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Sample records for previously unreported deep

  1. Poly-epiphyseal overgrowth: description of a previously unreported skeletal dysplasia

    Energy Technology Data Exchange (ETDEWEB)

    Pazzaglia, Ugo E.; Bonaspetti, Giovanni [University of Brescia, Orthopaedic Clinic, Brescia (Italy); Beluffi, Giampiero [Fondazione IRCCS Policlinico San Matteo, Department of Paediatric Radiology, Pavia (Italy); Marchi, Antonietta; Bozzola, Mauro; Savasta, Salvatore [Fondazione IRCCS Policlinico San Matteo, Paediatric Clinic, University of Pavia, Pavia (Italy)

    2007-10-15

    A skeletal dysplasia with previously unreported features is presented. Its evolution was characterized by growth abnormalities of bones without involvement of other organs. Advanced bone age, increased stature and irregular epiphyseal ossification with stippling of the main long bones were documented. Physeal overgrowth was massive in the left proximal humerus and femur. Furthermore, the hip joint appeared fused with an abundant mass of pathological calcific tissue extending from the femur to the ilium. Pathological epiphyses were characterized by anarchic cartilaginous proliferation with multiple ossification centres, while lamellar bone apposition and remodelling were normal. The observed bone changes were different from those in any previously reported syndrome, metabolic defect or bone dysplasia. However, they clearly indicated a defect of endochondral ossification with some resemblance to phenotypes observed in dysplasia epiphysealis hemimelica. (orig.)

  2. Poly-epiphyseal overgrowth: description of a previously unreported skeletal dysplasia

    International Nuclear Information System (INIS)

    Pazzaglia, Ugo E.; Bonaspetti, Giovanni; Beluffi, Giampiero; Marchi, Antonietta; Bozzola, Mauro; Savasta, Salvatore

    2007-01-01

    A skeletal dysplasia with previously unreported features is presented. Its evolution was characterized by growth abnormalities of bones without involvement of other organs. Advanced bone age, increased stature and irregular epiphyseal ossification with stippling of the main long bones were documented. Physeal overgrowth was massive in the left proximal humerus and femur. Furthermore, the hip joint appeared fused with an abundant mass of pathological calcific tissue extending from the femur to the ilium. Pathological epiphyses were characterized by anarchic cartilaginous proliferation with multiple ossification centres, while lamellar bone apposition and remodelling were normal. The observed bone changes were different from those in any previously reported syndrome, metabolic defect or bone dysplasia. However, they clearly indicated a defect of endochondral ossification with some resemblance to phenotypes observed in dysplasia epiphysealis hemimelica. (orig.)

  3. Wolff-Parkinson-White syndrome with cleft lip and palate: A rare, previously unreported association

    Directory of Open Access Journals (Sweden)

    K Kannan

    2008-01-01

    Full Text Available Wolff-Parkinson-White syndrome, also called Pre Excitation Syndrome, is characterized by an extra pathway that conducts the electrical impulses from the atria to the ventricles without the normal delay. We are reporting a case of WPW syndrome with a cleft lip and palate, which is a rare association and previously unreported in literature.

  4. Congenital costo-vertebral fibrous band and congenital kyphoscoliosis: a previously unreported combination.

    Science.gov (United States)

    Eid, Tony; Ghostine, Bachir; Kreichaty, Gaby; Daher, Paul; Ghanem, Ismat

    2013-05-01

    Congenital kyphoscoliosis (CKS) results from abnormal vertebral chondrification. Congenital fibrous bands occur in several locations with variable impact on vertebral development. We report a previously unreported case of a female infant with CKS presenting with an L2 hypoplastic vertebra and a costo-vertebral fibrous band extending to the skin in the form of a dimple. We also describe the therapeutic approach, consisting of surgical excision of the fibrous band and postoperative fulltime bracing, with a 7-year follow-up. We recommend a high index of suspicion in any unusual presentation of CKS and insist on case by case management in such cases.

  5. Mediastinal involvement in lymphangiomatosis: a previously unreported MRI sign

    Energy Technology Data Exchange (ETDEWEB)

    Shah, Vikas; Shah, Sachit; Barnacle, Alex; McHugh, Kieran [Great Ormond Street Hospital for Children, Department of Radiology, London (United Kingdom); Sebire, Neil J. [Great Ormond Street Hospital for Children, Department of Histopathology, London (United Kingdom); Brock, Penelope [Great Ormond Street Hospital for Children, Department of Oncology, London (United Kingdom); Harper, John I. [Great Ormond Street Hospital for Children, Department of Dermatology, London (United Kingdom)

    2011-08-15

    Multifocal lymphangiomatosis is a rare systemic disorder affecting children. Due to its rarity and wide spectrum of clinical, histological and imaging features, establishing the diagnosis of multifocal lymphangiomatosis can be challenging. The purpose of this study was to describe a new imaging sign in this disorder: paraspinal soft tissue and signal abnormality at MRI. We retrospectively reviewed the imaging, clinical and histopathological findings in a cohort of eight children with thoracic involvement from this condition. Evidence of paraspinal chest disease was identified at MRI and CT in all eight of these children. The changes comprise heterogeneous intermediate-to-high signal parallel to the thoracic vertebrae on T2-weighted sequences at MRI, with abnormal paraspinal soft tissue at CT and plain radiography. Multifocal lymphangiomatosis is a rare disorder with a broad range of clinicopathological and imaging features. MRI allows complete evaluation of disease extent without the use of ionising radiation and has allowed us to describe a previously unreported imaging sign in this disorder, namely, heterogeneous hyperintense signal in abnormal paraspinal tissue on T2-weighted images. (orig.)

  6. Previously unreported abnormalities in Wolfram Syndrome Type 2.

    Science.gov (United States)

    Akturk, Halis Kaan; Yasa, Seda

    2017-01-01

    Wolfram syndrome (WFS) is a rare autosomal recessive disease with non-autoimmune childhood onset insulin dependent diabetes and optic atrophy. WFS type 2 (WFS2) differs from WFS type 1 (WFS1) with upper intestinal ulcers, bleeding tendency and the lack ofdiabetes insipidus. Li-fespan is short due to related comorbidities. Only a few familieshave been reported with this syndrome with the CISD2 mutation. Here we report two siblings with a clinical diagnosis of WFS2, previously misdiagnosed with type 1 diabetes mellitus and diabetic retinopathy-related blindness. We report possible additional clinical and laboratory findings that have not been pre-viously reported, such as asymptomatic hypoparathyroidism, osteomalacia, growth hormone (GH) deficiency and hepatomegaly. Even though not a requirement for the diagnosis of WFS2 currently, our case series confirm hypogonadotropic hypogonadism to be also a feature of this syndrome, as reported before. © Polish Society for Pediatric Endocrinology and Diabetology.

  7. Response to deep TMS in depressive patients with previous electroconvulsive treatment.

    Science.gov (United States)

    Rosenberg, Oded; Zangen, Abraham; Stryjer, Rafael; Kotler, Moshe; Dannon, Pinhas N

    2010-10-01

    The efficacy of transcranial magnetic stimulation (TMS) in the treatment of major depression has already been shown. Novel TMS coils allowing stimulation of deeper brain regions have recently been developed and studied. Our study is aimed at exploring the possible efficacy of deep TMS in patients with resistant depression, who previously underwent electroconvalsive therapy (ECT). Using Brainsway's deep TMS H1 coil, six patients who previously underwent ECT, were treated with 120% power of the motor threshold at a frequency of 20 Hz. Patients underwent five sessions per week, up to 4 weeks. Before the study, patients were evaluated using the Hamilton depression rating scale (HDRS, 24 items), the Hamilton anxiety scale, and the Beck depression inventory and were again evaluated after 5, 10, 15, and 20 daily treatments. Response to treatment was considered a reduction in the HDRS of at least 50%, and remission was considered a reduction of the HDRS-24 below 10 points. Two of six patients responded to the treatment with deep TMS, including one who achieved full remission. Our results suggest the possibility of a subpopulation of depressed patients who may benefit from deep TMS treatment, including patients who did not respond to ECT previously. However, the power of the study is small and similar larger samples are needed. Copyright © 2010 Elsevier Inc. All rights reserved.

  8. Extensive forearm deep venous thrombosis following a severe infliximab infusion reaction.

    Science.gov (United States)

    Ryan, Barbara M; Romberg, Marielle; Wolters, Frank; Stockbrugger, Reinhold W

    2004-09-01

    Here we describe a patient with Crohn's disease who developed a severe infliximab infusion reaction (IIR), complicated 1 day later by severe swelling of the forearm and hand ipsilateral to the site of infliximab infusion. This proved to be extensive forearm deep venous thrombosis. The site of thrombosis and the chronological relationship with the IIR implicates a hypersensitivity to infliximab in the causation of the venous thrombosis in this case. With an increasing trend towards re-treating patients with known IIRs, clinicians should be aware of this potentially serious and previously unreported complication.

  9. Unreported employment and tax evasion in mid-transition : comparing developments and causes in the Baltic States

    OpenAIRE

    Jaanika Meriküll; Karsten Staehr

    2008-01-01

    This paper compares the prevalence and determinants of unreported employment in the three Baltic States in 1998 and 2002 using a hitherto little used dataset. The prevalence of unreported employment varies substantially across the three countries and across the two sampling years. Microeconometric estimations show that firm-related characteristics, such as sectoral activity, firm size and employment trends, are important determinants of unreported employment in all three countries, whereas th...

  10. Unreported location and presentation for a parasitic ovarian dermoid cyst: A case report

    Directory of Open Access Journals (Sweden)

    Amr Hassan Wahba

    2010-07-01

    Full Text Available Dermoid cysts are one of the most common ovarian tumors especially in young patients; however, parasitic dermoid cysts are extremely rare with the most common site being the omentum. This case demonstrates a new site for parasitic dermoid cyst; on the reflection of uterovesical pouch onto the anterior abdominal wall which is known anatomically as the median umbilical fold, as well as previously unreported clinical presentation which is the perception of something moving inside the abdomen, that can be explained by the presence of the parasitic dermoid cyst close to the anterior abdominal wall in this case.

  11. Prevalence of pre-diabetes and unreported diabetes mellitus in ...

    African Journals Online (AJOL)

    Background: Unreported diabetes mellitus and glucose intolerance have substantial clinical importance. Glucose intolerance precedes diabetes mellitus and it is associated with cardiovascular complications. Subjects with prediabetes have near normal glycated haemoglobin and may only be detected when oral glucose ...

  12. Using online reviews by restaurant patrons to identify unreported cases of foodborne illness - New York City, 2012-2013.

    Science.gov (United States)

    Harrison, Cassandra; Jorder, Mohip; Stern, Henri; Stavinsky, Faina; Reddy, Vasudha; Hanson, Heather; Waechter, HaeNa; Lowe, Luther; Gravano, Luis; Balter, Sharon

    2014-05-23

    While investigating an outbreak of gastrointestinal disease associated with a restaurant, the New York City Department of Health and Mental Hygiene (DOHMH) noted that patrons had reported illnesses on the business review website Yelp (http://www.yelp.com) that had not been reported to DOHMH. To explore the potential of using Yelp to identify unreported outbreaks, DOHMH worked with Columbia University and Yelp on a pilot project to prospectively identify restaurant reviews on Yelp that referred to foodborne illness. During July 1, 2012-March 31, 2013, approximately 294,000 Yelp restaurant reviews were analyzed by a software program developed for the project. The program identified 893 reviews that required further evaluation by a foodborne disease epidemiologist. Of the 893 reviews, 499 (56%) described an event consistent with foodborne illness (e.g., patrons reported diarrhea or vomiting after their meal), and 468 of those described an illness within 4 weeks of the review or did not provide a period. Only 3% of the illnesses referred to in the 468 reviews had also been reported directly to DOHMH via telephone and online systems during the same period. Closer examination determined that 129 of the 468 reviews required further investigation, resulting in telephone interviews with 27 reviewers. From those 27 interviews, three previously unreported restaurant-related outbreaks linked to 16 illnesses met DOHMH outbreak investigation criteria; environmental investigation of the three restaurants identified multiple food-handling violations. The results suggest that online restaurant reviews might help to identify unreported outbreaks of foodborne illness and restaurants with deficiencies in food handling. However, investigating reports of illness in this manner might require considerable time and resources.

  13. Family Business or Social Problem? The Cost of Unreported Domestic Violence

    Science.gov (United States)

    Carrell, Scott E.; Hoekstra, Mark

    2012-01-01

    Social interest in problems such as domestic violence is typically motivated by concerns regarding equity, rather than efficiency. However, we document that taking steps to reduce domestic violence by reporting it yields substantial benefits to external parties. Specifically, we find that although children exposed to as-yet-unreported domestic…

  14. Public and Private Sector Jobs, Unreported Income and Consumption Gap in India: Evidence from Micro-Data

    OpenAIRE

    Kar, Saibal; Roy, Poulomi; Saha, Sarani

    2012-01-01

    This paper tries to document the presence of unreported income among public sector employees in India. We investigate empirically the wage gap as well as consumption expenditure parity between public and private sector workers. It tests the hypothesis that despite a lower level of public sector income in some of the quantiles, if the level of durable goods consumption between the private and the public sector employees are similar, then it might be indicative of the presence of unreported inc...

  15. Deep brain stimulation of the subthalamic nucleus: effectiveness in advanced Parkinson's disease patients previously reliant on apomorphine

    OpenAIRE

    Varma, T; Fox, S; Eldridge, P; Littlechild, P; Byrne, P; Forster, A; Marshall, A; Cameron, H; McIver, K; Fletcher, N; Steiger, M

    2003-01-01

    Objectives: To assess the efficacy of bilateral subthalamic nucleus (STN) deep brain stimulation (DBS) in patients with advanced Parkinson's disease previously reliant on apomorphine as their main antiparkinsonian medication.

  16. Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials.gov.

    Science.gov (United States)

    Dunn, Adam G; Coiera, Enrico; Bourgeois, Florence T

    2018-03-01

    Trial registries can be used to measure reporting biases and support systematic reviews, but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed. We extracted terms and concepts from a data set of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles and tested methods for ranking articles across 16,005 reported links and 90 manually identified unreported links. Performance was measured by the median rank of matching articles and the proportion of unreported links that could be found by screening ranked candidate articles in order. The best-performing concept-based representation produced a median rank of 3 (interquartile range [IQR] 1-21) for reported links and 3 (IQR 1-19) for the manually identified unreported links, and term-based representations produced a median rank of 2 (1-20) for reported links and 2 (IQR 1-12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links. Leveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Cucullanid nematodes (Nematoda: Cucullanidae) from deep-sea marine fishes off New Caledonia, including Dichelyne etelidis n. sp.

    Science.gov (United States)

    Moravec, František; Justine, Jean-Lou

    2011-02-01

    Three nematode species of the family Cucullanidae, intestinal parasites of marine perciform fishes, are reported from off New Caledonia: Cucullanus bourdini Petter & Le Bel, 1992 from the crimson jobfish Pristipomoides filamentosus (Valenciennes) and the goldflag jobfish Pristipomoides auricilla (Jordan, Evermann & Tanaka) (new host record) (both Lutjanidae); Dichelyne etelidis n. sp. from the deep-water red snapper Etelis carbunculus Cuvier (type-host) and the deep-water longtail red snapper Etelis coruscans Valenciennes (both Lutjanidae); and Dichelyne sp. (only one female) from the trumpet emperor Lethrinus miniatus (Forster) (Lethrinidae). Detailed light and electron microscopical studies revealed in C. bourdini some taxonomically important, previously unreported features, such as the location of the excretory pore, nature of the vulva and the size of fully-developed eggs. The new species, D. etelidis, is characterised mainly by the length of the spicules (462-748 μm), a single intestinal caecum, the location of the deirids and excretory pore, the arrangement of the genital papillae and the host group.

  18. A previously unreported association between Nance-Horan syndrome and spontaneous dental abscesses.

    Science.gov (United States)

    Hibbert, Sally

    2005-02-01

    Atypical dentofacial structures may be the first indicator of other anomalies linked to a syndrome. This case describes the management of a 9-year-old girl referred for the routine management of supernumerary teeth. The anomalous form of her teeth, together with multiple supernumerary units and a history of congenital cataracts, were suggestive of a diagnosis of Nance-Horan syndrome. This is an X-linked disorder, in which females usually demonstrate mild expression; this case was unusual in respect to the marked phenotype expressed. Unusually, the girl developed 2 spontaneous abscesses of her noncarious upper incisor teeth; a feature never previously described in this syndrome. This report details the patient's dental management and discusses the possible pathogenesis of the dental abscesses, together with the genetic implications of this syndrome.

  19. PHACES syndrome: a review of eight previously unreported cases with late arterial occlusions

    International Nuclear Information System (INIS)

    Bhattacharya, J.J.; Luo, C.B.; Alvarez, H.; Rodesch, G.; Lasjaunias, P.L.; Pongpech, S.

    2004-01-01

    PHACE and PHACES are acronyms for a syndrome of variable expression comprising posterior cranial fossa malformations, facial haemangiomas, arterial anomalies, aortic coarctation and other cardiac disorders, ocular abnormalities and stenotic arterial disease. We review five girls and three boys aged 1 month-14 years with disorders from this spectrum. Six had large facial haemangiomas but recent reports suggest that small haemangiomas may occur; hence our inclusion of two possible cases. We also focus on the recently recognised feature of progressive intracranial arterial occlusions, present in four of our patients, later than previously recognised, from 4 to 14 years of age. We suggest that many elements of this disorder could reflect an abnormality of cell proliferation and apoptosis. (orig.)

  20. Duane retraction syndrome type 1 with Usher syndrome type 2: an unreported association.

    Science.gov (United States)

    Khurana, Bhawna Piplani; Khurana, Aruj Kumar; Grover, Sumit

    2015-05-07

    Duane retraction syndrome is characterized by globe retraction and palpebral fissure narrowing on adduction, with restriction of abduction, adduction, or both. Usher syndrome type 2 consists of congenital bilateral sensorineural hearing loss and retinitis pigmentosa. The authors present a case with a yet unreported association between Duane retraction syndrome type 1 and Usher syndrome type 2. Copyright 2015, SLACK Incorporated.

  1. Echinocandin Failure Case Due to a Previously Unreported FKS1 Mutation in Candida krusei

    DEFF Research Database (Denmark)

    Jensen, Rasmus Hare; Justesen, Ulrik Stenz; Rewes, Annika

    2014-01-01

    Echinocandins are the preferred therapy for invasive infections due to Candida krusei. We present here a case of clinical failure involving C. krusei with a characteristic FKS1 hot spot mutation not previously reported in C. krusei that was isolated after 14 days of treatment. Anidulafungin MICs...... were elevated by ≥5 dilution steps above the clinical breakpoint but by only 1 step for a Candida albicans isolate harboring the corresponding mutation, suggesting a notable species-specific difference in the MIC increase conferred by this mutation....

  2. An unreported type of coronary artery naomaly in congenitally corrected transposition of great arteries

    Energy Technology Data Exchange (ETDEWEB)

    Kwak, Min Kyu; Jeong, Yeon Joo; Lee, Gee Won; Lee, Nam Kyung; Choi, Jung Hyun; Lee, Ji Won [Medical Research Institute, Pusan National University Hospital, Busan (Korea, Republic of)

    2016-07-15

    Coronary artery variations are associated anomalies in 45% of congenitally corrected transposition of the great arteries (ccTGA) cases, and it is important to detect any coronary artery anomalies before cardiac surgery. We report a case of a 51-year-old woman with ccTGA and an unreported type of coronary artery anomaly.

  3. On detection of the possible use of VVERs for unreported production of plutonium. Final report for the period July 1988 - December 1989

    International Nuclear Information System (INIS)

    Simov, R.; Nelov, N.; Stoyanova, I.; Kovachev, N.; Yonchev, P.

    1989-01-01

    The study includes an analysis of the feasibility of unreported production of plutonium-239 in VVER-440 reactors. It is shown that for VVER-440 reactors 36 natural uranium oxide fuel assemblies in the peripheral region of the core need to be loaded to produce 8 kg of extra plutonium in one cycle. Substituting the peripheral fuel assemblies with natural uranium oxide fuel assemblies, the changes in the power peaking are negligible and do not affect reactor safety. Unreported production outside the core is not practical due to physical and mechanical constraints, low flux level, etc. The feasibility of unreported removal of irradiated material in spent fuel cask has been also assessed. After about a month cooling time, still within the refueling period, the irradiated natural uranium fuel assemblies could be removed off-site without significant health hazard to the workers. To improve the effectiveness of the safeguards objectives, additional inspection activities are suggested. 10 figs

  4. Deep vein thrombosis, an unreported first manifestation of polyglandular autoimmune syndrome type III

    Directory of Open Access Journals (Sweden)

    M Horsey

    2016-07-01

    Full Text Available A 71-year-old woman with severe right lower leg pain, edema and erythema was presented to the Emergency Department and was found to have an extensive deep vein thrombosis (DVT confirmed by ultrasound. She underwent an extensive evaluation due to her prior history of malignancy and new hypercoagulable state, but no evidence of recurrent disease was detected. Further investigation revealed pernicious anemia (PA, confirmed by the presence of a macrocytic anemia (MCV=115.8fL/red cell, Hgb=9.0g/dL, decreased serum B12 levels (56pg/mL, with resultant increased methylmalonic acid (5303nmol/L and hyperhomocysteinemia (131μmol/L, the presumed etiology of the DVT. The patient also suffered from autoimmune thyroid disease (AITD, and both antithyroglobulin and anti-intrinsic factor antibodies were detected. She responded briskly to anticoagulation with heparin and coumadin and treatment of PA with intramuscular vitamin B12 injections. Our case suggests that a DVT secondary to hyperhomocystenemia may represent the first sign of polyglandular autoimmune syndrome III-B (PAS III-B, defined as the coexistent autoimmune conditions AITD and PA. It is important to recognize this clinical entity, as patients may not only require acute treatment with vitamin B12 supplementation and prolonged anticoagulation, as in this patient, but may also harbor other autoimmune diseases.

  5. Unreported workplace violence in nursing.

    Science.gov (United States)

    Kvas, A; Seljak, J

    2014-09-01

    Workplace violence occurs on a frequent basis in nursing. Most violent acts remain unreported. Consequently, we do not know the actual frequency of the occurrence of workplace violence. This requires research of nurses' actions following workplace violence and identification of reasons why most victims do not report violent acts in the appropriate manner. To explore violence in nursing as experienced by nurses in Slovenia. A survey was carried out with a representative sample of nurses in Slovenia. The questionnaire Workplace Violence in Nursing was submitted to 3756 nurses, with 692 completing the questionnaire. A total of 61.6% of the nurses surveyed had been exposed to violence in the past year. Most victims were exposed to psychological (60.1%) and economic violence (28.9%). Victims reported acts of violence in formal written form in a range from 6.5% (psychological violence) to 10.9% (physical violence). The largest share of victims who did not report violence and did not speak to anyone about it were victims of sexual violence (17.9%). The main reason for not reporting the violence was the belief that reporting it would not change anything, followed by the fear of losing one's job. Only a small share of the respondents reported violence in written form, the main reason being the victims' belief that reporting it would not change anything. This represents a severe criticism of the system for preventing workplace violence for it reveals the failure of response by leadership structures in healthcare organizations. Professional associations and the education system must prepare nurses for the prevention of violence and appropriate actions in the event of violent acts. Healthcare organizations must ensure the necessary conditions for enabling and encouraging appropriate actions following violent acts according to relevant protocols. © 2014 International Council of Nurses.

  6. Heuristics in primary care for recognition of unreported vision loss in older people: a technology development study.

    Science.gov (United States)

    Wijeyekoon, Skanda; Kharicha, Kalpa; Iliffe, Steve

    2015-09-01

    To evaluate heuristics (rules of thumb) for recognition of undetected vision loss in older patients in primary care. Vision loss is associated with ageing, and its prevalence is increasing. Visual impairment has a broad impact on health, functioning and well-being. Unrecognised vision loss remains common, and screening interventions have yet to reduce its prevalence. An alternative approach is to enhance practitioners' skills in recognising undetected vision loss, by having a more detailed picture of those who are likely not to act on vision changes, report symptoms or have eye tests. This paper describes a qualitative technology development study to evaluate heuristics for recognition of undetected vision loss in older patients in primary care. Using a previous modelling study, two heuristics in the form of mnemonics were developed to aid pattern recognition and allow general practitioners to identify potential cases of unreported vision loss. These heuristics were then analysed with experts. Findings It was concluded that their implementation in modern general practice was unsuitable and an alternative solution should be sort.

  7. Central diabetes insipidus: a previously unreported side effect of temozolomide.

    Science.gov (United States)

    Faje, Alexander T; Nachtigall, Lisa; Wexler, Deborah; Miller, Karen K; Klibanski, Anne; Makimura, Hideo

    2013-10-01

    Temozolomide (TMZ) is an alkylating agent primarily used to treat tumors of the central nervous system. We describe 2 patients with apparent TMZ-induced central diabetes insipidus. Using our institution's Research Patient Database Registry, we identified 3 additional potential cases of TMZ-induced diabetes insipidus among a group of 1545 patients treated with TMZ. A 53-year-old male with an oligoastrocytoma and a 38-year-old male with an oligodendroglioma each developed symptoms of polydipsia and polyuria approximately 2 months after the initiation of TMZ. Laboratory analyses demonstrated hypernatremia and urinary concentrating defects, consistent with the presence of diabetes insipidus, and the patients were successfully treated with desmopressin acetate. Desmopressin acetate was withdrawn after the discontinuation of TMZ, and diabetes insipidus did not recur. Magnetic resonance imaging of the pituitary and hypothalamus was unremarkable apart from the absence of a posterior pituitary bright spot in both of the cases. Anterior pituitary function tests were normal in both cases. Using the Research Patient Database Registry database, we identified the 2 index cases and 3 additional potential cases of diabetes insipidus for an estimated prevalence of 0.3% (5 cases of diabetes insipidus per 1545 patients prescribed TMZ). Central diabetes insipidus is a rare but reversible side effect of treatment with TMZ.

  8. Normal endothelial function after meals rich in olive or safflower oil previously used for deep frying.

    Science.gov (United States)

    Williams, M J; Sutherland, W H; McCormick, M P; Yeoman, D; de Jong, S A; Walker, R J

    2001-06-01

    Polyunsaturated fats are more susceptible to oxidation during heating than monounsaturated fats but their effects on endothelial function when heated are unknown. The aim of this study was to compare the effect of meals rich in heat-modified safflower and olive oils on postprandial flow-mediated endothelium-dependent dilation (EDD) in healthy men. Flow-mediated EDD and glyceryltrinitrate-induced endothelium-independent dilation of the brachial artery were investigated in 14 subjects before and 4 hours after meals rich in olive oil and safflower oil used hourly for deep-frying for 8 hours in a double-blind crossover study design. There were high levels of lipid oxidation products (peroxides and carbonyls) in both heated oils. Plasma triglycerides were markedly increased at 4 hours after heated olive oil (1.26 +/- 0.43 vs 2.06 +/- 0.97 mmol/L) and heated safflower oil (1.44 +/- 0.63 vs 1.99 +/- 0.88 mmol/L). There was no change in EDD between fasting and postprandial studies and the response during the postprandial period was not significantly (p = 0.51) different between the meals (heated olive oil: 4.9 +/- 2.2% vs 4.9 +/- 2.5%; heated safflower oil: 5.1 +/- 3.1% vs 5.6 +/- 3.4%). Meals rich in olive and safflower oils previously used for deep frying and containing high levels of lipid oxidation products increase postprandial serum triglycerides without affecting endothelial function. These findings suggest that relatively short-term use of these vegetable oils for frying may not adversely affect postprandial endothelial function when foods containing the heat-modified oils are consumed.

  9. Confronting Illegal, Unreported and Unregulated (IUU) fishing with proper port and flagged states policies: The case of South Korea and European Union

    Czech Academy of Sciences Publication Activity Database

    Midani, Rahimi Amaj; Lee, S.G.

    2016-01-01

    Roč. 10, č. 3 (2016), s. 43-51 E-ISSN 1307-234X Institutional support: RVO:60077344 Keywords : illegal unreported and unregulated * South Korea * distant water fishing * European Union * normative power * market power Subject RIV: GL - Fishing www. fisheries sciences.com

  10. A previously unreported variant of the synostotic sagittal suture: Case report and review of salient literature

    Directory of Open Access Journals (Sweden)

    Madison Budinich

    2016-12-01

    Full Text Available Introduction: Sagittal synostosis is a rare congenital disease caused by the premature fusion of the sagittal suture. Craniosynostosis occurs for a variety of reasons, different for every case, and often the etiology is unclear but the anomaly can frequently be seen as part of Crouzon's or Apert's syndromes. Herein, we discuss a rare case of craniosynostosis where the patient presented with a, to our knowledge, a previously undescribed variant of sagittal synostosis. Case report: A 3-month-old female infant presented to a craniofacial clinic for a consultation regarding an abnormal head shape. Images of the skull were performed, demonstrating that the patient had craniosynostosis. The patient displayed no other significant symptoms besides abnormalities in head shape. The sagittal suture was found to extend into the occipital bone where it was synostotic. Conclusion: To our knowledge, a synostotic sagittal suture has not been reported that extended posteriorly it involve the occipital bone. Those who interpret imaging or operate on this part of the skull should consider such a variation. Keywords: Anatomy, Craniosynostosis, Skull, Malformation, Pediatrics

  11. Oral Bilateral Collagenous Fibroma: A previously unreported case and literature review.

    Science.gov (United States)

    Vasconcelos, Ana-Carolina; Gomes, Ana-Paula; Tarquinio, Sandra; Abduch-Rodrigues, Eduardo; Mesquita, Ricardo; Silva, Karine

    2018-01-01

    Collagenous fibroma, also known as desmoplastic fibroblastoma, is a rare benign slow growing tumor particularly uncommon in the oral cavity. The aim of this study was to analyze the clinical and histopathological features of an oral collagenous fibroma as well as to compare this data with those reported in an English-literature review. The thirteenth case of collagenous fibroma in the oral cavity and the first to present clinically as a bilateral mass was described. A 48-years-old female patient was referred to a School of Dentistry, complaining about an asymptomatic swelling on the hard palate, lasting around ten years. The intraoral examination revealed two well-defined mass, bilaterally in the hard palate. An excisional biopsy was performed. Microscopically, the connective tissue consisted of dense collagen bundles in which were seen scarcely distributed spindle-shaped to stellate fibroblastic cells. Blood vessels were few, as well as inflammatory cells. Immunohistochemical staining was positive for vimentin, α-smooth muscle actin and factor XIIIa and negative for S-100, CD68, CD34, HHF35, desmin and AE1/AE3. The patient remains disease-free 24 months after excision. In conclusion, oral collagenous fibroma should be included in the differential diagnosis of bilateral sessile nodules in the oral cavity. Key words: Connective tissue, mouth diseases, mouth neoplasms, oral diagnosis, oral pathology.

  12. Chronic pain in Noonan Syndrome: A previously unreported but common symptom.

    Science.gov (United States)

    Vegunta, Sravanthi; Cotugno, Richard; Williamson, Amber; Grebe, Theresa A

    2015-12-01

    Noonan syndrome (NS) is a multiple malformation syndrome characterized by pulmonic stenosis, cardiomyopathy, short stature, lymphatic dysplasia, craniofacial anomalies, cryptorchidism, clotting disorders, and learning disabilities. Eight genes in the RAS/MAPK signaling pathway are implicated in NS. Chronic pain is an uncommon feature. To investigate the prevalence of pain in NS, we distributed a two-part questionnaire about pain among NS individuals at the Third International Meeting on Genetic Syndromes of the Ras/MAPK Pathway. The first part of the questionnaire queried demographic information among all NS participants. The second part was completed by individuals with chronic pain. Questions included musculoskeletal problems and clinical features of pain. Forty-five questionnaires were analyzed; 53% of subjects were female. Mean age was 17 (2-48) years; 47% had a PTPN11 mutation. Sixty-two percent (28/45) of individuals with NS experienced chronic pain. There was a significant relationship between prevalence of pain and residing in a cold climate (P = 0.004). Pain occurred commonly in extremities/joints and head/trunk, but more commonly in extremities/joints (P = 0.066). Subjects with hypermobile joints were more likely to have pain (P = 0.052). Human growth hormone treatment was not statistically significant among subjects without chronic pain (P = 0.607). We conclude that pain is a frequent and under-recognized clinical feature of NS. Chronic pain may be associated with joint hypermobility and aggravated by colder climate. Our study is a preliminary investigation that should raise awareness about pain as a common symptom in children and adults with NS. © 2015 Wiley Periodicals, Inc.

  13. Erysipelothrix endocarditis with previous cutaneous lesion: report of a case and review of the literature

    Directory of Open Access Journals (Sweden)

    Marion P. Rocha

    1989-08-01

    Full Text Available This report describes the first documented case of Erysipelothrix rhusiopathiae endocarditis in Latin America. The patient was a 51-years-old male, moderate alcoholic, with a previous history of aortic failure. He was used to fishing and cooking as a hobby and had his left hand wounded by a fish-bone. The disease began with erysipeloid form and developed to septicemia and endocarditis. He was treated with antibiotics and surgery for aortic valve replacement. There are only 46 cases of E. rhusiopathiae endocarditis reported to date. The authors wonder if several other cases might go unreported for lack of microbiological laboratorial diagnosis.

  14. 77 FR 64077 - National Highway-Rail Crossing Inventory Reporting Requirements

    Science.gov (United States)

    2012-10-18

    ... about warning devices and signage, for each previously unreported and new public and private highway..., including current information about warning devices and signage, related to new and previously unreported... devices and signage * * * concerning each previously unreported crossing through which it operates or with...

  15. Managerial improvement efforts after finding unreported cracks in reactor components

    International Nuclear Information System (INIS)

    Kawamura, S.

    2006-01-01

    In 2002 TEPCO found that there were unreported cracks in reactor components, of which inspection records had been falsified. Stress Corrosion Cracking indications found in Core Shrouds and Primary Loop Re-circulation pipes at some plants were removed from the inspection records and not reported to the regulators. Top management of TEPCO took the responsibility and resigned, and recovery was started under the leadership of new management team. First of all, behavioral standards were reconstituted to strongly support safety-first value. Ethics education was introduced and corporate ethics committee was organized with participation of external experts. Independent assessment organization was established to enhance quality assurance. Information became more transparent through Non-conformance Control Program. As for the material management, prevention and mitigation programs for the Stress Corrosion Cracking of reactor components were re-established. In addition to the above immediate recovery actions, long term improvement initiatives have also been launched and driven by our aspiration to excellence in safe operation of nuclear power plants. Vision and core values were set to align the people. Organizational learning was enhanced by benchmark studies, better systematic use of operational experience, self-assessment and external assessment. Based on these foundation blocks and with strong sponsorship from the top management, work processes were analyzed and improved by Peer Groups. (author)

  16. More Far-Side Deep Moonquake Nests Discovered

    Science.gov (United States)

    Nakamura, Y.; Jackson, John A.; Jackson, Katherine G.

    2004-01-01

    As reported last year, we started to reanalyze the seismic data acquired from 1969 to 1977 with a network of stations established on the Moon during the Apollo mission. The reason for the reanalysis was because recent advances in computer technology make it possible to employ much more sophisticated analysis techniques than was possible previously. The primary objective of the reanalysis was to search for deep moonquakes on the far side of the Moon and, if found, to use them to infer the structure of the Moon's deep interior, including a possible central core. The first step was to identify any new deep moonquakes that escaped our earlier search by applying a combination of waveform cross-correlation and single-link cluster analysis, and then to see if any of them are from previously unknown nests of deep moonquakes. We positively identified 7245 deep moonquakes, more than a five-fold increase from the previous 1360. We also found at least 88 previously unknown deep-moonquake nests. The question was whether any of these newly discovered nets were on the far side of the Moon, and we now report that our analysis of the data indicates that some of them are indeed on the far side.

  17. Previously unreported intense absorption band and the pK/sub A/ of protonated triplet methylene blue

    Energy Technology Data Exchange (ETDEWEB)

    Ohno, T.; Osif, T.L.; Lichtin, N.N.

    1979-01-01

    Excitation by a Q-switched giant ruby laser (1.2 joule output at 694 nm, approx. 50 nsec flash) of 2-10 ..mu..M solutions of methylene blue in water, 30% ethanol in water or 50 v/v% water - CH/sub 3/CN at pH values in the range 2.0 - 9.3 converted the dye essentially completely to its T/sub 1/ state. The absorption spectrum of T/sub 1/ dye was measured in different media at pH 2.0 and 8.2 by kinetic spectrophotometry. Previously reported T-T absorption in the violet in acidic and alkaline solutions and in the near infrared in alkaline solution was confirmed. Values found for these absorptions in the present work with 30% ethanol in water as solvent are lambda/sub max/ approx. 370 nm, epsilon/sub max/ approx. 13,200 M/sup -1/ cm/sup -1/ at pH 2 and lambda/sub max/ approx. 420 nm, epsilon/sub max/ approx. 9,000 M/sup -1/ cm/sup -1/, lambda/sub max/ approx. 840 nm, epsilon/sub max/ approx. 20,000 M/sup -1/ cm/sup -1/ at pH 8.2. Long-wavelength T-T absorption in acidic solution is reported here for the first time: lambda/sub max/ approx. 680 nm, epsilon/sub max/ approx. 19,000 M/sup -1/ cm/sup -1/ in 30% ethanol in water at pH 2. Observation of a pH-independent isobestic point approx. 720 nm confirms that the long-wavelength absorptions are due to different protonated states of the same species, MB/sup +/(T/sub 1/) and MBH/sup 2 +/(T/sub 1/). The pK/sub A/ of MBH/sup 2 +/(T/sub 1/) in water was determined from the dependence on pH of absorption at 700 and 825 nm to be 7.1/sub 4/ +- .1 and from the kinetics of decay of triplet absorption to be 7.2. The specific rate of protonation of MB/sup +/(T/sub 1/) by H/sub 2/PO/sub 4//sup -/ in water at pH 4.4 was found to be 4.5 +- .4 x 10/sup 8/ M/sup -1/ sec/sup -1/.

  18. A deep learning method for lincRNA detection using auto-encoder algorithm.

    Science.gov (United States)

    Yu, Ning; Yu, Zeng; Pan, Yi

    2017-12-06

    RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly

  19. Thickening and enhancement of multiple cranial nerves in conjunction with cystic white matter lesions in early infantile Krabbe disease

    Energy Technology Data Exchange (ETDEWEB)

    Beslow, Lauren A.; Boennemann, Carsten G. [Children' s Hospital of Philadelphia, Division of Neurology, Philadelphia, PA (United States); Schwartz, Erin S. [Children' s Hospital of Philadelphia, Division of Neuroradiology, Philadelphia, PA (United States)

    2008-06-15

    We present serial MR findings in a child ultimately diagnosed with the early infantile form of Krabbe disease. MR showed typical features of Krabbe disease including cerebellar and brainstem hyperintensity, periventricular and deep white matter hyperintensity, and cerebral atrophy. In addition, the combination of both enlargement and enhancement of multiple cranial nerves in conjunction with unusual cystic lesions adjacent to the frontal horns of the lateral ventricles was previously unreported and expands the spectrum of imaging findings in early Krabbe disease. (orig.)

  20. New, previously unreported correlations between latent Toxoplasma gondii infection and excessive ethanol consumption.

    Science.gov (United States)

    Samojłowicz, Dorota; Borowska-Solonynko, Aleksandra; Kruczyk, Marcin

    2017-11-01

    A number of world literature reports indicate that a latent Toxoplasma gondii infection leads to development of central nervous system disorders, which in turn may lead to altered behavior in the affected individuals. T. gondii infection has been observed to play the greatest role in drivers, suicides, and psychiatric patients. Studies conducted for this manuscript involve a different, never before really reported correlation between latent T. gondii infection and ethanol abuse. A total of 538 decedents with a known cause of death were included in the study. These individuals were divided into three groups: the risky behavior group, inconclusively risky behavior group, and control group. The criterion for this division was the likely effect of the individual's behavior on the mechanism and cause of his/her death. The material used for analyses were blood samples collected during routine medico-legal examinations in these cases. The blood samples were used to measure anti-T. gondii IgG antibodies with an enzyme-linked immunosorbent assay (ELISA). Moreover, the following data were recorded for each decedent: sex, age, circumstances of death, cause of death, time from death to autopsy, and (if provided) substance abuse status (alcohol, illicit drugs). In those cases where blood alcohol level or toxicology tests were requested by the Prosecutor's Office, their results were also included in our analysis. Test results demonstrated a strong correlation between latent T. gondii infection and engaging in risky behaviors leading to death. Moreover, analyses demonstrated a positive correlation between the presence of anti-T. gondii IgG antibodies and psychoactive substance (especially ethanol) abuse, however, the causal relationship remains unclear. Due to the fact that alcohol abuse constitutes a significant social problem, searching for eliminable risk factors for addiction is extremely important. Our analyses provided new important information on the possible effects of latent T. gondii infection in humans. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

    Science.gov (United States)

    Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

    2017-03-01

    Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

  2. Deep learning in neural networks: an overview.

    Science.gov (United States)

    Schmidhuber, Jürgen

    2015-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

  3. DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.

    Science.gov (United States)

    Wang, Feng; Gong, Huichao; Liu, Gaochao; Li, Meijing; Yan, Chuangye; Xia, Tian; Li, Xueming; Zeng, Jianyang

    2016-09-01

    Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. AB072. Novel mutation in the hepatocyte nuclear factor 1b/maturity-onset diabetes of the young type 5 gene—unreported Vietnamese case

    OpenAIRE

    Dung, Vu Chi; Thao, Bui Phuong; Ngoc, Can Thi Bich; Khanh, Nguyen Ngoc; Ellard, Sian

    2015-01-01

    Maturity-onset diabetes of the young type 5 (MODY5), a type of dominantly inherited diabetes mellitus and nephropathy, has been associated with mutations of the hepatocyte nuclear factor-1 (HNF-1β) gene, mostly generating truncated protein. Various phenotypes are related to HNF-1β mutations. Our aim to describe clinical and genetic findings in the unreported Vietnamese case identified with HNF-1β mutations. The proband with kidney failure from 7.5 years of age and diabetes diagnosed at 13.5 y...

  5. Case report of deep vein thrombosis caused by artificial urinary sphincter reservoir compressing right external iliac vein

    Directory of Open Access Journals (Sweden)

    Marcus J Yip

    2015-01-01

    Full Text Available Artificial urinary sphincters (AUSs are commonly used after radical prostatectomy for those who are incontinent of urine. However, they are associated with complications, the most common being reservoir uprising or migration. We present a unique case of occlusive external iliac and femoral vein obstruction by the AUS reservoir causing thrombosis. Deflation of the reservoir and anticoagulation has, thus far, not been successful at decreasing thrombus burden. We present this case as a rare, but significant surgical complication; explore the risk factors that may have contributed, and other potential endovascular therapies to address this previously unreported AUS complication.

  6. DeepWind - from Idea to 5 MW Concept

    DEFF Research Database (Denmark)

    Schmidt Paulsen, Uwe; Aagaard Madsen, Helge; Kragh, Knud Abildgaard

    2014-01-01

    The DeepWind concept has been described previously on challenges and potentials, this new offshore floating technology can offer to the wind industry [1]. The paper describes state of the art design improvements, new simulation results of the DeepWind floating vertical axis wind turbine concept...

  7. Pulmonary carcinosarcoma initially presenting as invasive aspergillosis: a case report of previously unreported combination

    Directory of Open Access Journals (Sweden)

    Van Thien

    2010-01-01

    Full Text Available Abstract Carcinosarcoma of the lung is a malignant tumor composed of a mixture of carcinoma and sarcoma elements. The carcinomatous component is most commonly squamous followed by adenocarcinoma. The sarcomatous component commonly comprises the bulk of the tumor and shows poorly differentiated spindle cell features. Foci of differentiated sarcomatous elements such as chondrosarcoma and osteosarcoma may be seen. Aspergillus pneumonia is the most common form of invasive aspergillosis and occurs mainly in patients with malignancy, immunocompromizing or debilitating diseases. Patients with Aspergillus pneumonia present with fever, cough, chest pain and occasionally hemoptysis. Tissue examination is the most reliable method for diagnosis, and mortality rate is high. We describe a case of primary carcinosarcoma of the lung concurrently occurring with invasive pulmonary aspergillosis in a 66-year old patient.

  8. Deep Visual Attention Prediction

    Science.gov (United States)

    Wang, Wenguan; Shen, Jianbing

    2018-05-01

    In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

  9. Is Multitask Deep Learning Practical for Pharma?

    Science.gov (United States)

    Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

    2017-08-28

    Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

  10. Pregnancy after catheter-directed thrombolysis for acute iliofemoral deep venous thrombosis

    DEFF Research Database (Denmark)

    Jørgensen, M; Broholm, R; Bækgaard, N

    2013-01-01

    To assess the safety and efficacy of low-molecular-weight heparin (LMWH) in pregnancy and puerperium in women with previous acute iliofemoral deep venous thrombosis (DVT) treated with catheter-directed thrombolysis (CDT).......To assess the safety and efficacy of low-molecular-weight heparin (LMWH) in pregnancy and puerperium in women with previous acute iliofemoral deep venous thrombosis (DVT) treated with catheter-directed thrombolysis (CDT)....

  11. Active semi-supervised learning method with hybrid deep belief networks.

    Science.gov (United States)

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  12. Deep Learning for Computer Vision: A Brief Review

    Science.gov (United States)

    Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

    2018-01-01

    Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. PMID:29487619

  13. Deep Learning for Computer Vision: A Brief Review

    Directory of Open Access Journals (Sweden)

    Athanasios Voulodimos

    2018-01-01

    Full Text Available Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

  14. Deep Learning for Computer Vision: A Brief Review.

    Science.gov (United States)

    Voulodimos, Athanasios; Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

    2018-01-01

    Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

  15. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  16. Temporary Efficacy of Pyrimethamine in Juvenile-Onset Tay-Sachs Disease Caused by 2 Unreported HEXA Mutations in the Indian Population.

    Science.gov (United States)

    Udwadia-Hegde, Anaita; Hajirnis, Omkar

    2017-01-01

    Juvenile Tay-Sachs disease is rarer than other forms of Tay-Sachs disease and is usually seen in children between the age of 2 and 10 years. Pyrimethamine as a pharmacological chaperone was used to increase β-hexosaminidase A activity in this patient. We describe a patient with Tay-Sachs disease from the Indian population, a juvenile case who presented with developmental regression starting at the age of three, initially with motor followed by language regression. She is currently incapacitated with severe behavioral issues. This brief communication gives an insight into the efficacy of pharmacological chaperones. It also describes two unreported mutations in hexosaminidase A gene from the Indian population. After commencing Pyrimethamine, though initial benefits with increase in levels corresponded with briefly halting the motor regression, the observed increase was only transient and not associated with discernible beneficial neurological or psychiatric effects.

  17. WFIRST: Science from Deep Field Surveys

    Science.gov (United States)

    Koekemoer, Anton; Foley, Ryan; WFIRST Deep Field Working Group

    2018-01-01

    WFIRST will enable deep field imaging across much larger areas than those previously obtained with Hubble, opening up completely new areas of parameter space for extragalactic deep fields including cosmology, supernova and galaxy evolution science. The instantaneous field of view of the Wide Field Instrument (WFI) is about 0.3 square degrees, which would for example yield an Ultra Deep Field (UDF) reaching similar depths at visible and near-infrared wavelengths to that obtained with Hubble, over an area about 100-200 times larger, for a comparable investment in time. Moreover, wider fields on scales of 10-20 square degrees could achieve depths comparable to large HST surveys at medium depths such as GOODS and CANDELS, and would enable multi-epoch supernova science that could be matched in area to LSST Deep Drilling fields or other large survey areas. Such fields may benefit from being placed on locations in the sky that have ancillary multi-band imaging or spectroscopy from other facilities, from the ground or in space. The WFIRST Deep Fields Working Group has been examining the science considerations for various types of deep fields that may be obtained with WFIRST, and present here a summary of the various properties of different locations in the sky that may be considered for future deep fields with WFIRST.

  18. Disseminated Kaposi sarcoma with epithelioid morphology in an HIV/AIDS patient: A previously unreported variant.

    Science.gov (United States)

    Basra, Pukhraz; Paramo, Juan; Alexis, John

    2018-04-16

    Kaposi sarcoma is an oligoclonal HHV-8-driven vascular proliferation that was first described by a Viennese dermatologist Dr Moritz Kaposi. The disease has been seen in different clinical-epidemiological settings with a wide morphologic spectrum. We report a 52-year-old Caucasian man with HIV/AIDS and Kaposi sarcoma who presented with dyspnea and pleural effusion. He reported numerous tender subcutaneous nodules developing over the past few months on his chest, back and abdomen. An excisional biopsy of one of the nodules was performed. Touch preps revealed malignant cells in clusters. Microscopically, the neoplasm appeared undifferentiated with an epithelioid morphology, and involved the dermis and subcutaneous fat. Despite the medical history, Kaposi sarcoma was not considered foremost in the differential diagnosis. The malignant cells were positive for vimentin and negative for S100 protein, keratin AE1/3, CK7, CK20, napsin A, TTF-1 and synaptophysin. Additional stains revealed positivity for HHV-8, CD31 and D2-40, supporting the diagnosis of Kaposi sarcoma. Kaposi sarcoma has been well described with many variants that may cause diagnostic difficulty. An epithelioid variant has not been reported and consequently, may cause misinterpretation of an otherwise well-known entity that may become life threatening if appropriate treatment is not initiated in a timely manner. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  19. Concurrent Multilocular Cystic Renal Cell Carcinoma and Leiomyoma in the Same Kidney: Previously Unreported Association

    Directory of Open Access Journals (Sweden)

    Min Su Cheong

    2010-07-01

    Full Text Available We present an unusual case of concurrent occurrence of a multilocular cystic renal cell carcinoma and a leiomyoma in the same kidney of a patient with no evident clinical symptoms. A 38-year-old man was found incidentally to have a cystic right renal mass on computed tomography. Laparoscopic radical nephrectomy was performed under a preoperative diagnosis of cystic renal cell carcinoma. Histology revealed a multilocular cystic renal cell carcinoma and a leiomyoma. This is the first report of this kind of presentation.

  20. DeepPy: Pythonic deep learning

    DEFF Research Database (Denmark)

    Larsen, Anders Boesen Lindbo

    This technical report introduces DeepPy – a deep learning framework built on top of NumPy with GPU acceleration. DeepPy bridges the gap between highperformance neural networks and the ease of development from Python/NumPy. Users with a background in scientific computing in Python will quickly...... be able to understand and change the DeepPy codebase as it is mainly implemented using high-level NumPy primitives. Moreover, DeepPy supports complex network architectures by letting the user compose mathematical expressions as directed graphs. The latest version is available at http...

  1. Early severe scoliosis in a patient with atypical progressive pseudorheumatoid dysplasia (PPD): Identification of two WISP3 mutations, one previously unreported.

    Science.gov (United States)

    Montané, Lucia Sentchordi; Marín, Oliver R; Rivera-Pedroza, Carlos I; Vallespín, Elena; Del Pozo, Ángela; Heath, Karen E

    2016-06-01

    Progressive pseudorheumatoid dysplasia (PPD) is a rare autosomal recessive disorder characterized by spondyloepiphyseal dysplasia associated with pain and stiffness of multiple joints, enlargement of the interphalangeal joints, normal inflammatory parameters, and absence of extra-skeletal manifestations. Homozygous or compound heterozygous WISP3 mutations cause PPD. We report two siblings from a non-consanguineous Ecuadorian family with a late-onset spondyloepiphyseal dysplasia. Mutation screening was undertaken in the two affected siblings using a customized skeletal dysplasia next generation sequencing (NGS) panel and confirmed by Sanger sequencing. Two compound heterozygous mutations were identified in WISP3 exon 2, c.[190G>A];[197G>A] (p.[(Gly64Arg)];[(Ser66Asn)]) in the two siblings, both of which had been inherited. The p. (Gly64Arg) mutation has not been previously described whilst the p. (Ser66Asn) mutation has been reported in two PPD families. The two siblings presented with atypical PPD, as they presented during late childhood, yet the severity was different between them. The progression was particularly aggressive in the male sibling who suffered severe scoliosis by the age of 13 years. This case reaffirms the clinical heterogeneity of this disorder and the clinical utility of NGS to genetically diagnose skeletal dysplasias, enabling adequate management, monitorization, and genetic counseling. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  2. Temporary Efficacy of Pyrimethamine in Juvenile-Onset Tay-Sachs Disease Caused by 2 Unreported HEXA Mutations in the Indian Population

    Directory of Open Access Journals (Sweden)

    Anaita Udwadia-Hegde MD, DCH, MRCPCH

    2017-01-01

    Full Text Available Background: Juvenile Tay-Sachs disease is rarer than other forms of Tay-Sachs disease and is usually seen in children between the age of 2 and 10 years. Pyrimethamine as a pharmacological chaperone was used to increase β-hexosaminidase A activity in this patient. Patient: We describe a patient with Tay-Sachs disease from the Indian population, a juvenile case who presented with developmental regression starting at the age of three, initially with motor followed by language regression. She is currently incapacitated with severe behavioral issues. Conclusion: This brief communication gives an insight into the efficacy of pharmacological chaperones. It also describes two unreported mutations in hexosaminidase A gene from the Indian population. After commencing Pyrimethamine, though initial benefits with increase in levels corresponded with briefly halting the motor regression, the observed increase was only transient and not associated with discernible beneficial neurological or psychiatric effects.

  3. White blood cells identification system based on convolutional deep neural learning networks.

    Science.gov (United States)

    Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

    2017-11-16

    White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

  4. A Bottom-Up Understanding of Illegal, Unreported, and Unregulated Fishing in Lake Victoria

    Directory of Open Access Journals (Sweden)

    Joseph Luomba

    2016-10-01

    Full Text Available Illegal, unreported, and unregulated (IUU fishing is a major concern in fisheries management around the world. Several measures have been taken to address the problem. In Lake Victoria, the alleviation of IUU fishing is implemented through the Regional Plan of Action (RPOA-IUU, which restricts use of certain fishing gear, as well as prohibits fishing in closed areas and during closed seasons. Despite the long-term efforts to monitor and control what goes on in the fisheries, IUU fishing has persisted in Lake Victoria. Inspired by interactive governance theory, this paper argues that the persistence of IUU fishing could be due to different images that stakeholders have about the situation, rather than the lack of management competency. Through structured interviews with 150 fisheries stakeholders on Ijinga Island in the southeastern part of Lake Victoria, Tanzania, using paired comparison questionnaires, the study elicits stakeholders’ perspective about the severity of different locally-pertinent fishing-related activities. The results show that while fisheries stakeholder groups agree on their judgments about certain fishing gears, some differences are also apparent. For instance, fisheries managers and scientists do not always agree with fishing people about what activities cause the most damage to fisheries resources and ecosystem. Further, they tend to consider some IUU fishing-related activities less damaging than some non-IUU fishing. Such disparity creates governability challenges, pointing to the need to revisit relevant regulatory measures and to make them consistent with the knowledge and judgments of all stakeholders. Based on these findings, we discuss governing interventions that may contribute to addressing IUU fishing in Lake Victoria and elsewhere.

  5. Mutation Spectrum of the ABCA4 Gene in a Greek Cohort with Stargardt Disease: Identification of Novel Mutations and Evidence of Three Prevalent Mutated Alleles

    Directory of Open Access Journals (Sweden)

    Kamakari Smaragda

    2018-01-01

    Full Text Available Aim. To evaluate the frequency and pattern of disease-associated mutations of ABCA4 gene among Greek patients with presumed Stargardt disease (STGD1. Materials and Methods. A total of 59 patients were analyzed for ABCA4 mutations using the ABCR400 microarray and PCR-based sequencing of all coding exons and flanking intronic regions. MLPA analysis as well as sequencing of two regions in introns 30 and 36 reported earlier to harbor deep intronic disease-associated variants was used in 4 selected cases. Results. An overall detection rate of at least one mutant allele was achieved in 52 of the 59 patients (88.1%. Direct sequencing improved significantly the complete characterization rate, that is, identification of two mutations compared to the microarray analysis (93.1% versus 50%. In total, 40 distinct potentially disease-causing variants of the ABCA4 gene were detected, including six previously unreported potentially pathogenic variants. Among the disease-causing variants, in this cohort, the most frequent was c.5714+5G>A representing 16.1%, while p.Gly1961Glu and p.Leu541Pro represented 15.2% and 8.5%, respectively. Conclusions. By using a combination of methods, we completely molecularly diagnosed 48 of the 59 patients studied. In addition, we identified six previously unreported, potentially pathogenic ABCA4 mutations.

  6. Extracting Databases from Dark Data with DeepDive.

    Science.gov (United States)

    Zhang, Ce; Shin, Jaeho; Ré, Christopher; Cafarella, Michael; Niu, Feng

    2016-01-01

    DeepDive is a system for extracting relational databases from dark data : the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data - scientific papers, Web classified ads, customer service notes, and so on - were instead in a relational database, it would give analysts a massive and valuable new set of "big data." DeepDive is distinctive when compared to previous information extraction systems in its ability to obtain very high precision and recall at reasonable engineering cost; in a number of applications, we have used DeepDive to create databases with accuracy that meets that of human annotators. To date we have successfully deployed DeepDive to create data-centric applications for insurance, materials science, genomics, paleontologists, law enforcement, and others. The data unlocked by DeepDive represents a massive opportunity for industry, government, and scientific researchers. DeepDive is enabled by an unusual design that combines large-scale probabilistic inference with a novel developer interaction cycle. This design is enabled by several core innovations around probabilistic training and inference.

  7. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

    Science.gov (United States)

    Burt, Jeremy R; Torosdagli, Neslisah; Khosravan, Naji; RaviPrakash, Harish; Mortazi, Aliasghar; Tissavirasingham, Fiona; Hussein, Sarfaraz; Bagci, Ulas

    2018-04-10

    Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.

