Miracle-dz/UniFAR-FTUs huggingface dataset:
The mechanics of malaria parasite invasion of the human erythrocyte – towards a reassessment of the host cell contribution

Despite decades of research, we still know little about the mechanics of Plasmodium host cell invasion. Fundamentally, while the essential or non‐essential nature of different parasite proteins is becoming clearer, their actual function and how each comes together to govern invasion are poorly understood. Furthermore, in recent years an emerging world view is shifting focus away from the parasite actin–myosin motor being the sole force responsible for entry to an appreciation of host cell dynamics and forces and their contribution to the process. In this review, we discuss merozoite invasion of the erythrocyte, focusing on the complex set of pre‐invasion events and how these might prime the red cell to facilitate invasion. While traditionally parasite interactions at this stage have been viewed simplistically as mediating adhesion only, recent work makes it apparent that by interacting with a number of host receptors and signalling pathways, combined with secretion of parasite‐derived lipid material, that the merozoite may initiate cytoskeletal re‐arrangements and biophysical changes in the erythrocyte that greatly reduce energy barriers for entry. Seen in this light Plasmodium invasion may well turn out to be a balance between host and parasite forces, much like that of other pathogen infection mechanisms.
A Performance Analysis of Optical Code Division Multiple Access System

With the concept of 3G services OCDMA is one of the important technology, that provides a very high speed communication. In this technology multiple users can communicate simultaneously to transfer different kind of data. OCDMA enables the best use of available bandwidth and the spectrum to represent a cost efficient network. But in a network there is always the requirement to increase the security and the efficiency or the throughput of a multiuser communication in the OCDMA network. The proposed work is about to increase the efficiency in a Noisy channel. Here the work will be performed on impulse noised network. We are here proposing the orthogonal approach along with OCDMA to improve the throughput. We are trying to show the results in terms of noise ratio and the derived throughput. The proposed work is about to reduce this packet loss and to increase the throughput in the noisy network. To overcome this drawback and to increase the throughput we are using the concept of OFDM along with CDMA. This concept is represented as the Orthogonal CDMA.
Genome-Wide Association Analysis of Autoantibody Positivity in Type 1 Diabetes Cases

The genetic basis of autoantibody production is largely unknown outside of associations located in the major histocompatibility complex (MHC) human leukocyte antigen (HLA) region. The aim of this study is the discovery of new genetic associations with autoantibody positivity using genome-wide association scan single nucleotide polymorphism (SNP) data in type 1 diabetes (T1D) patients with autoantibody measurements. We measured two anti-islet autoantibodies, glutamate decarboxylase (GADA, n = 2,506), insulinoma-associated antigen 2 (IA-2A, n = 2,498), antibodies to the autoimmune thyroid (Graves') disease (AITD) autoantigen thyroid peroxidase (TPOA, n = 8,300), and antibodies against gastric parietal cells (PCA, n = 4,328) that are associated with autoimmune gastritis. Two loci passed a stringent genome-wide significance level (p<10−10): 1q23/FCRL3 with IA-2A and 9q34/ABO with PCA. Eleven of 52 non-MHC T1D loci showed evidence of association with at least one autoantibody at a false discovery rate of 16%: 16p11/IL27-IA-2A, 2q24/IFIH1-IA-2A and PCA, 2q32/STAT4-TPOA, 10p15/IL2RA-GADA, 6q15/BACH2-TPOA, 21q22/UBASH3A-TPOA, 1p13/PTPN22-TPOA, 2q33/CTLA4-TPOA, 4q27/IL2/TPOA, 15q14/RASGRP1/TPOA, and 12q24/SH2B3-GADA and TPOA. Analysis of the TPOA-associated loci in 2,477 cases with Graves' disease identified two new AITD loci (BACH2 and UBASH3A).
Diversity of Genes, Its Representation and its Use for Creating New Individuals

The paper presents a metric to express the degree of diversity in a population of genes and proposes a structural representation of a population's similarity respective diversity by a pattern set. However, diversity cannot be understood as the presence of various genotypes only, but also as their quantitative distribution. To quantify the degree of distribution, the entropy of the patterns is used. Based on these metrics, three algorithms are introduced here, namely one for generating a new individual, which differs from all other individuals in the population to a required degree (we call it "exotic" here), an algorithm, which creates a new individual, which improves the entropy measure, and an algorithm, which creates a new individual from the fittest pattern.
Data mining through sentiment analysis: Lexicon based sentiment analysis model using aspect catalogue

