Miracle-dz/UniFAR-FTUs huggingface dataset:
Innate Immune Genes Associated With Newcastle Disease Virus Load in Chick Embryos From Inbred and Outbred Lines

Newcastle disease virus (NDV) causes substantial economic losses to smallholder farmers in low- and middle-income countries with high levels of morbidity and mortality in poultry flocks. Previous investigations have suggested differing levels of susceptibility to NDV between specific inbred lines and amongst breeds of chickens, however, the mechanisms contributing to this remain poorly understood. Studies have shown that some of these differences in levels of susceptibility to NDV infection may be accounted for by variability in the innate immune response amongst various breeds of poultry to NDV infection. Recent studies, in inbred Fayoumi and Leghorn lines, uncovered conserved, breed-dependent, and subline-dependent responses. To better understand the role of innate immune genes in engendering a protective immune response, we assessed the transcriptional responses to NDV of three highly outbred Tanzanian local chicken ecotypes, the Kuchi, the Morogoro Medium, and the Ching’wekwe. Hierarchical clustering and principal coordinate analysis of the gene expression profiles of 21-day old chick embryos infected with NDV clustered in an ecotype-dependent manner and was consistent with the relative viral loads for each of the three ecotypes. The Kuchi and Morogoro Medium exhibit significantly higher viral loads than the Ching’wekwe. The results show that the outbred ecotypes with increased levels of expression of CCL4, NOS2, and SOCS1 also had higher viral loads. The higher expression of SOCS1 is inconsistent with the expression in inbred lines. These differences may uncover new mechanisms or pathways in these populations that may have otherwise been overlooked when examining the response in highly inbred lines. Taken together, our findings provide insights on the specific conserved and differentially expressed innate immune-related genes involved the response of highly outbred chicken lines to NDV. This also suggests that several of the specific innate immunity related genes identified in the current investigation may serve as markers for the selection of chickens with reduced susceptibility to NDV.
Changes in Centrality Frequency of the Default Mode Network in Individuals With Subjective Cognitive Decline

Despite subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), being widely studied in recent years, studies on centrality frequency in individuals with SCD are lacking. This study aimed to investigate the differences in centrality frequency between individuals with SCD and normal controls (NCs). Forty individuals with SCD and 53 well-matched NCs underwent a resting-state functional magnetic resonance imaging scan. We assessed individual dynamic functional connectivity using sliding window correlations. In each time window, brain regions with a high degree centrality were defined as hubs. Across the entire time window, the proportion of time that the hub appeared was characterized as centrality frequency. The centrality frequency correlated with cognitive performance differently in individuals with SCD and NCs. Our results revealed that in individuals with SCD, compared with NCs, correlations between centrality frequency of the anterior cortical regions and cognitive performance decreased (79.2% for NCs and 43.5% for individuals with SCD). In contrast, correlations between centrality frequency of the posterior cortical regions and cognitive performance increased in SCD individuals compared with NCs (20.8% for NCs and 56.5% for individuals with SCD). Moreover, the changes mainly focused on the anterior (93.3% for NCs and 45.5% for individuals with SCD) and posterior (6.7% for NCs and 54.5% for individuals with SCD) regions associated with the default mode network (DMN). In addition, we used absolute thresholds (correlation efficient r = 0.2, 0.25) and proportional thresholds (sparsity = 0.2, 0.25) to verify the results. Dynamic results are relative stable at absolute thresholds while static results are relative stable at proportional thresholds. Converging findings provide a new framework for the detection of the changes occurring in individuals with SCD via centrality frequency of the DMN.
Fast, sensitive protein sequence searches using iterative pairwise comparison of hidden Markov models.

state profile comparison Secondary structure and solvent accessibility have two important characteristics that make it useful for remote homology detection and alignment and hence also 3D struture prediction: (1) The state of a residue can be predicted from local windows of sequence profiles centered around the residue, typically of size 13 or 15 columns. (2) The states evolve slowly in time and hence are relatively similar even between remote homologs. The idea is to generate an alphabet of abstract states that has the two characteristics above. This alphabet should ideally contain almost all the information from the profile windows that obeys the two characteristics. We could then expect this alphabet to contain secondary structure and solvent accessability information implicitly, among other information. Hence it should be even more powerful for remote homology detection than those two alphabets (secondary structure states and solvent accessibility states) together. For example predicted secondary structure and solvent accessibility do not improve alignment quality a lot since neighboring states are mostly identical. In contrast, we can expect that Chapter 5: Conclusion and Outlook 78 the register of an amphipathic helix or a beta strand is quite well conserved in evolution and naturally differs between neighboring residues. By using a relatively small number of abstract states (64 or 256) we are able to construct a substitution matrix for the abstract states, just as for amino acids. One can thereby statistically describe the evolutionary behaviour of these states. The main improvement to the case of comparing amino acid distributions is that these abstract states depend on a whole window in the profile, whereas the statistical model of similarity between amino acid distributions is always column-by-column and does not merge together information over several neighboring columns.
Choosing Optimal Seed Nodes in Competitive Contagion

