Environmental Determinants of Chronic Disease and Medical Approaches: Recognition, Avoidance, Supportive Therapy, and Detoxification

International Nuclear Information System (INIS)

Sears, M.E.; Sears, M.E.; Genuis, S.J.

2012-01-01

The World Health Organization warns that chronic, non communicable diseases are rapidly becoming epidemic worldwide. Escalating rates of neuro cognitive, metabolic, autoimmune and cardiovascular diseases cannot be ascribed only to genetics, lifestyle, and nutrition; early life and ongoing exposures, and bio accumulated toxicants may also cause chronic disease. Contributors to ill health are summarized from multiple perspectives biological effects of classes of toxicants, mechanisms of toxicity, and a synthesis of toxic contributors to major diseases. Health care practitioners have wide-ranging roles in addressing environmental factors in policy and public health and clinical practice. Public health initiatives include risk recognition and chemical assessment then exposure reduction, remediation, monitoring, and avoidance. The complex web of disease and environmental contributors is amenable to some straightforward clinical approaches addressing multiple toxicants. Widely applicable strategies include nutrition and supplements to counter toxic effects and to support metabolism; as well as exercise and sweating, and possibly medication to enhance excretion. Addressing environmental health and contributors to chronic disease has broad implications for society, with large potential benefits from improved health and productivity.

Step detection and activity recognition accuracy of seven physical activity monitors.

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Fabio A Storm

Full Text Available The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts, Up (Jawbone, One (Fitbit, ActivPAL (PAL Technologies Ltd., Nike+ Fuelband (Nike Inc., Tractivity (Kineteks Corp. and Sensewear Armband Mini (Bodymedia. Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

Step detection and activity recognition accuracy of seven physical activity monitors.

Science.gov (United States)

Storm, Fabio A; Heller, Ben W; Mazzà, Claudia

2015-01-01

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

Effect of dopamine therapy on nonverbal affect burst recognition in Parkinson's disease.

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Julie Péron

Full Text Available BACKGROUND: Parkinson's disease (PD provides a model for investigating the involvement of the basal ganglia and mesolimbic dopaminergic system in the recognition of emotions from voices (i.e., emotional prosody. Although previous studies of emotional prosody recognition in PD have reported evidence of impairment, none of them compared PD patients at different stages of the disease, or ON and OFF dopamine replacement therapy, making it difficult to determine whether their impairment was due to general cognitive deterioration or to a more specific dopaminergic deficit. METHODS: We explored the involvement of the dopaminergic pathways in the recognition of nonverbal affect bursts (onomatopoeias in 15 newly diagnosed PD patients in the early stages of the disease, 15 PD patients in the advanced stages of the disease and 15 healthy controls. The early PD group was studied in two conditions: ON and OFF dopaminergic therapy. RESULTS: Results showed that the early PD patients performed more poorly in the ON condition than in the OFF one, for overall emotion recognition, as well as for the recognition of anger, disgust and fear. Additionally, for anger, the early PD ON patients performed more poorly than controls. For overall emotion recognition, both advanced PD patients and early PD ON patients performed more poorly than controls. Analysis of continuous ratings on target and nontarget visual analog scales confirmed these patterns of results, showing a systematic emotional bias in both the advanced PD and early PD ON (but not OFF patients compared with controls. CONCLUSIONS: These results i confirm the involvement of the dopaminergic pathways and basal ganglia in emotional prosody recognition, and ii suggest a possibly deleterious effect of dopatherapy on affective abilities in the early stages of PD.

The recognition of facial emotion expressions in Parkinson's disease.

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Assogna, Francesca; Pontieri, Francesco E; Caltagirone, Carlo; Spalletta, Gianfranco

2008-11-01

A limited number of studies in Parkinson's Disease (PD) suggest a disturbance of recognition of facial emotion expressions. In particular, disgust recognition impairment has been reported in unmedicated and medicated PD patients. However, the results are rather inconclusive in the definition of the degree and the selectivity of emotion recognition impairment, and an associated impairment of almost all basic facial emotions in PD is also described. Few studies have investigated the relationship with neuropsychiatric and neuropsychological symptoms with mainly negative results. This inconsistency may be due to many different problems, such as emotion assessment, perception deficit, cognitive impairment, behavioral symptoms, illness severity and antiparkinsonian therapy. Here we review the clinical characteristics and neural structures involved in the recognition of specific facial emotion expressions, and the plausible role of dopamine transmission and dopamine replacement therapy in these processes. It is clear that future studies should be directed to clarify all these issues.

Altered emotional recognition and expression in patients with Parkinson’s disease

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Jin Y

2017-11-01

Full Text Available Yazhou Jin,* Zhiqi Mao,* Zhipei Ling, Xin Xu, Zhiyuan Zhang, Xinguang Yu Department of Neurosurgery, People’s Liberation Army General Hospital, Beijing, People’s Republic of China *These authors contributed equally to this work Background: Parkinson’s disease (PD patients exhibit deficits in emotional recognition and expression abilities, including emotional faces and voices. The aim of this study was to explore emotional processing in pre-deep brain stimulation (pre-DBS PD patients using two sensory modalities (visual and auditory. Methods: Fifteen PD patients who needed DBS surgery and 15 healthy, age- and gender-matched controls were recruited as participants. All participants were assessed by the Karolinska Directed Emotional Faces database 50 Faces Recognition test. Vocal recognition was evaluated by the Montreal Affective Voices database 50 Voices Recognition test. For emotional facial expression, the participants were asked to imitate five basic emotions (neutral, happiness, anger, fear, and sadness. The subjects were required to express nonverbal vocalizations of the five basic emotions. Fifteen Chinese native speakers were recruited as decoders. We recorded the accuracy of the responses, reaction time, and confidence level. Results: For emotional recognition and expression, the PD group scored lower on both facial and vocal emotional processing than did the healthy control group. There were significant differences between the two groups in both reaction time and confidence level. A significant relationship was also found between emotional recognition and emotional expression when considering all participants between the two groups together. Conclusion: The PD group exhibited poorer performance on both the recognition and expression tasks. Facial emotion deficits and vocal emotion abnormalities were associated with each other. In addition, our data allow us to speculate that emotional recognition and expression may share a common

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

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Srdjan Sladojevic

2016-01-01

Full Text Available The latest generation of convolutional neural networks (CNNs has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

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Iddamalgoda, Lahiru; Das, Partha S.; Aponso, Achala; Sundararajan, Vijayaraghava S.; Suravajhala, Prashanth; Valadi, Jayaraman K.

2016-01-01

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited ...

Health monitoring of 90° bolted joints using fuzzy pattern recognition of ultrasonic signals

International Nuclear Information System (INIS)

Jalalpour, M; El-Osery, A I; Austin, E M; Reda Taha, M M

2014-01-01

Bolted joints are important parts for aerospace structures. However, there is a significant risk associated with assembling bolted joints due to potential human error during the assembly process. Such errors are expensive to find and correct if exposed during environmental testing, yet checking the integrity of individual fasteners after assembly would be a time consuming task. Recent advances in structural health monitoring (SHM) can provide techniques to not only automate this process but also make it reliable. This integrity monitoring requires damage features to be related to physical conditions representing the structural integrity of bolted joints. In this paper an SHM technique using ultrasonic signals and fuzzy pattern recognition to monitor the integrity of 90° bolted joints in aerospace structures is described. The proposed technique is based on normalized fast Fourier transform (NFFT) of transmitted signals and fuzzy pattern recognition. Moreover, experimental observations of a case study on an aluminum 90° bolted joint are presented. We demonstrate the ability of the proposed method to efficiently monitor and indicate bolted joint integrity. (paper)

Total recognition discriminability in Huntington's and Alzheimer's disease.

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Graves, Lisa V; Holden, Heather M; Delano-Wood, Lisa; Bondi, Mark W; Woods, Steven Paul; Corey-Bloom, Jody; Salmon, David P; Delis, Dean C; Gilbert, Paul E

2017-03-01

Both the original and second editions of the California Verbal Learning Test (CVLT) provide an index of total recognition discriminability (TRD) but respectively utilize nonparametric and parametric formulas to compute the index. However, the degree to which population differences in TRD may vary across applications of these nonparametric and parametric formulas has not been explored. We evaluated individuals with Huntington's disease (HD), individuals with Alzheimer's disease (AD), healthy middle-aged adults, and healthy older adults who were administered the CVLT-II. Yes/no recognition memory indices were generated, including raw nonparametric TRD scores (as used in CVLT-I) and raw and standardized parametric TRD scores (as used in CVLT-II), as well as false positive (FP) rates. Overall, the patient groups had significantly lower TRD scores than their comparison groups. The application of nonparametric and parametric formulas resulted in comparable effect sizes for all group comparisons on raw TRD scores. Relative to the HD group, the AD group showed comparable standardized parametric TRD scores (despite lower raw nonparametric and parametric TRD scores), whereas the previous CVLT literature has shown that standardized TRD scores are lower in AD than in HD. Possible explanations for the similarity in standardized parametric TRD scores in the HD and AD groups in the present study are discussed, with an emphasis on the importance of evaluating TRD scores in the context of other indices such as FP rates in an effort to fully capture recognition memory function using the CVLT-II.

Selection to outsmart the germs: The evolution of disease recognition and social cognition.

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Kessler, Sharon E; Bonnell, Tyler R; Byrne, Richard W; Chapman, Colin A

2017-07-01

The emergence of providing care to diseased conspecifics must have been a turning point during the evolution of hominin sociality. On a population level, care may have minimized the costs of socially transmitted diseases at a time of increasing social complexity, although individual care-givers probably incurred increased transmission risks. We propose that care-giving likely originated within kin networks, where the costs may have been balanced by fitness increases obtained through caring for ill kin. We test a novel hypothesis of hominin cognitive evolution in which disease may have selected for the cognitive ability to recognize when a conspecific is infected. Because diseases may produce symptoms that are likely detectable via the perceptual-cognitive pathways integral to social cognition, we suggest that disease recognition and social cognition may have evolved together. Using agent-based modeling, we test 1) under what conditions disease can select for increasing disease recognition and care-giving among kin, 2) whether providing care produces greater selection for cognition than an avoidance strategy, and 3) whether care-giving alters the progression of the disease through the population. The greatest selection was produced by diseases with lower risks to the care-giver and prevalences low enough not to disrupt the kin networks. When care-giving and avoidance strategies were compared, only care-giving reduced the severity of the disease outbreaks and subsequent population crashes. The greatest selection for increased cognitive abilities occurred early in the model runs when the outbreaks and population crashes were most severe. Therefore, over the course of human evolution, repeated introductions of novel diseases into naïve populations could have produced sustained selection for increased disease recognition and care-giving behavior, leading to the evolution of increased cognition, social complexity, and, eventually, medical care in humans. Finally, we lay

A Particle Swarm Optimization Algorithm for Neural Networks in Recognition of Maize Leaf Diseases

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Zhiyong ZHANG

2014-03-01

Full Text Available The neural networks have significance on recognition of crops disease diagnosis? but it has disadvantage of slow convergent speed and shortcoming of local optimum. In order to identify the maize leaf diseases by using machine vision more accurately, we propose an improved particle swarm optimization algorithm for neural networks. With the algorithm, the neural network property is improved. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in the image recognition. At last, an example of the emulation shows that neural network model based on recognizes significantly better than without optimization. Model accuracy has been improved to a certain extent to meet the actual needs of maize leaf diseases recognition.

Relative preservation of the recognition of positive facial expression "happiness" in Alzheimer disease.

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Maki, Yohko; Yoshida, Hiroshi; Yamaguchi, Tomoharu; Yamaguchi, Haruyasu

2013-01-01

Positivity recognition bias has been reported for facial expression as well as memory and visual stimuli in aged individuals, whereas emotional facial recognition in Alzheimer disease (AD) patients is controversial, with possible involvement of confounding factors such as deficits in spatial processing of non-emotional facial features and in verbal processing to express emotions. Thus, we examined whether recognition of positive facial expressions was preserved in AD patients, by adapting a new method that eliminated the influences of these confounding factors. Sensitivity of six basic facial expressions (happiness, sadness, surprise, anger, disgust, and fear) was evaluated in 12 outpatients with mild AD, 17 aged normal controls (ANC), and 25 young normal controls (YNC). To eliminate the factors related to non-emotional facial features, averaged faces were prepared as stimuli. To eliminate the factors related to verbal processing, the participants were required to match the images of stimulus and answer, avoiding the use of verbal labels. In recognition of happiness, there was no difference in sensitivity between YNC and ANC, and between ANC and AD patients. AD patients were less sensitive than ANC in recognition of sadness, surprise, and anger. ANC were less sensitive than YNC in recognition of surprise, anger, and disgust. Within the AD patient group, sensitivity of happiness was significantly higher than those of the other five expressions. In AD patient, recognition of happiness was relatively preserved; recognition of happiness was most sensitive and was preserved against the influences of age and disease.

Normal mere exposure effect with impaired recognition in Alzheimer's disease.

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Willems, Sylvie; Adam, Stéphane; Van der Linden, Martial

2002-02-01

We investigated the mere exposure effect and the explicit memory in Alzheimer's disease (AD) patients and elderly control subjects, using unfamiliar faces. During the exposure phase, the subjects estimated the age of briefly flashed faces. The mere exposure effect was examined by presenting pairs of faces (old and new) and asking participants to select the face they liked. The participants were then presented with a forced-choice explicit recognition task. Controls subjects exhibited above-chance preference and recognition scores for old faces. The AD patients also showed the mere exposure effect but no explicit recognition. These results suggest that the processes involved in the mere exposure effect are preserved in AD patients despite their impaired explicit recognition. The results are discussed in terms of Seamon et al.'s (1995) proposal that processes involved in the mere exposure effect are equivalent to those subserving perceptual priming. These processes would depend on extrastriate areas which are relatively preserved in AD patients.

Fault diagnosis and performance monitoring for pumps by means of vibration measurement and pattern recognition

International Nuclear Information System (INIS)

Grabner, A.; Weiss, F.P.

1984-12-01

In recent years the early detection of malfunctions with noise and vibration analysis techniques has become a more and more important method for increasing availability and safety of various components in technical plants. The possibility of pattern recognition assisted vibration monitoring and its practical realization are demonstrated by failure diagnosis and trend analysis of the condition of large centrifugal pumps in hydraulic circuits. Some problems as, e.g., the finding of dynamic failure models, signal analysis, feature extraction and statistical pattern recognition, which helps automatically to decide whether the pump works normally or not, are discussed in more detail. In the paper it is shown that for various types of machines the chance of success of condition based maintenance can be enhanced by such an automatic vibration monitoring. (author)

Music recognition in frontotemporal lobar degeneration and Alzheimer disease.

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Johnson, Julene K; Chang, Chiung-Chih; Brambati, Simona M; Migliaccio, Raffaella; Gorno-Tempini, Maria Luisa; Miller, Bruce L; Janata, Petr

2011-06-01

To compare music recognition in patients with frontotemporal dementia, semantic dementia, Alzheimer disease, and controls and to evaluate the relationship between music recognition and brain volume. Recognition of familiar music depends on several levels of processing. There are few studies about how patients with dementia recognize familiar music. Subjects were administered tasks that assess pitch and melody discrimination, detection of pitch errors in familiar melodies, and naming of familiar melodies. There were no group differences on pitch and melody discrimination tasks. However, patients with semantic dementia had considerable difficulty naming familiar melodies and also scored the lowest when asked to identify pitch errors in the same melodies. Naming familiar melodies, but not other music tasks, was strongly related to measures of semantic memory. Voxel-based morphometry analysis of brain magnetic resonance imaging showed that difficulty in naming songs was associated with the bilateral temporal lobes and inferior frontal gyrus, whereas difficulty in identifying pitch errors in familiar melodies correlated with primarily the right temporal lobe. The results support a view that the anterior temporal lobes play a role in familiar melody recognition, and that musical functions are affected differentially across forms of dementia.

Music Recognition in Frontotemporal Lobar Degeneration and Alzheimer Disease

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Johnson, Julene K; Chang, Chiung-Chih; Brambati, Simona M; Migliaccio, Raffaella; Gorno-Tempini, Maria Luisa; Miller, Bruce L; Janata, Petr

2013-01-01

Objective To compare music recognition in patients with frontotemporal dementia, semantic dementia, Alzheimer disease, and controls and to evaluate the relationship between music recognition and brain volume. Background Recognition of familiar music depends on several levels of processing. There are few studies about how patients with dementia recognize familiar music. Methods Subjects were administered tasks that assess pitch and melody discrimination, detection of pitch errors in familiar melodies, and naming of familiar melodies. Results There were no group differences on pitch and melody discrimination tasks. However, patients with semantic dementia had considerable difficulty naming familiar melodies and also scored the lowest when asked to identify pitch errors in the same melodies. Naming familiar melodies, but not other music tasks, was strongly related to measures of semantic memory. Voxel-based morphometry analysis of brain MRI showed that difficulty in naming songs was associated with the bilateral temporal lobes and inferior frontal gyrus, whereas difficulty in identifying pitch errors in familiar melodies correlated with primarily the right temporal lobe. Conclusions The results support a view that the anterior temporal lobes play a role in familiar melody recognition, and that musical functions are affected differentially across forms of dementia. PMID:21617528

Epidemiological Concepts Regarding Disease Monitoring and Surveillance

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Christensen Jette

2001-03-01

Full Text Available Definitions of epidemiological concepts regarding disease monitoring and surveillance can be found in textbooks on veterinary epidemiology. This paper gives a review of how the concepts: monitoring, surveillance, and disease control strategies are defined. Monitoring and surveillance systems (MO&SS involve measurements of disease occurrence, and the design of the monitoring determines which types of disease occurrence measures can be applied. However, the knowledge of the performance of diagnostic tests (sensitivity and specificity is essential to estimate the true occurrence of the disease. The terms, disease control programme (DCP or disease eradication programme (DEP, are defined, and the steps of DCP/DEP are described to illustrate that they are a process rather than a static MO&SS.

Degraded Impairment of Emotion Recognition in Parkinson's Disease Extends from Negative to Positive Emotions.

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Lin, Chia-Yao; Tien, Yi-Min; Huang, Jong-Tsun; Tsai, Chon-Haw; Hsu, Li-Chuan

2016-01-01

Because of dopaminergic neurodegeneration, patients with Parkinson's disease (PD) show impairment in the recognition of negative facial expressions. In the present study, we aimed to determine whether PD patients with more advanced motor problems would show a much greater deficit in recognition of emotional facial expressions than a control group and whether impairment of emotion recognition would extend to positive emotions. Twenty-nine PD patients and 29 age-matched healthy controls were recruited. Participants were asked to discriminate emotions in Experiment  1 and identify gender in Experiment  2. In Experiment  1, PD patients demonstrated a recognition deficit for negative (sadness and anger) and positive faces. Further analysis showed that only PD patients with high motor dysfunction performed poorly in recognition of happy faces. In Experiment  2, PD patients showed an intact ability for gender identification, and the results eliminated possible abilities in the functions measured in Experiment  2 as alternative explanations for the results of Experiment  1. We concluded that patients' ability to recognize emotions deteriorated as the disease progressed. Recognition of negative emotions was impaired first, and then the impairment extended to positive emotions.

Conceptual fluency at test shifts recognition response bias in Alzheimer's disease: implications for increased false recognition.

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Gold, Carl A; Marchant, Natalie L; Koutstaal, Wilma; Schacter, Daniel L; Budson, Andrew E

2007-09-20

The presence or absence of conceptual information in pictorial stimuli may explain the mixed findings of previous studies of false recognition in patients with mild Alzheimer's disease (AD). To test this hypothesis, 48 patients with AD were compared to 48 healthy older adults on a recognition task first described by Koutstaal et al. [Koutstaal, W., Reddy, C., Jackson, E. M., Prince, S., Cendan, D. L., & Schacter D. L. (2003). False recognition of abstract versus common objects in older and younger adults: Testing the semantic categorization account. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 499-510]. Participants studied and were tested on their memory for categorized ambiguous pictures of common objects. The presence of conceptual information at study and/or test was manipulated by providing or withholding disambiguating semantic labels. Analyses focused on testing two competing theories. The semantic encoding hypothesis, which posits that the inter-item perceptual details are not encoded by AD patients when conceptual information is present in the stimuli, was not supported by the findings. In contrast, the conceptual fluency hypothesis was supported. Enhanced conceptual fluency at test dramatically shifted AD patients to a more liberal response bias, raising their false recognition. These results suggest that patients with AD rely on the fluency of test items in making recognition memory decisions. We speculate that AD patients' over reliance upon fluency may be attributable to (1) dysfunction of the hippocampus, disrupting recollection, and/or (2) dysfunction of prefrontal cortex, disrupting post-retrieval processes.

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.

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Iddamalgoda, Lahiru; Das, Partha S; Aponso, Achala; Sundararajan, Vijayaraghava S; Suravajhala, Prashanth; Valadi, Jayaraman K

2016-01-01

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.

Recall and recognition of verbal paired associates in early Alzheimer's disease.

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Lowndes, G J; Saling, M M; Ames, D; Chiu, E; Gonzalez, L M; Savage, G R

2008-07-01

The primary impairment in early Alzheimer's disease (AD) is encoding/consolidation, resulting from medial temporal lobe (MTL) pathology. AD patients perform poorly on cued-recall paired associate learning (PAL) tasks, which assess the ability of the MTLs to encode relational memory. Since encoding and retrieval processes are confounded within performance indexes on cued-recall PAL, its specificity for AD is limited. Recognition paradigms tend to show good specificity for AD, and are well tolerated, but are typically less sensitive than recall tasks. Associate-recognition is a novel PAL task requiring a combination of recall and recognition processes. We administered a verbal associate-recognition test and cued-recall analogue to 22 early AD patients and 55 elderly controls to compare their ability to discriminate these groups. Both paradigms used eight arbitrarily related word pairs (e.g., pool-teeth) with varying degrees of imageability. Associate-recognition was equally effective as the cued-recall analogue in discriminating the groups, and logistic regression demonstrated classification rates by both tasks were equivalent. These preliminary findings provide support for the clinical value of this recognition tool. Conceptually it has potential for greater specificity in informing neuropsychological diagnosis of AD in clinical samples but this requires further empirical support.

Recognition of facial and musical emotions in Parkinson's disease.

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Saenz, A; Doé de Maindreville, A; Henry, A; de Labbey, S; Bakchine, S; Ehrlé, N

2013-03-01

Patients with amygdala lesions were found to be impaired in recognizing the fear emotion both from face and from music. In patients with Parkinson's disease (PD), impairment in recognition of emotions from facial expressions was reported for disgust, fear, sadness and anger, but no studies had yet investigated this population for the recognition of emotions from both face and music. The ability to recognize basic universal emotions (fear, happiness and sadness) from both face and music was investigated in 24 medicated patients with PD and 24 healthy controls. The patient group was tested for language (verbal fluency tasks), memory (digit and spatial span), executive functions (Similarities and Picture Completion subtests of the WAIS III, Brixton and Stroop tests), visual attention (Bells test), and fulfilled self-assessment tests for anxiety and depression. Results showed that the PD group was significantly impaired for recognition of both fear and sadness emotions from facial expressions, whereas their performance in recognition of emotions from musical excerpts was not different from that of the control group. The scores of fear and sadness recognition from faces were neither correlated to scores in tests for executive and cognitive functions, nor to scores in self-assessment scales. We attributed the observed dissociation to the modality (visual vs. auditory) of presentation and to the ecological value of the musical stimuli that we used. We discuss the relevance of our findings for the care of patients with PD. © 2012 The Author(s) European Journal of Neurology © 2012 EFNS.

Degraded Impairment of Emotion Recognition in Parkinson’s Disease Extends from Negative to Positive Emotions

Directory of Open Access Journals (Sweden)

Chia-Yao Lin

2016-01-01

Full Text Available Because of dopaminergic neurodegeneration, patients with Parkinson’s disease (PD show impairment in the recognition of negative facial expressions. In the present study, we aimed to determine whether PD patients with more advanced motor problems would show a much greater deficit in recognition of emotional facial expressions than a control group and whether impairment of emotion recognition would extend to positive emotions. Twenty-nine PD patients and 29 age-matched healthy controls were recruited. Participants were asked to discriminate emotions in Experiment  1 and identify gender in Experiment  2. In Experiment  1, PD patients demonstrated a recognition deficit for negative (sadness and anger and positive faces. Further analysis showed that only PD patients with high motor dysfunction performed poorly in recognition of happy faces. In Experiment  2, PD patients showed an intact ability for gender identification, and the results eliminated possible abilities in the functions measured in Experiment  2 as alternative explanations for the results of Experiment  1. We concluded that patients’ ability to recognize emotions deteriorated as the disease progressed. Recognition of negative emotions was impaired first, and then the impairment extended to positive emotions.

Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks

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Chen, Bo; Zang, Chuanzhi

2009-03-01

This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune recognition and learning mechanisms. The structure damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to generate recognition feature vectors that are able to match the training data. In addition, the immune learning algorithm can learn and remember various data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) Structural Health Monitoring Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms.

Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

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Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

2015-01-01

Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

Stress Prediction for Distributed Structural Health Monitoring Using Existing Measurements and Pattern Recognition.

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Lu, Wei; Teng, Jun; Zhou, Qiushi; Peng, Qiexin

2018-02-01

The stress in structural steel members is the most useful and directly measurable physical quantity to evaluate the structural safety in structural health monitoring, which is also an important index to evaluate the stress distribution and force condition of structures during structural construction and service phases. Thus, it is common to set stress as a measure in steel structural monitoring. Considering the economy and the importance of the structural members, there are only a limited number of sensors that can be placed, which means that it is impossible to obtain the stresses of all members directly using sensors. This study aims to develop a stress response prediction method for locations where there are insufficent sensors, using measurements from a limited number of sensors and pattern recognition. The detailed improved aspects are: (1) a distributed computing process is proposed, where the same pattern is recognized by several subsets of measurements; and (2) the pattern recognition using the subset of measurements is carried out by considering the optimal number of sensors and number of fusion patterns. The validity and feasibility of the proposed method are verified using two examples: the finite-element simulation of a single-layer shell-like steel structure, and the structural health monitoring of the space steel roof of Shenzhen Bay Stadium; for the latter, the anti-noise performance of this method is verified by the stress measurements from a real-world project.

Loneliness and the social monitoring system: Emotion recognition and eye gaze in a real-life conversation

NARCIS (Netherlands)

Lodder, G.M.A.; Scholte, R.H.J.; Goossens, L.; Engels, R.C.M.E.; Verhagen, M.

2016-01-01

Based on the belongingness regulation theory (Gardner et al., 2005, Pers. Soc. Psychol. Bull., 31, 1549), this study focuses on the relationship between loneliness and social monitoring. Specifically, we examined whether loneliness relates to performance on three emotion recognition tasks and

The role of reinstating generation operations in recognition memory and reality monitoring

Directory of Open Access Journals (Sweden)

Nieznański Marek

2014-09-01

Full Text Available The role of encoding/retrieval conditions compatibility was investigated in a reality-monitoring task. An experiment was conducted which showed a positive effect of reinstating distinctive encoding operations at test. That is, generation of a low-frequency (LF word from the same word fragment at study and test significantly enhanced item recognition memory. However, reinstating of relatively more automatic operations of reading or generating a highfrequency (HF word did not influence recognition performance. Moreover, LF words were better recognized than HF words, but memory for source did not depend on the encoding/retrieval match or on the word-frequency. In comparison with reading, generating an item at study significantly enhanced source memory but generating it at test had no effect. The data were analysed using a multinomial modelling approach which allowed ruling out the influence of a response bias on the measurement of memory ability.

Is it worth changing pattern recognition methods for structural health monitoring?

Science.gov (United States)

Bull, L. A.; Worden, K.; Cross, E. J.; Dervilis, N.

2017-05-01

The key element of this work is to demonstrate alternative strategies for using pattern recognition algorithms whilst investigating structural health monitoring. This paper looks to determine if it makes any difference in choosing from a range of established classification techniques: from decision trees and support vector machines, to Gaussian processes. Classification algorithms are tested on adjustable synthetic data to establish performance metrics, then all techniques are applied to real SHM data. To aid the selection of training data, an informative chain of artificial intelligence tools is used to explore an active learning interaction between meaningful clusters of data.

Altered Kinematics of Facial Emotion Expression and Emotion Recognition Deficits Are Unrelated in Parkinson?s Disease

OpenAIRE

Bologna, Matteo; Berardelli, Isabella; Paparella, Giulia; Marsili, Luca; Ricciardi, Lucia; Fabbrini, Giovanni; Berardelli, Alfredo

2016-01-01

Background Altered emotional processing, including reduced emotion facial expression and defective emotion recognition, has been reported in patients with Parkinson?s disease (PD). However, few studies have objectively investigated facial expression abnormalities in PD using neurophysiological techniques. It is not known whether altered facial expression and recognition in PD are related. Objective To investigate possible deficits in facial emotion expression and emotion recognition and their...

A comparison of the recognition of overwork-related cardiovascular disease in Japan, Korea, and Taiwan.

Science.gov (United States)

Park, Jungsun; Kim, Yangho; Cheng, Yawen; Horie, Seichi

2012-01-01

In Japan, Korea, and Taiwan, cerebrovascular and cardiovascular diseases (CVDs) caused by overwork are recognized by government as work-related. These three countries are the only countries in the world that officially recognize CVDs caused by psychosocial factors (e.g., overwork) as work-related cerebrovascular and cardiovascular diseases (WR-CVDs), and compensate employees accordingly. The present study compared the similarities and differences among the recognition of overwork-related CVDs in Japan, Korea, and Taiwan. The criteria by which WR-CVDs are identified are very similar in the three countries. However, in the interval surveyed (1996-2009), Korea had a remarkably larger number of recognized WR-CVD patients than did Japan or Taiwan. Recognition of occupational diseases is influenced by various factors, including socio-cultural values, the nature of occupational health care schemes, the extent of the social security umbrella, national health insurance policy, and scientific evidence. Our results show that social factors may be very different among the three countries studied, although the recognition criteria for WR-CVDs are quite similar.

Cognitive factors affecting free recall, cued recall, and recognition tasks in Alzheimer's disease.

Science.gov (United States)

Yamagishi, Takashi; Sato, Takuya; Sato, Atsushi; Imamura, Toru

2012-01-01

Our aim was to identify cognitive factors affecting free recall, cued recall, and recognition tasks in patients with Alzheimer's disease (AD). We recruited 349 consecutive AD patients who attended a memory clinic. Each patient was assessed using the Alzheimer's Disease Assessment Scale (ADAS) and the extended 3-word recall test. In this task, each patient was asked to freely recall 3 previously presented words. If patients could not recall 1 or more of the target words, the examiner cued their recall by providing the category of the target word and then provided a forced-choice recognition of the target word with 2 distracters. The patients were divided into groups according to the results of the free recall, cued recall, and recognition tasks. Multivariate logistic regression analysis for repeated measures was carried out to evaluate the net effects of cognitive factors on the free recall, cued recall, and recognition tasks after controlling for the effects of age and recent memory deficit. Performance on the ADAS Orientation task was found to be related to performance on the free and cued recall tasks, performance on the ADAS Following Commands task was found to be related to performance on the cued recall task, and performance on the ADAS Ideational Praxis task was found to be related to performance on the free recall, cued recall, and recognition tasks. The extended 3-word recall test reflects deficits in a wider range of memory and other cognitive processes, including memory retention after interference, divided attention, and executive functions, compared with word-list recall tasks. The characteristics of the extended 3-word recall test may be advantageous for evaluating patients' memory impairments in daily living.

The effects of aging and Alzheimer's disease on associative recognition memory.

Science.gov (United States)

Hanaki, Risa; Abe, Nobuhito; Fujii, Toshikatsu; Ueno, Aya; Nishio, Yoshiyuki; Hiraoka, Kotaro; Shimomura, Tatsuo; Iizuka, Osamu; Shinohara, Mayumi; Hirayama, Kazumi; Mori, Etsuro

2011-12-01

We investigated the effects of aging and Alzheimer's disease (AD) on item and associative recognition memory. Three groups of participants (younger adults, elderly adults, and AD patients) studied photographs of common objects that were located on either the left or the right side of a black computer screen inside either a red or a blue square. In a subsequent old/new recognition memory test, the participants were presented with four kinds of stimuli: "intact" stimuli, which were presented as they were during the study phase; "location-altered" stimuli, which were presented in a different location; "color-altered" stimuli, which were presented with a different surrounding color; and "new" stimuli, which consisted of photographs that had not been presented during the study phase. Compared with younger adults, the older adults showed equivalent performance in simple item recognition but worse performance in discriminating location-altered and color-altered stimuli. Compared with older adults, the AD patients showed equivalent performance in discriminating color-altered stimuli but worse performance in simple item recognition and the discrimination of location-altered stimuli. We speculate that distinct structural and functional changes in specific brain regions that are caused by aging and AD are responsible for the different patterns of memory impairment.

Application of neural network and pattern recognition software to the automated analysis of continuous nuclear monitoring of on-load reactors

Energy Technology Data Exchange (ETDEWEB)

Howell, J.A.; Eccleston, G.W.; Halbig, J.K.; Klosterbuer, S.F. [Los Alamos National Lab., NM (United States); Larson, T.W. [California Polytechnic State Univ., San Luis Obispo, CA (US)

1993-08-01

Automated analysis using pattern recognition and neural network software can help interpret data, call attention to potential anomalies, and improve safeguards effectiveness. Automated software analysis, based on pattern recognition and neural networks, was applied to data collected from a radiation core discharge monitor system located adjacent to an on-load reactor core. Unattended radiation sensors continuously collect data to monitor on-line refueling operations in the reactor. The huge volume of data collected from a number of radiation channels makes it difficult for a safeguards inspector to review it all, check for consistency among the measurement channels, and find anomalies. Pattern recognition and neural network software can analyze large volumes of data from continuous, unattended measurements, thereby improving and automating the detection of anomalies. The authors developed a prototype pattern recognition program that determines the reactor power level and identifies the times when fuel bundles are pushed through the core during on-line refueling. Neural network models were also developed to predict fuel bundle burnup to calculate the region on the on-load reactor face from which fuel bundles were discharged based on the radiation signals. In the preliminary data set, which was limited and consisted of four distinct burnup regions, the neural network model correctly predicted the burnup region with an accuracy of 92%.

Joint recognition-expression impairment of facial emotions in Huntington's disease despite intact understanding of feelings.

Science.gov (United States)

Trinkler, Iris; Cleret de Langavant, Laurent; Bachoud-Lévi, Anne-Catherine

2013-02-01

Patients with Huntington's disease (HD), a neurodegenerative disorder that causes major motor impairments, also show cognitive and emotional deficits. While their deficit in recognising emotions has been explored in depth, little is known about their ability to express emotions and understand their feelings. If these faculties were impaired, patients might not only mis-read emotion expressions in others but their own emotions might be mis-interpreted by others as well, or thirdly, they might have difficulties understanding and describing their feelings. We compared the performance of recognition and expression of facial emotions in 13 HD patients with mild motor impairments but without significant bucco-facial abnormalities, and 13 controls matched for age and education. Emotion recognition was investigated in a forced-choice recognition test (FCR), and emotion expression by filming participants while they mimed the six basic emotional facial expressions (anger, disgust, fear, surprise, sadness and joy) to the experimenter. The films were then segmented into 60 stimuli per participant and four external raters performed a FCR on this material. Further, we tested understanding of feelings in self (alexithymia) and others (empathy) using questionnaires. Both recognition and expression were impaired across different emotions in HD compared to controls and recognition and expression scores were correlated. By contrast, alexithymia and empathy scores were very similar in HD and controls. This might suggest that emotion deficits in HD might be tied to the expression itself. Because similar emotion recognition-expression deficits are also found in Parkinson's Disease and vascular lesions of the striatum, our results further confirm the importance of the striatum for emotion recognition and expression, while access to the meaning of feelings relies on a different brain network, and is spared in HD. Copyright © 2011 Elsevier Ltd. All rights reserved.

Devices for Ambulatory Monitoring of Sleep-Associated Disorders in Children with Neurological Diseases.

Science.gov (United States)

Ulate-Campos, Adriana; Tsuboyama, Melissa; Loddenkemper, Tobias

2017-12-25

Good sleep quality is essential for a child's wellbeing. Early sleep problems have been linked to the later development of emotional and behavioral disorders and can negatively impact the quality of life of the child and his or her family. Sleep-associated conditions are frequent in the pediatric population, and even more so in children with neurological problems. Monitoring devices can help to better characterize sleep efficiency and sleep quality. They can also be helpful to better characterize paroxysmal nocturnal events and differentiate between nocturnal seizures, parasomnias, and obstructive sleep apnea, each of which has a different management. Overnight ambulatory detection devices allow for a tolerable, low cost, objective assessment of sleep quality in the patient's natural environment. They can also be used as a notification system to allow for rapid recognition and prompt intervention of events like seizures. Optimal monitoring devices will be patient- and diagnosis-specific, but may include a combination of modalities such as ambulatory electroencephalograms, actigraphy, and pulse oximetry. We will summarize the current literature on ambulatory sleep devices for detecting sleep disorders in children with neurological diseases.

Normal mere exposure effect with impaired recognition in Alzheimer's disease

OpenAIRE

Willems, Sylvie; Adam, Stéphane; Van der Linden, Martial

2002-01-01

We investigated the mere exposure effect and the explicit memory in Alzheimer's disease (AD) patients and elderly control subjects, using unfamiliar faces. During the exposure phase, the subjects estimated the age of briefly flashed faces. The mere exposure effect was examined by presenting pairs of faces (old and new) and asking participants to select the face they liked. The participants were then presented with a forced-choice explicit recognition task. Controls subjects exhibited above-ch...

Emotional face recognition deficits and medication effects in pre-manifest through stage-II Huntington's disease.

Science.gov (United States)

Labuschagne, Izelle; Jones, Rebecca; Callaghan, Jenny; Whitehead, Daisy; Dumas, Eve M; Say, Miranda J; Hart, Ellen P; Justo, Damian; Coleman, Allison; Dar Santos, Rachelle C; Frost, Chris; Craufurd, David; Tabrizi, Sarah J; Stout, Julie C

2013-05-15

Facial emotion recognition impairments have been reported in Huntington's disease (HD). However, the nature of the impairments across the spectrum of HD remains unclear. We report on emotion recognition data from 344 participants comprising premanifest HD (PreHD) and early HD patients, and controls. In a test of recognition of facial emotions, we examined responses to six basic emotional expressions and neutral expressions. In addition, and within the early HD sample, we tested for differences on emotion recognition performance between those 'on' vs. 'off' neuroleptic or selective serotonin reuptake inhibitor (SSRI) medications. The PreHD groups showed significant (precognition, compared to controls, on fearful, angry and surprised faces; whereas the early HD groups were significantly impaired across all emotions including neutral expressions. In early HD, neuroleptic use was associated with worse facial emotion recognition, whereas SSRI use was associated with better facial emotion recognition. The findings suggest that emotion recognition impairments exist across the HD spectrum, but are relatively more widespread in manifest HD than in the premanifest period. Commonly prescribed medications to treat HD-related symptoms also appear to affect emotion recognition. These findings have important implications for interpersonal communication and medication usage in HD. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Information system for diagnosis of respiratory system diseases

Science.gov (United States)

Abramov, G. V.; Korobova, L. A.; Ivashin, A. L.; Matytsina, I. A.

2018-05-01

An information system is for the diagnosis of patients with lung diseases. The main problem solved by this system is the definition of the parameters of cough fragments in the monitoring recordings using a voice recorder. The authors give the recognition criteria of recorded cough moments, audio records analysis. The results of the research are systematized. The cough recognition system can be used by the medical specialists to diagnose the condition of the patients and to monitor the process of their treatment.

Emotion Recognition in Frontotemporal Dementia and Alzheimer's Disease: A New Film-Based Assessment

Science.gov (United States)

Goodkind, Madeleine S.; Sturm, Virginia E.; Ascher, Elizabeth A.; Shdo, Suzanne M.; Miller, Bruce L.; Rankin, Katherine P.; Levenson, Robert W.

2015-01-01

Deficits in recognizing others' emotions are reported in many psychiatric and neurological disorders, including autism, schizophrenia, behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD). Most previous emotion recognition studies have required participants to identify emotional expressions in photographs. This type of assessment differs from real-world emotion recognition in important ways: Images are static rather than dynamic, include only 1 modality of emotional information (i.e., visual information), and are presented absent a social context. Additionally, existing emotion recognition batteries typically include multiple negative emotions, but only 1 positive emotion (i.e., happiness) and no self-conscious emotions (e.g., embarrassment). We present initial results using a new task for assessing emotion recognition that was developed to address these limitations. In this task, respondents view a series of short film clips and are asked to identify the main characters' emotions. The task assesses multiple negative, positive, and self-conscious emotions based on information that is multimodal, dynamic, and socially embedded. We evaluate this approach in a sample of patients with bvFTD, AD, and normal controls. Results indicate that patients with bvFTD have emotion recognition deficits in all 3 categories of emotion compared to the other groups. These deficits were especially pronounced for negative and self-conscious emotions. Emotion recognition in this sample of patients with AD was indistinguishable from controls. These findings underscore the utility of this approach to assessing emotion recognition and suggest that previous findings that recognition of positive emotion was preserved in dementia patients may have resulted from the limited sampling of positive emotion in traditional tests. PMID:26010574

Emotion recognition in frontotemporal dementia and Alzheimer's disease: A new film-based assessment.

Science.gov (United States)

Goodkind, Madeleine S; Sturm, Virginia E; Ascher, Elizabeth A; Shdo, Suzanne M; Miller, Bruce L; Rankin, Katherine P; Levenson, Robert W

2015-08-01

Deficits in recognizing others' emotions are reported in many psychiatric and neurological disorders, including autism, schizophrenia, behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD). Most previous emotion recognition studies have required participants to identify emotional expressions in photographs. This type of assessment differs from real-world emotion recognition in important ways: Images are static rather than dynamic, include only 1 modality of emotional information (i.e., visual information), and are presented absent a social context. Additionally, existing emotion recognition batteries typically include multiple negative emotions, but only 1 positive emotion (i.e., happiness) and no self-conscious emotions (e.g., embarrassment). We present initial results using a new task for assessing emotion recognition that was developed to address these limitations. In this task, respondents view a series of short film clips and are asked to identify the main characters' emotions. The task assesses multiple negative, positive, and self-conscious emotions based on information that is multimodal, dynamic, and socially embedded. We evaluate this approach in a sample of patients with bvFTD, AD, and normal controls. Results indicate that patients with bvFTD have emotion recognition deficits in all 3 categories of emotion compared to the other groups. These deficits were especially pronounced for negative and self-conscious emotions. Emotion recognition in this sample of patients with AD was indistinguishable from controls. These findings underscore the utility of this approach to assessing emotion recognition and suggest that previous findings that recognition of positive emotion was preserved in dementia patients may have resulted from the limited sampling of positive emotion in traditional tests. (c) 2015 APA, all rights reserved).

Emotion recognition impairment and apathy after subthalamic nucleus stimulation in Parkinson's disease have separate neural substrates.

Science.gov (United States)

Drapier, D; Péron, J; Leray, E; Sauleau, P; Biseul, I; Drapier, S; Le Jeune, F; Travers, D; Bourguignon, A; Haegelen, C; Millet, B; Vérin, M

2008-09-01

To test the hypothesis that emotion recognition and apathy share the same functional circuit involving the subthalamic nucleus (STN). A consecutive series of 17 patients with advanced Parkinson's disease (PD) was assessed 3 months before (M-3) and 3 months (M+3) after STN deep brain stimulation (DBS). Mean (+/-S.D.) age at surgery was 56.9 (8.7) years. Mean disease duration at surgery was 11.8 (2.6) years. Apathy was measured using the Apathy Evaluation Scale (AES) at both M-3 and M3. Patients were also assessed using a computerised paradigm of facial emotion recognition [Ekman, P., & Friesen, W. V. (1976). Pictures of facial affect. Palo Alto: Consulting Psychologist Press] before and after STN DBS. Prior to this, the Benton Facial Recognition Test was used to check that the ability to perceive faces was intact. Apathy had significantly worsened at M3 (42.5+/-8.9, p=0.006) after STN-DBS, in relation to the preoperative assessment (37.2+/-5.5). There was also a significant reduction in recognition percentages for facial expressions of fear (43.1%+/-22.9 vs. 61.6%+/-21.4, p=0.022) and sadness (52.7%+/-19.1 vs. 67.6%+/-22.8, p=0.031) after STN DBS. However, the postoperative worsening of apathy and emotion recognition impairment were not correlated. Our results confirm that the STN is involved in both the apathy and emotion recognition networks. However, the absence of any correlation between apathy and emotion recognition impairment suggests that the worsening of apathy following surgery could not be explained by a lack of facial emotion recognition and that its behavioural and cognitive components should therefore also be taken into consideration.

Crack recognition on vertical rotors by means of frequency selective vibration monitoring

International Nuclear Information System (INIS)

Nink, A.; Stoelben, H.

1990-01-01

Shaft cracks on primary coolant pumps in pressurized water reactors have led to intensive vibration monitoring, in particular of vertically arranged rotors. However, the interpretation of shaft vibrations with respect to crack recognition proved to be very difficult. Appropriate experimental approaches resulted in an improved interpretation base. The article describes both the problems related to primary coolant pumps and first experimental experience gained from tests on a pre-cracked vertical rotor. Differential vectors of rotational speed harmonics provide an optimum description of the effect of a crack on shaft vibration. Diagnostics can be supported by observing the vectors, while purposefully changing axial loads. (orig.) [de

Verbal monitoring in Parkinson’s disease: A comparison between internal and external monitoring

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Mertens, Jolien; Mariën, Peter; Santens, Patrick; Pickut, Barbara A.; Hartsuiker, Robert J.

2017-01-01

Patients with Parkinson’s disease (PD) display a variety of impairments in motor and non-motor language processes; speech is decreased on motor aspects such as amplitude, prosody and speed and on linguistic aspects including grammar and fluency. Here we investigated whether verbal monitoring is impaired and what the relative contributions of the internal and external monitoring route are on verbal monitoring in patients with PD relative to controls. Furthermore, the data were used to investigate whether internal monitoring performance could be predicted by internal speech perception tasks, as perception based monitoring theories assume. Performance of 18 patients with Parkinson’s disease was measured on two cognitive performance tasks and a battery of 11 linguistic tasks, including tasks that measured performance on internal and external monitoring. Results were compared with those of 16 age-matched healthy controls. PD patients and controls generally performed similarly on the linguistic and monitoring measures. However, we observed qualitative differences in the effects of noise masking on monitoring and disfluencies and in the extent to which the linguistic tasks predicted monitoring behavior. We suggest that the patients differ from healthy subjects in their recruitment of monitoring channels. PMID:28832595

Design of environmental monitoring system of nuclear facility based on a method of pattern recognition

Energy Technology Data Exchange (ETDEWEB)

Katoh, N; Kiyose, R; Yamamoto, Y [Tokyo Univ. (Japan). Faculty of Engineering

1977-10-01

The problem to optimize the number and locations of environmental radiation monitoring detectors is formulated by taking the specifically defined distance measures as a performance index and solved numerically using heuristic programming such as branch and bound method. An ideal numerical example neglecting noises due to background radiation, shows that the desirable number and locations of detectors are determined mainly by the atmospheric conditions and are not significantly influenced by the variation of the rate and pattern of activity release from the nuclear facility. It is shown also that the appropriate and sufficient number of monitoring detectors to be located around the facility will be from three to six at most, if considered from the viewpoint of pattern recognition.

Neuroanatomical correlates of impaired decision-making and facial emotion recognition in early Parkinson's disease.

Science.gov (United States)

Ibarretxe-Bilbao, Naroa; Junque, Carme; Tolosa, Eduardo; Marti, Maria-Jose; Valldeoriola, Francesc; Bargallo, Nuria; Zarei, Mojtaba

2009-09-01

Decision-making and recognition of emotions are often impaired in patients with Parkinson's disease (PD). The orbitofrontal cortex (OFC) and the amygdala are critical structures subserving these functions. This study was designed to test whether there are any structural changes in these areas that might explain the impairment of decision-making and recognition of facial emotions in early PD. We used the Iowa Gambling Task (IGT) and the Ekman 60 faces test which are sensitive to the integrity of OFC and amygdala dysfunctions in 24 early PD patients and 24 controls. High-resolution structural magnetic resonance images (MRI) were also obtained. Group analysis using voxel-based morphometry (VBM) showed significant and corrected (P decision-making and recognition of facial emotions occurs at the early stages of PD, (ii) these neuropsychological deficits are accompanied by degeneration of OFC and amygdala, and (iii) bilateral OFC reductions are associated with impaired recognition of emotions, and GM volume loss in left lateral OFC is related to decision-making impairment in PD.

