Miracle-dz/UniFAR-FTUs huggingface dataset:
Dominant-Negative FADD Rescues the In Vivo Fitness of a Cytomegalovirus Lacking an Antiapoptotic Viral Gene

ABSTRACT Genes that inhibit apoptosis have been described for many DNA viruses. Herpesviruses often contain even more than one gene to control cell death. Apoptosis inhibition by viral genes is postulated to contribute to viral fitness, although a formal proof is pending. To address this question, we studied the mouse cytomegalovirus (MCMV) protein M36, which binds to caspase-8 and blocks death receptor-induced apoptosis. The growth of MCMV recombinants lacking M36 (ΔM36) was attenuated in vitro and in vivo. In vitro, caspase inhibition by zVAD-fmk blocked apoptosis in ΔM36-infected macrophages and rescued the growth of the mutant. In vivo, ΔM36 infection foci in liver tissue contained significantly more apoptotic hepatocytes and Kupffer cells than did revertant virus foci, and apoptosis occurred during the early phase of virus replication prior to virion assembly. To further delineate the mode of M36 function, we replaced the M36 gene with a dominant-negative FADD (FADDDN) in an MCMV recombinant. FADDDN was expressed in cells infected with the recombinant and blocked the death-receptor pathway, replacing the antiapoptotic function of M36. Most importantly, FADDDN rescued ΔM36 virus replication, both in vitro and in vivo. These findings have identified the biological role of M36 and define apoptosis inhibition as a key determinant of viral fitness.
Proceedings of Olkc 2007 – " Learning Fusion " Learning in Small and Medium Enterprises (smes) - Take Taiwanese Smes as Examples

As organisational learning in small and medium enterprises (SMEs) has been attracted by the academics in recent years, the relevant topics of organisational learning in SMEs are becoming an important issue both in the research of organisational learning and the investigation of SMEs. According to the previous literatures, the quantitative research showed that there are different characters between family business and non-family business, however, the in-depth research is still not much. In this paper, I discuss their different learning formation by comparing organisational learning in family-owned and non-family-owned SMEs. The initial investigation was conducted in five companies (two of them are family business). The analyses of these five cases are based on three themes: owner-managers, organisational culture and external relationship. The results show that the different organisational structure (family-owned and non-family own) may be result in the different formation of organisational learning. The findings of this pilot research have been discussed in this paper and it did need the further research for a more comprehensive conclusion. At the end, I also assume some relevant questions which may help the further discussion.
Evaluation of advanced language modeling techniques for the Slovak LVCSR

In this paper we compare several advanced language modeling techniques for the Slovak continuous speech recognition. Five different language modeling techniques were analyzed, considering their model size and perplexity, speech recognition performance and complexity of their usage in real conditions of speech recognition in Slovak. The preliminary experimental results show that the convenient n-gram models smoothed by the Witten-Bell back-off algorithm produce the best performance according to the model perplexity and recognition accuracy. Other modeling techniques including Maximum Entropy, Power Law Discounting, Hierarchical Pitman-Yor process, or Variable-order Kneser-Ney smoothed models achieved better results only in the model perplexity. However, the increased computational requirements and worse recognition performance limit their usage in the real speech recognition tasks in Slovak.
Vehicle License Plate Character Segmentation Method Based on Watershed Algorithm

Vehicle license plate (VLP) character segmentation is an important procession in the VLP recognition system (VLPRS). Firstly, the binarization image is pre-processed, in which those regions that areas are excessively small and connected with the boundary are eliminated; the left and right boundaries of the first character (Chinese character) and the last one (possibly Chinese character) in the VLP image are obtained, and the auxiliary lines are added to make the character become a connected region. Secondly, the image with the auxiliary lines added is done by Distance Transformation based on City Block to get the corresponding gray image, and then it is segmented by Watershed algorithm (WA). Finally, the boundaries, including left, right, top and bottom ones in the segmented characters, are precisely clipped. This algorithm can address the following problems. First, each part of the Chinese character has disconnected. Second, there is some tilt in the VLP image. Third, there are multi-Chinese characters in the image. The experimental results show the fast processing time and the good performance of the segmentation with respect to other preexisting techniques.
A clearing procedure as a niching method for genetic algorithms

