Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein sequence for clustering DNA based on Artificial Neural Networks

DNA is a nucleic acid that contains the genetic instructions used in the development and functioning of all known living organisms and some viruses. Clustering is a process that groups a set of objects into clusters so that the similarity among objects in the same cluster is high, while that among the objects in different clusters is low. In this paper, we proposed an approach for clustering DNA sequences using Self-Organizing Map (SOM) algorithm and Protein Sequence. The main objective is to analyze biological data and to bunch DNA to many clusters more easily and efficiently. We use the proposed approach to analyze both large and small amount of input DNA sequences. The results show that the similarity of the sequences does not depend on the amount of input sequences. Our approach depends on evaluating the degree of the DNA sequences similarity using the hierarchal representation Dendrogram. Representing large amount of data using hierarchal tree gives the ability to compare
Using microsoft solver foundation to analyse feature models and configurations

Feature Models are widely used in Software Product Lines to represent commonalities and variabilities in a family of products and to support the interactive configuration of these products. They comprise features and options that can be included in a product and constraints about which combinations of features are allowed. Libraries and frameworks such as SPLOT and FaMa help engineers to determine if a feature model is valid, detect errors in the models and validate if a configuration (i.e., a set of features selected by an user) does not contradict the constraints in the model. Regrettably, these libraries are based on Java and cannot be used in . Net platforms such as the recent Windows Phone systems. For that platforms, there is a Microsoft Solver Foundation (MSF) library that provides a set of solvers such as the used to analyse feature models. This paper explains (1) how to translate feature models and configurations into Constraint Satisfaction Problems in MSF, and (2) how to use that library to determine if a model is valid, enumerate all the valid configurations, and detect core and dead features that may exist in the model. In addition, we present a performance evaluation of the approach.
Experiment of Masking-Based &#946;-Order MMSE Speech Enhancement in a Car Noise Environment

In this paper, we investigate the use of masking-based beta-order MMSE (MbetaOM) speech enhancement algorithm as applied to a car noise environment. Based on the previous work on the beta-order MMSE speech enhancement, it is found that MbetaOM has a satisfactory performance in the case of speech corrupted by Volvo interior car noise from the NOISEX-92 database. In this paper, the adaptation of the original MbetaOM is modified according to the characteristics of the car noise. The particular type of the noise of a car moving in the highway is considered. The experimental results are shown in terms of both Itakura Saito (IS) distortion and modified bark spectral distortion (MBSD) measures
Multiple Logistic Regression Analysis on the Health Checkup Data and the Lifestyle Habits of Medicated Residents: A Population-Based Cohort Study

Background: The residents currently taking prescribed medication have been exempted from the special public health guidance conducted in the act in Japan. This study analyzed blood pressure taken during the special health checks from 2008 to 2011 in light of resident lifestyle, focusing on comparisons between medicated and non-medicated residents. Methods: Health checkup data in retrospective cohort of 4,734 residents undergoing special health checks in B-City from 2008 to 2011 were analyzed. The participants were categorized as taking medication (medicated residents, n=1,083) and others (non-medicated residents, n=3,651). The multiple logistic regression analysis provided odds ratios (ORs) and 95% confidence intervals (95% CI). Results: The medicated residents had higher systolic and diastolic blood pressure than the non-medicated in both 2008 and 2011. Factors on which the OR was significantly higher for the hypertension group (normal blood pressure group=1) were alcohol consumption (OR: 1.30 (95% CI: 1.12-1.50)), and weight gain (OR: 1.45 (95% CI: 1.26-1.67)). Factors on which the OR was significantly higher for the diabetes mellitus group (normal blood glucose group=1) were smoking (OR: 3.14 (95% CI:1.69-5.80)). Factors on which the OR was significantly higher for the neutral fat high-flying group (normal neutral fat group=1) were alcohol consumption (OR: 1.30 (95% CI: 1.12-1.50)) and weight gain (OR: 1.45 (95% CI: 1.26-1.67)). Compared to the group with hypertension only, the group with multiple conditions who consumed alcohol in both 2008 and 2011 had an OR of 1.49 (95% CI: 1.29-1.72), and those who had weight gain of 10 kg had an OR of 1.74 (95% CI: 1.50-2.02). Conclusion: No improvement was found in the lifestyle habits of medicated residents. This study suggested that an appropriate health guidance will be needed to improve the lifestyle habits in medicated residents.
Estimating states in an unobservable system

