Miracle-dz/UniFAR-FTUs huggingface dataset:
Widespread distribution of a lexA‐regulated DNA damage‐inducible multiple gene cassette in the Proteobacteria phylum

The SOS response comprises a set of cellular functions aimed at preserving bacterial cell viability in front of DNA injuries. The SOS network, negatively regulated by the LexA protein, is found in many bacterial species that have not suffered major reductions in their gene contents, but presents distinctly divergent LexA‐binding sites across the Bacteria domain. In this article, we report the identification and characterization of an imported multiple gene cassette in the Gamma Proteobacterium Pseudomonas putida that encodes a LexA protein, an inhibitor of cell division (SulA), an error‐prone polymerase (DinP) and the alpha subunit of DNA polymerase III (DnaE). We also demonstrate that these genes constitute a DNA damage‐inducible operon that is regulated by its own encoded LexA protein, and we establish that the latter is a direct derivative of the Gram‐positive LexA protein. In addition, in silico analyses reveal that this multiple gene cassette is also present in many Proteobacteria families, and that both its gene content and LexA‐binding sequence have evolved over time, ultimately giving rise to the lexA lineage of extant Gamma Proteobacteria.
The Multiplicities of Internet Addiction: The Misrecognition of Leisure and Learning

Contents: Introduction: addiction: it got your attention Internet addiction: contrasting viewpoints When do we say 'too much'? Being cautious about 'over-use' and virtual reality Technological development and childhood play: the changing nature of everyday leisure Bordieu's theory of practice Introducing some teenage technological experts: digital insiders The blur between leisure, learning and expertise Internet addiction in the lives of teenagers New forms of privilege Misrecognition of the practice of leisure Conclusion: reframing our gaze on internet addiction References Index.
Development of Two Steradian EDX System for the HD-2700 FE-STEM Equipped with Dual X-MaxN 100 TLE Large Area Windowless SDDs

The model HD-2700 [1] 200 kV spherical aberration (Cs) corrected dedicated Scanning Transmission Electron Microscope (STEM) has been used for analyzing nanoto subnano-area targets in the fields of nanoscience and nanotechnology with Energy Dispersive X-ray spectrometry (EDX). The Cs corrector [2] enables the formation of sub-nanometer probe size with several hundred to a thousand pico amperes of probe current, but still EDX detectors with much higher sensitivity are desired. Recent adaptation of Silicon Drift Detector (SDD) technology [3] accelerated the counting rate of detection and enhancement of detector active area. These features are suitable to improve analytical sensitivity. Using a windowless high solid angle SDD, high sensitivity elemental analysis can be achieved [4].
‘I Already Know the City, I Don't Have to Explore it’: Adjustments to ‘Sense of Place’ for ‘Local’ UK University Students

Recent conceptualisations of place have sought to reconsider place as being the sedentary equivalent to mobility, instead recognising its dynamism and its potential for evoking powerful emotional responses. These notions hold particular resonance in the realm of higher education, with discussions emerging of the important influence place may hold for students as they progress through university. Although this has been recognised from the perspective of ‘mobile’ students, what is less clear is how these notions of place might influence the trajectories of ‘local’ students, specifically how feelings of place disruption or identity dislocation might spill out into their non-student lives and their wider sense of ‘being’ students (or non-students) within what are often highly emotive and memory-laden places. This is important as the process of ‘re-sensing’ place through the lens of a student may challenge long-established conceptions of the city for ‘local’ students. Copyright © 2014 John Wiley & Sons, Ltd.
Variational nonparametric Bayesian Hidden Markov Model

The Hidden Markov Model (HMM) has been widely used in many applications such as speech recognition. A common challenge for applying the classical HMM is to determine the structure of the hidden state space. Based on the Dirichlet Process, a nonparametric Bayesian Hidden Markov Model is proposed, which allows an infinite number of hidden states and uses an infinite number of Gaussian components to support continuous observations. An efficient variational inference method is also proposed and applied on the model. Our experiments demonstrate that the variational Bayesian inference on the new model can discover the HMM hidden structure for both synthetic data and real-world applications.
Gay Male Aging: A Review of the Literature

Abstract A review of empirically-based publications on gay male gerontology was conducted. A total of 58 articles is presented. Findings indicate that the long-held negative stereotypes of gay male elders are unwarranted and have been repeatedly debunked. Happiness and successful adaptation to old age are possible and are frequently reported by older gay men. Substantial and unique competencies in old age that may be useful in addressing the needs of aging populations in general are identifiable in this population. A discussion of needs for future research and a brief methodological critique are given.
Using XDMoD to facilitate XSEDE operations, planning and analysis