  8. Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

    Science.gov (United States)

    Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

    2018-03-01

    We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

  9. COST-EFFECTIVE APPROACH TO ESTIMATE UNREPORTED DATA: REBUILDING HISTORY OF LIFT-NET FISHING IN KWANDANG WATERS

    Directory of Open Access Journals (Sweden)

    Andhika Prima Prasetyo

    2014-12-01

    Full Text Available This paper aims to develop cost-effective approach regarding the estimation unreported annual catch data of lift-net fishery using Google Earth imagery. Lift net fishery is one of the main fishing activities of coastal community in Kwandang Bay, it has been faced problem of uncertain fisheries status due to limited recorded data. Combination of a Monte Carlo procedure was applied by involving couple of assumptions on parameters such as estimate growth rate of the total number of lift-net per years (10%, day at sea per unit per month (21 days and operated lift-net per month (50% and 80%. The results showed that 101 units of lift-nets were found around Kwandang waters based on Google Earth imagery recorded in October, 7th 2010, and this were used as a benchmark of calculation. This prediction was 28 units higher than official data from North Gorontalo District of Marine Affairs and Fisheries Services (DKP Gorontalo Utara. Compared with capture fisheries statistics issued by Kwandang CFP, the estimated lift-net catches based on two-scenarios represent additional catches of 46 % and 86 %. These results suggested and could be used as a correction index to improve the reliability of Kwandang District officially reported fisheries statistics as a baseline to develop a local common fisheries policy.

  10. Deep inelastic scattering near the Coulomb barrier

    International Nuclear Information System (INIS)

    Gehring, J.; Back, B.; Chan, K.

    1995-01-01

    Deep inelastic scattering was recently observed in heavy ion reactions at incident energies near and below the Coulomb barrier. Traditional models of this process are based on frictional forces and are designed to predict the features of deep inelastic processes at energies above the barrier. They cannot be applied at energies below the barrier where the nuclear overlap is small and friction is negligible. The presence of deep inelastic scattering at these energies requires a different explanation. The first observation of deep inelastic scattering near the barrier was in the systems 124,112 Sn + 58,64 Ni by Wolfs et al. We previously extended these measurements to the system 136 Xe + 64 Ni and currently measured the system 124 Xe + 58 Ni. We obtained better statistics, better mass and energy resolution, and more complete angular coverage in the Xe + Ni measurements. The cross sections and angular distributions are similar in all of the Sn + Ni and Xe + Ni systems. The data are currently being analyzed and compared with new theoretical calculations. They will be part of the thesis of J. Gehring

  11. Global diversity and biogeography of deep-sea pelagic prokaryotes

    KAUST Repository

    Salazar, Guillem

    2015-08-07

    The deep-sea is the largest biome of the biosphere, and contains more than half of the whole ocean\\'s microbes. Uncovering their general patterns of diversity and community structure at a global scale remains a great challenge, as only fragmentary information of deep-sea microbial diversity exists based on regional-scale studies. Here we report the first globally comprehensive survey of the prokaryotic communities inhabiting the bathypelagic ocean using high-throughput sequencing of the 16S rRNA gene. This work identifies the dominant prokaryotes in the pelagic deep ocean and reveals that 50% of the operational taxonomic units (OTUs) belong to previously unknown prokaryotic taxa, most of which are rare and appear in just a few samples. We show that whereas the local richness of communities is comparable to that observed in previous regional studies, the global pool of prokaryotic taxa detected is modest (∼3600 OTUs), as a high proportion of OTUs are shared among samples. The water masses appear to act as clear drivers of the geographical distribution of both particle-attached and free-living prokaryotes. In addition, we show that the deep-oceanic basins in which the bathypelagic realm is divided contain different particle-attached (but not free-living) microbial communities. The combination of the aging of the water masses and a lack of complete dispersal are identified as the main drivers for this biogeographical pattern. All together, we identify the potential of the deep ocean as a reservoir of still unknown biological diversity with a higher degree of spatial complexity than hitherto considered.

  12. Global diversity and biogeography of deep-sea pelagic prokaryotes

    KAUST Repository

    Salazar, Guillem; Cornejo-Castillo, Francisco M.; Bení tez-Barrios, Veró nica; Fraile-Nuez, Eugenio; Á lvarez-Salgado, X. Antó n; Duarte, Carlos M.; Gasol, Josep M.; Acinas, Silvia G.

    2015-01-01

    The deep-sea is the largest biome of the biosphere, and contains more than half of the whole ocean's microbes. Uncovering their general patterns of diversity and community structure at a global scale remains a great challenge, as only fragmentary information of deep-sea microbial diversity exists based on regional-scale studies. Here we report the first globally comprehensive survey of the prokaryotic communities inhabiting the bathypelagic ocean using high-throughput sequencing of the 16S rRNA gene. This work identifies the dominant prokaryotes in the pelagic deep ocean and reveals that 50% of the operational taxonomic units (OTUs) belong to previously unknown prokaryotic taxa, most of which are rare and appear in just a few samples. We show that whereas the local richness of communities is comparable to that observed in previous regional studies, the global pool of prokaryotic taxa detected is modest (∼3600 OTUs), as a high proportion of OTUs are shared among samples. The water masses appear to act as clear drivers of the geographical distribution of both particle-attached and free-living prokaryotes. In addition, we show that the deep-oceanic basins in which the bathypelagic realm is divided contain different particle-attached (but not free-living) microbial communities. The combination of the aging of the water masses and a lack of complete dispersal are identified as the main drivers for this biogeographical pattern. All together, we identify the potential of the deep ocean as a reservoir of still unknown biological diversity with a higher degree of spatial complexity than hitherto considered.

  13. Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition

    OpenAIRE

    Li, Xiangang; Wu, Xihong

    2014-01-01

    Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed an...

  14. DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs.

    Science.gov (United States)

    Park, Bo-Yong; Lee, Mi Ji; Lee, Seung-Hak; Cha, Jihoon; Chung, Chin-Sang; Kim, Sung Tae; Park, Hyunjin

    2018-01-01

    Migraineurs show an increased load of white matter hyperintensities (WMHs) and more rapid deep WMH progression. Previous methods for WMH segmentation have limited efficacy to detect small deep WMHs. We developed a new fully automated detection pipeline, DEWS (DEep White matter hyperintensity Segmentation framework), for small and superficially-located deep WMHs. A total of 148 non-elderly subjects with migraine were included in this study. The pipeline consists of three components: 1) white matter (WM) extraction, 2) WMH detection, and 3) false positive reduction. In WM extraction, we adjusted the WM mask to re-assign misclassified WMHs back to WM using many sequential low-level image processing steps. In WMH detection, the potential WMH clusters were detected using an intensity based threshold and region growing approach. For false positive reduction, the detected WMH clusters were classified into final WMHs and non-WMHs using the random forest (RF) classifier. Size, texture, and multi-scale deep features were used to train the RF classifier. DEWS successfully detected small deep WMHs with a high positive predictive value (PPV) of 0.98 and true positive rate (TPR) of 0.70 in the training and test sets. Similar performance of PPV (0.96) and TPR (0.68) was attained in the validation set. DEWS showed a superior performance in comparison with other methods. Our proposed pipeline is freely available online to help the research community in quantifying deep WMHs in non-elderly adults.

  15. Effectiveness of a second deep TMS in depression: a brief report.

    Science.gov (United States)

    Rosenberg, O; Isserles, M; Levkovitz, Y; Kotler, M; Zangen, A; Dannon, P N

    2011-06-01

    Deep transcranial magnetic stimulation (DTMS) is an emerging and promising treatment for major depression. In our study, we explored the effectiveness of a second antidepressant course of deep TMS in major depression. We enrolled eight patients who had previously responded well to DTMS but relapsed within 1 year in order to evaluate whether a second course of DTMS would still be effective. Eight depressive patients who relapsed after a previous successful deep TMS course expressed their wish to be treated again. Upon their request, they were recruited and treated with 20 daily sessions of DTMS at 20 Hz using the Brainsway's H1 coil. The Hamilton depression rating scale (HDRS), Hamilton anxiety rating scale (HARS) and the Beck depression inventory (BDI) were used weekly to evaluate the response to treatment. Similar to the results obtained in the first course of treatment, the second course of treatment (after relapse) induced significant reductions in HDRS, HARS and BDI scores, compared to the ratings measured prior to treatment. The magnitude of response in the second course was smaller relative to that obtained in the first course of treatment. Our results suggest that depressive patients who previously responded well to deep TMS treatment are likely to respond again. However, the slight reduction in the magnitude of the response in the second treatment raises the question of whether tolerance or resistance to this treatment may eventually develop. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. Deep Echo State Network (DeepESN): A Brief Survey

    OpenAIRE

    Gallicchio, Claudio; Micheli, Alessio

    2017-01-01

    The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced deep Echo State Network (deepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of deepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions ...

  17. First biological measurements of deep-sea corals from the Red Sea.

    Science.gov (United States)

    Roder, C; Berumen, M L; Bouwmeester, J; Papathanassiou, E; Al-Suwailem, A; Voolstra, C R

    2013-10-03

    It is usually assumed that metabolic constraints restrict deep-sea corals to cold-water habitats, with 'deep-sea' and 'cold-water' corals often used as synonymous. Here we report on the first measurements of biological characters of deep-sea corals from the central Red Sea, where they occur at temperatures exceeding 20°C in highly oligotrophic and oxygen-limited waters. Low respiration rates, low calcification rates, and minimized tissue cover indicate that a reduced metabolism is one of the key adaptations to prevailing environmental conditions. We investigated four sites and encountered six species of which at least two appear to be undescribed. One species is previously reported from the Red Sea but occurs in deep cold waters outside the Red Sea raising interesting questions about presumed environmental constraints for other deep-sea corals. Our findings suggest that the present understanding of deep-sea coral persistence and resilience needs to be revisited.

  18. First biological measurements of deep-sea corals from the Red Sea.

    KAUST Repository

    Roder, Cornelia

    2013-10-03

    It is usually assumed that metabolic constraints restrict deep-sea corals to cold-water habitats, with \\'deep-sea\\' and \\'cold-water\\' corals often used as synonymous. Here we report on the first measurements of biological characters of deep-sea corals from the central Red Sea, where they occur at temperatures exceeding 20°C in highly oligotrophic and oxygen-limited waters. Low respiration rates, low calcification rates, and minimized tissue cover indicate that a reduced metabolism is one of the key adaptations to prevailing environmental conditions. We investigated four sites and encountered six species of which at least two appear to be undescribed. One species is previously reported from the Red Sea but occurs in deep cold waters outside the Red Sea raising interesting questions about presumed environmental constraints for other deep-sea corals. Our findings suggest that the present understanding of deep-sea coral persistence and resilience needs to be revisited.

  19. PC operated acoustic transient spectroscopy of deep levels in MIS structures

    International Nuclear Information System (INIS)

    Bury, P.; Jamnicky, I.

    1996-01-01

    A new version of acoustic deep-level transient spectroscopy is presented to study the traps at the insulator-semiconductor interface. The acoustic deep-level transient spectroscopy uses an acoustoelectric response signal produced by the MIS structure interface when a longitudinal acoustic wave propagates through a structure. The acoustoelectric response signal is extremely sensitive to external conditions of the structure and reflects any changes in the charge distribution, connected also with charged traps. In comparison with previous version of acoustic deep-level transient spectroscopy that closely coincides with the principle of the original deep-level transient spectroscopy technique, the present technique is based on the computer-evaluated isothermal transients and represents an improved, more efficient and time saving technique. Many tests on the software used for calculation as well as on experimental setup have been performed. The improved acoustic deep-level transient spectroscopy method has been applied for the Si(p) MIS structures. The deep-level parameters as activation energy and capture cross-section have been determined. (authors)

  20. Deep inelastic scattering near the Coulomb barrier

    Energy Technology Data Exchange (ETDEWEB)

    Gehring, J.; Back, B.; Chan, K. [and others

    1995-08-01

    Deep inelastic scattering was recently observed in heavy ion reactions at incident energies near and below the Coulomb barrier. Traditional models of this process are based on frictional forces and are designed to predict the features of deep inelastic processes at energies above the barrier. They cannot be applied at energies below the barrier where the nuclear overlap is small and friction is negligible. The presence of deep inelastic scattering at these energies requires a different explanation. The first observation of deep inelastic scattering near the barrier was in the systems {sup 124,112}Sn + {sup 58,64}Ni by Wolfs et al. We previously extended these measurements to the system {sup 136}Xe + {sup 64}Ni and currently measured the system {sup 124}Xe + {sup 58}Ni. We obtained better statistics, better mass and energy resolution, and more complete angular coverage in the Xe + Ni measurements. The cross sections and angular distributions are similar in all of the Sn + Ni and Xe + Ni systems. The data are currently being analyzed and compared with new theoretical calculations. They will be part of the thesis of J. Gehring.

  1. Radio-active waste disposal and deep-sea biology

    International Nuclear Information System (INIS)

    Rice, A.L.

    1978-01-01

    The deep-sea has been widely thought of as a remote, sparsely populated, and biologically inactive environment, well suited to receive the noxious products of nuclear fission processes. Much of what is known of abyssal biology tends to support this view, but there are a few disquieting contra-indications. The realisation, in recent years, that many animal groups show a previously unsuspected high species diversity in the deep-sea emphasized the paucity of our knowledge of this environment. More dramatically, the discovery of a large, active, and highly mobile abysso-bentho-pelagic fauna changed the whole concept of abyssal life. Finally, while there is little evidence for the existence of vertical migration patterns linking the deep-sea bottom communities with those of the overlying water layers, there are similarly too few negative results for the possibility of such transport mechanisms to be dismissed. In summary, biological knowledge of the abyss is insufficient to answer the questions raised in connection with deep-sea dumping, but in the absence of adequate answers it might be dangerous to ignore the questions

  2. Deep learning in medical imaging: General overview

    Energy Technology Data Exchange (ETDEWEB)

    Lee, June Goo; Jun, Sang Hoon; Cho, Young Won; Lee, Hyun Na; KIm, Guk Bae; Seo, Joon Beom; Kim, Nam Kug [University of Ulsan College of Medicine, Asan Medical Center, Seoul (Korea, Republic of)

    2017-08-01

    The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and health care, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

  3. Deep learning in jet reconstruction at CMS

    CERN Document Server

    Stoye, Markus

    2017-01-01

    Deep learning has led to several breakthroughs outside the field of high energy physics, yet in jet reconstruction for the CMS experiment at the CERN LHC it has not been used so far. This report shows results of applying deep learning strategies to jet reconstruction at the stage of identifying the original parton association of the jet (jet tagging), which is crucial for physics analyses at the LHC experiments. We introduce a custom deep neural network architecture for jet tagging. We compare the performance of this novel method with the other established approaches at CMS and show that the proposed strategy provides a significant improvement. The strategy provides the first multi-class classifier, instead of the few binary classifiers that previously were used, and thus yields more information and in a more convenient way. The performance results obtained with simulation imply a significant improvement for a large number of important physics analysis at the CMS experiment.

  4. Deep learning in medical imaging: General overview

    International Nuclear Information System (INIS)

    Lee, June Goo; Jun, Sang Hoon; Cho, Young Won; Lee, Hyun Na; KIm, Guk Bae; Seo, Joon Beom; Kim, Nam Kug

    2017-01-01

    The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and health care, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging

  5. Deep Learning in Medical Imaging: General Overview

    Science.gov (United States)

    Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae

    2017-01-01

    The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. PMID:28670152

  6. Deep Learning in Medical Imaging: General Overview.

    Science.gov (United States)

    Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae; Seo, Joon Beom; Kim, Namkug

    2017-01-01

    The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

  7. Why & When Deep Learning Works: Looking Inside Deep Learnings

    OpenAIRE

    Ronen, Ronny

    2017-01-01

    The Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) has been heavily supporting Machine Learning and Deep Learning research from its foundation in 2012. We have asked six leading ICRI-CI Deep Learning researchers to address the challenge of "Why & When Deep Learning works", with the goal of looking inside Deep Learning, providing insights on how deep networks function, and uncovering key observations on their expressiveness, limitations, and potential. The outp...

  8. DAPs: Deep Action Proposals for Action Understanding

    KAUST Repository

    Escorcia, Victor

    2016-09-17

    Object proposals have contributed significantly to recent advances in object understanding in images. Inspired by the success of this approach, we introduce Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos. We show how to take advantage of the vast capacity of deep learning models and memory cells to retrieve from untrimmed videos temporal segments, which are likely to contain actions. A comprehensive evaluation indicates that our approach outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize, i.e. to retrieve good quality temporal proposals of actions unseen in training.

  9. Deep-water polychaetes (Annelida from the southeastern Gulf of California, Mexico

    Directory of Open Access Journals (Sweden)

    Nuria Méndez

    2006-09-01

    Full Text Available Polychaetes inhabiting deep-sea soft bottoms from the southeastern Gulf of California were collected during four oceanographic cruises during 2000 and 2001. Sampling of benthic organisms was performed with a benthic sledge to collect epifauna and a Karling dredge for epifauna and infauna, in a depth range from 732 to 2 250 m. A list of the polychaetes that were collected and their distribution are presented here. A total of 73 species (distributed among 33 families were identified. Moreover, 11 species were identified only to genus level and 20 species only to family level. With the exception of Ancistrosyllis hartmanae and Melinnampharete eoa, all identified species have been previously reported in soft bottoms of the Gulf of California or in adjacent areas. Additional previously unreported information is provided herein regarding depth ranges, geographical distribution, morphology and tubes inhabited by the organisms. The morphology of the ampharetids Amage sp. and Samytha sp. does not coincide with that of other species in these genera reported for the Gulf of California, which suggests that they are probably undescribed species. Rev. Biol. Trop. 54 (3: 773-785. Epub 2006 Sept. 29.Se recolectaron anélidos poliquetos de fondos profundos del SE del golfo de California durante cuatro campañas oceanográficas entre 2000 y 2001. El muestreo de organismos bentónicos se llevó a cabo mediante una draga de arrastre bentónica para recolectar epifauna y una draga tipo Karling para epifauna y endofauna, en un intervalo de profundidad de 732 a 2 250 m. Se presenta un listado de poliquetos con su distribución dentro del área de estudio. En total se identificaron 73 especies (distribuidas en 33 familias. Además, 11 especies fueron identificadas a nivel genérico y 20 a nivel de familia. Con excepción de Ancistrosyllis hartmanae y Melinnampharete eoa, todas las especies habían sido registradas en fondos blandos del golfo de California o zonas

  10. The DEEP-South: Scheduling and Data Reduction Software System

    Science.gov (United States)

    Yim, Hong-Suh; Kim, Myung-Jin; Bae, Youngho; Moon, Hong-Kyu; Choi, Young-Jun; Roh, Dong-Goo; the DEEP-South Team

    2015-08-01

    The DEep Ecliptic Patrol of the Southern sky (DEEP-South), started in October 2012, is currently in test runs with the first Korea Microlensing Telescope Network (KMTNet) 1.6 m wide-field telescope located at CTIO in Chile. While the primary objective for the DEEP-South is physical characterization of small bodies in the Solar System, it is expected to discover a large number of such bodies, many of them previously unknown.An automatic observation planning and data reduction software subsystem called "The DEEP-South Scheduling and Data reduction System" (the DEEP-South SDS) is currently being designed and implemented for observation planning, data reduction and analysis of huge amount of data with minimum human interaction. The DEEP-South SDS consists of three software subsystems: the DEEP-South Scheduling System (DSS), the Local Data Reduction System (LDR), and the Main Data Reduction System (MDR). The DSS manages observation targets, makes decision on target priority and observation methods, schedules nightly observations, and archive data using the Database Management System (DBMS). The LDR is designed to detect moving objects from CCD images, while the MDR conducts photometry and reconstructs lightcurves. Based on analysis made at the LDR and the MDR, the DSS schedules follow-up observation to be conducted at other KMTNet stations. In the end of 2015, we expect the DEEP-South SDS to achieve a stable operation. We also have a plan to improve the SDS to accomplish finely tuned observation strategy and more efficient data reduction in 2016.

  11. Potential Osteoporosis Recovery by Deep Sea Water through Bone Regeneration in SAMP8 Mice

    Directory of Open Access Journals (Sweden)

    Hen-Yu Liu

    2013-01-01

    Full Text Available The aim of this study is to examine the therapeutic potential of deep sea water (DSW on osteoporosis. Previously, we have established the ovariectomized senescence-accelerated mice (OVX-SAMP8 and demonstrated strong recovery of osteoporosis by stem cell and platelet-rich plasma (PRP. Deep sea water at hardness (HD 1000 showed significant increase in proliferation of osteoblastic cell (MC3T3 by MTT assay. For in vivo animal study, bone mineral density (BMD was strongly enhanced followed by the significantly increased trabecular numbers through micro-CT examination after a 4-month deep sea water treatment, and biochemistry analysis showed that serum alkaline phosphatase (ALP activity was decreased. For stage-specific osteogenesis, bone marrow-derived stromal cells (BMSCs were harvested and examined. Deep sea water-treated BMSCs showed stronger osteogenic differentiation such as BMP2, RUNX2, OPN, and OCN, and enhanced colony forming abilities, compared to the control group. Interestingly, most untreated OVX-SAMP8 mice died around 10 months; however, approximately 57% of DSW-treated groups lived up to 16.6 months, a life expectancy similar to the previously reported life expectancy for SAMR1 24 months. The results demonstrated the regenerative potentials of deep sea water on osteogenesis, showing that deep sea water could potentially be applied in osteoporosis therapy as a complementary and alternative medicine (CAM.

  12. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

    Science.gov (United States)

    Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

    2017-08-01

    Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

  13. Drying/rewetting cycles mobilize old C from deep soils from a California annual grassland

    OpenAIRE

    Schimel, JP; Wetterstedt, JAM; Holden, PA; Trumbore, SE

    2011-01-01

    We measured the 14 C and 13 C signatures of CO 2 respired from surface and deep soils released through multiple dry/rewetting cycles in laboratory incubations. The C respired from surface soils included components fixed before and after the 1960s. However, that respired from deep soils was derived from organic matter with a mean turnover time estimated in the range of 650-850 years. This reinforces previous research suggesting that a substantial amount of deep soil C is chemically labile b...

  14. Angiographic findings of collateral vessels in cervicofacial vascular lesions with previously ligated carotid artery

    International Nuclear Information System (INIS)

    Na, Dong Gyu; Han, Moon Hee; Chang, Kee Hyun; Han, Gi Seok; Yeon, Kung Mo

    1995-01-01

    The purpose of this study is to describe the angiographic findings of collateral vessels in cervicofacial vascular lesions with previously ligated carotid arteries and to evaluate the extent of angiographic assessment needed before embolization. We retrospectively reviewed 10 cervicofacial vascular lesions with previously ligated carotid artery, which were 6 cases of arteriovenous malformation, 2 cases of carotid cavernous fistula, 1 case of hemangioma and 1 case of arteriovenous malformation with carotid cavernous fistula. The previously ligated arteries are proximal external carotid artery (n = 5), branches of external carotid artery (n = 2) and common carotid artery (n = 3). Common carotid artery or internal carotid artery (n = 9), vertebral artery (n = 5), ipsilateral external carotid artery (n = 4), contralateral external carotid artery (n = 5), costocervical trunk (n = 2), thyrocervical trunk (n = 2) were assessed by conventional angiography. Angiography of both carotid and vertebral arteries was performed in 5 cases. The collateral vascular channels were inferolateral trunk of internal carotid artery (n = 8), vertebral artery (n = 5), contralateral external carotid artery (n = 5), ipsilateral external carotid artery (n = 4), deep cervical artery (n = 2) and ascending cervical artery (n = 1). Embolization were performed in 9 cases with operative cannulation (n = 4), embolization via collateral branches of ipsilateral external carotid artery (n = 1), embolization via collateral branches of contralateral external carotid artery (n = 3) and balloon occlusion via direct puncture (n = 1). The collateral channels in cervicofacial vascular lesions with previously ligated carotid artery were inferolateral trunk of internal carotid artery, contralateral or ipsilateral external carotid artery, vertebral artery, deep cervical artery and ascending cervical artery on angiography. Complete angiographic assessment of possible collateral channels is mandatory for the

  15. Lateral cervical cleft: a previously unreported anomaly resulting from incomplete disappearance of the second pharyngeal (branchial) cleft.

    Science.gov (United States)

    Gürsoy, M H; Gedikoğlu, G; Tanyel, F C

    1999-03-01

    The authors present a 2-year-old boy with a skin defect located in the right lateral side of the neck. They suggest the defect is a partial failure of disappearance of the second pharyngeal (branchial) cleft and propose a name of lateral cervical cleft.

  16. Karnyothrips flavipes, a previously unreported predatory thrips of the coffee berry borer: DNA-based gut content analysis

    Science.gov (United States)

    A new predator of the coffee berry borer, Hypothenemus hampei, was found in the coffee growing area of Kisii in Western Kenya. Field observations, laboratory trials and gut content analysis using molecular tools have confirmed the role of the predatory thrips Karnyothrips flavipes Jones (Phlaeothrip...

  17. A Case of Acute Myeloid Leukemia with a Previously Unreported Translocation (14; 15 (q32; q13

    Directory of Open Access Journals (Sweden)

    Mohamad Khawandanah

    2014-01-01

    Full Text Available Background. We hereby describe what we believe to be the first reported case of t (14; 15 (q32; q13 associated with acute myeloid leukemia (AML. Methods. PubMed, Embase, and OVID search engines were used to review the related literature and similar published cases. Case. A47-year-old female presented in December 2011 with AML (acute myelomonocytic leukemia with normal cytogenetics; molecular testing revealed FLT-3 internal tandem duplication (ITD mutation, while no mutations involving FLT3 D385/I836, NPM1 exon 12, or KIT exons 8 and 17 were detected. She was induced with 7 + 3 (cytarabine + idarubicin and achieved complete remission after a second induction with high-dose cytarabine (HiDAC followed by uneventful consolidation. She presented 19 months after diagnosis with relapsed disease. Of note, at relapse cytogenetic analysis revealed t (14; 15 (q32; q13, while FLT-3 analysis showed a codon D835 mutation (no ITD mutation was detected. She proved refractory to the initial clofarabine-based regimen, so FLAG-idarubicin then was used. She continued to have persistent disease, and she was discharged on best supportive care. Conclusion. Based on this single case of AML with t (14; 15 (q32; q13, this newly reported translocation may be associated with refractory disease.

  18. Part-based deep representation for product tagging and search

    Science.gov (United States)

    Chen, Keqing

    2017-06-01

    Despite previous studies, tagging and indexing the product images remain challenging due to the large inner-class variation of the products. In the traditional methods, the quantized hand-crafted features such as SIFTs are extracted as the representation of the product images, which are not discriminative enough to handle the inner-class variation. For discriminative image representation, this paper firstly presents a novel deep convolutional neural networks (DCNNs) architect true pre-trained on a large-scale general image dataset. Compared to the traditional features, our DCNNs representation is of more discriminative power with fewer dimensions. Moreover, we incorporate the part-based model into the framework to overcome the negative effect of bad alignment and cluttered background and hence the descriptive ability of the deep representation is further enhanced. Finally, we collect and contribute a well-labeled shoe image database, i.e., the TBShoes, on which we apply the part-based deep representation for product image tagging and search, respectively. The experimental results highlight the advantages of the proposed part-based deep representation.

  19. Predicting healthcare trajectories from medical records: A deep learning approach.

    Science.gov (United States)

    Pham, Trang; Tran, Truyen; Phung, Dinh; Venkatesh, Svetha

    2017-05-01

    Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Deep learning classification in asteroseismology using an improved neural network

    DEFF Research Database (Denmark)

    Hon, Marc; Stello, Dennis; Yu, Jie

    2018-01-01

    Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic...... frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower...

  1. Deep iCrawl: An Intelligent Vision-Based Deep Web Crawler

    OpenAIRE

    R.Anita; V.Ganga Bharani; N.Nityanandam; Pradeep Kumar Sahoo

    2011-01-01

    The explosive growth of World Wide Web has posed a challenging problem in extracting relevant data. Traditional web crawlers focus only on the surface web while the deep web keeps expanding behind the scene. Deep web pages are created dynamically as a result of queries posed to specific web databases. The structure of the deep web pages makes it impossible for traditional web crawlers to access deep web contents. This paper, Deep iCrawl, gives a novel and vision-based app...

  2. A new macrofaunal limit in the deep biosphere revealed by extreme burrow depths in ancient sediments.

    Science.gov (United States)

    Cobain, S L; Hodgson, D M; Peakall, J; Wignall, P B; Cobain, M R D

    2018-01-10

    Macrofauna is known to inhabit the top few 10s cm of marine sediments, with rare burrows up to two metres below the seabed. Here, we provide evidence from deep-water Permian strata for a previously unrecognised habitat up to at least 8 metres below the sediment-water interface. Infaunal organisms exploited networks of forcibly injected sand below the seabed, forming living traces and reworking sediment. This is the first record that shows sediment injections are responsible for hosting macrofaunal life metres below the contemporaneous seabed. In addition, given the widespread occurrence of thick sandy successions that accumulate in deep-water settings, macrofauna living in the deep biosphere are likely much more prevalent than considered previously. These findings should influence future sampling strategies to better constrain the depth range of infaunal animals living in modern deep-sea sands. One Sentence Summary: The living depth of infaunal macrofauna is shown to reach at least 8 metres in new habitats associated with sand injections.

  3. 30 CFR 203.31 - If I have a qualified phase 2 or qualified phase 3 ultra-deep well, what royalty relief would...

    Science.gov (United States)

    2010-07-01

    ... water less than 400 meters deep (see § 203.30(a)), has no existing deep or ultra-deep wells and that the... depths partly or entirely less than 200 meters and has not previously produced from a deep well (§ 203.30... which is 16,000 feet TVD SS and your lease is located in water 100 meters deep. Then in 2008, you drill...

  4. Deep learning for plasma tomography using the bolometer system at JET

    Energy Technology Data Exchange (ETDEWEB)

    Matos, Francisco A. [Instituto Superior Técnico (IST), University of Lisbon (Portugal); Ferreira, Diogo R., E-mail: diogo.ferreira@tecnico.ulisboa.pt [Instituto Superior Técnico (IST), University of Lisbon (Portugal); Carvalho, Pedro J. [Instituto de Plasmas e Fusão Nuclear (IPFN), IST, University of Lisbon (Portugal)

    2017-01-15

    Highlights: • Plasma tomography is able to reconstruct the plasma profile from radiation measurements along several lines of sight. • The reconstruction can be performed with neural networks, but previous work focused on learning a parametric model. • Deep learning can be used to reconstruct the full 2D plasma profile with the same resolution as existing tomograms. • We introduce a deep neural network to generate an image from 1D projection data based on a series of up-convolutions. • After training on JET data, the network provides accurate reconstructions with an average pixel error as low as 2%. - Abstract: Deep learning is having a profound impact in many fields, especially those that involve some form of image processing. Deep neural networks excel in turning an input image into a set of high-level features. On the other hand, tomography deals with the inverse problem of recreating an image from a number of projections. In plasma diagnostics, tomography aims at reconstructing the cross-section of the plasma from radiation measurements. This reconstruction can be computed with neural networks. However, previous attempts have focused on learning a parametric model of the plasma profile. In this work, we use a deep neural network to produce a full, pixel-by-pixel reconstruction of the plasma profile. For this purpose, we use the overview bolometer system at JET, and we introduce an up-convolutional network that has been trained and tested on a large set of sample tomograms. We show that this network is able to reproduce existing reconstructions with a high level of accuracy, as measured by several metrics.

  5. Deep learning for plasma tomography using the bolometer system at JET

    International Nuclear Information System (INIS)

    Matos, Francisco A.; Ferreira, Diogo R.; Carvalho, Pedro J.

    2017-01-01

    Highlights: • Plasma tomography is able to reconstruct the plasma profile from radiation measurements along several lines of sight. • The reconstruction can be performed with neural networks, but previous work focused on learning a parametric model. • Deep learning can be used to reconstruct the full 2D plasma profile with the same resolution as existing tomograms. • We introduce a deep neural network to generate an image from 1D projection data based on a series of up-convolutions. • After training on JET data, the network provides accurate reconstructions with an average pixel error as low as 2%. - Abstract: Deep learning is having a profound impact in many fields, especially those that involve some form of image processing. Deep neural networks excel in turning an input image into a set of high-level features. On the other hand, tomography deals with the inverse problem of recreating an image from a number of projections. In plasma diagnostics, tomography aims at reconstructing the cross-section of the plasma from radiation measurements. This reconstruction can be computed with neural networks. However, previous attempts have focused on learning a parametric model of the plasma profile. In this work, we use a deep neural network to produce a full, pixel-by-pixel reconstruction of the plasma profile. For this purpose, we use the overview bolometer system at JET, and we introduce an up-convolutional network that has been trained and tested on a large set of sample tomograms. We show that this network is able to reproduce existing reconstructions with a high level of accuracy, as measured by several metrics.

  6. Transcriptome sequences resolve deep relationships of the grape family.

    Science.gov (United States)

    Wen, Jun; Xiong, Zhiqiang; Nie, Ze-Long; Mao, Likai; Zhu, Yabing; Kan, Xian-Zhao; Ickert-Bond, Stefanie M; Gerrath, Jean; Zimmer, Elizabeth A; Fang, Xiao-Dong

    2013-01-01

    Previous phylogenetic studies of the grape family (Vitaceae) yielded poorly resolved deep relationships, thus impeding our understanding of the evolution of the family. Next-generation sequencing now offers access to protein coding sequences very easily, quickly and cost-effectively. To improve upon earlier work, we extracted 417 orthologous single-copy nuclear genes from the transcriptomes of 15 species of the Vitaceae, covering its phylogenetic diversity. The resulting transcriptome phylogeny provides robust support for the deep relationships, showing the phylogenetic utility of transcriptome data for plants over a time scale at least since the mid-Cretaceous. The pros and cons of transcriptome data for phylogenetic inference in plants are also evaluated.

  7. Deep-inelastic scattering in 124,136Xe+58,64Ni at energies near the Coulomb barrier

    International Nuclear Information System (INIS)

    Gehring, J.; Back, B.B.; Chan, K.C.; Freer, M.; Henderson, D.; Jiang, C.L.; Rehm, K.E.; Schiffer, J.P.; Wolanski, M.; Wuosmaa, A.H.; Gehring, J.; Wolanski, M.

    1997-01-01

    Cross sections, angular distributions, and mass distributions have been measured for deep-inelastic scattering in 124 Xe+ 58 Ni and 136 Xe+ 64 Ni at laboratory energies in the vicinity of the Coulomb barrier. The mass distributions show distinct components due to deep-inelastic and fissionlike processes. The strength of deep-inelastic scattering is similar in the two systems measured and comparable to previous measurements in 58 Ni+ 112,124 Sn. copyright 1997 The American Physical Society

  8. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

    Science.gov (United States)

    Abràmoff, Michael David; Lou, Yiyue; Erginay, Ali; Clarida, Warren; Amelon, Ryan; Folk, James C; Niemeijer, Meindert

    2016-10-01

    To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists. We used the previously reported consensus reference standard of referable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/or macular edema (ME). Neither Messidor-2 images, nor the three retinal specialists setting the Messidor-2 reference standard were used for training IDx-DR version X2.1. Sensitivity, specificity, negative predictive value, area under the curve (AUC), and their confidence intervals (CIs) were calculated. Sensitivity was 96.8% (95% CI: 93.3%-98.8%), specificity was 87.0% (95% CI: 84.2%-89.4%), with 6/874 false negatives, resulting in a negative predictive value of 99.0% (95% CI: 97.8%-99.6%). No cases of severe NPDR, PDR, or ME were missed. The AUC was 0.980 (95% CI: 0.968-0.992). Sensitivity was not statistically different from published IDP sensitivity, which had a CI of 94.4% to 99.3%, but specificity was significantly better than the published IDP specificity CI of 55.7% to 63.0%. A deep-learning enhanced algorithm for the automated detection of DR, achieves significantly better performance than a previously reported, otherwise essentially identical, algorithm that does not employ deep learning. Deep learning enhanced algorithms have the potential to improve the efficiency of DR screening, and thereby to prevent visual loss and blindness from this devastating disease.

  9. Deep carbon storage potential of buried floodplain soils.

    Science.gov (United States)

    D'Elia, Amanda H; Liles, Garrett C; Viers, Joshua H; Smart, David R

    2017-08-15

    Soils account for the largest terrestrial pool of carbon and have the potential for even greater quantities of carbon sequestration. Typical soil carbon (C) stocks used in global carbon models only account for the upper 1 meter of soil. Previously unaccounted for deep carbon pools (>1 m) were generally considered to provide a negligible input to total C contents and represent less dynamic C pools. Here we assess deep soil C pools associated with an alluvial floodplain ecosystem transitioning from agricultural production to restoration of native vegetation. We analyzed the soil organic carbon (SOC) concentrations of 87 surface soil samples (0-15 cm) and 23 subsurface boreholes (0-3 m). We evaluated the quantitative importance of the burial process in the sequestration of subsurface C and found our subsurface soils (0-3 m) contained considerably more C than typical C stocks of 0-1 m. This deep unaccounted soil C could have considerable implications for global C accounting. We compared differences in surface soil C related to vegetation and land use history and determined that flooding restoration could promote greater C accumulation in surface soils. We conclude deep floodplain soils may store substantial quantities of C and floodplain restoration should promote active C sequestration.

  10. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

    Science.gov (United States)

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

  11. Deep Extragalactic VIsible Legacy Survey (DEVILS): Motivation, Design and Target Catalogue

    Science.gov (United States)

    Davies, L. J. M.; Robotham, A. S. G.; Driver, S. P.; Lagos, C. P.; Cortese, L.; Mannering, E.; Foster, C.; Lidman, C.; Hashemizadeh, A.; Koushan, S.; O'Toole, S.; Baldry, I. K.; Bilicki, M.; Bland-Hawthorn, J.; Bremer, M. N.; Brown, M. J. I.; Bryant, J. J.; Catinella, B.; Croom, S. M.; Grootes, M. W.; Holwerda, B. W.; Jarvis, M. J.; Maddox, N.; Meyer, M.; Moffett, A. J.; Phillipps, S.; Taylor, E. N.; Windhorst, R. A.; Wolf, C.

    2018-06-01

    The Deep Extragalactic VIsible Legacy Survey (DEVILS) is a large spectroscopic campaign at the Anglo-Australian Telescope (AAT) aimed at bridging the near and distant Universe by producing the highest completeness survey of galaxies and groups at intermediate redshifts (0.3 < z < 1.0). Our sample consists of ˜60,000 galaxies to Y<21.2 mag, over ˜6 deg2 in three well-studied deep extragalactic fields (Cosmic Origins Survey field, COSMOS, Extended Chandra Deep Field South, ECDFS and the X-ray Multi-Mirror Mission Large-Scale Structure region, XMM-LSS - all Large Synoptic Survey Telescope deep-drill fields). This paper presents the broad experimental design of DEVILS. Our target sample has been selected from deep Visible and Infrared Survey Telescope for Astronomy (VISTA) Y-band imaging (VISTA Deep Extragalactic Observations, VIDEO and UltraVISTA), with photometry measured by PROFOUND. Photometric star/galaxy separation is done on the basis of NIR colours, and has been validated by visual inspection. To maximise our observing efficiency for faint targets we employ a redshift feedback strategy, which continually updates our target lists, feeding back the results from the previous night's observations. We also present an overview of the initial spectroscopic observations undertaken in late 2017 and early 2018.

  12. Deep learning

    CERN Document Server

    Goodfellow, Ian; Courville, Aaron

    2016-01-01

    Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language proces...

  13. Deep brain stimulation reveals emotional impact processing in ventromedial prefrontal cortex

    DEFF Research Database (Denmark)

    Gjedde, Albert; Geday, Jacob

    2009-01-01

    We tested the hypothesis that modulation of monoaminergic tone with deep-brain stimulation (DBS) of subthalamic nucleus would reveal a site of reactivity in the ventromedial prefrontal cortex that we previously identified by modulating serotonergic and noradrenergic mechanisms by blocking serotonin......-noradrenaline reuptake sites. We tested the hypothesis in patients with Parkinson's disease in whom we had measured the changes of blood flow everywhere in the brain associated with the deep brain stimulation of the subthalamic nucleus. We determined the emotional reactivity of the patients as the average impact...

  14. Background rejection in NEXT using deep neural networks

    CERN Document Server

    Renner, J.

    2017-01-01

    We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.

  15. Background rejection in NEXT using deep neural networks

    International Nuclear Information System (INIS)

    Renner, J.; Farbin, A.; Vidal, J. Muñoz; Benlloch-Rodríguez, J. M.; Botas, A.

    2017-01-01

    Here, we investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.

  16. Bacteriological examination and biological characteristics of deep frozen bone preserved by gamma sterilization

    International Nuclear Information System (INIS)

    Pham Quang Ngoc; Le The Trung; Vo Van Thuan; Ho Minh Duc

    1999-01-01

    To promote the surgical success in Vietnam, we should supply bone allografts of different sizes. For this reason we have developed a standard procedure in procurement, deep freezing, packaging and radiation sterilization of massive bone. The achievement in this attempt will be briefly reported. The dose of 10-15 kGy is proved to be suitable for radiation sterilization of massive bone allografts being treated in clean condition and preserved in deep frozen. Neither deep freezing nor radiation sterilization cause any significant loss of biochemical stability of massive bone allografts especially when deep freezing combines with radiation. There were neither cross infection nor change of biological characteristics found after 6 months of storage since radiation treatment. In addition to results of the previous research and development of tissue grafts for medical care, the deep freezing radiation sterilization has been established for preservation of massive bone that is of high demand for surgery in Vietnam

  17. Randomized Clinical Trials on Deep Carious Lesions

    DEFF Research Database (Denmark)

    Bjørndal, Lars; Fransson, Helena; Bruun, Gitte

    2017-01-01

    nonselective carious removal to hard dentin with or without pulp exposure. The aim of this article was to report the 5-y outcome on these previously treated patients having radiographically well-defined carious lesions extending into the pulpal quarter of the dentin but with a well-defined radiodense zone...... pulp exposures per se were included as failures. Pulp exposure rate was significantly lower in the stepwise carious removal group (21.2% vs. 35.5%; P = 0.014). Irrespective of pulp exposure status, the difference (13.3%) was still significant when sustained pulp vitality without apical radiolucency......) in deep carious lesions in adults. In conclusion, the stepwise carious removal group had a significantly higher proportion of pulps with sustained vitality without apical radiolucency versus nonselective carious removal of deep carious lesions in adult teeth at 5-y follow-up (ClinicalTrials.gov NCT...

  18. Opportunities and obstacles for deep learning in biology and medicine

    Science.gov (United States)

    2018-01-01

    Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine. PMID:29618526

  19. Opportunities and obstacles for deep learning in biology and medicine.

    Science.gov (United States)

    Ching, Travers; Himmelstein, Daniel S; Beaulieu-Jones, Brett K; Kalinin, Alexandr A; Do, Brian T; Way, Gregory P; Ferrero, Enrico; Agapow, Paul-Michael; Zietz, Michael; Hoffman, Michael M; Xie, Wei; Rosen, Gail L; Lengerich, Benjamin J; Israeli, Johnny; Lanchantin, Jack; Woloszynek, Stephen; Carpenter, Anne E; Shrikumar, Avanti; Xu, Jinbo; Cofer, Evan M; Lavender, Christopher A; Turaga, Srinivas C; Alexandari, Amr M; Lu, Zhiyong; Harris, David J; DeCaprio, Dave; Qi, Yanjun; Kundaje, Anshul; Peng, Yifan; Wiley, Laura K; Segler, Marwin H S; Boca, Simina M; Swamidass, S Joshua; Huang, Austin; Gitter, Anthony; Greene, Casey S

    2018-04-01

    Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine. © 2018 The Authors.

  20. Transcriptome sequences resolve deep relationships of the grape family.

    Directory of Open Access Journals (Sweden)

    Jun Wen

    Full Text Available Previous phylogenetic studies of the grape family (Vitaceae yielded poorly resolved deep relationships, thus impeding our understanding of the evolution of the family. Next-generation sequencing now offers access to protein coding sequences very easily, quickly and cost-effectively. To improve upon earlier work, we extracted 417 orthologous single-copy nuclear genes from the transcriptomes of 15 species of the Vitaceae, covering its phylogenetic diversity. The resulting transcriptome phylogeny provides robust support for the deep relationships, showing the phylogenetic utility of transcriptome data for plants over a time scale at least since the mid-Cretaceous. The pros and cons of transcriptome data for phylogenetic inference in plants are also evaluated.

  1. Case report: accessory head of the deep forearm flexors

    Science.gov (United States)

    JONES, M.; ABRAHAMS, P. H.; SAÑUDO, J. R.

    1997-01-01

    In 1813 Gantzer described 2 accessory muscles in the human forearm which bear his name (Wood, 1868; Macalister, 1875) and these have subsequently been reported with variable attachments (Wood, 1868; Macalister, 1875; Turner, 1879; Schäfer & Thane, 1894; Le Double, 1897; Dykes & Anson, 1944; Mangini, 1960; Malhotra et al. 1982; Kida, 1988; Tountas & Bergman, 1993). The accessory heads of the deep flexors of the forearm (Gantzer's muscles) have been described as 2 different small bellies which insert either into FPL or FDP. There are no previous reports which have mentioned the existence of an accessory muscle which inserts into both of the 2 deep flexors of the forearm as in the case presented here. PMID:9306208

  2. Investigation of Secondary Neutron Production in Large Space Vehicles for Deep Space

    Science.gov (United States)

    Rojdev, Kristina; Koontz, Steve; Reddell, Brandon; Atwell, William; Boeder, Paul

    2016-01-01

    Future NASA missions will focus on deep space and Mars surface operations with large structures necessary for transportation of crew and cargo. In addition to the challenges of manufacturing these large structures, there are added challenges from the space radiation environment and its impacts on the crew, electronics, and vehicle materials. Primary radiation from the sun (solar particle events) and from outside the solar system (galactic cosmic rays) interact with materials of the vehicle and the elements inside the vehicle. These interactions lead to the primary radiation being absorbed or producing secondary radiation (primarily neutrons). With all vehicles, the high-energy primary radiation is of most concern. However, with larger vehicles, there is more opportunity for secondary radiation production, which can be significant enough to cause concern. In a previous paper, we embarked upon our first steps toward studying neutron production from large vehicles by validating our radiation transport codes for neutron environments against flight data. The following paper will extend the previous work to focus on the deep space environment and the resulting neutron flux from large vehicles in this deep space environment.

  3. Deep Incremental Boosting

    OpenAIRE

    Mosca, Alan; Magoulas, George D

    2017-01-01

    This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We show a set of experiments that outlines some preliminary results on some common Deep Learning datasets and discuss the potential improvements Deep In...

  4. Deep challenges for China's war on water pollution.

    Science.gov (United States)

    Han, Dongmei; Currell, Matthew J; Cao, Guoliang

    2016-11-01

    China's Central government has released an ambitious plan to tackle the nation's water pollution crisis. However, this is inhibited by a lack of data, particularly for groundwater. We compiled and analyzed water quality classification data from publicly available government sources, further revealing the scale and extent of the crisis. We also compiled nitrate data in shallow and deep groundwater from a range of literature sources, covering 52 of China's groundwater systems; the most comprehensive national-scale assessment yet. Nitrate pollution at levels exceeding the US EPA's maximum contaminant level (10 mg/L NO 3 N) occurs at the 90th percentile in 25 of 36 shallow aquifers and 10 out of 37 deep or karst aquifers. Isotopic compositions of groundwater nitrate (δ 15 N and δ 18 O NO3 values ranging from -14.9‰ to 35.5‰ and -8.1‰ to 51.0‰, respectively) indicate many nitrate sources including soil nitrogen, agricultural fertilizers, untreated wastewater and/or manure, and locally show evidence of de-nitrification. From these data, it is clear that contaminated groundwater is ubiquitous in deep aquifers as well as shallow groundwater (and surface water). Deep aquifers contain water recharged tens of thousands of years before present, long before widespread anthropogenic nitrate contamination. This groundwater has therefore likely been contaminated due to rapid bypass flow along wells or other conduits. Addressing the issue of well condition is urgently needed to stop further pollution of China's deep aquifers, which are some of China's most important drinking water sources. China's new 10-point Water Pollution Plan addresses previous shortcomings, however, control and remediation of deep groundwater pollution will take decades of sustained effort. Copyright © 2016. Published by Elsevier Ltd.

  5. Deep Super Learner: A Deep Ensemble for Classification Problems

    OpenAIRE

    Young, Steven; Abdou, Tamer; Bener, Ayse

    2018-01-01

    Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datase...

  6. Simulating deep convection with a shallow convection scheme

    Directory of Open Access Journals (Sweden)

    C. Hohenegger

    2011-10-01

    Full Text Available Convective processes profoundly affect the global water and energy balance of our planet but remain a challenge for global climate modeling. Here we develop and investigate the suitability of a unified convection scheme, capable of handling both shallow and deep convection, to simulate cases of tropical oceanic convection, mid-latitude continental convection, and maritime shallow convection. To that aim, we employ large-eddy simulations (LES as a benchmark to test and refine a unified convection scheme implemented in the Single-column Community Atmosphere Model (SCAM. Our approach is motivated by previous cloud-resolving modeling studies, which have documented the gradual transition between shallow and deep convection and its possible importance for the simulated precipitation diurnal cycle.

    Analysis of the LES reveals that differences between shallow and deep convection, regarding cloud-base properties as well as entrainment/detrainment rates, can be related to the evaporation of precipitation. Parameterizing such effects and accordingly modifying the University of Washington shallow convection scheme, it is found that the new unified scheme can represent both shallow and deep convection as well as tropical and mid-latitude continental convection. Compared to the default SCAM version, the new scheme especially improves relative humidity, cloud cover and mass flux profiles. The new unified scheme also removes the well-known too early onset and peak of convective precipitation over mid-latitude continental areas.

  7. Human-level control through deep reinforcement learning

    Science.gov (United States)

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-01

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  8. Human-level control through deep reinforcement learning.

    Science.gov (United States)

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-26

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  9. DeepRT: deep learning for peptide retention time prediction in proteomics

    OpenAIRE

    Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

    2017-01-01

    Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

  10. Multiple bilateral lower limb fractures in a 2-year-old child: previously unreported injury with a unique mechanism

    Directory of Open Access Journals (Sweden)

    Anuj Jain

    2014-10-01

    Full Text Available 【Abstract】Fall from height is a common cause of unintentional injuries in children and accounts for 6% of all trauma-related childhood deaths, usually from head injury. We report a case of a 2-year-old child with multiple fractures of the bilateral lower limbs due to this reason. A child fell from a height of around 15 feet after toppling from a alcony. He developed multiple fractures involving the right femoral shaft, right distal femoral epiphysis (Salter Harris type 2, right distal metaphysis of the tibia and fi bula, and undisplaced Salter Harris type 2 epiphyseal injury of the left distal tibia. There were no head, abdominal or spinal injuries. The patient was taken into emergency operation theatre after initial management which consisted of intravenous fl uids, blood transfusion, and splintage of both lower limbs. Fracture of the femoral shaft was treated by closed reduction and fixation using two titanium elastic nails. Distal femoral physeal injury required open eduction and fixation with K wires. Distal tibia fractures were closely reduced and managed nonoperatively in both the lower limbs. All the fractures united in four weeks. At the last follow-up, the child had no disability and was able to perform daily ctivities comfortably. We also proposed the unique mechanism of injury in this report. Key words: Multiple bilateral lower limb fractures; Fall; Child

  11. SAGE II observations of a previously unreported stratospheric volcanic aerosol cloud in the northern polar summer of 1990

    Science.gov (United States)

    Yue, Glenn K.; Veiga, Robert E.; Wang, Pi-Huan

    1994-01-01

    Analysis of aerosol extinction profiles obtained by the spaceborne SAGE II sensor reveals that there was an anomalous increase of aerosol extinction below 18.5 km at latitudes poleward of 50 deg N from July 28 to September 9, 1990. This widespread increase of aerosol extinction in the lower stratosphere was apparently due to a remote high-latitude volcanic eruption that has not been reported to date. The increase in stratospheric optical depth in the northern polar region was about 50% in August and had diminished by October 1990. This eruption caused an increase in stratospheric aerosol mass of about 0.33 x 10(exp 5) tons, assuming the aerosol was composed of sulfuric acid and water.

  12. DIEP flap customization using Fluobeam® indocyanine green tissue perfusion assessment with large previous abdominal scar

    Directory of Open Access Journals (Sweden)

    Michael A. Fallucco

    2017-06-01

    Full Text Available The Fluobeam® is a portable, near-infrared camera that is held and controlled by the surgeon to visualize tissue perfusion using indocyanine green (ICG fluorescence imaging. This case report describes how data obtained from ICG imaging allows intraoperative customization in a previously surgically scarred abdomen during autologous Deep Inferior Epigastric Artery Perforator (DIEP flap bilateral breast reconstruction. The outcome was successful breast mound recreation without fat necrosis.

  13. Analyses of the deep borehole drilling status for a deep borehole disposal system

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jong Youl; Choi, Heui Joo; Lee, Min Soo; Kim, Geon Young; Kim, Kyung Su [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    The purpose of disposal for radioactive wastes is not only to isolate them from humans, but also to inhibit leakage of any radioactive materials into the accessible environment. Because of the extremely high level and long-time scale radioactivity of HLW(High-level radioactive waste), a mined deep geological disposal concept, the disposal depth is about 500 m below ground, is considered as the safest method to isolate the spent fuels or high-level radioactive waste from the human environment with the best available technology at present time. Therefore, as an alternative disposal concept, i.e., deep borehole disposal technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general status of deep drilling technologies was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, very preliminary applicability of deep drilling technology for deep borehole disposal analyzed. In this paper, as one of key technologies of deep borehole disposal system, the general status of deep drilling technologies in oil industry, geothermal industry and geo scientific field was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, the very preliminary applicability of deep drilling technology for deep borehole disposal such as relation between depth and diameter, drilling time and feasibility classification was analyzed.

  14. Kelvin Wave Influence on the Shallow-to-Deep Transition Over the Amazon

    Science.gov (United States)

    Rowe, A.; Serra, Y. L.

    2017-12-01

    The suite of observations from GOAmazon and CHUVA offers a unique opportunity to examine land-based convective processes in the tropics, including the poorly represented shallow-to-deep transition. This study uses these data to investigate impacts of Kelvin waves on the the shallow-to-deep transition over the Central Amazon. The Kelvin waves that propagate over the region often originate over the tropical central and east Pacific, with local generation over the Andes also observed. The observed 15 m s-1 phase speed and 4500 km wave length during the two-year campaign are in agreement with previously published studies of these waves across the tropics. Also in agreement with previous studies, we find the waves are most active during the wet season (November-May) for this region. Using four separate convective event classes (clear-sky, nonprecipitating cumulus congestus, afternoon deep convection, and mesoscale convective systems), we examine how the convection preferentially develops for different phases of the Kelvin waves seen during GOAmazon. We additionally examine surface meteorological variables, the vertical thermodynamic and dynamic structure of the troposphere, vertical moist static stability, integrated column water vapor and liquid water, and surface energy fluxes within the context of these convective classes to identify the important environmental factors contributing to observed periods of enhanced deep convection related to the waves. Results suggest that the waves significantly modify the local environment, such as creating a deep layer of moisture throughout the troposphere, favoring more organized convection in the active than in the suppressed phase of the wave. The significance of wave-related environmental modifications are assessed by comparing local rainfall accumulations during Kelvin wave activity to that when the waves are not present. Future work will further explore the shallow-to-deep transition and its modulation by Kelvin wave activity

  15. Deep Space Telecommunications

    Science.gov (United States)

    Kuiper, T. B. H.; Resch, G. M.

    2000-01-01

    The increasing load on NASA's deep Space Network, the new capabilities for deep space missions inherent in a next-generation radio telescope, and the potential of new telescope technology for reducing construction and operation costs suggest a natural marriage between radio astronomy and deep space telecommunications in developing advanced radio telescope concepts.

  16. pathways to deep decarbonization - 2014 report

    International Nuclear Information System (INIS)

    Sachs, Jeffrey; Guerin, Emmanuel; Mas, Carl; Schmidt-Traub, Guido; Tubiana, Laurence; Waisman, Henri; Colombier, Michel; Bulger, Claire; Sulakshana, Elana; Zhang, Kathy; Barthelemy, Pierre; Spinazze, Lena; Pharabod, Ivan

    2014-09-01

    network comprising 15 Country Research Partners, and several Partner Organizations who develop and share methods, assumptions, and findings related to deep decarbonization. Each DDPP Country Research Team develops illustrative pathway analysis for the transition to a low-carbon economy, with the intent of taking into account national socio-economic conditions, development aspirations, infrastructure stocks, resource endowments, and other relevant factors. This document starts with a short outline of key results from previous global studies (discussed in chapter I to IV) and then turns to what is new and special about the country-level approach of the DDPP (explained in chapter V). It summarizes the main preliminary findings from the Deep Decarbonization Pathways (DDPs) developed by the Country Research Partners (included in chapter VI) and draws some lessons for the international negotiations leading up to the 21. Conference of the Parties (COP-21) of the UN Framework Convention on Climate Change (UNFCCC) to be held in Paris in December 2015

  17. Developing Deep Learning Applications for Life Science and Pharma Industry.

    Science.gov (United States)

    Siegismund, Daniel; Tolkachev, Vasily; Heyse, Stephan; Sick, Beate; Duerr, Oliver; Steigele, Stephan

    2018-06-01

    Deep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of the major technological innovation areas, predicted to replace lots of repetitive, but complex tasks of human labor within the next decade. It is also expected to be 'game changing' for research activities in pharma and life sciences, where large sets of similar yet complex data samples are systematically analyzed. Deep learning is currently conquering formerly expert domains especially in areas requiring perception, previously not amenable to standard machine learning. A typical example is the automated analysis of images which are typically produced en-masse in many domains, e. g., in high-content screening or digital pathology. Deep learning enables to create competitive applications in so-far defined core domains of 'human intelligence'. Applications of artificial intelligence have been enabled in recent years by (i) the massive availability of data samples, collected in pharma driven drug programs (='big data') as well as (ii) deep learning algorithmic advancements and (iii) increase in compute power. Such applications are based on software frameworks with specific strengths and weaknesses. Here, we introduce typical applications and underlying frameworks for deep learning with a set of practical criteria for developing production ready solutions in life science and pharma research. Based on our own experience in successfully developing deep learning applications we provide suggestions and a baseline for selecting the most suited frameworks for a future-proof and cost-effective development. © Georg Thieme Verlag KG Stuttgart · New York.

