Auditors' Professional Skepticism: Neutrality versus Presumptive Doubt

NARCIS (Netherlands)

Groot, T.L.C.M.; Quadackers, L.M.; Wright, A.

2014-01-01

Although skepticism is widely viewed as essential to audit quality, there is a debate about what form is optimal. The two prevailing perspectives that have surfaced are "neutrality" and "presumptive doubt." With neutrality, auditors neither believe nor disbelieve client management. With presumptive

Case report 486: Spondyloepiphyseal dysplasia tarda (SDT) (presumptively proved)

International Nuclear Information System (INIS)

Brown, D.D.; Childress, M.H.

1988-01-01

A 51 year old man with severe degenerative joint disease, short stature, barrel chest deformity, platyspondyly, a narrow pelvis, small iliac bones, dysplastic femoral heads and necks, notching of the patellae and flattening of the femoral intercondylar notches has been described as an example of Spondyloepiphyseal dysplasia tarda SDT. The entity was discussed in detail. The notching of the patellae has not been reported in association with SDT to the authors' knowledge. Characteristic features of SDT allow it to be differentiated from other arthropathies and dysplasias and these distinctions have been emphasized in the discussion. The diagnosis in this case can only be considered presumptively proved. (orig./MG)

The Presumption of Innocence as a Counterfactual Principle

NARCIS (Netherlands)

de Jong, F.; van Lent, L.

This article’s primary aim is to highlight the essentially critical potential of the presumption of innocence, as well as the need for this critical potential to be duly recognized. It is argued that the essential meaning of the presumption of innocence is best understood when approached from what

Cost-effectiveness analysis of rapid diagnostic test, microscopy and syndromic approach in the diagnosis of malaria in Nigeria: implications for scaling-up deployment of ACT

Directory of Open Access Journals (Sweden)

Onwujekwe Obinna E

2009-11-01

Full Text Available Abstract Background The diagnosis and treatment of malaria is often based on syndromic presentation (presumptive treatment and microscopic examination of blood films. Treatment based on syndromic approach has been found to be costly, and contributes to the development of drug resistance, while microscopic diagnosis of malaria is time-consuming and labour-intensive. Also, there is lack of trained microscopists and reliable equipment especially in rural areas of Nigeria. However, although rapid diagnostic tests (RDTs have improved the ease of appropriate diagnosis of malaria diagnosis, the cost-effectiveness of RDTs in case management of malaria has not been evaluated in Nigeria. The study hence compares the cost-effectiveness of RDT versus syndromic diagnosis and microscopy. Methods A total of 638 patients with fever, clinically diagnosed as malaria (presumptive malaria by health workers, were selected for examination with both RDT and microscopy. Patients positive on RDT received artemisinin-based combination therapy (ACT and febrile patients negative on RDT received an antibiotic treatment. Using a decision tree model for a hypothetical cohort of 100,000 patients, the diagnostic alternatives considered were presumptive treatment (base strategy, RDT and microscopy. Costs were based on a consumer and provider perspective while the outcome measure was deaths averted. Information on costs and malaria epidemiology were locally generated, and along with available data on effectiveness of diagnostic tests, adherence level to drugs for treatment, and drug efficacy levels, cost-effectiveness estimates were computed using TreeAge programme. Results were reported based on costs and effects per strategy, and incremental cost-effectiveness ratios. Results The cost-effectiveness analysis at 43.1% prevalence level showed an incremental cost effectiveness ratio (ICER of 221 per deaths averted between RDT and presumptive treatment, while microscopy is dominated

Magnetic resonance imaging of presumptive lumbosacral discospondylitis in a dog

International Nuclear Information System (INIS)

Kraft, S.L.; Mussman, J.M.; Smith, T.; Biller, D.S.; Hoskinson, J.J.

1998-01-01

three-year-old male Boxer dog had hyperesthesia, symmetrical epaxial, gluteal and hind limb muscular atrophy and rear limb ataxia. Neurological deficits included decreased conscious proprioception of the left hind limb, decreased withdrawal and increased patellar reflexes of both hind limbs. The dog had a urinary tract infection with positive culture for Staphylococcus intermedius. On survey radiography of the lumbosacral spine there was active bone proliferation spanning the L7 S1 intervertebral disc space with an epidural filling defect at the ventral aspect of the vertebral canal on epidurography. On magnetic resonance imaging (MRI), findings were similar to those described for human diskospondylitis including altered signal intensity and nonuniform contrast enhancement of the L7-S1 intervertebral disc, adjacent vertebral end plates and epidural and sublumbar soft tissues. Although skeletal radiography is usually sufficient to reach a diagnosis of discospondylitis, MRI of this patient made it possible to reach a presumptive diagnosis of discospondylitis prior to development of definitive radiographic abnormalities

Growth characteristics of liquid cultures increase the reliability of presumptive identification of Mycobacterium tuberculosis complex.

Science.gov (United States)

Pinhata, Juliana Maira Watanabe; Felippe, Isis Moreira; Gallo, Juliana Failde; Chimara, Erica; Ferrazoli, Lucilaine; de Oliveira, Rosangela Siqueira

2018-04-23

We evaluated the microscopic and macroscopic characteristics of mycobacteria growth indicator tube (MGIT) cultures for the presumptive identification of the Mycobacterium tuberculosis complex (MTBC) and assessed the reliability of this strategy for correctly directing isolates to drug susceptibility testing (DST) or species identification. A total of 1526 isolates of mycobacteria received at the Instituto Adolfo Lutz were prospectively subjected to presumptive identification by the observation of growth characteristics along with cord formation detection via microscopy. The presumptive identification showed a sensitivity, specificity and accuracy of 98.8, 92.5 and 97.9 %, respectively. Macroscopic analysis of MTBC isolates that would have been erroneously classified as non-tuberculous mycobacteria based solely on microscopic morphology enabled us to direct them rapidly to DST, representing a substantial gain to patients. In conclusion, the growth characteristics of mycobacteria in MGIT, when considered along with cord formation, increased the reliability of the presumptive identification, which has a great impact on the laboratory budget and turnaround times.

A Minimalist and Garantistic Conception of the Presumption of Innocence

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Jordi Ferrer Beltrán

2018-03-01

Full Text Available The article aims to address the multiple faces that the presumption of innocence incorporates in modern legal systems from a critical perspective. In this sense, an analytical methodology seeks to demonstrate that some of these faces overlap with other legal rights and institutes, which, far from increasing the guarantees of citizens, leads to confusion and lack of controllability of judicial decisions. Thus, it is defended the conceptual and practical convenience of thinking the presumption of innocence avoiding overlaps with other legal rights or concepts, as standards of proof or burden of proof rules. Hence the reference to a minimalist and guarantistic conception of the presumption of innocence and, and, as will be seen, the defense of the presumption of innocence as a second order rule whose application would make sense in contexts of uncertainty about the satisfaction of the standard of proof.

75 FR 13051 - Presumptions of Service Connection for Persian Gulf Service

Science.gov (United States)

2010-03-18

... response to ``RIN 2900-AN24--Presumptions of Service Connection for Persian Gulf Service.'' Copies of...) Cardiovascular signs or symptoms. (12) Abnormal weight loss. (13) Menstrual disorders. (c) Presumptive service...

Randomised primary health center based interventions to improve the diagnosis and treatment of undifferentiated fever and dengue in Vietnam

NARCIS (Netherlands)

Phuong, Hoang L.; Nga, Tran T. T.; Giao, Phan T.; Hung, Le Q.; Binh, Tran Q.; Nam, Nguyen V.; Nagelkerke, Nico; de Vries, Peter J.

2010-01-01

ABSTRACT: BACKGROUND: Fever is a common reason for attending primary health facilities in Vietnam. Response of health care providers to patients with fever commonly consists of making a presumptive diagnosis and proposing corresponding treatment. In Vietnam, where malaria was brought under control,

Presumption of Innocence in Criminal Procedure

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Tatiana Zbanca

2009-06-01

Full Text Available Presumption of innocence appears as a rule hardly in modern penal trial. For first timewas noted in legislation from the end of the XVIIIth century (United States of America legislationand Declaration of Human Rights and Citizens in 1789. This constituted a reaction compared toinquisitional report, which practically the one involved into a penal case was presumed alwaysguilty, reverting the obligation of proving own innocence. According to the U.S. Supreme Court,the presumption of the innocence of a criminal defendant is best described as an assumption ofinnocence that is indulged in the absence of contrary evidence. It is not considered evidence of thedefendant's innocence, and it does not require that a mandatory inference favorable to thedefendant be drawn from any facts in evidence.

Myths, presumptions, and facts about obesity

DEFF Research Database (Denmark)

Casazza, Krista; Fontaine, Kevin R; Astrup, Arne

2013-01-01

Many beliefs about obesity persist in the absence of supporting scientific evidence (presumptions); some persist despite contradicting evidence (myths). The promulgation of unsupported beliefs may yield poorly informed policy decisions, inaccurate clinical and public health recommendations, and a...

[Burden of proof in medical cases--presumption of fact and prima facie evidence. II. Presumption of fact and prima facie evidence].

Science.gov (United States)

Sliwka, Marcin

2004-01-01

The aim of this paper was to present the main rules concerning the burden of proof in polish civil trials, including medical cases. The standard rules were presented with all the important exclusions such as presumption of law and fact or prima facie evidence. The author analyses the effect of these institutions on burden of proof in medical cases. The difference between presumptions of fact and prima facie evidence was analysed and explained. This paper also describes the importance of the res ipsa loquitur rule in United Kingdom and USA. This paper includes numerous High Court sentences on evidential and medical issues.

Presumption of Innocence and Public Safety: A Possible Dialogue

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Ana Aguilar-Garcia

2014-12-01

Full Text Available In Mexico, increasing demands for public safety coupled with the need for a more effective criminal justice system resulted in the security and justice constitutional reform of 2008. The outcome was a constitutional framework with provisions based on the highest standards of human rights on the one hand, and on the other, exceptional measures that restrict rights in an attempt to improve public safety. Unfortunately, the crime rate and incidence of unreported crime have changed little. When public safety is demanded, a clear, rational and concrete response is required. Limiting the alternatives to pre-trial detention or increasing penalties is rarely the appropriate response. This paper focuses on pre-trial detention and non-custodial measures supported by the new criminal justice system, how they relate to the principle of the presumption of innocence and the tension between this and the punitive demands for increased imprisonment. In addition, this study discusses a technical solution, found in pre-trial services, which seeks to balance the presumption of innocence and the right to personal liberty with public safety.

Two Obese Patients with Presumptive Diagnosis of Anaphylactoid Syndrome of Pregnancy Presenting at a Community Hospital.

Science.gov (United States)

Kradel, Brian K; Hinson, Scarlett B; Smith, Carr J

2016-07-01

Anaphylactoid syndrome of pregnancy (ASP) is a rare but extremely serious complication, with an estimated incidence in North America of 1 in 15 200 deliveries. Despite its rarity, ASP is responsible for approximately 10% of all childbirth-associated deaths in the United States. At present, there is no validated biomarker or specific set of risk factors sufficiently predictive of ASP risk to incorporate into clinical practice. Toward the goal of developing a methodology predictive of an impending ASP event for use by obstetricians, anesthesiologists, and other practitioners participating in infant deliveries, physicians encountering an ASP event have been encouraged to report the occurrence of a case and its biologically plausible risk factors. Herein, we report on 2 patients who presented with a presumptive diagnosis of ASP to the delivery unit of a community hospital. Patient One was a 21-year-old, obese (5'11" tall, 250 lbs., BMI 34.9) white female, 1 pregnancy, no live births (G1P0), estimated gestational age (EGA) 40.2 weeks. Patient Two was a 29-year-old, obese (5'7" tall, 307 lbs., BMI 48.1) Hispanic female, second pregnancy, with 1 previous live birth via C-section (G2P1-0-0-1). Her pregnancy was at gestational age 38 weeks plus 2 days. Patient One had 2 possible risk factors: administration of Pitocin to induce labor and post-coital spotting from recent intercourse. Patient Two suffered premature rupture of the placental membranes. Both Patient One and Patient Two had very high body mass indices (BMIs), at the 97th and 99th percentiles, respectively. In the relatively few cases of anaphylactoid syndrome of pregnancy described to date, this is the first report of a possible association with high BMI.

Presumptive Eligibility for Medicaid and CHIP Coverage

Data.gov (United States)

U.S. Department of Health & Human Services — Health care providers and Head Start programs can play a major role in finding and enrolling uninsured children through presumptive eligibility. States can authorize...

15 CFR 990.13 - Rebuttable presumption.

Science.gov (United States)

2010-01-01

...) NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE OIL POLLUTION ACT REGULATIONS... assessment of damages to natural resources made by a Federal, State, or Indian trustee in accordance with this part shall have the force and effect of a rebuttable presumption on behalf of the trustee in any...

Multiplex polymerase chain reaction: Could change diagnosis of ...

African Journals Online (AJOL)

acquired infection in critically ill children. The increasing incidence of infections by antibiotic-resistant pathogens adds significantly to the cost of hospital care and to the length of hospital stays. Besides clinical prerequisites for presumptive diagnosis ...

38 CFR 1.18 - Guidelines for establishing presumptions of service connection for former prisoners of war.

Science.gov (United States)

2010-07-01

... establishing presumptions of service connection for former prisoners of war. 1.18 Section 1.18 Pensions... Guidelines for establishing presumptions of service connection for former prisoners of war. (a) Purpose. The Secretary of Veterans Affairs will establish presumptions of service connection for former prisoners of war...

Microbiological studies of blood specimen from presumptively ...

African Journals Online (AJOL)

Three hundred and fifteen blood samples were obtained from presumptively diagnosed typhoid patients who were referred for Widal Serological test at four diagnostic centres. The blood samples were subjected to bacteriological investigations. Salmonella and non-Salmonella organisms isolated were identified according ...

Detection of presumptive Bacillus cereus in the Irish dairy farm environment

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O’Connell A.

2016-12-01

Full Text Available The objective of the study was to isolate potential Bacillus cereus sensu lato (B. cereus s.l. from a range of farm environments. Samples of tap water, milking equipment rinse water, milk sediment filter, grass, soil and bulk tank milk were collected from 63 farms. In addition, milk liners were swabbed at the start and the end of milking, and swabs were taken from cows’ teats prior to milking. The samples were plated on mannitol egg yolk polymyxin agar (MYP and presumptive B. cereus s.l. colonies were isolated and stored in nutrient broth with 20% glycerol and frozen at -80 °C. These isolates were then plated on chromogenic medium (BACARA and colonies identified as presumptive B. cereus s.l. on this medium were subjected to 16S ribosomal RNA (rRNA sequencing. Of the 507 isolates presumed to be B. cereus s.l. on the basis of growth on MYP, only 177 showed growth typical of B. cereus s.l. on BACARA agar. The use of 16S rRNA sequencing to identify isolates that grew on BACARA confirmed that the majority of isolates belonged to B. cereus s.l. A total of 81 of the 98 isolates sequenced were tentatively identified as presumptive B. cereus s.l. Pulsed-field gel electrophoresis was carried out on milk and soil isolates from seven farms that were identified as having presumptive B. cereus s.l. No pulsotype was shared by isolates from soil and milk on the same farm. Presumptive B. cereus s.l. was widely distributed within the dairy farm environment.

Elastofibroma dorsi: MRI diagnosis in a young girl

Energy Technology Data Exchange (ETDEWEB)

Devaney, D. [Dept. of Histopathology, Hospital for Sick Children, London (United Kingdom); Livesley, P. [Dept. of Orthopaedics, Hospital for Sick Children, London (United Kingdom); Shaw, D. [Dept. of Paediatric Radiology, Hospital for Sick Children, London (United Kingdom)

1995-06-01

With indications for computerised imaging expanding, elastofibroma dorsi will probably be seen more frequently. This report describes an elastofibroma presenting in an 11-year-old girl and its appearance by magnetic resonance imaging. Presumptive diagnosis by magnetic resonance imaging may prevent unnecessary radical surgery. (orig.)

Elastofibroma dorsi: MRI diagnosis in a young girl

International Nuclear Information System (INIS)

Devaney, D.; Livesley, P.; Shaw, D.

1995-01-01

With indications for computerised imaging expanding, elastofibroma dorsi will probably be seen more frequently. This report describes an elastofibroma presenting in an 11-year-old girl and its appearance by magnetic resonance imaging. Presumptive diagnosis by magnetic resonance imaging may prevent unnecessary radical surgery. (orig.)

77 FR 12522 - Tentative Eligibility Determinations; Presumptive Eligibility for Psychosis and Other Mental Illness

Science.gov (United States)

2012-03-01

...; Presumptive Eligibility for Psychosis and Other Mental Illness AGENCY: Department of Veterans Affairs. ACTION... psychosis within specified time periods and for Persian Gulf War veterans who developed a mental illness... eligibility determinations; Presumptive eligibility for psychosis and other mental illness.'' Copies of...

HIV-infected presumptive tuberculosis patients without tuberculosis: How many are eligible for antiretroviral therapy in Karnataka, India?

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Ajay M.V. Kumar

2017-03-01

Full Text Available For certain subgroups within people living with the human immunodeficiency virus (HIV [active tuberculosis (TB, pregnant women, children <5 years old, and serodiscordant couples], the World Health Organization recommends antiretroviral therapy (ART irrespective of CD4 count. Another subgroup which has received increased attention is “HIV-infected presumptive TB patients without TB”. In this study, we assess the proportion of HIV-infected presumptive TB patients eligible for ART in Karnataka State (population 60 million, India. This was a cross-sectional analysis of data of HIV-infected presumptive TB patients diagnosed in May 2015 abstracted from national TB and HIV program records. Of 42,585 presumptive TB patients, 28,964 (68% were tested for HIV and 2262 (8% were HIV positive. Of the latter, 377 (17% had active TB. Of 1885 “presumptive TB patients without active TB”, 1100 (58% were already receiving ART. Of the remaining 785 who were not receiving ART, 617 (79% were assessed for ART eligibility and of those, 548 (89% were eligible for ART. About 90% of “HIV-infected presumptive TB patients without TB” were eligible for ART. This evidence supports a public health approach of starting all “HIV-infected presumptive TB patients without TB” on ART irrespective of CD4 count in line with global thinking about ‘test and treat’.

The presumption of innocence across national borders

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Lola Shehu

2018-03-01

Full Text Available The principle of the presumption of innocence is already an important principle in modern democracies, which have included the principle in their legal systems. Many international instruments also sanction this important principle. The presumption of innocence protects not only the defendant but also the suspect before fi ling charges against him. Human rights are never fully and completely protected. The obligation that state institutions have to respect them does not necessarily mean and in any case guarantee them. For this reason, the material and procedural means envisaged in the legislation of a country are intended to protect the rights of the fundamental rights when the individual has no other way to enjoy them. Violation of fundamental rights can be claimed at every stage of ordinary trial because courts are also obliged to enforce and respect human rights. The practice of the Court in conjunction with Article 6 of the ECHR is basically stated that it has consistently been in the line of the fact that the right to a fair trial occupies an important place in a democratic society in the sense of the European Convention on Human Rights. The right to a fair trial is a very broad right and in any case should be carefully scrutinized by the national courts, analyzing in detail all the facts that, in one form or another, would affect the material or procedural rights of the accused.” (Nowicki, 2003. The right to a fair trial is implemented from the moment of the court’s investment and until the execution of its final decision. The ECHR has emphasized that the principle of the presumption of innocence is considered to be overturned if a judicial decision belonging to a person charged with a criminal offense reflects an opinion that he is guilty before his guilt has been proven by law.

Misleading presumption of a generalized Hartman effect

International Nuclear Information System (INIS)

Simanjuntak, Herbert P.; Pereyra, Pedro

2007-07-01

We analyze different examples to show that the so-called generalized Hartman effect is an erroneous presumption. The results obtained for electron tunneling and transmission of electromagnetic waves through superlattices and Bragg gratings show clearly the resonant character of the phase time behavior where a generalized Hartman effect is expected. A reinterpretation of the experimental results in double Bragg gratings is proposed. (author)

Community referral for presumptive TB in Nigeria: a comparison of four models of active case finding

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A. O. Adejumo

2016-02-01

Full Text Available Abstract Background Engagement of communities and civil society organizations is a critical part of the Post-2015 End TB Strategy. Since 2007, many models of community referral have been implemented to boost TB case detection in Nigeria. Yet clear insights into the comparative TB yield from particular approaches have been limited. Methods We compared four models of active case finding in three Nigerian states. Data on presumptive TB case referral by community workers (CWs, TB diagnoses among referred clients, active case finding model characteristics, and CWs compensation details for 2012 were obtained from implementers and CWs via interviews and log book review. Self-reported performance data were triangulated against routine surveillance data to assess concordance. Analysis focused on assessing the predictors of presumptive TB referral. Results CWs referred 4–22 % of presumptive TB clients tested, and 4–24 % of the total TB cases detected. The annual median referral per CW ranged widely among the models from 1 to 48 clients, with an overall average of 13.4 referrals per CW. The highest median referrals (48 per CW/yr and mean TB diagnoses (7.1/yr per CW (H =70.850, p < 0.001 was obtained by the model with training supervision, and $80/quarterly payments (Comprehensive Quotas-Oriented model. The model with irregularly supervised, trained, and compensated CWs contributed the least to TB case detection with a median of 13 referrals per CW/yr and mean of 0.53 TB diagnoses per CW/yr. Hours spent weekly on presumptive TB referral made the strongest unique contribution (Beta = 0.514, p < 0.001 to explaining presumptive TB referral after controlling for other variables. Conclusion All community based TB case-finding projects studied referred a relative low number of symptomatic individuals. The study shows that incentivized referral, appropriate selection of CWs, supportive supervision, leveraged treatment support roles, and a

THE PRINCIPLE OF THE PRESUMPTION OF INNOCENCE AND ...

African Journals Online (AJOL)

SimenehKA

The central issue relating to the presumption of innocence and burden of proof in Ethiopia's ... (moral) costs in the application of the substantive law.6 Those moral costs for the acquittal of the ..... (New York: Aspen Law and Business), at 767. ..... Buhagiar,. William (last accessed 26 August 2009).

The Ethics of Fertility Preservation for Paediatric Cancer Patients: From Offer to Rebuttable Presumption.

Science.gov (United States)

McDougall, Rosalind

2015-11-01

Given advances in the science of fertility preservation and the link between fertility choices and wellbeing, it is time to reframe our ethical thinking around fertility preservation procedures for children and young people with cancer. The current framing of fertility preservation as a possible offer may no longer be universally appropriate. There is an increasingly pressing need to discuss the ethics of failing to preserve fertility, particularly for patient groups for whom established techniques exist. I argue that the starting point for deliberating about a particular patient should be a rebuttable presumption that fertility preservation ought to be attempted. Consideration of the harms applicable to that specific patient may then override this presumption. I outline the benefits of attempting fertility preservation; these justify a presumption in favour of the treatment. I then discuss the potential harms associated with fertility preservation procedures, which may justify failing to attempt fertility preservation in an individual patient's particular case. Moving from a framework of offer to one of rebuttable presumption in favour of fertility preservation would have significant implications for medical practice, healthcare organizations and the state. © 2015 John Wiley & Sons Ltd.

[Pleural effusion: diagnosis and management].

Science.gov (United States)

Pastré, J; Roussel, S; Israël Biet, D; Sanchez, O

2015-04-01

Pleural effusion management is a common clinical situation associated with numerous pulmonary, pleural or extra-pulmonary diseases. A systematic approach is needed to enable a rapid diagnosis and an appropriate treatment. Pleural fluid analysis is the first step to perform which allows a presumptive diagnosis in most cases. Otherwise, further analysis of the pleural fluid or thoracic imaging or pleural biopsy may be necessary. This review aims at highlighting the important elements of the work-up required by a pleural effusion. Copyright © 2014 Société nationale française de médecine interne (SNFMI). Published by Elsevier SAS. All rights reserved.

Considerations Regarding the Observance of the Presumption of Innocence in the Media

Directory of Open Access Journals (Sweden)

Sandra Gradinaru

2009-06-01

Full Text Available The presumption of innocence, the right of privacy, of an intimate and a family life, the freedomof speech, officials, the deontological code of the journalist Abstract: In the context of the Rule of Law, amodern governing guarantees to anyone the presumption of innocence until is delivered an unappealablecriminal decision. Nevertheless, in almost all the cases, the media, by virtue of freedom of speech, bringsprejudices to the dignity, the honor and image of the officials, investigated in criminal cases, having as aunique argument the fact that a media campaign, searching the sensational, does nothing else thanreproducing hostile manifestations - public servant - thus influencing the public opinion. They affect theprinciple of presumption of innocence, inducing unfortunate effects above the default of justice. Thus, themedia takes the information from prosecutors that operate within the courts, shading them by the depreciatingallegations addressed to the public persons as defendants in criminal cases, creating to the public opinion adistorted image of reality, before the justice has passed through a final criminal decision on guilt or theirinnocence.

Innocent until primed: mock jurors' racially biased response to the presumption of innocence.

Directory of Open Access Journals (Sweden)

Danielle M Young

Full Text Available BACKGROUND: Research has shown that crime concepts can activate attentional bias to Black faces. This study investigates the possibility that some legal concepts hold similar implicit racial cues. Presumption of innocence instructions, a core legal principle specifically designed to eliminate bias, may instead serve as an implicit racial cue resulting in attentional bias. METHODOLOGY/PRINCIPAL FINDINGS: The experiment was conducted in a courtroom with participants seated in the jury box. Participants first watched a video of a federal judge reading jury instructions that contained presumption of innocence instructions, or matched length alternative instructions. Immediately following this video a dot-probe task was administered to assess the priming effect of the jury instructions. Presumption of innocence instructions, but not the alternative instructions, led to significantly faster response times to Black faces when compared with White faces. CONCLUSIONS/SIGNIFICANCE: These findings suggest that the core principle designed to ensure fairness in the legal system actually primes attention for Black faces, indicating that this supposedly fundamental protection could trigger racial stereotypes.

Mixed Ehrlichia canis, Hepatozoon canis, and presumptive Anaplasma phagocytophilum infection in a dog.

Science.gov (United States)

Mylonakis, Mathio E; Koutinas, Alex F; Baneth, Gad; Polizopoulou, Zoe; Fytianou, Anna

2004-01-01

A 5-month-old, female, mongrel dog was admitted to the Clinic of Companion Animal Medicine, Aristotle University of Thessaloniki, Greece, with depression, anorexia, fever, peripheral lymphadenopathy, splenomegaly, oculonasal discharge, nonregenerative anemia, and mild thrombocytopenia. Cytology of Giemsa-stained buffy coat, bone marrow, and lymph node aspiration smears revealed numerous morulae in mononuclear leukocytes and in neutrophils, and Hepatozoon canis gamonts in neutrophils. The dog was seropositive to Ehrlichia canis (immunofluorescence assay [IFA]) and Hepatozoon canis (ELISA) but not to Anaplasma phagocytophilum (IFA). A nested polymerase chain reaction performed on bone marrow aspirates was positive for E canis. This method was not applied for the detection of A phagocytophilum. Treatment with doxycycline and imidocarb dipropionate resulted in both clinical and parasitologic cure. This is the first reported case of a mixed infection with E canis, H canis, and presumptive A phagocytophilum. The findings emphasize the value of cytology in offering a quick and inexpensive diagnosis in mixed tick-borne infections of dogs.

Differential diagnosis of nongap metabolic acidosis: value of a systematic approach.

Science.gov (United States)

Kraut, Jeffrey A; Madias, Nicolaos E

2012-04-01

Nongap metabolic acidosis is a common form of both acute and chronic metabolic acidosis. Because derangements in renal acid-base regulation are a common cause of nongap metabolic acidosis, studies to evaluate renal acidification often serve as the mainstay of differential diagnosis. However, in many cases, information obtained from the history and physical examination, evaluation of the electrolyte pattern (to determine if a nongap acidosis alone or a combined nongap and high anion gap metabolic acidosis is present), and examination of the serum potassium concentration (to characterize the disorder as hyperkalemic or hypokalemic in nature) is sufficient to make a presumptive diagnosis without more sophisticated studies. If this information proves insufficient, indirect estimates or direct measurement of urinary NH(4)(+) concentration, measurement of urine pH, and assessment of urinary HCO(3)(-) excretion can help in establishing the diagnosis. This review summarizes current information concerning the pathophysiology of this electrolyte pattern and the value and limitations of all of the diagnostic studies available. It also provides a systematic and cost-effective approach to the differential diagnosis of nongap metabolic acidosis.

Veterans Affairs: Presumptive Service Connection and Disability Compensation

Science.gov (United States)

2011-03-28

aggravation of disease) and third element (nexus between in-service occurrence/aggravation of disease and current disease) of the prima facie case for...occurring within two years of separation from active duty military service. In the following years, additions to the presumptive list were made by...the change of mission for U.S. forces in Iraq. 4 Veterans Benefits Disability Commission, Honoring the Call to Duty : Veterans’ Disability Benefits in

Computed tomography in the diagnosis of steroidal hepatopathy in a dog: case report

Energy Technology Data Exchange (ETDEWEB)

Oliveira, D.C; Costa, L.A.V.S.; Lopes, B.F.; Lanis, A.B.; Borlini, D.C.; Costa, F.S., E-mail: danielcapucho@gmail.co [Universidade Federal do Espirito Santo (UFES), Vitroria, ES (Brazil). Dept. de Medicina Veterinaria; Maia Junior, J.A. [Centro de Escolas de Formacao de Tecnicos em Radiologia, Vila Velha, ES (Brazil)

2011-02-15

It is reported a case of an eight-year-old Yorkshire Terrier dog, with a history of prolonged use of prednisone in a dosage of 1mg/kg of body weight each 24 hours during two years. The helical computed tomography revealed hepatomegaly associated to a hyper attenuation of the parenchyma, with a radiodensity value of 82.55 Hounsfield units (HU). The spleen presented a mean radiodensity of 57.17HU, and a radiodensity difference of 25.38HU was observed between the two organs. Based on the history and findings of imaging technique, it was determined the presumptive diagnosis of steroidal hepatopathy compatible with accumulation of hepatic glycogen. It was concluded that computed tomography enabled the characterization of hepatic injury and the presumed diagnosis of steroidal hepatopathy. (author)

Computed tomography in the diagnosis of steroidal hepatopathy in a dog: case report

International Nuclear Information System (INIS)

Oliveira, D.C; Costa, L.A.V.S.; Lopes, B.F.; Lanis, A.B.; Borlini, D.C.; Costa, F.S.

2011-01-01

It is reported a case of an eight-year-old Yorkshire Terrier dog, with a history of prolonged use of prednisone in a dosage of 1mg/kg of body weight each 24 hours during two years. The helical computed tomography revealed hepatomegaly associated to a hyper attenuation of the parenchyma, with a radiodensity value of 82.55 Hounsfield units (HU). The spleen presented a mean radiodensity of 57.17HU, and a radiodensity difference of 25.38HU was observed between the two organs. Based on the history and findings of imaging technique, it was determined the presumptive diagnosis of steroidal hepatopathy compatible with accumulation of hepatic glycogen. It was concluded that computed tomography enabled the characterization of hepatic injury and the presumed diagnosis of steroidal hepatopathy. (author)

Computed tomography in the diagnosis of steroidal hepatopathy in a dog: case report

Directory of Open Access Journals (Sweden)

D.C Oliveira

2011-02-01

Full Text Available It is reported a case of an eight-year-old Yorkshire Terrier dog, with a history of prolonged use of prednisone in a dosage of 1mg/kg of body weight each 24 hours during two years. The helical computed tomography revealed hepatomegaly associated to a hyperattenuation of the parenchyma, with a radiodensity value of 82.55 Hounsfield units (HU. The spleen presented a mean radiodensity of 57.17HU, and a radiodensity difference of 25.38HU was observed between the two organs. Based on the history and findings of imaging technique, it was determined the presumptive diagnosis of steroidal hepatopathy compatible with accumulation of hepatic glycogen. It was concluded that computed tomography enabled the characterization of hepatic injury and the presumed diagnosis of steroidal hepatopathy

The Onset of Action of the Presumption of Innocence Principle

Directory of Open Access Journals (Sweden)

Igor Y. Murashkin

2016-04-01

Full Text Available The author explores the problematic issues of the beginning of the principle of presumption of innocence. Critically evaluate the currently existing position of the origin of the right to protection against unjustified allegations guilty to the crime since the initiation of criminal proceedings. Grounded approach to the beginning of this principle since, when in fact it became prosecute.

Diagnosis of asthma: diagnostic testing.

Science.gov (United States)

Brigham, Emily P; West, Natalie E

2015-09-01

Asthma is a heterogeneous disease, encompassing both atopic and non-atopic phenotypes. Diagnosis of asthma is based on the combined presence of typical symptoms and objective tests of lung function. Objective diagnostic testing consists of 2 components: (1) demonstration of airway obstruction, and (2) documentation of variability in degree of obstruction. A review of current guidelines and literature was performed regarding diagnostic testing for asthma. Spirometry with bronchodilator reversibility testing remains the mainstay of asthma diagnostic testing for children and adults. Repetition of the test over several time points may be necessary to confirm airway obstruction and variability thereof. Repeated peak flow measurement is relatively simple to implement in a clinical and home setting. Bronchial challenge testing is reserved for patients in whom the aforementioned testing has been unrevealing but clinical suspicion remains, though is associated with low specificity. Demonstration of eosinophilic inflammation, via fractional exhaled nitric oxide measurement, or atopy, may be supportive of atopic asthma, though diagnostic utility is limited particularly in nonatopic asthma. All efforts should be made to confirm the diagnosis of asthma in those who are being presumptively treated but have not had objective measurements of variability in the degree of obstruction. Multiple testing modalities are available for objective confirmation of airway obstruction and variability thereof, consistent with a diagnosis of asthma in the appropriate clinical context. Providers should be aware that both these characteristics may be present in other disease states, and may not be specific to a diagnosis of asthma. © 2015 ARS-AAOA, LLC.

20 CFR 410.418 - Irrebuttable presumption of total disability due to pneumoconiosis.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Irrebuttable presumption of total disability due to pneumoconiosis. 410.418 Section 410.418 Employees' Benefits SOCIAL SECURITY ADMINISTRATION... of the Pneumoconioses of the International Labour Office, Extended Classification (1968) (which may...

75 FR 61356 - Presumptions of Service Connection for Persian Gulf Service; Correction

Science.gov (United States)

2010-10-05

... DEPARTMENT OF VETERANS AFFAIRS 38 CFR Part 3 RIN 2900-AN24 Presumptions of Service Connection for Persian Gulf Service; Correction AGENCY: Department of Veterans Affairs. ACTION: Correcting amendment. SUMMARY: The Department of Veterans Affairs (VA) published in the Federal Register of September 29, 2010...

Issues in practical model-based diagnosis

NARCIS (Netherlands)

Bakker, R.R.; Bakker, R.R.; van den Bempt, P.C.A.; van den Bempt, P.C.A.; Mars, Nicolaas; Out, D.-J.; Out, D.J.; van Soest, D.C.; van Soes, D.C.

1993-01-01

The model-based diagnosis project at the University of Twente has been directed at improving the practical usefulness of model-based diagnosis. In cooperation with industrial partners, the research addressed the modeling problem and the efficiency problem in model-based reasoning. Main results of

Serological diagnosis of brucellosis.

Science.gov (United States)

Nielsen, K; Yu, W L

2010-01-01

To present a review and to describe the most widely used laboratory tests for serology diagnosis of brucellosis along with their pros and cons. Review the recent literature on brucellosis serology diagnostic tests. The choice of the testing strategy depends on the prevailing brucellosis epidemiological situation and the goal of testing. The 'gold standard' for the diagnosis of brucellosis is isolation and identification of the causative bacterium, a member of Brucella sp. Isolation of Brucella sp. requires high security laboratory facilities (biological containment level 3), highly skilled personnel, an extended turnaround time for results and it is considered a hazardous procedure. Hence brucellosis is generally diagnosed by detection of an elevated level of antibody in serum or other body fluid. This is a presumptive diagnosis as other microorganisms and perhaps environmental factors can also cause increased antibody levels. A large number of serological tests for brucellosis have been devised over the 100+ years since its initial isolation, starting with a simple agglutination test and progressing to sophisticated primary binding assays available today. However, no test devised to date is 100% accurate so generally serological diagnosis consists of testing sera by several tests, usually a screening test of high sensitivity, followed by a confirmatory test of high specificity.

77 FR 42909 - Presumption of Insurable Interest for Same-Sex Domestic Partners

Science.gov (United States)

2012-07-20

... do not fall within the presumptive classes. The commenter suggested that OPM has merely replaced one... beneficiary's date of birth. * * * * * PART 842--FEDERAL EMPLOYEES RETIREMENT SYSTEM--BASIC ANNUITY 0 3. The.... 842.605 Election of insurable interest rate. * * * * * (e) An insurable interest rate may be elected...

The Principle of the Presumption of Innocence and its Challenges in ...

African Journals Online (AJOL)

The administration of the criminal justice system tries to strike a balance between the search for truth and the fairness of the process. To this end, the law should protect individual rights and impose various legal burdens on the state. One such tool is the principle of the presumption of innocence until proven guilty. This is a ...

Etiological Diagnosis of Undervirilized Male / XY Disorder of Sex Development

International Nuclear Information System (INIS)

Atta, I.; Ibrahim, M.; Parkash, A.; Lone, S. W.; Khan, Y. N.; Raza, J.

2014-01-01

Objective: To do clinical, hormonal and chromosomal analysis in undervirilized male / XY disorder of sex development and to make presumptive etiological diagnosis according to the new Disorder of Sex Development (DSD) classification system. Study Design: Case series. Place and Duration of Study: Endocrine Unit at National Institute of Child Health, Karachi, Pakistan, from January 2007 to December 2012. Methodology: Patients of suspected XY DSD / undervirilized male visiting endocrine clinic were enrolled in the study. Criteria suggested XY DSD include overt genital ambiguity, apparent female/male genitalia with inguinal/labial mass, apparent male genitalia with unilateral or bilateral non-palpable testes, micropenis and isolated hypospadias or with undescended testis. The older children who had delayed puberty were also evaluated with respect to DSD. As a part of evaluation of XY DSD, abdominopelvic ultrasound, karyotype, hormone measurement (testosterone, FSH, LH), FISH analysis with SRY probing, genitogram, laparoscopy, gonadal biopsy and HCG stimulation test were performed. Frequencies and percentages applied on categorical data whereas mean, median, standard deviation were calculated for continuous data. Results: A total of 187 patients met the criteria of XY DSD. Age ranged from 1 month to 15 years, 55 (29.4%) presented in infancy, 104 (55.6%) between 1 and 10 years and 28 (15%) older than 10 years. Twenty five (13.4%) were raised as female and 162 as (86.6%) male. The main complaints were ambiguous genitalia, unilateral cryptorchidism, bilateral cryptorchidism, micropenis, delayed puberty, hypospadias, female like genitalia with gonads, inguinal mass. The karyotype was 46 XY in 183 (97.9%), 46 XX in 2 (1.1%), 47 XXY in 1 (0.5%), 45 X/46 XY in 1 (0.5%) patient. HCG stimulation test showed low testosterone response in 43 (23 %), high testosterone response in 62 (33.2%), partial testosterone response in 32 (17.1%) and normal testosterone response in 50 (26

MRI findings in the patients with the presumptive clinical diagnosis of Tolosa-Hunt syndrome

Energy Technology Data Exchange (ETDEWEB)

Cakirer, Sinan [Department of Radiology, Neuroradiology Section, Istanbul Sisli Etfal Hospital, 81120 Istanbul (Turkey)

2003-01-01

The aim of this study was to present our experience in MRI diagnosis of 23 patients with the clinical findings suggesting Tolosa-Hunt syndrome (THS). Cranial MRI studies of the patients with a clinical history of at least one episode of unilateral or bilateral orbital and periorbital pain, and associated paresis of one or more of third to sixth cranial nerves, were performed on a 1.5-T MRI scanner. Whereas 5 patients had the diagnosis of THS, paracavernous meningiomas in 4 patients, pituitary macroadenomas with cavernous sinus infiltration in 3 patients, Meckel's cave neurinoma in 1 patient, and suprasellar epidermoid in 1 patient were surgically proven MRI findings. Other pathological MRI findings were leptomeningeal metastases in 3 patients, granulomatous pachymeningitis sequelae in 2 patients, and aneurysm with compression on cavernous sinus in 1 patient. Three patients had normal MRI findings. The incidence of radiologically proven diagnosis of THS among the patients with the clinical findings suggesting THS seemed to be low in our study. In conclusion, MRI is the most valuable imaging technique to distinguish THS from other THS-like entities, and permits a precise assessment, management, and therapeutic planning of the underlying pathological conditions. (orig.)

Knowledge-based diagnosis for aerospace systems

Science.gov (United States)

Atkinson, David J.

1988-01-01

The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

Presumptive risk factors for monkeypox in rural communities in the Democratic Republic of the Congo.

Directory of Open Access Journals (Sweden)

Claire A Quiner

Full Text Available Monkeypox virus (MPXV, a close relative of Variola virus, is a zoonotic virus with an unknown reservoir. Interaction with infected wildlife, bites from peri-domestic animals, and bushmeat hunting are hypothesized routes of infection from wildlife to humans. Using a Risk Questionnaire, performed in monkeypox-affected areas of rural Democratic Republic of the Congo, we describe the lifestyles and demographics associated with presumptive risk factors for MPXV infection. We generated two indices to assess risk: Household Materials Index (HMI, a proxy for socioeconomic status of households and Risk Activity Index (RAI, which describes presumptive risk for animal-to-human transmission of MPXV. Based on participant self-reported activity patterns, we found that people in this population are more likely to visit the forest than a market to fulfill material needs, and that the reported occupation is limited in describing behavior of individuals may participate. Being bitten by rodents in the home was commonly reported, and this was significantly associated with a low HMI. The highest scoring RAI sub-groups were 'hunters' and males aged ≥ 18 years; however, several activities involving MPXV-implicated animals were distributed across all sub-groups. The current analysis may be useful in identifying at-risk groups and help to direct education, outreach and prevention efforts more efficiently.

Naming and Shaming in Financial Market Regulations: A Violation of the Presumption of Innocence?

Directory of Open Access Journals (Sweden)

Juliette J.W. Pfaeltzer

2014-01-01

Full Text Available Naming and shaming in the financial markets has become a well-known enforcement tool by national supervisors both within and outside the EU. The Netherlands is one of the Member States which permits the publication of offences and administrative sanctions including the name of the offender. However, such publication practice might raise some concerns in the light of certain fundamental human rights. For instance, does naming and shaming violate the presumption of innocence? This article tries to answer this question by evaluating the Dutch publication regime under the Financial Supervision Act. Are the legal safeguards as provided under this Act sufficiently adequate to prevent an infringement of the presumption of innocence?

Diagnosis and Treatment of Pityriasis Rubra Pilaris

Directory of Open Access Journals (Sweden)

Kubanov Alexey

2014-12-01

Full Text Available The article deals with clinical diagnosis and treatment of pityriasis rubra pilaris (PRP. The authors analyze the diagnostic errors, present literature review, and their own observations. The clinical study included 23 patients with pityriasis rubra pilaris: 18 women and 5 men, average age of 54 ± 7.2. The clinical diagnosis of all examined patients was subsequently confirmed by histological analysis of the skin. The primary clinical diagnosis was psoriasis in 15 (65.2% patients, 6 (26% patients received treatment for toxic exanthema, and only 2 (8.8% patients were presumptively diagnosed with pityriasis rubra pilaris. In conclusion, pityriasis rubra pilaris was initially misdiagnosed in 91.2% of patients. Considering the great number of diagnostic errors, we analyzed the main diagnostic and differential diagnostic features of PRP. The most effective of all synthetic retinoids in PRP treatment is acitretin. Although symptomatic improvement in PRP occurs within a month, substantial improvement, even clearing is possible within 4 - 6 months.

Contribution of brain CT in the diagnosis of tuberculous meningitis: a case report from Djibouti.

Science.gov (United States)

Garetier, M; Roche, N C; Longin, C; Clapson, P; Benois, A; Rousset, J

2017-08-01

Tuberculous meningitis, a serious disease with high mortality and morbidity, remains frequent in countries with endemic tuberculosis. Its non-specific presentation often delays the introduction of appropriate treatment. Its definitive diagnosis requires isolation of Mycobacterium tuberculosis from cerebrospinal fluid, although this test may be negative without conclusively ruling out this diagnosis. A presumptive diagnosis should be reached as soon as possible through a body of clinical evidence, including the lumbar puncture findings. Brain computed tomography (CT) with and without contrast medium injection is helpful for the diagnosis of tuberculous meningitis and its complications. We discuss the features of CT and their value in relation to a case of tuberculous meningitis in Djibouti, as well as the role of CT in managing this disease.

PRESUMPTIVE DIAGNOSIS OF SCHISTOSOMA HAEMATOBIUM ...

