Miracle-dz/UniFAR-FTUs huggingface dataset:
Using shared genomic synteny and shared protein functions to enhance the identification of orthologous gene pairs

MOTIVATION The identification of orthologous gene pairs is generally based on sequence similarity. Gene pairs that are mutually 'best hits' between the genomes being compared are asserted to be orthologs. Although this method identifies most orthologous gene pairs with high confidence, it will miss a fraction of them, especially genes in duplicated gene families. In addition, the approach depends heavily on the completeness and quality of gene annotation. When the gene sequences are not correctly represented the approach is unlikely to find the correct ortholog. To overcome these limitations, we have developed an approach to identify orthologous gene pairs using shared chromosomal synteny and the annotation of protein function. RESULTS Assembled mouse and human genomes were used to identify the regions of conserved synteny between these genomes. 'Syntenic anchors' are conserved non-repetitive locations between mouse and human genomes. Using these anchors, we identified blocks of sequences that contain consistently ordered anchors between the two genomes (syntenic blocks). The synteny information has been used to help us identify orthologous gene pairs between mouse and human genomes. The approach combines the mutual selection of the best tBlastX hits between human and mouse transcripts, and inferring gene orthologous relationships based on sharing syntenic anchors, collocating in the same syntenic blocks and sharing the same annotated protein function. Using this approach, we were able to find 19,357 orthologous gene pairs between human and mouse genomes, a 20% increase in the number of orthologs identified by conventional approaches.
Material challenges for solar cells in the twenty-first century: directions in emerging technologies

Abstract Photovoltaic generation has stepped up within the last decade from outsider status to one of the important contributors of the ongoing energy transition, with about 1.7% of world electricity provided by solar cells. Progress in materials and production processes has played an important part in this development. Yet, there are many challenges before photovoltaics could provide clean, abundant, and cheap energy. Here, we review this research direction, with a focus on the results obtained within a Japan–French cooperation program, NextPV, working on promising solar cell technologies. The cooperation was focused on efficient photovoltaic devices, such as multijunction, ultrathin, intermediate band, and hot-carrier solar cells, and on printable solar cell materials such as colloidal quantum dots.
Bilingually-Constrained (Monolingual) Shift-Reduce Parsing

Jointly parsing two languages has been shown to improve accuracies on either or both sides. However, its search space is much bigger than the monolingual case, forcing existing approaches to employ complicated modeling and crude approximations. Here we propose a much simpler alternative, bilingually-constrained monolingual parsing, where a source-language parser learns to exploit reorderings as additional observation, but not bothering to build the target-side tree as well. We show specifically how to enhance a shift-reduce dependency parser with alignment features to resolve shift-reduce conflicts. Experiments on the bilingual portion of Chinese Treebank show that, with just 3 bilingual features, we can improve parsing accuracies by 0.6% (absolute) for both English and Chinese over a state-of-the-art baseline, with negligible (~6%) efficiency overhead, thus much faster than biparsing.
Gaussian Particle Filtering Approach for Carrier Frequency Offset Estimation in OFDM Systems

We propose Gaussian particle filtering (PF) approach for estimating carrier frequency offset (CFO) in OFDM systems. PF is more powerful especially for nonlinear problems where classical approaches (e.g., maximum likelihood estimators) may not show optimal performance. Standard PF undergoes the particle impoverishment (PI) problem resulting from resampling process for this static parameter (i.e., CFO) estimation. Gaussian PF (GPF) avoids the PI problem because resampling process is not needed in the algorithm. We show that GPF outperforms current approaches in this nonlinear estimation problem.
Reducing bacterial contamination of enteral feeds

