Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic diversity and expression of the [NiFe] hydrogenase large-subunit gene of Desulfovibrio spp. in environmental samples

The genetic diversity and expression of the [NiFe] hydrogenase large-subunit gene of Desulfovibrio spp. in environmental samples were determined in order to show in parallel the existing and active members of Desulfovibrio populations. DNA and total RNA were extracted from different anaerobic bioreactor samples; RNA was transcribed into cDNA. Subsequently, PCR was performed to amplify a ca. -440-bp fragment of the [NiFe] hydrogenase large-subunit gene and its mRNA. Denaturing gradient gel electrophoresis analysis was used to separate the PCR products according to their sequence and thereby to visualize the individual community members. Desulfovibrio strains corresponding to amplified [NiFe] hydrogenase transcripts were regarded as metabolically active, because in pure cultures transcripts were detectable in exponentially growing cells but not in cultures in the stationary phase. DNA sequencing and comparative sequence analysis were used to identify the detected organisms on the basis of their [NiFe] hydrogenase sequences. The genes of characterized Desulfovibrio spp. showed a considerable extent of divergence (ca. 30%), whereas sequences obtained from bacterial populations of the bioreactors showed a low level of variation and indicated the coexistence of closely related strains probably belonging to the species Desulfovibrio sulfodismutans. Under methanogenic conditions, all detected populations were active; under denitrifying conditions, no [NiFe] hydrogenase mRNA was visible. Changes in activity and composition of Desulfovibrio populations caused by changes in the environmental conditions could be monitored by using the approach described in this study.
The use of mutants to discern the degradation pathway of 3,4'-dichlorobiphenyl in Pseudomonas acidovorans M3GY.

Abstract Pseudomonas acidovorans strain M3GY is a recombinant bacterium with the novel ability to utilize 3,4'-dichlorobiphenyl (3,4'-DCBP) as a growth substrate. This strain was previously shown to oxidize the 3'-ring and produce 4-chlorobenzoate (4-CBa) through the standard biphenyl pathway. Although 4-CBa was metabolized through the meta-fission pathway, the genes encoding the ortho-chlorocatechol pathway were retained. Nevertheless, neither 3-CBa nor 3-chlorocatechol (3-CC) were detected as intermediates during metabolism of 3,4'-DCBP, nor was 4-CBa utilized as a sole carbon source, by this strain. Two mutant strains were produced to resolve these anomalies. Mutant strain M3GY-9 was obtained by Tn5 insertion and selection for growth on biphenyl, and was unable to grow on 3-CBa. It accumulated 3-CC from 3,4'-DCBP when grown on biphenyl. Thus, M3GY attacks both rings, and the failure to isolate 3-CBa or 3-CC is due to rapid turnover by the enzymes of the ortho-chlorocatechol pathway in the wild-type strain. Mutant strain M3GY-1 grew on 4-CBa, unlike the wild-type strain. Washed cell suspensions of mutant strain MEGY-1 consumed 4-fluorobenzoate, 4-bromobenzoate, and, to a lesser extent 4-iodobenzoate. The mutation that resulted in the ability of mutant strain M3GY-I to effectively utilize 4-CBa as a sole carbon source was associated with a transport mechanism.
Deleterious in late life mitochondrial alleles and aging: secrets of Japanese centenarians

Aging is associated with accumulation of somatic mutations⁠. This process is especially pronounced in mitochondrial genomes of postmitotic cells, which accumulate large-scale somatic mitochondrial deletions with time⁠, leading to neurodegeneration, muscular dystrophy and aging. Slowing down the rate of origin of these somatic deletions may benefit human lifespan and healthy aging. The main factors determining breakpoints of somatic mitochondrial deletions are direct nucleotide repeats⁠, which might be considered as Deleterious In Late Life (DILL) alleles. Correspondingly, the decreased amount of these DILL alleles might lead to low production of somatic deletions and increased lifespan. Intriguingly, in the Japanese D4a haplogroup, which is famous for an excess of centenarians and supercentenarians, we found that the longest direct repeat (“common repeat”) in the human mitochondrial genome has been disrupted by a point synonymous mutation. Thus we hypothesize that the disruption of the common repeat annuls common deletion (which is the most frequent among all somatic deletions) and at least partially may contribute to the extreme longevity of the D4a Japanese haplogroup⁠. Here, to better understand the mitochondrial components of longevity and potential causative links between repeats, deletions and longevity we discuss molecular, population and evolutionary factors affecting dynamics of mitochondrial direct repeats.
Language model adaptation for ASR of spoken translations using phrase-based translation models and named entity models