African Journals Online (AJOL)

boaz

AFRICAN JOURNAL OF CLINICAL AND EXPERIMENTAL MICROBIOLOGY .... study. Sample Collection and Processing. Two specimen containers were given to each subject and the ... false positive results and 24.3% true negative results.

Usefulness of additional fetal magnetic resonance imaging in the prenatal diagnosis of congenital abnormalities.

Science.gov (United States)

We, Ji Sun; Young, Lee; Park, In Yang; Shin, Jong Chul; Im, Soo Ah

2012-12-01

Our aim was to compare the value of fetal magnetic resonance imaging (MRI) with detailed ultrasound in the prenatal diagnosis of congenital abnormalities. This retrospective study reviewed the medical records of pregnant women and their neonates who, after ultrasound, were suspected to have congenital abnormalities. They then underwent a detailed ultrasound examination and a fetal MRI in our institutions. Fetal MRI was performed in 81 cases. Each prenatal presumptive diagnosis, based on detailed ultrasound examination and fetal MRI, was compared with the postnatal confirmed diagnosis. In 58 cases, the data collected were confirmed by the postnatal diagnosis. Supplemental information from fetal MRI was useful in 17 of the 22 cases involving the central nervous system (CNS), two of two cases involving the thorax, nine of nine cases involving the genitourinary system, two of eight cases involving the gastrointestinal system, and ten of ten cases involving complex malformations. Fetal MRI did not provide significantly useful information or facilitate a more accurate diagnosis except for CNS abnormalities. Fetal MRI was not superior to an ultrasound examination in the prenatal detection of congenital abnormalities. A detailed ultrasound examination performed by experienced obstetricians had satisfactory accuracy in the diagnosis of fetal abnormalities compared with fetal MRI. Fetal MRI might be useful in appropriate cases in Korea. Greater effort is required to increase the ultrasound knowledge and skill of competent obstetricians.

Mucocutaneous Leishmaniasis: clinical markers in presumptive diagnosis Leishmaniose mucosa: marcadores clínicos no diagnóstico presuntivo

Directory of Open Access Journals (Sweden)

João Luiz Cioglia Pereira Diniz

2011-06-01

Full Text Available Mucocutaneous Leishmaniasis (ML can lead to serious sequela; however, early diagnosis can prevent complications. AIM: To evaluate clinical markers for the early diagnosis of ML. MATERIALS AND METHODS: A series study of 21 cases of ML, which were evaluated through clinical interview, nasal endoscopy, biopsy and the Montenegro test. RESULTS: A skin scar and previous diagnosis of cutaneous leishmaniasis (CL were reported in 8(38% patients, and 13(62% of them denied having had previous CL and had no scar. Nasal/oral symptom onset until the ML diagnosis varied from 5 months to 20 years, mean value of 6 years. In the Montenegro test, the average size of the papule was 14.5 mm, which did not correlate with disease duration (p=0.87. The nose was the most often involved site and the extension of the injured mucosa did not correlate with disease duration. The parasite was found in 2 (9.52% biopsy specimens. CONCLUSIONS: ML diagnosis was late. Finding the parasite in the mucosa, cutaneous scar and/or previous diagnosis of CL were not clinical markers for ML. ML diagnosis must be based on the Montenegro test, chronic nasal and/or oral discharge and histological findings ruling out other granulomatous diseases.A leishmaniose cutâneo-mucosa (LM pode deixar sequelas graves. O diagnóstico precoce evita complicações. OBJETIVO: Avaliar marcadores clínicos para o diagnóstico precoce da LM. MATERIAL E MÉTODO: Estudo de série de 21 casos avaliados com diagnóstico confirmado de LM por meio de entrevista, endoscopia nasal, biópsia e teste cutâneo de Montenegro. RESULTADOS: A cicatriz cutânea ou história de leishmaniose cutânea foram observadas em 8 (38% pacientes e 13(62% negaram terem tido forma cutânea e não tinham cicatriz. O início dos sintomas nasais/orais até a definição do diagnóstico variou de 5 meses a 20 anos, média de 6 anos. No teste de Montenegro, o tamanho médio da pápula foi de 14,5mm e não se correlacionou com a duração da

Bloodstains on Leather: Examination of False Negatives in Presumptive Test and Human Hemoglobin Test.

Science.gov (United States)

Castelló, Ana; Francès, Francesc; Verdú, Fernando

2017-09-01

Presumptive tests for blood are very simple and sensitive tests used in the search for evidence. They also provide initial information on the nature of stains. A second test can confirm their nature. However, these tests can present false-negative results for different reasons. Some of those reasons have been studied, while others, those caused by the substrate material that contains the stain, are less well known. This work studies the effect of one component of a leather substrate-quebracho extract-on presumptive and human hemoglobin blood tests. Assays were performed using samples of blood dilutions contaminated with quebracho extract and others formed on a substrate containing the contaminant. Results show an undoubted interference that causes false negatives and even visible to the naked eye stains and also indicate that some tests (phenolphthalein) are more affected than others. Examiners should be taken into account when working on this kind of substrates. © 2017 American Academy of Forensic Sciences.

Ontology-Based Method for Fault Diagnosis of Loaders.

Science.gov (United States)

Xu, Feixiang; Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei

2018-02-28

This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

Neurotoxoplasmosis diagnosis for HIV-1 patients by real-time PCR of cerebrospinal fluid

Directory of Open Access Journals (Sweden)

Fábio Luís Nascimento Nogui

Full Text Available Encephalitis caused by Toxoplasma gondii is the most common cause of central nervous system damage in patients with acquired immunodeficiency syndrome (AIDS. Toxoplasma may infect any of the brain cells, thus leading to non-specific neurotoxoplasmosis clinical manifestations including focused or non-focused signs and symptoms of central nervous system malfunction. Clinical development ranges from insidious display during weeks to experiencing acute general confusion or ultimately fatal onset. Cerebral toxoplasmosis occurs in advanced stages of immunodeficiency, and the absence of anti-toxoplasmosis antibodies by the immunofluorescence method does not allow us to rule out its diagnosis. As specific therapy begins, diagnosis confirmation is sought through clinical and radiological response. There are few accurate diagnosis methods to confirm such cases. We present a method for T. gondii DNA detection by real time PCR-Multiplex. Fifty-one patients were evaluated; 16 patients had AIDS and a presumptive diagnosis for toxoplasmosis, 23 patients were HIV-positive with further morbidities except neurotoxoplasmosis, and 12 subjects were HIV-negative control patients. Real time PCR-Multiplex was applied to these patients' cephalorachidian liquid with a specific T. gondii genome sequence from the 529bp fragment. This test is usually carried out within four hours. Test sensitivity, specificity, positive predictive value, and negative predictive value were calculated according to applicable tables. Toxoplasma gondii assay by real time Multiplex of cephalorachidian fluid was positive for 11 out of 16 patients with AIDS and a presumptive diagnosis for cerebral toxoplasmosis, while none of the 35 control patients displayed such a result. Therefore, this method allowed us to achieve 68.8% sensitivity, 100% specificity, 100% positive predictive value, and 87.8% negative predictive value. Real time PCR on CSF allowed high specificity and good sensitivity among

78 FR 28140 - Tentative Eligibility Determinations; Presumptive Eligibility for Psychosis and Other Mental Illness

Science.gov (United States)

2013-05-14

...; Presumptive Eligibility for Psychosis and Other Mental Illness AGENCY: Department of Veterans Affairs. ACTION... time periods and for Persian Gulf War veterans who developed a mental illness other than psychosis... veterans, 38 CFR 17.37, to include veterans with psychosis or mental illness other than psychosis. We are...

47 CFR 51.230 - Presumption of acceptability for deployment of an advanced services loop technology.

Science.gov (United States)

2010-10-01

... an advanced services loop technology. 51.230 Section 51.230 Telecommunication FEDERAL COMMUNICATIONS... Carriers § 51.230 Presumption of acceptability for deployment of an advanced services loop technology. (a) An advanced services loop technology is presumed acceptable for deployment under any one of the...

Effects of surveillance on the rule of law, due process and the presumption of innocence

NARCIS (Netherlands)

Galetta, Antonella; de Hert, Paul; Wright, D.; Kreissl, R.

2015-01-01

This contribution focuses on the impact of surveillance on the rule of law, due process and the presumption of innocence, key values and principles of a democratic order. It illustrates how they are implemented and enforced in contemporary surveillance societies, while referring to European law and

New Diagnosis of AIDS Based on Salmonella enterica subsp. I (enterica Enteritidis (A Meningitis in a Previously Immunocompetent Adult in the United States

Directory of Open Access Journals (Sweden)

Andrew C. Elton

2017-01-01

Full Text Available Salmonella meningitis is a rare manifestation of meningitis typically presenting in neonates and the elderly. This infection typically associates with foodborne outbreaks in developing nations and AIDS-endemic regions. We report a case of a 19-year-old male presenting with altered mental status after 3-day absence from work at a Wisconsin tourist area. He was febrile, tachycardic, and tachypneic with a GCS of 8. The patient was intubated and a presumptive diagnosis of meningitis was made. Treatment was initiated with ceftriaxone, vancomycin, acyclovir, dexamethasone, and fluid resuscitation. A lumbar puncture showed cloudy CSF with Gram negative rods. He was admitted to the ICU. CSF culture confirmed Salmonella enterica subsp. I (enterica Enteritidis (A. Based on this finding, a 4th-generation HIV antibody/p24 antigen test was sent. When this returned positive, a CD4 count was obtained and showed 3 cells/mm3, confirming AIDS. The patient ultimately received 38 days of ceftriaxone, was placed on elvitegravir, cobicistat, emtricitabine, and tenofovir alafenamide (Genvoya for HIV/AIDS, and was discharged neurologically intact after a 44-day admission.

Performance based fault diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik

2002-01-01

Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...

Usefulness of gram staining of blood collected from total parenteral nutrition catheter for rapid diagnosis of catheter-related sepsis.

Science.gov (United States)

Moonens, F; el Alami, S; Van Gossum, A; Struelens, M J; Serruys, E

1994-01-01

The accuracy of Gram staining of blood drawn from catheters used to administer total parenteral nutrition was compared with paired quantitative blood cultures for the diagnosis of catheter-related sepsis. Gram staining was positive in 11 of 18 episodes of catheter-related sepsis documented by quantitative culture (sensitivity, 61%) but in none of the 5 episodes of fever unrelated to catheter infection. Thus, this procedure enabled the rapid presumptive diagnosis and guidance of antimicrobial therapy for total parenteral nutrition catheter sepsis, with a positive predictive value of 100% and a negative predictive value of 42%. PMID:7521359

76 FR 41696 - Presumptive Service Connection for Diseases Associated With Service in the Southwest Asia Theater...

Science.gov (United States)

2011-07-15

... disorders, among the ``Gulf War Seabees'' and that some also have neural damage as a result of vibration...: Functional Gastrointestinal Disorders AGENCY: Department of Veterans Affairs. ACTION: Final rule. SUMMARY... gastrointestinal disorders (FGIDs) and clarifies that FGIDs fall within the scope of the existing presumptions of...

Criminalisation of the Muslim community and the fight for the presumption of innocence

Directory of Open Access Journals (Sweden)

Iker Barbero González

2017-04-01

Full Text Available In parallel to the strategy of neo-Orientalising the Muslim community in Europe, acts of resistance emerge to condemn it. This article considers neo-Orientalisation not only as a strategy for exoticising and/or undermining the community, it demonstrates that it may be understood as an “agonistic Government strategy”. To this end, the paper presents the case of the “Raval 11” and bases its analysis on the interpretation of the resistance by family and activists to the arrests of 11 Pakistanis and Indians on charges of terrorism in Barcelona in 2008 as “acts of citizenship”. New political subjects were engaged: women, young people and children burst onto the scene demanding both freedom and the presumption of innocence for their relatives and dignity for the wider Muslim and migrant community criminalised by the dominant political and media discourses.

The Dsu Article 3.8 Presumption that an Infringement Constitutes a Prima Facie Case of Nullification or Impairment: When Does it Operate and Why?

OpenAIRE

Arwel Davies

2010-01-01

This article considers the origin, meaning and current relevance of the Dispute Settlement Understanding (DSU) Article 3.8 presumption that a government measure which infringes World Trade Organization (WTO) obligations constitutes a prima facie case of nullification or impairment. It is argued that the prevailing interpretation of this provision is inconsistent with its plain language and may have contributed to the tendency of respondent states to invoke the presumption in order to undermin...

Model-Based Methods for Fault Diagnosis: Some Guide-Lines

DEFF Research Database (Denmark)

Patton, R.J.; Chen, J.; Nielsen, S.B.

1995-01-01

This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties.......This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties....

The diagnosis of tuberculosis in dialysis patients

Directory of Open Access Journals (Sweden)

Hela Jebali

2017-01-01

Full Text Available The incidence of tuberculosis (TB is high in patients undergoing chronic dialysis than it is in the general population. The diagnosis of TB is often difficult and extrapulmonary involvement is predominant. This study investigates the spectrum of clinical presentations and outcome in dialysis patients during a nine-year period. TB was diagnosed in 41 patients. Anti-TB drugs, adverse effects of therapy, and outcome were noted. Thirty-eight patients (92.6% were on hemodialysis and three were on peritoneal dialysis (7.3%. The mean age at diagnosis was 50.8 years and the male/female ratio was 1.16. Four patients had a history of pulmonary TB. Extrapulmonary involvement was observed in 32 (78 % patients. The bacteriological confirmation was made in 41.46% and histological confirmation was made in 26.83%, and in the rest, the diagnosis was retained on the criterion presumption. Nineteen patients (46.34% developed adverse effects of antitubercular drugs. Eight patients (19.51% died during the study from TB or adverse effects of treatment. Low urea reduction ratio and female sex were associated with poor prognosis in our study. The clinical manifestations of TB in patients on dialysis are quite nonspecific, making timely diagnosis difficult, and delaying the initiation of curative treatment, which is a major determinant of the outcome.

Fault tolerant control based on active fault diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik

2005-01-01

An active fault diagnosis (AFD) method will be considered in this paper in connection with a Fault Tolerant Control (FTC) architecture based on the YJBK parameterization of all stabilizing controllers. The architecture consists of a fault diagnosis (FD) part and a controller reconfiguration (CR......) part. The FTC architecture can be applied for additive faults, parametric faults, and for system structural changes. Only parametric faults will be considered in this paper. The main focus in this paper is on the use of the new approach of active fault diagnosis in connection with FTC. The active fault...... diagnosis approach is based on including an auxiliary input in the system. A fault signature matrix is introduced in connection with AFD, given as the transfer function from the auxiliary input to the residual output. This can be considered as a generalization of the passive fault diagnosis case, where...

The reversed halo sign: update and differential diagnosis

Science.gov (United States)

Godoy, M C B; Viswanathan, C; Marchiori, E; Truong, M T; Benveniste, M F; Rossi, S; Marom, E M

2012-01-01

The reversed halo sign is characterised by a central ground-glass opacity surrounded by denser air–space consolidation in the shape of a crescent or a ring. It was first described on high-resolution CT as being specific for cryptogenic organising pneumonia. Since then, the reversed halo sign has been reported in association with a wide range of pulmonary diseases, including invasive pulmonary fungal infections, paracoccidioidomycosis, pneumocystis pneumonia, tuberculosis, community-acquired pneumonia, lymphomatoid granulomatosis, Wegener granulomatosis, lipoid pneumonia and sarcoidosis. It is also seen in pulmonary neoplasms and infarction, and following radiation therapy and radiofrequency ablation of pulmonary malignancies. In this article, we present the spectrum of neoplastic and non-neoplastic diseases that may show the reversed halo sign and offer helpful clues for assisting in the differential diagnosis. By integrating the patient's clinical history with the presence of the reversed halo sign and other accompanying radiological findings, the radiologist should be able to narrow the differential diagnosis substantially, and may be able to provide a presumptive final diagnosis, which may obviate the need for biopsy in selected cases, especially in the immunosuppressed population. PMID:22553298

77 FR 76170 - Presumption of Exposure to Herbicides for Blue Water Navy Vietnam Veterans Not Supported

Science.gov (United States)

2012-12-26

... during the Vietnam War. After careful review of the IOM report, the Secretary determines that the... served in deep-water naval vessels off the coast of Vietnam during the Vietnam War are referred to as... DEPARTMENT OF VETERANS AFFAIRS Presumption of Exposure to Herbicides for Blue Water Navy Vietnam...

Process fault diagnosis using knowledge-based systems

International Nuclear Information System (INIS)

Sudduth, A.L.

1991-01-01

Advancing technology in process plants has led to increased need for computer based process diagnostic systems to assist the operator. One approach to this problem is to use an embedded knowledge based system to interpret measurement signals. Knowledge based systems using only symptom based rules are inadequate for real time diagnosis of dynamic systems; therefore a model based approach is necessary. Though several forms of model based reasoning have been proposed, the use of qualitative causal models incorporating first principles knowledge of process behavior structure, and function appear to have the most promise as a robust modeling methodology. In this paper the structure of a diagnostic system is described which uses model based reasoning and conventional numerical methods to perform process diagnosis. This system is being applied to emergency diesel generator system in nuclear stations

Bronchoscopic diagnosis of pulmonary infiltrates in granulocytopenic patients with hematologic malignancies: BAL versus PSB and PBAL.

Science.gov (United States)

Boersma, Wim G; Erjavec, Zoran; van der Werf, Tjip S; de Vries-Hosper, Hilly G; Gouw, Annette S H; Manson, Willem L

2007-02-01

Treatment of patients with hematologic malignancies is often complicated by severe respiratory infections. Bronchoscopy is generally to be used as a diagnostic tool in order to find a causative pathogen. In a prospective study the combination of protected specimen brush (PSB) and protected bronchoalveolar lavage (PBAL) was compared with bronchoalveolar lavage (BAL) for evaluated feasibility and diagnostic yield in granulocytopenic patients with hematologic malignancies and pulmonary infiltrates. All specimens from 63 bronchoscopic procedures (35 BAL and 28 PSB-PBAL) were investigated by cytological examination and various microbiological tests. If clinically relevant and feasible, based on the clinical condition and/or the presence of thrombocytopenia, lung tissue samples were obtained. The majority of the 58 included patients were diagnosed as having acute myeloid leukaemia and developed a severe neutropenia (BAL-group: 27 days; PSB-PBAL group: 30 days). Microbiological and cytological examination of 63 bronchoscopic procedures (35 BAL and 28 PSB-PBAL) yielded causative pathogens in 9 (26%) patients of the BAL-group and 8 (29%) patients of the PSB-PBAL group (PSB and PBAL 4 each). Aspergillus fumigatus was the pathogen most frequently (13%) detected. Using all available examinations including the results of autopsy, a presumptive diagnosis was established in 43% of the patients in the BAL group and 57% of those in the PSB-PBAL group; in these cases microbial aetiology was correctly identified in 67% and 57%, respectively. The complication rate was of these procedures were low, and none of the patients experienced serious complications due to the invasive techniques. Our results showed that modern bronchoscopic techniques such as PSB and PBAL did not yield better diagnostic results compared to BAL in granulocytopenic patients with hematologic malignancies and pulmonary infiltrates. In approximately half of the cases a presumptive diagnosis was made by bronchoscopic

Organizational Diagnosis in Project-Based Companies

Directory of Open Access Journals (Sweden)

Behrouz Zarei

2014-05-01

Full Text Available The purpose of this article is to develop a new method for corporate diagnosis (CD. To this end, a method is developed for the diagnosis process of project-based companies. The article presents a case study in a large company where data have been collected through focus groups. Project delay, high project cost, and low profitability are examples of project deficiency in project-based companies. Such issues have made managers pay special attention to find effective solutions to improve them. Prominent factors are inappropriate strategy, structure, system, human resource management, and PMBOK(Project Management Body of Knowledge processes. Thus, CD and analysis is an important task in improvement of corporate performance. The CD model that is developed in this article could be used for project-based companies. The proposed method can be used for CD in any project-based company. This article provides an emphatic application of CD as a prerequisite for restructuring in project-based companies.

Rule - based Fault Diagnosis Expert System for Wind Turbine

Directory of Open Access Journals (Sweden)

Deng Xiao-Wen

2017-01-01

Full Text Available Under the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge base by using confidence production rules and expert system self-learning method. In Visual Studio 2013 platform, C # language is selected and ADO.NET technology is used to access the database. Development of Fault Diagnosis Expert System for Wind Turbine. The purpose of this paper is to realize on-line diagnosis of wind turbine fault through human-computer interaction, and to improve the diagnostic capability of the system through the continuous improvement of the knowledge base.

Failure diagnosis using deep belief learning based health state classification

International Nuclear Information System (INIS)

Tamilselvan, Prasanna; Wang, Pingfeng

2013-01-01

Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach

Diagnosis and treatment of gastroesophageal reflux disease

Science.gov (United States)

Badillo, Raul; Francis, Dawn

2014-01-01

Gastroesophageal reflux disease (GERD) is a common disease with a prevalence as high as 10%-20% in the western world. The disease can manifest in various symptoms which can be grouped into typical, atypical and extra-esophageal symptoms. Those with the highest specificity for GERD are acid regurgitation and heartburn. In the absence of alarm symptoms, these symptoms can allow one to make a presumptive diagnosis and initiate empiric therapy. In certain situations, further diagnostic testing is needed to confirm the diagnosis as well as to assess for complications or alternate causes for the symptoms. GERD complications include erosive esophagitis, peptic stricture, Barrett’s esophagus, esophageal adenocarcinoma and pulmonary disease. Management of GERD may involve lifestyle modification, medical therapy and surgical therapy. Lifestyle modifications including weight loss and/or head of bed elevation have been shown to improve esophageal pH and/or GERD symptoms. Medical therapy involves acid suppression which can be achieved with antacids, histamine-receptor antagonists or proton-pump inhibitors. Whereas most patients can be effectively managed with medical therapy, others may go on to require anti-reflux surgery after undergoing a proper pre-operative evaluation. The purpose of this review is to discuss the current approach to the diagnosis and treatment of gastroesophageal reflux disease. PMID:25133039

Learning-based diagnosis and repair

NARCIS (Netherlands)

Roos, Nico

2017-01-01

This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus

41 CFR 301-10.5 - What are the presumptions as to the most advantageous method of transportation?

Science.gov (United States)

2010-07-01

... presumptions as to the most advantageous method of transportation? 301-10.5 Section 301-10.5 Public Contracts... most advantageous method of transportation? (a) Common carrier. Travel by common carrier is presumed to be the most advantageous method of transportation and must be used when reasonably available. (b...

High yield of culture-based diagnosis in a TB-endemic setting

NARCIS (Netherlands)

Demers, Anne-Marie; Verver, Suzanne; Boulle, Andrew; Warren, Robin; van Helden, Paul; Behr, Marcel A.; Coetzee, David

2012-01-01

Background: In most of the world, microbiologic diagnosis of tuberculosis (TB) is limited to microscopy. Recent guidelines recommend culture-based diagnosis where feasible. Methods: In order to evaluate the relative and absolute incremental diagnostic yield of culture-based diagnosis in a

Research on Fault Diagnosis Method Based on Rule Base Neural Network

Directory of Open Access Journals (Sweden)

Zheng Ni

2017-01-01

Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

Decision tree and PCA-based fault diagnosis of rotating machinery

Science.gov (United States)

Sun, Weixiang; Chen, Jin; Li, Jiaqing

2007-04-01

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

Approximation Algorithms for Model-Based Diagnosis

NARCIS (Netherlands)

Feldman, A.B.

2010-01-01

Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation

MGP site remediation: Working toward presumptive remedies

International Nuclear Information System (INIS)

Larsen, B.R.

1996-01-01

Manufactured Gas Plants (MGPs) were prevalent in the United States during the 19th and first half of the 20th centuries. MGPs produced large quantities of waste by-products, which varied depending on the process used to manufacture the gas, but most commonly were tars and polynuclear aromatic hydrocarbons. There are an estimated 3,000 to 5,000 abandoned MGP sites across the United States. Because these sites are not concentrated in one geographic location and at least three different manufacturing processes were used, the waste characteristics are very heterogeneous. The question of site remediation becomes how to implement a cost-effective remediation with the variety of cleanup technologies available for these sites. Because of the significant expenditure required for characterization and cleanup of MGP sites, owners and regulatory agencies are beginning to look at standardizing cleanup technologies for these sites. This paper discusses applicable cleanup technologies and the attitude of state regulatory agencies towards the use of presumptive remedies, which can reduce the amount of characterization and detailed analysis necessary for any particular site. Additionally, this paper outlines the process of screening and evaluating candidate technologies, and the progress being made to match the technology to the site

An Event-Based Approach to Distributed Diagnosis of Continuous Systems

Science.gov (United States)

Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

2010-01-01

Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

correlation of who clinical staging with cd4 counts in adult hiv/aids

African Journals Online (AJOL)

2011-02-02

Feb 2, 2011 ... AIDS and HIV/AIDS case definitions for surveillance. (African region) (12). Laboratory and radiological methods were carried out if they were required to make a clinical diagnosis. In the majority of the cases, the HIV clinical events were presumptive diagnosis and based on the WHO clinical staging for ...

High Rates of Non-Tuberculous Mycobacteria Isolation in Mozambican Children with Presumptive Tuberculosis.

Directory of Open Access Journals (Sweden)

Elisa López-Varela

Full Text Available Non-tuberculous mycobacteria (NTM can cause disease which can be clinically and radiologically undistinguishable from tuberculosis (TB, posing a diagnostic and therapeutic challenge in high TB settings. We aim to describe the prevalence of NTM isolation and its clinical characteristics in children from rural Mozambique.This study was part of a community TB incidence study in children <3 years of age. Gastric aspirate and induced sputum sampling were performed in all presumptive TB cases and processed for smear testing using fluorochrome staining and LED Microscopy, liquid and solid culture, and molecular identification by GenoType® Mycobacterium CM/AS assays.NTM were isolated in 26.3% (204/775 of children. The most prevalent NTM species was M. intracellulare (N = 128, followed by M. scrofulaceum (N = 35 and M. fortuitum (N = 9. Children with NTM were significantly less symptomatic and less likely to present with an abnormal chest radiograph than those with M. tuberculosis. NTM were present in 21.6% of follow-up samples and 25 children had the same species isolated from ≥2 separate samples. All were considered clinically insignificant and none received specific treatment. Children with NTM isolates had equal all cause mortality and likelihood of TB treatment as those with negative culture although they were less likely to have TB ruled out.NTM isolation is frequent in presumptive TB cases but was not clinically significant in this patient cohort. However, it can contribute to TB misdiagnosis. Further studies are needed to understand the epidemiology and the clinical significance of NTM in children.

Analysis of operators' diagnosis tasks based on cognitive process

International Nuclear Information System (INIS)

Zhou Yong; Zhang Li

2012-01-01

Diagnosis tasks in nuclear power plants characterized as high-dynamic uncertainties are complex reasoning tasks. Diagnosis errors are the main causes for the error of commission. Firstly, based on mental model theory and perception/action cycle theory, a cognitive model for analyzing operators' diagnosis tasks is proposed. Then, the model is used to investigate a trip event which occurred at crystal river nuclear power plant. The application demonstrates typical cognitive bias and mistakes which operators may make when performing diagnosis tasks. They mainly include the strong confirmation tendency, difficulty to produce complete hypothesis sets, group mindset, non-systematic errors in hypothesis testing, and etc. (authors)

USE OF PRESUMPTIVE TAXATION IN FACILITATING SMALL BUSINESS TAX COMPLIANCE

Directory of Open Access Journals (Sweden)

Victoria IORDACHI

2016-07-01

Full Text Available The actuality of this article is determined by the necessity of implementing fiscal simplicity for increasing tax compliance through fiscal education of small business representatives. In many developing and transition countries, micro and small enterprises are the most rapidly growing business segment. Tax compliance attitude within this sector varies significantly because high conformation costs and difficult formalization procedures can determine many small enterprises to operate in the informal economy. Thus tax regulation of small enterprises is crucial in the process of small entrepreneurs fiscal education and tax simplification of SMEs in many countries becomes one of the most efficient instruments. The main research methods were systemic analysis and logic synthesis. The main results obtained in article, as a result of research, are identification, analysis and systematization of foreign countries’ practices in implementing presumptive tax design and elaboration of some recommendations on fiscal simplicity.

Sensor fault diagnosis of aero-engine based on divided flight status

Science.gov (United States)

Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

2017-11-01

Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

Sensor fault diagnosis of aero-engine based on divided flight status.

Science.gov (United States)

Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

2017-11-01

Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

Colour quantitation for chemical spot tests for a controlled substances presumptive test database.

Science.gov (United States)

Elkins, Kelly M; Weghorst, Alex C; Quinn, Alicia A; Acharya, Subrata

2017-02-01

Crime scene investigators (CSIs) often encounter unknown powders, capsules, tablets, and liquids at crime scenes, many of which are controlled substances. Because most drugs are white powders, however, visual determination of the chemical identity is difficult. Colourimetric tests are a well-established method of presumptive drug identification. Positive tests are often reported differently, however, because two analysts may perceive colour or record colourimetric results in different ways. In addition to perceiving colour differently, it is very common for there to be poor visibility conditions (e.g. rain, darkness) while performing these tests, further obscuring the results. In order to address these concerns and to create uniformity in the reporting of on-site colourimetric test results, this study has evaluated two of the state-of-the-art apps (ColorAssist® and Colorimeter®) for reporting the colour test results quantitatively in red-green-blue (RGB) format. The compiled library database of presumptive test results contains over 3300 data points including over 800 unique drug/test combinations. Variations observed between test replicates, from performing a test on different days, recording with a different device type (e.g. iPod Touch, iPhone models 4, 5c, 5s, or 6), and using different quantities of drug are discussed. Overall, the least variation in Euclidian norm was observed using ColorAssist® with the camera light (25.1±22.1) while the variation between replicates and data recorded using different devices was similar. The resulting library is uploaded to a smartphone application aimed to aid in identifying and interpreting suspected controlled substance evidence. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Long-term follow-up of surgical resection alone for primary ...

African Journals Online (AJOL)

After a presumptive diagnosis of intracranial neoplasia is established based on history, clinical ... the decision to treat and which treatment to choose must be considered. ... A negative correlation between the presence of a midline shift and ST ...

All Roads Lead to Fault Diagnosis : Model-Based Reasoning with LYDIA

NARCIS (Netherlands)

Feldman, A.B.; Pietersma, J.; Van Gemund, A.J.C.

2006-01-01

Model-Based Reasoning (MBR) over qualitative models of complex, real-world systems has proven succesful for automated fault diagnosis, control, and repair. Expressing a system under diagnosis in a formal model and infering a diagnosis given observations are both challenging problems. In this paper

Breath analysis based on micropreconcentrator for early cancer diagnosis

Science.gov (United States)

Lee, Sang-Seok

2018-02-01

We are developing micropreconcentrators based on micro/nanotechnology to detect trace levels of volatile organic compound (VOC) gases contained in human and canine exhaled breath. The possibility of using exhaled VOC gases as biomarkers for various cancer diagnoses has been previously discussed. For early cancer diagnosis, detection of trace levels of VOC gas is indispensable. Using micropreconcentrators based on MEMS technology or nanotechnology is very promising for detection of VOC gas. A micropreconcentrator based breath analysis technique also has advantages from the viewpoints of cost performance and availability for various cancers diagnosis. In this paper, we introduce design, fabrication and evaluation results of our MEMS and nanotechnology based micropreconcentrators. In the MEMS based device, we propose a flower leaf type Si microstructure, and its shape and configuration are optimized quantitatively by finite element method simulation. The nanotechnology based micropreconcentrator consists of carbon nanotube (CNT) structures. As a result, we achieve ppb level VOC gas detection with our micropreconcentrators and usual gas chromatography system that can detect on the order of ppm VOC in gas samples. In performance evaluation, we also confirm that the CNT based micropreconcentrator shows 115 times better concentration ratio than that of the Si based micropreconcentrator. Moreover, we discuss a commercialization idea for new cancer diagnosis using breath analysis. Future work and preliminary clinical testing in dogs is also discussed.

Research of Litchi Diseases Diagnosis Expertsystem Based on Rbr and Cbr

Science.gov (United States)

Xu, Bing; Liu, Liqun

To conquer the bottleneck problems existing in the traditional rule-based reasoning diseases diagnosis system, such as low reasoning efficiency and lack of flexibility, etc.. It researched the integrated case-based reasoning (CBR) and rule-based reasoning (RBR) technology, and put forward a litchi diseases diagnosis expert system (LDDES) with integrated reasoning method. The method use data mining and knowledge obtaining technology to establish knowledge base and case library. It adopt rules to instruct the retrieval and matching for CBR, and use association rule and decision trees algorithm to calculate case similarity.The experiment shows that the method can increase the system's flexibility and reasoning ability, and improve the accuracy of litchi diseases diagnosis.

A study on group decision-making based fault multi-symptom-domain consensus diagnosis

International Nuclear Information System (INIS)

He Yongyong; Chu Fulei; Zhong Binglin

2001-01-01

In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. In this paper, in order to utilize multiple symptom domains to improve the diagnosis accuracy, an idea of fault multi-symptom-domain consensus diagnosis is developed. From the point of view of the group decision-making, two particular multi-symptom-domain diagnosis strategies are proposed. The proposed strategies use BP (Back-Propagation) neural networks as diagnosis models in various symptom domains, and then combine the outputs of these networks by two combination schemes, which are based on Dempster-Shafer evidence theory and fuzzy integral theory, respectively. Finally, a case study pertaining to the fault diagnosis for rotor-bearing systems is given in detail, and the results show that the proposed diagnosis strategies are feasible and more efficient than conventional stacked-vector methods

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

Science.gov (United States)

Daigle, Matthew; Roychoudhury, Indranil

2010-01-01

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

Information Based Fault Diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2008-01-01

Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems. These ...

Restricting Access to ART on the Basis of Criminal Record : An Ethical Analysis of a State-Enforced "Presumption Against Treatment" With Regard to Assisted Reproductive Technologies.

Science.gov (United States)

Thompson, Kara; McDougall, Rosalind

2015-09-01

As assisted reproductive technologies (ART) become increasingly popular, debate has intensified over the ethical justification for restricting access to ART based on various medical and non-medical factors. In 2010, the Australian state of Victoria enacted world-first legislation that denies access to ART for all patients with certain criminal or child protection histories. Patients and their partners are identified via a compulsory police and child protection check prior to commencing ART and, if found to have a previous relevant conviction or child protection order, are given a "presumption against treatment." This article reviews the legislation and identifies arguments that may be used to justify restricting access to ART for various reasons. The arguments reviewed include limitations of reproductive rights, inheriting undesirable genetic traits, distributive justice, and the welfare of the future child. We show that none of these arguments justifies restricting access to ART in the context of past criminal history. We show that a "presumption against treatment" is an unjustified infringement on reproductive freedom and that it creates various inconsistencies in current social, medical, and legal policy. We argue that a state-enforced policy of restricting access to ART based on the non-medical factor of past criminal history is an example of unjust discrimination and cannot be ethically justified, with one important exception: in cases where ART treatment may be considered futile on the basis that the parents are not expected to raise the resulting child.

Neuron Types in the Presumptive Primary Somatosensory Cortex of the Florida Manatee (Trichechus manatus latirostris).

Science.gov (United States)

Reyes, Laura D; Stimpson, Cheryl D; Gupta, Kanika; Raghanti, Mary Ann; Hof, Patrick R; Reep, Roger L; Sherwood, Chet C

2015-01-01

Within afrotherians, sirenians are unusual due to their aquatic lifestyle, large body size and relatively large lissencephalic brain. However, little is known about the neuron type distributions of the cerebral cortex in sirenians within the context of other afrotherians and aquatic mammals. The present study investigated two cortical regions, dorsolateral cortex area 1 (DL1) and cluster cortex area 2 (CL2), in the presumptive primary somatosensory cortex (S1) in Florida manatees (Trichechus manatus latirostris) to characterize cyto- and chemoarchitecture. The mean neuron density for both cortical regions was 35,617 neurons/mm(3) and fell within the 95% prediction intervals relative to brain mass based on a reference group of afrotherians and xenarthrans. Densities of inhibitory interneuron subtypes labeled against calcium-binding proteins and neuropeptide Y were relatively low compared to afrotherians and xenarthrans and also formed a small percentage of the overall population of inhibitory interneurons as revealed by GAD67 immunoreactivity. Nonphosphorylated neurofilament protein-immunoreactive (NPNFP-ir) neurons comprised a mean of 60% of neurons in layer V across DL1 and CL2. DL1 contained a higher percentage of NPNFP-ir neurons than CL2, although CL2 had a higher variety of morphological types. The mean percentage of NPNFP-ir neurons in the two regions of the presumptive S1 were low compared to other afrotherians and xenarthrans but were within the 95% prediction intervals relative to brain mass, and their morphologies were comparable to those found in other afrotherians and xenarthrans. Although this specific pattern of neuron types and densities sets the manatee apart from other afrotherians and xenarthrans, the manatee isocortex does not appear to be explicitly adapted for an aquatic habitat. Many of the features that are shared between manatees and cetaceans are also shared with a diverse array of terrestrial mammals and likely represent highly conserved

On the presumption of evidentiary independence: can confessions corrupt eyewitness identifications?

Science.gov (United States)

Hasel, Lisa E; Kassin, Saul M

2009-01-01

A confession is potent evidence, persuasive to judges and juries. Is it possible that a confession can also affect other evidence? The present study tested the hypothesis that a confession will alter eyewitnesses' identification decisions. Two days after witnessing a staged theft and making an identification decision from a lineup that did not include the thief, participants were told that certain lineup members had confessed or denied guilt during a subsequent interrogation. Among those participants who had made a selection but were told that another lineup member confessed, 61% changed their identifications. Among those participants who had not made an identification, 50% went on to select the confessor when his identity was known. These findings challenge the presumption in law that different forms of evidence are independent and suggest an important overlooked mechanism by which innocent confessors are wrongfully convicted: Potentially exculpatory evidence is corrupted by a confession itself.

Bond graph model-based fault diagnosis of hybrid systems

CERN Document Server

Borutzky, Wolfgang

2015-01-01

This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...

Pituitary tuberculoma: A consideration in the differential diagnosis in a patient manifesting with pituitary apoplexy-like syndrome

Directory of Open Access Journals (Sweden)

Sasima Srisukh

2016-01-01

Full Text Available Pituitary tuberculoma is extremely rare, even in endemic regions of tuberculosis and much less frequently as a presentation of pituitary apoplexy. We describe a 25-year-old female presented with sudden onset of headache and vision loss of left eye which mimicking symptoms of pituitary apoplexy. MRI of the pituitary gland showed a rim-enhancing lesion at the intrasellar region extending into the suprasellar area, but absence of posterior bright spot with enhancement of the pituitary stalk. Pituitary hormonal evaluation revealed panhypopituitarism and diabetes insipidus. An urgent transphenoidal surgery of the pituitary gland was undertaken for which the histopathology showed necrotizing granulomatous inflammation with infarcted adjacent pituitary tissue. Despite negative fungal and AFB staining, pituitary tuberculoma was presumptively diagnosed based on imaging, pathology and the high incidence of tuberculosis in the country. After the course of anti-tuberculosis therapy, the clinical findings were dramatically improved, supporting the diagnosis. Pituitary tuberculoma is extremely rare in particular with an apoplexy-like presentation but should be one of the differential diagnosis list of intrasellar lesions in the patient presenting with sudden onset of headache and visual loss. The presence of diabetes insipidus and thickened with enhancement of pituitary stalk on MRI were very helpful in diagnosing pituitary tuberculosis.

Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections

Science.gov (United States)

Jrad, Nisrine; Grall-Maës, Edith; Beauseroy, Pierre

2009-01-01

Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant on each class and on each wrong or partially correct decision. It is based on ν-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers. PMID:19584932

Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

Science.gov (United States)

Cao, Yan; Sun, Fengru

2018-01-01

In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.

Comparative evaluation of six chromogenic media for presumptive yeast identification.

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Vecchione, Alessandra; Florio, Walter; Celandroni, Francesco; Barnini, Simona; Lupetti, Antonella; Ghelardi, Emilia

2017-12-01

The present study was undertaken to evaluate the discrimination ability of six chromogenic media in presumptive yeast identification. We analysed 108 clinical isolates and reference strains belonging to eight different species: Candida albicans , Candida dubliniensis , Candida tropicalis , Candida krusei , Candida glabrata , Candida parapsilosis , Candida lusitaniae and Trichosporon mucoides . C. albicans , C. tropicalis and C. krusei could be distinguished from one another in all the tested chromogenic media, as predicted by the manufacturers. In addition, C. albicans could be distinguished from C. dubliniensis on BBL CHROMagar Candida, Kima CHROMagar Candida and Brilliance Candida, and C. parapsilosis could be identified on CHROMATIC Candida agar, CHROMOGENIC Candida agar, and Brilliance Candida agar. Brilliance Candida provided the widest discrimination ability, being able to discriminate five out of the seven Candida species tested. Interestingly, C. tropicalis and C. krusei could be already distinguished from each other after 24 hours of incubation. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Gastroesophageal reflux disease in 2006. The imperfect diagnosis

International Nuclear Information System (INIS)

Boyle, John T.

2006-01-01

There continues to be significant controversy related to diagnostic testing for gastroesophageal reflux disease (GERD). Clearly, barium contrast fluoroscopy is superior to any other test in defining the anatomy of the upper gastrointestinal (UGI) tract. Although fluoroscopy can demonstrate gastroesophageal reflux (GER), this observation does not equate to GERD. Fluoroscopy time should not be prolonged to attempt to demonstrate GER during barium contrast radiography. There are no data to justify prolonging fluoroscopy time to perform provocative maneuvers to demonstrate reflux during barium contrast UGI series. Symptoms of GERD may be associated with physiologic esophageal acid exposure measured by intraesophageal pH monitoring, and a significant percentage of patients with abnormal esophageal acid exposure have no or minimal clinical symptoms of reflux. Abnormal acid exposure defined by pH monitoring over a 24-h period does not equate to GERD. In clinical practice presumptive diagnosis of GERD is reasonably assumed by substantial reduction or elimination of suspected reflux symptoms during therapeutic trial of acid reduction therapy. (orig.)

Gastroesophageal reflux disease in 2006. The imperfect diagnosis

Energy Technology Data Exchange (ETDEWEB)

Boyle, John T. [Children' s Hospital of Alabama, Division of Pediatric Gastroenterology, Birmingham, AL (United States); University of Alabama-Birmingham School of Medicine, Division of Pediatric Gastroenterology, Birmingham, AL (United States)

2006-09-15

There continues to be significant controversy related to diagnostic testing for gastroesophageal reflux disease (GERD). Clearly, barium contrast fluoroscopy is superior to any other test in defining the anatomy of the upper gastrointestinal (UGI) tract. Although fluoroscopy can demonstrate gastroesophageal reflux (GER), this observation does not equate to GERD. Fluoroscopy time should not be prolonged to attempt to demonstrate GER during barium contrast radiography. There are no data to justify prolonging fluoroscopy time to perform provocative maneuvers to demonstrate reflux during barium contrast UGI series. Symptoms of GERD may be associated with physiologic esophageal acid exposure measured by intraesophageal pH monitoring, and a significant percentage of patients with abnormal esophageal acid exposure have no or minimal clinical symptoms of reflux. Abnormal acid exposure defined by pH monitoring over a 24-h period does not equate to GERD. In clinical practice presumptive diagnosis of GERD is reasonably assumed by substantial reduction or elimination of suspected reflux symptoms during therapeutic trial of acid reduction therapy. (orig.)

Current status of gastroesophageal reflux disease : diagnosis and treatment.

Science.gov (United States)

Chuang, Tang-Wei; Chen, Shou-Chien; Chen, Kow-Tong

2017-01-01

The aim of this study was to explore the recent advances in diagnosis and treatment of gastroesophageal reflux disease (GERD). Previous studies were searched using the terms "gastroesophageal reflux disease" and "diagnosis" or "treatment" in Medline and Pubmed. Articles that were not published in the English language, manuscripts without an abstract, reviews, meta-analysis, and opinion articles were excluded from the review. After a preliminary screening, all of the articles were reviewed and synthesized to provide an overview of the contemporary approaches to GERD. GERD has a variety of symptomatic manifestations, which can be grouped into typical, atypical and extra-esophageal symptoms. Those with the highest specificity for GERD are acid regurgitation and heartburn. In the absence of other alarming symptoms, these symptoms allow one to make a presumptive diagnosis of GERD and initiate empiric therapy. GERD-associated complications include erosive esophagitis, peptic stricture, Barrett's esophagus, esophageal adenocarcinoma and pulmonary disease. Management of GERD may involve lifestyle modifications, medical and surgical therapy. Medical therapy involves acid suppression, which can be achieved with antacids, histamine-receptor antagonists or proton-pump inhibitors. Whereas most patients can be effectively managed with medical therapy, others may go on to require anti-reflux surgery after undergoing a proper pre-operative evaluation. The management of this disease requires a complex approach. Maintenance therapy of GERD after using anti-secretory drugs should be continuously monitored. © Acta Gastro-Enterologica Belgica.