OBJECTIVE It has previously been shown that microbial contamination of enteral feeds given to children in hospital and at home is common. This study therefore examined the effects of improvements in the enteral feeding protocol, coupled with an intensive staff training programme, on bacterial contamination. METHODS The enteral feeding protocol was modified by: priming the feeding set on an alcohol treated metal tray, spraying the bottle opener and top with 70% alcohol, wearing non-sterile disposable gloves, and filling the feeding reservoir with feed for up to 24 hours’ use rather than only four hours. Daily feeds samples were collected from 16 inpatients and home patients on enteral nutrition at the start and end of feeding. Seventy seven samples were cultured. Results were compared with previously published control data. RESULTS Enteral feed contamination rates were reduced significantly from 62% to 6% of feeds given at home (p < 0.001), and from 45% to 4% of feeds given in hospital (p < 0.001). CONCLUSIONS This study highlights the importance of using an appropriate enteral feeding protocol, and of regular staff training in reducing contamination rates of enteral feeds to an acceptable level.
A unified and discriminative model for query refinement

This paper addresses the issue of query refinement, which involves reformulating ill-formed search queries in order to enhance relevance of search results. Query refinement typically includes a number of tasks such as spelling error correction, word splitting, word merging, phrase segmentation, word stemming, and acronym expansion. In previous research, such tasks were addressed separately or through employing generative models. This paper proposes employing a unified and discriminative model for query refinement. Specifically, it proposes a Conditional Random Field (CRF) model suitable for the problem, referred to as Conditional Random Field for Query Refinement (CRF-QR). Given a sequence of query words, CRF-QR predicts a sequence of refined query words as well as corresponding refinement operations. In that sense, CRF-QR differs greatly from conventional CRF models. Two types of CRF-QR models, namely a basic model and an extended model are introduced. One merit of employing CRF-QR is that different refinement tasks can be performed simultaneously and thus the accuracy of refinement can be enhanced. Furthermore, the advantages of discriminative models over generative models can be fully leveraged. Experimental results demonstrate that CRF-QR can significantly outperform baseline methods. Furthermore, when CRF-QR is used in web search, a significant improvement of relevance can be obtained.
Nutraceutic Characteristics of the Extracts and Juice of Chayote (Sechium edule (Jacq.) Sw.) Fruits

Fruits of chayote [Sechium edule (Jacq.) Swartz] are a non-traditional vegetable widely consumed in Latin America, with the state of Veracruz, Mexico being the world’s main producer, but little is known about the nutraceutical potential. This study aimed to determine the chemical compositions and antioxidant activities from the juice fruits from two commercial varieties of chayote cultivated in Mexico, as well as a proposal for the elaboration of chayote juices with stevia leaves and pineapple juice. The physicochemical properties of juice from virens levis (VL) and nigrum spinosum (NS) varieties were determined using standard methods. The juice of the two varieties differ significantly regarding the concentrations of total soluble solids and total sugars, but not vitamin C. The total concentration of phenolics in NS extracts was slightly higher than in VL (1005 and 856 mg 100 g−1 dry-weight, respectively), but the total flavonoid contents were similar (27 and 26 mg 100 g−1 dry-weight, respectively). Cucurbitacin D was predominant in both varieties. The radical scavenging capacities of VL and NS extracts varied slightly (IC50 = 0.45 to 0.65 mg mL−1), while the antioxidant activities were similar (~80%). The NS variety is particularly promising regarding nutraceutical application. The chayote juice combined with stevia and pineapple maintained the original nutraceutical characteristics of the fruit, but enhanced the organoleptic characteristics like density and sugar/acidity balance.
Plenoptic modeling: an image-based rendering system

Image-based rendering is a powerful new approach for generating real-time photorealistic computer graphics. It can provide convincing animations without an explicit geometric representation. We use the "plenoptic function" of Adelson and Bergen to provide a concise problem statement for image-based rendering paradigms, such as morphing and view interpolation. The plenoptic function is a parameterized function for describing everything that is visible from a given point in space. We present an image-based rendering system based on sampling, reconstructing, and resampling the plenoptic function. In addition, we introduce a novel visible surface algorithm and a geometric invariant for cylindrical projections that is equivalent to the epipolar constraint defined for planar projections.
Development, implementation, and assessment of a web-based power electronics laboratory