  18. Greedy Deep Dictionary Learning

    OpenAIRE

    Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

    2016-01-01

    In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

  19. Patterns and trends of macrobenthic abundance, biomass and production in the deep Arctic Ocean

    Directory of Open Access Journals (Sweden)

    Renate Degen

    2015-08-01

    Full Text Available Little is known about the distribution and dynamics of macrobenthic communities of the deep Arctic Ocean. The few previous studies report low standing stocks and confirm a gradient with declining biomass from the slopes down to the basins, as commonly reported for deep-sea benthos. In this study, we investigated regional differences of faunal abundance and biomass, and made for the first time ever estimates of deep Arctic community production by using a multi-parameter artificial neural network model. The underlying data set combines data from recent field studies with published and unpublished data from the past 20 years, to analyse the influence of water depth, geographical latitude and sea-ice concentration on Arctic benthic communities. We were able to confirm the previously described negative relationship of macrofauna standing stock with water depth in the Arctic deep sea, while also detecting substantial regional differences. Furthermore, abundance, biomass and production decreased significantly with increasing sea-ice extent (towards higher latitudes down to values <200 ind m−2, <65 mg C m−2 and <73 mg C m−2 y−1, respectively. In contrast, stations under the seasonal ice zone regime showed much higher standing stock and production (up to 2500 mg C m−2 y−1, even at depths down to 3700 m. We conclude that particle flux is the key factor structuring benthic communities in the deep Arctic Ocean as it explains both the low values in the ice-covered Arctic basins and the higher values in the seasonal ice zone.

  20. DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning.

    Science.gov (United States)

    Laksshman, Sundaram; Bhat, Rajendra Rana; Viswanath, Vivek; Li, Xiaolin

    2017-09-01

    Bipolar disorder, also known as manic depression, is a brain disorder that affects the brain structure of a patient. It results in extreme mood swings, severe states of depression, and overexcitement simultaneously. It is estimated that roughly 3% of the population of the United States (about 5.3 million adults) suffers from bipolar disorder. Recent research efforts like the Twin studies have demonstrated a high heritability factor for the disorder, making genomics a viable alternative for detecting and treating bipolar disorder, in addition to the conventional lengthy and costly postsymptom clinical diagnosis. Motivated by this study, leveraging several emerging deep learning algorithms, we design an end-to-end deep learning architecture (called DeepBipolar) to predict bipolar disorder based on limited genomic data. DeepBipolar adopts the Deep Convolutional Neural Network (DCNN) architecture that automatically extracts features from genotype information to predict the bipolar phenotype. We participated in the Critical Assessment of Genome Interpretation (CAGI) bipolar disorder challenge and DeepBipolar was considered the most successful by the independent assessor. In this work, we thoroughly evaluate the performance of DeepBipolar and analyze the type of signals we believe could have affected the classifier in distinguishing the case samples from the control set. © 2017 Wiley Periodicals, Inc.

  1. Deep learning? What deep learning? | Fourie | South African ...

    African Journals Online (AJOL)

    In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability ...

  2. Model of hot-carrier induced degradation in ultra-deep sub-micrometer nMOSFET

    International Nuclear Information System (INIS)

    Lei Xiao-Yi; Liu Hong-Xia; Zhang Yue; Ma Xiao-Hua; Hao Yue

    2014-01-01

    The degradation produced by hot carrier (HC) in ultra-deep sub-micron n-channel metal oxide semiconductor field effect transistor (nMOSFET) has been analyzed in this paper. The generation of negatively charged interface states is the predominant mechanism for the ultra-deep sub-micron nMOSFET. According to our lifetime model of p-channel MOFET (pMOFET) that was reported in a previous publication, a lifetime prediction model for nMOSFET is presented and the parameters in the model are extracted. For the first time, the lifetime models of nMOFET and pMOSFET are unified. In addition, the model can precisely predict the lifetime of the ultra-deep sub-micron nMOSFET and pMOSFET. (condensed matter: electronic structure, electrical, magnetic, and optical properties)

  3. Identification of new deep sea sinuous channels in the eastern Arabian Sea.

    Science.gov (United States)

    Mishra, Ravi; Pandey, D K; Ramesh, Prerna; Clift, Peter D

    2016-01-01

    Deep sea channel systems are recognized in most submarine fans worldwide as well as in the geological record. The Indus Fan is the second largest modern submarine fan, having a well-developed active canyon and deep sea channel system. Previous studies from the upper Indus Fan have reported several active channel systems. In the present study, deep sea channel systems were identified within the middle Indus Fan using high resolution multibeam bathymetric data. Prominent morphological features within the survey block include the Raman Seamount and Laxmi Ridge. The origin of the newly discovered channels in the middle fan has been inferred using medium resolution satellite bathymetry data. Interpretation of new data shows that the highly sinuous deep sea channel systems also extend to the east of Laxmi Ridge, as well as to the west of Laxmi Ridge, as previously reported. A decrease in sinuosity southward can be attributed to the morphological constraints imposed by the elevated features. These findings have significance in determining the pathways for active sediment transport systems, as well as their source characterization. The geometry suggests a series of punctuated avulsion events leading to the present array of disconnected channels. Such channels have affected the Laxmi Basin since the Pliocene and are responsible for reworking older fan sediments, resulting in loss of the original erosional signature supplied from the river mouth. This implies that distal fan sediments have experienced significant signal shredding and may not represent the erosion and weathering conditions within the onshore basin at the time of sedimentation.

  4. A recursive Monte Carlo method for estimating importance functions in deep penetration problems

    International Nuclear Information System (INIS)

    Goldstein, M.

    1980-04-01

    A pratical recursive Monte Carlo method for estimating the importance function distribution, aimed at importance sampling for the solution of deep penetration problems in three-dimensional systems, was developed. The efficiency of the recursive method was investigated for sample problems including one- and two-dimensional, monoenergetic and and multigroup problems, as well as for a practical deep-penetration problem with streaming. The results of the recursive Monte Carlo calculations agree fairly well with Ssub(n) results. It is concluded that the recursive Monte Carlo method promises to become a universal method for estimating the importance function distribution for the solution of deep-penetration problems, in all kinds of systems: for many systems the recursive method is likely to be more efficient than previously existing methods; for three-dimensional systems it is the first method that can estimate the importance function with the accuracy required for an efficient solution based on importance sampling of neutron deep-penetration problems in those systems

  5. Deep Learning for Population Genetic Inference.

    Science.gov (United States)

    Sheehan, Sara; Song, Yun S

    2016-03-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

  6. Deep learning with Python

    CERN Document Server

    Chollet, Francois

    2018-01-01

    DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES • Practical code examples • In-depth introduction to Keras • Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural ...

  7. A deep sea community at the Kebrit brine pool in the Red Sea

    KAUST Repository

    Vestheim, Hege; Kaartvedt, Stein

    2015-01-01

    Approximately 25 deep sea brine pools occur along the mid axis of the Red Sea. These hypersaline, anoxic, and acidic environments have previously been reported to host diverse microbial communities. We visited the Kebrit brine pool in April 2013

  8. Deep learning evaluation using deep linguistic processing

    OpenAIRE

    Kuhnle, Alexander; Copestake, Ann

    2017-01-01

    We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value ...

  9. A previously unidentified deletion in G protein-coupled receptor 143 causing X-linked congenital nystagmus in a Chinese family

    Directory of Open Access Journals (Sweden)

    Jing Liu

    2016-01-01

    Full Text Available Background: Congenital nystagmus (CN is characterized by conjugated, spontaneous, and involuntary ocular oscillations. It is an inherited disease and the most common inheritance pattern is X-linked CN. In this study, our aim is to identify the disease-causing mutation in a large sixth-generation Chinese family with X-linked CN. Methods: It has been reported that mutations in four-point-one, ezrin, radixin, moesin domain-containing 7 gene (FRMD7 and G protein-coupled receptor 143 gene (GPR143 account for the majority patients of X-linked nystagmus. We collected 8 ml blood samples from members of a large sixth-generation pedigree with X-linked CN and 100 normal controls. FRMD7 and GPR143 were scanned by polymerase chain reaction (PCR-based DNA sequencing assays, and multiplex PCR assays were applied to detect deletions. Results: We identified a previously unreported deletion covering 7 exons in GPR143 in a Chinese family. The heterozygous deletion from exon 3 to exon 9 of GPR143 was detected in all affected males in the family, while it was not detected in other unaffected relatives or 100 normal controls. Conclusions: This is the first report of molecular characterization in GPR143 gene in the CN family. Our results expand the spectrum of GPR143 mutations causing CN and further confirm the role of GPR143 in the pathogenesis of CN.

  10. Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

    NARCIS (Netherlands)

    K.L. Groenland (Koen); S.M. Bohte (Sander)

    2016-01-01

    textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order

  11. A novel approach reveals high zooplankton standing stock deep in the sea

    Directory of Open Access Journals (Sweden)

    A. Vereshchaka

    2016-11-01

    Full Text Available In a changing ocean there is a critical need to understand global biogeochemical cycling, particularly regarding carbon. We have made strides in understanding upper ocean dynamics, but the deep ocean interior (> 1000 m is still largely unknown, despite representing the overwhelming majority of Earth's biosphere. Here we present a method for estimating deep-pelagic zooplankton biomass on an ocean-basin scale. We have made several new discoveries about the Atlantic, which likely apply to the world ocean. First, multivariate analysis showed that depth and Chl were the basic factors affecting the wet biomass of the main plankton groups. Wet biomass of all major groups was significantly correlated with Chl. Second, zooplankton biomass in the upper bathypelagic domain is higher than expected. Third, the majority of this biomass comprises macroplanktonic shrimps, which have been historically underestimated. These findings, coupled with recent findings of increased global deep-pelagic fish biomass, suggest that the contribution of the deep-ocean pelagic fauna for biogeochemical cycles may be more important than previously thought.

  12. Uptake and distribution of organo-iodine in deep-sea corals.

    Science.gov (United States)

    Prouty, Nancy G; Roark, E Brendan; Mohon, Leslye M; Chang, Ching-Chih

    2018-07-01

    Understanding iodine concentration, transport, and bioavailability is essential in evaluating iodine's impact to the environment and its effectiveness as an environmental biogeotracer. While iodine and its radionuclides have proven to be important tracers in geologic and biologic studies, little is known about transport of this element to the deep sea and subsequent uptake in deep-sea coral habitats. Results presented here on deep-sea black coral iodine speciation and iodine isotope variability provides key information on iodine behavior in natural and anthropogenic environments, and its geochemical pathway in the Gulf of Mexico. Organo-iodine is the dominant iodine species in the black corals, demonstrating that binding of iodine to organic matter plays an important role in the transport and transfer of iodine to the deep-sea corals. The identification of growth bands captured in high-resolution scanning electron images (SEM) with synchronous peaks in iodine variability suggest that riverine delivery of terrestrial-derived organo-iodine is the most plausible explanation to account for annual periodicity in the deep-sea coral geochemistry. Whereas previous studies have suggested the presence of annual growth rings in deep-sea corals, this present study provides a mechanism to explain the formation of annual growth bands. Furthermore, deep-sea coral ages based on iodine peak counts agree well with those ages derived from radiocarbon ( 14 C) measurements. These results hold promise for developing chronologies independent of 14 C dating, which is an essential component in constraining reservoir ages and using radiocarbon as a tracer of ocean circulation. Furthermore, the presence of enriched 129 I/ 127 I ratios during the most recent period of skeleton growth is linked to nuclear weapons testing during the 1960s. The sensitivity of the coral skeleton to record changes in surface water 129 I composition provides further evidence that iodine composition and isotope

  13. Uptake and distribution of organo-iodine in deep-sea corals

    Science.gov (United States)

    Prouty, Nancy G.; Roark, E. Brendan; Mohon, Leslye M.; Chang, Ching-Chih

    2018-01-01

    Understanding iodine concentration, transport, and bioavailability is essential in evaluating iodine's impact to the environment and its effectiveness as an environmental biogeotracer. While iodine and its radionuclides have proven to be important tracers in geologic and biologic studies, little is known about transport of this element to the deep sea and subsequent uptake in deep-sea coral habitats. Results presented here on deep-sea black coral iodine speciation and iodine isotope variability provides key information on iodine behavior in natural and anthropogenic environments, and its geochemical pathway in the Gulf of Mexico. Organo-iodine is the dominant iodine species in the black corals, demonstrating that binding of iodine to organic matter plays an important role in the transport and transfer of iodine to the deep-sea corals. The identification of growth bands captured in high-resolution scanning electron images (SEM) with synchronous peaks in iodine variability suggest that riverine delivery of terrestrial-derived organo-iodine is the most plausible explanation to account for annual periodicity in the deep-sea coral geochemistry. Whereas previous studies have suggested the presence of annual growth rings in deep-sea corals, this present study provides a mechanism to explain the formation of annual growth bands. Furthermore, deep-sea coral ages based on iodine peak counts agree well with those ages derived from radiocarbon (14C) measurements. These results hold promise for developing chronologies independent of 14C dating, which is an essential component in constraining reservoir ages and using radiocarbon as a tracer of ocean circulation. Furthermore, the presence of enriched 129I/127I ratios during the most recent period of skeleton growth is linked to nuclear weapons testing during the 1960s. The sensitivity of the coral skeleton to record changes in surface water 129I composition provides further evidence that iodine composition and isotope

  14. Clean subglacial access: prospects for future deep hot-water drilling

    Science.gov (United States)

    Pearce, David; Hodgson, Dominic A.; Smith, Andrew M.; Rose, Mike; Ross, Neil; Mowlem, Matt; Parnell, John

    2016-01-01

    Accessing and sampling subglacial environments deep beneath the Antarctic Ice Sheet presents several challenges to existing drilling technologies. With over half of the ice sheet believed to be resting on a wet bed, drilling down to this environment must conform to international agreements on environmental stewardship and protection, making clean hot-water drilling the most viable option. Such a drill, and its water recovery system, must be capable of accessing significantly greater ice depths than previous hot-water drills, and remain fully operational after connecting with the basal hydrological system. The Subglacial Lake Ellsworth (SLE) project developed a comprehensive plan for deep (greater than 3000 m) subglacial lake research, involving the design and development of a clean deep-ice hot-water drill. However, during fieldwork in December 2012 drilling was halted after a succession of equipment issues culminated in a failure to link with a subsurface cavity and abandonment of the access holes. The lessons learned from this experience are presented here. Combining knowledge gained from these lessons with experience from other hot-water drilling programmes, and recent field testing, we describe the most viable technical options and operational procedures for future clean entry into SLE and other deep subglacial access targets. PMID:26667913

  15. New High Pressure Phase of CaCO3: Implication for the Deep Diamond Formation

    Science.gov (United States)

    Mao, Z.; Li, X.; Zhang, Z.; Lin, J. F.; Ni, H.; Prakapenka, V.

    2017-12-01

    Surface carbon can be transported to the Earth's deep interior through sinking subduction slabs. Carbonates, including CaCO3, MgCO3 and MgCa(CO3)2, are important carbon carriers for the deep carbon cycle. Experimental studies on the phase stability of carbonates with coexisting mantle minerals at relevant pressure and temperature conditions are thus important for understanding the deep carbon cycle. In particular, recent petrological studies have revealed the evidence for the transportation of CaCO3 to the depth at least of the top lower mantle by analyzing the diamond inclusions. Yet the phase stability of CaCO3 at relevant pressure and temperature conditions of the top lower mantle is still unclear. Previous single-crystal study has shown that CaCO3 transforms from the CaCO3-III structure to CaCO3-VI at 15 GPa and 300 K. The CaCO3-VI is stable at least up to 40 GPa at 300 K. At high temperatures, CaCO3 in the aragonite structure will directly transform into the post-aragonite structure at 40 GPa. However, a recent theoretical study predicted a new phase of CaCO3 with a space group of P21/c between 32 and 48 GPa which is different from previous experimental results. In this study, we have investigated the phase stability of CaCO3 at high pressure-temperature conditions using synchrotron X-ray diffraction in laser-heated diamond anvil cells. We report the discovery of a new phase of CaCO3 at relevant pressure-temperature conditions of the top lower mantle which is consistent with previous theoretical predictions. This new phase is an important carrier for the transportation of carbon to the Earth's lower mantle and crucial for growing deep diamonds in the region.

  16. High efficacy with deep nurse-administered propofol sedation for advanced gastroenterologic endoscopic procedures

    DEFF Research Database (Denmark)

    Jensen, Jeppe Thue; Hornslet, Pernille; Konge, Lars

    2016-01-01

    was requested eight times (0.4 %). One patient was intubated due to suspected aspiration. CONCLUSIONS: Intermittent deep NAPS for advanced endoscopies in selected patients provided an almost 100 % success rate. However, the rate of hypoxia, hypotension and respiratory support was high compared with previously......BACKGROUND AND STUDY AIMS: Whereas data on moderate nurse-administered propofol sedation (NAPS) efficacy and safety for standard endoscopy is abundant, few reports on the use of deep sedation by endoscopy nurses during advanced endoscopy, such as Endoscopic Retrograde Cholangiopancreatography (ERCP......) and Endoscopic Ultrasound (EUS) are available and potential benefits or hazards remain unclear. The aims of this study were to investigate the efficacy of intermittent deep sedation with propofol for a large cohort of advanced endoscopies and to provide data on the safety. PATIENTS AND METHODS: All available...

  17. Bohring-Opitz (Oberklaid-Danks) syndrome: clinical study, review of the literature, and discussion of possible pathogenesis

    DEFF Research Database (Denmark)

    Hastings, Rob; Cobben, Jan-Maarten; Gillessen-Kaesbach, Gabriele

    2011-01-01

    Bohring-Opitz syndrome (BOS) is a rare congenital disorder of unknown etiology diagnosed on the basis of distinctive clinical features. We suggest diagnostic criteria for this condition, describe ten previously unreported patients, and update the natural history of four previously reported patients...

  18. DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

    Science.gov (United States)

    Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

    2018-04-01

    Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. Deep frying

    NARCIS (Netherlands)

    Koerten, van K.N.

    2016-01-01

    Deep frying is one of the most used methods in the food processing industry. Though practically any food can be fried, French fries are probably the most well-known deep fried products. The popularity of French fries stems from their unique taste and texture, a crispy outside with a mealy soft

  20. Potential contribution of surface-dwelling Sargassum algae to deep-sea ecosystems in the southern North Atlantic

    Science.gov (United States)

    Baker, Philip; Minzlaff, Ulrike; Schoenle, Alexandra; Schwabe, Enrico; Hohlfeld, Manon; Jeuck, Alexandra; Brenke, Nils; Prausse, Dennis; Rothenbeck, Marcel; Brix, Saskia; Frutos, Inmaculada; Jörger, Katharina M.; Neusser, Timea P.; Koppelmann, Rolf; Devey, Colin; Brandt, Angelika; Arndt, Hartmut

    2018-02-01

    Deep-sea ecosystems, limited by their inability to use primary production as a source of carbon, rely on other sources to maintain life. Sedimentation of organic carbon into the deep sea has been previously studied, however, the high biomass of sedimented Sargassum algae discovered during the VEMA Transit expedition in 2014/2015 to the southern North Atlantic, and its potential as a regular carbon input, has been an underestimated phenomenon. To determine the potential for this carbon flux, a literature survey of previous studies that estimated the abundance of surface water Sargassum was conducted. We compared these estimates with quantitative analyses of sedimented Sargassum appearing on photos taken with an autonomous underwater vehicle (AUV) directly above the abyssal sediment during the expedition. Organismal communities associated to Sargassum fluitans from surface waters were investigated and Sargassum samples collected from surface waters and the deep sea were biochemically analyzed (fatty acids, stable isotopes, C:N ratios) to determine degradation potential and the trophic significance within deep-sea communities. The estimated Sargassum biomass (fresh weight) in the deep sea (0.07-3.75 g/m2) was several times higher than that estimated from surface waters in the North Atlantic (0.024-0.84 g/m2). Biochemical analysis showed degradation of Sargassum occurring during sedimentation or in the deep sea, however, fatty acid and stable isotope analysis did not indicate direct trophic interactions between the algae and benthic organisms. Thus, it is assumed that components of the deep-sea microbial food web form an important link between the macroalgae and larger benthic organisms. Evaluation of the epifauna showed a diverse nano- micro-, meio, and macrofauna on surface Sargassum and maybe transported across the Atlantic, but we had no evidence for a vertical exchange of fauna components. The large-scale sedimentation of Sargassum forms an important trophic link

  1. Origins, characteristics, controls, and economic viabilities of deep- basin gas resources

    Science.gov (United States)

    Price, L.C.

    1995-01-01

    Dry-gas deposits (methane ???95% of the hydrocarbon (HC) gases) are thought to originate from in-reservoir thermal cracking of oil and C2+ HC gases to methane. However, because methanes from Anadarko Basin dry-gas deposits do not carry the isotopic signature characteristics of C15+ HC destruction, an origin of these methanes from this process is considered improbable. Instead, the isotopic signature of these methanes suggests that they were cogenerated with C15+ HC's. Only a limited resource of deep-basin gas deposits may be expected by the accepted model for the origin of dry-gas deposits because of a limited number of deep-basin oil deposits originally available to be thermally converted to dry gas. However, by the models of this paper (inefficient source-rock oil and gas expulsion, closed fluid systems in petroleum-basin depocenters, and most dry-gas methane cogenerated with C15+ HC's), very large, previously unrecognized, unconventional, deep-basin gas resources are expected. -from Author

  2. DeepPVP: phenotype-based prioritization of causative variants using deep learning

    KAUST Repository

    Boudellioua, Imene

    2018-05-02

    Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient\\'s phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

  3. Hot, deep origin of petroleum: deep basin evidence and application

    Science.gov (United States)

    Price, Leigh C.

    1978-01-01

    Use of the model of a hot deep origin of oil places rigid constraints on the migration and entrapment of crude oil. Specifically, oil originating from depth migrates vertically up faults and is emplaced in traps at shallower depths. Review of petroleum-producing basins worldwide shows oil occurrence in these basins conforms to the restraints of and therefore supports the hypothesis. Most of the world's oil is found in the very deepest sedimentary basins, and production over or adjacent to the deep basin is cut by or directly updip from faults dipping into the basin deep. Generally the greater the fault throw the greater the reserves. Fault-block highs next to deep sedimentary troughs are the best target areas by the present concept. Traps along major basin-forming faults are quite prospective. The structural style of a basin governs the distribution, types, and amounts of hydrocarbons expected and hence the exploration strategy. Production in delta depocenters (Niger) is in structures cut by or updip from major growth faults, and structures not associated with such faults are barren. Production in block fault basins is on horsts next to deep sedimentary troughs (Sirte, North Sea). In basins whose sediment thickness, structure and geologic history are known to a moderate degree, the main oil occurrences can be specifically predicted by analysis of fault systems and possible hydrocarbon migration routes. Use of the concept permits the identification of significant targets which have either been downgraded or ignored in the past, such as production in or just updip from thrust belts, stratigraphic traps over the deep basin associated with major faulting, production over the basin deep, and regional stratigraphic trapping updip from established production along major fault zones.

  4. Coated Particle Fuel and Deep Burn Program Monthly Highlights May 2011

    International Nuclear Information System (INIS)

    Snead, Lance Lewis; Bell, Gary L.; Besmann, Theodore M.

    2011-01-01

    During FY 2011 the CP and DB Program will report Highlights on a monthly basis, but will no longer produce Quarterly Progress Reports. Technical details that were previously included in the quarterly reports will be included in the appropriate Milestone Reports that are submitted to FCRD Program Management. These reports will also be uploaded to the Deep Burn website. The Monthly Highlights report for April 2011, ORNL/TM-2011/125, was distributed to program participants on May 10, 2011. As reported previously, the final Quarterly for FY 2010, Deep Burn Program Quarterly Report for July - September 2010, ORNL/TM-2010/301, was announced to program participants and posted to the website on December 28, 2010. This report discusses the following: (1) Fuel Performance Modeling - Fuel Performance Analysis; (2) Thermochemical Data and Model Development - (a) Thermochemical Modeling, (b) Thermomechanical Modeling, (c) Actinide and Fission Product Transport; (3) TRU (transuranic elements) TRISO (tri-structural isotropic) Development - (a) TRU Kernel Development, (b) Coating Development; and (4) LWR Fully Ceramic Fuel - (a) FCM Fabrication Development, (b) FCM Irradiation Testing.

  5. Coated Particle Fuel and Deep Burn Program Monthly Highlights June 2011

    International Nuclear Information System (INIS)

    Snead, Lance Lewis; Bell, Gary L.; Besmann, Theodore M.

    2011-01-01

    During FY 2011 the CP and DB Program will report Highlights on a monthly basis, but will no longer produce Quarterly Progress Reports. Technical details that were previously included in the quarterly reports will be included in the appropriate Milestone Reports that are submitted to FCRD Program Management. These reports will also be uploaded to the Deep Burn website. The Monthly Highlights report for May 2011, ORNL/TM-2011/126, was distributed to program participants on June 9, 2011. As reported previously, the final Quarterly for FY 2010, Deep Burn Program Quarterly Report for July - September 2010, ORNL/TM-2010/301, was announced to program participants and posted to the website on December 28, 2010. This report discusses the following: (1) Fuel Performance Modeling - Fuel Performance Analysis; (2) Thermochemical Data and Model Development - (a) Thermochemical Behavior, (b) Thermomechanical Modeling, (c) Actinide and Fission Product Transport; (3) TRU (transuranic elements) TRISO (tri-structural isotropic) Development - (a) TRU Kernel Development, (b) Coating Development; and (4) LWR Fully Ceramic Fuel - (a) FCM Fabrication Development, (b) FCM Irradiation Testing.

  6. Applications of Deep Learning and Reinforcement Learning to Biological Data.

    Science.gov (United States)

    Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

    2018-06-01

    Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

  7. Deep learning in bioinformatics.

    Science.gov (United States)

    Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

    2017-09-01

    In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  8. Objective aesthetic performance of Icon treatment by deep infiltration: a case report

    Directory of Open Access Journals (Sweden)

    Fabrizio Guerra

    2016-06-01

    Full Text Available Enamel hypomineralization is characterized by decreased refractive index of the lesion surface as compared to the surrounding sound enamel. Icon® treatment has been recently successfully used for the cosmetic treatment of enamel demineralizations seen in white spot lesions (WSLs and developmental defects of enamel (DDE. On the other hand, cases of molar incisal hypomineralization (MIH, deep lesions of traumatic origin, and those associated with severe fluorosis do not have the same response to this type of treatment. The application of a new deep Icon® infiltrative technique is proposed and the aesthetic results are validated by using a spectrophotometric approach as previously described by our group.

  9. Radiation recall cutaneous induced by chlorambucil. Case report

    International Nuclear Information System (INIS)

    Dei-Cas, Ignacio; Wright, Dolores; Rigo, Bettina; Cohen Sabban, Emilia; Lacasagne, Jorgelina; Pietropaolo, Nelida; Cabo, Horacio; Molina, Malena

    2005-01-01

    Radiation recall refers to a tissue reaction produced by the use of certain drugs, usually chemotherapeutic agents, in a previously irradiated area. We report a patient with cutaneous radiation recall associated with chlorambucil, drug previously unreported as a causative agent in the literature. (author) [es

  10. Seasonal copepod lipid pump promotes carbon sequestration in the deep North Atlantic

    DEFF Research Database (Denmark)

    Jonasdottir, Sigrun; Visser, Andre; Richardson, Katherine

    2015-01-01

    it is metabolized at a rate comparable to the carbon delivered by sinking detritus. This “lipid pump” has not been included in previous estimates of the deep-ocean carbon sequestration, which are based on either measurements of sinking fluxes of detritus, or estimates of new primary production. Unlike other...

  11. DeepSimulator: a deep simulator for Nanopore sequencing

    KAUST Repository

    Li, Yu

    2017-12-23

    Motivation: Oxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals. Results: Here we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection. Availability: The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator.

  12. Deep Learning for Population Genetic Inference.

    Directory of Open Access Journals (Sweden)

    Sara Sheehan

    2016-03-01

    Full Text Available Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data to the output (e.g., population genetic parameters of interest. We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history. Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

  13. Deep Learning for Population Genetic Inference

    Science.gov (United States)

    Sheehan, Sara; Song, Yun S.

    2016-01-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

  14. ¿El Caballo Viejo? Latin Genre Recognition with Deep Learning and Spectral Periodicity

    DEFF Research Database (Denmark)

    Sturm, Bob L.; Kereliuk, Corey Mose; Larsen, Jan

    2015-01-01

    as BALLROOM. In this paper, we reproduce the “winning” deep learning system using LMD, and measure the effects of time dilation on its performance. We find that tempo changes of at most ±6 % greatly diminish and improve its performance. Interpreted with the low-level nature of the input features......The “winning” system in the 2013 MIREX Latin Genre Classification Task was a deep neural network trained with simple features. An explanation for its winning performance has yet to be found. In previous work, we built similar systems using the BALLROOM music dataset, and found their performances...

  15. Deep learning relevance

    DEFF Research Database (Denmark)

    Lioma, Christina; Larsen, Birger; Petersen, Casper

    2016-01-01

    train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared...... to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all....

  16. Threshold-improved predictions for charm production in deep-inelastic scattering

    International Nuclear Information System (INIS)

    Lo Presti, N.A.; Kawamura, H.; Vogt, A.

    2010-08-01

    We have extended previous results on the threshold expansion of the gluon coefficient function for the charm contribution to the deep-inelastic structure function F 2 by deriving all thresholdenhanced contributions at the next-to-next-to-leading order. The size of these corrections is briefly illustrated, and a first step towards extending this improvement to more differential charmproduction cross sections is presented. (orig.)

  17. STIMULATION TECHNOLOGIES FOR DEEP WELL COMPLETIONS

    Energy Technology Data Exchange (ETDEWEB)

    Stephen Wolhart

    2003-06-01

    The Department of Energy (DOE) is sponsoring a Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a project to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. Phase 1 was recently completed and consisted of assessing deep gas well drilling activity (1995-2007) and an industry survey on deep gas well stimulation practices by region. Of the 29,000 oil, gas and dry holes drilled in 2002, about 300 were drilled in the deep well; 25% were dry, 50% were high temperature/high pressure completions and 25% were simply deep completions. South Texas has about 30% of these wells, Oklahoma 20%, Gulf of Mexico Shelf 15% and the Gulf Coast about 15%. The Rockies represent only 2% of deep drilling. Of the 60 operators who drill deep and HTHP wells, the top 20 drill almost 80% of the wells. Six operators drill half the U.S. deep wells. Deep drilling peaked at 425 wells in 1998 and fell to 250 in 1999. Drilling is expected to rise through 2004 after which drilling should cycle down as overall drilling declines.

  18. New initiative in studies of Earth's deep interior

    Science.gov (United States)

    Lay, Thorne

    A multidisciplinary U.S. research community is undertaking a new coordinated effort to study the state and dynamics of the Earth's deep mantle and core. At an open meeting held at the Massachusetts Institute of Technology, Cambridge, from September 11 to 12, 1992, over 120 Earth scientists gathered to discuss this new program, which is an outgrowth of activity during the previous year by an ad hoc steering committee. The research program will be coordinated by a community-based scientific organization and supported through competitive research proposals submitted to the National Science Foundation with the aim of facilitating cooperative research projects cutting across traditional disciplinary and institutional boundaries.The new organization is the U.S. Studies of the Earth's Deep Interior (SEDI) Coordinating Committee. This committee will facilitate communication among the U.S. SEDI research community, federal funding agencies, the AGU Committee for Studies of the Earth's Interior (SEI), the Union SEDI Committee of the International Union of Geodesy and Geophysics, and the general public (Figure 1).

  19. Deep learning in TMVA Benchmarking Benchmarking TMVA DNN Integration of a Deep Autoencoder

    CERN Document Server

    Huwiler, Marc

    2017-01-01

    The TMVA library in ROOT is dedicated to multivariate analysis, and in partic- ular oers numerous machine learning algorithms in a standardized framework. It is widely used in High Energy Physics for data analysis, mainly to perform regression and classication. To keep up to date with the state of the art in deep learning, a new deep learning module was being developed this summer, oering deep neural net- work, convolutional neural network, and autoencoder. TMVA did not have yet any autoencoder method, and the present project consists in implementing the TMVA autoencoder class based on the deep learning module. It also includes some bench- marking performed on the actual deep neural network implementation, in comparison to the Keras framework with Tensorflow and Theano backend.

  20. Shielded battery syndrome: a new hardware complication of deep brain stimulation.

    Science.gov (United States)

    Chelvarajah, Ramesh; Lumsden, Daniel; Kaminska, Margaret; Samuel, Michael; Hulse, Natasha; Selway, Richard P; Lin, Jean-Pierre; Ashkan, Keyoumars

    2012-01-01

    Deep brain stimulation hardware is constantly advancing. The last few years have seen the introduction of rechargeable cell technology into the implanted pulse generator design, allowing for longer battery life and fewer replacement operations. The Medtronic® system requires an additional pocket adaptor when revising a non-rechargeable battery such as their Kinetra® to their rechargeable Activa® RC. This additional hardware item can, if it migrates superficially, become an impediment to the recharging of the battery and negate the intended technological advance. To report the emergence of the 'shielded battery syndrome', which has not been previously described. We reviewed our deep brain stimulation database to identify cases of recharging difficulties reported by patients with Activa RC implanted pulse generators. Two cases of shielded battery syndrome were identified. The first required surgery to reposition the adaptor to the deep aspect of the subcutaneous pocket. In the second case, it was possible to perform external manual manipulation to restore the adaptor to its original position deep to the battery. We describe strategies to minimise the occurrence of the shielded battery syndrome and advise vigilance in all patients who experience difficulty with recharging after replacement surgery of this type for the implanted pulse generator. Copyright © 2012 S. Karger AG, Basel.

  1. Deep subsurface microbial processes

    Science.gov (United States)

    Lovley, D.R.; Chapelle, F.H.

    1995-01-01

    Information on the microbiology of the deep subsurface is necessary in order to understand the factors controlling the rate and extent of the microbially catalyzed redox reactions that influence the geophysical properties of these environments. Furthermore, there is an increasing threat that deep aquifers, an important drinking water resource, may be contaminated by man's activities, and there is a need to predict the extent to which microbial activity may remediate such contamination. Metabolically active microorganisms can be recovered from a diversity of deep subsurface environments. The available evidence suggests that these microorganisms are responsible for catalyzing the oxidation of organic matter coupled to a variety of electron acceptors just as microorganisms do in surface sediments, but at much slower rates. The technical difficulties in aseptically sampling deep subsurface sediments and the fact that microbial processes in laboratory incubations of deep subsurface material often do not mimic in situ processes frequently necessitate that microbial activity in the deep subsurface be inferred through nonmicrobiological analyses of ground water. These approaches include measurements of dissolved H2, which can predict the predominant microbially catalyzed redox reactions in aquifers, as well as geochemical and groundwater flow modeling, which can be used to estimate the rates of microbial processes. Microorganisms recovered from the deep subsurface have the potential to affect the fate of toxic organics and inorganic contaminants in groundwater. Microbial activity also greatly influences 1 the chemistry of many pristine groundwaters and contributes to such phenomena as porosity development in carbonate aquifers, accumulation of undesirably high concentrations of dissolved iron, and production of methane and hydrogen sulfide. Although the last decade has seen a dramatic increase in interest in deep subsurface microbiology, in comparison with the study of

  2. Fish product mislabelling: failings of traceability in the production chain and implications for illegal, unreported and unregulated (IUU) fishing.

    Science.gov (United States)

    Helyar, Sarah J; Lloyd, Hywel Ap D; de Bruyn, Mark; Leake, Jonathan; Bennett, Niall; Carvalho, Gary R

    2014-01-01

    Increasing consumer demand for seafood, combined with concern over the health of our oceans, has led to many initiatives aimed at tackling destructive fishing practices and promoting the sustainability of fisheries. An important global threat to sustainable fisheries is Illegal, Unreported and Unregulated (IUU) fishing, and there is now an increased emphasis on the use of trade measures to prevent IUU-sourced fish and fish products from entering the international market. Initiatives encompass new legislation in the European Union requiring the inclusion of species names on catch labels throughout the distribution chain. Such certification measures do not, however, guarantee accuracy of species designation. Using two DNA-based methods to compare species descriptions with molecular ID, we examined 386 samples of white fish, or products labelled as primarily containing white fish, from major UK supermarket chains. Species specific real-time PCR probes were used for cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) to provide a highly sensitive and species-specific test for the major species of white fish sold in the UK. Additionally, fish-specific primers were used to sequence the forensically validated barcoding gene, mitochondrial cytochrome oxidase I (COI). Overall levels of congruence between product label and genetic species identification were high, with 94.34% of samples correctly labelled, though a significant proportion in terms of potential volume, were mislabelled. Substitution was usually for a cheaper alternative and, in one case, extended to a tropical species. To our knowledge, this is the first published study encompassing a large-scale assessment of UK retailers, and if representative, indicates a potentially significant incidence of incorrect product designation.

  3. Fish product mislabelling: failings of traceability in the production chain and implications for illegal, unreported and unregulated (IUU fishing.

    Directory of Open Access Journals (Sweden)

    Sarah J Helyar

    Full Text Available Increasing consumer demand for seafood, combined with concern over the health of our oceans, has led to many initiatives aimed at tackling destructive fishing practices and promoting the sustainability of fisheries. An important global threat to sustainable fisheries is Illegal, Unreported and Unregulated (IUU fishing, and there is now an increased emphasis on the use of trade measures to prevent IUU-sourced fish and fish products from entering the international market. Initiatives encompass new legislation in the European Union requiring the inclusion of species names on catch labels throughout the distribution chain. Such certification measures do not, however, guarantee accuracy of species designation. Using two DNA-based methods to compare species descriptions with molecular ID, we examined 386 samples of white fish, or products labelled as primarily containing white fish, from major UK supermarket chains. Species specific real-time PCR probes were used for cod (Gadus morhua and haddock (Melanogrammus aeglefinus to provide a highly sensitive and species-specific test for the major species of white fish sold in the UK. Additionally, fish-specific primers were used to sequence the forensically validated barcoding gene, mitochondrial cytochrome oxidase I (COI. Overall levels of congruence between product label and genetic species identification were high, with 94.34% of samples correctly labelled, though a significant proportion in terms of potential volume, were mislabelled. Substitution was usually for a cheaper alternative and, in one case, extended to a tropical species. To our knowledge, this is the first published study encompassing a large-scale assessment of UK retailers, and if representative, indicates a potentially significant incidence of incorrect product designation.

  4. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

    Science.gov (United States)

    Katzman, Jared L; Shaham, Uri; Cloninger, Alexander; Bates, Jonathan; Jiang, Tingting; Kluger, Yuval

    2018-02-26

    Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

  5. Pathogenesis of deep endometriosis.

    Science.gov (United States)

    Gordts, Stephan; Koninckx, Philippe; Brosens, Ivo

    2017-12-01

    The pathophysiology of (deep) endometriosis is still unclear. As originally suggested by Cullen, change the definition "deeper than 5 mm" to "adenomyosis externa." With the discovery of the old European literature on uterine bleeding in 5%-10% of the neonates and histologic evidence that the bleeding represents decidual shedding, it is postulated/hypothesized that endometrial stem/progenitor cells, implanted in the pelvic cavity after birth, may be at the origin of adolescent and even the occasionally premenarcheal pelvic endometriosis. Endometriosis in the adolescent is characterized by angiogenic and hemorrhagic peritoneal and ovarian lesions. The development of deep endometriosis at a later age suggests that deep infiltrating endometriosis is a delayed stage of endometriosis. Another hypothesis is that the endometriotic cell has undergone genetic or epigenetic changes and those specific changes determine the development into deep endometriosis. This is compatible with the hereditary aspects, and with the clonality of deep and cystic ovarian endometriosis. It explains the predisposition and an eventual causal effect by dioxin or radiation. Specific genetic/epigenetic changes could explain the various expressions and thus typical, cystic, and deep endometriosis become three different diseases. Subtle lesions are not a disease until epi(genetic) changes occur. A classification should reflect that deep endometriosis is a specific disease. In conclusion the pathophysiology of deep endometriosis remains debated and the mechanisms of disease progression, as well as the role of genetics and epigenetics in the process, still needs to be unraveled. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

  6. DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters

    OpenAIRE

    Kim, Hanjoo; Park, Jaehong; Jang, Jaehee; Yoon, Sungroh

    2016-01-01

    The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operation...

  7. Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

    Science.gov (United States)

    Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

    2017-05-25

    Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

  8. Seasonal copepod lipid pump promotes carbon sequestration in the deep North Atlantic.

    Science.gov (United States)

    Jónasdóttir, Sigrún Huld; Visser, André W; Richardson, Katherine; Heath, Michael R

    2015-09-29

    Estimates of carbon flux to the deep oceans are essential for our understanding of global carbon budgets. Sinking of detrital material ("biological pump") is usually thought to be the main biological component of this flux. Here, we identify an additional biological mechanism, the seasonal "lipid pump," which is highly efficient at sequestering carbon into the deep ocean. It involves the vertical transport and metabolism of carbon rich lipids by overwintering zooplankton. We show that one species, the copepod Calanus finmarchicus overwintering in the North Atlantic, sequesters an amount of carbon equivalent to the sinking flux of detrital material. The efficiency of the lipid pump derives from a near-complete decoupling between nutrient and carbon cycling—a "lipid shunt," and its direct transport of carbon through the mesopelagic zone to below the permanent thermocline with very little attenuation. Inclusion of the lipid pump almost doubles the previous estimates of deep-ocean carbon sequestration by biological processes in the North Atlantic.

  9. Exploring fungal diversity in deep-sea sediments from Okinawa Trough using high-throughput Illumina sequencing

    Science.gov (United States)

    Zhang, Xiao-Yong; Wang, Guang-Hua; Xu, Xin-Ya; Nong, Xu-Hua; Wang, Jie; Amin, Muhammad; Qi, Shu-Hua

    2016-10-01

    The present study investigated the fungal diversity in four different deep-sea sediments from Okinawa Trough using high-throughput Illumina sequencing of the nuclear ribosomal internal transcribed spacer-1 (ITS1). A total of 40,297 fungal ITS1 sequences clustered into 420 operational taxonomic units (OTUs) with 97% sequence similarity and 170 taxa were recovered from these sediments. Most ITS1 sequences (78%) belonged to the phylum Ascomycota, followed by Basidiomycota (17.3%), Zygomycota (1.5%) and Chytridiomycota (0.8%), and a small proportion (2.4%) belonged to unassigned fungal phyla. Compared with previous studies on fungal diversity of sediments from deep-sea environments by culture-dependent approach and clone library analysis, the present result suggested that Illumina sequencing had been dramatically accelerating the discovery of fungal community of deep-sea sediments. Furthermore, our results revealed that Sordariomycetes was the most diverse and abundant fungal class in this study, challenging the traditional view that the diversity of Sordariomycetes phylotypes was low in the deep-sea environments. In addition, more than 12 taxa accounted for 21.5% sequences were found to be rarely reported as deep-sea fungi, suggesting the deep-sea sediments from Okinawa Trough harbored a plethora of different fungal communities compared with other deep-sea environments. To our knowledge, this study is the first exploration of the fungal diversity in deep-sea sediments from Okinawa Trough using high-throughput Illumina sequencing.

  10. DeepPVP: phenotype-based prioritization of causative variants using deep learning

    KAUST Repository

    Boudellioua, Imene; Kulmanov, Maxat; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert

    2018-01-01

    phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well

  11. Seawater Carbonate Chemistry of Deep-sea Coral Beds off the Northwestern Hawaiian Islands

    Science.gov (United States)

    Brooks, J.; Shamberger, K.; Roark, E. B.; Miller, K.; Baco-Taylor, A.

    2016-02-01

    Many species of deep-sea octocorals produce calcium carbonate (CaCO3) skeletons and form coral beds that support diverse ecosystems crucial to fisheries. The geochemistry of deep-sea coral skeletons can provide valuable paleoceanographic information on ocean circulation and nutrient cycling. Deep-sea corals in the older bottom waters of the Pacific are naturally exposed to higher carbon dioxide (CO2) concentrations and lower pH than in the Atlantic where much of the previous deep-sea coral work has occurred. Therefore, some Pacific deep-sea corals may live and calcify in waters that are corrosive to their skeletons, but there have been few current seawater carbonate chemistry measurements of the waters surrounding deep-sea coral beds to assess this. The input of anthropogenic atmospheric CO2 known as ocean acidification (OA) lowers ocean pH and causes an expansion of these corrosive waters. Seawater carbonate chemistry must be characterized before accurate predictions can be made for the effects of OA on these important ecosystems. Total Alkalinity (TA) and Dissolved Inorganic Carbon (DIC) samples were collected in the fall of 2014 and 2015 from the surface to 1450 m depth off the Northwestern Hawaiian Island chain where deep-sea octocorals are found. The partial pressure of CO2 increased and pH, calcite saturation state (Ωca) and aragonite saturation state (Ωar) decreased with increasing latitude and depth. Notably, waters were undersaturated with respect to calcite and aragonite (Ωca and Ωar less than 1) below 800 m and 500 m, respectively. Therefore, deep-sea corals below these depths must calcify in waters that are thermodynamically favorable for CaCO3 dissolution. How deep-sea octocorals cope with such adverse seawater chemistry is critical to understanding future effects of OA. It is not known whether OA is currently negatively impacting deep-sea octocorals, but their naturally acidified environments could make them particularly susceptible to OA.

  12. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

    Science.gov (United States)

    Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

    2018-02-01

    Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The

  13. Systematic review of the risk of uterine rupture with the use of amnioinfusion after previous cesarean delivery.

    Science.gov (United States)

    Hicks, Paul

    2005-04-01

    Amnioinfusion is commonly used for the intrapartum treatment of women with pregnancy complicated by thick meconium or oligohydramnios with deep variable fetal heart rate decelerations. Its benefit in women with previous cesarean deliveries is less known. Theoretically, rapid increases in intrauterine volume would lead to a higher risk of uterine rupture. Searches of the Cochrane Library from inception to the third quarter of 2001 and MEDLINE, 1966 to November 2001, were performed by using keywords "cesarean" and "amnioinfusion." Search terms were expanded to maximize results. All languages were included. Review articles, editorials, and data previously published in other sites were not analyzed. Four studies were retrieved having unduplicated data describing amnioinfusion in women who were attempting a trial of labor after previous cesarean section. As the studies were of disparate types, meta-analysis was not possible. The use of amnioinfusion in women with previous cesarean delivery who are undergoing a trial of labor may be a safe procedure, but confirmatory large, controlled prospective studies are needed before definitive recommendations can be made.

  14. ExpNet: Landmark-Free, Deep, 3D Facial Expressions

    OpenAIRE

    Chang, Feng-Ju; Tran, Anh Tuan; Hassner, Tal; Masi, Iacopo; Nevatia, Ram; Medioni, Gerard

    2018-01-01

    We describe a deep learning based method for estimating 3D facial expression coefficients. Unlike previous work, our process does not relay on facial landmark detection methods as a proxy step. Recent methods have shown that a CNN can be trained to regress accurate and discriminative 3D morphable model (3DMM) representations, directly from image intensities. By foregoing facial landmark detection, these methods were able to estimate shapes for occluded faces appearing in unprecedented in-the-...

  15. Stimulation Technologies for Deep Well Completions

    Energy Technology Data Exchange (ETDEWEB)

    None

    2003-09-30

    The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a study to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. An assessment of historical deep gas well drilling activity and forecast of future trends was completed during the first six months of the project; this segment of the project was covered in Technical Project Report No. 1. The second progress report covers the next six months of the project during which efforts were primarily split between summarizing rock mechanics and fracture growth in deep reservoirs and contacting operators about case studies of deep gas well stimulation.

  16. DeepQA: improving the estimation of single protein model quality with deep belief networks.

    Science.gov (United States)

    Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

    2016-12-05

    Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

  17. Sea-level and deep-sea-temperature variability over the past 5.3 million years.

    Science.gov (United States)

    Rohling, E J; Foster, G L; Grant, K M; Marino, G; Roberts, A P; Tamisiea, M E; Williams, F

    2014-04-24

    Ice volume (and hence sea level) and deep-sea temperature are key measures of global climate change. Sea level has been documented using several independent methods over the past 0.5 million years (Myr). Older periods, however, lack such independent validation; all existing records are related to deep-sea oxygen isotope (δ(18)O) data that are influenced by processes unrelated to sea level. For deep-sea temperature, only one continuous high-resolution (Mg/Ca-based) record exists, with related sea-level estimates, spanning the past 1.5 Myr. Here we present a novel sea-level reconstruction, with associated estimates of deep-sea temperature, which independently validates the previous 0-1.5 Myr reconstruction and extends it back to 5.3 Myr ago. We find that deep-sea temperature and sea level generally decreased through time, but distinctly out of synchrony, which is remarkable given the importance of ice-albedo feedbacks on the radiative forcing of climate. In particular, we observe a large temporal offset during the onset of Plio-Pleistocene ice ages, between a marked cooling step at 2.73 Myr ago and the first major glaciation at 2.15 Myr ago. Last, we tentatively infer that ice sheets may have grown largest during glacials with more modest reductions in deep-sea temperature.

  18. Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.

    Science.gov (United States)

    Shafiee, Mohammad Javad; Chung, Audrey G; Khalvati, Farzad; Haider, Masoom A; Wong, Alexander

    2017-10-01

    While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose an evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist's computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically proven diagnostic data from the LIDC-IDRI dataset. The EDRS shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.

  19. Stimulation Technologies for Deep Well Completions

    Energy Technology Data Exchange (ETDEWEB)

    Stephen Wolhart

    2005-06-30

    The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies conducted a study to evaluate the stimulation of deep wells. The objective of the project was to review U.S. deep well drilling and stimulation activity, review rock mechanics and fracture growth in deep, high-pressure/temperature wells and evaluate stimulation technology in several key deep plays. This report documents results from this project.

  20. Exploring the Earth Using Deep Learning Techniques

    Science.gov (United States)

    Larraondo, P. R.; Evans, B. J. K.; Antony, J.

    2016-12-01

    Research using deep neural networks have significantly matured in recent times, and there is now a surge in interest to apply such methods to Earth systems science and the geosciences. When combined with Big Data, we believe there are opportunities for significantly transforming a number of areas relevant to researchers and policy makers. In particular, by using a combination of data from a range of satellite Earth observations as well as computer simulations from climate models and reanalysis, we can gain new insights into the information that is locked within the data. Global geospatial datasets describe a wide range of physical and chemical parameters, which are mostly available using regular grids covering large spatial and temporal extents. This makes them perfect candidates to apply deep learning methods. So far, these techniques have been successfully applied to image analysis through the use of convolutional neural networks. However, this is only one field of interest, and there is potential for many more use cases to be explored. The deep learning algorithms require fast access to large amounts of data in the form of tensors and make intensive use of CPU in order to train its models. The Australian National Computational Infrastructure (NCI) has recently augmented its Raijin 1.2 PFlop supercomputer with hardware accelerators. Together with NCI's 3000 core high performance OpenStack cloud, these computational systems have direct access to NCI's 10+ PBytes of datasets and associated Big Data software technologies (see http://geonetwork.nci.org.au/ and http://nci.org.au/systems-services/national-facility/nerdip/). To effectively use these computing infrastructures requires that both the data and software are organised in a way that readily supports the deep learning software ecosystem. Deep learning software, such as the open source TensorFlow library, has allowed us to demonstrate the possibility of generating geospatial models by combining information from

  1. Deep and shallow water effects on developing preschoolers' aquatic skills.

    Science.gov (United States)

    Costa, Aldo M; Marinho, Daniel A; Rocha, Helena; Silva, António J; Barbosa, Tiago M; Ferreira, Sandra S; Martins, Marta

    2012-05-01

    The aim of the study was to assess deep and shallow water teaching methods in swimming lessons for preschool children and identify variations in the basic aquatic skills acquired. The study sample included 32 swimming instructors (16 from deep water programs and 16 from shallow water programs) and 98 preschool children (50 from deep water swimming pool and 48 from shallow water swimming pool). The children were also studied regarding their previous experience in swimming (6, 12 and 18 months or practice). Chi-Square test and Fisher's exact test were used to compare the teaching methodology. A discriminant analysis was conducted with Λ wilk's method to predict under what conditions students are better or worse (aquatic competence). Results suggest that regardless of the non-significant variations found in teaching methods, the water depth can affect aquatic skill acquisition - shallow water lessons seem to impose greater water competence particularly after 6 months of practice. The discriminant function revealed a significant association between groups and all predictors for 6 months of swimming practice (pdeep and shallow water programs for preschoolers is not significantly different. However, shallow water lessons could be preferable for the development of basic aquatic skills.

  2. DeepBase: annotation and discovery of microRNAs and other noncoding RNAs from deep-sequencing data.

    Science.gov (United States)

    Yang, Jian-Hua; Qu, Liang-Hu

    2012-01-01

    Recent advances in high-throughput deep-sequencing technology have produced large numbers of short and long RNA sequences and enabled the detection and profiling of known and novel microRNAs (miRNAs) and other noncoding RNAs (ncRNAs) at unprecedented sensitivity and depth. In this chapter, we describe the use of deepBase, a database that we have developed to integrate all public deep-sequencing data and to facilitate the comprehensive annotation and discovery of miRNAs and other ncRNAs from these data. deepBase provides an integrative, interactive, and versatile web graphical interface to evaluate miRBase-annotated miRNA genes and other known ncRNAs, explores the expression patterns of miRNAs and other ncRNAs, and discovers novel miRNAs and other ncRNAs from deep-sequencing data. deepBase also provides a deepView genome browser to comparatively analyze these data at multiple levels. deepBase is available at http://deepbase.sysu.edu.cn/.

  3. Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2016-10-15

    In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea.

  4. Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

    International Nuclear Information System (INIS)

    Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu

    2016-01-01

    In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea

  5. A Pseudoaneurysm of the Deep Palmar Arch After Penetrating Trauma to the Hand: Successful Exclusion by Ultrasound Guided Percutaneous Thrombin Injection

    Directory of Open Access Journals (Sweden)

    A. Bosman

    Full Text Available : Introduction: Pseudoaneurysm of the hand is a rare condition; most are treated surgically. Ultrasound guided thrombin injection has not previously been reported as a treatment option for pseudoaneurysms of the deep palmar arch. Report: A man was referred to the emergency department with a swollen, painful hand after penetrating trauma. On physical examination, a pulsating tumor was found on the dorsum of the hand. Imaging revealed a pseudoaneurysm vascularized by the deep palmar arch. Ultrasound guided percutaneous thrombin injection was successfully performed. Conclusion: Thrombin injection might be a safe alternative option in the treatment of pseudoaneurysm of the deep palmar arch. Keywords: Deep palmar arch, Pseudoaneurysm, Thrombin injection

  6. Automated embolic signal detection using Deep Convolutional Neural Network.

    Science.gov (United States)

    Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

    2017-07-01

    This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

  7. Deep Mapping and Spatial Anthropology

    Directory of Open Access Journals (Sweden)

    Les Roberts

    2016-01-01

    Full Text Available This paper provides an introduction to the Humanities Special Issue on “Deep Mapping”. It sets out the rationale for the collection and explores the broad-ranging nature of perspectives and practices that fall within the “undisciplined” interdisciplinary domain of spatial humanities. Sketching a cross-current of ideas that have begun to coalesce around the concept of “deep mapping”, the paper argues that rather than attempting to outline a set of defining characteristics and “deep” cartographic features, a more instructive approach is to pay closer attention to the multivalent ways deep mapping is performatively put to work. Casting a critical and reflexive gaze over the developing discourse of deep mapping, it is argued that what deep mapping “is” cannot be reduced to the otherwise a-spatial and a-temporal fixity of the “deep map”. In this respect, as an undisciplined survey of this increasing expansive field of study and practice, the paper explores the ways in which deep mapping can engage broader discussion around questions of spatial anthropology.

  8. Hourly air pollution concentrations and their important predictors over Houston, Texas using deep neural networks: case study of DISCOVER-AQ time period

    Science.gov (United States)

    Eslami, E.; Choi, Y.; Roy, A.

    2017-12-01

    Air quality forecasting carried out by chemical transport models often show significant error. This study uses a deep-learning approach over the Houston-Galveston-Brazoria (HGB) area to overcome this forecasting challenge, for the DISCOVER-AQ period (September 2013). Two approaches, deep neural network (DNN) using a Multi-Layer Perceptron (MLP) and Restricted Boltzmann Machine (RBM) were utilized. The proposed approaches analyzed input data by identifying features abstracted from its previous layer using a stepwise method. The approaches predicted hourly ozone and PM in September 2013 using several predictors of prior three days, including wind fields, temperature, relative humidity, cloud fraction, precipitation along with PM, ozone, and NOx concentrations. Model-measurement comparisons for available monitoring sites reported Indexes of Agreement (IOA) of around 0.95 for both DNN and RBM. A standard artificial neural network (ANN) (IOA=0.90) with similar architecture showed poorer performance than the deep networks, clearly demonstrating the superiority of the deep approaches. Additionally, each network (both deep and standard) performed significantly better than a previous CMAQ study, which showed an IOA of less than 0.80. The most influential input variables were identified using their associated weights, which represented the sensitivity of ozone to input parameters. The results indicate deep learning approaches can achieve more accurate ozone forecasting and identify the important input variables for ozone predictions in metropolitan areas.

