Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of mini-tyrosyl-tRNA synthetase on ischemic angiogenesis, leukocyte recruitment, and vascular permeability.

Mini-tyrosyl-tRNA synthetase (mini-TyrRS), the N-terminal domain of tyrosyl-tRNA synthetase, is a recently identified protein released by endothelial cells that exhibits angiogenic and leukocyte chemoattractant, ELR-motif (Glu-Leu-Arg)-dependent activities in vitro. We sought to determine whether exogenous mini-TyrRS exerts these and other cytokine-like actions in physiological and pathological settings in vivo. High-dose mini-TyrRS (600 microg.kg(-1). day(-1)) augmented while low-dose mini-TyrRS (3 microg.kg(-1). day(-1)), unexpectedly, inhibited angiogenesis in the ischemic mouse ear. Enhanced angiogenesis was associated with increased CD45- and CD4-positive leukocyte accumulation. Mini-TyrRS also had biphasic actions on both basal and mustard oil-evoked and VEGF-evoked leakage of Evan's blue dye-albumin in nonischemic ear and in endothelial cell monolayers, that is, low-dose inhibited and high-dose augmented leakage. Mutation of the ELR motif of mini-TyrRS abolished the above activities. Mini-TyrRS was reduced (immunoblot) in extracts of ischemic calf muscle and in thoracic aorta explants exposed to hypoxia or VEGF. Inhibition of VEGF with a soluble Flt1 "trap" protein abolished this hypoxic-induced reduction in mini-TyrRS in aorta explants. These data show that mini-TyrRS has dose-dependent biphasic effects on ischemic angiogenesis and vascular permeability in vivo, that is, antiangiogenic and antipermeability activities at low concentration and proangiogenic, propermeability activities at high concentrations.
Bulbar Microcircuit Model Predicts Connectivity and Roles of Interneurons in Odor Coding

Stimulus encoding by primary sensory brain areas provides a data-rich context for understanding their circuit mechanisms. The vertebrate olfactory bulb is an input area having unusual two-layer dendro-dendritic connections whose roles in odor coding are unclear. To clarify these roles, we built a detailed compartmental model of the rat olfactory bulb that synthesizes a much wider range of experimental observations on bulbar physiology and response dynamics than has hitherto been modeled. We predict that superficial-layer inhibitory interneurons (periglomerular cells) linearize the input-output transformation of the principal neurons (mitral cells), unlike previous models of contrast enhancement. The linearization is required to replicate observed linear summation of mitral odor responses. Further, in our model, action-potentials back-propagate along lateral dendrites of mitral cells and activate deep-layer inhibitory interneurons (granule cells). Using this, we propose sparse, long-range inhibition between mitral cells, mediated by granule cells, to explain how the respiratory phases of odor responses of sister mitral cells can be sometimes decorrelated as observed, despite receiving similar receptor input. We also rule out some alternative mechanisms. In our mechanism, we predict that a few distant mitral cells receiving input from different receptors, inhibit sister mitral cells differentially, by activating disjoint subsets of granule cells. This differential inhibition is strong enough to decorrelate their firing rate phases, and not merely modulate their spike timing. Thus our well-constrained model suggests novel computational roles for the two most numerous classes of interneurons in the bulb.
A COMPARISON OF THE SENSITIVITYOF DIFFERENT STRAINS OF MICE TO SARIN

Summary Poisoning from chemical warfare agents (CWAs) such as sarin is associated with neuronal degeneration. This damage is thought to result from glutamatergic excitotoxicity such as seen following kainic acid induced seizures. In order to search for novel neuroprotectants it is necessary to select good mouse models for susceptibility to nerve agent-induced seizures and the resulting neurodegeneration. The mouse strains tested (C57BL/6, ICR, DBA/2, SW, and FVB/N, Harlan Laboratory) had widely different sensitivity to sarin as shown by differences in the dose required resulting in 50% mortality, LD 50 . Differences also were observed among the strains in Fluoro-Jade C staining with the C57BL/6 and DBA having little to no staining when euthanized at 7 days whereas the other strains did. Differences in acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) activity were found among the strains as well. The ICR strain was excluded from the FOB and weight data due to difficulty getting a consistent LD 50 . Weight loss and FOB scores were similar for all strains. All strains had inhibited AChE activity after sarin exposure and exhibited inhibition of CNS BuChE after sarin exposure but only ICR and SW reached significance.
Dynamic migration in multiple ant colonies

