Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Technique for Signal Processing to Identify the Brain Disorder for Epilepsy Captures Using Fuzzy Systems

The new direction of understand the signal that is created from the brain organization is one of the main chores in the brain signal processing. Amid all the neurological disorders the human brain epilepsy is measured as one of the extreme prevalent and then programmed artificial intelligence detection technique is an essential due to the crooked and unpredictable nature of happening of epileptic seizures. We proposed an Improved Fuzzy firefly algorithm, which would enhance the classification of the brain signal efficiently with minimum iteration. An important bunching technique created on fuzzy logic is the Fuzzy C means. Together in the feature domain with the spatial domain the features gained after multichannel EEG signals remained combined by means of fuzzy algorithms. And for better precision segmentation process the firefly algorithm is applied to optimize the Fuzzy C-means membership function. Simultaneously for the efficient clustering method the convergence criteria are set. On the whole the proposed technique yields more accurate results and that gives an edge over other techniques. This proposed algorithm result compared with other algorithms like fuzzy c means algorithm and PSO algorithm.
Comparative protein binding of naringin and its aglycone naringenin in rat, dog and human plasma

Naringin and its aglycone naringenin, widely distributed in traditional Chinese medicines, had received extensive studies on their bioactivities. However, the plasma protein binding ratio, which played a very important role on pharmacokinetic and pharmacodynamic studies, remained unclear. In this present study, the protein binding of naringin and naringenin in rat, dog and human plasma was investigated using equilibrium dialysis and a liquid chromatography with electrospray ionization tandem mass/mass spectrometry (LC-ESI-MS/MS). The analytical time was less than 5 min without sacrificing resolution using ZORBAX Eclipse Plus-C18 (2.1 × 100 mm, 1.8 μm) column and MS/MS mode performed for multiple reaction monitoring. The plasma protein binding ratios of naringin were 83.30 to 84.56%, 72.14 to 74.06% and 48.17 to 51.33% in rat, human and dog plasma, respectively. No obvious concentration dependence was observed across the entire concentration range. On the other hand, the binding ratios for aglycone naringenin in three species plasma exceed 94% over all concentration studied, which suggested that naringenin existed predominantly in the bound form in the circulation blood at a wide range of naringenin concentration. The results may be speculated and make further impacts on their pharmacokinetic study. Key words: Plasma protein binding, naringin, naringenin.
BURNS—A TEAM APPROACH

hensive review is most welcome. Immunological Aspects of Infectious Diseases provides a well-written, multiauthored book on the subject, from basics on specific and non-specific defence against infection, to most recent research on the immunopathology ofviral, bacterial, fungal and protozoal infections.Interesting reviews on the relation between infections and allergy, autoimmunity, tissue damaging immune complexes as well as granulomatous lesions are presented. Some overlapping between the chapters may be inevitable, but two chapters on immunodeficiency seems unnecessary. A few minor deficiencies exist such as the reference to clinical effects of transfer factor treatment, although such are not well supported by controlled studies. Likewise, the role of Zn in certain immunodeficiencies is not mentioned. This up-to-date book should find readers among medical students as well as practitioners and researchers.
Huatan Qingre Prescription Treatment of Chronic Obstructive Pulmonary Disease with Acute Exacerbation and Syndrome of Heat-phelgm Stagnating the Lung the Effects of Cytokines