  9. Deep Vein Thrombosis

    African Journals Online (AJOL)

    OWNER

    Deep Vein Thrombosis: Risk Factors and Prevention in Surgical Patients. Deep Vein ... preventable morbidity and mortality in hospitalized surgical patients. ... the elderly.3,4 It is very rare before the age ... depends on the risk level; therefore an .... but also in the post-operative period. ... is continuing uncertainty regarding.

  10. Bilateral Deep Vein Thrombosis Associated with Inferior Vena Cava Agenesis in a Young Patient Manifesting as Low Back Pain

    Directory of Open Access Journals (Sweden)

    Felipe Langer

    2017-04-01

    Full Text Available Congenital absence of the inferior vena cava is a rare vascular anomaly, and most cases are asymptomatic. Nevertheless, patients with inferior vena cava malformations may have increased risk of deep venous thrombosis. Particularly, cases of bilateral deep venous thrombosis may arise owing to an insufficient collateral venous drainage from the lower limbs. We hereby describe a case of a previously healthy young male patient presenting with bilateral lower limb deep venous thrombosis as the initial clinical manifestation of congenital inferior vena cava agenesis. We conclude that in young patients presenting with deep venous thrombosis, especially when thrombosis occurs spontaneously, bilaterally, or recurrently, inferior vena cava anomalies should be thoroughly investigated and ruled out as appropriate.

  11. Ventilation of the deep Greenland and Norwegian seas: evidence from krypton-85, tritium, carbon-14 and argon-39

    International Nuclear Information System (INIS)

    Smethie, W.M. Jr.; Ostlund, H.G.; Loosli, H.H.

    1986-01-01

    On leg 5 of the TTO expedition, the distributions of 85 Kr, tritium, 14 C, 39 Ar, temperature, salinity, oxygen, carbon dioxide and nutrients were measured in the Greenland and Norwegian seas. These observations support previous observations that Greenland Sea Deep Water is formed by a deep convective process within the Greenland gyre. They also support AAGAARD et al.'s (1985, Journal of Geophysical Research, 90, 4833-4846) new hypothesis that Norwegian Sea Deep Water forms from a mixture of Greenland Sea Deep Water and Eurasian Basin Deep Water. Volume transports estimated from the distributions of 85 Kr, tritium, 14 C and 39 Ar range from 0.53 to 0.74 Sv for exchange between the surface and deep Greenland Sea and from 0.9 to 1.47 Sv for exchange between the deep Greenland and deep Norwegian Seas. The residence time of water and the deep Greenland Sea with respect to exchange with surface water ranges from 24 to 34 years reported by PETERSON and ROOTH (1976, Deep-Sea Research, 23, 273-283) and 35-42 years reported by BULLISTER and WEISS (1983, Science, 221, 265-268). The residence time of water in the deep Norwegian Sea with respect to exchange with the deep Greenland Sea ranges from 19 to 30 years compared to 97-107 years reported by PETERSON and ROOTH (1976) and 10-28 years reported by BULLISTER and WEISS (1983). The oxygen consumption rate was estimated to be at most 1.04 μM kg -1 y -1 for the deep Greenland Sea and to be between 0.47 and 0.79 μM kg -1 y -1 for the deep Norwegian Sea. (author)

  12. Limitations of microbial hydrocarbon degradation at the Amon mud volcano (Nile deep-sea fan)

    NARCIS (Netherlands)

    Felden, J.; Lichtschlag, A.; Wenzhöfer, F.; de Beer, D.; Feseker, T.; Pop Ristova, P.; de Lange, G.; Boetius, A.

    2013-01-01

    The Amon mud volcano (MV), located at 1250m water depth on the Nile deep-sea fan, is known for its active emission of methane and non-methane hydrocarbons into the hydrosphere. Previous investigations showed a low efficiency of hydrocarbon-degrading anaerobic microbial communities inhabiting the

  13. Oral Microbiome of Deep and Shallow Dental Pockets In Chronic Periodontitis

    Science.gov (United States)

    Ge, Xiuchun; Rodriguez, Rafael; Trinh, My; Gunsolley, John; Xu, Ping

    2013-01-01

    We examined the subgingival bacterial biodiversity in untreated chronic periodontitis patients by sequencing 16S rRNA genes. The primary purpose of the study was to compare the oral microbiome in deep (diseased) and shallow (healthy) sites. A secondary purpose was to evaluate the influences of smoking, race and dental caries on this relationship. A total of 88 subjects from two clinics were recruited. Paired subgingival plaque samples were taken from each subject, one from a probing site depth >5 mm (deep site) and the other from a probing site depth ≤3mm (shallow site). A universal primer set was designed to amplify the V4–V6 region for oral microbial 16S rRNA sequences. Differences in genera and species attributable to deep and shallow sites were determined by statistical analysis using a two-part model and false discovery rate. Fifty-one of 170 genera and 200 of 746 species were found significantly different in abundances between shallow and deep sites. Besides previously identified periodontal disease-associated bacterial species, additional species were found markedly changed in diseased sites. Cluster analysis revealed that the microbiome difference between deep and shallow sites was influenced by patient-level effects such as clinic location, race and smoking. The differences between clinic locations may be influenced by racial distribution, in that all of the African Americans subjects were seen at the same clinic. Our results suggested that there were influences from the microbiome for caries and periodontal disease and these influences are independent. PMID:23762384

  14. MRI finding of ethylmalonic encephalopathy: case report

    International Nuclear Information System (INIS)

    Kim, Jin Yong; Lee, Shi Kyung; Han, Chun Hwan; Rho, Eun Jin

    2002-01-01

    Ethylmalonic encephalopathy is a rare syndrom characterized by developmental delay, acrocyanosis, petechiae, chronic diarrhea, and ethylmalonic, lactic, and methylsuccinic aciduria. We report the MRI finding of ethylmalonic encephalopathy including previously unreported intracranial hematoma

  15. pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

    Science.gov (United States)

    Zhou, Xie-Xuan; Zeng, Wen-Feng; Chi, Hao; Luo, Chunjie; Liu, Chao; Zhan, Jianfeng; He, Si-Min; Zhang, Zhifei

    2017-12-05

    In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

  16. A PILOT FOR A VERY LARGE ARRAY H I DEEP FIELD

    International Nuclear Information System (INIS)

    Fernández, Ximena; Van Gorkom, J. H.; Schiminovich, David; Hess, Kelley M.; Pisano, D. J.; Kreckel, Kathryn; Momjian, Emmanuel; Popping, Attila; Oosterloo, Tom; Chomiuk, Laura; Verheijen, M. A. W.; Henning, Patricia A.; Bershady, Matthew A.; Wilcots, Eric M.; Scoville, Nick

    2013-01-01

    High-resolution 21 cm H I deep fields provide spatially and kinematically resolved images of neutral hydrogen at different redshifts, which are key to understanding galaxy evolution across cosmic time and testing predictions of cosmological simulations. Here we present results from a pilot for an H I deep field done with the Karl G. Jansky Very Large Array (VLA). We take advantage of the newly expanded capabilities of the telescope to probe the redshift interval 0 < z < 0.193 in one observation. We observe the COSMOS field for 50 hr, which contains 413 galaxies with optical spectroscopic redshifts in the imaged field of 34' × 34' and the observed redshift interval. We have detected neutral hydrogen gas in 33 galaxies in different environments spanning the probed redshift range, including three without a previously known spectroscopic redshift. The detections have a range of H I and stellar masses, indicating the diversity of galaxies we are probing. We discuss the observations, data reduction, results, and highlight interesting detections. We find that the VLA's B-array is the ideal configuration for H I deep fields since its long spacings mitigate radio frequency interference. This pilot shows that the VLA is ready to carry out such a survey, and serves as a test for future H I deep fields planned with other Square Kilometer Array pathfinders.

  17. A PILOT FOR A VERY LARGE ARRAY H I DEEP FIELD

    Energy Technology Data Exchange (ETDEWEB)

    Fernandez, Ximena; Van Gorkom, J. H.; Schiminovich, David [Department of Astronomy, Columbia University, New York, NY 10027 (United States); Hess, Kelley M. [Department of Astronomy, Astrophysics, Cosmology and Gravity Centre, University of Cape Town, Private Bag X3, Rondebosch 7701 (South Africa); Pisano, D. J. [Department of Physics, West Virginia University, P.O. Box 6315, Morgantown, WV 26506 (United States); Kreckel, Kathryn [Max Planck Institute for Astronomy, Koenigstuhl 17, D-69117 Heidelberg (Germany); Momjian, Emmanuel [National Radio Astronomy Observatory, Socorro, NM 87801 (United States); Popping, Attila [International Centre for Radio Astronomy Research (ICRAR), The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009 (Australia); Oosterloo, Tom [Netherlands Institute for Radio Astronomy (ASTRON), Postbus 2, NL-7990 AA Dwingeloo (Netherlands); Chomiuk, Laura [Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 (United States); Verheijen, M. A. W. [Kapteyn Astronomical Institute, University of Groningen, Postbus 800, NL-9700 AV Groningen (Netherlands); Henning, Patricia A. [Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM 87131 (United States); Bershady, Matthew A.; Wilcots, Eric M. [Department of Astronomy, University of Wisconsin-Madison, Madison, WI 53706 (United States); Scoville, Nick, E-mail: ximena@astro.columbia.edu [Department of Astronomy, California Institute of Technology, Pasadena, CA 91125 (United States)

    2013-06-20

    High-resolution 21 cm H I deep fields provide spatially and kinematically resolved images of neutral hydrogen at different redshifts, which are key to understanding galaxy evolution across cosmic time and testing predictions of cosmological simulations. Here we present results from a pilot for an H I deep field done with the Karl G. Jansky Very Large Array (VLA). We take advantage of the newly expanded capabilities of the telescope to probe the redshift interval 0 < z < 0.193 in one observation. We observe the COSMOS field for 50 hr, which contains 413 galaxies with optical spectroscopic redshifts in the imaged field of 34' Multiplication-Sign 34' and the observed redshift interval. We have detected neutral hydrogen gas in 33 galaxies in different environments spanning the probed redshift range, including three without a previously known spectroscopic redshift. The detections have a range of H I and stellar masses, indicating the diversity of galaxies we are probing. We discuss the observations, data reduction, results, and highlight interesting detections. We find that the VLA's B-array is the ideal configuration for H I deep fields since its long spacings mitigate radio frequency interference. This pilot shows that the VLA is ready to carry out such a survey, and serves as a test for future H I deep fields planned with other Square Kilometer Array pathfinders.

  18. Luminescence and deep-level transient spectroscopy of grown dislocation-rich Si layers

    Directory of Open Access Journals (Sweden)

    I. I. Kurkina

    2012-09-01

    Full Text Available The charge deep-level transient spectroscopy (Q-DLTS is applied to the study of the dislocation-rich Si layers grown on a surface composed of dense arrays of Ge islands prepared on the oxidized Si surface. This provides revealing three deep-level bands located at EV + 0.31 eV, EC – 0.35 eV and EC – 0.43 eV using the stripe-shaped p-i-n diodes fabricated on the basis of these layers. The most interesting observation is the local state recharging process which proceeds with low activation energy (∼50 meV or without activation. The recharging may occur by carrier tunneling within deep-level bands owing to the high dislocation density ∼ 1011 - 1012 cm-2. This result is in favor of the suggestion on the presence of carrier transport between the deep states, which was previously derived from the excitation dependence of photoluminescence (PL intensity. Electroluminescence (EL spectra measured from the stripe edge of the same diodes contain two peaks centered near 1.32 and 1.55 μm. Comparison with PL spectra indicates that the EL peaks are generated from arsenic-contaminated and pure areas of the layers, respectively.

  19. Deep learning for EEG-Based preference classification

    Science.gov (United States)

    Teo, Jason; Hou, Chew Lin; Mountstephens, James

    2017-10-01

    Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

  20. Clinical analysis of 28 patients with deep neck infection

    International Nuclear Information System (INIS)

    Takeda, Shoichiro; Kobayashi, Taisuke; Nakamura, Koshiro

    2008-01-01

    Although the number of patients with deep neck infection has been decreasing with the development of antibiotics, this condition sometimes shows a poor prognosis. Early diagnosis and appropriate treatment are important for good results. In this study, 28 patients (21 males, 7 females) with deep neck infection treated between April 1991 and March 2004 at Department of Otolaryngology, Ehime Prefectural Central Hospital were investigated retrospectively. Twenty-seven of those patients resulted in good recovery, while one patient died the day after admission because of disseminated intravascular coagulation (DIC). Surgical drainage was performed for 19 of 28 patients and tracheostomy or postoperative endotracheal intubation was performed in eight patients. The average interval between admission and surgery was 1.2 days, which was shorter than that in other previous reports. Early diagnosis using CT with enhancement and surgical drainage are important in order to prevent progression of abscess into the mediastinum, which is sometimes fatal. Tracheostomy or postoperative endotracheal intubation is necessary when upper aiway stenosis is occurs. (author)

  1. Salinity of deep groundwater in California: Water quantity, quality, and protection

    Science.gov (United States)

    Kang, Mary; Jackson, Robert B.

    2016-01-01

    Deep groundwater aquifers are poorly characterized but could yield important sources of water in California and elsewhere. Deep aquifers have been developed for oil and gas extraction, and this activity has created both valuable data and risks to groundwater quality. Assessing groundwater quantity and quality requires baseline data and a monitoring framework for evaluating impacts. We analyze 938 chemical, geological, and depth data points from 360 oil/gas fields across eight counties in California and depth data from 34,392 oil and gas wells. By expanding previous groundwater volume estimates from depths of 305 m to 3,000 m in California’s Central Valley, an important agricultural region with growing groundwater demands, fresh [groundwater volume is almost tripled to 2,700 km3, most of it found shallower than 1,000 m. The 3,000-m depth zone also provides 3,900 km3 of fresh and saline water, not previously estimated, that can be categorized as underground sources of drinking water (USDWs; freshwater zones and USDWs, respectively, in the eight counties. Deeper activities, such as wastewater injection, may also pose a potential threat to groundwater, especially USDWs. Our findings indicate that California’s Central Valley alone has close to three times the volume of fresh groundwater and four times the volume of USDWs than previous estimates suggest. Therefore, efforts to monitor and protect deeper, saline groundwater resources are needed in California and beyond. PMID:27354527

  2. Review of geoscientific data of relevance to disposal of spent nuclear fuel in deep boreholes in crystalline rock

    International Nuclear Information System (INIS)

    Marsic, Nico; Grundfelt, Bertil

    2013-09-01

    In this report a compilation of recent geoscientific data of relevance to disposal of spent nuclear fuel in deep boreholes in Sweden is presented. The goal of the study has been limited to identifying and briefly describing such geoscientific information of relevance to disposal in deep boreholes that was not available at the time when previous compilations were made. Hence, the study is not to be regarded as a general up-date of new geoscientific information. Disposal of spent nuclear fuel in deep boreholes has been studied in Sweden since the second half of the 1980s. The currently studied concept has been proposed by Sandia National Laboratories in the USA. In this concept the spent fuel elements are encapsulated in cylindrical steel canisters that are joined together in strings of 40 canisters and lowered into five kilometres deep boreholes. Ten such strings are stacked between three and five kilometres depth separated from each other by concrete plugs. The study started with a review of boreholes that have been reported after the previous reviews that were published in 1998 and 2004. A total of 12 boreholes of potential relevance were identified. Further study showed that only four out of these holes penetrated into crystalline rock. Two of these were deemed to be less relevant because they were drilled in areas with much higher geothermal gradient than in the parts of the Fennoscandian shield that realistically could host a Swedish deep borehole repository. Of the two remaining boreholes, only one, a geoscientific hole drilled at Outokumpu in Finland, is associated with a reasonably complete geoscientific data set. It is worth mentioning that a large part of this hole is drilled through meta sedimentary rock (mica schist) rather than granitic rock. The information collected and reviewed has been gathered under the headings hydraulic conditions, geothermal conditions, hydrogeochemical conditions, bacteriological activity and rock mechanical properties. Only

  3. Review of geoscientific data of relevance to disposal of spent nuclear fuel in deep boreholes in crystalline rock

    Energy Technology Data Exchange (ETDEWEB)

    Marsic, Nico; Grundfelt, Bertil [Kemakta Konsult AB, Stockholm (Sweden)

    2013-09-15

    In this report a compilation of recent geoscientific data of relevance to disposal of spent nuclear fuel in deep boreholes in Sweden is presented. The goal of the study has been limited to identifying and briefly describing such geoscientific information of relevance to disposal in deep boreholes that was not available at the time when previous compilations were made. Hence, the study is not to be regarded as a general up-date of new geoscientific information. Disposal of spent nuclear fuel in deep boreholes has been studied in Sweden since the second half of the 1980s. The currently studied concept has been proposed by Sandia National Laboratories in the USA. In this concept the spent fuel elements are encapsulated in cylindrical steel canisters that are joined together in strings of 40 canisters and lowered into five kilometres deep boreholes. Ten such strings are stacked between three and five kilometres depth separated from each other by concrete plugs. The study started with a review of boreholes that have been reported after the previous reviews that were published in 1998 and 2004. A total of 12 boreholes of potential relevance were identified. Further study showed that only four out of these holes penetrated into crystalline rock. Two of these were deemed to be less relevant because they were drilled in areas with much higher geothermal gradient than in the parts of the Fennoscandian shield that realistically could host a Swedish deep borehole repository. Of the two remaining boreholes, only one, a geoscientific hole drilled at Outokumpu in Finland, is associated with a reasonably complete geoscientific data set. It is worth mentioning that a large part of this hole is drilled through meta sedimentary rock (mica schist) rather than granitic rock. The information collected and reviewed has been gathered under the headings hydraulic conditions, geothermal conditions, hydrogeochemical conditions, bacteriological activity and rock mechanical properties. Only

  4. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

    Science.gov (United States)

    Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

    2017-11-13

    Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

  5. Deep learning for image classification

    Science.gov (United States)

    McCoppin, Ryan; Rizki, Mateen

    2014-06-01

    This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

  6. Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures.

    Science.gov (United States)

    Trullo, Roger; Petitjean, Caroline; Nie, Dong; Shen, Dinggang; Ruan, Su

    2017-09-01

    Computed Tomography (CT) is the standard imaging technique for radiotherapy planning. The delineation of Organs at Risk (OAR) in thoracic CT images is a necessary step before radiotherapy, for preventing irradiation of healthy organs. However, due to low contrast, multi-organ segmentation is a challenge. In this paper, we focus on developing a novel framework for automatic delineation of OARs. Different from previous works in OAR segmentation where each organ is segmented separately, we propose two collaborative deep architectures to jointly segment all organs, including esophagus, heart, aorta and trachea. Since most of the organ borders are ill-defined, we believe spatial relationships must be taken into account to overcome the lack of contrast. The aim of combining two networks is to learn anatomical constraints with the first network, which will be used in the second network, when each OAR is segmented in turn. Specifically, we use the first deep architecture, a deep SharpMask architecture, for providing an effective combination of low-level representations with deep high-level features, and then take into account the spatial relationships between organs by the use of Conditional Random Fields (CRF). Next, the second deep architecture is employed to refine the segmentation of each organ by using the maps obtained on the first deep architecture to learn anatomical constraints for guiding and refining the segmentations. Experimental results show superior performance on 30 CT scans, comparing with other state-of-the-art methods.

  7. Deep learning for computational chemistry

    Energy Technology Data Exchange (ETDEWEB)

    Goh, Garrett B. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Hodas, Nathan O. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Vishnu, Abhinav [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354

    2017-03-08

    The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.

  8. Deep learning for computational chemistry.

    Science.gov (United States)

    Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

    2017-06-15

    The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  9. The Next Era: Deep Learning in Pharmaceutical Research.

    Science.gov (United States)

    Ekins, Sean

    2016-11-01

    Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

  10. What Really is Deep Learning Doing?

    OpenAIRE

    Xiong, Chuyu

    2017-01-01

    Deep learning has achieved a great success in many areas, from computer vision to natural language processing, to game playing, and much more. Yet, what deep learning is really doing is still an open question. There are a lot of works in this direction. For example, [5] tried to explain deep learning by group renormalization, and [6] tried to explain deep learning from the view of functional approximation. In order to address this very crucial question, here we see deep learning from perspect...

  11. Taoism and Deep Ecology.

    Science.gov (United States)

    Sylvan, Richard; Bennett, David

    1988-01-01

    Contrasted are the philosophies of Deep Ecology and ancient Chinese. Discusses the cosmology, morality, lifestyle, views of power, politics, and environmental philosophies of each. Concludes that Deep Ecology could gain much from Taoism. (CW)

  12. Clustered deep shadow maps for integrated polyhedral and volume rendering

    KAUST Repository

    Bornik, Alexander

    2012-01-01

    This paper presents a hardware-accelerated approach for shadow computation in scenes containing both complex volumetric objects and polyhedral models. Our system is the first hardware accelerated complete implementation of deep shadow maps, which unifies the computation of volumetric and geometric shadows. Up to now such unified computation was limited to software-only rendering . Previous hardware accelerated techniques can handle only geometric or only volumetric scenes - both resulting in the loss of important properties of the original concept. Our approach supports interactive rendering of polyhedrally bounded volumetric objects on the GPU based on ray casting. The ray casting can be conveniently used for both the shadow map computation and the rendering. We show how anti-aliased high-quality shadows are feasible in scenes composed of multiple overlapping translucent objects, and how sparse scenes can be handled efficiently using clustered deep shadow maps. © 2012 Springer-Verlag.

  13. Top tagging with deep neural networks [Vidyo

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Recent literature on deep neural networks for top tagging has focussed on image based techniques or multivariate approaches using high level jet substructure variables. Here, we take a sequential approach to this task by using anordered sequence of energy deposits as training inputs. Unlike previous approaches, this strategy does not result in a loss of information during pixelization or the calculation of high level features. We also propose new preprocessing methods that do not alter key physical quantities such as jet mass. We compare the performance of this approach to standard tagging techniques and present results evaluating the robustness of the neural network to pileup.

  14. deepTools2: a next generation web server for deep-sequencing data analysis.

    Science.gov (United States)

    Ramírez, Fidel; Ryan, Devon P; Grüning, Björn; Bhardwaj, Vivek; Kilpert, Fabian; Richter, Andreas S; Heyne, Steffen; Dündar, Friederike; Manke, Thomas

    2016-07-08

    We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. Deep ensemble learning of sparse regression models for brain disease diagnosis.

    Science.gov (United States)

    Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

    2017-04-01

    Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Bacterial community diversity of the deep-sea octocoral Paramuricea placomus

    Directory of Open Access Journals (Sweden)

    Christina A. Kellogg

    2016-09-01

    Full Text Available Compared to tropical corals, much less is known about deep-sea coral biology and ecology. Although the microbial communities of some deep-sea corals have been described, this is the first study to characterize the bacterial community associated with the deep-sea octocoral, Paramuricea placomus. Samples from five colonies of P. placomus were collected from Baltimore Canyon (379–382 m depth in the Atlantic Ocean off the east coast of the United States of America. DNA was extracted from the coral samples and 16S rRNA gene amplicons were pyrosequenced using V4-V5 primers. Three samples sequenced deeply (>4,000 sequences each and were further analyzed. The dominant microbial phylum was Proteobacteria, but other major phyla included Firmicutes and Planctomycetes. A conserved community of bacterial taxa held in common across the three P. placomus colonies was identified, comprising 68–90% of the total bacterial community depending on the coral individual. The bacterial community of P. placomus does not appear to include the genus Endozoicomonas, which has been found previously to be the dominant bacterial associate in several temperate and tropical gorgonians. Inferred functionality suggests the possibility of nitrogen cycling by the core bacterial community.

  17. The deep hydrogeologic flow system underlying the Oak Ridge Reservation

    International Nuclear Information System (INIS)

    Nativ, R.; Hunley, A.E.

    1993-07-01

    The deep hydrogeologic system underlying the Oak Ridge Reservation contains some areas contaminated with radionuclides, heavy metals, nitrates, and organic compounds. The groundwater at that depth is saline and has previously been considered stagnant. On the basis of existing and newly collected data, the nature of flow of the saline groundwater and its potential discharge into shallow, freshwater systems was assessed. Data used for this purpose included (1) spatial and temporal pressures and hydraulic heads measured in the deep system, (2) hydraulic parameters of the formations in question, (3) spatial temperature variations, and (4) spatial and temporal chemical and isotopic composition of the saline groundwater. In addition, chemical analyses of brine in adjacent areas in Tennessee, Kentucky, Ohio, Pennsylvania, and West Virginia were compared with the deep water underlying the reservation to help assess the origin of the brine. Preliminary conclusions suggest that the saline water contained at depth is old but not isolated (in terms of recharge and discharge) from the overlying active and freshwater-bearing units. The confined water (along with dissolved solutes) moves along open fractures (or man-made shortcuts) at relatively high velocity into adjacent, more permeable units. Groundwater volumes involved in this flow probably are small

  18. Vertical migrations of a deep-sea fish and its prey.

    Directory of Open Access Journals (Sweden)

    Pedro Afonso

    Full Text Available It has been speculated that some deep-sea fishes can display large vertical migrations and likely doing so to explore the full suite of benthopelagic food resources, especially the pelagic organisms of the deep scattering layer (DSL. This would help explain the success of fishes residing at seamounts and the increased biodiversity found in these features of the open ocean. We combined active plus passive acoustic telemetry of blackspot seabream with in situ environmental and biological (backscattering data collection at a seamount to verify if its behaviour is dominated by vertical movements as a response to temporal changes in environmental conditions and pelagic prey availability. We found that seabream extensively migrate up and down the water column, that these patterns are cyclic both in short-term (tidal, diel as well as long-term (seasonal scales, and that they partially match the availability of potential DSL prey components. Furthermore, the emerging pattern points to a more complex spatial behaviour than previously anticipated, suggesting a seasonal switch in the diel behaviour mode (benthic vs. pelagic of seabream, which may reflect an adaptation to differences in prey availability. This study is the first to document the fine scale three-dimensional behaviour of a deep-sea fish residing at seamounts.

  19. Assessing microscope image focus quality with deep learning.

    Science.gov (United States)

    Yang, Samuel J; Berndl, Marc; Michael Ando, D; Barch, Mariya; Narayanaswamy, Arunachalam; Christiansen, Eric; Hoyer, Stephan; Roat, Chris; Hung, Jane; Rueden, Curtis T; Shankar, Asim; Finkbeiner, Steven; Nelson, Philip

    2018-03-15

    Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of

  20. From Parkinsonian thalamic activity to restoring thalamic relay using deep brain stimulation: new insights from computational modeling

    NARCIS (Netherlands)

    Meijer, Hil Gaétan Ellart; Krupa, M.; Cagnan, H.; Lourens, Marcel Antonius Johannes; Heida, Tjitske; Martens, H.C.F.; Bour, L.J.; van Gils, Stephanus A.

    2011-01-01

    We present a computational model of a thalamocortical relay neuron for exploring basal ganglia thalamocortical loop behavior in relation to Parkinson's disease and deep brain stimulation (DBS). Previous microelectrode, single-unit recording studies demonstrated that oscillatory interaction within

  1. Radio variability in the Phoenix Deep Survey at 1.4 GHz

    Science.gov (United States)

    Hancock, P. J.; Drury, J. A.; Bell, M. E.; Murphy, T.; Gaensler, B. M.

    2016-09-01

    We use archival data from the Phoenix Deep Survey to investigate the variable radio source population above 1 mJy beam-1 at 1.4 GHz. Given the similarity of this survey to other such surveys we take the opportunity to investigate the conflicting results which have appeared in the literature. Two previous surveys for variability conducted with the Very Large Array (VLA) achieved a sensitivity of 1 mJy beam-1. However, one survey found an areal density of radio variables on time-scales of decades that is a factor of ˜4 times greater than a second survey which was conducted on time-scales of less than a few years. In the Phoenix deep field we measure the density of variable radio sources to be ρ = 0.98 deg-2 on time-scales of 6 months to 8 yr. We make use of Wide-field Infrared Survey Explorer infrared cross-ids, and identify all variable sources as an active galactic nucleus of some description. We suggest that the discrepancy between previous VLA results is due to the different time-scales probed by each of the surveys, and that radio variability at 1.4 GHz is greatest on time-scales of 2-5 yr.

  2. DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian

    Directory of Open Access Journals (Sweden)

    Chao Li

    2017-02-01

    Full Text Available Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.. Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way.

  3. Invited talk: Deep Learning Meets Physics

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, text and vision benchmarks. Consequently it evolved into the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. The main obstacle to learning deep neural networks is the vanishing gradient problem. The vanishing gradient impedes credit assignment to the first layers of a deep network or to early elements of a sequence, therefore limits model selection. Major advances in Deep Learning can be related to avoiding the vanishing gradient like stacking, ReLUs, residual networks, highway networks, and LSTM. For Deep Learning, we suggested self-normalizing neural networks (SNNs) which automatica...

  4. Long-Term Efficacy of Constant Current Deep Brain Stimulation in Essential Tremor.

    Science.gov (United States)

    Rezaei Haddad, Ali; Samuel, Michael; Hulse, Natasha; Lin, Hsin-Ying; Ashkan, Keyoumars

    2017-07-01

    Ventralis intermedius deep brain stimulation is an established intervention for medication-refractory essential tremor. Newer constant current stimulation technology offers theoretical advantage over the traditional constant voltage systems in terms of delivering a more biologically stable therapy. There are no previous reports on the outcomes of constant current deep brain stimulation in the treatment of essential tremor. This study aimed to evaluate the long-term efficacy of ventralis intermedius constant current deep brain stimulation in patients diagnosed with essential tremor. Essential tremor patients implanted with constant current deep brain stimulation for a minimum of three years were evaluated. Clinical outcomes were assessed using the Fahn-Tolosa-Marin tremor rating scale at baseline and postoperatively at the time of evaluation. The quality of life in the patients was assessed using the Quality of Life in Essential Tremor questionnaire. Ten patients were evaluated with a median age at evaluation of 74 years (range 66-79) and a mean follow up time of 49.7 (range 36-78) months since starting stimulation. Constant current ventralis intermedius deep brain stimulation was well tolerated and effective in all patients with a mean score improvement from 50.7 ± 5.9 to 17.4 ± 5.7 (p = 0.0020) in the total Fahn-Tolosa-Marin rating scale score (65.6%). Furthermore, the total combined mean Quality of Life in Essential Tremor score was improved from 56.2 ± 4.9 to 16.8 ± 3.5 (p value = 0.0059) (70.1%). This report shows that long-term constant current ventralis intermedius deep brain stimulation is a safe and effective intervention for essential tremor patients. © 2017 International Neuromodulation Society.

  5. Predicted deep-sea coral habitat suitability for the U.S. West coast.

    Directory of Open Access Journals (Sweden)

    John M Guinotte

    Full Text Available Regional scale habitat suitability models provide finer scale resolution and more focused predictions of where organisms may occur. Previous modelling approaches have focused primarily on local and/or global scales, while regional scale models have been relatively few. In this study, regional scale predictive habitat models are presented for deep-sea corals for the U.S. West Coast (California, Oregon and Washington. Model results are intended to aid in future research or mapping efforts and to assess potential coral habitat suitability both within and outside existing bottom trawl closures (i.e. Essential Fish Habitat (EFH and identify suitable habitat within U.S. National Marine Sanctuaries (NMS. Deep-sea coral habitat suitability was modelled at 500 m×500 m spatial resolution using a range of physical, chemical and environmental variables known or thought to influence the distribution of deep-sea corals. Using a spatial partitioning cross-validation approach, maximum entropy models identified slope, temperature, salinity and depth as important predictors for most deep-sea coral taxa. Large areas of highly suitable deep-sea coral habitat were predicted both within and outside of existing bottom trawl closures and NMS boundaries. Predicted habitat suitability over regional scales are not currently able to identify coral areas with pin point accuracy and probably overpredict actual coral distribution due to model limitations and unincorporated variables (i.e. data on distribution of hard substrate that are known to limit their distribution. Predicted habitat results should be used in conjunction with multibeam bathymetry, geological mapping and other tools to guide future research efforts to areas with the highest probability of harboring deep-sea corals. Field validation of predicted habitat is needed to quantify model accuracy, particularly in areas that have not been sampled.

  6. Deep geothermics

    International Nuclear Information System (INIS)

    Anon.

    1995-01-01

    The hot-dry-rocks located at 3-4 km of depth correspond to low permeable rocks carrying a large amount of heat. The extraction of this heat usually requires artificial hydraulic fracturing of the rock to increase its permeability before water injection. Hot-dry-rocks geothermics or deep geothermics is not today a commercial channel but only a scientific and technological research field. The Soultz-sous-Forets site (Northern Alsace, France) is characterized by a 6 degrees per meter geothermal gradient and is used as a natural laboratory for deep geothermal and geological studies in the framework of a European research program. Two boreholes have been drilled up to 3600 m of depth in the highly-fractured granite massif beneath the site. The aim is to create a deep heat exchanger using only the natural fracturing for water transfer. A consortium of german, french and italian industrial companies (Pfalzwerke, Badenwerk, EdF and Enel) has been created for a more active participation to the pilot phase. (J.S.). 1 fig., 2 photos

  7. Stable architectures for deep neural networks

    Science.gov (United States)

    Haber, Eldad; Ruthotto, Lars

    2018-01-01

    Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

  8. Carbon mineralization and oxygen dynamics in sediments with deep oxygen penetration, Lake Superior

    DEFF Research Database (Denmark)

    Li, Jiying; Crowe, Sean Andrew; Miklesh, David

    2012-01-01

    To understand carbon and oxygen dynamics in sediments with deep oxygen penetration, we investigated eight locations (160–318-m depth) throughout Lake Superior. Despite the 2–4 weight percent organic carbon content, oxygen penetrated into the sediment by 3.5 to > 12 cm at all locations. Such deep ...... volume-specific carbon degradation rates were 0.3–1.5 µmol cm−3 d−1; bioturbation coefficient near the sediment surface was 3–8 cm2 yr−1. These results indicate that carbon cycling in large freshwater systems conforms to many of the same trends as in marine systems.......To understand carbon and oxygen dynamics in sediments with deep oxygen penetration, we investigated eight locations (160–318-m depth) throughout Lake Superior. Despite the 2–4 weight percent organic carbon content, oxygen penetrated into the sediment by 3.5 to > 12 cm at all locations. Such deep......, suggesting that temporal variability in deeply oxygenated sediments may be greater than previously acknowledged. The oxygen uptake rates (4.4–7.7 mmol m−2 d−1, average 6.1 mmol m−2 d−1) and carbon mineralization efficiency (∼ 90% of deposited carbon) were similar to those in marine hemipelagic and pelagic...

  9. Estimation of the Critical Temperatures of Some More Deep Eutectic Solvents from Their Surface Tensions

    Directory of Open Access Journals (Sweden)

    Yizhak Marcus

    2018-01-01

    Full Text Available The critical temperatures of two dozen deep eutectic solvents, for only some of which these have been estimated previously, were estimated from the temperature dependences of their surface tensions and densities available in the literature according to the Eötvös and the Guggenheim expressions.

  10. Mesonerilla neridae, n. sp. (Nerillidae): First meiofaunal annelid from deep-sea hydrothermal vents

    DEFF Research Database (Denmark)

    Worsaae, Katrine; Rouse, Greg W

    2009-01-01

    Though most common in coastal sandy bottoms, nerillid annelids have been found in a broad variety of habitats around the world and two genera have previously been reported from the deep sea. During a cruise to the southern East Pacific Rise and northern Pacific Antarctic Ridge (near Easter Island...

  11. Aplasia cutis congenita, skull defect, brain heterotopia, and intestinal lymphangiectasia

    NARCIS (Netherlands)

    Bonioli, Eugenio; Hennekam, Raoul C.; Spena, Gianantonio; Morcaldi, Guido; Di Stefano, Antonio; Serra, Giovanni; Bellini, Carlo

    2005-01-01

    We describe a female infant with a previously unreported combination of manifestations characterized by aplasia cutis, skull defect, brain heterotopia, mild congenital lymphedema, and intestinal lymphangiectasia. The association of intestinal lymphangiectasia and aplasia cutis, and the association

  12. Expertize of hydrochemical investigation Know-how for deep underground

    International Nuclear Information System (INIS)

    Iwatsuki, Teruki; Mizuno, Takashi; Amano, Yuki; Kunimaru, Takanori; Semba, Takeshi

    2012-03-01

    This report summarizes technical basis and the Know-how on hydrochemical investigations for deep underground as a part of METI project 'Development of Information Synthesis and Interpretation System (ISIS)'. We describe the procedures and methods of hydrochemical investigation in following stages; 1) initial analysis of previous information, 2) planning of borehole investigation, 3) borehole investigation at field, and 4) construct the 'hydrochemical model' representing hydrochemical condition and the evolution process. The contents of this report are inputted to 'Expert system' developed by METI project and are available on WEB system (internet). (author)

  13. Impact disruption and recovery of the deep subsurface biosphere

    DEFF Research Database (Denmark)

    Cockell, Charles S.; Voytek, Mary A.; Gronstal, Aaaron L

    2012-01-01

    the 35 million-year-old Chesapeake Bay impact structure, USA, with robust contamination control. Microbial enumerations displayed a logarithmic downward decline, but the different gradient, when compared to previously studied sites, and the scatter of the data are consistent with a microbiota influenced......Although a large fraction of the world's biomass resides in the subsurface, there has been no study of the effects of catastrophic disturbance on the deep biosphere and the rate of its subsequent recovery. We carried out an investigation of the microbiology of a 1.76 km drill core obtained from...

  14. Large-eddy simulation of maritime deep tropical convection

    Directory of Open Access Journals (Sweden)

    Peter A Bogenschutz

    2009-12-01

    Full Text Available This study represents an attempt to apply Large-Eddy Simulation (LES resolution to simulate deep tropical convection in near equilibrium for 24 hours over an area of about 205 x 205 km2, which is comparable to that of a typical horizontal grid cell in a global climate model. The simulation is driven by large-scale thermodynamic tendencies derived from mean conditions during the GATE Phase III field experiment. The LES uses 2048 x 2048 x 256 grid points with horizontal grid spacing of 100 m and vertical grid spacing ranging from 50 m in the boundary layer to 100 m in the free troposphere. The simulation reaches a near equilibrium deep convection regime in 12 hours. The simulated vertical cloud distribution exhibits a trimodal vertical distribution of deep, middle and shallow clouds similar to that often observed in Tropics. A sensitivity experiment in which cold pools are suppressed by switching off the evaporation of precipitation results in much lower amounts of shallow and congestus clouds. Unlike the benchmark LES where the new deep clouds tend to appear along the edges of spreading cold pools, the deep clouds in the no-cold-pool experiment tend to reappear at the sites of the previous deep clouds and tend to be surrounded by extensive areas of sporadic shallow clouds. The vertical velocity statistics of updraft and downdraft cores below 6 km height are compared to aircraft observations made during GATE. The comparison shows generally good agreement, and strongly suggests that the LES simulation can be used as a benchmark to represent the dynamics of tropical deep convection on scales ranging from large turbulent eddies to mesoscale convective systems. The effect of horizontal grid resolution is examined by running the same case with progressively larger grid sizes of 200, 400, 800, and 1600 m. These runs show a reasonable agreement with the benchmark LES in statistics such as convective available potential energy, convective inhibition

  15. Deep Web Search Interface Identification: A Semi-Supervised Ensemble Approach

    OpenAIRE

    Hong Wang; Qingsong Xu; Lifeng Zhou

    2014-01-01

    To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML) form) or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tediousmanual work, while unlabeled HTML forms are abundant and easy to obtain. In this research, we consider the plausibility of using both labeled and unlabeled data to train better models to...

  16. Fault plane orientations of deep earthquakes in the Izu-Bonin-Marianas subduction zone system

    Science.gov (United States)

    Myhill, R.; Warren, L. M.

    2011-12-01

    We present the results of directivity analysis on 45 deep earthquakes within the Izu-Bonin-Marianas subduction zone between 1993 and 2011. The age of the subducting Pacific plate increases from north to south along the trench, from 120 Ma offshore Tokyo to over 150 Ma east of the Mariana Islands. The dip of the deep slab generally increases from north to south, and is steep to overturned beneath the southern Bonin Islands and Marianas. Between 34 and 26 degrees north, a peak in seismicity at 350-450 km depth marks a decrease in dip as the slab approaches the base of the upper mantle. We observe directivity for around 60 percent of the analysed earthquakes, and use the propagation characteristics to find the best fitting rupture vector. In 60-70 percent of cases with well constrained rupture directivity, the best fitting rupture vector allows discrimination of the fault plane and the auxiliary plane of the focal mechanism. The identified fault planes between 100 km and 500 km are predominantly near-horizontal or south-southwest dipping. Rotated into the plane of the slab, the fault plane poles form a single cluster, since the more steeply dipping fault planes are found within more steeply dipping sections of slab. The dominance of near-horizontal fault planes at intermediate depth agrees with results from previous studies of the Tonga and Middle-America subduction zones. However, the presence of a single preferred fault plane orientation for large deep-focus earthquakes has not been previously reported, and contrasts with the situation for deep-focus earthquakes in the Tonga-Kermadec subduction system. Ruptures tend to propagate away from the top surface of the slab. We discuss potential causes of preferred fault plane orientations within subducting slabs in the light of existing available data, and the implications for mechanisms of faulting at great depths within the Earth.

  17. Deep Seawater Intrusion Enhanced by Geothermal Through Deep Faults in Xinzhou Geothermal Field in Guangdong, China

    Science.gov (United States)

    Lu, G.; Ou, H.; Hu, B. X.; Wang, X.

    2017-12-01

    This study investigates abnormal sea water intrusion from deep depth, riding an inland-ward deep groundwater flow, which is enhanced by deep faults and geothermal processes. The study site Xinzhou geothermal field is 20 km from the coast line. It is in southern China's Guangdong coast, a part of China's long coastal geothermal belt. The geothermal water is salty, having fueled an speculation that it was ancient sea water retained. However, the perpetual "pumping" of the self-flowing outflow of geothermal waters might alter the deep underground flow to favor large-scale or long distant sea water intrusion. We studied geochemical characteristics of the geothermal water and found it as a mixture of the sea water with rain water or pore water, with no indication of dilution involved. And we conducted numerical studies of the buoyancy-driven geothermal flow in the deep ground and find that deep down in thousand meters there is favorable hydraulic gradient favoring inland-ward groundwater flow, allowing seawater intrude inland for an unusually long tens of kilometers in a granitic groundwater flow system. This work formed the first in understanding geo-environment for deep ground water flow.

  18. Deep Learning in Neuroradiology.

    Science.gov (United States)

    Zaharchuk, G; Gong, E; Wintermark, M; Rubin, D; Langlotz, C P

    2018-02-01

    Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method. © 2018 by American Journal of Neuroradiology.

  19. Computer-aided classification of lung nodules on computed tomography images via deep learning technique

    Directory of Open Access Journals (Sweden)

    Hua KL

    2015-08-01

    Full Text Available Kai-Lung Hua,1 Che-Hao Hsu,1 Shintami Chusnul Hidayati,1 Wen-Huang Cheng,2 Yu-Jen Chen3 1Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 2Research Center for Information Technology Innovation, Academia Sinica, 3Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan Abstract: Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. Keywords: nodule classification, deep learning, deep belief network, convolutional neural network

  20. miRBase: integrating microRNA annotation and deep-sequencing data.

    Science.gov (United States)

    Kozomara, Ana; Griffiths-Jones, Sam

    2011-01-01

    miRBase is the primary online repository for all microRNA sequences and annotation. The current release (miRBase 16) contains over 15,000 microRNA gene loci in over 140 species, and over 17,000 distinct mature microRNA sequences. Deep-sequencing technologies have delivered a sharp rise in the rate of novel microRNA discovery. We have mapped reads from short RNA deep-sequencing experiments to microRNAs in miRBase and developed web interfaces to view these mappings. The user can view all read data associated with a given microRNA annotation, filter reads by experiment and count, and search for microRNAs by tissue- and stage-specific expression. These data can be used as a proxy for relative expression levels of microRNA sequences, provide detailed evidence for microRNA annotations and alternative isoforms of mature microRNAs, and allow us to revisit previous annotations. miRBase is available online at: http://www.mirbase.org/.

  1. A deep sea community at the Kebrit brine pool in the Red Sea

    KAUST Repository

    Vestheim, Hege

    2015-02-26

    Approximately 25 deep sea brine pools occur along the mid axis of the Red Sea. These hypersaline, anoxic, and acidic environments have previously been reported to host diverse microbial communities. We visited the Kebrit brine pool in April 2013 and found macrofauna present just above the brine–seawater interface (~1465 m). In particular, inactive sulfur chimneys had associated epifauna of sea anemones, sabellid type polychaetes, and hydroids, and infauna consisting of capitellid polychaetes, gastropods of the genus Laeviphitus (fam. Elachisinidae), and top snails of the family Cocculinidae. The deep Red Sea generally is regarded as extremely poor in benthos. We hypothesize that the periphery along the Kebrit holds increased biomass and biodiversity that are sustained by prokaryotes associated with the brine pool or co-occurring seeps.

  2. Unveiling the Biodiversity of Deep-Sea Nematodes through Metabarcoding: Are We Ready to Bypass the Classical Taxonomy?

    Science.gov (United States)

    Dell'Anno, Antonio; Carugati, Laura; Corinaldesi, Cinzia; Riccioni, Giulia; Danovaro, Roberto

    2015-01-01

    Nematodes inhabiting benthic deep-sea ecosystems account for >90% of the total metazoan abundances and they have been hypothesised to be hyper-diverse, but their biodiversity is still largely unknown. Metabarcoding could facilitate the census of biodiversity, especially for those tiny metazoans for which morphological identification is difficult. We compared, for the first time, different DNA extraction procedures based on the use of two commercial kits and a previously published laboratory protocol and tested their suitability for sequencing analyses of 18S rDNA of marine nematodes. We also investigated the reliability of Roche 454 sequencing analyses for assessing the biodiversity of deep-sea nematode assemblages previously morphologically identified. Finally, intra-genomic variation in 18S rRNA gene repeats was investigated by Illumina MiSeq in different deep-sea nematode morphospecies to assess the influence of polymorphisms on nematode biodiversity estimates. Our results indicate that the two commercial kits should be preferred for the molecular analysis of biodiversity of deep-sea nematodes since they consistently provide amplifiable DNA suitable for sequencing. We report that the morphological identification of deep-sea nematodes matches the results obtained by metabarcoding analysis only at the order-family level and that a large portion of Operational Clustered Taxonomic Units (OCTUs) was not assigned. We also show that independently from the cut-off criteria and bioinformatic pipelines used, the number of OCTUs largely exceeds the number of individuals and that 18S rRNA gene of different morpho-species of nematodes displayed intra-genomic polymorphisms. Our results indicate that metabarcoding is an important tool to explore the diversity of deep-sea nematodes, but still fails in identifying most of the species due to limited number of sequences deposited in the public databases, and in providing quantitative data on the species encountered. These aspects

  3. Early-Onset Oligohydramnios Complicated with Hypertension, Hyperthyroidism and Coexisting Elevated Urine Vanillylmandelic Acid of Unknown Origin, Mimicking a Pheochromocytoma

    Directory of Open Access Journals (Sweden)

    Joung-Liang Wu

    2004-12-01

    Conclusion: A combination of hypertension, oligohydramnios and hyperthyroidism is rare. An elevated 24- hour urine vanillylmandelic acid of unknown origin is a previously unreported finding, to the best of our knowledge, associated with hyperthyroidism and pregnancy-induced hypertension.

  4. Temperature impacts on deep-sea biodiversity.

    Science.gov (United States)

    Yasuhara, Moriaki; Danovaro, Roberto

    2016-05-01

    Temperature is considered to be a fundamental factor controlling biodiversity in marine ecosystems, but precisely what role temperature plays in modulating diversity is still not clear. The deep ocean, lacking light and in situ photosynthetic primary production, is an ideal model system to test the effects of temperature changes on biodiversity. Here we synthesize current knowledge on temperature-diversity relationships in the deep sea. Our results from both present and past deep-sea assemblages suggest that, when a wide range of deep-sea bottom-water temperatures is considered, a unimodal relationship exists between temperature and diversity (that may be right skewed). It is possible that temperature is important only when at relatively high and low levels but does not play a major role in the intermediate temperature range. Possible mechanisms explaining the temperature-biodiversity relationship include the physiological-tolerance hypothesis, the metabolic hypothesis, island biogeography theory, or some combination of these. The possible unimodal relationship discussed here may allow us to identify tipping points at which on-going global change and deep-water warming may increase or decrease deep-sea biodiversity. Predicted changes in deep-sea temperatures due to human-induced climate change may have more adverse consequences than expected considering the sensitivity of deep-sea ecosystems to temperature changes. © 2014 Cambridge Philosophical Society.

  5. Malignant peritoneal mesothelioma associated with deep vein thrombosis following radiotherapy for seminoma of the testis

    International Nuclear Information System (INIS)

    Sato, Fuminori; Yamazaki, Hajime; Ataka, Ken; Mashima, Ichiro; Suzuki, Kenta; Takahashi, Toru; Umezu, Hajime; Gejyo, Fumitake

    2000-01-01

    A 52-year-old man developed malignant peritoneal mesothelioma 17 years after radiotherapy for seminoma of the testis. Although asbestos exposure is considered to be the major risk factor for the development of malignant mesothelioma, prior therapeutic radiation has also been postulated as a causative factor. The unexplained appearance of ascites or pleural effusion within a previously irradiated area should be considered suggestive of malignant mesothelioma in any long-term survivor of cancer. In addition, the patient suffered a deep vein thrombosis four years before the diagnosis of mesothelioma. Deep vein thrombosis is a common complication of malignant disease, and is often the first clue to occult malignancy. (author)

  6. Malignant peritoneal mesothelioma associated with deep vein thrombosis following radiotherapy for seminoma of the testis

    Energy Technology Data Exchange (ETDEWEB)

    Sato, Fuminori; Yamazaki, Hajime; Ataka, Ken; Mashima, Ichiro; Suzuki, Kenta; Takahashi, Toru; Umezu, Hajime; Gejyo, Fumitake [Niigata Univ. (Japan). School of Medicine

    2000-11-01

    A 52-year-old man developed malignant peritoneal mesothelioma 17 years after radiotherapy for seminoma of the testis. Although asbestos exposure is considered to be the major risk factor for the development of malignant mesothelioma, prior therapeutic radiation has also been postulated as a causative factor. The unexplained appearance of ascites or pleural effusion within a previously irradiated area should be considered suggestive of malignant mesothelioma in any long-term survivor of cancer. In addition, the patient suffered a deep vein thrombosis four years before the diagnosis of mesothelioma. Deep vein thrombosis is a common complication of malignant disease, and is often the first clue to occult malignancy. (author)

  7. Deep ocean communities impacted by changing climate over 24 y in the abyssal northeast Pacific Ocean.

    Science.gov (United States)

    Smith, Kenneth L; Ruhl, Henry A; Kahru, Mati; Huffard, Christine L; Sherman, Alana D

    2013-12-03

    The deep ocean, covering a vast expanse of the globe, relies almost exclusively on a food supply originating from primary production in surface waters. With well-documented warming of oceanic surface waters and conflicting reports of increasing and decreasing primary production trends, questions persist about how such changes impact deep ocean communities. A 24-y time-series study of sinking particulate organic carbon (food) supply and its utilization by the benthic community was conducted in the abyssal northeast Pacific (~4,000-m depth). Here we show that previous findings of food deficits are now punctuated by large episodic surpluses of particulate organic carbon reaching the sea floor, which meet utilization. Changing surface ocean conditions are translated to the deep ocean, where decadal peaks in supply, remineralization, and sequestration of organic carbon have broad implications for global carbon budget projections.

  8. Reduced vaginal elasticity, reduced lubrication, and deep and superficial dyspareunia in irradiated gynecological cancer survivors.

    Science.gov (United States)

    Stinesen Kollberg, Karin; Waldenström, Ann-Charlotte; Bergmark, Karin; Dunberger, Gail; Rossander, Anna; Wilderäng, Ulrica; Åvall-Lundqvist, Elisabeth; Steineck, Gunnar

    2015-05-01

    The purpose of this study was to examine whether or not vaginal elasticity or lack of lubrication is associated with deep or superficial dyspareunia. We investigated gynecological cancer survivors treated with radiation therapy. In a population-based study with 616 women answering a questionnaire (participation rate 78%) and who were treated with radiotherapy for gynecological cancer, we analyzed information from 243 women (39%) who reported that they had had intercourse during the previous six months. Analyses included log-binomial regression (relative risks) and multiple imputations by chained equations in combination with Bayesian Model Averaging, yielding a posterior probability value. Age range of this cancer recurrent-free group of women was 29-80. Dyspareunia affected 164 of 243 of the women (67%). One hundred thirty-four women (55%) reported superficial pain, 97 women (40%) reported deep pain, and 87 women (36%) reported both types of dyspareunia. The relative risk (RR) of deep dyspareunia was 1.87 (CI 1.41-2.49) with impaired vaginal elasticity compared to normal vaginal elasticity. Age and lower abdominal swelling were separate risk factors for deep dyspareunia. However, effects remain after adjusting for these factors. The relative risk of deep dyspareunia was almost twice as high with impaired vaginal elasticity compared to normal vaginal elasticity. If we wish to treat or even prevent deep dyspareunia in women with gynecological cancer, we may use our knowledge of the pathophysiology of deep dyspareunia and increasingly provide dilators together with instructions on how to use them for stretching exercises in order to retain vaginal elasticity. Results highlight the need for studies with more precise questions distinguishing superficial from deep dyspareunia so that in the future we may be able to primarily try to avoid reduced vaginal elasticity and secondarily reduce the symptoms.

  9. Extreme Longevity in Proteinaceous Deep-Sea Corals

    Energy Technology Data Exchange (ETDEWEB)

    Roark, E B; Guilderson, T P; Dunbar, R B; Fallon, S J; Mucciarone, D A

    2009-02-09

    Deep-sea corals are found on hard substrates on seamounts and continental margins world-wide at depths of 300 to {approx}3000 meters. Deep-sea coral communities are hotspots of deep ocean biomass and biodiversity, providing critical habitat for fish and invertebrates. Newly applied radiocarbon age date from the deep water proteinaceous corals Gerardia sp. and Leiopathes glaberrima show that radial growth rates are as low as 4 to 35 {micro}m yr{sup -1} and that individual colony longevities are on the order of thousands of years. The management and conservation of deep sea coral communities is challenged by their commercial harvest for the jewelry trade and damage caused by deep water fishing practices. In light of their unusual longevity, a better understanding of deep sea coral ecology and their interrelationships with associated benthic communities is needed to inform coherent international conservation strategies for these important deep-sea ecosystems.

  10. Release of arsenic to deep groundwater in the Mekong Delta, Vietnam, linked to pumping-induced land subsidence.

    Science.gov (United States)

    Erban, Laura E; Gorelick, Steven M; Zebker, Howard A; Fendorf, Scott

    2013-08-20

    Deep aquifers in South and Southeast Asia are increasingly exploited as presumed sources of pathogen- and arsenic-free water, although little is known of the processes that may compromise their long-term viability. We analyze a large area (>1,000 km(2)) of the Mekong Delta, Vietnam, in which arsenic is found pervasively in deep, Pliocene-Miocene-age aquifers, where nearly 900 wells at depths of 200-500 m are contaminated. There, intensive groundwater extraction is causing land subsidence of up to 3 cm/y as measured using satellite-based radar images from 2007 to 2010 and consistent with transient 3D aquifer simulations showing similar subsidence rates and total subsidence of up to 27 cm since 1988. We propose a previously unrecognized mechanism in which deep groundwater extraction is causing interbedded clays to compact and expel water containing dissolved arsenic or arsenic-mobilizing solutes (e.g., dissolved organic carbon and competing ions) to deep aquifers over decades. The implication for the broader Mekong Delta region, and potentially others like it across Asia, is that deep, untreated groundwater will not necessarily remain a safe source of drinking water.

  11. Processing speed and working memory span: their differential role in superficial and deep memory processes in schizophrenia.

    Science.gov (United States)

    Brébion, Gildas; Bressan, Rodrigo A; Pilowsky, Lyn S; David, Anthony S

    2011-05-01

    Previous work has suggested that decrement in both processing speed and working memory span plays a role in the memory impairment observed in patients with schizophrenia. We undertook a study to examine simultaneously the effect of these two factors. A sample of 49 patients with schizophrenia and 43 healthy controls underwent a battery of verbal and visual memory tasks. Superficial and deep encoding memory measures were tallied. We conducted regression analyses on the various memory measures, using processing speed and working memory span as independent variables. In the patient group, processing speed was a significant predictor of superficial and deep memory measures in verbal and visual memory. Working memory span was an additional significant predictor of the deep memory measures only. Regression analyses involving all participants revealed that the effect of diagnosis on all the deep encoding memory measures was reduced to non-significance when processing speed was entered in the regression. Decreased processing speed is involved in verbal and visual memory deficit in patients, whether the task require superficial or deep encoding. Working memory is involved only insofar as the task requires a certain amount of effort.