Language model adaptation based on Machine Translation (MT) is a recently proposed approach to improve the Automatic Speech Recognition (ASR) of spoken translations that does not suffer from a common problem in approaches based on rescoring i.e. errors made during recognition cannot be recovered by the MT system. In previous work we presented an efficient implementation for MT-based language model adaptation using a word-based translation model. By omitting renormalization and employing weighted updates, the implementation exhibited virtually no adaptation overhead, enabling its use in a real-time setting. In this paper we investigate whether we can improve recognition accuracy without sacrificing the achieved efficiency. More precisely, we investigate the effect of both state-of-the-art phrase-based translation models and named entity probability estimation. We report relative WER reductions of 6.2% over a word-based LM adaptation technique and 25.3% over an unadapted 3-gram baseline on an English-to-Dutch dataset.
Second‐site suppression of regulatory phosphorylation in Escherichia coli isocitrate dehydrogenase

Inactivation of Escherichia coli isocitrate dehydrogenase upon phosphorylation at S113 depends upon the direct electrostatic repulsion of the negatively charged γ‐carboxylate of isocitrate by the negatively charged phosphoserine. The effect is mimicked by replacing S113 with aspartate or glutamate, which reduce performance (kcat/Ki‐isocitrat/KmNADP) by a factor of 107. Here, we demonstrate that the inactivating effects of the electrostatic repulsion are completely eliminated by a second‐site mutation, and provide the structural basis for this striking example of intragenic suppression. N115 is adjacent to S113 on one face of the D‐helix, interacts with isocitrate and NADP+, and has been postulated to serve in both substrate binding and in catalysis. The single N115L substitution reduces affinity for isocitrate by a factor of 50 and performance by a factor of 500. However, the N115L substitution completely suppresses the inactivating electrostatic effects of S113D or S113E: the performance of the double mutants is 105 higher than the S113D and S113E single mutants. These mutations have little effect on the kinetics of alternative substrates, which lack the charged γ‐carboxylate of isocitrate. Both glutamate and aspartate at site 113 remain fully ionized in the presence of leucine. In the crystal structure of the N115L mutant, the leucine adopts a different conformer from the wild‐type asparagine. Repacking around the leucine forces the amino‐terminus of the D‐helix away from the rest of the active site. The hydrogen bond between E113 and N115 in the S113E single mutant is broken in the S113E/N115L mutant, allowing the glutamate side chain to move away from the γ‐carboxylate of isocitrate. These movements increase the distance between the carboxylates, diminish the electrostatic repulsion, and lead to the remarkably high activity of the S113E/N115L mutant.
Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier

Reports a result on neural spike sorting under conditions where the signal-to-noise ratio is very low. The use of nonlinear energy operator enables the detection of an action potential, even when the SNR is so poor that a typical amplitude thresholding method cannot be applied. The superior detection ability facilitates the collection of a training set under lower SNR than that of the methods which employ simple amplitude thresholding. Thus, the statistical characteristics of the input vectors can be better represented in the neural-network classifier The trained neural-network classifiers yield the correct classification ratio higher than 90% when the SNR is as low as 1.2 (0.8 dB) when applied to data obtained from extracellular recording from Aplysia abdominal ganglia using a semiconductor microelectrode array.
Learning with cost intervals

Existing cost-sensitive learning methods require that the unequal misclassification costs should be given as precise values. In many real-world applications, however, it is generally difficult to have a precise cost value since the user maybe only knows that one type of mistake is much more severe than another type, yet it is infeasible to give a precise description. In such situations, it is more meaningful to work with a cost interval instead of a precise cost value. In this paper we report the first study along this direction. We propose the CISVM method, a support vector machine, to work with cost interval information. Experiments show that when there are only cost intervals available, CISVM is significantly superior to standard cost-sensitive SVMs using any of the minimal cost, mean cost and maximal cost to learn. Moreover, considering that in some cases other information about costs can be obtained in addition to cost intervals, such as the distribution of costs, we propose a general approach CODIS for using the distribution information to help improve performance. Experiments show that this approach can reduce 60% more risks than the standard cost-sensitive SVM which assumes the expected cost is the true value.
Q-RoSA: QoE-aware routing for SVC video streaming over ad-hoc networks

Relaying packets through multi-hop ad-hoc networks and the scarcity of wireless resources can severely deteriorate the quality of service. As a result, one of the major challenges in video streaming over ad-hoc networks is enhancing users experience and network utilization. An extension of H.264 standard, named scalable video coding (SVC), enables smooth adaptation of video quality to networks' status. In this paper, we formulate the problem of optimizing quality of experience (QoE) for SVC under time-constraints. The problem itself is a mixed integer linear programming which is NP-hard, and therefore we propose a heuristic algorithm to solve it. Simulation results show that the proposed algorithm can provide the similar video quality as optimal solutions with shorter calculation time.
BPEX: Localizing BitTorrent Traffic via Biased Peer Exchange