Cognitive Factors Affecting Free Recall, Cued Recall, and Recognition Tasks in Alzheimer’s Disease

Directory of Open Access Journals (Sweden)

Takashi Yamagishi

2012-07-01

Full Text Available Background/Aims: Our aim was to identify cognitive factors affecting free recall, cued recall, and recognition tasks in patients with Alzheimer’s disease (AD. Subjects: We recruited 349 consecutive AD patients who attended a memory clinic. Methods: Each patient was assessed using the Alzheimer’s Disease Assessment Scale (ADAS and the extended 3-word recall test. In this task, each patient was asked to freely recall 3 previously presented words. If patients could not recall 1 or more of the target words, the examiner cued their recall by providing the category of the target word and then provided a forced-choice recognition of the target word with 2 distracters. The patients were divided into groups according to the results of the free recall, cued recall, and recognition tasks. Multivariate logistic regression analysis for repeated measures was carried out to evaluate the net effects of cognitive factors on the free recall, cued recall, and recognition tasks after controlling for the effects of age and recent memory deficit. Results: Performance on the ADAS Orientation task was found to be related to performance on the free and cued recall tasks, performance on the ADAS Following Commands task was found to be related to performance on the cued recall task, and performance on the ADAS Ideational Praxis task was found to be related to performance on the free recall, cued recall, and recognition tasks. Conclusion: The extended 3-word recall test reflects deficits in a wider range of memory and other cognitive processes, including memory retention after interference, divided attention, and executive functions, compared with word-list recall tasks. The characteristics of the extended 3-word recall test may be advantageous for evaluating patients’ memory impairments in daily living.

Flexible Piezoelectric Sensor-Based Gait Recognition

Directory of Open Access Journals (Sweden)

Youngsu Cha

2018-02-01

Full Text Available Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

The effects of healthy aging, amnestic mild cognitive impairment, and Alzheimer's disease on recollection, familiarity and false recognition, estimated by an associative process-dissociation recognition procedure.

Science.gov (United States)

Pitarque, Alfonso; Meléndez, Juan C; Sales, Alicia; Mayordomo, Teresa; Satorres, Encar; Escudero, Joaquín; Algarabel, Salvador

2016-10-01

Given the uneven experimental results in the literature regarding whether or not familiarity declines with healthy aging and cognitive impairment, we compare four samples (healthy young people, healthy older people, older people with amnestic mild cognitive impairment - aMCI -, and older people with Alzheimer's disease - AD -) on an associative recognition task, which, following the logic of the process-dissociation procedure, allowed us to obtain corrected estimates of recollection, familiarity and false recognition. The results show that familiarity does not decline with healthy aging, but it does with cognitive impairment, whereas false recognition increases with healthy aging, but declines significantly with cognitive impairment. These results support the idea that the deficits detected in recollection, familiarity, or false recognition in older people could be used as early prodromal markers of cognitive impairment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Word recognition in Alzheimer's disease: Effects of semantic degeneration.

Science.gov (United States)

Cuetos, Fernando; Arce, Noemí; Martínez, Carmen; Ellis, Andrew W

2017-03-01

Impairments of word recognition in Alzheimer's disease (AD) have been less widely investigated than impairments affecting word retrieval and production. In particular, we know little about what makes individual words easier or harder for patients with AD to recognize. We used a lexical selection task in which participants were shown sets of four items, each set consisting of one word and three non-words. The task was simply to point to the word on each trial. Forty patients with mild-to-moderate AD were significantly impaired on this task relative to matched controls who made very few errors. The number of patients with AD able to recognize each word correctly was predicted by the frequency, age of acquisition, and imageability of the words, but not by their length or number of orthographic neighbours. Patient Mini-Mental State Examination and phonological fluency scores also predicted the number of words recognized. We propose that progressive degradation of central semantic representations in AD differentially affects the ability to recognize low-imageability, low-frequency, late-acquired words, with the same factors affecting word recognition as affecting word retrieval. © 2015 The British Psychological Society.

Application of the pattern recognition technique to fast reactor acoustic monitoring

International Nuclear Information System (INIS)

Brunet, M.; Val, M.

1981-10-01

The early detection of operating anomalies is an aim involving the safety of fast reactors. The most likely accident is the complete or partial blocking up of an assembly, one of the signs of which is the boiling of the sodium contained in it. This boiling is accompanied by the production of acoustic waves, the detection of which by appropriate sensors appears to be an efficient way of monitoring the core. For a number of years the CEA has been conducting an experimental programme for studying the detection of boiling by acoustic means. The text presents the various different experiments undertaken and then draws a parallel between the results obtained by conventional processing procedures and those obtained by applying the shape recognition method to the same basic data [fr

Comparing source-based and gist-based false recognition in aging and Alzheimer's disease.

Science.gov (United States)

Pierce, Benton H; Sullivan, Alison L; Schacter, Daniel L; Budson, Andrew E

2005-07-01

This study examined 2 factors contributing to false recognition of semantic associates: errors based on confusion of source and errors based on general similarity information or gist. The authors investigated these errors in patients with Alzheimer's disease (AD), age-matched control participants, and younger adults, focusing on each group's ability to use recollection of source information to suppress false recognition. The authors used a paradigm consisting of both deep and shallow incidental encoding tasks, followed by study of a series of categorized lists in which several typical exemplars were omitted. Results showed that healthy older adults were able to use recollection from the deep processing task to some extent but less than that used by younger adults. In contrast, false recognition in AD patients actually increased following the deep processing task, suggesting that they were unable to use recollection to oppose familiarity arising from incidental presentation. (c) 2005 APA, all rights reserved.

Recognition memory span in autopsy-confirmed Dementia with Lewy Bodies and Alzheimer's Disease.

Science.gov (United States)

Salmon, David P; Heindel, William C; Hamilton, Joanne M; Vincent Filoteo, J; Cidambi, Varun; Hansen, Lawrence A; Masliah, Eliezer; Galasko, Douglas

2015-08-01

Evidence from patients with amnesia suggests that recognition memory span tasks engage both long-term memory (i.e., secondary memory) processes mediated by the diencephalic-medial temporal lobe memory system and working memory processes mediated by fronto-striatal systems. Thus, the recognition memory span task may be particularly effective for detecting memory deficits in disorders that disrupt both memory systems. The presence of unique pathology in fronto-striatal circuits in Dementia with Lewy Bodies (DLB) compared to AD suggests that performance on the recognition memory span task might be differentially affected in the two disorders even though they have quantitatively similar deficits in secondary memory. In the present study, patients with autopsy-confirmed DLB or AD, and Normal Control (NC) participants, were tested on separate recognition memory span tasks that required them to retain increasing amounts of verbal, spatial, or visual object (i.e., faces) information across trials. Results showed that recognition memory spans for verbal and spatial stimuli, but not face stimuli, were lower in patients with DLB than in those with AD, and more impaired relative to NC performance. This was despite similar deficits in the two patient groups on independent measures of secondary memory such as the total number of words recalled from long-term storage on the Buschke Selective Reminding Test. The disproportionate vulnerability of recognition memory span task performance in DLB compared to AD may be due to greater fronto-striatal involvement in DLB and a corresponding decrement in cooperative interaction between working memory and secondary memory processes. Assessment of recognition memory span may contribute to the ability to distinguish between DLB and AD relatively early in the course of disease. Copyright © 2015 Elsevier Ltd. All rights reserved.

Emotion recognition in early Parkinson's disease patients undergoing deep brain stimulation or dopaminergic therapy: a comparison to healthy participants

Directory of Open Access Journals (Sweden)

Lindsey G. McIntosh

2015-01-01

Full Text Available Parkinson’s disease (PD is traditionally regarded as a neurodegenerative movement disorder, however, nigrostriatal dopaminergic degeneration is also thought to disrupt non-motor loops connecting basal ganglia to areas in frontal cortex involved in cognition and emotion processing. PD patients are impaired on tests of emotion recognition, but it is difficult to disentangle this deficit from the more general cognitive dysfunction that frequently accompanies disease progression. Testing for emotion recognition deficits early in the disease course, prior to cognitive decline, better assesses the sensitivity of these non-motor corticobasal ganglia-thalamocortical loops involved in emotion processing to early degenerative change in basal ganglia circuits. In addition, contrasting this with a group of healthy aging individuals demonstrates changes in emotion processing specific to the degeneration of basal ganglia circuitry in PD. Early PD patients (EPD were recruited from a randomized clinical trial testing the safety and tolerability of deep brain stimulation of the subthalamic nucleus (STN-DBS in early-staged PD. EPD patients were previously randomized to receive optimal drug therapy only (ODT, or drug therapy plus STN-DBS (ODT+DBS. Matched healthy elderly controls (HEC and young controls (HYC also participated in this study. Participants completed two control tasks and three emotion recognition tests that varied in stimulus domain. EPD patients were impaired on all emotion recognition tasks compared to HEC. Neither therapy type (ODT or ODT+DBS nor therapy state (ON/OFF altered emotion recognition performance in this study. Finally, HEC were impaired on vocal emotion recognition relative to HYC, suggesting a decline related to healthy aging. This study supports the existence of impaired emotion recognition early in the PD course, implicating an early disruption of fronto-striatal loops mediating emotional function.

Global disease monitoring and forecasting with Wikipedia.

Science.gov (United States)

Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y; Priedhorsky, Reid

2014-11-01

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

Global disease monitoring and forecasting with Wikipedia.

Directory of Open Access Journals (Sweden)

Nicholas Generous

2014-11-01

Full Text Available Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

Long-term odor recognition memory in unipolar major depression and Alzheimer׳s disease.

Science.gov (United States)

Naudin, Marine; Mondon, Karl; El-Hage, Wissam; Desmidt, Thomas; Jaafari, Nematollah; Belzung, Catherine; Gaillard, Philippe; Hommet, Caroline; Atanasova, Boriana

2014-12-30

Major depression and Alzheimer׳s disease (AD) are often observed in the elderly. The identification of specific markers for these diseases could improve their screening. The aim of this study was to investigate long-term odor recognition memory in depressed and AD patients, with a view to identifying olfactory markers of these diseases. We included 20 patients with unipolar major depressive episodes (MDE), 20 patients with mild to moderate AD and 24 healthy subjects. We investigated the cognitive profile and olfactory memory capacities (ability to recognize familiar and unfamiliar odors) of these subjects. Olfactory memory test results showed that AD and depressed patients were characterized by significantly less correct responses and more wrong responses than healthy controls. Detection index did not differ significantly between patients with major depression and those with AD when the results were analyzed for all odors. However, MDE patients displayed an impairment of olfactory memory for both familiar and unfamiliar odors, whereas AD subjects were impaired only in the recognition of unfamiliar odors, with respect to healthy subjects. If preservation of olfactory memory for familiar stimuli in patients with mild to moderate AD is confirmed, this test could be used in clinical practice as a complementary tool for diagnosis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition

OpenAIRE

Fuentes, Alvaro; Yoon, Sook; Kim, Sang Cheol; Park, Dong Sun

2017-01-01

Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using ...

Evaluating Recall and Recognition Memory Using the Montreal Cognitive Assessment: Applicability for Alzheimer's and Huntington's Diseases.

Science.gov (United States)

Van Liew, Charles; Santoro, Maya S; Goldstein, Jody; Gluhm, Shea; Gilbert, Paul E; Corey-Bloom, Jody

2016-12-01

We sought to investigate whether the Montreal Cognitive Assessment (MoCA) could provide a brief assessment of recall and recognition using Huntington disease (HD) and Alzheimer disease (AD) as disorders characterized by different memory deficits. This study included 80 participants with HD, 64 participants with AD, and 183 community-dwelling control participants. Random-effects hierarchical logistic regressions were performed to assess the relative performance of the normal control (NC), participants with HD, and participants with AD on verbal free recall, cued recall, and multiple-choice recognition on the MoCA. The NC participants performed significantly better than participants with AD at all the 3 levels of assessment. No difference existed between participants with HD and NC for cued recall, but NC participants performed significantly better than participants with HD on free recall and recognition. The participants with HD performed significantly better than participants with AD at all the 3 levels of assessment. The MoCA appears to be a valuable, brief cognitive assessment capable of identifying specific memory deficits consistent with known differences in memory profiles. © The Author(s) 2016.

Scanning patterns of faces do not explain impaired emotion recognition in Huntington Disease: Evidence for a high level mechanism

Directory of Open Access Journals (Sweden)

Marieke evan Asselen

2012-02-01

Full Text Available Previous studies in patients with amygdala lesions suggested that deficits in emotion recognition might be mediated by impaired scanning patterns of faces. Here we investigated whether scanning patterns also contribute to the selective impairment in recognition of disgust in Huntington disease (HD. To achieve this goal, we recorded eye movements during a two-alternative forced choice emotion recognition task. HD patients in presymptomatic (n=16 and symptomatic (n=9 disease stages were tested and their performance was compared to a control group (n=22. In our emotion recognition task, participants had to indicate whether a face reflected one of six basic emotions. In addition, and in order to define whether emotion recognition was altered when the participants were forced to look at a specific component of the face, we used a second task where only limited facial information was provided (eyes/mouth in partially masked faces. Behavioural results showed no differences in the ability to recognize emotions between presymptomatic gene carriers and controls. However, an emotion recognition deficit was found for all 6 basic emotion categories in early stage HD. Analysis of eye movement patterns showed that patient and controls used similar scanning strategies. Patterns of deficits were similar regardless of whether parts of the faces were masked or not, thereby confirming that selective attention to particular face parts is not underlying the deficits. These results suggest that the emotion recognition deficits in symptomatic HD patients cannot be explained by impaired scanning patterns of faces. Furthermore, no selective deficit for recognition of disgust was found in presymptomatic HD patients.

Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review.

Science.gov (United States)

Block, Valerie A J; Pitsch, Erica; Tahir, Peggy; Cree, Bruce A C; Allen, Diane D; Gelfand, Jeffrey M

2016-01-01

To perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps. Studies were systematically identified in PubMed/MEDLINE, CINAHL and SCOPUS from January 2004 to December 2014 that monitored physical activity for ≥24 hours in adults with neurological diseases. Studies that measured only involuntary motor activity (tremor, seizures), energy expenditure or sleep were excluded. Feasibility, findings, and protocols were examined. 137 studies met inclusion criteria in multiple sclerosis (MS) (61 studies); stroke (41); Parkinson's Disease (PD) (20); dementia (11); traumatic brain injury (2) and ataxia (1). Physical activity levels measured by remote monitoring are consistently low in people with MS, stroke and dementia, and patterns of physical activity are altered in PD. In MS, decreased ambulatory activity assessed via remote monitoring is associated with greater disability and lower quality of life. In stroke, remote measures of upper limb function and ambulation are associated with functional recovery following rehabilitation and goal-directed interventions. In PD, remote monitoring may help to predict falls. In dementia, remote physical activity measures correlate with disease severity and can detect wandering. These studies show that remote physical activity monitoring is feasible in neurological diseases, including in people with moderate to severe neurological disability. Remote monitoring can be a psychometrically sound and responsive way to assess physical activity in neurological disease. Further research is needed to ensure these tools provide meaningful information in the context of specific neurological disorders and patterns of neurological disability.

Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review

Science.gov (United States)

Block, Valerie A. J.; Pitsch, Erica; Tahir, Peggy; Cree, Bruce A. C.; Allen, Diane D.; Gelfand, Jeffrey M.

2016-01-01

Objective To perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps. Methods Studies were systematically identified in PubMed/MEDLINE, CINAHL and SCOPUS from January 2004 to December 2014 that monitored physical activity for ≥24 hours in adults with neurological diseases. Studies that measured only involuntary motor activity (tremor, seizures), energy expenditure or sleep were excluded. Feasibility, findings, and protocols were examined. Results 137 studies met inclusion criteria in multiple sclerosis (MS) (61 studies); stroke (41); Parkinson's Disease (PD) (20); dementia (11); traumatic brain injury (2) and ataxia (1). Physical activity levels measured by remote monitoring are consistently low in people with MS, stroke and dementia, and patterns of physical activity are altered in PD. In MS, decreased ambulatory activity assessed via remote monitoring is associated with greater disability and lower quality of life. In stroke, remote measures of upper limb function and ambulation are associated with functional recovery following rehabilitation and goal-directed interventions. In PD, remote monitoring may help to predict falls. In dementia, remote physical activity measures correlate with disease severity and can detect wandering. Conclusions These studies show that remote physical activity monitoring is feasible in neurological diseases, including in people with moderate to severe neurological disability. Remote monitoring can be a psychometrically sound and responsive way to assess physical activity in neurological disease. Further research is needed to ensure these tools provide meaningful information in the context of specific neurological disorders and patterns of neurological disability. PMID:27124611

NCBI disease corpus: a resource for disease name recognition and concept normalization.

Science.gov (United States)

Doğan, Rezarta Islamaj; Leaman, Robert; Lu, Zhiyong

2014-02-01

knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/. Published by Elsevier Inc.

Multitasking During Degraded Speech Recognition in School-Age Children.

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Grieco-Calub, Tina M; Ward, Kristina M; Brehm, Laurel

2017-01-01

Multitasking requires individuals to allocate their cognitive resources across different tasks. The purpose of the current study was to assess school-age children's multitasking abilities during degraded speech recognition. Children (8 to 12 years old) completed a dual-task paradigm including a sentence recognition (primary) task containing speech that was either unprocessed or noise-band vocoded with 8, 6, or 4 spectral channels and a visual monitoring (secondary) task. Children's accuracy and reaction time on the visual monitoring task was quantified during the dual-task paradigm in each condition of the primary task and compared with single-task performance. Children experienced dual-task costs in the 6- and 4-channel conditions of the primary speech recognition task with decreased accuracy on the visual monitoring task relative to baseline performance. In all conditions, children's dual-task performance on the visual monitoring task was strongly predicted by their single-task (baseline) performance on the task. Results suggest that children's proficiency with the secondary task contributes to the magnitude of dual-task costs while multitasking during degraded speech recognition.

Cognitive Factors Affecting Free Recall, Cued Recall, and Recognition Tasks in Alzheimer’s Disease

OpenAIRE

Takashi Yamagishi; Takuya Sato; Atsushi Sato; Toru Imamura

2012-01-01

Background/Aims: Our aim was to identify cognitive factors affecting free recall, cued recall, and recognition tasks in patients with Alzheimer’s disease (AD). Subjects: We recruited 349 consecutive AD patients who attended a memory clinic. Methods: Each patient was assessed using the Alzheimer’s Disease Assessment Scale (ADAS) and the extended 3-word recall test. In this task, each patient was asked to freely recall 3 previously presented words. If patients could not recall 1 or more of the ...

Facial Emotion Recognition Impairment in Patients with Parkinson's Disease and Isolated Apathy

Directory of Open Access Journals (Sweden)

Mercè Martínez-Corral

2010-01-01

Full Text Available Apathy is a frequent feature of Parkinson's disease (PD, usually related with executive dysfunction. However, in a subgroup of PD patients apathy may represent the only or predominant neuropsychiatric feature. To understand the mechanisms underlying apathy in PD, we investigated emotional processing in PD patients with and without apathy and in healthy controls (HC, assessed by a facial emotion recognition task (FERT. We excluded PD patients with cognitive impairment, depression, other affective disturbances and previous surgery for PD. PD patients with apathy scored significantly worse in the FERT, performing worse in fear, anger, and sadness recognition. No differences, however, were found between nonapathetic PD patients and HC. These findings suggest the existence of a disruption of emotional-affective processing in cognitive preserved PD patients with apathy. To identify specific dysfunction of limbic structures in PD, patients with isolated apathy may have therapeutic and prognostic implications.

Yes/No Versus Forced-Choice Recognition Memory in Mild Cognitive Impairment and Alzheimer’s Disease: Patterns of Impairment and Associations with Dementia Severity

Science.gov (United States)

Clark, Lindsay R.; Stricker, Nikki H.; Libon, David J.; Delano-Wood, Lisa; Salmon, David P.; Delis, Dean C.; Bondi, Mark W.

2012-01-01

Memory tests are sensitive to early identification of Alzheimer’s disease (AD) but less useful as the disease advances. However, assessing particular types of recognition memory may better characterize dementia severity in later stages of AD. We sought to examine patterns of recognition memory deficits in individuals with AD and mild cognitive impairment (MCI). Memory performance and global cognition data were collected from participants with AD (n=37), MCI (n=37), and cognitively intact older adults (normal controls, NC; n=35). One-way analyses of variance (ANOVAs) examined differences between groups on yes/no and forced-choice recognition measures. Individuals with amnestic MCI performed worse than NC and nonamnestic MCI participants on yes/no recognition, but were comparable on forced-choice recognition. AD patients were more impaired across yes/no and forced-choice recognition tasks. Individuals with mild AD (≥120 Dementia Rating Scale, DRS) performed better than those with moderate-to-severe AD (recognition, but were equally impaired on yes/no recognition. There were differences in the relationships between learning, recall, and recognition performance across groups. Although yes/no recognition testing may be sensitive to MCI, forced-choice procedures may provide utility in assessing severity of anterograde amnesia in later stages of AD. Implications for assessment of insufficient effort and malingering are also discussed. PMID:23030301

Meta-Analysis of Facial Emotion Recognition in Behavioral Variant Frontotemporal Dementia: Comparison With Alzheimer Disease and Healthy Controls.

Science.gov (United States)

Bora, Emre; Velakoulis, Dennis; Walterfang, Mark

2016-07-01

Behavioral disturbances and lack of empathy are distinctive clinical features of behavioral variant frontotemporal dementia (bvFTD) in comparison to Alzheimer disease (AD). The aim of this meta-analytic review was to compare facial emotion recognition performances of bvFTD with healthy controls and AD. The current meta-analysis included a total of 19 studies and involved comparisons of 288 individuals with bvFTD and 329 healthy controls and 162 bvFTD and 147 patients with AD. Facial emotion recognition was significantly impaired in bvFTD in comparison to the healthy controls (d = 1.81) and AD (d = 1.23). In bvFTD, recognition of negative emotions, especially anger (d = 1.48) and disgust (d = 1.41), were severely impaired. Emotion recognition was significantly impaired in bvFTD in comparison to AD in all emotions other than happiness. Impairment of emotion recognition is a relatively specific feature of bvFTD. Routine assessment of social-cognitive abilities including emotion recognition can be helpful in better differentiating between cortical dementias such as bvFTD and AD. © The Author(s) 2016.

Monitoring caustic injuries from emergency department databases using automatic keyword recognition software.

Science.gov (United States)

Vignally, P; Fondi, G; Taggi, F; Pitidis, A

2011-03-31

In Italy the European Union Injury Database reports the involvement of chemical products in 0.9% of home and leisure accidents. The Emergency Department registry on domestic accidents in Italy and the Poison Control Centres record that 90% of cases of exposure to toxic substances occur in the home. It is not rare for the effects of chemical agents to be observed in hospitals, with a high potential risk of damage - the rate of this cause of hospital admission is double the domestic injury average. The aim of this study was to monitor the effects of injuries caused by caustic agents in Italy using automatic free-text recognition in Emergency Department medical databases. We created a Stata software program to automatically identify caustic or corrosive injury cases using an agent-specific list of keywords. We focused attention on the procedure's sensitivity and specificity. Ten hospitals in six regions of Italy participated in the study. The program identified 112 cases of injury by caustic or corrosive agents. Checking the cases by quality controls (based on manual reading of ED reports), we assessed 99 cases as true positive, i.e. 88.4% of the patients were automatically recognized by the software as being affected by caustic substances (99% CI: 80.6%- 96.2%), that is to say 0.59% (99% CI: 0.45%-0.76%) of the whole sample of home injuries, a value almost three times as high as that expected (p < 0.0001) from European codified information. False positives were 11.6% of the recognized cases (99% CI: 5.1%- 21.5%). Our automatic procedure for caustic agent identification proved to have excellent product recognition capacity with an acceptable level of excess sensitivity. Contrary to our a priori hypothesis, the automatic recognition system provided a level of identification of agents possessing caustic effects that was significantly much greater than was predictable on the basis of the values from current codifications reported in the European Database.

Using Recall to Reduce False Recognition: Diagnostic and Disqualifying Monitoring

Science.gov (United States)

Gallo, David A.

2004-01-01

Whether recall of studied words (e.g., parsley, rosemary, thyme) could reduce false recognition of related lures (e.g., basil) was investigated. Subjects studied words from several categories for a final recognition memory test. Half of the subjects were given standard test instructions, and half were instructed to use recall to reduce false…

Altered Kinematics of Facial Emotion Expression and Emotion Recognition Deficits Are Unrelated in Parkinson's Disease.

Science.gov (United States)

Bologna, Matteo; Berardelli, Isabella; Paparella, Giulia; Marsili, Luca; Ricciardi, Lucia; Fabbrini, Giovanni; Berardelli, Alfredo

2016-01-01

Altered emotional processing, including reduced emotion facial expression and defective emotion recognition, has been reported in patients with Parkinson's disease (PD). However, few studies have objectively investigated facial expression abnormalities in PD using neurophysiological techniques. It is not known whether altered facial expression and recognition in PD are related. To investigate possible deficits in facial emotion expression and emotion recognition and their relationship, if any, in patients with PD. Eighteen patients with PD and 16 healthy controls were enrolled in this study. Facial expressions of emotion were recorded using a 3D optoelectronic system and analyzed using the facial action coding system. Possible deficits in emotion recognition were assessed using the Ekman test. Participants were assessed in one experimental session. Possible relationship between the kinematic variables of facial emotion expression, the Ekman test scores, and clinical and demographic data in patients were evaluated using the Spearman's test and multiple regression analysis. The facial expression of all six basic emotions had slower velocity and lower amplitude in patients in comparison to healthy controls (all P s facial expression kinematics and emotion recognition deficits were unrelated in patients (all P s > 0.05). Finally, no relationship emerged between kinematic variables of facial emotion expression, the Ekman test scores, and clinical and demographic data in patients (all P s > 0.05). The results in this study provide further evidence of altered emotional processing in PD. The lack of any correlation between altered facial emotion expression kinematics and emotion recognition deficits in patients suggests that these abnormalities are mediated by separate pathophysiological mechanisms.

[Wireless device for monitoring the patients with chronic disease].

Science.gov (United States)

Ciorap, R; Zaharia, D; Corciovă, C; Ungureanu, Monica; Lupu, R; Stan, A

2008-01-01

Remote monitoring of chronic diseases can improve health outcomes and potentially lower health care costs. The high number of the patients, suffering of chronically diseases, who wish to stay at home rather then in a hospital increasing the need of homecare monitoring and have lead to a high demand of wearable medical devices. Also, extended patient monitoring during normal activity has become a very important target. In this paper are presented the design of the wireless monitoring devices based on ultra low power circuits, high storage memory flash, bluetooth communication and the firmware for the management of the monitoring device. The monitoring device is built using an ultra low power microcontroller (MSP430 from Texas Instruments) that offers the advantage of high integration of some circuits. The custom made electronic boards used for biosignal acquisition are also included modules for storage device (SD/MMC card) with FAT32 file system and Bluetooth device for short-range communication used for data transmission between monitoring device and PC or PDA. The work was focused on design and implementation of an ultra low power wearable device able to acquire patient vital parameters, causing minimal discomfort and allowing high mobility. The proposed wireless device could be used as a warning system for monitoring during normal activity.

Emerging Animal Parasitic Diseases: A Global Overview and Appropriate Strategies for their Monitoring and Surveillance in Nigeria.

Science.gov (United States)

Atehmengo, Ngongeh L; Nnagbo, Chiejina S

2014-01-01

Emerging animal parasitic diseases are reviewed and appropriate strategies for efficient monitoring and surveillance in Nigeria are outlined. Animal and human parasitic infections are distinguished. Emerging diseases have been described as those diseases that are being recognised for the first time or diseases that are already recorded but their frequency and/or geographic range is being increased tremendously. Emergence of new diseases may be due to a number of factors such as the spread of a new infectious agent, recognition of an infection that has been in existence but undiagnosed, or when it is realised that an established disease has an infectious origin. The terms could also be used to describe the resurgence of a known infection after its incidence had been known to have declined. Emerging infections are compounding the control of infectious diseases and huge resources are being channeled to alleviate the rising challenge. The diseases are numerous and include helminth, protozoal / rickettsial and entomological. A list of parasitic emerging diseases in Nigeria is included. Globally occurring emerging parasitic diseases are also outlined. Emerging and re-emerging infections can be brought about by many factors including climate change and global warming, changes in biodiversity, population mobility, movement of animals, globalisation of commerce/trade and food supply, social and cultural factors such as food eating habits, religious beliefs, farming practices, trade of infected healthy animals, reduction in the available land for animals, immune-suppressed host and host density and misuse or over use of some drugs leading to drug resistance.

Emerging Animal Parasitic Diseases: A Global Overview and Appropriate Strategies for their Monitoring and Surveillance in Nigeria

Science.gov (United States)

Atehmengo, Ngongeh L; Nnagbo, Chiejina S

2014-01-01

Emerging animal parasitic diseases are reviewed and appropriate strategies for efficient monitoring and surveillance in Nigeria are outlined. Animal and human parasitic infections are distinguished. Emerging diseases have been described as those diseases that are being recognised for the first time or diseases that are already recorded but their frequency and/or geographic range is being increased tremendously. Emergence of new diseases may be due to a number of factors such as the spread of a new infectious agent, recognition of an infection that has been in existence but undiagnosed, or when it is realised that an established disease has an infectious origin. The terms could also be used to describe the resurgence of a known infection after its incidence had been known to have declined. Emerging infections are compounding the control of infectious diseases and huge resources are being channeled to alleviate the rising challenge. The diseases are numerous and include helminth, protozoal / rickettsial and entomological. A list of parasitic emerging diseases in Nigeria is included. Globally occurring emerging parasitic diseases are also outlined. Emerging and re-emerging infections can be brought about by many factors including climate change and global warming, changes in biodiversity, population mobility, movement of animals, globalisation of commerce/trade and food supply, social and cultural factors such as food eating habits, religious beliefs, farming practices, trade of infected healthy animals, reduction in the available land for animals, immune-suppressed host and host density and misuse or over use of some drugs leading to drug resistance. PMID:25328553

Recognition and Clinical Presentation of Invasive Fungal Disease in Neonates and Children.

Science.gov (United States)

King, Jill; Pana, Zoi-Dorothea; Lehrnbecher, Thomas; Steinbach, William J; Warris, Adilia

2017-09-01

Invasive fungal diseases (IFDs) are devastating opportunistic infections that result in significant morbidity and death in a broad range of pediatric patients, particularly those with a compromised immune system. Recognizing them can be difficult, because nonspecific clinical signs and symptoms or isolated fever are frequently the only presenting features. Therefore, a high index of clinical suspicion is necessary in patients at increased risk of IFD, which requires knowledge of the pediatric patient population at risk, additional predisposing factors within this population, and the clinical signs and symptoms of IFD. With this review, we aim to summarize current knowledge regarding the recognition and clinical presentation of IFD in neonates and children. © The Author 2017. Published by Oxford University Press on behalf of The Journal of the Pediatric Infectious Diseases Society.

Assessment of disease named entity recognition on a corpus of annotated sentences.

Science.gov (United States)

Jimeno, Antonio; Jimenez-Ruiz, Ernesto; Lee, Vivian; Gaudan, Sylvain; Berlanga, Rafael; Rebholz-Schuhmann, Dietrich

2008-04-11

In recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns suffers from low recall (e.g., Whatizit) other solutions make use of morpho-syntactic features to better cover the full scope of terminological variability (e.g., MetaMap). Currently, MetaMap that is provided from the National Library of Medicine (NLM) is the state of the art solution for the annotation of concepts from UMLS (Unified Medical Language System) in the literature. Nonetheless, its performance has not yet been assessed on an annotated corpus. In addition, little effort has been invested so far to generate an annotated dataset that links disease entities in text to disease entries in a database, thesaurus or ontology and that could serve as a gold standard to benchmark text mining solutions. As part of our research work, we have taken a corpus that has been delivered in the past for the identification of associations of genes to diseases based on the UMLS Metathesaurus and we have reprocessed and re-annotated the corpus. We have gathered annotations for disease entities from two curators, analyzed their disagreement (0.51 in the kappa-statistic) and composed a single annotated corpus for public use. Thereafter, three solutions for disease named entity recognition including MetaMap have been applied to the corpus to automatically annotate it with UMLS Metathesaurus concepts. The resulting annotations have been benchmarked to compare their performance. The annotated corpus is publicly available at ftp://ftp.ebi.ac.uk/pub/software/textmining/corpora/diseases and can serve as a benchmark to other systems. In addition, we found

Internet-based surveillance systems for monitoring emerging infectious diseases.

Science.gov (United States)

Milinovich, Gabriel J; Williams, Gail M; Clements, Archie C A; Hu, Wenbiao

2014-02-01

Emerging infectious diseases present a complex challenge to public health officials and governments; these challenges have been compounded by rapidly shifting patterns of human behaviour and globalisation. The increase in emerging infectious diseases has led to calls for new technologies and approaches for detection, tracking, reporting, and response. Internet-based surveillance systems offer a novel and developing means of monitoring conditions of public health concern, including emerging infectious diseases. We review studies that have exploited internet use and search trends to monitor two such diseases: influenza and dengue. Internet-based surveillance systems have good congruence with traditional surveillance approaches. Additionally, internet-based approaches are logistically and economically appealing. However, they do not have the capacity to replace traditional surveillance systems; they should not be viewed as an alternative, but rather an extension. Future research should focus on using data generated through internet-based surveillance and response systems to bolster the capacity of traditional surveillance systems for emerging infectious diseases. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Performance Assessment of the Detector for the Portable Environmental Radiation Distribution Monitoring System with Rapid Nuclide Recognition

International Nuclear Information System (INIS)

Lee, Uk Jae; Kim, Hee Reyoung

2015-01-01

The environment radiation distribution monitoring system measures the radiation using a portable detector and display the overall radiation distribution. Bluetooth and RS-232 communications are used for constructing monitoring system. However RS-232 serial communication is known to be more stable than Bluetooth and also it can use the detector's raw data which will be used for getting the activity of each artificial nuclide. In the present study, the detection and communication performance of the developed detector with RS-232 method is assessed by using standard sources for the real application to the urban or rural environment. Assessment of the detector for the portable environmental radiation distribution monitoring system with rapid nuclide recognition was carried out. It was understood that the raw data of detector could be effectively treated by using RS-232 method and the measurement showed a good agreement with the calculation within the relative error of 0.4 % in maximum

The Performance Assessment of the Detector for the Portable Environmental Radiation Distribution Monitoring System with Rapid Nuclide Recognition

Energy Technology Data Exchange (ETDEWEB)

Lee, Uk Jae; Kim, Hee Reyoung [Ulsan National Institute of Science and Technology, Ulsan (Korea, Republic of)

2015-05-15

The environment radiation distribution monitoring system measures the radiation using a portable detector and display the overall radiation distribution. Bluetooth and RS-232 communications are used for constructing monitoring system. However RS-232 serial communication is known to be more stable than Bluetooth and also it can use the detector's raw data which will be used for getting the activity of each artificial nuclide. In the present study, the detection and communication performance of the developed detector with RS-232 method is assessed by using standard sources for the real application to the urban or rural environment. Assessment of the detector for the portable environmental radiation distribution monitoring system with rapid nuclide recognition was carried out. It was understood that the raw data of detector could be effectively treated by using RS-232 method and the measurement showed a good agreement with the calculation within the relative error of 0.4 % in maximum.

False recognition in behavioural variant frontotemporal dementia and Alzheimer’s disease – disinhibition or amnesia?

Directory of Open Access Journals (Sweden)

Emma C Flanagan

2016-07-01

Full Text Available Episodic memory recall processes in Alzheimer’s disease (AD and behavioural variant frontotemporal dementia (bvFTD can be similarly impaired, whereas recognition performance is more variable. A potential reason for this variability could be false-positive errors made on recognition trials and whether these errors are due to amnesia per se or a general over-endorsement of recognition items regardless of memory. The current study addressed this issue by analysing recognition performance on the Rey Auditory Verbal Learning Test (RAVLT in 39 bvFTD, 77 AD and 61 control participants from two centres (India, Australia, as well as disinhibition assessed using the Hayling test. Whereas both AD and bvFTD patients were comparably impaired on delayed recall, bvFTD patients showed intact recognition performance in terms of the number of correct hits. However, both patient groups endorsed significantly more false-positives than controls, and bvFTD and AD patients scored equally poorly on a sensitivity index (correct hits - false-positives. Furthermore, measures of disinhibition were significantly associated with false positives in both groups, with a stronger relationship with false-positives in bvFTD. Voxel-based morphometry analyses revealed similar neural correlates of false positive endorsement across bvFTD and AD, with both patient groups showing involvement of prefrontal and Papez circuitry regions, such as medial temporal and thalamic regions, and a DTI analysis detected an emerging but non-significant trend between false positives and decreased fornix integrity in bvFTD only. These findings suggest that false-positive errors on recognition tests relate to similar mechanisms in bvFTD and AD, reflecting deficits in episodic memory processes and disinhibition. These findings highlight that current memory tests are not sufficient to accurately distinguish between bvFTD and AD patients.

Through your eyes or mine? The neural correlates of mental state recognition in Huntington's disease.

Science.gov (United States)

Eddy, Clare M; Rickards, Hugh E; Hansen, Peter C

2018-03-01

Huntington's disease (HD) can impair social cognition. This study investigated whether patients with HD exhibit neural differences to healthy controls when they are considering mental and physical states relating to the static expressions of human eyes. Thirty-two patients with HD and 28 age-matched controls were scanned with fMRI during two versions of the Reading the Mind in the Eyes Task: The standard version requiring mental state judgments, and a comparison version requiring judgments about age. HD was associated with behavioral deficits on only the mental state eyes task. Contrasting the two versions of the eyes task (mental state > age judgment) revealed hypoactivation within left middle frontal gyrus and supramarginal gyrus in HD. Subgroup analyses comparing premanifest HD patients to age-matched controls revealed reduced activity in right supramarginal gyrus and increased activity in anterior cingulate during mental state recognition in these patients, while manifest HD was associated with hypoactivity in left insula and left supramarginal gyrus. When controlling for the effects of healthy aging, manifest patients exhibited declining activation within areas including right temporal pole. Our findings provide compelling evidence for a selective impairment of internal emotional status when patients with HD appraise facial features in order to make social judgements. Differential activity in temporal and anterior cingulate cortices may suggest that poor emotion regulation and emotional egocentricity underlie impaired mental state recognition in premanifest patients, while more extensive mental state recognition impairments in manifest disease reflect dysfunction in neural substrates underlying executive functions, and the experience and interpretation of emotion. © 2017 Wiley Periodicals, Inc.

Did depressive symptoms affect recognition of emotional prosody in Parkinson’s disease?

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Adriana Vélez Feijó

2008-06-01

Full Text Available Adriana Vélez Feijó1, Carlos RM Rieder3, Márcia LF Chaves21Medical Sciences Post-Graduate Course; 2Internal Medicine Department, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; 3Movement Disorders Clinic Coordinator, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, BrazilObjective: Evaluate the influence of depressive symptoms on the recognition of emotional prosody in Parkinson’s disease (PD patients, and identify types of emotion on spoken sentences.Methods: Thirty-five PD patients and 65 normal participants were studied. Dementia was checked with the Mini Mental State Examination, Clinical Dementia Rating scale, and DSM IV. Recognition of emotional prosody was tested by asking subjects to listen to 12 recorded statements with neutral affective content that were read with a strong affective expression. Subjects had to recognize the correct emotion by one of four descriptors (angry, sad, cheerful, and neutral. The Beck Depression Inventory (BDI was employed to rate depressive symptoms with the cutoff 14.Results: Total ratings of emotions correctly recognized by participants below and above the BDI cutoff were similar among PD patients and normal individuals. PD patients who correctly identified neutral and anger inflections presented higher rates of depressive symptoms (p = 0.011 and 0.044, respectively. No significant differences were observed in the normal group.Conclusions: Depression may modify some modalities of emotional prosody perception in PD, by increasing the perception of non-pleasant emotions or lack of affection, such as anger or indifference.Keywords: emotional prosody, Parkinson’s disease, depression, emotion

Image Analysis-Based Food Recognition and Volume Estimation for Diet Monitoring

OpenAIRE

Hassannejadh, Hamid

2017-01-01

Food intake and eating habits have a significant impact on people's health. Widespread diseases, such as diabetes and obesity, are directly related to eating habits. Therefore, monitoring diet can be an effective way of promoting the adoption of a healthy lifestyle and of improving personal and national health economy. Studies have demonstrated that manual reporting of food intake is inaccurate and often impractical. Thus, several methods have been proposed to automate the process. This thesi...

Does Facial Amimia Impact the Recognition of Facial Emotions? An EMG Study in Parkinson’s Disease

Science.gov (United States)

Argaud, Soizic; Delplanque, Sylvain; Houvenaghel, Jean-François; Auffret, Manon; Duprez, Joan; Vérin, Marc; Grandjean, Didier; Sauleau, Paul

2016-01-01

According to embodied simulation theory, understanding other people’s emotions is fostered by facial mimicry. However, studies assessing the effect of facial mimicry on the recognition of emotion are still controversial. In Parkinson’s disease (PD), one of the most distinctive clinical features is facial amimia, a reduction in facial expressiveness, but patients also show emotional disturbances. The present study used the pathological model of PD to examine the role of facial mimicry on emotion recognition by investigating EMG responses in PD patients during a facial emotion recognition task (anger, joy, neutral). Our results evidenced a significant decrease in facial mimicry for joy in PD, essentially linked to the absence of reaction of the zygomaticus major and the orbicularis oculi muscles in response to happy avatars, whereas facial mimicry for expressions of anger was relatively preserved. We also confirmed that PD patients were less accurate in recognizing positive and neutral facial expressions and highlighted a beneficial effect of facial mimicry on the recognition of emotion. We thus provide additional arguments for embodied simulation theory suggesting that facial mimicry is a potential lever for therapeutic actions in PD even if it seems not to be necessarily required in recognizing emotion as such. PMID:27467393

Therapeutic Drug Monitoring in Rheumatic Diseases

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NG Hoi-Yan Alexandra

2016-12-01

Full Text Available The ultimate goal of treating rheumatic disease is to achieve rapid suppression of inflammation, while at the same time minimizing the toxicities from rheumatic drugs. Different patients have different individual pharmacokinetics that can affect the drug level. Moreover, different factors, such as renal function, age or even different underlying diseases, can affect the drug level. Therefore, giving the same dosage of drugs to different patients may result in different drug levels. This article will review the usefulness of therapeutic drug monitoring in maximizing drug efficacy, while reducing the risk of toxicities in Hydroxychloroquine, Mycophenolate Mofetil, Tacrolimus and Tumor Necrosis Factor inhibitors (TNF Inhibitors.

Human action recognition with depth cameras

CERN Document Server

Wang, Jiang; Wu, Ying

2014-01-01

Action recognition technology has many real-world applications in human-computer interaction, surveillance, video retrieval, retirement home monitoring, and robotics. The commoditization of depth sensors has also opened up further applications that were not feasible before. This text focuses on feature representation and machine learning algorithms for action recognition from depth sensors. After presenting a comprehensive overview of the state of the art, the authors then provide in-depth descriptions of their recently developed feature representations and machine learning techniques, includi

Automatic anatomy recognition on CT images with pathology

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Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

2016-03-01

Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

Dynamic generalized linear models for monitoring endemic diseases

DEFF Research Database (Denmark)

Lopes Antunes, Ana Carolina; Jensen, Dan; Hisham Beshara Halasa, Tariq

2016-01-01

The objective was to use a Dynamic Generalized Linear Model (DGLM) based on abinomial distribution with a linear trend, for monitoring the PRRS (Porcine Reproductive and Respiratory Syndrome sero-prevalence in Danish swine herds. The DGLM was described and its performance for monitoring control...... and eradication programmes based on changes in PRRS sero-prevalence was explored. Results showed a declining trend in PRRS sero-prevalence between 2007 and 2014 suggesting that Danish herds are slowly eradicating PRRS. The simulation study demonstrated the flexibility of DGLMs in adapting to changes intrends...... in sero-prevalence. Based on this, it was possible to detect variations in the growth model component. This study is a proof-of-concept, demonstrating the use of DGLMs for monitoring endemic diseases. In addition, the principles stated might be useful in general research on monitoring and surveillance...

Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor

OpenAIRE

CHIKH, Mohamed Amine; SAIDI, Meryem; SETTOUTI, Nesma

2012-01-01

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disea...

Facial Emotion Recognition and Expression in Parkinson's Disease: An Emotional Mirror Mechanism?

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Ricciardi, Lucia; Visco-Comandini, Federica; Erro, Roberto; Morgante, Francesca; Bologna, Matteo; Fasano, Alfonso; Ricciardi, Diego; Edwards, Mark J; Kilner, James

2017-01-01

Parkinson's disease (PD) patients have impairment of facial expressivity (hypomimia) and difficulties in interpreting the emotional facial expressions produced by others, especially for aversive emotions. We aimed to evaluate the ability to produce facial emotional expressions and to recognize facial emotional expressions produced by others in a group of PD patients and a group of healthy participants in order to explore the relationship between these two abilities and any differences between the two groups of participants. Twenty non-demented, non-depressed PD patients and twenty healthy participants (HC) matched for demographic characteristics were studied. The ability of recognizing emotional facial expressions was assessed with the Ekman 60-faces test (Emotion recognition task). Participants were video-recorded while posing facial expressions of 6 primary emotions (happiness, sadness, surprise, disgust, fear and anger). The most expressive pictures for each emotion were derived from the videos. Ten healthy raters were asked to look at the pictures displayed on a computer-screen in pseudo-random fashion and to identify the emotional label in a six-forced-choice response format (Emotion expressivity task). Reaction time (RT) and accuracy of responses were recorded. At the end of each trial the participant was asked to rate his/her confidence in his/her perceived accuracy of response. For emotion recognition, PD reported lower score than HC for Ekman total score (pemotions sub-scores happiness, fear, anger, sadness (pfacial emotion expressivity task, PD and HC significantly differed in the total score (p = 0.05) and in the sub-scores for happiness, sadness, anger (all pemotions. There was a significant positive correlation between the emotion facial recognition and expressivity in both groups; the correlation was even stronger when ranking emotions from the best recognized to the worst (R = 0.75, p = 0.004). PD patients showed difficulties in recognizing emotional

Epidemiological monitoring for emerging infectious diseases

Science.gov (United States)

Greene, Marjorie

2010-04-01

The Homeland Security News Wire has been reporting on new ways to fight epidemics using digital tools such as iPhone, social networks, Wikipedia, and other Internet sites. Instant two-way communication now gives consumers the ability to complement official reports on emerging infectious diseases from health authorities. However, there is increasing concern that these communications networks could open the door to mass panic from unreliable or false reports. There is thus an urgent need to ensure that epidemiological monitoring for emerging infectious diseases gives health authorities the capability to identify, analyze, and report disease outbreaks in as timely and efficient a manner as possible. One of the dilemmas in the global dissemination of information on infectious diseases is the possibility that information overload will create inefficiencies as the volume of Internet-based surveillance information increases. What is needed is a filtering mechanism that will retrieve relevant information for further analysis by epidemiologists, laboratories, and other health organizations so they are not overwhelmed with irrelevant information and will be able to respond quickly. This paper introduces a self-organizing ontology that could be used as a filtering mechanism to increase relevance and allow rapid analysis of disease outbreaks as they evolve in real time.

Facial emotion recognition in Parkinson's disease: A review and new hypotheses

Science.gov (United States)

Vérin, Marc; Sauleau, Paul; Grandjean, Didier

2018-01-01

Abstract Parkinson's disease is a neurodegenerative disorder classically characterized by motor symptoms. Among them, hypomimia affects facial expressiveness and social communication and has a highly negative impact on patients' and relatives' quality of life. Patients also frequently experience nonmotor symptoms, including emotional‐processing impairments, leading to difficulty in recognizing emotions from faces. Aside from its theoretical importance, understanding the disruption of facial emotion recognition in PD is crucial for improving quality of life for both patients and caregivers, as this impairment is associated with heightened interpersonal difficulties. However, studies assessing abilities in recognizing facial emotions in PD still report contradictory outcomes. The origins of this inconsistency are unclear, and several questions (regarding the role of dopamine replacement therapy or the possible consequences of hypomimia) remain unanswered. We therefore undertook a fresh review of relevant articles focusing on facial emotion recognition in PD to deepen current understanding of this nonmotor feature, exploring multiple significant potential confounding factors, both clinical and methodological, and discussing probable pathophysiological mechanisms. This led us to examine recent proposals about the role of basal ganglia‐based circuits in emotion and to consider the involvement of facial mimicry in this deficit from the perspective of embodied simulation theory. We believe our findings will inform clinical practice and increase fundamental knowledge, particularly in relation to potential embodied emotion impairment in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. PMID:29473661

A Review on Video-Based Human Activity Recognition

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Shian-Ru Ke

2013-06-01

Full Text Available This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems.