  12. Radon concentration distributions in shallow and deep groundwater around the Tachikawa fault zone.

    Science.gov (United States)

    Tsunomori, Fumiaki; Shimodate, Tomoya; Ide, Tomoki; Tanaka, Hidemi

    2017-06-01

    Groundwater radon concentrations around the Tachikawa fault zone were surveyed. The radon concentrations in shallow groundwater samples around the Tachikawa fault segment are comparable to previous studies. The characteristics of the radon concentrations on both sides of the segment are considered to have changed in response to the decrease in groundwater recharge caused by urbanization on the eastern side of the segment. The radon concentrations in deep groundwater samples collected around the Naguri and the Tachikawa fault segments are the same as those of shallow groundwater samples. However, the radon concentrations in deep groundwater samples collected from the bedrock beside the Naguri and Tachikawa fault segments are markedly higher than the radon concentrations expected from the geology on the Kanto plane. This disparity can be explained by the development of fracture zones spreading on both sides of the two segments. The radon concentration distribution for deep groundwater samples from the Naguri and the Tachikawa fault segments suggests that a fault exists even at the southern part of the Tachikawa fault line. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. New optimized drill pipe size for deep-water, extended reach and ultra-deep drilling

    Energy Technology Data Exchange (ETDEWEB)

    Jellison, Michael J.; Delgado, Ivanni [Grant Prideco, Inc., Hoston, TX (United States); Falcao, Jose Luiz; Sato, Ademar Takashi [PETROBRAS, Rio de Janeiro, RJ (Brazil); Moura, Carlos Amsler [Comercial Perfuradora Delba Baiana Ltda., Rio de Janeiro, RJ (Brazil)

    2004-07-01

    A new drill pipe size, 5-7/8 in. OD, represents enabling technology for Extended Reach Drilling (ERD), deep water and other deep well applications. Most world-class ERD and deep water wells have traditionally been drilled with 5-1/2 in. drill pipe or a combination of 6-5/8 in. and 5-1/2 in. drill pipe. The hydraulic performance of 5-1/2 in. drill pipe can be a major limitation in substantial ERD and deep water wells resulting in poor cuttings removal, slower penetration rates, diminished control over well trajectory and more tendency for drill pipe sticking. The 5-7/8 in. drill pipe provides a significant improvement in hydraulic efficiency compared to 5-1/2 in. drill pipe and does not suffer from the disadvantages associated with use of 6-5/8 in. drill pipe. It represents a drill pipe assembly that is optimized dimensionally and on a performance basis for casing and bit programs that are commonly used for ERD, deep water and ultra-deep wells. The paper discusses the engineering philosophy behind 5-7/8 in. drill pipe, the design challenges associated with development of the product and reviews the features and capabilities of the second-generation double-shoulder connection. The paper provides drilling case history information on significant projects where the pipe has been used and details results achieved with the pipe. (author)

  14. Fine-grained leukocyte classification with deep residual learning for microscopic images.

    Science.gov (United States)

    Qin, Feiwei; Gao, Nannan; Peng, Yong; Wu, Zizhao; Shen, Shuying; Grudtsin, Artur

    2018-08-01

    Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past development of machine learning techniques, for example, extracting distinctive features from raw microscopic images are difficult, the widely used SVM classifier only has relative few parameters to tune, these methods cannot efficiently handle fine-grained classification cases when the white blood cells have up to 40 categories. Based on deep learning theory, a systematic study is conducted on finer leukocyte classification in this paper. A deep residual neural network based leukocyte classifier is constructed at first, which can imitate the domain expert's cell recognition process, and extract salient features robustly and automatically. Then the deep neural network classifier's topology is adjusted according to the prior knowledge of white blood cell test. After that the microscopic image dataset with almost one hundred thousand labeled leukocytes belonging to 40 categories is built, and combined training strategies are adopted to make the designed classifier has good generalization ability. The proposed deep residual neural network based classifier was tested on microscopic image dataset with 40 leukocyte categories. It achieves top-1 accuracy of 77.80%, top-5 accuracy of 98.75% during the training procedure. The average accuracy on the test set is nearly 76.84%. This paper presents a fine-grained leukocyte classification method for microscopic images, based on deep residual learning theory and medical domain knowledge. Experimental results validate the feasibility and effectiveness of our approach. Extended experiments support that the fine-grained leukocyte classifier could be used in real medical applications, assist doctors in diagnosing diseases, reduce human power significantly. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. Deep Reinforcement Learning: An Overview

    OpenAIRE

    Li, Yuxi

    2017-01-01

    We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsuperv...

  16. On interpreting studies of tracer transport by deep cumulus convection and its effects on atmospheric chemistry

    Directory of Open Access Journals (Sweden)

    M. G. Lawrence

    2008-10-01

    Full Text Available Global chemistry-transport models (CTMs and chemistry-GCMs (CGCMs generally simulate vertical tracer transport by deep convection separately from the advective transport by the mean winds, even though a component of the mean transport, for instance in the Hadley and Walker cells, occurs in deep convective updrafts. This split treatment of vertical transport has various implications for CTM simulations. In particular, it has led to a misinterpretation of several sensitivity simulations in previous studies in which the parameterized convective transport of one or more tracers is neglected. We describe this issue in terms of simulated fluxes and fractions of these fluxes representing various physical and non-physical processes. We then show that there is a significant overlap between the convective and large-scale mean advective vertical air mass fluxes in the CTM MATCH, and discuss the implications which this has for interpreting previous and future sensitivity simulations, as well as briefly noting other related implications such as numerical diffusion.

  17. Nano-Satellite Secondary Spacecraft on Deep Space Missions

    Science.gov (United States)

    Klesh, Andrew T.; Castillo-Rogez, Julie C.

    2012-01-01

    NanoSat technology has opened Earth orbit to extremely low-cost science missions through a common interface that provides greater launch accessibility. They have also been used on interplanetary missions, but these missions have used one-off components and architectures so that the return on investment has been limited. A natural question is the role that CubeSat-derived NanoSats could play to increase the science return of deep space missions. We do not consider single instrument nano-satellites as likely to complete entire Discovery-class missions alone,but believe that nano-satellites could augment larger missions to significantly increase science return. The key advantages offered by these mini-spacecrafts over previous planetary probes is the common availability of advanced subsystems that open the door to a large variety of science experiments, including new guidance, navigation and control capabilities. In this paper, multiple NanoSat science applications are investigated, primarily for high risk/high return science areas. We also address the significant challenges and questions that remain as obstacles to the use of nano-satellites in deep space missions. Finally, we provide some thoughts on a development roadmap toward interplanetary usage of NanoSpacecraft.

  18. DeepMirTar: a deep-learning approach for predicting human miRNA targets.

    Science.gov (United States)

    Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

    2018-06-01

    MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

  19. Munchausen Syndrome by Proxy: Unusual Manifestations and Disturbing Sequelae.

    Science.gov (United States)

    Porter, Gerald E.; And Others

    1994-01-01

    This study documents previously unreported findings in cases of Munchausen Syndrome by Proxy (in which a mother fabricates an illness in her child). In the reported case, esophageal perforation, retrograde intussusception, tooth loss, and bradycardia were found. (Author/DB)

  20. Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues

    Science.gov (United States)

    Tzoumas, Stratis; Nunes, Antonio; Olefir, Ivan; Stangl, Stefan; Symvoulidis, Panagiotis; Glasl, Sarah; Bayer, Christine; Multhoff, Gabriele; Ntziachristos, Vasilis

    2016-06-01

    Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation, an effect that causes spectral corruption. Spectral corruption has limited the quantification accuracy of optical and optoacoustic spectroscopic methods, and impeded the goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical goal for the assessment of oxygenation in physiological processes and disease. Here we describe light fluence in the spectral domain and introduce eigenspectra multispectral optoacoustic tomography (eMSOT) to account for wavelength-dependent light attenuation, and estimate blood sO2 within deep tissue. We validate eMSOT in simulations, phantoms and animal measurements and spatially resolve sO2 in muscle and tumours, validating our measurements with histology data. eMSOT shows substantial sO2 accuracy enhancement over previous optoacoustic methods, potentially serving as a valuable tool for imaging tissue pathophysiology.

  1. Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues.

    Science.gov (United States)

    Tzoumas, Stratis; Nunes, Antonio; Olefir, Ivan; Stangl, Stefan; Symvoulidis, Panagiotis; Glasl, Sarah; Bayer, Christine; Multhoff, Gabriele; Ntziachristos, Vasilis

    2016-06-30

    Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation, an effect that causes spectral corruption. Spectral corruption has limited the quantification accuracy of optical and optoacoustic spectroscopic methods, and impeded the goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical goal for the assessment of oxygenation in physiological processes and disease. Here we describe light fluence in the spectral domain and introduce eigenspectra multispectral optoacoustic tomography (eMSOT) to account for wavelength-dependent light attenuation, and estimate blood sO2 within deep tissue. We validate eMSOT in simulations, phantoms and animal measurements and spatially resolve sO2 in muscle and tumours, validating our measurements with histology data. eMSOT shows substantial sO2 accuracy enhancement over previous optoacoustic methods, potentially serving as a valuable tool for imaging tissue pathophysiology.

  2. Organic carbon accumulation in modern sediments of the Angola basin influenced by the Congo deep-sea fan

    Science.gov (United States)

    Baudin, François; Martinez, Philippe; Dennielou, Bernard; Charlier, Karine; Marsset, Tania; Droz, Laurence; Rabouille, Christophe

    2017-08-01

    Geochemical data (total organic carbon-TOC content, δ13Corg, C:N, Rock-Eval analyses) were obtained on 150 core tops from the Angola basin, with a special focus on the Congo deep-sea fan. Combined with the previously published data, the resulting dataset (322 stations) shows a good spatial and bathymetric representativeness. TOC content and δ13Corg maps of the Angola basin were generated using this enhanced dataset. The main difference in our map with previously published ones is the high terrestrial organic matter content observed downslope along the active turbidite channel of the Congo deep-sea fan till the distal lobe complex near 5000 m of water-depth. Interpretation of downslope trends in TOC content and organic matter composition indicates that lateral particle transport by turbidity currents is the primary mechanism controlling supply and burial of organic matter in the bathypelagic depths.

  3. Deep Unfolding for Topic Models.

    Science.gov (United States)

    Chien, Jen-Tzung; Lee, Chao-Hsi

    2018-02-01

    Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.

  4. Gradual DropIn of Layers to Train Very Deep Neural Networks

    OpenAIRE

    Smith, Leslie N.; Hand, Emily M.; Doster, Timothy

    2015-01-01

    We introduce the concept of dynamically growing a neural network during training. In particular, an untrainable deep network starts as a trainable shallow network and newly added layers are slowly, organically added during training, thereby increasing the network's depth. This is accomplished by a new layer, which we call DropIn. The DropIn layer starts by passing the output from a previous layer (effectively skipping over the newly added layers), then increasingly including units from the ne...

  5. Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce

    OpenAIRE

    Majumder, Bodhisattwa Prasad; Subramanian, Aditya; Krishnan, Abhinandan; Gandhi, Shreyansh; More, Ajinkya

    2018-01-01

    Extracting accurate attribute qualities from product titles is a vital component in delivering eCommerce customers with a rewarding online shopping experience via an enriched faceted search. We demonstrate the potential of Deep Recurrent Networks in this domain, primarily models such as Bidirectional LSTMs and Bidirectional LSTM-CRF with or without an attention mechanism. These have improved overall F1 scores, as compared to the previous benchmarks (More et al.) by at least 0.0391, showcasing...

  6. Impact disruption and recovery of the deep subsurface biosphere

    Science.gov (United States)

    Cockell, Charles S.; Voytek, Mary A.; Gronstal, Aaron L.; Finster, Kai; Kirshtein, Julie D.; Howard, Kieren; Reitner, Joachim; Gohn, Gregory S.; Sanford, Ward E.; Horton, J. Wright; Kallmeyer, Jens; Kelly, Laura; Powars, David S.

    2012-01-01

    Although a large fraction of the world's biomass resides in the subsurface, there has been no study of the effects of catastrophic disturbance on the deep biosphere and the rate of its subsequent recovery. We carried out an investigation of the microbiology of a 1.76 km drill core obtained from the ~35 million-year-old Chesapeake Bay impact structure, USA, with robust contamination control. Microbial enumerations displayed a logarithmic downward decline, but the different gradient, when compared to previously studied sites, and the scatter of the data are consistent with a microbiota influenced by the geological disturbances caused by the impact. Microbial abundance is low in buried crater-fill, ocean-resurge, and avalanche deposits despite the presence of redox couples for growth. Coupled with the low hydraulic conductivity, the data suggest the microbial community has not yet recovered from the impact ~35 million years ago. Microbial enumerations, molecular analysis of microbial enrichment cultures, and geochemical analysis showed recolonization of a deep region of impact-fractured rock that was heated to above the upper temperature limit for life at the time of impact. These results show how, by fracturing subsurface rocks, impacts can extend the depth of the biosphere. This phenomenon would have provided deep refugia for life on the more heavily bombarded early Earth, and it shows that the deeply fractured regions of impact craters are promising targets to study the past and present habitability of Mars.

  7. A Method for Improving Reliability of Radiation Detection using Deep Learning Framework

    International Nuclear Information System (INIS)

    Chang, Hojong; Kim, Tae-Ho; Han, Byunghun; Kim, Hyunduk; Kim, Ki-duk

    2017-01-01

    Radiation detection is essential technology for overall field of radiation and nuclear engineering. Previously, technology for radiation detection composes of preparation of the table of the input spectrum to output spectrum in advance, which requires simulation of numerous predicted output spectrum with simulation using parameters modeling the spectrum. In this paper, we propose new technique to improve the performance of radiation detector. The software in the radiation detector has been stagnant for a while with possible intrinsic error of simulation. In the proposed method, to predict the input source using output spectrum measured by radiation detector is performed using deep neural network. With highly complex model, we expect that the complex pattern between data and the label can be captured well. Furthermore, the radiation detector should be calibrated regularly and beforehand. We propose a method to calibrate radiation detector using GAN. We hope that the power of deep learning may also reach to radiation detectors and make huge improvement on the field. Using improved radiation detector, the reliability of detection would be confident, and there are many tasks remaining to solve using deep learning in nuclear engineering society.

  8. Initial study of deep inelastic scattering with ZEUS at HERA

    Science.gov (United States)

    Derrick, M.; Krakauer, D.; Magill, S.; Musgrave, B.; Repond, J.; Repond, S.; Stanek, R.; Talaga, R. L.; Thron, J.; Arzarello, F.; Ayad, R.; Barbagli, G.; Bari, G.; Basile, M.; Bellagamba, L.; Boscherini, D.; Bruni, A.; Bruni, G.; Bruni, P.; Cara Romeo, G.; Castellini, G.; Chiarini, M.; Cifarelli, L.; Cindolo, F.; Ciralli, F.; Contin, A.; D'Auria, S.; del Papa, C.; Frasconi, F.; Giusti, P.; Iacobucci, G.; Laurenti, G.; Levi, G.; Lin, Q.; Lisowski, B.; Maccarrone, G.; Margotti, A.; Massam, T.; Nania, R.; Nemoz, C.; Palmonari, F.; Sartorelli, G.; Timellini, R.; Zamora Garcia, Y.; Zichichi, A.; Bargende, A.; Crittenden, J.; Dabbous, H.; Desch, K.; Diekmann, B.; Doeker, T.; Geerts, M.; Geitz, G.; Gutjahr, B.; Hartmann, H.; Hartmann, J.; Haun, D.; Heinloth, K.; Hilger, E.; Jakob, H.-P.; Kramarczyk, S.; Kückes, M.; Mass, A.; Mengel, S.; Mollen, J.; Monaldi, D.; Müsch, H.; Paul, E.; Schattevoy, R.; Schneider, J.-L.; Wedemeyer, R.; Cassidy, A.; Cussans, D. G.; Dyce, N.; Fawcett, H. F.; Foster, B.; Gilmore, R.; Heath, G. P.; Lancaster, M.; Llewellyn, T. J.; Malos, J.; Morgado, C. J. S.; Tapper, R. J.; Wilson, S. S.; Rau, R. R.; Barillari, T.; Schioppa, M.; Susinno, G.; Bernstein, A.; Caldwell, A.; Gialas, I.; Parsons, J. A.; Ritz, S.; Sciulli, F.; Straub, P. B.; Wai, L.; Yang, S.; Burkot, W.; Eskreys, A.; Piotrzkowski, K.; Zachara, M.; Zawiejski, L.; Borzemski, P.; Jeleń, K.; Kisielewska, D.; Kowalski, T.; Rulikowska-Zerȩbska, E.; Suszycki, L.; Zajc, J.; Kȩdzierski, T.; Kotański, A.; Przybycień, M.; Bauerdick, L. A. T.; Behrens, U.; Bienlein, J. K.; Coldewey, C.; Dannemann, A.; Dierks, K.; Dorth, W.; Drews, G.; Erhard, P.; Flasiński, M.; Fleck, I.; Fürtjes, A.; Gläser, R.; Göttlicher, P.; Hass, T.; Hagge, L.; Hain, W.; Hasell, D.; Hultschig, H.; Jahnen, G.; Joos, P.; Kasemann, M.; Klanner, R.; Koch, W.; Kötz, U.; Kowalski, H.; Labs, J.; Ladage, A.; Löhr, B.; Lüke, D.; Mainusch, J.; Manczak, O.; Momayezi, M.; Ng, J. S. T.; Nicel, S.; Notz, D.; Park, I. H.; Pösnecker, K.-U.; Rohde, M.; Ros, E.; Schneekloth, S.; Schroeder, J.; Schulz, W.; Selonke, F.; Stiliaris, E.; Tscheslog, E.; Tsurugai, T.; Turkot, F.; Vogel, W.; Woeniger, T.; Wolf, G.; Youngman, C.; Grabosch, H. J.; Leich, A.; Meyer, A.; Rethfeldt, C.; Schlensthdt, S.; Casalbuoni, R.; de Curtis, S.; Dominici, D.; Francescato, A.; Nuti, M.; Pelfer, P.; Anzivino, G.; Casaccia, R.; de Pasquale, S.; Qian, S.; Votano, L.; Bamberger, A.; Freidhof, A.; Poser, T.; Söldner-Rembold, S.; Theisen, G.; Trefzger, T.; Brook, N. H.; Bussey, P. J.; Doyle, A. T.; Forbes, J. R.; Jamieson, V. A.; Raine, C.; Saxon, D. H.; Brückmann, H.; Gloth, G.; Holm, U.; Kammerdocher, H.; Krebs, B.; Neumann, T.; Wick, K.; Hofmann, A.; Kröger, W.; Krüger, J.; Lohrmann, E.; Milewski, J.; Nakahata, M.; Pavel, N.; Poelz, G.; Salomon, R.; Seidman, A.; Schott, W.; Wiik, B. H.; Zetsche, F.; Bacon, T. C.; Butterworth, I.; Markou, C.; McQuillan, D.; Miller, D. B.; Mobayyen, M. M.; Prinias, A.; Vorvolakos, A.; Bienz, T.; Kreutzmann, H.; Mallik, U.; McCliment, E.; Roco, M.; Wang, M. Z.; Cloth, P.; Filges, D.; Chen, L.; Imlay, R.; Kartik, S.; Kim, H.-J.; McNeil, R. R.; Metcalf, W.; Barreiro, F.; Cases, G.; Hervás, L.; Labarga, L.; del Peso, J.; Roldán, J.; Terrón, J.; de Trocóniz, J. F.; Ikraiam, F.; Mayer, J. K.; Smith, G. R.; Corriveau, F.; Gilkinson, D. J.; Hanna, D. S.; Hung, L. W.; Mitchell, J. W.; Patel, P. M.; Sinclair, L. E.; Stairs, D. G.; Ullmann, R.; Bashindzhagyan, G. L.; Ermolov, P. F.; Golubkov, Y. A.; Kuzmin, V. A.; Kuznetsov, E. N.; Savin, A. A.; Voronin, A. G.; Zotov, N. P.; Bentvelsen, S.; Dake, A.; Engelen, J.; de Jong, P.; de Jong, S.; de Kamps, M.; Kooijman, P.; Kruse, A.; van der Lugt, H.; O'dell, V.; Straver, J.; Tenner, A.; Tiecke, H.; Uijterwaal, H.; Vermeulen, J.; Wiggers, L.; de Wolf, E.; van Woudenberg, R.; Yoshida, R.; Bylsma, B.; Durkin, L. S.; Li, C.; Ling, T. Y.; McLean, K. W.; Murray, W. N.; Park, S. K.; Romanowski, T. A.; Seidlein, R.; Blair, G. A.; Butterworth, J. M.; Byrne, A.; Cashmore, R. J.; Cooper-Sarkar, A. M.; Devenish, R. C. E.; Gingrich, D. M.; Hallam-Baker, P. M.; Harnew, N.; Khatri, T.; Long, K. R.; Luffman, P.; McArthur, I.; Morawitz, P.; Nash, J.; Smith, S. J. P.; Roocroft, N. C.; Wilson, F. F.; Abbiendi, G.; Brugnera, R.; Carlin, R.; dal Corso, F.; de Giorgi, M.; Dosselli, U.; Gasparini, F.; Limentani, S.; Morandin, M.; Posocco, M.; Stanco, L.; Stroili, R.; Voci, C.; Field, G.; Lim, J. N.; Oh, B. Y.; Whitmore, J.; Contino, U.; D'Agostini, G.; Guida, M.; Iori, M.; Mari, S. M.; Marini, G.; Mattioli, M.; Nigro, A.; Hart, J. C.; McCubbin, N. A.; Shah, T. P.; Short, T. L.; Barberis, E.; Cartiglia, N.; Heusch, C.; Hubbard, B.; Leslie, J.; O'Shaughnessy, K.; Sadrozinski, H. F.; Seiden, A.; Badura, E.; Biltzinger, J.; Chaves, H.; Rost, M.; Seifert, R. J.; Walenta, A. H.; Weihs, W.; Zech, G.; Dagan, S.; Levy, A.; Zer-Zion, D.; Hasegawa, T.; Hazumi, M.; Ishii, T.; Kasai, S.; Kuze, M.; Nagasawa, Y.; Nakao, M.; Okuno, H.; Tokushuku, K.; Watanabe, T.; Yamada, S.; Chiba, M.; Hamatsu, R.; Hirose, T.; Kitamura, S.; Nagayama, S.; Nakamitsu, Y.; Arneodo, M.; Costa, M.; Ferrero, M. I.; Lamberti, L.; Maselli, S.; Peroni, C.; Solano, A.; Staiano, A.; Dardo, M.; Bailey, D. C.; Bandyopadhyay, D.; Benard, F.; Bhadra, S.; Brkic, M.; Burow, B. D.; Chlebana, F. S.; Crombie, M. B.; Hartner, G. F.; Levman, G. M.; Martin, J. F.; Orr, R. S.; Prentice, J. D.; Sampson, C. R.; Stairs, G. G.; Teuscher, R. J.; Yoon, T.-S.; Bullock, F. W.; Catterall, C. D.; Giddings, J. C.; Jones, T. W.; Khan, A. M.; Lane, J. B.; Makkar, P. L.; Shaw, D.; Shulman, J.; Blankenship, K.; Gibaut, D. B.; Kochocki, J.; Lu, B.; Mo, L. W.; Charchula, K.; Ciborowski, J.; Gajewski, J.; Grzelak, G.; Kasprzak, M.; Krzyżanowski, M.; Muchorowski, K.; Nowak, R. J.; Pawlak, J. M.; Stojda, K.; Stopczyński, A.; Szwed, R.; Tymieniecka, T.; Walczak, R.; Wróblewski, A. K.; Zakrzewski, J. A.; Zarnecki, A. F.; Adamus, M.; Abramowicz, H.; Eisenberg, Y.; Glasman, C.; Karshon, U.; Montag, A.; Revel, D.; Shapira, A.; Ali, I.; Behrens, B.; Camerini, U.; Dasu, S.; Fordham, C.; Foudas, C.; Goussiou, A.; Lomperski, M.; Loveless, R. J.; Nylander, P.; Ptacek, M.; Reeder, D. D.; Smith, W. H.; Silverstein, S.; Frisken, W. R.; Furutani, K. M.; Iga, Y.

    1993-04-01

    Results are presented on neutral current, deep inelastic scattering measured in collisions of 26.7 GeV electrons and 820 GeV protons. The events typically populate a range in Q2 from 10 to 100 GeV2. The values of x extend down to x ~ 10-4 which is two orders of magnitude lower than previously measured at such Q2 values in fixed target experiments. The measured cross sections are in accord with the extrapolations of current parametrisations of parton distributions.

  9. Docker Containers for Deep Learning Experiments

    OpenAIRE

    Gerke, Paul K.

    2017-01-01

    Deep learning is a powerful tool to solve problems in the area of image analysis. The dominant compute platform for deep learning is Nvidia’s proprietary CUDA, which can only be used together with Nvidia graphics cards. The nivida-docker project allows exposing Nvidia graphics cards to docker containers and thus makes it possible to run deep learning experiments in docker containers.In our department, we use deep learning to solve problems in the area of medical image analysis and use docker ...

  10. Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    2016-01-01

    Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...... faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets....

  11. Accelerating Deep Learning with Shrinkage and Recall

    OpenAIRE

    Zheng, Shuai; Vishnu, Abhinav; Ding, Chris

    2016-01-01

    Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Lea...

  12. Deep Sludge Gas Release Event Analytical Evaluation

    International Nuclear Information System (INIS)

    Sams, Terry L.

    2013-01-01

    Long Abstract. Full Text. The purpose of the Deep Sludge Gas Release Event Analytical Evaluation (DSGRE-AE) is to evaluate the postulated hypothesis that a hydrogen GRE may occur in Hanford tanks containing waste sludges at levels greater than previously experienced. There is a need to understand gas retention and release hazards in sludge beds which are 200 -300 inches deep. These sludge beds are deeper than historical Hanford sludge waste beds, and are created when waste is retrieved from older single-shell tanks (SST) and transferred to newer double-shell tanks (DST).Retrieval of waste from SSTs reduces the risk to the environment from leakage or potential leakage of waste into the ground from these tanks. However, the possibility of an energetic event (flammable gas accident) in the retrieval receiver DST is worse than slow leakage. Lines of inquiry, therefore, are (1) can sludge waste be stored safely in deep beds; (2) can gas release events (GRE) be prevented by periodically degassing the sludge (e.g., mixer pump); or (3) does the retrieval strategy need to be altered to limit sludge bed height by retrieving into additional DSTs? The scope of this effort is to provide expert advice on whether or not to move forward with the generation of deep beds of sludge through retrieval of C-Farm tanks. Evaluation of possible mitigation methods (e.g., using mixer pumps to release gas, retrieving into an additional DST) are being evaluated by a second team and are not discussed in this report. While available data and engineering judgment indicate that increased gas retention (retained gas fraction) in DST sludge at depths resulting from the completion of SST 241-C Tank Farm retrievals is not expected and, even if gas releases were to occur, they would be small and local, a positive USQ was declared (Occurrence Report EM-RP--WRPS-TANKFARM-2012-0014, 'Potential Exists for a Large Spontaneous Gas Release Event in Deep Settled Waste Sludge'). The purpose of this technical

  13. Consolidated Deep Actor Critic Networks (DRAFT)

    NARCIS (Netherlands)

    Van der Laan, T.A.

    2015-01-01

    The works [Volodymyr et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.] and [Volodymyr et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.] have demonstrated the power of combining deep neural networks with

  14. Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks

    OpenAIRE

    Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Hassanien, Aboul Ella; Selim, I. M.

    2017-01-01

    In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep galaxies architecture consists of 8 layers, one main convolutional layer for features extraction with 96 filters, followed by two principles fully connected layers for classification. It is trained over 1356 images and achieved 97.272% in testing accuracy. A c...

  15. Sceliphrolactam, a polyene macrocyclic lactam from a wasp-associated Streptomyces sp

    DEFF Research Database (Denmark)

    Oh, Dong-Chan; Poulsen, Michael; Currie, Cameron R

    2011-01-01

    A previously unreported 26-membered polyene macrocyclic lactam, sceliphrolactam, was isolated from an actinomycete, Streptomyces sp., associated with the mud dauber, Sceliphron caementarium. Sceliphrolactam's structure was determined by 1D- and 2D-NMR, MS, UV, and IR spectral analysis. Sceliphrol...

  16. Cytoskeletal actin dynamics shape a ramifying actin network underpinning immunological synapse formation

    DEFF Research Database (Denmark)

    Fritzsche, Marco; Fernandes, Ricardo A.; Chang, Veronica T.

    2017-01-01

    optical microscopes to analyze resting and activated T cells, we show that, following contact formation with activating surfaces, these cells sequentially rearrange their cortical actin across the entire cell, creating a previously unreported ramifying actin network above the immunological synapse...

  17. Molecular analysis of clinical isolates previously diagnosed as Mycobacterium intracellulare reveals incidental findings of "Mycobacterium indicus pranii" genotypes in human lung infection.

    Science.gov (United States)

    Kim, Su-Young; Park, Hye Yun; Jeong, Byeong-Ho; Jeon, Kyeongman; Huh, Hee Jae; Ki, Chang-Seok; Lee, Nam Yong; Han, Seung-Jung; Shin, Sung Jae; Koh, Won-Jung

    2015-09-30

    Mycobacterium intracellulare is a major cause of Mycobacterium avium complex lung disease in many countries. Molecular studies have revealed several new Mycobacteria species that are closely related to M. intracellulare. The aim of this study was to re-identify and characterize clinical isolates from patients previously diagnosed with M. intracellulare lung disease at the molecular level. Mycobacterial isolates from 77 patients, initially diagnosed with M. intracellulare lung disease were re-analyzed by multi-locus sequencing and pattern of insertion sequences. Among the 77 isolates, 74 (96 %) isolates were designated as M. intracellulare based on multigene sequence-based analysis. Interestingly, the three remaining strains (4 %) were re-identified as "Mycobacterium indicus pranii" according to distinct molecular phylogenetic positions in rpoB and hsp65 sequence-based typing. In hsp65 sequevar analysis, code 13 was found in the majority of cases and three unreported codes were identified. In 16S-23S rRNA internal transcribed spacer (ITS) sequevar analysis, all isolates of both species were classified within the Min-A ITS sequevar. Interestingly, four of the M. intracellulare isolates harbored IS1311, a M. avium-specific element. Two of three patients infected with "M. indicus pranii" had persistent positive sputum cultures after antibiotic therapy, indicating the clinical relevance of this study. This analysis highlights the importance of precise identification of clinical isolates genetically close to Mycobacterium species, and suggests that greater attention should be paid to nontuberculous mycobacteria lung disease caused by "M. indicus pranii".

  18. Effect of the type of ammonium salt on the extractive desulfurization of fuels using deep eutectic solvents

    NARCIS (Netherlands)

    Warrag, Samah E.E.; Adeyemi, Idowu; Rodriguez, Nerea R.; Nashef, Inas M.; van Sint Annaland, Martin; Kroon, Maaike C.; Peters, Cor J.

    2018-01-01

    In a previous work, we proved that the deep eutectic solvents (DESs) consisting of mixtures of tetraalkylammonium salts with polyols are promising candidates for oil desulfurization based on the obtained liquid-liquid equilibrium (LLE) data. In this study, the capability of DESs containing other

  19. Long-term effects of pallidal or subthalamic deep brain stimulation on quality of life in Parkinson's disease

    NARCIS (Netherlands)

    Volkmann, Jens; Albanese, Alberto; Kulisevsky, Jaime; Tornqvist, Aana-Lena; Houeto, Jean-Luc; Pidoux, Bernard; Bonnet, Anne-Marie; Mendes, Alexandre; Benabid, Alim-Louis; Fraix, Valerie; van Blercom, Nadege; Xie, Jing; Obeso, José; Rodriguez-Oroz, Maria Cruz; Guridi, Jurge; Schnitzler, Alfons; Timmermann, Lars; Gironell, Alexandre A.; Molet, Juan; Pascual-Sedano, Benta; Rehncrona, Stig; Moro, Elena; Lang, Anthony C.; Lozano, Andres M.; Bentivoglio, Anna Rita; Scerrati, Massimo; Contarino, Maria Fiorella; Romito, Luigi; Janssens, Marc; Agid, Yves

    2009-01-01

    We assessed the effects of deep brain stimulation of the subthalamic nucleus (STN-DBS) or internal pallidum (GPi-DBS) on health-related quality of life (HrQoL) in patients with advanced Parkinson's disease participating in a previously reported multicenter trial. Sickness Impact Profile (SIP)

  20. Deep Feature Consistent Variational Autoencoder

    OpenAIRE

    Hou, Xianxu; Shen, Linlin; Sun, Ke; Qiu, Guoping

    2016-01-01

    We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural net...

  1. Unreported births and deaths, a severe obstacle for improved neonatal survival in low-income countries; a population based study

    Directory of Open Access Journals (Sweden)

    Wallin Lars

    2008-03-01

    Full Text Available Abstract Background In order to improve child survival there is a need to target neonatal mortality. In this pursuit, valid local and national statistics on child health are essential. We analyze to what extent births and neonatal deaths are unreported in a low-income country and discuss the consequences at local and international levels for efforts to save newborn lives. Methods Information on all births and neonatal deaths in Quang Ninh province in Northern Vietnam in 2005 was ascertained by systematic inventory through group interviews with key informants, questionnaires and examination of health facility records. Health care staff at 187 Community Health Centers (CHC and 18 hospitals, in addition to 1372 Village Health Workers (VHW, were included in the study. Results were compared with the official reports of the Provincial Health Bureau. Results The neonatal mortality rate (NMR was 16/1000 (284 neonatal deaths/17 519 births, as compared to the official rate of 4.2/1000. The NMR varied between 44/1000 and 10/1000 in the different districts of the province. The under-reporting was mainly attributable to a dysfunctional reporting system and the fact that families, not the health system, were made responsible to register births and deaths. This under-reporting has severe consequences at local, national and international levels. At a local level, it results in a lack of awareness of the magnitude and differentials in NMR, leading to an indifference towards the problem. At a national and international level the perceived low mortality rate is manifested in a lack of investments in perinatal health programs. Conclusion This example of a faulty health information system is reportedly not unique in low and middle income countries where needs for neonatal health reforms are greatest. Improving reporting systems on births and neonatal deaths is a matter of human rights and a prerequisite for reducing neonatal mortality in order to reach the fourth

  2. Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

    Science.gov (United States)

    Shi, Bibo; Grimm, Lars J; Mazurowski, Maciej A; Baker, Jay A; Marks, Jeffrey R; King, Lorraine M; Maley, Carlo C; Hwang, E Shelley; Lo, Joseph Y

    2018-03-01

    The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. In this retrospective study, digital mammographic magnification views were collected for 99 subjects with DCIS at biopsy, 25 of which were subsequently upstaged to invasive cancer. A deep convolutional neural network model that was pretrained on nonmedical images (eg, animals, plants, instruments) was used as the feature extractor. Through a statistical pooling strategy, deep features were extracted at different levels of convolutional layers from the lesion areas, without sacrificing the original resolution or distorting the underlying topology. A multivariate classifier was then trained to predict which tumors contain occult invasive disease. This was compared with the performance of traditional "handcrafted" computer vision (CV) features previously developed specifically to assess mammographic calcifications. The generalization performance was assessed using Monte Carlo cross-validation and receiver operating characteristic curve analysis. Deep features were able to distinguish DCIS with occult invasion from pure DCIS, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval, 0.68-0.73). This performance was comparable with the handcrafted CV features (area under the curve = 0.68; 95% confidence interval, 0.66-0.71) that were designed with prior domain knowledge. Despite being pretrained on only nonmedical images, the deep features extracted from digital mammograms demonstrated comparable performance with handcrafted CV features for the challenging task of predicting DCIS upstaging. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  3. The XMM deep survey in the CDF-S. X. X-ray variability of bright sources

    Science.gov (United States)

    Falocco, S.; Paolillo, M.; Comastri, A.; Carrera, F. J.; Ranalli, P.; Iwasawa, K.; Georgantopoulos, I.; Vignali, C.; Gilli, R.

    2017-12-01

    Aims: We aim to study the variability properties of bright hard X-ray selected active galactic nuclei (AGN) in the redshift range between 0.3 and 1.6 detected in the Chandra Deep Field South (XMM-CDFS) by a long ( 3 Ms) XMM observation. Methods: Taking advantage of the good count statistics in the XMM CDFS, we search for flux and spectral variability using the hardness ratio (HR) techniques. We also investigate the spectral variability of different spectral components (photon index of the power law, column density of the local absorber, and reflection intensity). The spectra were merged in six epochs (defined as adjacent observations) and in high and low flux states to understand whether the flux transitions are accompanied by spectral changes. Results: The flux variability is significant in all the sources investigated. The HRs in general are not as variable as the fluxes, in line with previous results on deep fields. Only one source displays a variable HR, anti-correlated with the flux (source 337). The spectral analysis in the available epochs confirms the steeper when brighter trend consistent with Comptonisation models only in this source at 99% confidence level. Finding this trend in one out of seven unabsorbed sources is consistent, within the statistical limits, with the 15% of unabsorbed AGN in previous deep surveys. No significant variability in the column densities, nor in the Compton reflection component, has been detected across the epochs considered. The high and low states display in general different normalisations but consistent spectral properties. Conclusions: X-ray flux fluctuations are ubiquitous in AGN, though in some cases the data quality does not allow for their detection. In general, the significant flux variations are not associated with spectral variability: photon index and column densities are not significantly variable in nine out of the ten AGN over long timescales (from three to six and a half years). Photon index variability is

  4. How Stressful Is "Deep Bubbling"?

    Science.gov (United States)

    Tyrmi, Jaana; Laukkanen, Anne-Maria

    2017-03-01

    Water resistance therapy by phonating through a tube into the water is used to treat dysphonia. Deep submersion (≥10 cm in water, "deep bubbling") is used for hypofunctional voice disorders. Using it with caution is recommended to avoid vocal overloading. This experimental study aimed to investigate how strenuous "deep bubbling" is. Fourteen subjects, half of them with voice training, repeated the syllable [pa:] in comfortable speaking pitch and loudness, loudly, and in strained voice. Thereafter, they phonated a vowel-like sound both in comfortable loudness and loudly into a glass resonance tube immersed 10 cm into the water. Oral pressure, contact quotient (CQ, calculated from electroglottographic signal), and sound pressure level were studied. The peak oral pressure P(oral) during [p] and shuttering of the outer end of the tube was measured to estimate the subglottic pressure P(sub) and the mean P(oral) during vowel portions to enable calculation of transglottic pressure P(trans). Sensations during phonation were reported with an open-ended interview. P(sub) and P(oral) were higher in "deep bubbling" and P(trans) lower than in loud syllable phonation, but the CQ did not differ significantly. Similar results were obtained for the comparison between loud "deep bubbling" and strained phonation, although P(sub) did not differ significantly. Most of the subjects reported "deep bubbling" to be stressful only for respiratory and lip muscles. No big differences were found between trained and untrained subjects. The CQ values suggest that "deep bubbling" may increase vocal fold loading. Further studies should address impact stress during water resistance exercises. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

  5. Evaluation of the DeepWind concept

    DEFF Research Database (Denmark)

    Schmidt Paulsen, Uwe; Borg, Michael; Gonzales Seabra, Luis Alberto

    The report describes the DeepWind 5 MW conceptual design as a baseline for results obtained in the scientific and technical work packages of the DeepWind project. A comparison of DeepWi nd with existing VAWTs and paper projects are carried out and the evaluation of the concept in terms of cost...

  6. Simulator Studies of the Deep Stall

    Science.gov (United States)

    White, Maurice D.; Cooper, George E.

    1965-01-01

    Simulator studies of the deep-stall problem encountered with modern airplanes are discussed. The results indicate that the basic deep-stall tendencies produced by aerodynamic characteristics are augmented by operational considerations. Because of control difficulties to be anticipated in the deep stall, it is desirable that adequate safeguards be provided against inadvertent penetrations.

  7. Deep Learning

    DEFF Research Database (Denmark)

    Jensen, Morten Bornø; Bahnsen, Chris Holmberg; Nasrollahi, Kamal

    2018-01-01

    I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning.......I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning....

  8. Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Daoyu Lin

    2018-01-01

    Full Text Available Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such as faces and bedrooms. In this paper, we propose a multiscale deep neural network to transform sketches into Chinese paintings. To synthesize more realistic imagery, we train the generative network by using both L1 loss and adversarial loss. Additionally, users can control the process of the synthesis since the generative network is feed-forward. This network can also be treated as neural style transfer by adding an edge detector. Furthermore, additional experiments on image colorization and image super-resolution demonstrate the universality of our proposed approach.

  9. Cultivating the Deep Subsurface Microbiome

    Science.gov (United States)

    Casar, C. P.; Osburn, M. R.; Flynn, T. M.; Masterson, A.; Kruger, B.

    2017-12-01

    Subterranean ecosystems are poorly understood because many microbes detected in metagenomic surveys are only distantly related to characterized isolates. Cultivating microorganisms from the deep subsurface is challenging due to its inaccessibility and potential for contamination. The Deep Mine Microbial Observatory (DeMMO) in Lead, SD however, offers access to deep microbial life via pristine fracture fluids in bedrock to a depth of 1478 m. The metabolic landscape of DeMMO was previously characterized via thermodynamic modeling coupled with genomic data, illustrating the potential for microbial inhabitants of DeMMO to utilize mineral substrates as energy sources. Here, we employ field and lab based cultivation approaches with pure minerals to link phylogeny to metabolism at DeMMO. Fracture fluids were directed through reactors filled with Fe3O4, Fe2O3, FeS2, MnO2, and FeCO3 at two sites (610 m and 1478 m) for 2 months prior to harvesting for subsequent analyses. We examined mineralogical, geochemical, and microbiological composition of the reactors via DNA sequencing, microscopy, lipid biomarker characterization, and bulk C and N isotope ratios to determine the influence of mineralogy on biofilm community development. Pre-characterized mineral chips were imaged via SEM to assay microbial growth; preliminary results suggest MnO2, Fe3O4, and Fe2O3 were most conducive to colonization. Solid materials from reactors were used as inoculum for batch cultivation experiments. Media designed to mimic fracture fluid chemistry was supplemented with mineral substrates targeting metal reducers. DNA sequences and microscopy of iron oxide-rich biofilms and fracture fluids suggest iron oxidation is a major energy source at redox transition zones where anaerobic fluids meet more oxidizing conditions. We utilized these biofilms and fluids as inoculum in gradient cultivation experiments targeting microaerophilic iron oxidizers. Cultivation of microbes endemic to DeMMO, a system

  10. Departures from the impulse approximation in deep inelastic neutron scattering

    International Nuclear Information System (INIS)

    Mayers, J.

    1989-01-01

    A new formulation of the impulse approximation (IA) in deep inelastic neutron scattering is developed. It is shown that observed departures from the IA at intermediate momentum transfers are caused by the quantum nature of the initial state rather than final state effects, as has previously been assumed and that these effects become small at high temperatures. It is also argued that final state broadening is significant for He liquids in all feasible experiments, but that in other systems the IA is approached at high momentum transfers. (author)

  11. DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

    Science.gov (United States)

    Wachinger, Christian; Reuter, Martin; Klein, Tassilo

    2018-04-15

    We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Deep Learning and Its Applications in Biomedicine.

    Science.gov (United States)

    Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

    2018-02-01

    Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

  13. Improved process robustness by using closed loop control in deep drawing applications

    Science.gov (United States)

    Barthau, M.; Liewald, M.; Christian, Held

    2017-09-01

    The production of irregular shaped deep-drawing parts with high quality requirements, which are common in today’s automotive production, permanently challenges production processes. High requirements on lightweight construction of passenger car bodies following European regulations until 2020 have been massively increasing the use of high strength steels substantially for years and are also leading to bigger challenges in sheet metal part production. Of course, the more and more complex shapes of today’s car body shells also intensify the issue due to modern and future design criteria. The metal forming technology tries to meet these challenges by developing a highly sophisticated layout of deep drawing dies that consider part quality requirements, process robustness and controlled material flow during the deep or stretch drawing process phase. A new method for controlling material flow using a closed loop system was developed at the IFU Stuttgart. In contrast to previous approaches, this new method allows a control intervention during the deep-drawing stroke. The blank holder force around the outline of the drawn part is used as control variable. The closed loop is designed as trajectory follow up with feed forward control. The used command variable is the part-wall stress that is measured with a piezo-electric measuring pin. In this paper the used control loop will be described in detail. The experimental tool that was built for testing the new control approach is explained here with its features. A method for gaining the follow up trajectories from simulation will also be presented. Furthermore, experimental results considering the robustness of the deep drawing process and the gain in process performance with developed control loop will be shown. Finally, a new procedure for the industrial application of the new control method of deep drawing will be presented by using a new kind of active element to influence the local blank holder pressure onto part

  14. Deep Water Acoustics

    Science.gov (United States)

    2016-06-28

    the Deep Water project and participate in the NPAL Workshops, including Art Baggeroer (MIT), J. Beron- Vera (UMiami), M. Brown (UMiami), T...Kathleen E . Wage. The North Pacific Acoustic Laboratory deep-water acoustic propagation experiments in the Philippine Sea. J. Acoust. Soc. Am., 134(4...estimate of the angle α during PhilSea09, made from ADCP measurements at the site of the DVLA. Sim. A B1 B2 B3 C D E F Prof. # 0 4 4 4 5 10 16 20 α

  15. Bioherbicidal potential of a strain of Xanthomonas spp. for control of common cockelbur, (Santium strumarium)

    Science.gov (United States)

    Several isolates of a previously unreported disease were discovered on common cocklebur seedlings in Chicot County, Arkansas and Washington County, Mississippi. Diseased plants in nature exhibited angular-shaped leaf spotting symptoms on leaf margins and central leaf areas. The isolates were cultu...

  16. Asymptomatic parental mosaicism for osteogenesis imperfect associated with a new splice site mutation in COL1A2

    DEFF Research Database (Denmark)

    Frederiksen, Anja Lisbeth; Dunø, Morten; Johnsen, Iben Birgit Gade

    2016-01-01

    Recurrent lethal perinatal osteogenesis imperfecta may result from asymptomatic parental mosaicism. A previously unreported mutation in COL1A2 leads to recurrent cases of fetal osteogenesis imperfecta Sillence type IIA, which emphasizes the importance of clinical and genetic evaluation of mosaicism...

  17. Genomic Dissection of Travel-Associated Extended-Spectrum-Beta-Lactamase-Producing Salmonella enterica Serovar Typhi Isolates Originating from the Philippines: a One-Off Occurrence or a Threat to Effective Treatment of Typhoid Fever?

    DEFF Research Database (Denmark)

    Hendriksen, Rene S.; Leekitcharoenphon, Pimlapas; Mikoleit, Matthew

    2015-01-01

    One unreported case of extended-spectrum-beta-lactamase (ESBL)-producing Salmonella enterica serovar Typhi was identified, whole-genome sequence typed, among other analyses, and compared to other available genomes of S. Typhi. The reported strain was similar to a previously published strain harbo...

  18. A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs.

    Science.gov (United States)

    Wu, Zifeng; Huang, Yongzhen; Wang, Liang; Wang, Xiaogang; Tan, Tieniu

    2017-02-01

    This paper studies an approach to gait based human identification via similarity learning by deep convolutional neural networks (CNNs). With a pretty small group of labeled multi-view human walking videos, we can train deep networks to recognize the most discriminative changes of gait patterns which suggest the change of human identity. To the best of our knowledge, this is the first work based on deep CNNs for gait recognition in the literature. Here, we provide an extensive empirical evaluation in terms of various scenarios, namely, cross-view and cross-walking-condition, with different preprocessing approaches and network architectures. The method is first evaluated on the challenging CASIA-B dataset in terms of cross-view gait recognition. Experimental results show that it outperforms the previous state-of-the-art methods by a significant margin. In particular, our method shows advantages when the cross-view angle is large, i.e., no less than 36 degree. And the average recognition rate can reach 94 percent, much better than the previous best result (less than 65 percent). The method is further evaluated on the OU-ISIR gait dataset to test its generalization ability to larger data. OU-ISIR is currently the largest dataset available in the literature for gait recognition, with 4,007 subjects. On this dataset, the average accuracy of our method under identical view conditions is above 98 percent, and the one for cross-view scenarios is above 91 percent. Finally, the method also performs the best on the USF gait dataset, whose gait sequences are imaged in a real outdoor scene. These results show great potential of this method for practical applications.

  19. Prediction of successful trial of labour in patients with a previous caesarean section

    International Nuclear Information System (INIS)

    Shaheen, N.; Khalil, S.; Iftikhar, P.

    2014-01-01

    Objective: To determine the prediction rate of success in trial of labour after one previous caesarean section. Methods: The cross-sectional study was conducted at the Department of Obstetrics and Gynaecology, Cantonment General Hospital, Rawalpindi, from January 1, 2012 to January 31, 2013, and comprised women with one previous Caesarean section and with single alive foetus at 37-41 weeks of gestation. Women with more than one Caesarean section, unknown site of uterine scar, bony pelvic deformity, placenta previa, intra-uterine growth restriction, deep transverse arrest in previous labour and non-reassuring foetal status at the time of admission were excluded. Intrapartum risk assessment included Bishop score at admission, rate of cervical dilatation and scar tenderness. SPSS 21 was used for statistical analysis. Results: Out of a total of 95 women, the trial was successful in 68 (71.6%). Estimated foetal weight and number of prior vaginal deliveries had a high predictive value for successful trial of labour after Caesarean section. Estimated foetal weight had an odds ratio of 0.46 (p<0.001), while number of prior vaginal deliveries had an odds ratio of 0.85 with (p=0.010). Other factors found to be predictive of successful trial included Bishop score at the time of admission (p<0.037) and rate of cervical dilatation in the first stage of labour (p<0.021). Conclusion: History of prior vaginal deliveries, higher Bishop score at the time of admission, rapid rate of cervical dilatation and lower estimated foetal weight were predictive of a successful trial of labour after Caesarean section. (author)

  20. Overview of deep learning in medical imaging.

    Science.gov (United States)

    Suzuki, Kenji

    2017-09-01

    The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a

  1. Supercement for Annular Seal and Long-Term Integrity in Deep, Hot Wells "DeepTrek"

    Energy Technology Data Exchange (ETDEWEB)

    CSI Technologies

    2007-08-31

    The purpose of this project is to formulate a 'Supercement' designed for improving the long-term sealing integrity in HPHT wells. Phase I concentrated on chemistry studies and screening tests to design and evaluate Portland-based, hybrid Portland, and non-Portland-based cement systems suitable for further scale-up testing. Phase II work concentrated on additional lab and field testing to reduce the candidate materials list to two systems, as well as scaleup activities aimed at verifying performance at the field scale. Phase II was extended thorough a proposal to develop additional testing capabilities aimed at quantifying cementing material properties and performance that were previously not possible. Phase III focused on bringing the material(s) developed in previous Phases to commercialization, through Field Trials, Cost/Benefit Analysis, and Technology Transfer. Extensive development and testing work throughout the project led to Phase III commercialization of two very different materials: (1) Highly-expansive cement (Portland-based), patent pending as 'PRESTRESSED CEMENT'; and (2) Epoxy Resin (non-Portland-based), patent pending. Trade name is Ultra Seal-R. In Phase III, work concentrated on application of the Supercement materials in various increasingly-challenging wells. Previous testing revealed that PRESTRESSED CEMENT, when applied in weak or unconsolidated formations, tends to expand away from the central pipe, restricting the applicability of this material to competent formations. Tests were devised to quantify this effect so the material could be applied in appropriate wells. Additionally, the testing was needed because of industry resistance to expansive cements, due to previous marketing attempts with other materials that were less than successful. Field trials with the Epoxy Resin currently numbers in the hundreds of jobs at up to 295 deg F, with a large percentage being completely successful. Both the PRESTRESSED CEMENT as well

  2. Deep Web Search Interface Identification: A Semi-Supervised Ensemble Approach

    Directory of Open Access Journals (Sweden)

    Hong Wang

    2014-12-01

    Full Text Available To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML form or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tediousmanual work, while unlabeled HTML forms are abundant and easy to obtain. In this research, we consider the plausibility of using both labeled and unlabeled data to train better models to identify search interfaces more effectively. We present a semi-supervised co-training ensemble learning approach using both neural networks and decision trees to deal with the search interface identification problem. We show that the proposed model outperforms previous methods using only labeled data. We also show that adding unlabeled data improves the effectiveness of the proposed model.

  3. TOPIC MODELING: CLUSTERING OF DEEP WEBPAGES

    OpenAIRE

    Muhunthaadithya C; Rohit J.V; Sadhana Kesavan; E. Sivasankar

    2015-01-01

    The internet is comprised of massive amount of information in the form of zillions of web pages.This information can be categorized into the surface web and the deep web. The existing search engines can effectively make use of surface web information.But the deep web remains unexploited yet. Machine learning techniques have been commonly employed to access deep web content.

  4. DeepSimulator: a deep simulator for Nanopore sequencing

    KAUST Repository

    Li, Yu; Han, Renmin; Bi, Chongwei; Li, Mo; Wang, Sheng; Gao, Xin

    2017-01-01

    or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments

  5. Memo on Some Target of Previous Meeting in 2004 and Now

    International Nuclear Information System (INIS)

    Shibata, Heki

    2014-01-01

    The necessity of deep borehole observation was pointed out in a 2002 WS entitled 'Physics of Active Faults' organized by USGS and NIED, and discussion on the utilization of deep borehole observation for design basis ground motion development was then was deepened at the 2004 OECD/NEED Tsukuba WS. In addition to this study history, a series of WSs on Seismic Observation in Deep Boreholes and its Application organized by OECD, IAEA and JNES was planned. The author also explained that at the first SODB WS, he expected deep borehole observation to be used to observe the activity of specific faults with high accuracy, to detect the detailed structure of those specific faults, and to estimate the asperity distribution in order to establish a practical approach to design basis earthquakes for nuclear power plant design. Recently, however, he has come to think that SODB will help find hidden faults and help observe the conditions of old faults. He also mentioned that he believes that in the future SODB can be used for re-activity evaluation of tertiary faults and evaluation of the movement of local fracture zones

  6. Building Program Vector Representations for Deep Learning

    OpenAIRE

    Mou, Lili; Li, Ge; Liu, Yuxuan; Peng, Hao; Jin, Zhi; Xu, Yan; Zhang, Lu

    2014-01-01

    Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, whi...

  7. [Deep vein thrombosis prophylaxis.

    Science.gov (United States)

    Sandoval-Chagoya, Gloria Alejandra; Laniado-Laborín, Rafael

    2013-01-01

    Background: despite the proven effectiveness of preventive therapy for deep vein thrombosis, a significant proportion of patients at risk for thromboembolism do not receive prophylaxis during hospitalization. Our objective was to determine the adherence to thrombosis prophylaxis guidelines in a general hospital as a quality control strategy. Methods: a random audit of clinical charts was conducted at the Tijuana General Hospital, Baja California, Mexico, to determine the degree of adherence to deep vein thrombosis prophylaxis guidelines. The instrument used was the Caprini's checklist for thrombosis risk assessment in adult patients. Results: the sample included 300 patient charts; 182 (60.7 %) were surgical patients and 118 were medical patients. Forty six patients (15.3 %) received deep vein thrombosis pharmacologic prophylaxis; 27.1 % of medical patients received deep vein thrombosis prophylaxis versus 8.3 % of surgical patients (p < 0.0001). Conclusions: our results show that adherence to DVT prophylaxis at our hospital is extremely low. Only 15.3 % of our patients at risk received treatment, and even patients with very high risk received treatment in less than 25 % of the cases. We have implemented strategies to increase compliance with clinical guidelines.

  8. Contemporary deep recurrent learning for recognition

    Science.gov (United States)

    Iftekharuddin, K. M.; Alam, M.; Vidyaratne, L.

    2017-05-01

    Large-scale feed-forward neural networks have seen intense application in many computer vision problems. However, these networks can get hefty and computationally intensive with increasing complexity of the task. Our work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks such as maze traversal and image processing when compared to generic feed forward networks. While deep neural networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical structure has largely been absent in neural networks with recurrency. Further, our work introduces deep hierarchy in SRN for object recognition. The simultaneous recurrency results in an unfolding effect of the SRN through time, potentially enabling the design of an arbitrarily deep network. This paper shows experiments using face, facial expression and character recognition tasks using novel deep recurrent model and compares recognition performance with that of generic deep feed forward model. Finally, we demonstrate the flexibility of incorporating our proposed deep SRN based recognition framework in a humanoid robotic platform called NAO.

  9. Larval transport modeling of deep-sea invertebrates can aid the search for undiscovered populations.

    Directory of Open Access Journals (Sweden)

    Jon M Yearsley

    Full Text Available BACKGROUND: Many deep-sea benthic animals occur in patchy distributions separated by thousands of kilometres, yet because deep-sea habitats are remote, little is known about their larval dispersal. Our novel method simulates dispersal by combining data from the Argo array of autonomous oceanographic probes, deep-sea ecological surveys, and comparative invertebrate physiology. The predicted particle tracks allow quantitative, testable predictions about the dispersal of benthic invertebrate larvae in the south-west Pacific. PRINCIPAL FINDINGS: In a test case presented here, using non-feeding, non-swimming (lecithotrophic trochophore larvae of polyplacophoran molluscs (chitons, we show that the likely dispersal pathways in a single generation are significantly shorter than the distances between the three known population centres in our study region. The large-scale density of chiton populations throughout our study region is potentially much greater than present survey data suggest, with intermediate 'stepping stone' populations yet to be discovered. CONCLUSIONS/SIGNIFICANCE: We present a new method that is broadly applicable to studies of the dispersal of deep-sea organisms. This test case demonstrates the power and potential applications of our new method, in generating quantitative, testable hypotheses at multiple levels to solve the mismatch between observed and expected distributions: probabilistic predictions of locations of intermediate populations, potential alternative dispersal mechanisms, and expected population genetic structure. The global Argo data have never previously been used to address benthic biology, and our method can be applied to any non-swimming larvae of the deep-sea, giving information upon dispersal corridors and population densities in habitats that remain intrinsically difficult to assess.

