Miracle-dz/UniFAR-FTUs huggingface dataset:
Concordance between semen‐derived HIV‐1 proviral DNA and viral RNA hypervariable region 3 (V3) envelope sequences in cases where semen populations are distinct from those present in blood

Sequence analysis of the third hypervariable region (V3) of the envelope gene of the HIV‐1 was carried out on HIV proviral and viral populations present in blood and semen. Phylogenetically distinct populations of virus were observed in three of the 10 patients analysed. Although the majority of the viruses were predicted to have an R5 phenotype, amino acid differences between blood and semen‐derived virus and provirus sequences were observed at sites previously shown to affect cell tropism. Importantly, the semen proviral population was representative of that observed for cell‐free virus. This indicates that seminal fluid mononuclear cells are possible sources for the cell‐free virus in found in semen. J. Med. Virol. 67:9–19, 2002. © 2002 Wiley‐Liss, Inc.
Inducible Defenses with a "Twist": Daphnia barbata Abandons Bilateral Symmetry in Response to an Ancient Predator

Predation is one of the most important drivers of natural selection. In consequence a huge variety of anti-predator defenses have evolved in prey species. Under unpredictable and temporally variable predation pressure, the evolution of phenotypically plastic defensive traits is favored. These “inducible defenses”, range from changes in behavior, life history, physiology to morphology and can be found in almost all taxa from bacteria to vertebrates. An important group of model organisms in ecological, evolutionary and environmental research, water fleas of the genus Daphnia (Crustacea: Cladocera), are well known for their ability to respond to predators with an enormous variety of inducible morphological defenses. Here we report on the “twist”, a body torsion, as a so far unrecognized inducible morphological defense in Daphnia, expressed by Daphnia barbata exposed to the predatory tadpole shrimp Triops cancriformis. This defense is realized by a twisted carapace with the helmet and the tail spine deviating from the body axis into opposing directions, resulting in a complete abolishment of bilateral symmetry. The twisted morphotype should considerably interfere with the feeding apparatus of the predator, contributing to the effectiveness of the array of defensive traits in D. barbata. As such this study does not only describe a completely novel inducible defense in the genus Daphnia but also presents the first report of a free living Bilateria to flexibly respond to predation risk by abandoning bilateral symmetry.
Mapping the SUMOylated landscape

SUMOylation is a post‐translational modification that regulates a multitude of cellular processes, including replication, cell‐cycle progression, protein transport and the DNA damage response. Similar to ubiquitin, SUMO (small ubiquitin‐like modifier) is covalently attached to target proteins in a reversible process via an enzymatic cascade. SUMOylation is essential for nearly all eukaryotic organisms, and deregulation of the SUMO system is associated with human diseases such as cancer and neurodegenerative diseases. Therefore, it is of great interest to understand the regulation and dynamics of this post‐translational modification. Within the last decade, mass spectrometry analyses of SUMO proteomes have overcome several obstacles, greatly expanding the number of known SUMO target proteins. In this review, we briefly outline the basic concepts of the SUMO system, and discuss the potential of proteomic approaches to decipher SUMOylation patterns in order to understand the role of SUMO in health and disease.
Single input optimal control for globally coupled neuron networks

We consider the problem of desynchronizing a network of synchronized, globally (all-to-all) coupled neurons using an input to a single neuron. This is done by applying the discrete time dynamic programming method to reduced phase models for neural populations. This technique numerically minimizes a certain cost function over the whole state space, and is applied to a Kuramoto model and a reduced phase model for Hodgkin–Huxley neurons with electrotonic coupling. We evaluate the effectiveness of control inputs obtained by averaging over results obtained for different coupling strengths. We also investigate the applicability of this method to Hodgkin–Huxley models driven by multiplicative stimuli.
DIPPER: Description and Formalisation of an Information-State Update Dialogue System Architecture

The DIPPER architecture is a collection of software agents for prototyping spoken dialogue systems. Implemented on top of the Open Agent Architecture (OAA), it comprises agents for speech input and output, dialogue management, and further supporting agents. We deﬁne a formal syntax and semantics for the DIP-PER information state update language. The language is independent of particular programming languages, and incorporates procedural attachments for access to ex-ternal resources using OAA.
Les systèmes agroalimentaires localisés (SYAL) : introduction