Designing a Low-Resolution Face Recognition System for Long-Range Surveillance

NARCIS (Netherlands)

Peng, Y.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

2016-01-01

Most face recognition systems deal well with high-resolution facial images, but perform much worse on low-resolution facial images. In low-resolution face recognition, there is a specific but realistic surveillance scenario: a surveillance camera monitoring a large area. In this scenario, usually

A multi-view face recognition system based on cascade face detector and improved Dlib

Science.gov (United States)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Use of UV-vis-NIR spectroscopy to monitor label-free interaction between molecular recognition elements and erythropoietin on a gold-coated polycarbonate platform.

Science.gov (United States)

Citartan, Marimuthu; Gopinath, Subash C B; Tominaga, Junji; Chen, Yeng; Tang, Thean-Hock

2014-08-01

Label-free-based detection is pivotal for real-time monitoring of biomolecular interactions and to eliminate the need for labeling with tags that can occupy important binding sites of biomolecules. One simplest form of label-free-based detection is ultraviolet-visible-near-infrared (UV-vis-NIR) spectroscopy, which measure changes in reflectivity as a means to monitor immobilization and interaction of biomolecules with their corresponding partners. In biosensor development, the platform used for the biomolecular interaction should be suitable for different molecular recognition elements. In this study, gold (Au)-coated polycarbonate was used as a platform and as a proof-of-concept, erythropoietin (EPO), a doping substance widely abused by the athletes was used as the target. The interaction of EPO with its corresponding molecular recognition elements (anti-EPO monoclonal antibody and anti-EPO DNA aptamer) is monitored by UV-vis-NIR spectroscopy. Prior to this, to show that UV-vis-NIR spectroscopy is a suitable method for measuring biomolecular interaction, the interaction between biotin and streptavidin was demonstrated via this strategy and reflectivity of this interaction decreased by 25%. Subsequent to this, interaction of the EPO with anti-EPO monoclonal antibody and anti-EPO DNA aptamer resulted in the decrease of reflectivity by 5% and 10%, respectively. The results indicated that Au-coated polycarbonate could be an ideal biosensor platform for monitoring biomolecular interactions using UV-vis-NIR spectroscopy. A smaller version of the Au-coated polycarbonate substrates can be derived from the recent set-up, to be applied towards detecting EPO abuse among atheletes. Copyright © 2014 Elsevier B.V. All rights reserved.

Recognition Memory in Amnestic-Mild Cognitive Impairment: Insights from Event-Related Potentials

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David A Wolk

2013-12-01

Full Text Available Episodic memory loss is the hallmark cognitive dysfunction associated with Alzheimer’s Disease (AD. Amnestic Mild Cognitive Impairment (a-MCI frequently represents a transitional stage between normal aging and early AD. A better understanding of the qualitative features of memory loss in a-MCI may have important implications for predicting those most likely to harbor AD-related pathology and for disease monitoring. Dual process models of memory argue that recognition memory is subserved by the dissociable processes of recollection and familiarity. Work studying recognition memory in a-MCI from this perspective has been controversial, particularly with regard to the integrity of familiarity. Event-related potentials (ERPs offer an alternative means for assessing these functions without the associated assumptions of behavioral estimation methods. ERPs were recorded while a-MCI patients and cognitively normal (CN age-matched adults performed a recognition memory task. When retrieval success was measured (hits versus correct rejections in which performance was matched by group, a-MCI patients displayed similar neural correlates to that of the CN group, including modulation of the FN400 and the late parietal complex (LPC which are thought to index familiarity and recollection, respectively. Alternatively, when the integrity of these components were measured based on retrieval attempts (studied versus unstudied items, a-MCI patients displayed a reduced FN400 and LPC. Furthermore, modulation of the FN400 correlated with a behavioral estimate of familiarity and the LPC with a behavioral estimates of recollection obtained in a separate experiment in the same individuals, consistent with the proposed mappings of these indices. These results support a global decline of recognition memory in a-MCI, which suggests that the memory loss of prodromal AD may be qualitatively distinct from normal aging.

Prescribing of Electronic Activity Monitors in Cardiometabolic Diseases: Qualitative Interview-Based Study.

Science.gov (United States)

Bellicha, Alice; Macé, Sandrine; Oppert, Jean-Michel

2017-09-23

The prevalence of noncommunicable diseases, including those such as type 2 diabetes, obesity, dyslipidemia, and hypertension, so-called cardiometabolic diseases, is high and is increasing worldwide. Strong evidence supports the role of physical activity in management of these diseases. There is general consensus that mHealth technology, including electronic activity monitors, can potentially increase physical activity in patients, but their use in clinical settings remains limited. Practitioners' requirements when prescribing electronic activity monitors have been poorly described. The aims of this qualitative study were (1) to explore how specialist physicians prescribe electronic activity monitors to patients presenting with cardiometabolic conditions, and (2) to better understand their motivation for and barriers to prescribing such monitors. We conducted qualitative semistructured interviews in March to May 2016 with 11 senior physicians from a public university hospital in France with expertise in management of cardiometabolic diseases (type 1 and type 2 diabetes, obesity, hypertension, and dyslipidemia). Interviews lasted 45 to 60 minutes and were audiotaped, transcribed verbatim, and analyzed using directed content analysis. We report our findings following the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist. Most physicians we interviewed had never prescribed electronic activity monitors, whereas they frequently prescribed blood glucose or blood pressure self-monitoring devices. Reasons for nonprescription included lack of interest in the data collected, lack of evidence for data accuracy, concern about work overload possibly resulting from automatic data transfer, and risk of patients becoming addicted to data. Physicians expected future marketing of easy-to-use monitors that will accurately measure physical activity duration and intensity and provide understandable motivating feedback. Features of electronic activity monitors

Memory Distortion and Its Avoidance: An Event-Related Potentials Study on False Recognition and Correct Rejection.

Science.gov (United States)

Cadavid, Sara; Beato, Maria Soledad

2016-01-01

Memory researchers have long been captivated by the nature of memory distortions and have made efforts to identify the neural correlates of true and false memories. However, the underlying mechanisms of avoiding false memories by correctly rejecting related lures remains underexplored. In this study, we employed a variant of the Deese/Roediger-McDermott paradigm to explore neural signatures of committing and avoiding false memories. ERP were obtained for True recognition, False recognition, Correct rejection of new items, and, more importantly, Correct rejection of related lures. With these ERP data, early-frontal, left-parietal, and late right-frontal old/new effects (associated with familiarity, recollection, and monitoring processes, respectively) were analysed. Results indicated that there were similar patterns for True and False recognition in all three old/new effects analysed in our study. Also, False recognition and Correct rejection of related lures activities seemed to share common underlying familiarity-based processes. The ERP similarities between False recognition and Correct rejection of related lures disappeared when recollection processes were examined because only False recognition presented a parietal old/new effect. This finding supported the view that actual false recollections underlie false memories, providing evidence consistent with previous behavioural research and with most ERP and neuroimaging studies. Later, with the onset of monitoring processes, False recognition and Correct rejection of related lures waveforms presented, again, clearly dissociated patterns. Specifically, False recognition and True recognition showed more positive going patterns than Correct rejection of related lures signal and Correct rejection of new items signature. Since False recognition and Correct rejection of related lures triggered familiarity-recognition processes, our results suggest that deciding which items are studied is based more on recollection

Memory Distortion and Its Avoidance: An Event-Related Potentials Study on False Recognition and Correct Rejection.

Directory of Open Access Journals (Sweden)

Sara Cadavid

Full Text Available Memory researchers have long been captivated by the nature of memory distortions and have made efforts to identify the neural correlates of true and false memories. However, the underlying mechanisms of avoiding false memories by correctly rejecting related lures remains underexplored. In this study, we employed a variant of the Deese/Roediger-McDermott paradigm to explore neural signatures of committing and avoiding false memories. ERP were obtained for True recognition, False recognition, Correct rejection of new items, and, more importantly, Correct rejection of related lures. With these ERP data, early-frontal, left-parietal, and late right-frontal old/new effects (associated with familiarity, recollection, and monitoring processes, respectively were analysed. Results indicated that there were similar patterns for True and False recognition in all three old/new effects analysed in our study. Also, False recognition and Correct rejection of related lures activities seemed to share common underlying familiarity-based processes. The ERP similarities between False recognition and Correct rejection of related lures disappeared when recollection processes were examined because only False recognition presented a parietal old/new effect. This finding supported the view that actual false recollections underlie false memories, providing evidence consistent with previous behavioural research and with most ERP and neuroimaging studies. Later, with the onset of monitoring processes, False recognition and Correct rejection of related lures waveforms presented, again, clearly dissociated patterns. Specifically, False recognition and True recognition showed more positive going patterns than Correct rejection of related lures signal and Correct rejection of new items signature. Since False recognition and Correct rejection of related lures triggered familiarity-recognition processes, our results suggest that deciding which items are studied is based more on

Mobile Monitoring and Reasoning Methods to Prevent Cardiovascular Diseases

Directory of Open Access Journals (Sweden)

Diego López-de-Ipiña

2013-05-01

Full Text Available With the recent technological advances, it is possible to monitor vital signs using Bluetooth-enabled biometric mobile devices such as smartphones, tablets or electric wristbands. In this manuscript, we present a system to estimate the risk of cardiovascular diseases in Ambient Assisted Living environments. Cardiovascular disease risk is obtained from the monitoring of the blood pressure by means of mobile devices in combination with other clinical factors, and applying reasoning techniques based on the Systematic Coronary Risk Evaluation Project charts. We have developed an end-to-end software application for patients and physicians and a rule-based reasoning engine. We have also proposed a conceptual module to integrate recommendations to patients in their daily activities based on information proactively inferred through reasoning techniques and context-awareness. To evaluate the platform, we carried out usability experiments and performance benchmarks.

[Evaluation and analysis of monitoring and early warning functions of the occupational disease reporting system in China].

Science.gov (United States)

Zhu, Xiaojun; Li, Tao; Liu, Mengxuan

2015-06-01

To evaluate the monitoring and early warning functions of the occupational disease reporting system right now in China, and to analyze their influencing factors. An improved audit tool (ODIT) was used to score the monitoring and early warning functions with a total score of 10. The nine indices were completeness of information on the reporting form, coverage of the reporting system, accessibility of criteria or guidelines for diagnosis, education and training for physicians, completeness of the reporting system, statistical methods, investigation of special cases, release of monitoring information, and release of early warning information. According to the evaluation, the occupational disease reporting system in China had a score of 5.5 in monitoring existing occupational diseases with a low score for release of monitoring information; the reporting system had a score of 6.5 in early warning of newly occurring occupational diseases with low scores for education and training for physicians as well as completeness of the reporting system. The occupational disease reporting system in China still does not have full function in monitoring and early warning. It is the education and participation of physicians from general hospitals in the diagnosis and treatment of occupational diseases and suspected occupational diseases that need to be enhanced. In addition, the problem of monitoring the incidence of occupational diseases needs to be solved as soon as possible.

Quality of chronic kidney disease management in primary care: a retrospective study.

Science.gov (United States)

Van Gelder, Vincent A; Scherpbier-De Haan, Nynke D; De Grauw, Wim J C; Vervoort, Gerald M M; Van Weel, Chris; Biermans, Marion C J; Braspenning, Jozé C C; Wetzels, Jack F M

2016-01-01

Early detection and appropriate management of chronic kidney disease (CKD) in primary care are essential to reduce morbidity and mortality. To assess the quality of care (QoC) of CKD in primary healthcare in relation to patient and practice characteristics in order to tailor improvement strategies. Retrospective study using data between 2008 and 2011 from 47 general practices (207 469 patients of whom 162 562 were adults). CKD management of patients under the care of their general practitioner (GP) was qualified using indicators derived from the Dutch interdisciplinary CKD guideline for primary care and nephrology and included (1) monitoring of renal function, albuminuria, blood pressure, and glucose, (2) monitoring of metabolic parameters, and alongside the guideline: (3) recognition of CKD. The outcome indicator was (4) achieving blood pressure targets. Multilevel logistic regression analysis was applied to identify associated patient and practice characteristics. Kidney function or albuminuria data were available for 59 728 adult patients; 9288 patients had CKD, of whom 8794 were under GP care. Monitoring of disease progression was complete in 42% of CKD patients, monitoring of metabolic parameters in 2%, and blood pressure target was reached in 43.1%. GPs documented CKD in 31.4% of CKD patients. High QoC was strongly associated with diabetes, and to a lesser extent with hypertension and male sex. Room for improvement was found in all aspects of CKD management. As QoC was higher in patients who received structured diabetes care, future CKD care may profit from more structured primary care management, e.g. according to the chronic care model. Quality of care for chronic kidney disease patients in primary care can be improved. In comparison with guideline advice, adequate monitoring of disease progression was observed in 42%, of metabolic parameters in 2%, correct recognition of impaired renal function in 31%, and reaching blood pressure targets in 43% of chronic

Semantic Activity Recognition

OpenAIRE

Thonnat , Monique

2008-01-01

International audience; Extracting automatically the semantics from visual data is a real challenge. We describe in this paper how recent work in cognitive vision leads to significative results in activity recognition for visualsurveillance and video monitoring. In particular we present work performed in the domain of video understanding in our PULSAR team at INRIA in Sophia Antipolis. Our main objective is to analyse in real-time video streams captured by static video cameras and to recogniz...

Investigation of an expert health monitoring system for aeronautical structures based on pattern recognition and acousto-ultrasonics

Science.gov (United States)

Tibaduiza-Burgos, Diego Alexander; Torres-Arredondo, Miguel Angel

2015-08-01

Aeronautical structures are subjected to damage during their service raising the necessity for periodic inspection and maintenance of their components so that structural integrity and safe operation can be guaranteed. Cost reduction related to minimizing the out-of-service time of the aircraft, together with the advantages offered by real-time and safe-life service monitoring, have led to a boom in the design of inexpensive and structurally integrated transducer networks comprising actuators, sensors, signal processing units and controllers. These kinds of automated systems are normally referred to as smart structures and offer a multitude of new solutions to engineering problems and multi-functional capabilities. It is thus expected that structural health monitoring (SHM) systems will become one of the leading technologies for assessing and assuring the structural integrity of future aircraft. This study is devoted to the development and experimental investigation of an SHM methodology for the detection of damage in real scale complex aeronautical structures. The work focuses on each aspect of the SHM system and highlights the potentialities of the health monitoring technique based on acousto-ultrasonics and data-driven modelling within the concepts of sensor data fusion, feature extraction and pattern recognition. The methodology is experimentally demonstrated on an aircraft skin panel and fuselage panel for which several damage scenarios are analysed. The detection performance in both structures is quantified and presented.

Differential Recognition of Mycobacterium tuberculosis-Specific Epitopes as a Function of Tuberculosis Disease History.

Science.gov (United States)

Scriba, Thomas J; Carpenter, Chelsea; Pro, Sebastian Carrasco; Sidney, John; Musvosvi, Munyaradzi; Rozot, Virginie; Seumois, Grégory; Rosales, Sandy L; Vijayanand, Pandurangan; Goletti, Delia; Makgotlho, Edward; Hanekom, Willem; Hatherill, Mark; Peters, Bjoern; Sette, Alessandro; Arlehamn, Cecilia S Lindestam

2017-09-15

Individuals with a history of tuberculosis (TB) disease are at elevated risk of disease recurrence. The underlying cause is not known, but one explanation is that previous disease results in less-effective immunity against Mycobacterium tuberculosis (Mtb). We hypothesized that the repertoire of Mtb-derived epitopes recognized by T cells from individuals with latent Mtb infection differs as a function of previous diagnosis of active TB disease. T-cell responses to peptide pools in samples collected from an adult screening and an adolescent validation cohort were measured by IFN-γ enzyme-linked immunospot assay or intracellular cytokine staining. We identified a set of "type 2" T-cell epitopes that were recognized at 10-fold-lower levels in Mtb-infected individuals with a history of TB disease less than 6 years ago than in those without previous TB. By contrast, "type 1" epitopes were recognized equally well in individuals with or without previous TB. The differential epitope recognition was not due to differences in HLA class II binding, memory phenotypes, or gene expression in the responding T cells. Instead, "TB disease history-sensitive" type 2 epitopes were significantly (P < 0.0001) more homologous to sequences from bacteria found in the human microbiome than type 1 epitopes. Preferential loss of T-cell reactivity to Mtb epitopes that are homologous to bacteria in the microbiome in persons with previous TB disease may reflect long-term effects of antibiotic TB treatment on the microbiome.

Facial Emotion Recognition and Expression in Parkinson’s Disease: An Emotional Mirror Mechanism?

Science.gov (United States)

Ricciardi, Lucia; Visco-Comandini, Federica; Erro, Roberto; Morgante, Francesca; Bologna, Matteo; Fasano, Alfonso; Ricciardi, Diego; Edwards, Mark J.; Kilner, James

2017-01-01

Background and aim Parkinson’s disease (PD) patients have impairment of facial expressivity (hypomimia) and difficulties in interpreting the emotional facial expressions produced by others, especially for aversive emotions. We aimed to evaluate the ability to produce facial emotional expressions and to recognize facial emotional expressions produced by others in a group of PD patients and a group of healthy participants in order to explore the relationship between these two abilities and any differences between the two groups of participants. Methods Twenty non-demented, non-depressed PD patients and twenty healthy participants (HC) matched for demographic characteristics were studied. The ability of recognizing emotional facial expressions was assessed with the Ekman 60-faces test (Emotion recognition task). Participants were video-recorded while posing facial expressions of 6 primary emotions (happiness, sadness, surprise, disgust, fear and anger). The most expressive pictures for each emotion were derived from the videos. Ten healthy raters were asked to look at the pictures displayed on a computer-screen in pseudo-random fashion and to identify the emotional label in a six-forced-choice response format (Emotion expressivity task). Reaction time (RT) and accuracy of responses were recorded. At the end of each trial the participant was asked to rate his/her confidence in his/her perceived accuracy of response. Results For emotion recognition, PD reported lower score than HC for Ekman total score (pemotions sub-scores happiness, fear, anger, sadness (pfacial emotion expressivity task, PD and HC significantly differed in the total score (p = 0.05) and in the sub-scores for happiness, sadness, anger (all pemotions. There was a significant positive correlation between the emotion facial recognition and expressivity in both groups; the correlation was even stronger when ranking emotions from the best recognized to the worst (R = 0.75, p = 0.004). Conclusions PD

Automated pattern recognition system for noise analysis

International Nuclear Information System (INIS)

Sides, W.H. Jr.; Piety, K.R.

1980-01-01

A pattern recognition system was developed at ORNL for on-line monitoring of noise signals from sensors in a nuclear power plant. The system continuousy measures the power spectral density (PSD) values of the signals and the statistical characteristics of the PSDs in unattended operation. Through statistical comparison of current with past PSDs (pattern recognition), the system detects changes in the noise signals. Because the noise signals contain information about the current operational condition of the plant, a change in these signals could indicate a change, either normal or abnormal, in the operational condition

A sensor and video based ontology for activity recognition in smart environments.

Science.gov (United States)

Mitchell, D; Morrow, Philip J; Nugent, Chris D

2014-01-01

Activity recognition is used in a wide range of applications including healthcare and security. In a smart environment activity recognition can be used to monitor and support the activities of a user. There have been a range of methods used in activity recognition including sensor-based approaches, vision-based approaches and ontological approaches. This paper presents a novel approach to activity recognition in a smart home environment which combines sensor and video data through an ontological framework. The ontology describes the relationships and interactions between activities, the user, objects, sensors and video data.

Gas bubble disease monitoring and research of juvenile salmonids

International Nuclear Information System (INIS)

Maule, A.G.; Beeman, J.; Hans, K.M.; Mesa, M.G.; Haner, P.; Warren, J.J.

1997-10-01

This document describes the project activities 1996--1997 contract year. This report is composed of three chapters which contain data and analyses of the three main elements of the project: field research to determine the vertical distribution of migrating juvenile salmonids, monitoring of juvenile migrants at dams on the Snake and Columbia rivers, and laboratory experiments to describe the progression of gas bubble disease signs leading to mortality. The major findings described in this report are: A miniature pressure-sensitive radio transmitter was found to be accurate and precise and, after compensation for water temperature, can be used to determine the depth of tagged-fish to within 0.32 m of the true depth (Chapter 1). Preliminary data from very few fish suggest that depth protects migrating juvenile steelhead from total dissolved gas supersaturation (Chapter 1). As in 1995, few fish had any signs of gas bubble disease, but it appeared that prevalence and severity increased as fish migrated downstream and in response to changing gas supersaturation (Chapter 2). It appeared to gas bubble disease was not a threat to migrating juvenile salmonids when total dissolved gas supersaturation was < 120% (Chapter 2). Laboratory studies suggest that external examinations are appropriate for determining the severity of gas bubble disease in juvenile salmonids (Chapter 3). The authors developed a new method for examining gill arches for intravascular bubbles by clamping the ventral aorta to reduce bleeding when arches were removed (Chapter 3). Despite an outbreak of bacterial kidney disease in the experimental fish, the data indicate that gas bubble disease is a progressive trauma that can be monitored (Chapter 3)

Disease named entity recognition from biomedical literature using a novel convolutional neural network.

Science.gov (United States)

Zhao, Zhehuan; Yang, Zhihao; Luo, Ling; Wang, Lei; Zhang, Yin; Lin, Hongfei; Wang, Jian

2017-12-28

Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated. In this paper, we propose a novel multiple label convolutional neural network (MCNN) based disease NER approach. In this approach, instead of the CRF layer, a multiple label strategy (MLS) first introduced by us, is employed. First, the character-level embedding, word-level embedding and lexicon feature embedding are concatenated. Then several convolutional layers are stacked over the concatenated embedding. Finally, MLS strategy is applied to the output layer to capture the correlation information between neighboring labels. As shown by the experimental results, MCNN can achieve the state-of-the-art performance on both NCBI and CDR corpora. The proposed MCNN based disease NER method achieves the state-of-the-art performance with little feature engineering. And the experimental results show the MLS strategy's effectiveness of capturing the correlation information between labels in the neighborhood.

Long-term cannabidiol treatment prevents the development of social recognition memory deficits in Alzheimer's disease transgenic mice.

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Cheng, David; Spiro, Adena S; Jenner, Andrew M; Garner, Brett; Karl, Tim

2014-01-01

Impairments in cognitive ability and widespread pathophysiological changes caused by neurotoxicity, neuroinflammation, oxidative damage, and altered cholesterol homeostasis are associated with Alzheimer's disease (AD). Cannabidiol (CBD) has been shown to reverse cognitive deficits of AD transgenic mice and to exert neuroprotective, anti-oxidative, and anti-inflammatory properties in vitro and in vivo. Here we evaluate the preventative properties of long-term CBD treatment in male AβPPSwe/PS1ΔE9 (AβPP × PS1) mice, a transgenic model of AD. Control and AD transgenic mice were treated orally from 2.5 months of age with CBD (20 mg/kg) daily for 8 months. Mice were then assessed in the social preference test, elevated plus maze, and fear conditioning paradigms, before cortical and hippocampal tissues were analyzed for amyloid load, oxidative damage, cholesterol, phytosterols, and inflammation. We found that AβPP × PS1 mice developed a social recognition deficit, which was prevented by CBD treatment. CBD had no impact on anxiety or associative learning. The prevention of the social recognition deficit was not associated with any changes in amyloid load or oxidative damage. However, the study revealed a subtle impact of CBD on neuroinflammation, cholesterol, and dietary phytosterol retention, which deserves further investigation. This study is the first to demonstrate CBD's ability to prevent the development of a social recognition deficit in AD transgenic mice. Our findings provide the first evidence that CBD may have potential as a preventative treatment for AD with a particular relevance for symptoms of social withdrawal and facial recognition.

A prospective study of monitoring practices for metabolic disease in antipsychotic-treated community psychiatric patients

Directory of Open Access Journals (Sweden)

Watkinson Helen MO

2007-06-01

Full Text Available Abstract Background Patients with severe mental illness are at increased risk for metabolic and cardiovascular disease. A number of recent guidelines and consensus statements recommend stringent monitoring of metabolic function in individuals receiving antipsychotic drugs. Methods We conducted a prospective cohort study of 106 community-treated psychiatric patients from across the diagnostic spectrum from the Northeast of England to investigate changes in metabolic status and monitoring practices for metabolic and cardiovascular disease. We undertook detailed anthropometric and metabolic assessment at baseline and follow-up, and examined clinical notes and hospital laboratory records to ascertain monitoring practices. Results A high prevalence of undiagnosed and untreated metabolic disease was present at baseline assessment. Mean follow-up time was 599.3 (SD ± 235.4 days. Body mass index (p 50% of subjects had neither blood glucose nor lipids monitored during the follow-up period. Conclusion This cohort has a high prevalence of metabolic disease and heightened cardiovascular risk. Despite the publication of a number of recommendations regarding physical health screening in this population, monitoring rates are poor, and physical health worsened during the follow-up period.

[Emotion recognition rehabilitation combined with cognitive stimulation for people with Alzheimer's disease. Efficacy for cognition and functional aspects].

Science.gov (United States)

Garcia-Casal, J A; Goni-Imizcoz, M; Perea-Bartolome, M V; Garcia-Moja, C; Calvo-Simal, S; Cardelle-Garcia, F; Franco-Martin, M

2017-08-01

The ability to recognize facial emotional expression is essential for social interactions and adapting to the environment. Emotion recognition is impaired in people with Alzheimer's disease (AD), thus rehabilitation of these skills has the potential to elicit significant benefits. To assess the efficacy of a combined treatment of rehabilitation of emotion recognition (RER) and cognitive stimulation (CS) for people with AD, due to its potential implications for more effective psychosocial interventions. 36 patients were assigned to one of three experimental conditions: an experimental group (EG) that received 20 sessions of RER and 20 sessions of CS; a control group (CG) that received 40 sessions of CS, and a treatment as usual group (TAU). 32 patients completed the treatment (77.53 ± 5.43 years). Significant differences were found in MMSE30 (F = 5.10; p = 0.013), MMSE35 (F = 4.16; p = 0.026), affect recognition (Z = -2.81; p = 0.005) and basic activities of daily living (Z = -2.27; p = 0.018) favouring the efficacy of the combined treatment. The TAU group showed a decline in depression (Z = -1.99; p = 0.048), apathy (Z = -2.30; p = 0.022) and anosognosia (Z = -2.19; p = 0.028). The combined treatment of RER + CS was more effective than TAU and CS alone for the treatment of patients with AD. This is the first study about the rehabilitation of affect recognition in AD.

Levels-of-processing effect on internal source monitoring in schizophrenia.

Science.gov (United States)

Ragland, J Daniel; McCarthy, Erin; Bilker, Warren B; Brensinger, Colleen M; Valdez, Jeffrey; Kohler, Christian; Gur, Raquel E; Gur, Ruben C

2006-05-01

Recognition can be normalized in schizophrenia by providing patients with semantic organizational strategies through a levels-of-processing (LOP) framework. However, patients may rely primarily on familiarity effects, making recognition less sensitive than source monitoring to the strength of the episodic memory trace. The current study investigates whether providing semantic organizational strategies can also normalize patients' internal source-monitoring performance. Sixteen clinically stable medicated patients with schizophrenia and 15 demographically matched healthy controls were asked to identify the source of remembered words following an LOP-encoding paradigm in which they alternated between processing words on a 'shallow' perceptual versus a 'deep' semantic level. A multinomial analysis provided orthogonal measures of item recognition and source discrimination, and bootstrapping generated variance to allow for parametric analyses. LOP and group effects were tested by contrasting recognition and source-monitoring parameters for words that had been encoded during deep versus shallow processing conditions. As in a previous study there were no group differences in LOP effects on recognition performance, with patients and controls benefiting equally from deep versus shallow processing. Although there were no group differences in internal source monitoring, only controls had significantly better performance for words processed during the deep encoding condition. Patient performance did not correlate with clinical symptoms or medication dose. Providing a deep processing semantic encoding strategy significantly improved patients' recognition performance only. The lack of a significant LOP effect on internal source monitoring in patients may reflect subtle problems in the relational binding of semantic information that are independent of strategic memory processes.

SME2EM: Smart mobile end-to-end monitoring architecture for life-long diseases.

Science.gov (United States)

Serhani, Mohamed Adel; Menshawy, Mohamed El; Benharref, Abdelghani

2016-01-01

Monitoring life-long diseases requires continuous measurements and recording of physical vital signs. Most of these diseases are manifested through unexpected and non-uniform occurrences and behaviors. It is impractical to keep patients in hospitals, health-care institutions, or even at home for long periods of time. Monitoring solutions based on smartphones combined with mobile sensors and wireless communication technologies are a potential candidate to support complete mobility-freedom, not only for patients, but also for physicians. However, existing monitoring architectures based on smartphones and modern communication technologies are not suitable to address some challenging issues, such as intensive and big data, resource constraints, data integration, and context awareness in an integrated framework. This manuscript provides a novel mobile-based end-to-end architecture for live monitoring and visualization of life-long diseases. The proposed architecture provides smartness features to cope with continuous monitoring, data explosion, dynamic adaptation, unlimited mobility, and constrained devices resources. The integration of the architecture׳s components provides information about diseases׳ recurrences as soon as they occur to expedite taking necessary actions, and thus prevent severe consequences. Our architecture system is formally model-checked to automatically verify its correctness against designers׳ desirable properties at design time. Its components are fully implemented as Web services with respect to the SOA architecture to be easy to deploy and integrate, and supported by Cloud infrastructure and services to allow high scalability, availability of processes and data being stored and exchanged. The architecture׳s applicability is evaluated through concrete experimental scenarios on monitoring and visualizing states of epileptic diseases. The obtained theoretical and experimental results are very promising and efficiently satisfy the proposed

Object and event recognition for stroke rehabilitation

Science.gov (United States)

Ghali, Ahmed; Cunningham, Andrew S.; Pridmore, Tony P.

2003-06-01

Stroke is a major cause of disability and health care expenditure around the world. Existing stroke rehabilitation methods can be effective but are costly and need to be improved. Even modest improvements in the effectiveness of rehabilitation techniques could produce large benefits in terms of quality of life. The work reported here is part of an ongoing effort to integrate virtual reality and machine vision technologies to produce innovative stroke rehabilitation methods. We describe a combined object recognition and event detection system that provides real time feedback to stroke patients performing everyday kitchen tasks necessary for independent living, e.g. making a cup of coffee. The image plane position of each object, including the patient"s hand, is monitored using histogram-based recognition methods. The relative positions of hand and objects are then reported to a task monitor that compares the patient"s actions against a model of the target task. A prototype system has been constructed and is currently undergoing technical and clinical evaluation.

Memory evaluation in mild cognitive impairment using recall and recognition tests.

Science.gov (United States)

Bennett, Ilana J; Golob, Edward J; Parker, Elizabeth S; Starr, Arnold

2006-11-01

Amnestic mild cognitive impairment (MCI) is a selective episodic memory deficit that often indicates early Alzheimer's disease. Episodic memory function in MCI is typically defined by deficits in free recall, but can also be tested using recognition procedures. To assess both recall and recognition in MCI, MCI (n = 21) and older comparison (n = 30) groups completed the USC-Repeatable Episodic Memory Test. Subjects memorized two verbally presented 15-item lists. One list was used for three free recall trials, immediately followed by yes/no recognition. The second list was used for three-alternative forced-choice recognition. Relative to the comparison group, MCI had significantly fewer hits and more false alarms in yes/no recognition, and were less accurate in forced-choice recognition. Signal detection analysis showed that group differences were not due to response bias. Discriminant function analysis showed that yes/no recognition was a better predictor of group membership than free recall or forced-choice measures. MCI subjects recalled fewer items than comparison subjects, with no group differences in repetitions, intrusions, serial position effects, or measures of recall strategy (subjective organization, recall consistency). Performance deficits on free recall and recognition in MCI suggest a combination of both tests may be useful for defining episodic memory impairment associated with MCI and early Alzheimer's disease.

Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative.

Science.gov (United States)

Russo, María J; Campos, Jorge; Vázquez, Silvia; Sevlever, Gustavo; Allegri, Ricardo F

2017-01-01

Background: Ongoing research is focusing on the identification of those individuals with mild cognitive impairment (MCI) who are most likely to convert to Alzheimer's disease (AD). We investigated whether recognition memory tasks in combination with delayed recall measure of episodic memory and CSF biomarkers can predict MCI to AD conversion at 24-month follow-up. Methods: A total of 397 amnestic-MCI subjects from Alzheimer's disease Neuroimaging Initiative were included. Logistic regression modeling was done to assess the predictive value of all RAVLT measures, risk factors such as age, sex, education, APOE genotype, and CSF biomarkers for progression to AD. Estimating adjusted odds ratios was used to determine which variables would produce an optimal predictive model, and whether adding tests of interaction between the RAVLT Delayed Recall and recognition measures (traditional score and d-prime) would improve prediction of the conversion from a-MCI to AD. Results: 112 (28.2%) subjects developed dementia and 285 (71.8%) subjects did not. Of the all included variables, CSF Aβ1-42 levels, RAVLT Delayed Recall, and the combination of RAVLT Delayed Recall and d-prime were predictive of progression to AD (χ 2 = 38.23, df = 14, p < 0.001). Conclusions: The combination of RAVLT Delayed Recall and d-prime measures may be predictor of conversion from MCI to AD in the ADNI cohort, especially in combination with amyloid biomarkers. A predictive model to help identify individuals at-risk for dementia should include not only traditional episodic memory measures (delayed recall or recognition), but also additional variables (d-prime) that allow the homogenization of the assessment procedures in the diagnosis of MCI.

Automatic ingestion monitor: a novel wearable device for monitoring of ingestive behavior.

Science.gov (United States)

Fontana, Juan M; Farooq, Muhammad; Sazonov, Edward

2014-06-01

Objective monitoring of food intake and ingestive behavior in a free-living environment remains an open problem that has significant implications in study and treatment of obesity and eating disorders. In this paper, a novel wearable sensor system (automatic ingestion monitor, AIM) is presented for objective monitoring of ingestive behavior in free living. The proposed device integrates three sensor modalities that wirelessly interface to a smartphone: a jaw motion sensor, a hand gesture sensor, and an accelerometer. A novel sensor fusion and pattern recognition method was developed for subject-independent food intake recognition. The device and the methodology were validated with data collected from 12 subjects wearing AIM during the course of 24 h in which both the daily activities and the food intake of the subjects were not restricted in any way. Results showed that the system was able to detect food intake with an average accuracy of 89.8%, which suggests that AIM can potentially be used as an instrument to monitor ingestive behavior in free-living individuals.

[Impact of facial emotional recognition alterations in Dementia of the Alzheimer type].

Science.gov (United States)

Rubinstein, Wanda; Cossini, Florencia; Politis, Daniel

2016-07-01

Face recognition of basic emotions is independent of other deficits in dementia of the Alzheimer type. Among these deficits, there is disagreement about what emotions are more difficult to recognize. Our aim was to study the presence of alterations in the process of facial recognition of basic emotions, and to investigate if there were differences in the recognition of each type of emotion in Alzheimer's disease. With three tests of recognition of basic facial emotions we evaluated 29 patients who had been diagnosed with dementia of the Alzheimer type and 18 control subjects. Significant differences were obtained in tests of recognition of basic facial emotions and between each. Since the amygdala, one of the brain structures responsible for emotional reaction, is affected in the early stages of this disease, our findings become relevant to understand how this alteration of the process of emotional recognition impacts the difficulties these patients have in both interpersonal relations and behavioral disorders.

Low, slow, small target recognition based on spatial vision network

Science.gov (United States)

Cheng, Zhao; Guo, Pei; Qi, Xin

2018-03-01

Traditional photoelectric monitoring is monitored using a large number of identical cameras. In order to ensure the full coverage of the monitoring area, this monitoring method uses more cameras, which leads to more monitoring and repetition areas, and higher costs, resulting in more waste. In order to reduce the monitoring cost and solve the difficult problem of finding, identifying and tracking a low altitude, slow speed and small target, this paper presents spatial vision network for low-slow-small targets recognition. Based on camera imaging principle and monitoring model, spatial vision network is modeled and optimized. Simulation experiment results demonstrate that the proposed method has good performance.

Diagnosis of diabetes diseases using an Artificial Immune Recognition System2 (AIRS2) with fuzzy K-nearest neighbor.

Science.gov (United States)

Chikh, Mohamed Amine; Saidi, Meryem; Settouti, Nesma

2012-10-01

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.

What tools are useful for monitoring endemic diseases? A simulation study based on different time-series components

DEFF Research Database (Denmark)

Lopes Antunes, Ana Carolina; Jensen, D; Hisham Beshara Halasa, Tariq

2017-01-01

Control and eradication programs play an important role in disease monitoring and surveillance. It is important to follow up on implemented strategies to reduce and/or eliminate a specific disease. The objectives of this study were to investigate the performance of different detection methods......, including methods commonly used in biosurveillance as well as state space models, for monitoring the effect of endemic disease control and eradication programs. We simulated 16 different scenarios of changes in disease sero-prevalence, inspired by real-world data from the Danish PRRS (Porcine Reproductive...... and Respiratory Syndrome) monitoring program. The changes included increases, decreases and/or constant sero-prevalence levels in different combinations. Two state space models were used to model the simulated data and different monitoring methods, such as univariate process control algorithms (UPCA...

Clinical skills: cardiac rhythm recognition and monitoring.

Science.gov (United States)

Sharman, Joanna

With technological advances, changes in provision of healthcare services and increasing pressure on critical care services, ward patients' severity of illness is ever increasing. As such, nurses need to develop their skills and knowledge to care for their client group. Competency in cardiac rhythm monitoring is beneficial to identify changes in cardiac status, assess response to treatment, diagnosis and post-surgical monitoring. This paper describes the basic anatomy and physiology of the heart and its conduction system, and explains a simple and easy to remember process of analysing cardiac rhythms (Resuscitation Council UK, 2000) that can be used in first-line assessment to assist healthcare practitioners in providing care to their patients.

Pattern recognition receptors and the inflammasome in kidney disease

NARCIS (Netherlands)

Leemans, Jaklien C.; Kors, Lotte; Anders, Hans-Joachim; Florquin, Sandrine

2014-01-01

Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain receptors (NLRs) are families of pattern recognition receptors that, together with inflammasomes, sense and respond to highly conserved pathogen motifs and endogenous molecules released upon cell damage or stress. Evidence

Monitoring diseases based on register data: Methods and application in the Danish swine production

DEFF Research Database (Denmark)

Lopes Antunes, Ana Carolina

The spread of diseases is one of the most important threats to animal production and public health. Disease spread causes considerable economic losses for the agricultural sector and constitutes trade-limiting factors, as transmission to countries free from disease should beavoided. Monitoring...

Behavioral and Neuroimaging Evidence for Facial Emotion Recognition in Elderly Korean Adults with Mild Cognitive Impairment, Alzheimer's Disease, and Frontotemporal Dementia.

Science.gov (United States)

Park, Soowon; Kim, Taehoon; Shin, Seong A; Kim, Yu Kyeong; Sohn, Bo Kyung; Park, Hyeon-Ju; Youn, Jung-Hae; Lee, Jun-Young

2017-01-01

Background: Facial emotion recognition (FER) is impaired in individuals with frontotemporal dementia (FTD) and Alzheimer's disease (AD) when compared to healthy older adults. Since deficits in emotion recognition are closely related to caregiver burden or social interactions, researchers have fundamental interest in FER performance in patients with dementia. Purpose: The purpose of this study was to identify the performance profiles of six facial emotions (i.e., fear, anger, disgust, sadness, surprise, and happiness) and neutral faces measured among Korean healthy control (HCs), and those with mild cognitive impairment (MCI), AD, and FTD. Additionally, the neuroanatomical correlates of facial emotions were investigated. Methods: A total of 110 (33 HC, 32 MCI, 32 AD, 13 FTD) older adult participants were recruited from two different medical centers in metropolitan areas of South Korea. These individuals underwent an FER test that was used to assess the recognition of emotions or absence of emotion (neutral) in 35 facial stimuli. Repeated measures two-way analyses of variance were used to examine the distinct profiles of emotional recognition among the four groups. We also performed brain imaging and voxel-based morphometry (VBM) on the participants to examine the associations between FER scores and gray matter volume. Results: The mean score of negative emotion recognition (i.e., fear, anger, disgust, and sadness) clearly discriminated FTD participants from individuals with MCI and AD and HC [ F (3,106) = 10.829, p < 0.001, η 2 = 0.235], whereas the mean score of positive emotion recognition (i.e., surprise and happiness) did not. A VBM analysis showed negative emotions were correlated with gray matter volume of anterior temporal regions, whereas positive emotions were related to gray matter volume of fronto-parietal regions. Conclusion: Impairment of negative FER in patients with FTD is cross-cultural. The discrete neural correlates of FER indicate that emotional

Bite weight prediction from acoustic recognition of chewing

NARCIS (Netherlands)

Amft, O.D.; Kusserow, M.; Tröster, G.

2009-01-01

Automatic dietary monitoring (ADM) offers new perspectives to reduce the self-reporting burden for participants in diet coaching programs. This paper presents an approach to predict weight of individual bites taken. We utilize a pattern recognition procedure to spot chewing cycles and food type in

Patient cloth with motion recognition sensors based on flexible piezoelectric materials.

Science.gov (United States)

Youngsu Cha; Kihyuk Nam; Doik Kim

2017-07-01

In this paper, we introduce a patient cloth for position monitoring using motion recognition sensors based on flexible piezoelectric materials. The motion recognition sensors are embedded in three parts, which are the knee, hip and back, in the patient cloth. We use polyvinylidene fluoride (PVDF) as the flexible piezoelectric material for the sensors. By using the piezoelectric effect of the PVDF, we detect electrical signals when the cloth is bent or extended. We analyze the sensing values for our human motions by processing the sensor outputs in a custom-made program. Specifically, we focus on the transitions between standing and sitting, and sitting knee extension and supine position, which are important motions for patient monitoring.

Management system of occupational diseases in Korea: statistics, report and monitoring system.

Science.gov (United States)

Rhee, Kyung Yong; Choe, Seong Weon

2010-12-01

The management system of occupational diseases in Korea can be assessed from the perspective of a surveillance system. Workers' compensation insurance reports are used to produce official statistics on occupational diseases in Korea. National working conditions surveys are used to monitor the magnitude of work-related symptoms and signs in the labor force. A health examination program was introduced to detect occupational diseases through both selective and mass screening programs. The Working Environment Measurement Institution assesses workers' exposure to hazards in the workplace. Government regulates that the employer should do health examinations and working conditions measurement through contracted private agencies and following the Occupational Safety and Health Act. It is hoped that these institutions may be able to effectively detect and monitor occupational diseases and hazards in the workplace. In view of this, the occupational management system in Korea is well designed, except for the national survey system. In the future, national surveys for detection of hazards and ill-health outcomes in workers should be developed. The existing surveillance system for occupational disease can be improved by providing more refined information through statistical analysis of surveillance data.

Human Activity Recognition in AAL Environments Using Random Projections

Directory of Open Access Journals (Sweden)

Robertas Damaševičius

2016-01-01

Full Text Available Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject’s body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL, for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD data are presented.

Human Activity Recognition in AAL Environments Using Random Projections.

Science.gov (United States)

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

2016-01-01

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

Neutron monitoring system

International Nuclear Information System (INIS)

Okido, Fumiyasu; Arita, Setsuo.

1994-01-01

The present invention concerns neutron monitoring for monitoring reactor power, and presents a generation state of abnormal signals by monitoring output signals from neutron sensors, judges abnormal signals at an excessively high level outputted from the sensors to a measuring operator or a reactor operator. That is, a threshold value judging means judges whether a sensor signal exceeds a predetermined threshold value or not. When it exceeds the value, recognition signals are outputted to a memory means. The memory means memorizes the times of input of the recognition signals on every period of interval signals outputted from a reference signal generation means. The memory content of the memory means and the previously inputted hysteresis of the sensor are compared and judged, to determine the extent of the degradation of the sensors and output the result of the judgement and hysteresis information to the display means. The input means accesses to the judging means and the memory means to retrieve and correct the content of the memory means and the hysteresis information inputted to the judging means. (I.S.)

Source Monitoring in Alzheimer's Disease

Science.gov (United States)

El Haj, Mohamad; Fasotti, Luciano; Allain, Philippe

2012-01-01

Source monitoring is the process of making judgments about the origin of memories. There are three categories of source monitoring: reality monitoring (discrimination between self- versus other-generated sources), external monitoring (discrimination between several external sources), and internal monitoring (discrimination between two types of…

Tool wear and breakage monitoring in machining

International Nuclear Information System (INIS)

Madl, J.

1992-01-01

Risk minimization of metal cutting operations is one of the main problems of metal cutting technology. This paper describes some aspects in monitoring and control of machining processes. Tool monitoring is the fokus of machining process monitoring. Tool breakage and tool life recognition are the main problems of tool monitoring. All problems of this type of monitoring have not yet been fully solved. (orig.)

Acute kidney injury risk factor recognition in three teaching hospitals ...

African Journals Online (AJOL)

developing countries, which lack access to renal replacement therapy, ... To examine the relationship between AKI risk factor recognition and monitoring of renal ..... Travel costs were supported by the award of a Baxter Clinical Evidence.

Restoration of Dopamine Release Deficits during Object Recognition Memory Acquisition Attenuates Cognitive Impairment in a Triple Transgenic Mice Model of Alzheimer's Disease

Science.gov (United States)

Guzman-Ramos, Kioko; Moreno-Castilla, Perla; Castro-Cruz, Monica; McGaugh, James L.; Martinez-Coria, Hilda; LaFerla, Frank M.; Bermudez-Rattoni, Federico

2012-01-01

Previous findings indicate that the acquisition and consolidation of recognition memory involves dopaminergic activity. Although dopamine deregulation has been observed in Alzheimer's disease (AD) patients, the dysfunction of this neurotransmitter has not been investigated in animal models of AD. The aim of this study was to assess, by in vivo…

Misattribution, false recognition and the sins of memory.

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Schacter, D L; Dodson, C S

2001-09-29

Memory is sometimes a troublemaker. Schacter has classified memory's transgressions into seven fundamental 'sins': transience, absent-mindedness, blocking, misattribution, suggestibility, bias and persistence. This paper focuses on one memory sin, misattribution, that is implicated in false or illusory recognition of episodes that never occurred. We present data from cognitive, neuropsychological and neuroimaging studies that illuminate aspects of misattribution and false recognition. We first discuss cognitive research examining possible mechanisms of misattribution associated with false recognition. We also consider ways in which false recognition can be reduced or avoided, focusing in particular on the role of distinctive information. We next turn to neuropsychological research concerning patients with amnesia and Alzheimer's disease that reveals conditions under which such patients are less susceptible to false recognition than are healthy controls, thus providing clues about the brain mechanisms that drive false recognition. We then consider neuroimaging studies concerned with the neural correlates of true and false recognition, examining when the two forms of recognition can and cannot be distinguished on the basis of brain activity. Finally, we argue that even though misattribution and other memory sins are annoying and even dangerous, they can also be viewed as by-products of adaptive features of memory.

8 CFR 1292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

Science.gov (United States)

2010-01-01

...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 1292.2...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization...

Robust Peak Recognition in Intracranial Pressure Signals

Directory of Open Access Journals (Sweden)

Bergsneider Marvin

2010-10-01

Full Text Available Abstract Background The waveform morphology of intracranial pressure pulses (ICP is an essential indicator for monitoring, and forecasting critical intracranial and cerebrovascular pathophysiological variations. While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses. Methods This paper provides two contributions to this problem. First, it introduces MOCAIP++, a generic ICP pulse processing framework that generalizes MOCAIP (Morphological Clustering and Analysis of ICP Pulse. Its strength is to integrate several peak recognition methods to describe ICP morphology, and to exploit different ICP features to improve peak recognition. Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models. Results Experiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions. Conclusion The proposed framework allows to extract more reliable statistics about the ICP waveform morphology on challenging pulses to investigate the predictive power of these pulses on the condition of the patient.

