Miracle-dz/UniFAR-FTUs huggingface dataset:
AUTOMATIC DETECTION OF LEARNER'S AFFECT FROM GROSS BODY LANGUAGE

We explored the reliability of detecting learners' affect by monitoring their gross body language (body position and arousal) during interactions with an intelligent tutoring system called AutoTutor. Training and validation data on affective states were collected in a learning session with AutoTutor, after which the learners' affective states (i.e., emotions) were rated by the learner, a peer, and two trained judges. An automated body pressure measurement system was used to capture the pressure exerted by the learner on the seat and back of a chair during the tutoring session. We extracted two sets of features from the pressure maps. The first set focused on the average pressure exerted, along with the magnitude and direction of changes in the pressure during emotional experiences. The second set of features monitored the spatial and temporal properties of naturally occurring pockets of pressure. We constructed five data sets that temporally integrated the affective judgments with the two sets of pressure features. The first four datasets corresponded to judgments of the learner, a peer, and two trained judges, whereas the final data set integrated judgments of the two trained judges. Machine-learning experiments yielded affect detection accuracies of 73%, 72%, 70%, 83%, and 74%, respectively (chance = 50%) in detecting boredom, confusion, delight, flow, and frustration, from neutral. Accuracies involving discriminations between two, three, four, and five affective states (excluding neutral) were 71%, 55%, 46%, and 40% with chance rates being 50%, 33%, 25%, and 20%, respectively.
Apopain/CPP32 cleaves proteins that are essential for cellular repair: a fundamental principle of apoptotic death

Proteolysis mediated by the interleukin 1 beta-converting enzyme (ICE) homologues is an important mechanism of the apoptotic process. The ICE homologue apopain/CPP-32/Yama (subsequently referred to as apopain) cleaves poly(ADP-ribose)polymerase (PARP) early during apoptosis. Additional apoptosis-specific protein cleavages have been observed in which the direct involvement of ICE-like proteases has been postulated. These substrates include the 70-kD protein component of the U1- ribonucleoprotein (U1-70kD), and the catalytic subunit of the DNA- dependent protein kinase (DNA-PKcs). The present studies demonstrate that U1-70kD and DNA-PKcs are excellent substrates for apopain, with cleavage occurring at sites that are highly similar to the cleavage site within PARP. The fragments generated from isolated protein substrates by apopain are identical to those observed in intact apoptotic cells, in apoptotic cell extracts, and in normal cell extracts to which apopain has been added. Like PARP, cleavage of these substrates in apoptotic cell extracts is abolished by nanomolar concentrations of Ac-DEVD-CHO and micromolar amounts of Ac-YVAD-CHO, confirming the involvement of apopain or an apopain-like activity. We propose that a central function of apopain or similar homologues in apoptosis is the cleavage of nuclear repair proteins, thereby abolishing their critical homeostatic functions.
Mechanisms of Resistance to Chloramphenicol in Pseudomonas putida KT2440

ABSTRACT Pseudomonas putida KT2440 is a chloramphenicol-resistant bacterium that is able to grow in the presence of this antibiotic at a concentration of up to 25 μg/ml. Transcriptomic analyses revealed that the expression profile of 102 genes changed in response to this concentration of chloramphenicol in the culture medium. The genes that showed altered expression include those involved in general metabolism, cellular stress response, gene regulation, efflux pump transporters, and protein biosynthesis. Analysis of a genome-wide collection of mutants showed that survival of a knockout mutant in the TtgABC resistance-nodulation-division (RND) efflux pump and mutants in the biosynthesis of pyrroloquinoline (PQQ) were compromised in the presence of chloramphenicol. The analysis also revealed that an ABC extrusion system (PP2669/PP2668/PP2667) and the AgmR regulator (PP2665) were needed for full resistance toward chloramphenicol. Transcriptional arrays revealed that AgmR controls the expression of the pqq genes and the operon encoding the ABC extrusion pump from the promoter upstream of open reading frame (ORF) PP2669.
Quantile regression with varying coefficients