Keeping in mind the peculiarity about product reviews, we have proposed a method that can help in determining the sentiment associated with the product in any review. Our method is based on `Lexicon based approach for Sentiment Analysis' with an additional feature of Aspect Catalogue. In our approach we first look for the keywords present in aspect catalogue to identify those sentences in which features of any product are mentioned. Taking into account only these sentences we further work on determining what opinion the speaker holds about features or aspects of the product. Aspect catalogue is referred again to find degree of importance corresponding to the feature with respect to the product. This is followed by calculation of numerical value of sentiment involved by taking the average of all the weightage of the features present in the text.
Layered photo pop-up

A common technique in documentaries is to animate photographs by panning across them slowly. More recently, it has become popular to divide such photographs into layers, and to animate these layers as moving over each other to create a motion parallax effect, commonly known as the "3D Ken Burns effect". Producing this effect involves a laborious manual process that requires hours of manual rotoscoping, clone-brushing, positioning in 3D, and adjusting the panning speeds of individual layers.
2016 Ieee International Conference on Big Data (big Data) Exploiting Temporal Divergence of Topic Distributions for Event Detection

—In a collection of documents, such as news articles or tweets, various events take place over time. The event detection problem aims at discovering significant events that have not been mentioned before the detection time. When these events occur, we observe that topic distributions of documents will diverge notably. However, event detection from such divergence may be hampered by noises. In this paper, we propose T opicDiver, a novel method to address the event detection problem. T opicDiver models topic distributions of documents over time and filters out noises while capturing the useful divergence between such distributions. The direct exploitation of topic distribution over time sets our work apart from existing studies on event detection. We conduct comprehensive experiments under different settings on news and Twitter data. The experimental results demonstrate that T opicDiver outperforms the baseline models in the measures for accuracy across various settings.
Cerebral Blood Flow Changes in Multiple Sclerosis and Neuromyelitis Optica and Their Correlations With Clinical Disability

Distinguishing relapsing-remitting multiple sclerosis (RRMS) and neuromyelitis optica (NMO) is clinically important because they differ in prognosis and treatment. This study aimed to identify perfusion abnormalities in RRMS and NMO and their correlations with gray matter volume (GMV) atrophy and clinical parameters. Structural and arterial spin labeling MRI scans were performed in 39 RRMS patients, 62 NMO patients, and 73 healthy controls. The gray matter cerebral blood flow (CBF) values were voxel-wisely compared among the three groups with and without GMV correction. The regional CBF changes were correlated with the Expanded Disability Status Scale scores in the corresponding patient groups. Although multiple brain regions showed CBF differences among the three groups without GMV correction, only three of these regions remained significant after GMV correction. Specifically, both the RRMS and NMO groups showed reduced CBF in the occipital cortex and increased CBF in the right putamen compared to the control group. The RRMS group had increased CBF only in the medial prefrontal cortex compared to the other two groups. The occipital CBF was negatively correlated with clinical disability in the NMO group; however, the CBF in the right putamen was positively correlated with clinical disability in both patient groups. These findings suggest that there are perfusion alterations independent of GMV atrophy in RRMS and NMO patients. The regional CBF in the occipital cortex and putamen could be used as imaging features to objectively assess clinical disability in these patients.
Comparable High Rates of Extended-Spectrum-Beta-Lactamase-Producing Escherichia coli in Birds of Prey from Germany and Mongolia

Frequent contact with human waste and liquid manure from intensive livestock breeding, and the increased loads of antibiotic-resistant bacteria that result, are believed to be responsible for the high carriage rates of ESBL-producing E. coli found in birds of prey (raptors) in Central Europe. To test this hypothesis against the influence of avian migration, we initiated a comparative analysis of faecal samples from wild birds found in Saxony-Anhalt in Germany and the Gobi-Desert in Mongolia, regions of dissimilar human and livestock population characteristics and agricultural practices. We sampled a total of 281 wild birds, mostly raptors with primarily north-to-south migration routes. We determined antimicrobial resistance, focusing on ESBL production, and unravelled the phylogenetic and clonal relatedness of identified ESBL-producing E. coli isolates using multi-locus sequence typing (MLST) and macrorestriction analyses. Surprisingly, the overall carriage rates (approximately 5%) and the proportion of ESBL-producers among E. coli (Germany: 13.8%, Mongolia: 10.8%) were similar in both regions. Whereas bla CTX-M-1 predominated among German isolates (100%), bla CTX-M-9 was the most prevalent in Mongolian isolates (75%). We identified sequence types (STs) that are well known in human and veterinary clinical ESBL-producing E. coli (ST12, ST117, ST167, ST648) and observed clonal relatedness between a Mongolian avian ESBL-E. coli (ST167) and a clinical isolate of the same ST that originated in a hospitalised patient in Europe. Our data suggest the influence of avian migratory species in the transmission of ESBL-producing E. coli and challenge the prevailing assumption that reducing human influence alone invariably leads to lower rates of antimicrobial resistance.
The cytoplasmic filament system in critical point-dried whole mounts and plastic-embedded sections