Threshold models of recognition and the recognition heuristic

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Edgar Erdfelder

2011-02-01

Full Text Available According to the recognition heuristic (RH theory, decisions follow the recognition principle: Given a high validity of the recognition cue, people should prefer recognized choice options compared to unrecognized ones. Assuming that the memory strength of choice options is strongly correlated with both the choice criterion and recognition judgments, the RH is a reasonable strategy that approximates optimal decisions with a minimum of cognitive effort (Davis-Stober, Dana, and Budescu, 2010. However, theories of recognition memory are not generally compatible with this assumption. For example, some threshold models of recognition presume that recognition judgments can arise from two types of cognitive states: (1 certainty states in which judgments are almost perfectly correlated with memory strength and (2 uncertainty states in which recognition judgments reflect guessing rather than differences in memory strength. We report an experiment designed to test the prediction that the RH applies to certainty states only. Our results show that memory states rather than recognition judgments affect use of recognition information in binary decisions.

Application Of t-Cherry Junction Trees in Pattern Recognition

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Edith Kovacs

2010-06-01

Full Text Available Pattern recognition aims to classify data (patterns based ei-
ther on a priori knowledge or on statistical information extracted from the data. In this paper we will concentrate on statistical pattern recognition using a new probabilistic approach which makes possible to select the so called 'informative' features. We develop a pattern recognition algorithm which is based on the conditional independence structure underlying the statistical data. Our method was succesfully applied on a real problem of recognizing Parkinson's disease on the basis of voice disorders.

Enhanced surveillance strategies for detecting and monitoring chronic wasting disease in free-ranging cervids

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Walsh, Daniel P.

2012-01-01

The purpose of this document is to provide wildlife management agencies with the foundation upon which they can build scientifically rigorous and cost-effective surveillance and monitoring programs for chronic wasting disease (CWD) or refine their existing programs. The first chapter provides an overview of potential demographic and spatial risk factors of susceptible wildlife populations that may be exploited for CWD surveillance and monitoring. The information contained in this chapter explores historic as well as recent developments in our understanding of CWD disease dynamics. It also contains many literature references for readers who may desire a more thorough review of the topics or CWD in general. The second chapter examines methods for enhancing efforts to detect CWD on the landscape where it is not presently known to exist and focuses on the efficiency and cost-effectiveness of the surveillance program. Specifically, it describes the means of exploiting current knowledge of demographic and spatial risk factors, as described in the first chapter, through a two-stage surveillance scheme that utilizes traditional design-based sampling approaches and novel statistical methods to incorporate information about the attributes of the landscape, environment, populations and individual animals into CWD surveillance activities. By accounting for these attributes, efficiencies can be gained and cost-savings can be realized. The final chapter is unique in relation to the first two chapters. Its focus is on designing programs to monitor CWD once it is discovered within a jurisdiction. Unlike the prior chapters that are more detailed or prescriptive, this chapter by design is considerably more general because providing comprehensive direction for creating monitoring programs for jurisdictions without consideration of their monitoring goals, sociopolitical constraints, or their biological systems, is not possible. Therefore, the authors draw upon their collective

Increasing trend of wearables and multimodal interface for human activity monitoring: A review.

Science.gov (United States)

Kumari, Preeti; Mathew, Lini; Syal, Poonam

2017-04-15

Activity recognition technology is one of the most important technologies for life-logging and for the care of elderly persons. Elderly people prefer to live in their own houses, within their own locality. If, they are capable to do so, several benefits can follow in terms of society and economy. However, living alone may have high risks. Wearable sensors have been developed to overcome these risks and these sensors are supposed to be ready for medical uses. It can help in monitoring the wellness of elderly persons living alone by unobtrusively monitoring their daily activities. The study aims to review the increasing trends of wearable devices and need of multimodal recognition for continuous or discontinuous monitoring of human activity, biological signals such as Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG) and parameters along with other symptoms. This can provide necessary assistance in times of ominous need, which is crucial for the advancement of disease-diagnosis and treatment. Shared control architecture with multimodal interface can be used for application in more complex environment where more number of commands is to be used to control with better results in terms of controlling. Copyright © 2016 Elsevier B.V. All rights reserved.

Cough Recognition Based on Mel Frequency Cepstral Coefficients and Dynamic Time Warping

Science.gov (United States)

Zhu, Chunmei; Liu, Baojun; Li, Ping

Cough recognition provides important clinical information for the treatment of many respiratory diseases, but the assessment of cough frequency over a long period of time remains unsatisfied for either clinical or research purpose. In this paper, according to the advantage of dynamic time warping (DTW) and the characteristic of cough recognition, an attempt is made to adapt DTW as the recognition algorithm for cough recognition. The process of cough recognition based on mel frequency cepstral coefficients (MFCC) and DTW is introduced. Experiment results of testing samples from 3 subjects show that acceptable performances of cough recognition are obtained by DTW with a small training set.

Cross domain self-monitoring in anosognosia for memory loss in Alzheimer's disease.

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Chapman, Silvia; Colvin, Leigh E; Vuorre, Matti; Cocchini, Gianna; Metcalfe, Janet; Huey, Edward D; Cosentino, Stephanie

2018-04-01

Anosognosia for memory loss is a common feature of Alzheimer's disease (AD). Recent theories have proposed that anosognosia, a disruption in awareness at a global level, may reflect specific deficits in self-monitoring, or local awareness. Though anosognosia for memory loss has been shown to relate to memory self-monitoring, it is not clear if it relates to self-monitoring deficits in other domains (i.e., motor). The current study examined this question by analyzing the relationship between anosognosia for memory loss, memory monitoring, and motor monitoring in 35 individuals with mild to moderate AD. Anosognosia was assessed via clinical interview before participants completed a metamemory task to measure memory monitoring, and a computerized agency task to measure motor monitoring. Cognitive and psychological measures included memory, executive functions, and mood. Memory monitoring was associated with motor monitoring; however, anosognosia was associated only with memory monitoring, and not motor monitoring. Cognition and mood related differently to each measure of self-awareness. Results are interpreted within a hierarchical model of awareness in which local self-monitoring processes are associated across domain, but appear to only contribute to a global level awareness in a domain-specific fashion. Copyright © 2018 Elsevier Ltd. All rights reserved.

Behavioral and Neuroimaging Evidence for Facial Emotion Recognition in Elderly Korean Adults with Mild Cognitive Impairment, Alzheimer’s Disease, and Frontotemporal Dementia

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Soowon Park

2017-11-01

Full Text Available Background: Facial emotion recognition (FER is impaired in individuals with frontotemporal dementia (FTD and Alzheimer’s disease (AD when compared to healthy older adults. Since deficits in emotion recognition are closely related to caregiver burden or social interactions, researchers have fundamental interest in FER performance in patients with dementia.Purpose: The purpose of this study was to identify the performance profiles of six facial emotions (i.e., fear, anger, disgust, sadness, surprise, and happiness and neutral faces measured among Korean healthy control (HCs, and those with mild cognitive impairment (MCI, AD, and FTD. Additionally, the neuroanatomical correlates of facial emotions were investigated.Methods: A total of 110 (33 HC, 32 MCI, 32 AD, 13 FTD older adult participants were recruited from two different medical centers in metropolitan areas of South Korea. These individuals underwent an FER test that was used to assess the recognition of emotions or absence of emotion (neutral in 35 facial stimuli. Repeated measures two-way analyses of variance were used to examine the distinct profiles of emotional recognition among the four groups. We also performed brain imaging and voxel-based morphometry (VBM on the participants to examine the associations between FER scores and gray matter volume.Results: The mean score of negative emotion recognition (i.e., fear, anger, disgust, and sadness clearly discriminated FTD participants from individuals with MCI and AD and HC [F(3,106 = 10.829, p < 0.001, η2 = 0.235], whereas the mean score of positive emotion recognition (i.e., surprise and happiness did not. A VBM analysis showed negative emotions were correlated with gray matter volume of anterior temporal regions, whereas positive emotions were related to gray matter volume of fronto-parietal regions.Conclusion: Impairment of negative FER in patients with FTD is cross-cultural. The discrete neural correlates of FER indicate that

Behavioral and Neuroimaging Evidence for Facial Emotion Recognition in Elderly Korean Adults with Mild Cognitive Impairment, Alzheimer’s Disease, and Frontotemporal Dementia

Science.gov (United States)

Park, Soowon; Kim, Taehoon; Shin, Seong A; Kim, Yu Kyeong; Sohn, Bo Kyung; Park, Hyeon-Ju; Youn, Jung-Hae; Lee, Jun-Young

2017-01-01

Background: Facial emotion recognition (FER) is impaired in individuals with frontotemporal dementia (FTD) and Alzheimer’s disease (AD) when compared to healthy older adults. Since deficits in emotion recognition are closely related to caregiver burden or social interactions, researchers have fundamental interest in FER performance in patients with dementia. Purpose: The purpose of this study was to identify the performance profiles of six facial emotions (i.e., fear, anger, disgust, sadness, surprise, and happiness) and neutral faces measured among Korean healthy control (HCs), and those with mild cognitive impairment (MCI), AD, and FTD. Additionally, the neuroanatomical correlates of facial emotions were investigated. Methods: A total of 110 (33 HC, 32 MCI, 32 AD, 13 FTD) older adult participants were recruited from two different medical centers in metropolitan areas of South Korea. These individuals underwent an FER test that was used to assess the recognition of emotions or absence of emotion (neutral) in 35 facial stimuli. Repeated measures two-way analyses of variance were used to examine the distinct profiles of emotional recognition among the four groups. We also performed brain imaging and voxel-based morphometry (VBM) on the participants to examine the associations between FER scores and gray matter volume. Results: The mean score of negative emotion recognition (i.e., fear, anger, disgust, and sadness) clearly discriminated FTD participants from individuals with MCI and AD and HC [F(3,106) = 10.829, p emotion recognition (i.e., surprise and happiness) did not. A VBM analysis showed negative emotions were correlated with gray matter volume of anterior temporal regions, whereas positive emotions were related to gray matter volume of fronto-parietal regions. Conclusion: Impairment of negative FER in patients with FTD is cross-cultural. The discrete neural correlates of FER indicate that emotional recognition processing is a multi-modal system in the brain

Radiological imaging in pediatric rheumatic diseases

International Nuclear Information System (INIS)

Matuszewska, Genowefa; Zaniewicz-Kaniewska, Katarzyna; Włodkowska-Korytkowska, Monika; Smorawińska, Patrycja; Saied, Fadhil; Kunisz, Wojciech; Sudoł-Szopińska, Iwona

2014-01-01

Radiological imaging plays a fundamental role in the diagnosis and monitoring of rheumatic diseases. The basic method of imaging is a classic X-ray picture, which for many years has been used as a single method for the recognition and evaluation of the effects of disease management. In today’s modern day treatment of rheumatic diseases, ultrasonography and magnetic resonance are more commonly performed for early detection of inflammatory changes in the region of soft tissue, subchondral bone and bone marrow. In spite of their usefulness and fundamental role in the diagnosis, X-ray still remains an essential tool in the diagnosis of rheumatoid arthritis in children and is complementary to today’s methods of imaging diagnostics. In clinical practice, X-ray imaging is still an important examination performed not only to recognize the disorders, but also to provide a differential diagnosis. It helps estimate disease progression and is used to monitor the effects of treatment and the development of possible complications. Differential diagnosis of rheumatic diseases is performed on the basis of localization and type of radiographic changes. The surrounding periarticular soft tissues, bone structures, joint space, with special attention to articular bone surfaces and epiphyses, are analyzed. The aim of this work is to describe characteristic inflammatory changes present on X-ray imaging typical for the most commonly diagnosed rheumatic diseases in children, such as juvenile idiopathic arthritis, systemic lupus erythematosus, systemic scleroderma, mixed connective tissue disease, juvenile dermatomyositis, juvenile spondyloarthropathy and systemic vascular disease

Behavioral Biometrics in Assisted Living: A Methodology for Emotion Recognition

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S. Xefteris

2016-08-01

Full Text Available Behavioral biometrics aim at providing algorithms for the automatic recognition of individual behavioral traits, stemming from a person’s actions, attitude, expressions and conduct. In the field of ambient assisted living, behavioral biometrics find an important niche. Individuals suffering from the early stages of neurodegenerative diseases (MCI, Alzheimer’s, dementia need supervision in their daily activities. In this context, an unobtrusive system to monitor subjects and alert formal and informal carers providing information on both physical and emotional status is of great importance and positively affects multiple stakeholders. The primary aim of this paper is to describe a methodology for recognizing the emotional status of a subject using facial expressions and to identify its uses, in conjunction with pre-existing risk-assessment methodologies, for its integration into the context of a smart monitoring system for subjects suffering from neurodegenerative diseases. Paul Ekman’s research provided the background on the universality of facial expressions as indicators of underlying emotions. The methodology then makes use of computational geometry, image processing and graph theory algorithms for the detection of regions of interest and then a neural network is used for the final classification. Findings are coupled with previous published work for risk assessment and alert generation in the context of an ambient assisted living environment based on Service oriented architecture principles, aimed at remote web-based estimation of the cognitive and physical status of MCI and dementia patients.

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

Science.gov (United States)

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

Monitoring the efficacy of drugs for neglected tropical diseases controlled by preventive chemotherapy.

Science.gov (United States)

Albonico, M; Levecke, B; LoVerde, P T; Montresor, A; Prichard, R; Vercruysse, J; Webster, J P

2015-12-01

In the last decade, pharmaceutical companies, governments and global health organisations under the leadership of the World Health Organization (WHO) have pledged large-scale donations of anthelmintic drugs, including ivermectin (IVM), praziquantel (PZQ), albendazole (ALB) and mebendazole (MEB). This worldwide scale-up in drug donations calls for strong monitoring systems to detect any changes in anthelmintic drug efficacy. This review reports on the outcome of the WHO Global Working Group on Monitoring of Neglected Tropical Diseases Drug Efficacy, which consists of three subgroups: (i) soil-transmitted helminthiases (ALB and MEB); (ii) onchocerciasis and lymphatic filariasis (IVM); and (iii) schistosomiasis (PZQ). Progress of ongoing work, challenges and research needs for each of the four main drugs used in helminthic preventive chemotherapy (PC) are reported, laying the ground for appropriate implementation of drug efficacy monitoring programmes under the co-ordination and guidelines of the WHO. Best practices for monitoring drug efficacy should be made available and capacity built as an integral part of neglected tropical disease (NTD) programme monitoring. Development of a disease-specific model to predict the impact of PC programmes, to detect outliers and to solicit responses is essential. Research studies on genetic polymorphisms in relation to low-efficacy phenotypes should be carried out to identify markers of putative resistance against all NTD drugs and ultimately to develop diagnostic assays. Development of combination and co-administration of NTD drugs as well as of new drug entities to boost the armamentarium of the few drugs available for NTD control and elimination should be pursued in parallel. Copyright © 2015 International Society for Chemotherapy of Infection and Cancer. Published by Elsevier Ltd. All rights reserved.

Some Important Diseases of Tree Fruits - Diseases of Vegetable Crops - Diseases of Grapes - Diseases of Tree Nuts.

Science.gov (United States)

Petersen, Donald H.; And Others

This agriculture extension service publication from Pennsylvania State University consists of four sections on plant disease recognition and control. The titles of these four sections are: (1) Some Important Diseases of Tree Fruits; (2) Diseases of Vegetable Crops; (3) Diseases of Crops; and (4) Diseases of Tree Nuts. The first section discusses…

The functional neuroanatomy of verbal memory in Alzheimer's disease: [18F]-Fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) correlates of recency and recognition memory.

Science.gov (United States)

Staffaroni, Adam M; Melrose, Rebecca J; Leskin, Lorraine P; Riskin-Jones, Hannah; Harwood, Dylan; Mandelkern, Mark; Sultzer, David L

2017-09-01

The objective of this study was to distinguish the functional neuroanatomy of verbal learning and recognition in Alzheimer's disease (AD) using the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word Learning task. In 81 Veterans diagnosed with dementia due to AD, we conducted a cluster-based correlation analysis to assess the relationships between recency and recognition memory scores from the CERAD Word Learning Task and cortical metabolic activity measured using [ 18 F]-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET). AD patients (Mini-Mental State Examination, MMSE mean = 20.2) performed significantly better on the recall of recency items during learning trials than of primacy and middle items. Recency memory was associated with cerebral metabolism in the left middle and inferior temporal gyri and left fusiform gyrus (p recognition memory was correlated with metabolic activity in two clusters: (a) a large cluster that included the left hippocampus, parahippocampal gyrus, entorhinal cortex, anterior temporal lobe, and inferior and middle temporal gyri; (b) the bilateral orbitofrontal cortices (OFC). The present study further informs our understanding of the disparate functional neuroanatomy of recency memory and recognition memory in AD. We anticipated that the recency effect would be relatively preserved and associated with temporoparietal brain regions implicated in short-term verbal memory, while recognition memory would be associated with the medial temporal lobe and possibly the OFC. Consistent with our a priori hypotheses, list learning in our AD sample was characterized by a reduced primacy effect and a relatively spared recency effect; however, recency memory was associated with cerebral metabolism in inferior and lateral temporal regions associated with the semantic memory network, rather than regions associated with short-term verbal memory. The correlates of recognition memory included the medial temporal lobe

A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition.

Science.gov (United States)

Fuentes, Alvaro; Yoon, Sook; Kim, Sang Cheol; Park, Dong Sun

2017-09-04

Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called "deep learning meta-architectures". We combine each of these meta-architectures with "deep feature extractors" such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant's surrounding area.

A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition

Directory of Open Access Journals (Sweden)

Alvaro Fuentes

2017-09-01

Full Text Available Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN, Region-based Fully Convolutional Network (R-FCN, and Single Shot Multibox Detector (SSD, which for the purpose of this work are called “deep learning meta-architectures”. We combine each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network (ResNet. We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant’s surrounding area.

Available data sources for monitoring non-communicable diseases and their risk factors in South Africa

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M Wandai

2017-04-01

Full Text Available Background. Health information systems for monitoring chronic non-communicable diseases (NCDs in South Africa (SA are relatively less advanced than those for infectious diseases (particularly tuberculosis and HIV and for maternal and child health. NCDs are now the largest cause of premature mortality owing to exposure to risk factors arising from obesity that include physical inactivity and accessible, cheap but unhealthy diets. The National Strategic Plan for the Prevention and Control of Non-Communicable Diseases 2013 - 17 developed by the SA National Department of Health outlines targets and monitoring priorities. Objectives. To assess data sources relevant for monitoring NCDs and their risk factors by identifying the strengths and weaknesses, including usability and availability, of surveys and routine systems focusing at national and certain sub-national levels. Methods. Publicly available survey and routine data sources were assessed for variables collected, their characteristics, frequency of data collection, geographical coverage and data availability. Results. Survey data sources were found to be quite different in the way data variables are collected, their geographical coverage and also availability, while the main weakness of routine data sources was poor quality of data. Conclusions. To provide a sound basis for monitoring progress of NCDs and related risk factors, we recommend harmonising and strengthening available SA data sources in terms of data quality, definitions, categories used, timeliness, disease coverage and biomarker measurement.

A participatory approach to design monitoring indicators of production diseases in organic dairy farms.

Science.gov (United States)

Duval, J E; Fourichon, C; Madouasse, A; Sjöström, K; Emanuelson, U; Bareille, N

2016-06-01

Production diseases have an important negative effect on the health and welfare of dairy cows. Although organic animal production systems aim for high animal health levels, compliance with European organic farming regulations does not guarantee that this is achieved. Herd health and production management (HHPM) programs aim at optimizing herd health by preventing disease and production problems, but as yet they have not been consistently implemented by farmers. We hypothesize that one reason is the mismatch between what scientists propose as indicators for herd health monitoring and what farmers would like to use. Herd health monitoring is a key element in HHPM programs as it permits a regular assessment of the functioning of the different components of the production process. Planned observations or measurements of these components are indispensable for this monitoring. In this study, a participatory approach was used to create an environment in which farmers could adapt the indicators proposed by scientists for monitoring the five main production diseases on dairy cattle farms. The adaptations of the indicators were characterized and the farmers' explanations for the changes made were described. The study was conducted in France and Sweden, which differ in terms of their national organic regulations and existing advisory services. In both countries, twenty certified organic dairy farmers and their animal health management advisors participated in the study. All of the farmers adapted the initial monitoring plan proposed by scientists to specific production and animal health situation on their farm. This resulted in forty unique and farm-specific combinations of indicators for herd health monitoring. All but three farmers intended to monitor five health topics simultaneously using the constructed indicators. The qualitative analysis of the explanations given by farmers for their choices enabled an understanding of farmers' reasons for selecting and adapting

Pattern recognition

CERN Document Server

Theodoridis, Sergios

2003-01-01

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

Automatic anatomy recognition in whole-body PET/CT images

Energy Technology Data Exchange (ETDEWEB)

Wang, Huiqian [College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China and Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey; Tong, Yubing; Torigian, Drew A. [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Zhao, Liming [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 and Research Center of Intelligent System and Robotics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China)

2016-01-15

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

Automatic anatomy recognition in whole-body PET/CT images

International Nuclear Information System (INIS)

Wang, Huiqian; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.; Zhao, Liming

2016-01-01

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

Medical Named Entity Recognition for Indonesian Language Using Word Representations

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Rahman, Arief

2018-03-01

Nowadays, Named Entity Recognition (NER) system is used in medical texts to obtain important medical information, like diseases, symptoms, and drugs. While most NER systems are applied to formal medical texts, informal ones like those from social media (also called semi-formal texts) are starting to get recognition as a gold mine for medical information. We propose a theoretical Named Entity Recognition (NER) model for semi-formal medical texts in our medical knowledge management system by comparing two kinds of word representations: cluster-based word representation and distributed representation.

Recognition for reaching the most vulnerable populations in Burkina ...

International Development Research Centre (IDRC) Digital Library (Canada)

Recognition for reaching the most vulnerable populations in Burkina Faso. 07 novembre 2016. An IDRC supported project was recognized for its efforts to improve health service provision and the monitoring of pregnant women, new mothers, children, and people living with HIV in Burkina Faso's Nouna district. Dr Maurice ...

The Value of Fecal Markers in Predicting Relapse in Inflammatory Bowel Diseases

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Bianca J. Galgut

2018-01-01

Full Text Available The inflammatory bowel diseases (IBDs are lifelong chronic illnesses that place an immense burden on patients. The primary aim of therapy is to reduce disease burden and prevent relapse. However, the occurrence of relapses is often unpredictable. Current disease monitoring is primarily by way of clinical indices, with relapses often only recognized once the inflammatory episode is established with subsequent symptoms and gut damage. The window between initial upregulation of the inflammatory response and the recognition of symptoms may provide an opportunity to prevent the relapse and associated morbidity. This review will describe the existing literature surrounding predictive indicators of relapse of IBD with a specific focus on fecal biomarkers. Fecal biomarkers offer promise as a convenient, non-invasive, low cost option for disease monitoring that is predictive of subsequent relapse. To exploit the potential of fecal biomarkers in this role, further research is now required. This research needs to assess multiple fecal markers in context with demographics, disease phenotype, genetics, and intestinal microbiome composition, to build disease behavior models that can provide the clinician with sufficient confidence to intervene and change the long-term disease course.

The role of nitric oxide in the object recognition memory.

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Pitsikas, Nikolaos

2015-05-15

The novel object recognition task (NORT) assesses recognition memory in animals. It is a non-rewarded paradigm that it is based on spontaneous exploratory behavior in rodents. This procedure is widely used for testing the effects of compounds on recognition memory. Recognition memory is a type of memory severely compromised in schizophrenic and Alzheimer's disease patients. Nitric oxide (NO) is sought to be an intra- and inter-cellular messenger in the central nervous system and its implication in learning and memory is well documented. Here I intended to critically review the role of NO-related compounds on different aspects of recognition memory. Current analysis shows that both NO donors and NO synthase (NOS) inhibitors are involved in object recognition memory and suggests that NO might be a promising target for cognition impairments. However, the potential neurotoxicity of NO would add a note of caution in this context. Copyright © 2014 Elsevier B.V. All rights reserved.

Gait Recognition and Walking Exercise Intensity Estimation

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Bor-Shing Lin

2014-04-01

Full Text Available Cardiovascular patients consult doctors for advice regarding regular exercise, whereas obese patients must self-manage their weight. Because a system for permanently monitoring and tracking patients’ exercise intensities and workouts is necessary, a system for recognizing gait and estimating walking exercise intensity was proposed. For gait recognition analysis, αβ filters were used to improve the recognition of athletic attitude. Furthermore, empirical mode decomposition (EMD was used to filter the noise of patients’ attitude to acquire the Fourier transform energy spectrum. Linear discriminant analysis was then applied to this energy spectrum for training and recognition. When the gait or motion was recognized, the walking exercise intensity was estimated. In addition, this study addressed the correlation between inertia and exercise intensity by using the residual function of the EMD and quadratic approximation to filter the effect of the baseline drift integral of the acceleration sensor. The increase in the determination coefficient of the regression equation from 0.55 to 0.81 proved that the accuracy of the method for estimating walking exercise intensity proposed by Kurihara was improved in this study.

Advertisement recognition using mode voting acoustic fingerprint

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Fahmi, Reza; Abedi Firouzjaee, Hosein; Janalizadeh Choobbasti, Ali; Mortazavi Najafabadi, S. H. E.; Safavi, Saeid

2017-12-01

Emergence of media outlets and public relations tools such as TV, radio and the Internet since the 20th century provided the companies with a good platform for advertising their goods and services. Advertisement recognition is an important task that can help companies measure the efficiency of their advertising campaigns in the market and make it possible to compare their performance with competitors in order to get better business insights. Advertisement recognition is usually performed manually with help of human labor or is done through automated methods that are mainly based on heuristics features, these methods usually lack abilities such as scalability, being able to be generalized and be used in different situations. In this paper, we present an automated method for advertisement recognition based on audio processing method that could make this process fairly simple and eliminate the human factor out of the equation. This method has ultimately been used in Miras information technology in order to monitor 56 TV channels to detect all ad video clips broadcast over some networks.

Detection of recurrent Cushing's disease: proposal for standardized patient monitoring following transsphenoidal surgery.

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Ayala, Alejandro; Manzano, Alex J

2014-09-01

Transsphenoidal surgery (TSS) is first-line treatment for Cushing's disease (CD), a devastating disorder of hypercortisolism resulting from overproduction of adrenocorticotropic hormone by a pituitary adenoma. Surgical success rates vary widely and disease may recur years after remission is achieved. Recognizing CD recurrence can be challenging; although there is general acceptance among endocrinologists that patients need lifelong follow-up, there are currently no standardized monitoring guidelines. To begin addressing this need we created a novel, systematic algorithm by integrating information from literature on relapse rates in surgically-treated CD patients and our own clinical experiences. Reported recurrence rates range from 3 to 47 % (mean time to recurrence 16-49 months), emphasizing the need for careful post-surgical patient monitoring. We recommend that patients with post-operative serum cortisol surgery) be monitored semiannually for 3 years and annually thereafter. Patients with post-operative cortisol between 2 and 5 µg/dL may experience persistent or subclinical CD and should be evaluated every 2-3 months until biochemical control is achieved or additional treatment is initiated. Post-operative cortisol >5 µg/dL often signifies persistent disease and second-line treatment (e.g., immediate repeat pituitary surgery, radiotherapy, and/or medical therapy) may be considered. This follow-up algorithm aims to (a) enable early diagnosis and treatment of recurrent CD, thereby minimizing the detrimental effects of hypercortisolism, and (b) begin addressing the need for standardized guidelines for vigilant monitoring of CD patients treated by TSS, as demonstrated by the reported rates of recurrence.

8 CFR 292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

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2010-01-01

...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 292.2...; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization established in the United...

Type 2 diabetes and impaired glucose tolerance are associated with word memory source monitoring recollection deficits but not simple recognition familiarity deficits following water, low glycaemic load, and high glycaemic load breakfasts.

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Lamport, Daniel J; Lawton, Clare L; Mansfield, Michael W; Moulin, Chris A J; Dye, Louise

2014-01-30

It has been established that type 2 diabetes, and to some extent, impaired glucose tolerance (IGT), are associated with general neuropsychological impairments in episodic memory. However, the effect of abnormalities in glucose metabolism on specific retrieval processes such as source monitoring has not been investigated. The primary aim was to investigate the impact of type 2 diabetes and IGT on simple word recognition (familiarity) and complex source monitoring (recollection). A secondary aim was to examine the effect of acute breakfast glycaemic load manipulations on episodic memory. Data are presented from two separate studies; (i) 24 adults with type 2 diabetes and 12 controls aged 45-75years, (ii) 18 females with IGT and 47 female controls aged 30-50years. Controls were matched for age, IQ, BMI, waist circumference, and depression. Recognition of previously learned words and memory for specifically which list a previously learned word had appeared in (source monitoring) was examined at two test sessions during the morning after consumption of low glycaemic load, high glycaemic load and water breakfasts according to a counterbalanced, crossover design. Type 2 diabetes (pglucose metabolism are not detrimental for global episodic memory processes. This enhances our understanding of how metabolic disorders are associated with memory impairments. © 2013.

Monitoring endemic livestock diseases using laboratory diagnostic data: A simulation study to evaluate the performance of univariate process monitoring control algorithms.

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Lopes Antunes, Ana Carolina; Dórea, Fernanda; Halasa, Tariq; Toft, Nils

2016-05-01

Surveillance systems are critical for accurate, timely monitoring and effective disease control. In this study, we investigated the performance of univariate process monitoring control algorithms in detecting changes in seroprevalence for endemic diseases. We also assessed the effect of sample size (number of sentinel herds tested in the surveillance system) on the performance of the algorithms. Three univariate process monitoring control algorithms were compared: Shewart p Chart(1) (PSHEW), Cumulative Sum(2) (CUSUM) and Exponentially Weighted Moving Average(3) (EWMA). Increases in seroprevalence were simulated from 0.10 to 0.15 and 0.20 over 4, 8, 24, 52 and 104 weeks. Each epidemic scenario was run with 2000 iterations. The cumulative sensitivity(4) (CumSe) and timeliness were used to evaluate the algorithms' performance with a 1% false alarm rate. Using these performance evaluation criteria, it was possible to assess the accuracy and timeliness of the surveillance system working in real-time. The results showed that EWMA and PSHEW had higher CumSe (when compared with the CUSUM) from week 1 until the end of the period for all simulated scenarios. Changes in seroprevalence from 0.10 to 0.20 were more easily detected (higher CumSe) than changes from 0.10 to 0.15 for all three algorithms. Similar results were found with EWMA and PSHEW, based on the median time to detection. Changes in the seroprevalence were detected later with CUSUM, compared to EWMA and PSHEW for the different scenarios. Increasing the sample size 10 fold halved the time to detection (CumSe=1), whereas increasing the sample size 100 fold reduced the time to detection by a factor of 6. This study investigated the performance of three univariate process monitoring control algorithms in monitoring endemic diseases. It was shown that automated systems based on these detection methods identified changes in seroprevalence at different times. Increasing the number of tested herds would lead to faster

Pervasive mobile healthcare systems for chronic disease monitoring.

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Huzooree, Geshwaree; Kumar Khedo, Kavi; Joonas, Noorjehan

2017-05-01

Pervasive mobile healthcare system has the potential to improve healthcare and the quality of life of chronic disease patients through continuous monitoring. Recently, many articles related to pervasive mobile healthcare system focusing on health monitoring using wireless technologies have been published. The main aim of this review is to evaluate the state-of-the-art pervasive mobile healthcare systems to identify major technical requirements and design challenges associated with the realization of a pervasive mobile healthcare system. A systematic literature review was conducted over IEEE Xplore Digital Library to evaluate 20 pervasive mobile healthcare systems out of 683 articles from 2011 to 2016. The classification of the pervasive mobile healthcare systems and other important factors are discussed. Potential opportunities and challenges are pointed out for the further deployment of effective pervasive mobile healthcare systems. This article helps researchers in health informatics to have a holistic view toward understanding pervasive mobile healthcare systems and points out new technological trends and design challenges that researchers have to consider when designing such systems for better adoption, usability, and seamless integration.

Embedded Face Detection and Recognition

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Göksel Günlü

2012-10-01

Full Text Available The need to increase security in open or public spaces has in turn given rise to the requirement to monitor these spaces and analyse those images on-site and on-time. At this point, the use of smart cameras – of which the popularity has been increasing – is one step ahead. With sensors and Digital Signal Processors (DSPs, smart cameras generate ad hoc results by analysing the numeric images transmitted from the sensor by means of a variety of image-processing algorithms. Since the images are not transmitted to a distance processing unit but rather are processed inside the camera, it does not necessitate high-bandwidth networks or high processor powered systems; it can instantaneously decide on the required access. Nonetheless, on account of restricted memory, processing power and overall power, image processing algorithms need to be developed and optimized for embedded processors. Among these algorithms, one of the most important is for face detection and recognition. A number of face detection and recognition methods have been proposed recently and many of these methods have been tested on general-purpose processors. In smart cameras – which are real-life applications of such methods – the widest use is on DSPs. In the present study, the Viola-Jones face detection method – which was reported to run faster on PCs – was optimized for DSPs; the face recognition method was combined with the developed sub-region and mask-based DCT (Discrete Cosine Transform. As the employed DSP is a fixed-point processor, the processes were performed with integers insofar as it was possible. To enable face recognition, the image was divided into sub-regions and from each sub-region the robust coefficients against disruptive elements – like face expression, illumination, etc. – were selected as the features. The discrimination of the selected features was enhanced via LDA (Linear Discriminant Analysis and then employed for recognition. Thanks to its

Available data sources for monitoring non-communicable diseases and their risk factors in South Africa

DEFF Research Database (Denmark)

Wandai, M.; Aagaard-Hansen, Jens; Day, C.

2017-01-01

Background. Health information systems for monitoring chronic non-communicable diseases (NCDs) in South Africa (SA) are relatively less advanced than those for infectious diseases (particularly tuberculosis and HIV) and for maternal and child health. NCDs are now the largest cause of premature mo...

Patterns of False Memory in Patients with Huntington's Disease.

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Chen, I-Wen; Chen, Chiung-Mei; Wu, Yih-Ru; Hua, Mau-Sun

2017-06-01

Increased false memory recognition in patients with Huntington's disease (HD) has been widely reported; however, the underlying memory constructive processes remain unclear. The present study explored gist memory, item-specific memory, and monitoring ability in patients with HD. Twenty-five patients (including 13 patients with mild HD and 12 patients with moderate-to-severe HD) and 30 healthy comparison participants (HC) were recruited. We used the Deese-Roediger-McDermott (DRM) paradigm to investigate participants' false recognition patterns, along with neuropsychological tests to assess general cognitive function. Both mild and moderate-to-severe patients with HD showed significant executive functioning and episodic memory impairment. On the DRM tasks, both HD patient groups showed significantly impaired performance in tasks assessing unrelated false recognition and item-specific memory as compared to the HC group; moderate-to-severe patients performed more poorly than mild patients did. Only moderate-severe patients exhibited significantly poorer related false recognition index scores than HCs in the verbal DRM task; performance of HD patient groups was comparable to the HC group on the pictorial DRM task. It appears that diminished verbatim memory and monitoring ability are early signs of cognitive decline during the HD course. Conversely, gist memory is relatively robust, with only partial decline during advanced-stage HD. Our findings suggest that medial temporal lobe function is relatively preserved compared to that of frontal-related structures in early HD. Thus, gist-based memory rehabilitation programs might be beneficial for patients with HD. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Thiopurine monitoring in children with inflammatory bowel disease: a systematic review.

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Konidari, Anastasia; Anagnostopoulos, Antonios; Bonnett, Laura J; Pirmohamed, Munir; El-Matary, Wael

2014-09-01

The aim was to systematically review the evidence on the clinical usefulness of thiopurine metabolite and white blood count (WBC) monitoring in the assessment of clinical outcomes in children with inflammatory bowel disease (IBD). Medline, Embase, Cochrane Central Register of controlled trials and http://www.clinicaltrials.gov were screened in adherence to the PRISMA statement by two independent reviewers for identification of eligible studies. Eligible studies were randomized controlled trials (RCTs), cohort studies and large case series of children with inflammatory bowel disease (IBD) (6MMPR) as an indicator of hepatotoxicity. Low thiopurine metabolite concentration may be indicative of non-compliance. Thiopurine metabolite testing does not safely predict clinical outcome, but may facilitate toxicity surveillance and treatment optimization in poor responders. Current evidence favours the combination of thiopurine metabolite/WBC monitoring and clinic follow-up for prompt identification of haematologic/hepatic toxicity safe dose adjustment, and treatment modification in cases of suboptimal clinical outcome or non-compliance. Well designed RCTs for the identification of robust surrogate markers of thiopurine efficacy and toxicity are required. © 2014 The British Pharmacological Society.

Study on Analysis and Pattern Recognition of the Manifestation of the Pulse Detection of Cerebrovascular Disease

Energy Technology Data Exchange (ETDEWEB)

Jing, J; Wang, Y C; Hong, W X; Zhang, W P [Department of Biomedical Engineering, University of Yanshan, Qinhuangdao, Hebei Province, 066004 (China)

2006-10-15

Cerebrovascular Disease (CVD) is also called stroke in Traditional Chinese Medicine (TCM). CVD is a kind of frequent diseases with high incidence, high death rate, high deformity rate and high relapse rate. The pathogenesis of CVD has relation to many factors. In modern medicine, we can make use of various instruments to check many biochemical parameters. However, at present, the early detection of CVD can mostly be done artificially by specialists. In TCM the salted expert can detect the state of a CVD patient by felling his (or her) pulse. It is significant to apply the modern information and engineering techniques to the early discovery of CVD. It is also a challenge to do this in fact. In this paper, the authors presented a detection method of CVD basing on analysis and pattern recognition of Manifestation of the Pulse of TCM using wavelet technology and Neural Networks. Pulse signals from normal health persons and CVD patients were studied comparatively. This research method is flexible to deal with other physiological signals.

Wearable-Based Human Activity Recognition Using an IoT Approach

Directory of Open Access Journals (Sweden)

Diego Castro

2017-11-01

Full Text Available This paper presents a novel system based on the Internet of Things (IoT to Human Activity Recognition (HAR by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog. Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio.

Gesture recognition for smart home applications using portable radar sensors.

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Wan, Qian; Li, Yiran; Li, Changzhi; Pal, Ranadip

2014-01-01

In this article, we consider the design of a human gesture recognition system based on pattern recognition of signatures from a portable smart radar sensor. Powered by AAA batteries, the smart radar sensor operates in the 2.4 GHz industrial, scientific and medical (ISM) band. We analyzed the feature space using principle components and application-specific time and frequency domain features extracted from radar signals for two different sets of gestures. We illustrate that a nearest neighbor based classifier can achieve greater than 95% accuracy for multi class classification using 10 fold cross validation when features are extracted based on magnitude differences and Doppler shifts as compared to features extracted through orthogonal transformations. The reported results illustrate the potential of intelligent radars integrated with a pattern recognition system for high accuracy smart home and health monitoring purposes.

The effects of initial testing on false recall and false recognition in the social contagion of memory paradigm.

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Huff, Mark J; Davis, Sara D; Meade, Michelle L

2013-08-01

In three experiments, participants studied photographs of common household scenes. Following study, participants completed a category-cued recall test without feedback (Exps. 1 and 3), a category-cued recall test with feedback (Exp. 2), or a filler task (no-test condition). Participants then viewed recall tests from fictitious previous participants that contained erroneous items presented either one or four times, and then completed final recall and source recognition tests. The participants in all conditions reported incorrect items during final testing (a social contagion effect), and across experiments, initial testing had no impact on false recall of erroneous items. However, on the final source-monitoring recognition test, initial testing had a protective effect against false source recognition: Participants who were initially tested with and without feedback on category-cued initial tests attributed fewer incorrect items to the original event on the final source-monitoring recognition test than did participants who were not initially tested. These data demonstrate that initial testing may protect individuals' memories from erroneous suggestions.

HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

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Linlin Guo

2018-01-01

Full Text Available The joint of WiFi-based and vision-based human activity recognition has attracted increasing attention in the human-computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi-based activity recognition dataset named WiAR to provide a benchmark for WiFi-based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton-based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than 93% using WiAR dataset.

Weight preserving image registration for monitoring disease progression in lung CT

DEFF Research Database (Denmark)

Gorbunova, Vladlena; Lo, Pechin Chien Pau; Haseem, Ashraf

2008-01-01

We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan...... the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans...

Dual-Recognition Förster Resonance Energy Transfer Based Platform for One-Step Sensitive Detection of Pathogenic Bacteria Using Fluorescent Vancomycin-Gold Nanoclusters and Aptamer-Gold Nanoparticles.

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Yu, Mengqun; Wang, Hong; Fu, Fei; Li, Linyao; Li, Jing; Li, Gan; Song, Yang; Swihart, Mark T; Song, Erqun

2017-04-04

The effective monitoring, identification, and quantification of pathogenic bacteria is essential for addressing serious public health issues. In this study, we present a universal and facile one-step strategy for sensitive and selective detection of pathogenic bacteria using a dual-molecular affinity-based Förster (fluorescence) resonance energy transfer (FRET) platform based on the recognition of bacterial cell walls by antibiotic and aptamer molecules, respectively. As a proof of concept, Vancomycin (Van) and a nucleic acid aptamer were employed in a model dual-recognition scheme for detecting Staphylococcus aureus (Staph. aureus). Within 30 min, by using Van-functionalized gold nanoclusters and aptamer-modified gold nanoparticles as the energy donor and acceptor, respectively, the FRET signal shows a linear variation with the concentration of Staph. aureus in the range from 20 to 10 8 cfu/mL with a detection limit of 10 cfu/mL. Other nontarget bacteria showed negative results, demonstrating the good specificity of the approach. When employed to assay Staph. aureus in real samples, the dual-recognition FRET strategy showed recoveries from 99.00% to the 109.75% with relative standard derivations (RSDs) less than 4%. This establishes a universal detection platform for sensitive, specific, and simple pathogenic bacteria detection, which could have great impact in the fields of food/public safety monitoring and infectious disease diagnosis.

Pattern Recognition of the Multiple Sclerosis Syndrome

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Stewart, Renee; Healey, Kathleen M.

2017-01-01

During recent decades, the autoimmune disease neuromyelitis optica spectrum disorder (NMOSD), once broadly classified under the umbrella of multiple sclerosis (MS), has been extended to include autoimmune inflammatory conditions of the central nervous system (CNS), which are now diagnosable with serum serological tests. These antibody-mediated inflammatory diseases of the CNS share a clinical presentation to MS. A number of practical learning points emerge in this review, which is geared toward the pattern recognition of optic neuritis, transverse myelitis, brainstem/cerebellar and hemispheric tumefactive demyelinating lesion (TDL)-associated MS, aquaporin-4-antibody and myelin oligodendrocyte glycoprotein (MOG)-antibody NMOSD, overlap syndrome, and some yet-to-be-defined/classified demyelinating disease, all unspecifically labeled under MS syndrome. The goal of this review is to increase clinicians’ awareness of the clinical nuances of the autoimmune conditions for MS and NMSOD, and to highlight highly suggestive patterns of clinical, paraclinical or imaging presentations in order to improve differentiation. With overlay in clinical manifestations between MS and NMOSD, magnetic resonance imaging (MRI) of the brain, orbits and spinal cord, serology, and most importantly, high index of suspicion based on pattern recognition, will help lead to the final diagnosis. PMID:29064441

Atypical evening cortisol profile induces visual recognition memory deficit in healthy human subjects

Directory of Open Access Journals (Sweden)

Gilpin Heather

2008-08-01

Full Text Available Abstract Background Diurnal rhythm-mediated endogenous cortisol levels in humans are characterised by a peak in secretion after awakening that declines throughout the day to an evening trough. However, a significant proportion of the population exhibits an atypical cycle of diurnal cortisol due to shift work, jet-lag, aging, and mental illness. Results The present study has demonstrated a correlation between elevation of cortisol in the evening and deterioration of visual object recognition memory. However, high evening cortisol levels have no effect on spatial memory. Conclusion This study suggests that atypical evening salivary cortisol levels have an important role in the early deterioration of recognition memory. The loss of recognition memory, which is vital for everyday life, is a major symptom of the amnesic syndrome and early stages of Alzheimer's disease. Therefore, this study will promote a potential physiologic marker of early deterioration of recognition memory and a possible diagnostic strategy for Alzheimer's disease.

How consumer physical activity monitors could transform human physiology research

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Hall Brown, Tyish S.; Collier, Scott R.; Sandberg, Kathryn

2017-01-01

A sedentary lifestyle and lack of physical activity are well-established risk factors for chronic disease and adverse health outcomes. Thus, there is enormous interest in measuring physical activity in biomedical research. Many consumer physical activity monitors, including Basis Health Tracker, BodyMedia Fit, DirectLife, Fitbit Flex, Fitbit One, Fitbit Zip, Garmin Vivofit, Jawbone UP, MisFit Shine, Nike FuelBand, Polar Loop, Withings Pulse O2, and others have accuracies similar to that of research-grade physical activity monitors for measuring steps. This review focuses on the unprecedented opportunities that consumer physical activity monitors offer for human physiology and pathophysiology research because of their ability to measure activity continuously under real-life conditions and because they are already widely used by consumers. We examine current and potential uses of consumer physical activity monitors as a measuring or monitoring device, or as an intervention in strategies to change behavior and predict health outcomes. The accuracy, reliability, reproducibility, and validity of consumer physical activity monitors are reviewed, as are limitations and challenges associated with using these devices in research. Other topics covered include how smartphone apps and platforms, such as the Apple ResearchKit, can be used in conjunction with consumer physical activity monitors for research. Lastly, the future of consumer physical activity monitors and related technology is considered: pattern recognition, integration of sleep monitors, and other biosensors in combination with new forms of information processing. PMID:28052867

How consumer physical activity monitors could transform human physiology research.

Science.gov (United States)

Wright, Stephen P; Hall Brown, Tyish S; Collier, Scott R; Sandberg, Kathryn

2017-03-01

A sedentary lifestyle and lack of physical activity are well-established risk factors for chronic disease and adverse health outcomes. Thus, there is enormous interest in measuring physical activity in biomedical research. Many consumer physical activity monitors, including Basis Health Tracker, BodyMedia Fit, DirectLife, Fitbit Flex, Fitbit One, Fitbit Zip, Garmin Vivofit, Jawbone UP, MisFit Shine, Nike FuelBand, Polar Loop, Withings Pulse O 2 , and others have accuracies similar to that of research-grade physical activity monitors for measuring steps. This review focuses on the unprecedented opportunities that consumer physical activity monitors offer for human physiology and pathophysiology research because of their ability to measure activity continuously under real-life conditions and because they are already widely used by consumers. We examine current and potential uses of consumer physical activity monitors as a measuring or monitoring device, or as an intervention in strategies to change behavior and predict health outcomes. The accuracy, reliability, reproducibility, and validity of consumer physical activity monitors are reviewed, as are limitations and challenges associated with using these devices in research. Other topics covered include how smartphone apps and platforms, such as the Apple ResearchKit, can be used in conjunction with consumer physical activity monitors for research. Lastly, the future of consumer physical activity monitors and related technology is considered: pattern recognition, integration of sleep monitors, and other biosensors in combination with new forms of information processing. Copyright © 2017 the American Physiological Society.

A Malaysian Vehicle License Plate Localization and Recognition System

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Ganapathy Velappa

2008-02-01

Full Text Available Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed license plate localization algorithm is based on a combination of morphological processes with a modified Hough Transform approach and the recognition of the license plates is achieved by the implementation of the feed-forward backpropagation artificial neural network. Experimental results show an average of 95% successful license plate localization and recognition in a total of 589 images captured from a complex outdoor environment.

Grey Incidence analyze of Environment Monitoring Data and Research on the Disease Prevention Measures of Longmen Grottoes

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LeiLei, Zheng; XueZhi, Fu; Fei, Chu

2018-05-01

Longmen Grottoes was afflicted with many diseases for a long period such as weathering, seepage water and organism growth. Those adverse factors were threatening to preserve cultural relic. Longmen Grottoes conservation and restoration project being put into effect by UNESCO in 2002. The Longmen Grottoes area environmental monitoring system was built in order to comprehensively master the distribution law of environmental factors over the Longmen Grottoes. The monitoring items contains temperature, humidity, wind direction, wind speed, precipitation, light intensity,water content in soil, the rock surface temperature and so on. At the same time, monitoring three experiment caves, monitoring the inside temperature, humidity, seepage water and the wall face temperature etc. So as to analyze the relationship between cave environment and regional environment. We statistical and arrange the data using Excel software, Kgraph software and DPS software. Through the grey incidence analyze, the incidence matrix and the correlation degree of the environmental factors was obtained[1]. The main environment factors for the formation of the disease had been researched. Based on the existing environmental monitor data, the relevance of seepage water and fracture displacement with other environmental factors had been studied, and the relational order was obtained. Corresponding preventive measures were put forward by the formation mechanism analyze of the disease.