  10. Towards deep learning with segregated dendrites.

    Science.gov (United States)

    Guerguiev, Jordan; Lillicrap, Timothy P; Richards, Blake A

    2017-12-05

    Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

  11. The deep ocean under climate change

    Science.gov (United States)

    Levin, Lisa A.; Le Bris, Nadine

    2015-11-01

    The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems.

  12. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    Directory of Open Access Journals (Sweden)

    Francisco Javier Ordóñez

    2016-01-01

    Full Text Available Human activity recognition (HAR tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i is suitable for multimodal wearable sensors; (ii can perform sensor fusion naturally; (iii does not require expert knowledge in designing features; and (iv explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

  13. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

    Science.gov (United States)

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-18

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

  14. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    Science.gov (United States)

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

  15. Effects of defibrotide in patients with chronic deep insufficiency. The PROVEDIS study.

    Science.gov (United States)

    Coccheri, S; Andreozzi, G M; D'Addato, M; Gensini, G F

    2004-06-01

    In the present study the effect of defibrotide, an antithrombotic and profibrinolytic agent, was investigated in patients with chronic venous insufficiency (CVI) due to deep vein obstruction and/or reflux (chronic deep vein insufficiency, CDVI). The study was a multicenter, randomized, double blind placebo controlled trial in which only patients with CDVI confirmed by ultrasound were enrolled. All patients were treated with adequate elastic compression and randomized to receive either oral defibrotide (800 mg/die) or matching placebo for 1 year. Patients with active or previous leg ulcer were excluded. A total of 288 patients were randomized and 159 completed the study. At baseline ultrasound investigation, obstructive changes were found in 2/3 of all patients thus ascertaining a post-thrombotic syndrome (PTS). The primary endpoint, ankle circumference, was significantly reduced under defibrotide from day 120 throughout 360. Scores for pain and edema were improved. The number of episodes of superficial thrombophlebitis and deep vein thrombosis was significantly lower under defibrotide (n=2) than under placebo (n=10). The majority of these events occurred in the subset of patients with documented PTS. Treatment with defibrotide in addition to elastic compression in patients with objectively assessed CDVI, mostly due to PTS, resulted in clinical benefits and prevented thrombotic complications harmful to the limb conditions.

  16. NATURAL GAS RESOURCES IN DEEP SEDIMENTARY BASINS

    Energy Technology Data Exchange (ETDEWEB)

    Thaddeus S. Dyman; Troy Cook; Robert A. Crovelli; Allison A. Henry; Timothy C. Hester; Ronald C. Johnson; Michael D. Lewan; Vito F. Nuccio; James W. Schmoker; Dennis B. Riggin; Christopher J. Schenk

    2002-02-05

    From a geological perspective, deep natural gas resources are generally defined as resources occurring in reservoirs at or below 15,000 feet, whereas ultra-deep gas occurs below 25,000 feet. From an operational point of view, ''deep'' is often thought of in a relative sense based on the geologic and engineering knowledge of gas (and oil) resources in a particular area. Deep gas can be found in either conventionally-trapped or unconventional basin-center accumulations that are essentially large single fields having spatial dimensions often exceeding those of conventional fields. Exploration for deep conventional and unconventional basin-center natural gas resources deserves special attention because these resources are widespread and occur in diverse geologic environments. In 1995, the U.S. Geological Survey estimated that 939 TCF of technically recoverable natural gas remained to be discovered or was part of reserve appreciation from known fields in the onshore areas and State waters of the United. Of this USGS resource, nearly 114 trillion cubic feet (Tcf) of technically-recoverable gas remains to be discovered from deep sedimentary basins. Worldwide estimates of deep gas are also high. The U.S. Geological Survey World Petroleum Assessment 2000 Project recently estimated a world mean undiscovered conventional gas resource outside the U.S. of 844 Tcf below 4.5 km (about 15,000 feet). Less is known about the origins of deep gas than about the origins of gas at shallower depths because fewer wells have been drilled into the deeper portions of many basins. Some of the many factors contributing to the origin of deep gas include the thermal stability of methane, the role of water and non-hydrocarbon gases in natural gas generation, porosity loss with increasing thermal maturity, the kinetics of deep gas generation, thermal cracking of oil to gas, and source rock potential based on thermal maturity and kerogen type. Recent experimental simulations

  17. Survey and analysis of deep water mineral deposits using nuclear methods

    International Nuclear Information System (INIS)

    Staehle, C.M.; Noakes, J.E.; Spaulding, J.

    1991-01-01

    Present knowledge of the location, quality, quantity and recoverability of sea floor minerals is severely limited, particularly in the abyssal depths and deep water within the 200 mile Exclusion Economic Zone (EEZ) surrounding the U.S. Pacific Islands. To improve this understanding and permit exploitation of these mineral reserves much additional data is needed. This paper will discuss a sponsored program for extending existing proven nuclear survey methods currently used on the shallow continental margins of the Atlantic and Gulf of Mexico into the deeper waters of the Pacific. This nuclear technology can be readily integrated and extended to depths of 2000 m using the existing RCV-150 remotely operated vehicle (ROV) and the PISCESE V manned deep submersible vehicle (DSV) operated by The University of Hawaii's, Hawaii Underseas Research Laboratory (HURL). Previous papers by the authors have also proposed incorporating these nuclear analytical methods for survey of the deep ocean through the use of Autonomous Underwater Vehicle (AUX). Such a vehicle could extend the use of passive nuclear instrument operation, in addition to conventional analytical methods, into the abyssal depths and do so with speed and economy not otherwise possible. The natural radioactivity associated with manganese nodules and crustal deposits is sufficiently above normal background levels to allow discrimination and quantification in near real time

  18. Applying work flow control in make-to-order job shops

    DEFF Research Database (Denmark)

    Harrod, Steven; Kanet, John J.

    2013-01-01

    of a relatively new work flow control method, "paired overlapping loops of cards" or POLCA. Additionally, this paper explains "lockup," a previously unreported terminal system blocking behavior. A management method to prevent occurrence of lockup is provided. (C) 2012 Elsevier B.V. All rights reserved....

  19. First LOCSMITH locations of deep moonquakes

    Science.gov (United States)

    Hempel, S.; Knapmeyer, M.; Sens-Schönfelder, C.; Oberst, J.

    2008-09-01

    stacking we developed a complex multiparameter correlation algorithm to calculate the optimum time shift. Results We present relocations of selected deep moonquakes in context of data availability and quality. Previous locations are often contained in our location clouds, but realistic location uncertainties allow large deviations from the best fitting solutions, including locations on the far side of the Moon. Perspective By developing new methods for data processing and using the LOCSMITH locating algorithm we hope to reduce the location uncertainty sufficiently to make sure that all sources are on the near side, or to prove a far side origin of some of them. This would answer questions of hemispheric symmetry of lunar deep seismicity and the Moon's internal structure. References [1] Knapmeyer (2008) accepted to GJI. [2] Nakamura (2005) JGR, 110, E01001. [3] Lognonné (2003) EPSL, 211, 2744. [4] Bulow (2005) JGR, 110, E10003. [5] Sonnemann (2005) EGU05A07960. [6] Hempel, Knapmeyer, Oberst (2008) EGU2008A07989.

  20. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

    Science.gov (United States)

    Xu, Kele; Feng, Dawei; Mi, Haibo

    2017-11-23

    The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches.

  1. Diverse deep-sea fungi from the South China Sea and their antimicrobial activity.

    Science.gov (United States)

    Zhang, Xiao-Yong; Zhang, Yun; Xu, Xin-Ya; Qi, Shu-Hua

    2013-11-01

    We investigated the diversity of fungal communities in nine different deep-sea sediment samples of the South China Sea by culture-dependent methods followed by analysis of fungal internal transcribed spacer (ITS) sequences. Although 14 out of 27 identified species were reported in a previous study, 13 species were isolated from sediments of deep-sea environments for the first report. Moreover, these ITS sequences of six isolates shared 84-92 % similarity with their closest matches in GenBank, which suggested that they might be novel phylotypes of genera Ajellomyces, Podosordaria, Torula, and Xylaria. The antimicrobial activities of these fungal isolates were explored using a double-layer technique. A relatively high proportion (56 %) of fungal isolates exhibited antimicrobial activity against at least one pathogenic bacterium or fungus among four marine pathogenic microbes (Micrococcus luteus, Pseudoaltermonas piscida, Aspergerillus versicolor, and A. sydowii). Out of these antimicrobial fungi, the genera Arthrinium, Aspergillus, and Penicillium exhibited antibacterial and antifungal activities, while genus Aureobasidium displayed only antibacterial activity, and genera Acremonium, Cladosporium, Geomyces, and Phaeosphaeriopsis displayed only antifungal activity. To our knowledge, this is the first report to investigate the diversity and antimicrobial activity of culturable deep-sea-derived fungi in the South China Sea. These results suggest that diverse deep-sea fungi from the South China Sea are a potential source for antibiotics' discovery and further increase the pool of fungi available for natural bioactive product screening.

  2. Deep smarts.

    Science.gov (United States)

    Leonard, Dorothy; Swap, Walter

    2004-09-01

    When a person sizes up a complex situation and rapidly comes to a decision that proves to be not just good but brilliant, you think, "That was smart." After you watch him do this a few times, you realize you're in the presence of something special. It's not raw brainpower, though that helps. It's not emotional intelligence, either, though that, too, is often involved. It's deep smarts. Deep smarts are not philosophical--they're not"wisdom" in that sense, but they're as close to wisdom as business gets. You see them in the manager who understands when and how to move into a new international market, in the executive who knows just what kind of talk to give when her organization is in crisis, in the technician who can track a product failure back to an interaction between independently produced elements. These are people whose knowledge would be hard to purchase on the open market. Their insight is based on know-how more than on know-what; it comprises a system view as well as expertise in individual areas. Because deep smarts are experienced based and often context specific, they can't be produced overnight or readily imported into an organization. It takes years for an individual to develop them--and no time at all for an organization to lose them when a valued veteran walks out the door. They can be taught, however, with the right techniques. Drawing on their forthcoming book Deep Smarts, Dorothy Leonard and Walter Swap say the best way to transfer such expertise to novices--and, on a larger scale, to make individual knowledge institutional--isn't through PowerPoint slides, a Web site of best practices, online training, project reports, or lectures. Rather, the sage needs to teach the neophyte individually how to draw wisdom from experience. Companies have to be willing to dedicate time and effort to such extensive training, but the investment more than pays for itself.

  3. Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

    Science.gov (United States)

    Sadeghi, Zahra

    2016-09-01

    In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

  4. The SCUBA-2 Cosmology Legacy Survey: the EGS deep field - I. Deep number counts and the redshift distribution of the recovered cosmic infrared background at 450 and 850 μ m

    Science.gov (United States)

    Zavala, J. A.; Aretxaga, I.; Geach, J. E.; Hughes, D. H.; Birkinshaw, M.; Chapin, E.; Chapman, S.; Chen, Chian-Chou; Clements, D. L.; Dunlop, J. S.; Farrah, D.; Ivison, R. J.; Jenness, T.; Michałowski, M. J.; Robson, E. I.; Scott, Douglas; Simpson, J.; Spaans, M.; van der Werf, P.

    2017-01-01

    We present deep observations at 450 and 850 μm in the Extended Groth Strip field taken with the SCUBA-2 camera mounted on the James Clerk Maxwell Telescope as part of the deep SCUBA-2 Cosmology Legacy Survey (S2CLS), achieving a central instrumental depth of σ450 = 1.2 mJy beam-1 and σ850 = 0.2 mJy beam-1. We detect 57 sources at 450 μm and 90 at 850 μm with signal-to-noise ratio >3.5 over ˜70 arcmin2. From these detections, we derive the number counts at flux densities S450 > 4.0 mJy and S850 > 0.9 mJy, which represent the deepest number counts at these wavelengths derived using directly extracted sources from only blank-field observations with a single-dish telescope. Our measurements smoothly connect the gap between previous shallower blank-field single-dish observations and deep interferometric ALMA results. We estimate the contribution of our SCUBA-2 detected galaxies to the cosmic infrared background (CIB), as well as the contribution of 24 μm-selected galaxies through a stacking technique, which add a total of 0.26 ± 0.03 and 0.07 ± 0.01 MJy sr-1, at 450 and 850 μm, respectively. These surface brightnesses correspond to 60 ± 20 and 50 ± 20 per cent of the total CIB measurements, where the errors are dominated by those of the total CIB. Using the photometric redshifts of the 24 μm-selected sample and the redshift distributions of the submillimetre galaxies, we find that the redshift distribution of the recovered CIB is different at each wavelength, with a peak at z ˜ 1 for 450 μm and at z ˜ 2 for 850 μm, consistent with previous observations and theoretical models.

  5. Climate, carbon cycling, and deep-ocean ecosystems.

    Science.gov (United States)

    Smith, K L; Ruhl, H A; Bett, B J; Billett, D S M; Lampitt, R S; Kaufmann, R S

    2009-11-17

    Climate variation affects surface ocean processes and the production of organic carbon, which ultimately comprises the primary food supply to the deep-sea ecosystems that occupy approximately 60% of the Earth's surface. Warming trends in atmospheric and upper ocean temperatures, attributed to anthropogenic influence, have occurred over the past four decades. Changes in upper ocean temperature influence stratification and can affect the availability of nutrients for phytoplankton production. Global warming has been predicted to intensify stratification and reduce vertical mixing. Research also suggests that such reduced mixing will enhance variability in primary production and carbon export flux to the deep sea. The dependence of deep-sea communities on surface water production has raised important questions about how climate change will affect carbon cycling and deep-ocean ecosystem function. Recently, unprecedented time-series studies conducted over the past two decades in the North Pacific and the North Atlantic at >4,000-m depth have revealed unexpectedly large changes in deep-ocean ecosystems significantly correlated to climate-driven changes in the surface ocean that can impact the global carbon cycle. Climate-driven variation affects oceanic communities from surface waters to the much-overlooked deep sea and will have impacts on the global carbon cycle. Data from these two widely separated areas of the deep ocean provide compelling evidence that changes in climate can readily influence deep-sea processes. However, the limited geographic coverage of these existing time-series studies stresses the importance of developing a more global effort to monitor deep-sea ecosystems under modern conditions of rapidly changing climate.

  6. Deep diving odontocetes foraging strategies and their prey field as determined by acoustic techniques

    Science.gov (United States)

    Giorli, Giacomo

    Deep diving odontocetes, like sperm whales, beaked whales, Risso's dolphins, and pilot whales are known to forage at deep depths in the ocean on squid and fish. These marine mammal species are top predators and for this reason are very important for the ecosystems they live in, since they can affect prey populations and control food web dynamics through top-down effects. The studies presented in this thesis investigate deep diving odontocetes. foraging strategies, and the density and size of their potential prey in the deep ocean using passive and active acoustic techniques. Ecological Acoustic Recorders (EAR) were used to monitor the foraging activity of deep diving odontocetes at three locations around the world: the Josephine Seamount High Sea Marine Protected Area (JHSMPA), the Ligurian Sea, and along the Kona coast of the island of Hawaii. In the JHSMPA, sperm whales. and beaked whales. foraging rates do not differ between night-time and day-time. However, in the Ligurian Sea, sperm whales switch to night-time foraging as the winter approaches, while beaked whales alternate between hunting mainly at night, and both at night and at day. Spatial differences were found in deep diving odontocetes. foraging activity in Hawaii where they forage most in areas with higher chlorophyll concentrations. Pilot whales (and false killer whales, clustered together in the category "blackfishes") and Risso's dolphins forage mainly at night at all locations. These two species adjust their foraging activity with the length of the night. The density and size of animals living in deep sea scattering layers was studied using a DIDSON imaging sonar at multiple stations along the Kona coast of Hawaii. The density of animals was affected by location, depth, month, and the time of day. The size of animals was influenced by station and month. The DIDSON proved to be a successful, non-invasive technique to study density and size of animals in the deep sea. Densities were found to be an

  7. The deep ocean under climate change.

    Science.gov (United States)

    Levin, Lisa A; Le Bris, Nadine

    2015-11-13

    The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems. Copyright © 2015, American Association for the Advancement of Science.

  8. SEDS: THE SPITZER EXTENDED DEEP SURVEY. SURVEY DESIGN, PHOTOMETRY, AND DEEP IRAC SOURCE COUNTS

    International Nuclear Information System (INIS)

    Ashby, M. L. N.; Willner, S. P.; Fazio, G. G.; Huang, J.-S.; Hernquist, L.; Hora, J. L.; Arendt, R.; Barmby, P.; Barro, G.; Faber, S.; Guhathakurta, P.; Bell, E. F.; Bouwens, R.; Cattaneo, A.; Croton, D.; Davé, R.; Dunlop, J. S.; Egami, E.; Finlator, K.; Grogin, N. A.

    2013-01-01

    The Spitzer Extended Deep Survey (SEDS) is a very deep infrared survey within five well-known extragalactic science fields: the UKIDSS Ultra-Deep Survey, the Extended Chandra Deep Field South, COSMOS, the Hubble Deep Field North, and the Extended Groth Strip. SEDS covers a total area of 1.46 deg 2 to a depth of 26 AB mag (3σ) in both of the warm Infrared Array Camera (IRAC) bands at 3.6 and 4.5 μm. Because of its uniform depth of coverage in so many widely-separated fields, SEDS is subject to roughly 25% smaller errors due to cosmic variance than a single-field survey of the same size. SEDS was designed to detect and characterize galaxies from intermediate to high redshifts (z = 2-7) with a built-in means of assessing the impact of cosmic variance on the individual fields. Because the full SEDS depth was accumulated in at least three separate visits to each field, typically with six-month intervals between visits, SEDS also furnishes an opportunity to assess the infrared variability of faint objects. This paper describes the SEDS survey design, processing, and publicly-available data products. Deep IRAC counts for the more than 300,000 galaxies detected by SEDS are consistent with models based on known galaxy populations. Discrete IRAC sources contribute 5.6 ± 1.0 and 4.4 ± 0.8 nW m –2 sr –1 at 3.6 and 4.5 μm to the diffuse cosmic infrared background (CIB). IRAC sources cannot contribute more than half of the total CIB flux estimated from DIRBE data. Barring an unexpected error in the DIRBE flux estimates, half the CIB flux must therefore come from a diffuse component.

  9. Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements

    Science.gov (United States)

    Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K.; Cai, Chang; Nagarajan, Srikantan S.

    2018-06-01

    Objective. Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. Approach. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Main results. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. Significance. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

  10. The deep lymphatic anatomy of the hand.

    Science.gov (United States)

    Ma, Chuan-Xiang; Pan, Wei-Ren; Liu, Zhi-An; Zeng, Fan-Qiang; Qiu, Zhi-Qiang

    2018-04-03

    The deep lymphatic anatomy of the hand still remains the least described in medical literature. Eight hands were harvested from four nonembalmed human cadavers amputated above the wrist. A small amount of 6% hydrogen peroxide was employed to detect the lymphatic vessels around the superficial and deep palmar vascular arches, in webs from the index to little fingers, the thenar and hypothenar areas. A 30-gauge needle was inserted into the vessels and injected with a barium sulphate compound. Each specimen was dissected, photographed and radiographed to demonstrate deep lymphatic distribution of the hand. Five groups of deep collecting lymph vessels were found in the hand: superficial palmar arch lymph vessel (SPALV); deep palmar arch lymph vessel (DPALV); thenar lymph vessel (TLV); hypothenar lymph vessel (HTLV); deep finger web lymph vessel (DFWLV). Each group of vessels drained in different directions first, then all turned and ran towards the wrist in different layers. The deep lymphatic drainage of the hand has been presented. The results will provide an anatomical basis for clinical management, educational reference and scientific research. Copyright © 2018 Elsevier GmbH. All rights reserved.

  11. Deep erosions of the palmar aspect of the navicular bone diagnosed by standing magnetic resonance imaging.

    Science.gov (United States)

    Sherlock, C; Mair, T; Blunden, T

    2008-11-01

    Erosion of the palmar (flexor) aspect of the navicular bone is difficult to diagnose with conventional imaging techniques. To review the clinical, magnetic resonance (MR) and pathological features of deep erosions of the palmar aspect of the navicular bone. Cases of deep erosions of the palmar aspect of the navicular bone, diagnosed by standing low field MR imaging, were selected. Clinical details, results of diagnostic procedures, MR features and pathological findings were reviewed. Deep erosions of the palmar aspect of the navicular bone were diagnosed in 16 mature horses, 6 of which were bilaterally lame. Sudden onset of lameness was recorded in 63%. Radiography prior to MR imaging showed equivocal changes in 7 horses. The MR features consisted of focal areas of intermediate or high signal intensity on T1-, T2*- and T2-weighted images and STIR images affecting the dorsal aspect of the deep digital flexor tendon, the fibrocartilage of the palmar aspect, subchondral compact bone and medulla of the navicular bone. On follow-up, 7/16 horses (44%) had been subjected to euthanasia and only one was being worked at its previous level. Erosions of the palmar aspect of the navicular bone were confirmed post mortem in 2 horses. Histologically, the lesions were characterised by localised degeneration of fibrocartilage with underlying focal osteonecrosis and fibroplasia. The adjacent deep digital flexor tendon showed fibril formation and fibrocartilaginous metaplasia. Deep erosions of the palmar aspect of the navicular bone are more easily diagnosed by standing low field MR imaging than by conventional radiography. The lesions involve degeneration of the palmar fibrocartilage with underlying osteonecrosis and fibroplasia affecting the subchondral compact bone and medulla, and carry a poor prognosis for return to performance. Diagnosis of shallow erosive lesions of the palmar fibrocartilage may allow therapeutic intervention earlier in the disease process, thereby preventing

  12. DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks.

    Science.gov (United States)

    Kim, Lok-Won

    2018-05-01

    Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).

  13. Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

    Science.gov (United States)

    Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

    2018-02-08

    Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

  14. Case of Small Vessel Disease Associated with COL4A1 Mutations following Trauma

    Directory of Open Access Journals (Sweden)

    Joao McONeil Plancher

    2015-06-01

    Full Text Available With this case report, we would like to heighten the awareness of clinicians about COL4A1 as a single-gene disorder causing cerebral small vessel disease and describe a previously unreported pathogenic missense substitution in COL4A1 (p.Gly990Val and a new clinical presentation. We identified a heterozygous putatively pathogenic mutation of COL4A1 in a 50-year-old female with a history of congenital cataracts and glaucoma who presented with multiple diffusion-positive infarcts and areas of contrast enhancement following mild head trauma. We believe that this presentation of multiple areas of acute brain and vascular injury in the setting of mild head trauma is a new manifestation of this genetic disorder. Imaging findings of multiple acute infarcts and regions of contrast enhancement with associated asymptomatic old deep microhemorrhages and leukomalacia in adults after head trauma should raise a high suspicion for a COL4A1 genetic disorder. Radiographic patterns of significant leukoaraiosis and deep microhemorrhages can also be seen in patients with long-standing vasculopathy associated with hypertension, which our patient lacked. Our findings demonstrate the utility of genetic screening for COL4A1 mutations in young patients who have small vessel vasculopathy on brain imaging but who do not have significant cardiovascular risk factors.

  15. Deep inelastic electron and muon scattering

    International Nuclear Information System (INIS)

    Taylor, R.E.

    1975-07-01

    From the review of deep inelastic electron and muon scattering it is concluded that the puzzle of deep inelastic scattering versus annihilation was replaced with the challenge of the new particles, that the evidence for the simplest quark-algebra models of deep inelastic processes is weaker than a year ago. Definite evidence of scale breaking was found but the specific form of that scale breaking is difficult to extract from the data. 59 references

  16. Fast, Distributed Algorithms in Deep Networks

    Science.gov (United States)

    2016-05-11

    shallow networks, additional work will need to be done in order to allow for the application of ADMM to deep nets. The ADMM method allows for quick...Quock V Le, et al. Large scale distributed deep networks. In Advances in Neural Information Processing Systems, pages 1223–1231, 2012. [11] Ken-Ichi...A TRIDENT SCHOLAR PROJECT REPORT NO. 446 Fast, Distributed Algorithms in Deep Networks by Midshipman 1/C Ryan J. Burmeister, USN

  17. Deep-sea Lebensspuren of the Australian continental margins

    Science.gov (United States)

    Przeslawski, Rachel; Dundas, Kate; Radke, Lynda; Anderson, Tara J.

    Much of the deep sea comprises soft-sediment habitats dominated by comparatively low abundances of species-rich macrofauna and meiofauna. Although often not observed, these animals bioturbate the sediment during feeding and burrowing, leaving signs of their activities called Lebensspuren ('life traces'). In this study, we use still images to quantify Lebensspuren from the eastern (1921 images, 13 stations, 1300-2200 m depth) and western (1008 images, 11 stations, 1500-4400 m depth) Australian margins using a univariate measure of trace richness and a multivariate measure of Lebensspuren assemblages. A total of 46 Lebensspuren types were identified, including those matching named trace fossils and modern Lebensspuren found elsewhere in the world. Most traces could be associated with waste, crawling, dwellings, organism tests, feeding, or resting, but the origin of 15% of trace types remains unknown. Assemblages were significantly different between the two regions and depth profiles, with five Lebensspuren types accounting for over 95% of the differentiation (ovoid pinnate trace, crater row, spider trace, matchstick trace, mesh trace). Lebensspuren richness showed no strong relationships with depth, total organic carbon, or mud, although there was a positive correlation to chlorin index (i.e., organic freshness) in the eastern margin, with richness increasing with organic freshness. Lebensspuren richness was not related to epifauna either, indicating that epifauna may not be the primary source of Lebensspuren. Despite the abundance and distinctiveness of several traces both in the current and previous studies (e.g., ovoid pinnate, mesh, spider), their origin and distribution remains a mystery. We discuss this and several other considerations in the identification and quantification of Lebensspuren. This study represents the first comprehensive catalogue of deep-sea Lebensspuren in Australian waters and highlights the potential of Lebensspuren as valuable and often

  18. Deep Learning from Crowds

    DEFF Research Database (Denmark)

    Rodrigues, Filipe; Pereira, Francisco Camara

    Over the last few years, deep learning has revolutionized the field of machine learning by dramatically improving the stateof-the-art in various domains. However, as the size of supervised artificial neural networks grows, typically so does the need for larger labeled datasets. Recently...... networks from crowds. We begin by describing an EM algorithm for jointly learning the parameters of the network and the reliabilities of the annotators. Then, a novel general-purpose crowd layer is proposed, which allows us to train deep neural networks end-to-end, directly from the noisy labels......, crowdsourcing has established itself as an efficient and cost-effective solution for labeling large sets of data in a scalable manner, but it often requires aggregating labels from multiple noisy contributors with different levels of expertise. In this paper, we address the problem of learning deep neural...

  19. Deep learning methods for protein torsion angle prediction.

    Science.gov (United States)

    Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

    2017-09-18

    Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

  20. Deep Learning in Gastrointestinal Endoscopy.

    Science.gov (United States)

    Patel, Vivek; Armstrong, David; Ganguli, Malika; Roopra, Sandeep; Kantipudi, Neha; Albashir, Siwar; Kamath, Markad V

    2016-01-01

    Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

  1. Turnover of microbial lipids in the deep biosphere and growth of benthic archaeal populations.

    Science.gov (United States)

    Xie, Sitan; Lipp, Julius S; Wegener, Gunter; Ferdelman, Timothy G; Hinrichs, Kai-Uwe

    2013-04-09

    Deep subseafloor sediments host a microbial biosphere with unknown impact on global biogeochemical cycles. This study tests previous evidence based on microbial intact polar lipids (IPLs) as proxies of live biomass, suggesting that Archaea dominate the marine sedimentary biosphere. We devised a sensitive radiotracer assay to measure the decay rate of ([(14)C]glucosyl)-diphytanylglyceroldiether (GlcDGD) as an analog of archaeal IPLs in continental margin sediments. The degradation kinetics were incorporated in model simulations that constrained the fossil fraction of subseafloor IPLs and rates of archaeal turnover. Simulating the top 1 km in a generic continental margin sediment column, we estimated degradation rate constants of GlcDGD being one to two orders of magnitude lower than those of bacterial IPLs, with half-lives of GlcDGD increasing with depth to 310 ky. Given estimated microbial community turnover times of 1.6-73 ky in sediments deeper than 1 m, 50-96% of archaeal IPLs represent fossil signals. Consequently, previous lipid-based estimates of global subseafloor biomass probably are too high, and the widely observed dominance of archaeal IPLs does not rule out a deep biosphere dominated by Bacteria. Reverse modeling of existing concentration profiles suggest that archaeal IPL synthesis rates decline from around 1,000 pg⋅mL(-1) sediment⋅y(-1) at the surface to 0.2 pg⋅mL(-1)⋅y(-1) at 1 km depth, equivalent to production of 7 × 10(5) to 140 archaeal cells⋅mL(-1) sediment⋅y(-1), respectively. These constraints on microbial growth are an important step toward understanding the relationship between the deep biosphere and the carbon cycle.

  2. Neuromorphic Deep Learning Machines

    OpenAIRE

    Neftci, E; Augustine, C; Paul, S; Detorakis, G

    2017-01-01

    An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide...

  3. Ecology and Distribution of Thaumarchaea in the Deep Hypolimnion of Lake Maggiore

    Directory of Open Access Journals (Sweden)

    Manuela Coci

    2015-01-01

    Full Text Available Ammonia-oxidizing Archaea (AOA play an important role in the oxidation of ammonia in terrestrial, marine, and geothermal habitats, as confirmed by a number of studies specifically focused on those environments. Much less is known about the ecological role of AOA in freshwaters. In order to reach a high resolution at the Thaumarchaea community level, the probe MGI-535 was specifically designed for this study and applied to fluorescence in situ hybridization and catalyzed reporter deposition (CARD-FISH analysis. We then applied it to a fine analysis of diversity and relative abundance of AOA in the deepest layers of the oligotrophic Lake Maggiore, confirming previous published results of AOA presence, but showing differences in abundance and distribution within the water column without significant seasonal trends with respect to Bacteria. Furthermore, phylogenetic analysis of AOA clone libraries from deep lake water and from a lake tributary, River Maggia, suggested the riverine origin of AOA of the deep hypolimnion of the lake.

  4. Analysis of well test data from selected intervals in Leuggern deep borehole

    International Nuclear Information System (INIS)

    Karasaki, K.

    1990-07-01

    Applicability of the PTST technique was verified by conducting a sensitivity study to the various parameters. The study showed that for ranges of skin parameters the true formation permeability was still successfully estimated using the PTST analysis technique. The analysis technique was then applied to field data from the deep borehole in Leuggern, Northern Switzerland. The analysis indicated that the formation permeability may be as much as one order of magnitude larger than the value based on no-skin analysis. Swabbing data from the Leuggern deep borehole were also analyzed assuming that they are constant pressure tests. The analysis of the swabbing data indicates that the formation transmissivity is as much as 20 times larger than the previously obtained value. This study is part of an investigation of the feasibility of geologic isolation of nuclear wastes being carried out by the US Department of Energy and the National Cooperative for the Storage of Radioactive Waste of Switzerland

  5. Deep Restricted Kernel Machines Using Conjugate Feature Duality.

    Science.gov (United States)

    Suykens, Johan A K

    2017-08-01

    The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

  6. Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jong Youl; Lee, Min Soo; Choi, Heui Joo; Kim, Geon Young; Kim, Kyung Su [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2016-06-15

    Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested.

  7. Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

    International Nuclear Information System (INIS)

    Lee, Jong Youl; Lee, Min Soo; Choi, Heui Joo; Kim, Geon Young; Kim, Kyung Su

    2016-01-01

    Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested

  8. Toolkits and Libraries for Deep Learning.

    Science.gov (United States)

    Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

    2017-08-01

    Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

  9. Rectal duplication cyst: a combined abdominal and endoanal operative approach.

    Science.gov (United States)

    Rees, Clare M; Woodward, Mark; Grier, David; Cusick, Eleri

    2007-04-01

    Rectal duplication cysts are rare, comprising duplications. Early excision is the treatment of choice and a number of surgical approaches have been described. We present a 3-week-old infant with a 3 cm cyst that was excised using a previously unreported combined abdominal and endoanal approach.

  10. Multiple isoforms of the human pentraxin serum amyloid P component

    DEFF Research Database (Denmark)

    Sørensen, Inge Juul; Andersen, Ove; Nielsen, EH

    1995-01-01

    major and several minor subpopulations of SAP. IEF of all SAP isolates showed a previously unreported degree of heterogeneity with six isoelectric forms (pKi range 5.5-6.1) and with minor interindividual differences in respect of isoelectric points. Total enzymatic deglycosylation of SAP reduced...

  11. Deep-sea coral research and technology program: Alaska deep-sea coral and sponge initiative final report

    Science.gov (United States)

    Rooper, Chris; Stone, Robert P.; Etnoyer, Peter; Conrath, Christina; Reynolds, Jennifer; Greene, H. Gary; Williams, Branwen; Salgado, Enrique; Morrison, Cheryl L.; Waller, Rhian G.; Demopoulos, Amanda W.J.

    2017-01-01

    Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska’s marine waters. In some places, such as the central and western Aleutian Islands, deep-sea coral and sponge resources can be extremely diverse and may rank among the most abundant deep-sea coral and sponge communities in the world. Many different species of fishes and invertebrates are associated with deep-sea coral and sponge communities in Alaska. Because of their biology, these benthic invertebrates are potentially impacted by climate change and ocean acidification. Deepsea coral and sponge ecosystems are also vulnerable to the effects of commercial fishing activities. Because of the size and scope of Alaska’s continental shelf and slope, the vast majority of the area has not been visually surveyed for deep-sea corals and sponges. NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) sponsored a field research program in the Alaska region between 2012–2015, referred to hereafter as the Alaska Initiative. The priorities for Alaska were derived from ongoing data needs and objectives identified by the DSCRTP, the North Pacific Fishery Management Council (NPFMC), and Essential Fish Habitat-Environmental Impact Statement (EFH-EIS) process.This report presents the results of 15 projects conducted using DSCRTP funds from 2012-2015. Three of the projects conducted as part of the Alaska deep-sea coral and sponge initiative included dedicated at-sea cruises and fieldwork spread across multiple years. These projects were the eastern Gulf of Alaska Primnoa pacifica study, the Aleutian Islands mapping study, and the Gulf of Alaska fish productivity study. In all, there were nine separate research cruises carried out with a total of 109 at-sea days conducting research. The remaining projects either used data and samples collected by the three major fieldwork projects or were piggy-backed onto existing research programs at the Alaska Fisheries Science Center (AFSC).

  12. Image Captioning with Deep Bidirectional LSTMs

    OpenAIRE

    Wang, Cheng; Yang, Haojin; Bartz, Christian; Meinel, Christoph

    2016-01-01

    This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are propose...

  13. An overview of latest deep water technologies

    International Nuclear Information System (INIS)

    Anon.

    1995-01-01

    The 8th Deep Offshore Technology Conference (DOT VIII, Rio de Janeiro, October 30 - November 3, 1995) has brought together renowned specialists in deep water development projects, as well as managers from oil companies and engineering/service companies to discuss state-of-the-art technologies and ongoing projects in the deep offshore. This paper is a compilation of the session summaries about sub sea technologies, mooring and dynamic positioning, floaters (Tension Leg Platforms (TLP) and Floating Production Storage and Off loading (FPSO)), pipelines and risers, exploration and drilling, and other deep water techniques. (J.S.)

  14. Assessment of genetic variation for the LINE-1 retrotransposon from next generation sequence data

    Directory of Open Access Journals (Sweden)

    Ramos Kenneth

    2010-10-01

    Full Text Available Abstract Background In humans, copies of the Long Interspersed Nuclear Element 1 (LINE-1 retrotransposon comprise 21% of the reference genome, and have been shown to modulate expression and produce novel splice isoforms of transcripts from genes that span or neighbor the LINE-1 insertion site. Results In this work, newly released pilot data from the 1000 Genomes Project is analyzed to detect previously unreported full length insertions of the retrotransposon LINE-1. By direct analysis of the sequence data, we have identified 22 previously unreported LINE-1 insertion sites within the sequence data reported for a mother/father/daughter trio. Conclusions It is demonstrated here that next generation sequencing data, as well as emerging high quality datasets from individual genome projects allow us to assess the amount of heterogeneity with respect to the LINE-1 retrotransposon amongst humans, and provide us with a wealth of testable hypotheses as to the impact that this diversity may have on the health of individuals and populations.

  15. Unique Case of Imperforate Hymen.

    Science.gov (United States)

    Coppola, Lynn

    2016-02-01

    Imperforate hymen typically presents in adolescence with pain, hematocolpometra and primary amenorrhea. This case documents a previously unreported etiology for an atypical presentation with a history of recent menstruation. A female adolescent presented with symptoms of urinary retention and leg pain. She reported a history of irregular, painful menses. Clinical examination revealed a pelvic mass and imperforate hymen. Sonography was consistent with hematocolpometra. Before a planned hymenectomy, the patient began to pass dark blood through a fistulous opening in her vulva. Hymenectomy resulted in complete resolution of the pain and hematocolpometra. Identification of the fistulous tract explained the patient's history of menstrual bleeding despite an imperforate hymen. Spontaneous rupture of hematocolpometra through a fistulous tract to the vulva is a previously unreported atypical presentation of imperforate hymen in a "menstruating" adolescent with pain and a pelvic mass. Copyright © 2016 North American Society for Pediatric and Adolescent Gynecology. Published by Elsevier Inc. All rights reserved.

  16. Deep processing activates the medial temporal lobe in young but not in old adults.

    Science.gov (United States)

    Daselaar, Sander M; Veltman, Dick J; Rombouts, Serge A R B; Raaijmakers, Jeroen G W; Jonker, Cees

    2003-11-01

    Age-related impairments in episodic memory have been related to a deficiency in semantic processing, based on the finding that elderly adults typically benefit less than young adults from deep, semantic as opposed to shallow, nonsemantic processing of study items. In the present study, we tested the hypothesis that elderly adults are not able to perform certain cognitive operations under deep processing conditions. We further hypothesised that this inability does not involve regions commonly associated with lexical/semantic retrieval processes, but rather involves a dysfunction of the medial temporal lobe (MTL) memory system. To this end, we used functional MRI on rather extensive groups of young and elderly adults to compare brain activity patterns obtained during a deep (living/nonliving) and a shallow (uppercase/lowercase) classification task. Common activity in relation to semantic classification was observed in regions that have been previously related to semantic retrieval, including mainly left-lateralised activity in the inferior prefrontal, middle temporal, and middle frontal/anterior cingulate gyrus. Although the young adults showed more activity in some of these areas, the finding of mainly overlapping activation patterns during semantic classification supports the idea that lexical/semantic retrieval processes are still intact in elderly adults. This received further support by the finding that both groups showed similar behavioural performances as well on the deep and shallow classification tasks. Importantly, though, the young revealed significantly more activity than the elderly adults in the left anterior hippocampus during deep relative to shallow classification. This finding is in line with the idea that age-related impairments in episodic encoding are, at least partly, due to an under-recruitment of the medial temporal lobe memory system.

  17. Combining shallow and deep processing for a robust, fast, deep-linguistic dependency parser

    OpenAIRE

    Schneider, G

    2004-01-01

    This paper describes Pro3Gres, a fast, robust, broad-coverage parser that delivers deep-linguistic grammatical relation structures as output, which are closer to predicate-argument structures and more informative than pure constituency structures. The parser stays as shallow as is possible for each task, combining shallow and deep-linguistic methods by integrating chunking and by expressing the majority of long-distance dependencies in a context-free way. It combines statistical and rule-base...

  18. Waste Handling and Emplacement Options for Disposal of Radioactive Waste in Deep Boreholes.

    Energy Technology Data Exchange (ETDEWEB)

    Cochran, John R.; Hardin, Ernest

    2015-11-01

    Traditional methods cannot be used to handle and emplace radioactive wastes in boreholes up to 16,400 feet (5 km) deep for disposal. This paper describes three systems that can be used for handling and emplacing waste packages in deep borehole: (1) a 2011 reference design that is based on a previous study by Woodward–Clyde in 1983 in which waste packages are assembled into “strings” and lowered using drill pipe; (2) an updated version of the 2011 reference design; and (3) a new concept in which individual waste packages would be lowered to depth using a wireline. Emplacement on coiled tubing was also considered, but not developed in detail. The systems described here are currently designed for U.S. Department of Energy-owned high-level waste (HLW) including the Cesium- 137/Strontium-90 capsules from the Hanford Facility and bulk granular HLW from fuel processing in Idaho.

  19. Attenuation of deep semantic processing during mind wandering: an event-related potential study.

    Science.gov (United States)

    Xu, Judy; Friedman, David; Metcalfe, Janet

    2018-03-21

    Although much research shows that early sensory and attentional processing is affected by mind wandering, the effect of mind wandering on deep (i.e. semantic) processing is relatively unexplored. To investigate this relation, we recorded event-related potentials as participants studied English-Spanish word pairs, one at a time, while being intermittently probed for whether they were 'on task' or 'mind wandering'. Both perceptual processing, indexed by the P2 component, and deep processing, indexed by a late, sustained slow wave maximal at parietal electrodes, was attenuated during periods preceding participants' mind wandering reports. The pattern when participants were on task, rather than mind wandering, is similar to the subsequent memory or difference in memory effect. These results support previous findings of sensory attenuation during mind wandering, and extend them to a long-duration slow wave by suggesting that the deeper and more sustained levels of processing are also disrupted.

  20. Desalination Economic Evaluation Program (DEEP-3.0). User's manual

    International Nuclear Information System (INIS)

    2006-01-01

    DEEP is a Desalination Economic Evaluation Program developed by the International Atomic Energy Agency (IAEA) and made freely available for download, under a license agreement (www.iaea.org/nucleardesalination). The program is based on linked Microsoft Excel spreadsheets and can be useful for evaluating desalination strategies by calculating estimates of technical performance and costs for various alternative energy and desalination technology configurations. Desalination technology options modelled, include multi-stage flashing (MSF), multi-effect distillation (MED), reverse osmosis (RO) and hybrid options (RO-MSF, RO-MED) while energy source options include nuclear, fossil, renewables and grid electricity (stand-alone RO). Version 3 of DEEP (DEEP 3.0) features important changes from previous versions, including upgrades in thermal and membrane performance and costing models, the coupling configuration matrix and the user interface. Changes in the thermal performance model include a revision of the gain output ratio (GOR) calculation and its generalization to include thermal vapour compression effects. Since energy costs continue to represent an important fraction of seawater desalination costs, the lost shaft work model has been generalized to properly account for both backpressure and extraction systems. For RO systems, changes include improved modelling of system recovery, feed pressure and permeate salinity, taking into account temperature, feed salinity and fouling correction factors. The upgrade to the coupling technology configuration matrix includes a re-categorization of the energy sources to follow turbine design (steam vs. gas) and cogeneration features (dual-purpose vs. heat-only). In addition, cost data has also been updated to reflect current practice and the user interface has been refurbished and made user-friendlier

  1. Building Better Biosensors for Exploration into Deep-Space, Using Humanized Yeast

    Science.gov (United States)

    Liddell, Lauren; Santa Maria, Sergio; Tieze, Sofia; Bhattacharya, Sharmila

    2017-01-01

    1.BioSentinel is 1 of 13 secondary payloads hitching a ride beyond Low Earth Orbit on Exploration Mission 1 (EM-1), set to launch from NASAs Space Launch System in 2019. EM-1 is our first opportunity to investigate the effects of the deep space environment on a eukaryotic biological system, the budding yeast S. cerevisiae. Though separated by a billion years of evolution we share hundreds of genes important for basic cell function, including responses to DNA damage. Thus, yeast is an ideal biosensor for detecting typesextent of damage induced by deep-space radiation.We will fly desiccated cells, then rehydrate to wake them up when the automated payload is ready to initiate the experiment. Rehydration solution contains SC (Synthetic Complete) media and alamarBlue, an indicator for changes in growth and metabolism. Telemetry of LED readings will then allow us to detect how cells respond throughout the mission. The desiccation-rehydration process can be extremely damaging to cells, and can severely diminish our ability to accurately measure and model cellular responses to deep-space radiation. The aim of this study is to develop a better biosensor: yeast strains that are more resistant to desiccation stress. We will over-express known cellular protectants, including hydrophilin Sip18, the protein disaggregase Hsp104, and thioredoxin Trx2, a responder to oxidative stress, then measure cell viability after desiccation to determine which factors improve stress tolerance. Over-expression of SIP18 in wine yeast starter cultures was previously reported to increase viability following desiccation stress by up to 70. Thus, we expect similar improvements in our space-yeast strains. By designing better yeast biosensors we can better prepare for and mitigate the potential dangers of deep-space radiation for future missions.This work is funded by NASAs AES program.

  2. Reflection imaging of the Moon's interior using deep-moonquake seismic interferometry

    Science.gov (United States)

    Nishitsuji, Yohei; Rowe, C. A.; Wapenaar, Kees; Draganov, Deyan

    2016-04-01

    The internal structure of the Moon has been investigated over many years using a variety of seismic methods, such as travel time analysis, receiver functions, and tomography. Here we propose to apply body-wave seismic interferometry to deep moonquakes in order to retrieve zero-offset reflection responses (and thus images) beneath the Apollo stations on the nearside of the Moon from virtual sources colocated with the stations. This method is called deep-moonquake seismic interferometry (DMSI). Our results show a laterally coherent acoustic boundary around 50 km depth beneath all four Apollo stations. We interpret this boundary as the lunar seismic Moho. This depth agrees with Japan Aerospace Exploration Agency's (JAXA) SELenological and Engineering Explorer (SELENE) result and previous travel time analysis at the Apollo 12/14 sites. The deeper part of the image we obtain from DMSI shows laterally incoherent structures. Such lateral inhomogeneity we interpret as representing a zone characterized by strong scattering and constant apparent seismic velocity at our resolution scale (0.2-2.0 Hz).

  3. Measurement of D* production in diffractive deep inelastic scattering at HERA

    Energy Technology Data Exchange (ETDEWEB)

    Andreev, V.; Belousov, A.; Fomenko, A.; Gogitidze, N.; Lebedev, A.; Malinovski, E.; Soloviev, Y.; Vazdik, Y. [Lebedev Physical Institute, Moscow (Russian Federation); Baghdasaryan, A.; Zohrabyan, H. [Yerevan Physics Institute, Yerevan (Armenia); Begzsuren, K.; Ravdandorj, T. [Academy of Sciences, Institute of Physics and Technology of the Mongolian, Ulaanbaatar (Mongolia); Bolz, A.; Huber, F.; Sauter, M.; Schoening, A. [Universitaet Heidelberg, Physikalisches Institut, Heidelberg (Germany); Boudry, V.; Specka, A. [LLR, Ecole Polytechnique, CNRS/IN2P3, Palaiseau (France); Brandt, G. [Universitaet Goettingen, II. Physikalisches Institut, Goettingen (Germany); Brisson, V.; Jacquet, M.; Pascaud, C.; Zhang, Z.; Zomer, F. [LAL, Universite Paris-Sud, CNRS/IN2P3, Orsay (France); Britzger, D.; Campbell, A.J.; Dodonov, V.; Eckerlin, G.; Elsen, E.; Fleischer, M.; Gayler, J.; Ghazaryan, S.; Haidt, D.; Jung, A.; Jung, H.; Katzy, J.; Kleinwort, C.; Kruecker, D.; Krueger, K.; Levonian, S.; Lipka, K.; List, B.; List, J.; Meyer, A.B.; Meyer, J.; Niebuhr, C.; Olsson, J.E.; Pirumov, H.; Pitzl, D.; Placakyte, R.; Schmitt, S.; Sefkow, F.; South, D.; Steder, M.; Wuensch, E. [DESY, Hamburg (Germany); Buniatyan, A.; Newman, P.R.; Thompson, P.D. [University of Birmingham, School of Physics and Astronomy, Birmingham (United Kingdom); Bylinkin, A. [Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region (Russian Federation); Bystritskaya, L.; Fedotov, A. [Institute for Theoretical and Experimental Physics, Moscow (Russian Federation); Avila, K.B.C.; Contreras, J.G. [CINVESTAV, Departamento de Fisica Aplicada, Merida, Yucatan (Mexico); Cerny, K.; Jansova, M.; Salek, D.; Valkarova, A.; Zacek, J.; Zlebcik, R. [Charles University, Faculty of Mathematics and Physics, Prague (Czech Republic); Chekelian, V.; Grindhammer, G.; Kiesling, C.; Lobodzinski, B. [Max-Planck-Institut fuer Physik, Munich (Germany); Cvach, J.; Hladky, J.; Reimer, P. [Academy of Sciences of the Czech Republic, Institute of Physics, Prague (Czech Republic); Dainton, J.B.; Gabathuler, E.; Greenshaw, T.; Klein, M.; Kostka, P.; Kretzschmar, J.; Laycock, P.; Maxfield, S.J.; Mehta, A.; Patel, G.D. [University of Liverpool, Department of Physics, Liverpool (United Kingdom); Daum, K.; Meyer, H. [Fachbereich C, Universitaet Wuppertal, Wuppertal (Germany); Diaconu, C.; Hoffmann, D.; Vallee, C. [Aix Marseille Universite, CNRS/IN2P3, CPPM UMR 7346, Marseille (France); Dobre, M.; Rotaru, M. [Horia Hulubei National Institute for R and D in Physics and Nuclear Engineering (IFIN-HH), Bucharest (Romania); Egli, S.; Horisberger, R.; Ozerov, D. [Paul Scherrer Institute, Villigen (Switzerland); Favart, L.; Grebenyuk, A.; Hreus, T.; Janssen, X.; Roosen, R.; Mechelen, P. van [Brussels and Universiteit Antwerpen, Inter-University Institute for High Energies ULB-VUB, Antwerp (Belgium); Feltesse, J.; Schoeffel, L. [Irfu/SPP, CE Saclay, Gif-sur-Yvette (France); Ferencei, J. [Nuclear Physics Institute of the CAS, Rez (Czech Republic); Goerlich, L.; Mikocki, S.; Nowak, G.; Sopicki, P. [Institute of Nuclear Physics, Polish Academy of Sciences, Krakow (Poland); Gouzevitch, M.; Petrukhin, A. [IPNL, Universite Claude Bernard Lyon 1, CNRS/IN2P3, Villeurbanne (France); Grab, C. [Institut fuer Teilchenphysik, ETH, Zurich (Switzerland); Henderson, R.C.W. [University of Lancaster, Department of Physics, Liverpool (United Kingdom); Kapichine, M.; Morozov, A.; Spaskov, V. [Joint Institute for Nuclear Research, Dubna (Russian Federation); Kogler, R. [Universitaet Hamburg, Institut fuer Experimentalphysik, Hamburg (Germany); Landon, M.P.J.; Rizvi, E.; Traynor, D. [University of London, School of Physics and Astronomy, London (United Kingdom); Lange, W.; Naumann, T. [DESY, Zeuthen (Germany); Martyn, H.U. [I. Physikalisches Institut der RWTH, Aachen (Germany); Mueller, K.; Robmann, P.; Straumann, U.; Truoel, P. [Physik-Institut der Universitaet Zuerich, Zurich (Switzerland); Perez, E. [CERN, Geneva (Switzerland); Picuric, I.; Raicevic, N. [University of Montenegro, Faculty of Science, Podgorica (Montenegro); Polifka, R. [Charles University, Faculty of Mathematics and Physics, Prague (Czech Republic); University of Toronto, Department of Physics, Toronto, ON (Canada); Radescu, V. [Oxford University, Department of Physics, Oxford (United Kingdom); Rostovtsev, A. [Institute for Information Transmission Problems RAS, Moscow (Russian Federation); Sankey, D.P.C. [STFC, Rutherford Appleton Laboratory, Didcot, Oxfordshire (United Kingdom); Sauvan, E. [Aix Marseille Universite, CNRS/IN2P3, CPPM UMR 7346, Marseille (France); Universite de Savoie, CNRS/IN2P3, LAPP, Annecy-le-Vieux (France); Shushkevich, S. [Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow (Russian Federation); Stella, B. [Dipartimento di Fisica Universita di Roma Tre (Italy); INFN Roma 3, Rome (Italy); Sykora, T. [Brussels and Universiteit Antwerpen, Inter-University Institute for High Energies ULB-VUB, Antwerp (Belgium); Charles University, Faculty of Mathematics and Physics, Prague (Czech Republic); Tsakov, I. [Institute for Nuclear Research and Nuclear Energy, Sofia (Bulgaria); Tseepeldorj, B. [Academy of Sciences, Institute of Physics and Technology of the Mongolian, Ulaanbaatar (Mongolia); Ulaanbaatar University, Ulaanbaatar (Mongolia); Wegener, D. [Institut fuer Physik, TU Dortmund, Dortmund (Germany)

    2017-05-15

    Measurements of D*(2010) meson production in diffractive deep inelastic scattering (5 < Q{sup 2} < 100 GeV{sup 2}) are presented which are based on HERA data recorded at a centre-of-mass energy √(s) = 319 GeV with an integrated luminosity of 287 pb{sup -1}. The reaction ep → eXY is studied, where the system X, containing at least one D*(2010) meson, is separated from a leading low-mass proton dissociative system Y by a large rapidity gap. The kinematics of D* candidates are reconstructed in the D* → Kππ decay channel. The measured cross sections compare favourably with next-to-leading order QCD predictions, where charm quarks are produced via boson-gluon fusion. The charm quarks are then independently fragmented to the D* mesons. The calculations rely on the collinear factorisation theorem and are based on diffractive parton densities previously obtained by H1 from fits to inclusive diffractive cross sections. The data are further used to determine the diffractive to inclusive D* production ratio in deep inelastic scattering. (orig.)

  4. PRE-ACTIVITY MODULATION OF LOWER EXTREMITY MUSCLES WITHIN DIFFERENT TYPES AND HEIGHTS OF DEEP JUMP

    Directory of Open Access Journals (Sweden)

    Vladimir Mrdakovic

    2008-06-01

    Full Text Available The purpose of this study was to determine modulation of pre- activity related to different types and heights of deep jump. Sixteen male soccer players without experience in deep jumps training (the national competition; 15.0 ± 0.5yrs; weight 61.9 ± 6.1kg; height 1.77 ± 0.07m, who participated in the study, performed three types of deep jump (bounce landing, counter landing, and bounce drop jump from three different heights (40cm, 60cm, and 80cm. Surface EMG device (1000Hz was used to estimate muscle activity (maximal amplitude of EMG - AmaxEMG; integral EMG signal - iEMG of five muscles (mm.gastrocnemii, m.soleus, m.tibialis anterior, m.vastus lateralis within 150ms before touchdown. All the muscles, except m. gastrocnemius medialis, showed systematic increase in pre-activity when platform height was raised. For most of the lower extremity muscles, the most significant differences were between values of pre-activity obtained for 40 cm and 80 cm platforms. While the amount of muscle pre-activity in deep jumps from the heights above and beneath the optimal one did not differ significantly from that generated in deep jumps from the optimal drop height of 60 cm, the patterns of muscle pre-activity obtained for the heights above the optimal one did differ from those obtained for the optimal drop height. That suggests that deep jumps from the heights above the optimal one do not seem to be an adequate exercise for adjusting muscle activity for the impact. Muscle pre-activity in bounce drop jumps differed significantly from that in counter landing and bounce landing respectively, which should indicate that a higher amount of pre-activity generated during bounce drop jumps was used for performing take-offs. As this study included the subjects who were not familiar with deep jumps training, the prospective studies should reveal the results of athletes with previous experience

  5. DeepVel: Deep learning for the estimation of horizontal velocities at the solar surface

    Science.gov (United States)

    Asensio Ramos, A.; Requerey, I. S.; Vitas, N.

    2017-07-01

    Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have direct spectroscopic access to the components that are perpendicular to the line of sight. These components are typically estimated using methods based on local correlation tracking. We have designed DeepVel, an end-to-end deep neural network that produces an estimation of the velocity at every single pixel, every time step, and at three different heights in the atmosphere from just two consecutive continuum images. We confront DeepVel with local correlation tracking, pointing out that they give very similar results in the time and spatially averaged cases. We use the network to study the evolution in height of the horizontal velocity field in fragmenting granules, supporting the buoyancy-braking mechanism for the formation of integranular lanes in these granules. We also show that DeepVel can capture very small vortices, so that we can potentially expand the scaling cascade of vortices to very small sizes and durations. The movie attached to Fig. 3 is available at http://www.aanda.org

  6. Dislocation loops in spinel crystals irradiated successively with deep and shallow ion implants

    International Nuclear Information System (INIS)

    Ai, R.X.; Cooper, E.A.; Sickafus, K.E.; Nastasi, M.; Bordes, N.; Ewing, R.C.

    1993-01-01

    This study examines the influence of microstructural defects on irradiation damage accumulation in the oxide spinel. Single crystals of the compound MgAl 2 O 4 with surface normal [111] were irradiated under cryogenic temperature (100K) either with 50 keV Ne ions (fluence 5.0 x 10 12 /cm 2 ), 400 keV Ne ions (fluence 6.7 x 10 13 /cm 2 ) or successively with 400 keV Ne ions followed by 50 keV Ne ions. The projected range of 50 keV Ne ions in spinel is ∼50 mn (''shallow'') while the projected range of 400 keV Ne ions is ∼500 mn (''deep''). Transmission electron microscopy (TEM) was used to examine dislocation loops/defect clusters formed by the implantation process. Measurements of the dislocation loop size were made using weak-beam imaging technique on cross-sectional TEM ion-implanted specimens. Defect clusters were observed in both deep and shallow implanted specimens, while dislocation loops were observed in the shallow implanted sample that was previously irradiated by 400 keV Ne ions. Cluster size was seen to increase for shallow implants in crystals irradiated with a deep implant (size ∼8.5 nm) as compared to crystals treated only to a shallow implant (size ∼3.1 nm)

  7. Running of the charm-quark mass from HERA deep-inelastic scattering data

    International Nuclear Information System (INIS)

    Gizhko, A.; Geiser, A.; Moch, S.