The XDMoD auditing tool provides, for the first time, a comprehensive tool to measure both utilization and performance of high-end cyberinfrastructure (CI), with initial focus on XSEDE. Here, we demonstrate, through several case studies, its utility for providing important metrics regarding resource utilization and performance of TeraGrid/XSEDE that can be used for detailed analysis and planning as well as improving operational efficiency and performance. Measuring the utilization of high-end cyberinfrastructure such as XSEDE helps provide a detailed understanding of how a given CI resource is being utilized and can lead to improved performance of the resource in terms of job throughput or any number of desired job characteristics. In the case studies considered here, a detailed historical analysis of XSEDE usage data using XDMoD clearly demonstrates the tremendous growth in the number of users, overall usage, and scale of the simulations routinely carried out. Not surprisingly, physics, chemistry, and the engineering disciplines are shown to be heavy users of the resources. However, as the data clearly show, molecular biosciences are now a significant and growing user of XSEDE resources, accounting for more than 20 percent of all SUs consumed in 2012. XDMoD shows that the resources required by the various scientific disciplines are very different. Physics, Astronomical sciences, and Atmospheric sciences tend to solve large problems requiring many cores. Molecular biosciences applications on the other hand, require many cycles but do not employ core counts that are as large. Such distinctions are important in guiding future cyberinfrastructure design decisions. XDMoD's implementation of a novel application kernel-based auditing system to measure overall CI system performance and quality of service is shown, through several examples, to provide a useful means to automatically detect under performing hardware and software. This capability is especially critical given the complex composition of today's advanced CI. Examples include an application kernel based on a widely used quantum chemistry program that uncovered a software bug in the I/O stack of a commercial parallel file system, which was subsequently fixed by the vendor in the form of a software patch that is now part of their standard release. This error, which resulted in dramatically increased execution times as well as outright job failure, would likely have gone unnoticed for sometime and was only uncovered as a result of implementation of XDMoD's suite of application kernels.
Entropy-based subspace clustering for mining numerical data

Mining numerical data is a relatively difficult problem in data mining. Clustering is one of the techniques. We consider a database with numerical attributes, in which each transaction is viewed as a multi-dimensional vector. By studying the clusters formed by these vectors, we can discover certain behaviors hidden in the data. Traditional clustering algorithms find clusters in the full space of the data sets. This results in high dimensional clusters, which are poorly comprehensible to human. One important task in this setting is the ability to discover clusters embedded in the subspaces of a high-dimensional data set. This problem is known as subspace clustering. We follow the basic assumptions of previous work CLIQUE. It is found that the number of subspaces with clustering is very large, and a criterion called the coverage is proposed in CLIQUE for the pruning. In addition to coverage, we identify new useful criteria for this problem and propose an entropybased algorithm called ENCLUS to handle the criteria. Our major contributions are: (1) identify new meaningful criteria of high density and correlation of dimensions for goodness of clustering in subspaces, (2) introduce the use of entropy and provide evidence to support its use, (3) make use of two closure properties based on entropy to prune away uninteresting subspaces efficiently, (4) propose a mechanism to mine non-minimally correlated subspaces which are of interest because of strong clustering, (5) experiments are carried out to show the effectiveness of the proposed method.
A novel benchmark on human activity recognition using WiFi signals

WiFi-based Human activity recognition has attracted attention in the human-computer interaction, smart homes, and security monitoring fields. We first construct a WiFi-based activity dataset, namely WiAR, to provide a benchmark for existing works. Then, we leverage the moving variance of CSI to detect the start and end of activity. Moreover, we present K-means-based subcarrier selection mechanism according to subcarrier's sensitivity on human activity to enhance the robustness of human activity recognition. Finally, we leverage several classification algorithms to evaluate the performance of WiAR. Our results show that WiAR satisfies primary demand and achieves an average accuracy of greater than 93% using SVM, 80% using kNN, Random forest, and Decision tree.
Modal Specifications of Trace-Based Security Properties

We introdu e a multi-modal logi that ombines omplementary features of authenti ation logi s and tra e-based approa hes. Our logi ontains two kinds of modalities: impli it belief, whi h formalizes the view of an external agent reasoning about interleaved proto ol exe utions, and expli it belief, whi h uses awareness to model the resour e-bounded reasoning of the agents involved in the exe utions. We employ these modalities to formalize extensional and intensional spe i ations of proto ols and their properties, and use these formalizations to hara terize and reason about atta ks. As an example, we onsider the Needham-S hroeder Publi Key proto ol and use our logi to demonstrate the existen e of the well-known man-in-the-middle atta k, and also show the equivalen e of our modal spe i ation to one based on an interleaved tra e semanti s.
Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the "Always Connected Era"

The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes, have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users. By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of "always connected" terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users' locations.
Infusing Gender and Diversity Issues into Educational Leadership Programs: Transformational Learning and Resistance.

