Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting Rac1 Signaling Inhibits Streptococcal M1 Protein-Induced CXC Chemokine Formation, Neutrophil Infiltration and Lung Injury

Infections with Streptococcus pyogenes exhibit a wide spectrum of infections ranging from mild pharyngitis to severe Streptococcal toxic shock syndrome (STSS). The M1 serotype of Streptococcus pyogenes is most commonly associated with STSS. In the present study, we hypothesized that Rac1 signaling might regulate M1 protein-induced lung injury. We studied the effect of a Rac1 inhibitor (NSC23766) on M1 protein-provoked pulmonary injury. Male C57BL/6 mice received NSC23766 prior to M1 protein challenge. Bronchoalveolar fluid and lung tissue were harvested for quantification of neutrophil recruitment, edema and CXC chemokine formation. Neutrophil expression of Mac-1 was quantified by use of flow cytometry. Quantitative RT-PCR was used to determine gene expression of CXC chemokines in alveolar macrophages. Treatment with NSC23766 decreased M1 protein-induced neutrophil infiltration, edema formation and tissue injury in the lung. M1 protein challenge markedly enhanced Mac-1 expression on neutrophils and CXC chemokine levels in the lung. Inhibition of Rac1 activity had no effect on M1 protein-induced expression of Mac-1 on neutrophils. However, Rac1 inhibition markedly decreased M1 protein-evoked formation of CXC chemokines in the lung. Moreover, NSC23766 completely inhibited M1 protein-provoked gene expression of CXC chemokines in alveolar macrophages. We conclude that these novel results suggest that Rac1 signaling is a significant regulator of neutrophil infiltration and CXC chemokine production in the lung. Thus, targeting Rac1 activity might be a potent strategy to attenuate streptococcal M1 protein-triggered acute lung damage.
Two-Stage Hypotheses Generation for Spoken Language Translation

Spoken Language Translation (SLT) is the research area that focuses on the translation of speech or text between two spoken languages. Phrase-based and syntax-based methods represent the state-of-the-art for statistical machine translation (SMT). The phrase-based method specializes in modeling local reorderings and translations of multiword expressions. The syntax-based method is enhanced by using syntactic knowledge, which can better model long word reorderings, discontinuous phrases, and syntactic structure. In this article, we leverage on the strength of these two methods and propose a strategy based on multiple hypotheses generation in a two-stage framework for spoken language translation. The hypotheses are generated in two stages, namely, decoding and regeneration. In the decoding stage, we apply state-of-the-art, phrase-based, and syntax-based methods to generate basic translation hypotheses. Then in the regeneration stage, much more hypotheses that cannot be captured by the decoding algorithms are produced from the basic hypotheses. We study three regeneration methods: redecoding, n-gram expansion, and confusion network in the second stage. Finally, an additional reranking pass is introduced to select the translation outputs by a linear combination of rescoring models. Experimental results on the Chinese-to-English IWSLT-2006 challenge task of translating the transcription of spontaneous speech show that the proposed mechanism achieves significant improvements over the baseline of about 2.80 BLEU-score.
Two Phase Extraction Method for Multi-label Classification of Real Life Tweets

Recently, many users share their daily events and opinions on Twitter. Some are beneficial and comment on several aspects of a user's real life, i.e., eating, traffic, weather, disasters, and so on. Such posts as "The train is not coming! " are categorized in the "Traffic" aspect and will support users who want to ride the train. Such tweets as "The train is not coming due to heavy rain" are categorized in both the "Traffic" and "Weather" aspects. In this paper, we propose a multi-label method that estimates appropriate aspects against unknown tweets by extending the two phase extraction method. In it, many topics are extracted from a sea of tweets using Latent Dirichlet Allocation (LDA). Associations among many topics and fewer aspects are built using a small set of labeled tweets. Aspect scores for unknown tweets are calculated using the associations among the topics and the aspects based on the extracted terms. Appropriate aspects are labeled for unknown tweets by averaging of the aspect scores. Using a large amount of actual tweets, our sophisticated experimental evaluations demonstrate the high efficiency of our proposed multi-label classification method. When an aspect score is much larger than others, that aspect is estimated against the tweet. When several aspect scores are large within similar values, these aspects are estimated. Based on the experimental evaluation results, our prototype system demonstrates that our proposed method can appropriately estimate some aspects of each unknown tweets.
PAuthKey: A Pervasive Authentication Protocol and Key Establishment Scheme for Wireless Sensor Networks in Distributed IoT Applications