The clearing procedure is a niching method inspired by the principle stated by J.H. Holland (1975) - that of sharing limited resources within subpopulations of individuals characterized by some similarities - but instead of evenly sharing the available resources among the individuals of a subpopulation, the clearing procedure supplies these resources only to the best individuals of each subpopulation. The clearing is naturally adapted to elitist strategies. This can significantly improve the performance of genetic algorithms (GAs) applied to multimodal optimization. Moreover, the clearing procedure allows a GA to efficiently reduce the genetic drift when used with an appropriate selection operator. Some experimental results are presented for a massively multimodal deceptive function optimization.
Enabling Combined Software and Data Engineering: the ALIGNED Suite of Ontologies

Effective, collaborative integration of software and big data engineering for Web-scale systems, is now a crucial technical and economic challenge. This requires new combined data and software engineering processes and tools. Semantic metadata standards and linked data principles, provide a technical grounding for such integrated systems given an appropriate model of the domain. In this paper we introduce the ALIGNED suite of ontologies specifically designed to model the information exchange needs of combined software and data engineering. The models have been deployed to enable: tool-chain integration, such as the exchange of data quality reports; cross-domain communication, such as interlinked data and software unit testing; mediation of the system design process through the capture of design intents and as a source of context for model-driven software engineering processes. These ontologies are deployed in web-scale, data-intensive, system development environments in both the commercial and academic domains. We exemplify the usage of the suite on a complex collaborative software and data engineering scenario from the legal information system domain.
RNA Interference of GADD153 Protects Photoreceptors from Endoplasmic Reticulum Stress-Mediated Apoptosis after Retinal Detachment

Background Apoptosis of photoreceptors plays a critical role in the vision loss caused by retinal detachment (RD). Pharmacologic inhibition of photoreceptor cell death may prevent RD. This study investigated the role of GADD153 that participates in endoplasmic reticulum (ER) stress-mediated apoptosis of photoreceptor cells after RD. Methods Retinal detachment was created in Wistar rats by subretinal injection of hyaluronic acid. The rats were then randomly divided into four groups: normal control group, RD group, GADD153 RNAi group and vehicle group. RNA interference of GADD153 was performed using short hairpin RNA (shRNA). Expressions of GADD153 mRNA and protein were examined by RT-PCR and Western blotting analysis, respectively. GADD153 protein distribution in the retinal cells was observed using immunofluorescence confocal laser scanning microscopy. Apoptosis of retinal cells was determined by TdT-mediated fluorescein-16-dUTP nick-end labeling (TUNEL) assay. Results Lentivirus GADD153 shRNA with the most effective silencing effect was chosen for in vivo animal study and was successfully delivered into the retinal tissues. GADD153 mRNA and protein expressions in GADD153 RNAi group were significantly lower than those in the RD group. Silencing of GADD153 by RNAi protected photoreceptors from ER stress-induced apoptosis. Conclusion ER stress-mediated pathway is involved in photoreceptor cell apoptosis after RD. GADD153 is a key regulatory molecule regulating ER-stress pathways and plays a crucial role in the apoptosis of photoreceptor cells after RD.
User-centered Mobile Technology Intelligence System

Technology Intelligence is an activity that enables companies to identify strategies for their survival and future growth in the market. We introduce a new version of the technology intelligence system, InSciTe adaptive (2012). The system has major approaches of catching users’ intention, giving intuitive insight to users, supporting mobile environment in the technology intelligence field. Finally, this paper presents some improved services and a new service in comparison with the last version of the system, InSciTe advanced (2011).
The wavenumber shift in SAR interferometry

