Miracle-dz/UniFAR-FTUs huggingface dataset:
Conservation and Diversity of Influenza A H1N1 HLA-Restricted T Cell Epitope Candidates for Epitope-Based Vaccines

Background The immune-related evolution of influenza viruses is exceedingly complex and current vaccines against influenza must be reformulated for each influenza season because of the high degree of antigenic drift among circulating influenza strains. Delay in vaccine production is a serious problem in responding to a pandemic situation, such as that of the current H1N1 strain. Immune escape is generally attributed to reduced antibody recognition of the viral hemagglutinin and neuraminidase proteins whose rate of mutation is much greater than that of the internal non-structural proteins. As a possible alternative, vaccines directed at T cell epitope domains of internal influenza proteins, that are less susceptible to antigenic variation, have been investigated. Methodology/Principal Findings HLA transgenic mouse strains expressing HLA class I A*0201, A*2402, and B*0702, and class II DRB1*1501, DRB1*0301 and DRB1*0401 were immunized with 196 influenza H1N1 peptides that contained residues of highly conserved proteome sequences of the human H1N1, H3N2, H1N2, H5N1, and avian influenza A strains. Fifty-four (54) peptides that elicited 63 HLA-restricted peptide-specific T cell epitope responses were identified by IFN-γ ELISpot assay. The 54 peptides were compared to the 2007–2009 human H1N1 sequences for selection of sequences in the design of a new candidate H1N1 vaccine, specifically targeted to highly-conserved HLA-restricted T cell epitopes. Conclusions/Significance Seventeen (17) T cell epitopes in PB1, PB2, and M1 were selected as vaccine targets based on sequence conservation over the past 30 years, high functional avidity, non-identity to human peptides, clustered localization, and promiscuity to multiple HLA alleles. These candidate vaccine antigen sequences may be applicable to any avian or human influenza A virus.
COMPARATIVE STUDY ON MCPWM STRATEGIES FOR 15 LEVEL ASYMMETRIC INVERTER

This paper focuses on a new topology of multilevel inverter with a reversing-voltage technique. This topology requires fewer components compared to conventional inverters and requires fewer carrier signals and gate drives. Multilevel inverter is triggered using Unipolar Sine Pulse Width Modulating (USPWM) strategies using sine reference and trapezoidal reference with triangular carriers. It include Phase Disposition (PD) strategy, Alternate Phase Opposition Disposition (APOD) strategy, Carrier Overlapping (CO) strategy and Variable Frequency (VF) strategy. The performance measures like Total Harmonic Distortion (THD), VRMS (fundamental), crest factor, form factor and distortion factor are evaluated for various modulation indices. Simulation is performed using MATLAB-SIMULINK.
Robust defect detection algorithm based classificat ion on features extracted from MFL signals

ANALYTICAL models at recent years are developed to determine surface-breaking defects along the applied field when using the magnetic flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation o f defects. Permeability variations were neglected b y employing a flux density close to sample saturation . Three different defect geometries were experiment ally investigated and the validity of the analytical mod el was verified. Different Feature extractor functi ons are applied in this paper to yield fast decision and mo re accurate. Indeed more accuracy is because of dec ision on different features that yields by employing two kinds of feature extractors, PCA and DCT. Both feature extraction and Multilayer perceptron (MLP) methods for identifying erosion defects are described and employed in this paper. Great accuracy rate in comp are between results of related approaches suggests that this Method can be used as an algorithm of MFL data interpretation technique.
Model Based Vehicle Tracking in Urban Environments

Situational awareness is crucial for autonomous driving in urban environments. We present the moving vehicle tracking module we developed for our autonomous driving robot Junior. The robot won second place in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The module provides reliable detection and tracking of moving vehicles from a high-speed moving platform using laser range finders. Our approach models both dynamic and geometric properties of the tracked vehicles and estimates them using a single Bayes filter per vehicle. We show how to build consistent and efficient 2D representations out of 3D range data and how to detect poorly visible black vehicles. Experimental validation includes the most challenging conditions presented at the Urban Grand Challenge as well as other urban settings.
Analysis of detection algorithm of Sinkhole attack & QoS on AODV for MANET