A Web-based laboratory exercise with remote access is presented, through which a student of Electrical/Electronic Engineering is introduced in both a theoretical and practical way, to many fundamental aspects of power electronics. The system is flexible and can expand the range of laboratory exercises where full-scale laboratories are not feasible. In the electrical environment, limits can be placed on voltages and currents for safety reasons. Prelaboratory investigations allow students to take an active involvement in the learning process by addressing some challenging and critical aspects of the design before approaching the physical system. Further understanding is gained by studying the circuit in a Web-based, interactive power electronics seminar (iPES) by simulating the circuit using PSpice and then analyzing the control and feedback issues with MATLAB. In the final stage, a real power converter is tested remotely over the Web, and the cycle of design, simulation, and test is completed using Web-based tools.
Social media distribution: a data-driven approach

The Internet has recently witnessed the convergence of online social network services and online video services: users import videos from content sharing sites, and propagate them along the social connections by re-sharing them. Such social behaviors have dramatically reshaped how videos are disseminated, and the users are now actively engaged to be part of the social ecosystem, rather than being passively consumers. Despite the increasingly abundant bandwidth and computation resources, the ever increasing data volume of user generated video content and the boundless coverage of socialized sharing have presented unprecedented challenges. In this paper, we first presents the challenges in social-aware video delivery. Then, we present a principal framework for data-driven social video delivery approaches. Moreover, we identify the unique characteristics of social-aware video access and the social content propagation, and closely reveal the design of individual modules and their integration towards enhancing users' experience in the social network context.
TREC-2003 Novelty and Web Track at ICT ( Draft ) +

In this paper, we will present our approaches and experiments on the following two tracks of TREC-2003: Novelty track and Web track. The novelty track can be treated as a binary classification problem: relevant sentences vs. irrelevant sentences, or new vs. non-new. In this way, we applied variants of techniques that have been employed for text categorization problem. To retrieve the relevant sentences, we compute the similarity between the topic and sentences using vector space model (VSM). If the similarity exceeds a certain threshold, the sentence is considered as relevant. In addition, we tried several techniques in an attempt to improve the performance: using narrative field and adopting dynamic threshold for different docs. We also have implemented the KNN algorithm and Winnow algorithm for classifying the sentences into relevant and irrelevant sentences in the novelty task 3. To detect the new sentences from the relevant sentences, we used Maximum Marginal Relevance (MMR) measure, Winnow algorithm and word overlapping within sentences. In addition, we attempted to detect novelty by computing semantic distance between sentences using WordNet. For the Web track, we improved the basic SMART system, and the Lnu-Ltu weighting method was introduced into the system. The improved system has been proved to be effective in last year’s task. In addition, we implemented a simple retrieval system using the probability model that is adopted by Okapi. The structure of the paper is as follows: The section 2 reports the approaches and experiments in novelty track. The section 3 describes the experiments in web track. Finally, in section 4, we conclude by summarizing our experiments and presenting the future work.
5 Web Page Classification * 5.1 Introduction 5.1.1 Motivation

This chapter describes systems that automatically classify web pages into meaningful categories. It first defines two types of web page classification: subject based and genre based classifications. It then describes the state of the art techniques and subsystems used to build automatic web page classification systems, including web page representations, dimensionality reductions, web page classifiers, and evaluation of web page classifiers. Such systems are essential tools for Web Mining and for the future of Semantic Web.
Interrater Reliability of Algometry in Measuring Pressure Pain Thresholds in Healthy Humans, Using Multiple Raters

ObjectiveTo evaluate interrater reliability using 5 newly trained observers in the assessment of pressure pain threshold (PPT) using a fixed-angle algometer. MethodsThe study design comprised 2 phases. Phase 1: 5 undergraduate physical therapists were trained in algometry at a predefined angle, at a rate of 5 Newtons (N)/s, to the first dorsal interosseous muscle. Each observer then underwent a competency test of the application speed. The aim was to achieve repeated applications at 5 N/s without visual feedback from the algometer. Phase 2: the 5 observers measured PPT of 13 healthy volunteers, at the first dorsal interosseous muscle. The sequence of observer measurements for each participant was randomized. Mean PPT values for each observer were analyzed using repeated measures analysis of variance, intraclass correlation coefficient (ICC2,1), and standard error of measurement, with 95% confidence intervals (CIs). ResultsNo significant differences between observers' mean values were found (P=0.094), suggesting no bias. The ICC was 0.91 (95% CI 0.82, 0.97). The standard error of measurement value was 6.27 N/cm2 (95% CI 5.35, 7.59). Differences in PPT measurements of more than 17.39 N/cm2 (1.77 kg/cm2) are likely to exceed the magnitude of measurement error, and could be used to indicate true change. This margin of error is, however, somewhat larger than a previously proposed minimum clinically important difference in PPT of 14.71 N/cm2 (1.5 kg/cm2). DiscussionThis study provides new evidence that trained observers can apply an algometer at a consistent rate and provide highly reliable measures of PPT in healthy humans, when PPT is calculated as the mean of 3 trials.
A comparative study on the effectiveness of part-of-speech tagging techniques on bug reports