SAR surveys from separate passes show relative shifts of the ground wavenumber spectra that depend on the local slope and the off-nadir angle. The authors discuss the exploitation of this spectral shift for different applications: 1) generation of "low noise" interferograms benefiting phase unwrapping, 2) generation of quick-look interferograms, 3) decorrelation reduction by means of tunable SAR systems (TINSAR), 4) range resolution enhancement, and 5) the combination of SAR data gathered by different platforms (airborne and satellite) for a "long-time coherence" study. >
Scalable Read-mostly Synchronization Using Passive Reader-Writer Locks

Reader-writer locks (rwlocks) aim to maximize parallelism among readers, but many existing rwlocks either cause readers to contend, or significantly extend writer latency, or both. Further, some scalable rwlocks cannot cope with OS semantics like sleeping inside critical sections, preemption and conditional wait. Though truly scalable rwlocks exist, some of them cannot handle preemption, sleeping inside critical sections, or other important functions required inside OS kernels. This paper describes a new rwlock called the passive reader-writer lock (prwlock) that provides scalable read-side performance as well as small writer latency for TSO architectures. The key of prwlock is a version-based consensus protocol between multiple non-communicating readers and a pending writer. Prwlock leverages bounded staleness of memory consistency to avoid atomic instructions and memory barriers in readers' common paths, and uses message-passing (e.g., IPI) for straggling readers so that writer lock acquisition latency can be bounded. Evaluation on a 64-core machine shows that prwlock significantly boosts the performance of the Linux virtual memory subsystem, a concurrent hashtable and an in-memory database.
Motivations for Public Equity Offers: An International Perspective

This paper examines the extent to which investment financing and market-timing explanations motivate public equity offers. We consider a sample of 16,958 initial public offerings and 12,373 seasoned equity offerings from 38 countries between 1990 and 2003. We provide estimates of the change in each accounting variable for each dollar raised in an equity offer, and for each dollar of internally generated cash. Our estimates imply that firms invest 18.8 cents in R&D and 7.3 cents in capital expenditures for an incremental dollar raised in an equity offer during the year following the offer, rising to 84.8 cents and 14.3 cents when the change is measured over a four-year period. These findings are consistent with one motive for the equity offer being to raise capital for investment. However, firms also hold onto much of the cash they raised, and this fraction is higher when the firm has a high q. In addition, firms are more likely to issue secondary shares, which are usually sold by insiders, when q is high, enabling insiders to benefit personally from potential overvaluation. These results suggest that market timing as well as investment financing is a motivation for equity offers.
Redefining College Readiness.

The purpose of this paper is to provide an operational definition of college readiness that differs in scope from current representations of this concept. The paper suggests that, although much has been learned about this phenomenon, particularly during the past  years, few systematic attempts have been made to integrate the various aspects or components of college readiness that have been investigated during this period. Consequently, the term college readiness continues to be defined primarily in terms of high school courses taken and grades received, combined with scores on national tests. Recent research has shed light on several other key components of college success. Most relevant for this paper are a range of cognitive and metacognitive capabilities, often referred to as key cognitive strategies, which have been consistently and emphatically identified by those who teach entry-level college courses as being as of equal or greater importance than any specific content knowledge taught in high school. Examples of key cognitive strategies include analysis, interpretation, precision and accuracy, problem solving, and reasoning. Nearly as important are specific types of content knowledge. Several studies have led to college readiness standards that identify key content knowledge associated with college success. The ability to write well is the single academic skill most closely associated with college success, but the " big ideas " of each content area are also very important elements. Of equal importance are the attitudes and behavioral attributes that successful college students tend to possess. Among these are study skills, time management, awareness of one's performance, persistence, and the ability to utilize study groups. These are both specific skills and more general attitudes, but all of them require high levels of self-awareness and intentionality on the part of students as they enter college. Finally, an increasing number of studies have highlighted the contextual knowledge that a student must possess to be prepared for college. These studies describe the need for students to understand how to apply to college, how to manage financial aid issues, and, perhaps most important, how to adjust to college once they arrive. The transition to college includes an element of culture shock for students, which is more severe for students from some communities than for others. Access to information about the culture of college helps students understand how to interact with professors and peers in college and how to navigate college as a social system and learning environment. …
Web Based Decision Support System for Kidney Exchange