Dance-the-Music: an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

Science.gov (United States)

Maes, Pieter-Jan; Amelynck, Denis; Leman, Marc

2012-12-01

In this article, a computational platform is presented, entitled "Dance-the-Music", that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers' models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method-can determine the quality of a student's performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures.

Emotional Faces in Context: Age Differences in Recognition Accuracy and Scanning Patterns

Science.gov (United States)

Noh, Soo Rim; Isaacowitz, Derek M.

2014-01-01

While age-related declines in facial expression recognition are well documented, previous research relied mostly on isolated faces devoid of context. We investigated the effects of context on age differences in recognition of facial emotions and in visual scanning patterns of emotional faces. While their eye movements were monitored, younger and older participants viewed facial expressions (i.e., anger, disgust) in contexts that were emotionally congruent, incongruent, or neutral to the facial expression to be identified. Both age groups had highest recognition rates of facial expressions in the congruent context, followed by the neutral context, and recognition rates in the incongruent context were worst. These context effects were more pronounced for older adults. Compared to younger adults, older adults exhibited a greater benefit from congruent contextual information, regardless of facial expression. Context also influenced the pattern of visual scanning characteristics of emotional faces in a similar manner across age groups. In addition, older adults initially attended more to context overall. Our data highlight the importance of considering the role of context in understanding emotion recognition in adulthood. PMID:23163713

The importance of monitoring minimal residual disease in childhood acute lymphoblastic leukemia

International Nuclear Information System (INIS)

Kolenova, A.; Subova, Z.; Cizmar, A.; Sejnova, D.; Kaiserova, E.; Hikkel, I.; Hikkelova, M.; Bubanska, E.; Oravkinova, I.

2012-01-01

Since the strong correlation between minimal residual disease (MRD) levels and risk of relapse in childhood acute lymphoblastic leukemia, monitoring of MRD provides unique information regarding treatment response. Because the significance of MRD monitoring has been strongly supported by several studies and because it has been implemented in the latest protocols, there has been a significant effort to develop MRD monitoring in the Slovak Republic. Between 1. 10. 2006 and 31. 12. 2009, 50 children with ALL who were treated at three Slovak centers were included in the RQ PCR MRD pilot project. Based on MRD stratification, we identified 26 patients who were stratified into the HRG (high risk group) 3 patients (11,5 %), IRG (intermediate risk group), 14 p. 54 % and SRG (standard risk group), 9 p. (34,5 %). (author)

Prenatal Remote Monitoring of Women With Gestational Hypertensive Diseases: Cost Analysis.

Science.gov (United States)

Lanssens, Dorien; Vandenberk, Thijs; Smeets, Christophe Jp; De Cannière, Hélène; Vonck, Sharona; Claessens, Jade; Heyrman, Yenthel; Vandijck, Dominique; Storms, Valerie; Thijs, Inge M; Grieten, Lars; Gyselaers, Wilfried

2018-03-26

Remote monitoring in obstetrics is relatively new; some studies have shown its effectiveness for both mother and child. However, few studies have evaluated the economic impact compared to conventional care, and no cost analysis of a remote monitoring prenatal follow-up program for women diagnosed with gestational hypertensive diseases (GHD) has been published. The aim of this study was to assess the costs of remote monitoring versus conventional care relative to reported benefits. Patient data from the Pregnancy Remote Monitoring (PREMOM) study were used. Health care costs were calculated from patient-specific hospital bills of Ziekenhuis Oost-Limburg (Genk, Belgium) in 2015. Cost comparison was made from three perspectives: the Belgian national health care system (HCS), the National Institution for Insurance of Disease and Disability (RIZIV), and costs for individual patients. The calculations were made for four major domains: prenatal follow-up, prenatal admission to the hospital, maternal and neonatal care at and after delivery, and total amount of costs. A simulation exercise was made in which it was calculated how much could be demanded of RIZIV for funding the remote monitoring service. A total of 140 pregnancies were included, of which 43 received remote monitoring (30.7%) and 97 received conventional care (69.2%). From the three perspectives, there were no differences in costs for prenatal follow-up. Compared to conventional care, remote monitoring patients had 34.51% less HCS and 41.72% less RIZIV costs for laboratory test results (HCS: mean €0.00 [SD €55.34] vs mean €38.28 [SD € 44.08], Pmonitoring than conventional care (mean €209.22 [SD €213.32] vs mean €231.32 [SD 67.09], P=.02), but were 0.69% higher for RIZIV (mean €122.60 [SD €92.02] vs mean €121.78 [SD €20.77], Pmonitoring were mean €4233.31 (SD €3463.31) per person and mean €4973.69 (SD €5219.00) per person for conventional care (P=.82), a reduction of €740.38 (14

Monitoring emerging diseases of fish and shellfish using electronic sources.

Science.gov (United States)

Thrush, M A; Dunn, P L; Peeler, E J

2012-10-01

New and emerging fish and shellfish diseases represent an important constraint to the growth and sustainability of many aquaculture sectors and have also caused substantial economic and environmental impacts in wild stocks. This paper details the results of 8 years of a monitoring programme for emerging aquatic animal diseases reported around the world. The objectives were to track global occurrences and, more specifically, to identify and provide advanced warning of disease threats that may affect wild and farmed fish stocks in the UK. A range of electronic information sources, including Internet newsletters, alerting services and news agency releases, was systematically searched for reports of new diseases, new presentations of known pathogens and known diseases occurring in new geographic locations or new host species. A database was established to log the details of key findings, and 250 emerging disease events in 52 countries were recorded during the period of study. These included 14 new diseases and a further 16 known diseases in new species. Viruses and parasites accounted for the majority of reports (55% and 24%, respectively), and known diseases occurring in new locations were the most important emerging disease category (in which viruses were dominant). Emerging diseases were reported disproportionally in salmonid species (33%), in farmed populations (62%) and in Europe and North America (80%). The lack of reports from some regions with significant aquaculture or fishery production may indicate that emerging diseases are not being recognized in these areas owing to insufficient surveillance or testing or that these events are being under-reported. The results are discussed in relation to processes underpinning disease emergence in the aquatic environment. © 2011 Crown Copyright. Reproduced with the permission of the Controller of Her Majesty’s Stationery Office and Centre for Environment Fisheries & Aquaculture Science.

Human Activity Recognition from Body Sensor Data using Deep Learning.

Science.gov (United States)

Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

2018-04-16

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Patient perceptions of a remote monitoring intervention for chronic disease management.

Science.gov (United States)

Wakefield, Bonnie J; Holman, John E; Ray, Annette; Scherubel, Melody

2011-04-01

Use of telecommunications technology to provide remote monitoring for people with chronic disease is becoming increasingly accepted as a means to improve patient outcomes and reduce resource use. The purpose of this project was to evaluate patient perceptions of a nurse-managed remote monitoring intervention to improve outcomes in veterans with comorbid diabetes and hypertension. Postintervention evaluation data were collected using a 12-item questionnaire and an open-ended question. Participants rated the program as generally positive on the questionnaire, but responses to the open-ended question revealed criticisms and suggestions for improvement not captured on the questionnaire. Interviewing participants in these programs may offer richer data for identifying areas for program improvement. Copyright 2011, SLACK Incorporated.

Distinct roles of basal forebrain cholinergic neurons in spatial and object recognition memory

OpenAIRE

Kana Okada; Kayo Nishizawa; Tomoko Kobayashi; Shogo Sakata; Kazuto Kobayashi

2015-01-01

Recognition memory requires processing of various types of information such as objects and locations. Impairment in recognition memory is a prominent feature of amnesia and a symptom of Alzheimer?s disease (AD). Basal forebrain cholinergic neurons contain two major groups, one localized in the medial septum (MS)/vertical diagonal band of Broca (vDB), and the other in the nucleus basalis magnocellularis (NBM). The roles of these cell groups in recognition memory have been debated, and it remai...

Feature selection in classification of eye movements using electrooculography for activity recognition.

Science.gov (United States)

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

Nuclear and thermal power plant components monitoring by pattern recognition methods

International Nuclear Information System (INIS)

Chehade, M.

1981-05-01

This study deals with the monitoring of complex systems with the aim of diagnosing failures or degradation of operation. The different monitoring and diagnostic techniques are reviewed and a statistical analyses of data is presented. The hardware and software for the acquisition and processing of data are presented. The method of monitoring is applied to the extraction valve and the pressurizer discharge isolation valve surveillance [fr

Optical Pattern Recognition

Science.gov (United States)

Yu, Francis T. S.; Jutamulia, Suganda

2008-10-01

Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

Event Recognition Based on Deep Learning in Chinese Texts.

Directory of Open Access Journals (Sweden)

Yajun Zhang

Full Text Available Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM. Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN, then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

Event Recognition Based on Deep Learning in Chinese Texts.

Science.gov (United States)

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

A new selective developmental deficit: Impaired object recognition with normal face recognition.

Science.gov (United States)

Germine, Laura; Cashdollar, Nathan; Düzel, Emrah; Duchaine, Bradley

2011-05-01

Studies of developmental deficits in face recognition, or developmental prosopagnosia, have shown that individuals who have not suffered brain damage can show face recognition impairments coupled with normal object recognition (Duchaine and Nakayama, 2005; Duchaine et al., 2006; Nunn et al., 2001). However, no developmental cases with the opposite dissociation - normal face recognition with impaired object recognition - have been reported. The existence of a case of non-face developmental visual agnosia would indicate that the development of normal face recognition mechanisms does not rely on the development of normal object recognition mechanisms. To see whether a developmental variant of non-face visual object agnosia exists, we conducted a series of web-based object and face recognition tests to screen for individuals showing object recognition memory impairments but not face recognition impairments. Through this screening process, we identified AW, an otherwise normal 19-year-old female, who was then tested in the lab on face and object recognition tests. AW's performance was impaired in within-class visual recognition memory across six different visual categories (guns, horses, scenes, tools, doors, and cars). In contrast, she scored normally on seven tests of face recognition, tests of memory for two other object categories (houses and glasses), and tests of recall memory for visual shapes. Testing confirmed that her impairment was not related to a general deficit in lower-level perception, object perception, basic-level recognition, or memory. AW's results provide the first neuropsychological evidence that recognition memory for non-face visual object categories can be selectively impaired in individuals without brain damage or other memory impairment. These results indicate that the development of recognition memory for faces does not depend on intact object recognition memory and provide further evidence for category-specific dissociations in visual

Tracking and position recognition applied to remote monitoring to be used in integrated safeguards

Energy Technology Data Exchange (ETDEWEB)

Bonino, Anibal D; Perez, Adrian C; Krimer, Mario J; Teira, Ruben O; Vigile, Rodolfo S; Valentino, Lucia I; Giordano, Luis A; Ferro, Juan M [Autoridad Regulatoria Nuclear, Buenos Aires (Argentina)

2001-07-01

In the framework of the Strengthening and integrated Safeguards Systems new measures and tools are available to meet the safeguards objective. The credible assurance on the absence of undeclared nuclear material and activities derived from the implementation of the Additional Protocol has an impact on the current safeguards approach to declared facilities thus their through review is advisable. Among these tools, a more intensive use of unattended systems and remote transmission of safeguards relevant information are considered, specifically for On Load Reactors (ORLs). A Remote Monitoring Systems (RMS) to cover the transfers of spent fuels from the ponds to a dry storage is being tested at Embalse nuclear power plant. In connection with the RMS, this paper describes some of the technologies involved: the Global Position System (GPS) and the Radio Frequency IDentification (RFID), which were implemented due to the requirement to ascertain the position of valuable elements. The main objective of this design aimed at safeguarding the spent fuels transfers from the welding cell to the silos field by a strict surveillance of the whereabouts. The bases for the development were settled by the specifications imposed by the integrated Safeguards of the Nuclear Regulatory Authority in Argentina. The resultant tracking and position recognition system is based on GPS receivers operating in Differential Mode, with the aid of Radio Frequency Identification. In compliance with the safeguard requirement the whole system is able to operate in a continuous and remote mode, what means without human being attention. (author)

Quantitative EEG Applying the Statistical Recognition Pattern Method

DEFF Research Database (Denmark)

Engedal, Knut; Snaedal, Jon; Hoegh, Peter

2015-01-01

BACKGROUND/AIM: The aim of this study was to examine the discriminatory power of quantitative EEG (qEEG) applying the statistical pattern recognition (SPR) method to separate Alzheimer's disease (AD) patients from elderly individuals without dementia and from other dementia patients. METHODS...

Individual Self-monitoring &Peer-monitoring In One Classroom in Writing Activities: Who Is at Disadvantage?

Directory of Open Access Journals (Sweden)

Zohreh Zare Toofan

2014-02-01

Full Text Available Writing is an important experience through which we are able to share ideas, arouse feelings, persuade and convince other people (white & Arndt, 1991. It is important to view writing not solely as the product of an individual, but as a cognitive, social and cultural act. Writing is an act that takes place within a context, that accomplishes a particular purpose and that is appropriately shaped for its intended audience (Hamplyones & Condon, 1989. Here, the present research considers the significance effects of two important independent variables self-monitoring and peer-monitoring in writing activities on Iranian EFL learners. In this research it was supposed to study new effects of two Meta cognitive strategies self-monitoring and peer-monitoring on 173 male and female learners' writing activities whose age ranged between the age 16-27, and they had a composing description writing paragraph as pre & post test in the same conditions. Although many studies have been conducted on the effects of self-monitoring with a variety of students across a variety of settings (Amato-Zech, Hoff, & Doepke, 2006 Cooper et al., 2007, Dunlap, Dunlap, Koegel, & Koegel 1991. But goal of this study was to increase the participant’s on-task behavior in self & peer-monitoring (E. Johnson, 2007, Self &Peer-monitoring added. Although both of them were useful for providing challengeable students, and became useful for prosocial life, but self-monitoring helped them to become awareness of their weaknesses and strengths to increase positive way of the quality and quantity of their learning in written task, and peer-monitoring occurred when the students achieved recognition level to evaluate the other peers' behavior, and it was obviously understood that it needed more training time to arrive at the level of recognition of each others' behavior.

Weight preserving image registration for monitoring disease progression in lung CT.

Science.gov (United States)

Gorbunova, Vladlena; Lol, Pechin; Ashraf, Haseem; Dirksen, Asger; Nielsen, Mads; de Bruijne, Marleen

2008-01-01

We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures.

Recognition of power quality events by using multiwavelet-based neural networks

Energy Technology Data Exchange (ETDEWEB)

Kaewarsa, Suriya; Attakitmongcol, Kitti; Kulworawanichpong, Thanatchai [School of Electrical Engineering, Suranaree University of Technology, 111 University Avenue, Muang District, Nakhon Ratchasima 30000 (Thailand)

2008-05-15

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency. (author)

Recognition of clinical deterioration: a clinical leadership opportunity for nurse executive.

Science.gov (United States)

Swartz, Colleen

2013-01-01

Recognition and avoidance of further clinical deterioration can be termed a critical success factor in every care delivery model. As care resources become more constrained and allocated to the most critical of patients, some patients are being shifted to less intense and costly care settings where continuous physiologic monitoring may not be an option. Nurse executives are facing these complex issues as they work with clinical experts to develop systems of safety in the patient care arena. A systematic review of the literature related to the recognition of clinical deterioration is needed to identify areas for further leadership, research, and practice advancements.

The role of nanotechnology and nano and micro-electronics in monitoring and control of cardiovascular diseases and neurological disorders

Science.gov (United States)

Varadan, Vijay K.

2007-04-01

Nanotechnology has been broadly defined as the one for not only the creation of functional materials and devices as well as systems through control of matter at the scale of 1-100 nm, but also the exploitation of novel properties and phenomena at the same scale. Growing needs in the point-of-care (POC) that is an increasing market for improving patient's quality of life, are driving the development of nanotechnologies for diagnosis and treatment of various life threatening diseases. This paper addresses the recent development of nanodiagnostic sensors and nanotherapeutic devices with functionalized carbon nanotube and/or nanowire on a flexible organic thin film electronics to monitor and control of the three leading diseases namely 1) neurodegenerative diseases, 2) cardiovascular diseases, and 3) diabetes and metabolic diseases. The sensors developed include implantable and biocompatible devices, light weight wearable devices in wrist-watches, hats, shoes and clothes. The nanotherapeutics devices include nanobased drug delivery system. Many of these sensors are integrated with the wireless systems for the remote physiological monitoring. The author's research team has also developed a wireless neural probe using nanowires and nanotubes for monitoring and control of Parkinson's disease. Light weight and compact EEG, EOG and EMG monitoring system in a hat developed is capable of monitoring real time epileptic patients and patients with neurological and movement disorders using the Internet and cellular network. Physicians could be able to monitor these signals in realtime using portable computers or cell phones and will give early warning signal if these signals cross a pre-determined threshold level. In addition the potential impact of nanotechnology for applications in medicine is that, the devices can be designed to interact with cells and tissues at the molecular level, which allows high degree of functionality. Devices engineered at nanometer scale imply a

Man-system interface based on automatic speech recognition: integration to a virtual control desk

Energy Technology Data Exchange (ETDEWEB)

Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C., E-mail: calexandre@ien.gov.b, E-mail: mol@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: mag@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Nomiya, Diogo V., E-mail: diogonomiya@gmail.co [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

2009-07-01

This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

Man-system interface based on automatic speech recognition: integration to a virtual control desk

International Nuclear Information System (INIS)

Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C.; Nomiya, Diogo V.

2009-01-01

This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

On the Relevance of Using Bayesian Belief Networks in Wireless Sensor Networks Situation Recognition

Directory of Open Access Journals (Sweden)

Marco Zennaro

2010-12-01

Full Text Available Achieving situation recognition in ubiquitous sensor networks (USNs is an important issue that has been poorly addressed by both the research and practitioner communities. This paper describes some steps taken to address this issue by effecting USN middleware intelligence using an emerging situation awareness (ESA technology. We propose a situation recognition framework where temporal probabilistic reasoning is used to derive and emerge situation awareness in ubiquitous sensor networks. Using data collected from an outdoor environment monitoring in the city of Cape Town, we illustrate the use of the ESA technology in terms of sensor system operating conditions and environmental situation recognition.

Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.

Science.gov (United States)

Urbain, Jay

2015-12-01

We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time. The system was originally developed for the 2014 i2b2 Challenges in Natural Language in Clinical Data. The system's strengths included a high level of accuracy for identifying named entities associated with heart disease risk factor events. The system's primary weakness was due to inaccuracies when characterizing the attributes of some events. For example, determining the relative time of an event with respect to the record date, whether an event is attributable to the patient's history or the patient's family history, and differentiating between current and prior smoking status. We believe these inaccuracies were due in large part to the lack of an effective approach for integrating context into our event detection model. To address these inaccuracies, we explore the addition of a distributional semantic model for characterizing contextual evidence of heart disease risk factor events. Using this semantic model, we raise our initial 2014 i2b2 Challenges in Natural Language of Clinical data F1 score of 0.838 to 0.890 and increased precision by 10.3% without use of any lexicons that might bias our results. Copyright © 2015 Elsevier Inc. All rights reserved.

In-vitro and in-vivo phenotype of type Asia 1 foot-and-mouth disease viruses utilizing two non-RGD receptor recognition sites

Science.gov (United States)

2011-01-01

Background Foot-and-mouth disease virus (FMDV) uses a highly conserved Arg-Gly-Asp (RGD) triplet for attachment to host cells and this motif is believed to be essential for virus viability. Previous sequence analyses of the 1D-encoding region of an FMDV field isolate (Asia1/JS/CHA/05) and its two derivatives indicated that two viruses, which contained an Arg-Asp-Asp (RDD) or an Arg-Ser-Asp (RSD) triplet instead of the RGD integrin recognition motif, were generated serendipitously upon short-term evolution of field isolate in different biological environments. To examine the influence of single amino acid substitutions in the receptor binding site of the RDD-containing FMD viral genome on virus viability and the ability of non-RGD FMDVs to cause disease in susceptible animals, we constructed an RDD-containing FMDV full-length cDNA clone and derived mutant molecules with RGD or RSD receptor recognition motifs. Following transfection of BSR cells with the full-length genome plasmids, the genetically engineered viruses were examined for their infectious potential in cell culture and susceptible animals. Results Amino acid sequence analysis of the 1D-coding region of different derivatives derived from the Asia1/JS/CHA/05 field isolate revealed that the RDD mutants became dominant or achieved population equilibrium with coexistence of the RGD and RSD subpopulations at an early phase of type Asia1 FMDV quasispecies evolution. Furthermore, the RDD and RSD sequences remained genetically stable for at least 20 passages. Using reverse genetics, the RDD-, RSD-, and RGD-containing FMD viruses were rescued from full-length cDNA clones, and single amino acid substitution in RDD-containing FMD viral genome did not affect virus viability. The genetically engineered viruses replicated stably in BHK-21 cells and had similar growth properties to the parental virus. The RDD parental virus and two non-RGD recombinant viruses were virulent to pigs and bovines that developed typical

In-vitro and in-vivo phenotype of type Asia 1 foot-and-mouth disease viruses utilizing two non-RGD receptor recognition sites

Directory of Open Access Journals (Sweden)

Yin Hong

2011-06-01

Full Text Available Abstract Background Foot-and-mouth disease virus (FMDV uses a highly conserved Arg-Gly-Asp (RGD triplet for attachment to host cells and this motif is believed to be essential for virus viability. Previous sequence analyses of the 1D-encoding region of an FMDV field isolate (Asia1/JS/CHA/05 and its two derivatives indicated that two viruses, which contained an Arg-Asp-Asp (RDD or an Arg-Ser-Asp (RSD triplet instead of the RGD integrin recognition motif, were generated serendipitously upon short-term evolution of field isolate in different biological environments. To examine the influence of single amino acid substitutions in the receptor binding site of the RDD-containing FMD viral genome on virus viability and the ability of non-RGD FMDVs to cause disease in susceptible animals, we constructed an RDD-containing FMDV full-length cDNA clone and derived mutant molecules with RGD or RSD receptor recognition motifs. Following transfection of BSR cells with the full-length genome plasmids, the genetically engineered viruses were examined for their infectious potential in cell culture and susceptible animals. Results Amino acid sequence analysis of the 1D-coding region of different derivatives derived from the Asia1/JS/CHA/05 field isolate revealed that the RDD mutants became dominant or achieved population equilibrium with coexistence of the RGD and RSD subpopulations at an early phase of type Asia1 FMDV quasispecies evolution. Furthermore, the RDD and RSD sequences remained genetically stable for at least 20 passages. Using reverse genetics, the RDD-, RSD-, and RGD-containing FMD viruses were rescued from full-length cDNA clones, and single amino acid substitution in RDD-containing FMD viral genome did not affect virus viability. The genetically engineered viruses replicated stably in BHK-21 cells and had similar growth properties to the parental virus. The RDD parental virus and two non-RGD recombinant viruses were virulent to pigs and bovines that

Miniaturized Bio-and Chemical-Sensors for Point-of-Care Monitoring of Chronic Kidney Diseases.

Science.gov (United States)

Tricoli, Antonio; Neri, Giovanni

2018-03-22

This review reports the latest achievements in point-of-care (POC) sensor technologies for the monitoring of ammonia, creatinine and urea in patients suffering of chronic kidney diseases (CKDs). Abnormal levels of these nitrogen biomarkers are found in the physiological fluids, such as blood, urine and sweat, of CKD patients. Delocalized at-home monitoring of CKD biomarkers via integration of miniaturized, portable, and low cost chemical- and bio-sensors in POC devices, is an emerging approach to improve patients' health monitoring and life quality. The successful monitoring of CKD biomarkers, performed on the different body fluids by means of sensors having strict requirements in term of size, cost, large-scale production capacity, response time and simple operation procedures for use in POC devices, is reported and discussed.

Assessment of motor function, sensory motor gating and recognition memory in a novel BACHD transgenic rat model for huntington disease.

Science.gov (United States)

Abada, Yah-Se K; Nguyen, Huu Phuc; Schreiber, Rudy; Ellenbroek, Bart

2013-01-01

Huntington disease (HD) is frequently first diagnosed by the appearance of motor symptoms; the diagnosis is subsequently confirmed by the presence of expanded CAG repeats (> 35) in the HUNTINGTIN (HTT) gene. A BACHD rat model for HD carrying the human full length mutated HTT with 97 CAG-CAA repeats has been established recently. Behavioral phenotyping of BACHD rats will help to determine the validity of this model and its potential use in preclinical drug discovery studies. The present study seeks to characterize the progressive emergence of motor, sensorimotor and cognitive deficits in BACHD rats. Wild type and transgenic rats were tested from 1 till 12 months of age. Motor tests were selected to measure spontaneous locomotor activity (open field) and gait coordination. Sensorimotor gating was assessed in acoustic startle response paradigms and recognition memory was evaluated in an object recognition test. Transgenic rats showed hyperactivity at 1 month and hypoactivity starting at 4 months of age. Motor coordination imbalance in a Rotarod test was present at 2 months and gait abnormalities were seen in a Catwalk test at 12 months. Subtle sensorimotor changes were observed, whereas object recognition was unimpaired in BACHD rats up to 12 months of age. The current BACHD rat model recapitulates certain symptoms from HD patients, especially the marked motor deficits. A subtle neuropsychological phenotype was found and further studies are needed to fully address the sensorimotor phenotype and the potential use of BACHD rats for drug discovery purposes.

Speech recognition by means of a three-integrated-circuit set

Energy Technology Data Exchange (ETDEWEB)

Zoicas, A.

1983-11-03

The author uses pattern recognition methods for detecting word boundaries, and monitors incoming speech at 12 millisecond intervals. Frequency is divided into eight bands and analysis is achieved in an analogue interface integrated circuit, a pipeline digital processor and a control integrated circuit. Applications are suggested, including speech input to personal computers. 3 references.

Gesture recognition based on computer vision and glove sensor for remote working environments

Energy Technology Data Exchange (ETDEWEB)

Chien, Sung Il; Kim, In Chul; Baek, Yung Mok; Kim, Dong Su; Jeong, Jee Won; Shin, Kug [Kyungpook National University, Taegu (Korea)

1998-04-01

In this research, we defined a gesture set needed for remote monitoring and control of a manless system in atomic power station environments. Here, we define a command as the loci of a gesture. We aim at the development of an algorithm using a vision sensor and glove sensors in order to implement the gesture recognition system. The gesture recognition system based on computer vision tracks a hand by using cross correlation of PDOE image. To recognize the gesture word, the 8 direction code is employed as the input symbol for discrete HMM. Another gesture recognition based on sensor has introduced Pinch glove and Polhemus sensor as an input device. The extracted feature through preprocessing now acts as an input signal of the recognizer. For recognition 3D loci of Polhemus sensor, discrete HMM is also adopted. The alternative approach of two foregoing recognition systems uses the vision and and glove sensors together. The extracted mesh feature and 8 direction code from the locus tracking are introduced for further enhancing recognition performance. MLP trained by backpropagation is introduced here and its performance is compared to that of discrete HMM. (author). 32 refs., 44 figs., 21 tabs.

A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

Science.gov (United States)

Cippitelli, Enea; Gasparrini, Samuele; Gambi, Ennio; Spinsante, Susanna

2016-01-01

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

Directory of Open Access Journals (Sweden)

Enea Cippitelli

2016-01-01

Full Text Available The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Directory of Open Access Journals (Sweden)

Ahmad Jalal

2017-08-01

Full Text Available Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments

Directory of Open Access Journals (Sweden)

Ahmad Jalal

2014-07-01

Full Text Available Recent advancements in depth video sensors technologies have made human activity recognition (HAR realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments.

Science.gov (United States)

Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin

2014-07-02

Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

Chronic kidney disease

African Journals Online (AJOL)

disease, together with other related non -communicable diseases. (NCDs), poses not only a threat ... but because if we do not act against NCDs we will also be increasing individual and ... respiratory diseases and cancer. This is in recognition ...

Electrophysiological correlates of word recognition memory process in patients with ischemic left ventricular dysfunction.

Science.gov (United States)

Giovannelli, Fabio; Simoni, David; Gavazzi, Gioele; Giganti, Fiorenza; Olivotto, Iacopo; Cincotta, Massimo; Pratesi, Alessandra; Baldasseroni, Samuele; Viggiano, Maria Pia

2016-09-01

The relationship between left ventricular ejection fraction (LVEF) and cognitive performance in patients with coronary artery disease without overt heart failure is still under debate. In this study we combine behavioral measures and event-related potentials (ERPs) to verify whether electrophysiological correlates of recognition memory (old/new effect) are modulated differently as a function of LVEF. Twenty-three male patients (12 without [LVEF>55%] and 11 with [LVEF25 were enrolled. ERPs were recorded while participants performed an old/new visual word recognition task. A late positive ERP component between 350 and 550ms was differentially modulated in the two groups: a clear old/new effect (enhanced mean amplitude for old respect to new items) was observed in patients without LVEF dysfunction; whereas patients with overt LVEF dysfunction did not show such effect. In contrast, no significant differences emerged for behavioral performance and neuropsychological evaluations. These data suggest that ERPs may reveal functional brain abnormalities that are not observed at behavioral level. Detecting sub-clinical measures of cognitive decline may contribute to set appropriate treatments and to monitor asymptomatic or mildly symptomatic patients with LVEF dysfunction. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

Science.gov (United States)

Swartz, R. Andrew

2013-01-01

This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate. PMID:24191136

Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

Directory of Open Access Journals (Sweden)

Wenjia Liu

2013-01-01

Full Text Available This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate.

Speech Recognition

Directory of Open Access Journals (Sweden)

Adrian Morariu

2009-01-01

Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

Miniaturized Bio-and Chemical-Sensors for Point-of-Care Monitoring of Chronic Kidney Diseases

Directory of Open Access Journals (Sweden)

Antonio Tricoli

2018-03-01

Full Text Available This review reports the latest achievements in point-of-care (POC sensor technologies for the monitoring of ammonia, creatinine and urea in patients suffering of chronic kidney diseases (CKDs. Abnormal levels of these nitrogen biomarkers are found in the physiological fluids, such as blood, urine and sweat, of CKD patients. Delocalized at-home monitoring of CKD biomarkers via integration of miniaturized, portable, and low cost chemical- and bio-sensors in POC devices, is an emerging approach to improve patients’ health monitoring and life quality. The successful monitoring of CKD biomarkers, performed on the different body fluids by means of sensors having strict requirements in term of size, cost, large-scale production capacity, response time and simple operation procedures for use in POC devices, is reported and discussed.

Recognition and Toleration

DEFF Research Database (Denmark)

Lægaard, Sune

2010-01-01

Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms...... or interpretations of recognition and toleration are considered, confusing and problematic uses of the terms are noted, and the compatibility of toleration and recognition is discussed. The article argues that there is a range of legitimate and importantly different conceptions of both toleration and recognition...

Human body contour data based activity recognition.

Science.gov (United States)

Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

2013-01-01

This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

Remote Video Monitor of Vehicles in Cooperative Information Platform

Science.gov (United States)

Qin, Guofeng; Wang, Xiaoguo; Wang, Li; Li, Yang; Li, Qiyan

Detection of vehicles plays an important role in the area of the modern intelligent traffic management. And the pattern recognition is a hot issue in the area of computer vision. An auto- recognition system in cooperative information platform is studied. In the cooperative platform, 3G wireless network, including GPS, GPRS (CDMA), Internet (Intranet), remote video monitor and M-DMB networks are integrated. The remote video information can be taken from the terminals and sent to the cooperative platform, then detected by the auto-recognition system. The images are pretreated and segmented, including feature extraction, template matching and pattern recognition. The system identifies different models and gets vehicular traffic statistics. Finally, the implementation of the system is introduced.

Daily Living Movement Recognition for Pedestrian Dead Reckoning Applications

Directory of Open Access Journals (Sweden)

Alessio Martinelli

2016-01-01

Full Text Available Nowadays, activity recognition is a central topic in numerous applications such as patient and sport activity monitoring, surveillance, and navigation. By focusing on the latter, in particular Pedestrian Dead Reckoning navigation systems, activity recognition is generally exploited to get landmarks on the map of the buildings in order to permit the calibration of the navigation routines. The present work aims to provide a contribution to the definition of a more effective movement recognition for Pedestrian Dead Reckoning applications. The signal acquired by a belt-mounted triaxial accelerometer is considered as the input to the movement segmentation procedure which exploits Continuous Wavelet Transform to detect and segment cyclic movements such as walking. Furthermore, the segmented movements are provided to a supervised learning classifier in order to distinguish between activities such as walking and walking downstairs and upstairs. In particular, four supervised learning classification families are tested: decision tree, Support Vector Machine, k-nearest neighbour, and Ensemble Learner. Finally, the accuracy of the considered classification models is evaluated and the relative confusion matrices are presented.

Indications of 24-h esophageal pH monitoring, capsule pH monitoring, combined pH monitoring with multichannel impedance, esophageal manometry, radiology and scintigraphy in gastroesophageal reflux disease?

Science.gov (United States)

Vardar, Rukiye; Keskin, Muharrem

2017-12-01

Ambulatory esophageal pH monitoring is an essential method in patients exhibiting signs of non-erosive reflux disease (NERD) to make an objective diagnosis. Intra-esophageal pH monitoring is important in patients who are non-responsive to medications and in those with extraesophageal symptoms, particularly in NERD, before surgical interventions. With the help of the wireless capsule pH monitoring, measurements can be made under more physiological conditions as well as longer recordings can be performed because the investigation can be better tolerated by patients. Ambulatory esophageal pH monitoring can be detected within normal limits in 17%-31.4% of the patients with endoscopic esophagitis; therefore, normal pH monitoring cannot exclude the diagnosis of gastroesophageal reflux disease (GERD). Multi-channel intraluminal impedance pH (MII-pH) technology have been developed and currently the most sensitive tool to evaluate patients with both typical and atypical reflux symptoms. The sensitivity of a pH catheter test is 58% for the detection of acid reflux compared with MII-pH monitoring; further, its sensitivity is 28% for the detection of weak acid reflux compared with MII-pH monitoring. By adding impedance to pH catheter in patients with reflux symptoms, particularly in those receiving PPIs, it has been demonstrated that higher rates of diagnoses and symptom analyses can be obtained than those using only pH catheter. Esophageal manometry is used in the evaluation of patients with functional dysphagia and unexplained noncardiac chest pain and prior to antireflux surgery. The use of esophageal manometry is suitable for the detection of esophageal motor patterns and extreme motor abnormalities (e.g., achalasia and extreme hypomotility). Esophageal manometry and ambulatory pH monitoring are often used in assessments prior to laparoscopic antireflux surgery and in patients with reflux symptoms refractory to medical treatment. Although the esophageal motility is

Can systemically generated reactive oxygen species help to monitor disease activity in generalized vitiligo? A pilot study

Directory of Open Access Journals (Sweden)

Richeek Pradhan

2014-01-01

Full Text Available Background: Generalized vitiligo is a disease with unpredictable bursts of activity, goal of treatment during the active phase being to stabilize the lesions. This emphasizes the need for a prospective marker for monitoring disease activity to help decide the duration of therapy. Aims and Objectives: In the present study, we examined whether reactive oxygen species (ROS generated in erythrocytes can be translated into a marker of activity in vitiligo. Materials and Methods: Level of intracellular ROS was measured flow cytometrically in erythrocytes from venous blood of 21 patients with generalized vitiligo and 21 healthy volunteers using the probe dichlorodihydrofluorescein diacetate. Results: The levels of ROS differed significantly between patients and healthy controls, as well as between active versus stable disease groups. In the active disease group, ROS levels were significantly lower in those being treated with systemic steroids than those that were not. ROS levels poorly correlated with disease duration or body surface area involved. Conclusion: A long-term study based on these findings can be conducted to further validate the potential role of ROS in monitoring disease activity vitiligo.

Infliximab ameliorates AD-associated object recognition memory impairment.

Science.gov (United States)

Kim, Dong Hyun; Choi, Seong-Min; Jho, Jihoon; Park, Man-Seok; Kang, Jisu; Park, Se Jin; Ryu, Jong Hoon; Jo, Jihoon; Kim, Hyun Hee; Kim, Byeong C

2016-09-15

Dysfunctions in the perirhinal cortex (PRh) are associated with visual recognition memory deficit, which is frequently detected in the early stage of Alzheimer's disease. Muscarinic acetylcholine receptor-dependent long-term depression (mAChR-LTD) of synaptic transmission is known as a key pathway in eliciting this type of memory, and Tg2576 mice expressing enhanced levels of Aβ oligomers are found to have impaired mAChR-LTD in this brain area at as early as 3 months of age. We found that the administration of Aβ oligomers in young normal mice also induced visual recognition memory impairment and perturbed mAChR-LTD in mouse PRh slices. In addition, when mice were treated with infliximab, a monoclonal antibody against TNF-α, visual recognition memory impaired by pre-administered Aβ oligomers dramatically improved and the detrimental Aβ effect on mAChR-LTD was annulled. Taken together, these findings suggest that Aβ-induced inflammation is mediated through TNF-α signaling cascades, disturbing synaptic transmission in the PRh, and leading to visual recognition memory deficits. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated recognition system for power quality disturbances

Science.gov (United States)

Abdelgalil, Tarek

The application of deregulation policies in electric power systems has resulted in the necessity to quantify the quality of electric power. This fact highlights the need for a new monitoring strategy which is capable of tracking, detecting, classifying power quality disturbances, and then identifying the source of the disturbance. The objective of this work is to design an efficient and reliable power quality monitoring strategy that uses the advances in signal processing and pattern recognition to overcome the deficiencies that exist in power quality monitoring devices. The purposed monitoring strategy has two stages. The first stage is to detect, track, and classify any power quality violation by the use of on-line measurements. In the second stage, the source of the classified power quality disturbance must be identified. In the first stage, an adaptive linear combiner is used to detect power quality disturbances. Then, the Teager Energy Operator and Hilbert Transform are utilized for power quality event tracking. After the Fourier, Wavelet, and Walsh Transforms are employed for the feature extraction, two approaches are then exploited to classify the different power quality disturbances. The first approach depends on comparing the disturbance to be classified with a stored set of signatures for different power quality disturbances. The comparison is developed by using Hidden Markov Models and Dynamic Time Warping. The second approach depends on employing an inductive inference to generate the classification rules directly from the data. In the second stage of the new monitoring strategy, only the problem of identifying the location of the switched capacitor which initiates the transients is investigated. The Total Least Square-Estimation of Signal Parameters via Rotational Invariance Technique is adopted to estimate the amplitudes and frequencies of the various modes contained in the voltage signal measured at the facility entrance. After extracting the

Memory evaluation in mild cognitive impairment using recall and recognition tests

OpenAIRE

Bennett, IJ; Golob, EJ; Parker, ES; Starr, A

2006-01-01

Amnestic mild cognitive impairment (MCI) is a selective episodic memory deficit that often indicates early Alzheimer's disease. Episodic memory function in MCI is typically defined by deficits in free recall, but can also be tested using recognition procedures. To assess both recall and recognition in MCI, MCI (n = 21) and older comparison (n = 30) groups completed the USC-Repeatable Episodic Memory Test. Subjects memorized two verbally presented 15-item lists. One list was used for three fre...

Investigating strength and frequency effects in recognition memory using type-2 signal detection theory.

Science.gov (United States)

Higham, Philip A; Perfect, Timothy J; Bruno, Davide

2009-01-01

Criterion- versus distribution-shift accounts of frequency and strength effects in recognition memory were investigated with Type-2 signal detection receiver operating characteristic (ROC) analysis, which provides a measure of metacognitive monitoring. Experiment 1 demonstrated a frequency-based mirror effect, with a higher hit rate and lower false alarm rate, for low frequency words compared with high frequency words. In Experiment 2, the authors manipulated item strength with repetition, which showed an increased hit rate but no effect on the false alarm rate. Whereas Type-1 indices were ambiguous as to whether these effects were based on a criterion- or distribution-shift model, the two models predict opposite effects on Type-2 distractor monitoring under some assumptions. Hence, Type-2 ROC analysis discriminated between potential models of recognition that could not be discriminated using Type-1 indices alone. In Experiment 3, the authors manipulated Type-1 response bias by varying the number of old versus new response categories to confirm the assumptions made in Experiments 1 and 2. The authors conclude that Type-2 analyses are a useful tool for investigating recognition memory when used in conjunction with more traditional Type-1 analyses.

Re-thinking employee recognition: understanding employee experiences of recognition

OpenAIRE

Smith, Charlotte

2013-01-01

Despite widespread acceptance of the importance of employee recognition for both individuals and organisations and evidence of its increasing use in organisations, employee recognition has received relatively little focused attention from academic researchers. Particularly lacking is research exploring the lived experience of employee recognition and the interpretations and meanings which individuals give to these experiences. Drawing on qualitative interviews conducted as part of my PhD rese...

Movement monitoring device

International Nuclear Information System (INIS)

Ichikawa, Takashi; Yoneda, Yasuaki; Hanatsumi, Masaharu.

1997-01-01

The present invention provides a device suitable to accurate recognition for the moving state of reactor core fuels as an object to be monitored in a nuclear power plant. Namely, the device of the present invention prepares each of scheduled paths for the movement of the object to be monitored and executed moving paths along with the movement based on the information of the movement obtained from scheduled information for the movement of the reactor core fuels as a object to be monitored and the actual movement of the object to be monitored. The results of the preparation are outputted. As an output mode, (1) the results of preparation for each of the paths for movement and the results of the monitoring obtained by monitoring the state of the object to be monitored are jointed and outputted, (2) images showing each of the paths for the movement are formed, and the formed images are displayed on a screen, and (3) each of the moving paths is prepared as an image, and the image is displayed together with the image of the regions before and after the movement of the object to be monitored. In addition, obtained images of each of the paths for the movement and the monitored images obtained by monitoring the state of the object to be monitored are joined and displayed. (I.S.)

Use of the recognition heuristic depends on the domain's recognition validity, not on the recognition validity of selected sets of objects.

Science.gov (United States)

Pohl, Rüdiger F; Michalkiewicz, Martha; Erdfelder, Edgar; Hilbig, Benjamin E

2017-07-01

According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success-and thus usefulness-of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set's recognition validity), or on the underlying domain from which the objects were drawn (the domain's recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain's recognition validity, not on the set's recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.

Patterns of source monitoring bias in incarcerated youths with and without conduct problems.

Science.gov (United States)

Morosan, Larisa; Badoud, Deborah; Salaminios, George; Eliez, Stephan; Van der Linden, Martial; Heller, Patrick; Debbané, Martin

2018-01-01

Antisocial individuals present behaviours that violate the social norms and the rights of others. In the present study, we examine whether biases in monitoring the self-generated cognitive material might be linked to antisocial manifestations during adolescence. We further examine the association with psychopathic traits and conduct problems (CPs). Sixty-five incarcerated adolescents (IAs; M age = 15.85, SD = 1.30) and 88 community adolescents (CAs; M age = 15.78, SD = 1.60) participated in our study. In the IA group, 28 adolescents presented CPs (M age = 16.06, SD = 1.41) and 19 did not meet the diagnostic criteria for CPs (M age = 15.97, SD = 1.20). Source monitoring was assessed through a speech-monitoring task, using items requiring different levels of cognitive effort; recognition and source-monitoring bias scores (internalising and externalising biases) were calculated. Between-group comparisons indicate greater overall biases and different patterns of biases in the source monitoring. IA participants manifest a greater externalising bias, whereas CA participants present a greater internalising bias. In addition, IA with CPs present different patterns of item recognition. These results indicate that the two groups of adolescents present different types of source-monitoring bias for self-generated speech. In addition, the IAs with CPs present impairments in item recognition. Future studies may examine the developmental implications of self-monitoring biases in the perseverance of antisocial behaviours from adolescence to adulthood.

Analysis of circulating tumour DNA to monitor disease burden following colorectal cancer surgery

DEFF Research Database (Denmark)

Reinert, Thomas; Schøler, Lone Vedel; Thomsen, Rune

2016-01-01

in 151 serial plasma samples from six relapsing and five non-relapsing colorectal cancer (CRC) patients by droplet digital PCR, and correlated to clinical findings. RESULTS: Up to six personalised assays were designed for each patient. Our approach enabled efficient temporal assessment of disease status...... relapse were 100%. CONCLUSIONS: We show that assessment of ctDNA is a non-invasive, exquisitely specific and highly sensitive approach for monitoring disease load, which has the potential to provide clinically relevant lead times compared with conventional methods. Furthermore, we provide a low...

Monitoring and diagnosis of Alzheimer's disease using noninvasive compressive sensing EEG

Science.gov (United States)

Morabito, F. C.; Labate, D.; Morabito, G.; Palamara, I.; Szu, H.

2013-05-01

The majority of elderly with Alzheimer's Disease (AD) receive care at home from caregivers. In contrast to standard tethered clinical settings, a wireless, real-time, body-area smartphone-based remote monitoring of electroencephalogram (EEG) can be extremely advantageous for home care of those patients. Such wearable tools pave the way to personalized medicine, for example giving the opportunity to control the progression of the disease and the effect of drugs. By applying Compressive Sensing (CS) techniques it is in principle possible to overcome the difficulty raised by smartphones spatial-temporal throughput rate bottleneck. Unfortunately, EEG and other physiological signals are often non-sparse. In this paper, it is instead shown that the EEG of AD patients becomes actually more compressible with the progression of the disease. EEG of Mild Cognitive Impaired (MCI) subjects is also showing clear tendency to enhanced compressibility. This feature favor the use of CS techniques and ultimately the use of telemonitoring with wearable sensors.

Graph-based Geospatial Prediction and Clustering for Situation Recognition

OpenAIRE

Tang, Mengfan

2017-01-01

Big data continues to grow and diversify at an increasing pace. To understand constantly evolving situations, data is collected from various location-based sensors as well as people using effective participatory sensing. Static sensors are placed at particular locations, monitoring and measuring important variables from the environment. Additionally, people contribute data in the form of mobile streams through participatory sensing. To process such disparate data for situation recognition, we...

Graphical symbol recognition

OpenAIRE

K.C. , Santosh; Wendling , Laurent

2015-01-01

International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...

Object recognition as a measure of memory in 1–2 years old transgenic minipigs carrying the APPsw mutation for Alzheimer’s disease

DEFF Research Database (Denmark)

Søndergaard, Lene Vammen; Ladewig, Jan; Dagnæs-Hansen, Frederik

2012-01-01

Alzheimer’s disease (AD) is a disabling, fatal disease, where animal models potentially can enable investigation of aetiology and treatment. The first litter of Göttingen minipigs carrying a mutation for human AD was born in 2007, showing transgene expression. In human AD patients, memory...... impairment is the most striking and consistent feature. The aim of the present study was to examine effects of the APPsw transgene on memory of AD minipigs compared with non-transgenic controls at two ages (1–2 years) using the spontaneous object recognition test (SORT), which is based on behavioural...... using the SORT, we were not able to show memory impairment in APPsw carrying minipigs. Being an age-dependent disease, the transgene is expected to cause AD-like symptoms in this porcine model, and the SORT should be repeated at older ages...

Evaluation of neopterin as a biomarker for the monitoring of Gaucher disease patients.

Science.gov (United States)

Drugan, Cristina; Drugan, Tudor C; Miron, Nicolae; Grigorescu-Sido, Paula; Naşcu, Ioana; Cătană, Cristina

2016-07-01

Biomarker research is an important area of investigation in Gaucher disease, caused by an inherited deficiency of a lysosomal enzyme, glucocerebrosidase. We evaluated the usefulness of neopterin, as a novel biomarker reflecting chronic inflammation and immune system activation in Gaucher disease and analysed its evolution in response to enzyme replacement therapy (ERT). Circulating plasma neopterin levels in 31 patients with non-neuronopathic Gaucher disease were measured before and after the onset of ERT and were compared with those of 18 healthy controls. Plasma chitotriosidase activity was also monitored, as a reference biomarker, against which we evaluated the evolution of neopterin. Neopterin levels were significantly increased in treatment-naïve patients (mean 11.90 ± 5.82 nM) compared with controls (6.63 ± 5.59 nM, Mann-Whitney U test P = 0.001), but returned to normal levels (6.92 ± 4.66 nM) following ERT. Investigating the diagnostic value of neopterin by receiver operating characteristic analysis, we found a cut-off value of 7.613 nM that corresponds to an area under the curve of 0.780 and indicates a good discrimination capacity, with a sensitivity of 0.774 and a specificity of 0.778. Our results suggest that measurement of circulating neopterin may be considered as a novel test for the confirmation of diagnosis and monitoring of the efficacy of therapeutic intervention in Gaucher disease. Plasma neopterin levels reflect the global accumulation and activation of Gaucher cells and the extent of chronic immune activation in this disorder. Neopterin may be an alternative storage cell biomarker in Gaucher disease, especially in chitotriosidase-deficient patients.