La notion de Systeme agroalimentaire localise (SYAL) est apparue, il y a une dizaine d'annees, dans un contexte de crise des societes rurales, d'aggravation des problemes environnementaux et des nouveaux defis alimentaires poses aux differentes societes des pays du Sud et du Nord, tant du point de vue quantitatif que qualitatif. La premiere definition a ete avancee en 1996 pour chercher a rendre compte, dans ce contexte, de regroupements geographiques d'entreprises agroalimentaires qui resistaient ou innovaient a partir de strategies de valorisation de ressources et produits locaux. Les SYAL sont alors definis comme des ensembles "d'organisations de production et de service (unites de production agricole, entreprises agroalimentaires, commerciales, de services, restauration) associees a un territoire specifique par leurs caracteristiques et leur fonctionnement. Le milieu, les produits, les hommes, leurs institutions, leur savoir-faire, leurs comportements alimentaires, leurs reseaux de relations, se combinent dans un territoire pour produire une forme d'organisation agroalimentaire a une echelle spatiale donnee" [CIRAD-SAR (1996)]. (Resume d'auteur)
Glial Wingless/Wnt Regulates Glutamate Receptor Clustering and Synaptic Physiology at the Drosophila Neuromuscular Junction

Glial cells are emerging as important regulators of synapse formation, maturation, and plasticity through the release of secreted signaling molecules. Here we use chromatin immunoprecipitation along with Drosophila genomic tiling arrays to define potential targets of the glial transcription factor Reversed polarity (Repo). Unexpectedly, we identified wingless (wg), a secreted morphogen that regulates synaptic growth at the Drosophila larval neuromuscular junction (NMJ), as a potential Repo target gene. We demonstrate that Repo regulates wg expression in vivo and that local glial cells secrete Wg at the NMJ to regulate glutamate receptor clustering and synaptic function. This work identifies Wg as a novel in vivo glial-secreted factor that specifically modulates assembly of the postsynaptic signaling machinery at the Drosophila NMJ.
Broadband Resonant-Type Metamaterial Transmission Lines

In this letter, it is shown that, contrary to previous assumptions, the broadband characteristics of metamaterial transmission lines are not exclusive of the so called CL loaded composite right/left handed (CRLH) structures. In the balance condition, the typical frequency gap between the left handed (LH) and right handed (RH) transmission bands of these CRLH lines collapses, the characteristic impedance varies smoothly in the vicinity of the transition frequency, and broadband response results. However, through an appropriate design, similar behavior can be obtained in resonant type metamaterial transmission lines, namely transmission lines loaded with complementary split rings resonators. A detailed analysis of the structures, based on the equivalent circuit model is provided, and implications of balancing are pointed out. In this letter, it is clearly demonstrated that broadband balanced CRLH lines can also be implemented by means of the resonant type approach
Application isolation in the Java Virtual Machine

To date, systems offering multitasking for the Java&trade; programming language either use one process or one class loader for each application. Both approaches are unsatisfactory. Using operating system processes is expensive, scales poorly and does not fully exploit the protection features inherent in a safe language. Class loaders replicate application code, obscure the type system, and non-uniformly treat 'trusted' and 'untrusted' classes, which leads to subtle, but nevertheless, potentially harmful forms of undesirable inter-application interaction.In this paper we propose a novel, simple yet powerful solution. The new model improves on existing designs in terms of resource utilization while offering strong isolation among applications. The approach is applicable both on high-end servers and on small devices. The main idea is to maintain only one copy of every class, regardless of how many applications use it. Classes are transparently and automatically modified, so that each application has a separate copy of its static fields. Two prototypes are described and selected performance data is analyzed. Various aspects of the proposed architectural changes to the Java Virtual Machine are discussed.
Energy-aware geo-routing for delay tolerant networks in disaster areas

Routing in Delay Tolerant Networks (DTNs) is characterized by the inexistence of the end-to-end path. This feature can be applied in the disaster area where infrastructures are destroyed. Using existing mobile platform such as mobile phone to build an energy aware communication network is an attractive problem. In this paper, a geographical routing protocol called GeoRescue is proposed, enhanced with the prediction based routing and transmission control mechanism. By using a real cartography of Sendai area, GeoRescue increases the delivery rate and decrease the latency compared with two benchmark protocols, and greatly decreases the routing overhead. The hop count of GeoRescue is more approximate to the epidemic routing than the others.
Learning to Model Students: Using Theory Refinement to Detect Misconceptions

A new student modeling system called ASSERT is described. ASSERT is a general purpose algorithm which uses domain independent techniques to perform student modeling and to automatically construct libraries of common bugs. For its modeling component, ASSERT uses a machine learning technique which is an extension of the EITHER theory refinement algorithm. A common library of bugs is constructed by extracting commonalities across multiple student models. Initial experimental data suggests that ASSERT is a more effective modeling system than other techniques previously explored, and that the automatic bug library construction significantly enhances subsequent modeling efforts.
Rhythmic Episodes of Subthreshold Membrane Potential Oscillations in the Rat Inferior Olive Nuclei In Vivo