High voltage electron microscopy of intact cells prepared by the critical point drying (CPD) procedure has become an important tool in the study of three-dimensional relationships between cytoplasmic organelles. It has been claimed that critical point-dried specimens reveal a structure that is not visible in sections of plastic-embedded material; it has also been claimed that this structure, in association with known cytoplasmic filaments, forms a meshwork of tapering threads ("microtrabecular lattice"). Alternatively, this structure might be a surface tension artifact produced during CPD. To test possible sources of artifacts during CPD, model fiber systems of known structure were used. It was found that traces of water or ethanol in the CO2 caused distortions and fusion of fibers in pure muscle actin, fibrin, collagen, chromatin, and microtubules that produce a structure very similar to the proposed "microtrabecular lattice. " These structures were, however, well preserved if water and ethanol were totally excluded from the CO2. The same results were obtained with whole mounts of cultured cells. A "microtrabecular lattice" was obtained if some water or ethanol was present in the pressure chamber. On the other hand, when water or ethanol were totally excluded from the CO2 during CPD, cytoplasmic filaments were uniform in thickness similar to their appearance in sections of plastic-embedded cells. It is concluded that the "microtrabecular lattice" is a distorted image of the cytoplasmic filament network produced during CPD by traces of water or ethanol in the CO2.
Online Searching

We consider the problem of searching m branches which, with the exception of a common source s, are disjoint hereafter called concurrent branches. A searcher, starting at s, must find a given “exit” t whose location, unknown to the searcher, is on one of the m branches. The problem is to find a strategy that minimizes the worst-case ratio between the total distance traveled and the length of the shortest path from s to t. Additional information may be available to the searcher before he begins his search. In addition to finding optimal or near optimal deterministic online algorithms for these problems, this paper addresses the “value” of getting additional information before starting the search.
Performance of k-NN classifier for emotion detection using EEG signals

This paper describes the performance of k-NN classifier to classify the different emotions. The human brain is a superimposition of the diverse processes. This complex structure of brain is recognized through EEG signals. EEG signals indicate the changes in the state of brain. Electroencephalograph (EEG) measurements are commonly used in different research areas under the field of medical. Data acquisition is done for different emotions with the help of ADInsruments' power lab instrument. The real life EEG signals are collected with the help of Ground Truth Method. In this paper, proposed method consists of four steps, viz., acquisition of data, Pre-processing, Feature extraction and Classification. Subjects are stimulated for Sad and Happy emotions. Statistical features are then given to a k-NN classifier. The k Nearest Neighbor classifier gives different accuracy of classification for different combinations of training and testing dataset. The system has been tested on number of subjects to observe the performance of k-NN classifier.
Path planning for robust image-based control

Vision feedback control loop techniques are efficient for a large class of applications, but they come up against difficulties when the initial and desired robot positions are distant. Classical approaches are based on the regulation to zero of an error function computed from the current measurement and a constant desired one. By using such an approach, it is not obvious how to introduce any constraint in the realized trajectories or to ensure the convergence for all the initial configurations. In this paper, we propose a new approach to resolve these difficulties by coupling path planning in image space and image-based control. Constraints such that the object remains in the camera field of view or the robot avoids its joint limits can be taken into account at the task planning level. Furthermore, by using this approach, current measurements always remain close to their desired value, and a control by image-based servoing ensures robustness with respect to modeling errors. The proposed method is based on the potential field approach and is applied whether the object shape and dimensions are known or not, and when the calibration parameters of the camera are well or badly estimated. Finally, real-time experimental results using an eye-in-hand robotic system are presented and confirm the validity of our approach.
A scheme discriminating between synthetic speech and normal speech

This paper develops a system to automatically distinguish natural speech from synthetic speech. The issue of feature selection is considered. We take commonly used feature Mel-Frequency Cepstrum Coefficient (MFCC) in consideration, as well as other features such as Relative Phase Shift (RPS) and pitch tuned for Automatically Speech Recognition (ASR). We found some features are complimentary in the task of discriminating synthetic and natural speech. Gaussian Mixture Model Support Vector Machine (GMM-SVM) system is applied as classifier with feature input modified and compared to that of feature is applied in speaker recognition. Experiment on Librespeech versus online Text-to-Speech (TTS) speech synthesis platforms data set verified the effectiveness of the combination of these features.
Path-finding in Multi-Agent, unexplored And Dynamic Military Environment Using Genetic Algorithm