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

Directory of Open Access Journals (Sweden)

Chen Lu

Full Text Available Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

Science.gov (United States)

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

Presumptive Ischemic Brain Infarction in a Dog with Evans’ Syndrome

Directory of Open Access Journals (Sweden)

Angelo Pasquale Giannuzzi

2014-01-01

Full Text Available A ten-year-old neutered female mixed breed dog was referred for pale mucous membrane and acute onset of right prosencephalic clinical signs. Brain magnetic resonance imaging was suggestive for right middle cerebral artery ischemic stroke. Based on cell blood count, serum biochemistry and serologic tests and flow cytometric detection of anti-platelets and anti-red blood cells antibodies, a diagnosis of immunomediated haemolytic anemia associated with thrombocytopenia of suspected immunomediated origin was done. Immunosuppresive therapy with prednisone was started and the dog clinically recovered. Two months later complete normalization of CBC and serum biochemistry was documented. The dog remained stable for 7 months without therapy; then she relapsed. CBC revealed mild regenerative anemia with spherocytosis and thrombocytopenia. A conclusive Evans’ syndrome diagnosis was done and prednisone and cyclosporine treatment led to normalization of physical and CBC parameters. The dog is still alive at the time the paper submitted. Possible thrombotic etiopathogenetic mechanisms are illustrated in the paper and the authors suggest introducing Evans’ syndrome in the differential diagnosis list for brain ischemic stroke in dogs.

Model-based fault diagnosis in PEM fuel cell systems

Energy Technology Data Exchange (ETDEWEB)

Escobet, T; de Lira, S; Puig, V; Quevedo, J [Automatic Control Department (ESAII), Universitat Politecnica de Catalunya (UPC), Rambla Sant Nebridi 10, 08222 Terrassa (Spain); Feroldi, D; Riera, J; Serra, M [Institut de Robotica i Informatica Industrial (IRI), Consejo Superior de Investigaciones Cientificas (CSIC), Universitat Politecnica de Catalunya (UPC) Parc Tecnologic de Barcelona, Edifici U, Carrer Llorens i Artigas, 4-6, Planta 2, 08028 Barcelona (Spain)

2009-07-01

In this work, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals, indicators that are obtained comparing measured inputs and outputs with analytical relationships, which are obtained by system modelling. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator proposed in the literature is used, with modifications, to include a set of fault scenarios proposed in this work. Finally, it is presented the diagnosis results corresponding to these fault scenarios. It is remarkable that with this methodology it is possible to diagnose and isolate all the faults in the proposed set in contrast with other well known methodologies which use the binary signature matrix of analytical residuals and faults. (author)

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

Science.gov (United States)

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-07-06

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

Hereditary familial polyposis and Gardner's syndrome: contribution of the odonto-stomatology examination in its diagnosis and a case description.

Science.gov (United States)

Chimenos-Küstner, Eduardo; Pascual, Montserrat; Blanco, Ignacio; Finestres, Fernando

2005-01-01

Familial Adenomatous Polyposis (FAP) and its phenotype variant, Gardner's syndrome, constitute a rare autosomal dominant inherited disorder. They are characterised by the development, generally during the second and third decades of life, of multiple adenomatous polyps in the colon and rectum. These polyps have a high risk of subsequently becoming malignant, which normally occurs in the third and fourth decades of life. The phenotypical features of FAP can be very variable. As well as colorectal polyps, these individuals can present with extra-colonic symptoms, among which are particularly: gastro-duodenal polyps, dermoid and epidermoid cysts, desmoid tumours, congenital hypertrophy of the retinal pigment epithelium, disorders of the maxillary and skeletal bones and dental anomalies. In this paper the most important aspects of this syndrome are reviewed, showing an example based on a well documented clinical case. The importance of odonto-stomatological examinations should be pointed out, among others, as a means of reaching a presumptive diagnosis, whose confirmation is vital to the patient.

International non-governmental organizations' provision of community-based tuberculosis care for hard-to-reach populations in Myanmar, 2013-2014.

Science.gov (United States)

Soe, Kyaw Thu; Saw, Saw; van Griensven, Johan; Zhou, Shuisen; Win, Le; Chinnakali, Palanivel; Shah, Safieh; Mon, Myo Myo; Aung, Si Thu

2017-03-24

National tuberculosis (TB) programs increasingly engage with international non-governmental organizations (INGOs), especially to provide TB care in complex settings where community involvement might be required. In Myanmar, however, there is limited data on how such INGO community-based programs are organized and how effective they are. In this study, we describe four INGO strategies for providing community-based TB care to hard-to-reach populations in Myanmar, and assess their contribution to TB case detection. We conducted a descriptive study using program data from four INGOs and the National TB Program (NTP) in 2013-2014. For each INGO, we extracted information on its approach and key activities, the number of presumptive TB cases referred and undergoing TB testing, and the number of patients diagnosed with TB and their treatment outcomes. The contribution of INGOs to TB diagnosis in their selected townships was calculated as the proportion of INGO-diagnosed new TB cases out of the total NTP-diagnosed new TB cases in the same townships. All four INGOs implemented community-based TB care in challenging contexts, targeting migrants, post-conflict areas, the urban poor, and other vulnerable populations. Two recruited community volunteers via existing community health volunteers or health structures, one via existing community leaderships, and one directly involved TB infected/affected individuals. Two INGOs compensated volunteers via performance-based financing, and two provided financial and in-kind initiatives. All relied on NTP laboratories for diagnosis and TB drugs, but provided direct observation treatment support and treatment follow-up. A total of 21 995 presumptive TB cases were referred for TB diagnosis, with 7 383 (34%) new TB cases diagnosed and almost all (98%) successfully treated. The four INGOs contributed to the detection of, on average, 36% (7 383/20 663) of the total new TB cases in their respective townships (range: 15-52%). Community-based TB

Integrated Knowledge Based Expert System for Disease Diagnosis System

Science.gov (United States)

Arbaiy, Nureize; Sulaiman, Shafiza Eliza; Hassan, Norlida; Afizah Afip, Zehan

2017-08-01

The role and importance of healthcare systems to improve quality of life and social welfare in a society have been well recognized. Attention should be given to raise awareness and implementing appropriate measures to improve health care. Therefore, a computer based system is developed to serve as an alternative for people to self-diagnose their health status based on given symptoms. This strategy should be emphasized so that people can utilize the information correctly as a reference to enjoy healthier life. Hence, a Web-based Community Center for Healthcare Diagnosis system is developed based on expert system technique. Expert system reasoning technique is employed in the system to enable information about treatment and prevention of the diseases based on given symptoms. At present, three diseases are included which are arthritis, thalassemia and pneumococcal. Sets of rule and fact are managed in the knowledge based system. Web based technology is used as a platform to disseminate the information to users in order for them to optimize the information appropriately. This system will benefit people who wish to increase health awareness and seek expert knowledge on the diseases by performing self-diagnosis for early disease detection.

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review.

Science.gov (United States)

Huang, Wentao; Sun, Hongjian; Wang, Weijie

2017-06-03

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

Method of fault diagnosis in nuclear power plant base on genetic algorithm and knowledge base

International Nuclear Information System (INIS)

Zhou Yangping; Zhao Bingquan

2000-01-01

Via using the knowledge base, combining Genetic Algorithm and classical probability and contraposing the characteristic of the fault diagnosis of NPP. The authors put forward a method of fault diagnosis. In the process of fault diagnosis, this method contact the state of NPP with the colony in GA and transform the colony to get the individual that adapts to the condition. On the 950MW full size simulator in Beijing NPP simulation training center, experimentation shows it has comparative adaptability to the imperfection of expert knowledge, illusive signal and other instance

Fault diagnosis and fault-tolerant control based on adaptive control approach

CERN Document Server

Shen, Qikun; Shi, Peng

2017-01-01

This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering. .

MRI-based decision tree model for diagnosis of biliary atresia.

Science.gov (United States)

Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

2018-02-23

To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

Ontology based decision system for breast cancer diagnosis

Science.gov (United States)

Trabelsi Ben Ameur, Soumaya; Cloppet, Florence; Wendling, Laurent; Sellami, Dorra

2018-04-01

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters

Science.gov (United States)

Lu, Yunsong; Wang, Fuli; Jia, Mingxing; Qi, Yuanchen

2016-12-01

This paper concerns fault diagnosis of centrifugal compressor based on thermal parameters. An improved qualitative simulation (QSIM) based fault diagnosis method is proposed to diagnose the faults of centrifugal compressor in a gas-steam combined-cycle power plant (CCPP). The qualitative models under normal and two faulty conditions have been built through the analysis of the principle of centrifugal compressor. To solve the problem of qualitative description of the observations of system variables, a qualitative trend extraction algorithm is applied to extract the trends of the observations. For qualitative states matching, a sliding window based matching strategy which consists of variables operating ranges constraints and qualitative constraints is proposed. The matching results are used to determine which QSIM model is more consistent with the running state of system. The correct diagnosis of two typical faults: seal leakage and valve stuck in the centrifugal compressor has validated the targeted performance of the proposed method, showing the advantages of fault roots containing in thermal parameters.

Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition.

Science.gov (United States)

Cheng, Yujie; Zhou, Bo; Lu, Chen; Yang, Chao

2017-05-25

Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.

NN-Es Fault Diagnosis Method in Nuclear Power Equipment Based on Concept Lattice

International Nuclear Information System (INIS)

Liu Yongkuo; Xie Chunli; Xia Hong

2010-01-01

In order to improve the fault diagnosis accuracy of nuclear power plant,neural network and expert systems were combined to give full play to their advantages. In this paper, the concept lattice was applied to get the object properties, extracting the core attributes, dispensable attributes and relative necessary attributes from a large number raw data of fault symptoms.Based on these attributes, neural networks with different levels of importance were designed to improve the learning speed and diagnosis accuracy, and the diagnosis results of the neural networks were verified by using rule-based reasoning expert system. To verify the accuracy of this method, some simulation experiments about the typical faults of nuclear power plant were conducted. And the simulation results show that it is feasible to diagnose nuclear power plant faults with the confederation diagnosis methods combined the neural networks based on the concept lattice theory and expert system, with the distinctive features such as the efficiency of neural network learning, less calculation and reliability of diagnosis results and so on. (authors)

Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

DEFF Research Database (Denmark)

Huang, Sijia; Chong, Nicole; Lewis, Nathan

2016-01-01

diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. Results: The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under.......993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. Conclusions: We have successfully developed a new type of pathway-based model to study...... metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease...

Model-based sensor diagnosis

International Nuclear Information System (INIS)

Milgram, J.; Dormoy, J.L.

1994-09-01

Running a nuclear power plant involves monitoring data provided by the installation's sensors. Operators and computerized systems then use these data to establish a diagnostic of the plant. However, the instrumentation system is complex, and is not immune to faults and failures. This paper presents a system for detecting sensor failures using a topological description of the installation and a set of component models. This model of the plant implicitly contains relations between sensor data. These relations must always be checked if all the components are functioning correctly. The failure detection task thus consists of checking these constraints. The constraints are extracted in two stages. Firstly, a qualitative model of their existence is built using structural analysis. Secondly, the models are formally handled according to the results of the structural analysis, in order to establish the constraints on the sensor data. This work constitutes an initial step in extending model-based diagnosis, as the information on which it is based is suspect. This work will be followed by surveillance of the detection system. When the instrumentation is assumed to be sound, the unverified constraints indicate errors on the plant model. (authors). 8 refs., 4 figs

Diagnosis of Food Allergy Based on Oral Food Challenge Test

Directory of Open Access Journals (Sweden)

Komei Ito

2009-01-01

Full Text Available Diagnosis of food allergy should be based on the observation of allergic symptoms after intake of the suspected food. The oral food challenge test (OFC is the most reliable clinical procedure for diagnosing food allergy. The OFC is also applied for the diagnosis of tolerance of food allergy. The Japanese Society of Pediatric Allergy and Clinical Immunology issued the 'Japanese Pediatric Guideline for Oral Food Challenge Test in Food Allergy 2009' in April 2009, to provide information on a safe and standardized method for administering the OFC. This review focuses on the clinical applications and procedure for the OFC, based on the Japanese OFC guideline.

Landmark-based deep multi-instance learning for brain disease diagnosis.

Science.gov (United States)

Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

2018-01-01

In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.

A knowledge based system for plant diagnosis

International Nuclear Information System (INIS)

Motoda, H.; Yamada, N.; Yoshida, K.

1984-01-01

A knowledge based system for plant diagnosis is proposed in which both event-oriented and function-oriented knowledge are used. For the proposed system to be of practical use, these two types of knowledge are represented by mutually nested four frames, i.e. the component, causality, criteriality, and simulator frames, and production rules. The system provides fast inference capability for use as both a production system and a formal reasoning system, with uncertainty of knowledge taken into account in the former. Event-oriented knowledge is used in both diagnosis and guidance and function-oriented knowledge, in diagnosis only. The inference capability required is forward chaining in the former and resolution in the latter. The causality frame guides in the use of event-oriented knowledge, whereas the criteriality frame does so for function-oriented knowledge. Feedback nature of the plant requires the best first search algorithm that uses histories in the resolution process. The inference program is written in Lisp and the plant simulator and the process I/O control programs in Fortran. Fast data transfer between these two languages is realized by enhancing the memory management capability of Lisp to control the numerical data in the global memory. Simulation applications to a BWR plant demonstrated its diagnostic capability

Prevention and control of sexually transmissible infections among hotel-based female sex workers in Dhaka, Bangladesh.

Science.gov (United States)

McCormick, Duncan F; Rahman, Motiur; Zadrozny, Sabrina; Alam, Anadil; Ashraf, Lutfa; Neilsen, Graham A; Kelly, Robert; Menezes, Prema; Miller, William C; Hoffman, Irving F

2013-12-01

Hotel-based sex workers in Bangladesh have high rates of sexually transmissible infections (STIs), high client turnover and low condom use. Two monthly clinic-based strategies were compared: periodic presumptive treatment (PPT) and enhanced syndromic management (ESM) - one round of presumptive treatment followed by treatment based on assessment and laboratory tests. A randomised controlled trial compared PPT and ESM by prevalence and incidence, behaviour, retention, cost and STI incidence and prevalence. Demographic, behavioural and clinical data were collected from women at two clinics in Dhaka. All women received presumptive treatment and were randomised to receive PPT or ESM at nine monthly visits. In total, 549 women (median age: control. PPT offered a feasible, low-cost alternative to ESM. Educational aspects led to a reduction in coercion and fewer sessions. Implementation studies are needed to improve condom use and retention.

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

Science.gov (United States)

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Commercial surrogacy in India: The presumption of adaptive preference formation, the possibility of autonomy and the persistence of exploitation

OpenAIRE

Fellowes, Melanie G.

2017-01-01

India’s proposed 2016 Bill on the regulation of surrogacy is its latest attempt to respond to criticism regarding the lack of protection given to those entering into a commercial surrogacy arrangement. Adaptive preference theorists presume that a decision made in an oppressive environment, which is inconsistent with the woman’s well-being, is not autonomous and that she is therefore exploited. This article challenges this presumption, arguing that some decisions may be suspected as adaptive p...

Unilateral lichen planus pigmentosus mimicking acral lentiginous melanoma.

Science.gov (United States)

Bickle, Kelly; Smithberger, Erica; Lien, Mary H; Fenske, Neil Alan

2010-07-01

The authors report a case of a Latin American woman who developed progressive pigmentation primarily involving two digits of her right hand. She was scheduled for amputation based on a presumptive histologic diagnosis of melanoma with regression. Dermatology consultation with repeat biopsies disclosed a lichenoid tissue reaction with marked pigment incontinence and no evidence of melanoma. This report should prompt physicians to include lichen planus pigmentosus in the differential diagnosis of acral lentiginous melanoma.

Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance

Directory of Open Access Journals (Sweden)

Detang Zeng

2018-01-01

Full Text Available It is essential to monitor and to diagnose faults in rotating machinery with a high thrust–weight ratio and complex structure for a variety of industrial applications, for which reliable signal measurements are required. However, the measured values consist of the true values of the parameters, the inertia of measurements, random errors and systematic errors. Such signals cannot reflect the true performance state and the health state of rotating machinery accurately. High-quality, steady-state measurements are necessary for most current diagnostic methods. Unfortunately, it is hard to obtain these kinds of measurements for most rotating machinery. Diagnosis based on transient performance is a useful tool that can potentially solve this problem. A model-based fault diagnosis method for gas turbines based on transient performance is proposed in this paper. The fault diagnosis consists of a dynamic simulation model, a diagnostic scheme, and an optimization algorithm. A high-accuracy, nonlinear, dynamic gas turbine model using a modular modeling method is presented that involves thermophysical properties, a component characteristic chart, and system inertial. The startup process is simulated using this model. The consistency between the simulation results and the field operation data shows the validity of the model and the advantages of transient accumulated deviation. In addition, a diagnostic scheme is designed to fulfill this process. Finally, cuckoo search is selected to solve the optimization problem in fault diagnosis. Comparative diagnostic results for a gas turbine before and after washing indicate the improved effectiveness and accuracy of the proposed method of using data from transient processes, compared with traditional methods using data from the steady state.

Imaging diagnosis of Granulocytic Sarcoma in the skull base

International Nuclear Information System (INIS)

Zheng Shaoyan; Xie Jiming; Yang Zhiyun; Zhou Zhou; Li Shurong

2010-01-01

Objective: To improve the understanding and imaging diagnosis of granulocytic sarcoma in the skull base. Methods: Three cases of granulocytic sarcomas in the skull base are reported. The clinical features and imaging findings were analyzed. Results: The three cases occurred in children with acute myeloid leukemia. Two patients presented with oculomotor paralysis before the diagnosis of leukemia, the third patient with history of leukemia presented with headache. Diffuse infiltration of basal skull bone marrow and extracranial soft tissue masses were shown on MRI. The signal intensities of the masses were similar to that of gray matter on T 1 WI and T 2 WI with marked contrast enhancement. The soft tissue masses were located in the para-sellar region and surrounded the lateral wall of the maxillary sinus in one case. The soft tissue mass of the second case infiltrated the orbital cavity, cavernous sinus and oculomotor nerve. Tumor infiltrating the meninges, cranial nerves and paranasal sinuses was seen in the third patient. Conclusion: Cranial nerve paralysis can be the presenting symptom of basal skull granulocytic sarcoma in children. Granulocytic sarcoma should be considered in the different diagnosis when diffuse abnormal signal intensities in the basal skull bone marrow with solitary or multiple soft tissue masses are shown on MRI. (authors)

Invasive candidiasis: future directions in non-culture based diagnosis.

Science.gov (United States)

Posch, Wilfried; Heimdörfer, David; Wilflingseder, Doris; Lass-Flörl, Cornelia

2017-09-01

Delayed initial antifungal therapy is associated with high mortality rates caused by invasive candida infections, since accurate detection of the opportunistic pathogenic yeast and its identification display a diagnostic challenge. diagnosis of candida infections relies on time-consuming methods such as blood cultures, serologic and histopathologic examination. to allow for fast detection and characterization of invasive candidiasis, there is a need to improve diagnostic tools. trends in diagnostics switch to non-culture-based methods, which allow specified diagnosis within significantly shorter periods of time in order to provide early and appropriate antifungal treatment. Areas covered: within this review comprise novel pathogen- and host-related testing methods, e.g. multiplex-PCR analyses, T2 magnetic resonance, fungus-specific DNA microarrays, microRNA characterization or analyses of IL-17 as biomarker for early detection of invasive candidiasis. Expert commentary: Early recognition and diagnosis of fungal infections is a key issue for improved patient management. As shown in this review, a broad range of novel molecular based tests for the detection and identification of Candida species is available. However, several assays are in-house assays and lack standardization, clinical validation as well as data on sensitivity and specificity. This underscores the need for the development of faster and more accurate diagnostic tests.

A hierarchical fuzzy rule-based approach to aphasia diagnosis.

Science.gov (United States)

Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid

2007-10-01

Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.

Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

Directory of Open Access Journals (Sweden)

Jinde Zheng

2014-01-01

Full Text Available A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE, Laplacian score (LS, and support vector machines (SVMs is proposed in this paper. Permutation entropy (PE was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

Combustion engine diagnosis model-based condition monitoring of gasoline and diesel engines and their components

CERN Document Server

Isermann, Rolf

2017-01-01

This book offers first a short introduction to advanced supervision, fault detection and diagnosis methods. It then describes model-based methods of fault detection and diagnosis for the main components of gasoline and diesel engines, such as the intake system, fuel supply, fuel injection, combustion process, turbocharger, exhaust system and exhaust gas aftertreatment. Additionally, model-based fault diagnosis of electrical motors, electric, pneumatic and hydraulic actuators and fault-tolerant systems is treated. In general series production sensors are used. It includes abundant experimental results showing the detection and diagnosis quality of implemented faults. Written for automotive engineers in practice, it is also of interest to graduate students of mechanical and electrical engineering and computer science. The Content Introduction.- I SUPERVISION, FAULT DETECTION AND DIAGNOSIS METHODS.- Supervision, Fault-Detection and Fault-Diagnosis Methods - a short Introduction.- II DIAGNOSIS OF INTERNAL COMBUST...

Utility of diffusion-weighted imaging in the presurgical diagnosis of an infected urachal cyst

International Nuclear Information System (INIS)

Chouhan, Manil; Cuckow, Peter; Humphries, Paul D.

2011-01-01

Urachal cysts are one of a spectrum of urachal abnormalities that occur following failure of regression of the allantois and presumptive bladder between 4 weeks and 6 weeks of gestation. Infection is the most common complication of this rare congenital anomaly. The nonspecific presentation may mimic other pathological processes, underlining their clinical and radiological significance. Imaging investigations typically include US and CT, both of which are limited in their ability to characterize lesions. We report the case of a 5-year-old presenting with macroscopic haematuria in whom diffusion-weighted MRI (DWI) suggested the diagnosis of an infected urachal cyst, which was confirmed surgically. We discuss the radiological findings in multiple imaging modalities and present the application of DWI in this context as a means of improving the radiological diagnostic yield. (orig.)

Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

Directory of Open Access Journals (Sweden)

Kaijuan Yuan

2016-01-01

Full Text Available Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.

Diagnosis of Food Allergy Based on Oral Food Challenge Test

OpenAIRE

Komei Ito; Atsuo Urisu

2009-01-01

Diagnosis of food allergy should be based on the observation of allergic symptoms after intake of the suspected food. The oral food challenge test (OFC) is the most reliable clinical procedure for diagnosing food allergy. The OFC is also applied for the diagnosis of tolerance of food allergy. The Japanese Society of Pediatric Allergy and Clinical Immunology issued the 'Japanese Pediatric Guideline for Oral Food Challenge Test in Food Allergy 2009' in April 2009, to provide information on a sa...

Should the diagnosis of COPD be based on a single spirometry test?

NARCIS (Netherlands)

Schermer, T.R.; Robberts, B.; Crockett, A.J.; Thoonen, B.P.; Lucas, A.; Grootens, J.; Smeele, I.J.; Thamrin, C.; Reddel, H.K.

2016-01-01

Clinical guidelines indicate that a chronic obstructive pulmonary disease (COPD) diagnosis is made from a single spirometry test. However, long-term stability of diagnosis based on forced expiratory volume in 1 s over forced vital capacity (FEV1/FVC) ratio has not been reported. In primary care

PACS-Based Computer-Aided Detection and Diagnosis

Science.gov (United States)

Huang, H. K. (Bernie); Liu, Brent J.; Le, Anh HongTu; Documet, Jorge

The ultimate goal of Picture Archiving and Communication System (PACS)-based Computer-Aided Detection and Diagnosis (CAD) is to integrate CAD results into daily clinical practice so that it becomes a second reader to aid the radiologist's diagnosis. Integration of CAD and Hospital Information System (HIS), Radiology Information System (RIS) or PACS requires certain basic ingredients from Health Level 7 (HL7) standard for textual data, Digital Imaging and Communications in Medicine (DICOM) standard for images, and Integrating the Healthcare Enterprise (IHE) workflow profiles in order to comply with the Health Insurance Portability and Accountability Act (HIPAA) requirements to be a healthcare information system. Among the DICOM standards and IHE workflow profiles, DICOM Structured Reporting (DICOM-SR); and IHE Key Image Note (KIN), Simple Image and Numeric Report (SINR) and Post-processing Work Flow (PWF) are utilized in CAD-HIS/RIS/PACS integration. These topics with examples are presented in this chapter.

Appropriate targeting of artemisinin-based combination therapy by community health workers using malaria rapid diagnostic tests

DEFF Research Database (Denmark)

Ndyomugyenyi, Richard; Magnussen, Pascal; Lal, Sham

2016-01-01

OBJECTIVE: To compare the impact of malaria rapid diagnostic tests (mRDTs), used by community health workers (CHWs), on the proportion of children ...-randomized trials were conducted in two contrasting areas of moderate-to-high and low malaria transmission in rural Uganda. Each trial examined the effectiveness of mRDTs in the management of malaria and targeting of ACTs by CHWs comparing two diagnostic approaches: (i) presumptive clinical diagnosis of malaria...

Heartbeat-based error diagnosis framework for distributed embedded systems

Science.gov (United States)

Mishra, Swagat; Khilar, Pabitra Mohan

2012-01-01

Distributed Embedded Systems have significant applications in automobile industry as steer-by-wire, fly-by-wire and brake-by-wire systems. In this paper, we provide a general framework for fault detection in a distributed embedded real time system. We use heartbeat monitoring, check pointing and model based redundancy to design a scalable framework that takes care of task scheduling, temperature control and diagnosis of faulty nodes in a distributed embedded system. This helps in diagnosis and shutting down of faulty actuators before the system becomes unsafe. The framework is designed and tested using a new simulation model consisting of virtual nodes working on a message passing system.

Study of fault diagnosis software design for complex system based on fault tree

International Nuclear Information System (INIS)

Yuan Run; Li Yazhou; Wang Jianye; Hu Liqin; Wang Jiaqun; Wu Yican

2012-01-01

Complex systems always have high-level reliability and safety requirements, and same does their diagnosis work. As a great deal of fault tree models have been acquired during the design and operation phases, a fault diagnosis method which combines fault tree analysis with knowledge-based technology has been proposed. The prototype of fault diagnosis software has been realized and applied to mobile LIDAR system. (authors)

Presumptive intraperitoneal envenomation resulting in hemoperitoneum and acute abdominal pain in a dog.

Science.gov (United States)

Istvan, Stephanie A; Walker, Julie M; Hansen, Bernard D; Hanel, Rita M; Marks, Steven L

2015-01-01

To describe the clinical features, diagnostic findings, treatment, and outcome of a dog with acute abdominal pain and hemoperitoneum secondary to a presumptive intraperitoneal (IP) snakebite. A 10-month-old castrated male mixed-breed dog was evaluated for suspected snake envenomation. The dog presented recumbent and tachycardic with signs of severe abdominal pain. Two cutaneous puncture wounds and hemoperitoneum were discovered during evaluation. Ultrasonographic examination revealed communication of the wounds with the peritoneal cavity. The dog was treated with supportive care, parenteral analgesia, packed red blood cell and fresh frozen plasma transfusions, crotalid antivenom, and placement of an IP catheter to provide local analgesia. The dog recovered fully and was discharged 5 days after initial presentation. To our knowledge, this is the first report of IP envenomation accompanied by hemorrhage treated with continuous IP analgesia in the veterinary literature. © Veterinary Emergency and Critical Care Society 2015.

Product quality management based on CNC machine fault prognostics and diagnosis

Science.gov (United States)

Kozlov, A. M.; Al-jonid, Kh M.; Kozlov, A. A.; Antar, Sh D.

2018-03-01

This paper presents a new fault classification model and an integrated approach to fault diagnosis which involves the combination of ideas of Neuro-fuzzy Networks (NF), Dynamic Bayesian Networks (DBN) and Particle Filtering (PF) algorithm on a single platform. In the new model, faults are categorized in two aspects, namely first and second degree faults. First degree faults are instantaneous in nature, and second degree faults are evolutional and appear as a developing phenomenon which starts from the initial stage, goes through the development stage and finally ends at the mature stage. These categories of faults have a lifetime which is inversely proportional to a machine tool's life according to the modified version of Taylor’s equation. For fault diagnosis, this framework consists of two phases: the first one is focusing on fault prognosis, which is done online, and the second one is concerned with fault diagnosis which depends on both off-line and on-line modules. In the first phase, a neuro-fuzzy predictor is used to take a decision on whether to embark Conditional Based Maintenance (CBM) or fault diagnosis based on the severity of a fault. The second phase only comes into action when an evolving fault goes beyond a critical threshold limit called a CBM limit for a command to be issued for fault diagnosis. During this phase, DBN and PF techniques are used as an intelligent fault diagnosis system to determine the severity, time and location of the fault. The feasibility of this approach was tested in a simulation environment using the CNC machine as a case study and the results were studied and analyzed.

A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

Directory of Open Access Journals (Sweden)

Gang Ma

2015-01-01

Full Text Available Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

Fuzzy Concurrent Object Oriented Expert System for Fault Diagnosis in 8085 Microprocessor Based System Board

OpenAIRE

Mr.D. V. Kodavade; Dr. Mrs.S.D.Apte

2014-01-01

With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations...

Diagnosis-based and external cause-based criteria to identify adverse drug reactions in hospital ICD-coded data: application to an Australia population-based study

Directory of Open Access Journals (Sweden)

Wei Du

2017-04-01

Full Text Available Objectives: External cause International Classification of Diseases (ICD codes are commonly used to ascertain adverse drug reactions (ADRs related to hospitalisation. We quantified ascertainment of ADR-related hospitalisation using external cause codes and additional ICD-based hospital diagnosis codes. Methods: We reviewed the scientific literature to identify different ICD-based criteria for ADR-related hospitalisations, developed algorithms to capture ADRs based on candidate hospital ICD-10 diagnoses and external cause codes (Y40–Y59, and incorporated previously published causality ratings estimating the probability that a specific diagnosis was ADR related. We applied the algorithms to the NSW Admitted Patient Data Collection records of 45 and Up Study participants (2011–2013. Results: Of 493 442 hospitalisations among 267 153 study participants during 2011–2013, 18.8% (n = 92 953 had hospital diagnosis codes that were potentially ADR related; 1.1% (n = 5305 had high/very high–probability ADR-related diagnosis codes (causality ratings: A1 and A2; and 2.0% (n = 10 039 had ADR-related external cause codes. Overall, 2.2% (n = 11 082 of cases were classified as including an ADR-based hospitalisation on either external cause codes or high/very high–probability ADR-related diagnosis codes. Hence, adding high/very high–probability ADR-related hospitalisation codes to standard external cause codes alone (Y40–Y59 increased the number of hospitalisations classified as having an ADR-related diagnosis by 10.4%. Only 6.7% of cases with high-probability ADR-related mental symptoms were captured by external cause codes. Conclusion: Selective use of high-probability ADR-related hospital diagnosis codes in addition to external cause codes yielded a modest increase in hospitalised ADR incidence, which is of potential clinical significance. Clinically validated combinations of diagnosis codes could potentially further enhance capture.

Stroke Diagnosis using Microstrip Patch Antennas Based on Microwave Tomography Systems

Directory of Open Access Journals (Sweden)

Sakthisudhan K

2017-03-01

Full Text Available Microwave tomography (MT based on stroke diagnosis is one of the alternative methods for determinations of the haemorrhagic, ischemic and stroke in brain nervous systems. It is focusing on the brain imaging, continuous monitoring, and preclinical applications. It provides cost effective system and able to use the rural and urban medical clinics that lack the necessary resources in effective stroke diagnosis during emerging applications in road accident and pre-ambulance clinical treatment. In the early works, the design of microstrip patch antennas (MPAs involved the implementation of MT system. Consequently, the MT system presented a few limitations since it required an efficient MPA design with appropriate parameters. Moreover, there were no specific diagnosis modules and body centric features in it. The present research proposes the MPA designs in the forms of diagnosis modules and implements it on the MT system.

Nanotechnology-Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer

Science.gov (United States)

2017-08-01

AWARD NUMBER: W81XWH-15-1-0157 TITLE: Nanotechnology -Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer PRINCIPAL...TITLE AND SUBTITLE Nanotechnology -Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer 5a. CONTRACT NUMBER 5b. GRANT NUMBER...identify novel differentially expressed miRNAs in the body fluids (blood, urine, etc.) for an early detection of PCa. Advances in nanotechnology and

Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

Science.gov (United States)

Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

2016-01-01

Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A knowledge-based diagnosis system for welding machine problem solving

International Nuclear Information System (INIS)

Bonnieres, P. de; Boutes, J.L.; Calas, M.A.; Para, S.

1986-06-01

This paper presents a knowledge-based diagnosis system which can be a valuable aid in resolving malfunctions and failures encountered using the automatic hot-wire TIG weld cladding process. This knowledge-based system is currently under evaluation by welding operators at the Framatome heavy fabricating facility. Extension to other welding processes is being considered

Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

OpenAIRE

Yuehai Wang; Yongzheng Yan; Qinyong Wang

2016-01-01

Fault diagnosis for analog circuit has become a prominent factor in improving the reliability of integrated circuit due to its irreplaceability in modern integrated circuits. In fact fault diagnosis based on intelligent algorithms has become a popular research topic as efficient feature extraction and selection are a critical and intricate task in analog fault diagnosis. Further, it is extremely important to propose some general guidelines for the optimal feature extraction and selection. In ...

Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

International Nuclear Information System (INIS)

Ben Rabah, N; Saddem, R; Carre-Menetrier, V; Ben Hmida, F; Tagina, M

2017-01-01

Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach. (paper)

A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

Directory of Open Access Journals (Sweden)

Songrong Luo

2016-01-01

Full Text Available The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD technique based local characteristic-scale decomposition (LCD was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN and mean impact value (MIV, ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.

A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine

Directory of Open Access Journals (Sweden)

Xiao-hui He

2016-01-01

Full Text Available To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM. Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form representation of the distribution underlying the training data, and it is very convenient for modeling high-dimensional real-valued data. Experiments on 10 different data sets verify the performance of the proposed method. The superiority of Gaussian RBM classifier is also confirmed by comparing with other classifiers, such as extreme learning machine, support vector machine, and deep belief network. The robustness of the proposed method is also studied in this paper. It can be concluded that the proposed method can realize the bearing fault diagnosis accurately and effectively.

Qualified Presumption of Safety (QPS) is a generic risk assessment approach applied by the European Food Safety Authority (EFSA)

DEFF Research Database (Denmark)

Leuschner, R. G. K.; Robinson, T. P.; Hugas, M.

2010-01-01

Qualified Presumption of Safety (QPS) is a generic risk assessment approach applied by the European Food Safety Authority (EFSA) to notified biological agents aiming at simplifying risk assessments across different scientific Panels and Units. The aim of this review is to outline the implementation...... and value of the QPS assessment for EFSA and to explain its principles such as the unambiguous identity of a taxonomic unit, the body of knowledge including potential safety concerns and how these considerations lead to a list of biological agents recommended for QPS which EFSA keeps updated through...

Gastrointestinal stromal tumors as an incidental finding in patients with a presumptive diagnosis of ovarian cancer.

Science.gov (United States)

Muñoz, Mario; Ramirez, Pedro T; Echeverri, Carolina; Alvarez, Luis Guillermo; Palomino, Maria Alejandra; Pareja, Luis René

2012-01-01

To report the clinical presentation and oncologic outcomes of a series of patients who presented with an abdominal or pelvic mass and were diagnosed with a gastrointestinal stromal tumor (GIST). Data were obtained on all patients who presented with an abdominal or pelvic mass between September 2007 and June 2010 and who were ultimately diagnosed with a GIST. The patients' medical records were reviewed. A literature review was also conducted. Six patients were identified who met the inclusion criteria. All six patients had a tumor in the intestinal tract arising from the small bowel. The mean tumor size was 12 cm (range, 6 to 22 cm). A complete resection was achieved in five of the six patients. There were no intraoperative complications; one patient had a postoperative complication. Two patients were treated with imatinib after surgery. The mean follow-up time was 32 months (range, 0.3 to 40 months). At the last follow-up, five of the six patients were without any evidence of disease. One patient died of an unrelated hepatic encephalopathy. The incidence in our institution is 3%. GISTs are uncommon; however, they should be considered in the differential diagnosis of patients presenting with an abdominal or pelvic mass.

A Fault Diagnosis Model of Surface to Air Missile Equipment Based on Wavelet Transformation and Support Vector Machine

Directory of Open Access Journals (Sweden)

Zhheng Ni

2016-01-01

Full Text Available At present, the fault signals of surface to air missile equipment are hard to collect and the accuracy of fault diagnosis is very low. To solve the above problems, based on the superiority of wavelet transformation on processing non-stationary signals and the advantage of SVM on pattern classification, this paper proposes a fault diagnosis model and takes the typical analog circuit diagnosis of one power distribution system as an example to verify the fault diagnosis model based on Wavelet Transformation and SVM. The simulation results show that the model is able to achieve fault diagnosis based on a small amount of training samples, which improves the accuracy of fault diagnosis.

Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

Directory of Open Access Journals (Sweden)

Yu Yuan

2015-01-01

Full Text Available The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid type J and order parameter q in algorithm, the value range of J and the cut-off condition of q were optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

Precarity and Preparedness: Non-Adherence as Institutional Work in Diagnosing and Treating Malaria in Uganda.

Science.gov (United States)

Umlauf, René

2017-07-01

Access to anti-malarial drugs is increasingly governed by novel regulation technologies like rapid diagnostic tests (RDTs). However, high rates of non-adherence particularly to negative RDT results have been reported, threatening the cost-effectiveness of the two interrelated goals of improving diagnosis and reducing the over-prescription of expensive anti-malarial drugs. Below I set out to reconstruct prior treatment forms like presumptive treatment of malaria by paying particular attention to their institutional groundings. I show how novel regulation technologies affect existing institutions of care and argue that the institutional work of presumptive treatment goes beyond the diagnosis and treatment of a currently observed fever episode. Instead, in contexts of precarity, through what I will call "practices of preparedness," presumptive treatment includes a variety of practices, performances, temporalities, and opportunities that allow individuals to prepare for future episodes of fever.

Scattering transform and LSPTSVM based fault diagnosis of rotating machinery

Science.gov (United States)

Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng

2018-05-01

This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

Directory of Open Access Journals (Sweden)

Xianfeng Yuan

2015-01-01

presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel support vector machine (SVM and Dempster-Shafer (D-S fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

A case-oriented web-based training system for breast cancer diagnosis.

Science.gov (United States)

Huang, Qinghua; Huang, Xianhai; Liu, Longzhong; Lin, Yidi; Long, Xingzhang; Li, Xuelong

2018-03-01

Breast cancer is still considered as the most common form of cancer as well as the leading causes of cancer deaths among women all over the world. We aim to provide a web-based breast ultrasound database for online training inexperienced radiologists and giving computer-assisted diagnostic information for detection and classification of the breast tumor. We introduce a web database which stores breast ultrasound images from breast cancer patients as well as their diagnostic information. A web-based training system using a feature scoring scheme based on Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed. A computer-aided diagnosis (CAD) subsystem was developed to assist the radiologists to make scores on the BI-RADS features for an input case. The training system possesses 1669 scored cases, where 412 cases are benign and 1257 cases are malignant. It was tested by 31 users including 12 interns, 11 junior radiologists, and 8 experienced senior radiologists. This online training system automatically creates case-based exercises to train and guide the newly employed or resident radiologists for the diagnosis of breast cancer using breast ultrasound images based on the BI-RADS. After the trainings, the interns and junior radiologists show significant improvement in the diagnosis of the breast tumor with ultrasound imaging (p-value  .05). The online training system can improve the capabilities of early-career radiologists in distinguishing between the benign and malignant lesions and reduce the misdiagnosis of breast cancer in a quick, convenient and effective manner. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

Science.gov (United States)

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-08

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

Directory of Open Access Journals (Sweden)

Ke Li

2016-01-01

Full Text Available A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF and Diagnostic Bayesian Network (DBN is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO. To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA is proposed to evaluate the sensitiveness of symptom parameters (SPs for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

Science.gov (United States)

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-01

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

Fault Diagnosis for Compensating Capacitors of Jointless Track Circuit Based on Dynamic Time Warping

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Wei Dong

2014-01-01

Full Text Available Aiming at the problem of online fault diagnosis for compensating capacitors of jointless track circuit, a dynamic time warping (DTW based diagnosis method is proposed in this paper. Different from the existing related works, this method only uses the ground indoor monitoring signals of track circuit to locate the faulty compensating capacitor, not depending on the shunt current of inspection train, which is an indispensable condition for existing methods. So, it can be used for online diagnosis of compensating capacitor, which has not yet been realized by existing methods. To overcome the key problem that track circuit cannot obtain the precise position of the train, the DTW method is used for the first time in this situation to recover the function relationship between receiver’s peak voltage and shunt position. The necessity, thinking, and procedure of the method are described in detail. Besides the classical DTW based method, two improved methods for improving classification quality and reducing computation complexity are proposed. Finally, the diagnosis experiments based on the simulation model of track circuit show the effectiveness of the proposed methods.

Nuclear power plant monitoring and fault diagnosis methods based on the artificial intelligence technique

International Nuclear Information System (INIS)

Yoshikawa, S.; Saiki, A.; Ugolini, D.; Ozawa, K.

1996-01-01

The main objective of this paper is to develop an advanced diagnosis system based on the artificial intelligence technique to monitor the operation and to improve the operational safety of nuclear power plants. Three different methods have been elaborated in this study: an artificial neural network local diagnosis (NN ds ) scheme that acting at the component level discriminates between normal and abnormal transients, a model-based diagnostic reasoning mechanism that combines a physical causal network model-based knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge. Although the three methods have been developed and verified independently, they are highly correlated and, when connected together, form a effective and robust diagnosis and monitoring tool. (authors)

A Clinical Approach to the Diagnosis of Acid-Base Disorders

OpenAIRE

Bear, Robert A.

1986-01-01

The ability to diagnose and manage acid-base disorders rapidly and effectively is essential to the care of critically ill patients. This article presents an approach to the diagnosis of pure and mixed acid-base disorders, metabolic or respiratory. The approach taken is based on using the law of mass-action equation as it applies to the bicarbonate buffer system (Henderson equation), using sub-classifications for diagnostic purposes of causes of metabolic acidosis and metabolic alkalosis, and ...

Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

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Mingtao Ge

2017-12-01

Full Text Available This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT and kernel density estimation classifier (KDEC, which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean square, kurtosis, and skewness of the F modes were computed and combined into the feature vector. According to the characteristics of kernel density estimation, a classifier based on kernel density estimation and mutual information was proposed. Then, the feature vectors were input into the KDEC for training and testing. The experimental results indicated that the proposed method can effectively identify three different operative conditions of rolling element bearings, and the accuracy rates was higher than support vector machine (SVM classifier and back-propagation (BP neural network classifier.

Meckel-Gruber Syndrome: Autopsy Based Approach to Diagnosis

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Asaranti Kar

2016-01-01

Full Text Available Meckel-Gruber syndrome (MGS is a rare lethal congenital malformation affecting 1 in 13,250-140,000 live births. The classical diagnostic triad comprises multicystic dysplastic kidneys, occipital encephalocele, and postaxial polydactyly. It can variably be associated with other malformations such as cleft lip and palate, pulmonary hypoplasia, hepatic fibrosis, and anomalies of central nervous system. A 20 weeks fetus was diagnosed as MGS with classical features along with many other congenital abnormalities such as microcephaly, microphthalmia, hypertelorism, cleft lip and palate, neonatal teeth, and the right side club foot which were detected only after doing autopsy. This case is reported because of its rarity emphasizing the importance of neonatal autopsy in every case of fetal death, especially where the antenatal diagnosis has not been made previously. A systematic approach to accurate diagnosis of MGS based on autopsy will be described here which can allow recurrence risk counseling and proper management in future pregnancies.