In vitro studies of inferior olive neurons demonstrate that they are intrinsically active, generating periodic spatiotemporal patterns. These self-generated patterns of activity extend the role of olivary neurons beyond that of a deliverer of teaching or error signals. However, autorhythmicity or patterned activity of complex spikes in the cerebellar cortex was observed in only a few studies. This discrepancy between the self-generated rhythmicity in the inferior olive observed in vitro and the sporadic reports on rhythmicity of complex spikes can be reconciled by recording intracellularly from inferior olive neurons in situ. To this end, we recorded intracellularly from olivary neurons of anesthetized rats. We demonstrate that, in vivo, olivary neurons show both slow and fast rhythmic processes. The slow process (0.2–2 Hz) is expressed as rhythmic transitions from quiescent periods to periods of fast rhythm, manifested as subthreshold oscillations of 6–12 Hz. Spikes, if they occur, are locked to the depolarized phase of these subthreshold oscillations and, therefore, hold and transfer rhythmic information. The transient nature of these oscillatory epochs accounts for the difficulties to uncover them by prolonged recordings of complex spikes activity in the cerebellar cortex.
A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis

Summary Objectives: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. Methods: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. Results: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. Conclusions: The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.
Comparison of F-Measure, BER and PSNR of Tumor Detection using Hybridization of Fuzzy and Region Growing

This paper has dedicated to brain tumor detection algorithm. The majority of the existing work with tumor detection has neglected the using object-based segmentation. Thus this paper has planned an effective brain tumor detection using the feature detection and roundness metric. To boost the tumor detection rate further we've incorporated the proposed hybridization of fuzzy C-means and region growing segmentation based tumor detection with the use of trilateral filter in its preprocessing stage. The planned method has the capability to generate efficient results even in the event of large occurrence of the noise. The experimental results have obviously shown that the planned method outperforms over the available techniques.
2016 Ieee International Conference on Big Data (big Data) Automated It System Failure Prediction: a Deep Learning Approach

—In mission critical IT services, system failure prediction becomes increasingly important; it prevents unexpected system downtime, and assures service reliability for end users. While operational console logs record rich and descriptive information on the health status of those IT systems, existing system management technologies mostly use them in a labor-intensive forensics approach, i.e., identifying what went wrong after the fact. Recent efforts on log-based system management take an automation approach with text mining techniques, such as term frequency-inverse document frequency (TF-IDF). However, those techniques lead to a high-dimensional feature space, and are not easily generalizable to heterogeneous log formats. In this paper, we present a novel system that automatically parses streamed console logs and detects early warning signals for IT system failure prediction. In particular, our solution includes a log pattern extraction method by clustering together logs with similar format and content. We then resemble the TF-IDF idea by considering each pattern as a word and the set of patterns in each discretized epoch as a document. This leads to a feature space with significantly lower dimensionality that can provide robust signals for the status of the system. As system failures tend to occur very rare, we apply a recurrent neural network, namely, Long Short-Term Memory (LSTM), to deal with the " rarity " of labeled data in the training process. LSTM is able to capture the long-range dependency across sequences, therefore outperforms traditional supervised learning methods in our application domain. We evaluated and compared our proposed technology with state-of-the-art machine learning approaches using real log traces from two large enterprise systems. The results showed the advantage and potentials of our system in prediction of complex IT failures. To our knowledge, our work is the first that employs LSTM for log-based system failure prediction.
Bridging or buffering?: The impact of schools’ adaptive strategies on student achievement

Purpose – Rational and open system theories offer divergent sets of tactics on how best to deal with factors outside the boundary of the school. This study compared two competing strategies that emerge from these theories: bridging and buffering. The impact of how schools interact with their environments was examined in relation to student achievement.Design/methodology/approach – The competing theories were operationalized into two survey measures that tapped teacher perceptions of their schools’ orientations toward the environment. Using schools as the unit of analysis, two competing perspectives were contrasted together with their relative impact on student achievement on standardized tests.Findings – Multiple regression was used to assess the relative weight of each of these constructs in explaining the variance in student achievement. Bridging strategies explained a greater proportion of the variance than buffering.Research limitations/implications – Data were limited to teacher perceptions of the st.
Inference of Gene Tree Discordance and Recombination