paper provides a framework for web implementation of the different matching algorithms for two way kidney paired donation (KPD). The first accept match, weighted Edmond"s algorithm and optimized matching algorithm are used to test the matching process in KPD. The results of matching algorithms are obtained in terms of number of matches (transplants) that can be made between incompatible patient donor pairs. Also, currently in India, only cadaver transplant program (CTP) and live related donations are legally acceptable. Hence, this study indicates that the number of patients on waiting list can be enormously decreased, if KPD is legalized in India.
Finding optimal local path for mobile robot with kinematic constraints: How to find the safest path for nonholonomic robot

The paper deals with the optimization of local path for the mobile robot, whereas the safety of the route is the major criterion. The robot equipped with differential chassis with kinematic constraints is considered for the task. The optimization is based on genetic algorithm (GA), optimizing the polyline representation of the path. The core idea of the method presented is to sequentially increase the number of key points in the path during the optimization. This way the computational expenses are kept reasonable and resulting path quickly converges to the optimal one, with respect to the actual number of parameters to be optimized. The cost function is defined as the lowest distance from the obstacle along the path. Sequential refinement of key path proved to speed up the search.
Large-treewidth graph decompositions and applications

Treewidth is a graph parameter that plays a fundamental role in several structural and algorithmic results. We study the problem of decomposing a given graph G into node-disjoint subgraphs, where each subgraph has sufficiently large treewidth. We prove two theorems on the tradeoff between the number of the desired subgraphs h, and the desired lower bound r on the treewidth of each subgraph. The theorems assert that, given a graph G with treewidth k, a decomposition with parameters h,r is feasible whenever hr2 ≤ k/polylog(k), or h3r ≤ k/polylog(k) holds. We then show a framework for using these theorems to bypass the well-known Grid-Minor Theorem of Robertson and Seymour in some applications. In particular, this leads to substantially improved parameters in some Erdos-Posa-type results, and faster algorithms for some fixed-parameter tractable problems.
Unsupervised feature learning for content-based histopathology image retrieval

This paper proposes a strategy for content-based image retrieval, which combines unsupervised feature learning (UFL) with the classical bag-of-features (BOF) representation. In BOF, patches are usually represented using standard classical descriptors (i.e., SIFT, SURF, DCT, among others). We propose to use UFL to learn the patch representation itself. This is achieved by applying a topographic UFL method, which automatically learns visual invariance properties of color, scale and rotation from an image collection. The learned image representation is used as input for a multimodal latent semantic indexing system, which enriches the visual representation with semantics from image annotations. The overall strategy is evaluated in a particular histopathology image collection retrieval task, showing that the learned representation has a positive impact in retrieval performance for this particular task.
Translation of Sindbis Subgenomic mRNA is Independent of eIF 2 , eIF 2 A and eIF 2 D

Translation of Sindbis virus subgenomic mRNA (sgmRNA) can occur after inactivation of eIF2 by phosphorylation in mammalian cells. Several studies have suggested that eIF2 can be replaced by eIF2A or eIF2D. HAP1 human cell lines knocked-out for eIF2A, eIF2D or both by CRISPR/Cas9 genome engineering were compared with wild-type (WT) cells to test the potential role of eIF2A and eIF2D in translation. Sindbis virus infection was comparable between the four cell lines. Moreover, synthesis of viral proteins during late stage infection was similar in all four cell lines despite the fact that eIF2α became phosphorylated. These findings demonstrate that eIF2A and eIF2D are not required for the translation of sgmRNA when eIF2α is phosphorylated. Moreover, silencing of eIF2A or eIF2D by transfection of the corresponding siRNAs in HAP1 WT, HAP1-eIF2A− and HAP1-eIF2D− cells had little effect on the synthesis of viral proteins late in infection. Modification of AUGi to other codons in sgmRNA failed to abrogate translation. Sindbis virus replicons containing these sgmRNA variants could still direct the synthesis of viral proteins. No significant differences were found between the cell lines assayed, suggesting that neither eIF2A nor eIF2D are involved in the translation of this sgmRNA bearing non-AUG codons.
Addressing Community Question Answering in English and Arabic