Advanced neural network-based computational schemes for robust fault diagnosis

CERN Document Server

Mrugalski, Marcin

2014-01-01

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

Intelligence system based classification approach for medical disease diagnosis

Science.gov (United States)

Sagir, Abdu Masanawa; Sathasivam, Saratha

2017-08-01

The prediction of breast cancer in women who have no signs or symptoms of the disease as well as survivability after undergone certain surgery has been a challenging problem for medical researchers. The decision about presence or absence of diseases depends on the physician's intuition, experience and skill for comparing current indicators with previous one than on knowledge rich data hidden in a database. This measure is a very crucial and challenging task. The goal is to predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system. A framework describes methodology for designing and evaluation of classification performances of two discrete ANFIS systems of hybrid learning algorithms least square estimates with Modified Levenberg-Marquardt and Gradient descent algorithms that can be used by physicians to accelerate diagnosis process. The proposed method's performance was evaluated based on training and test datasets with mammographic mass and Haberman's survival Datasets obtained from benchmarked datasets of University of California at Irvine's (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity is examined. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

Non-tuberculous mycobacterial lung disease: diagnosis based on computed tomography of the chest

Energy Technology Data Exchange (ETDEWEB)

Kwak, Nakwon; Han, Sung Koo; Yim, Jae-Joon [Seoul National University College of Medicine, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul (Korea, Republic of); Lee, Chang Hyun; Lee, Hyun-Ju [Seoul National University College of Medicine, Department of Radiology, and Institute of Radiation Medicine, Seoul (Korea, Republic of); Kang, Young Ae [Yonsei University College of Medicine, Division of Pulmonology, Department of Internal Medicine, Severance Hospital, Institute of Chest Diseases, Seoul (Korea, Republic of); Lee, Jae Ho [Seoul National University Bundang Hospital, Department of Internal Medicine, Seongnam, Gyeonggi-do (Korea, Republic of)

2016-12-15

To elucidate the accuracy and inter-observer agreement of non-tuberculous mycobacterial lung disease (NTM-LD) diagnosis based on chest CT findings. Two chest radiologists and two pulmonologists interpreted chest CTs of 66 patients with NTM-LD, 33 with pulmonary tuberculosis and 33 with non-cystic fibrosis bronchiectasis. These observers selected one of these diagnoses for each case without knowing any clinical information except age and sex. Sensitivity and specificity were calculated according to degree of observer confidence. Inter-observer agreement was assessed using Fleiss' κ values. Multiple logistic regression was performed to elucidate which radiological features led to the correct diagnosis. The sensitivity of NTM-LD diagnosis was 56.4 % (95 % CI 47.9-64.7) and specificity 80.3 % (73.1-86.0). The specificity of NTM-LD diagnosis increased with confidence: 44.4 % (20.5-71.3) for possible, 77.4 % (67.4-85.0) for probable, 95.2 % (87.2-98.2) for definite (P < 0.001) diagnoses. Inter-observer agreement for NTM-LD diagnosis was moderate (κ = 0.453). Tree-in-bud pattern (adjusted odds ratio [aOR] 6.24, P < 0.001), consolidation (aOR 1.92, P = 0.036) and atelectasis (aOR 3.73, P < 0.001) were associated with correct NTM-LD diagnoses, whereas presence of pleural effusion (aOR 0.05, P < 0.001) led to false diagnoses. NTM-LD diagnosis based on chest CT findings is specific but not sensitive. (orig.)

Neural network based expert system for fault diagnosis of particle accelerators

International Nuclear Information System (INIS)

Dewidar, M.M.

1997-01-01

Particle accelerators are generators that produce beams of charged particles, acquiring different energies, depending on the accelerator type. The MGC-20 cyclotron is a cyclic particle accelerator used for accelerating protons, deuterons, alpha particles, and helium-3 to different energies. Its applications include isotope production, nuclear reaction, and mass spectroscopy studies. It is a complicated machine, it consists of five main parts, the ion source, the deflector, the beam transport system, the concentric and harmonic coils, and the radio frequency system. The diagnosis of this device is a very complex task. it depends on the conditions of 27 indicators of the control panel of the device. The accurate diagnosis can lead to a high system reliability and save maintenance costs. so an expert system for the cyclotron fault diagnosis is necessary to be built. In this thesis , a hybrid expert system was developed for the fault diagnosis of the MGC-20 cyclotron. Two intelligent techniques, multilayer feed forward back propagation neural network and the rule based expert system, are integrated as a pre-processor loosely coupled model to build the proposed hybrid expert system. The architecture of the developed hybrid expert system consists of two levels. The first level is two feed forward back propagation neural networks, used for isolating the faulty part of the cyclotron. The second level is the rule based expert system, used for troubleshooting the faults inside the isolated faulty part. 4-6 tabs., 4-5 figs., 36 refs

Missed Opportunities for the Diagnosis of Colorectal Cancer

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Laura A. Siminoff

2015-01-01

Full Text Available Objective. To examine patient and medical characteristics which predict a missed diagnostic opportunity (MDO for colorectal cancer (CRC. Methods. The sample consisted of 252 patients diagnosed with Stages 1–4 CRC who were diagnosed in the prior six months, had experienced symptoms prior to diagnosis, and were not diagnosed through routine screening. Systematic review of all medical records prior to patients’ diagnosis was conducted. An MDO was defined as a clinical encounter where, even in the presence of presumptive CRC symptoms, the CRC diagnostic process is not started. Results. 92 patients (36.5% experienced an MDO. Almost 80% of alternate diagnoses were other GI-GU diseases, including hemorrhoids and diverticulitis. Stomach pain, anemia, and constipation were the most common symptoms experienced by the MDO group. These symptoms, and weight loss and vomiting, were more likely to be noted in the charts of the MDO patients (P<0.04. Independent risk factors for MDO included age (<50 [OR = 2.29 (1.14–4.60, P=0.02] and female sex [OR = 2.19 (1.16–4.16, P=0.03]. Each additional physician seen, more than doubled the MDO risk [OR = 2.05 (1.53–2.74, P<0.001]. Conclusions. Females, younger patients, and those consulting more physicians were all more likely to experience an MDO. Continued increased training of physicians to enhance knowledge of who is vulnerable to CRC is needed in addition to an increased focus to adherence to screening recommendations.

The PCR-Based Diagnosis of Central Nervous System Tuberculosis: Up to Date

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Teruyuki Takahashi

2012-01-01

Full Text Available Central nervous system (CNS tuberculosis, particularly tuberculous meningitis (TBM, is the severest form of Mycobacterium tuberculosis (M.Tb infection, causing death or severe neurological defects in more than half of those affected, in spite of recent advancements in available anti-tuberculosis treatment. The definitive diagnosis of CNS tuberculosis depends upon the detection of M.Tb bacilli in the cerebrospinal fluid (CSF. At present, the diagnosis of CNS tuberculosis remains a complex issue because the most widely used conventional “gold standard” based on bacteriological detection methods, such as direct smear and culture identification, cannot rapidly detect M.Tb in CSF specimens with sufficient sensitivity in the acute phase of TBM. Recently, instead of the conventional “gold standard”, the various molecular-based methods including nucleic acid amplification (NAA assay technique, particularly polymerase chain reaction (PCR assay, has emerged as a promising new method for the diagnosis of CNS tuberculosis because of its rapidity, sensitivity and specificity. In addition, the innovation of nested PCR assay technique is worthy of note given its contribution to improve the diagnosis of CNS tuberculosis. In this review, an overview of recent progress of the NAA methods, mainly highlighting the PCR assay technique, was presented.

Direct costs of emergency medical care: a diagnosis-based case-mix classification system.

Science.gov (United States)

Baraff, L J; Cameron, J M; Sekhon, R

1991-01-01

To develop a diagnosis-based case mix classification system for emergency department patient visits based on direct costs of care designed for an outpatient setting. Prospective provider time study with collection of financial data from each hospital's accounts receivable system and medical information, including discharge diagnosis, from hospital medical records. Three community hospital EDs in Los Angeles County during selected times in 1984. Only direct costs of care were included: health care provider time, ED management and clerical personnel excluding registration, nonlabor ED expense including supplies, and ancillary hospital services. Indirect costs for hospitals and physicians, including depreciation and amortization, debt service, utilities, malpractice insurance, administration, billing, registration, and medical records were not included. Costs were derived by valuing provider time based on a formula using annual income or salary and fringe benefits, productivity and direct care factors, and using hospital direct cost to charge ratios. Physician costs were based on a national study of emergency physician income and excluded practice costs. Patients were classified into one of 216 emergency department groups (EDGs) on the basis of the discharge diagnosis, patient disposition, age, and the presence of a limited number of physician procedures. Total mean direct costs ranged from $23 for follow-up visit to $936 for trauma, admitted, with critical care procedure. The mean total direct costs for the 16,771 nonadmitted patients was $69. Of this, 34% was for ED costs, 45% was for ancillary service costs, and 21% was for physician costs. The mean total direct costs for the 1,955 admitted patients was $259. Of this, 23% was for ED costs, 63% was for ancillary service costs, and 14% was for physician costs. Laboratory and radiographic services accounted for approximately 85% of all ancillary service costs and 38% of total direct costs for nonadmitted patients

Taxing bads by taxing goods. Towards efficient pollution control with presumptive charges

International Nuclear Information System (INIS)

Eskeland, G.S.; Devarajan, S.

1995-01-01

A strong case is made for relying on a mix of indirect pollution control instruments - those which tax or regulate activities associated with emissions - rather than taxing the emissions themselves. They show that indirect instruments that reduce the scale of output (such as a tax on output or on polluting inputs) can be important complementary measures to emissions standards that reduce the level of emissions per unit of output. In this way, the effects of an optimal emission fee can be mimicked fairly well. The optimal mix of indirect instruments, however, requires knowledge of the 'cleaner' technologies (the ease with which emissions per unit of output can be reduced) as well as the sensitivity of demand to prices (the ease with which the scale of output can be reduced). This contrasts with the optimal emission fee, which relies only on information about emissions. The authors present empirically-based case studies to illustrate the consequences of employing a combination of presumptive charges and emissions standards. A recurring theme throughout their contribution is that the taxation of fuel use, due to the interaction between fuel use and emissions, can serve as a powerful indirect instrument to supplement pollution standards in controlling air pollution. In the case of automobiles, for example, they show that failing to employ gasoline taxes (which ensure that emissions are cut through not only cleaner cars but also fewer trips) in Mexico City would significantly harm welfare, even when regulatory standards (catalytic converters) are in place. In the case of point-source pollution, they calculate that significant potential exists for altering the fuel mix of industries in Indonesia and Chile by taxing 'dirtier' fuels. Furthermore, they show that, in the case of Indonesia, the general-equilibrium consequences of such a change in the tax structure are similar, though somewhat dampened, compared to what is indicated by partial-equilibrium models

Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

KAUST Repository

Busbait, Monther I.

2014-05-01

We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum depth of decision tree for diagnosis of constant faults depending on the number of edges in a contact network over that basis. Also, we obtain asymptotic bounds on the depth of decision trees for diagnosis of each type of constant faults depending on the number of edges in contact networks in the worst case per basis. We study the set of indecomposable contact networks with up to 10 edges and obtain sharp coefficients for the linear upper bound for diagnosis of constant faults in contact networks over bases of these indecomposable contact networks. We use a set of algorithms, including one that we create, to obtain the sharp coefficients.

Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

Science.gov (United States)

Li, Chaoshun; Zhou, Jianzhong

2014-09-01

Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Community health workers adherence to referral guidelines

DEFF Research Database (Denmark)

Lal, Sham; Ndyomugenyi, Richard; Paintain, Lucy

2016-01-01

artemisinin-based combination therapy (ACT) and recognize symptoms in children that required immediate referral to the nearest health centre. Intervention arm CHWs had additional training on how to conduct an RDT; CHWs in the control arm used a presumptive diagnosis for malaria using clinical signs......Background Many malaria-endemic countries have implemented national community health worker (CHW) programmes to serve remote populations that have poor access to malaria diagnosis and treatment. Despite mounting evidence of CHWs’ ability to adhere to malaria rapid diagnostic tests (RDTs...

Diagnosis and surgical management of malignant ovarian teratoma in a green iguana (Iguana iguana).

Science.gov (United States)

Bel, Lucia; Tecilla, Marco; Borza, Gabriel; Pestean, Cosmin; Purdoiu, Robert; Ober, Ciprian; Oana, Liviu; Taulescu, Marian

2016-07-19

Ovarian tumors in reptiles are uncommonly reported in the literature and for green iguanas previously reported cases include teratomas, one adenocarcinoma and one papillary cystadenocarcinoma. The present report is the first of a malignant ovarian teratoma in a green iguana. Complete and detailed pathological features, differential diagnosis and surgical management of malignant ovarian teratoma are discussed in this paper. A 9-year-old intact female green iguana (Iguana iguana) with a clinical history of persistent anorexia and progressive abdominal distension was referred to the surgery department. On physical examination, a presumptive diagnosis of follicular stasis was established. Radiographic evaluation showed a large radioopaque mass within the abdomen, which was visible both in latero-lateral and ventro-dorsal exposures. Abdominal ultrasonography showed a large intra-abdominal mass, with numerous cyst-like structures filled with liquid and a heterogeneous aspect with hypoechoic areas. Exploratory laparatomy was thus suggested and the mass was removed surgically. The histologic findings of the neoplasm were consistent with those of ovarian malignant teratoma. Surgical excision of the mass in our case was considered curative and after a follow-up period of 6 months the animal has recovered completely. A malignant ovarian teratoma has not been previously reported in green iguana and should be included in the list of differential diagnosis of ovarian tumors in this species. This report will contribute to a better understanding of the pathology of this rare tumor in green iguanas.

Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

Directory of Open Access Journals (Sweden)

Hao Sun

2014-01-01

Full Text Available The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO is proposed. Geronimo, Hardin, and Massopust (GHM multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.

Condition based monitoring, diagnosis and maintenance on operating equipments of a hydraulic generator unit

International Nuclear Information System (INIS)

Liu, X T; Feng, F Z; Si, A W

2012-01-01

According to performance characteristics of operating equipments in a hydraulic generator unit (HGU), the relative techniques on condition monitoring and fault diagnosis (CMFD) are introduced in this paper, especially the key technologies are emphasized, such as equipment monitoring, expert system (ES), intelligent diagnosis and condition based maintenance (CBM). Meanwhile, according to the instructor on CBM proposed by State electric power corporation, based on integrated mode, the main steps on implementation of CBM are discussed in this paper.

Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics

International Nuclear Information System (INIS)

Chen, Zhicong; Wu, Lijun; Cheng, Shuying; Lin, Peijie; Wu, Yue; Lin, Wencheng

2017-01-01

Highlights: •An improved Simulink based modeling method is proposed for PV modules and arrays. •Key points of I-V curves and PV model parameters are used as the feature variables. •Kernel extreme learning machine (KELM) is explored for PV arrays fault diagnosis. •The parameters of KELM algorithm are optimized by the Nelder-Mead simplex method. •The optimized KELM fault diagnosis model achieves high accuracy and reliability. -- Abstract: Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to insufficient monitored information; fault diagnosis models are not efficient to be trained and updated; labeled fault data samples are hard to obtain by field experiments. To address these issues, this paper makes contribution in the following three aspects: (1) based on the key points and model parameters extracted from monitored I-V characteristic curves and environment condition, an effective and efficient feature vector of seven dimensions is proposed as the input of the fault diagnosis model; (2) the emerging kernel based extreme learning machine (KELM), which features extremely fast learning speed and good generalization performance, is utilized to automatically establish the fault diagnosis model. Moreover, the Nelder-Mead Simplex (NMS) optimization method is employed to optimize the KELM parameters which affect the classification performance; (3) an improved accurate Simulink based PV modeling approach is proposed for a laboratory PV array to facilitate the fault simulation and data sample acquisition. Intensive fault experiments are

Fault diagnosis for tilting-pad journal bearing based on SVD and LMD

Directory of Open Access Journals (Sweden)

Zhang Xiaotao

2016-01-01

Full Text Available Aiming at fault diagnosis for tilting-pad journal bearing with fluid support developed recently, a new method based on singular value decomposition (SVD and local mean decomposition (LMD is proposed. First, the phase space reconstruction of Hankel matrix and SVD method are used as pre-filter process unit to reduce the random noises in the original signal. Then the purified signal is decomposed by LMD into a series of production functions (PFs. Based on PFs, time frequency map and marginal spectrum can be obtained for fault diagnosis. Finally, this method is applied to numerical simulation and practical experiment data. The results show that the proposed method can effectively detect fault features of tilting-pad journal bearing.

Five-year risk of HIV diagnosis subsequent to 147 hospital-based indicator diseases

DEFF Research Database (Denmark)

Omland, Lars Haukali; Legarth, Rebecca; Ahlström, Magnus Glindvad

2016-01-01

. To estimate the risk of HIV diagnosis in the general population without any indicator diseases, we calculated the FYRHD starting at age 25, 35, 45, and 55 years. RESULTS: The risk in the male general population was substantially higher than the female general population, and the risk was lower in the older...... with relevant indicator diseases are nonexistent. METHODS: In a nationwide population-based cohort study encompassing all Danish residents aged 20-60 years during 1994-2013, we estimated the 5-year risk of an HIV diagnosis (FYRHD) after a first-time diagnosis of 147 prespecified potential indicator diseases...

Paradoxical embolism: computed tomography demonstration

International Nuclear Information System (INIS)

Kaye, J.; Hayward, M.

2001-01-01

Paradoxical emboli are rare and often presumptively diagnosed. A case of paradoxical embolism, in which both the arterial and venous emboli were documented on CT, is described. While paradoxical emboli are not infrequently diagnosed clinically on a presumptive basis, it is rare to document them with imaging such as CT which, in the present case, confirmed the diagnosis. Copyright (2001) Blackwell Science Pty Ltd

Diagnosis of pregnancy in dairy cows based on the progesterone ...

African Journals Online (AJOL)

Diagnosis of pregnancy in dairy cows based on the progesterone content of milk. Part 1. ... best overall classification of dairy cows into pregnant and non-pregnant groups (confirmed by rectal palpation). Progesterone levels ... Teen 'n diskriminante progesteroonwaarde van 5 ng/ml melk het hierdie funksie 98,0% van alle ...

Performance of a Highly Sensitive Mycobacterium tuberculosis Complex Real-Time PCR Assay for Diagnosis of Pulmonary Tuberculosis in a Low-Prevalence Setting: a Prospective Intervention Study.

Science.gov (United States)

Vinuesa, Víctor; Borrás, Rafael; Briones, María Luisa; Clari, María Ángeles; Cresencio, Vicenta; Giménez, Estela; Muñoz, Carmen; Oltra, Rosa; Servera, Emilio; Scheelje, Talia; Tornero, Carlos; Navarro, David

2018-05-01

The potential impact of routine real-time PCR testing of respiratory specimens from patients with presumptive tuberculosis in terms of diagnostic accuracy and time to tuberculosis treatment inception in low-prevalence settings remains largely unexplored. We conducted a prospective intervention cohort study. Respiratory specimens from 1,020 patients were examined by acid-fast bacillus smear microscopy, tested by a real-time Mycobacterium tuberculosis complex PCR assay (Abbott RealTi me MTB PCR), and cultured in mycobacterial media. Seventeen patients tested positive by PCR (5 were acid-fast bacillus smear positive and 12 acid-fast bacillus smear negative), and Mycobacterium tuberculosis was recovered from cultures for 12 of them. Patients testing positive by PCR and negative by culture ( n = 5) were treated and deemed to have responded to antituberculosis therapy. There were no PCR-negative/culture-positive cases, and none of the patients testing positive for nontuberculous mycobacteria ( n = 20) yielded a positive PCR result. The data indicated that routine testing of respiratory specimens from patients with presumptive tuberculosis by the RealTi me MTB PCR assay improves the tuberculosis diagnostic yield and may reduce the time to antituberculosis treatment initiation. On the basis of our data, we propose a novel mycobacterial laboratory algorithm for tuberculosis diagnosis. Copyright © 2018 American Society for Microbiology.

Learning and case-based reasoning for faults diagnosis-aiding in nuclear power plants

International Nuclear Information System (INIS)

Nicolini, C.

1998-01-01

The aim of this thesis is the design of a faults diagnosis-aiding system in a nuclear facility of the Cea. Actually the existing system allows the optimization of the production processes in regular operating conditions. Meanwhile during accidental events, the alarms, managed by threshold, are bringing no relevant information. To increase the reliability and the safety, the human operator needs a faults diagnosis-aiding system. The developed system, SECAPI, combines problem solving techniques and automatic learning techniques, that allow the diagnosis and the the simulation of various faults happening on nuclear facilities. Its reasoning principle uses case-based and rules-based techniques. SECAPI owns a learning module which reads out knowledge connected with faults. It can then simulate various faults, using the inductive logical computing. SECAPI has been applied on a radioactive tritium treatment operating channel, at the Cea with good results. (A.L.B.)

Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

Directory of Open Access Journals (Sweden)

Jose M. Bernal-de-Lázaro

2016-05-01

Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

[Definition of the Diagnosis Osteomyelitis-Osteomyelitis Diagnosis Score (ODS)].

Science.gov (United States)

Schmidt, H G K; Tiemann, A H; Braunschweig, R; Diefenbeck, M; Bühler, M; Abitzsch, D; Haustedt, N; Walter, G; Schoop, R; Heppert, V; Hofmann, G O; Glombitza, M; Grimme, C; Gerlach, U-J; Flesch, I

2011-08-01

The disease "osteomyelitis" is characterised by different symptoms and parameters. Decisive roles in the development of the disease are played by the causative bacteria, the route of infection and the individual defense mechanisms of the host. The diagnosis is based on different symptoms and findings from the clinical history, clinical symptoms, laboratory results, diagnostic imaging, microbiological and histopathological analyses. While different osteomyelitis classifications have been published, there is to the best of our knowledge no score that gives information how sure the diagnosis "osteomyelitis" is in general. For any scientific study of a disease a valid definition is essential. We have developed a special osteomyelitis diagnosis score for the reliable classification of clinical, laboratory and technical findings. The score is based on five diagnostic procedures: 1) clinical history and risk factors, 2) clinical examination and laboratory results, 3) diagnostic imaging (ultrasound, radiology, CT, MRI, nuclear medicine and hybrid methods), 4) microbiology, and 5) histopathology. Each diagnostic procedure is related to many individual findings, which are weighted by a score system, in order to achieve a relevant value for each assessment. If the sum of the five diagnostic criteria is 18 or more points, the diagnosis of osteomyelitis can be viewed as "safe" (diagnosis class A). Between 8-17 points the diagnosis is "probable" (diagnosis class B). Less than 8 points means that the diagnosis is "possible, but unlikely" (class C diagnosis). Since each parameter can score six points at a maximum, a reliable diagnosis can only be achieved if at least 3 parameters are scored with 6 points. The osteomyelitis diagnosis score should help to avoid the false description of a clinical presentation as "osteomyelitis". A safe diagnosis is essential for the aetiology, treatment and outcome studies of osteomyelitis. © Georg Thieme Verlag KG Stuttgart · New York.

Clinical diagnosis versus autopsy diagnosis in head trauma

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Velnic Andreea-Alexandra

2017-12-01

Full Text Available The correct and complete diagnosis is essential for the adequate care and the favourable clinical evolution of the patients with head trauma. Purpose: To identify the error rate in the clinical diagnosis of head injuries as shown in comparison with the autopsy diagnosis and to identify the most common sources of error. Material and method: We performed a retrospective study based on data from the medical files and the autopsy reports of patients with head trauma who died in the hospital and underwent forensic autopsy. We collected: demographic data, clinical and laboratory data and autopsy findings. To quantify the concordance rate between the clinical diagnosis of death and the autopsy diagnosis we used a 4 classes classification, which ranged from 100% concordance (C1 to total discordance (C4 and two classes of partial discordance: C2 (partial discordance in favour of the clinical diagnosis- missing injuries in the autopsy reports and C3 (partial discordance in favor of the necroptic diagnosis- missing injuries in the medical files. Data were analyzed with SPSS version 20.0. Results: We analyzed 194 cases of death due to head injuries. We found a total concordance between the clinical death diagnosis and autopsy diagnosis in 30.4% of cases and at least one discrepancy in 69.6% of cases. Increasing the duration of hospitalization directly correlates with the amount of the imaging investigations and these in turn correlates with an increased rate of diagnosis concordance. Among the patients with stage 3 coma who associated a spinal cord injury, we found a partial diagnosis discordance in 50% of cases and a total discordance in 50% of cases, possibly due to the need for conducting emergency imaging investigation and the need for surgical treatment. In cases with partial and total discordant diagnosis, at least one lesion was omitted in 45.1% of the cases. The most commonly omitted injuries in C2 cases were subdural hematoma, intracerebral

47 CFR 63.23 - Resale-based international common carriers.

Science.gov (United States)

2010-10-01

... presumption that they lack market power in particular foreign points are available on the International Bureau... 47 Telecommunication 3 2010-10-01 2010-10-01 false Resale-based international common carriers. 63... Supplements § 63.23 Resale-based international common carriers. The following conditions apply to carriers...

Utility of health facility-based malaria data for malaria surveillance.

Directory of Open Access Journals (Sweden)

Yaw A Afrane

Full Text Available Currently, intensive malaria control programs are being implemented in Africa to reduce the malaria burden. Clinical malaria data from hospitals are valuable for monitoring trends in malaria morbidity and for evaluating the impacts of these interventions. However, the reliability of hospital-based data for true malaria incidence is often questioned because of diagnosis accuracy issues and variation in access to healthcare facilities among sub-groups of the population. This study investigated how diagnosis and treatment practices of malaria cases in hospitals affect reliability of hospital malaria data.The study was undertaken in health facilities in western Kenya. A total of 3,569 blood smears were analyzed after being collected from patients who were requested by clinicians to go to the hospital's laboratory for malaria testing. We applied several quality control measures for clinical malaria diagnosis. We compared our slide reading results with those from the hospital technicians. Among the 3,390 patients whose diagnoses were analyzed, only 36% had clinical malaria defined as presence of any level of parasitaemia and fever. Sensitivity and specificity of clinicians' diagnoses were 60.1% (95% CI: 61.1-67.5 and 75.0% (95% CI: 30.8-35.7, respectively. Among the 980 patients presumptively treated with an anti-malarial by the clinicians without laboratory diagnosis, only 47% had clinical malaria.These findings revealed substantial over-prescription of anti-malarials and misdiagnosis of clinical malaria. More than half of the febrile cases were not truly clinical malaria, but were wrongly diagnosed and treated as such. Deficiency in malaria diagnosis makes health facility data unreliable for monitoring trends in malaria morbidity and for evaluating impacts of malaria interventions. Improving malaria diagnosis should be a top priority in rural African health centers.

Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders.

Science.gov (United States)

Liu, Han; Zhou, Jianzhong; Zheng, Yang; Jiang, Wei; Zhang, Yuncheng

2018-04-19

As the rolling bearings being the key part of rotary machine, its healthy condition is quite important for safety production. Fault diagnosis of rolling bearing has been research focus for the sake of improving the economic efficiency and guaranteeing the operation security. However, the collected signals are mixed with ambient noise during the operation of rotary machine, which brings great challenge to the exact diagnosis results. Using signals collected from multiple sensors can avoid the loss of local information and extract more helpful characteristics. Recurrent Neural Networks (RNN) is a type of artificial neural network which can deal with multiple time sequence data. The capacity of RNN has been proved outstanding for catching time relevance about time sequence data. This paper proposed a novel method for bearing fault diagnosis with RNN in the form of an autoencoder. In this approach, multiple vibration value of the rolling bearings of the next period are predicted from the previous period by means of Gated Recurrent Unit (GRU)-based denoising autoencoder. These GRU-based non-linear predictive denoising autoencoders (GRU-NP-DAEs) are trained with strong generalization ability for each different fault pattern. Then for the given input data, the reconstruction errors between the next period data and the output data generated by different GRU-NP-DAEs are used to detect anomalous conditions and classify fault type. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method achieves satisfactory performance with strong robustness and high classification accuracy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Identifying Psoriasis and Psoriatic Arthritis Patients in Retrospective Databases When Diagnosis Codes Are Not Available: A Validation Study Comparing Medication/Prescriber Visit-Based Algorithms with Diagnosis Codes.

Science.gov (United States)

Dobson-Belaire, Wendy; Goodfield, Jason; Borrelli, Richard; Liu, Fei Fei; Khan, Zeba M

2018-01-01

Using diagnosis code-based algorithms is the primary method of identifying patient cohorts for retrospective studies; nevertheless, many databases lack reliable diagnosis code information. To develop precise algorithms based on medication claims/prescriber visits (MCs/PVs) to identify psoriasis (PsO) patients and psoriatic patients with arthritic conditions (PsO-AC), a proxy for psoriatic arthritis, in Canadian databases lacking diagnosis codes. Algorithms were developed using medications with narrow indication profiles in combination with prescriber specialty to define PsO and PsO-AC. For a 3-year study period from July 1, 2009, algorithms were validated using the PharMetrics Plus database, which contains both adjudicated medication claims and diagnosis codes. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of the developed algorithms were assessed using diagnosis code as the reference standard. Chosen algorithms were then applied to Canadian drug databases to profile the algorithm-identified PsO and PsO-AC cohorts. In the selected database, 183,328 patients were identified for validation. The highest PPVs for PsO (85%) and PsO-AC (65%) occurred when a predictive algorithm of two or more MCs/PVs was compared with the reference standard of one or more diagnosis codes. NPV and specificity were high (99%-100%), whereas sensitivity was low (≤30%). Reducing the number of MCs/PVs or increasing diagnosis claims decreased the algorithms' PPVs. We have developed an MC/PV-based algorithm to identify PsO patients with a high degree of accuracy, but accuracy for PsO-AC requires further investigation. Such methods allow researchers to conduct retrospective studies in databases in which diagnosis codes are absent. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Estudio en niños con diagnóstico presuntivo de toxocariasis en Santa Fe, Argentina Analysis of children with a presumptive diagnosis of toxocariasis in Santa Fe, Argentina

Directory of Open Access Journals (Sweden)

Ubaldo O. Martín

2008-10-01

the larvae migrate through the capillaries, taking up residence in different tissues. Clinical manifestations are associated with mechanical and/or reaction damage caused by these parasites larvae. Clinical diagnosis is difficult. The method applied in this work is the demonstration of antibodies against the helminth in the blood of children, target host population of this parasitic disease. An ELISA test was performed using T. canis larval excretory-secretory products as antigen. A total of 100 children presumptively diagnosed of toxocariasis that had been derived from different services of the Regional Children’s Hospital for complementary studies, were included in the analysis. The test detected two different populations: infected (59 and non-infected (41. The statistical analysis showed a non significant association between infection and sex (p = 0.279. Infected subjects tended to be older than the non infected (p = 0.009. Eosinophilia was detected in 100% of seropositive children and in 85.2% of the seronegative. There was no significant association between infection and leucocytosis ( = 0.950. The association of these two parameters was significantly higher among infected patients (R = 0.918. Respiratory symptoms and signs were more frequently detected in the positive population (p = 0.05. Dogs tenancy was as frequent among infected as in the non infected homes (p = 0.53. According to these results, prevention, early diagnosis and opportune treatment for toxocariasis should be considered as prioritary health activities in this region.

Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

KAUST Repository

Busbait, Monther I.

2014-01-01

We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum

An Approach of Diagnosis Based On The Hidden Markov Chains Model

Directory of Open Access Journals (Sweden)

Karim Bouamrane

2008-07-01

Full Text Available Diagnosis is a key element in industrial system maintenance process performance. A diagnosis tool is proposed allowing the maintenance operators capitalizing on the knowledge of their trade and subdividing it for better performance improvement and intervention effectiveness within the maintenance process service. The Tool is based on the Markov Chain Model and more precisely the Hidden Markov Chains (HMC which has the system failures determination advantage, taking into account the causal relations, stochastic context modeling of their dynamics and providing a relevant diagnosis help by their ability of dubious information use. Since the FMEA method is a well adapted artificial intelligence field, the modeling with Markov Chains is carried out with its assistance. Recently, a dynamic programming recursive algorithm, called 'Viterbi Algorithm', is being used in the Hidden Markov Chains field. This algorithm provides as input to the HMC a set of system observed effects and generates at exit the various causes having caused the loss from one or several system functions.

Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

Science.gov (United States)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

2008-03-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Fei Gao

2016-01-01

Full Text Available Single-Stage Extreme Learning Machine (SS-ELM is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

UIO-based Fault Diagnosis for Hydraulic Automatic Gauge Control System of Magnesium Sheet Mill

Directory of Open Access Journals (Sweden)

Li-Ping FAN

2014-02-01

Full Text Available Hydraulic automatic gauge control system of magnesium sheet mill is a complex integrated control system, which including mechanical, hydraulic and electrical comprehensive information. The failure rate of AGC system always is high, and its fault reasons are always complex. Based on analyzing the fault of main components of the automatic gauge control system, unknown input observer is used to realize fault diagnosis and isolation. Simulation results show that the fault diagnosis method based on the unknown input observer for the hydraulic automatic gauge control system of magnesium sheet mill is effective.

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

Science.gov (United States)

Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

2018-03-01

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

Considerations in the Diagnosis and Accelerated Return to Sport of a Professional Basketball Player With a Triceps Surae Injury: A Case Report.

Science.gov (United States)

Anloague, Philip A; Strack, Donald S

2018-05-01

Study Design Case report. Background Acute injuries of the triceps surae and Achilles tendon are common in sports. Rupture of the plantaris tendon can be challenging to diagnose. There is limited evidence detailing the diagnosis, rehabilitation, and accelerated return to sport of elite professional basketball players who have sustained calf injuries. Case Description A 25-year-old male professional basketball player sustained an injury to his calf during a professional basketball game. This case report details the presumptive diagnosis, graduated progression of intervention, and return to play of a professional athlete with a likely isolated plantaris tendon tear. Outcomes The patient returned to postseason competition 10 days post injury. Objective measures were tracked throughout rehabilitation and compared to baseline assessments. Before returning to play, the athlete showed improvements beyond the minimal clinically important difference for calf girth (2 cm) and numeric pain-rating scale score (4 points, 0-10 scale). Functional testing was conducted that included the Y Balance Test lower quarter and the Functional Movement Screen, with results that exceeded or returned the athlete to preseason levels. Discussion This report details the case of a professional basketball player who returned to competitive play in an accelerated time frame following injury to his calf. Diagnosing a plantaris tendon rupture can be challenging, and anatomical variations of this muscle should be considered. It was demonstrated in this case that physical therapy rehabilitation was helpful in making a treatment-based clinical diagnosis when imaging was unclear. Level of Evidence Therapy, level 5. J Orthop Sports Phys Ther 2018;48(5):388-397. Epub 6 Apr 2018. doi:10.2519/jospt.2018.7192.

Observer agreement in the diagnosis of interstitial lung diseases based on HRCT scans

International Nuclear Information System (INIS)

Antunes, Viviane Baptista; Meirelles, Gustavo de Souza Portes; Jasinowodolinski, Dany; Verrastro, Carlos Gustavo Yuji; Torlai, Fabiola Goda

2010-01-01

Objective: to determine the interobserver and intraobserver agreement in the diagnosis of interstitial lung diseases (ILDs) based on HRCT scans and the impact of observer expertise, clinical data and confidence level on such agreement. Methods: two thoracic radiologists and two general radiologists independently reviewed the HRCT images of 58 patients with ILDs on two distinct occasions: prior to and after the clinical anamnesis. The radiologists selected up to three diagnostic hypotheses for each patient and defined the confidence level for these hypotheses. One of the thoracic and one of the general radiologists re-evaluated the same images up to three months after the first readings. In the coefficient analyses, the kappa statistic was used. Results: the thoracic and general radiologists, respectively, agreed on at least one diagnosis for each patient in 91.4% and 82.8% of the patients. The thoracic radiologists agreed on the most likely diagnosis in 48.3% (κ = 0.42) and 62.1% (κ = 0.58) of the cases, respectively, prior to and after the clinical anamnesis; likewise, the general radiologists agreed on the most likely diagnosis in 37.9% (κ 0.32) and 36.2% (κ = 0.30) of the cases. For the thoracic radiologist, the intraobserver agreement on the most likely diagnosis was 0.73 and 0.63 prior to and after the clinical anamnesis, respectively. That for the general radiologist was 0.38 and 0.42.The thoracic radiologists presented almost perfect agreement for the diagnostic hypotheses defined with the high confidence level. Conclusions: Interobserver and intraobserver agreement in the diagnosis of ILDs based on HRCT scans ranged from fair to almost perfect and was influenced by radiologist expertise, clinical history and confidence level. (author)

Fault diagnosis method for nuclear power plants based on neural networks and voting fusion

International Nuclear Information System (INIS)

Zhou Gang; Ge Shengqi; Yang Li

2010-01-01

A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs. (authors)

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Directory of Open Access Journals (Sweden)

Muhammad Sohaib

2017-12-01

Full Text Available Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE-based deep neural networks (DNNs to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs and backpropagation neural networks (BPNNs.

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis.

Science.gov (United States)

Sohaib, Muhammad; Kim, Cheol-Hong; Kim, Jong-Myon

2017-12-11

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

A Case-Based Study with Radiologists Performing Diagnosis Tasks in Virtual Reality.

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Venson, José Eduardo; Albiero Berni, Jean Carlo; Edmilson da Silva Maia, Carlos; Marques da Silva, Ana Maria; Cordeiro d'Ornellas, Marcos; Maciel, Anderson

2017-01-01

In radiology diagnosis, medical images are most often visualized slice by slice. At the same time, the visualization based on 3D volumetric rendering of the data is considered useful and has increased its field of application. In this work, we present a case-based study with 16 medical specialists to assess the diagnostic effectiveness of a Virtual Reality interface in fracture identification over 3D volumetric reconstructions. We developed a VR volume viewer compatible with both the Oculus Rift and handheld-based head mounted displays (HMDs). We then performed user experiments to validate the approach in a diagnosis environment. In addition, we assessed the subjects' perception of the 3D reconstruction quality, ease of interaction and ergonomics, and also the users opinion on how VR applications can be useful in healthcare. Among other results, we have found a high level of effectiveness of the VR interface in identifying superficial fractures on head CTs.

Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

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Xiaoming Xu

2017-01-01

Full Text Available In traditional principle component analysis (PCA, because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

Rare Cause of Pleuropnemonia: Tularemia Disease.

Science.gov (United States)

Agca, Meltem; Duman, Dildar; Sulu, Ebru; Ozbaki, Fatma; Barkay, Orcun; Ozturk, Derya; Yarkin, Tulay

2017-09-01

Tularemia is a zoonotic infection which is caused by gram negative coccobacilli, Francisella tularensis. The disease occurs after contact with blood and body fluids of infected animals, bites and ingestion of infected food and water. Although it commonly presents with skin lesions, there may also be serious organ involvements. A55-year woman was consulted for presumptive diagnosis of tuberculosis. Multiple lymphadenopathy in right cervical area was present on physical examination. Pleural effusion on left side was detected with computed tomography. In detailed history, knowledge of a family member with the diagnosis of tularemia was obtained. Both of them had the history of contact with infected animals. Diagnosis of tularemia was confirmed with microagglutination test. With this patient who was initially presumptively diagnosed as tuberculosis, we aim to draw attention to diagnosis of tularemia in the presence of pleuropnemonia and peripheral lymphadenopathy and emphasize importance of detailed patient history.

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

Science.gov (United States)

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

Computer-Aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review.

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Bharti, Puja; Mittal, Deepti; Ananthasivan, Rupa

2016-04-19

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis. © The Author(s) 2016.

Endometriosis of the liver: Findings in imaging diagnosis

International Nuclear Information System (INIS)

Nakanishi, K.; Bohndorf, K.; Lindemann, F.; Leipprand, E.

1994-01-01

Endometriosis of the liver is an extremely rare disease. To our knowledge, no more than three such cases were so far mentioned in the relevant literature. Moreover, we understand that nmr findings to prove the presence of hepatic endometriosis have not yet been described. We consider nmr imaging to be a suitable tool to establish a presumptive, if not firm, diagnosis of hepatic endometriosis. A sign strongly suggestive of the disorder is the irregular pattern of blood constituents of different ages that can invariably be visualized using this method. Due to the great amounts of free methaemoglobin found in subacute haemorrhages in increase insignal intensity can be observed for T 1 -weighted and T 2 -weighted SE sequences. The residues of former bleedings into the stroma, which are histologically confirmed by haemosiderin deposits, account for the greatly diminished signal intensity in T 1 -weighted images. An unusual finding here was the comparatively high signal intensity observed for T 2 -weighted images in those areas, where signals were practically absent in T 1 -weighted images. In our opinion, this can be explained by scattered subacute bleedings, which are probably too small in amount to produce signals in T 1 -weighted pictures. (orig./MG) [de

Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

International Nuclear Information System (INIS)

Wang, M; Hu, N Q; Qin, G J

2011-01-01

In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

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Wang, M; Hu, N Q; Qin, G J, E-mail: hnq@nudt.edu.cn, E-mail: wm198063@yahoo.com.cn [School of Mechatronic Engineering and Automation, National University of Defense Technology, ChangSha, Hunan, 410073 (China)

2011-07-19

In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI

DEFF Research Database (Denmark)

Bron, Esther E.; Smits, Marion; van der Flier, Wiesje M.

2015-01-01

algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease...... of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume......Abstract Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform...

Deterministic versus evidence-based attitude towards clinical diagnosis.

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Soltani, Akbar; Moayyeri, Alireza

2007-08-01

Generally, two basic classes have been proposed for scientific explanation of events. Deductive reasoning emphasizes on reaching conclusions about a hypothesis based on verification of universal laws pertinent to that hypothesis, while inductive or probabilistic reasoning explains an event by calculation of some probabilities for that event to be related to a given hypothesis. Although both types of reasoning are used in clinical practice, evidence-based medicine stresses on the advantages of the second approach for most instances in medical decision making. While 'probabilistic or evidence-based' reasoning seems to involve more mathematical formulas at the first look, this attitude is more dynamic and less imprisoned by the rigidity of mathematics comparing with 'deterministic or mathematical attitude'. In the field of medical diagnosis, appreciation of uncertainty in clinical encounters and utilization of likelihood ratio as measure of accuracy seem to be the most important characteristics of evidence-based doctors. Other characteristics include use of series of tests for refining probability, changing diagnostic thresholds considering external evidences and nature of the disease, and attention to confidence intervals to estimate uncertainty of research-derived parameters.

A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

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Chen Bor-Sen

2011-01-01

Full Text Available Abstract Background Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective. Methods In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples. Results Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs. In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer. Conclusions A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

[COMPLICATED AMOEBIC APENDICITIS.REPORT OF A CASE

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Casavilca Zambrano, Sandro; Gomez Anchante, Victor; Cisneros Gallegos, Eduardo

2000-01-01

We report a case of acute abdomen that is operated with the presumptive diagnosis of complicated acute appendicitis. In the histologic examination we make the diagnosis of complicated amoebic appendicitis. We discuss clinical manifestations and histopathologic findings of this unusual presentation of amoebic infection.

Presumptions of effective operation of diesel engines running on rme biodiesel. Research on kinetics of combustion of RME biodiesel

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A. Vaicekauskas

2007-06-01

Full Text Available The results of experimental research on kinetics of fuel combustion of diesel engine A41are presented in the publication. The change of characteristics of indicated work (in-cylinder pressure and temperature, period of induction, heat release and heat release rate and fuel injection (fuel injection pressure, fuel injection phases was determined in diesel engine running on RME biodiesel being compared to diesel fuel. The results of researches were used to explain experimentally determined changes of operational and ecological characteristics of diesel engine running on RME biodiesel. In addition, the reliability of diesel engine A41 running on RME biodiesel was evaluated. The presumptions of effective operation of diesel engines running on RME biodiesel were formulated.

Decision of Habeas Corpus n. 126.292: Relativization of the Principle of Presumption of Innocence and the Constitutional Jurisdiction in Perspective

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Hamilton da Cunha Iribure Júnior

2016-10-01

Full Text Available The article aims to analyze the breach of the presumption of innocence with the anticipation of the sentence before the final judgment of conviction, in a recent decision of the Supreme Court. Adopted documentary analytical methodology. Assumes that fundamental rights are not absolute and must be relativized. Deals with the constitutional jurisdiction in this perspective and the limits of the judicial role in the exercise of interpretation of the law. One of the conclusions is that the relativization of fundamental rights in Brazil follows tendency to give in proceedings other than the Constitutional Court.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.

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Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J Peter; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir; Jonas, Karyn; Chan, R V Paul; Ostmo, Susan; Chiang, Michael F

2015-11-01

We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system. Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

Metabolic profile at first-time schizophrenia diagnosis: a population-based cross-sectional study

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Horsdal HT

2017-02-01

Full Text Available Henriette Thisted Horsdal,1,2 Michael Eriksen Benros,2,3 Ole Köhler-Forsberg,2–4 Jesper Krogh,3 Christiane Gasse1,2,5 1National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus BSS, Aarhus University, Aarhus, 2The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, 3Faculty of Health Sciences, Mental Health Centre Copenhagen, University of Copenhagen, Copenhagen, 4Psychosis Research Unit, Aarhus University Hospital, Risskov, 5Centre for Integrated Register-Based Research, Aarhus University, Aarhus, Denmark Objective: Schizophrenia and/or antipsychotic drug use are associated with metabolic abnormalities; however, knowledge regarding metabolic status and physician’s monitoring of metabolic status at first schizophrenia diagnosis is sparse. We assessed the prevalence of monitoring for metabolic blood abnormalities and characterized the metabolic profiles in people with a first-time schizophrenia diagnosis. Methods: This is a population-based cross-sectional study including all adults born in Denmark after January 1, 1955, with their first schizophrenia diagnosis between 2000 and 2012 in the Central Denmark Region. Information on metabolic parameters was obtained from a clinical laboratory information system. Associations were calculated using Wilcoxon rank-sum tests, chi-square tests, logistic regression, and Spearman’s correlation coefficients. Results: A total of 2,452 people with a first-time schizophrenia diagnosis were identified, of whom 1,040 (42.4% were monitored for metabolic abnormalities. Among those monitored, 58.4% had an abnormal lipid profile and 13.8% had an abnormal glucose profile. People who had previously filled prescription(s for antipsychotic drugs were more likely to present an abnormal lipid measure (65.7% vs 46.8%, P<0.001 and abnormal glucose profile (16.4% vs 10.1%, P=0.01. Conclusion: Metabolic abnormalities are common at first

Parent-based diagnosis of ADHD is as accurate as a teacher-based diagnosis of ADHD.