Wireless Mobile ad-hoc network is quite vulnerable to many security compromising attacks because of their open deployment architecture. These attacks could involve wormhole attack, message replay or tampering, identity spoofing, black hole attack, eavesdropping, and so on. In the Sinkhole attack, malicious node tries to attract data packets of network using its fake routing information in network. Using sink whole attack, data packets can be dropped and routing information can be altered. This research presents an Individual Trust Managing Technique to prevent against sink-hole attack. In this research sinkhole attack is implemented for analyzing different effects on performance of network due to increasing the mobility and probability of attacks. A detection technique is also analyzed for effective detection and removal of attacker node. The proposed analysis is simulated using network simulator NS3. In this way, the ad-hoc networks are exploited by routing protocol design. So, there is need of methods to make MANET routing protocols resistant to Sinkhole attack. In this research work, the Sinkhole attack has been performed over AODV. The prevention technique is significantly successful in handling the attack while restoring the performance of network and reduces the effect of attack from the network.
A Post-Matching Doherty Power Amplifier Employing Low-Order Impedance Inverters for Broadband Applications

This paper presents a modified Doherty configuration with extended bandwidth. The narrow band feature of the conventional Doherty amplifier is discussed in the view of the broadband matching. To extend the bandwidth, the post-matching architecture is employed in the proposed design. Meanwhile, broadband low-order impedance inverters are adopted to replace the quarter-wavelength transmission lines. Low-pass filter topologies are used to realize both the post matching network and the impedance inverters. A modified Doherty Power amplifier was designed and fabricated based on commercial GaN HEMT devices to validate the broadband characteristics of this configuration. The 6-dB backoff efficiencies of 47%-57% are obtained from 1.7 to 2.6 GHz (41.9% fractional bandwidth) and the measured maximum output power ranges from 44.9 to 46.3 dBm in the designed band. In particular, more than 40% efficiencies are measured at 10-dB backoff throughout the operation band.
Interrogating the Plasmodium Sporozoite Surface: Identification of Surface-Exposed Proteins and Demonstration of Glycosylation on CSP and TRAP by Mass Spectrometry-Based Proteomics

Malaria parasite infection is initiated by the mosquito-transmitted sporozoite stage, a highly motile invasive cell that targets hepatocytes in the liver for infection. A promising approach to developing a malaria vaccine is the use of proteins located on the sporozoite surface as antigens to elicit humoral immune responses that prevent the establishment of infection. Very little of the P. falciparum genome has been considered as potential vaccine targets, and candidate vaccines have been almost exclusively based on single antigens, generating the need for novel target identification. The most advanced malaria vaccine to date, RTS,S, a subunit vaccine consisting of a portion of the major surface protein circumsporozoite protein (CSP), conferred limited protection in Phase III trials, falling short of community-established vaccine efficacy goals. In striking contrast to the limited protection seen in current vaccine trials, sterilizing immunity can be achieved by immunization with radiation-attenuated sporozoites, suggesting that more potent protection may be achievable with a multivalent protein vaccine. Here, we provide the most comprehensive analysis to date of proteins located on the surface of or secreted by Plasmodium falciparum salivary gland sporozoites. We used chemical labeling to isolate surface-exposed proteins on sporozoites and identified these proteins by mass spectrometry. We validated several of these targets and also provide evidence that components of the inner membrane complex are in fact surface-exposed and accessible to antibodies in live sporozoites. Finally, our mass spectrometry data provide the first direct evidence that the Plasmodium surface proteins CSP and TRAP are glycosylated in sporozoites, a finding that could impact the selection of vaccine antigens.
'just enough' sensing to ENLITEN: a preliminary demonstration of sensing strategy for the 'energy literacy through an intelligent home energy advisor' (ENLITEN) project