    2017-04-01

    Combined HERA data on charm production in deep-inelastic scattering have previously been used to determine the charm-quark running mass m_c(m_c) in the MS renormalisation scheme. Here, the same data are used as a function of the photon virtuality Q"2 to evaluate the charm-quark running mass at different scales to one-loop order, in the context of a next-to-leading order QCD analysis. The scale dependence of the mass is found to be consistent with QCD expectations.

  8. Deep Learning in Drug Discovery.

    Science.gov (United States)

    Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert

    2016-01-01

    Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Iris Transponder-Communications and Navigation for Deep Space

    Science.gov (United States)

    Duncan, Courtney B.; Smith, Amy E.; Aguirre, Fernando H.

    2014-01-01

    The Jet Propulsion Laboratory has developed the Iris CubeSat compatible deep space transponder for INSPIRE, the first CubeSat to deep space. Iris is 0.4 U, 0.4 kg, consumes 12.8 W, and interoperates with NASA's Deep Space Network (DSN) on X-Band frequencies (7.2 GHz uplink, 8.4 GHz downlink) for command, telemetry, and navigation. This talk discusses the Iris for INSPIRE, it's features and requirements; future developments and improvements underway; deep space and proximity operations applications for Iris; high rate earth orbit variants; and ground requirements, such as are implemented in the DSN, for deep space operations.

  10. Deep-Sea Trench Microbiology Down to 10.9 Kilometers Below the Surface

    Science.gov (United States)

    Bartlett, D. H.

    2012-12-01

    Deep-sea trenches, extending to more than 10.9 km below the sea surface, are among the most remote and infrequently sampled habitats. As a result a global perspective of microbial diversity and adaptation is lacking in these extreme settings. I will present the results of studies of deep-sea trench microbes collected in the Puerto Rico Trench (PRT), Tonga Trench, New Britain Trench and Mariana Trench. The samples collected include sediment, seawater and animals in baited traps. The analyses to be described include microbial community activity and viability measurements as a function of hydrostatic pressure, microbial culturing at high pressure under various physiological conditions, phylogenetics and metagenome and single-cell genome characterizations. Most of the results to date stem from samples recovered from the PRT. The deep-sea PRT Trench microbes have more in common at the species level with other deep-sea microbial communities previously characterized in the Pacific Ocean and the Mediterranean Sea than with the microbial populations above them in shallow waters. They also harbor larger genomes with more genes assigned to signal transduction, transcription, replication, recombination and repair and inorganic ion transport. The overrepresented transporters in the PRT metagenome include di- and tri-carboxylate transporters that correspond to the prevailing catabolic processes such as butanoate, glyoxylate and dicarboxylate metabolism. A surprisingly high abundance of sulfatases for the degradation of sulfated polysaccharides were also present in the PRT. But, perhaps the most dramatic adaptational feature of the PRT microbes is heavy metal resistance, as reflected in the high numbers of metal efflux systems present. Single-cell genomics approaches have proven particularly useful for placing PRT metagenomic data into context.

  11. Smoking cessation induced by deep repetitive transcranial magnetic stimulation of the prefrontal and insular cortices: a prospective, randomized controlled trial.

    Science.gov (United States)

    Dinur-Klein, Limor; Dannon, Pinhas; Hadar, Aviad; Rosenberg, Oded; Roth, Yiftach; Kotler, Moshe; Zangen, Abraham

    2014-11-01

    Tobacco smoking is the leading cause of preventable death in developed countries. Our previous studies in animal models and humans suggest that repeated activation of cue-induced craving networks followed by electromagnetic stimulation of the dorsal prefrontal cortex (PFC) can cause lasting reductions in drug craving and consumption. We hypothesized that disruption of these circuitries by deep transcranial magnetic stimulation (TMS) of the PFC and insula bilaterally can induce smoking cessation. Adults (N = 115) who smoke at least 20 cigarettes/day and failed previous treatments were recruited from the general population. Participants were randomized to receive 13 daily sessions of high-frequency, low-frequency or sham stimulation following, or without, presentation of smoking cues. Deep TMS was administered using an H-coil version targeting the lateral PFC and insula bilaterally. Cigarette consumption was evaluated during the treatment by measuring cotinine levels in urine samples and recording participants' self-reports as a primary outcome variable. Dependence and craving were assessed using standardized questionnaires. High (but not low) frequency deep TMS treatment significantly reduced cigarette consumption and nicotine dependence. The combination of this treatment with exposure to smoking cues enhanced reduction in cigarette consumption leading to an abstinence rate of 44% at the end of the treatment and an estimated 33% 6 months following the treatment. This study further implicates the lateral PFC and insula in nicotine addiction and suggests the use of deep high-frequency TMS of these regions following presentation of smoking cues as a promising treatment strategy. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  12. Context and Deep Learning Design

    Science.gov (United States)

    Boyle, Tom; Ravenscroft, Andrew

    2012-01-01

    Conceptual clarification is essential if we are to establish a stable and deep discipline of technology enhanced learning. The technology is alluring; this can distract from deep design in a surface rush to exploit the affordances of the new technology. We need a basis for design, and a conceptual unit of organization, that are applicable across…

  13. Deep Generative Models for Molecular Science

    DEFF Research Database (Denmark)

    Jørgensen, Peter Bjørn; Schmidt, Mikkel Nørgaard; Winther, Ole

    2018-01-01

    Generative deep machine learning models now rival traditional quantum-mechanical computations in predicting properties of new structures, and they come with a significantly lower computational cost, opening new avenues in computational molecular science. In the last few years, a variety of deep...... generative models have been proposed for modeling molecules, which differ in both their model structure and choice of input features. We review these recent advances within deep generative models for predicting molecular properties, with particular focus on models based on the probabilistic autoencoder (or...

  14. Does surgery for deep infiltrating bowel endometriosis improve fertility? A systematic review.

    Science.gov (United States)

    Iversen, Maja L; Seyer-Hansen, Mikkel; Forman, Axel

    2017-06-01

    Reduced fertility is a major concern in women with endometriosis. The influence of surgery of deep infiltrating endometriosis (DIE) affecting the bowel wall on fertility is controversial and the literature on this field is heterogeneous. In this review we addressed whether surgery for bowel DIE improves the spontaneous pregnancy rate, and the results of in vitro fertilization (IVF), and the potential risk of such surgery. We conducted a literature search including the terms "deep", "deep infiltrating", "bowel", rectovaginal", "endometriosis", "fertility", "infertility" and "IVF" in PubMed. No randomized controlled studies were found. Other publications of relevance included four retrospective and three prospective observational studies. Moreover, one retrospective study compared results of IVF treatment with or without previous surgery for bowel DIE. All studies reported detailed data on surgical complications. The poor data quality precluded firm conclusions. The results indicate, however, the possibility that surgery for bowel DIE may improve the spontaneous pregnancy rate, and positive effects on IVF outcome cannot be excluded. Such surgery will be associated with risk of major complications. The lack of proper data precludes conclusions on the potential role for bowel DIE surgery to improve the spontaneous pregnancy rate and results of IVF treatment. Positive effects cannot be excluded, but the definite risk of major complications must be taken into account. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.

  15. Too Deep or Not Too Deep?: A Propensity-Matched Comparison of the Analgesic Effects of a Superficial Versus Deep Serratus Fascial Plane Block for Ambulatory Breast Cancer Surgery.

    Science.gov (United States)

    Abdallah, Faraj W; Cil, Tulin; MacLean, David; Madjdpour, Caveh; Escallon, Jaime; Semple, John; Brull, Richard

    2018-07-01

    Serratus fascial plane block can reduce pain following breast surgery, but the question of whether to inject the local anesthetic superficial or deep to the serratus muscle has not been answered. This cohort study compares the analgesic benefits of superficial versus deep serratus plane blocks in ambulatory breast cancer surgery patients at Women's College Hospital between February 2014 and December 2016. We tested the joint hypothesis that deep serratus block is noninferior to superficial serratus block for postoperative in-hospital (pre-discharge) opioid consumption and pain severity. One hundred sixty-six patients were propensity matched among 2 groups (83/group): superficial and deep serratus blocks. The cohort was used to evaluate the effect of blocks on postoperative oral morphine equivalent consumption and area under the curve for rest pain scores. We considered deep serratus block to be noninferior to superficial serratus block if it were noninferior for both outcomes, within 15 mg morphine and 4 cm·h units margins. Other outcomes included intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and incidence of postoperative nausea and vomiting. Deep serratus block was associated with postoperative morphine consumption and pain scores area under the curve that were noninferior to those of the superficial serratus block. Intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and postoperative nausea and vomiting were not different between blocks. The postoperative in-hospital analgesia associated with deep serratus block is as effective (within an acceptable margin) as superficial serratus block following ambulatory breast cancer surgery. These new findings are important to inform both current clinical practices and future prospective studies.

  16. A deep learning framework for causal shape transformation.

    Science.gov (United States)

    Lore, Kin Gwn; Stoecklein, Daniel; Davies, Michael; Ganapathysubramanian, Baskar; Sarkar, Soumik

    2018-02-01

    Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Desiccation-crack-induced salinization in deep clay sediment

    Directory of Open Access Journals (Sweden)

    S. Baram

    2013-04-01

    Full Text Available A study on water infiltration and solute transport in a clayey vadose zone underlying a dairy farm waste source was conducted to assess the impact of desiccation cracks on subsurface evaporation and salinization. The study is based on five years of continuous measurements of the temporal variation in the vadose zone water content and on the chemical and isotopic composition of the sediment and pore water in it. The isotopic composition of water stable isotopes (δ18O and δ2H in water and sediment samples, from the area where desiccation crack networks prevail, indicated subsurface evaporation down to ~ 3.5 m below land surface, and vertical and lateral preferential transport of water, following erratic preferential infiltration events. Chloride (Cl− concentrations in the vadose zone pore water substantially increased with depth, evidence of deep subsurface evaporation and down flushing of concentrated solutions from the evaporation zones during preferential infiltration events. These observations led to development of a desiccation-crack-induced salinization (DCIS conceptual model. DCIS suggests that thermally driven convective air flow in the desiccation cracks induces evaporation and salinization in relatively deep sections of the subsurface. This conceptual model supports previous conceptual models on vadose zone and groundwater salinization in fractured rock in arid environments and extends its validity to clayey soils in semi-arid environments.

  18. Nurses' training and confidence on deep venous catheterization.

    Science.gov (United States)

    Liachopoulou, A P; Synodinou-Kamilou, E E; Deligiannidi, P G; Giannakopoulou, M; Birbas, K N

    2008-01-01

    The rough estimation of the education and the self-confidence of nurses, both students and professionals, regarding deep venous catheterization in adult patients, the evaluation of the change in self-confidence of one team of students who were trained with a simulator on deep venous catheterization and the correlation of their self-confidence with their performance recorded by the simulator. Seventy-six nurses and one hundred twenty-four undergraduate students participated in the study. Fourty-four University students took part in a two-day educational seminar and were trained on subclavian and femoral vein paracentesis with a simulator and an anatomical model. Three questionnaires were filled in by the participants: one from nurses, one from students of Technological institutions, while the University students filled in the previous questionnaire before their attendance of the seminar, and another questionnaire after having attended it. Impressive results in improving the participants' self-confidence were recorded. However, the weak correlation of their self-confidence with the score automatically provided by the simulator after each user's training obligates us to be particularly cautious about the ability of the users to repeat the action successfully in a clinical environment. Educational courses and simulators are useful educational tools that are likely to shorten but in no case can efface the early phase of the learning curve in clinical setting, substituting the clinical training of inexperienced users.

  19. Coated Particle and Deep Burn Fuels Monthly Highlights December 2010

    International Nuclear Information System (INIS)

    Snead, Lance Lewis; Bell, Gary L.; Besmann, Theodore M.

    2011-01-01

    During FY 2011 the CP and DB Program will report Highlights on a monthly basis, but will no longer produce Quarterly Progress Reports. Technical details that were previously included in the quarterly reports will be included in the appropriate Milestone Reports that are submitted to FCRD Program Management. These reports will also be uploaded to the Deep Burn website. The Monthly Highlights report for November 2010, ORNL/TM-2010/323, was distributed to program participants on December 9, 2010. The final Quarterly for FY 2010, Deep Burn Program Quarterly Report for July - September 2010, ORNL/TM-2010/301, was announced to program participants and posted to the website on December 28, 2010. This report discusses the following: (1) Thermochemical Data and Model Development - (a) Thermochemical Modeling, (b) Core Design Optimization in the HTR (high temperature helium-cooled reactor) Pebble Bed Design (INL), (c) Radiation Damage and Properties; (2) TRISO (tri-structural isotropic) Development - (a) TRU (transuranic elements) Kernel Development, (b) Coating Development; (3) LWR Fully Ceramic Fuel - (a) FCM Fabrication Development, (b) FCM Irradiation Testing (ORNL); (4) Fuel Performance and Analytical Analysis - Fuel Performance Modeling (ORNL).

  20. Horner’s syndrome associated with parotid duct obstruction in a sheep

    OpenAIRE

    Loste, Araceli; Ramos, Juan J.; Ferrer, Luis M.; Climent, Salvador; Latre, María V.

    2006-01-01

    A 9-year old, Rasa Aragonesa ewe was presented with a left-sided, facial, soft fluctuant swelling. The postmortem examination showed grass awns filling the entire length of the parotid gland duct. The presence of parotid duct obstruction with Horner’s syndrome, previously unreported in sheep, is discussed.

  1. Redescription of Schizostheturs lyriformis (McGraw and Farrier, 1969) (Parasitiformes: Parasitidae), with revision of the genus

    Science.gov (United States)

    Fahad Al-Atawi; Hana Klompen; John C. Moser

    2002-01-01

    Schizosthetus lyrifomis (McGraw and Farrier, 1969) is redescribed for all inslars, emphasizing ontogenetic changes in sensillar and gland patterns of all body parts This approach allows recognition of some previously unreported patterns. Major positional shifts of lyrifissures over ontogeny appear correlated with the formation of the peritremes, and...

  2. Globus Pallidus Interna Deep Brain Stimulation in a Patient with Medically Intractable Meige Syndrome

    Directory of Open Access Journals (Sweden)

    Dae-Woong Bae

    2014-10-01

    Full Text Available Medical therapies in patients with Meige syndrome, including botulinum toxin injection, have been limited because of incomplete response or adverse side effects. We evaluated a patient with Meige syndrome who was successfully treated with deep brain stimulation (DBS in the globus pallidus interna (GPi. This case report and other previous reports suggest that bilateral GPi DBS may be an effective treatment for medically refractory Meige syndrome, without significant adverse effects.

  3. Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

    Science.gov (United States)

    He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

    2017-03-01

    Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

  4. Landform partitioning and estimates of deep storage of soil organic matter in Zackenberg, Greenland

    Science.gov (United States)

    Palmtag, Juri; Cable, Stefanie; Christiansen, Hanne H.; Hugelius, Gustaf; Kuhry, Peter

    2018-05-01

    Soils in the northern high latitudes are a key component in the global carbon cycle, with potential feedback on climate. This study aims to improve the previous soil organic carbon (SOC) and total nitrogen (TN) storage estimates for the Zackenberg area (NE Greenland) that were based on a land cover classification (LCC) approach, by using geomorphological upscaling. In addition, novel organic carbon (OC) estimates for deeper alluvial and deltaic deposits (down to 300 cm depth) are presented. We hypothesise that landforms will better represent the long-term slope and depositional processes that result in deep SOC burial in this type of mountain permafrost environments. The updated mean SOC storage for the 0-100 cm soil depth is 4.8 kg C m-2, which is 42 % lower than the previous estimate of 8.3 kg C m-2 based on land cover upscaling. Similarly, the mean soil TN storage in the 0-100 cm depth decreased with 44 % from 0.50 kg (± 0.1 CI) to 0.28 (±0.1 CI) kg TN m-2. We ascribe the differences to a previous areal overestimate of SOC- and TN-rich vegetated land cover classes. The landform-based approach more correctly constrains the depositional areas in alluvial fans and deltas with high SOC and TN storage. These are also areas of deep carbon storage with an additional 2.4 kg C m-2 in the 100-300 cm depth interval. This research emphasises the need to consider geomorphology when assessing SOC pools in mountain permafrost landscapes.

  5. Numerical and Experimental Study on Manufacture of a Novel High-Capacity Engine Oil Pan Subjected to Hydro-Mechanical Deep Drawing

    Science.gov (United States)

    Chen, D. Y.; Xu, Y.; Zhang, S. H.; El-Aty, A. Abd; Ma, Y.

    2017-09-01

    The oil pan is equipped at the bottom of engine crankcase of the automobile to prevent impurity and collect the lubrication oil from the surfaces of the engine which is helpful for heat dissipation and oxidation prevention. The present study aims at manufacturing a novel high-capacity engine oil pan, which is considered as a complex shaped component with features of thin wall, large size and asymmetric deep cavity through both numerical and experimental methods. The result indicated that it is difficult to form the current part through the common deep drawing process. Accordingly, the hydro-mechanical deep drawing technology was conducted, which consisted of two steps, previous local drawing and the final integral deep drawing with hydraulic pressure. The finite element analysis (FEA) was carried out to investigate the influence of initial blank dimension and the key process parameters such as loading path, draw-bead force and fillet radius on the formability of the sheet blank. Compared with the common deep drawing, the limit drawing ratio by hydro-mechanical deep drawing can be increased from 2.34 to 2.77, while the reduction in blank wall thickness can be controlled in the range of 28%. The formability is greatly improved without any defects such as crack and wrinkle by means of parameters optimisation. The results gained from simulation keep a reasonable agreement with that obtained from experiment trials.

  6. Linking white matter and deep gray matter alterations in premanifest Huntington disease

    Directory of Open Access Journals (Sweden)

    Andreia V. Faria

    2016-01-01

    Full Text Available Huntington disease (HD is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i regions of interest surrounding these structures, using (ii tractography-based analysis, and using (iii whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores, and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be

  7. Behaviour of Steel Fibre Reinforced Rubberized Continuous Deep Beams

    Science.gov (United States)

    Sandeep, MS; Nagarajan, Praveen; Shashikala, A. P.

    2018-03-01

    Transfer girders and pier caps, which are in fact deep beams, are critical structural elements present in high-rise buildings and bridges respectively. During an earthquake, failure of lifeline structures like bridges and critical structural members like transfer girders will result in severe catastrophes. Ductility is the key factor that influences the resistance of any structural member against seismic action. Structural members cast using materials having higher ductility will possess higher seismic resistance. Previous research shows that concrete having rubber particles (rubcrete) possess better ductility and low density in comparison to ordinary concrete. The main hindrance to the use of rubcrete is the reduction in compressive and tensile strength of concrete due to the presence of rubber. If these undesirable properties of rubcrete can be controlled, a new cementitious composite with better ductility, seismic performance and economy can be developed. A combination of rubber particles and steel fibre has the potential to reduce the undesirable effect of rubcrete. In this paper, the effect of rubber particles and steel fibre in the behaviour of two-span continuous deep beams is studied experimentally. Based on the results, optimum proportions of steel fibre and rubber particles for getting good ductile behaviour with less reduction in collapse load is found out.

  8. Magma Transport from Deep to Shallow Crust and Eruption

    Science.gov (United States)

    White, R. S.; Greenfield, T. S.; Green, R. G.; Brandsdottir, B.; Hudson, T.; Woods, J.; Donaldson, C.; Ágústsdóttir, T.

    2016-12-01

    We have mapped magma transport paths from the deep (20 km) to the shallow (6 km) crust and in two cases to eventual surface eruption under several Icelandic volcanoes (Askja, Bardarbunga, Eyjafjallajokull, Upptyppingar). We use microearthquakes caused by brittle fracture to map magma on the move and tomographic seismic studies of velocity perturbations beneath volcanoes to map the magma storage regions. High-frequency brittle failure earthquakes with magnitudes of typically 0-2 occur where melt is forcing its way through the country rock, or where previously frozen melt is repeatedly re-broken in conduits and dykes. The Icelandic crust on the rift zones where these earthquakes occur is ductile at depths greater than 7 km beneath the surface, so the occurrence of brittle failure seismicity at depths as great as 20 km is indicative of high strain rates, for which magma movement is the most likely explanation. We suggest that high volatile pressures caused by the exsolution of carbon dioxide in the deep crust is driving the magma movement and seismicity at depths of 15-20 km. Eruptions from shallow crustal storage areas are likewise driven by volatile exsolution, though additional volatiles, and in particular water are also involved in the shallow crust.

  9. Molecular analysis of deep subsurface bacteria

    International Nuclear Information System (INIS)

    Jimenez Baez, L.E.

    1989-09-01

    Deep sediments samples from site C10a, in Appleton, and sites, P24, P28, and P29, at the Savannah River Site (SRS), near Aiken, South Carolina were studied to determine their microbial community composition, DNA homology and mol %G+C. Different geological formations with great variability in hydrogeological parameters were found across the depth profile. Phenotypic identification of deep subsurface bacteria underestimated the bacterial diversity at the three SRS sites, since bacteria with the same phenotype have different DNA composition and less than 70% DNA homology. Total DNA hybridization and mol %G+C analysis of deep sediment bacterial isolates suggested that each formation is comprised of different microbial communities. Depositional environment was more important than site and geological formation on the DNA relatedness between deep subsurface bacteria, since more 70% of bacteria with 20% or more of DNA homology came from the same depositional environments. Based on phenotypic and genotypic tests Pseudomonas spp. and Acinetobacter spp.-like bacteria were identified in 85 million years old sediments. This suggests that these microbial communities might have been adapted during a long period of time to the environmental conditions of the deep subsurface

  10. Origin and evolution of the deep thermochemical structure beneath Eurasia.

    Science.gov (United States)

    Flament, N; Williams, S; Müller, R D; Gurnis, M; Bower, D J

    2017-01-18

    A unique structure in the Earth's lowermost mantle, the Perm Anomaly, was recently identified beneath Eurasia. It seismologically resembles the large low-shear velocity provinces (LLSVPs) under Africa and the Pacific, but is much smaller. This challenges the current understanding of the evolution of the plate-mantle system in which plumes rise from the edges of the two LLSVPs, spatially fixed in time. New models of mantle flow over the last 230 million years reproduce the present-day structure of the lower mantle, and show a Perm-like anomaly. The anomaly formed in isolation within a closed subduction network ∼22,000 km in circumference prior to 150 million years ago before migrating ∼1,500 km westward at an average rate of 1 cm year -1 , indicating a greater mobility of deep mantle structures than previously recognized. We hypothesize that the mobile Perm Anomaly could be linked to the Emeishan volcanics, in contrast to the previously proposed Siberian Traps.

  11. A new methanogen “Methanobrevibacter massiliense” isolated in a case of severe periodontitis

    OpenAIRE

    Huynh, Hong T. T.; Pignoly, Marion; Drancourt, Michel; Aboudharam, Gérard

    2017-01-01

    Background A few methanogens have been previously recovered from periodontitis lesions, yet their repertoire may not be completed. We recovered a previously unreported methanogen species in this situation. Case presentation A 64-year-old Caucasian woman was diagnosed with chronic, severe generalized periodontitis. In the presence of negative controls, an 18-month culture of periodontal pockets in anaerobe Hungate tube yielded “Methanobrevibacter massiliense” and Pyramidobacter piscolens. Conc...

  12. Investigation of Lithium Metal Hydride Materials for Mitigation of Deep Space Radiation

    Science.gov (United States)

    Rojdev, Kristina; Atwell, William

    2016-01-01

    Radiation exposure to crew, electronics, and non-metallic materials is one of many concerns with long-term, deep space travel. Mitigating this exposure is approached via a multi-faceted methodology focusing on multi-functional materials, vehicle configuration, and operational or mission constraints. In this set of research, we are focusing on new multi-functional materials that may have advantages over traditional shielding materials, such as polyethylene. Metal hydride materials are of particular interest for deep space radiation shielding due to their ability to store hydrogen, a low-Z material known to be an excellent radiation mitigator and a potential fuel source. We have previously investigated 41 different metal hydrides for their radiation mitigation potential. Of these metal hydrides, we found a set of lithium hydrides to be of particular interest due to their excellent shielding of galactic cosmic radiation. Given these results, we will continue our investigation of lithium hydrides by expanding our data set to include dose equivalent and to further understand why these materials outperformed polyethylene in a heavy ion environment. For this study, we used HZETRN 2010, a one-dimensional transport code developed by NASA Langley Research Center, to simulate radiation transport through the lithium hydrides. We focused on the 1977 solar minimum Galactic Cosmic Radiation environment and thicknesses of 1, 5, 10, 20, 30, 50, and 100 g/cm2 to stay consistent with our previous studies. The details of this work and the subsequent results will be discussed in this paper.

  13. Joint Training of Deep Boltzmann Machines

    OpenAIRE

    Goodfellow, Ian; Courville, Aaron; Bengio, Yoshua

    2012-01-01

    We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at a time, or do not perform well on classifi- cation tasks.

  14. THE 2012 HUBBLE ULTRA DEEP FIELD (UDF12): OBSERVATIONAL OVERVIEW

    Energy Technology Data Exchange (ETDEWEB)

    Koekemoer, Anton M. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Ellis, Richard S.; Schenker, Matthew A. [Department of Astrophysics, California Institute of Technology, MS 249-17, Pasadena, CA 91125 (United States); McLure, Ross J.; Dunlop, James S.; Bowler, Rebecca A. A.; Rogers, Alexander B.; Curtis-Lake, Emma; Cirasuolo, Michele; Wild, V.; Targett, T. [Institute for Astronomy, University of Edinburgh, Royal Observatory, Edinburgh EH9 3HJ (United Kingdom); Robertson, Brant E.; Schneider, Evan; Stark, Daniel P. [Department of Astronomy and Steward Observatory, University of Arizona, Tucson, AZ 85721 (United States); Ono, Yoshiaki; Ouchi, Masami [Institute for Cosmic Ray Research, University of Tokyo, Kashiwa City, Chiba 277-8582 (Japan); Charlot, Stephane [UPMC-CNRS, UMR7095, Institut d' Astrophysique de Paris, F-75014, Paris (France); Furlanetto, Steven R. [Department of Physics and Astronomy, University of California, Los Angeles, CA 90095 (United States)

    2013-11-01

    We present the 2012 Hubble Ultra Deep Field campaign (UDF12), a large 128 orbit Cycle 19 Hubble Space Telescope program aimed at extending previous Wide Field Camera 3 (WFC3)/IR observations of the UDF by quadrupling the exposure time in the F105W filter, imaging in an additional F140W filter, and extending the F160W exposure time by 50%, as well as adding an extremely deep parallel field with the Advanced Camera for Surveys (ACS) in the F814W filter with a total exposure time of 128 orbits. The principal scientific goal of this project is to determine whether galaxies reionized the universe; our observations are designed to provide a robust determination of the star formation density at z ∼> 8, improve measurements of the ultraviolet continuum slope at z ∼ 7-8, facilitate the construction of new samples of z ∼ 9-10 candidates, and enable the detection of sources up to z ∼ 12. For this project we committed to combining these and other WFC3/IR imaging observations of the UDF area into a single homogeneous dataset to provide the deepest near-infrared observations of the sky. In this paper we present the observational overview of the project and describe the procedures used in reducing the data as well as the final products that were produced. We present the details of several special procedures that we implemented to correct calibration issues in the data for both the WFC3/IR observations of the main UDF field and our deep 128 orbit ACS/WFC F814W parallel field image, including treatment for persistence, correction for time-variable sky backgrounds, and astrometric alignment to an accuracy of a few milliarcseconds. We release the full, combined mosaics comprising a single, unified set of mosaics of the UDF, providing the deepest near-infrared blank-field view of the universe currently achievable, reaching magnitudes as deep as AB ∼ 30 mag in the near-infrared, and yielding a legacy dataset on this field.

  15. THE 2012 HUBBLE ULTRA DEEP FIELD (UDF12): OBSERVATIONAL OVERVIEW

    International Nuclear Information System (INIS)

    Koekemoer, Anton M.; Ellis, Richard S.; Schenker, Matthew A.; McLure, Ross J.; Dunlop, James S.; Bowler, Rebecca A. A.; Rogers, Alexander B.; Curtis-Lake, Emma; Cirasuolo, Michele; Wild, V.; Targett, T.; Robertson, Brant E.; Schneider, Evan; Stark, Daniel P.; Ono, Yoshiaki; Ouchi, Masami; Charlot, Stephane; Furlanetto, Steven R.

    2013-01-01

    We present the 2012 Hubble Ultra Deep Field campaign (UDF12), a large 128 orbit Cycle 19 Hubble Space Telescope program aimed at extending previous Wide Field Camera 3 (WFC3)/IR observations of the UDF by quadrupling the exposure time in the F105W filter, imaging in an additional F140W filter, and extending the F160W exposure time by 50%, as well as adding an extremely deep parallel field with the Advanced Camera for Surveys (ACS) in the F814W filter with a total exposure time of 128 orbits. The principal scientific goal of this project is to determine whether galaxies reionized the universe; our observations are designed to provide a robust determination of the star formation density at z ∼> 8, improve measurements of the ultraviolet continuum slope at z ∼ 7-8, facilitate the construction of new samples of z ∼ 9-10 candidates, and enable the detection of sources up to z ∼ 12. For this project we committed to combining these and other WFC3/IR imaging observations of the UDF area into a single homogeneous dataset to provide the deepest near-infrared observations of the sky. In this paper we present the observational overview of the project and describe the procedures used in reducing the data as well as the final products that were produced. We present the details of several special procedures that we implemented to correct calibration issues in the data for both the WFC3/IR observations of the main UDF field and our deep 128 orbit ACS/WFC F814W parallel field image, including treatment for persistence, correction for time-variable sky backgrounds, and astrometric alignment to an accuracy of a few milliarcseconds. We release the full, combined mosaics comprising a single, unified set of mosaics of the UDF, providing the deepest near-infrared blank-field view of the universe currently achievable, reaching magnitudes as deep as AB ∼ 30 mag in the near-infrared, and yielding a legacy dataset on this field

  16. Discoveries and Conservation Efforts of Extensive Deep-Sea Coral Habitat off the Southeastern U.S.

    Science.gov (United States)

    Reed, J. K.; Messing, C. G.; Walker, B. K.; Farrington, S.; Brooke, S.; Correa, T.; Brouwer, M.

    2012-12-01

    The deep-sea floor of the Western Atlantic off the southeastern U.S. supports a variety of deep-sea coral ecosystem (DSCE) habitats, including: coral mounds, rock terraces (Miami and Pourtalès Terraces), canyons (Agassiz and Tortugas Valleys), and island slopes (western Bahamas and northern Cuba). We used NOAA bathymetric contour maps and digital elevation models to identify and delineate the areal extent of potential DSCE habitat (50-1000 m) from northeastern Florida through the Straits of Florida. Recently, shipboard and AUV side-scan and multibeam sonar have further documented portions of the region. The resulting maps have been ground-truthed with over 250 submersible and remotely operated vehicle (ROV) dives, revealing that high-relief topographic features, including steep escarpments and rocky terraces, are good predictors of DSCE habitat in this region. The benthic biota is diverse but locally variable; for example, Lophelia and Enallopsammia stony corals dominate the deep-water mounds, whereas stylasterid corals dominate the rocky terraces where Lophelia is sporadic. Octocorals, black corals, and sponges are common at most sites but different species exhibit site-specific distributional variability. In 2011, the first of two NOAA-sponsored cruises using sonar mapping and an ROV discovered the southernmost Lophelia coral mound in the continental United States, south of the Florida Keys, offering the possibility that more Lophelia mounds may exist in this region where they were previously thought to be absent. The second cruise discovered that deep-water Oculina varicosa coral reefs extend over 70 nmi north of the current boundaries of the Oculina Habitat Area of Particular Concern (OHAPC), which was first designated as a marine protected area in 1984. These studies indicate that cold-water coral mounds are significantly more diverse and abundant in this region than previously thought. These research results were presented to NOAA and the South Atlantic

  17. Deep boreholes; Tiefe Bohrloecher

    Energy Technology Data Exchange (ETDEWEB)

    Bracke, Guido [Gesellschaft fuer Anlagen- und Reaktorsicherheit gGmbH Koeln (Germany); Charlier, Frank [NSE international nuclear safety engineering gmbh, Aachen (Germany); Geckeis, Horst [Karlsruher Institut fuer Technologie (Germany). Inst. fuer Nukleare Entsorgung; and others

    2016-02-15

    The report on deep boreholes covers the following subject areas: methods for safe enclosure of radioactive wastes, requirements concerning the geological conditions of possible boreholes, reversibility of decisions and retrievability, status of drilling technology. The introduction covers national and international activities. Further chapters deal with the following issues: basic concept of the storage in deep bore holes, status of the drilling technology, safe enclosure, geomechanics and stability, reversibility of decisions, risk scenarios, compliancy with safe4ty requirements and site selection criteria, research and development demand.

  18. Deep Reactive Ion Etching (DRIE) of High Aspect Ratio SiC Microstructures using a Time-Multiplexed Etch-Passivate Process

    Science.gov (United States)

    Evans, Laura J.; Beheim, Glenn M.

    2006-01-01

    High aspect ratio silicon carbide (SiC) microstructures are needed for microengines and other harsh environment micro-electro-mechanical systems (MEMS). Previously, deep reactive ion etching (DRIE) of low aspect ratio (AR less than or = 1) deep (greater than 100 micron) trenches in SiC has been reported. However, existing DRIE processes for SiC are not well-suited for definition of high aspect ratio features because such simple etch-only processes provide insufficient control over sidewall roughness and slope. Therefore, we have investigated the use of a time-multiplexed etch-passivate (TMEP) process, which alternates etching with polymer passivation of the etch sidewalls. An optimized TMEP process was used to etch high aspect ratio (AR greater than 5) deep (less than 100 micron) trenches in 6H-SiC. Power MEMS structures (micro turbine blades) in 6H-SiC were also fabricated.

  19. Oil desulfurization using deep eutectic solvents as sustainable an economical extractants via liquid-liquid extraction: Experimental and PC-SAFT predictions

    NARCIS (Netherlands)

    Warrag, S.E.E.; Pototzki, Clarissa; Rodriguez Rodriguez, N.; van Sint Annaland, M.; Kroon, M.C.; Held, Christoph; Sadowski, G.; Peters, Cor

    2018-01-01

    The reduction of the sulfur content in crude oil is of utmost importance in order to meet the stringent environmental regulations. Thiophene and its derivatives are considered key substances to be separated from the crude oil. In previous works, six deep eutectic solvents (DESs) based on

  20. Waste Handling and Emplacement Options for Disposal of Radioactive Waste in Deep Boreholes

    International Nuclear Information System (INIS)

    Cochran, John R.; Hardin, Ernest

    2015-01-01

    Traditional methods cannot be used to handle and emplace radioactive wastes in boreholes up to 16,400 feet (5 km) deep for disposal. This paper describes three systems that can be used for handling and emplacing waste packages in deep borehole: (1) a 2011 reference design that is based on a previous study by Woodward-Clyde in 1983 in which waste packages are assembled into ''strings'' and lowered using drill pipe; (2) an updated version of the 2011 reference design; and (3) a new concept in which individual waste packages would be lowered to depth using a wireline. Emplacement on coiled tubing was also considered, but not developed in detail. The systems described here are currently designed for U.S. Department of Energy-owned high-level waste (HLW) including the Cesium- 137/Strontium-90 capsules from the Hanford Facility and bulk granular HLW from fuel processing in Idaho.

  1. DeepLoc: prediction of protein subcellular localization using deep learning

    DEFF Research Database (Denmark)

    Almagro Armenteros, Jose Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    2017-01-01

    The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from...... knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict...... current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc . Example code is available at https://github.com/JJAlmagro/subcellular_localization . The dataset is available at http...

  2. Pre-cementation of deep shaft

    Science.gov (United States)

    Heinz, W. F.

    1988-12-01

    Pre-cementation or pre-grouting of deep shafts in South Africa is an established technique to improve safety and reduce water ingress during shaft sinking. The recent completion of several pre-cementation projects for shafts deeper than 1000m has once again highlighted the effectiveness of pre-grouting of shafts utilizing deep slimline boreholes and incorporating wireline technique for drilling and conventional deep borehole grouting techniques for pre-cementation. Pre-cementation of deep shaft will: (i) Increase the safety of shaft sinking operation (ii) Minimize water and gas inflow during shaft sinking (iii) Minimize the time lost due to additional grouting operations during sinking of the shaft and hence minimize costly delays and standing time of shaft sinking crews and equipment. (iv) Provide detailed information of the geology of the proposed shaft site. Informations on anomalies, dykes, faults as well as reef (gold bearing conglomerates) intersections can be obtained from the evaluation of cores of the pre-cementation boreholes. (v) Provide improved rock strength for excavations in the immediate vicinity of the shaft area. The paper describes pre-cementation techniques recently applied successfully from surface and some conclusions drawn for further considerations.

  3. A sigh of relief or a sigh to relieve: The psychological and physiological relief effect of deep breaths.

    Science.gov (United States)

    Vlemincx, Elke; Van Diest, Ilse; Van den Bergh, Omer

    2016-10-15

    Both animal and human research have revealed important associations between sighs and relief. Previously we argued to conceive of sighs as resetters which temporarily induce relief. The present study aimed to investigate the psychological and physiological relief effect of sighs by instructed deep breaths and spontaneous sighs compared to a control breathing maneuver. Participants completed three blocks of 40 trials during which uncertainty cues were followed by either safety cues followed by a positive picture, or danger cues followed by a negative picture. One block was presented without breathing instructions, two subsequent blocks with breathing instructions. During the presentation of the safety and danger cues, an instruction was given to either 'take a deep breath' or 'postpone the next inhalation for 2 s (breath hold). Continuously, participants rated relief and Frontalis electromyography was recorded. Trait anxiety sensitivity was assessed by the Anxiety Sensitivity Index. Self-reported relief and physiological tension were compared 5s before and after instructed deep breaths and breath holds, and before and after spontaneous deep breaths and breath holds in the respective blocks. Results show that self-reported relief following an instructed deep breath was higher than before. Physiological tension decreased following a spontaneous sigh in high anxiety sensitive persons and following a spontaneous breath hold in low anxiety sensitive persons. These results are the first to show that a deep breath relieves and, in anxiety sensitive persons, reduces physiological tension. These findings support the hypothesis that sighs are psychological and physiological resetters. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Integrated study of Mediterranean deep canyons: Novel results and future challenges

    Science.gov (United States)

    Canals, M.; Company, J. B.; Martín, D.; Sànchez-Vidal, A.; Ramírez-Llodrà, E.

    2013-11-01

    This volume compiles a number of scientific papers resulting from a sustained multidisciplinary research effort of the deep-sea ecosystem in the Mediterranean Sea. This started 20 years ago and peaked over the last few years thanks to a number of Spanish and European projects such as PROMETEO, DOS MARES, REDECO, GRACCIE, HERMES, HERMIONE and PERSEUS, amongst others. The geographic focus of most papers is on the NW Mediterranean Sea including the Western Gulf of Lion and the North Catalan margin, with a special attention to submarine canyons, in particular the Blanes and Cap de Creus canyons. This introductory article to the Progress in Oceanography special issue on “Mediterranean deep canyons” provides background information needed to better understand the individual papers forming the volume, comments previous reference papers related to the main topics here addressed, and finally highlights the existing relationships between atmospheric forcing, oceanographic processes, seafloor physiography, ecosystem response, and litter and chemical pollution. This article also aims at constituting a sort of glue, in terms of existing knowledge and concepts and novel findings, linking together the other twenty papers in the volume, also including some illustrative figures. The main driving ideas behind this special issue, particularly fitting to the study area of the NW Mediterranean Sea, could be summarized as follows: (i) the atmosphere and the deep-sea ecosystem are connected through oceanographic processes originating in the coastal area and the ocean surface, which get activated at the occasion of high-energy events leading to fast transfers of matter and energy to the deep; (ii) shelf indented submarine canyons play a pivotal role in such transfers, which involve dense water, sedimentary particles, organic matter, litter and chemical pollutants; (iii) lateral inputs (advection) from the upper continental margin contributes significantly to the formation of

  5. Charged particle production in high Q deep-inelastic scattering at HERA

    Science.gov (United States)

    H1 Collaboration; Aaron, F. D.; Aktas, A.; Alexa, C.; Andreev, V.; Antunovic, B.; Aplin, S.; Asmone, A.; Astvatsatourov, A.; Backovic, S.; Baghdasaryan, A.; Baranov, P.; Barrelet, E.; Bartel, W.; Baudrand, S.; Beckingham, M.; Begzsuren, K.; Behnke, O.; Behrendt, O.; Belousov, A.; Berger, N.; Bizot, J. C.; Boenig, M.-O.; Boudry, V.; Bozovic-Jelisavcic, I.; Bracinik, J.; Brandt, G.; Brinkmann, M.; Brisson, V.; Bruncko, D.; Büsser, F. W.; Bunyatyan, A.; Buschhorn, G.; Bystritskaya, L.; Campbell, A. J.; Avila, K. B. Cantun; Cassol-Brunner, F.; Cerny, K.; Cerny, V.; Chekelian, V.; Cholewa, A.; Contreras, J. G.; Coughlan, J. A.; Cozzika, G.; Cvach, J.; Dainton, J. B.; Daum, K.; Deak, M.; de Boer, Y.; Delcourt, B.; Del Degan, M.; Delvax, J.; de Roeck, A.; de Wolf, E. A.; Diaconu, C.; Dodonov, V.; Dubak, A.; Eckerlin, G.; Efremenko, V.; Egli, S.; Eichler, R.; Eisele, F.; Eliseev, A.; Elsen, E.; Essenov, S.; Falkiewicz, A.; Faulkner, P. J. W.; Favart, L.; Fedotov, A.; Felst, R.; Feltesse, J.; Ferencei, J.; Finke, L.; Fleischer, M.; Fomenko, A.; Franke, G.; Frisson, T.; Gabathuler, E.; Gayler, J.; Ghazaryan, S.; Ginzburgskaya, S.; Glazov, A.; Glushkov, I.; Goerlich, L.; Goettlich, M.; Gogitidze, N.; Gorbounov, S.; Gouzevitch, M.; Grab, C.; Greenshaw, T.; Gregori, M.; Grell, B. R.; Grindhammer, G.; Habib, S.; Haidt, D.; Hansson, M.; Heinzelmann, G.; Helebrant, C.; Henderson, R. C. W.; Henschel, H.; Herrera, G.; Hildebrandt, M.; Hiller, K. H.; Hoffmann, D.; Horisberger, R.; Hovhannisyan, A.; Hreus, T.; Jacquet, M.; Janssen, M. E.; Janssen, X.; Jemanov, V.; Jönsson, L.; Johnson, D. P.; Jung, A. W.; Jung, H.; Kapichine, M.; Katzy, J.; Kenyon, I. R.; Kiesling, C.; Klein, M.; Kleinwort, C.; Klimkovich, T.; Kluge, T.; Knutsson, A.; Korbel, V.; Kostka, P.; Kraemer, M.; Krastev, K.; Kretzschmar, J.; Kropivnitskaya, A.; Krüger, K.; Landon, M. P. J.; Lange, W.; Laštovička-Medin, G.; Laycock, P.; Lebedev, A.; Leibenguth, G.; Lendermann, V.; Levonian, S.; Li, G.; Lindfeld, L.; Lipka, K.; Liptaj, A.; List, B.; List, J.; Loktionova, N.; Lopez-Fernandez, R.; Lubimov, V.; Lucaci-Timoce, A.-I.; Lytkin, L.; Makankine, A.; Malinovski, E.; Marage, P.; Marti, Ll.; Martisikova, M.; Martyn, H.-U.; Maxfield, S. J.; Mehta, A.; Meier, K.; Meyer, A. B.; Meyer, H.; Meyer, H.; Meyer, J.; Michels, V.; Mikocki, S.; Milcewicz-Mika, I.; Mohamed, A.; Moreau, F.; Morozov, A.; Morris, J. V.; Mozer, M. U.; Müller, K.; Murín, P.; Nankov, K.; Naroska, B.; Naumann, Th.; Newman, P. R.; Niebuhr, C.; Nikiforov, A.; Nowak, G.; Nowak, K.; Nozicka, M.; Oganezov, R.; Olivier, B.; Olsson, J. E.; Osman, S.; Ozerov, D.; Palichik, V.; Panagoulias, I.; Pandurovic, M.; Papadopoulou, Th.; Pascaud, C.; Patel, G. D.; Peng, H.; Perez, E.; Perez-Astudillo, D.; Perieanu, A.; Petrukhin, A.; Picuric, I.; Piec, S.; Pitzl, D.; Plačakytė, R.; Polifka, R.; Povh, B.; Preda, T.; Prideaux, P.; Radescu, V.; Rahmat, A. J.; Raicevic, N.; Ravdandorj, T.; Reimer, P.; Risler, C.; Rizvi, E.; Robmann, P.; Roland, B.; Roosen, R.; Rostovtsev, A.; Rurikova, Z.; Rusakov, S.; Salek, D.; Salvaire, F.; Sankey, D. P. C.; Sauter, M.; Sauvan, E.; Schmidt, S.; Schmitt, S.; Schmitz, C.; Schoeffel, L.; Schöning, A.; Schultz-Coulon, H.-C.; Sefkow, F.; Shaw-West, R. N.; Sheviakov, I.; Shtarkov, L. N.; Sloan, T.; Smiljanic, I.; Smirnov, P.; Soloviev, Y.; South, D.; Spaskov, V.; Specka, A.; Staykova, Z.; Steder, M.; Stella, B.; Stiewe, J.; Straumann, U.; Sunar, D.; Sykora, T.; Tchoulakov, V.; Thompson, G.; Thompson, P. D.; Toll, T.; Tomasz, F.; Tran, T. H.; Traynor, D.; Trinh, T. N.; Truöl, P.; Tsakov, I.; Tseepeldorj, B.; Tsipolitis, G.; Tsurin, I.; Turnau, J.; Tzamariudaki, E.; Urban, K.; Utkin, D.; Valkárová, A.; Vallée, C.; van Mechelen, P.; Trevino, A. Vargas; Vazdik, Y.; Vinokurova, S.; Volchinski, V.; Weber, G.; Weber, R.; Wegener, D.; Werner, C.; Wessels, M.; Wissing, Ch.; Wolf, R.; Wünsch, E.; Xella, S.; Yeganov, V.; Žáček, J.; Zálešák, J.; Zhang, Z.; Zhelezov, A.; Zhokin, A.; Zhu, Y. C.; Zimmermann, T.; Zohrabyan, H.; Zomer, F.

    2007-10-01

    The average charged track multiplicity and the normalised distribution of the scaled momentum, x, of charged final state hadrons are measured in deep-inelastic ep scattering at high Q in the Breit frame of reference. The analysis covers the range of photon virtuality 100previous results presented by HERA experiments this analysis has a significantly higher statistical precision and extends the phase space to higher Q and to the full range of x. The results are compared with ee annihilation data and with various calculations based on perturbative QCD using different models of the hadronisation process.

  6. Achieving deep reductions in US transport greenhouse gas emissions: Scenario analysis and policy implications

    International Nuclear Information System (INIS)

    McCollum, David; Yang, Christopher

    2009-01-01

    This paper investigates the potential for making deep cuts in US transportation greenhouse gas (GHG) emissions in the long-term (50-80% below 1990 levels by 2050). Scenarios are used to envision how such a significant decarbonization might be achieved through the application of advanced vehicle technologies and fuels, and various options for behavioral change. A Kaya framework that decomposes GHG emissions into the product of four major drivers is used to analyze emissions and mitigation options. In contrast to most previous studies, a relatively simple, easily adaptable modeling methodology is used which can incorporate insights from other modeling studies and organize them in a way that is easy for policymakers to understand. Also, a wider range of transportation subsectors is considered here-light- and heavy-duty vehicles, aviation, rail, marine, agriculture, off-road, and construction. This analysis investigates scenarios with multiple options (increased efficiency, lower-carbon fuels, and travel demand management) across the various subsectors and confirms the notion that there are no 'silver bullet' strategies for making deep cuts in transport GHGs. If substantial emission reductions are to be made, considerable action is needed on all fronts, and no subsectors can be ignored. Light-duty vehicles offer the greatest potential for emission reductions; however, while deep reductions in other subsectors are also possible, there are more limitations in the types of fuels and propulsion systems that can be used. In all cases travel demand management strategies are critical; deep emission cuts will not likely be possible without slowing growth in travel demand across all modes. Even though these scenarios represent only a small subset of the potential futures in which deep reductions might be achieved, they provide a sense of the magnitude of changes required in our transportation system and the need for early and aggressive action if long-term targets are to be met.

  7. Applications of Deep Learning in Biomedicine.

    Science.gov (United States)

    Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

    2016-05-02

    Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

  8. A Deep Learning based Approach to Reduced Order Modeling of Fluids using LSTM Neural Networks

    Science.gov (United States)

    Mohan, Arvind; Gaitonde, Datta

    2017-11-01

    Reduced Order Modeling (ROM) can be used as surrogates to prohibitively expensive simulations to model flow behavior for long time periods. ROM is predicated on extracting dominant spatio-temporal features of the flow from CFD or experimental datasets. We explore ROM development with a deep learning approach, which comprises of learning functional relationships between different variables in large datasets for predictive modeling. Although deep learning and related artificial intelligence based predictive modeling techniques have shown varied success in other fields, such approaches are in their initial stages of application to fluid dynamics. Here, we explore the application of the Long Short Term Memory (LSTM) neural network to sequential data, specifically to predict the time coefficients of Proper Orthogonal Decomposition (POD) modes of the flow for future timesteps, by training it on data at previous timesteps. The approach is demonstrated by constructing ROMs of several canonical flows. Additionally, we show that statistical estimates of stationarity in the training data can indicate a priori how amenable a given flow-field is to this approach. Finally, the potential and limitations of deep learning based ROM approaches will be elucidated and further developments discussed.

  9. Post-Palaeozoic evolution of weathered landsurfaces in Uganda by tectonically controlled deep weathering and stripping

    Science.gov (United States)

    Taylor, R. G.; Howard, K. W. F.

    1998-11-01

    A model for the evolution of weathered landsurfaces in Uganda is developed using available geotectonic, climatic, sedimentological and chronological data. The model demonstrates the pivotal role of tectonic uplift in inducing cycles of stripping, and tectonic quiescence for cycles of deep weathering. It is able to account for the development of key landforms, such as inselbergs and duricrust-capped plateaux, which previous hypotheses of landscape evolution that are based on climatic or eustatic controls are unable to explain. Development of the Ugandan landscape is traced back to the Permian. Following late Palaeozoic glaciation, a trend towards warmer and more humid climates through the Mesozoic enabled deep weathering of the Jurassic/mid-Cretaceous surface in Uganda during a period of prolonged tectonic quiescence. Uplift associated with the opening South Atlantic Ocean terminated this cycle and instigated a cycle of stripping between the mid-Cretaceous and early Miocene. Deep weathering on the succeeding Miocene to recent (African) surface has occurred from Miocene to present but has been interrupted in the areas adjacent to the western rift where development of a new drainage base level has prompted cycles of stripping in the Miocene and Pleistocene.

  10. Deep transcranial magnetic stimulation for the treatment of auditory hallucinations: a preliminary open-label study.

    Science.gov (United States)

    Rosenberg, Oded; Roth, Yiftach; Kotler, Moshe; Zangen, Abraham; Dannon, Pinhas

    2011-02-09

    Schizophrenia is a chronic and disabling disease that presents with delusions and hallucinations. Auditory hallucinations are usually expressed as voices speaking to or about the patient. Previous studies have examined the effect of repetitive transcranial magnetic stimulation (TMS) over the temporoparietal cortex on auditory hallucinations in schizophrenic patients. Our aim was to explore the potential effect of deep TMS, using the H coil over the same brain region on auditory hallucinations. Eight schizophrenic patients with refractory auditory hallucinations were recruited, mainly from Beer Ya'akov Mental Health Institution (Tel Aviv university, Israel) ambulatory clinics, as well as from other hospitals outpatient populations. Low-frequency deep TMS was applied for 10 min (600 pulses per session) to the left temporoparietal cortex for either 10 or 20 sessions. Deep TMS was applied using Brainsway's H1 coil apparatus. Patients were evaluated using the Auditory Hallucinations Rating Scale (AHRS) as well as the Scale for the Assessment of Positive Symptoms scores (SAPS), Clinical Global Impressions (CGI) scale, and the Scale for Assessment of Negative Symptoms (SANS). This preliminary study demonstrated a significant improvement in AHRS score (an average reduction of 31.7% ± 32.2%) and to a lesser extent improvement in SAPS results (an average reduction of 16.5% ± 20.3%). In this study, we have demonstrated the potential of deep TMS treatment over the temporoparietal cortex as an add-on treatment for chronic auditory hallucinations in schizophrenic patients. Larger samples in a double-blind sham-controlled design are now being preformed to evaluate the effectiveness of deep TMS treatment for auditory hallucinations. This trial is registered with clinicaltrials.gov (identifier: NCT00564096).

  11. Effectiveness of deep cleaning followed by hydrogen peroxide decontamination during high Clostridium difficile infection incidence.

    Science.gov (United States)

    Best, E L; Parnell, P; Thirkell, G; Verity, P; Copland, M; Else, P; Denton, M; Hobson, R P; Wilcox, M H

    2014-05-01

    Clostridium difficile infection (CDI) remains an infection control challenge, especially when environmental spore contamination and suboptimal cleaning may increase transmission risk. To substantiate the long-term effectiveness throughout a stroke rehabilitation unit (SRU) of deep cleaning and hydrogen peroxide decontamination (HPD), following a high incidence of CDI. Extensive environmental sampling (342 sites on each occasion) for C. difficile using sponge wipes was performed: before and after deep cleaning with detergent/chlorine agent; immediately following HPD; and on two further occasions, 19 days and 20 weeks following HPD. C. difficile isolates underwent polymerase chain reaction ribotyping and multi-locus variable repeat analysis (MLVA). C. difficile was recovered from 10.8%, 6.1%, 0.9%, 0% and 3.5% of sites at baseline, following deep cleaning, immediately after HPD, and 19 days and 20 weeks after HPD, respectively. C. difficile ribotypes recovered after deep cleaning matched those from CDI cases in the SRU during the previous 10 months. Similarly, 10/12 of the positive sites identified at 20 weeks post-HPD harboured the same C. difficile ribotype (002) and MLVA pattern as the isolate from the first post-HPD CDI case. CDI incidence [number of cases on SRU per 10 months (January-October 2011)] declined from 20 before to seven after the intervention. HPD, after deep cleaning with a detergent/chlorine agent, was highly effective for removing environmental C. difficile contamination. Long-term follow-up demonstrated that a CDI symptomatic patient can rapidly recontaminate the immediate environment. Determining a role for HPD should include long-term cost-effectiveness evaluations. Copyright © 2014 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

  12. Deep Complementary Bottleneck Features for Visual Speech Recognition

    NARCIS (Netherlands)

    Petridis, Stavros; Pantic, Maja

    Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

  13. Producing deep-water hydrocarbons

    International Nuclear Information System (INIS)

    Pilenko, Thierry

    2011-01-01

    Several studies relate the history and progress made in offshore production from oil and gas fields in relation to reserves and the techniques for producing oil offshore. The intention herein is not to review these studies but rather to argue that the activities of prospecting and producing deep-water oil and gas call for a combination of technology and project management and, above all, of devotion and innovation. Without this sense of commitment motivating men and women in this industry, the human adventure of deep-water production would never have taken place

  14. Deep venous thrombosis after saphenous endovenous radiofrequency ablation: is it predictable?

    Science.gov (United States)

    Jacobs, Chad E; Pinzon, Maria Mora; Orozco, Jennifer; Hunt, Peter J B; Rivera, Aksim; McCarthy, Walter J

    2014-04-01

    Endovenous radiofrequency ablation (RFA) is a safe and effective treatment for varicose veins caused by saphenous reflux. Deep venous thrombosis (DVT) is a known complication of this procedure. The purpose of this study is to describe the frequency of DVT after RFA and the associated predisposing factors. A retrospective analysis was performed using prospectively collected data from December 2008 to December 2011; a total of 277 consecutive office-based RFA procedures were performed at a single institution using the VNUS ClosureFast catheter (VNUS Medical Technologies, San Jose, CA). Duplex ultrasonography scans were completed 2 weeks postprocedure in all patients. Risk factors assessed for the development of DVT included: great versus small saphenous vein (SSV) treated, right versus left side treated, number of radiofrequency cycles used, hypercoagulable state, history of DVT, tobacco use, medications (i.e., oral contraceptives, aspirin, warfarin, and clopidogrel), and vein diameter at the junction of the superficial and deep systems. Seventy-two percent of the patients were women, 56% were treated on the right side, and 86% were performed on the great saphenous vein (GSV). The mean age was 54 ± 14 years (range: 23-88 years). Three percent of patients had a preprocedure diagnosis of hypercoagulable state, and 8% had a history of previous DVT. On postprocedural ultrasound, thrombus protrusion into the deep system without occlusion (endovenous heat-induced thrombosis) was present in 11 patients (4%). DVT, as defined by thrombus protrusion with complete occlusion of the femoral or popliteal vein, was identified in 2 patients (0.7%). Previous DVT was the only factor associated with postprocedural DVT (P = 0.018). Although not statistically significant, there was a trend toward a higher risk of DVT in SSV-treated patients. Factors associated with endovascular heat-induced thrombosis alone were male sex (P = 0.02), SSV treatment (P = 0.05), aspirin use (P = 0.008), and

  15. Deep inelastic processes and the parton model

    International Nuclear Information System (INIS)

    Altarelli, G.