Quantile regression provides a framework for modeling statistical quantities of interest other than the conditional mean. The regression methodology is well developed for linear models, but less so for nonparametric models. We consider conditional quantiles with varying coefficients and propose a methodology for their estimation and assessment using polynomial splines. The proposed estimators are easy to compute via standard quantile regression algorithms and a stepwise knot selection algorithm. The proposed Rao-score-type test that assesses the model against a linear model is also easy to implement. We provide asymptotic results on the convergence of the estimators and the null distribution of the test statistic. Empirical results are also provided, including an application of the methodology to forced expiratory volume (FEV) data.
Efficient And Robust Shape Signatures For Object Recognition

Content Based Image Retrieval (CBIR) is an important issue in the computer vision community. Both visual and textual descriptions are employed when the user formulates his queries. Shape feature is one of the most important visual features. The shape feature is essential as it corresponds to the region of interest in images. Consequently, the shape representation is fundamental. The shape comparisons must be compact and accurate, and must own properties of invariance to several geometric transformations such as translation, rotation and scaling, though the representation itself may be variant to rotation. This paper presents simple, efficient and shape descriptors for efficient image mining. The main strength of the method is its simplicity. KeywordsDistance mapping, Image mining, Moment invariance, Object Recognition, Shape Descriptor, Shape Recognition, Shape signature.
Characterization of a Novel Recombinant Hyaluronan Binding Protein for Tissue Hyaluronan Detection

Tumor necrosis factor-Stimulated Gene 6 protein (TSG-6) is a hyaluronan (HA)-binding glycoprotein containing an HA-binding Link module. Because of its well-defined structure, HA binding properties and small size, TSG-6 is an excellent candidate as an alternative to animal-derived HA-binding protein (HABP) for the detection of HA. The present work describes the generation and characterization of a novel recombinant HA-binding probe obtained by fusion of a modified TSG-6 Link module with mutationally inactivated heparin-binding sequence and the Fc portion of human IgG1 (TSG-6-ΔHep-Fc) for tissue HA detection in histological samples. Direct binding assays indicated strong binding of TSG-6-ΔHep-Fc to HA, with little residual binding to heparin. Histolocalization of HA in formalin-fixed, paraffin-embedded tissue sections using biotin-TSG-6-ΔHep-Fc resulted in hyaluronidase-sensitive staining patterns similar to those obtained with biotin-HABP, but with improved sensitivity. HA was detected in many human tissues, and was most abundant in soft connective tissues such as the skin dermis and the stroma of various glands. Digital image analysis revealed a linear correlation between biotin-HABP and biotin-TSG-6-ΔHep-Fc staining intensity in a subset of normal and malignant human tissues. These results demonstrate that TSG-6-ΔHep-Fc is a sensitive and specific probe for the detection of HA by histological methods.
An Approach for Classification of Dysfluent and Fluent Speech Using K-NN And SVM

This paper presents a new approach for classification of dysfluent and fluent speech using Mel-Frequency Cepstral Coefficient (MFCC). The speech is fluent when person's speech flows easily and smoothly. Sounds combine into syllable, syllables mix together into words and words link into sentences with little effort. When someone's speech is dysfluent, it is irregular and does not flow effortlessly. Therefore, a dysfluency is a break in the smooth, meaningful flow of speech. Stuttering is one such disorder in which the fluent flow of speech is disrupted by occurrences of dysfluencies such as repetitions, prolongations, interjections and so on. In this work we have considered three types of dysfluencies such as repetition, prolongation and interjection to characterize dysfluent speech. After obtaining dysfluent and fluent speech, the speech signals are analyzed in order to extract MFCC features. The k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers are used to classify the speech as dysfluent and fluent speech. The 80% of the data is used for training and 20% for testing. The average accuracy of 86.67% and 93.34% is obtained for dysfluent and fluent speech respectively.
Detecting Gene Duplications in the Human Lineage