The ENLITEN project aims to reduce carbon emissions attributable to energy use within buildings -- particularly homes -- by understanding and influencing occupants' habits and behaviours with respect to energy use. To achieve this we are developing a system based on a whole building energy model that, uniquely, integrates (i) a thermal model, (ii) a model of occupants' habits and requirements and (iii) a disaggregated model of energy use in the dwelling. A "minimal sensor set" is being identified for delivery of the live data feed to the whole building energy model. This will be complemented by collection and analysis of occupant data that will be used to construct a model of occupants' energy-related attitudes, behaviours and habits. The whole building energy model will contribute to an interactive in-building tool to help occupants identify and break poor energy habits, form better ones and reduce energy demand and carbon emissions. This system is to be deployed in 200 homes for a period of 2 years in the city of Exeter (UK). This poster and demo presents our prototype sensor deployment and the visualisation of live data collected from the trial home group, through which the minimal sensor set is being developed.
Learning Scale Free Network by Node Specific Degree Prior

Learning the network structure underlying data is an important problem in machine learning. This paper introduces a novel prior to study the inference of scale-free networks, which are widely used to model social and biological networks. The prior not only favors a desirable global node degree distribution, but also takes into consideration the relative strength of all the possible edges adjacent to the same node and the estimated degree of each individual node. To fulfill this, ranking is incorporated into the prior, which makes the problem challenging to solve. We employ an ADMM (alternating direction method of multipliers) framework to solve the Gaussian Graphical model regularized by this prior. Our experiments on both synthetic and real data show that our prior not only yields a scale-free network, but also produces many more correctly predicted edges than the others such as the scale-free inducing prior, the hub-inducing prior and the l1 norm.
The Role of Students' Personality Traits in the Effective Use of Social Networking Sites in the Educational Context

By critically reviewing the theory and previous research in the domains of education, personality psychology and social networking sites (SNS) use, this chapter investigates the implications of educational SNS use for students with different personality structures. Conscientiousness is shown to be crucial for academic performance, with indications that neuroticism, agreeableness and openness are important as well. With regard to SNS use in schools, we give a short review of the existing studies, which yielded contradictory findings when it comes to SNS's effect on academic achievement, but are fairly in agreement about students' positive attitudes toward their use in schools. As our main purpose, we present personality-related findings and make predictions about the benefits of educational SNS use for introverted and highly neurotic students, and those with low self-esteem. We conclude that introducing SNS into the educational context would be valuable for all students, especially with regard to giving them equal chances in realizing their potential.
Predicting GPCR and enzymes function with a global approach based on LCS

The families of G-Protein Coupled Receptor (GPCR) and enzymes are among the main protein family. They represent to the scientific and medical communities, a significant target for bioactive and drug discovery programs. The model of classification of enzymes and GPCR is characterized by its hierarchical structure in format of tree and this makes more difficult its prediction. In this work we propose an adapted version of Learning Classifier Systems (LCS) data mining algorithm which tends to be more efficient than statistical methods based on homology used in tool such as PSI-BLAST. Hence, a new global model approach, called HLCS (Hierarchical Learning Classifier System) is used to predict the function of enzymes and GPCR, respecting its organizational structure of classes throughout the model development. The HLCS is expressed as a set of IF-THEN classification rules, which have the advantage of representing comprehensible knowledge to biologist users. The HLCS is evaluated with eight datasets from enzymes and GPCR, and compared with a Global Naive Bayes algorithm, named GMNB. In the tests realized the HLCS outperformed the GMNB in the databases of the GPCR proteins group type.
Television documentary, history and memory. An analysis of Sergio Zavoli's The Gardens of Abel