Wireless sensor Networks (WSNs) deployed in distributed Internet of Things (IoT) applications should be integrated into the Internet. According to the distributed architecture, sensor nodes measure data, process, exchange information, and perform collaboratively with other sensor nodes and end-users, which can be internal or external to the network. In order to maintain the trustworthy connectivity and the accessibility of distributed IoT, it is important to establish secure links for end-to-end communication with a strong pervasive authentication mechanism. However, due to the resource constraints and heterogeneous characteristics of the devices, traditional authentication and key management schemes are not effective for such applications. This paper proposes a pervasive lightweight authentication and keying mechanism for WSNs in distributed IoT applications, in which the sensor nodes can establish secured links with peer sensor nodes and end-users. The established authentication scheme PAuthKey is based on implicit certificates and it provides application level end-to-end security. A comprehensive description for the scenario based behavior of the protocol is presented. With the performance evaluation and the security analysis, it is justified that the proposed scheme is viable to deploy in the resource constrained WSNs.
Measurement of W boson angular distributions in events with high transverse momentum jets at √ s = 8 TeV using the ATLAS detector

Article history: Received 23 September 2016 Received in revised form 30 November 2016 Accepted 2 December 2016 Available online 6 December 2016 Editor: W. -D. Schlatter The W boson angular distribution in events with high transverse momentum jets is measured using data collected by the ATLAS experiment from proton–proton collisions at a centre-of-mass energy √ s = 8 TeV at the Large Hadron Collider, corresponding to an integrated luminosity of 20.3 fb−1. The focus is on the contributions to W + jets processes from real W emission, which is achieved by studying events where a muon is observed close to a high transverse momentum jet. At small angular separations, these contributions are expected to be large. Various theoretical models of this process are compared to the data in terms of the absolute cross-section and the angular distributions of the muon from the leptonic W decay. © 2016 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3.
APPLICATIONS OF CELLULAR AUTOMATA IN COMPLEX SYSTEM STUDY

In recent years, cellular automata have been used widely to study complex systems, such as biological systems, tra-c systems, economic systems, environmental systems, engineering systems, social systems, industrial systems, and a lot of remarkable results have been gained. Due to its underlying prop- erties, cellular automata approach has become a new promising method for studying complex systems. First, this paper analyzes the relationship between cellular automata and complex systems based on their own characteristics. Then, some applications of cellular automata in complex systems' modeling, analyzing and controlling are discussed. Finally, some suggestions for future development of cellular automata approach in studying complex systems are given.
Link State Routing Protocol with Cluster Based Flooding for Mobile Ad-hoc Computer Networks

Ability of a routing algorithm to converge quickly when network topology changes frequently is a critical requirement for routing in multi-hop wireless networks. Due to its exceptional convergence performance, Link State routing technology is the state of the art in wired network routing. There is, however, a concern that Link State routing will generate too much overhead traffic in bandwidth-depleted wireless networks. This paper presents a protocol for Link State routing in a mobile wireless ad-hoc network. The protocol is designed to operate in small to medium size ad-hoc wireless networks, with each node being a router at the same time. While most routing algorithms for ad-hoc networks use minimum hop count as their routing metric, our protocol uses link costs that are proportional to the power that the node needs to generate to reach the next hop. Although this approach produces routes with more hops, it allows to minimize the congestion on the link layer (OSI reference architecture), preserves battery power, and creates routes that are less likely to be broken due to node mobility. In order to ensure efficient and reliable flooding of Link State Updates, the cluster based algorithm is used. Once global up-to-date information is delivered to every router, Shortest Path First algorithm ensures that the optimal route is selected.
The Procrastination Related Indicators in e-Learning Platforms