New automated procedure to assess context recognition memory in mice.

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Reiss, David; Walter, Ondine; Bourgoin, Lucie; Kieffer, Brigitte L; Ouagazzal, Abdel-Mouttalib

2014-11-01

Recognition memory is an important aspect of human declarative memory and is one of the routine memory abilities altered in patients with amnestic syndrome and Alzheimer's disease. In rodents, recognition memory has been most widely assessed using the novel object preference paradigm, which exploits the spontaneous preference that animals display for novel objects. Here, we used nose-poke units instead of objects to design a simple automated method for assessing context recognition memory in mice. In the acquisition trial, mice are exposed for the first time to an operant chamber with one blinking nose-poke unit. In the choice session, a novel nonblinking nose-poke unit is inserted into an empty spatial location and the number of nose poking dedicated to each set of nose-poke unit is used as an index of recognition memory. We report that recognition performance varies as a function of the length of the acquisition period and the retention delay and is sensitive to conventional amnestic treatments. By manipulating the features of the operant chamber during a brief retrieval episode (3-min long), we further demonstrate that reconsolidation of the original contextual memory depends on the magnitude and the type of environmental changes introduced into the familiar spatial environment. These results show that the nose-poke recognition task provides a rapid and reliable way for assessing context recognition memory in mice and offers new possibilities for the deciphering of the brain mechanisms governing the reconsolidation process.

Lipids in hepatic glycogen storage diseases: pathophysiology, monitoring of dietary management and future directions.

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Derks, Terry G J; van Rijn, Margreet

2015-05-01

Hepatic glycogen storage diseases (GSD) underscore the intimate relationship between carbohydrate and lipid metabolism. The hyperlipidemias in hepatic GSD reflect perturbed intracellular metabolism, providing biomarkers in blood to monitor dietary management. In different types of GSD, hyperlipidemias are of a different origin. Hypertriglyceridemia is most prominent in GSD type Ia and associated with long-term outcome morbidity, like pancreatitis and hepatic adenomas. In the ketotic subtypes of GSD, hypertriglyceridemia reflects the age-dependent fasting intolerance, secondary lipolysis and increased mitochondrial fatty acid oxidation. The role of high protein diets is established for ketotic types of GSD, but non-traditional dietary interventions (like medium-chain triglycerides and the ketogenic diet) in hepatic GSD are still controversial and necessitate further studies. Patients with these rare inherited disorders of carbohydrate metabolism meet several criteria of the metabolic syndrome, therefore close monitoring for cardiovascular diseases in ageing GSD patients may be justified.

NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

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Mezgec, Simon; Koroušić Seljak, Barbara

2017-06-27

Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86 . 72 % , along with an accuracy of 94 . 47 % on a detection dataset containing 130 , 517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson's disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55 % , which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson's disease patients.

Recognition of lysophosphatidylcholine by type II NKT cells and protection from an inflammatory liver disease.

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Maricic, Igor; Girardi, Enrico; Zajonc, Dirk M; Kumar, Vipin

2014-11-01

Lipids presented by the MHC class I-like molecule, CD1d, are recognized by NK T (NKT) cells, which can be broadly categorized into two subsets. The well-characterized type I NKT cells express a semi-invariant TCR and can recognize both α- and β-linked glycolipids, whereas type II NKT cells are less well studied, express a relatively diverse TCR repertoire, and recognize β-linked lipids. Recent structural studies have shown a distinct mode of recognition of a self-glycolipid sulfatide bound to CD1d by a type II NKT TCR. To further characterize Ag recognition by these cells, we have used the structural data and screened other small molecules able to bind to CD1d and activate type II NKT cells. Using plate-bound CD1d and APC-based Ag presentation assay, we found that phospholipids such as lysophosphatidylcholine (LPC) can stimulate the sulfatide-reactive type II NKT hybridoma Hy19.3 in a CD1d-dependent manner. Using plasmon resonance studies, we found that this type II NKT TCR binds with CD1d-bound LPC with micromolar affinities similar to that for sulfatide. Furthermore, LPC-mediated activation of type II NKT cells leads to anergy induction in type I NKT cells and affords protection from Con A-induced hepatitis. These data indicate that, in addition to self-glycolipids, self-lysophospholipids are also recognized by type II NKT cells. Because lysophospholipids are involved during inflammation, our findings have implications for not only understanding activation of type II NKT cells in physiological settings, but also for the development of immune intervention in inflammatory diseases. Copyright © 2014 by The American Association of Immunologists, Inc.

Aplikasi sistem pakar diagnosis penyakit ispa berbasis speech recognition menggunakan metode naive bayes classifier

Directory of Open Access Journals (Sweden)

Mariam Marlina

2017-05-01

Full Text Available AbstrakISPA (Infeksi Saluran Pernafasan Akut adalah suatu penyakit gangguan saluran pernapasan yang dapat menimbulkan berbagai spektrum penyakit mulai dari penyakit tanpa gejala, infeksi ringan sampai penyakit yang parah dan mematikan akibat faktor lingkungan. Kurangnya pengetahuan masyarakat mengenai gejala dan cara penanganan penyakit ISPA merupakan salah satu faktor penyebab tingginya angka kematian akibat ISPA. Peran sistem pakar yang disediakan dalam bentuk aplikasi sangat diperlukan untuk membantu seseorang dalam melakukan diagnosa penyakit ISPA secara mudah dan cepat. Dengan berusaha mengadopsi pengetahuan manusia ke komputer, sistem pakar mampu menyelesaikan permasalahan seperti yang dilakukan oleh seorang pakar. Oleh Karena itu, Aplikasi Sistem Pakar Diagnosis Penyakit ISPA Berbasis Speech Recognition Menggunakan Metode Naive Bayes Classifier dapat digunakan untuk mendiagnosis penyakit ISPA terhadap seseorang berdasarkan konversi hasil deteksi suara pengguna. Dengan aplikasi ini pengguna seakan berkonsultasi kepada seorang dokter/pakar yang menangani penyakit ISPA. Aplikasi dibangun berbasis android dengan menggunakan bahasa pemrograman Java dan database MySQL. Kata kunci : Sistem pakar, speech recognition, ISPA, metode naïve bayes classifier, Android. AbstractISPA (Acute Respiratory Tract Infection is a respiratory disorder disease that can lead to a wide spectrum of diseases ranging from asymptomatic disease, mild infection to severe and deadly disease due to environmental factors. So if someone complains of respiratory disorders not necessarily just have regular respiratory problems because it could be the person has ARI disease. The role of expert systems provided in the form of an application is needed to help a person in the diagnosis of ARI disease easily and quickly. By trying to adopt human knowledge into a computer, an expert system is capable of solving problems like that of an expert. Therefore, the Application of Expert

Statistical Pattern Recognition

CERN Document Server

Webb, Andrew R

2011-01-01

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

User-Independent Motion State Recognition Using Smartphone Sensors.

Science.gov (United States)

Gu, Fuqiang; Kealy, Allison; Khoshelham, Kourosh; Shang, Jianga

2015-12-04

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users' data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people's motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human's motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

User-Independent Motion State Recognition Using Smartphone Sensors

Directory of Open Access Journals (Sweden)

Fuqiang Gu

2015-12-01

Full Text Available The recognition of locomotion activities (e.g., walking, running, still is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

Facial Emotion Recognition Impairments are Associated with Brain Volume Abnormalities in Individuals with HIV

Science.gov (United States)

Clark, Uraina S.; Walker, Keenan A.; Cohen, Ronald A.; Devlin, Kathryn N.; Folkers, Anna M.; Pina, Mathew M.; Tashima, Karen T.

2015-01-01

Impaired facial emotion recognition abilities in HIV+ patients are well documented, but little is known about the neural etiology of these difficulties. We examined the relation of facial emotion recognition abilities to regional brain volumes in 44 HIV-positive (HIV+) and 44 HIV-negative control (HC) adults. Volumes of structures implicated in HIV− associated neuropathology and emotion recognition were measured on MRI using an automated segmentation tool. Relative to HC, HIV+ patients demonstrated emotion recognition impairments for fearful expressions, reduced anterior cingulate cortex (ACC) volumes, and increased amygdala volumes. In the HIV+ group, fear recognition impairments correlated significantly with ACC, but not amygdala volumes. ACC reductions were also associated with lower nadir CD4 levels (i.e., greater HIV-disease severity). These findings extend our understanding of the neurobiological substrates underlying an essential social function, facial emotion recognition, in HIV+ individuals and implicate HIV-related ACC atrophy in the impairment of these abilities. PMID:25744868

Biology, diversity and strategies for the monitoring and control of triatomines--Chagas disease vectors.

Science.gov (United States)

Costa, Jane; Lorenzo, Marcelo

2009-07-01

Despite the relevant achievements in the control of the main Chagas disease vectors Triatoma infestans and Rhodnius prolixus, several factors still promote the risk of infection. The disease is a real threat to the poor rural regions of several countries in Latin America. The current situation in Brazil requires renewed attention due to its high diversity of triatomine species and to the rapid and drastic environmental changes that are occurring. Using the biology, behaviour and diversity of triatomines as a basis for new strategies for monitoring and controlling the vectorial transmission are discussed here. The importance of ongoing long-term monitoring activities for house infestations by T. infestans, Triatoma brasiliensis, Panstrongylus megistus, Triatoma rubrovaria and R. prolixus is also stressed, as well as understanding the invasion by sylvatic species. Moreover, the insecticide resistance is analysed. Strong efforts to sustain and improve surveillance procedures are crucial, especially when the vectorial transmission is considered interrupted in many endemic areas.

Immunogenicity and T cell recognition in swine of foot-and-mouth disease virus polymerase 3D

International Nuclear Information System (INIS)

Garcia-Briones, Maria M.; Blanco, Esther; Chiva, Cristina; Andreu, David; Ley, Victoria; Sobrino, Francisco

2004-01-01

Immunization of domestic pigs with a vaccinia virus (VV) recombinant expressing foot-and-mouth disease virus (FMDV) 3D protein conferred partial protection against challenge with infectious virus. The severity reduction of the clinical symptoms developed by the challenged animals occurred in the absence of significant levels of anti-3D circulating antibodies. This observation suggested that the partial protection observed was mediated by the induction of a 3D-specific cellular immune response. To gain information on the T cell recognition of FMDV 3D protein, we conducted in vitro proliferative assays using lymphocytes from outbred pigs experimentally infected with FMDV and 90 overlapping peptides spanning the complete 3D sequence. The use of pools of two to three peptides allowed the identification of T cell epitopes that were efficiently recognized by lymphocytes from at least four of the five animals analyzed. This recognition was heterotypic because anti-peptide responses increased upon reinfection of animals with a FMDV isolate from a different serotype. The results obtained with individual peptides confirmed the antigenicity observed with peptide pools. Detection of cytokine mRNAs by RT-PCR in lymphocytes stimulated in vitro by individual 3D peptides revealed that IFN-γ mRNA was the most consistently induced, suggesting that the activated T cells belong to the Th 1 subset. These results indicate that 3D protein contains epitopes that can be efficiently recognized by porcine T lymphocytes from different infected animals, both upon primary and secondary (heterotypic) FMDV infection. These epitopes can extend the repertoire of viral T cell epitopes to be included in subunit and synthetic FMD vaccines

Actigraphy monitoring of symptoms in patients with Parkinson's disease.

Science.gov (United States)

Pan, Weidong; Kwak, Shin; Li, Fuzhong; Wu, Chunlan; Chen, Yiyun; Yamamoto, Yoshiharu; Cai, Dingfang

2013-07-02

Although the Unified Parkinson's Disease Rating Scale (UPDRS) is the "gold-standard" tool in assessing the severity of symptoms in patients with Parkinson's disease (PD), not all activity-related disease symptoms can be accurately captured by the well-established clinical rating scale. Using an alternative approach, this study examined the level of physical activity measured by actigraphy over time and whether change in physical activity was associated with disease severity assessed by UPDRS. We used a longitudinal design in which physical activity and disease severity were assessed repeatedly during a 4-month interval, over a 3-year observational period, in a sample of 61 patients with idiopathic PD and a control group of 32 neurologically intact individuals. Physical activity data during awake-time were analyzed using the power-law exponent (PLE) method. Correlational relationships between changes in maxima values of PLE and scores of total UPDRS, UPDRS-part II (Activities of Daily Living), and UPDRS-part III (Motor Examination) in patients with PD were examined. Results show an increase in maxima values of PLE and the UPDRS total score in PD patients and that there is a positive association between changes in maxima values and total UPDRS score (r=0.746, p=0.032), UPDRS-part II score (r=0.687, p=0.027), and UPDRS-part III score (r=0.893, p=0.018). There was no significant change in the level of physical activity over time for the controls. Findings from this study indicate that change in physical activity, as captured by actigraphy, is associated with increased severity in patients' clinical symptoms of PD over time. Thus, these data suggest that, when used in conjunction with the conventional UPDRS measure, an actigraphic measure of physical activity may provide clinicians an adjunct measurement approach to monitor patients' activity-based disease progression or responses to treatment in outpatient clinic settings. Copyright © 2013 The Authors. Published by

Rotation-invariant neural pattern recognition system with application to coin recognition.

Science.gov (United States)

Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

1992-01-01

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

The ELISA-measured increase in cerebrospinal fluid tau that discriminates Alzheimer's disease from other neurodegenerative disorders is not attributable to differential recognition of tau assembly forms.

Science.gov (United States)

O'Dowd, Seán T; Ardah, Mustafa T; Johansson, Per; Lomakin, Aleksey; Benedek, George B; Roberts, Kinley A; Cummins, Gemma; El Agnaf, Omar M; Svensson, Johan; Zetterberg, Henrik; Lynch, Timothy; Walsh, Dominic M

2013-01-01

Elevated cerebrospinal fluid concentrations of tau discriminate Alzheimer's disease from other neurodegenerative conditions. The reasons for this are unclear. While commercial assay kits are widely used to determine total-tau concentrations, little is known about their ability to detect different aggregation states of tau. We demonstrate that the leading commercial enzyme-linked immunosorbent assay reliably detects aggregated and monomeric tau and evinces good recovery of both species when added into cerebrospinal fluid. Hence, the disparity between total-tau levels encountered in Alzheimer's disease and other neurodegenerative conditions is not due to differential recognition of tau assembly forms or the extent of degeneration.

Multivariate Variables Recognition using Hotelling’s T2 and MEWMA via ANN’s

Directory of Open Access Journals (Sweden)

Chiñas-Sánchez Pamela

2014-01-01

Full Text Available In this article, a method for multivariate pattern recognition using artificial neural networks (ANN is proposed. The method is useful for monitoring multiple variables during the statistical process control. It employs descriptive statistics and multivariate control techniques. Three different ANN’s are evaluated to identify the network with higher efficiency during pattern recognition of multivariate variables tasks from data bases. Two data bases are analyzed; the first one is generated by simulation using the Montecarlo method, and the second data base was obtained from a public data base repository. The method consists of three stages: multivariate variables generation, multivariate analysis and pattern recognition using ANN’s. Several multivariate scenarios were generated using a combination of 2, 3 and 4 patterns in multivariate variables for the Hotelling’s T2 and MEWMA statistics that were analyzed to know its behavior and to determine their statistical characteristics. The pattern recognition task was evaluated using the ANN. In both study cases, experimental results showed an improved efficiency when using the Perceptron and the Backpropagation networks compared to the RBF network.

Measuring Gait Quality in Parkinson’s Disease through Real-Time Gait Phase Recognition

Directory of Open Access Journals (Sweden)

Ilaria Mileti

2018-03-01

Full Text Available Monitoring gait quality in daily activities through wearable sensors has the potential to improve medical assessment in Parkinson’s Disease (PD. In this study, four gait partitioning methods, two based on thresholds and two based on a machine learning approach, considering the four-phase model, were compared. The methods were tested on 26 PD patients, both in OFF and ON levodopa conditions, and 11 healthy subjects, during walking tasks. All subjects were equipped with inertial sensors placed on feet. Force resistive sensors were used to assess reference time sequence of gait phases. Goodness Index (G was evaluated to assess accuracy in gait phases estimation. A novel synthetic index called Gait Phase Quality Index (GPQI was proposed for gait quality assessment. Results revealed optimum performance (G < 0.25 for three tested methods and good performance (0.25 < G < 0.70 for one threshold method. The GPQI resulted significantly higher in PD patients than in healthy subjects, showing a moderate correlation with clinical scales score. Furthermore, in patients with severe gait impairment, GPQI was found higher in OFF than in ON state. Our results unveil the possibility of monitoring gait quality in PD through real-time gait partitioning based on wearable sensors.

Facial Analysis: Looking at Biometric Recognition and Genome-Wide Association

DEFF Research Database (Denmark)

Fagertun, Jens

The goal of this Ph.D. project is to present selected challenges regarding facial analysis within the fields of Human Biometrics and Human Genetics. In the course of the Ph.D. nine papers have been produced, eight of which have been included in this thesis. Three of the papers focus on face...... and gender recognition, where in the gender recognition papers the process of human perception of gender is analyzed and used to improve machine learning algorithms. One paper addresses the issues of variability in human annotation of facial landmarks, which most papers regard as a static “gold standard...... on genetic information, a new area that holds great potential. Two papers explore the connection between minor physical anomalies in the face and schizophrenic disorders. Schizophrenia is a life long disease, but early discovery and treatment can have a significant impact on the course of the disease...

The ebb and flow of airborne pathogens: Monitoring and use in disease management decisions

Science.gov (United States)

Perhaps the earliest form of monitoring the regional spread of plant disease was a group of growers gathering together at the market and discussing what they see in their crops. This type of reporting continues to this day through regional extension blogs, by crop consultants and more formal scoutin...

Sitting is the new smoking : online complex human activity recognition with smartphones and wearables

NARCIS (Netherlands)

Shoaib, Muhammad

2017-01-01

Human activity recognition plays an important role in fitness tracking, health monitoring, context-aware feedback and self-management of smartphones and wearable devices. These devices are equipped with different sensors which can be used to recognize various human activities. A significant amount of

Effectiveness of Intraoperative Parathyroid Monitoring (ioPTH) in predicting a multiglandular or malignant parathyroid disease.

Science.gov (United States)

Dobrinja, C; Santandrea, G; Giacca, M; Stenner, Elisabetta; Ruscio, Maurizio; de Manzini, Nicolò

2017-05-01

The main goal of our study was to confirm the usefulness of intra-operative parathyroid hormone (PTH) monitoring (ioPTH) when using minimally invasive techniques for treatment of sporadic Primary hyperparathyroidism (pHTP). Furthermore, we aimed to evaluate if ioPTH monitoring may help to predict the etiology of primary hyperparathyroidism, especially in malignant or multiglandular parathyroid disease. A retrospective review of 125 consecutive patients with pHPT who underwent parathyroidectomy between 2001 and 2016 at the Department of General Surgery was performed. For each patient, the specific preoperative work-up consisted of: high-resolution US of the neck by a skilled sonographer, sestamibi parathyroid scan, laryngoscopy, and serum measurement of PTH, serum calcium levels, and serum 25(OH)D levels. The study included 125 consecutive patients who underwent surgery for pHPT. At the histological examination, we registered 113 patients with simple adenomatous pathology (90,4%), 5 atypical adenomas (4%), 3 cases of parathyroid carcinoma (2,4%),, , and 4 histological exams of different nature (3,2%). Overall, 6 cases (4,8%) of multiglandular disease were found. We reported 10 cases (8%) of recurrent/persistent hyperparathyroidism: 1/10 in a patient affected by atypical adenoma, 9/10 in patients with benign pathology. Regarding these 10 cases, in three (30%) patients, ioPTH wasn't dosed (only frozen section (FS) exam was taken), in 5 cases (50%) ioPTH dropped more than 50% compared to basal value (false negative results), and in 2 (20%) cases, ioPTH did not drop >50% from the first samples taken, the extemporary exam had confirmed the presence of adenoma and the probable second hyperfunctioning adenoma was not found. IoPTH determinations ensure operative success of surgical resection in almost all hyperfunctioning tissue; in particular it is very important during minimally invasive parathyroidectomy, as it allows avoiding bilateral neck exploration. The use of io

Monitoring Human Activity through Portable Devices

Directory of Open Access Journals (Sweden)

G. Sebestyen

2012-06-01

Full Text Available Monitoring human activity may be useful for medical supervision and for prophylactic purposes. Mobile devices like intelligent phones or watches have multiple sensors and wireless communication capabilities which can be used for this purpose. This paper presents some integrated solutions for determining and continuous monitoring of a person’s state. Aspects taken into consideration are: activity detection and recognition based on acceleration sensors, wireless communication protocols for data acquisition, web monitoring, alerts generation and statistical processing of multiple sensorial data. As practical implementations two case studies are presented, one using an intelligent phone and another using a mixed signal processor integrated in a watch.

Word-stem priming and recognition in type 2 diabetes mellitus, Alzheimer's disease patients and healthy older adults.

Science.gov (United States)

Redondo, María Teresa; Beltrán-Brotóns, José Luís; Reales, José Manuel; Ballesteros, Soledad

2015-11-01

The present study investigated (a) whether the pattern of performance on implicit and explicit memory of patients with type 2 diabetes mellitus (DM2) is more similar to those of patients with Alzheimer's disease (AD) or to cognitively normal older adults and (b) whether glycosylated hemoglobin levels (a measure of glucose regulation) are related to performance on the two memory tasks, implicit word-stem completion and "old-new" recognition. The procedures of both memory tasks included encoding and memory test phases separated by a short delay. Three groups of participants (healthy older adults, DM2 patients and AD patients) completed medical and psychological assessments and performed both memory tasks on a computer. The results of the word-stem completion task showed similar implicit memory in the three groups. By contrast, explicit recognition of the three groups differed. Implicit memory was not affected by either normal or pathological aging, but explicit memory deteriorated in the two groups of patients, especially in AD patients, showing a severe impairment compared to the cognitively healthy older adults. Importantly, glycosylated hemoglobin levels were not related to performance on either implicit or explicit memory tasks. These findings revealed a clear dissociation between explicit and implicit memory tasks in normal and pathological aging. Neuropsychologists and clinicians working with TM2 patients should be aware that the decline of voluntary, long-term explicit memory could have a negative impact on their treatment management. By contrast, the intact implicit memory of the two clinical groups could be used in rehabilitation.

Energy efficient smartphone-based activity recognition using fixed-point arithmetic

OpenAIRE

Anguita, Davide; Ghio, Alessandro; Oneto, Luca; Llanas Parra, Francesc Xavier; Reyes Ortiz, Jorge Luis

2013-01-01

In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre- serve the smartphone battery lifetime with respect to the conventional flo...

Low cost sensing technology for type 2 diabetes monitoring

Science.gov (United States)

Sarswat, Prashant; Free, Michael

2015-03-01

Alpha-hydroxybutyrate (2-hydroxybutyrate or α-HB) is becoming more widely recognized as an important metabolic biomarker that has been shown to be highly correlated with prediabetes and other metabolic diseases. In 2012 there were 86 million Americans with prediabetes, many of whom are not aware they have prediabetes, but could be diagnosed and treated to prevent type 2 diabetes if a simple, low-cost, convenient test were available. We have developed new, low-cost, accurate α-HB detection methods that can be used for the detection and monitoring of diseases such as prediabetes, type 2 diabetes, β-cell dysfunction, and early hyperglycemia. The new sensing method utilizes a diol recognition moiety, additives and a photoinitiator to detect α-HB at levels near 1 micro g/l in the presence of serum compounds such as lactic acid, sodium pyruvate, and glucose. The objective of this research is to improve the understanding of the interactions that enhance α-HB detection to enable additional improvements in α-HB detection as well as improvements in other biosensor applications.

Response monitoring in de novo patients with Parkinson's disease.

Directory of Open Access Journals (Sweden)

Rita Willemssen

Full Text Available BACKGROUND: Parkinson's disease (PD is accompanied by dysfunctions in a variety of cognitive processes. One of these is error processing, which depends upon phasic decreases of medial prefrontal dopaminergic activity. Until now, there is no study evaluating these processes in newly diagnosed, untreated patients with PD ("de novo PD". METHODOLOGY/PRINCIPAL FINDINGS: Here we report large changes in performance monitoring processes using event-related potentials (ERPs in de novo PD-patients. The results suggest that increases in medial frontal dopaminergic activity after an error (Ne are decreased, relative to age-matched controls. In contrast, neurophysiological processes reflecting general motor response monitoring (Nc are enhanced in de novo patients. CONCLUSIONS/SIGNIFICANCE: It may be hypothesized that the Nc-increase is at costs of dopaminergic activity after an error; on a functional level errors may not always be detected and correct responses sometimes be misinterpreted as errors. This pattern differs from studies examining patients with a longer history of PD and may reflect compensatory processes, frequently occurring in pre-manifest stages of PD. From a clinical point of view the clearly attenuated Ne in the de novo PD patients may prove a useful additional tool for the early diagnosis of basal ganglia dysfunction in PD.

A Malaysian Vehicle License Plate Localization and Recognition System

OpenAIRE

Ganapathy Velappa; Dennis LUI Wen Lik

2008-01-01

Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services,...

A RECOGNITION METHOD FOR AIRPLANE TARGETS USING 3D POINT CLOUD DATA

Directory of Open Access Journals (Sweden)

M. Zhou

2012-07-01

Full Text Available LiDAR is capable of obtaining three dimension coordinates of the terrain and targets directly and is widely applied in digital city, emergent disaster mitigation and environment monitoring. Especially because of its ability of penetrating the low density vegetation and canopy, LiDAR technique has superior advantages in hidden and camouflaged targets detection and recognition. Based on the multi-echo data of LiDAR, and combining the invariant moment theory, this paper presents a recognition method for classic airplanes (even hidden targets mainly under the cover of canopy using KD-Tree segmented point cloud data. The proposed algorithm firstly uses KD-tree to organize and manage point cloud data, and makes use of the clustering method to segment objects, and then the prior knowledge and invariant recognition moment are utilized to recognise airplanes. The outcomes of this test verified the practicality and feasibility of the method derived in this paper. And these could be applied in target measuring and modelling of subsequent data processing.

Mapping face recognition information use across cultures

Directory of Open Access Journals (Sweden)

Sébastien eMiellet

2013-02-01

Full Text Available Face recognition is not rooted in a universal eye movement information-gathering strategy. Western observers favor a local facial feature sampling strategy, whereas Eastern observers prefer sampling face information from a global, central fixation strategy. Yet, the precise qualitative (the diagnostic and quantitative (the amount information underlying these cultural perceptual biases in face recognition remains undetermined.To this end, we monitored the eye movements of Western and Eastern observers during a face recognition task, with a novel gaze-contingent technique: the Expanding Spotlight. We used 2° Gaussian apertures centered on the observers' fixations expanding dynamically at a rate of 1° every 25ms at each fixation - the longer the fixation duration, the larger the aperture size. Identity-specific face information was only displayed within the Gaussian aperture; outside the aperture, an average face template was displayed to facilitate saccade planning. Thus, the Expanding Spotlight simultaneously maps out the facial information span at each fixation location.Data obtained with the Expanding Spotlight technique confirmed that Westerners extract more information from the eye region, whereas Easterners extract more information from the nose region. Interestingly, this quantitative difference was paired with a qualitative disparity. Retinal filters based on spatial frequency decomposition built from the fixations maps revealed that Westerners used local high-spatial frequency information sampling, covering all the features critical for effective face recognition (the eyes and the mouth. In contrast, Easterners achieved a similar result by using global low-spatial frequency information from those facial features.Our data show that the face system flexibly engages into local or global eye movement strategies across cultures, by relying on distinct facial information span and culturally tuned spatially filtered information. Overall, our

Investigating Strength and Frequency Effects in Recognition Memory Using Type-2 Signal Detection Theory

Science.gov (United States)

Higham, Philip A.; Perfect, Timothy J.; Bruno, Davide

2009-01-01

Criterion- versus distribution-shift accounts of frequency and strength effects in recognition memory were investigated with Type-2 signal detection receiver operating characteristic (ROC) analysis, which provides a measure of metacognitive monitoring. Experiment 1 demonstrated a frequency-based mirror effect, with a higher hit rate and lower…

An alpha-synuclein MRM assay with diagnostic potential for Parkinson's disease and monitoring disease progression

Energy Technology Data Exchange (ETDEWEB)

Yang, Li [Department of Pathology, University of Washington, Seattle WA USA; Stewart, Tessandra [Department of Pathology, University of Washington, Seattle WA USA; Shi, Min [Department of Pathology, University of Washington, Seattle WA USA; Pottiez, Gwenael [Department of Pathology, University of Washington, Seattle WA USA; Dator, Romel [Department of Pathology, University of Washington, Seattle WA USA; Wu, Rui [Department of Pathology, University of Washington, Seattle WA USA; Department of Pathology, No. 3 Hospital of Beijing University, Beijing China; Aro, Patrick [Department of Pathology, University of Washington, Seattle WA USA; Schuster, Robert J. [Department of Pathology, University of Washington, Seattle WA USA; Ginghina, Carmen [Department of Pathology, University of Washington, Seattle WA USA; Pan, Catherine [Department of Pathology, University of Washington, Seattle WA USA; Gao, Yuqian [Pacific Northwest National Laboratory, Richland WA USA; Qian, Weijun [Pacific Northwest National Laboratory, Richland WA USA; Zabetian, Cyrus P. [Parkinson' s Disease Research and Geriatric Research, Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle WA USA; Department of Neurology, University of Washington School of Medicine, Seattle WA USA; Hu, Shu-Ching [Department of Neurology, University of Washington School of Medicine, Seattle WA USA; Quinn, Joseph F. [Department of Neurology, Oregon Health and Science University, Portland OR USA; Zhang, Jing [Department of Pathology, University of Washington, Seattle WA USA; Department of Pathology, Peking University Health Science Centre and Third Hospital, Beijing 100083 China

2017-04-19

Aim: The alpha-synuclein (α-syn) level in human cerebrospinal fluid (CSF), as measured by immunoassays, is promising as a Parkinson’s disease (PD) biomarker. However, the levels of total α-syn are inconsistent among studies with large cohorts and different measurement platforms. Total α-syn level also does not correlate with disease severity or progression. Here, we developed a highly sensitive Multiple Reaction Monitoring (MRM) method to measure absolute CSF α-syn peptide concentrations without prior enrichment or fractionation, aiming to discover new candidate biomarkers. Results: Six peptides covering 73% of protein sequence were reliably identified, and two were consistently quantified in cross-sectional and longitudinal cohorts. Absolute concentration of α-syn in human CSF was determined to be 2.1ng/mL. A unique α-syn peptide, TVEGAGSIAAATGFVK (81-96), displayed excellent correlation with previous immunoassay results in two independent PD cohorts (p < 0.001), correlated with disease severity, and its changes significantly tracked the disease progression longitudinally. Conclusions: An MRM assay to quantify human CSF α-syn was developed and optimized. Sixty clinical samples from cross-sectional and longitudinal PD cohorts were analyzed with this approach. Although further larger-scale validation is needed, the results suggest that α-syn peptide could serve as a promising biomarker in PD diagnosis and progression.

Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

Science.gov (United States)

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate. PMID:22368464

Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.

Science.gov (United States)

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

Directory of Open Access Journals (Sweden)

Ning Yu

2011-12-01

Full Text Available In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

Familiar Person Recognition: Is Autonoetic Consciousness More Likely to Accompany Face Recognition Than Voice Recognition?

Science.gov (United States)

Barsics, Catherine; Brédart, Serge

2010-11-01

Autonoetic consciousness is a fundamental property of human memory, enabling us to experience mental time travel, to recollect past events with a feeling of self-involvement, and to project ourselves in the future. Autonoetic consciousness is a characteristic of episodic memory. By contrast, awareness of the past associated with a mere feeling of familiarity or knowing relies on noetic consciousness, depending on semantic memory integrity. Present research was aimed at evaluating whether conscious recollection of episodic memories is more likely to occur following the recognition of a familiar face than following the recognition of a familiar voice. Recall of semantic information (biographical information) was also assessed. Previous studies that investigated the recall of biographical information following person recognition used faces and voices of famous people as stimuli. In this study, the participants were presented with personally familiar people's voices and faces, thus avoiding the presence of identity cues in the spoken extracts and allowing a stricter control of frequency exposure with both types of stimuli (voices and faces). In the present study, the rate of retrieved episodic memories, associated with autonoetic awareness, was significantly higher from familiar faces than familiar voices even though the level of overall recognition was similar for both these stimuli domains. The same pattern was observed regarding semantic information retrieval. These results and their implications for current Interactive Activation and Competition person recognition models are discussed.

Validation of the Actiheart Activity Monitor for Measurement of Activity Energy Expenditure in Children and Adolescents with Chronic Disease.

OpenAIRE

2010-01-01

Abstract Introduction: The purpose of this study was to develop an activity energy expenditure (AEE) prediction equation for the Actiheart activity monitor (AH) for use in children with chronic disease. Methods: 63 children, aged 8-18 years with different types of chronic disease (Juvenile Arthritis, Hemophilia, Dermatomyositis, neuromuscular disease, Cystic Fibrosis or Congenital Heart Disease) participated in an activity testing session which consisted of a resting protocol, ...

Late recognition of pregnancy as a predictor of adverse birth outcomes.

Science.gov (United States)

Ayoola, Adejoke B; Stommel, Manfred; Nettleman, Mary D

2009-08-01

We examined the relationship between the time of recognition of pregnancy and birth outcomes, such as premature births, low birthweight (LBW), admission to the neonatal intensive care unit (NICU), and infant mortality. A secondary analysis was performed using the Pregnancy Risk Assessment and Monitoring System (PRAMS) multistate data from 2000-2004. The sample consisted of 136,373 women who had a live childbirth. Analysis involved multiple logistic regression models, appropriately weighted for point and variance estimation to reflect the complex survey design of the PRAMS using STATA 9.2 (Stata Corp, College Station, TX). Approximately 27.6% recognized their pregnancy late (after 6 weeks of gestation). Late recognition was significantly associated with an increased odds of having premature births (odds ratio [OR], 1.09; 99% confidence interval [CI], 1.01-1.19), LBW (OR, 1.08; 99% CI, 1.01-1.15), and NICU admissions (OR, 1.12; 99% CI, 1.03-1.21). These results provide a rationale and an impetus for developing interventions that promote early recognition of pregnancy.

Management and monitoring recommendations for the use of eliglustat in adults with type 1 Gaucher disease in Europe.

Science.gov (United States)

Belmatoug, Nadia; Di Rocco, Maja; Fraga, Cristina; Giraldo, Pilar; Hughes, Derralynn; Lukina, Elena; Maison-Blanche, Pierre; Merkel, Martin; Niederau, Claus; Plӧckinger, Ursula; Richter, Johan; Stulnig, Thomas M; Vom Dahl, Stephan; Cox, Timothy M

2017-01-01

In Gaucher disease, diminished activity of the lysosomal enzyme, acid β-glucosidase, leads to accumulation of glucosylceramides and related substrates, primarily in the spleen, liver, and bone marrow. Eliglustat is an oral substrate reduction therapy approved in the European Union and the United States as a first-line treatment for adults with type 1 Gaucher disease who have compatible CYP2D6 metabolism phenotypes. A European Advisory Council of experts in Gaucher disease describes the characteristics of eliglustat that are distinct from enzyme augmentation therapy (the standard of care) and miglustat (the other approved substrate reduction therapy) and recommends investigations and monitoring for patients on eliglustat therapy within the context of current recommendations for Gaucher disease management. Eliglustat is a selective, potent inhibitor of glucosylceramide synthase, the enzyme responsible for biosynthesis of glucosylceramides which accumulate in Gaucher disease. Extensive metabolism of eliglustat by CYP2D6, and, to a lesser extent, CYP3A of the cytochrome P450 pathway, necessitates careful consideration of the patient's CYP2D6 metaboliser status and use of concomitant medications which share metabolism by these pathways. Guidance on specific assessments and monitoring required for eliglustat therapy, including an algorithm to determine eligibility for eliglustat, are provided. As a first-line therapy for type 1 Gaucher disease, eliglustat offers eligible patients a daily oral therapy alternative to biweekly infusions of enzyme therapy. Physicians will need to carefully assess individual Gaucher patients to determine their appropriateness for eliglustat therapy. The therapeutic response to eliglustat and use of concomitant medications will require long-term monitoring. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Errors in radiographic recognition in the emergency room

International Nuclear Information System (INIS)

Britton, C.A.; Cooperstein, L.A.

1986-01-01

For 6 months we monitored the frequency and type of errors in radiographic recognition made by radiology residents on call in our emergency room. A relatively low error rate was observed, probably because the authors evaluated cognitive errors only, rather than include those of interpretation. The most common missed finding was a small fracture, particularly on the hands or feet. First-year residents were most likely to make an error, but, interestingly, our survey revealed a small subset of upper-level residents who made a disproportionate number of errors

[Prosopagnosia and facial expression recognition].

Science.gov (United States)

Koyama, Shinichi

2014-04-01

This paper reviews clinical neuropsychological studies that have indicated that the recognition of a person's identity and the recognition of facial expressions are processed by different cortical and subcortical areas of the brain. The fusiform gyrus, especially the right fusiform gyrus, plays an important role in the recognition of identity. The superior temporal sulcus, amygdala, and medial frontal cortex play important roles in facial-expression recognition. Both facial recognition and facial-expression recognition are highly intellectual processes that involve several regions of the brain.

Eye-movement strategies in developmental prosopagnosia and "super" face recognition.

Science.gov (United States)

Bobak, Anna K; Parris, Benjamin A; Gregory, Nicola J; Bennetts, Rachel J; Bate, Sarah

2017-02-01

Developmental prosopagnosia (DP) is a cognitive condition characterized by a severe deficit in face recognition. Few investigations have examined whether impairments at the early stages of processing may underpin the condition, and it is also unknown whether DP is simply the "bottom end" of the typical face-processing spectrum. To address these issues, we monitored the eye-movements of DPs, typical perceivers, and "super recognizers" (SRs) while they viewed a set of static images displaying people engaged in naturalistic social scenarios. Three key findings emerged: (a) Individuals with more severe prosopagnosia spent less time examining the internal facial region, (b) as observed in acquired prosopagnosia, some DPs spent less time examining the eyes and more time examining the mouth than controls, and (c) SRs spent more time examining the nose-a measure that also correlated with face recognition ability in controls. These findings support previous suggestions that DP is a heterogeneous condition, but suggest that at least the most severe cases represent a group of individuals that qualitatively differ from the typical population. While SRs seem to merely be those at the "top end" of normal, this work identifies the nose as a critical region for successful face recognition.

Development of a brain MRI-based hidden Markov model for dementia recognition.

Science.gov (United States)

Chen, Ying; Pham, Tuan D

2013-01-01

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

Altered brain mechanisms of emotion processing in pre-manifest Huntington's disease.

Science.gov (United States)

Novak, Marianne J U; Warren, Jason D; Henley, Susie M D; Draganski, Bogdan; Frackowiak, Richard S; Tabrizi, Sarah J

2012-04-01

Huntington's disease is an inherited neurodegenerative disease that causes motor, cognitive and psychiatric impairment, including an early decline in ability to recognize emotional states in others. The pathophysiology underlying the earliest manifestations of the disease is not fully understood; the objective of our study was to clarify this. We used functional magnetic resonance imaging to investigate changes in brain mechanisms of emotion recognition in pre-manifest carriers of the abnormal Huntington's disease gene (subjects with pre-manifest Huntington's disease): 16 subjects with pre-manifest Huntington's disease and 14 control subjects underwent 1.5 tesla magnetic resonance scanning while viewing pictures of facial expressions from the Ekman and Friesen series. Disgust, anger and happiness were chosen as emotions of interest. Disgust is the emotion in which recognition deficits have most commonly been detected in Huntington's disease; anger is the emotion in which impaired recognition was detected in the largest behavioural study of emotion recognition in pre-manifest Huntington's disease to date; and happiness is a positive emotion to contrast with disgust and anger. Ekman facial expressions were also used to quantify emotion recognition accuracy outside the scanner and structural magnetic resonance imaging with voxel-based morphometry was used to assess the relationship between emotion recognition accuracy and regional grey matter volume. Emotion processing in pre-manifest Huntington's disease was associated with reduced neural activity for all three emotions in partially separable functional networks. Furthermore, the Huntington's disease-associated modulation of disgust and happiness processing was negatively correlated with genetic markers of pre-manifest disease progression in distributed, largely extrastriatal networks. The modulated disgust network included insulae, cingulate cortices, pre- and postcentral gyri, precunei, cunei, bilateral putamena

Effect of Self-monitoring and Medication Self-titration on Systolic Blood Pressure in Hypertensive Patients at High Risk of Cardiovascular Disease

OpenAIRE

McManus, Richard J.; Mant, Jonathan; Haque, M. Sayeed; Bray, Emma P.; Bryan, Stirling; Greenfield, Sheila M.; Jones, Miren I.; Jowett, Sue; Little, Paul; Penaloza, Cristina; Schwartz, Claire; Shackleford, Helen; Shovelton, Claire; Varghese, Jinu; Williams, Bryan

2014-01-01

IMPORTANCE: Self-monitoring of blood pressure with self-titration of antihypertensives (self-management) results in lower blood pressure in patients with hypertension, but there are no data about patients in high-risk groups.\\ud \\ud OBJECTIVE: To determine the effect of self-monitoring with self-titration of antihypertensive medication compared with usual care on systolic blood pressure among patients with cardiovascular disease, diabetes, or chronic kidney disease.\\ud \\ud DESIGN, SETTING, AN...

Bihippocampal damage with emotional dysfunction: impaired auditory recognition of fear.

Science.gov (United States)

Ghika-Schmid, F; Ghika, J; Vuilleumier, P; Assal, G; Vuadens, P; Scherer, K; Maeder, P; Uske, A; Bogousslavsky, J

1997-01-01

A right-handed man developed a sudden transient, amnestic syndrome associated with bilateral hemorrhage of the hippocampi, probably due to Urbach-Wiethe disease. In the 3rd month, despite significant hippocampal structural damage on imaging, only a milder degree of retrograde and anterograde amnesia persisted on detailed neuropsychological examination. On systematic testing of recognition of facial and vocal expression of emotion, we found an impairment of the vocal perception of fear, but not that of other emotions, such as joy, sadness and anger. Such selective impairment of fear perception was not present in the recognition of facial expression of emotion. Thus emotional perception varies according to the different aspects of emotions and the different modality of presentation (faces versus voices). This is consistent with the idea that there may be multiple emotion systems. The study of emotional perception in this unique case of bilateral involvement of hippocampus suggests that this structure may play a critical role in the recognition of fear in vocal expression, possibly dissociated from that of other emotions and from that of fear in facial expression. In regard of recent data suggesting that the amygdala is playing a role in the recognition of fear in the auditory as well as in the visual modality this could suggest that the hippocampus may be part of the auditory pathway of fear recognition.

Pupil dilation during recognition memory: Isolating unexpected recognition from judgment uncertainty.

Science.gov (United States)

Mill, Ravi D; O'Connor, Akira R; Dobbins, Ian G

2016-09-01

Optimally discriminating familiar from novel stimuli demands a decision-making process informed by prior expectations. Here we demonstrate that pupillary dilation (PD) responses during recognition memory decisions are modulated by expectations, and more specifically, that pupil dilation increases for unexpected compared to expected recognition. Furthermore, multi-level modeling demonstrated that the time course of the dilation during each individual trial contains separable early and late dilation components, with the early amplitude capturing unexpected recognition, and the later trailing slope reflecting general judgment uncertainty or effort. This is the first demonstration that the early dilation response during recognition is dependent upon observer expectations and that separate recognition expectation and judgment uncertainty components are present in the dilation time course of every trial. The findings provide novel insights into adaptive memory-linked orienting mechanisms as well as the general cognitive underpinnings of the pupillary index of autonomic nervous system activity. Copyright © 2016 Elsevier B.V. All rights reserved.

The CC chemokine receptor 5 regulates olfactory and social recognition in mice.

Science.gov (United States)

Kalkonde, Y V; Shelton, R; Villarreal, M; Sigala, J; Mishra, P K; Ahuja, S S; Barea-Rodriguez, E; Moretti, P; Ahuja, S K

2011-12-01

Chemokines are chemotactic cytokines that regulate cell migration and are thought to play an important role in a broad range of inflammatory diseases. The availability of chemokine receptor blockers makes them an important therapeutic target. In vitro, chemokines are shown to modulate neurotransmission. However, it is not very clear if chemokines play a role in behavior and cognition. Here we evaluated the role of CC chemokine receptor 5 (CCR5) in various behavioral tasks in mice using Wt (Ccr5⁺/⁺) and Ccr5-null (Ccr5⁻/⁻)mice. Ccr5⁻/⁻ mice showed enhanced social recognition. Administration of CC chemokine ligand 3 (CCL3), one of the CCR5-ligands, impaired social recognition. Since the social recognition task is dependent on the sense of olfaction, we tested olfactory recognition for social and non-social scents in these mice. Ccr5⁻/⁻ mice had enhanced olfactory recognition for both these scents indicating that enhanced performance in social recognition task could be due to enhanced olfactory recognition in these mice. Spatial memory and aversive memory were comparable in Wt and Ccr5⁻/⁻ mice. Collectively, these results suggest that chemokines/chemokine receptors might play an important role in olfactory recognition tasks in mice and to our knowledge represents the first direct demonstration of an in vivo role of CCR5 in modulating social behavior in mice. These studies are important as CCR5 blockers are undergoing clinical trials and can potentially modulate behavior. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Development of advanced methods for signal processing in the monitoring of sodium-cooled reactors

International Nuclear Information System (INIS)

Schleisiek, K.; Aberle, J.; Massier, H.; Scherer, K.P.; Vaeth, W.; Leder, H.J.; Schade, H.J.

1987-01-01

Selected examples (acoustic boiling detection, pattern recognition method, identification of fuel element vibrations, diagnosis system for KNK II) are used to demonstrate the benefits of up-to-date information technology in the monitoring of nuclear facilities. The methods used range from intelligent frequency analysis to AI methods like pattern recognition and expert systems. (DG) [de

Kernel learning algorithms for face recognition

CERN Document Server

Li, Jun-Bao; Pan, Jeng-Shyang

2013-01-01

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

A telemedicine instrument for Internet-based home monitoring of thoracoabdominal motion in patients with respiratory diseases

Science.gov (United States)

da Silva Junior, Evert Pereira; Esteves, Guilherme Pompeu; Dames, Karla Kristine; Melo, Pedro Lopes de

2011-01-01

Changes in thoracoabdominal motion are highly prevalent in patients with chronic respiratory diseases. Home care services that use telemedicine techniques and Internet-based monitoring have the potential to improve the management of these patients. However, there is no detailed description in the literature of a system for Internet-based monitoring of patients with disturbed thoracoabdominal motion. The purpose of this work was to describe the development of a new telemedicine instrument for Internet-based home monitoring of thoracoabdominal movement. The instrument directly measures changes in the thorax and abdomen circumferences and transfers data through a transmission control protocol/Internet protocol connection. After the design details are described, the accuracy of the electronic and software processing units of the instrument is evaluated by using electronic signals simulating normal subjects and individuals with thoracoabdominal motion disorders. The results obtained during in vivo studies on normal subjects simulating thoracoabdominal motion disorders showed that this new system is able to detect a reduction in abdominal movement that is associated with abnormal thoracic breathing (p telemedicine scenarios, which can reduce the costs of assistance offered to patients with respiratory diseases.

Monitoring of facial stress during space flight: Optical computer recognition combining discriminative and generative methods

Science.gov (United States)

Dinges, David F.; Venkataraman, Sundara; McGlinchey, Eleanor L.; Metaxas, Dimitris N.