Gene duplications represent an important class of evolutionary events that is likely to have contributed to the unique human phenotype in the short evolutionary time since the human‐chimpanzee divergence. With the availability of both human and chimpanzee genome drafts in high coverage re‐sequencing assemblies and the high annotation quality of most human genes, it should now be possible to identify all human lineage‐specific gene duplication events (human inparalogues) and a few pioneering studies have attempted to do that. However, the different levels of coverage in the human and chimpanzee's genomes assemblies, and the differing levels of gene annotation, have led to problematic assumptions and oversimplifications in the algorithms and the datasets used to detect human lineage‐specific gene duplications. In this study, we have developed a set of bioinformatic tools to overcome a number of the conceptual problems that are prevalent in previous studies and have collected a reliable and representative set of human inparalogues.
An approach to classify polarimetric P-band SAR images for land use and land cover mapping in the

In this paper the potentiality of polarimetric P-band SAR data for Amazon tropical forest land cover mapping is assessed. The classifying approach is based on the Iterative Conditional Mode (ICM) algorithm, taking into account several specific distributions to SAR data. Distinct land cover classes are modeled considering different distributions. The results show that the P-band data is not capable to discriminate the nine classes initially used. However this capability improves significantly when classes having similar vegetation structure are grouped. The HV image is effective in differentiating primary and very old regeneration forest areas from other land cover classes, while W image increases the classification of bare soil and crop/pasture areas. The results show the importance of polarimetric information for the classification of several land use classes.
Evidence for Multiple Stages in the Processing of Ambiguous Words in Syntactic Contexts

A variable time delay naming latency paradigm was used to investigate the processing of noun-verb lexical ambiguities (e.g., watch) in syntactic contexts which either biased the noun or verb reading (e.g., I bought the watch; I will watch). Target words related to either the noun or verb reading were presented at 0,200, and 600 msec following the sentence-final ambiguous word:. At 0 msec, naming latencies related to either reading were facilitated regardless of the biasing context. By 200msec, facilitation obtained only for targets related to the reading of the ambiguous word biased by the context. The results support a two-stage model in which all readings of ambiguous words are initially accessed and then the inappropriate readings are rapidly suppressed.
Is Ecb Communication Effective? Acknowledgements the Authors Thank Seminar Participants at the Lse Money/macro Work-in-progress and Especially

In its Monthly Bulletin of November 2002, the European Central Bank (ECB) stated that the monthly press conference held by its President represents one of its most important communication channels and that it provides a comprehensive summary of the policy relevant assessment of economic developments. After providing a glossary to translate the qualitative information of the press conferences into an ordered scale, we verify empirically whether and to what extent market expectations react to the information released by the ECB. We found that the public not only understand but also believe the signals sent by the European monetary authority.
Design, Synthesis, Antimycobacterial Evaluation, and In Silico Studies of 3-(Phenylcarbamoyl)-pyrazine-2-carboxylic Acids

Pyrazinamide, the first-line antitubercular drug, has been regarded the basic component of tuberculosis treatment for over sixty years. Researchers have investigated its effect on Mycobacterium tuberculosis for this long time, and as a result, new potential targets of pyrazinamide or its active form, pyrazinoic acid, have been found. We have designed and prepared 3-(phenyl-carbamoyl)pyrazine-2-carboxylic acids as more lipophilic derivatives of pyrazinoic acid. We also prepared methyl and propyl derivatives as prodrugs with further increased lipophilicity. Antimycobacterial, antibacterial and antifungal growth inhibiting activity was investigated in all prepared compounds. 3-[(4-Nitrophenyl)carbamoyl]pyrazine-2-carboxylic acid (16) exerted high antimycobacterial activity against Mycobacterium tuberculosis H37Rv with MIC = 1.56 μg·mL−1 (5 μM). Propyl 3-{[4-(trifluoromethyl)phenyl]carbamoyl}pyrazine-2-carboxylate (18a) showed also high antimycobacterial activity against Mycobacterium tuberculosis H37Rv with MIC = 3.13 μg·mL−1. In vitro cytotoxicity of the active compounds was investigated and no significant cytotoxic effect was observed. Based to structural similarity to known inhibitors of decaprenylphosphoryl-β-d-ribose oxidase, DprE1, we performed molecular docking of the prepared acids to DprE1. These in silico experiments indicate that modification of the linker connecting aromatic parts of molecule does not have any negative influence on the binding.
pH-dependent Substrate Preference of Pig Heart Lipoamide Dehydrogenase Varies with Oligomeric State