Multi-population-based bio-inspired computation may use migration among groups to increase the search diversity. Through good solutions exchanged among sub-populations, better solutions may be found with a high probability. In this paper, we propose two algorithms to dynamically adjust the two primary parameters, migration interval and migration rate, for flexibly reflect solution situation for effective migration. The first algorithm only dynamically changes the migration interval, and the second considers both interval and rate. We will examine how the dynamic migration strategies affect the quality of solutions in the experiments.
MazG, a Nucleoside Triphosphate Pyrophosphohydrolase, Interacts with Era, an Essential GTPase in Escherichia coli

ABSTRACT Era is an essential GTPase in Escherichia coli, and Era has been implicated in a number of cellular functions. Homologues of Era have been identified in various bacteria and some eukaryotes. Using the era gene as bait in the yeast two-hybrid system to screen E. coli genomic libraries, we discovered that Era interacts with MazG, a protein of unknown function which is highly conserved among bacteria. The direct interaction between Era and MazG was also confirmed in vitro, being stronger in the presence of GDP than in the presence of GTPγS. MazG was characterized as a nucleoside triphosphate pyrophosphohydrolase which can hydrolyze all eight of the canonical ribo- and deoxynucleoside triphosphates to their respective monophosphates and PPi, with a preference for deoxynucleotides. A mazG deletion strain of E. coli was constructed by replacing the mazG gene with a kanamycin resistance gene. Unlike mutT, a gene for another conserved nucleotide triphosphate pyrophosphohydrolase that functions as a mutator gene, the mazG deletion did not result in a mutator phenotype in E. coli.
HIV-1 infection and antibodies to Plasmodium falciparum in adults.

BACKGROUND Coinfection with human immunodeficiency virus (HIV) may increase susceptibility to malaria by compromising naturally acquired immunity. METHODS In 339 adults (64% HIV infected), we measured antibodies to Plasmodium falciparum variant surface antigens (VSA) and antibodies that opsonise infected erythrocytes using parasite lines FCR3, E8B, and R29, and antibodies to merozoite antigens AMA-1 and MSP2. We determined the relationship between malaria antibodies, HIV infection, markers of immune compromise, and risk of incident parasitemia. RESULTS HIV-infected adults had significantly lower mean levels of opsonizing antibody to all parasite lines (P < .0001), and lower levels of antibody to AMA-1 (P = .01) and MSP2 (P < .0001). Levels of immunoglobulin G (IgG) to VSA were not affected by HIV status. Opsonising antibody titres against some isolates were positively correlated with CD4 count. There were negative associations between human immunodeficiency virus type 1 (HIV-1) viral load and opsonizing antibodies to FCR3 (P = .04), and levels of IgG to AMA-1 (P ≤ .03) and MSP2-3D7 (P = .05). Lower opsonizing antibody levels on enrollment were seen in those who became parasitemic during follow-up, independent of HIV infection (P ≤ .04 for each line). CONCLUSIONS HIV-1 infection decreases opsonizing antibodies to VSA, and antibody to merozoite antigens. Opsonizing antibodies were associated with lack of parasitemia during follow up, suggesting a role in protection.
The impact of simple feature engineering in multilingual medical NER