This article examines a celebrated documentary made for Italian state TV in 1968 and transmitted in 1969 to an audience of millions. The programme – The Gardens of Abel – looked at changes introduced by the radical psychiatrist Franco Basaglia in an asylum in the north-east of Italy (Gorizia). The article examines the content of this programme for the first time, questions some of the claims that have been made for it, and outlines the sources used by the director, Sergio Zavoli. The article argues that the film was as much an expression of Zavoli's vision and ideas as it was linked to those of Franco Basaglia himself. Finally, the article highlights the way that this programme has become part of historical discourse and popular memory.
Costs of resistance: genetic correlations and potential trade‐offs in an insect immune System

Theory predicts that natural selection will erode additive genetic variation in fitness‐related traits. However, numerous studies have found considerable heritable variation in traits related to immune function, which should be closely linked to fitness. This could be due to trade‐offs maintaining variation in these traits. We used the Egyptian cotton leafworm, Spodoptera littoralis, as a model system to examine the quantitative genetics of insect immune function. We estimated the heritabilities of several different measures of innate immunity and the genetic correlations between these immune traits and a number of life history traits. Our results provide the first evidence for a potential genetic trade‐off within the insect immune system, with antibacterial activity (lysozyme‐like) exhibiting a significant negative genetic correlation with haemocyte density, which itself is positively genetically correlated with both haemolymph phenoloxidase activity and cuticular melanization. We speculate on a potential trade‐off between defence against parasites and predators, mediated by larval colour, and its role in maintaining genetic variation in traits under natural selection.
ROBOROACH - an experiment in legged locomotion

This paper describes the development of a legged mobile robot. Legged locomotion has several advantages over wheels. The major advantage is the ability to traverse varied surfaces. However, legs must carry the full weight of the robot and this places great demands on the leg actuators, requiring a light robot structure. In most cases, the actuators contribute a large proportion of the weight, and usually at least two are required per leg. This project aims to reduce weight by using only one actuator per leg. Another aim is to improve walking performance on rough ground. Roboroach is a six legged insect robot, suited to traversing uneven terrain and having potential biomedical applications, with emphasis on areas such as environmental monitoring and search and rescue. The robot's legs each employ a single servo actuator and rotate a full 360°. This creates a novel walking style, which can negotiate large obstacles. The legs are compliant, which makes locomotion smoother, and gives passive adaptability to uneven surfaces. It is fitted with obstacle and tilt detection, and autonomously integrates these behaviours with walking and turning.
World Health Organization internal study of somatoform disorders : background and rationale

The World Health Organization (WHO) has recently launched a study of the characteristics of somatoform disorders in different cultures. An expert review of current knowledge on somatization and of issues that require future research had been produced prior to the study. These issues include the diagnosis classification assessment, cross-cultural variation and treatment of somatization. The main objectives of the WHO study are to test the cross-cultural applicability of the diagllostic criteria for somatoform disorders and develop instruments for their assessment; to assess the rates and other epidemiological indicators of the somatic expression of psychological disorders in different cultures; to produce and evaluate a set of educational tools and a programme of intervention for somatoform disorders in different parts of the world
Detection of attributes for Bengali phoneme in continuous speech using deep neural network

Hidden Markov Model (HMM) has contributed greatly in the area of speech recognition during last two decades. However, in recent days, detection-based, bottom-up speech recognition techniques achieve high success rate. In this detection-based, bottom-up approach of speech recognition, first step is detection of speech attributes like place and manner of articulation of the phonemes. This paper describes about the detection of attributes which leads to identification of place and manner of articulation of Bengali phonemes using Deep Neural Network (DNN).
Association between Serum Perfluorooctanoic Acid (PFOA) and Thyroid Disease in the U.S. National Health and Nutrition Examination Survey