Purpose – The purpose of this article is to consider the impact of incorporating a set of readings focused on issues of gender, diversity, leadership, and feminist thought into the curriculum of a statewide educational leadership doctoral program.Design/methodology/approach – Based data from open‐ended surveys, semi‐structured interviews, and reflection statements, the article presents a qualitative analysis of how students react to, learn from, and resist social justice‐oriented curricula and teaching strategies, particularly those related to gender issues.Findings – The analysis of the data collected in this research suggests that, after a year of exposure to readings and written assignments about gender and other diversity issues, few students had undergone significant transformations in their learning regarding gender issues. Moreover, it was found that many students demonstrated resistance to reading, reflecting on and discussing gender issues.Originality/value – Programs and professors that endeavor.
Applications of nonverbal behavioral theories and research

Contents: Preface. Part I: Social Relationships. R.E. Riggio, Social Interaction Skills and Nonverbal Behavior. P. Noller, Nonverbal Communication in Marriage. Part II: Business and the Law. P.J. DePaulo, Applications of Nonverbal Behavior Research in Marketing and Management. P.D. Blanck, R. Rosenthal, Nonverbal Behavior in the Courtroom. Part III: Health. D.B. Buller, R.L. Street, Jr., Physician-Patient Relationships. L. Tickle-Degnen, R. Rosenthal, Nonverbal Aspects of Therapeutic Rapport. Part IV: Education. E. Babad, Teacher Expectancies and Nonverbal Behavior. P. Philippot, R.S. Feldman, G. McGee, Nonverbal Behavioral Skills in an Educational Context: Typical and Atypical Populations. Part V: Society and Culture. G.R. Miller, J.B. Stiff, Applied Issues in Studying Deceptive Communication. M.E. Lee, D. Matsumoto, M. Kobayashi, D. Krupp, E.F. Maniatis, W. Roberts, Cultural Influences on Nonverbal Behavior in Applied Settings.
Throughput maximization for online request admissions in mobile cloudlets

In mobile cloud computing (MCC) paradigm, cloud service providers not only offer powerful cloud data centers but also provide small-scale cloudlets in some strategic locations for mobile users to access their rich resources. Due to the flexibility and locality of cloudlets, most requests of mobile users can be processed locally. However, the cloudlets usually have limited resources and processing abilities, which implies that they may not be capable to process every incoming request. Instead, some resource-intensive requests need to be sent to remote data centers for processing and such a processing is transparent to users. In this paper, we address the online request admission issue in a cloudlet with an objective to maximize the system throughput, for which we first propose a novel admission cost model to model critical resource consumptions. We then devise efficient control algorithms for online request admissions. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithms. Experimental results indicate that the proposed algorithms are promising and outperform other heuristics.
Signal-specialized parameterization for piecewise linear reconstruction

We propose a metric for surface parameterization specialized to its signal that can be used to create more efficient, high-quality texture maps. Derived from Taylor expansion of signal error, our metric predicts the signal approximation error - the difference between the original surface signal and its reconstruction from the sampled texture. Unlike previous methods, our metric assumes piecewise-linear reconstruction, and thus makes a good approximation to bilinear reconstruction employed in graphics hardware. We achieve significant savings in texture area for a desired signal accuracy compared to the signal-specialized parameterization metric proposed by Sander et al. in the 2002 Eurographics Workshop on Rendering.
Genetic Alterations in p 53 , INK 4 A , and HRas Genes along Defined Stages of Malignant Transformation