In general, research confirms that learning is more effective when students obtain feedback regarding their learning progress. Currently, new versions of e-learning platforms include indicators that provide some static feedback mechanisms and help both learners and educators in planning their learning strategies. This paper explains the usage of indicators in current e-learning systems, generates a taxonomy for their classification, and studies their influence on student performance. Also, it provides a study which is based on the combination of a user-based evaluation process that facilitates data collection and data mining algorithms to infer association rules between learning variables and performance. The results highlight how procrastination influences negative learning performance and how time-related indicators are tightly coupled with students' performance in e-learning platforms.
EngageMeter: A System for Implicit Audience Engagement Sensing Using Electroencephalography

Obtaining information about audience engagement in presentations is a valuable asset for presenters in many domains. Prior literature mostly utilized explicit methods of collecting feedback which induce distractions, add workload on audience and do not provide objective information to presenters. We present EngageMeter - a system that allows fine-grained information on audience engagement to be obtained implicitly from multiple brain-computer interfaces (BCI) and to be fed back to presenters for real time and post-hoc access. Through evaluation during an HCI conference (Naudience=11, Npresenters=3) we found that EngageMeter provides value to presenters (a) in real-time, since it allows reacting to current engagement scores by changing tone or adding pauses, and (b) in post-hoc, since presenters can adjust their slides and embed extra elements. We discuss how EngageMeter can be used in collocated and distributed audience sensing as well as how it can aid presenters in long term use.
Endoplasmic reticulum derangement in hypothalamic neurons of rats expressing a familial neurohypophyseal diabetes insipidus mutant vasopressin transgene

Human familial neurohypophyseal diabetes insipidus (FNDI) is an autosomal dominant endocrine disorder that presents in early childhood as excessive drinking and urination as a consequence of a progressive loss of secretion of vasopressin (VP) from posterior pituitary nerve terminals. Mutations in the VP gene have been implicated as the cause of FNDI, but the mechanisms by which these mutants manifest their pathology, and prevent the secretion of the co‐expressed wild‐type protein, are unknown. One hypothesis suggests that mutant precursors are toxic, and stop the synthesis of wild‐type VP by killing expressing cells. Another hypothesis suggests that aberrant interactions between mutant and wild‐type precursors might directly inhibit the elaboration or secretion of the products of the normal allele. We have tested these hypotheses using new animal models‐‐transgenic rats that express an FNDI mutant VP gene that encodes a truncated precursor (Cys67stop). Cell‐specific and inducible expression of the Cys67stop mutation in rat VP hypothalamic neurons does not result in cell death or atrophy. Rather, expression of the FNDI mutant causes a neuronal pathology characterized by distorted structures in the cell body that are labeled by antisera that recognize endoplasmic reticulum (ER) markers, and that accumulate both mutant and wild‐type VP gene products. This is accompanied by an increase in the abundance of the mannose‐6‐phosphate receptor (MPR), a marker of endosome‐lysosome activity. We suggest that FNDI in humans may be initiated, as in our transgenic rat model, by the trapping of wild‐type VP gene products within an ER, which is targeted for lysosomal degradation by autophagy.
I-Vector / PLDA Variants for Text-Dependent Speaker Recognition