In recent years, BitTorrent traffic accounts for a substantial percentage of Internet traffic in the world. However, the current BitTorrent protocols disregards traffic cost at the underlying network and generate a large amount of inter-AS traffic. As a result, BitTorrent peers may suffer from slow download of files due to long delays. Moreover, ISPs may limit BitTorrent traffic to reduce inter-AS traffic. In this paper, we propose a new approach localizing BitTorrent traffic within neighboring ASes, gBPEXh, in which a controller and peers directly exchange lists of peer in the vicinity through BitTorrent's PEX protocol. Conducting experiments on Planet Lab, we show that BPEX significantly reduces inter-AS traffic and peers' download completion time.
Gender Classification Based on the Non-Lexical Cues of Emergency Calls with Recurrent Neural Networks (RNN)

Automatic gender classification in speech is a challenging research field with a wide range of applications in HCI (humancomputer interaction). A couple of decades of research have shown promising results, but there is still a need for improvement. Until now, gender classification has been made using differences in the spectral characteristics of males and females. We assumed that a neutral margin exists between the male and female spectral range. This margin causes misclassification of gender. To address this limitation, we studied three non-lexical speech features (fillers, overlapping, and lengthening). From the statistical analysis, we found that overlapping and lengthening are effective in gender classification. Next, we performed gender classification using overlapping, lengthening, and the baseline acoustic feature, Mel Frequency Cepstral Coefficient (MFCC). We have tried to achieve the best results by using various combinations of features at the same time or sequentially. We used two types of machine-learning methods, support vector machine (SVM) and recurrent neural networks (RNN), to classify the gender. We achieved 89.61% with RNN using a feature set including MFCC, overlapping, and lengthening at the same time. Also, we have reclassified using non-lexical features with only data belonging to the neutral margin which was empirically selected based on the result of gender classification with only MFCC. As a result, we determined that the accuracy of classification with RNN using lengthening was 1.83% better than when MFCC alone was used. We concluded that new speech features could be effective in improving gender classification through a behavioral approach, notably including emergency calls.
Excitation of Confined Modes in Silicon Slotted Waveguides and Microring Resonators for Sensing Purposes

An important class of biochemical sensors are based on slotted waveguides having an increased sensitivity due to the confinement of the optical field in the slot region. The most sensitive devices of such a type contain microrings, and in these devices, the resonant frequency shift due to presence of the analyte in the microring slot is converted to the sensing signal. However, to the best of our knowledge, in none of the publications dealing with slotted microring sensors, there is no numerical or experimental analysis if the light is actually confined in the ring slot region. Here, we investigate numerically the conditions for the excitation of the modes confined in the slot of the ring. The 3-D finite element method (FEM) is used to calculate the optical power transmission coefficients of all-pass configuration (slotted ring +bus waveguide) in wide spectral range, while the eigenfrequency spectrum of slotted rings and its dependence on the top cladding refractive index is analyzed by means of 2-D axisymmetric FEM approach. It is shown that for the sensors based on slotted microrings sensitivity attains much higher value, up to four times, when the mode is confined in the slot of the ring is excited. This can be important for the design of different biochemical sensors.
Book Reviews: Advances in Information Retrieval: Recent Research from the Center for Intelligent Information Retrieval

The recent advances in information retrieval (IR) in this collection of ten original papers reflect the wide range of research topics developed at the Center for Intelligent Information Retrieval (CIIR) at the University of Massachusetts, Amherst. W. Bruce Croft, the Director of CIIR and the editor of this volume, presents in the preface both the history and the impressive research track of the center. The preface also lists the topics covered in the collection, reflecting the fact that the majority of the papers deal with research in traditional IR or with architecture and implementation issues. Only one of the papers tackles new areas, namely the topic detection and tracking problem. Surprisingly, none of the papers address one of the most exciting new IR tasks: the open-domain textual question-answering task. As one might expect, the papers in this volume are of varying quality. However, both the IR researcher and the computational linguist will find at least two of the papers outstanding. Warren Greiff's paper "The use of exploratory data analysis in information retrieval research" reports on a new line of research that uncovers statistical regularities in novel ways. By analyzing data using the notion of weight of evidence, Greiff obtains a new formulation of the inverse document frequency (IDF) that generates results of the same quality as those obtained using traditional IDF measures. Moreover, this data-driven approach is extended to other IR measures, such as term frequency and document length. This approach, comprising four incremental models, generates a ranking formula that is shown to perform similarly to the INQUERY system. Greiff's data-driven model has special promise as it allows natural extensions based on additional sources of evidence such as thesaurus terms or phrases. This technique is an ideal research vehicle for traditional and modern IR. The second paper that presents outstanding new research is "Topic detection and tracking: Event clustering as a basis for first story detection" by Ron Papka and James Allan. This paper captivates the reader by presenting an overview of the topic detection and tracking (TDT) problem, whose purpose is to organize broadcast news stories by the real-world events that they discuss. The paper describes the research problems considered in all three phases of the TDT and focuses on the solutions developed at CIIR for one of the problems, namely the detection problem--that is, identifying when new topics have appeared in the news stream. The approach discussed is both practical and technically interesting as it presents both new algorithms and modifications of existing IR techniques for the TDT problem.
On the Construction of Selection Systems.