In recent years there has been a growing interest in simulating competitive markets to find out the efficient ways to advertise a product or spread an ideology. Along this line, we consider a binary competitive contagion process where two infections, A and B, interact with each other and diffuse simultaneously in a network. We investigate which is the best centrality measure to find out the seed nodes a company should adopt in the presence of rivals so that it can maximize its influence. These nodes can be used as the initial spreaders or advertisers by firms when two firms compete with each other. Each node is assigned a price tag to become an initial advertiser which varies according to their importance in the network. Considering their fixed budgets, they initially determine the payoff of their products and the number of their initial seeds in the network. Under this setting, we study the question of whether to choose a small number of influential nodes or a larger number of less influential nodes.
Meta-Brokering requirements and research directions in state-of-the-art Grid Resource Management

Grid resource management is probably the research (cid:2)eld mostly affected by user demands. Though well-designed, evaluated and widely used resource brokers, meta-schedulers have been developed, new capabilities are required, such as agreement and interoperability support. Existing solutions cannot cross the border of current middleware systems that are lacking the support of these requirements. In this paper we examine and compare different research directions followed by researchers in the (cid:2)eld of Grid Resource Management regarding the level on top of brokering approaches. Our proposed meta-brokering approach means a higher level resource management by enabling communication among existing Grid Brokers and utilizing them. We state the requirements of this novel middleware service, and de(cid:2)ne a general meta-brokering architecture to be implemented as a Service Oriented Knowledge Utility (SOKU) in future generation grids.
Switching Assistance for Exoskeletons During Cyclic Motions

This paper proposes a novel control algorithm for torque-controlled exoskeletons assisting cyclic movements. The control strategy is based on the injection of energy parcels into the human-robot system with a timing that minimizes perturbations, i.e., when the angular momentum is maximum. Electromyographic activity of main flexor-extensor knee muscles showed that the proposed controller mostly favors extensor muscles during extension, with a statistically significant reduction in muscular activity in the range of 10–20% in 60 out of 72 trials (i.e., 83%), while no effect related to swinging speed was recorded (speed variation was lower than 10% in 92% of the trials). In the remaining cases muscular activity increment, when statistically significant, was less than 10%. These results showed that the proposed algorithm reduced muscular effort during the most energetically demanding part of the movement (the extension of the knee against gravity) without perturbing the spatio-temporal characteristics of the task and making it particularly suitable for application in exoskeleton-assisted cyclic motions.
STING Polymer Structure Reveals Mechanisms for Activation, Hyperactivation, and Inhibition

How the central innate immune protein, STING, is activated by its ligands remains unknown. Here, using structural biology and biochemistry, we report that the metazoan second messenger 2’3’-cGAMP induces closing of the human STING homodimer and release of the STING C-terminal tail, which exposes a polymerization interface on the STING dimer and leads to the formation of disulfide-linked polymers via cysteine residue 148. Disease-causing hyperactive STING mutations either flank C148 and depend on disulfide formation or reside in the C-terminal tail binding site and cause constitutive C-terminal tail release and polymerization. Finally, bacterial cyclic-di-GMP induces an alternative active STING conformation, activates STING in a cooperative manner, and acts as a partial antagonist of 2’3’-cGAMP signaling. Our insights explain the tight control of STING signaling given varying background activation signals and provide a novel therapeutic hypothesis for autoimmune syndrome treatment.
JiST : An efficient approach to simulation using virtual machines †

Discrete event simulators are important scientific tools and their efficient design and execution is the subject of much research. In this paper, we propose a new approach for constructing simulators that leverages virtual machines and combines advantages from the traditional systems-based and language-based simulator designs. We introduce JiST, a Java-based simulation system that executes discrete event simulations both efficiently and transparently by embedding simulation semantics directly into the Java execution model. The system provides standard benefits that the modern Java runtime affords. In addition, JiST is efficient, out-performing existing highly optimized simulation runtimes. As a case study, we illustrate the practicality of the JiST framework by applying it to the construction of SWANS, a scalable wireless ad hoc network simulator. We simulate million node wireless networks, which represents two orders of magnitude increase in scale over what existing simulators can achieve on equivalent hardware and at the same level of detail.
An SSR-SNP Linkage Map of the Parasitic Weed Orobanche cumana Wallr. Including a Gene for Plant Pigmentation