Objective:Based on clinical efficacy evidence of the treatment with Qingre Huatan prescriptions of AECOPD with syndrome of heat-phelgm stagnating the lung,this paper aims to discuss the mechanism of the treatment with Qingre Huatan prescription of AECOPD with syndrome of heat-phelgm stagnating the lung,by detecting level changes in peripheral blood of patients with Interleukin-8 (IL-8),tumor necrosis factor(TNF-α),intercellular adhesion molecule-1(ICAM-1),endothelin-1(ET-1),calcitonin gene-related peptide (CGRP) before and after treatment.Methods:140 cases of patients who meet the inclusion criteria are selected and randomly divided into two groups.Before and after the course,the serum of the two groups is retained to detect the level of IL-8,TNF-α and ICAM-1,and the plasma is retained to detect the level of ET-1 and CGRP.Results:After treatment,TNF-α,ICAM-1,and IL-8 and ET-1 of the two groups are significantly lower then before (P0.01,P0.05),CGRP levels are significantly higher (P0.01);TNF-α,ICAM-1,IL-8 and ET-1 levels in pilot group is significantly lower than the control group after treatment(P0.05),CGRP is significantly higher than the control group(P0.05). Conclusion:Cytokines such as IL-8,TNF-α,ICAM-1,ET-1,and CGRP are involved in the acute exacerbation and the formation of COPD.Qingre Huatan AECOPD prescription can reduce the high-level expression of TNF-α,ICAM-I,IL-8 and ET -1,and increase the expression level of CGRP.This may be one of the mechanisms which functions in the treatment of AECOPD with Qingre Huatan prescription.
A comparative analysis of animal chiropractic regulation within seven countries

vi TABLE OF CONTENTS . ix LIST OF FIGURES . xix LIST OF TABLES . xxi LIST OF APPENDICES . xxiii GLOSSARY . xxiv ABBREVIATIONS . xxvii CHAPTER 1 : INTRODUCTION . 1 1.1 Background and context of the study . 1 1.2 Research question . 2 1.3 Research aim . 2 1.4 Research objectives. 2 1.5 Rationale of this study . 3 1.6 Significance of the study . 3 1.7 Outline of chapters . 4 CHAPTER 2 : LITERATURE REVIEW . 5 2.
Change Detection with Global Viewpoint Localization

In this study, we address the problem of change detection in robotic mapping and localization (e.g., SLAM) applications from the novel perspective of global viewpoint uncertainty. Our goal is to develop a generic framework that can be used to detect changes under multi-modal viewpoint estimation by global viewpoint localization. To achieve this goal, we introduce a global viewpoint localization module powered by deep local convolutional features with bag-of-local-features scene model. We then study the effects of global viewpoint uncertainty in change detection tasks and develop two complementary approaches: feature matching (i.e., no-change detection) and anomaly detection (i.e., change detection). We evaluate the effectiveness of individual techniques as well as their combinations using highly dynamic scenes obtained from the publicly available Malaga dataset.
Studies on filed and compensated claims for patient injuries

13 Abstract in Finnish . 15 Abstract in Swedish . 17
Discriminative Transfer Learning Siamese CNN for Person Re-identification

Person re-identification (Re-ID) has become an increasingly popular computer vision problem. It remains challenging, especially when there are non-overlapping cameras. In this paper, we review the two representative architecture, i.e., identification and verification models. They both have their advancements and limitations. We present a novel method to address the Re-ID problem. First, combine the two models to consist a more effective fusion loss function. Second, we find that CNNs which are pre-trained on large image datasets learn more discriminative knowledge with objective semantic, which can be transferred to subsequent layers to promote accuracy significantly. Experiments on four benchmark datasets show the superiority of our method over the state-of-the-art alternatives.
Impact of Ageing on EEG Based Biometric Systems