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Bied, Adam; Biederman, Joseph; Faraone, Stephen

2017-04-01

To review the literature evaluating the psychometric properties of parent and teacher informants relative to a gold-standard ADHD diagnosis in pediatric populations. We included studies that included both a parent and teacher informant, a gold-standard diagnosis, and diagnostic accuracy metrics. Potential confounds were evaluated. We also assessed the 'OR' and the 'AND' rules for combining informant reports. Eight articles met inclusion criteria. The diagnostic accuracy for predicting gold standard ADHD diagnoses did not differ between parents and teachers. Sample size, sample type, participant drop-out, participant age, participant gender, geographic area of the study, and date of study publication were assessed as potential confounds. Parent and teachers both yielded moderate to good diagnostic accuracy for ADHD diagnoses. Parent reports were statistically indistinguishable from those of teachers. The predictive features of the 'OR' and 'AND' rules are useful in evaluating approaches to better integrating information from these informants.

Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles

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Peng Liu

2018-01-01

Full Text Available The battery is a key component and the major fault source in electric vehicles (EVs. Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis.

Brain medical image diagnosis based on corners with importance-values.

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Gao, Linlin; Pan, Haiwei; Li, Qing; Xie, Xiaoqin; Zhang, Zhiqiang; Han, Jinming; Zhai, Xiao

2017-11-21

Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection

Review of Diagnosis-Related Group-Based Financing of Hospital Care

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Natasa Mihailovic

2016-05-01

Full Text Available Since the 1990s, diagnosis-related group (DRG-based payment systems were gradually introduced in many countries. The main design characteristics of a DRG-based payment system are an exhaustive patient case classification system (ie, the system of diagnosis-related groupings and the payment formula, which is based on the base rate multiplied by a relative cost weight specific for each DRG. Cases within the same DRG code group are expected to undergo similar clinical evolution. Consecutively, they should incur the costs of diagnostics and treatment within a predefined scale. Such predictability was proven in a number of cost-of-illness studies conducted on major prosperity diseases alongside clinical trials on efficiency. This was the case with risky pregnancies, chronic obstructive pulmonary disease, diabetes, depression, alcohol addiction, hepatitis, and cancer. This article presents experience of introduced DRG-based payments in countries of western and eastern Europe, Scandinavia, United States, Canada, and Australia. This article presents the results of few selected reviews and systematic reviews of the following evidence: published reports on health system reforms by World Health Organization, World Bank, Organization for Economic Co-operation and Development, Canadian Institute for Health Information, Canadian Health Services Research Foundation, and Centre for Health Economics University of York. Diverse payment systems have different strengths and weaknesses in relation to the various objectives. The advantages of the DRG payment system are reflected in the increased efficiency and transparency and reduced average length of stay. The disadvantage of DRG is creating financial incentives toward earlier hospital discharges. Occasionally, such polices are not in full accordance with the clinical benefit priorities.

A diagnosis-based clinical decision rule for spinal pain part 2: review of the literature

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Hurwitz Eric L

2008-08-01

Full Text Available Abstract Background Spinal pain is a common and often disabling problem. The research on various treatments for spinal pain has, for the most part, suggested that while several interventions have demonstrated mild to moderate short-term benefit, no single treatment has a major impact on either pain or disability. There is great need for more accurate diagnosis in patients with spinal pain. In a previous paper, the theoretical model of a diagnosis-based clinical decision rule was presented. The approach is designed to provide the clinician with a strategy for arriving at a specific working diagnosis from which treatment decisions can be made. It is based on three questions of diagnosis. In the current paper, the literature on the reliability and validity of the assessment procedures that are included in the diagnosis-based clinical decision rule is presented. Methods The databases of Medline, Cinahl, Embase and MANTIS were searched for studies that evaluated the reliability and validity of clinic-based diagnostic procedures for patients with spinal pain that have relevance for questions 2 (which investigates characteristics of the pain source and 3 (which investigates perpetuating factors of the pain experience. In addition, the reference list of identified papers and authors' libraries were searched. Results A total of 1769 articles were retrieved, of which 138 were deemed relevant. Fifty-one studies related to reliability and 76 related to validity. One study evaluated both reliability and validity. Conclusion Regarding some aspects of the DBCDR, there are a number of studies that allow the clinician to have a reasonable degree of confidence in his or her findings. This is particularly true for centralization signs, neurodynamic signs and psychological perpetuating factors. There are other aspects of the DBCDR in which a lesser degree of confidence is warranted, and in which further research is needed.

Laboratory Diagnosis of Carbohydrate Metabolism Disorders. Diagnosis Algorithm in Hyperglycemic States

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V.I. Pankiv

2014-04-01

Full Text Available The article is devoted to the laboratory diagnosis of disorders of carbohydrate metabolism. Presents criteria for diagnosis of diabetes, an algorithm for oral glucose tolerance test, determine type of diabetes based on clinical and laboratory data. The article also raised the issues of diagnosis of gestational diabetes and a diagnostic algorithm of hyperglycemia conditions during pregnancy.

Proposed diagnosis criteria for inflicted head injury of children younger than two years of age

International Nuclear Information System (INIS)

Fujiwara, Takeo; Okuyama, Makiko; Matsumoto, Tsutomu; Aritaki, Kentarou; Yotani, Nobuyuki; Miyasaka, Mikiko; Nishina, Sachiko

2008-01-01

It is difficult to distinguish whether children's head injuries are due to physical abuse or unintentional accidents. However, in the literature, medical findings specific to infant physical abuse were identified. Thus, we developed diagnostic criteria for inflicted head injury (IHI) and assessed its validity. Subjects were collected from all patients who were less than two years old when they visited National Center for Child Health (NCCHD) and Development and underwent head CT scan to assess head trauma from March 1, 2002 to December 31, 2005. Diagnostic criteria for IHI were developed based on definitions of Duhaime et al (1992) and Reece et al (2001). Validity of diagnosis criteria was assessed by comparing the official report to the Child Guidance Center (CGC) from NCCHD to the disposition decided by the CGC. Two-hundred and sixty cases were collected and diagnosed. There was a 86.5% match of the number of cases which were diagnosed as IHI or non-IHI using the IHI diagnostic criteria with official reports to CGC from NCCHD. Among the cases which were diagnosed as presumptive IHI and also reported to the CGC, 20 cases (83.3%) were regarded as abused cases by the CGC. The diagnostic criteria for IHI were valid and would be useful for pediatricians not to condone inflicted head injury. (author)

[Status of diagnosis and treatment devices of acupuncture based on SooPAT and bibliometrics in China].

Science.gov (United States)

Bai, Lin; Ren, Yulan; Guo, Taipin; Chen, Lin; Zhou, Yumei; Feng, Shuwei; Li, Ji; Liang, Fanrong

2016-11-12

To perform a bibliometrics analysis on patent literature regarding diagnosis and treatment devices of acupuncture in China, aiming to provide references for the development of diagnosis and treatment devices of acupuncture. Based on SooPAT, a patent database, the patent literature regarding diagnosis and treatment devices of acupuncture in China was collected. With bibliometrics methods, the annual distribution of type, quantity, classification and content of diagnosis and treatment devices of acupuncture were analyzed. The number of acupuncture diagnosis and treatment devices reached its peak in 2012 and 2013 in China. The A61N in patent and utility model patent were the most, which were mainly related to electrotherapy, magnetic therapy, radioactive therapy and ultrasound therapy, etc. The main content was acupuncture treatment devices and meridian treatment devices. The 24-01 in design patent was the most, involving fixation devices used by doctors, hospitals and laboratories, etc. Currently the majority of diagnosis and treatment devices of acupuncture is therapeutic apparatus, while the acupuncture diagnosis devices are needed.

A fault diagnosis and operation advising cooperative expert system based on multi-agent technology

Energy Technology Data Exchange (ETDEWEB)

Zhao, W.; Bai, X.; Ding, J.; Fang, Z.; Li, Z. [China Electric Power Research Inst., Haidian District, Beijing (China)

2006-07-01

Power systems are becoming more and more complex. In addition, the amount of real-time alarm messages from the supervisory control and data acquisition, energy management systems and wide area measurement systems about switchgear and protection are also increasing to a point far beyond the operator's capacity to digest the information. Research and development of a fault diagnosis system is necessary for the timely identification of fault or malfunctioning devices and for realizing the automation functions of dynamic supervisory control system. The prevailing fault diagnosis approaches in power systems include the expert system, artificial neural network, and fault diagnosis based on optimal theory. This paper discussed the advantages and disadvantages of each of these approaches for diagnosing faults. The paper also proposed a new fault diagnosis and operational processing approach based on a cooperative expert system combined with a multi-agent architecture. For solving complex and correlative faults, the cooperative expert system can overcome the deficiency of a single expert system. It can be used not only for diagnosing complex faults in real time but also in providing timely operational advice. The proposed system has been used successfully in a district power grid in China's Shangdong province for a year. 9 refs., 4 figs.

Diagnostic Performance of Tuberculosis-Specific IgG Antibody Profiles in Patients with Presumptive Tuberculosis from Two Continents.

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Broger, Tobias; Basu Roy, Robindra; Filomena, Angela; Greef, Charles H; Rimmele, Stefanie; Havumaki, Joshua; Danks, David; Schneiderhan-Marra, Nicole; Gray, Christen M; Singh, Mahavir; Rosenkrands, Ida; Andersen, Peter; Husar, Gregory M; Joos, Thomas O; Gennaro, Maria L; Lochhead, Michael J; Denkinger, Claudia M; Perkins, Mark D

2017-04-01

Development of rapid diagnostic tests for tuberculosis is a global priority. A whole proteome screen identified Mycobacterium tuberculosis antigens associated with serological responses in tuberculosis patients. We used World Health Organization (WHO) target product profile (TPP) criteria for a detection test and triage test to evaluate these antigens. Consecutive patients presenting to microscopy centers and district hospitals in Peru and to outpatient clinics at a tuberculosis reference center in Vietnam were recruited. We tested blood samples from 755 HIV-uninfected adults with presumptive pulmonary tuberculosis to measure IgG antibody responses to 57 M. tuberculosis antigens using a field-based multiplexed serological assay and a 132-antigen bead-based reference assay. We evaluated single antigen performance and models of all possible 3-antigen combinations and multiantigen combinations. Three-antigen and multiantigen models performed similarly and were superior to single antigens. With specificity set at 90% for a detection test, the best sensitivity of a 3-antigen model was 35% (95% confidence interval [CI], 31-40). With sensitivity set at 85% for a triage test, the specificity of the best 3-antigen model was 34% (95% CI, 29-40). The reference assay also did not meet study targets. Antigen performance differed significantly between the study sites for 7/22 of the best-performing antigens. Although M. tuberculosis antigens were recognized by the IgG response during tuberculosis, no single antigen or multiantigen set performance approached WHO TPP criteria for clinical utility among HIV-uninfected adults with presumed tuberculosis in high-volume, urban settings in tuberculosis-endemic countries. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

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Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

2014-01-01

The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

Directory of Open Access Journals (Sweden)

Hong Yin

2014-01-01

Full Text Available The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

Tuberculosis among transhumant pastoralist and settled communities of south-eastern Mauritania

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Aissata Lô

2016-05-01

Full Text Available Background: Transhumant pastoralists of Mauritania were assumed to have a high prevalence of tuberculosis (TB because of reduced access to diagnostic testing. No population-based survey on TB has been published for Mauritania. Objective: The goal of this study was to estimate the prevalence of presumptive TB cases among mobile pastoralists and villagers in a remote zone of Mauritania. Design: In the south-eastern province of Hodh Ech Chargui, 250 adult pastoralists and 250 villagers were randomly enrolled using multistage cluster sampling in February 2012. A TB centre nurse examined participants using a standard clinical protocol, and a participant questionnaire was completed. Focus group discussions and interviews were conducted with community members and health personnel, respectively. Results: Fourteen new presumptive TB cases were identified, leading to an overall prevalence of 2.8%, (95% confidence interval (CI 1.5–4.7%. The prevalence was non-significantly higher among villagers than pastoralists (3.6% vs. 2.0%. Assuming illness duration was 3 years and all presumptive cases started treatment, an overall crude incidence of 933 cases/100,000 was derived. Five of six presumptive cases in Djiguenni were confirmed by sputum smear microscopy, but none out of eight presumptive cases were confirmed in Néma, although the same nurse performed all clinical examinations in both departments. This result was attributed to the use of expired reagents in Néma. Communities mentioned distance rather than lack of information as the main constraint to seeking diagnosis, but poor diagnostic centre performance also delayed decision-making. Conclusions: TB prevalences were high among both pastoralists and villagers. None of the 14 presumptive cases sought prior diagnostic testing. TB diagnostic centres in the remote rural study zone were poorly equipped. These centres must remain in operation to reduce TB incidence in vulnerable communities in insecure

Enhanced DET-Based Fault Signature Analysis for Reliable Diagnosis of Single and Multiple-Combined Bearing Defects

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In-Kyu Jeong

2015-01-01

Full Text Available To early identify cylindrical roller bearing failures, this paper proposes a comprehensive bearing fault diagnosis method, which consists of spectral kurtosis analysis for finding the most informative subband signal well representing abnormal symptoms about the bearing failures, fault signature calculation using this subband signal, enhanced distance evaluation technique- (EDET- based fault signature analysis that outputs the most discriminative fault features for accurate diagnosis, and identification of various single and multiple-combined cylindrical roller bearing defects using the simplified fuzzy adaptive resonance map (SFAM. The proposed comprehensive bearing fault diagnosis methodology is effective for accurate bearing fault diagnosis, yielding an average classification accuracy of 90.35%. In this paper, the proposed EDET specifically addresses shortcomings in the conventional distance evaluation technique (DET by accurately estimating the sensitivity of each fault signature for each class. To verify the efficacy of the EDET-based fault signature analysis for accurate diagnosis, a diagnostic performance comparison is carried between the proposed EDET and the conventional DET in terms of average classification accuracy. In fact, the proposed EDET achieves up to 106.85% performance improvement over the conventional DET in average classification accuracy.

Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

Science.gov (United States)

Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun

2017-11-01

The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

Development of diagnosis and maintenance support system for nuclear power plants with flexible inference function and knowledge base edition support function

International Nuclear Information System (INIS)

Fujii, Makoto; Seki, Eiji; Tai, Ichiro; Morioka, Toshihiko

1988-01-01

For the reliable and efficient diagnosis and inspection work of the nuclear power plant equipments, 'Diagnosis and Maintenance Support System' has been developed. This system has functions to assist operators or engineers to observe and evaluate equipment conditions based on the experts' knowledge. These functions are carried out through dialogue between the system and users. This system has two subsystems: diagnosis subsystem and knowledge base edition support subsystem. To achieve the functions of diagnosis subsystem, a new method of knowledge processing for equipment diagnosis is adopted. This method is based on the concept of 'Cause Generation and Checking'. Knowledge for diagnosis is represented with modularized production rules. And each rule module consists of four different type rules with hierarchical structure. With this approach, the system is equipped with sufficient performance not only in diagnosis function but also in flexible man-machine interface. Knowledge base edition support subsystem (Graphical Rule Editor) is provided for this system. This editor has functions to display and edit the contents of knowledge base with tree structures through the graphic display. With these functions, the efficiency of constructing expert system is highly improved. By applying this system to the maintenance support of neutron monitoring system, it is proved that this system has satisfactory performance as a diagnosis and maintenance support system. (author)

A misleading urethral smear with polymorphonuclear leucocytes and intracellular diplococci; case report of urethritis caused by Neisseria meningitidis.

Science.gov (United States)

Genders, R E; Spitaels, D; Jansen, C L; van den Akker, Th W; Quint, K D

2013-12-01

The primary pathogens found in men with urethritis are Chlamydia trachomatis and Neisseria gonorrhoeae. Rapid diagnosis of N. gonorrhoeae infection can be made based on a Gram- or methylene blue-stained urethral smear. We describe a case of a man with purulent penile discharge, in which microscopic examination led to the presumptive diagnosis of gonorrhoea. A nucleic acid amplification test was negative for N. gonorrhoeae but positive for C. trachomatis. Culture showed Gram-negative diplococci which were identified as Neisseria meningitidis. N. meningitidis can be sporadically pathogenic in the genito-urinary tract and mimicks gonococcal urethritis, and appears identical by microscopy. When a gonococcal urethritis is suspected based on clinical signs and microscopic examination, but investigatory tests cannot confirm the diagnosis, a N. meningitidis infection should be considered.

Image standards in Tissue-Based Diagnosis (Diagnostic Surgical Pathology

Directory of Open Access Journals (Sweden)

Vollmer Ekkehard

2008-04-01

Full Text Available Abstract Background Progress in automated image analysis, virtual microscopy, hospital information systems, and interdisciplinary data exchange require image standards to be applied in tissue-based diagnosis. Aims To describe the theoretical background, practical experiences and comparable solutions in other medical fields to promote image standards applicable for diagnostic pathology. Theory and experiences Images used in tissue-based diagnosis present with pathology – specific characteristics. It seems appropriate to discuss their characteristics and potential standardization in relation to the levels of hierarchy in which they appear. All levels can be divided into legal, medical, and technological properties. Standards applied to the first level include regulations or aims to be fulfilled. In legal properties, they have to regulate features of privacy, image documentation, transmission, and presentation; in medical properties, features of disease – image combination, human – diagnostics, automated information extraction, archive retrieval and access; and in technological properties features of image acquisition, display, formats, transfer speed, safety, and system dynamics. The next lower second level has to implement the prescriptions of the upper one, i.e. describe how they are implemented. Legal aspects should demand secure encryption for privacy of all patient related data, image archives that include all images used for diagnostics for a period of 10 years at minimum, accurate annotations of dates and viewing, and precise hardware and software information. Medical aspects should demand standardized patients' files such as DICOM 3 or HL 7 including history and previous examinations, information of image display hardware and software, of image resolution and fields of view, of relation between sizes of biological objects and image sizes, and of access to archives and retrieval. Technological aspects should deal with image

Model-Based Diagnosis and Prognosis of a Water Recycling System

Science.gov (United States)

Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

2013-01-01

A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

Rolling bearing fault diagnosis based on information fusion using Dempster-Shafer evidence theory

Science.gov (United States)

Pei, Di; Yue, Jianhai; Jiao, Jing

2017-10-01

This paper presents a fault diagnosis method for rolling bearing based on information fusion. Acceleration sensors are arranged at different position to get bearing vibration data as diagnostic evidence. The Dempster-Shafer (D-S) evidence theory is used to fuse multi-sensor data to improve diagnostic accuracy. The efficiency of the proposed method is demonstrated by the high speed train transmission test bench. The results of experiment show that the proposed method in this paper improves the rolling bearing fault diagnosis accuracy compared with traditional signal analysis methods.

Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Directory of Open Access Journals (Sweden)

Mariela Cerrada

2015-09-01

Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

A fuzzy-logic based diagnosis and control of a reactor performing complete autotrophic nitrogen removal

DEFF Research Database (Denmark)

Mauricio Iglesias, Miguel; Vangsgaard, Anna Katrine; Gernaey, Krist

2013-01-01

Diagnosis and control modules based on fuzzy set theory were tested for novel bioreactor monitoring and control. Two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation in d...... autotrophic nitrogen removal process. The whole module is evaluated by dynamic simulation....

Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

Directory of Open Access Journals (Sweden)

Wei-Li Qin

2016-01-01

Full Text Available Roller bearings are one of the most commonly used components in rotational machines. The fault diagnosis of roller bearings thus plays an important role in ensuring the safe functioning of the mechanical systems. However, in most cases of bearing fault diagnosis, there are limited number of labeled data to achieve a proper fault diagnosis. Therefore, exploiting unlabeled data plus few labeled data, this paper proposed a roller bearing fault diagnosis method based on tritraining to improve roller bearing diagnosis performance. To overcome the noise brought by wrong labeling into the classifiers training process, the cut edge weight confidence is introduced into the diagnosis framework. Besides a small trick called suspect principle is adopted to avoid overfitting problem. The proposed method is validated in two independent roller bearing fault experiment vibrational signals that both include three types of faults: inner-ring fault, outer-ring fault, and rolling element fault. The results demonstrate the desirable diagnostic performance improvement by the proposed method in the extreme situation where there is only limited number of labeled data.

Diagnosis of Short-Circuit Fault in Large-Scale Permanent-Magnet Wind Power Generator Based on CMAC

Directory of Open Access Journals (Sweden)

Chin-Tsung Hsieh

2013-01-01

Full Text Available This study proposes a method based on the cerebellar model arithmetic controller (CMAC for fault diagnosis of large-scale permanent-magnet wind power generators and compares the results with Error Back Propagation (EBP. The diagnosis is based on the short-circuit faults in permanent-magnet wind power generators, magnetic field change, and temperature change. Since CMAC is characterized by inductive ability, associative ability, quick response, and similar input signals exciting similar memories, it has an excellent effect as an intelligent fault diagnosis implement. The experimental results suggest that faults can be diagnosed effectively after only training CMAC 10 times. In comparison to training 151 times for EBP, CMAC is better than EBP in terms of training speed.

Fault diagnosis and performance evaluation for high current LIA based on radial basis function neural network

International Nuclear Information System (INIS)

Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin

2006-01-01

High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)

Impact of presumed service-connected diagnosis on the Department of Veterans Affairs healthcare utilization patterns of Vietnam-Theater Veterans

Science.gov (United States)

Fried, Dennis A.; Rajan, Mangala; Tseng, Chin-lin; Helmer, Drew

2018-01-01

Abstract During the Vietnam War, the US military sprayed almost 20 million gallons of Agent Orange (AO), an herbicide contaminated with dioxin, over Vietnam. Approximately, 2.7 million US military personnel may have been exposed to AO during their deployment. Ordinarily, veterans who can demonstrate a nexus between a diagnosed condition and military service are eligible for Department of Veterans Affairs (VA) service-connected disability compensation. Vietnam Veterans have had difficulty, however, establishing a nexus between AO exposure and certain medical conditions that developed many years after the war. In response, VA has designated certain conditions as “presumed service connected” for Vietnam Veterans who were present and possibly exposed. Veterans with any of these designated conditions do not have to document AO exposure, making it easier for them to access the VA disability system. The extent to which VA healthcare utilization patterns reflect easier access afforded those with diagnosed presumptive conditions remains unknown. In this cross-sectional study, we hypothesized that Vietnam Veterans with diagnosed presumptive conditions would be heavier users of the VA healthcare system than those without these conditions. In our analysis of 85,699 Vietnam Veterans, we used binary and cumulative logit multivariable regression to assess associations between diagnosed presumptive conditions and VA healthcare utilization in 2013. We found that diagnosed presumptive conditions were associated with higher odds of 5+ VHA primary care visits (OR = 2.01, 95% CI: 1.93–2.07), 5+ specialty care visits (OR = 2.11, 95% CI: 2.04–2.18), emergency department use (OR = 1.22, 95% CI: 1.11–1.34), and hospitalization (OR = 1.23, 95% CI: 1.17–1.29). Consistent with legislative intent, presumptive policies appear to facilitate greater VA system utilization for Vietnam Veterans who may have been exposed to AO. PMID:29742706

Fault diagnosis

Science.gov (United States)

Abbott, Kathy

1990-01-01

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to

The Analysis of Organizational Diagnosis on Based Six Box Model in Universities

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Hamid, Rahimi; Siadat, Sayyed Ali; Reza, Hoveida; Arash, Shahin; Ali, Nasrabadi Hasan; Azizollah, Arbabisarjou

2011-01-01

Purpose: The analysis of organizational diagnosis on based six box model at universities. Research method: Research method was descriptive-survey. Statistical population consisted of 1544 faculty members of universities which through random strafed sampling method 218 persons were chosen as the sample. Research Instrument were organizational…

Diagnosis of clinical samples spotted on FTA cards using PCR-based methods.

Science.gov (United States)

Jamjoom, Manal; Sultan, Amal H

2009-04-01

The broad clinical presentation of Leishmaniasis makes the diagnosis of current and past cases of this disease rather difficult. Differential diagnosis is important because diseases caused by other aetiologies and a clinical spectrum similar to that of leishmaniasis (e.g. leprosy, skin cancers and tuberculosis for CL; malaria and schistosomiasis for VL) are often present in endemic areas of endemicity. Presently, a variety of methods have been developed and tested to aid the identification and diagnosis of Leishmania. The advent of the PCR technology has opened new channels for the diagnosis of leishmaniasis in a variety of clinical materials. PCR is a simple, rapid procedure that has been adapted for diagnosis of leishmaniasis. A range of tools is currently available for the diagnosis and identification of leishmaniasis and Leishmania species, respectively. However, none of these diagnostic tools are examined and tested using samples spotted on FTA cards. Three different PCR-based approaches were examined including: kDNA minicircle, Leishmania 18S rRNA gene and PCR-RFLP of Intergenic region of ribosomal protein. PCR primers were designed that sit within the coding sequences of genes (relatively well conserved) but which amplify across the intervening intergenic sequence (relatively variable). These were used in PCR-RFLP on reference isolates of 10 of the most important Leishmania species: L. donovani, L. infantum, L. major & L. tropica. Digestion of PCR products with restriction enzymes produced species-specific restriction patterns allowed discrimination of reference isolates. The kDNA minicircle primers are highly sensitive in diagnosis of both bone marrow and skin smears from FTA cards. Leishmania 18S rRNA gene conserved region is sensitive in identification of bone marrow smear but less sensitive in diagnosing skin smears. The intergenic nested PCR-RFLP using P5 & P6 as well as P1 & P2 newly designed primers showed high level of reproducibility and sensitivity

Infrared thermography based on artificial intelligence as a screening method for carpal tunnel syndrome diagnosis.

Science.gov (United States)

Jesensek Papez, B; Palfy, M; Mertik, M; Turk, Z

2009-01-01

This study further evaluated a computer-based infrared thermography (IRT) system, which employs artificial neural networks for the diagnosis of carpal tunnel syndrome (CTS) using a large database of 502 thermal images of the dorsal and palmar side of 132 healthy and 119 pathological hands. It confirmed the hypothesis that the dorsal side of the hand is of greater importance than the palmar side when diagnosing CTS thermographically. Using this method it was possible correctly to classify 72.2% of all hands (healthy and pathological) based on dorsal images and > 80% of hands when only severely affected and healthy hands were considered. Compared with the gold standard electromyographic diagnosis of CTS, IRT cannot be recommended as an adequate diagnostic tool when exact severity level diagnosis is required, however we conclude that IRT could be used as a screening tool for severe cases in populations with high ergonomic risk factors of CTS.

Discovering mammography-based machine learning classifiers for breast cancer diagnosis.

Science.gov (United States)

Ramos-Pollán, Raúl; Guevara-López, Miguel Angel; Suárez-Ortega, Cesar; Díaz-Herrero, Guillermo; Franco-Valiente, Jose Miguel; Rubio-Del-Solar, Manuel; González-de-Posada, Naimy; Vaz, Mario Augusto Pires; Loureiro, Joana; Ramos, Isabel

2012-08-01

This work explores the design of mammography-based machine learning classifiers (MLC) and proposes a new method to build MLC for breast cancer diagnosis. We massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views, providing BI-RADS diagnosis. Previously, appropriate combinations of image processing and normalization techniques were applied to reduce image artifacts and increase mammograms details. The method can be used under different data acquisition circumstances and exploits computer clusters to select well performing MLC configurations. We evaluated 286 cases extracted from the repository owned by HSJ-FMUP, where specialized radiologists segmented regions on CC and/or MLO images (biopsies provided the golden standard). Around 20,000 MLC configurations were evaluated, obtaining classifiers achieving an area under the ROC curve of 0.996 when combining features vectors extracted from CC and MLO views of the same case.

Highly sensitive dendrimer-based nanoplasmonic biosensor for drug allergy diagnosis.

Science.gov (United States)

Soler, Maria; Mesa-Antunez, Pablo; Estevez, M-Carmen; Ruiz-Sanchez, Antonio Jesus; Otte, Marinus A; Sepulveda, Borja; Collado, Daniel; Mayorga, Cristobalina; Torres, Maria Jose; Perez-Inestrosa, Ezequiel; Lechuga, Laura M

2015-04-15

A label-free biosensing strategy for amoxicillin (AX) allergy diagnosis based on the combination of novel dendrimer-based conjugates and a recently developed nanoplasmonic sensor technology is reported. Gold nanodisks were functionalized with a custom-designed thiol-ending-polyamido-based dendron (d-BAPAD) peripherally decorated with amoxicilloyl (AXO) groups (d-BAPAD-AXO) in order to detect specific IgE generated in patient's serum against this antibiotic during an allergy outbreak. This innovative strategy, which follows a simple one-step immobilization procedure, shows exceptional results in terms of sensitivity and robustness, leading to a highly-reproducible and long-term stable surface which allows achieving extremely low limits of detection. Moreover, the viability of this biosensor approach to analyze human biological samples has been demonstrated by directly analyzing and quantifying specific anti-AX antibodies in patient's serum without any sample pretreatment. An excellent limit of detection (LoD) of 0.6ng/mL (i.e. 0.25kU/L) has been achieved in the evaluation of clinical samples evidencing the potential of our nanoplasmonic biosensor as an advanced diagnostic tool to quickly identify allergic patients. The results have been compared and validated with a conventional clinical immunofluorescence assay (ImmunoCAP test), confirming an excellent correlation between both techniques. The combination of a novel compact nanoplasmonic platform and a dendrimer-based strategy provides a highly sensitive label free biosensor approach with over two times better detectability than conventional SPR. Both the biosensor device and the carrier structure hold great potential in clinical diagnosis for biomarker analysis in whole serum samples and other human biological samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Presumptive binge eating disorder in type 2 diabetes mellitus patients and its effect in metabolic control

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Sandra Soares Melo

2009-09-01

Full Text Available Objective: This study sought to determine the presence of diagnosis suggestive of binge eating disorder in individuals with type 2 diabetes mellitus, and to evaluate the influence of such disorder on the metabolic control. Methods: sixty-three patients with type 2 diabetes mellitus and registered  at the Diabetes and Hypertension Program of a Health Unit in the town of Balneário Camboriú, Santa Catarina, Brazil, were evaluated. The diagnosis of binge eating disorder was made by analysis of the Questionnaire on Eating and Weight Patterms – Revised. For the evaluation of metabolic control, 10 ml of blood was collected, and the serum glucose, glycated hemoglobin, tryglicerides, cholestrol and fractions were determined. Weight and height were determined for evaluation of national nutritional state, according to the body mass index. Rresults: Among the evaluated individuals, 29% presented a diagnosis suggestive of binge eating disorder, with higher prevalence among females. The individuals with diagnosis suggestive of binge eating disorder presented a higher average body mass index value than the group without diagnosis. The serum concentrations of glycated hemoglobin (p = 0.02 and triglicerides (p = 0.03 were statistically higher in the group with diagnosis suggestive of binge eating disorder. Cconclusions: Based on the results of this study, it is possible to conclude that the presence of binge eating disorder in individuals with type 2 diabetes mellitus favors an increase in body weight and has a negative influence on metabolic control, contributing to the early emergence of complications related to the disease.

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine(SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

Diagnosis of vulvovaginitis: comparison of clinical and microbiological diagnosis.

Science.gov (United States)

Esim Buyukbayrak, Esra; Kars, Bulent; Karsidag, Ayse Yasemin Karageyim; Karadeniz, Bernan Ilkay; Kaymaz, Ozge; Gencer, Serap; Pirimoglu, Zehra Meltem; Unal, Orhan; Turan, Mehmet Cem

2010-11-01

The purpose of the present study was to compare the current diagnostic clinical and laboratory approaches to women with vulvovaginal discharge complaint. The secondary outcomes were to determine the prevalence of infections in our setting and to look for the relation between vulvovaginal infections and predisposing factors if present. Premenopausal women applying to our gynecology outpatient clinic with vaginal discharge complaint were enrolled prospectively into the study. Each patient evaluated clinically with direct observation of vaginal secretions, wet mount examination, whiff test, vaginal pH testing and chlamydia rapid antigen test. Each patient also evaluated microbiologically with vaginal discharge culture and gram staining. Clinical diagnosis was compared with the microbiological diagnosis (the gold standard). Diagnostic accuracy was measured with sensitivity, specificity, positive (ppv) and negative predictive values (npv). 460 patients were included in the study. 89.8% of patients received a clinical diagnosis whereas only 36% of them had microbiological diagnosis. The sensitivity, specificity, ppv, npv of clinical diagnosis over microbiological culture results were 95, 13, 38, 82%, respectively. The most commonly encountered microorganisms by culture were Candida species (17.4%) and Gardnerella vaginalis (10.2%). Clinically, the most commonly made diagnoses were mixed infection (34.1%), bacterial vaginosis (32.4%) and fungal infection (14.1%). Symptoms did not predict laboratory results. Predisposing factors (DM, vaginal douching practice, presence of IUD and usage of oral contraceptive pills) were not found to be statistically important influencing factors for vaginal infections. Clinical diagnosis based on combining symptoms with office-based testing improves diagnostic accuracy but is insufficient. The most effective approach also incorporates laboratory testing as an adjunct when a diagnosis is in question or treatment is failing.

A systems biology-based classifier for hepatocellular carcinoma diagnosis.

Directory of Open Access Journals (Sweden)

Yanqiong Zhang

Full Text Available AIM: The diagnosis of hepatocellular carcinoma (HCC in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71% and area under ROC curve (approximating 1.0, and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier.

An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming.

Science.gov (United States)

Zhang, Li; Chen, Jiasheng; Gao, Chunming; Liu, Chuanmiao; Xu, Kuihua

2018-03-16

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model to diagnose HCC based on gene expression programming (GEP). GEP is an evolutionary algorithm and a domain-independent problem-solving technique. Clinical data show that six serum biomarkers, including gamma-glutamyl transferase, C-reaction protein, carcinoembryonic antigen, alpha-fetoprotein, carbohydrate antigen 153, and carbohydrate antigen 199, are related to HCC characteristics. In this study, the prediction of HCC was made based on these six biomarkers (195 HCC patients and 215 non-HCC controls) by setting up optimal joint models with GEP. The GEP model discriminated 353 out of 410 subjects, representing a determination coefficient of 86.28% (283/328) and 85.37% (70/82) for training and test sets, respectively. Compared to the results from the support vector machine, the artificial neural network, and the multilayer perceptron, GEP showed a better outcome. The results suggested that GEP modeling was a promising and excellent tool in diagnosis of hepatocellular carcinoma, and it could be widely used in HCC auxiliary diagnosis. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma.

[Presumptive organ donations for transplants agreement of the Ethics Committee of the University of Chile Medical School].

Science.gov (United States)

Roa, A; Rosselot, E

1995-04-01

The ethics committee of the Faculty of Medicine, University of Chile was consulted about the ethical aspects of presumptive organ donation for transplantation. After analyzing the problem, the committee concluded that every human being has the right to make use of his organs freely, voluntarily and according to his own discernment. The society has no right to make obligatory this donation, even after death. The foundations of this agreement were laid in a series of reasons. In fact, the corpse is not a juridical but a ethical asset and deserves respect for whom it was. It cannot be commercialized and is the only non-religious object susceptible of profanation. It is also object of popular affective and religious manifestations. Beliefs and affects must be respected. Organ donation is an act of charity and cannot be compulsory. The organ donation consent must be explicit, voluntary and solemn.

Discrete event systems diagnosis and diagnosability

CERN Document Server

Sayed-Mouchaweh, Moamar

2014-01-01

Discrete Event Systems: Diagnosis and Diagnosability addresses the problem of fault diagnosis of Discrete Event Systems (DES). This book provides the basic techniques and approaches necessary for the design of an efficient fault diagnosis system for a wide range of modern engineering applications. The different techniques and approaches are classified according to several criteria such as: modeling tools (Automata, Petri nets) that is used to construct the model; the information (qualitative based on events occurrences and/or states outputs, quantitative based on signal processing and data analysis) that is needed to analyze and achieve the diagnosis; the decision structure (centralized, decentralized) that is required to achieve the diagnosis. The goal of this classification is to select the efficient method to achieve the fault diagnosis according to the application constraints. This book focuses on the centralized and decentralized event based diagnosis approaches using formal language and automata as mode...

Fault diagnosis in spur gears based on genetic algorithm and random forest

Science.gov (United States)

Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

2016-03-01

There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

An Integrated Model-Based Distributed Diagnosis and Prognosis Framework

Data.gov (United States)

National Aeronautics and Space Administration — Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec- tion, isolation...

A novel diagnosis method for a Hall plates-based rotary encoder with a magnetic concentrator.

Science.gov (United States)

Meng, Bumin; Wang, Yaonan; Sun, Wei; Yuan, Xiaofang

2014-07-31

In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly.

Technical advances in flow cytometry-based diagnosis and monitoring of paroxysmal nocturnal hemoglobinuria

Science.gov (United States)

Correia, Rodolfo Patussi; Bento, Laiz Cameirão; Bortolucci, Ana Carolina Apelle; Alexandre, Anderson Marega; Vaz, Andressa da Costa; Schimidell, Daniela; Pedro, Eduardo de Carvalho; Perin, Fabricio Simões; Nozawa, Sonia Tsukasa; Mendes, Cláudio Ernesto Albers; Barroso, Rodrigo de Souza; Bacal, Nydia Strachman

2016-01-01

ABSTRACT Objective: To discuss the implementation of technical advances in laboratory diagnosis and monitoring of paroxysmal nocturnal hemoglobinuria for validation of high-sensitivity flow cytometry protocols. Methods: A retrospective study based on analysis of laboratory data from 745 patient samples submitted to flow cytometry for diagnosis and/or monitoring of paroxysmal nocturnal hemoglobinuria. Results: Implementation of technical advances reduced test costs and improved flow cytometry resolution for paroxysmal nocturnal hemoglobinuria clone detection. Conclusion: High-sensitivity flow cytometry allowed more sensitive determination of paroxysmal nocturnal hemoglobinuria clone type and size, particularly in samples with small clones. PMID:27759825

Utilizing DMAIC six sigma and evidence-based medicine to streamline diagnosis in chest pain.

Science.gov (United States)

Kumar, Sameer; Thomas, Kory M

2010-01-01

The purpose of this study was to quantify the difference between the current process flow model for a typical patient workup for chest pain and development of a new process flow model that incorporates DMAIC (define, measure, analyze, improve, control) Six Sigma and evidence-based medicine in a best practices model for diagnosis and treatment. The first stage, DMAIC Six Sigma, is used to highlight areas of variability and unnecessary tests in the current process flow for a patient presenting to the emergency department or physician's clinic with chest pain (also known as angina). The next stage, patient process flow, utilizes DMAIC results in the development of a simulated model that represents real-world variability in the diagnosis and treatment of a patient presenting with angina. The third and final stage is used to analyze the evidence-based output and quantify the factors that drive physician diagnosis accuracy and treatment, as well as review the potential for a broad national evidence-based database. Because of the collective expertise captured within the computer-oriented evidence-based model, the study has introduced an innovative approach to health care delivery by bringing expert-level care to any physician triaging a patient for chest pain anywhere in the world. Similar models can be created for other ailments as well, such as headache, gastrointestinal upset, and back pain. This updated way of looking at diagnosing patients stemming from an evidence-based best practice decision support model may improve workflow processes and cost savings across the health care continuum.

A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery.

Science.gov (United States)

Xue, Xiaoming; Zhou, Jianzhong

2017-01-01

To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

An MRI-based diagnostic framework for early diagnosis of dyslexia

International Nuclear Information System (INIS)

El-Baz, A.; Casanova, M.; Mott, M.; Switala, A.; Gimel'farb, G.

2008-01-01

A computer-aided diagnosis (CAD) system for early diagnosis of dyslexia was developed and tested. Dyslexia can severely impair the learning abilities of children so improved diagnostic methods are needed. Neuropathological studies show abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We sought to develop an MRI-based macroscopic neuropathological correlate to the minicolumnopathy of dyslexia that relates to cortical connectivity: the gyral window. The brains of dyslexic patients often exhibit decreased gyrifications, so the thickness of gyral CWM for dyslexic subjects is greater than for normal subjects. We developed an MRI-based method for assessment of gyral CWM thickness with automated recognition of abnormal (e.g., dyslexic) brains. In vivo data was collected from 16 right-handed dyslexic men aged 18-40 years, and a group of 14 controls matched for gender, age, educational level, socioeconomic background, handedness and general intelligence. All the subjects were physically healthy and free of history of neurological diseases and head injury. Images were acquired with the same 1.5T MRI scanner (GE, Milwaukee, WI, USA) with voxel resolution 0.9375 x 0.9375 x 1.5 mm using a T1-weighted imaging sequence protocol. The ''ground truth'' diagnosis to evaluate the classification accuracy for each patient was given by the clinicians. The accuracy of diagnosis/classification of both the training and test subjects was evaluated using the Chi-square test at the three confidence levels - 85, 90 and 95% - in order to examine significant differences in the Levy distances. As expected, the 85% confidence level yielded the best results, the system correctly classified 16 out of 16 dyslexic subjects (a 100% accuracy) and 14 out of 14 control subjects (a 100% accuracy). At the 90% confidence level, 16 out of 16 dyslexic subjects were still classified correctly; however, only 13 out of 14 control subjects were correct, bringing the accuracy rate for the

An MRI-based diagnostic framework for early diagnosis of dyslexia

Energy Technology Data Exchange (ETDEWEB)

El-Baz, A. [University of Louisville, Bioengineering Department, Louisville, KY (United States); Casanova, M.; Mott, M.; Switala, A. [University of Louisville, Department of Psychiatry and Behavioral Science, Louisville, KY (United States); Gimel' farb, G. [University of Auckland, Computer Science Department, Auckland (New Zealand)

2008-09-15

A computer-aided diagnosis (CAD) system for early diagnosis of dyslexia was developed and tested. Dyslexia can severely impair the learning abilities of children so improved diagnostic methods are needed. Neuropathological studies show abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We sought to develop an MRI-based macroscopic neuropathological correlate to the minicolumnopathy of dyslexia that relates to cortical connectivity: the gyral window. The brains of dyslexic patients often exhibit decreased gyrifications, so the thickness of gyral CWM for dyslexic subjects is greater than for normal subjects. We developed an MRI-based method for assessment of gyral CWM thickness with automated recognition of abnormal (e.g., dyslexic) brains. In vivo data was collected from 16 right-handed dyslexic men aged 18-40 years, and a group of 14 controls matched for gender, age, educational level, socioeconomic background, handedness and general intelligence. All the subjects were physically healthy and free of history of neurological diseases and head injury. Images were acquired with the same 1.5T MRI scanner (GE, Milwaukee, WI, USA) with voxel resolution 0.9375 x 0.9375 x 1.5 mm using a T1-weighted imaging sequence protocol. The ''ground truth'' diagnosis to evaluate the classification accuracy for each patient was given by the clinicians. The accuracy of diagnosis/classification of both the training and test subjects was evaluated using the Chi-square test at the three confidence levels - 85, 90 and 95% - in order to examine significant differences in the Levy distances. As expected, the 85% confidence level yielded the best results, the system correctly classified 16 out of 16 dyslexic subjects (a 100% accuracy) and 14 out of 14 control subjects (a 100% accuracy). At the 90% confidence level, 16 out of 16 dyslexic subjects were still classified correctly; however, only 13 out of 14 control subjects were correct, bringing the

129 microbiological studies of blood specimen from presumptively ...

African Journals Online (AJOL)

patient' serum for salmonella antibodies is a rapid tool in the diagnosis of enteric fever, but can afford an indirect ... which various dilutions of patient's serum are mixed with drops of either O or H-antigen of Salm. Typhi .... biochemical characterization and sero-typing are essential for complete identification of salmonella.

An expert fitness diagnosis system based on elastic cloud computing.

Science.gov (United States)

Tseng, Kevin C; Wu, Chia-Chuan

2014-01-01

This paper presents an expert diagnosis system based on cloud computing. It classifies a user's fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user's physiological data, such as age, gender, and body mass index (BMI). In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.