Many software artifacts are written in natural language or contain substantial amount of natural language contents. Thus these artifacts could be analyzed using text analysis techniques from the natural language processing (NLP) community, e.g., the part-of-speech (POS) tagging technique that assigns POS tags (e.g., verb, noun, etc.) to words in a sentence. In the literature, several studies have already applied POS tagging technique on software artifacts to recover important words in them, which are then used for automating various tasks, e.g., locating buggy files for a given bug report, etc. There are many POS tagging techniques proposed and they are trained and evaluated on non software engineering corpus (documents). Thus it is unknown whether they can correctly identify the POS of a word in a software artifact and which of them performs the best. To fill this gap, in this work, we investigate the effectiveness of seven POS taggers on bug reports. We randomly sample 100 bug reports from Eclipse and Mozilla project and create a text corpus that contains 21,713 words. We manually assign POS tags to these words and use them to evaluate the studied POS taggers. Our comparative study shows that the state-of-the-art POS taggers achieve an accuracy of 83.6%-90.5% on bug reports and the Stanford POS tagger and the TreeTagger achieve the highest accuracy on the sampled bug reports. Our findings show that researchers could use these POS taggers to analyze software artifacts, if an accuracy of 80-90% is acceptable for their specific needs, and we recommend using the Stanford POS tagger or the TreeTagger.
In vitro antiestrogenic effects of aryl methyl sulfone metabolites of polychlorinated biphenyls and 2,2-bis(4-chlorophenyl)-1,1-dichloroethene on 17beta-estradiol-induced gene expression in several bioassay systems.

Methylsulfonyl (MeSO(2)) metabolites of polychlorinated biphenyls (PCBs) and 2,2-bis(4-chlorophenyl)-1,1-dichloroethene (4,4'-DDE), itself a metabolite of the insecticide 4,4'-DDT, are emerging as a major class of contaminants in the tissues of wildlife and humans. We investigated the antiestrogenic capacity and potencies of 3'- and 4'-MeSO(2)-2,2',4,5,5'-pentachlorobiphenyl (CB101) and -2,2',4,5'-tetrachlorobiphenyl (CB49), which are among the most environmentally persistent MeSO(2)-PCBs, and 3-MeSO(2)-4,4'-DDE on estrogen receptor (ER)-dependent gene expression in four cell-based bioassay systems. Congener- and concentration-dependent antagonism of 17beta-estradiol (E2)-induced gene expression, rather than induction of ER-dependent gene expression, was observed for the MeSO(2)-PCBs on lucifierase activity in stably transfected human breast adenocarcinoma T47D cells (ER-CALUX) and vitellogenin (vtg) production in primary hepatocytes from male carp fish (Cyprinus carpio) (CARP-HEP/vtg). 4'-MeSO(2)-CB101 and -CB49 had the highest antagonistic potency (i.e., maximum inhibition of about 70%, LOECs of 1.0 microM and 2.5 microM), whereas 3'-MeSO(2)-CB101 and -CB49 were less antagonistic; the precursor CB101 and MeSO(2)-PCB analog MeSO(2)-2,5-dichlorobenzene had no effect. Relative to the 4-MeSO(2)-PCBs, tamoxifen (IC(50), 0.06 microM and 0.7 microM) was about 40 and 7 times more potent in the ER-CALUX and CARP-HEP/vtg assays, respectively. Congener- and concentration-dependent effects on aryl hydrocarbon receptor-mediated induction of EROD activity (carp hepatocytes), luciferase expression (H4IIE rat hepatoma [H4IIE.luc] cell line), or cell viability were not observed. 3-MeSO(2)-4,4'-DDE was neither estrogenic nor antiestrogenic in either of the bioassays. Inhibitory trends for the MeSO(2)-PCBs in a bioassay based on stably transfected human embryonic kidney cell (HEK293-ERalpha-ERE) were similar to the ER-CALUX and CARP-HEP/vtg bioassays, whereas the antagonism was weaker in a related HEK293-ERbeta-ERE bioassay. Our findings suggest that the 4'-MeSO(2)-PCBs are antiestrogenic in vitro via a reversible or surmountable interaction with fish or human ER, and that the interaction with human ERalpha is apparently favored over ERbeta. MeSO(2)-PCB metabolites are persistent and bioaccumulative contaminants, and therefore, could be potentially active as environmental antiestrogens in wildlife and humans.
Leveraging POMDPs Trained with User Simulations and Rule-based Dialogue Management in a Spoken Dialogue System