2007-02-01

Astronauts are required to perform mission-critical tasks at a high level of functional capability throughout spaceflight. Stressors can compromise their ability to do so, making early objective detection of neurobehavioral problems in spaceflight a priority. Computer optical approaches offer a completely unobtrusive way to detect distress during critical operations in space flight. A methodology was developed and a study completed to determine whether optical computer recognition algorithms could be used to discriminate facial expressions during stress induced by performance demands. Stress recognition from a facial image sequence is a subject that has not received much attention although it is an important problem for many applications beyond space flight (security, human-computer interaction, etc.). This paper proposes a comprehensive method to detect stress from facial image sequences by using a model-based tracker. The image sequences were captured as subjects underwent a battery of psychological tests under high- and low-stress conditions. A cue integration-based tracking system accurately captured the rigid and non-rigid parameters of different parts of the face (eyebrows, lips). The labeled sequences were used to train the recognition system, which consisted of generative (hidden Markov model) and discriminative (support vector machine) parts that yield results superior to using either approach individually. The current optical algorithm methods performed at a 68% accuracy rate in an experimental study of 60 healthy adults undergoing periods of high-stress versus low-stress performance demands. Accuracy and practical feasibility of the technique is being improved further with automatic multi-resolution selection for the discretization of the mask, and automated face detection and mask initialization algorithms.

A Scoping Review of Economic Evaluations Alongside Randomised Controlled Trials of Home Monitoring in Chronic Disease Management.

Science.gov (United States)

Kidholm, Kristian; Kristensen, Mie Borch Dahl

2018-04-01

Many countries have considered telemedicine and home monitoring of patients as a solution to the demographic challenges that health-care systems face. However, reviews of economic evaluations of telemedicine have identified methodological problems in many studies as they do not comply with guidelines. The aim of this study was to examine economic evaluations alongside randomised controlled trials of home monitoring in chronic disease management and hereby to explore the resources included in the programme costs, the types of health-care utilisation that change as a result of home monitoring and discuss the value of economic evaluation alongside randomised controlled trials of home monitoring on the basis of the studies identified. A scoping review of economic evaluations of home monitoring of patients with chronic disease based on randomised controlled trials and including information on the programme costs and the costs of equipment was carried out based on a Medline (PubMed) search. Nine studies met the inclusion criteria. All studies include both costs of equipment and use of staff, but there is large variation in the types of equipment and types of tasks for the staff included in the costs. Equipment costs constituted 16-73% of the total programme costs. In six of the nine studies, home monitoring resulted in a reduction in primary care or emergency contacts. However, in total, home monitoring resulted in increased average costs per patient in six studies and reduced costs in three of the nine studies. The review is limited by the small number of studies found and the restriction to randomised controlled trials, which can be problematic in this area due to lack of blinding of patients and healthcare professionals and the difficulty of implementing organisational changes in hospital departments for the limited period of a trial. Furthermore, our results may be based on assessments of older telemedicine interventions.

Working memory affects older adults' use of context in spoken-word recognition.

Science.gov (United States)

Janse, Esther; Jesse, Alexandra

2014-01-01

Many older listeners report difficulties in understanding speech in noisy situations. Working memory and other cognitive skills may modulate older listeners' ability to use context information to alleviate the effects of noise on spoken-word recognition. In the present study, we investigated whether verbal working memory predicts older adults' ability to immediately use context information in the recognition of words embedded in sentences, presented in different listening conditions. In a phoneme-monitoring task, older adults were asked to detect as fast and as accurately as possible target phonemes in sentences spoken by a target speaker. Target speech was presented without noise, with fluctuating speech-shaped noise, or with competing speech from a single distractor speaker. The gradient measure of contextual probability (derived from a separate offline rating study) affected the speed of recognition. Contextual facilitation was modulated by older listeners' verbal working memory (measured with a backward digit span task) and age across listening conditions. Working memory and age, as well as hearing loss, were also the most consistent predictors of overall listening performance. Older listeners' immediate benefit from context in spoken-word recognition thus relates to their ability to keep and update a semantic representation of the sentence content in working memory.

Pelvic insufficiency fractures associated with radiation atrophy: clinical recognition and diagnostic evaluation

International Nuclear Information System (INIS)

Mumber, M.P.; Greven, K.M.; Haygood, T.M.

1997-01-01

Pelvic bone injuries are infrequent complications of radiotherapy. However, insufficiency fractures in irradiated pelvic bones may be underdetected, particularly in postmenopausal women. We describe the clinical presentation, radiologic evaluation, and course of disease in three patients with postradiation pelvic insufficiency fractures. Differential diagnosis included metastatic disease, tumor recurrence, and second malignancy. Recognition of radiographic features may prevent unnecessary, possibly morbid treatments. (orig.). With 6 figs

State Recognition and Visualization of Hoisting Motor of Quayside Container Crane Based on SOFM

Science.gov (United States)

Yang, Z. Q.; He, P.; Tang, G.; Hu, X.

2017-07-01

The neural network structure and algorithm of self-organizing feature map (SOFM) are researched and analysed. The method is applied to state recognition and visualization of the quayside container crane hoisting motor. By using SOFM, the clustering and visualization of attribute reduction of data are carried out, and three kinds motor states are obtained with Root Mean Square(RMS), Impulse Index and Margin Index, and the simulation visualization interface is realized by MATLAB. Through the processing of the sample data, it can realize the accurate identification of the motor state, thus provide better monitoring of the quayside container crane hoisting motor and a new way for the mechanical state recognition.

Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals.

Science.gov (United States)

Vanrell, Sebastian Rodrigo; Milone, Diego Humberto; Rufiner, Hugo Leonardo

2017-07-03

Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several \\azul{classic} techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, based on homomorphic analysis, a new type of feature extraction stage is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.

Sudden Event Recognition: A Survey

Directory of Open Access Journals (Sweden)

Mohd Asyraf Zulkifley

2013-08-01

Full Text Available Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1 the importance of a sudden event over a general anomalous event; (2 frameworks used in sudden event recognition; (3 the requirements and comparative studies of a sudden event recognition system and (4 various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

Inflammatory Bowel Disease in Primary Immunodeficiencies.

Science.gov (United States)

Kelsen, Judith R; Sullivan, Kathleen E

2017-08-01

Inflammatory bowel disease is most often a polygenic disorder with contributions from the intestinal microbiome, defects in barrier function, and dysregulated host responses to microbial stimulation. There is, however, increasing recognition of single gene defects that underlie a subset of patients with inflammatory bowel disease, particularly those with early-onset disease, and this review focuses on the primary immunodeficiencies associated with early-onset inflammatory bowel disease. The advent of next-generation sequencing has led to an improved recognition of single gene defects underlying some cases of inflammatory bowel disease. Among single gene defects, immune response genes are the most frequent category identified. This is also true of common genetic variants associated with inflammatory bowel disease, supporting a pivotal role for host responses in the pathogenesis. This review focuses on practical aspects related to diagnosis and management of children with inflammatory bowel disease who have underlying primary immunodeficiencies.

Image simulation for automatic license plate recognition

Science.gov (United States)

Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

2012-01-01

Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

Thermal Super-Pixels for Bimodal Stress Recognition

DEFF Research Database (Denmark)

Irani, Ramin; Nasrollahi, Kamal; Dhall, Abhinav

2016-01-01

to be in touch with the body which is not always practical. Contact-free monitoring of the stress by a camera [1, 2] can be an alternative. These systems usually utilize only an RGB or a thermal camera to recognize stress. To the best of our knowledge, the only work on fusion of these two modalities for stress......Stress is a response to time pressure or negative environmental conditions. If its stimulus iterates or stays for a long time, it affects health conditions. Thus, stress recognition is an important issue. Traditional systems for this purpose are mostly contact-based, i.e., they require a sensor...

Efficient Active Sensing with Categorized Further Explorations for a Home Behavior-Monitoring Robot

Directory of Open Access Journals (Sweden)

Wenwei Yu

2017-01-01

Full Text Available Mobile robotics is a potential solution to home behavior monitoring for the elderly. For a mobile robot in the real world, there are several types of uncertainties for its perceptions, such as the ambiguity between a target object and the surrounding objects and occlusions by furniture. The problem could be more serious for a home behavior-monitoring system, which aims to accurately recognize the activity of a target person, in spite of these uncertainties. It detects irregularities and categorizes situations requiring further explorations, which strategically maximize the information needed for activity recognition while minimizing the costs. Two schemes of active sensing, based on two irregularity detections, namely, heuristic-based and template-matching-based irregularity detections, were implemented and examined for body contour-based activity recognition. Their time cost and accuracy in activity recognition were evaluated through experiments in both a controlled scenario and a home living scenario. Experiment results showed that the categorized further explorations guided the robot system to sense the target person actively. As a result, with the proposed approach, the robot system has achieved higher accuracy of activity recognition.

The Improvement of Behavior Recognition Accuracy of Micro Inertial Accelerometer by Secondary Recognition Algorithm

Directory of Open Access Journals (Sweden)

Yu Liu

2014-05-01

Full Text Available Behaviors of “still”, “walking”, “running”, “jumping”, “upstairs” and “downstairs” can be recognized by micro inertial accelerometer of low cost. By using the features as inputs to the well-trained BP artificial neural network which is selected as classifier, those behaviors can be recognized. But the experimental results show that the recognition accuracy is not satisfactory. This paper presents secondary recognition algorithm and combine it with BP artificial neural network to improving the recognition accuracy. The Algorithm is verified by the Android mobile platform, and the recognition accuracy can be improved more than 8 %. Through extensive testing statistic analysis, the recognition accuracy can reach 95 % through BP artificial neural network and the secondary recognition, which is a reasonable good result from practical point of view.

Performance Monitoring Of A Computer Numerically Controlled (CNC) Lathe Using Pattern Recognition Techniques

Science.gov (United States)

Daneshmend, L. K.; Pak, H. A.

1984-02-01

On-line monitoring of the cutting process in CNC lathe is desirable to ensure unattended fault-free operation in an automated environment. The state of the cutting tool is one of the most important parameters which characterises the cutting process. Direct monitoring of the cutting tool or workpiece is not feasible during machining. However several variables related to the state of the tool can be measured on-line. A novel monitoring technique is presented which uses cutting torque as the variable for on-line monitoring. A classifier is designed on the basis of the empirical relationship between cutting torque and flank wear. The empirical model required by the on-line classifier is established during an automated training cycle using machine vision for off-line direct inspection of the tool.

Nonnecrotizing anterior scleritis mimicking orbital inflammatory disease

Directory of Open Access Journals (Sweden)

Lynch MC

2013-08-01

Full Text Available Michelle Chen Lynch,1 Andrew B Mick21Optometry Clinic, Ocala West Veterans Affairs Specialty Clinic, Ocala, FL, USA; 2Eye Clinic, San Francisco VA Medical Center, San Francisco, CA, USABackground: Anterior scleritis is an uncommon form of ocular inflammation, often associated with coexisting autoimmune disease. With early recognition and aggressive systemic therapy, prognosis for resolution is good. The diagnosis of underlying autoimmune disease involves a multidisciplinary approach.Case report: A 42-year-old African American female presented to the Eye Clinic at the San Francisco Veteran Affairs Medical Center, with a tremendously painful left eye, worse on eye movement, with marked injection of conjunctiva. There was mild swelling of the upper eyelid. Visual acuity was unaffected, but there was a mild red cap desaturation. The posterior segment was unremarkable. The initial differential diagnoses included anterior scleritis and orbital inflammatory disease. Oral steroid treatment was initiated with rapid resolution over a few days. Orbital imaging was unremarkable, and extensive laboratory work-up was positive only for antinuclear antibodies. The patient was diagnosed with idiopathic diffuse, nonnecrotizing anterior scleritis and has been followed for over 5 years without recurrence. The rheumatology clinic monitors the patient closely, as suspicion remains for potential arthralgias including human leukocyte antigen-B27-associated arthritis, lupus-associated arthritis, seronegative rheumatoid arthritis, recurrent juvenile idiopathic arthritis, and scleroderma, based on her constitutional symptoms and clinical presentation, along with a positive anti-nuclear antibody lab result.Conclusion: Untreated anterior scleritis can progress to formation of cataracts, glaucoma, uveitis, corneal melting, and posterior segment disease with significant risk of vision loss. Patients with anterior scleritis must be aggressively treated with systemic anti

Molecularly imprinted titania nanoparticles for selective recognition and assay of uric acid

Science.gov (United States)

Mujahid, Adnan; Khan, Aimen Idrees; Afzal, Adeel; Hussain, Tajamal; Raza, Muhammad Hamid; Shah, Asma Tufail; uz Zaman, Waheed

2015-06-01

Molecularly imprinted titania nanoparticles are su ccessfully synthesized by sol-gel method for the selective recognition of uric acid. Atomic force microscopy is used to study the morphology of uric acid imprinted titania nanoparticles with diameter in the range of 100-150 nm. Scanning electron microscopy images of thick titania layer indicate the formation of fine network of titania nanoparticles with uniform distribution. Molecular imprinting of uric acid as well as its subsequent washing is confirmed by Fourier transformation infrared spectroscopy measurements. Uric acid rebinding studies reveal the recognition capability of imprinted particles in the range of 0.01-0.095 mmol, which is applicable in monitoring normal to elevated levels of uric acid in human blood. The optical shift (signal) of imprinted particles is six times higher in comparison with non-imprinted particles for the same concentration of uric acid. Imprinted titania particles have shown substantially reduced binding affinity toward interfering and structurally related substances, e.g. ascorbic acid and guanine. These results suggest the possible application of titania nanoparticles in uric acid recognition and quantification in blood serum.

Mobile-based text recognition from water quality devices

Science.gov (United States)

Dhakal, Shanti; Rahnemoonfar, Maryam

2015-03-01

Measuring water quality of bays, estuaries, and gulfs is a complicated and time-consuming process. YSI Sonde is an instrument used to measure water quality parameters such as pH, temperature, salinity, and dissolved oxygen. This instrument is taken to water bodies in a boat trip and researchers note down different parameters displayed by the instrument's display monitor. In this project, a mobile application is developed for Android platform that allows a user to take a picture of the YSI Sonde monitor, extract text from the image and store it in a file on the phone. The image captured by the application is first processed to remove perspective distortion. Probabilistic Hough line transform is used to identify lines in the image and the corner of the image is then obtained by determining the intersection of the detected horizontal and vertical lines. The image is warped using the perspective transformation matrix, obtained from the corner points of the source image and the destination image, hence, removing the perspective distortion. Mathematical morphology operation, black-hat is used to correct the shading of the image. The image is binarized using Otsu's binarization technique and is then passed to the Optical Character Recognition (OCR) software for character recognition. The extracted information is stored in a file on the phone and can be retrieved later for analysis. The algorithm was tested on 60 different images of YSI Sonde with different perspective features and shading. Experimental results, in comparison to ground-truth results, demonstrate the effectiveness of the proposed method.

RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

Directory of Open Access Journals (Sweden)

Z. Wang

2017-09-01

Full Text Available In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

Pattern Recognition-Based Analysis of COPD in CT

DEFF Research Database (Denmark)

Sørensen, Lauge Emil Borch Laurs

recognition part is used to turn the texture measures, measured in a CT image of the lungs, into a quantitative measure of disease. This is done by applying a classifier that is trained on a training set of data examples with known lung tissue patterns. Different classification systems are considered, and we...... will in particular use the pattern recognition concepts of supervised learning, multiple instance learning, and dissimilarity representation-based classification. The proposed texture-based measures are applied to CT data from two different sources, one comprising low dose CT slices from subjects with manually...... annotated regions of emphysema and healthy tissue, and one comprising volumetric low dose CT images from subjects that are either healthy or suffer from COPD. Several experiments demonstrate that it is clearly beneficial to take the lung tissue texture into account when classifying or quantifying emphysema...

Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets.

Science.gov (United States)

Salau, J; Haas, J H; Thaller, G; Leisen, M; Junge, W

2016-09-01

Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69.

Development and Validation of an Inflammatory Bowel Diseases Monitoring Index for Use With Mobile Health Technologies

NARCIS (Netherlands)

van Deen, Welmoed K.; van der Meulen-de Jong, Andrea E.; Parekh, Nimisha K.; Kane, Ellen; Zand, Aria; DiNicola, Courtney A.; Hall, Laurin; Inserra, Elizabeth K.; Choi, Jennifer M.; Ha, Christina Y.; Esrailian, Eric; van Oijen, Martijn G. H.; Hommes, Daniel W.

2016-01-01

Mobile health technologies are advancing rapidly as smartphone use increases. Patients with inflammatory bowel disease (IBD) might be managed remotely through smartphone applications, but no tools are yet available. We tested the ability of an IBD monitoring tool, which can be used with mobile

State Recognition of High Voltage Isolation Switch Based on Background Difference and Iterative Search

Science.gov (United States)

Xu, Jiayuan; Yu, Chengtao; Bo, Bin; Xue, Yu; Xu, Changfu; Chaminda, P. R. Dushantha; Hu, Chengbo; Peng, Kai

2018-03-01

The automatic recognition of the high voltage isolation switch by remote video monitoring is an effective means to ensure the safety of the personnel and the equipment. The existing methods mainly include two ways: improving monitoring accuracy and adopting target detection technology through equipment transformation. Such a method is often applied to specific scenarios, with limited application scope and high cost. To solve this problem, a high voltage isolation switch state recognition method based on background difference and iterative search is proposed in this paper. The initial position of the switch is detected in real time through the background difference method. When the switch starts to open and close, the target tracking algorithm is used to track the motion trajectory of the switch. The opening and closing state of the switch is determined according to the angle variation of the switch tracking point and the center line. The effectiveness of the method is verified by experiments on different switched video frames of switching states. Compared with the traditional methods, this method is more robust and effective.

Serum tryptase monitoring in indolent systemic mastocytosis: association with disease features and patient outcome.

Directory of Open Access Journals (Sweden)

Almudena Matito

Full Text Available BACKGROUND: Serum baseline tryptase (sBT is a minor diagnostic criterion for systemic mastocytosis (SM of undetermined prognostic impact. We monitored sBT levels in indolent SM (ISM patients and investigated its utility for predicting disease behaviour and outcome. METHODS: In total 74 adult ISM patients who were followed for ≥48 months and received no cytoreductive therapy were retrospectively studied. Patients were classified according to the pattern of evolution of sBT observed. RESULTS: Overall 16/74 (22% cases had decreasing sBT levels, 48 (65% patients showed increasing sBT levels and 10 (13% patients showed a fluctuating pattern. Patients with significantly increasing sBT (sBT slope ≥0.15 after 48 months of follow-up showed a slightly greater rate of development of diffuse bone sclerosis (13% vs. 2% and hepatomegaly plus splenomegaly (16% vs. 5%, as well as a significantly greater frequency of multilineage vs. mast cells (MC-restricted KIT mutation (p = 0.01 together with a greater frequency of cases with progression of ISM to smouldering and aggressive SM (p = 0.03, and a shorter progression-free survival (p = 0.03. CONCLUSIONS: Monitoring of sBT in ISM patients is closely associated with poor prognosis disease features as well as with disease progression, pointing out the need for a closer follow-up in ISM patients with progressively increasing sBT values.

Robust Radio Broadcast Monitoring Using a Multi-Band Spectral Entropy Signature

Science.gov (United States)

Camarena-Ibarrola, Antonio; Chávez, Edgar; Tellez, Eric Sadit

Monitoring media broadcast content has deserved a lot of attention lately from both academy and industry due to the technical challenge involved and its economic importance (e.g. in advertising). The problem pose a unique challenge from the pattern recognition point of view because a very high recognition rate is needed under non ideal conditions. The problem consist in comparing a small audio sequence (the commercial ad) with a large audio stream (the broadcast) searching for matches.

Mood recognition in bipolar patients through the PSYCHE platform: preliminary evaluations and perspectives.

Science.gov (United States)

Valenza, Gaetano; Gentili, Claudio; Lanatà, Antonio; Scilingo, Enzo Pasquale

2013-01-01

Bipolar disorders are characterized by a series of both depressive and manic or hypomanic episodes. Although common and expensive to treat, the clinical assessment of bipolar disorder is still ill-defined. In the current literature several correlations between mood disorders and dysfunctions involving the autonomic nervous system (ANS) can be found. The objective of this work is to develop a novel mood recognition system based on a pervasive, wearable and personalized monitoring system using ANS-related biosignals. The monitoring platform used in this study is the core sensing system of the personalized monitoring systems for care in mental health (PSYCHE) European project. It is comprised of a comfortable sensorized t-shirt that can acquire the inter-beat interval time series, the heart rate, and the respiratory dynamics for long-term monitoring during the day and overnight. In this study, three bipolar patients were followed for a period of 90 days during which up to six monitoring sessions and psychophysical evaluations were performed for each patient. Specific signal processing techniques and artificial intelligence algorithms were applied to analyze more than 120 h of data. Experimental results are expressed in terms of confusion matrices and an exhaustive descriptive statistics of the most relevant features is reported as well. A classification accuracy of about 97% is achieved for the intra-subject analysis. Such an accuracy was found in distinguishing relatively good affective balance state (euthymia) from severe clinical states (severe depression and mixed state) and is lower in distinguishing euthymia from the milder states (accuracy up to 88%). The PSYCHE platform could provide a viable decision support system in order to improve mood assessment in patient care. Evidences about the correlation between mood disorders and ANS dysfunctions were found and the obtained results are promising for an effective biosignal-based mood recognition. Copyright © 2012

The Legal Recognition of Sign Languages

Science.gov (United States)

De Meulder, Maartje

2015-01-01

This article provides an analytical overview of the different types of explicit legal recognition of sign languages. Five categories are distinguished: constitutional recognition, recognition by means of general language legislation, recognition by means of a sign language law or act, recognition by means of a sign language law or act including…

Human activity recognition and prediction

CERN Document Server

2016-01-01

This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .

Anatomical entity recognition with a hierarchical framework augmented by external resources.

Directory of Open Access Journals (Sweden)

Yan Xu

Full Text Available References to anatomical entities in medical records consist not only of explicit references to anatomical locations, but also other diverse types of expressions, such as specific diseases, clinical tests, clinical treatments, which constitute implicit references to anatomical entities. In order to identify these implicit anatomical entities, we propose a hierarchical framework, in which two layers of named entity recognizers (NERs work in a cooperative manner. Each of the NERs is implemented using the Conditional Random Fields (CRF model, which use a range of external resources to generate features. We constructed a dictionary of anatomical entity expressions by exploiting four existing resources, i.e., UMLS, MeSH, RadLex and BodyPart3D, and supplemented information from two external knowledge bases, i.e., Wikipedia and WordNet, to improve inference of anatomical entities from implicit expressions. Experiments conducted on 300 discharge summaries showed a micro-averaged performance of 0.8509 Precision, 0.7796 Recall and 0.8137 F1 for explicit anatomical entity recognition, and 0.8695 Precision, 0.6893 Recall and 0.7690 F1 for implicit anatomical entity recognition. The use of the hierarchical framework, which combines the recognition of named entities of various types (diseases, clinical tests, treatments with information embedded in external knowledge bases, resulted in a 5.08% increment in F1. The resources constructed for this research will be made publicly available.

Monitoring and accountability for the Pacific response to the non-communicable diseases crisis

Directory of Open Access Journals (Sweden)

Hilary Tolley

2016-09-01

Full Text Available Abstract Background Non-communicable diseases (NCD are the leading cause of premature death and disability in the Pacific. In 2011, Pacific Forum Leaders declared “a human, social and economic crisis” due to the significant and growing burden of NCDs in the region. In 2013, Pacific Health Ministers’ commitment to ‘whole of government’ strategy prompted calls for the development of a robust, sustainable, collaborative NCD monitoring and accountability system to track, review and propose remedial action to ensure progress towards the NCD goals and targets. The purpose of this paper is to describe a regional, collaborative framework for coordination, innovation and application of NCD monitoring activities at scale, and to show how they can strengthen accountability for action on NCDs in the Pacific. A key component is the Dashboard for NCD Action which aims to strengthen mutual accountability by demonstrating national and regional progress towards agreed NCD policies and actions. Discussion The framework for the Pacific Monitoring Alliance for NCD Action (MANA draws together core country-level components of NCD monitoring data (mortality, morbidity, risk factors, health system responses, environments, and policies and identifies key cross-cutting issues for strengthening national and regional monitoring systems. These include: capacity building; a regional knowledge exchange hub; innovations (monitoring childhood obesity and food environments; and a robust regional accountability system. The MANA framework is governed by the Heads of Health and operationalised by a multi-agency technical Coordination Team. Alliance membership is voluntary and non-conditional, and aims to support the 22 Pacific Island countries and territories to improve the quality of NCD monitoring data across the region. In establishing a common vision for NCD monitoring, the framework combines data collected under the WHO Global Framework for NCDs with a set of action

Monitoring and accountability for the Pacific response to the non-communicable diseases crisis.

Science.gov (United States)

Tolley, Hilary; Snowdon, Wendy; Wate, Jillian; Durand, A Mark; Vivili, Paula; McCool, Judith; Novotny, Rachel; Dewes, Ofa; Hoy, Damian; Bell, Colin; Richards, Nicola; Swinburn, Boyd

2016-09-10

Non-communicable diseases (NCD) are the leading cause of premature death and disability in the Pacific. In 2011, Pacific Forum Leaders declared "a human, social and economic crisis" due to the significant and growing burden of NCDs in the region. In 2013, Pacific Health Ministers' commitment to 'whole of government' strategy prompted calls for the development of a robust, sustainable, collaborative NCD monitoring and accountability system to track, review and propose remedial action to ensure progress towards the NCD goals and targets. The purpose of this paper is to describe a regional, collaborative framework for coordination, innovation and application of NCD monitoring activities at scale, and to show how they can strengthen accountability for action on NCDs in the Pacific. A key component is the Dashboard for NCD Action which aims to strengthen mutual accountability by demonstrating national and regional progress towards agreed NCD policies and actions. The framework for the Pacific Monitoring Alliance for NCD Action (MANA) draws together core country-level components of NCD monitoring data (mortality, morbidity, risk factors, health system responses, environments, and policies) and identifies key cross-cutting issues for strengthening national and regional monitoring systems. These include: capacity building; a regional knowledge exchange hub; innovations (monitoring childhood obesity and food environments); and a robust regional accountability system. The MANA framework is governed by the Heads of Health and operationalised by a multi-agency technical Coordination Team. Alliance membership is voluntary and non-conditional, and aims to support the 22 Pacific Island countries and territories to improve the quality of NCD monitoring data across the region. In establishing a common vision for NCD monitoring, the framework combines data collected under the WHO Global Framework for NCDs with a set of action-orientated indicators captured in a NCD Dashboard for

Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons.

Science.gov (United States)

Filippoupolitis, Avgoustinos; Oliff, William; Takand, Babak; Loukas, George

2017-05-27

Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation.

A simulation study to evaluate the performance of five statistical monitoring methods when applied to different time-series components in the context of control programs for endemic diseases

DEFF Research Database (Denmark)

Lopes Antunes, Ana Carolina; Jensen, Dan; Hisham Beshara Halasa, Tariq

2017-01-01

Disease monitoring and surveillance play a crucial role in control and eradication programs, as it is important to track implemented strategies in order to reduce and/or eliminate a specific disease. The objectives of this study were to assess the performance of different statistical monitoring......, decreases and constant sero-prevalence levels (referred as events). Two space-state models were used to model the time series, and different statistical monitoring methods (such as univariate process control algorithms–Shewart Control Chart, Tabular Cumulative Sums, and the V-mask- and monitoring...... of noise in the baseline was greater for the Shewhart Control Chart and Tabular Cumulative Sums than for the V-Mask and trend-based methods. The performance of the different statistical monitoring methods varied when monitoring increases and decreases in disease sero-prevalence. Combining two of more...

A simulation study to evaluate the performance of five statistical monitoring methods when applied to different time-series components in the context of control programs for endemic diseases

DEFF Research Database (Denmark)

Lopes Antunes, Ana Carolina; Jensen, Dan; Hisham Beshara Halasa, Tariq

2017-01-01

, decreases and constant sero-prevalence levels (referred as events). Two space-state models were used to model the time series, and different statistical monitoring methods (such as univariate process control algorithms–Shewart Control Chart, Tabular Cumulative Sums, and the V-mask- and monitoring......Disease monitoring and surveillance play a crucial role in control and eradication programs, as it is important to track implemented strategies in order to reduce and/or eliminate a specific disease. The objectives of this study were to assess the performance of different statistical monitoring...... of noise in the baseline was greater for the Shewhart Control Chart and Tabular Cumulative Sums than for the V-Mask and trend-based methods. The performance of the different statistical monitoring methods varied when monitoring increases and decreases in disease sero-prevalence. Combining two of more...

Environmental monitoring by means of remote sensing

International Nuclear Information System (INIS)

Theilen-Willige, B.

1993-01-01

Aircraft and satellite aerial photographs represent indispensible tools for environmental observation today. They contribute to a systematic inventory of important environmental parameters such as climate, vegetation or surface water. Their great importance lies in the continuous monitoring of large regions so that changes in environmental conditions are quickly detected. This book provides an overview of the capabilities of remote sensing in environmental monitoring and in the recognition of environmental problems as well as of the usefulness of remote sensing data for environmental planning. Also addressed is the role of remote sensing in the monitoring of natural hazards such as earthquakes and volcano eruptions as well as problems of remote sensing technology transfer to developing countries. (orig.) [de

Radar automatic target recognition (ATR) and non-cooperative target recognition (NCTR)

CERN Document Server

Blacknell, David

2013-01-01

The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition. Topics covered include the problem as applied to th

Recognition and Toleration

DEFF Research Database (Denmark)

Lægaard, Sune

2010-01-01

Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms or inter...

Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

Science.gov (United States)

Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

2017-06-01

Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

Recognition of familiar people with a mobile cloud architecture for Alzheimer patients.

Science.gov (United States)

Fardoun, Habib M; Mashat, Abdullah A; Ramirez Castillo, Jaime

2017-02-01

This article aims to the evaluation of a prototypal assistive technology for Alzheimer's disease (AD) patients that helps them to remember personal details of familiar people they meet in their daily lives. An architecture is proposed for a personal information system powered by face recognition, where the main AD patient's interaction is performed in a smart watch device and the face recognition is carried out on the Cloud. A prototype was developed to perform some tests in a real-life scenario. The prototype showed correct results as a personal information system based on face recognition. However, usability flaws were identified in the interaction with the smart watch. Our architecture showed correct performance and we realized that it could be introduced in other fields, apart from assistive technology. However, when being targeted to patients with dementia some usability problems appeared, such as difficulties to read information in a small screen or take a proper photo. These problems should be addressed in further research. Implications for Rehabilitation This article presents a prototypal assistive technology for Alzheimer's disease (AD) patients. It targets AD patients to recognize their familiars, especially in medium-advanced stages of the disease. Analysing pictures taken by a smart watch, which the patient carries, the person in front is recognized and information about him is sent to the watch. This technology enables patients to have all the information of any close person, as a remainder, easing their daily lives, improving their self-esteem and stimulating the patient with novel technology.

Super-recognition in development: A case study of an adolescent with extraordinary face recognition skills.

Science.gov (United States)

Bennetts, Rachel J; Mole, Joseph; Bate, Sarah

2017-09-01

Face recognition abilities vary widely. While face recognition deficits have been reported in children, it is unclear whether superior face recognition skills can be encountered during development. This paper presents O.B., a 14-year-old female with extraordinary face recognition skills: a "super-recognizer" (SR). O.B. demonstrated exceptional face-processing skills across multiple tasks, with a level of performance that is comparable to adult SRs. Her superior abilities appear to be specific to face identity: She showed an exaggerated face inversion effect and her superior abilities did not extend to object processing or non-identity aspects of face recognition. Finally, an eye-movement task demonstrated that O.B. spent more time than controls examining the nose - a pattern previously reported in adult SRs. O.B. is therefore particularly skilled at extracting and using identity-specific facial cues, indicating that face and object recognition are dissociable during development, and that super recognition can be detected in adolescence.

Disease management: atrial fibrillation and home monitoring.

Science.gov (United States)

Ricci, Renato Pietro

2013-06-01

Device-detected atrial fibrillation (AF) episodes predict poor clinical outcome regardless of symptoms. Potential benefits of remote monitoring are early arrhythmia detection and patient continuous monitoring. Several studies of device remote monitoring consistently demonstrated that AF represents the most common clinical alert and that detailed information on arrhythmia onset, duration, and burden as well as on the ventricular rate may be early available for clinical evaluation. Reaction time to AF alerts was very short in all series involving either pacemakers or defibrillators and action ability of AF alerts was very high. In the Home Guide Registry, in which 1650 patients were enrolled, AF was detected in 16.3% of patients and represented 36% of all cardiovascular events during the follow-up. Timely anticoagulation introduction in asymptomatic patients may impact on the stroke rate. According to the results of repeated Monte Carlo simulations based on a real population of 166 patients, daily monitoring may reduce the 2-year stroke risk by 9-18% with an absolute reduction of 0.2-0.6%, compared with conventional inter-visit intervals of 6-12 months. In the COMPAS trial, the incidence of hospitalizations for atrial arrhythmias and related stroke was significantly higher in the control group than in the remote monitoring group. Major questions will be addressed by the ongoing IMPACT trial in which a remote monitoring guided anticoagulation strategy based on AF detection will be compared with a physician-directed standard strategy. In patients with heart failure, AF early detection combined with other indexes may help prevent hospitalizations.

Damage-recognition proteins as a potential indicator of DNA-damage-mediated sensitivity or resistance of human cells to ultraviolet radiation

International Nuclear Information System (INIS)

Chao, C.C.-K.

1992-01-01

The authors compared damage-recognition proteins in cells expressing different sensitivities to DNA damage. An increase in damage-recognition proteins and an enhancement of plasmid re-activation were detected in HeLa cells resistant to cisplatin and u.v. However, repair-defective cells derived from xeroderma-pigmentosum (a rare skin disease) patients did not express less cisplatin damage-recognition proteins than repair-competent cells, suggesting that damage-recognition-protein expression may not be related to DNA repair. By contrast, cells resistant to DNA damage consistently expressed high levels of u.v.-modified-DNA damage-recognition proteins. The results support the notion that u.v. damage-recognition proteins are different from those that bind to cisplatin. Findings also suggest that the damage-recognition proteins identified could be used as potential indicators of the sensitivity or resistance of cells to u.v. (author)

Alterations in Resting-State Activity Relate to Performance in a Verbal Recognition Task

Science.gov (United States)

López Zunini, Rocío A.; Thivierge, Jean-Philippe; Kousaie, Shanna; Sheppard, Christine; Taler, Vanessa

2013-01-01

In the brain, resting-state activity refers to non-random patterns of intrinsic activity occurring when participants are not actively engaged in a task. We monitored resting-state activity using electroencephalogram (EEG) both before and after a verbal recognition task. We show a strong positive correlation between accuracy in verbal recognition and pre-task resting-state alpha power at posterior sites. We further characterized this effect by examining resting-state post-task activity. We found marked alterations in resting-state alpha power when comparing pre- and post-task periods, with more pronounced alterations in participants that attained higher task accuracy. These findings support a dynamical view of cognitive processes where patterns of ongoing brain activity can facilitate –or interfere– with optimal task performance. PMID:23785436

A motivational determinant of facial emotion recognition: regulatory focus affects recognition of emotions in faces.

Science.gov (United States)

Sassenrath, Claudia; Sassenberg, Kai; Ray, Devin G; Scheiter, Katharina; Jarodzka, Halszka

2014-01-01

Two studies examined an unexplored motivational determinant of facial emotion recognition: observer regulatory focus. It was predicted that a promotion focus would enhance facial emotion recognition relative to a prevention focus because the attentional strategies associated with promotion focus enhance performance on well-learned or innate tasks - such as facial emotion recognition. In Study 1, a promotion or a prevention focus was experimentally induced and better facial emotion recognition was observed in a promotion focus compared to a prevention focus. In Study 2, individual differences in chronic regulatory focus were assessed and attention allocation was measured using eye tracking during the facial emotion recognition task. Results indicated that the positive relation between a promotion focus and facial emotion recognition is mediated by shorter fixation duration on the face which reflects a pattern of attention allocation matched to the eager strategy in a promotion focus (i.e., striving to make hits). A prevention focus did not have an impact neither on perceptual processing nor on facial emotion recognition. Taken together, these findings demonstrate important mechanisms and consequences of observer motivational orientation for facial emotion recognition.

Pattern recognition & machine learning

CERN Document Server

Anzai, Y

1992-01-01

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

A REVIEW: OPTICAL CHARACTER RECOGNITION

OpenAIRE

Swati Tomar*1 & Amit Kishore2

2018-01-01

This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determine that have been proposed to realize the center of character recognition in an optical character recognition system. Even though, sufficient studies and papers are describes the techniques for converting textual content from a paper document into machine readable form. Optical character recognition is a process where the computer understands automatically the image of handwritten ...

Influence of auditory attention on sentence recognition captured by the neural phase.

Science.gov (United States)

Müller, Jana Annina; Kollmeier, Birger; Debener, Stefan; Brand, Thomas

2018-03-07

The aim of this study was to investigate whether attentional influences on speech recognition are reflected in the neural phase entrained by an external modulator. Sentences were presented in 7 Hz sinusoidally modulated noise while the neural response to that modulation frequency was monitored by electroencephalogram (EEG) recordings in 21 participants. We implemented a selective attention paradigm including three different attention conditions while keeping physical stimulus parameters constant. The participants' task was either to repeat the sentence as accurately as possible (speech recognition task), to count the number of decrements implemented in modulated noise (decrement detection task), or to do both (dual task), while the EEG was recorded. Behavioural analysis revealed reduced performance in the dual task condition for decrement detection, possibly reflecting limited cognitive resources. EEG analysis revealed no significant differences in power for the 7 Hz modulation frequency, but an attention-dependent phase difference between tasks. Further phase analysis revealed a significant difference 500 ms after sentence onset between trials with correct and incorrect responses for speech recognition, indicating that speech recognition performance and the neural phase are linked via selective attention mechanisms, at least shortly after sentence onset. However, the neural phase effects identified were small and await further investigation. © 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

SAR: Stroke Authorship Recognition

KAUST Repository

Shaheen, Sara; Rockwood, Alyn; Ghanem, Bernard

2015-01-01

Are simple strokes unique to the artist or designer who renders them? If so, can this idea be used to identify authorship or to classify artistic drawings? Also, could training methods be devised to develop particular styles? To answer these questions, we propose the Stroke Authorship Recognition (SAR) approach, a novel method that distinguishes the authorship of 2D digitized drawings. SAR converts a drawing into a histogram of stroke attributes that is discriminative of authorship. We provide extensive classification experiments on a large variety of data sets, which validate SAR's ability to distinguish unique authorship of artists and designers. We also demonstrate the usefulness of SAR in several applications including the detection of fraudulent sketches, the training and monitoring of artists in learning a particular new style and the first quantitative way to measure the quality of automatic sketch synthesis tools. © 2015 The Eurographics Association and John Wiley & Sons Ltd.

SAR: Stroke Authorship Recognition

KAUST Repository

Shaheen, Sara

2015-10-15

Are simple strokes unique to the artist or designer who renders them? If so, can this idea be used to identify authorship or to classify artistic drawings? Also, could training methods be devised to develop particular styles? To answer these questions, we propose the Stroke Authorship Recognition (SAR) approach, a novel method that distinguishes the authorship of 2D digitized drawings. SAR converts a drawing into a histogram of stroke attributes that is discriminative of authorship. We provide extensive classification experiments on a large variety of data sets, which validate SAR\\'s ability to distinguish unique authorship of artists and designers. We also demonstrate the usefulness of SAR in several applications including the detection of fraudulent sketches, the training and monitoring of artists in learning a particular new style and the first quantitative way to measure the quality of automatic sketch synthesis tools. © 2015 The Eurographics Association and John Wiley & Sons Ltd.

Interfamily transfer of a plant pattern-recognition receptor confers broad-spectrum bacterial resistance

NARCIS (Netherlands)

Lacombe, S.; Rougon-Cardoso, A.; Sherwood, E.; Peeters, N.; Dahlbeck, D.; Esse, van H.P.; Smoker, M.; Rallapalli, G.; Thomma, B.P.H.J.; Staskawicz, B.; Jones, J.D.G.; Zipfel, C.

2010-01-01

Plant diseases cause massive losses in agriculture. Increasing the natural defenses of plants may reduce the impact of phytopathogens on agricultural productivity. Pattern-recognition receptors (PRRs) detect microbes by recognizing conserved pathogen-associated molecular patterns (PAMPs)1, 2, 3.

Antiphospholipid syndrome and kidney disease.

Science.gov (United States)

Bienaimé, Frank; Legendre, Christophe; Terzi, Fabiola; Canaud, Guillaume

2017-01-01

The antiphospholipid syndrome is a common autoimmune disease caused by pathogenic antiphospholipid antibodies, leading to recurrent thrombosis and/or obstetrical complications. Importantly for nephrologists, antiphospholipid antibodies are associated with various renal manifestations including large renal vessel thrombosis, renal artery stenosis, and a constellation of intrarenal lesions that has been termed antiphospholipid nephropathy. This last condition associates various degrees of acute thrombotic microangiopathy, proliferative and fibrotic lesions of the intrarenal vessels, and ischemic modifications of the renal parenchyma. The course of the disease can range from indolent nephropathy to devastating acute renal failure. The pejorative impact of antiphospholipid antibody-related renal complication is well established in the context of systemic lupus erythematous or after renal transplantation. In contrast, the exact significance of isolated antiphospholipid nephropathy remains uncertain. The evidence to guide management of the renal complications of antiphospholipid syndrome is limited. However, the recent recognition of the heterogeneous molecular mechanisms underlying the progression of intrarenal vascular lesions in antiphospholipid syndrome have opened promising tracks for patient monitoring and targeted therapeutic intervention. Copyright © 2016 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

Science.gov (United States)

Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui

2016-01-01

In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct

Challenging ocular image recognition

Science.gov (United States)

Pauca, V. Paúl; Forkin, Michael; Xu, Xiao; Plemmons, Robert; Ross, Arun A.

2011-06-01

Ocular recognition is a new area of biometric investigation targeted at overcoming the limitations of iris recognition performance in the presence of non-ideal data. There are several advantages for increasing the area beyond the iris, yet there are also key issues that must be addressed such as size of the ocular region, factors affecting performance, and appropriate corpora to study these factors in isolation. In this paper, we explore and identify some of these issues with the goal of better defining parameters for ocular recognition. An empirical study is performed where iris recognition methods are contrasted with texture and point operators on existing iris and face datasets. The experimental results show a dramatic recognition performance gain when additional features are considered in the presence of poor quality iris data, offering strong evidence for extending interest beyond the iris. The experiments also highlight the need for the direct collection of additional ocular imagery.

Emerging technologies with potential for objectively evaluating speech recognition skills.

Science.gov (United States)

Rawool, Vishakha Waman

2016-01-01

Work-related exposure to noise and other ototoxins can cause damage to the cochlea, synapses between the inner hair cells, the auditory nerve fibers, and higher auditory pathways, leading to difficulties in recognizing speech. Procedures designed to determine speech recognition scores (SRS) in an objective manner can be helpful in disability compensation cases where the worker claims to have poor speech perception due to exposure to noise or ototoxins. Such measures can also be helpful in determining SRS in individuals who cannot provide reliable responses to speech stimuli, including patients with Alzheimer's disease, traumatic brain injuries, and infants with and without hearing loss. Cost-effective neural monitoring hardware and software is being rapidly refined due to the high demand for neurogaming (games involving the use of brain-computer interfaces), health, and other applications. More specifically, two related advances in neuro-technology include relative ease in recording neural activity and availability of sophisticated analysing techniques. These techniques are reviewed in the current article and their applications for developing objective SRS procedures are proposed. Issues related to neuroaudioethics (ethics related to collection of neural data evoked by auditory stimuli including speech) and neurosecurity (preservation of a person's neural mechanisms and free will) are also discussed.

Evidence for Anger Saliency during the Recognition of Chimeric Facial Expressions of Emotions in Underage Ebola Survivors

Directory of Open Access Journals (Sweden)

Martina Ardizzi

2017-06-01

Full Text Available One of the crucial features defining basic emotions and their prototypical facial expressions is their value for survival. Childhood traumatic experiences affect the effective recognition of facial expressions of negative emotions, normally allowing the recruitment of adequate behavioral responses to environmental threats. Specifically, anger becomes an extraordinarily salient stimulus unbalancing victims’ recognition of negative emotions. Despite the plethora of studies on this topic, to date, it is not clear whether this phenomenon reflects an overall response tendency toward anger recognition or a selective proneness to the salience of specific facial expressive cues of anger after trauma exposure. To address this issue, a group of underage Sierra Leonean Ebola virus disease survivors (mean age 15.40 years, SE 0.35; years of schooling 8.8 years, SE 0.46; 14 males and a control group (mean age 14.55, SE 0.30; years of schooling 8.07 years, SE 0.30, 15 males performed a forced-choice chimeric facial expressions recognition task. The chimeric facial expressions were obtained pairing upper and lower half faces of two different negative emotions (selected from anger, fear and sadness for a total of six different combinations. Overall, results showed that upper facial expressive cues were more salient than lower facial expressive cues. This priority was lost among Ebola virus disease survivors for the chimeric facial expressions of anger. In this case, differently from controls, Ebola virus disease survivors recognized anger regardless of the upper or lower position of the facial expressive cues of this emotion. The present results demonstrate that victims’ performance in the recognition of the facial expression of anger does not reflect an overall response tendency toward anger recognition, but rather the specific greater salience of facial expressive cues of anger. Furthermore, the present results show that traumatic experiences deeply modify

Cataract influence on iris recognition performance

Science.gov (United States)

Trokielewicz, Mateusz; Czajka, Adam; Maciejewicz, Piotr

2014-11-01

This paper presents the experimental study revealing weaker performance of the automatic iris recognition methods for cataract-affected eyes when compared to healthy eyes. There is little research on the topic, mostly incorporating scarce databases that are often deficient in images representing more than one illness. We built our own database, acquiring 1288 eye images of 37 patients of the Medical University of Warsaw. Those images represent several common ocular diseases, such as cataract, along with less ordinary conditions, such as iris pattern alterations derived from illness or eye trauma. Images were captured in near-infrared light (used in biometrics) and for selected cases also in visible light (used in ophthalmological diagnosis). Since cataract is a disorder that is most populated by samples in the database, in this paper we focus solely on this illness. To assess the extent of the performance deterioration we use three iris recognition methodologies (commercial and academic solutions) to calculate genuine match scores for healthy eyes and those influenced by cataract. Results show a significant degradation in iris recognition reliability manifesting by worsening the genuine scores in all three matchers used in this study (12% of genuine score increase for an academic matcher, up to 175% of genuine score increase obtained for an example commercial matcher). This increase in genuine scores affected the final false non-match rate in two matchers. To our best knowledge this is the only study of such kind that employs more than one iris matcher, and analyzes the iris image segmentation as a potential source of decreased reliability

Agreement Between Home-Based Measurement of Stool Calprotectin and ELISA Results for Monitoring Inflammatory Bowel Disease Activity

NARCIS (Netherlands)

Heida, Anke; Knol, Mariska; Kobold, Anneke Muller; Bootsman, Josette; Dijkstra, Gerard; van Rheenen, Patrick F

2017-01-01

BACKGROUND & AIMS: An increasing number of physicians use repeated measurements of stool calprotectin to monitor intestinal inflammation in patients with inflammatory bowel diseases (IBDs). A lateral flow-based rapid test allows patients to measure their own stool calprotectin values at home. The

Genetic specificity of face recognition.

Science.gov (United States)

Shakeshaft, Nicholas G; Plomin, Robert

2015-10-13

Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.

Towards a European Framework to Monitor Infectious Diseases among Migrant Populations: Design and Applicability

Directory of Open Access Journals (Sweden)

Flavia Riccardo

2015-09-01

Full Text Available There are limitations in our capacity to interpret point estimates and trends of infectious diseases occurring among diverse migrant populations living in the European Union/European Economic Area (EU/EEA. The aim of this study was to design a data collection framework that could capture information on factors associated with increased risk to infectious diseases in migrant populations in the EU/EEA. The authors defined factors associated with increased risk according to a multi-dimensional framework and performed a systematic literature review in order to identify whether those factors well reflected the reported risk factors for infectious disease in these populations. Following this, the feasibility of applying this framework to relevant available EU/EEA data sources was assessed. The proposed multidimensional framework is well suited to capture the complexity and concurrence of these risk factors and in principle applicable in the EU/EEA. The authors conclude that adopting a multi-dimensional framework to monitor infectious diseases could favor the disaggregated collection and analysis of migrant health data.