An Expert Fitness Diagnosis System Based on Elastic Cloud Computing

Directory of Open Access Journals (Sweden)

Kevin C. Tseng

2014-01-01

Full Text Available This paper presents an expert diagnosis system based on cloud computing. It classifies a user’s fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user’s physiological data, such as age, gender, and body mass index (BMI. In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8% and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.

Surface plasmon resonance based biosensor: A new platform for rapid diagnosis of livestock diseases

Directory of Open Access Journals (Sweden)

Pravas Ranjan Sahoo

2016-12-01

Full Text Available Surface plasmon resonance (SPR based biosensors are the most advanced and developed optical label-free biosensor technique used for powerful detection with vast applications in environmental protection, biotechnology, medical diagnostics, drug screening, food safety, and security as well in livestock sector. The livestock sector which contributes the largest economy of India, harbors many bacterial, viral, and fungal diseases impacting a great loss to the production and productive potential which is a major concern in both small and large ruminants. Hence, an accurate, sensitive, and rapid diagnosis is required for prevention of these above-mentioned diseases. SPR based biosensor assay may fulfill the above characteristics which lead to a greater platform for rapid diagnosis of different livestock diseases. Hence, this review may give a detail idea about the principle, recent development of SPR based biosensor techniques and its application in livestock sector.

Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine

Science.gov (United States)

Antoni, Jérôme; Griffaton, Julien; André, Hugo; Avendaño-Valencia, Luis David; Bonnardot, Frédéric; Cardona-Morales, Oscar; Castellanos-Dominguez, German; Daga, Alessandro Paolo; Leclère, Quentin; Vicuña, Cristián Molina; Acuña, David Quezada; Ompusunggu, Agusmian Partogi; Sierra-Alonso, Edgar F.

2017-12-01

This paper presents the content and outcomes of the Safran contest organized during the International Conference Surveillance 8, October 20-21, 2015, at the Roanne Institute of Technology, France. The contest dealt with the diagnosis of a civil aircraft engine based on vibration data measured in a transient operating mode and provided by Safran. Based on two independent exercises, the contest offered the possibility to benchmark current diagnostic methods on real data supplemented with several challenges. Outcomes of seven competing teams are reported and discussed. The object of the paper is twofold. It first aims at giving a picture of the current state-of-the-art in vibration-based diagnosis of rolling-element bearings in nonstationary operating conditions. Second, it aims at providing the scientific community with a benchmark and some baseline solutions. In this respect, the data used in the contest are made available as supplementary material.

Novel fiber optic-based needle redox imager for cancer diagnosis

Science.gov (United States)

Kanniyappan, Udayakumar; Xu, He N.; Tang, Qinggong; Gaitan, Brandon; Liu, Yi; Li, Lin Z.; Chen, Yu

2018-02-01

Despite various technological advancements in cancer diagnosis, the mortality rates were not decreased significantly. We aim to develop a novel optical imaging tool to assist cancer diagnosis effectively. Fluorescence spectroscopy/imaging is a fast, rapid, and minimally invasive technique which has been successfully applied to diagnosing cancerous cells/tissues. Recently, the ratiometric imaging of intrinsic fluorescence of reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD), as pioneered by Britton Chance and the co-workers in 1950-70's, has gained much attention to quantify the physiological parameters of living cells/tissues. The redox ratio, i.e., FAD/(FAD+NADH) or FAD/NADH, has been shown to be sensitive to various metabolic changes in in vivo and in vitro cells/tissues. Optical redox imaging has also been investigated for providing potential imaging biomarkers for cancer transformation, aggressiveness, and treatment response. Towards this goal, we have designed and developed a novel fiberoptic-based needle redox imager (NRI) that can fit into an 11G clinical coaxial biopsy needle for real time imaging during clinical cancer surgery. In the present study, the device is calibrated with tissue mimicking phantoms of FAD and NADH along with various technical parameters such as sensitivity, dynamic range, linearity, and spatial resolution of the system. We also conducted preliminary imaging of tissues ex vivo for validation. We plan to test the NRI on clinical breast cancer patients. Once validated this device may provide an effective tool for clinical cancer diagnosis.

Laryngeal obstruction caused by lymphoma in an adult dairy cow

OpenAIRE

Lardé, Hélène; Nichols, Sylvain; Babkine, Marie; Chénier, Sonia

2014-01-01

A Holstein cow was presented for inspiratory dyspnea. Endoscopic evaluation revealed swollen arytenoids and a presumptive diagnosis of bilateral arytenoidal chondritis was made. A partial arytenoidectomy was performed, the right arytenoid was submitted for histopathology, and a diagnosis of laryngeal lymphoma was made. Due to the poor prognosis, the cow was euthanized.

Effects of questionnaire-based diagnosis and training on inter-rater reliability among practitioners of traditional Chinese medicine.

Science.gov (United States)

Mist, Scott; Ritenbaugh, Cheryl; Aickin, Mikel

2009-07-01

To investigate whether a training process that focused on a questionnaire-based diagnosis in Traditional Chinese Medicine (TCM), and developing diagnostic consensus, would improve the agreement of TCM diagnoses among 10 TCM practitioners evaluating patients with temporomandibular joint disorder (TMJD). Evaluation of a diagnostic training program at the Department of Family and Community Medicine, University of Arizona, Tucson, Arizona, and the Oregon College of Oriental Medicine, Portland, Oregon. Screened participants for a study of TCM for TMJD. PRACTITIONERS: Ten (10) licensed acupuncturists with a minimum of 5 years licensure and education in Chinese herbs. A training session using a questionnaire-based diagnostic form was conducted, followed by waves of diagnostic sessions. Between sessions, practitioners discussed the results of the previous round of participants with a focus on reducing variability in primary diagnosis and severity rating of each diagnosis: 3 waves of 5 patients were assessed by 4 practitioner pairs for a total of 120 diagnoses. At 18 months, practitioners completed a recalibration exercise with a similar format with a total of 32 diagnoses. These diagnoses were then examined with respect to the rate of agreement among the 10 practitioners using inter-rater correlations and kappas. The inter-rater correlation with respect to the TCM diagnoses among the 10 practitioners increased from 0.112 to 0.618 with training. Statistically significant improvements were found between the baseline and 18 month exercises (p reliability of TCM diagnosis may be improved through a training process and a questionnaire-based diagnosis process. The improvements varied by diagnosis, with the greatest congruence among primary and more severe diagnoses. Future TCM studies should consider including calibration training to improve the validity of results.

The standard diagnosis, treatment, and follow-up of gastrointestinal stromal tumors based on guidelines.

Science.gov (United States)

Nishida, Toshirou; Blay, Jean-Yves; Hirota, Seiichi; Kitagawa, Yuko; Kang, Yoon-Koo

2016-01-01

Although gastrointestinal stromal tumors (GISTs) are a rare type of cancer, they are the commonest sarcoma in the gastrointestinal tract. Molecularly targeted therapy, such as imatinib therapy, has revolutionized the treatment of advanced GIST and facilitates scientific research on GIST. Nevertheless, surgery remains a mainstay of treatment to obtain a permanent cure for GIST even in the era of targeted therapy. Many GIST guidelines have been published to guide the diagnosis and treatment of the disease. We review current versions of GIST guidelines published by the National Comprehensive Cancer Network, by the European Society for Medical Oncology, and in Japan. All clinical practice guidelines for GIST include recommendations based on evidence as well as on expert consensus. Most of the content is very similar, as represented by the following examples: GIST is a heterogeneous disease that may have mutations in KIT, PDGFRA, HRAS, NRAS, BRAF, NF1, or the succinate dehydrogenase complex, and these subsets of tumors have several distinctive features. Although there are some minor differences among the guidelines--for example, in the dose of imatinib recommended for exon 9-mutated GIST or the efficacy of antigen retrieval via immunohistochemistry--their common objectives regarding diagnosis and treatment are not only to improve the diagnosis of GIST and the prognosis of patients but also to control medical costs. This review describes the current standard diagnosis, treatment, and follow-up of GISTs based on the recommendations of several guidelines and expert consensus.

An intelligent system based on fuzzy probabilities for medical diagnosis – a study in aphasia diagnosis

Directory of Open Access Journals (Sweden)

Majid Moshtagh Khorasani

2009-04-01

Full Text Available

• BACKGROUND: Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with  mprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease.
• METHODS: Fuzzy probability is proposed here as the basic framework for handling the uncertainties in medical diagnosis and particularly aphasia diagnosis. To efficiently construct this fuzzy probabilistic mapping, statistical analysis is performed that constructs input membership functions as well as determines an effective set of input features.
• RESULTS: Considering the high sensitivity of performance measures to different distribution of testing/training sets, a statistical t-test of significance is applied to compare fuzzy approach results with NN  esults as well as author’s earlier work using fuzzy logic. The proposed fuzzy probability estimator approach clearly provides better diagnosis for both classes of data sets. Specifically, for the first and second type of fuzzy probability classifiers, i.e. spontaneous speech and comprehensive model, P-values are 2.24E-08 and 0.0059, espectively, strongly rejecting the null hypothesis.
• CONCLUSIONS: The technique is applied and compared on both comprehensive and spontaneous speech test data for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. Statistical analysis confirms that the proposed approach can significantly improve accuracy using fewer Aphasia features.
• KEYWORDS: Aphasia, fuzzy probability, fuzzy logic, medical diagnosis, fuzzy rules.

Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

Science.gov (United States)

Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

2014-01-01

A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

Weight-based nutritional diagnosis of Mexican children and adolescents with neuromotor disabilities.

Science.gov (United States)

Vega-Sanchez, Rodrigo; de la Luz Gomez-Aguilar, Maria; Haua, Karime; Rozada, Guadalupe

2012-07-04

Nutrition related problems are increasing worldwide but they have scarcely been evaluated in people with neuromotor disabilities, particularly in developing countries. In this study our aim was to describe the weight-based nutritional diagnoses of children and adolescents with neuromotor disabilities who attended a private rehabilitation center in Mexico City. Data from the first visit's clinical records of 410 patients who attended the Nutrition department at the Teleton Center for Children Rehabilitation, between 1999 and 2008, were analyzed. Sex, age, weight and height, length or segmental length data were collected and used to obtain the nutritional diagnosis based on international growth charts, as well as disability-specific charts. Weight for height was considered the main indicator. Cerebral palsy was the most frequent diagnosis, followed by spina bifida, muscular dystrophy, and Down's syndrome. Children with cerebral palsy showed a higher risk of presenting low weight/undernutrition (LW/UN) than children with other disabilities, which was three times higher in females. In contrast, children with spina bifida, particularly males, were more likely to be overweight/obese (OW/OB), especially after the age of 6 and even more after 11. Patients with muscular dystrophy showed a significantly lower risk of LW/UN than patients with other disabilities. In patients with Down's syndrome neither LW/UN nor OW/OB were different between age and sex. This is the first study that provides evidence of the nutritional situation of children and adolescents with neuromotor disabilities in Mexico, based on their weight status. Low weight and obesity affect a large number of these patients due to their disability, age and sex. Early nutritional diagnosis must be considered an essential component in the treatment of these patients to prevent obesity and malnutrition, and improve their quality of life.

Cost of presumptive source term Remedial Actions Laboratory for energy-related health research, University of California, Davis

International Nuclear Information System (INIS)

Last, G.V.; Bagaasen, L.M.; Josephson, G.B.; Lanigan, D.C.; Liikala, T.L.; Newcomer, D.R.; Pearson, A.W.; Teel, S.S.

1995-12-01

A Remedial Investigation/Feasibility Study (RI/FS) is in progress at the Laboratory for Energy Related Health Research (LEHR) at the University of California, Davis. The purpose of the RI/FS is to gather sufficient information to support an informed risk management decision regarding the most appropriate remedial actions for impacted areas of the facility. In an effort to expedite remediation of the LEHR facility, the remedial project managers requested a more detailed evaluation of a selected set of remedial actions. In particular, they requested information on both characterization and remedial action costs. The US Department of Energy -- Oakland Office requested the assistance of the Pacific Northwest National Laboratory to prepare order-of-magnitude cost estimates for presumptive remedial actions being considered for the five source term operable units. The cost estimates presented in this report include characterization costs, capital costs, and annual operation and maintenance (O ampersand M) costs. These cost estimates are intended to aid planning and direction of future environmental remediation efforts

Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles

International Nuclear Information System (INIS)

Chen, Zeyu; Xiong, Rui; Tian, Jinpeng; Shang, Xiong; Lu, Jiahuan

2016-01-01

Highlights: • The characteristics of ESC fault of lithium-ion battery are investigated experimentally. • The proposed method to simulate the electrical behavior of ESC fault is viable. • Ten parameters in the presented fault model were optimized using a DPSO algorithm. • A two-layer model-based fault diagnosis approach for battery ESC is proposed. • The effective and robustness of the proposed algorithm has been evaluated. - Abstract: This study investigates the external short circuit (ESC) fault characteristics of lithium-ion battery experimentally. An experiment platform is established and the ESC tests are implemented on ten 18650-type lithium cells considering different state-of-charges (SOCs). Based on the experiment results, several efforts have been made. (1) The ESC process can be divided into two periods and the electrical and thermal behaviors within these two periods are analyzed. (2) A modified first-order RC model is employed to simulate the electrical behavior of the lithium cell in the ESC fault process. The model parameters are re-identified by a dynamic-neighborhood particle swarm optimization algorithm. (3) A two-layer model-based ESC fault diagnosis algorithm is proposed. The first layer conducts preliminary fault detection and the second layer gives a precise model-based diagnosis. Four new cells are short-circuited to evaluate the proposed algorithm. It shows that the ESC fault can be diagnosed within 5 s, the error between the model and measured data is less than 0.36 V. The effectiveness of the fault diagnosis algorithm is not sensitive to the precision of battery SOC. The proposed algorithm can still make the correct diagnosis even if there is 10% error in SOC estimation.

Diagnosis of constant faults in read-once contact networks over finite bases

KAUST Repository

Busbait, Monther I.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

2015-01-01

We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

Diagnosis of constant faults in read-once contact networks over finite bases

KAUST Repository

Busbait, Monther I.

2015-03-01

We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

Has introduction of rapid drug susceptibility testing at diagnosis impacted treatment outcomes among previously treated tuberculosis patients in Gujarat, India?

Directory of Open Access Journals (Sweden)

Paresh Dave

Full Text Available Revised National TB Control Programme (RNTCP in India recommends that all previously-treated TB (PT patients are offered drug susceptibility testing (DST at diagnosis, using rapid diagnostics and screened out for rifampicin resistance before being treated with standardized, eight-month, retreatment regimen. This is intended to improve the early diagnosis of rifampicin resistance and its appropriate management and improve the treatment outcomes among the rest of the patients. In this state-wide study from Gujarat, India, we assess proportion of PT patients underwent rapid DST at diagnosis and the impact of this intervention on their treatment outcomes.This is a retrospective cohort study involving review of electronic patient-records maintained routinely under RNTCP. All PT patients registered for treatment in Gujarat during January-June 2013 were included. Information on DST and treatment outcomes were extracted from 'presumptive DR-TB patient register' and TB treatment register respectively. We performed a multivariate analysis to assess if getting tested is independently associated with unfavourable outcomes (death, loss-to-follow-up, failure, transfer out.Of 5,829 PT patients, 5306(91% were tested for drug susceptibility with rapid diagnostics. Overall, 71% (4,113 TB patients were successfully treated - 72% among tested versus 60% among non-tested. Patients who did not get tested at diagnosis had a 34% higher risk of unsuccessful outcomes as compared to those who got tested (aRR - 1.34; 95% CI 1.20-1.50 after adjusting for age, sex, HIV status and type of TB. Unfavourable outcomes (particularly failure and switched to category IV were higher among INH-resistant patients (39% as compared to INH-sensitive (29%.Offering DST at diagnosis improved the treatment outcomes among PT patients. However, even among tested, treatment outcomes remained suboptimal and were related to INH resistance and high loss-to-follow-up. These need to be addressed

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

Directory of Open Access Journals (Sweden)

Ridha Djemal

2017-01-01

Full Text Available Autism spectrum disorder (ASD is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD of autism ‎based on electroencephalography (EEG signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT, entropy (En, and artificial neural network (ANN. DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

Tuberculosis mimicking lung cancer

Directory of Open Access Journals (Sweden)

I. Hammen

2015-01-01

Our case report presents two patients, who were referred to the Thorax diagnostic centre at the Department of Respiratory Medicine, Odense University Hospital, with presumptive diagnosis of neoplasm and had proved lung TB with no evidence of malignancy instead. In the first case diagnosis was confirmed after thoracotomy, in the second case after bronchoscopy.

Development of a component centered fault monitoring and diagnosis knowledge based system for space power system

Science.gov (United States)

Lee, S. C.; Lollar, Louis F.

1988-01-01

The overall approach currently being taken in the development of AMPERES (Autonomously Managed Power System Extendable Real-time Expert System), a knowledge-based expert system for fault monitoring and diagnosis of space power systems, is discussed. The system architecture, knowledge representation, and fault monitoring and diagnosis strategy are examined. A 'component-centered' approach developed in this project is described. Critical issues requiring further study are identified.

Plant diagnosis device

International Nuclear Information System (INIS)

Tozuka, Shin-ichi.

1996-01-01

Standard data approximately defined are inputted as 1:1 functional data between at least two or more plant data and each of plant data are inputted. Diagnosis data corresponding to each of process data are formed based on the functional data. Limit value data to be a threshold value which determines whether the diagnosis data are in a predetermined state or not are formed. The diagnosis data and the limit value data are displayed in a recognizable state. If diagnosis data of a plurality of plants are displayed simultaneously, all of the plant data are substantially the same value with one standard datum if the plant is in a normal state. When abnormality should occur in the plant, the difference between the diagnosis data and the standard data is remarkable, and the difference between the diagnosis data of other normal plant data and the standard data are also made remarkably, accordingly, the display of a plurality of diagnosis data is scattered thereby capable of diagnosing the abnormality of the plant. (N.H.)

Schwannoma of the left brachial plexus mimicking a ...

African Journals Online (AJOL)

Schwannoma of the left brachial plexus mimicking a cervicomediastinal ... Her voice was hoarse but there was no eye signs suggestive of thyrotoxicosis. ... A presumptive diagnosis of thyroid carcinoma with retrosternal extension was made.

OPTICAL AND DIELECTRIC SENSORS BASED ON ANTIMICROBIAL PEPTIDES FOR MICROORGANISMS DIAGNOSIS

Directory of Open Access Journals (Sweden)

Rafael Ramos Silva

2014-08-01

Full Text Available Antimicrobial peptides (AMPs are natural compounds isolated from a wide variety of organisms that include microorganisms, insects, amphibians, plants and humans. These biomolecules are considered as part of the innate immune system and are known as natural antibiotics, presenting a broad spectrum of activities against bacteria, fungi and/or viruses. Technological innovations have enabled AMPs to be utilized for the development of novel biodetection devices. Advances in nanotechnology, such as the synthesis of nanocomposites, nanoparticles, and nanotubes have permitted the development of nanostructured platforms with biocompatibility and greater surface areas for the immobilization of biocomponents, arising as additional tools for obtaining more efficient biosensors. Diverse AMPs have been used as biological recognition elements for obtaining biosensors with more specificity and lower detection limits, whose analytical response can be evaluated through electrochemical impedance and fluorescence spectroscopies. AMP-based biosensors have shown potential for applications such as supplementary tools for conventional diagnosis methods of microorganisms. In this review, conventional methods for microorganism diagnosis as well new strategies using AMPs for the development of impedimetric and fluorescent biosensors are highlighted. AMP-based biosensors show promise as methods for diagnosing infections and bacterial contaminations as well as applications in quality control for clinical analyses and microbiological laboratories.

Calcium-based biomaterials for diagnosis, treatment, and theranostics.

Science.gov (United States)

Qi, Chao; Lin, Jing; Fu, Lian-Hua; Huang, Peng

2018-01-22

Calcium-based (CaXs) biomaterials including calcium phosphates, calcium carbonates, calcium silicate and calcium fluoride have been widely utilized in the biomedical field owing to their excellent biocompatibility and biodegradability. In recent years, CaXs biomaterials have been strategically integrated with imaging contrast agents and therapeutic agents for various molecular imaging modalities including fluorescence imaging, magnetic resonance imaging, ultrasound imaging or multimodal imaging, as well as for various therapeutic approaches including chemotherapy, gene therapy, hyperthermia therapy, photodynamic therapy, radiation therapy, or combination therapy, even imaging-guided therapy. Compared with other inorganic biomaterials such as silica-, carbon-, and gold-based biomaterials, CaXs biomaterials can dissolve into nontoxic ions and participate in the normal metabolism of organisms. Thus, they offer safer clinical solutions for disease theranostics. This review focuses on the state-of-the-art progress in CaXs biomaterials, which covers from their categories, characteristics and preparation methods to their bioapplications including diagnosis, treatment, and theranostics. Moreover, the current trends and key problems as well as the future prospects and challenges of CaXs biomaterials are also discussed at the end.

Radiologic diagnosis of bone tumours using Webonex, a web-based artificial intelligence program

International Nuclear Information System (INIS)

Rasuli, P.; Rasouli, F.; Rasouli, T.

2001-01-01

Knowledge-based system is a decision support system in which an expert's knowledge and reasoning can be applied to problems in bounded knowledge domains. These systems, using knowledge and inference techniques, mimic human reasoning to solve problems. Knowledge-based systems are said to be 'intelligent' because they possess massive stores of information and exhibit many attributes commonly associated with human experts performing difficult tasks and using specialized knowledge and sophisticated problem-solving strategies. Knowledge-based systems differ from conventional software such as database systems in that they are able to reason about data and draw conclusions employing heuristic rules. Heuristics embody human expertise in some knowledge domain and are sometimes characterized as the 'rules of thumb' that one acquires through practical experience and uses to solve everyday problems. Knowledge-based systems have been developed in a variety of fields, including medical disciplines. A decision support system has been assisting clinicians in areas such as infectious disease therapy for many years. For example, these systems can help radiologists formulate and evaluate diagnostic hypotheses by recalling associations between diseases and imaging findings. Although radiologic technology relies heavily on computers, it has been slow to develop a knowledge-based system to aid in diagnoses. These systems can be valuable interactive educational tools for medical students. In 1992, we developed a DOS-based Bonex, a menu-driven expert system for the differential diagnosis of bone tumours using PDC Prolog. It was a rule-based expert system that led the user through a menu of questions and generated a hard copy report and a list of diagnoses with an estimate of the likelihood of each. Bonex was presented at the 1992 Annual Meeting of the Radiological Society of North America (RSNA) in Chicago. We also developed an expert system for the differential diagnosis of brain lesions

Radiologic diagnosis of bone tumours using Webonex, a web-based artificial intelligence program

Energy Technology Data Exchange (ETDEWEB)

Rasuli, P. [Univ. of Ottawa, Dept. of Radiology, Ottawa Hospital, Ottawa, Ontario (Canada); Rasouli, F. [Research, Development and Engineering Center, PMUSA, Richmond, VA (United States); Rasouli, T. [Johns Hopkins Univ., Dept. of Cognitive Science, Baltimore, Maryland (United States)

2001-08-01

Knowledge-based system is a decision support system in which an expert's knowledge and reasoning can be applied to problems in bounded knowledge domains. These systems, using knowledge and inference techniques, mimic human reasoning to solve problems. Knowledge-based systems are said to be 'intelligent' because they possess massive stores of information and exhibit many attributes commonly associated with human experts performing difficult tasks and using specialized knowledge and sophisticated problem-solving strategies. Knowledge-based systems differ from conventional software such as database systems in that they are able to reason about data and draw conclusions employing heuristic rules. Heuristics embody human expertise in some knowledge domain and are sometimes characterized as the 'rules of thumb' that one acquires through practical experience and uses to solve everyday problems. Knowledge-based systems have been developed in a variety of fields, including medical disciplines. A decision support system has been assisting clinicians in areas such as infectious disease therapy for many years. For example, these systems can help radiologists formulate and evaluate diagnostic hypotheses by recalling associations between diseases and imaging findings. Although radiologic technology relies heavily on computers, it has been slow to develop a knowledge-based system to aid in diagnoses. These systems can be valuable interactive educational tools for medical students. In 1992, we developed a DOS-based Bonex, a menu-driven expert system for the differential diagnosis of bone tumours using PDC Prolog. It was a rule-based expert system that led the user through a menu of questions and generated a hard copy report and a list of diagnoses with an estimate of the likelihood of each. Bonex was presented at the 1992 Annual Meeting of the Radiological Society of North America (RSNA) in Chicago. We also developed an expert system for the differential

FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

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Pelin GORGEL

2013-01-01

Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.

Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

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Zhou Qing

2017-01-01

Full Text Available Sensorless Drive Diagnosis can be used to assess the process data without the need for additional cost-intensive sensor technology, and you can understand the synchronous motor and connecting parts of the damaged state. Considering the number of features involved in the process data, it is necessary to perform feature selection and reduce the data dimension in the process of fault detection. In this paper, the MOEA / D algorithm based on multi-objective optimization is used to obtain the weight vector of all the features in the original data set. It is more suitable to classify or make decisions based on these features. In order to ensure the fastness and convenience sensorless drive diagnosis, in this paper, the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature in sensorless drive, which makes the fault detection and elimination process more targeted.

Clinical Assessment of a Nocardia PCR-Based Assay for Diagnosis of Nocardiosis.

Science.gov (United States)

Rouzaud, Claire; Rodriguez-Nava, Véronica; Catherinot, Emilie; Méchaï, Frédéric; Bergeron, Emmanuelle; Farfour, Eric; Scemla, Anne; Poirée, Sylvain; Delavaud, Christophe; Mathieu, Daniel; Durupt, Stéphane; Larosa, Fabrice; Lengelé, Jean-Philippe; Christophe, Jean-Louis; Suarez, Felipe; Lortholary, Olivier; Lebeaux, David

2018-06-01

The diagnosis of nocardiosis, a severe opportunistic infection, is challenging. We assessed the specificity and sensitivity of a 16S rRNA Nocardia PCR-based assay performed on clinical samples. In this multicenter study (January 2014 to April 2015), patients who were admitted to three hospitals and had an underlying condition favoring nocardiosis, clinical and radiological signs consistent with nocardiosis, and a Nocardia PCR assay result for a clinical sample were included. Patients were classified as negative control (NC) (negative Nocardia culture results and proven alternative diagnosis or improvement at 6 months without anti- Nocardia treatment), positive control (PC) (positive Nocardia culture results), or probable nocardiosis (positive Nocardia PCR results, negative Nocardia culture results, and no alternative diagnosis). Sixty-eight patients were included; 47 were classified as NC, 8 as PC, and 13 as probable nocardiosis. PCR results were negative for 35/47 NC patients (74%). For the 12 NC patients with positive PCR results, the PCR assay had been performed with respiratory samples. These NC patients had chronic bronchopulmonary disease more frequently than did the NC patients with negative PCR results (8/12 patients [67%] versus 11/35 patients [31%]; P = 0.044). PCR results were positive for 7/8 PC patients (88%). There were 13 cases of probable nocardiosis, diagnosed solely using the PCR results; 9 of those patients (69%) had lung involvement (consolidation or nodule). Nocardia PCR testing had a specificity of 74% and a sensitivity of 88% for the diagnosis of nocardiosis. Nocardia PCR testing may be helpful for the diagnosis of nocardiosis in immunocompromised patients but interpretation of PCR results from respiratory samples is difficult, because the PCR assay may also detect colonization. Copyright © 2018 American Society for Microbiology.

The Usefulness of Clinical-Practice-Based Laboratory Data in Facilitating the Diagnosis of Dengue Illness

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Jien-Wei Liu

2013-01-01

Full Text Available Alertness to dengue and making a timely diagnosis is extremely important in the treatment of dengue and containment of dengue epidemics. We evaluated the complementary role of clinical-practice-based laboratory data in facilitating suspicion/diagnosis of dengue. One hundred overall dengue (57 dengue fever [DF] and 43 dengue hemorrhagic fever [DHF] cases and another 100 nondengue cases (78 viral infections other than dengue, 6 bacterial sepsis, and 16 miscellaneous diseases were analyzed. We separately compared individual laboratory variables (platelet count [PC] , prothrombin time [PT], activated partial thromboplastin time [APTT], alanine aminotransferase [ALT], and aspartate aminotransferase [AST] and varied combined variables of DF and/or DHF cases with the corresponding ones of nondengue cases. The sensitivity, specificity, accuracy, positive predictive value (PPV, and negative predictive value (NPV in the diagnosis of DF and/or DHF were measured based on these laboratory variables. While trade-off between sensitivity and specificity, and/or suboptimal PPV/NPV was found at measurements using these variables, prolonged APTT + normal PT + PC < 100 × 109 cells/L had a favorable sensitivity, specificity, PPV, and NPV in diagnosis of DF and/or DHF. In conclusion, these data suggested that prolonged APTT + normal PT + PC < 100 × 109 cells/L is useful in evaluating the likelihood of DF and/or DHF.

Cost-effectiveness analysis of malaria rapid diagnostic tests for appropriate treatment of malaria at the community level in Uganda

DEFF Research Database (Denmark)

Hansen, Kristian S; Ndyomugyenyi, Richard; Magnussen, Pascal

2017-01-01

was a cost-effectiveness analysis of the introduction of malaria rapid diagnostic tests (mRDTs) performed by CHWs in two areas of moderate-to-high and low malaria transmission in rural Uganda. CHWs were trained to perform mRDTs and treat children with artemisinin-based combination therapy (ACT......) in the intervention arm while CHWs offered treatment based on presumptive diagnosis in the control arm. Data on the proportion of children with fever 'appropriately treated for malaria with ACT' were captured from a randomised trial. Health sector costs included: training of CHWs, community sensitisation, supervision...

Research on bearing fault diagnosis of large machinery based on mathematical morphology

Science.gov (United States)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

Computer-aided diagnosis workstation and telemedicine network system for chest diagnosis based on multislice CT images

Science.gov (United States)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki

2009-02-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The functions to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and "Success in login" effective. As a result, patients' private information is protected. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and

Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.

Science.gov (United States)

Wu, Yixiao; Yang, Ran; Jia, Sen; Li, Zhanjun; Zhou, Zhiyang; Lou, Ting

2014-01-01

This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.

A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

International Nuclear Information System (INIS)

Liu, Yong-Kuo; Wu, Guo-Hua; Xie, Chun-Li; Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun

2016-01-01

Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.

A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

Energy Technology Data Exchange (ETDEWEB)

Liu, Yong-Kuo, E-mail: LYK08@126.com [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Wu, Guo-Hua [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Institute of Nuclear Energy Technology, Tsinghua University, Beijing 100084 (China); Xie, Chun-Li [Traffic College, Northeast Forestry University, Harbin, 150040 (China); Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China)

2016-02-15

Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.

Guidelines for the diagnosis and management of gastroesophageal reflux disease: an evidence-based consensus.

Science.gov (United States)

Moraes-Filho, Joaquim Prado P; Navarro-Rodriguez, Tomas; Barbuti, Ricardo; Eisig, Jaime; Chinzon, Decio; Bernardo, Wanderley

2010-01-01

Gastroesophageal reflux disease (GERD) is one of the most common disorders in medical practice. A number of guidelines and recommendations for the diagnosis and management of GERD have been published in different countries, but a Brazilian accepted directive by the standards of evidence-based medicine is still lacking. As such, the aim of the Brazilian GERD Consensus Group was to develop guidelines for the diagnosis and management of GERD, strictly using evidence-based medicine methodology that could be clinically used by primary care physicians and specialists and would encompass the needs of physicians, investigators, insurance and regulatory bodies. A total of 30 questions were proposed. Systematic literature reviews, which defined inclusion and/or exclusion criteria, were conducted to identify and grade the available evidence to support each statement. A total of 11,069 papers on GERD were selected, of which 6,474 addressed the diagnosis and 4,595, therapeutics. Regarding diagnosis, 51 met the requirements for the analysis of evidence-based medicine: 19 of them were classified as grade A and 32 as grade B. As for therapeutics, 158 met the evidence-based medicine criteria; 89 were classified as grade A and 69 as grade B. In the topic Diagnosis, answers supported by publications grade A and B were accepted. In the topic Treatment only publications grade A were accepted: answers supported by publications grade B were submitted to the voting by the Consensus Group. The present publication presents the most representative studies that responded to the proposed questions, followed by pertinent comments. Follow examples. In patients with atypical manifestations, the conventional esophageal pH-metry contributes little to the diagnosis of GERD. The sensitivity, however, increases with the use of double-channel pH-metry. In patients with atypical manifestations, the impedance-pH-metry substantially contributes to the diagnosis of GERD. The examination, however, is costly

Development of a GIS-Based Decision Support System for Diagnosis of River System Health and Restoration

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Jihong Xia

2014-10-01

Full Text Available The development of a decision support system (DSS to inform policy making has been progressing rapidly. This paper presents a generic framework and the development steps of a decision tool prototype of geographic information systems (GIS-based decision support system of river health diagnosis (RHD-DSS. This system integrates data, calculation models, and human knowledge of river health status assessment, causal factors diagnosis, and restoration decision making to assist decision makers during river restoration and management in Zhejiang Province, China. Our RHD-DSS is composed of four main elements: the graphical user interface (GUI, the database, the model base, and the knowledge base. It has five functional components: the input module, the database management, the diagnostic indicators management, the assessment and diagnosis, and the visual result module. The system design is illustrated with particular emphasis on the development of the database, model schemas, diagnosis and analytical processing techniques, and map management design. Finally, the application of the prototype RHD-DSS is presented and implemented for Xinjiangtang River of Haining County in Zhejiang Province, China. This case study is used to demonstrate the advantages gained by the application of this system. We conclude that there is great potential for using the RHD-DSS to systematically manage river basins in order to effectively mitigate environmental issues. The proposed approach will provide river managers and designers with improved insight into river degradation conditions, thereby strengthening the assessment process and the administration of human activities in river management.

Verification test for on-line diagnosis algorithm based on noise analysis

International Nuclear Information System (INIS)

Tamaoki, T.; Naito, N.; Tsunoda, T.; Sato, M.; Kameda, A.

1980-01-01

An on-line diagnosis algorithm was developed and its verification test was performed using a minicomputer. This algorithm identifies the plant state by analyzing various system noise patterns, such as power spectral densities, coherence functions etc., in three procedure steps. Each obtained noise pattern is examined by using the distances from its reference patterns prepared for various plant states. Then, the plant state is identified by synthesizing each result with an evaluation weight. This weight is determined automatically from the reference noise patterns prior to on-line diagnosis. The test was performed with 50 MW (th) Steam Generator noise data recorded under various controller parameter values. The algorithm performance was evaluated based on a newly devised index. The results obtained with one kind of weight showed the algorithm efficiency under the proper selection of noise patterns. Results for another kind of weight showed the robustness of the algorithm to this selection. (orig.)

Rapid Flow Cytometry-Based Test for the Diagnosis of Lipopolysaccharide Responsive Beige-Like Anchor (LRBA Deficiency

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Laura Gámez-Díaz

2018-04-01

Full Text Available The diagnosis of lipopolysaccharide-responsive beige-like-anchor-protein (LRBA deficiency currently relies on gene sequencing approaches that do not support a timely diagnosis and clinical management. We developed a rapid and sensitive test for clinical implementation based on the detection of LRBA protein by flow cytometry in peripheral blood cells after stimulation. LRBA protein was assessed in a prospective cohort of 54 healthy donors and 57 patients suspected of LRBA deficiency. Receiver operating characteristics analysis suggested an LRBA:MFI ratio cutoff point of 2.6 to identify LRBA-deficient patients by FACS with 94% sensitivity and 80% specificity and to discriminate them from patients with a similar clinical picture but other disease-causing mutations. This easy flow cytometry-based assay allows a fast screening of patients with suspicion of LRBA deficiency reducing therefore the number of patients requiring LRBA sequencing and accelerating the treatment implementation. Detection of biallelic mutations in LRBA is however required for a definitive diagnosis.

Rapid Flow Cytometry-Based Test for the Diagnosis of Lipopolysaccharide Responsive Beige-Like Anchor (LRBA) Deficiency.

Science.gov (United States)

Gámez-Díaz, Laura; Sigmund, Elena C; Reiser, Veronika; Vach, Werner; Jung, Sophie; Grimbacher, Bodo

2018-01-01

The diagnosis of lipopolysaccharide-responsive beige-like-anchor-protein (LRBA) deficiency currently relies on gene sequencing approaches that do not support a timely diagnosis and clinical management. We developed a rapid and sensitive test for clinical implementation based on the detection of LRBA protein by flow cytometry in peripheral blood cells after stimulation. LRBA protein was assessed in a prospective cohort of 54 healthy donors and 57 patients suspected of LRBA deficiency. Receiver operating characteristics analysis suggested an LRBA:MFI ratio cutoff point of 2.6 to identify LRBA-deficient patients by FACS with 94% sensitivity and 80% specificity and to discriminate them from patients with a similar clinical picture but other disease-causing mutations. This easy flow cytometry-based assay allows a fast screening of patients with suspicion of LRBA deficiency reducing therefore the number of patients requiring LRBA sequencing and accelerating the treatment implementation. Detection of biallelic mutations in LRBA is however required for a definitive diagnosis.

Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

Directory of Open Access Journals (Sweden)

Junjie Lu

2018-01-01

Full Text Available Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughly understanding the components’ performance, which is described by component characteristic maps and the fault of each component can be regarded as the change of characteristic maps. In this paper, a novel method based on a Levenberg–Marquardt (LM algorithm is proposed to enhance the fidelity of the performance estimation and the credibility of the fault diagnosis for the turbofan engine. The presented method utilizes the LM algorithm to figure out the operating point in the characteristic maps, preparing for performance estimation and fault diagnosis. The accuracy of the proposed method is evaluated for estimating performance parameters in the transient case with Rayleigh process noise and Gaussian measurement noise. The comparison among the extended Kalman filter (EKF method, the particle filter (PF method and the proposed method is implemented in the abrupt fault case and the gradual degeneration case and it has been shown that the proposed method has the capability to lead to more accurate result for performance estimation and fault diagnosis of turbofan engine than current popular EKF and PF diagnosis methods.

Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

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Yu Ding

2018-01-01

Full Text Available Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.

Matrix Failure Modes and Effects Analysis as a Knowledge Base for a Real Time Automated Diagnosis Expert System

Science.gov (United States)

Herrin, Stephanie; Iverson, David; Spukovska, Lilly; Souza, Kenneth A. (Technical Monitor)

1994-01-01

Failure Modes and Effects Analysis contain a wealth of information that can be used to create the knowledge base required for building automated diagnostic Expert systems. A real time monitoring and diagnosis expert system based on an actual NASA project's matrix failure modes and effects analysis was developed. This Expert system Was developed at NASA Ames Research Center. This system was first used as a case study to monitor the Research Animal Holding Facility (RAHF), a Space Shuttle payload that is used to house and monitor animals in orbit so the effects of space flight and microgravity can be studied. The techniques developed for the RAHF monitoring and diagnosis Expert system are general enough to be used for monitoring and diagnosis of a variety of other systems that undergo a Matrix FMEA. This automated diagnosis system was successfully used on-line and validated on the Space Shuttle flight STS-58, mission SLS-2 in October 1993.

Polymorphisms in the presumptive promoter region of the SLC2A9 gene are associated with gout in a Chinese male population.

Science.gov (United States)

Li, Changgui; Chu, Nan; Wang, Binbin; Wang, Jing; Luan, Jian; Han, Lin; Meng, Dongmei; Wang, Yunlong; Suo, Peisu; Cheng, Longfei; Ma, Xu; Miao, Zhimin; Liu, Shiguo

2012-01-01

Glucose transporter 9 (GLUT9) is a high-capacity/low-affinity urate transporter. To date, several recent genome-wide association studies (GWAS) and follow-up studies have identified genetic variants of SLC2A9 associated with urate concentrations and susceptibility to gout. We therefore investigated associations between gout and polymorphisms and haplotypes in the presumptive promoter region of GLUT9 in Chinese males. The approximately 2000 bp presumptive promoter region upstream of the start site of exon 1 of GLUT9 was sequenced and subjected to genetic analysis. A genotype-phenotype correlation was performed and polymorphisms-induced changes in transcription factor binding sites were predicted. Of 21 SNPs identified in GLUT9, five had not been previously reported. Two of the SNPs (rs13124007 and rs6850166) were associated with susceptibility to gout (p = 0.009 and p = 0.042, respectively). The C allele of rs13124007 appeared to be the risk allele for predisposition to gout (p = 0.006, OR 1.709 [95% CI 1.162-2.514]). For rs6850166, an increased risk of gout was associated with the A allele (p = 0.029, OR 1.645 [95% CI 1.050-2.577]). After Bonferroni correction, there was statistically difference in rs13124007 allele frequencies between gout cases and controls (P = 0.042). Haplotype analyses showed that haplotype GG was a protective haplotype (p = 0.0053) and haplotype CA was associated with increased risk of gout (p = 0.0326). Genotype-phenotype analysis among gout patients revealed an association of rs13124007 with serum triglycerides levels (P = 0.001). The C to G substitution in polymorphism rs13124007 resulted in a loss of a binding site for transcription factor interferon regulatory factor 1 (IRF-1). Polymorphisms rs13124007 and rs6850166 are associated with susceptibility to gout in Chinese males.

Adaptive PCA based fault diagnosis scheme in imperial smelting process.

Science.gov (United States)

Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

2014-09-01

In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Diagnosis of Giardia infections by PCR-based methods in children of an endemic area

Directory of Open Access Journals (Sweden)

EB David

2011-01-01

Full Text Available The present study was designed to estimate the prevalence of Giardia infection in preschool- and school-aged children living in an endemic area. Fecal samples from 573 children were processed by zinc sulfate centrifugal flotation, centrifugal sedimentation (using a commercial device for fecal concentration - TF-Test kit® and polymerase chain reaction (PCR-based methods. Of the stool samples assessed, 277 (48.3% were positive for intestinal parasites and/or commensal protozoa. Centrifugal flotation presented the highest diagnostic sensitivity for Giardia infections. The kappa index revealed that both coproparasitological techniques closely agreed on the Giardia diagnosis (86% versus satisfactory (72% and poor (35% concordances for commensal protozoan and helminth infections, respectively. Concerning Giardia molecular diagnosis, from the 71 microscopy-positive samples, specific amplification of gdh and tpi fragments was noted in 68 (95.7% and 64 (90% samples, respectively. Amplification of gdh and tpi genes was observed, respectively, in 95.7% and 90% of microscopy-positive Giardia samples. For 144 microscopy-negative samples, gdh and tpi gene amplification products were obtained from 8.3% and 35.9% samples, respectively. The agreement between these genes was about 40%. The centrifuge-flotation based method was the most suitable means of Giardia diagnosis assessed in the present study by combining accuracy and low cost.

An Event-based Distributed Diagnosis Framework using Structural Model Decomposition

Data.gov (United States)

National Aeronautics and Space Administration — Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis...

A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

Science.gov (United States)

Round, A. R.; Wilkinson, S. J.; Hall, C. J.; Rogers, K. D.; Glatter, O.; Wess, T.; Ellis, I. O.

2005-09-01

Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.

A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

Energy Technology Data Exchange (ETDEWEB)

Round, A R [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Wilkinson, S J [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Hall, C J [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Rogers, K D [Department of Materials and Medical Sciences, Cranfield University, Swindon, SN6 8LA (United Kingdom); Glatter, O [Department of Chemistry, University of Graz (Austria); Wess, T [School of Optometry and Vision Sciences, Cardiff University, Cardiff CF10 3NB, Wales (United Kingdom); Ellis, I O [Nottingham City Hospital, Nottingham (United Kingdom)

2005-09-07

Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.

A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

International Nuclear Information System (INIS)

Round, A R; Wilkinson, S J; Hall, C J; Rogers, K D; Glatter, O; Wess, T; Ellis, I O

2005-01-01

Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique

Distributed bearing fault diagnosis based on vibration analysis

Science.gov (United States)

Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

2016-01-01

Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

Novel Agent Based-approach for Industrial Diagnosis: A Combined use Between Case-based Reasoning and Similarity Measure

Directory of Open Access Journals (Sweden)

Fatima Zohra Benkaddour

2016-12-01

Full Text Available In spunlace nonwovens industry, the maintenance task is very complex, it requires experts and operators collaboration. In this paper, we propose a new approach integrating an agent- based modelling with case-based reasoning that utilizes similarity measures and preferences module. The main purpose of our study is to compare and evaluate the most suitable similarity measure for our case. Furthermore, operators that are usually geographically dispersed, have to collaborate and negotiate to achieve mutual agreements, especially when their proposals (diagnosis lead to a conflicting situation. The experimentation shows that the suggested agent-based approach is very interesting and efficient for operators and experts who collaborate in INOTIS enterprise.

Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

Science.gov (United States)

Zhang, Han

Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis

Directory of Open Access Journals (Sweden)

Yi Cao

2013-06-01

Full Text Available A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD and the relative crossing information (RCI methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO clustering algorithm, the synthesizing symptom parameters (SSP for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP, and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

Science.gov (United States)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

International Nuclear Information System (INIS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-01-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis

Computer aided diagnosis based on medical image processing and artificial intelligence methods

Energy Technology Data Exchange (ETDEWEB)

Stoitsis, John [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)]. E-mail: stoitsis@biosim.ntua.gr; Valavanis, Ioannis [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Mougiakakou, Stavroula G. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Golemati, Spyretta [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Nikita, Alexandra [University of Athens, Medical School 152 28 Athens (Greece); Nikita, Konstantina S. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)

2006-12-20

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings

Directory of Open Access Journals (Sweden)

Sharif Uddin

2016-01-01

Full Text Available An enhanced k-nearest neighbor (k-NN classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditional k-NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size, k. This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposed k-NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE signals. Experimental results demonstrate that the proposed scheme, which uses the enhanced k-NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size, k.

Fault diagnosis of main coolant pump in the nuclear power station based on the principal component analysis

International Nuclear Information System (INIS)

Feng Junting; Xu Mi; Wang Guizeng

2003-01-01

The fault diagnosis method based on principal component analysis is studied. The fault character direction storeroom of fifteen parameters abnormity is built in the simulation for the main coolant pump of nuclear power station. The measuring data are analyzed, and the results show that it is feasible for the fault diagnosis system of main coolant pump in the nuclear power station

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

Data.gov (United States)

National Aeronautics and Space Administration — Diagnosis and prognosis are necessary tasks for system re- configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation...

Diagnosis and microecological characteristics of aerobic vaginitis in outpatients based on preformed enzymes

OpenAIRE

Wang, Zhi-liang; Fu, Lan-yong; Xiong, Zheng-ai; Qin, Qin; Yu, Teng-hua; Wu, Yu-tong; Hua, Yuan-yuan; Zhang, Yong-hong

2016-01-01

Objective: Aerobic vaginitis (AV) is a recently proposed term for genital tract infection in women. The diagnosis of AV is mainly based on descriptive diagnostic criteria proposed by Donders and co-workers. The objective of this study is to report AV prevalence in southwest China using an objective assay kit based on preformed enzymes and also to determine its characteristics. Materials and methods: A total of 1948 outpatients were enrolled and tested by a commercial diagnostic kit to inve...

Impact of presumed service-connected diagnosis on the Department of Veterans Affairs healthcare utilization patterns of Vietnam-Theater Veterans: A cross-sectional study.

Science.gov (United States)

Fried, Dennis A; Rajan, Mangala; Tseng, Chin-Lin; Helmer, Drew

2018-05-01

During the Vietnam War, the US military sprayed almost 20 million gallons of Agent Orange (AO), an herbicide contaminated with dioxin, over Vietnam. Approximately, 2.7 million US military personnel may have been exposed to AO during their deployment. Ordinarily, veterans who can demonstrate a nexus between a diagnosed condition and military service are eligible for Department of Veterans Affairs (VA) service-connected disability compensation. Vietnam Veterans have had difficulty, however, establishing a nexus between AO exposure and certain medical conditions that developed many years after the war. In response, VA has designated certain conditions as "presumed service connected" for Vietnam Veterans who were present and possibly exposed. Veterans with any of these designated conditions do not have to document AO exposure, making it easier for them to access the VA disability system. The extent to which VA healthcare utilization patterns reflect easier access afforded those with diagnosed presumptive conditions remains unknown. In this cross-sectional study, we hypothesized that Vietnam Veterans with diagnosed presumptive conditions would be heavier users of the VA healthcare system than those without these conditions. In our analysis of 85,699 Vietnam Veterans, we used binary and cumulative logit multivariable regression to assess associations between diagnosed presumptive conditions and VA healthcare utilization in 2013. We found that diagnosed presumptive conditions were associated with higher odds of 5+ VHA primary care visits (OR = 2.01, 95% CI: 1.93-2.07), 5+ specialty care visits (OR = 2.11, 95% CI: 2.04-2.18), emergency department use (OR = 1.22, 95% CI: 1.11-1.34), and hospitalization (OR = 1.23, 95% CI: 1.17-1.29). Consistent with legislative intent, presumptive policies appear to facilitate greater VA system utilization for Vietnam Veterans who may have been exposed to AO.

Space-Based Diagnosis of Surface Ozone Sensitivity to Anthropogenic Emissions

Science.gov (United States)

Martin, Randall V.; Fiore, Arlene M.; VanDonkelaar, Aaron

2004-01-01

We present a novel capability in satellite remote sensing with implications for air pollution control strategy. We show that the ratio of formaldehyde columns to tropospheric nitrogen dioxide columns is an indicator of the relative sensitivity of surface ozone to emissions of nitrogen oxides (NO(x) = NO + NO2) and volatile organic compounds (VOCs). The diagnosis from these space-based observations is highly consistent with current understanding of surface ozone chemistry based on in situ observations. The satellite-derived ratios indicate that surface ozone is more sensitive to emissions of NO(x) than of VOCs throughout most continental regions of the Northern Hemisphere during summer. Exceptions include Los Angeles and industrial areas of Germany. A seasonal transition occurs in the fall when surface ozone becomes less sensitive to NOx and more sensitive to VOCs.

Knowledge-based fault diagnosis system for refuse collection vehicle

International Nuclear Information System (INIS)

Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

2015-01-01

The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle

Knowledge-based fault diagnosis system for refuse collection vehicle

Energy Technology Data Exchange (ETDEWEB)

Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y. [Centre of Advanced Research on Energy, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka (Malaysia)

2015-05-15

The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

Equine deep stromal abscesses (51 cases - 2004-2009) - Part 2

DEFF Research Database (Denmark)

Henriksen, Michala de Linde; Andersen, Pia Haubro; Mietelka, Kristy

2014-01-01

To investigate histopathologic and immunohistochemical aspects of equine deep stromal abscesses (DSA) with a focus on the histopathologic diagnosis, presumptive etiology, and the immunohistochemical expression of three angiogenesis-related factors: vascular endothelial growth factor-A (VEGF...

A fault diagnosis system for PV power station based on global partitioned gradually approximation method

Science.gov (United States)

Wang, S.; Zhang, X. N.; Gao, D. D.; Liu, H. X.; Ye, J.; Li, L. R.

2016-08-01

As the solar photovoltaic (PV) power is applied extensively, more attentions are paid to the maintenance and fault diagnosis of PV power plants. Based on analysis of the structure of PV power station, the global partitioned gradually approximation method is proposed as a fault diagnosis algorithm to determine and locate the fault of PV panels. The PV array is divided into 16x16 blocks and numbered. On the basis of modularly processing of the PV array, the current values of each block are analyzed. The mean current value of each block is used for calculating the fault weigh factor. The fault threshold is defined to determine the fault, and the shade is considered to reduce the probability of misjudgments. A fault diagnosis system is designed and implemented with LabVIEW. And it has some functions including the data realtime display, online check, statistics, real-time prediction and fault diagnosis. Through the data from PV plants, the algorithm is verified. The results show that the fault diagnosis results are accurate, and the system works well. The validity and the possibility of the system are verified by the results as well. The developed system will be benefit for the maintenance and management of large scale PV array.

Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools

CERN Document Server

Ding, Steven X

2013-01-01

Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: ·         new material on fault isolation and identification, and fault detection in feedback control loops; ·         extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and ·         enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagno...

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network

Directory of Open Access Journals (Sweden)

Muhammad Sohaib

2018-01-01

Full Text Available Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing, but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder- (SSAE- based deep neural networks (DNNs, a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE-DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.

Fault Detection and Diagnosis System in Process industry Based on Big Data and WeChat

Directory of Open Access Journals (Sweden)

Sun Zengqiang

2017-01-01

Full Text Available The fault detection and diagnosis information in process industry can be received, anytime and anywhere, based on bigdata and WeChat with mobile phone, which got rid of constraints that can only check Distributed Control System (DCS in the central control room or look over in office. Then, fault detection, diagnosis information sharing can be provided, and what’s more, fault detection alarm range, code and inform time can be personalized. The pressure of managers who worked on process industry can be release with the mobile information system.

Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network

Directory of Open Access Journals (Sweden)

Shu-zhi Gao

2013-01-01

Full Text Available Polyvinyl chloride (PVC polymerizing production process is a typical complex controlled object, with complexity features, such as nonlinear, multivariable, strong coupling, and large time-delay. Aiming at the real-time fault diagnosis and optimized monitoring requirements of the large-scale key polymerization equipment of PVC production process, a real-time fault diagnosis strategy is proposed based on rough sets theory with the improved discernibility matrix and BP neural networks. The improved discernibility matrix is adopted to reduct the attributes of rough sets in order to decrease the input dimensionality of fault characteristics effectively. Levenberg-Marquardt BP neural network is trained to diagnose the polymerize faults according to the reducted decision table, which realizes the nonlinear mapping from fault symptom set to polymerize fault set. Simulation experiments are carried out combining with the industry history datum to show the effectiveness of the proposed rough set neural networks fault diagnosis method. The proposed strategy greatly increased the accuracy rate and efficiency of the polymerization fault diagnosis system.

European evidence-based recommendations for diagnosis and treatment of paediatric antiphospholipid syndrome: the SHARE initiative.

Science.gov (United States)

Groot, Noortje; de Graeff, Nienke; Avcin, Tadej; Bader-Meunier, Brigitte; Dolezalova, Pavla; Feldman, Brian; Kenet, Gili; Koné-Paut, Isabelle; Lahdenne, Pekka; Marks, Stephen D; McCann, Liza; Pilkington, Clarissa A; Ravelli, Angelo; van Royen-Kerkhof, Annet; Uziel, Yosef; Vastert, Sebastiaan J; Wulffraat, Nico M; Ozen, Seza; Brogan, Paul; Kamphuis, Sylvia; Beresford, Michael W

2017-10-01

Antiphospholipid syndrome (APS) is rare in children, and evidence-based guidelines are sparse. Consequently, management is mostly based on observational studies and physician's experience, and treatment regimens differ widely. The Single Hub and Access point for paediatric Rheumatology in Europe (SHARE) initiative was launched to develop diagnostic and management regimens for children and young adults with rheumatic diseases. Here, we developed evidence-based recommendations for diagnosis and treatment of paediatric APS. Evidence-based recommendations were developed using the European League Against Rheumatism standard operating procedure. Following a detailed systematic review of the literature, a committee of paediatric rheumatologists and representation of paediatric haematology with expertise in paediatric APS developed recommendations. The literature review yielded 1473 articles, of which 15 were valid and relevant. In total, four recommendations for diagnosis and eight for treatment of paediatric APS (including paediatric Catastrophic Antiphospholipid Syndrome) were accepted. Additionally, two recommendations for children born to mothers with APS were accepted. It was agreed that new classification criteria for paediatric APS are necessary, and APS in association with childhood-onset systemic lupus erythematosus should be identified by performing antiphospholipid antibody screening. Treatment recommendations included prevention of thrombotic events, and treatment recommendations for venous and/or arterial thrombotic events. Notably, due to the paucity of studies on paediatric APS, level of evidence and strength of the recommendations is relatively low. The SHARE initiative provides international, evidence-based recommendations for diagnosis and treatment for paediatric APS, facilitating improvement and uniformity of care. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use

Rapid diagnosis of sepsis with TaqMan-Based multiplex real-time PCR.

Science.gov (United States)

Liu, Chang-Feng; Shi, Xin-Ping; Chen, Yun; Jin, Ye; Zhang, Bing

2018-02-01

The survival rate of septic patients mainly depends on a rapid and reliable diagnosis. A rapid, broad range, specific and sensitive quantitative diagnostic test is the urgent need. Thus, we developed a TaqMan-Based Multiplex real-time PCR assays to identify bloodstream pathogens within a few hours. Primers and TaqMan probes were designed to be complementary to conserved regions in the 16S rDNA gene of different kinds of bacteria. To evaluate accurately, sensitively, and specifically, the known bacteria samples (Standard strains, whole blood samples) are determined by TaqMan-Based Multiplex real-time PCR. In addition, 30 blood samples taken from patients with clinical symptoms of sepsis were tested by TaqMan-Based Multiplex real-time PCR and blood culture. The mean frequency of positive for Multiplex real-time PCR was 96% at a concentration of 100 CFU/mL, and it was 100% at a concentration greater than 1000 CFU/mL. All the known blood samples and Standard strains were detected positively by TaqMan-Based Multiplex PCR, no PCR products were detected when DNAs from other bacterium were used in the multiplex assay. Among the 30 patients with clinical symptoms of sepsis, 18 patients were confirmed positive by Multiplex real-time PCR and seven patients were confirmed positive by blood culture. TaqMan-Based Multiplex real-time PCR assay with highly sensitivity, specificity and broad detection range, is a rapid and accurate method in the detection of bacterial pathogens of sepsis and should have a promising usage in the diagnosis of sepsis. © 2017 Wiley Periodicals, Inc.

The development of a theory-based intervention to promote appropriate disclosure of a diagnosis of dementia

Directory of Open Access Journals (Sweden)

Bamford Claire

2007-12-01

Full Text Available Abstract Background The development and description of interventions to change professional practice are often limited by the lack of an explicit theoretical and empirical basis. We set out to develop an intervention to promote appropriate disclosure of a diagnosis of dementia based on theoretical and empirical work. Methods We identified three key disclosure behaviours: finding out what the patient already knows or suspects about their diagnosis; using the actual words 'dementia' or 'Alzheimer's disease' when talking to the patient; and exploring what the diagnosis means to the patient. We conducted a questionnaire survey of older peoples' mental health teams (MHTs based upon theoretical constructs from the Theory of Planned Behaviour (TPB and Social Cognitive Theory (SCT and used the findings to identify factors that predicted mental health professionals' intentions to perform each behaviour. We selected behaviour change techniques likely to alter these factors. Results The change techniques selected were: persuasive communication to target subjective norm; behavioural modelling and graded tasks to target self-efficacy; persuasive communication to target attitude towards the use of explicit terminology when talking to the patient; and behavioural modelling by MHTs to target perceived behavioural control for finding out what the patient already knows or suspects and exploring what the diagnosis means to the patient. We operationalised these behaviour change techniques using an interactive 'pen and paper' intervention designed to increase intentions to perform the three target behaviours. Conclusion It is feasible to develop an intervention to change professional behaviour based upon theoretical models, empirical data and evidence based behaviour change techniques. The next step is to evaluate the effect of such an intervention on behavioural intention. We argue that this approach to development and reporting of interventions will contribute to

Fault diagnosis of an intelligent hydraulic pump based on a nonlinear unknown input observer

Directory of Open Access Journals (Sweden)

Zhonghai MA

2018-02-01

Full Text Available Hydraulic piston pumps are commonly used in aircraft. In order to improve the viability of aircraft and energy efficiency, intelligent variable pressure pump systems have been used in aircraft hydraulic systems more and more widely. Efficient fault diagnosis plays an important role in improving the reliability and performance of hydraulic systems. In this paper, a fault diagnosis method of an intelligent hydraulic pump system (IHPS based on a nonlinear unknown input observer (NUIO is proposed. Different from factors of a full-order Luenberger-type unknown input observer, nonlinear factors of the IHPS are considered in the NUIO. Firstly, a new type of intelligent pump is presented, the mathematical model of which is established to describe the IHPS. Taking into account the real-time requirements of the IHPS and the special structure of the pump, the mechanism of the intelligent pump and failure modes are analyzed and two typical failure modes are obtained. Furthermore, a NUIO of the IHPS is performed based on the output pressure and swashplate angle signals. With the residual error signals produced by the NUIO, online intelligent pump failure occurring in real-time can be detected. Lastly, through analysis and simulation, it is confirmed that this diagnostic method could accurately diagnose and isolate those typical failure modes of the nonlinear IHPS. The method proposed in this paper is of great significance in improving the reliability of the IHPS. Keywords: Fault diagnosis, Hydraulic piston pump, Model-based, Nonlinear unknown input observer (NUIO, Residual error

Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

Directory of Open Access Journals (Sweden)

Yunxiao Fu

2016-06-01

Full Text Available Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM based correntropy extracted from the Intrinsic Mode Functions (IMFs, which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD and employ Least Square Support Vector Machine (LSSVM to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.

Development of a knowledge-based system for loop diagnosis

International Nuclear Information System (INIS)

Liao, L.Y.; Tang, H.C.; Chen, S.S.

1987-01-01

An accident diagnostic system is developed as an attempt to provide a useful aid for the operators of an experimental loop or a nuclear power plant in the case of emergency condition. Because the current practices in the system diagnosis are not satisfactory, there is an increasing demand on the establishment of various operator decision support systems. The knowledge based system is a new and promising technique which can be used to fulfill this demand. With the capability of automatic reasoning and by incorporating the information about system status, the knowledge based system can simulate the process of human thinking and serve as a good decision support system. This knowledge based decision support system can be helpful for both a fast, violent accident and a slowly developed accident. Specifically, a fast diagnostic report can be provided for a fast and violent accident of which time is the main concern and a complete diagnostic report can be provided for a slowly developed accident of which complexity is the main concern. Such a knowledge based decision support system also provides many other equally important advantages, such as the elimination of human error, the automatic validation of signal readings, the establishment of human error, the automatic validation of signal readings, and the establishment of a simulation environment

Knowledge-based and integrated monitoring and diagnosis in autonomous power systems

Science.gov (United States)

Momoh, J. A.; Zhang, Z. Z.

1990-01-01

A new technique of knowledge-based and integrated monitoring and diagnosis (KBIMD) to deal with abnormalities and incipient or potential failures in autonomous power systems is presented. The KBIMD conception is discussed as a new function of autonomous power system automation. Available diagnostic modelling, system structure, principles and strategies are suggested. In order to verify the feasibility of the KBIMD, a preliminary prototype expert system is designed to simulate the KBIMD function in a main electric network of the autonomous power system.

Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis

Science.gov (United States)

Zheng, Jinde; Pan, Haiyang; Yang, Shubao; Cheng, Junsheng

2018-01-01

Multiscale permutation entropy (MPE) is a recently proposed nonlinear dynamic method for measuring the randomness and detecting the nonlinear dynamic change of time series and can be used effectively to extract the nonlinear dynamic fault feature from vibration signals of rolling bearing. To solve the drawback of coarse graining process in MPE, an improved MPE method called generalized composite multiscale permutation entropy (GCMPE) was proposed in this paper. Also the influence of parameters on GCMPE and its comparison with the MPE are studied by analyzing simulation data. GCMPE was applied to the fault feature extraction from vibration signal of rolling bearing and then based on the GCMPE, Laplacian score for feature selection and the Particle swarm optimization based support vector machine, a new fault diagnosis method for rolling bearing was put forward in this paper. Finally, the proposed method was applied to analyze the experimental data of rolling bearing. The analysis results show that the proposed method can effectively realize the fault diagnosis of rolling bearing and has a higher fault recognition rate than the existing methods.

Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

Science.gov (United States)

Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

2017-06-01

This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

Impact of a training course on the quality of malaria diagnosis by microscopy in Angola.

Science.gov (United States)

Moura, Sofia; Fançony, Cláudia; Mirante, Clara; Neves, Marcela; Bernardino, Luís; Fortes, Filomeno; Sambo, Maria do Rosário; Brito, Miguel

2014-11-18

In Angola, malaria is an endemic disease having a major impact on the economy. The WHO recommends testing for all suspected malaria cases, to avoid the presumptive treatment of this disease. In malaria endemic regions laboratory technicians must be very comfortable with microscopy, the golden standard for malaria diagnosis, to avoid the incorrect diagnosis. The improper use of medication promotes drug resistance and undesirable side effects. The present study aims to assess the impact of a three-day refresher course on the knowledge of technicians, quality of blood smears preparation and accuracy of microscopy malaria diagnosis, using qPCR as reference method. This study was implemented in laboratories from three hospitals in different provinces of Angola: Bengo, Benguela and Luanda. In each laboratory samples were collected before and after the training course (slide with thin and thick blood smears, a dried blood spot and a form). The impact of the intervention was evaluated through a written test, the quality of slide preparation and the performance of microscopy. It was found a significant increase on the written test median score, from 52.5% to 65.0%. A total of 973 slides were analysed to evaluate the quality of thick and thin blood smears. Considering all laboratories there was a significant increase in quality of thick and thin blood smears. To determine the performance of microscopy using qPCR as the reference method we used 1,028 samples. Benguela presented the highest values for specificity, 92.9% and 98.8% pre and post-course, respectively and for sensitivity the best pre-course was Benguela (75.9%) and post-course Luanda (75.0%). However, no significant increase in sensitivity and specificity after the training course was registered in any laboratory analysed. The findings of this study support the need of continuous refresher training for microscopists and other laboratory staff. The laboratories should have a quality control programme to supervise

Fault Features Extraction and Identification based Rolling Bearing Fault Diagnosis

International Nuclear Information System (INIS)

Qin, B; Sun, G D; Zhang L Y; Wang J G; HU, J

2017-01-01

For the fault classification model based on extreme learning machine (ELM), the diagnosis accuracy and stability of rolling bearing is greatly influenced by a critical parameter, which is the number of nodes in hidden layer of ELM. An adaptive adjustment strategy is proposed based on vibrational mode decomposition, permutation entropy, and nuclear kernel extreme learning machine to determine the tunable parameter. First, the vibration signals are measured and then decomposed into different fault feature models based on variation mode decomposition. Then, fault feature of each model is formed to a high dimensional feature vector set based on permutation entropy. Second, the ELM output function is expressed by the inner product of Gauss kernel function to adaptively determine the number of hidden layer nodes. Finally, the high dimension feature vector set is used as the input to establish the kernel ELM rolling bearing fault classification model, and the classification and identification of different fault states of rolling bearings are carried out. In comparison with the fault classification methods based on support vector machine and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability. (paper)

[Overcoming the limitations of the descriptive and categorical approaches in psychiatric diagnosis: a proposal based on Bayesian networks].

Science.gov (United States)

Sorias, Soli

2015-01-01

Efforts to overcome the problems of descriptive and categorical approaches have not yielded results. In the present article, psychiatric diagnosis using Bayesian networks is proposed. Instead of a yes/no decision, Bayesian networks give the probability of diagnostic category inclusion, thereby yielding both a graded, i.e., dimensional diagnosis, and a value of the certainty of the diagnosis. With the use of Bayesian networks in the diagnosis of mental disorders, information about etiology, associated features, treatment outcome, and laboratory results may be used in addition to clinical signs and symptoms, with each of these factors contributing proportionally to their own specificity and sensitivity. Furthermore, a diagnosis (albeit one with a lower probability) can be made even with incomplete, uncertain, or partially erroneous information, and patients whose symptoms are below the diagnostic threshold can be evaluated. Lastly, there is no need of NOS or "unspecified" categories, and comorbid disorders become different dimensions of the diagnostic evaluation. Bayesian diagnoses allow the preservation of current categories and assessment methods, and may be used concurrently with criteria-based diagnoses. Users need not put in extra effort except to collect more comprehensive information. Unlike the Research Domain Criteria (RDoC) project, the Bayesian approach neither increases the diagnostic validity of existing categories nor explains the pathophysiological mechanisms of mental disorders. It, however, can be readily integrated to present classification systems. Therefore, the Bayesian approach may be an intermediate phase between criteria-based diagnosis and the RDoC ideal.

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

Science.gov (United States)

Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

2018-04-11

In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

A Privacy-Preserving Intelligent Medical Diagnosis System Based on Oblivious Keyword Search

Directory of Open Access Journals (Sweden)

Zhaowen Lin

2017-01-01

Full Text Available One of the concerns people have is how to get the diagnosis online without privacy being jeopardized. In this paper, we propose a privacy-preserving intelligent medical diagnosis system (IMDS, which can efficiently solve the problem. In IMDS, users submit their health examination parameters to the server in a protected form; this submitting process is based on Paillier cryptosystem and will not reveal any information about their data. And then the server retrieves the most likely disease (or multiple diseases from the database and returns it to the users. In the above search process, we use the oblivious keyword search (OKS as a basic framework, which makes the server maintain the computational ability but cannot learn any personal information over the data of users. Besides, this paper also provides a preprocessing method for data stored in the server, to make our protocol more efficient.

A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

Science.gov (United States)

Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

2015-08-01

Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS

Directory of Open Access Journals (Sweden)

Quan Zhou

2017-07-01

Full Text Available The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method.

A qualified presumption of safety approach for the safety assessment of Grana Padano whey starters.

Science.gov (United States)

Rossetti, Lia; Carminati, Domenico; Zago, Miriam; Giraffa, Giorgio

2009-03-15

A Qualified Presumption of Safety (QPS) approach was applied to dominant lactic acid bacteria (LAB) associated with Grana Padano cheese whey starters. Thirty-two strains belonging to Lactobacillus helveticus, Lactobacillus delbrueckii subsp. lactis, Streptococcus thermophilus, and Lactobacillus fermentum, and representing the overall genotypic LAB diversity associated with 24 previously collected whey starters [Rossetti, L., Fornasari, M.E., Gatti, M., Lazzi, C., Neviani, E., Giraffa, G., 2008. Grana Padano cheese whey starters: microbial composition and strain distribution. International Journal of Food Microbiology 127, 168-171], were analyzed. All L. helveticus, L. delbrueckii subsp. lactis, and S. thermophilus isolates were susceptible to four (i.e. vancomycin, gentamicin, tetracycline, and erythromycin) of the clinically most relevant antibiotics. One L. fermentum strain displayed phenotypic resistance to tetracycline (Tet(R)), with MIC of 32 microg/ml, and gentamycin (Gm(R)), with MIC of 32 microg/ml. PCR was applied to this strain to test the presence of genes tet(L), tet(M), tet(S), and aac(6')-aph(2')-Ia, which are involved in horizontal transfer of Tet(R) and Gm(R), respectively but no detectable amplification products were observed. According to QPS criteria, we conclude that Grana cheese whey starters do not present particular safety concerns.

A presumptive case of Baylisascaris procyonis in a feral green-cheeked Amazon parrot (Amazona viridigenalis).

Science.gov (United States)

Done, Lisa B; Tamura, Yoko

2014-03-01

A feral green-cheeked Amazon parrot (Amazona viridigenalis), also known as the red-crowned Amazon, with generalized neurologic symptoms was found in Pasadena in Southern California and brought in for treatment. The bird was refractory to a wide variety of medications and supportive treatment. Tests for polyoma virus, psittacine beak and feather disease virus, and West Nile virus as well as Chlamydophila psittaci were negative. Hospitalized and home care continued for a total of 69 days. The bird was rehospitalized on day 66 for increasing severity of clinical signs and found 3 days later hanging with its head down, in respiratory arrest. Resuscitation was unsuccessful. There were no gross pathologic lesions. Histopathology showed a focal subcutaneous fungal caseous granuloma under the skin of the dorsum. Many sarcocysts morphologically consistent with Sarcocystis falcatula were found in the cytoplasm of the skeletal myofibers from skeletal muscles of different locations of this bird, a finding that was considered an incidental, clinically nonsignificant finding in this case. Necrosis with microscopic lesions typical of Baylisascaris spp. neural larva migrans was in the brain. Although multiple histologic serial sections of the brain were examined and a brain squash performed and analyzed, no Baylisascaris larvae were found. This is the first presumptive case of Baylisascaris in a feral psittacine.

Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation

Directory of Open Access Journals (Sweden)

Yuning Qian

2016-01-01

Full Text Available This paper presents a single sensor based blind source separation approach, namely, the wavelet-assisted stationary subspace analysis (WSSA, for gearbox fault diagnosis in a wind turbine. Continuous wavelet transform (CWT is used as a preprocessing tool to decompose a single sensor measurement data into a set of wavelet coefficients to meet the multidimensional requirement of the stationary subspace analysis (SSA. The SSA is a blind source separation technique that can separate the multidimensional signals into stationary and nonstationary source components without the need for independency and prior information of the source signals. After that, the separated nonstationary source component with the maximum kurtosis value is analyzed by the enveloping spectral analysis to identify potential fault-related characteristic frequencies. Case studies performed on a wind turbine gearbox test system verify the effectiveness of the WSSA approach and indicate that it outperforms independent component analysis (ICA and empirical mode decomposition (EMD, as well as the spectral-kurtosis-based enveloping, for wind turbine gearbox fault diagnosis.

Admissions to emergency department may be classified into specific complaint categories

DEFF Research Database (Denmark)

Carter-Storch, Rasmus; Frydkjær-Olsen, Ulrik; Mogensen, Christian Backer

2014-01-01

INTRODUCTION: In the emergency departments (ED), a heterogeneous mix of patients is seen. The aim of this study was to establish a limited number of categories of complaints and symptoms covering the majority of admissions in a Danish ED and to quantify the volume of cases in each category...... covering all patient complaints was produced. Presumptive diagnoses and categories with frequencies less than 1% were pooled with other groups, unless keeping them was clinically relevant. RESULTS: Among the 9,863 patients, 49% were medical, 31% surgical, 15% orthopaedic and 5% vascular surgical patients....... In 35% of cases, the patients were referred with a presumptive diagnosis, in 65% with a complaint or a symptom; and 11,031 complaints were placed in 13 main categories, 77 subcategories and 44 presumptive diagnoses. This aggregation resulted in 99 groups holding less than 1% of the patients' complaints...

Obtaining of a rapid diagnostic test for Cholera, based on latex particles coupled with a monoclonal antibody against Vibrio cholera O1 lipopolysaccharide

Directory of Open Access Journals (Sweden)

Fátima Reyes-López

2015-08-01

Full Text Available Cholera is an acute contagious intestinal disease caused by ingestion of food or water contaminated with O1 and O139 serotypes of the bacterium Vibrio cholerae. Cholera is characterized by abundant secretory diarrhea leading to dehydration. Death occurs within hours without treatment, so early diagnosis is very important, especially at the beginning of the disease, because it is difficult to differentiate from other acute diarrheal diseases. The diagnostic golden test is the stool culture; however, it does not guarantee a rapid detection of the disease. Rapid tests have been recently developed; they are based on test strips and agglutination with latex particles, which are very effective, but difficult to acquire for their high prices. The objective of this research was to obtain a quick assay based on latex particles coupled with a monoclonal antibody (mAb against V. cholerae O1 lipopolysaccharide obtained in Finlay Institute. Latex particles of 0.8 µm were used in a 10% suspension, and they were coupled to the mAb (0.25 mg/ml for 2 hours at 37°C. The sensitivity, specificity and performance were evaluated in 84 stool samples from patients with presumptive diagnosis of cholera. The diagnostic test obtained showed no cross-reactivity against no-O1 strains and other enteropathogens. Latex diagnostic test showed values of sensitivity, specificity and efficacy of 97.87; 97.29 and 97.6% respectively, very similar to the commercial diagnostic test CTK- Biotech. The latex reagent obtained can be used in the rapid diagnosis of the disease.

Reasoning based in cases applied to diagnosis of electric generators; Razonamiento basado en casos aplicado al diagnostico de generadores electricos

Energy Technology Data Exchange (ETDEWEB)

De la Torre Vega, H. Octavio; Garcia Tevillo, Arturo; Campuzano Martinez, Roberto [Instituto de Investigaciones Electricas, Temixco, Morelos (Mexico); Lopez Azamar, Ernesto [Comision Federal de Electricidad (Mexico)

2000-07-01

The development of a system for the diagnosis of electrical generators that apply techniques of artificial intelligence, is presented, as it is the reasoning based on cases, to support the work of the diagnosis engineer. This system is part of a system called CADIS, dedicated to the diagnosis of electrical generators out of line and reason of previous articles. In this occasion the characteristics of the reasoning module based on experiences (SirBE) are emphasized, indicating how to make a diagnosis using similar cases and how to edit the system base of experience, using the interactive editor of cases. It is included, in addition, a summarized example which represents a case for SirBE and how the system helps to make a diagnosis. [Spanish] Se presenta el desarrollo de un sistema de diagnostico de generadores electricos que aplica tecnicas de inteligencia artificial, como es el razonamiento basado en casos, para apoyar la labor del ingeniero de diagnostico. Este sistema es parte de un sistema denominado CADIS, dedicado al diagnostico de generadores electricos fuera de linea y motivo de articulos anteriores. En esta ocasion se resaltan las caracteristicas del modulo de razonamiento basado en experiencias (SirBE), indicando como realizar un diagnostico utilizando casos similares y como editar la base de experiencia del sistema utilizando el editor interactivo de casos. Se incluye, ademas, un ejemplo resumido de lo que representa un caso para SiRBE y como el sistema ayuda a realizar un diagnostico.

Computer-aided diagnosis based on enhancement of degraded fundus photographs.

Science.gov (United States)

Jin, Kai; Zhou, Mei; Wang, Shaoze; Lou, Lixia; Xu, Yufeng; Ye, Juan; Qian, Dahong

2018-05-01

Retinal imaging is an important and effective tool for detecting retinal diseases. However, degraded images caused by the aberrations of the eye can disguise lesions, so that a diseased eye can be mistakenly diagnosed as normal. In this work, we propose a new image enhancement method to improve the quality of degraded images. A new method is used to enhance degraded-quality fundus images. In this method, the image is converted from the input RGB colour space to LAB colour space and then each normalized component is enhanced using contrast-limited adaptive histogram equalization. Human visual system (HVS)-based fundus image quality assessment, combined with diagnosis by experts, is used to evaluate the enhancement. The study included 191 degraded-quality fundus photographs of 143 subjects with optic media opacity. Objective quality assessment of image enhancement (range: 0-1) indicated that our method improved colour retinal image quality from an average of 0.0773 (variance 0.0801) to an average of 0.3973 (variance 0.0756). Following enhancement, area under curves (AUC) were 0.996 for the glaucoma classifier, 0.989 for the diabetic retinopathy (DR) classifier, 0.975 for the age-related macular degeneration (AMD) classifier and 0.979 for the other retinal diseases classifier. The relatively simple method for enhancing degraded-quality fundus images achieves superior image enhancement, as demonstrated in a qualitative HVS-based image quality assessment. This retinal image enhancement may, therefore, be employed to assist ophthalmologists in more efficient screening of retinal diseases and the development of computer-aided diagnosis. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Nuclear Power Plants Fault Diagnosis Method Based on Data Fusion

International Nuclear Information System (INIS)

Xie Chunli; Liu Yongkuo; Xia Hong

2009-01-01

The data fusion is a method suit for complex system fault diagnosis such as nuclear power plants, which is multisource information processing technology. This paper uses data fusion information hierarchical thinking and divides nuclear power plants fault diagnosis into three levels. Data level adopts data mining method to handle data and reduction attributes. Feature level uses three parallel neural networks to deal with attributes of data level reduction and the outputs of three networks are as the basic probability assignment of Dempster-Shafer (D-S) evidence theory. The improved D-S evidence theory synthesizes the outputs of neural networks in decision level, which conquer the traditional D-S evidence theory limitation which can't dispose conflict information. The diagnosis method was tested using correlation data of literature. The test results indicate that the data fusion diagnosis system can diagnose nuclear power plants faults accurately and the method has application value. (authors)

A GC/MS-based metabolomic approach for reliable diagnosis of phenylketonuria.

Science.gov (United States)

Xiong, Xiyue; Sheng, Xiaoqi; Liu, Dan; Zeng, Ting; Peng, Ying; Wang, Yichao

2015-11-01

), which showed that phenylacetic acid may be used as a reliable discriminator for the diagnosis of PKU. The low false positive rate (1-specificity, 0.064) can be eliminated or at least greatly reduced by simultaneously referring to other markers, especially phenylpyruvic acid, a unique marker in PKU. Additionally, this standard was obtained with high sensitivity and specificity in a less invasive manner for diagnosing PKU compared with the Phe/Tyr ratio. Therefore, we conclude that urinary metabolomic information based on the improved oximation-silylation method together with GC/MS may be reliable for the diagnosis and differential diagnosis of PKU.

International veterinary epilepsy task force consensus proposal

DEFF Research Database (Denmark)

De Risio, Luisa; Bhatti, Sofie; Muñana, Karen

2015-01-01

years, inter-ictal neurological abnormalities consistent with intracranial neurolocalisation, status epilepticus or cluster seizure at epileptic seizure onset, or a previous presumptive diagnosis of IE and drug-resistance with a single antiepileptic drug titrated to the highest tolerable dose...

The radiological and histopathological differential diagnosis of chordoid neoplasms in skull base

Directory of Open Access Journals (Sweden)

PAN Bin-cai

2013-07-01

Full Text Available Background Chordoid neoplasms refer to tumors appearing to have histological features of embryonic notochord, which is characterized by cords and lobules of neoplastic cells arranged within myxoid matrix. Because of radiological and histological similarities with myxoid matrix and overlapping immunohistochemical profile, chordoma, chordoid meningioma, chordoid glioma, and rare extraskeletal myxoid chondrosarcoma enter in the radiological and histological differential diagnosis at the site of skull base. However, there is always a great challenge for histopathologists to make an accurate diagnosis when encountering a chordoid neoplasm within or near the central nervous system. The aim of this study is to investigate and summarize the radiological, histological features and immunohistochemical profiles of chordoid neoplasms in skull base, and to find a judicious panel of immunostains to unquestionably help in diagnostically challenging cases. Methods A total of 23 cases of chordoid neoplasms in skull base, including 10 chordomas, 5 chordoid meningiomas, 3 chordoid gliomas and 5 extraskeletal myxoid chondrosarcomas, were collected from the First Affiliated Hospital, Sun Yat-sen University and Guangdong Tongjiang Hospital. MRI examination was performed on the patients before surgical treatment. Microscopical examination and immunohistochemical staining study using vimentin (Vim, pan-cytokeratin (PCK, epithelial membrane antigen (EMA, S?100 protein (S-100, glial fibrillary acidic protein (GFAP, D2-40, Galectin-3, CD3, CD20, Ki-67 were performed on the samples of cases. The clinicopathological data of the patients was also analyzed retrospectively. Results Most of chordomas were localized in the clivus with heterogeneous hyperintensity on T2WI scanning. The breakage of clivus was observed in most cases. Histologically, the tumor cells of chordoma exhibited bland nuclear features and some contained abundant vacuolated cytoplasm (the so

Validation of a clinical practice-based algorithm for the diagnosis of autosomal recessive cerebellar ataxias based on NGS identified cases.

Science.gov (United States)

Mallaret, Martial; Renaud, Mathilde; Redin, Claire; Drouot, Nathalie; Muller, Jean; Severac, Francois; Mandel, Jean Louis; Hamza, Wahiba; Benhassine, Traki; Ali-Pacha, Lamia; Tazir, Meriem; Durr, Alexandra; Monin, Marie-Lorraine; Mignot, Cyril; Charles, Perrine; Van Maldergem, Lionel; Chamard, Ludivine; Thauvin-Robinet, Christel; Laugel, Vincent; Burglen, Lydie; Calvas, Patrick; Fleury, Marie-Céline; Tranchant, Christine; Anheim, Mathieu; Koenig, Michel

2016-07-01

Establishing a molecular diagnosis of autosomal recessive cerebellar ataxias (ARCA) is challenging due to phenotype and genotype heterogeneity. We report the validation of a previously published clinical practice-based algorithm to diagnose ARCA. Two assessors performed a blind analysis to determine the most probable mutated gene based on comprehensive clinical and paraclinical data, without knowing the molecular diagnosis of 23 patients diagnosed by targeted capture of 57 ataxia genes and high-throughput sequencing coming from a 145 patients series. The correct gene was predicted in 61 and 78 % of the cases by the two assessors, respectively. There was a high inter-rater agreement [K = 0.85 (0.55-0.98) p < 0.001] confirming the algorithm's reproducibility. Phenotyping patients with proper clinical examination, imaging, biochemical investigations and nerve conduction studies remain crucial for the guidance of molecular analysis and to interpret next generation sequencing results. The proposed algorithm should be helpful for diagnosing ARCA in clinical practice.

A dynamic integrated fault diagnosis method for power transformers.

Science.gov (United States)

Gao, Wensheng; Bai, Cuifen; Liu, Tong

2015-01-01

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.

A Dynamic Integrated Fault Diagnosis Method for Power Transformers

Science.gov (United States)

Gao, Wensheng; Liu, Tong

2015-01-01

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified. PMID:25685841

Diagnosis of three types of constant faults in read-once contact networks over finite bases

KAUST Repository

Busbait, Monther I.; Moshkov, Mikhail

2016-01-01

We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound

Validation of a Blood-Based Laboratory Test to Aid in the Confirmation of a Diagnosis of Schizophrenia

NARCIS (Netherlands)

E. Schwartz (Emanuel); R. Izmailov (Rauf); M. Spain (Michael); A. Barnes (Anthony); J.P. Mapes (James); P.C. Guest (Paul); H. Rahmoune (Hassan); S. Pietsch (Sandra); F.M. Leweke (Marcus); M. Rothermundt (Matthias); J. Steiner (Johann); D. Koethe (Dagmar); L. Kranaster (Laura); P. Ohrmann (Patricia); T. Suslow (Thomas); Y. Levin (Yishai); B. Bogerts (Bernhard); N.J.M. van Beveren (Nico); G. McAllister (George); N. Weber (Natalya); D. Niebuhr (David); D. Cowan (David); R.H. Yolken (Robert); S. Bahn (Sabine)

2010-01-01

textabstractAbstract: We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51

OCT for diagnosis of periodontal disease

Science.gov (United States)

Colston, Bill W., Jr.; Everett, Matthew J.; Da Silva, Luiz B.; Otis, Linda L.

1998-04-01

We have developed a hand-held in vivo scanning device for use in the oral cavity. We produced, using this scanning device, in vivo OCT images of dental tissues in human volunteers. All the OCT images were analyzed for the presence of clinically relevant anatomical structures. The gingival margin, periodontal sulcus, and dento-enamel junction were visible in all the images. The cemento-enamel junction was discernible in 64% of the images and the alveolar bone presumptively identified for 71% of the images. These images represent, to our knowledge, the first in vivo OCT images of human dental tissue.

OCT for diagnosis of periodontal disease

Energy Technology Data Exchange (ETDEWEB)

Colston, B.W., LLNL

1998-01-01

We have developed a hand-held in vivo scanning device for use in the oral cavity. We produced, using this scanning device, in vivo OCT images of dental tissues in human volunteers. All the OCT images were analyzed for the presence of clinically relevant anatomical structures. The gingival margin, periodontal sulcus, and dento-enamel junction were visible in all the images. The cemento-enamel junction was discernible in 64% of the images and the alveolar bone presumptively identified for 71% of the images. These images represent, to our knowledge, the first in vivo OCT images of human dental tissue.

Comparing Measures of Late HIV Diagnosis in Washington State

Directory of Open Access Journals (Sweden)

Laura Saganic

2012-01-01

Full Text Available As more US HIV surveillance programs routinely use late HIV diagnosis to monitor and characterize HIV testing patterns, there is an increasing need to standardize how late HIV diagnosis is measured. In this study, we compared two measures of late HIV diagnosis, one based on time between HIV and AIDS, the other based on initial CD4+ results. Using data from Washington's HIV/AIDS Reporting System, we used multivariate logistic regression to identify predictors of late HIV diagnosis. We also conducted tests for trend to determine whether the proportion of cases diagnosed late has changed over time. Both measures lead us to similar conclusions about late HIV diagnosis, suggesting that being male, older, foreign-born, or heterosexual increase the likelihood of late HIV diagnosis. Our findings reaffirm the validity of a time-based definition of late HIV diagnosis, while at the same time demonstrating the potential value of a lab-based measure.

Why tell children: A synthesis of the global literature on reasons for disclosing or not disclosing an HIV diagnosis to children 12 and under

Directory of Open Access Journals (Sweden)

Beatrice J. Krauss

2016-09-01

Full Text Available While the psychological and health benefits of knowing one’s HIV diagnosis have been documented for adults and adolescents, practice is still in development for younger children. Moderating conditions for whether or not to tell a child he/she has HIV vary by region and local context. They include accessibility of treatment, consideration of HIV as a stigmatizing condition, prevalence of HIV and an accompanying presumption that any illness is HIV-related, parent or caregiver concerns about child reactions, child’s worsening health, assumptions about childhood and child readiness to know a diagnosis, and lack of policies such as those that would prevent bullying of affected children in schools. In this systematic review of the global literature, we summarize the reasons caregivers give for telling or not telling children 12 and under their HIV diagnosis. We also include articles in which children reflect on their desires for being told. While a broad number of reasons are given for telling a child—e.g., to aid in prevention, adaptation to illness (e.g., primarily to promote treatment adherence, understanding social reactions, and maintaining the child-adult relationship—a narrower range of reasons, often related to immediate child or caregiver well-being or discomfort, are given for not telling. Recommendations are made to improve the context for disclosure by providing supports before, during and after disclosure and to advance the research agenda by broadening samples and refining approaches.