Sunflower broomrape (Orobanche cumana Wallr.) is a holoparasitic plant that causes major yield losses to sunflower crops in the Old World. Efforts to understand how this parasitic weed recognizes and interacts with sunflowers are important for developing long-term genetic resistance strategies. However, such studies are hampered by the lack of genetic tools for O. cumana. The objectives of this research were to construct a genetic linkage map of this species using SSR and SNP markers, and mapping the Pg locus that is involved in plant pigmentation. The genetic map was developed from the progenies of a cross between the O. cumana inbred lines EK-12 and EK-A1, which originated from populations belonging to two distant and geographically separated gene pools identified in Spain. The inbred lines also differed in plant pigmentation, with EK-A1 lacking anthocyanin pigmentation (pgpg genotype). A genetic map comprising 26 SSR and 701 SNP markers was constructed, which displayed 19 linkage groups (LGs), corresponding to the 19 chromosome pairs of O. cumana. The total length of the map was 1795.7 cM, with an average distance between two adjacent positions of 2.5 cM and a maximum map distance of 41.9 cM. The Pg locus mapped to LG19 between the SNP markers OS02468 and OS01653 at 7.5 and 3.4 cM, respectively. This study constitutes the first linkage map and trait mapping study in Orobanche spp. , laying a key foundation for further genome characterization and providing a basis for mapping additional traits such as those having a key role in parasitism.
No Country for Oldowan Men: Emerging Factors in Language Evolution

Language evolution has long been researched. I will review a number of broad, emerging research directions which arguably have the potential to contribute to our understanding of language evolution. Emerging topics in genomics and neurolinguistics are explored, and human-specific levels of braincase globularity – and the broader process of self-domestication within which globularity seems capable of being encapsulated – will be argued to be the central pillars of any satisfactory and interdisciplinary model of language evolution.
Supervised Methods For Ranking Relations In Web Search

In this paper we propose an efﬁcient technique for ranking triples of knowledge base using information of full text. We devise supervised machine learning algorithms to compute the relevance scores for item-property pairs where an item can have more than one value.Such a score measures the degree to which an entity belongs to a type, and this plays an important role in ranking the search results. The problem is, in itself, new and not explored so much in the literature, possibly because of the heterogeneous behaviors of both semantic knowledge base and full-text articles. The classiﬁers exploit statistical features computed from the Wikipedia articles and the semantic information obtained from the word embedding concepts. We develop models based on traditional supervised models like Suport Vector Machine (SVM) and Random Forest (RF); and then using deep Convolution Neural Network (CNN). We perform experiments as provided by WSDM cup 2017, which provides about 1k human judgments of person-profession pairs. Evaluation shows that machine learning based approaches produce encouraging performance with the highest accuracy of 71%. The contributions of the current work are two-fold, viz. we focus on a problem that has not been explored much, and show the usage of powerful word-embedding features that produce promising results.
POS Tagging For Resource Poor Languages Through Feature Projection

We present an approach for POS tagging with out any labeled data. Our method requires translated sentences from a pair of languages. We used feature transfer from a resource rich language to resource poor languages. Across 8 different Indian Languages, we achieved encouraging accuracies without any knowledge of the target language and any human annotation. This will help us in creating annotated corpora for resource poor Indian languages.
The Impact of a Dot: Case Studies of a Noise Metamorphic Relation Pattern

We propose a "noise" metamorphic relation pattern (MRP), which is a sub-pattern under the more general MRP "symmetry. " We conduct case studies with real-life systems in three different application domains (obstacle perception in autonomous systems, machine translation, and named entity recognition) to show the usefulness of the "noise" MRP for software verification and validation.
End to End Dialog System for Telugu

This paper describes an end to end dialog system created using sequence to sequence learning and memory networks for Telugu, a low-resource language. We automatically generate dialog data for Tel-ugu in the tourist domain, using a knowledge base that provides tourist place, type, tour time, etc. Using this data, we train a sequence to sequence model to learn system responses in the dialog. In order to add the query prediction for information retrieval (through API calls), we train a memory network. We also handle cases requiring updation of API calls and querying for additional information. Using the combination of sequence to sequence learning and memory network, we successfully create an end to end dialog system for Telugu.
The limits of Spanglish?