The goal of this paper is to examine the impact of simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical NER. Sometimes papers using scientifically sound techniques present raw baselines that could be improved adding simple and cheap features. This work focuses on entity recognition for the clinical domain for three languages: English, Swedish and Spanish. The task is tackled using simple features, starting from the window size, capitalization, prefixes, and moving to POS and semantic tags. This work demonstrates that a simple initial step of feature engineering can improve the baseline results significantly. Hence, the contributions of this paper are: first, a short list of guidelines well supported with experimental results on three languages and, second, a detailed description of the relevance of these features for medical NER.
Leveraging data lifetime for energy-aware last level non-volatile SRAM caches using redundant store elimination

NVM has commonly been used to address increasingly large last-level caches (LLCs) requirements by reducing leakage. However, frequent data-writing operations result in increased energy consumption. In this context, a promising memory technology, Non-volatile SRAM (nvSRAM), enables normal and standby operation modes which can be used to store various types of data. However, nvSRAM suffers from high dynamic energy usage due to frequent switching between operation modes. In this paper, we propose a redundant store elimination (RSE) scheme which, on average, discards 94% of needless bit-write operations. Moreover, we present a retention-aware cache management policy to reduce data updates of cache blocks, based on the correlation between data lifetime and cache types. Experimental results demonstrate that our proposal can improve energy consumption of SRAM-based and RRAM-based LLCs by 57% and 31%, respectively.
Knowledge interaction with manufacturing clients and innovation of knowledge-intensive business services firms

Abstract The existing literature on knowledge-intensive business services (KlBS) predominantly focuses on their role as innovation agents in innovation systems, with limited attention to innovation of KIBS firms in its own right. Using a sample of 181 miaBS firms in Singapore, this paper examines the key determinants of innovation behavior ofKlBS firms. We find that knowledge interaction with manufacturing clients is positively associated with KlBS firms’ own innovation. We also find that export intensity, a strategic focus on marketing and communications, and human capital intensity are positively related to KlBS firms’ own innovation.
Event Prediction With Learning Algorithms-A Study of Events Surrounding the Egyptian Revolution of 2011 on the Basis of Micro Blog Data

We aim to predict activities of political nature influencing or reflecting societal-scale behavior and beliefs by applying learning algorithms to Twitter data. This study focuses on capturing domestic events in Egypt from November 2009 to November 2013. To this extent we study un­derlying communication patterns by evaluating content and metadata of 1.3 million tweets through computationally supported classification, with­out targeting specific keywords or users from the Twitter stream. Support Vector Machine (SVM) and Support Distribution Machine (SDM) classification algorithms are applied to detect and predict societal-scale unrest. Latent Dirichlet Allocation (LDA) is used to create content-based input patterns for the SVM while the SDM is used to classify sets of features created from meta-data. The experiments reveal that user cen­tric approaches based on meta-data outperform methods employing content-based input despite the use of well established natural language pro­cessing algorithms. The results show that distri­butions over user centric meta information pro­vide an important signal when detecting and pre­dicting events. Applying this approach can assist policymakers and stakeholders in their efforts toward proactive community management.
Direct combination

This paper reports on Direct Combination, a new user interactiontechnique. Direct Combination may be viewed variously as: asystematic extension to Direct Manipulation; a concise navigationalframework to help users find the operations they need; and as aframework to make a greater range and variety of operationsavailable to the user, without overburdening user or interfacedesigner. While Direct Combination may be seen as an extension ofDirect Manipulation, it may also be applied to a wide range of userinteraction styles, including even command line interfaces.Examples from various hypothetical systems and from an implementedsystem are presented. This paper argues that Direct Combination isapplicable not just to problem seeking or design oriented domains(where the technique originated) but is generally applicable. Avariety of new interaction styles for Direct Combination arepresented. The generalisation of Direct Combination to then-dimensional case is presented.
Predicting future locations using prediction-by-partial-match