Cloning of the koi herpesvirus (KHV gene encoding thymidine kinase and its use for a highly sensitive PCR based diagnosis

Directory of Open Access Journals (Sweden)

Gilad Oren

2005-03-01

Full Text Available Abstract Background Outbreaks with mass mortality among common carp Cyprinus carpio carpio and koi Cyprinus carpio koi have occurred worldwide since 1998. The herpes-like virus isolated from diseased fish is different from Herpesvirus cyprini and channel catfish virus and was accordingly designated koi herpesvirus (KHV. Diagnosis of KHV infection based on viral isolation and current PCR assays has a limited sensitivity and therefore new tools for the diagnosis of KHV infections are necessary. Results A robust and sensitive PCR assay based on a defined gene sequence of KHV was developed to improve the diagnosis of KHV infection. From a KHV genomic library, a hypothetical thymidine kinase gene (TK was identified, subcloned and expressed as a recombinant protein. Preliminary characterization of the recombinant TK showed that it has a kinase activity using dTTP but not dCTP as a substrate. A PCR assay based on primers selected from the defined DNA sequence of the TK gene was developed and resulted in a 409 bp amplified fragment. The TK based PCR assay did not amplify the DNAs of other fish herpesviruses such as Herpesvirus cyprini (CHV and the channel catfish virus (CCV. The TK based PCR assay was specific for the detection of KHV and was able to detect as little as 10 fentograms of KHV DNA corresponding to 30 virions. The TK based PCR was compared to previously described PCR assays and to viral culture in diseased fish and was shown to be the most sensitive method of diagnosis of KHV infection. Conclusion The TK based PCR assay developed in this work was shown to be specific for the detection of KHV. The TK based PCR assay was more sensitive for the detection of KHV than previously described PCR assays; it was as sensitive as virus isolation which is the golden standard method for KHV diagnosis and was able to detect as little as 10 fentograms of KHV DNA corresponding to 30 virions.

Trackside acoustic diagnosis of axle box bearing based on kurtosis-optimization wavelet denoising

Science.gov (United States)

Peng, Chaoyong; Gao, Xiaorong; Peng, Jianping; Wang, Ai

2018-04-01

As one of the key components of railway vehicles, the operation condition of the axle box bearing has a significant effect on traffic safety. The acoustic diagnosis is more suitable than vibration diagnosis for trackside monitoring. The acoustic signal generated by the train axle box bearing is an amplitude modulation and frequency modulation signal with complex train running noise. Although empirical mode decomposition (EMD) and some improved time-frequency algorithms have proved to be useful in bearing vibration signal processing, it is hard to extract the bearing fault signal from serious trackside acoustic background noises by using those algorithms. Therefore, a kurtosis-optimization-based wavelet packet (KWP) denoising algorithm is proposed, as the kurtosis is the key indicator of bearing fault signal in time domain. Firstly, the geometry based Doppler correction is applied to signals of each sensor, and with the signal superposition of multiple sensors, random noises and impulse noises, which are the interference of the kurtosis indicator, are suppressed. Then, the KWP is conducted. At last, the EMD and Hilbert transform is applied to extract the fault feature. Experiment results indicate that the proposed method consisting of KWP and EMD is superior to the EMD.

Medicare Program; Inpatient Rehabilitation Facility Prospective Payment System for Federal Fiscal Year 2018. Final rule.

Science.gov (United States)

2017-08-03

This final rule updates the prospective payment rates for inpatient rehabilitation facilities (IRFs) for federal fiscal year (FY) 2018 as required by the statute. As required by section 1886(j)(5) of the Social Security Act (the Act), this rule includes the classification and weighting factors for the IRF prospective payment system's (IRF PPS) case-mix groups and a description of the methodologies and data used in computing the prospective payment rates for FY 2018. This final rule also revises the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis codes that are used to determine presumptive compliance under the "60 percent rule," removes the 25 percent payment penalty for inpatient rehabilitation facility patient assessment instrument (IRF-PAI) late transmissions, removes the voluntary swallowing status item (Item 27) from the IRF-PAI, summarizes comments regarding the criteria used to classify facilities for payment under the IRF PPS, provides for a subregulatory process for certain annual updates to the presumptive methodology diagnosis code lists, adopts the use of height/weight items on the IRF-PAI to determine patient body mass index (BMI) greater than 50 for cases of single-joint replacement under the presumptive methodology, and revises and updates measures and reporting requirements under the IRF quality reporting program (QRP).

Issue Definition in Rights-Based Policy Focused on the Experiences of Individuals with Disabilities: An Examination of Canadian Parliamentary Discourse

Science.gov (United States)

Baker, Dana Lee

2008-01-01

In issue definition in rights-based policy Canada stereotypically embraces a more positive, human rights-centered approach as compared with the American stereotype associated with the USA's more presumptively negative, civil rights-based tack. Since exclusionary infrastructures violate the core values of democratic governance, a failure to address…

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

Science.gov (United States)

Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

2017-01-01

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

Application of energies of optimal frequency bands for fault diagnosis based on modified distance function

Energy Technology Data Exchange (ETDEWEB)

Zamanian, Amir Hosein [Southern Methodist University, Dallas (United States); Ohadi, Abdolreza [Amirkabir University of Technology (Tehran Polytechnic), Tehran (Iran, Islamic Republic of)

2017-06-15

Low-dimensional relevant feature sets are ideal to avoid extra data mining for classification. The current work investigates the feasibility of utilizing energies of vibration signals in optimal frequency bands as features for machine fault diagnosis application. Energies in different frequency bands were derived based on Parseval's theorem. The optimal feature sets were extracted by optimization of the related frequency bands using genetic algorithm and a Modified distance function (MDF). The frequency bands and the number of bands were optimized based on the MDF. The MDF is designed to a) maximize the distance between centers of classes, b) minimize the dispersion of features in each class separately, and c) minimize dimension of extracted feature sets. The experimental signals in two different gearboxes were used to demonstrate the efficiency of the presented technique. The results show the effectiveness of the presented technique in gear fault diagnosis application.

Research on Fault Diagnosis of HTR-PM Based on Multilevel Flow Model

International Nuclear Information System (INIS)

Zhang Yong; Zhou Yangping

2014-01-01

In this paper, we focus on the application of Multilevel Flow Model (MFM) in the automatic real-time fault diagnosis of High Temperature Gas-cooled Reactor Pebble-bed Module (HTR-PM) accidents. In the MFM, the plant process is described abstractly in function level by mass, energy and information flows, which reveal the interaction between different components and capacitate the causal reasoning between functions according to the flow properties. Thus, in the abnormal status, a goal-function-component oriented fault diagnosis can be performed with the model at a very quick speed and abnormal alarms can be also precisely explained by the reasoning relationship of the model. By using MFM, a fault diagnosis model of HTR-PM plant is built, and the detailed process of fault diagnosis is also shown by the flowcharts. Due to lack of simulation data about HTR-PM, experiments are not conducted to evaluate the fault diagnosis performance, but analysis of algorithm feasibility and complexity shows that the diagnosis system will have a good ability to detect and diagnosis accidents timely. (author)

CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency.

Science.gov (United States)

Bhavani, Selvaraj Rani; Senthilkumar, Jagatheesan; Chilambuchelvan, Arul Gnanaprakasam; Manjula, Dhanabalachandran; Krishnamoorthy, Ramasamy; Kannan, Arputharaj

2015-03-27

The Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking. We investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called "CIMIDx", based on representative association rules that support the diagnosis of medical images (mammograms). The proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype's classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user's perspective. We performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information from 150 breast cancer survivors from hospitals

A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

Science.gov (United States)

Khawaja, Taimoor Saleem

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

Disposable Electrochemical Immunosensor Diagnosis Device Based on Nanoparticle Probe and Immunochromatographic Strip

Energy Technology Data Exchange (ETDEWEB)

Liu, Guodong; Lin, Ying-Ying; Wang, Jun; Wu, Hong; Wai, Chien M.; Lin, Yuehe

2007-10-15

We describe a disposable electrochemical immunosensor diagnosis device that is based on the immunochromatographic strip technique and an electrochemical immunoassay based on quantum dot (QD, CdS@ZnS) labels. The device takes advantage of the speed and low-cost of the conventional immunochromatographic strip test and the high-sensitivity of the nanoparticle-based electrochemical immunoassay. A sandwich immunoreaction was performed on the immunochromatographic strip, and the captured QD labels in the test zone were determined by highly sensitive stripping voltammetric measurement of the dissolved metallic component (cadmium) with a disposable-screen-printed electrode, which is embedded underneath the membrane on the test zone. The new device coupled with a portable electrochemical analyzer shows great promise for in-field and point-of-care quantitative testing of disease-related protein biomarkers. The parameters (e.g., voltammetric measurement of QD labels, antibody immobilization, the loading amount of QD-antibody, and the immunoreaction time) that govern the sensitivity and reproducibility of the device were optimized with IgG model analyte. The voltammetric response of the optimized device is highly linear over the range of 0.1 to 10 ng mL-1 IgG, and the limit of detection is estimated to be 30 pg mL-1 in association with a 7-min immunoreaction time. The detection limit was improved to 10 pg mL-1 using a 20-min immunoreaction time. The new disposable electrochemical diagnosis device thus provides a more user-friendly, rapid, clinically accurate, less expensive, and quantitative tool for protein detection.

Efficacy of storage phosphor-based digital mammography in diagnosis of breast cancer

International Nuclear Information System (INIS)

Kitahama, Hiroyuki

1991-01-01

The aim of this study is to present efficacy of storage phosphor-based digital mammography (CR-mammography) in diagnosis of breast cancer. Ninety-seven cases with breast cancer including 44 cases less than 2 cm in macroscopic size (t1 cases) were evaluated using storage phosphor-based digital mammography (2000 x 2510 pixels by 10 bits). Abnormal findings on CR-mammography were detected in 86 cases (88.7%) of 97 women with breast cancer. Sensitivity of CR-mammography was 88.7%. It was superior to that of film-screen mammography. On t1 breast cancer cases, sensitivity on CR-mammography was 88.6%. False negative rate in t1 breast cancer cases was reduced by image processing using CR-mammography. To evaluate microcalcifications, CR-mammograms and film-screen mammograms were investigated in 22 cases of breast cancer proven pathologically the existence of microcalcifications and 11 paraffin tissue blocks of breast cancer. CR-mammography was superior to film-screen mammography in recognizing of microcalcifications. As regards the detectability for the number and the shape of microcalcifications, CR-mammography was equivalent to film-screen mammography. Receiver operating characteristic (ROC) analysis by eight observers was performed for CR-mammography and film-screen mammography with 54 breast cancer patients and 54 normal cases. The detectability of abnormal findings of breast cancer on CR-mammography (ROC area=0.91) was better than that on film-screen mammography (ROC area=0.88) (p<0.05). Efficacy of storage phosphor-based digital mammography in diagnosis of breast cancer was discussed and demonstrated in this study. (author)

Active fault diagnosis based on stochastic tests

DEFF Research Database (Denmark)

Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2008-01-01

The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output...

Conceptual Diagnosis Model Based on Distinct Knowledge Dyads for Interdisciplinary Environments

Directory of Open Access Journals (Sweden)

Cristian VIZITIU

2014-06-01

Full Text Available The present paper has a synergic dual purpose of bringing a psychological and neuroscience related perspective oriented towards decision making and knowledge creation diagnosis in the frame of Knowledge Management. !e conceptual model is built by means ofCognitive-Emotional and Explicit-Tacit knowledge dyads and structured on Analytic Hierarchy Process (AHP according to the hypothesis which designates the first dyad as an accessing mechanism of knowledge stored in the second dyad. Due to the well acknowledged needsconcerning new advanced decision making instruments and enhanced knowledge creation processes in the field of technical space projects emphasized by a high level of complexity, the herein study tries also to prove the relevance of the proposed conceptual diagnosis modelin Systems Engineering (SE methodology which foresees at its turn concurrent engineering within interdisciplinary working environments. !e theoretical model, entitled DiagnoSE, has the potential to provide practical implications to space/space related business sector butnot merely, and on the other hand, to trigger and inspire other knowledge management related researches for refining and testing the proposed instrument in SE or other similar decision making based working environment.

Star polymer-based unimolecular micelles and their application in bio-imaging and diagnosis.

Science.gov (United States)

Jin, Xin; Sun, Pei; Tong, Gangsheng; Zhu, Xinyuan

2018-02-03

As a novel kind of polymer with covalently linked core-shell structure, star polymers behave in nanostructure in aqueous medium at all concentration range, as unimolecular micelles at high dilution condition and multi-micelle aggregates in other situations. The unique morphologies endow star polymers with excellent stability and functions, making them a promising platform for bio-application. A variety of functions including imaging and therapeutics can be achieved through rational structure design of star polymers, and the existence of plentiful end-groups on shell offers the opportunity for further modification. In the last decades, star polymers have become an attracting platform on fabrication of novel nano-systems for bio-imaging and diagnosis. Focusing on the specific topology and physicochemical properties of star polymers, we have reviewed recent development of star polymer-based unimolecular micelles and their bio-application in imaging and diagnosis. The main content of this review summarizes the synthesis of integrated architecture of star polymers and their self-assembly behavior in aqueous medium, focusing especially on the recent advances on their bio-imaging application and diagnosis use. Finally, we conclude with remarks and give some outlooks for further exploration in this field. Copyright © 2018 Elsevier Ltd. All rights reserved.

DNA Sensors for Malaria Diagnosis

DEFF Research Database (Denmark)

Hede, Marianne Smedegaard; Fjelstrup, Søren; Knudsen, Birgitta R.

2015-01-01

In the field of malaria diagnosis much effort is put into the development of faster and easier alternatives to the gold standard, blood smear microscopy. Nucleic acid amplification based techniques pose some of the most promising upcoming diagnostic tools due to their potential for high sensitivity......, robustness and user-friendliness. In the current review, we will discuss some of the different DNA-based sensor systems under development for the diagnosis of malaria....

Privacy-Preserving Self-Helped Medical Diagnosis Scheme Based on Secure Two-Party Computation in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yi Sun

2014-01-01

Full Text Available With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients’ health information and doctors’ diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions.

Comparison of ICD code-based diagnosis of obesity with measured obesity in children and the implications for health care cost estimates.

Science.gov (United States)

Kuhle, Stefan; Kirk, Sara F L; Ohinmaa, Arto; Veugelers, Paul J

2011-12-21

Administrative health databases are a valuable research tool to assess health care utilization at the population level. However, their use in obesity research limited due to the lack of data on body weight. A potential workaround is to use the ICD code of obesity to identify obese individuals. The objective of the current study was to investigate the sensitivity and specificity of an ICD code-based diagnosis of obesity from administrative health data relative to the gold standard measured BMI. Linkage of a population-based survey with anthropometric measures in elementary school children in 2003 with longitudinal administrative health data (physician visits and hospital discharges 1992-2006) from the Canadian province of Nova Scotia. Measured obesity was defined based on the CDC cut-offs applied to the measured BMI. An ICD code-based diagnosis obesity was defined as one or more ICD-9 (278) or ICD-10 code (E66-E68) of obesity from a physician visit or a hospital stay. Sensitivity and specificity were calculated and health care cost estimates based on measured obesity and ICD-based obesity were compared. The sensitivity of an ICD code-based obesity diagnosis was 7.4% using ICD codes between 2002 and 2004. Those correctly identified had a higher BMI and had higher health care utilization and costs. An ICD diagnosis of obesity in Canadian administrative health data grossly underestimates the true prevalence of childhood obesity and overestimates the health care cost differential between obese and non-obese children.

Classification and Clinical Diagnosis of Fibromyalgia Syndrome: Recommendations of Recent Evidence-Based Interdisciplinary Guidelines

Directory of Open Access Journals (Sweden)

Mary-Ann Fitzcharles

2013-01-01

Full Text Available Objectives. Fibromyalgia syndrome (FMS, characterized by subjective complaints without physical or biomarker abnormality, courts controversy. Recommendations in recent guidelines addressing classification and diagnosis were examined for consistencies or differences. Methods. Systematic searches from January 2008 to February 2013 of the US-American National Guideline Clearing House, the Scottish Intercollegiate Guidelines Network, Guidelines International Network, and Medline for evidence-based guidelines for the management of FMS were conducted. Results. Three evidence-based interdisciplinary guidelines, independently developed in Canada, Germany, and Israel, recommended that FMS can be clinically diagnosed by a typical cluster of symptoms following a defined evaluation including history, physical examination, and selected laboratory tests, to exclude another somatic disease. Specialist referral is only recommended when some other physical or mental illness is reasonably suspected. The diagnosis can be based on the (modified preliminary American College of Rheumatology (ACR 2010 diagnostic criteria. Discussion. Guidelines from three continents showed remarkable consistency regarding the clinical concept of FMS, acknowledging that FMS is neither a distinct rheumatic nor mental disorder, but rather a cluster of symptoms, not explained by another somatic disease. While FMS remains an integral part of rheumatology, it is not an exclusive rheumatic condition and spans a broad range of medical disciplines.

Web-based diagnosis and therapy of auditory prerequisites for reading and spelling

Directory of Open Access Journals (Sweden)

Krammer, Sandra

2006-11-01

Full Text Available Cognitive deficits in auditory or visual processing or in verbal short-term-memory are amongst others risk factors for the development of dyslexia (reading and spelling disability. By early identification and intervention (optimally before school entry, detrimental effects of these cognitive deficits on reading and spelling might be prevented. The goal of the CASPAR-project is to develop and evaluate web-based tools for diagnosis and therapy of cognitive prerequisites for reading and spelling, which are appropriate for kindergarten children. In the first approach CASPAR addresses auditory processing disorders. This article describes a computerized and web-based approach for screening and testing phoneme discrimination and for promoting phoneme discrimination abilities through interactive games in kindergarteners.

Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings

NARCIS (Netherlands)

Beyene, Tariku Jibat; Eshetu, Amanuel; Abdu, Amina; Wondimu, Etenesh; Beyi, Ashenafi Feyisa; Tufa, Takele Beyene; Ibrahim, Sami

2017-01-01

Background: The recent rise in mobile phone use and increased signal coverage has created opportunities for growth of the mobile Health sector in many low resource settings. This pilot study explores the use of a smartphone-based application, VetAfrica-Ethiopia, in assisting diagnosis of cattle

A Cough-Based Algorithm for Automatic Diagnosis of Pertussis

Science.gov (United States)

Pramono, Renard Xaviero Adhi; Imtiaz, Syed Anas; Rodriguez-Villegas, Esther

2016-01-01

Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. PMID:27583523

A Method of Rotating Machinery Fault Diagnosis Based on the Close Degree of Information Entropy

Institute of Scientific and Technical Information of China (English)

GENG Jun-bao; HUANG Shu-hong; JIN Jia-shan; CHEN Fei; LIU Wei

2006-01-01

This paper presents a method of rotating machinery fault diagnosis based on the close degree of information entropy. In the view of the information entropy, we introduce four information entropy features of the rotating machinery, which describe the vibration condition of the machinery. The four features are, respectively, denominated as singular spectrum entropy, power spectrum entropy, wavelet space state feature entropy and wavelet power spectrum entropy. The value scopes of the four information entropy features of the rotating machinery in some typical fault conditions are gained by experiments, which can be acted as the standard features of fault diagnosis. According to the principle of the shorter distance between the more similar models, the decision-making method based on the close degree of information entropy is put forward to deal with the recognition of fault patterns. We demonstrate the effectiveness of this approach in an instance involving the fault pattern recognition of some rotating machinery.

Stripe-PZT Sensor-Based Baseline-Free Crack Diagnosis in a Structure with a Welded Stiffener

Directory of Open Access Journals (Sweden)

Yun-Kyu An

2016-09-01

Full Text Available This paper proposes a stripe-PZT sensor-based baseline-free crack diagnosis technique in the heat affected zone (HAZ of a structure with a welded stiffener. The proposed technique enables one to identify and localize a crack in the HAZ using only current data measured using a stripe-PZT sensor. The use of the stripe-PZT sensor makes it possible to significantly improve the applicability to real structures and minimize man-made errors associated with the installation process by embedding multiple piezoelectric sensors onto a printed circuit board. Moreover, a new frequency-wavenumber analysis-based baseline-free crack diagnosis algorithm minimizes false alarms caused by environmental variations by avoiding simple comparison with the baseline data accumulated from the pristine condition of a target structure. The proposed technique is numerically as well as experimentally validated using a plate-like structure with a welded stiffener, reveling that it successfully identifies and localizes a crack in HAZ.

Stripe-PZT Sensor-Based Baseline-Free Crack Diagnosis in a Structure with a Welded Stiffener.

Science.gov (United States)

An, Yun-Kyu; Shen, Zhiqi; Wu, Zhishen

2016-09-16

This paper proposes a stripe-PZT sensor-based baseline-free crack diagnosis technique in the heat affected zone (HAZ) of a structure with a welded stiffener. The proposed technique enables one to identify and localize a crack in the HAZ using only current data measured using a stripe-PZT sensor. The use of the stripe-PZT sensor makes it possible to significantly improve the applicability to real structures and minimize man-made errors associated with the installation process by embedding multiple piezoelectric sensors onto a printed circuit board. Moreover, a new frequency-wavenumber analysis-based baseline-free crack diagnosis algorithm minimizes false alarms caused by environmental variations by avoiding simple comparison with the baseline data accumulated from the pristine condition of a target structure. The proposed technique is numerically as well as experimentally validated using a plate-like structure with a welded stiffener, reveling that it successfully identifies and localizes a crack in HAZ.

Consistency between referral diagnosis and post-ENMG diagnosis in children

International Nuclear Information System (INIS)

Komur, M.; Okuyaz, C.; Makharoblidze, K.

2014-01-01

Objective: To evaluate the degree of consistency between the referral diagnosis and that based on electroneuromyography. Methods: The retrospective study was conducted at the Paediatric Neurology Laboratory of Mersin University School of Medicine, Turkey, and comprised all electroneuromyographies carried out between January 2005 and December 2010. Demographic data, referral diagnosis and post-procedure diagnosis were recorded for each patient, and were classified into groups. Consistency between the two groups was compared using SPSS 13. Results: Of the total 294 patients, polyneuropathy was the reason for referral in 104 (35.4%), peripheral nerve injury in 54 (18.4%), brachial plexus injury in 52 (17.7%), myopathy in 52 (17.7%), hypotonia in 23 (7.8%), and facial paralysis in 9 (3.0%) patients. There was consistency between the two diagnoses in 179 (60.9%) patients. Conclusion: Electroneuromyography is an uneasy, painful and stressfull procedure for children, and, therefore, it should be recommended only in cases where the result may be beneficial in the diagnosis, treatment and follow-up of a patient. (author)

Cost-effectiveness analysis of introducing malaria diagnostic testing in drug shops

DEFF Research Database (Denmark)

Hansen, Kristian Schultz; Clarke, Siân E.; Lal, Sham

2017-01-01

Background Private sector drug shops are an important source of malaria treatment in Africa, yet diagnosis without parasitological testing is common among these providers. Accurate rapid diagnostic tests for malaria (mRDTs) require limited training and present an opportunity to increase access...... to correct diagnosis. The present study was a cost-effectiveness analysis of the introduction of mRDTs in Ugandan drug shops. Methods Drug shop vendors were trained to perform and sell subsidised mRDTs and artemisinin-based combination therapies (ACTs) in the intervention arm while vendors offered ACTs...... following presumptive diagnosis of malaria in the control arm. The effect on the proportion of customers with fever ‘appropriately treated of malaria with ACT’ was captured during a randomised trial in drug shops in Mukono District, Uganda. Health sector costs included: training of drug shop vendors...

Imaging basilar skull fractures in the horse: a review

International Nuclear Information System (INIS)

Ramirez, O. III; Jorgensen, J.S.; Thrall, D.E.

1998-01-01

Due to the complex nature of the anatomy of the equine head, superimposition of numerous structures, and poor soft tissue differentiation, radiography may be of limited value in the diagnosis of basilar skull fractures. However, in many horses radiographic changes such as soft tissue opacification of the guttural pouch region, irregular bone margination at the sphenooccipital line, attenuation of the nasopharynx, ventral displacement of the dorsal pharyngeal wall and the presence of irregularly shaped bone fragments in the region of the guttural pouches are suggestive of a fracture of the skull base. These findings in conjunction with physical examination findings and historical information may lead to a presumptive diagnosis of a fracture. When available and when the patient will accommodate the equipment, computed tomography may give a definitive diagnosis owing to its superior resolution and differentiation of soft tissue structures

Development of a fluorescence-based sensor for rapid diagnosis of cyanide exposure.

Science.gov (United States)

Jackson, Randy; Oda, Robert P; Bhandari, Raj K; Mahon, Sari B; Brenner, Matthew; Rockwood, Gary A; Logue, Brian A

2014-02-04

Although commonly known as a highly toxic chemical, cyanide is also an essential reagent for many industrial processes in areas such as mining, electroplating, and synthetic fiber production. The "heavy" use of cyanide in these industries, along with its necessary transportation, increases the possibility of human exposure. Because the onset of cyanide toxicity is fast, a rapid, sensitive, and accurate method for the diagnosis of cyanide exposure is necessary. Therefore, a field sensor for the diagnosis of cyanide exposure was developed based on the reaction of naphthalene dialdehyde, taurine, and cyanide, yielding a fluorescent β-isoindole. An integrated cyanide capture "apparatus", consisting of sample and cyanide capture chambers, allowed rapid separation of cyanide from blood samples. Rabbit whole blood was added to the sample chamber, acidified, and the HCN gas evolved was actively transferred through a stainless steel channel to the capture chamber containing a basic solution of naphthalene dialdehyde (NDA) and taurine. The overall analysis time (including the addition of the sample) was cyanide exposure. Most importantly, the sensor was 100% accurate in diagnosing cyanide poisoning for acutely exposed rabbits.

Developing a semantic web model for medical differential diagnosis recommendation.

Science.gov (United States)

Mohammed, Osama; Benlamri, Rachid

2014-10-01

In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.

Space nuclear reactor system diagnosis: Knowledge-based approach

International Nuclear Information System (INIS)

Ting, Y.T.D.

1990-01-01

SP-100 space nuclear reactor system development is a joint effort by the Department of Energy, the Department of Defense and the National Aeronautics and Space Administration. The system is designed to operate in isolation for many years, and is possibly subject to little or no remote maintenance. This dissertation proposes a knowledge based diagnostic system which, in principle, can diagnose the faults which can either cause reactor shutdown or lead to another serious problem. This framework in general can be applied to the fully specified system if detailed design information becomes available. The set of faults considered herein is identified based on heuristic knowledge about the system operation. The suitable approach to diagnostic problem solving is proposed after investigating the most prevalent methodologies in Artificial Intelligence as well as the causal analysis of the system. Deep causal knowledge modeling based on digraph, fault-tree or logic flowgraph methodology would present a need for some knowledge representation to handle the time dependent system behavior. A proposed qualitative temporal knowledge modeling methodology, using rules with specified time delay among the process variables, has been proposed and is used to develop the diagnostic sufficient rule set. The rule set has been modified by using a time zone approach to have a robust system design. The sufficient rule set is transformed to a sufficient and necessary one by searching the whole knowledge base. Qualitative data analysis is proposed in analyzing the measured data if in a real time situation. An expert system shell - Intelligence Compiler is used to develop the prototype system. Frames are used for the process variables. Forward chaining rules are used in monitoring and backward chaining rules are used in diagnosis

Catching the missing million: experiences in enhancing TB & DR-TB detection by providing upfront Xpert MTB/RIF testing for people living with HIV in India.

Directory of Open Access Journals (Sweden)

Neeraj Raizada

Full Text Available A critical challenge in providing TB care to People Living with HIV (PLHIV is establishing an accurate bacteriological diagnosis. Xpert MTB/RIF, a highly sensitive and specific rapid tool, offers a promising solution in addressing these challenges. This study presents results from PLHIV taking part in a large demonstration study across India wherein upfront Xpert MTB/RIF testing was offered to all presumptive PTB cases in public health facilities.The study covered a population of 8.8 million across 18 sub-district level tuberculosis units (TU, with one Xpert MTB/RIF platform established at each TU. All HIV-infected patients suspected of TB (both TB and Drug Resistant TB (DR-TB accessing public health facilities in study area were prospectively enrolled and provided upfront Xpert MTB/RIF testing.2,787 HIV-infected presumptive pulmonary TB cases were enrolled and 867 (31.1%, 95% Confidence Interval (CI 29.4‒32.8 HIV-infected TB cases were diagnosed under the study. Overall 27.6% (CI 25.9-29.3 of HIV-infected presumptive PTB cases were positive by Xpert MTB/RIF, compared with 12.9% (CI 11.6-14.1 who had positive sputum smears. Upfront Xpert MTB/RIF testing of presumptive PTB and DR-TB cases resulted in diagnosis of 73 (9.5%, CI 7.6‒11.8 and 16 (11.2%, CI 6.7‒17.1 rifampicin resistance cases, respectively. Positive predictive value (PPV for rifampicin resistance detection was high 97.7% (CI 89.3‒99.8, with no significant difference with or without prior history of TB treatment.The study results strongly demonstrate limitations of using smear microscopy for TB diagnosis in PLHIV, leading to low TB and DR-TB detection which can potentially lead to either delayed or sub-optimal TB treatment. Our findings demonstrate the usefulness and feasibility of addressing this diagnostic gap with upfront of Xpert MTB/RIF testing, leading to overall strengthening of care and support package for PLHIV.

A Model Based Approach to Sample Size Estimation in Recent Onset Type 1 Diabetes

Science.gov (United States)

Bundy, Brian; Krischer, Jeffrey P.

2016-01-01

The area under the curve C-peptide following a 2-hour mixed meal tolerance test from 481 individuals enrolled on 5 prior TrialNet studies of recent onset type 1 diabetes from baseline to 12 months after enrollment were modelled to produce estimates of its rate of loss and variance. Age at diagnosis and baseline C-peptide were found to be significant predictors and adjusting for these in an ANCOVA resulted in estimates with lower variance. Using these results as planning parameters for new studies results in a nearly 50% reduction in the target sample size. The modelling also produces an expected C-peptide that can be used in Observed vs. Expected calculations to estimate the presumption of benefit in ongoing trials. PMID:26991448

Diagnosis of three types of constant faults in read-once contact networks over finite bases

KAUST Repository

Busbait, Monther I.

2016-03-24

We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in networks. For bases containing networks with at most 10 edges, we find sharp coefficients for linear bounds.

MDD diagnosis based on partial-brain functional connection network

Science.gov (United States)

Yan, Gaoliang; Hu, Hailong; Zhao, Xiang; Zhang, Lin; Qu, Zehui; Li, Yantao

2018-04-01

Artificial intelligence (AI) is a hotspot in computer science research nowadays. To apply AI technology in all industries has been the developing direction for researchers. Major depressive disorder (MDD) is a common disease of serious mental disorders. The World Health Organization (WHO) reports that MDD is projected to become the second most common cause of death and disability by 2020. At present, the way of MDD diagnosis is single. Applying AI technology to MDD diagnosis and pathophysiological research will speed up the MDD research and improve the efficiency of MDD diagnosis. In this study, we select the higher degree of brain network functional connectivity by statistical methods. And our experiments show that the average accuracy of Logistic Regression (LR) classifier using feature filtering reaches 88.48%. Compared with other classification methods, both the efficiency and accuracy of this method are improved, which will greatly improve the process of MDD diagnose. In these experiments, we also define the brain regions associated with MDD, which plays a vital role in MDD pathophysiological research.

Online Open Circuit Fault Diagnosis for Rail Transit Traction Converter Based on Object-Oriented Colored Petri Net Topology Reasoning

Directory of Open Access Journals (Sweden)

Lei Wang

2016-01-01

Full Text Available For online open circuit fault diagnosis of the traction converter in rail transit vehicles, conventional approaches depend heavily on component parameters and circuit layouts. For better universality and less parameter sensitivity during the diagnosis, this paper proposes a novel topology analysis approach to diagnose switching device open circuit failures. During the diagnosis, the topology is analyzed with fault reasoning mechanism, which is based on object-oriented Petri net (OOCPN. The OOCPN model takes in digitalized current inputs as fault signatures, and dynamical transitions between discrete switching states of a circuit with broken device are symbolized with the dynamical transitions of colored tokens in OOCPN. Such transitions simulate natural reasoning process of an expert’s brain during diagnosis. The dependence on component parameters and on circuit layouts is finally eliminated by such circuit topology reasoning process. In the last part, the proposed online reasoning and diagnosis process is exemplified with the case of a certain switching device failure in the power circuit of traction converter.

Referral patterns of community health workers diagnosing and treating malaria

DEFF Research Database (Denmark)

Lal, Sham; Ndyomugenyi, Richard; Magnussen, Pascal

2016-01-01

Malaria-endemic countries have implemented community health worker (CHW) programs to provide malaria diagnosis and treatment to populations living beyond the reach of health systems. However, there is limited evidence describing the referral practices of CHWs. We examined the impact of malaria...... rapid diagnostic tests (mRDTs) on CHW referral in two cluster-randomized trials, one conducted in a moderate-to-high malaria transmission setting and one in a low-transmission setting in Uganda, between January 2010 and July 2012. All CHWs were trained to prescribe artemisinin-based combination therapy...... (ACT) for malaria and recognize signs and symptoms for referral to health centers. CHWs in the control arm used a presumptive diagnosis for malaria based on clinical symptoms, whereas intervention arm CHWs used mRDTs. CHWs recorded ACT prescriptions, mRDT results, and referral inpatient registers...

Genotype, phenotype and in silico pathogenicity analysis of HEXB mutations: Panel based sequencing for differential diagnosis of gangliosidosis.

Science.gov (United States)

Mahdieh, Nejat; Mikaeeli, Sahar; Tavasoli, Ali Reza; Rezaei, Zahra; Maleki, Majid; Rabbani, Bahareh

2018-04-01

Gangliosidosis is an inherited metabolic disorder causing neurodegeneration and motor regression. Preventive diagnosis is the first choice for the affected families due to lack of straightforward therapy. Genetic studies could confirm the diagnosis and help families for carrier screening and prenatal diagnosis. An update of HEXB gene variants concerning genotype, phenotype and in silico analysis are presented. Panel based next generation sequencing and direct sequencing of four cases were performed to confirm the clinical diagnosis and for reproductive planning. Bioinformatic analyses of the HEXB mutation database were also performed. Direct sequencing of HEXA and HEXB genes showed recurrent homozygous variants at c.509G>A (p.Arg170Gln) and c.850C>T (p.Arg284Ter), respectively. A novel variant at c.416T>A (p.Leu139Gln) was identified in the GLB1 gene. Panel based next generation sequencing was performed for an undiagnosed patient which showed a novel mutation at c.1602C>A (p.Cys534Ter) of HEXB gene. Bioinformatic analysis of the HEXB mutation database showed 97% consistency of in silico genotype analysis with the phenotype. Bioinformatic analysis of the novel variants predicted to be disease causing. In silico structural and functional analysis of the novel variants showed structural effect of HEXB and functional effect of GLB1 variants which would provide fast analysis of novel variants. Panel based studies could be performed for overlapping symptomatic patients. Consequently, genetic testing would help affected families for patients' management, carrier detection, and family planning's. Copyright © 2018 Elsevier B.V. All rights reserved.

Fault Diagnosis of an Advanced Wind Turbine Benchmark using Interval-based ARRs and Observers

DEFF Research Database (Denmark)

Sardi, Hector Eloy Sanchez; Escobet, Teressa; Puig, Vicenc

2015-01-01

This paper proposes a model-based fault diagnosis (FD) approach for wind turbines and its application to a realistic wind turbine FD benchmark. The proposed FD approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown...... turbine and noise/parameter uncertainty bounds. Fault isolation is based on considering a set of ARRs obtained from the structural analysis of the wind turbine model and a fault signature matrix that considers the relation of ARRs and faults. The proposed FD approach has been validated on a 5-MW wind...

Autism in Preschoolers: Does Individual Clinician’s First Visit Diagnosis Agree with Final Comprehensive Diagnosis?

Directory of Open Access Journals (Sweden)

Gunilla Westman Andersson

2013-01-01

Full Text Available Comprehensive clinical diagnosis based on all available information is considered the “gold standard” in autism spectrum disorders (ASD. We examined agreement across independent assessments (clinical judgment of 34 young children (age 24–46 months with suspected ASD, assessed by a multidisciplinary team, and final comprehensive clinical diagnosis. Agreement across settings and between each clinician’s assessment and final diagnosis was moderate. The poorest fit was found at assessment in connection with psychological evaluation and the best with preschool observation and parent interview. Some individual clinicians had good and others had poor fit with final diagnosis. Disagreement across assessments was pronounced for girls. The findings suggest that multidisciplinary assessments remain important and that comprehensive clinical diagnosis should still be regarded as the gold standard in ASD.

Value representations: a value based dialogue tool

DEFF Research Database (Denmark)

Petersen, Marianne Graves; Rasmussen, Majken Kirkegaard

2011-01-01

Stereotypic presumptions about gender affect the design process, both in relation to how users are understood and how products are designed. As a way to decrease the influence of stereotypic presumptions in design process, we propose not to disregard the aspect of gender in the design process......, as the perspective brings valuable insights on different approaches to technology, but instead to view gender through a value lens. Contributing to this perspective, we have developed Value Representations as a design-oriented instrument for staging a reflective dialogue with users. Value Representations...

Research on method of nuclear power plant operation fault diagnosis based on a combined artificial neural network

International Nuclear Information System (INIS)

Liu Feng; Yu Ren; Li Fengyu; Zhang Meng

2007-01-01

To solve the online real-time diagnosis problem of the nuclear power plant in operating condition, a method based on a combined artificial neural network is put forward in the paper. Its main principle is: using the BP neural network for the fast group diagnosis, and then using the RBF neural network for distinguishing and verifying the diagnostic result. The accuracy of the method is verified using the simulation values of the key parameters in normal status and malfunction status of a nuclear power plant. The results show that the method combining the advantages of the two neural networks can not only diagnose the learned faults in similar power level of the nuclear power plant quickly and accurately, but also can identify the faults in different power status, as well as the unlearned faults. The outputs of the diagnosis system are in form of the reliability of the faults, and are changing with the lasting of the operation time of the plant. This makes the diagnosis results be more acceptable to operators. (authors)

Network Fault Diagnosis Using DSM

Institute of Scientific and Technical Information of China (English)

Jiang Hao; Yan Pu-liu; Chen Xiao; Wu Jing

2004-01-01

Difference similitude matrix (DSM) is effective in reducing information system with its higher reduction rate and higher validity. We use DSM method to analyze the fault data of computer networks and obtain the fault diagnosis rules. Through discretizing the relative value of fault data, we get the information system of the fault data. DSM method reduces the information system and gets the diagnosis rules. The simulation with the actual scenario shows that the fault diagnosis based on DSM can obtain few and effective rules.

Determining the analytical specificity of PCR-based assays for the diagnosis of IA: What is Aspergillus?

NARCIS (Netherlands)

Morton, C.O.; White, P.L.; Barnes, R.A.; Klingspor, L.; Cuenca-Estrella, M.; Lagrou, K.; Bretagne, S.; Melchers, W.J.; Mengoli, C.; Caliendo, A.M.; Cogliati, M.; Debets-Ossenkopp, Y.; Gorton, R.; Hagen, F.; Halliday, C.; Hamal, P.; Harvey-Wood, K.; Jaton, K.; Johnson, G.; Kidd, S.; Lengerova, M.; Lass-Florl, C.; Linton, C.; Millon, L.; Morrissey, C.O.; Paholcsek, M.; Talento, A.F.; Ruhnke, M.; Willinger, B.; Donnelly, J.P.; Loeffler, J.

2017-01-01

A wide array of PCR tests has been developed to aid the diagnosis of invasive aspergillosis (IA), providing technical diversity but limiting standardisation and acceptance. Methodological recommendations for testing blood samples using PCR exist, based on achieving optimal assay sensitivity to help

Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

Science.gov (United States)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

2006-03-01

Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

Directory of Open Access Journals (Sweden)

Chin-Tsung Hsieh

2014-01-01

Full Text Available The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

Cost-effectiveness of malaria microscopy and rapid diagnostic tests versus presumptive diagnosis

DEFF Research Database (Denmark)

Batwala, Vincent; Magnussen, Pascal; Hansen, Kristian Schultz

2011-01-01

.9) than in low transmission setting (US$1.78). At a willingness to pay of US$2.8, RDT remained cost effective up to a threshold value of the cost of treatment of US$4.7. CONCLUSION: RDT was cost effective in both low and high transmission settings. With a global campaign to reduce the costs of AL and RDT......ABSTRACT: BACKGROUND: Current Uganda National Malaria treatment guidelines recommend parasitological confirmation either by microscopy or rapid diagnostic test (RDT) before treatment with artemether-lumefantrine (AL). However, the cost-effectiveness of these strategies has not been assessed...... departments were enrolled from March 2010 to February 2011. Of these, a random sample of 1,627 was selected to measure additional socio-economic characteristics. Costing was performed following the standard step-down cost allocation and the ingredients approach. Effectiveness was measured as the number...

Fault diagnosis of nuclear-powered equipment based on HMM and SVM

International Nuclear Information System (INIS)

Yue Xia; Zhang Chunliang; Zhu Houyao; Quan Yanming

2012-01-01

For the complexity and the small fault samples of nuclear-powered equipment, a hybrid HMM/SVM method was introduced in fault diagnosis. The hybrid method has two steps: first, HMM is utilized for primary diagnosis, in which the range of possible failure is reduced and the state trends can be observed; then faults can be recognized taking the advantage of the generalization ability of SVM. Experiments on the main pump failure simulator show that the HMM/SVM system has a high recognition rate and can be used in the fault diagnosis of nuclear-powered equipment. (authors)

Common variable immunodeficiency in three horses with presumptive bacterial meningitis.

Science.gov (United States)

Pellegrini-Masini, Alessandra; Bentz, Amy I; Johns, Imogen C; Parsons, Corrina S; Beech, Jill; Whitlock, Robert H; Flaminio, M Julia B F

2005-07-01

Three adult horses were evaluated for signs of musculoskeletal pain, dullness, ataxia, and seizures. A diagnosis of bacterial meningitis was made on the basis of results of CSF analysis. Because primary bacterial meningitis is so rare in adult horses without any history of generalized sepsis or trauma, immune function testing was pursued. Flow cytometric phenotyping of peripheral blood lymphocytes was performed, and proliferation of peripheral blood lymphocytes in response to concanavalin A, phytohemagglutinin, pokeweed mitogen, and lipopolysaccharide was determined. Serum IgA, IgM, and IgG concentrations were measured by means of radial immunodiffusion, and serum concentrations of IgG isotypes were assessed with a capture antibody ELISA. Serum tetanus antibody concentrations were measured before and 1 month after tetanus toxoid administration. Phagocytosis and oxidative burst activity of isolated peripheral blood phagocytes were evaluated by means of simultaneous flow cytometric analysis. Persistent B-cell lymphopenia, hypogammaglobulinemia, and abnormal in vitro responses to mitogens were detected in all 3 horses, and a diagnosis of common variable immunodeficiency was made.

Clinical usefulness of normal data bases comparisons for the SPECT diagnosis of Alzheimer's disease

International Nuclear Information System (INIS)

Darcourt, J.; Koulibaly, P.M.; Migneco, O.; Dygai, I.; Robert, P.H.; Nobili, F.; Ebmeir, K.

2002-01-01

Aim. The possible added value of voxel by voxel comparisons to normal data bases has not been evaluated for the diagnosis of Alzheimer's disease (AD). We conducted a prospective comparison of the diagnostic performances of 2 software packages: Statistical Parametric Mapping (SPM) (Friston et al.) and NeuroGam (NGam) (Segami Corporation). Materials and methods. A total of 152 subjects (age ≥ 50 years) were included: 93 AD, 28 depressed patients and 31 normal controls (NC). They were studied in 4 centers as part of a European project 'SPECT in dementia' BMH4-98-3130. NC were used to build the normal data bases and the total population was submitted to the readers for the diagnosis of AD. AD final diagnosis was based on NINCDS/ADRDA criteria for probable AD and DSM-IV criteria for dementia of AD type. SPECT scans were obtained in each center with dedicated cameras 30 to 90 min after i.v. injection of 250 to 925 MBq of 99mTc-HMPAO. All data were reconstructed on the same workstation by filtered backprojection with attenuation correction. The 4.7 mm thick cuts (CUTS) were displayed in the transverse, sagittal and coronal planes with the same color scale. They also were submitted to the 2 packages tested. For SPM, we used SPM'96 for Windows'95. For each individual scan we computed the corresponding z-map by comparison to the NC data base. We used p<0.01 to threshold the t-maps and a p corrected value <0.01 on intensity for cluster selection. For NGam, the same NC were used to build the normal data base. Each individual scan was then compared to this base and the results consisted in a 3D parametric image of voxel by voxel standard deviations form the normal mean value. 4 expert readers (more than 3 years experience; more than 5 SPECT per week) were asked to class the scans as AD or not with a 4 degree of confidence. They reviewed the CUTS alone, CUTS+SPM and CUTS+NGam. ROC analysis was performed and the areas under curves (AUC) statistically compared. Results. Average