The i-vector/PLDA approach currently dominates the field of text-independent speaker recognition and the question of how to translate this methodology to the text-dependent domain has recently become an active area of research. The essential difference between the two fields is that it is possible to do speaker recognition with enrollment and test utterances of very short duration in the text-dependent case but not in the text-independent case. The i-vector representation of short utterances turns out to be very sensitive to their phonetic content and this introduces a major source of nuisance variability when i-vectors are used in text-dependent speaker recognition. We show how, despite this complication, i-vector extractors can be successfully trained on short utterances (rather than on whole conversation sides as is usually done) and how this source of nuisance variability can be dealt with successfully in a PLDA classifier by making the PLDA model parameters phrase-dependent. Our results show that this phrase dependent version of PLDA is capable of outperforming the speaker-phrase version of PLDA prePreprint submitted to Computer, Speech and Language November 6, 2013 sented in [8] on the RSR2015 dataset. We also give a detailed account of uncertainty propagation in PLDA and we show that it combines very successfully with phrase-dependent PLDA.
Localization of the Drosophila Rad9 Protein to the Nuclear Membrane Is Regulated by the C-Terminal Region and Is Affected in the Meiotic Checkpoint

Rad9, Rad1, and Hus1 (9-1-1) are part of the DNA integrity checkpoint control system. It was shown previously that the C-terminal end of the human Rad9 protein, which contains a nuclear localization sequence (NLS) nearby, is critical for the nuclear transport of Rad1 and Hus1. In this study, we show that in Drosophila, Hus1 is found in the cytoplasm, Rad1 is found throughout the entire cell and that Rad9 (DmRad9) is a nuclear protein. More specifically, DmRad9 exists in two alternatively spliced forms, DmRad9A and DmRad9B, where DmRad9B is localized at the cell nucleus, and DmRad9A is found on the nuclear membrane both in Drosophila tissues and also when expressed in mammalian cells. Whereas both alternatively spliced forms of DmRad9 contain a common NLS near the C terminus, the 32 C-terminal residues of DmRad9A, specific to this alternative splice form, are required for targeting the protein to the nuclear membrane. We further show that activation of a meiotic checkpoint by a DNA repair gene defect but not defects in the anchoring of meiotic chromosomes to the oocyte nuclear envelope upon ectopic expression of non-phosphorylatable Barrier to Autointegration Factor (BAF) dramatically affects DmRad9A localization. Thus, by studying the localization pattern of DmRad9, our study reveals that the DmRad9A C-terminal region targets the protein to the nuclear membrane, where it might play a role in response to the activation of the meiotic checkpoint.
Object-Oriented Requirements Engineering for GIS Applications

We stress the importance of requirements engineering (RE) for the development of large scale software in general and for GIS-applications in particular. RE analyses the problem domain and denes the requirements upon the software to be developed. We discuss two GIS-application scenarios, the so-called topological scenario and the network scenario, we frequently encountered in real life projects. Our investigations reveal that conventional RE-methods like e.g. Coad/Yourdon's Object-Oriented Analysis (OOA) are not capable to model GIS-specic requirements appropriately. To overcome the deciencies of conventional OOA we introduce GeoOOA which complements OOA by suitable geoprimitives and allows for a more adequate treatment of GISspeci c requirements. We provide GeoOOA-models of a broadcasting and a power supply domain as rst validation steps for the usefulness of GeoOOA.
A low-jitter and low-power clock generator

This paper presents a phase-locked loop (PLL) for a clock generator in data communication system. The PLL exhibits simultaneously low jitter and low power consumption. Fully integrated loop filter simplifies the peripheral circuit. It has been fabricated using a 0.35 µm BiCMOS process, occupying 0.07 mm2 of silicon area. For a 50 MHz output frequency, the circuit features a 119 ps peak-to-peak jitter. At that frequency, the PLL consumes less than 6mW from a supply voltage of 3.3 V. The clock generator has been successfully applied in a receiver.
Random versus Deterministic Descent in RNA Energy Landscape Analysis