We implemented the Prediction-by-Partial-Match data compression algorithm as a predictor of future locations. Positioning was done using IEEE 802.11 wireless access logs. Several experiments were run to determine how to divide the data for training and testing and how to best represent the data as a string of symbols. Our test data consisted of 198 datasets containing over 28,000 <time, location> pairs, obtained from the UCSD Wireless Topology Discovery project. Tests of a first-order PPM model revealed a 90% success rate in predicting a user's location given the time. The third-order model, which is given the previous time and location and asked to predict the location at a given time, is correct 92% of the time.
On Exploiting Structures for Constraint Solving

The constraint satisfaction problem (CSP) instances modeling problems occurring in practice often have structures which can be exploited to speed-up the constraint solving process. This thesis explores the exploitation of three such structures. The three structures explored in this thesis are: 1. Tree-like relationships: Several CSP instances have tree-like relationships between the variables in them. Such a relationship can be observed by using the tree-width parameter. Given a CSP instance with low tree-width, implying close to tree-like relationship, a tree-decomposition of the instance could be used to speed-up the constraint solving process. 2. Compact BDD representation: For some finite-domain constraint problem instances, the solutions of each constraint in those instances might be represented by a compact Binary Decision Diagram (BDD). In which case, it is advantageous to represent the constraints using BDDs. This might lead to speeding-up the propagation and other reasoning steps in a constraint solver. 3. Abundance of infrequent variables: In some constraint problem instances, the modeling process might introduce a lot of variables to simplify the encoding of a problem instance. Such introduction of new variables might lead to abundance of variables occurring in only a few constraints. In such cases, the existential quantification of those infrequent variables in a preprocessing step might reduce the effort required to determine the satisfiability of the instance. This thesis explores toward exploiting these three ideas on several problem instances. Suggestions are made toward incorporation of these ideas and those suggestions are supported by empirical data. Since these kind of structures won’t be found in randomly generated CSP instances, the experiments in this thesis were done on instances either mostly from practice or on those close to those occurring in practice. For most of the experiments reported in this thesis, the source code or the executable code used, along with the input data are publicly available for further research. Some highlights of the results in this thesis are:
Patent citation network analysis for measuring the ICT patent progress in India

Patent citation network analysis is a useful method of identifying the knowledge flow of a technology from one country, institution or corporate to another. Whereas patent citation analysis involves statistical study of citations; patent citation network analysis involves the study of network and its different aspects identical to the parameters applied in the study of social network analysis. In the current research paper we have formed patent citation network from the rough data available for patents. Study focuses on the growth and advancement of `Information and Communication technology' patents in India. The scope of study focuses on patents which are filed in Indian Patent Office by the Indian assignees.
Location Awareness in Ad Hoc Wireless Mobile Networks

Networks composed of dynamically repositioning mobile hosts require location awareness to provide new geographic services and to maximize routing efficiency and quality of service. Because wireless networks can operate in a 3D physical environment, exploiting mobile hosts' location information is both natural and inevitable. Emerging geographic services based on mobile ad-hoc networks (manets) must confront several challenges, including how to increase positioning accuracy and how to establish a connection from location information to the vast body of Web data, as in a tour-guide system for example.
Higher education reform in Germany

Purpose – The purpose of this paper is to provide information about the current reform of higher education in Germany, which can be described as German reading of the Bologna process, about the problems and deficits occurring in this area, and about ways to correct unwelcome developments.Design/methodology/approach – The paper starts with a review of appropriate programmatic documents, on the one hand, and of the results of relevant empirical studies on the other. On this basis deficits and risks of the German higher education policy are identified and feasible solutions discussed.Findings – The overloading of the Bologna process with political ambitions, which no longer have a lot in common with the fundamental thoughts formed at a European level, leads in Germany to unwelcome and counter‐productive effects. One of them is that the space for cross‐border activities for learners and lecturers is not expanding but diminishing.Practical implications – In Germany, it seems to be time to give up the priority .
A hierarchical motion trajectory signature descriptor