We have developed a complete spoken dialogue framework that includes rule-based and trainable dialogue managers, speech recognition, spoken language understanding and generation modules, and a comprehensive web visualization interface. We present a spoken dialogue system based on Reinforcement Learning that goes beyond standard rule based models and computes on-line decisions of the best dialogue moves. Bridging the gap between handcrafted (e.g. rule-based) and adaptive (e.g. based on Partially Observable Markov Decision Processes - POMDP) dialogue models, this prototype is able to learn high rewarding policies in a number of dialogue situations.
A study of certain morphological structures of Kazakh and their impact on the machine translation quality

This paper describes a morphological analysis of the Kazakh language for Kazakh-English statistical machine translation through changing the compound words of Kazakh language, and explores the effect of using the modified input on translation quality with a large number of training sentences. Word alignment problem would become more serious for translation from morphologically rich language such as Kazakh to morphologically simple one such as English, due to the problem of data sparseness on translation word forms in many different morphological variants. We present our investigations on unsupervised Kazakh morphological segmentation over newspaper corpus and compare unsupervised segmentation against rule-based language processing tools. In our experiments, the results show that our proposed method can improve word alignment and translation quality.
Left-Right Facial Orientation of Familiar Faces: Developmental Aspects of « the Mere Exposure Hypothesis »

We investigated the developmental aspect of sensitivity to the orientation of familiar faces by asking 38 adults and 72 children from 3 to 12 years old to make a preference choice between standard and mirror images of themselves and of familiar faces, presented side-by-side or successively. When familiar (parental) faces were presented simultaneously, 3- to 5-year-olds showed no preference, but by age 5–7 years an adult-like preference for the standard image emerged. Similarly, the adult-like preference for the mirror image of their own face emerged by 5–7 years of age. When familiar or self faces were presented successively, 3- to 7-year-olds showed no preference, and adult-like preference for the standard image emerged by age 7–12 years. These results suggest the occurrence of a developmental process in the perception of familiar face asymmetries which is retained in memory related to knowledge about faces.
KODAMA project

This paper describes the design and implementation of a worldwide distributed multi-agent system, called KODAMA (Kyushu University Open & Distributed Autonomous Multi-Agent). Historically, research on multi-agent systems is a natural extension of the evolution of programming, as well as communications and control technologies. Through our work on the KODAMA project, we concentrate on enabling technologies for building scalable, flexible and secure multi-agent systems, which can operate in a worldwide computing environment. The innovative points of KODAMA are the approaches it takes to formalizing a global distributed computing architecture based on agent-oriented programming. The core concept behind our design is a separation principle that mandates the separation of application-level logic from agent-level logic and the separation of agent-level logic from network-level logic. In this way, the inherent distribution and complexity that the worldwide computing environment involve can be abstracted, encapsulated and tackled appropriately at different levels . In line with the separation principle, this article gives a general introduction to the key abstraction models that form the basis of KODAMA.