With the development of sensor technology, Electroencephalography (EEG) has been a popular area of interest in recent years. Also, a great degree of changes with age have been found in face, voice, fingerprint or other physiological based biometric identifier systems. The distinct characteristic of neuro-signals have focused the attention of research community towards building a user identification system which is resistant to vulnerable attacks. However, the permanence issue of the brain signals has been studied sporadically. In this paper, we investigate the robustness of EEG signals to address the longitudinal stability issue and its effectiveness in user identification systems. Discrete Wavelet Transform (DWT) signal decomposition technique has been applied to extract Alpha-band waves. Further, two statistical features, namely, Root Mean Square (RMS) and Integrated EEG (IEEG) have been calculated for the band waves. Person identification has been performed using three well-known classification techniques, namely, Random Forest (RF), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). EEG data from 10 users has been recorded in 6 different sessions within a period of 6 months. Finally, a decision fusion scheme, majority voting has been applied to boost the system performance. An average accuracy of 80% has been recorded using decision fusion. The results highlight a significant amount of variations across sessions, which shows various factors could effect the state of the mind with temporal With the development of sensor technology, Electroencephalography (EEG) has been a popular area of interest in recent years. Also, a great degree of changes with age have been found in face, voice, fingerprint or other physiological based biometric identifier systems. The distinct characteristic of neuro-signals have focused the attention of research community towards building a user identification system which is resistant to vulnerable attacks. However, the permanence issue of the brain signals has been studied sporadically. In this paper, we investigate the robustness of EEG signals to address the longitudinal stability issue and its effectiveness in user identification systems. Discrete Wavelet Transform (DWT) signal decomposition technique has been applied to extract Alpha-band waves. Further, two statistical features, namely, Root Mean Square (RMS) and Integrated EEG (IEEG) have been calculated for the band waves. Person identification has been performed using three well-known classification techniques, namely, Random Forest (RF), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). EEG data from 10 users has been recorded in 6 different sessions within a period of 6 months. Finally, a decision fusion scheme, majority voting has been applied to boost the system performance. An average accuracy of 80% has been recorded using decision fusion. The results highlight a significant amount of variations across sessions, which shows various factors could effect the state of the mind with temporal distance. Keywords: EEG, Biometrics, Ageing, Random Forest, Majority Voting.
Sequence-to-Sequence Learning for Human Pose Correction in Videos

The power of ConvNets has been demonstrated in a wide variety of vision tasks including pose estimation. But they often produce absurdly erroneous predictions in videos due to unusual poses, challenging illumination, blur, self-occlusions etc. These erroneous predictions can be refined by leveraging previous and future predictions as the temporal smoothness constrain in the videos. In this paper, we present a generic approach for pose correction in videos using sequence learning that makes minimal assumptions on the sequence structure. The proposed model is generic, fast and surpasses the state-of-the-art on benchmark datasets. We use a generic pose estimator for initial pose estimates, which are further refined using our method. The proposed architecture uses Long Short-Term Memory (LSTM) encoder-decoder model to encode the temporal context and refine the estimations. We show 3.7% gain over the baseline Yang & Ramanan (YR) and 2.07% gain over Spatial Fusion Network (SFN) on a new challenging YouTube Pose Subset dataset.
Adaptive Energy Selection for Content-Aware Image Resizing

Content-aware image resizing aims to reduce the size of an image without touching important objects and regions. In seam carving, this is done by assessing the importance of each pixel by an energy function and repeatedly removing a string of pixels avoiding pixels with high energy. However, there is no single energy function that is best for all images: the optimal energy function is itself a function of the image. In this paper, we present a method for predicting the quality of the results of resizing an image with different energy functions, so as to select the energy best suited for that particular image. We formulate the selection as a classification problem; i.e., we 'classify' the input into the class of images for which one of the energies works best. The standard approach would be to use a CNN for the classification. However, the existence of a fully connected layer forces us to resize the input to a fixed size, which obliterates useful information, especially lower-level features that more closely relate to the energies used for seam carving. Instead, we extract a feature from internal convolutional layers, which results in a fixed-length vector regardless of the input size, making it amenable to classification with a Support Vector Machine. This formulation of the algorithm selection as a classification problem can be used whenever there are multiple approaches for a specific image processing task. We validate our approach with a user study, where our method outperforms recent seam carving approaches.
Deep Face Recognition under Eyeglass and Scale Variation Using Extended Siamese Network

Face recognition has attracted much attention from researchers for past decades. Recently, with the development of deep learning, a deep neural network is adopted by face recognition system and better performance is obtained. Many works on metric learning have been done in the deep neural network. Meanwhile, there are several variation problems existing in face recognition, such as profile face image, low-resolution face image, different age of face image, face image wearing eyeglass, etc. In this paper, targeting at different kinds of variation problems, we proposed a novel network structure, called Extended Siamese Network. Another contribution is that a new loss function is proposed, to further take inter-class information into account based on the center loss function. The experiments show that recognition accuracy is improved in comparison with the other state-of-art methods.
The interplay between gait and cognitive function in elderly people.