Motion trajectory is a compact clue for motion characterization. However, it is normally used directly in its raw data form in most work and effective trajectory description is lacking. In this paper, we propose a novel hierarchical motion trajectory signature descriptor, which can not only fully capture motion features for detailed perception, but also can be used for probabilistic fast recognition. The hierarchy enables the signature to exhibit high functional adaptability meeting different application requirements. At the first-level, differential invariants are employed to describe trajectory features and a nonlinear signature warping method is developed to perceive and recognize trajectories. The second-level signature is the condensation of the first-level signature by applying PCA based dimension optimization. It behaves more efficiently in recognition based on the Gaussian Mixture modeling and Bayesian classifier. The conducted experiments verified the signature's effectiveness.
Grammar Inference Automata Induction and Language Acquisition

The natural language learning problem has attracted the attention of researchers for several decades Computational and formal models of language acquisition have provided some preliminary yet promising insights of how children learn the lan guage of their community Further these formal models also provide an operational framework for the numerous practical applications of language learning We will survey some of the key results in formal language learning In particular we will discuss the prominent computational approaches for learning di erent classes of for mal languages and discuss how these t in the broad context of natural language learning
PROMETHEUS: A Corpus of Proverbs Annotated with Metaphors

Proverbs are commonly metaphoric in nature and the mapping across domains is commonly established in proverbs. The abundance of proverbs in terms of metaphors makes them an extremely valuable linguistic resource since they can be utilized as a gold standard for various metaphor related linguistic tasks such as metaphor identification or interpretation. Besides, a collection of proverbs fromvarious languages annotated with metaphors would also be essential for social scientists to explore the cultural differences betweenthose languages. In this paper, we introduce PROMETHEUS, a dataset consisting of English proverbs and their equivalents in Italian.In addition to the word-level metaphor annotations for each proverb, PROMETHEUS contains other types of information such as the metaphoricity degree of the overall proverb, its meaning, the century that it was first recorded in and a pair of subjective questions responded by the annotators. To the best of our knowledge, this is the first multi-lingual and open-domain corpus of proverbs annotated with word-level metaphors.
Machine Learning Techniques for Topic Spotting

Automatically choosing topics for text documents that describe the document contents, is a useful technique for text categorization. For example queries sent on the web can use this technique to identify the query topic and accordingly forward query to small group of people. Similarly online blogs can be categorized according to the topics they are related to. In this paper we applied machine learning techniques to the problem of topic spotting. We used supervised learning techniques which are highly dependent on training data and the particular training algorithm used. Our approach differs from automatic text clustering which uses unsupervised learning for clustering the text. Secondly the topics are known in advance and come from an exhaustive list of words. The machine learning techniques we applied are 1) neural network. , 2) Naive Bayes Classifier, 3) Instance based learning using k-nearest neighbours and 4) Decision Tree method. We used Reuters-21578 text categorization dataset for our experiments.
A fuzzy commitment scheme

We combine well-known techniques from the areas of error-correcting codes and cryptography to achieve a new type of cryptographic primitive that we refer to as a fuzzy commitment scheme. Like a conventional cryptographic commitment scheme, our fuzzy commitment scheme is both concealing and binding: it is infeasible for an attacker to learn the committed value, and also for the committer to decommit a value in more than one way. In a conventional scheme, a commitment must be opened using a unique witness, which acts, essentially, as a decryption key. By contrast, our scheme is fuzzy in the sense that it accepts a witness that is close to the original encrypting witness in a suitable metric, but not necessarily identical. This characteristic of our fuzzy commitment scheme makes it useful for applications such as biometric authentication systems, in which data is subject to random noise. Because the scheme is tolerant of error, it is capable of protecting biometric data just as conventional cryptographic techniques, like hash functions, are used to protect alphanumeric passwords. This addresses a major outstanding problem in the theory of biometric authentication. We prove the security characteristics of our fuzzy commitment scheme relative to the properties of an underlying cryptographic hash function.