Background Perfluorooctanoic acid (PFOA, also known as C8) and perfluorooctane sulfonate (PFOS) are stable compounds with many industrial and consumer uses. Their persistence in the environment plus toxicity in animal models has raised concern over low-level chronic exposure effects on human health. Objectives We estimated associations between serum PFOA and PFOS concentrations and thyroid disease prevalence in representative samples of the U.S. general population. Methods Analyses of PFOA/PFOS versus disease status in the National Health and Nutrition Examination Survey (NHANES) for 1999–2000, 2003–2004, and 2005–2006 included 3,974 adults with measured concentrations for perfluorinated chemicals. Regression models were adjusted for age, sex, race/ethnicity, education, smoking status, body mass index, and alcohol intake. Results The NHANES-weighted prevalence of reporting any thyroid disease was 16.18% (n = 292) in women and 3.06% (n = 69) in men; prevalence of current thyroid disease with related medication was 9.89% (n = 163) in women and 1.88% (n = 46) in men. In fully adjusted logistic models, women with PFOA ≥ 5.7 ng/mL [fourth (highest) population quartile] were more likely to report current treated thyroid disease [odds ratio (OR) = 2.24; 95% confidence interval (CI), 1.38–3.65; p = 0.002] compared with PFOA ≤ 4.0 ng/mL (quartiles 1 and 2); we found a near significant similar trend in men (OR = 2.12; 95% CI, 0.93–4.82; p = 0.073). For PFOS, in men we found a similar association for those with PFOS ≥ 36.8 ng/mL (quartile 4) versus ≤ 25.5 ng/mL (quartiles 1 and 2: OR for treated disease = 2.68; 95% CI, 1.03–6.98; p = 0.043); in women this association was not significant. Conclusions Higher concentrations of serum PFOA and PFOS are associated with current thyroid disease in the U.S. general adult population. More work is needed to establish the mechanisms involved and to exclude confounding and pharmacokinetic explanations.
Design of a Fault-tolerant Job-flow Manager for Grid Environments Using Standard Technologies, Job-flow Patterns, and a Transparent Proxy

The execution of job flow applications is a reality today in academic and industrial domains. Current approaches to execution of job flows often follow proprietary solutions on expressing the job flows and do not leverage recurrent job-flow patterns to address faults in Grid computing environments. In this paper, we provide a design solution to development of job-flow managers that uses standard technologies such as BPEL and JSDL to express job flows and employs a two-layer peer-to-peer architecture with interoperable protocols for crossdomain interactions among job-flow mangers. In addition, we identify a number of recurring job-flow patterns and introduce their corresponding fault-tolerant patterns to address runtime faults and exceptions. Finally, to keep the business logic of job flows separate from their fault-tolerant behavior, we use a transparent proxy that intercepts job-flow execution at runtime to handle potential faults using a growing knowledge base that contains the most recently identified job-flow patterns and their corresponding fault-tolerant patterns.
Robot reaching movement synthesis by human demonstration

Reaching to an object is a fundamental skill to a robot. The purpose of robot reaching aims at bringing the robot hands to the object location without obstacle collision. Since implementing such robot reaching movement is a tedious task, this paper proposes a method to synthesize robot reaching movement by human demonstration. However, human demonstrations are inconsistent, this paper adopts Gaussian Mixture Model (GMM) to obtain generalized reaching movement which is then used to synthesize new movement to avoid the obstacle by adjusting the parameters of its Gaussian model. With the proposed method, robot reaching movement is goal-directed, collision-free, and human-like. We validated the proposed method on a Aldebaran Robotics NAO humanoid robot with 25 degrees of freedom. The results showed that the NAO robot was able to perform skillful reaching movement to the object.
Improved evaluation of DRAM transistors and accurate resistance measurement for real chip contacts by nano-probing technique

In this study we have measured and analyzed characteristics of real transistors on dynamic random access memories (DRAM) including cell transistor by using nano-probing system for improved failure analysis. Measuring results of the conventional pad probing and nano-probing were compared on test element group (TEG) patterns of large transistors. The transistor characteristics of nano-probing results were evaluated for the each layer of DRAM structure with comparing the TEGs pad probing results. We also have measured sheet resistance (Rs) and contact resistance (Rc) on source and drain of real transistor bit line contacts (BLC) by nano-probing with transmission line model (TLM) method. We could find the effect of floating BLC was negligible and the effective resistance was only depending on the facing length of the contact plug bottom.