Linguistic code-switching (C-S) is common in oral bilingual vernacular speech. When used in literature, C-S becomes an artistic choice that can mirror the patterns of bilingual interactions. But it can also potentially exceed them. What are the limits of C-S? We model features of C-S in corpora of contemporary U.S. Spanish-English literary and conversational data to analyze why some critics view the ‘Spanglish’ texts of Ilan Stavans as deviating from a C-S norm.
Natural Language Inference with Monotonicity

This paper describes a working system which performs natural language inference using polarity-marked parse trees. The system handles all of the instances of monotonicity inference in the FraCaS data set. Except for the initial parse, it is entirely deterministic. It handles multi-premise arguments, and the kind of inference performed is essentially “logical”, but it goes beyond what is representable in first-order logic. In any case, the system works on surface forms rather than on representations of any kind.
Multi - model partitioning the multi - model evolutionary framework for intelligent control

The nature of control problems of great current interest is highly complex, nonlinear, stochastic, time-varying, and partially known at best. The difficulties of control design are compounded further because the control goals and control laws may not be completely defined either because they are not known at design time or because they change unexpectedly [ 1 I 11. As such, the modern control engineering design problems to be solved are highly complex, large-scale, and computationally intractable. Their practical solution requires systedalgorithms that are: a) adaptive, in order to cope with significant unmodeled and unanticipated changes in the plant, in the environment and in the control objectives; b) learning, in order to increase its knowledge of the plant and its environment; and c) intelligent and autonomous. Moreover, the resulting intelligent controls must be realizable in decentralized/parallel/distributed processing implementations. Indeed, for the complex control problems of today, the modular, paralleVdistributed processing architectures are absolutely necessary in order to meet, at reasonable cost, the time-processing constraints imposed by the complexity of the applications. The design of optimal controls. especially for large-scale, complex applications, depends integrally on the model of the physical problem. Indeed, the model constitutes the vital link between the “physical” problem in which the optimal control will be used, and the mathematical realm in which it must be designed. The effectiveness, and applicability of the designed system depends strongly on the realism with which the associated mathematical model represents the underlying “physical problem. However, the more realistic the model, the greater its complexity, and consequently the greater the difficulty of the associated control design problem, and the greater is the difficulty in implementing the designed control. The difficulties are compounded further by the fact that as the model realism increases so does the ignorance of the model. In most applications, complete knowledge of complex models is neither available nor readily forthcoming and one is confronted with the design of an optimal control in the face of incomplete model
Online trained neuro-controller with a modified error function

A modified error function to improve the performance of a neuro-controller trained online by the backpropagation (BP) algorithm is presented. The performance is significantly improved with the proposed error function compared to that with the traditional error function used in the BP algorithm. Based on this modified error function, the structure of the network can be greatly simplified leading to a very simple neuro-controller. Performance of the simple network based neuro-controller to control a second order system is presented.
Earley-based stochastic context-free grammar estimation from bracketed corpora and its use in a hybrid languaje model

In this paper, we study the problem of estimating Stochastic Context-Free Grammars (SCFGs) in general format and their use in a hybrid language model. In this work, we propose the estimation of a SCFG by means of a new bracketed version of the Earley algorithm. A hybrid language model is defined as a combination of a word-based n-gram, which is used to capture the local relations between words, and a category-based SCFG with a word distribution in categories, which is defined to represent the long-term relations between these categories. Experiments on the UPenn Treebank corpus are reported. These experiments have been carried out in terms of the test set perplexity and the word error rate in a speech recognition experiment.
Sign Clustering and Topic Extraction in Proto-Elamite

We describe a first attempt at using techniques from computational linguistics to analyze the undeciphered proto-Elamite script. Using hierarchical clustering, n-gram frequencies, and LDA topic models, we both replicate results obtained by manual decipherment and reveal previously-unobserved relationships between signs. This demonstrates the utility of these techniques as an aid to manual decipherment.
Implementing IoT Systems in Service-Oriented Architecture

The article presents the analysis of the existing Internet of Things (IoT) systems, identifies three levels of application: household, civil and industrial. The shortcomings of the implementations associated with the popular client-server architecture are revealed. The possibility of building scalable solutions in a service-oriented architecture is considered. Argued the merits of such decisions, providing compatibility, fault tolerance and security at any level of the application of the IoT system. The description of the developed automatic room ventilation system and intersection counting system with data transmission over a radio channel are given as examples.