The evolutionary history of a group of organisms is important for studying their genomes. Many biological processes can cause the evolutionary histories of different genes to vary, and such incongruent histories may mislead the inference of a species tree. New methods for the reconstruction of gene trees and species trees from molecular data have been enabled by recent sequencing and computing advances. I examine here the sensitivity of two such Bayesian methods, BEST and BUCKy, to the violation of their assumptions in the presence of two biological processes: incomplete lineage sorting (ILS) and horizontal gene transfer (HGT). BEST assumes gene tree discordance is due to ILS based on the coalescent model. Instead, BUCKy uses a non-parametric clustering prior distribution on gene trees. The species tree was found to be more accurately reconstructed by BUCKy than BEST in the presence of HGT. Both methods performed similarly in most other cases. A test is proposed to determine if the coalescent model alone can explain gene tree discordance. This test uses the concordance factor (CF) of clades, i.e. the proportion of genes that have the clade
Lessons Learnt from Field Trials of a Robotic Sweet Pepper Harvester

In this paper, we present the lessons learnt during the development of a new robotic harvester (Harvey) that can autonomously harvest sweet pepper (capsicum) in protected cropping environments. Robotic harvesting offers an attractive potential solution to reducing labour costs while enabling more regular and selective harvesting, optimising crop quality, scheduling and therefore profit. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. We demonstrate a simple and effective vision-based algorithm for crop detection, a grasp selection method, and a novel end-effector design for harvesting. To reduce the complexity of motion planning and to minimise occlusions we focus on picking sweet peppers in a protected cropping environment where plants are grown on planar trellis structures. Initial field trials in protected cropping environments, with two cultivars, demonstrate the efficacy of this approach. The results show that the robot harvester can successfully detect, grasp, and detach crop from the plant within a real protected cropping system. The novel contributions of this work have resulted in significant and encouraging improvements in sweet pepper picking success rates compared with the state-of-the-art. Future work will look at detecting sweet pepper peduncles and improving the total harvesting cycle time for each sweet pepper. The methods presented in this paper provide steps towards the goal of fully autonomous and reliable crop picking systems that will revolutionise the horticulture industry by reducing labour costs, maximising the quality of produce, and ultimately improving the sustainability of farming enterprises.
Measuring Privacy in Vehicular Networks

Vehicular communication plays a key role in nearfuture automotive transport, promising features like increased traffic safety or wireless software updates. However, vehicular communication can expose driver locations and thus poses important privacy risks. Many schemes have been proposed to protect privacy in vehicular communication, and their effectiveness is usually shown using privacy metrics. However, to the best of our knowledge, (1) different privacy metrics have never been compared to each other, and (2) it is unknown how strong the metrics are. In this paper, we argue that privacy metrics should be monotonic, i.e. that they indicate decreasing privacy for increasing adversary strength, and we evaluate the monotonicity of 32 privacy metrics on real and synthetic traffic with state-ofthe- art adversary models. Our results indicate that most privacy metrics are weak at least in some situations. We therefore recommend to use metrics suites, i.e. combinations of privacy metrics, when evaluating new privacy-enhancing technologies.
Organizational commitment and psychological attachment: The effects of compliance, identification, and internalization on prosocial behavior.

Previous research on organizational commitment has typically not focused on the underlying dimensions of psychological attachment to the organization. Results of two studies using university employees (N = 82) and students (N = 162) suggest that psychological attachment may be predicated on compliance, identification, and internalization (e.g., Kelman, 19S8). Identification and internalization are positively related to prosocial behaviors and negatively related to turnover. Internalization is predictive of financial donations to a fund-raising campaign. Overall, the results suggest the importance of clearly specifying the underlying dimensions of commitment using notions of psychological attachment and the various forms such attachment can take. In the past decade, the construct of organizational commitment has occupied a prominent place in organizational behavior research (Mowday, Porter, & Steers, 1982; Salancik, 1977; Staw & Ross, 1978). Unfortunately, as Morrow (1983, p. 486) has pointed out, "the growth in commitment related concepts has not been accompanied by a careful segmentation of commitment's theoretical domain in terms of intended meaning of each concept or the concepts' relationships among each other. " By her count, there are over 25 commitment-related concepts and measures. Staw (1977), for instance, has noted that the value of commitment as a separate construct distinct from other psychological concepts such as motivation, involvement, or behavioral intention remains to be demonstrated. What is needed are theoretical and operational definitions that clearly differentiate commitment and its components from other re
Practical Language Processing for Virtual Humans

NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses a statistical language classification technology for mapping from user's text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.