Path-finding in multi-agent, unexplored and dynamic military environment is one of the most important issues for solving in simulators. In this article a substructure of agent Path-finding is created which these agents are placed in an unexplored environment, also they communicate each other and use proposed methods to find targets to reach them. The most important aim of this article is to consider the targets behavior to decrease the required cost for reaching to the targets by improving agent's behavior in a dynamic military environment and improving agent's interactions. In the proposed method each agent have movement ability in a dynamic environment autonomously and they have ability to finding targets between obstacles. Therefore to solve this problem all of the necessary constraints to find path in a dynamic and unexplored environment are considered and Genetic algorithm is used.
Autonomous Agents in Future Energy Markets: The 2012 Power Trading Agent Competition

Sustainable energy systems of the future will need more than efficient, clean, and low-cost energy sources. They will also need efficient price signals that motivate sustainable energy consumption behaviors and a tight real-time alignment of energy demand with supply from renewable and traditional sources. The Power Trading Agent Competition (Power TAC) is a rich, competitive, open-source simulation platform for future retail power markets built on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making as well as the robustness of proposed market designs. Power TAC invites researchers to develop autonomous electricity broker agents and to pit them against best-in-class strategies in global competitions, the first of which will be held at AAAI 2013. Power TAC competitions provide compelling, actionable information for policy makers and industry leaders. We describe the competition scenario, demonstrate the realism of the Power TAC platform, and analyze key characteristics of successful brokers in one of our 2012 pilot competitions between seven research groups from five different countries.
Design of a pilot-centered visual decision-support system for airborne collision avoidance

In this work we present a pilot-centered visual decision support system for flight critical collision avoidance using “dynamic 4D trajectory management”. The system utilizes an underlying automation support system, which is connected to the onboard avionics, and the NextGen/SESAR enhanced flight deck technologies enabling new Communication, Navigation and Surveillance (CNS) and System Wide Information Management (SWIM) services. The overall automation support system which embeds “dynamic 4D trajectory management” is envisioned to a) provide the pilots with alternative trajectories as tunnels-in-the-sky through avionics displays on the console and head-up/augmented reality displays in real-time, b) provide the flight crew with quantified and visual understanding of collision risks in terms of time, directions, and countermeasures, and c) provide autonomous conflict resolution as an autopilot mode. Thus, ensuring highly responsive and adaptive airborne collision avoidance in face of ever challenging scenarios that involve blunders, weather/ terrain/ obstacle/ new conflict hazards. The designed visual decision support system does not increase workload of the pilot and dilutes “tunneling effect” in interaction while increasing situational awareness. These algorithms and tools developed are currently being integrated on an Automation Support System for implementation on a Boeing 737-NG FNPT II Flight Simulator with synthetic vision and augmented reality.
MOCS: Multi-objective Clustering Selection Evolutionary Algorithm

In this paper, we describe a multi-objective evolutionary algorithm, that uses clustering selection and does not need any additional parameter like others. It clusters the population into a flexible number of clusters employing x-means from [Pelleg and Moore, 2000]. First, the selective fitness is assigned to clusters and in second place to individuals of clusters. We show three hybrid variants incorporating additional mechanisms from other elitist multi-objective evolutionary algorithms in order to increase selection pressure. Using the test functions from Deb's T suite (T1-T6), from Schaffer, Kursawe and Quagliarella we evaluate the performance and the quality of our approach against the most recent and performant elitist multi-objective evolutionary algorithms, NSGA2, SPEA2 and PESA2. The comparison yields promising results for region-based selection using clustering in combination with additional crowding strategies.
LIG at CLEF 2015 SBS Lab

This paper describes the work achieved by the MRIM research group of Grenoble, using some data from the LaHC of SaintEtienne, in a way to test personalized retrieval of books for the Social Book Search Lab of CLEF 2015. Our proposal rely on a biased fusion of content-only retrieval, using BM25F and LGD retrieval models, user non-social profile based on the catalog of the requester, and social profiles using user/user links generated from their catalogs and ratings on books. The official results obtained show a clear positive impact of user profile, and a small positive impact of the social elements we used. Post official results that present non biased fusion scores are also presented.
A beamspace sparse-overcomplete signal representation method for bearing estimation by sensor array

Recently, the sparse overcomplete signal representations have been introduced to solve the direction-of-arrival (DOA) estimation problem by sensor array. However, it still has high computational complexity, and as the dictionary, i.e. a collection of possible direction response vectors, is required to be “overcomplete”, it has to cover all the possible DOAs both of the interesting signals and uninteresting interferences to get the signal DOA estimation. This not only takes more program waiting time, but also makes the results unstable when the arrival angles of interferences are varying rapidly. In this paper, with the extension of the sparse overcomplete representation method (M-SBL) due to D. P. Wipf and B. D. Rao in beamspace bearing estimation, the beamspace M-SBL (BM-SBL) method is proposed. Since its dictionary contains only the possible DOAs of interest and taking advantages of beamspace conversion, the proposed method takes much less computational cost. On the other hand, simulation results show that the BM-SBL outperforms its element space counterpart M-SBL in both resolution and accuracy.