This paper studies the impact of different types of features applied to learning to re-rank questions in community Question Answering. We tested our models on two datasets released in SemEval-2016 Task 3 on "Community Question Answering". Task 3 targeted real-life Web fora both in English and Arabic. Our models include bag-of-words features (BoW), syntactic tree kernels (TKs), rank features, embeddings, and machine translation evaluation features. To the best of our knowledge, structural kernels have barely been applied to the question reranking task, where they have to model paraphrase relations. In the case of the English question re-ranking task, we compare our learning to rank (L2R) algorithms against a strong baseline given by the Google-generated ranking (GR). The results show that i) the shallow structures used in our TKs are robust enough to noisy data and ii) improving GR is possible, but effective BoW features and TKs along with an accurate model of GR features in the used L2R algorithm are required. In the case of the Arabic question re-ranking task, for the first time we applied tree kernels on syntactic trees of Arabic sentences. Our approaches to both tasks obtained the second best results on SemEval-2016 subtasks B on English and D on Arabic.
Power Control in Opportunistic Spectrum Access Cognitive Radio with Sensing Information at Transmitter

In an opportunistic spectrum access based cognitive radio network, the secondary user (SU) does not transmit when the primary user (PU) is detected to be present. If the SU detects that the PU is inactive during a sensing period, it is usually assumed that the SU will transmit its data at peak power in the following data transmission frame. However, when the PU is detected to be inactive, the PU may not definitely be inactive as it may be a case of miss-detection. If it is a miss-detection, the SU's data rate for that particular data frame will be reduced due to interference from the PU. In this paper, power allocation strategies are designed for each data frame based on the spectrum sensing information (SSI) gathered during the sensing period. Using the SSI, the miss-detection probability for a data frame is taken into consideration to maximize the average data rate and minimize the outage probability of the SU while ensuring that the probability of detecting the PU is above a targeted threshold and the SU satisfies the long-term transmit power budget. Our simulation results showed that the transmit power of the SU should indeed be a function of the SSI in order to maximize the average data rate and minimize the outage probability.
A Robust and Compact Fiber Bragg Grating Vibration Sensor for Seismic Measurement

A compact fiber Bragg grating (FBG) vibration sensor consisting a flat diaphragm and two L-shaped rigid cantilever beams for seismic measurement has been proposed and experimentally demonstrated. The specially designed sensing configuration contributes many desirable features such as a wide frequency response range (10-120 Hz), an extremely high sensitivity coefficient (~100pm/g) together with a robust metal package for improving the mechanical strength and a decreased transverse sensitivity (<; 5%), making it a good candidate for in-field seismic wave measurement in oil and gas exploration.
Estimation and Hypothesis Testing for the Loglinear Model

In recent years, the loglinear model has been proposed and used for analysing frequency data in multidimensional contingency tables. The primary focus of the literature has been on model building and only secondarily on hypothesis testing and estimation. This paper extends Kennedy's (1988) description by presenting post hoc procedures for statistically evaluating treatment effects, contrasts, and confidence intervals. It illustrates methods for main effect and interaction contrasts and pays special attention to odds ratios and their interval estimates. Procedures are described for treating the variables as interdependent and for the case where there are independent and dependent variables—both ordered and unordered.