This article examines the structure and components of information storage and retrieval systems and information filtering systems. Analysis of the tasks performed in such selection systems leads to the identification of 13 components. Eight are necessarily present in all such systems, mechanized or not; the others may, but need not be, present. The authors argue that all selection systems can be represented in terms of combinations of these components. The components are of only two types: representations of data objects and functions that operate on them. Further, the functional components, or rules, reduce to two basic types: 1) transformation, making or modifying the members of a set of representations, and 2) sorting or partitioning. The representational transformations may be in the form of copies, excerpts, descriptions, abstractions, or mere identifying references. By partitioning, we mean dividing a set of objects by using matching, sorting, ranking, selecting, and other logically equivalent operations. The typical multiplicity of knowledge sources and of system vocabularies is noted. Some of the implications for the study, use, and design of information storage and retrieval systems are discussed.
Reduction of Ripple in a single phase buck converter by Fuzzy logic control

This paper presents the reduction of ripple by the use of fuzzy logic controller in a single phase buck converter. In Electrical science ripple means the unwanted residual periodic variation of the direct current output of a power supply which has been derived from an alternating current source. The ripple is due to incomplete suppression of the alternating current waveform within the power supply. As the ripple causes reduction in resolution of the measuring instruments, it should be reduced up to possible extent. When comparing with the other control techniques available for a DC-DC buck converter like Sliding mode control, Current mode control and Voltage mode control, Fuzzy logic control is the better one as it is mainly based on heuristic knowledge of the system. In this paper simulation results are also presented with and without fuzzy control to better understand the necessity of the fuzzy logic controller.
Knowledge of medical professionalism in medical students and physicians at Shahid Beheshti University of Medical Sciences and affiliated hospitals—Iran

AbstractAlthough medical professionalism is a fundamental aspect of competence in medicine and a distinct facet of physicians’ competence, evidence suggests that the subject of professionalism is not taught or assessed as part of medical students’ curricula in Iran and many other countries. Assessing the knowledge of medical students and physicians about medical professionalism seems to be helpful in identifying the weaknesses of training in the field of professionalism and devise plans for future training on the subject.The present cross-sectional, quantitative, observational, and prevalence study recruited 149 medical interns, clinical residents, physicians, and professors working in hospitals selected through stratified random sampling using a questionnaire designed by the researchers and confirmed for its validity and reliability. The results were analyzed by Stata at a significance level of 0.05. Out of 149 cases, 61.64% were male with the mean age of 30.81 years. A total of 66 participants (44.29%) (95% confidence interval [CI]: 36.44%–52.44%) had heard and 83 (55.70%) (95% CI: 47.55%–63.55%) had not heard the term “medical professionalism” before the study. After adjusting for potential confounders, age and degree did not have statistically significant difference in assessed knowledge of medical professionalism, but sex had (mean difference: 5.88, P = 0.045), and the mean of the female was significantly higher than that of the male participants. The mean percentage of correct answers was 47.67. The present study demonstrated that the medical professionals working in the national healthcare system have an unfavorable theoretical knowledge about medical professionalism in Iran; although this does not indicate that their practices are unethical, it should be noted that one of the prerequisites of possessing a high level of medical professionalism and for establishing a proper relationship between the medical community and the patients is to have a proper knowledge of this concept. Improving behaviors and performances in medical professions requires adequate training on the concepts of medical professionalism and consequently the assessment of the levels of professionalism achieved in medical professionals.
Syntactic and Semantic Features For Code-Switching Factored Language Models

This paper presents our latest investigations on different features for factored language models for Code-Switching speech and their effect on automatic speech recognition (ASR) performance. We focus on syntactic and semantic features which can be extracted from Code-Switching text data and integrate them into factored language models. Different possible factors, such as words, part-of-speech tags, Brown word clusters, open class words and clusters of open class word embeddings are explored. The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model containing Brown word clusters and part-of-speech tags and the model also including clusters of open class word embeddings yield the best mixed error rate results. In summary, the best language model can significantly reduce the perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed error rate by up to 3.4% relative.