Cycling of intracellular pH has recently been shown to play a critical role in ischemia-reperfusion injury. Ischemia-reperfusion also leads to mitochondrial matrix acidification and dysfunction. However, the mechanism by which matrix acidification contributes to mitochondrial dysfunction, oxidative stress, and the resultant cellular injury has not been elucidated. We observe pH-dependent equilibria between monomeric, dimeric, and a previously undescribed tetrameric form of pig heart lipoamide dehydrogenase (LADH), a mitochondrial matrix enzyme. Dynamic light scattering studies of native LADH in aqueous solution indicate that lowering pH favors a shift in average molecular mass from higher oligomeric states to monomer. Sedimentation velocity of LADH entrapped in reverse micelles reveals dimer and tetramer at both pH 5.8 and 7.5, but monomer was observed only at pH 5.8. Enzyme activity measurements in reverse Aerosol OT micelles in octane indicate that LADH dimer and tetramer possess lipoamide dehydrogenase and diaphorase activities at pH 7.5. Upon acidification to pH 5.8 only the LADH monomer is active and only the diaphorase activity is observed. These results indicate a correlation between pH-dependent changes in the LADH reaction specificity and its oligomeric state. The acidification of mitochondrial matrix that occurs during ischemia-reperfusion injury is sufficient to alter the structure and enzymatic specificity of LADH, thereby reducing mitochondrial defenses, increasing oxidative stress, and slowing the recovery of energy metabolism. Matrix acidification may also disrupt the quaternary structure of other mitochondrial protein complexes critical for cellular homeostasis and survival.
Proactive behavior of an autonomous mobile robot for human-assisted learning

During the last decade, there has been a growing interest in making autonomous social robots able to interact with people. However, there are still many open issues regarding the social capabilities that robots should have in order to perform these interactions more naturally. In this paper we present the results of several experiments conducted at the Barcelona Robot Lab in the campus of the “Universitat Politècnica de Catalunya” in which we have analyzed different important aspects of the interaction between a mobile robot and nontrained human volunteers. First, we have proposed different robot behaviors to approach a person and create an engagement with him/her. In order to perform this task we have provided the robot with several perception and action capabilities, such as that of detecting people, planning an approach and verbally communicating its intention to initiate a conversation. Once the initial engagement has been created, we have developed further communication skills in order to let people assist the robot and improve its face recognition system. After this assisted and online learning stage, the robot becomes able to detect people under severe changing conditions, which, in turn enhances the number and the manner that subsequent human-robot interactions are performed.
Children and schooling : issues in childhood socialisation

Acknowledgements - Introduction - 1. Perspectives of Childhood: Historical Overview - 2. Schooling - 3. Psychology and Education - 4. Language, Communication and the Curriculum - 5. Socialisation and Achievement - 6. Constraints on the Curriculum - Social-Psychological Issues7. Self-Esteem and Social Learning.Appendix A: A Rough Linguistic/Cognitive Socialisation Chart Showing Changes in Conceptual Growth and Development. Appendix B: The CARALOC Pupil Questionnaire. Appendix C: Twenty Official Rules of the Schools in USSR. Index
Building Attorney Resources: Helping New Lawyers Succeed Through Psychological Capital