Identifying sets of metastable conformations is a major research topic in RNA energy landscape analysis, and recently several methods have been proposed for finding local minima in landscapes spawned by RNA secondary structures. An important and time-critical component of such methods is steepest, or gradient, descent in attraction basins of local minima. We analyse the speed-up achievable by randomised descent in attraction basins in the context of large sample sets where the size has an order of magnitude in the region of ~106. While the gain for each individual sample might be marginal, the overall run-time improvement can be significant. Moreover, for the two nongradient methods we analysed for partial energy landscapes induced by ten different RNA sequences, we obtained that the number of observed local minima is on average larger by 7.3% and 3.5%, respectively. The run-time improvement is approximately 16.6% and 6.8% on average over the ten partial energy landscapes. For the large sample size we selected for descent procedures, the coverage of local minima is very high up to energy values of the region where the samples were randomly selected from the partial energy landscapes; that is, the difference to the total set of local minima is mainly due to the upper area of the energy landscapes.
Characterizing phonetic convergence with speaker recognition techniques

Speakers are known to accommodate to each other’s behavior when interacting. Information circulates via multiple sensory-motor loops operating at various levels of the interaction and this closed-loop process induces modifications in all levels of representation from social and psychological evaluation to low-level gestural behaviors such as gaze, respiratory patterns, or speech. Various authors have proposed that these representations tend to converge or diverge according to cognitive demand. While quite plausible, claimed observations of such behavior in speech are extremely controversial. The effects are rather small, and are difficult to capture and characterize objectively. This paper focuses on the study of the convergence between phonetic representations – spectral realizations of speech sounds – using automatic classification techniques developed for speech and speaker recognition. Using data collected during a novel language game we term ‘verbal dominoes’, we show that scores are comparable between global techniques and a more fine-grained analysis focused on vocalic segments.
Does the development of non-cash payments affect monetary policy transmission?

The different degree of development of retail payment systems generates significant differences in the structure and operations of the financial system. These differences affect the degree of efficiency of the mechanisms of monetary policy transmission, including the bank-lending channel. Taking the case of Spain for 1992-2000, we show that banks appear to have taken advantage of the non-cash instruments to increase deposits and, therefore, to adjust their loan supply in the face of monetary policy tightening (76 words).
Uses of Online Digital Libraries-a User-Centric Approach

This article presents a study of online digital library (DL) uses. Based on three data sources, it combines social sciences methods and, innovative technology for capturing and processing IP traffic data. The study shows that DL users differ from average Internet users as well as from classical library users, and that their practices involve particular contexts, among which personal researches and bibliophily. These results lead us to reconsider the status of online documents, as well as the relationship between merchant and non-merchant Web sites.
Recognition of Conative and Affective Behavior in Web Learning using Digital Gestures

It is reasonably easy to understand a student when in a classroom. Learners can be uniquely identified, content can be specifically presented, and advancement can be individually supervised, maintained, and reviewed on a regular basis. In addition, study has found that conventional classroom (mainly of cognitive type) solution, are not always workable in the online setting. Lately, companies offering web-learning courses often tend to elucidate on conative and affective attributes of learners. These attributes are more stable over different online learning circumstances. These companies are discovering the requirement to set their focal point on the conative and affective factors that influence learning. Many contemporary researchers have extended their research on learning and memory constructs (and associated measures) to include conative and affective but very few have successfully deciphered these perspectives into technology. Human gestures are nothing but psychosomatic motions, which evolve due to the communication between the mind and body. These Human gestures give rise to digital gestures in an online environment. Keyboard press, mouse movement, page tracking, hyperlink usage, scrolling rate etc. are some of the web usage characteristics. Web Mining is a way to search for "interesting" relationships in web data. It has immense potential in discovering some decisive knowledge like conative and affective elements that will help the company provide learners with better learning experience. Primarily they need to identify the dominant power of emotions and intentions on learning, and then, seek personalized solutions to revolutionize the presentation of learning. This paper highlights on how conative and affective attributes of a learner can be transformed to real-time data and analyzed using Web Mining. Finally, our approach would be to discover several learners with similar psychological characteristics and their grouping will help us achieve mass customization.