Application of robust face recognition in video surveillance systems

Science.gov (United States)

Zhang, De-xin; An, Peng; Zhang, Hao-xiang

2018-03-01

In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis (FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.

Coronary Heart Disease: Pandemic in a True Sense

Directory of Open Access Journals (Sweden)

Saurabh RamBihariLal Shrivastava

2013-09-01

Full Text Available Cardiovascular diseases are caused because of abnormalities in the heart and blood vessels. Recent trends reveal that the incidence of coronary heart disease (CHD has gradually decreased in many developed countries, but the situation remains quite challenging in developing nations that account for more than 60% of the global burden. Multiple socio-demographic, personal, physician related and healthcare delivery system related factors have been identified which act in variable combinations to either influence the incidence of CHD or affect the short/long-term outcome of the disease. Of all CHD cases who succumb within 28 days of onset of symptoms, almost 67% fail to reach even a hospital. This clearly signifies the importance of primary prevention and early recognition of the warning signs in averting cause-specific mortality. The main priority is to develop cost-effective equitable health care innovations in CHD prevention and to monitor the trend of CHD so that evidence-based interventions can be formulated. To conclude, inculcating health-promoting behaviors in school children and the general population by means of community-based health screening and education interventions could avert many more deaths attributed to CHDs.

Coronary heart disease: pandemic in a true sense.

Science.gov (United States)

Rambiharilal Shrivastava, Saurabh; Saurabh Shrivastava, Prateek; Ramasamy, Jegadeesh

2013-01-01

Cardiovascular diseases are caused because of abnormalities in the heart and blood vessels. Recent trends reveal that the incidence of coronary heart disease (CHD) has gradually decreased in many developed countries, but the situation remains quite challenging in developing nations that account for more than 60% of the global burden. Multiple socio-demographic, personal, physician related and healthcare delivery system related factors have been identified which act in variable combinations to either influence the incidence of CHD or affect the short/long-term outcome of the disease. Of all CHD cases who succumb within 28 days of onset of symptoms, almost 67% fail to reach even a hospital. This clearly signifies the importance of primary prevention and early recognition of the warning signs in averting cause-specific mortality. The main priority is to develop cost-effective equitable health care innovations in CHD prevention and to monitor the trend of CHD so that evidence-based interventions can be formulated. To conclude, inculcating health-promoting behaviors in school children and the general population by means of community-based health screening and education interventions could avert many more deaths attributed to CHDs.

Face Detection and Recognition

National Research Council Canada - National Science Library

Jain, Anil K

2004-01-01

This report describes research efforts towards developing algorithms for a robust face recognition system to overcome many of the limitations found in existing two-dimensional facial recognition systems...

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion

Science.gov (United States)

Jonnagaddala, Jitendra; Jue, Toni Rose; Chang, Nai-Wen; Dai, Hong-Jie

2016-01-01

The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13. Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract PMID:27504009

Forensic Face Recognition: A Survey

NARCIS (Netherlands)

Ali, Tauseef; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Quaglia, Adamo; Epifano, Calogera M.

2012-01-01

The improvements of automatic face recognition during the last 2 decades have disclosed new applications like border control and camera surveillance. A new application field is forensic face recognition. Traditionally, face recognition by human experts has been used in forensics, but now there is a

Nocturnal hypoglycemia identified by a continuous glucose monitoring system in patients with primary adrenal insufficiency (Addison's Disease).

Science.gov (United States)

Meyer, Gesine; Hackemann, Annika; Reusch, Juergen; Badenhoop, Klaus

2012-05-01

Hypoglycemia can be a symptom in patients with Addison's disease. The common regimen of replacement therapy with oral glucocorticoids results in unphysiological low cortisol levels in the early morning, the time of highest insulin sensitivity. Therefore patients with Addison's disease are at risk for unrecognized and potentially severe nocturnal hypoglycemia also because of a disturbed counterregulatory function. Use of a continuous glucose monitoring system (CGMS) could help to adjust hydrocortisone treatment and to avoid nocturnal hypoglycemia in these patients. Thirteen patients with Addison's disease were screened for hypoglycemia wearing a CGMS for 3-5 days. In one patient we identified a hypoglycemic episode at 3:45 a.m. with a blood glucose level of 46 mg/dL, clearly beneath the 95% tolerance interval of minimal glucose levels between 2 and 4 a.m. (53.84 mg/dL). After the hydrocortisone replacement scheme was changed, the minimum blood glucose level between 2 and 4 a.m. normalized to 87 mg/dL. Continuous glucose monitoring can detect nocturnal hypoglycemia in patients with primary adrenal insufficiency and hence prevent in these patients an impaired quality of life and even serious adverse effects.

Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation

Directory of Open Access Journals (Sweden)

Carlos Fernández-Llatas

2013-10-01

Full Text Available Born in the early nineteen nineties, evidence-based medicine (EBM is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized.

Voice Recognition in Face-Blind Patients

Science.gov (United States)

Liu, Ran R.; Pancaroglu, Raika; Hills, Charlotte S.; Duchaine, Brad; Barton, Jason J. S.

2016-01-01

Right or bilateral anterior temporal damage can impair face recognition, but whether this is an associative variant of prosopagnosia or part of a multimodal disorder of person recognition is an unsettled question, with implications for cognitive and neuroanatomic models of person recognition. We assessed voice perception and short-term recognition of recently heard voices in 10 subjects with impaired face recognition acquired after cerebral lesions. All 4 subjects with apperceptive prosopagnosia due to lesions limited to fusiform cortex had intact voice discrimination and recognition. One subject with bilateral fusiform and anterior temporal lesions had a combined apperceptive prosopagnosia and apperceptive phonagnosia, the first such described case. Deficits indicating a multimodal syndrome of person recognition were found only in 2 subjects with bilateral anterior temporal lesions. All 3 subjects with right anterior temporal lesions had normal voice perception and recognition, 2 of whom performed normally on perceptual discrimination of faces. This confirms that such lesions can cause a modality-specific associative prosopagnosia. PMID:25349193

Invariant Face recognition Using Infrared Images

International Nuclear Information System (INIS)

Zahran, E.G.

2012-01-01

Over the past few decades, face recognition has become a rapidly growing research topic due to the increasing demands in many applications of our daily life such as airport surveillance, personal identification in law enforcement, surveillance systems, information safety, securing financial transactions, and computer security. The objective of this thesis is to develop a face recognition system capable of recognizing persons with a high recognition capability, low processing time, and under different illumination conditions, and different facial expressions. The thesis presents a study for the performance of the face recognition system using two techniques; the Principal Component Analysis (PCA), and the Zernike Moments (ZM). The performance of the recognition system is evaluated according to several aspects including the recognition rate, and the processing time. Face recognition systems that use visual images are sensitive to variations in the lighting conditions and facial expressions. The performance of these systems may be degraded under poor illumination conditions or for subjects of various skin colors. Several solutions have been proposed to overcome these limitations. One of these solutions is to work in the Infrared (IR) spectrum. IR images have been suggested as an alternative source of information for detection and recognition of faces, when there is little or no control over lighting conditions. This arises from the fact that these images are formed due to thermal emissions from skin, which is an intrinsic property because these emissions depend on the distribution of blood vessels under the skin. On the other hand IR face recognition systems still have limitations with temperature variations and recognition of persons wearing eye glasses. In this thesis we will fuse IR images with visible images to enhance the performance of face recognition systems. Images are fused using the wavelet transform. Simulation results show that the fusion of visible and

End-Stop Exemplar Based Recognition

DEFF Research Database (Denmark)

Olsen, Søren I.

2003-01-01

An approach to exemplar based recognition of visual shapes is presented. The shape information is described by attributed interest points (keys) detected by an end-stop operator. The attributes describe the statistics of lines and edges local to the interest point, the position of neighboring int...... interest points, and (in the training phase) a list of recognition names. Recognition is made by a simple voting procedure. Preliminary experiments indicate that the recognition is robust to noise, small deformations, background clutter and partial occlusion....

Speech Recognition on Mobile Devices

DEFF Research Database (Denmark)

Tan, Zheng-Hua; Lindberg, Børge

2010-01-01

in the mobile context covering motivations, challenges, fundamental techniques and applications. Three ASR architectures are introduced: embedded speech recognition, distributed speech recognition and network speech recognition. Their pros and cons and implementation issues are discussed. Applications within......The enthusiasm of deploying automatic speech recognition (ASR) on mobile devices is driven both by remarkable advances in ASR technology and by the demand for efficient user interfaces on such devices as mobile phones and personal digital assistants (PDAs). This chapter presents an overview of ASR...

Markov Models for Handwriting Recognition

CERN Document Server

Plotz, Thomas

2011-01-01

Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden

Study on road sign recognition in LabVIEW

Science.gov (United States)

Panoiu, M.; Rat, C. L.; Panoiu, C.

2016-02-01

Road and traffic sign identification is a field of study that can be used to aid the development of in-car advisory systems. It uses computer vision and artificial intelligence to extract the road signs from outdoor images acquired by a camera in uncontrolled lighting conditions where they may be occluded by other objects, or may suffer from problems such as color fading, disorientation, variations in shape and size, etc. An automatic means of identifying traffic signs, in these conditions, can make a significant contribution to develop an Intelligent Transport Systems (ITS) that continuously monitors the driver, the vehicle, and the road. Road and traffic signs are characterized by a number of features which make them recognizable from the environment. Road signs are located in standard positions and have standard shapes, standard colors, and known pictograms. These characteristics make them suitable for image identification. Traffic sign identification covers two problems: traffic sign detection and traffic sign recognition. Traffic sign detection is meant for the accurate localization of traffic signs in the image space, while traffic sign recognition handles the labeling of such detections into specific traffic sign types or subcategories [1].

Recognition and Treatment of Nongonococcal Urethritis in Clinical Practice

Science.gov (United States)

Isiadinso, O. O. A.

1980-01-01

Nongonococcal urethritis is a relatively common disorder in sexually active individuals. The incidence is almost as high, if not higher, than gonorrhea. This syndrome may present with signs and symptoms indistinguishable from acute gonococcal urethritis. It is essential to differentiate the two diseases, as treatment protocols are different. Early recognition of nongonococcal urethritis and proper therapy will often lead to complete resolution and prevention of annoying complications. PMID:6999164

Examining ERP correlates of recognition memory: Evidence of accurate source recognition without recollection

Science.gov (United States)

Addante, Richard, J.; Ranganath, Charan; Yonelinas, Andrew, P.

2012-01-01

Recollection is typically associated with high recognition confidence and accurate source memory. However, subjects sometimes make accurate source memory judgments even for items that are not confidently recognized, and it is not known whether these responses are based on recollection or some other memory process. In the current study, we measured event related potentials (ERPs) while subjects made item and source memory confidence judgments in order to determine whether recollection supported accurate source recognition responses for items that were not confidently recognized. In line with previous studies, we found that recognition memory was associated with two ERP effects: an early on-setting FN400 effect, and a later parietal old-new effect [Late Positive Component (LPC)], which have been associated with familiarity and recollection, respectively. The FN400 increased gradually with item recognition confidence, whereas the LPC was only observed for highly confident recognition responses. The LPC was also related to source accuracy, but only for items that had received a high confidence item recognition response; accurate source judgments to items that were less confidently recognized did not exhibit the typical ERP correlate of recollection or familiarity, but rather showed a late, broadly distributed negative ERP difference. The results indicate that accurate source judgments of episodic context can occur even when recollection fails. PMID:22548808

Evaluating music emotion recognition:Lessons from music genre recognition?

OpenAIRE

Sturm, Bob L.

2013-01-01

A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER syste...

Validation of Marek's disease diagnosis and monitoring of Marek's disease vaccines from samples collected in FTA cards.

Science.gov (United States)

Cortes, Aneg L; Montiel, Enrique R; Gimeno, Isabel M

2009-12-01

The use of Flinders Technology Associates (FTA) filter cards to quantify Marek's disease virus (MDV) DNA for the diagnosis of Marek's disease (MD) and to monitor MD vaccines was evaluated. Samples of blood (43), solid tumors (14), and feather pulp (FP; 36) collected fresh and in FTA cards were analyzed. MDV DNA load was quantified by real-time PCR. Threshold cycle (Ct) ratios were calculated for each sample by dividing the Ct value of the internal control gene (glyceraldehyde-3-phosphate dehydrogenase) by the Ct value of the MDV gene. Statistically significant correlation (P FTA cards by using Pearson's correlation test. Load of serotype 1 MDV DNA was quantified in 24 FP, 14 solid tumor, and 43 blood samples. There was a statistically significant correlation between FP (r = 0.95), solid tumor (r = 0.94), and blood (r = 0.9) samples collected fresh and in FTA cards. Load of serotype 2 MDV DNA was quantified in 17 FP samples, and the correlation between samples collected fresh and in FTA cards was also statistically significant (Pearson's coefficient, r = 0.96); load of serotype 3 MDV DNA was quantified in 36 FP samples, and correlation between samples taken fresh and in FTA cards was also statistically significant (r = 0.84). MDV DNA samples extracted 3 days (t0) and 8 months after collection (t1) were used to evaluate the stability of MDV DNA in archived samples collected in FTA cards. A statistically significant correlation was found for serotype 1 (r = 0.96), serotype 2 (r = 1), and serotype 3 (r = 0.9). The results show that FTA cards are an excellent media to collect, transport, and archive samples for MD diagnosis and to monitor MD vaccines. In addition, FTA cards are widely available, inexpensive, and adequate for the shipment of samples nationally and internationally.

Word Recognition in Auditory Cortex

Science.gov (United States)

DeWitt, Iain D. J.

2013-01-01

Although spoken word recognition is more fundamental to human communication than text recognition, knowledge of word-processing in auditory cortex is comparatively impoverished. This dissertation synthesizes current models of auditory cortex, models of cortical pattern recognition, models of single-word reading, results in phonetics and results in…

[Comparative studies of face recognition].

Science.gov (United States)

Kawai, Nobuyuki

2012-07-01

Every human being is proficient in face recognition. However, the reason for and the manner in which humans have attained such an ability remain unknown. These questions can be best answered-through comparative studies of face recognition in non-human animals. Studies in both primates and non-primates show that not only primates, but also non-primates possess the ability to extract information from their conspecifics and from human experimenters. Neural specialization for face recognition is shared with mammals in distant taxa, suggesting that face recognition evolved earlier than the emergence of mammals. A recent study indicated that a social insect, the golden paper wasp, can distinguish their conspecific faces, whereas a closely related species, which has a less complex social lifestyle with just one queen ruling a nest of underlings, did not show strong face recognition for their conspecifics. Social complexity and the need to differentiate between one another likely led humans to evolve their face recognition abilities.

The recognition of occupational diseases attributed to heavy workloads: experiences in Japan, Korea, and Taiwan.

Science.gov (United States)

Cheng, Yawen; Park, Jungsun; Kim, Yangho; Kawakami, Norito

2012-10-01

Health problems caused by long working hours and work stress have gained growing concerns in Japan, Korea, and Taiwan. In all the three countries, cardiovascular, cerebrovascular, and mental disorders attributed to heavy workloads or stressful work events are considered compensable occupational diseases by workers' compensation systems. This study compared the trends of such cases and correlated the trends with changes in working hours during the period from 1980 to 2010. Data on occupational diseases were obtained from official statistics of the workers' compensation systems. Information on working hours was obtained from official statistics and national surveys of employees. While occupational cardiovascular, cerebrovascular, and mental disorders attributed to work stress were increasingly compensated in all the three countries, the averaged working hours and the percentage of employees with long working hours had been in decline discordantly. Findings of this study suggested that reducing working hours alone is unlikely to reduce the problems of work stress. There is an urgent need to monitor and regulate a wider range of psychosocial work hazards. Especially, precarious employment and its associated health risks should be targeted for effective prevention of stress-related health problems in the workplace.

Clinical application of 99Tcm-TRODAT-1 SPECT imaging of dopamine transporter in monitoring the state of Parkinson's disease

International Nuclear Information System (INIS)

Deng Huaifu; Hu Ping

2005-01-01

To discuss the applicability of 99 Tc m -TRODAT-1 SPECT imaging of dopamine transporter in monitoring the state of Parkinson's disease (PD), 20 patients with PD and a control group of 14 healthy subjects were chosen to conduct dopamine transporter (DAT) imaging by SPECT with 99 Tc m -TRODAT-1. The radioactive ratio between bilateral striatum and cerebellum and the asymmetry index (Al) of bilateral striatum were computed by using the region of interest (ROI) technology. Meanwhile, the PD patients were classified by the improved Hoehn-Yahr Disability Score and then evaluated by Unified Parkinson's Disease Rating Scale (UPDRS). The findings show that there is a negative correlation between the bilateral ST/CB mean of the PD and the Hoehn-Yahr grading of the patients' state of illness, the UPDRS score, the patients' self-caring ability, the ability to move around. As for the asymmetry index AI PD , there was a positive correlation with the duration of disease, and a significant difference between the PD and the control group, with the former much higher than the latter. Therefore, the dopamine transporter imaging by SPECT with 99 Tc m -TRODAT-1 can monitor the state of Parkinson's disease, and show the symptom severity of Parkinson's disease. (authors)

Brain correlates of musical and facial emotion recognition: evidence from the dementias.

Science.gov (United States)

Hsieh, S; Hornberger, M; Piguet, O; Hodges, J R

2012-07-01

The recognition of facial expressions of emotion is impaired in semantic dementia (SD) and is associated with right-sided brain atrophy in areas known to be involved in emotion processing, notably the amygdala. Whether patients with SD also experience difficulty recognizing emotions conveyed by other media, such as music, is unclear. Prior studies have used excerpts of known music from classical or film repertoire but not unfamiliar melodies designed to convey distinct emotions. Patients with SD (n = 11), Alzheimer's disease (n = 12) and healthy control participants (n = 20) underwent tests of emotion recognition in two modalities: unfamiliar musical tunes and unknown faces as well as volumetric MRI. Patients with SD were most impaired with the recognition of facial and musical emotions, particularly for negative emotions. Voxel-based morphometry showed that the labelling of emotions, regardless of modality, correlated with the degree of atrophy in the right temporal pole, amygdala and insula. The recognition of musical (but not facial) emotions was also associated with atrophy of the left anterior and inferior temporal lobe, which overlapped with regions correlating with standardized measures of verbal semantic memory. These findings highlight the common neural substrates supporting the processing of emotions by facial and musical stimuli but also indicate that the recognition of emotions from music draws upon brain regions that are associated with semantics in language. Copyright © 2012 Elsevier Ltd. All rights reserved.

Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

Science.gov (United States)

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2017-12-01

The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Diagnostic Potential of Novel Salivary Host Biomarkers as Candidates for the Immunological Diagnosis of Tuberculosis Disease and Monitoring of Tuberculosis Treatment Response.

Science.gov (United States)

Jacobs, Ruschca; Maasdorp, Elizna; Malherbe, Stephanus; Loxton, Andre G; Stanley, Kim; van der Spuy, Gian; Walzl, Gerhard; Chegou, Novel N

2016-01-01

There is an urgent need for new tools for the early diagnosis of TB disease and monitoring of the response to treatment, especially in resource-constrained settings. We investigated the usefulness of host markers detected in saliva as candidate biomarkers for the immunological diagnosis of TB disease and monitoring of treatment response. We prospectively collected saliva samples from 51 individuals that presented with signs and symptoms suggestive of TB disease at a health centre in Cape Town, South Africa, prior to the establishment of a clinical diagnosis. Patients were later classified as having TB disease or other respiratory disease (ORD), using a combination of clinical, radiological and laboratory findings. We evaluated the concentrations of 69 host markers in saliva samples using a multiplex cytokine platform, and assessed the diagnostic potentials of these markers by receiver operator characteristics (ROC) curve analysis, and general discriminant analysis. Out of the 51 study participants, 18 (35.4%) were diagnosed with TB disease and 12 (23.5%) were HIV infected. Only two of the 69 host markers that were evaluated (IL-16 and IL-23) diagnosed TB disease individually with area under the ROC curve ≥0.70. A five-marker biosignature comprising of IL-1β, IL-23, ECM-1, HCC1 and fibrinogen diagnosed TB disease with a sensitivity of 88.9% (95% CI,76.7-99.9%) and specificity of 89.7% (95% CI, 60.4-96.6%) after leave-one-out cross validation, regardless of HIV infection status. Eight-marker biosignatures performed with a sensitivity of 100% (95% CI, 83.2-100%) and specificity of 95% (95% CI, 68.1-99.9%) in the absence of HIV infection. Furthermore, the concentrations of 11 of the markers changed during treatment, indicating that they may be useful in monitoring of TB treatment response. We have identified novel salivary biosignatures which may be useful in the diagnosis of TB disease and monitoring of the response to TB treatment. Our findings require further

USHERING IN THE STUDY AND TREATMENT OF PRECLINICAL ALZHEIMER DISEASE

Science.gov (United States)

Langbaum, Jessica B.S.; Fleisher, Adam S.; Chen, Kewei; Ayutyanont, Napatkamon; Lopera, Francisco; Quiroz, Yakeel T.; Caselli, Richard J.; Tariot, Pierre N.; Reiman, Eric M.

2014-01-01

Researchers have begun to characterize the subtle biological and cognitive processes that precede the clinical onset of Alzheimer disease (AD), and to set the stage for accelerated evaluation of experimental treatments to delay the onset, reduce the risk of or completely prevent clinical decline. Here, we provide an overview of the experimental strategies, and brain imaging and cerebrospinal fluid biomarker measures that are used in early detection and tracking of AD, highlighting at-risk individuals who could be suitable for preclinical monitoring. We discuss how these advances have contributed to reconceptualization of AD as a sequence of biological changes that occur during progression from preclinical AD, to mild cognitive impairment and finally dementia, and we review recently proposed research criteria for preclinical AD. Advances in the study of preclinical AD have driven the recognition that efficacy of at least some AD therapies may depend on initiation of treatment before clinical manifestation of disease, leading to a new era of AD prevention research. PMID:23752908

Quantitative optical diagnostics in pathology recognition and monitoring of tissue reaction to PDT

Science.gov (United States)

Kirillin, Mikhail; Shakhova, Maria; Meller, Alina; Sapunov, Dmitry; Agrba, Pavel; Khilov, Alexander; Pasukhin, Mikhail; Kondratieva, Olga; Chikalova, Ksenia; Motovilova, Tatiana; Sergeeva, Ekaterina; Turchin, Ilya; Shakhova, Natalia

2017-07-01

Optical coherence tomography (OCT) is currently actively introduced into clinical practice. Besides diagnostics, it can be efficiently employed for treatment monitoring allowing for timely correction of the treatment procedure. In monitoring of photodynamic therapy (PDT) traditionally employed fluorescence imaging (FI) can benefit from complementary use of OCT. Additional diagnostic efficiency can be derived from numerical processing of optical diagnostics data providing more information compared to visual evaluation. In this paper we report on application of OCT together with numerical processing for clinical diagnostic in gynecology and otolaryngology, for monitoring of PDT in otolaryngology and on OCT and FI applications in clinical and aesthetic dermatology. Image numerical processing and quantification provides increase in diagnostic accuracy. Keywords: optical coherence tomography, fluorescence imaging, photod

Wireless Monitoring for Patients with Cardiovascular Diseases and Parkinson's Disease.

Science.gov (United States)

Kefaliakos, Antonios; Pliakos, Ioannis; Charalampidou, Martha; Diomidous, Marianna

2016-01-01

The use of applications for mobile devices and wireless sensors is common for the sector of telemedicine. Recently various studies and systems were developed in order to help patients suffering from severe diseases such as cardiovascular diseases and Parkinson's disease. They present a challenge for the sector because such systems demand the flow of accurate data in real time and the use of specialized sensors. In this review will be presented some very interesting applications developed for patients with cardiovascular diseases and Parkinson's disease.

Recognition of the Environmental Costs of Fossil Fuel Plants

Directory of Open Access Journals (Sweden)

Hakkı FINDIK

2015-12-01

Full Text Available Environment that is the natural residential area of live life is among the interests of the various sciences. Within the scope of accounting science, the concept of social awareness requires a social responsibility based approach and this causes some additional environmental costs emerged when interaction of business with their environment considered. In the Uniform Accounting Plan there exists a special account relating with monitoring, controlling and managing of environmental costs. This study deals with environmental accounting for enterprises and introduces determination and recognition of the environmental costs of fossil fuel plants that use coal as a fuel

Behavioral features recognition and oestrus detection based on fast approximate clustering algorithm in dairy cows

Science.gov (United States)

Tian, Fuyang; Cao, Dong; Dong, Xiaoning; Zhao, Xinqiang; Li, Fade; Wang, Zhonghua

2017-06-01

Behavioral features recognition was an important effect to detect oestrus and sickness in dairy herds and there is a need for heat detection aid. The detection method was based on the measure of the individual behavioural activity, standing time, and temperature of dairy using vibrational sensor and temperature sensor in this paper. The data of behavioural activity index, standing time, lying time and walking time were sent to computer by lower power consumption wireless communication system. The fast approximate K-means algorithm (FAKM) was proposed to deal the data of the sensor for behavioral features recognition. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible.

Visual Recognition Memory across Contexts

Science.gov (United States)

Jones, Emily J. H.; Pascalis, Olivier; Eacott, Madeline J.; Herbert, Jane S.

2011-01-01

In two experiments, we investigated the development of representational flexibility in visual recognition memory during infancy using the Visual Paired Comparison (VPC) task. In Experiment 1, 6- and 9-month-old infants exhibited recognition when familiarization and test occurred in the same room, but showed no evidence of recognition when…

Determining T-cell specificity to understand and treat disease

DEFF Research Database (Denmark)

Hadrup, Sine Reker; Newell, Evan W.

2017-01-01

Adaptive immune responses and immunopathogeneses are based on the ability of T cells to respond to specific antigens. Consequently, understanding T-cell recognition patterns in health and disease involves studying the complexity and genetic heterogeneity of the antigen recognition pathway, which...

Hematopoietic stem cell transplantation monitoring in childhood. Hematological diseases in Serbia: STR-PCR techniques

Directory of Open Access Journals (Sweden)

Krstić Aleksandra D.

2007-01-01

Full Text Available Hematopoietic stem cell transplantation (HSCT is a very successful method of treatment for children with different aquired or inborn diseases. The main goal of post-transplantation chimerism monitoring in HSCT is to predict negative events (such as disease relapse and graft rejection, in order to intervene with appropriate therapy and improve the probability of long-term DFS (disease free survival. In this context, by quantifying the relative amounts of donor and recipient cells present in the peripheral blood sample, it can be determined if engraftment has taken place at all, or if full or mixed chimerism exists. In a group of patients who underwent hematopoietic stem cell transplantation at the Mother and Child Health Care Institute, we decided to use standard human identfication tests based on multiplex PCR analyses of short tandem repeats (STRs, as they are highly informative, sensitive, and fast and therefore represent an optimal methodological approach to engraftment analysis.

Flexible and wearable electronic silk fabrics for human physiological monitoring

Science.gov (United States)

Mao, Cuiping; Zhang, Huihui; Lu, Zhisong

2017-09-01

The development of textile-based devices for human physiological monitoring has attracted tremendous interest in recent years. However, flexible physiological sensing elements based on silk fabrics have not been realized. In this paper, ZnO nanorod arrays are grown in situ on reduced graphene oxide-coated silk fabrics via a facile electro-deposition method for the fabrication of silk-fabric-based mechanical sensing devices. The data show that well-aligned ZnO nanorods with hexagonal wurtzite crystalline structures are synthesized on the conductive silk fabric surface. After magnetron sputtering of gold electrodes, silk-fabric-based devices are produced and applied to detect periodic bending and twisting. Based on the electric signals, the deformation and release processes can be easily differentiated. Human arterial pulse and respiration can also be real-time monitored to calculate the pulse rate and respiration frequency, respectively. Throat vibrations during coughing and singing are detected to demonstrate the voice recognition capability. This work may not only help develop silk-fabric-based mechanical sensing elements for potential applications in clinical diagnosis, daily healthcare monitoring and voice recognition, but also provide a versatile method for fabricating textile-based flexible electronic devices.

Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

Science.gov (United States)

Alshurafa, Nabil; Eastwood, Jo-Ann; Pourhomayoun, Mohammad; Liu, Jason J; Sarrafzadeh, Majid

2014-01-01

Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified. In this paper, we analyze results from an RHM system deployed in a six month Women's Heart Health study of 90 patients, and apply advanced feature selection and machine learning algorithms to identify patients' key baseline contextual features and build effective prediction models that help determine RHM outcome success. We introduce Wanda-CVD, a smartphone-based RHM system designed to help participants with cardiovascular disease risk factors by motivating participants through wireless coaching using feedback and prompts as social support. We analyze key contextual features that secure positive patient outcomes in both physiological parameters and lipid profiles. Results from the Women's Heart Health study show that health threat of heart disease, quality of life, family history, stress factors, social support, and anxiety at baseline all help predict patient RHM outcome success.

Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons

Directory of Open Access Journals (Sweden)

Avgoustinos Filippoupolitis

2017-05-01

Full Text Available Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation.

Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants.

Science.gov (United States)

Capela, N A; Lemaire, E D; Baddour, N; Rudolf, M; Goljar, N; Burger, H

2016-01-20

Mobile health monitoring using wearable sensors is a growing area of interest. As the world's population ages and locomotor capabilities decrease, the ability to report on a person's mobility activities outside a hospital setting becomes a valuable tool for clinical decision-making and evaluating healthcare interventions. Smartphones are omnipresent in society and offer convenient and suitable sensors for mobility monitoring applications. To enhance our understanding of human activity recognition (HAR) system performance for able-bodied and populations with gait deviations, this research evaluated a custom smartphone-based HAR classifier on fifteen able-bodied participants and fifteen participants who suffered a stroke. Participants performed a consecutive series of mobility tasks and daily living activities while wearing a BlackBerry Z10 smartphone on their waist to collect accelerometer and gyroscope data. Five features were derived from the sensor data and used to classify participant activities (decision tree). Sensitivity, specificity and F-scores were calculated to evaluate HAR classifier performance. The classifier performed well for both populations when differentiating mobile from immobile states (F-score > 94 %). As activity recognition complexity increased, HAR system sensitivity and specificity decreased for the stroke population, particularly when using information derived from participant posture to make classification decisions. Human activity recognition using a smartphone based system can be accomplished for both able-bodied and stroke populations; however, an increase in activity classification complexity leads to a decrease in HAR performance with a stroke population. The study results can be used to guide smartphone HAR system development for populations with differing movement characteristics.

Near-infrared fluorescence molecular imaging of amyloid beta species and monitoring therapy in animal models of Alzheimer’s disease

Science.gov (United States)

Zhang, Xueli; Tian, Yanli; Zhang, Can; Tian, Xiaoyu; Ross, Alana W.; Moir, Robert D.; Sun, Hongbin; Tanzi, Rudolph E.; Moore, Anna; Ran, Chongzhao

2015-01-01

Near-infrared fluorescence (NIRF) molecular imaging has been widely applied to monitoring therapy of cancer and other diseases in preclinical studies; however, this technology has not been applied successfully to monitoring therapy for Alzheimer’s disease (AD). Although several NIRF probes for detecting amyloid beta (Aβ) species of AD have been reported, none of these probes has been used to monitor changes of Aβs during therapy. In this article, we demonstrated that CRANAD-3, a curcumin analog, is capable of detecting both soluble and insoluble Aβ species. In vivo imaging showed that the NIRF signal of CRANAD-3 from 4-mo-old transgenic AD (APP/PS1) mice was 2.29-fold higher than that from age-matched wild-type mice, indicating that CRANAD-3 is capable of detecting early molecular pathology. To verify the feasibility of CRANAD-3 for monitoring therapy, we first used the fast Aβ-lowering drug LY2811376, a well-characterized beta-amyloid cleaving enzyme-1 inhibitor, to treat APP/PS1 mice. Imaging data suggested that CRANAD-3 could monitor the decrease in Aβs after drug treatment. To validate the imaging capacity of CRANAD-3 further, we used it to monitor the therapeutic effect of CRANAD-17, a curcumin analog for inhibition of Aβ cross-linking. The imaging data indicated that the fluorescence signal in the CRANAD-17–treated group was significantly lower than that in the control group, and the result correlated with ELISA analysis of brain extraction and Aβ plaque counting. It was the first time, to our knowledge, that NIRF was used to monitor AD therapy, and we believe that our imaging technology has the potential to have a high impact on AD drug development. PMID:26199414

Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

Science.gov (United States)

Li, Baopu; Meng, Max Q-H

2012-05-01

Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

Non Audio-Video gesture recognition system

DEFF Research Database (Denmark)

Craciunescu, Razvan; Mihovska, Albena Dimitrova; Kyriazakos, Sofoklis

2016-01-01

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current research focus includes on the emotion...... recognition from the face and hand gesture recognition. Gesture recognition enables humans to communicate with the machine and interact naturally without any mechanical devices. This paper investigates the possibility to use non-audio/video sensors in order to design a low-cost gesture recognition device...

Spirometric and hygienic criteria in recognition of occupational COPD in Poland - A retrospective analysis of medical records.

Science.gov (United States)

Kleniewska, Aneta; Walusiak-Skorupa, Jolanta; Lipińska-Ojrzanowska, Agnieszka; Szcześniak, Kamila; Wiszniewska, Marta

2018-01-07

Chronic obstructive pulmonary disease (COPD) may be work-related. It has been estimated that 15% of the population burden of COPD is attributable to occupational exposure. However, in Poland COPD is rarely recognized as an occupational disease. The aim of the study has been to analyze the causes of the low prevalence of work-related COPD in the context of the existing criteria as well as to analyze which part of the assessment - clinical or hygienic one - is responsible for such a low rate of occupational COPD recognitions. The study group included 150 patients hospitalized with a suspicion of occupational COPD. Each patient underwent a clinical examination, spirometry and reversibility test using bronchodilator. Moreover, hygienic evaluation of work conditions was performed in all the considered cases. In the case of the patients who fulfilled the criteria for COPD diagnosis in accordance with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) occupational origins of the disease, the disease was not recognized because 24.1% of the individuals did not meet spirometric criteria included in a definition of COPD in the Polish list of occupational diseases, while 27.8% of the individuals did not fulfill the criterion of a documented exposure to dusts and irritant gases. None of these criteria was fulfilled by 42.6% of the patients. In our country, both clinical and hygienic criteria result in limitations in recognition of occupational COPD. There is the need to establish new guidelines for the recognition of COPD as a compensable disease in Poland. Int J Occup Med Environ Health 2018;31(2):139-150. This work is available in Open Access model and licensed under a CC BY-NC 3.0 PL license.

Management of Graves' disease during pregnancy: the key role of fetal thyroid gland monitoring.

Science.gov (United States)

Luton, Dominique; Le Gac, Isabelle; Vuillard, Edith; Castanet, Mireille; Guibourdenche, Jean; Noel, Michèle; Toubert, Marie-Elisabeth; Léger, Juliane; Boissinot, Christine; Schlageter, Marie-Hélène; Garel, Catherine; Tébeka, Brigitte; Oury, Jean-François; Czernichow, Paul; Polak, Michel

2005-11-01

Fetuses from mothers with Graves' disease may experience hypothyroidism or hyperthyroidism due to transplacental transfer of antithyroid drugs (ATD) or anti-TSH receptor antibodies, respectively. Little is known about the fetal consequences. Early diagnosis is essential to successful management. We investigated a new approach to the fetal diagnosis of thyroid dysfunction and validated the usefulness of fetal thyroid ultrasonograms. Seventy-two mothers with past or present Graves' disease and their fetuses were monitored monthly from 22 wk gestation. Fetal thyroid size and Doppler signals, and fetal bone maturation were determined on ultrasonograms, and thyroid function was evaluated at birth. Thyroid function and ATD dosage were monitored in the mothers. The 31 fetuses whose mothers were anti-TSH receptor antibody negative and took no ATDs during late pregnancy had normal test results. Of the 41 other fetuses, 30 had normal test results at 32 wk, 29 were euthyroid at birth, and one had moderate hypothyroidism on cord blood tests. In the remaining 11 fetuses, goiter was visualized by ultrasonography at 32 wk, and fetal thyroid dysfunction was diagnosed and treated; there was one death, in a late referral, and 10 good outcomes with normal or slightly altered thyroid function at birth. The sensitivity and specificity of fetal thyroid ultrasound at 32 wk for the diagnosis of clinically relevant fetal thyroid dysfunction were 92 and 100%, respectively. In pregnant women with past or current Graves' disease, ultrasonography of the fetal thyroid gland by an experienced ultrasonographer is an excellent diagnostic tool. This tool in conjunction with close teamwork among internists, endocrinologists, obstetricians, echographists, and pediatricians can ensure normal fetal thyroid function.

Comparing Face Detection and Recognition Techniques

OpenAIRE

Korra, Jyothi

2016-01-01

This paper implements and compares different techniques for face detection and recognition. One is find where the face is located in the images that is face detection and second is face recognition that is identifying the person. We study three techniques in this paper: Face detection using self organizing map (SOM), Face recognition by projection and nearest neighbor and Face recognition using SVM.

Cotinine improves visual recognition memory and decreases cortical Tau phosphorylation in the Tg6799 mice.

Science.gov (United States)

Grizzell, J Alex; Patel, Sagar; Barreto, George E; Echeverria, Valentina

2017-08-01

Alzheimer's disease (AD) is associated with the progressive aggregation of hyperphosphorylated forms of the microtubule associated protein Tau in the central nervous system. Cotinine, the main metabolite of nicotine, reduced working memory deficits, synaptic loss, and amyloid β peptide aggregation into oligomers and plaques as well as inhibited the cerebral Tau kinase, glycogen synthase 3β (GSK3β) in the transgenic (Tg)6799 (5XFAD) mice. In this study, the effect of cotinine on visual recognition memory and cortical Tau phosphorylation at the GSK3β sites Serine (Ser)-396/Ser-404 and phospho-CREB were investigated in the Tg6799 and non-transgenic (NT) littermate mice. Tg mice showed short-term visual recognition memory impairment in the novel object recognition test, and higher levels of Tau phosphorylation when compared to NT mice. Cotinine significantly improved visual recognition memory performance increased CREB phosphorylation and reduced cortical Tau phosphorylation. Potential mechanisms underlying theses beneficial effects are discussed. Copyright © 2017. Published by Elsevier Inc.

Exemplar Based Recognition of Visual Shapes

DEFF Research Database (Denmark)

Olsen, Søren I.

2005-01-01

This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoint....... The matching is insensitive to rotations, limited scalings and small deformations. The recognition is robust to noise, background clutter and partial occlusion. Recognition is possible from few training images and improve with the number of training images.......This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoints...

Superficial Priming in Episodic Recognition

Science.gov (United States)

Dopkins, Stephen; Sargent, Jesse; Ngo, Catherine T.

2010-01-01

We explored the effect of superficial priming in episodic recognition and found it to be different from the effect of semantic priming in episodic recognition. Participants made recognition judgments to pairs of items, with each pair consisting of a prime item and a test item. Correct positive responses to the test item were impeded if the prime…

Specification for projects of radiogeologic recognition

International Nuclear Information System (INIS)

1979-01-01

This instruction is a guidance to achievement of radiogeologic recognition projects. The radiogeologic recognition is a prospecting method that join the classic geologic recognition with measures of rock radioactivity. (C.M.)

Why recognition is rational

Directory of Open Access Journals (Sweden)

Clintin P. Davis-Stober

2010-07-01

Full Text Available The Recognition Heuristic (Gigerenzer and Goldstein, 1996; Goldstein and Gigerenzer, 2002 makes the counter-intuitive prediction that a decision maker utilizing less information may do as well as, or outperform, an idealized decision maker utilizing more information. We lay a theoretical foundation for the use of single-variable heuristics such as the Recognition Heuristic as an optimal decision strategy within a linear modeling framework. We identify conditions under which over-weighting a single predictor is a mini-max strategy among a class of a priori chosen weights based on decision heuristics with respect to a measure of statistical lack of fit we call ``risk''. These strategies, in turn, outperform standard multiple regression as long as the amount of data available is limited. We also show that, under related conditions, weighting only one variable and ignoring all others produces the same risk as ignoring the single variable and weighting all others. This approach has the advantage of generalizing beyond the original environment of the Recognition Heuristic to situations with more than two choice options, binary or continuous representations of recognition, and to other single variable heuristics. We analyze the structure of data used in some prior recognition tasks and find that it matches the sufficient conditions for optimality in our results. Rather than being a poor or adequate substitute for a compensatory model, the Recognition Heuristic closely approximates an optimal strategy when a decision maker has finite data about the world.

Infant visual attention and object recognition.

Science.gov (United States)

Reynolds, Greg D

2015-05-15

This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and object recognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant object recognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and object recognition that occur across infancy. Copyright © 2015 Elsevier B.V. All rights reserved.

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion.

Science.gov (United States)

Jonnagaddala, Jitendra; Jue, Toni Rose; Chang, Nai-Wen; Dai, Hong-Jie

2016-01-01

The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13.Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract. © The Author(s) 2016. Published by Oxford University Press.

An intelligent condition monitoring system for on-line classification of machine tool wear

Energy Technology Data Exchange (ETDEWEB)

Pan, Fu; Hope, A D; Javed, M [Systems Engineering Faculty, Southampton Institute (United Kingdom)

1998-12-31

The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.

An intelligent condition monitoring system for on-line classification of machine tool wear

Energy Technology Data Exchange (ETDEWEB)

Fu Pan; Hope, A.D.; Javed, M. [Systems Engineering Faculty, Southampton Institute (United Kingdom)

1997-12-31

The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.

Dual Recognition Strategy for Specific and Sensitive Detection of Bacteria Using Aptamer-Coated Magnetic Beads and Antibiotic-Capped Gold Nanoclusters.

Science.gov (United States)

Cheng, Dan; Yu, Mengqun; Fu, Fei; Han, Weiye; Li, Gan; Xie, Jianping; Song, Yang; Swihart, Mark T; Song, Erqun

2016-01-05

Food poisoning and infectious diseases caused by pathogenic bacteria such as Staphylococcus aureus (SA) are serious public health concerns. A method of specific, sensitive, and rapid detection of such bacteria is essential and important. This study presents a strategy that combines aptamer and antibiotic-based dual recognition units with magnetic enrichment and fluorescent detection to achieve specific and sensitive quantification of SA in authentic specimens and in the presence of much higher concentrations of other bacteria. Aptamer-coated magnetic beads (Apt-MB) were employed for specific capture of SA. Vancomycin-stabilized fluorescent gold nanoclusters (AuNCs@Van) were prepared by a simple one-step process and used for sensitive quantification of SA in the range of 32-10(8) cfu/mL with the detection limit of 16 cfu/mL via a fluorescence intensity measurement. And using this strategy, about 70 cfu/mL of SA in complex samples (containing 3 × 10(8) cfu/mL of other different contaminated bacteria) could be successfully detected. In comparison to prior studies, the developed strategy here not only simplifies the preparation procedure of the fluorescent probes (AuNCs@Van) to a great extent but also could sensitively quantify SA in the presence of much higher concentrations of other bacteria directly with good accuracy. Moreover, the aptamer and antibiotic used in this strategy are much less expensive and widely available compared to common-used antibodies, making it cost-effective. This general aptamer- and antibiotic-based dual recognition strategy, combined with magnetic enrichment and fluorescent detection of trace bacteria, shows great potential application in monitoring bacterial food contamination and infectious diseases.

Probabilistic Open Set Recognition

Science.gov (United States)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary

Hemispheric lateralization of linguistic prosody recognition in comparison to speech and speaker recognition.

Science.gov (United States)

Kreitewolf, Jens; Friederici, Angela D; von Kriegstein, Katharina

2014-11-15

Hemispheric specialization for linguistic prosody is a controversial issue. While it is commonly assumed that linguistic prosody and emotional prosody are preferentially processed in the right hemisphere, neuropsychological work directly comparing processes of linguistic prosody and emotional prosody suggests a predominant role of the left hemisphere for linguistic prosody processing. Here, we used two functional magnetic resonance imaging (fMRI) experiments to clarify the role of left and right hemispheres in the neural processing of linguistic prosody. In the first experiment, we sought to confirm previous findings showing that linguistic prosody processing compared to other speech-related processes predominantly involves the right hemisphere. Unlike previous studies, we controlled for stimulus influences by employing a prosody and speech task using the same speech material. The second experiment was designed to investigate whether a left-hemispheric involvement in linguistic prosody processing is specific to contrasts between linguistic prosody and emotional prosody or whether it also occurs when linguistic prosody is contrasted against other non-linguistic processes (i.e., speaker recognition). Prosody and speaker tasks were performed on the same stimulus material. In both experiments, linguistic prosody processing was associated with activity in temporal, frontal, parietal and cerebellar regions. Activation in temporo-frontal regions showed differential lateralization depending on whether the control task required recognition of speech or speaker: recognition of linguistic prosody predominantly involved right temporo-frontal areas when it was contrasted against speech recognition; when contrasted against speaker recognition, recognition of linguistic prosody predominantly involved left temporo-frontal areas. The results show that linguistic prosody processing involves functions of both hemispheres and suggest that recognition of linguistic prosody is based on

Facial emotion recognition in Chinese with schizophrenia at early and chronic stages of illness.

Science.gov (United States)

Leung, Joey Shuk-Yan; Lee, Tatia M C; Lee, Chi-Chiu

2011-12-30

Deficits in facial emotion recognition have been recognised in Chinese patients diagnosed with schizophrenia. This study examined the relationship between chronicity of illness and performance of facial emotion recognition in Chinese with schizophrenia. There were altogether four groups of subjects matched for age and gender composition. The first and second groups comprised medically stable outpatients with first-episode schizophrenia (n=50) and their healthy controls (n=26). The third and fourth groups were patients with chronic schizophrenic illness (n=51) and their controls (n=28). The ability to recognise the six prototypical facial emotions was examined using locally validated coloured photographs from the Japanese and Caucasian Facial Expressions of Emotion. Chinese patients with schizophrenia, in both the first-episode and chronic stages, performed significantly worse than their control counterparts on overall facial emotion recognition, (Pemotion did not appear to have worsened over the course of disease progression, suggesting that recognition of facial emotion is a rather stable trait of the illness. The emotion-specific deficit may have implications for understanding the social difficulties in schizophrenia. Copyright © 2011 Elsevier Ltd. All rights reserved.

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.

Directory of Open Access Journals (Sweden)

Andre F Marquand

Full Text Available Progressive supranuclear palsy (PSP, multiple system atrophy (MSA and idiopathic Parkinson's disease (IPD can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs. An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i a subcortical motor network; (ii each of its component regions and (iii the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.

Science.gov (United States)

Rundo, Francesco; Conoci, Sabrina; Ortis, Alessandro; Battiato, Sebastiano

2018-01-30

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

Inconsistency in the Diagnosis of Functional Heartburn: Usefulness of Prolonged Wireless pH Monitoring in Patients With Proton Pump Inhibitor Refractory Gastroesophageal Reflux Disease

Science.gov (United States)

Penagini, Roberto; Sweis, Rami; Mauro, Aurelio; Domingues, Gerson; Vales, Andres; Sifrim, Daniel

2015-01-01

Background/Aims The diagnosis of functional heartburn is important for management, however it stands on fragile pH monitoring variables, ie, acid exposure time varies from day to day and symptoms are often few or absent. Aim of this study was to investigate consistency of the diagnosis of functional heartburn in subsequent days using prolonged wireless pH monitoring and its impact on patients’ outcome. Methods Fifty proton pump inhibitotor refractory patients (11 male, 48 years [range, 38–57 years]) with a diagnosis of functional heart-burn according to Rome III in the first 24 hours of wireless pH monitoring were reviewed. pH variables were analysed in the following 24-hour periods to determine if tracings were indicative of diagnosis of non-erosive reflux disease (either acid exposure time > 5% or normal acid exposure time and symptom index ≥ 50%). Outcome was assessed by review of hospital files and/or telephone interview. Results Fifteen out of 50 patients had a pathological acid exposure time after the first day of monitoring (10 in the second day and 5 in subsequent days), which changed their diagnosis from functional heartburn to non-erosive reflux disease. Fifty-four percent of non-erosive reflux disease vs 11% of functional heartburn patients (P heartburn patients (P heartburn at 24-hour pH-monitoring can be re-classified as non-erosive reflux disease after a more prolonged pH recording period. This observation has a positive impact on patients’ management. PMID:25843078

Silent Speech Recognition as an Alternative Communication Device for Persons with Laryngectomy.

Science.gov (United States)

Meltzner, Geoffrey S; Heaton, James T; Deng, Yunbin; De Luca, Gianluca; Roy, Serge H; Kline, Joshua C