In the next 10 years the number of Dutch people aged 65 and over will increase with 650,000 to 3.0 million people. The prevalence of multiple and often interacting problems in elderly people is high and have many adverse effects on health and quality of life. However, most research and guidelines are directed at isolated health problems. Therefore the research in this thesis is directed towards the interplay between gait and cognition, which both are highly prevalent in old age, have a great impact on the quality of life and functioning of older people and their carers and increase the risk of falling. In this thesis methods to investigate gait and cognitive function and their possible interplay are investigated. It can be concluded that impairments in gait and cognition too often co-occur to be just determined by coincidence. Literature together with the results of this thesis supports the hypothesis that gait and cognition influence each other in older people. They probably have a shared pathophysiology and treatment given for a cognitive problem, for example antipsychotics, can also influence gait. Cognitive functions that seem involved in the coordination of gait and balance are division of attention and insight in cognitive and physical abilities in the cognitive regulation of walking. Executive functions, such as planning and mental flexibility, are also involved and better executive functioning is associated with better gait and balance performance during walking with a cognitive dual task. Although numerous questions are still open, enough knowledge is already present to be able to recommend: If gait is impaired also assess cognitive function and if cognitive function is impaired, also assess gait!
Deep Feature Similarity for Generative Adversarial Networks

We propose a new way to train generative adversarial networks (GANs) based on pretrained deep convolutional neural network (CNN). Instead of directly using the generated images and the real images in pixel space, the corresponding deep features extracted from pretrained networks are used to train the generator and discriminator. We enforce the deep feature similarity of the generated and real images to stabilize the training and generate more natural visual images. Testing on face and flower image dataset, we show that the generated samples are clearer and have higher visual quality than traditional GANs. The human evaluation demonstrates that humans cannot easily distinguish the fake from real face images.
Enlarging Effective Receptive Field of Convolutional Neural Networks for Better Semantic Segmentation

Recently, convolutional neural networks have shown powerful capability in different fields of computer vision, and have become the most effective means for dense prediction problems such as semantic segmentation. However, methods based on fully convolution network(FCN) are inherently limited to the size of the receptive field for each pixel, which leads to the bad performance of predicting object boundary. In this paper, we propose a novel deep neural network module, namely group dilated convolution(GDC), to effectively enlarge the receptive field, and a top-to-down pathway network is exploited simultaneously. The idea is that dilation convolution with different ratios can cover features of different scales, which shows a significant Mean IOU improvement in comparison with the baseline network.
Logistic Regression Modeling for Evaluation of Factors Affecting Trauma Outcome in a Level I Trauma Center in Shiraz