    The lecture was intended as an elementary introduction to the physics of deep inelastic phenomena from the point of view of theory. General formulae and facts concerning inclusive deep inelastic processes in the form: l+N→l'+hadrons (electroproduction, neutrino scattering) are first recalled. The deep inelastic annihilation e + e - →hadrons is then envisaged. The light cone approach, the parton model and their relation are mainly emphasized

  16. Life Support for Deep Space and Mars

    Science.gov (United States)

    Jones, Harry W.; Hodgson, Edward W.; Kliss, Mark H.

    2014-01-01

    How should life support for deep space be developed? The International Space Station (ISS) life support system is the operational result of many decades of research and development. Long duration deep space missions such as Mars have been expected to use matured and upgraded versions of ISS life support. Deep space life support must use the knowledge base incorporated in ISS but it must also meet much more difficult requirements. The primary new requirement is that life support in deep space must be considerably more reliable than on ISS or anywhere in the Earth-Moon system, where emergency resupply and a quick return are possible. Due to the great distance from Earth and the long duration of deep space missions, if life support systems fail, the traditional approaches for emergency supply of oxygen and water, emergency supply of parts, and crew return to Earth or escape to a safe haven are likely infeasible. The Orbital Replacement Unit (ORU) maintenance approach used by ISS is unsuitable for deep space with ORU's as large and complex as those originally provided in ISS designs because it minimizes opportunities for commonality of spares, requires replacement of many functional parts with each failure, and results in substantial launch mass and volume penalties. It has become impractical even for ISS after the shuttle era, resulting in the need for ad hoc repair activity at lower assembly levels with consequent crew time penalties and extended repair timelines. Less complex, more robust technical approaches may be needed to meet the difficult deep space requirements for reliability, maintainability, and reparability. Developing an entirely new life support system would neglect what has been achieved. The suggested approach is use the ISS life support technologies as a platform to build on and to continue to improve ISS subsystems while also developing new subsystems where needed to meet deep space requirements.

  17. Like Father, Like Daughter-inherited cutis aplasia occurring in a family with Marfan syndrome: a case report.

    Science.gov (United States)

    Islam, Yasmin Florence Khodeja; Williams, Charles A; Schoch, Jennifer Jane; Andrews, Israel David

    2017-01-01

    We present the case of a newborn with co-occurrence of Marfan syndrome and aplasia cutis congenita (ACC) and a family history significant for Marfan syndrome and ACC in the father. This case details a previously unreported mutation in Marfan syndrome and describes a novel coinheritance of Marfan syndrome and ACC.

  18. Sudden oak death: disease trends in Marin county plots after one year

    Science.gov (United States)

    Brice A. McPherson; David L. Wood; Andrew J. Storer; Nina Maggi Kelly; Richard B. Standiford

    2002-01-01

    Sudden oak death has emerged as a major threat to the oak forests of California. In oaks and tanoak, this disease complex consists of a previously unreported fungus-like pathogen, Phytophthora ramorum, insects (bark and ambrosia beetles), and a secondary fungus, Hypoxylon thouarsianum. Species monitored in this study were coast...

  19. The use of alfaxalone and remifentanil total intravenous anesthesia in a dog undergoing a craniectomy for tumor resection.

    Science.gov (United States)

    Warne, Leon N; Beths, Thierry; Fogal, Sandra; Bauquier, Sébastien H

    2014-11-01

    A 7-year-old castrated border collie dog was anesthetised for surgical resection of a hippocampal mass. Anesthesia was maintained using a previously unreported TIVA protocol for craniectomy consisting of alfaxalone and remifentanil. Recovery was uneventful, and the patient was discharged from hospital. We describe the anesthetic management of this case.

  20. Deep Invasive Fungal Infection of the Hand in a Child Mimicking a Local Gigantism.

    Science.gov (United States)

    Chatterjee, Anirban; Chatterjee, Shamita

    2018-04-01

    Subcutaneous and deep fungal infections in the hand are rare among children. These are usually found in immunocompromised adults or in persons engaged in soil handling activities, due to direct exposure, especially in the tropics. Delay in diagnosis is usual because pyogenic and other granulomatous infections are considered first. The authors present the case of a healthy, immunocompetent 2½-year-old child who presented with progressive swelling of the right hand mimicking a localized gigantism of the entire hand. Multiple operative drainage procedures done previously had failed to resolve the condition. A biopsy established the presence of fungal hyphae, thus confirming the diagnosis of deep fungal infection of the hand and guided proper therapeutic intervention. A strong index of suspicion needs to be maintained in cases not responding to conventional antibacterial therapy, and both microbiologic and histopathologic samples need to be obtained to establish the diagnosis.

  1. Automatic Segmentation and Deep Learning of Bird Sounds

    NARCIS (Netherlands)

    Koops, Hendrik Vincent; Van Balen, J.M.H.; Wiering, F.

    2015-01-01

    We present a study on automatic birdsong recognition with deep neural networks using the BIRDCLEF2014 dataset. Through deep learning, feature hierarchies are learned that represent the data on several levels of abstraction. Deep learning has been applied with success to problems in fields such as

  2. Deep Learning: A Primer for Radiologists.

    Science.gov (United States)

    Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

    2017-01-01

    Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

  3. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    Science.gov (United States)

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-11

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

  4. Stratification-Based Outlier Detection over the Deep Web.

    Science.gov (United States)

    Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

    2016-01-01

    For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

  5. Deep Learning and Bayesian Methods

    Directory of Open Access Journals (Sweden)

    Prosper Harrison B.

    2017-01-01

    Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.

  6. Eric Davidson and deep time.

    Science.gov (United States)

    Erwin, Douglas H

    2017-10-13

    Eric Davidson had a deep and abiding interest in the role developmental mechanisms played in generating evolutionary patterns documented in deep time, from the origin of the euechinoids to the processes responsible for the morphological architectures of major animal clades. Although not an evolutionary biologist, Davidson's interests long preceded the current excitement over comparative evolutionary developmental biology. Here I discuss three aspects at the intersection between his research and evolutionary patterns in deep time: First, understanding the mechanisms of body plan formation, particularly those associated with the early diversification of major metazoan clades. Second, a critique of early claims about ancestral metazoans based on the discoveries of highly conserved genes across bilaterian animals. Third, Davidson's own involvement in paleontology through a collaborative study of the fossil embryos from the Ediacaran Doushantuo Formation in south China.

  7. EMPIRICAL PREDICTIONS FOR (SUB-)MILLIMETER LINE AND CONTINUUM DEEP FIELDS

    Energy Technology Data Exchange (ETDEWEB)

    Da Cunha, Elisabete; Walter, Fabian; Decarli, Roberto; Rix, Hans-Walter [Max-Planck-Institut fuer Astronomie, Koenigstuhl 17, D-69117 Heidelberg (Germany); Bertoldi, Frank [Argelander Institute for Astronomy, University of Bonn, Auf dem Huegel 71, D-53121 Bonn (Germany); Carilli, Chris [National Radio Astronomy Observatory, Pete V. Domenici Array Science Center, P.O. Box O, Socorro, NM 87801 (United States); Daddi, Emanuele; Elbaz, David; Sargent, Mark [Laboratoire AIM, CEA/DSM-CNRS-Universite Paris Diderot, Irfu/Service d' Astrophysique, CEA Saclay, Orme des Merisiers, F-91191 Gif-sur-Yvette Cedex (France); Ivison, Rob [UK Astronomy Technology Centre, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ (United Kingdom); Maiolino, Roberto [Cavendish Laboratory, University of Cambridge, 19 J.J. Thomson Avenue, Cambridge CB3 0HE (United Kingdom); Riechers, Dominik [Astronomy Department, California Institute of Technology, MC 249-17, 1200 East California Boulevard, Pasadena, CA 91125 (United States); Smail, Ian [Institute for Computational Cosmology, Durham University, South Road, Durham DH1 3LE (United Kingdom); Weiss, Axel, E-mail: cunha@mpia.de [Max-Planck-Institut fuer Radioastronomie, Auf dem Huegel 69, D-53121 Bonn (Germany)

    2013-03-01

    Modern (sub-)millimeter/radio interferometers such as ALMA, JVLA, and the PdBI successor NOEMA will enable us to measure the dust and molecular gas emission from galaxies that have luminosities lower than the Milky Way, out to high redshifts and with unprecedented spatial resolution and sensitivity. This will provide new constraints on the star formation properties and gas reservoir in galaxies throughout cosmic times through dedicated deep field campaigns targeting the CO/[C II] lines and dust continuum emission in the (sub-)millimeter regime. In this paper, we present empirical predictions for such line and continuum deep fields. We base these predictions on the deepest available optical/near-infrared Advanced Camera for Surveys and NICMOS data on the Hubble Ultra Deep Field (over an area of about 12 arcmin{sup 2}). Using a physically motivated spectral energy distribution model, we fit the observed optical/near-infrared emission of 13,099 galaxies with redshifts up to z = 5, and obtain median-likelihood estimates of their stellar mass, star formation rate, dust attenuation, and dust luminosity. We combine the attenuated stellar spectra with a library of infrared emission models spanning a wide range of dust temperatures to derive statistical constraints on the dust emission in the infrared and (sub-)millimeter which are consistent with the observed optical/near-infrared emission in terms of energy balance. This allows us to estimate, for each galaxy, the (sub-)millimeter continuum flux densities in several ALMA, PdBI/NOEMA, and JVLA bands. As a consistency check, we verify that the 850 {mu}m number counts and extragalactic background light derived using our predictions are consistent with previous observations. Using empirical relations between the observed CO/[C II] line luminosities and the infrared luminosity of star-forming galaxies, we infer the luminosity of the CO(1-0) and [C II] lines from the estimated infrared luminosity of each galaxy in our sample

  8. Secondary omental and pectoralis major double flap reconstruction following aggressive sternectomy for deep sternal wound infections after cardiac surgery

    Directory of Open Access Journals (Sweden)

    Shirasawa Bungo

    2011-04-01

    Full Text Available Abstract Background Deep sternal wound infection after cardiac surgery carries high morbidity and mortality. Our strategy for deep sternal wound infection is aggressive strenal debridement followed by vacuum-assisted closure (VAC therapy and omental-muscle flap reconstrucion. We describe this strategy and examine the outcome and long-term quality of life (QOL it achieves. Methods We retrospectively examined 16 patients treated for deep sternal wound infection between 2001 and 2007. The most recent nine patients were treated with total sternal resection followed by VAC therapy and secondary closure with omental-muscle flap reconstruction (recent group; whereas the former seven patients were treated with sternal preservation if possible, without VAC therapy, and four of these patients underwent primary closure (former group. We assessed long-term quality of life after DSWI by using the Short Form 36-Item Health Survey, Version 2 (SF36v2. Results One patient died and four required further surgery for recurrence of deep sternal wound infection in the former group. The duration of treatment for deep sternal wound infection in the recent group was significantly shorter than that in previous group (63.4 ± 54.1 days vs. 120.0 ± 31.8 days, respectively; p = 0.039. Despite aggressive sternal resection, the QOL of patients treated for DSWI was only minimally compromised compared with age-, sex-, surgical procedures-matched patients without deep sternal wound infection. Conclusions Aggressive sternal debridement followed by VAC therapy and secondary closure with an omental-muscle flap is effective for deep sternal wound infection. In this series, it resulted in a lower incidence of recurrent infection, shorter hospitalization, and it did not compromise long-term QOL greatly.

  9. A Preliminary Assessment of a Deep Borehole disposal of Spent Fuels

    International Nuclear Information System (INIS)

    Lee, Younmyoung; Jeon, Jongtae

    2014-01-01

    Deep borehole disposal (DBD) of such radioactive waste as spent nuclear fuels (SFs) and other waste forms has been investigating mainly at Sandia National Labs for the US DOE as an alternative option. DBD can give advantages over less deep geological disposal since the disposal of wastes at a great depth where a low degree of permeability in the potentially steady rock condition will be beneficial for nuclide movement. Groundwater in the deep basement rock can even have salinity and less chance to mix with groundwater above. The DBD concept is quite straightforward and even simple: Waste canisters are simply emplaced in the lower 2 km part of the borehole down to 5 km deep. Through this study, a conceptual DBD is assessed for a similar case as the US DOE's approach, in which 400 SF canisters are to be emplaced at a deep bottom between 3km and 5km depths, upon which an additional 1km-thick compacted bentonite is overbuffered, and the remaining upper part of the borehole is backfilled again with a mixture of crushed rock and bentonite. Then, the total 5km-deep borehole has three zones: a disposal zone at the bottom 2km, a buffer zone at the next 1km, and backfill zone at the rest top 2km, as illustrated conceptually in Fig. 1. To demonstrate the feasibility in view of long-term radiological safety, a rough model for a safety assessment of this conceptual deep borehole repository system, providing detailed models for nuclide transport in and around the geosphere and biosphere under normal nuclide release scenarios that can occur after a closure of the repository, has been developed using GoldSim. A simple preliminary result in terms of the dose exposure rate from a safety assessment of the DBD is also presented and compared to the case of direct disposal of SFs in a KBS-3V vertical type repository, carried out in previous studies. For different types and shapes of repositories at each different depth, direct comparison between a DBD and a KBS-3 type disposal of

  10. A Preliminary Assessment of a Deep Borehole disposal of Spent Fuels

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Younmyoung; Jeon, Jongtae [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2014-05-15

    Deep borehole disposal (DBD) of such radioactive waste as spent nuclear fuels (SFs) and other waste forms has been investigating mainly at Sandia National Labs for the US DOE as an alternative option. DBD can give advantages over less deep geological disposal since the disposal of wastes at a great depth where a low degree of permeability in the potentially steady rock condition will be beneficial for nuclide movement. Groundwater in the deep basement rock can even have salinity and less chance to mix with groundwater above. The DBD concept is quite straightforward and even simple: Waste canisters are simply emplaced in the lower 2 km part of the borehole down to 5 km deep. Through this study, a conceptual DBD is assessed for a similar case as the US DOE's approach, in which 400 SF canisters are to be emplaced at a deep bottom between 3km and 5km depths, upon which an additional 1km-thick compacted bentonite is overbuffered, and the remaining upper part of the borehole is backfilled again with a mixture of crushed rock and bentonite. Then, the total 5km-deep borehole has three zones: a disposal zone at the bottom 2km, a buffer zone at the next 1km, and backfill zone at the rest top 2km, as illustrated conceptually in Fig. 1. To demonstrate the feasibility in view of long-term radiological safety, a rough model for a safety assessment of this conceptual deep borehole repository system, providing detailed models for nuclide transport in and around the geosphere and biosphere under normal nuclide release scenarios that can occur after a closure of the repository, has been developed using GoldSim. A simple preliminary result in terms of the dose exposure rate from a safety assessment of the DBD is also presented and compared to the case of direct disposal of SFs in a KBS-3V vertical type repository, carried out in previous studies. For different types and shapes of repositories at each different depth, direct comparison between a DBD and a KBS-3 type disposal of

  11. DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

    Directory of Open Access Journals (Sweden)

    Peter Christiansen

    2016-11-01

    Full Text Available Convolutional neural network (CNN-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN. In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m than RCNN. RCNN has a similar performance at a short range (0–30 m. However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms = a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit.

  12. Homoacetogenesis in Deep-Sea Chloroflexi, as Inferred by Single-Cell Genomics, Provides a Link to Reductive Dehalogenation in Terrestrial Dehalococcoidetes.

    Science.gov (United States)

    Sewell, Holly L; Kaster, Anne-Kristin; Spormann, Alfred M

    2017-12-19

    The deep marine subsurface is one of the largest unexplored biospheres on Earth and is widely inhabited by members of the phylum Chloroflexi In this report, we investigated genomes of single cells obtained from deep-sea sediments of the Peruvian Margin, which are enriched in such Chloroflexi 16S rRNA gene sequence analysis placed two of these single-cell-derived genomes (DscP3 and Dsc4) in a clade of subphylum I Chloroflexi which were previously recovered from deep-sea sediment in the Okinawa Trough and a third (DscP2-2) as a member of the previously reported DscP2 population from Peruvian Margin site 1230. The presence of genes encoding enzymes of a complete Wood-Ljungdahl pathway, glycolysis/gluconeogenesis, a Rhodobacter nitrogen fixation (Rnf) complex, glyosyltransferases, and formate dehydrogenases in the single-cell genomes of DscP3 and Dsc4 and the presence of an NADH-dependent reduced ferredoxin:NADP oxidoreductase (Nfn) and Rnf in the genome of DscP2-2 imply a homoacetogenic lifestyle of these abundant marine Chloroflexi We also report here the first complete pathway for anaerobic benzoate oxidation to acetyl coenzyme A (CoA) in the phylum Chloroflexi (DscP3 and Dsc4), including a class I benzoyl-CoA reductase. Of remarkable evolutionary significance, we discovered a gene encoding a formate dehydrogenase (FdnI) with reciprocal closest identity to the formate dehydrogenase-like protein (complex iron-sulfur molybdoenzyme [CISM], DET0187) of terrestrial Dehalococcoides/Dehalogenimonas spp. This formate dehydrogenase-like protein has been shown to lack formate dehydrogenase activity in Dehalococcoides/Dehalogenimonas spp. and is instead hypothesized to couple HupL hydrogenase to a reductive dehalogenase in the catabolic reductive dehalogenation pathway. This finding of a close functional homologue provides an important missing link for understanding the origin and the metabolic core of terrestrial Dehalococcoides/Dehalogenimonas spp. and of reductive

  13. Deep levels in as-grown and electron-irradiated n-type GaN studied by deep level transient spectroscopy and minority carrier transient spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Duc, Tran Thien [Department of Physics, Chemistry and Biology (IFM), Linköping University, S-581 83 Linköping (Sweden); School of Engineering Physics, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi (Viet Nam); Pozina, Galia; Son, Nguyen Tien; Kordina, Olof; Janzén, Erik; Hemmingsson, Carl [Department of Physics, Chemistry and Biology (IFM), Linköping University, S-581 83 Linköping (Sweden); Ohshima, Takeshi [Japan Atomic Energy Agency (JAEA), Takasaki, Gunma 370-1292 (Japan)

    2016-03-07

    Development of high performance GaN-based devices is strongly dependent on the possibility to control and understand defects in material. Important information about deep level defects is obtained by deep level transient spectroscopy and minority carrier transient spectroscopy on as-grown and electron irradiated n-type bulk GaN with low threading dislocation density produced by halide vapor phase epitaxy. One hole trap labelled H1 (E{sub V} + 0.34 eV) has been detected on as-grown GaN sample. After 2 MeV electron irradiation, the concentration of H1 increases and at fluences higher than 5 × 10{sup 14 }cm{sup −2}, a second hole trap labelled H2 is observed. Simultaneously, the concentration of two electron traps, labelled T1 (E{sub C} – 0.12 eV) and T2 (E{sub C} – 0.23 eV), increases. By studying the increase of the defect concentration versus electron irradiation fluence, the introduction rate of T1 and T2 using 2 MeV- electrons was determined to be 7 × 10{sup −3 }cm{sup −1} and 0.9 cm{sup −1}, respectively. Due to the low introduction rate of T1, it is suggested that the defect is associated with a complex. The high introduction rate of trap H1 and T2 suggests that the defects are associated with primary intrinsic defects or complexes. Some deep levels previously observed in irradiated GaN layers with higher threading dislocation densities are not detected in present investigation. It is therefore suggested that the absent traps may be related to primary defects segregated around dislocations.

  14. A VERY DEEP CHANDRA OBSERVATION OF A2052: BUBBLES, SHOCKS, AND SLOSHING

    International Nuclear Information System (INIS)

    Blanton, E. L.; Douglass, E. M.; Randall, S. W.; McNamara, B. R.; Clarke, T. E.; Sarazin, C. L.; McDonald, M.

    2011-01-01

    We present the first results from a very deep (∼650 ks) Chandra X-ray observation of A2052, as well as archival Very Large Array radio observations. The data reveal detailed structure in the inner parts of the cluster, including bubbles evacuated by radio lobes of the active galactic nucleus (AGN), compressed bubble rims, filaments, and loops. Two concentric shocks are seen, and a temperature rise is measured for the innermost one. On larger scales, we report the first detection of an excess surface brightness spiral feature. The spiral has cooler temperatures, lower entropies, and higher abundances than its surroundings, and is likely the result of sloshing gas initiated by a previous cluster-cluster or sub-cluster merger. Initial evidence for previously unseen bubbles at larger radii related to earlier outbursts from the AGN is presented.

  15. Learning Transferable Features with Deep Adaptation Networks

    OpenAIRE

    Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

    2015-01-01

    Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

  16. Theory of deep inelastic lepton-hadron scattering

    International Nuclear Information System (INIS)

    Geyer, B.; Robaschik, D.; Wieczorek, E.

    1979-01-01

    The description of deep inelastic lepton-nucleon scattering in the lowest order of the electromagnetic and weak coupling constants leads to a study of virtual Compton amplitudes and their absorptive parts. Some aspects of quantum chromodynamics are discussed. Deep inelastic scattering enables a central quantity of quantum field theory, namely the light cone behaviour of the current commutator. The moments of structure functions are used for the description of deep inelastic scattering. (author)

  17. DeepQA: Improving the estimation of single protein model quality with deep belief networks

    OpenAIRE

    Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

    2016-01-01

    Background Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. Results We introduce a novel single-model quality assessment method DeepQA based on deep belie...

  18. DeepDive: Declarative Knowledge Base Construction.

    Science.gov (United States)

    De Sa, Christopher; Ratner, Alex; Ré, Christopher; Shin, Jaeho; Wang, Feiran; Wu, Sen; Zhang, Ce

    2016-03-01

    The dark data extraction or knowledge base construction (KBC) problem is to populate a SQL database with information from unstructured data sources including emails, webpages, and pdf reports. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems. The key idea in DeepDive is that statistical inference and machine learning are key tools to attack classical data problems in extraction, cleaning, and integration in a unified and more effective manner. DeepDive programs are declarative in that one cannot write probabilistic inference algorithms; instead, one interacts by defining features or rules about the domain. A key reason for this design choice is to enable domain experts to build their own KBC systems. We present the applications, abstractions, and techniques of DeepDive employed to accelerate construction of KBC systems.

  19. Pathways to deep decarbonization - 2015 report

    International Nuclear Information System (INIS)

    Ribera, Teresa; Colombier, Michel; Waisman, Henri; Bataille, Chris; Pierfederici, Roberta; Sachs, Jeffrey; Schmidt-Traub, Guido; Williams, Jim; Segafredo, Laura; Hamburg Coplan, Jill; Pharabod, Ivan; Oury, Christian

    2015-12-01

    In September 2015, the Deep Decarbonization Pathways Project published the Executive Summary of the Pathways to Deep Decarbonization: 2015 Synthesis Report. The full 2015 Synthesis Report was launched in Paris on December 3, 2015, at a technical workshop with the Mitigation Action Plans and Scenarios (MAPS) program. The Deep Decarbonization Pathways Project (DDPP) is a collaborative initiative to understand and show how individual countries can transition to a low-carbon economy and how the world can meet the internationally agreed target of limiting the increase in global mean surface temperature to less than 2 degrees Celsius (deg. C). Achieving the 2 deg. C limit will require that global net emissions of greenhouse gases (GHG) approach zero by the second half of the century. In turn, this will require a profound transformation of energy systems by mid-century through steep declines in carbon intensity in all sectors of the economy, a transition we call 'deep decarbonization'

  20. Vertical structure, biomass and topographic association of deep-pelagic fishes in relation to a mid-ocean ridge system

    Science.gov (United States)

    Sutton, T. T.; Porteiro, F. M.; Heino, M.; Byrkjedal, I.; Langhelle, G.; Anderson, C. I. H.; Horne, J.; Søiland, H.; Falkenhaug, T.; Godø, O. R.; Bergstad, O. A.

    2008-01-01

    The assemblage structure and vertical distribution of deep-pelagic fishes relative to a mid-ocean ridge system are described from an acoustic and discrete-depth trawling survey conducted as part of the international Census of Marine Life field project MAR-ECO . The 36-station, zig-zag survey along the northern Mid-Atlantic Ridge (MAR; Iceland to the Azores) covered the full depth range (0 to >3000 m), from the surface to near the bottom, using a combination of gear types to gain a more comprehensive understanding of the pelagic fauna. Abundance per volume of deep-pelagic fishes was highest in the epipelagic zone and within the benthic boundary layer (BBL; 0-200 m above the seafloor). Minimum fish abundance occurred at depths below 2300 m but above the BBL. Biomass per volume of deep-pelagic fishes over the MAR reached a maximum within the BBL, revealing a previously unknown topographic association of a bathypelagic fish assemblage with a mid-ocean ridge system. With the exception of the BBL, biomass per volume reached a water column maximum in the bathypelagic zone between 1500 and 2300 m. This stands in stark contrast to the general "open-ocean" paradigm that biomass decreases exponentially from the surface downwards. As much of the summit of the MAR extends into this depth layer, a likely explanation for this mid-water maximum is ridge association. Multivariate statistical analyses suggest that the dominant component of deep-pelagic fish biomass over the northern MAR was a wide-ranging bathypelagic assemblage that was remarkably consistent along the length of the ridge from Iceland to the Azores. Integrating these results with those of previous studies in oceanic ecosystems, there appears to be adequate evidence to conclude that special hydrodynamic and biotic features of mid-ocean ridge systems cause changes in the ecological structure of deep-pelagic fish assemblages relative to those at the same depths over abyssal plains. Lacking terrigenous input of

  1. WHATS-3: An improved flow-through multi-bottle fluid sampler for deep-sea geofluid research

    Science.gov (United States)

    Miyazaki, Junichi; Makabe, Akiko; Matsui, Yohei; Ebina, Naoya; Tsutsumi, Saki; Ishibashi, Jun-ichiro; Chen, Chong; Kaneko, Sho; Takai, Ken; Kawagucci, Shinsuke

    2017-06-01

    Deep-sea geofluid systems, such as hydrothermal vents and cold seeps, are key to understanding subseafloor environments of Earth. Fluid chemistry, especially, provides crucial information towards elucidating the physical, chemical and biological processes that occur in these ecosystems. To accurately assess fluid and gas properties of deep-sea geofluids, well-designed pressure-tight fluid samplers are indispensable and as such they are important assets of deep-sea geofluid research. Here, the development of a new flow-through, pressure-tight fluid sampler capable of four independent sampling events (two subsamples for liquid and gas analyses from each) is reported. This new sampler, named WHATS-3, is a new addition to the WHATS-series samplers and a major upgrade from the previous WHATS-2 sampler with improvements in sample number, valve operational time, physical robustness, and ease of maintenance. Routine laboratory-based pressure tests proved that it is suitable for operation up to 35 MPa pressure. Successful field tests of the new sampler were also carried out in five hydrothermal fields, two in Indian Ocean and three in Okinawa Trough (max. depth 3,300 m). Relations of Mg and major ion species demonstrated bimodal mixing trends between a hydrothermal fluid and seawater, confirming the high-quality of fluids sampled. The newly developed WHATS-3 sampler is well-balanced in sampling capability, field usability, and maintenance feasibility, and can serve as one of the best geofluid samplers available at present to conduct efficient research of deep-sea geofluid systems.

  2. In-situ formation compaction monitoring in deep reservoirs by use of fiber optics

    Directory of Open Access Journals (Sweden)

    H. Ikeda

    2015-11-01

    Full Text Available We have devised a new in situ monitoring method for the amount of stratified compaction in borehole drilled several hundred meters underground. This newly developed epoch-making monitoring system differs from conventional monitoring methods for land subsidence in that it is designed to continuously monitor the amounts of displacement in several intervals separately, using optical fibers fitted in the sensor assembly. This report presents results from a deep observation well. This is a continued report from the previous one on EISOLS 2010.

  3. Calcium hydroxyapatite crystal deposition with intraosseous penetration involving the posterior aspect of the cervical spine: a previously unreported cause of neck pain.

    Science.gov (United States)

    Urrutia, Julio; Contreras, Oscar

    2017-05-01

    Calcific tendinitis is a frequent disorder caused by hydroxyapatite crystal deposition; however, bone erosions from calcific tendinitis are unusual. The spinal manifestation of this disease is calcific tendinitis of the longus colli muscle; this disease has never been described in the posterior aspect of the spine. We report a case of calcium hydroxyapatite crystal deposition involving the posterior cervical spine eroding the bone cortex. A 57-year-old woman presented with a 5-month history of left-sided neck pain. Radiographs showed C4-C5 interspinous calcification with lytic compromise of the posterior arch of C4. Magnetic resonance imaging confirmed a lytic lesion of the posterior arch of C4, with a soft tissue mass extending to the C4-C5 interspinous space; calcifications were observed as very low signal intensity areas on T1 and T2 sequences, surrounded by gadolinium-enhanced soft tissues. A computed tomography (CT) scan confirmed the bone erosions and the soft tissue calcifications. A CT-guided needle biopsy was performed; it showed vascularized connective tissue with inflammatory histiocytic infiltration and multinucleated giant cells; Alizarin Red stain confirmed the presence of hydroxyapatite crystals. The patient was treated with anti-inflammatories for 2 weeks. She has been asymptomatic in a 6-month follow-up; a CT scan at the last follow-up revealed reparative remodeling of bone erosions. This is the first report of calcium hydroxyapatite crystal deposition with intraosseous penetration involving the posterior aspect of the cervical spine. Considering that this unusual lesion can be misinterpreted as a tumor or infection, high suspicion is required to avoid unnecessary surgical procedures.

  4. Molecular diagnosis of a previously unreported predator-prey association in coffee: Karnyothrips flavipes Jones (Thysanoptera: Phlaeothripidae) predation on the coffee berry borer

    Science.gov (United States)

    Jaramillo, Juliana; Chapman, Eric G.; Vega, Fernando E.; Harwood, James D.

    2010-03-01

    The coffee berry borer, Hypothenemus hampei, is the most important pest of coffee throughout the world, causing losses estimated at US 500 million/year. The thrips Karnyothrips flavipes was observed for the first time feeding on immature stages of H. hampei in April 2008 from samples collected in the Kisii area of Western Kenya. Since the trophic interactions between H. hampei and K. flavipes are carried out entirely within the coffee berry, and because thrips feed by liquid ingestion, we used molecular gut-content analysis to confirm the potential role of K. flavipes as a predator of H. hampei in an organic coffee production system. Species-specific COI primers designed for H. hampei were shown to have a high degree of specificity for H. hampei DNA and did not produce any PCR product from DNA templates of the other insects associated with the coffee agroecosystems. In total, 3,327 K. flavipes emerged from 17,792 H. hampei-infested berries collected from the field between April and September 2008. Throughout the season, 8.3% of K. flavipes tested positive for H. hampei DNA, although at times this figure approached 50%. Prey availability was significantly correlated with prey consumption, thus indicating the potential impact on H. hampei populations.

  5. Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection

    Directory of Open Access Journals (Sweden)

    Yi-Shan Lin

    2017-01-01

    Full Text Available Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU or the graphic processing unit (GPU were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA. In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method.

  6. Text feature extraction based on deep learning: a review.

    Science.gov (United States)

    Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

    2017-01-01

    Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

  7. Deep-seated sarcomas of the penis

    Directory of Open Access Journals (Sweden)

    Alberto A. Antunes

    2005-06-01

    Full Text Available Mesenchymal neoplasias represent 5% of tumors affecting the penis. Due to the rarity of such tumors, there is no agreement concerning the best method for staging and managing these patients. Sarcomas of the penis can be classified as deep-seated if they derive from the structures forming the spongy body and the cavernous bodies. Superficial lesions are usually low-grade and show a small tendency towards distant metastasis. In contrast, deep-seated lesions usually show behavior that is more aggressive and have poorer prognosis. The authors report 3 cases of deep-seated primary sarcomas of the penis and review the literature on this rare and aggressive neoplasia.

  8. A Case Study of Deep Vein Thrombosis of the Right Internal Jugular Vein in a Healthy 21-Year-Old Male

    Directory of Open Access Journals (Sweden)

    Javier Corral

    2016-01-01

    Full Text Available We are reporting a case of a healthy 21-year-old male, with no significant past medical history, who was found to have an incidental nonocclusive deep vein thrombosis in the right internal jugular vein detected on a head MRI previously ordered for work-up of headaches. A follow-up upper extremity venous Doppler ultrasound confirmed the presence of a partially occlusive deep vein thrombosis in the right jugular vein. The case presented is unique for the reason that the patient is young and has no prior risk factor, personal or familial, for venous thrombosis except for associated polycythemia on clinical presentation.

  9. In Brief: Deep-sea observatory

    Science.gov (United States)

    Showstack, Randy

    2008-11-01

    The first deep-sea ocean observatory offshore of the continental United States has begun operating in the waters off central California. The remotely operated Monterey Accelerated Research System (MARS) will allow scientists to monitor the deep sea continuously. Among the first devices to be hooked up to the observatory are instruments to monitor earthquakes, videotape deep-sea animals, and study the effects of acidification on seafloor animals. ``Some day we may look back at the first packets of data streaming in from the MARS observatory as the equivalent of those first words spoken by Alexander Graham Bell: `Watson, come here, I need you!','' commented Marcia McNutt, president and CEO of the Monterey Bay Aquarium Research Institute, which coordinated construction of the observatory. For more information, see http://www.mbari.org/news/news_releases/2008/mars-live/mars-live.html.

  10. Eccentricity from transit photometry

    DEFF Research Database (Denmark)

    Van Eylen, Vincent; Albrecht, Simon

    2015-01-01

    and can be described by a Rayleigh distribution with $\\sigma$ = 0.049 $\\pm$ 0.013. This is in full agreement with solar system eccentricities, but in contrast to the eccentricity distributions previously derived for exoplanets from radial velocity studies. Our findings are helpful in identifying which...... (TTVs), and we present some previously unreported TTVs. Finally transit durations help distinguish between false positives and true planets and we use our measurements to confirm six new exoplanets....

  11. Landform partitioning and estimates of deep storage of soil organic matter in Zackenberg, Greenland

    Directory of Open Access Journals (Sweden)

    J. Palmtag

    2018-05-01

    Full Text Available Soils in the northern high latitudes are a key component in the global carbon cycle, with potential feedback on climate. This study aims to improve the previous soil organic carbon (SOC and total nitrogen (TN storage estimates for the Zackenberg area (NE Greenland that were based on a land cover classification (LCC approach, by using geomorphological upscaling. In addition, novel organic carbon (OC estimates for deeper alluvial and deltaic deposits (down to 300 cm depth are presented. We hypothesise that landforms will better represent the long-term slope and depositional processes that result in deep SOC burial in this type of mountain permafrost environments. The updated mean SOC storage for the 0–100 cm soil depth is 4.8 kg C m−2, which is 42 % lower than the previous estimate of 8.3 kg C m−2 based on land cover upscaling. Similarly, the mean soil TN storage in the 0–100 cm depth decreased with 44 % from 0.50 kg (± 0.1 CI to 0.28 (±0.1 CI kg TN m−2. We ascribe the differences to a previous areal overestimate of SOC- and TN-rich vegetated land cover classes. The landform-based approach more correctly constrains the depositional areas in alluvial fans and deltas with high SOC and TN storage. These are also areas of deep carbon storage with an additional 2.4 kg C m−2 in the 100–300 cm depth interval. This research emphasises the need to consider geomorphology when assessing SOC pools in mountain permafrost landscapes.

  12. Is deep dreaming the new collage?

    Science.gov (United States)

    Boden, Margaret A.

    2017-10-01

    Deep dreaming (DD) can combine and transform images in surprising ways. But, being based in deep learning (DL), it is not analytically understood. Collage is an art form that is constrained along various dimensions. DD will not be able to generate collages until DL can be guided in a disciplined fashion.

  13. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2018-02-01

    Full Text Available Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples. Therefore, a presentation attack detection (PAD method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP, local ternary pattern (LTP, and histogram of oriented gradients (HOG. As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN method to extract deep image features and the multi-level local binary pattern (MLBP method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

  14. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.

    Science.gov (United States)

    Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

    2018-02-26

    Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

  15. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

    Science.gov (United States)

    Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

    2018-01-01

    Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. PMID:29495417

  16. Late prostatic metastasis of an uveal melanoma in a miniature Schnauzer dog.

    Science.gov (United States)

    Delgado, Esmeralda; Silva, João X; Pissarra, Hugo; Peleteiro, Maria C; Dubielzig, Richard R

    2016-07-01

    This manuscript describes a previously unreported clinical case of canine uveal melanoma in a miniature Schnauzer dog with an unusual location of metastasis (prostate) and delayed occurrence (3 years after primary tumor diagnosis and enucleation). Immunohistochemical labeling of both tumors with Melan A, Ki-67, and c-kit added some valuable information.

  17. Observations of X-ray sources in the Large Magellanic cloud by the OSO-7 satellite

    International Nuclear Information System (INIS)

    Markert, T.H.; Clark, G.W.

    1975-01-01

    Observations of the Large Magellanic Cloud with the 1-40 keV X-ray detectors on the OSO-7 satellite are reported. Results include the discovery of a previously unreported source LMC X-5, measurements of the spectral characteristics of four sources, and observations of their variability on time scales of months

  18. Unique unbalanced X;X translocation (Xq22;p11.2) in a woman with primary amenorrhea but without Ullrich-Turner syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Letterie, G.S. [Virginia Mason Medical Center, Seattle, WA (United States)

    1995-12-04

    This is a report of a patient with delayed puberty and a previously unreported translocation 46,X,-X,+der(X),t(X;X) (q22;p11.2) without any manifestations of Ullrich-Turner syndrome. The relationship of this unbalanced translocation to the critical region hypothesis is discussed. 6 refs., 3 figs.

  19. Splenic irradiation for hairy cell leukaemia

    Energy Technology Data Exchange (ETDEWEB)

    Al-Moundhri, A.; Graham, P.H. [St George Hospital, Kogarah, NSW, (Australia). Department of Radiation Oncology

    1997-11-01

    Splenic irradiation in the management of hairy cell leukaemia is previously unreported. A case is presented here to illustrate that splenic irradiation may be a useful addition to systemic therapies. It achieved local splenic and blood picture response and remission similar to splenectomy without any significant toxicity. (authors). 7 refs., 2 figs.

  20. Density functionals from deep learning

    OpenAIRE

    McMahon, Jeffrey M.

    2016-01-01

    Density-functional theory is a formally exact description of a many-body quantum system in terms of its density; in practice, however, approximations to the universal density functional are required. In this work, a model based on deep learning is developed to approximate this functional. Deep learning allows computational models that are capable of naturally discovering intricate structure in large and/or high-dimensional data sets, with multiple levels of abstraction. As no assumptions are ...

  1. Evaluation of the sustainability of deep groundwater as an arsenic-safe resource in the Bengal Basin

    OpenAIRE

    Michael, Holly A.; Voss, Clifford I.

    2008-01-01

    Tens of millions of people in the Bengal Basin region of Bangladesh and India drink groundwater containing unsafe concentrations of arsenic. This high-arsenic groundwater is produced from shallow (150 m where groundwater arsenic concentrations are nearly uniformly low, and many more wells are needed, however, the sustainability of deep, arsenic-safe groundwater has not been previously assessed. Deeper pumping could induce downward migration of dissolved arsenic, permanently destroying the dee...

  2. Stripped Elliptical Galaxies as Probes of ICM Physics. III. Deep Chandra Observations of NGC 4552: Measuring the Viscosity of the Intracluster Medium

    Energy Technology Data Exchange (ETDEWEB)

    Kraft, R. P.; Roediger, E.; Machacek, M.; Forman, W. R.; Nulsen, P. E. J.; Jones, C.; Randall, S.; Su, Y. [Harvard/Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Churazov, E. [MPI für Astrophysik, Karl-Schwarzschild-Str. 1, Garching D-85741 (Germany); Sheardown, A., E-mail: rkraft@cfa.harvard.edu [E. A. Milne Center for Astrophysics, Department of Physics and Mathematics, University of Hull, Hull, HU6 7RX (United Kingdom)

    2017-10-10

    We present results from a deep (200 ks) Chandra observation of the early-type galaxy NGC 4552 (M89), which is falling into the Virgo cluster. Previous shallower X-ray observations of this galaxy showed a remnant gas core, a tail to the South of the galaxy, and twin “horns” attached to the northern edge of the gas core. In our deeper data, we detect a diffuse, low surface brightness extension to the previously known tail, and measure the temperature structure within the tail. We combine the deep Chandra data with archival XMM-Newton observations to put a strong upper limit on the diffuse emission of the tail out to a large distance (10× the radius of the remnant core) from the galaxy center. In our two previous papers, we presented the results of hydrodynamical simulations of ram pressure stripping specifically for M89 falling into the Virgo cluster and investigated the effect of intracluster medium (ICM) viscosity. In this paper, we compare our deep data with our specifically tailored simulations and conclude that the observed morphology of the stripped tail in NGC 4552 is most similar to the inviscid models. We conclude that, to the extent the transport processes can be simply modeled as a hydrodynamic viscosity, the ICM viscosity is negligible. More generally, any micro-scale description of the transport processes in the high- β plasma of the cluster ICM must be consistent with the efficient mixing observed in the stripped tail on macroscopic scales.

  3. Deep Time Data Infrastructure: Integrating Our Current Geologic and Biologic Databases

    Science.gov (United States)

    Kolankowski, S. M.; Fox, P. A.; Ma, X.; Prabhu, A.

    2016-12-01

    As our knowledge of Earth's geologic and mineralogical history grows, we require more efficient methods of sharing immense amounts of data. Databases across numerous disciplines have been utilized to offer extensive information on very specific Epochs of Earth's history up to its current state, i.e. Fossil record, rock composition, proteins, etc. These databases could be a powerful force in identifying previously unseen correlations such as relationships between minerals and proteins. Creating a unifying site that provides a portal to these databases will aid in our ability as a collaborative scientific community to utilize our findings more effectively. The Deep-Time Data Infrastructure (DTDI) is currently being defined as part of a larger effort to accomplish this goal. DTDI will not be a new database, but an integration of existing resources. Current geologic and related databases were identified, documentation of their schema was established and will be presented as a stage by stage progression. Through conceptual modeling focused around variables from their combined records, we will determine the best way to integrate these databases using common factors. The Deep-Time Data Infrastructure will allow geoscientists to bridge gaps in data and further our understanding of our Earth's history.

  4. A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

    OpenAIRE

    Wang, Haohan; Raj, Bhiksha

    2015-01-01

    This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On t...

  5. Assessment of deep geological environment condition

    International Nuclear Information System (INIS)

    Bae, Dae Seok; Han, Kyung Won; Joen, Kwan Sik

    2003-05-01

    The main tasks of geoscientific study in the 2nd stage was characterized focusing mainly on a near-field condition of deep geologic environment, and aimed to generate the geologic input data for a Korean reference disposal system for high level radioactive wastes and to establish site characterization methodology, including neotectonic features, fracture systems and mechanical properties of plutonic rocks, and hydrogeochemical characteristics. The preliminary assessment of neotectonics in the Korean peninsula was performed on the basis of seismicity recorded, Quarternary faults investigated, uplift characteristics studied on limited areas, distribution of the major regional faults and their characteristics. The local fracture system was studied in detail from the data obtained from deep boreholes in granitic terrain. Through this deep drilling project, the geometrical and hydraulic properties of different fracture sets are statistically analysed on a block scale. The mechanical properties of intact rocks were evaluated from the core samples by laboratory testing and the in-situ stress conditions were estimated by a hydro fracturing test in the boreholes. The hydrogeochemical conditions in the deep boreholes were characterized based on hydrochemical composition and isotopic signatures and were attempted to assess the interrelation with a major fracture system. The residence time of deep groundwater was estimated by C-14 dating. For the travel time of groundwater between the boreholes, the methodology and equipment for tracer test were established

  6. Deep Carbon Observatory investigates Carbon from Crust to Core: An Academic Record of the History of Deep Carbon Science

    Science.gov (United States)

    Mitton, S. A.

    2017-12-01

    Carbon plays an unparalleled role in our lives: as the element of life, as the basis of most of society's energy, as the backbone of most new materials, and as the central focus in efforts to understand Earth's variable and uncertain climate. Yet in spite of carbon's importance, scientists remain largely ignorant of the physical, chemical, and biological behavior of many of Earth's carbon-bearing systems. The Deep Carbon Observatory (DCO) is a global research program to transform our understanding of carbon in Earth. At its heart, DCO is a community of scientists, from biologists to physicists, geoscientists to chemists, and many others whose work crosses these disciplinary lines, forging a new, integrative field of deep carbon science. As a historian of science, I specialise in the history of planetary science and astronomy since 1900. This is directed toward understanding of the history of the steps on the road to discovering the internal dynamics of our planet. Within a framework that describes the historical background to the new field of Earth System Science, I present the first history of deep carbon science. This project will identifies the key discoveries of deep carbon science. It will assess the impact of new knowledge on geochemistry, geodynamics, and geobiology. The project will lead to publication, in book form in 2019, of an illuminating narrative that will highlight the engaging human stories of many remarkable scientists and natural philosophers from whom we have learned about the complexity of Earth's internal world. On this journey of discovery we will encounter not just the pioneering researchers of deep carbon science, but also their institutions, their instrumental inventiveness, and their passion for exploration. The book is organised thematically around the four communities of the Deep Carbon Observatory: Deep Life, Extreme Physics and Chemistry, Reservoirs and Fluxes, and Deep Energy. The presentation has a gallery and list of Deep Carbon

  7. Deep Energy Retrofit

    DEFF Research Database (Denmark)

    Zhivov, Alexander; Lohse, Rüdiger; Rose, Jørgen

    Deep Energy Retrofit – A Guide to Achieving Significant Energy User Reduction with Major Renovation Projects contains recommendations for characteristics of some of core technologies and measures that are based on studies conducted by national teams associated with the International Energy Agency...... Energy Conservation in Buildings and Communities Program (IEA-EBC) Annex 61 (Lohse et al. 2016, Case, et al. 2016, Rose et al. 2016, Yao, et al. 2016, Dake 2014, Stankevica et al. 2016, Kiatreungwattana 2014). Results of these studies provided a base for setting minimum requirements to the building...... envelope-related technologies to make Deep Energy Retrofit feasible and, in many situations, cost effective. Use of energy efficiency measures (EEMs) in addition to core technologies bundle and high-efficiency appliances will foster further energy use reduction. This Guide also provides best practice...

  8. LUMINOUS AND HIGH STELLAR MASS CANDIDATE GALAXIES AT z ≈ 8 DISCOVERED IN THE COSMIC ASSEMBLY NEAR-INFRARED DEEP EXTRAGALACTIC LEGACY SURVEY

    International Nuclear Information System (INIS)

    Yan Haojing; Finkelstein, Steven L.; Huang, Kuang-Han; Ryan, Russell E.; Ferguson, Henry C.; Koekemoer, Anton M.; Grogin, Norman A.; Dickinson, Mark; Newman, Jeffrey A.; Somerville, Rachel S.; Davé, Romeel; Faber, S. M.; Papovich, Casey; Guo Yicheng; Giavalisco, Mauro; Lee, Kyoung-soo; Reddy, Naveen; Siana, Brian D.; Cooray, Asantha R.; Hathi, Nimish P.

    2012-01-01

    One key goal of the Hubble Space Telescope Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey is to track galaxy evolution back to z ≈ 8. Its two-tiered ''wide and deep'' strategy bridges significant gaps in existing near-infrared surveys. Here we report on z ≈ 8 galaxy candidates selected as F105W-band dropouts in one of its deep fields, which covers 50.1 arcmin 2 to 4 ks depth in each of three near-infrared bands in the Great Observatories Origins Deep Survey southern field. Two of our candidates have J 1 mag brighter than any previously known F105W-dropouts. We derive constraints on the bright end of the rest-frame ultraviolet luminosity function of galaxies at z ≈ 8, and show that the number density of such very bright objects is higher than expected from the previous Schechter luminosity function estimates at this redshift. Another two candidates are securely detected in Spitzer Infrared Array Camera images, which are the first such individual detections at z ≈ 8. Their derived stellar masses are on the order of a few × 10 9 M ☉ , from which we obtain the first measurement of the high-mass end of the galaxy stellar mass function at z ≈ 8. The high number density of very luminous and very massive galaxies at z ≈ 8, if real, could imply a large stellar-to-halo mass ratio and an efficient conversion of baryons to stars at such an early time.

  9. Laparoscopy After Previous Laparotomy

    Directory of Open Access Journals (Sweden)

    Zulfo Godinjak

    2006-11-01

    Full Text Available Following the abdominal surgery, extensive adhesions often occur and they can cause difficulties during laparoscopic operations. However, previous laparotomy is not considered to be a contraindication for laparoscopy. The aim of this study is to present that an insertion of Veres needle in the region of umbilicus is a safe method for creating a pneumoperitoneum for laparoscopic operations after previous laparotomy. In the last three years, we have performed 144 laparoscopic operations in patients that previously underwent one or two laparotomies. Pathology of digestive system, genital organs, Cesarean Section or abdominal war injuries were the most common causes of previouslaparotomy. During those operations or during entering into abdominal cavity we have not experienced any complications, while in 7 patients we performed conversion to laparotomy following the diagnostic laparoscopy. In all patients an insertion of Veres needle and trocar insertion in the umbilical region was performed, namely a technique of closed laparoscopy. Not even in one patient adhesions in the region of umbilicus were found, and no abdominal organs were injured.

  10. Deep-well injection of radioactive waste in Russia

    International Nuclear Information System (INIS)

    Hoek, J.

    1998-01-01

    In the Russian federation, deep borehole injection of liquid radioactive waste has been established practice since at least 1963. The liquid is injected into sandy or other formations with high porosity, which are isolated by water-tight layers. This technique has also been used elsewhere for toxic liquid waste and residues from mining operations. Deep-well injection of radioactive waste is not currently used in any of the European Commission (EC) countries. In this paper the results of a EC-funded study were presented. The study is entitled 'Measurements, modelling of migration and possible radiological consequences at deep well injection sites for liquid radioactive waste in Russia', COSU-CT94-0099-UK. The study was carried out jointly by AEA Technology, CAG and the Research Institute for Nuclear Reactors (NIIAR) at Dimitrovgrad. Many scientists have contributed to the results reported here. The aims of the study are: Provision of extensive information on the deep-well injection repositories and their use in the former Soviet Union; Provision of a methodology to assess safety aspects of deep-well injection of liquid radioactive waste in deep geological formations; This will allow evaluation of proposals to use deep-well injection techniques in other regions; Support for Russian regulatory bodies through evaluation of the suitability of the sites, including estimates of the maximum amount of waste that can be safely stored in them; and Provision of a methodology to assess the use of deep-well injection repositories as an alternative disposal technique for EC countries. 7 refs

  11. DEEP: a general computational framework for predicting enhancers

    KAUST Repository

    Kleftogiannis, Dimitrios A.

    2014-11-05

    Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer\\'s properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

  12. DEEP: a general computational framework for predicting enhancers

    KAUST Repository

    Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.

    2014-01-01

    Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

  13. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

    Science.gov (United States)

    Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

    2017-01-01

    In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

  14. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

    Science.gov (United States)

    Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

    2017-01-01

    In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

  15. Striations, duration, migration and tidal response in deep tremor.

    Science.gov (United States)

    Ide, Satoshi

    2010-07-15

    Deep tremor in subduction zones is thought to be caused by small repeating shear slip events on the plate interface with significant slow components. It occurs at a depth of about 30 kilometres and provides valuable information on deep plate motion and shallow stress accumulation on the fault plane of megathrust earthquakes. Tremor has been suggested to repeat at a regular interval, migrate at various velocities and be modulated by tidal stress. Here I show that some time-invariant interface property controls tremor behaviour, using precise location of tremor sources with event duration in western Shikoku in the Nankai subduction zone, Japan. In areas where tremor duration is short, tremor is more strongly affected by tidal stress and migration is inhibited. Where tremor lasts longer, diffusive migration occurs with a constant diffusivity of 10(4) m(2) s(-1). The control property may be the ratio of brittle to ductile areas, perhaps determined by the influence of mantle wedge serpentinization on the plate interface. The spatial variation of the controlling property seems to be characterized by striations in tremor source distribution, which follows either the current or previous plate subduction directions. This suggests that the striations and corresponding interface properties are formed through the subduction of inhomogeneous structure, such as seamounts, for periods as long as ten million years.

  16. Deep transcranial magnetic stimulation for the treatment of auditory hallucinations: a preliminary open-label study

    Directory of Open Access Journals (Sweden)

    Zangen Abraham

    2011-02-01

    Full Text Available Abstract Background Schizophrenia is a chronic and disabling disease that presents with delusions and hallucinations. Auditory hallucinations are usually expressed as voices speaking to or about the patient. Previous studies have examined the effect of repetitive transcranial magnetic stimulation (TMS over the temporoparietal cortex on auditory hallucinations in schizophrenic patients. Our aim was to explore the potential effect of deep TMS, using the H coil over the same brain region on auditory hallucinations. Patients and methods Eight schizophrenic patients with refractory auditory hallucinations were recruited, mainly from Beer Ya'akov Mental Health Institution (Tel Aviv university, Israel ambulatory clinics, as well as from other hospitals outpatient populations. Low-frequency deep TMS was applied for 10 min (600 pulses per session to the left temporoparietal cortex for either 10 or 20 sessions. Deep TMS was applied using Brainsway's H1 coil apparatus. Patients were evaluated using the Auditory Hallucinations Rating Scale (AHRS as well as the Scale for the Assessment of Positive Symptoms scores (SAPS, Clinical Global Impressions (CGI scale, and the Scale for Assessment of Negative Symptoms (SANS. Results This preliminary study demonstrated a significant improvement in AHRS score (an average reduction of 31.7% ± 32.2% and to a lesser extent improvement in SAPS results (an average reduction of 16.5% ± 20.3%. Conclusions In this study, we have demonstrated the potential of deep TMS treatment over the temporoparietal cortex as an add-on treatment for chronic auditory hallucinations in schizophrenic patients. Larger samples in a double-blind sham-controlled design are now being preformed to evaluate the effectiveness of deep TMS treatment for auditory hallucinations. Trial registration This trial is registered with clinicaltrials.gov (identifier: NCT00564096.

  17. Size and Carbon Content of Sub-seafloor Microbial Cells at Landsort Deep, Baltic Sea

    DEFF Research Database (Denmark)

    Braun, Stefan; Morono, Yuki; Littmann, Sten

    2016-01-01

    determined the volume and the carbon content of microbial cells from a marine sediment drill core retrieved by the Integrated Ocean Drilling Program (IODP), Expedition 347, at Landsort Deep, Baltic Sea. To determine their shape and volume, cells were separated from the sediment matrix by multi-layer density......-specific carbon content was 19–31 fg C cell−1, which is at the lower end of previous estimates that were used for global estimates of microbial biomass. The cell-specific carbon density increased with sediment depth from about 200 to 1000 fg C μm−3, suggesting that cells decrease their water content and grow...... small cell sizes as adaptation to the long-term subsistence at very low energy availability in the deep biosphere. We present for the first time depth-related data on the cell volume and carbon content of sedimentary microbial cells buried down to 60 m below the seafloor. Our data enable estimates...

  18. Deep-Sea Corals: A New Oceanic Archive

    National Research Council Canada - National Science Library

    Adkins, Jess

    1998-01-01

    Deep-sea corals are an extraordinary new archive of deep ocean behavior. The species Desmophyllum cristagalli is a solitary coral composed of uranium rich, density banded aragonite that I have calibrated for several paleoclimate tracers...

  19. Discovery of asphalt seeps in the deep Southwest Atlantic off Brazil

    Science.gov (United States)

    Fujikura, Katsunori; Yamanaka, Toshiro; Sumida, Paulo Y. G.; Bernardino, Angelo F.; Pereira, Olivia S.; Kanehara, Toshiyuki; Nagano, Yuriko; Nakayama, Cristina R.; Nobrega, Marcos; Pellizari, Vivian H.; Shigeno, Shuichi; Yoshida, Takao; Zhang, Jing; Kitazato, Hiroshi

    2017-12-01

    The discovery and description of cold seeps with deep-sea chemosynthetic communities in the Southwest Atlantic Ocean are still incomplete, despite the large proven oil and gas reserves off the coast of Brazil. In the southeastern Brazilian continental margin, where over 71% of the country's oil and gas production takes place, there are previous geological and qualitative biological evidence of seep biota associated with pockmarks on the upper slope of the Santos Basin. In order to further study seep ecosystems on the Brazilian margin, a deep-sea investigation named Iatá-Piúna cruise was conducted using the human-occupied vehicle Shinkai 6500 off Brazil's southeast continental margin. Asphalt seeps were discovered on the seafloor of the North São Paulo Plateau from depths of 2652-2752 m, representing only the third discovery of this type of seep worldwide, following those in the Gulf of Mexico and off Angola. Video and isotopic analyses indicated a number of megabenthic animals in the asphalt seeps in the North São Paulo Plateau and revealed typical deep-sea heterotrophic and photosynthesis-based fauna occupying hard substrates provided by the asphalt seep. There was no evidence of chemosynthesis-based megabenthic fauna such as vesicomyid clams, Bathymodiolus mussels, and siboglinid tube worms, or any sediment bacterial mats, gas seepage, and carbonate rock in/around the seeps. The benthic fauna was composed mainly of sponges (ca. 15 species), such as the hexactinellids Caulophacus sp., Poliopogon amadou, Saccocalyx pedunculatus, Farrea occa and cf. Chonelasma choanoides; besides typical deep-sea isidid octocorals, brisingid starfishes and galatheid crabs. The δ13C values of poriferan sponges suggested a heterotrophic and pelagic nutrition. Geochemical analyses of asphalt revealed a heavy biodegradation of hydrocarbon molecules, supported by the depletion of light n-alkanes and other labile compounds. This advanced asphalt biodegradation is the likely reason

  20. [Research of electroencephalography representational emotion recognition based on deep belief networks].

    Science.gov (United States)

    Yang, Hao; Zhang, Junran; Jiang, Xiaomei; Liu, Fei

    2018-04-01

    In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and