The difficulty to dissect a complex phenotype of established malignant cells to several critical transcriptional programs greatly impends our understanding of the malignant transformation. The genetic elements required to transform some primary human cells to a tumorigenic state were described in several recent studies. We took the advantage of the global genomic profiling approach and tried to go one step further in the dissection of the transformation network. We sought to identify the genetic signatures and key target genes, which underlie the genetic alterations in p53 , Ras , INK4A locus, and telomerase , introduced in a stepwise manner into primary human fibroblasts. Here, we show that these are the minimally required genetic alterations for sarcomagenesis in vivo . A genome-wide expression profiling identified distinct genetic signatures corresponding to the genetic alterations listed above. Most importantly, unique transformation hallmarks, such as differentiation block, aberrant mitotic progression, increased angiogenesis, and invasiveness, were identified and coupled with genetic signatures assigned for the genetic alterations in the p53 , INK4A locus, and H-Ras , respectively. Furthermore, a transcriptional program that defines the cellular response to p53 inactivation was an excellent predictor of metastasis development and bad prognosis in breast cancer patients. Deciphering these transformation fingerprints, which are affected by the most common oncogenic mutations, provides considerable insight into regulatory circuits controlling malignant transformation and will hopefully open new avenues for rational therapeutic decisions. (Cancer Res 2005; 65(11): 4530-43)
The Role of the Hippocampus in Avoidance Learning and Anxiety Vulnerability

The hippocampus has been implicated in anxiety disorders and post-traumatic stress disorder (PTSD); human studies suggest that a dysfunctional hippocampus may be a vulnerability factor for the development of PTSD. In the current study, we examined the effect of hippocampal damage in avoidance learning, as avoidance is a core symptom of all anxiety disorders. First, the effect of hippocampal damage on avoidance learning was investigated in outbred Sprague Dawley (SD) rats. Second, the function of the hippocampus in Wistar-Kyoto (WKY) rats was compared to SD rats. The WKY rat is an animal model of behavioral inhibition, a risk factor for anxiety, and demonstrates abnormal avoidance learning, marked by facilitated avoidance acquisition and resistance to extinction. The results of the current study indicate that hippocampal damage in SD rats leads to impaired extinction of avoidance learning similar to WKY rats. Furthermore, WKY rats have reduced hippocampal volume and impaired hippocampal synaptic plasticity as compared to SD rats. These results suggest that hippocampal dysfunction enhances the development of persistent avoidance responding and, thus, may confer vulnerability to the development of anxiety disorders and PTSD.
Emotions and fashion recommendations: evaluating the predictive power of affective information for the prediction of fashion product preferences in cold-start scenarios

Emotions have a significant impact on the purchasing process. Due to novel affective computing approaches, affective information of users can be acquired in implicit and therefore non-intrusive manner. Recent research in the field of recommender systems indicates that the incorporation of affective user information in the prediction model has a positive impact on the recommender systems accuracy. Existing research mainly focused on product recommendations in the movie anfd music domain. Our paper investigates the impact of affective emotions on fashion products, which is one of the largest consumer industries. We integrate the users' mood and their emotion in the prediction model, and the results are compared to the baseline model using rating data only. For this, we generate a dataset with 337 participants, 64 products, and 10816 ratings. We determine the mood information using the PANAS questionnaire, and the emotion by using the SAM self-assessment method. The affective information is integrated leveraging Factorization Machines. The evaluation of the offline experiments reveals that in new item cold-start scenarios the mood information has a positive impact on the prediction accuracy, whereas the emotion information has a negative impact.
A scoping review of rehabilitation interventions that reduce fatigue among adults with multiple sclerosis

Abstract Objective: To identify and summarize available research literature about rehabilitation interventions for multiple sclerosis (MS) fatigue management. Methods: Database searches (PubMed, EMBASE, CINAHL and PsychINFO) were conducted in October 2011 and last updated in July 2013. To be included, studies must have been published in a peer-reviewed scientific journal, written in English and included an intervention to manage MS fatigue. Effect size (ES) were calculated for the quantitative studies to estimate intervention effects, and major themes were summarized for the qualitative studies. Results: Thirty-eight studies were included in this review. A variety of exercise and behavior change interventions were prescribed to adults with MS. The two most common interventions were progressive resistive training and fatigue management programs. Three exercise intervention studies and nine behavior change intervention studies with quantitative data presented significant ES. Four studies with qualitative data supported the positive impact of certain exercise and behavior change interventions. Conclusions: This review identified a variety of exercise and behavior change interventions for MS fatigue management. While the findings may provide helpful information to inform practice, future researchers need to develop and evaluate knowledge translation strategies to facilitate the application of this evidence to daily practice to advance MS rehabilitation care. Implications for Rehabilitation Both exercise and behavior change interventions demonstrate some degree of effectiveness for managing MS fatigue. Effect sizes for exercise and behavior change interventions are similar, although the populations examined are different. Overall, evidence for exercise focuses on people who are less disabled, while evidence for behavior change interventions includes a broader population. Future researchers need to develop and evaluate knowledge translation strategies that facilitate application of evidence in daily practice in order to advance MS rehabilitation.