Background: Since injury-related mortality is preventable, identifying factors that inversely affect trauma outcome are important initial steps towards reducing injury burden. Objectives: This study aims to determine independent risk factors of early/late in-hospital mortality among adult trauma victims with equal injury characteristics and severity at Shahid Rajaee (Emtiaz) Hospital during 2013 and 2014. PatientsandMethods: A cross-sectional study of adult trauma patients (age≥ 15 years) sustaining injury through traffic accidents, violence, and unintentional incidents was conducted. Information was retrieved from three hospital administrative databases. Data on demographics, injury mechanisms, injured body regions, injury descriptions, outcomes of hospitalization, and development of nosocomial infections were recorded. Injury severity score was calculated by cross walking from international classification of diseases (ICD-10) injury diagnosis codes to abbreviated injury scale (AIS-98) severity codes. Two multiple logistic regression models were employed to reflect the partial effect of each covariate on early (within 48 hours) and late (beyond 48 hours) deaths. Results: There were 47,295 hospitalized patients (male/female ratio: 2.7:1.0) with a median age of 30 years (interquartile range 23 44 years). A crude mortality rate of 1% (454 cases) was observed and 52% of deaths occurred within 48 hours of hospital arrival. One percent developed a nosocomial infection in the course of admission. After adjusting for covariates, sustaining a thoracic injury (OR 8.5, 95% CI [4.7 15.2]), ISS over 16 (OR 6.4, 95% CI [3.6 11.4]) and age over 65 years (OR 5.1, 95% CI [3.0 8.8]) were the most important independent risk factors of early trauma death. Presence of a hospital-acquired infection (OR 12.7, 95% CI [8.9 18.1]), age over 65 years (OR 7.4 95% CI [4.5 12.1]), and ISS of more than 16 (OR 14.6, 95% CI [6.2 34.3]) were independent predictors of late death. Conclusions: Age, injury severity, injured body region, and hospital-acquired infections are important determinants of trauma outcome in our center. Timely recognition of factors affecting trauma mortality is crucial for monitoring changes of trauma quality of care. Our findings suggest the need to allocate resources for trauma prevention along with a potential focus on reducing inhospital complications.
Diabetic clinic defaulters — who are they and why do they default?

To investigate the problem of diabetic clinic defaulting, we studied patients who had lapsed from follow-up for at least 12 months and were referred back during one calendar year. Of the 37 investigated in detail, sex distribution, age, diabetes duration, treatment and BMI were not significantly different from a control group of regular clinic attenders. However, defaulters had a higher HbA1 (10.0 ± 2.5% v 8.5 ± 2.0%, p <0.01) and more micro-and macrovascular complications (20 v 11, p < 0.05). Most complications had not been present at the time of default from clinic (mean period of lapse was 26 ± 14 months). Reasons given for default included: overcrowded clinic; prolonged waiting; seeing different doctors; and not seeing the consultant often enough. Defaulting is thus a common problem and is associated with poor glycaemic control and increased complication rates. Better education and more “user-friendly” clinics may reduce the problem.
Assessment of Knowledge, Attitude and Practices Concerning Food Safety among Restaurant Workers in Putrajaya, Malaysia

The aim of this study is to evaluate the knowledge, attitudes and practices on food safety held by food handlers working in restaurants in Putrajaya, Malaysia. Inappropriate food handling is the main factor contributing to food borne disease outbreaks. A total of 127 food handlers were randomly selected from 23 restaurants and the data collected in September 2013 through self-administered questionnaires were analyzed using the SPSS version 16. Generally knowledge of food safety was good with the mean score 90.3 ± 7.787. However, respondents lacked knowledge about the hazards of reheating cooked food (75.1 ± 25.662) and the safe temperature of cooked food (71.9 ± 33.548). Knowledge differed significantly by age groups (F=2.530; p =0.044). Respondents had positive attitudes about food safety, with a mean score of 93.9 ± 6.813, although there were significant differences between trained and untrained workers (t=2.406; p =0.018); Malaysian ethnic groups (F=2.502; p =0.034); Malaysians and non-Malaysians (t=3.273; p =0.001) and due to differences in education levels (F=6.057; p =0.003). The mean score for practice was 92.9 ± 7.647 and again there were significant differences related to education levels (F=1.345; p=0.003), gender (t=-2.120; p =0.036) and ethnicity of Malaysian workers (F=2.502; p =0.034). A strong relationship was found between knowledge and attitudes about food safety (r=0.266; p =0.002) and between knowledge and practice (r=0.203; p =0.022). In conclusion, this study suggests that food handlers in Putrajaya restaurants displayed good knowledge, a positive attitude and an excellent practices regarding food safety. Yet results showed the food workers still lack basic knowledge of food safety, particularly related to reheated food and safe temperatures for cooked food. Keywords: Knowledge, Attitude, Practices, food safety, Putrajaya, Malaysia.