Law firms seeking a competitive advantage in the marketplace would do well to consider that the positive psychological resources of firm lawyers are just as important to individual and organizational performance as their intellectual resources. There is growing evidence from the fields of positive psychology and positive organizational behavior that the resources of hope, optimism, self-efficacy and resilience shape the underlying attitudes and behaviors associated with increased performance. These resources may even buffer lawyers against the occupational hazards of the profession that cut against long-term success – hazards that include depression, anxiety, substance abuse and suicide. In this capstone I advocate for traditional law firm associate training programs to be enhanced to include the development of psychological capital (or “PsyCap”), a construct consisting of the components of hope, self-efficacy, resilience, and optimism. I discuss how policies that promote lawyer strengths and well-being are good for the lawyer, good for the law firm and ultimately good for business, and I propose a brief training intervention to boost lawyer PsyCap.
Dynamic performance tuning for speculative threads

In response to the emergence of multicore processors, various novel and sophisticated execution models have been introduced to fully utilize these processors. One such execution model is Thread-Level Speculation (TLS), which allows potentially dependent threads to execute speculatively in parallel. While TLS offers significant performance potential for applications that are otherwise non-parallel, extracting efficient speculative threads in the presence of complex control flow and ambiguous data dependences is a real challenge. This task is further complicated by the fact that the performance of speculative threads is often architecture-dependent, input-sensitive, and exhibits phase behaviors. Thus we propose dynamic performance tuning mechanisms that determine where and how to create speculative threads at runtime. This paper describes the design, implementation, and evaluation of hardware and software support that takes advantage of runtime performance profiles to extract efficient speculative threads. In our proposed framework, speculative threads are monitored by hardware-based performance counters and their performance impact is estimated. The creation of speculative threads is adjusted based on the estimation. This paper proposes speculative threads performance estimation techniques, that are capable of correctly determining whether speculation can improve performance for loops that corresponds to 83.8% of total loop execution time across all benchmarks. This paper also examines several dynamic performance tuning policies and finds that the best tuning policy achieves an overall speedup of 36.8%on a set of benchmarks from SPEC2000 suite, which outperforms static thread management by 9.5%.
Test‐data generation using genetic algorithms

This paper presents a technique that uses a genetic algorithm for automatic test‐data generation. A genetic algorithm is a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem. In the test‐data generation application, the solution sought by the genetic algorithm is test data that causes execution of a given statement, branch, path, or definition–use pair in the program under test. The test‐data‐generation technique was implemented in a tool called TGen, in which parallel processing was used to improve the performance of the search. To experiment with TGen, a random test‐data generator called Random was also implemented. Both Tgen and Random were used to experiment with the generation of test‐data for statement and branch coverage of six programs. Copyright © 1999 John Wiley & Sons, Ltd.
Does the Addition of Writing Into a Pharmacy Communication Skills Course Significantly Impact Student Communicative Learning Outcomes? A Pilot Study

The objective of this study was to determine if the addition of a reflective writing component in a fourth year (P-2) pharmacy communication skills course would significantly affect 2 measures of learning: (1) objective multiple choice examination questions and (2) a patient counseling Objective Structured Clinical Examination (OSCE) score. Using a nonequivalent group quasi-experimental retrospective comparison design, 98 randomly selected final examination scores from students taking a nonwriting intensive (NWI) communication skills course were compared with 112 randomly selected final examination scores from students that took a communication skills course in which students engaged in several reflective writing assignments. In addition, 91 randomly selected patient counseling OSCE scores from a NWI course were statistically compared with 112 scores from students that took the writing intensive (WI) course. There were statistically significant improvements in multiple choice examination scores in the group that took the reflective writing communication skills course. There was not a statistically significant difference in patient counseling OSCE scores after students completed the WI course. Studying the effects of using reflective writing assignments in communication skills courses may improve the retention and retrieval of information presented within the course.