Run-to-Run control methods as applied in semiconductor manufacturing can greatly benefit from partitioning the observed disturbance state into separate components of tool and product disturbance states. Obtaining sufficiently accurate initial estimates of tool and product disturbance terms from historical data greatly improves the effectiveness of this method. Such an estimation method is developed in this work based on the just-in-time adaptive disturbance estimator (JADE) using random, high frequency sampling of historical data.
Fusion of Audio and Temporal Multimodal Data by Spreading Activation for Dweller Localisation in a Smart Home

In this paper, an approach to locate a person using non visual sensors in a smart home is presented. The information extracted from these sensors gives uncertain evidence about the location of a person. To improve robustness of location, audio informa- tion (used for voice command) is fused with classi- cal domotic sensor data using a two-level dynamic network and using an adapted spreading activation method that considers the temporal dimension to deal with evidence that expire. The automatic lo- cation was tested within two different smart homes using data from experiments involving 25 partici- pants. The preliminary results show that an accu- racy of 90% can be reached using several uncertain sources.
Advice on Abductive Logic

It’s not usual for citations or footnotes to be included in abstracts. One of our purposes here is to expose something of the elementary logical structure of abductive reasoning, and to do so in a way that helps orient theorists to the various tasks that a logic of abduction should concern itself with. We are mindful of criticisms that have been levelled against the very idea of a logic of abduction; so we think it prudent to proceed with a certain diffidence. That our own account of abduction is itself abductive is methodological expression of this diffidence. A second objective is to test our conception of abduction’s logical structure against some of the more promising going accounts of abductive reasoning. We offer our various suggestions in a benignly advisory way. The primary targets of our advice is ourselves, meant as guides to work we have yet to complete or, in some instances, start. It is possible that our colleagues in the abduction research communities will find our counsel to be of some interest. But we repeat that our first concern is to try to get ourselves straight about what a logic of abduction should encompass.
Action-based sensor space categorization for robot learning

Robot learning such as reinforcement learning generally needs a well-defined state space in order to converge. However, to build such a state space is one of the main issues of the robot learning because of the inter-dependence between state and action spaces, which resembles to the well known "chicken and egg" problem. This paper proposes a method of action-based state space construction for vision-based mobile robots. Basic ideas to cope with the inter-dependence are that we define a state as a cluster of input vectors from which the robot can reach the goal state or the state already obtained by a sequence of one kind action primitive regardless of its length, and that this sequence is defined as one action. To realize these ideas, we need many data (experiences) of the robot and cluster the input vectors as hyper ellipsoids so that the whole state space is segmented into a state transition map in terms of action from which the optimal action sequence is obtained. To show the validity of the method, we apply it to a soccer robot which tries to shoot a ball into a goal. The simulation and real experiments are shown.
Location-aware coordinated multipoint transmission in OFDMA networks

We propose a novel Location-Aware multicell Cooperation (LAC) scheme for downlink transmission in OFDMA-based networks. Compared to the traditional multicell cooperation, the proposed scheme uses coordinated multipoint (CoMP) transmission to serve only users with poor signal-to-interference-plus-noise ratio (SINR). On the other hand, users with good SINR conditions are served via multiuser MIMO by a single base station (BS). The proposed scheme uses a joint zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) transmission along with optimized power allocation in a semi-distributed manner to maximize the overall system energy efficiency (i.e., the average data rate per unit power [bps/Watt], or equivalently, average number of successfully transmitted bits per energy unit [bit/Joule]). Numerical results show that the proposed scheme outperforms the scheme that uses cooperation to serve all users, in terms of energy efficiency as well as system capacity and fairness.
Performing systematic literature review in software engineering

In the field of software engineering (SE) the first primary studies about Systematic Literature Review (SLR) have been conducted during the last decade. By adapting guidelines from other disciplines Kitchenham in 2004 created guidelines on performing the SLR in a field of SE. Since then the SLR is used in SE regularly, but many obstacles still remain. This paper aims to give an short but complete overview of all phases and stages that should be undertaken in SLR, and although based on mentioned guidelines, this paper takes into consideration feedback and experiences reported by other authors and discusses the possible approaches along with advantages and dissadvantages of the method.
Mining Frequent Correlated-Quasi-Cliques from PPI Networks

Many of the previous studies show convincing arguments that mining frequent subgraphs is especially useful. Many hidden frequent patterns which are very interesting can not be found by mining single graph. Previous studies as Quasi-Clique have little success with the hub problem. In this paper, we introduce a new conception Correlated-Quasi-Clique and develop a novel algorithm, CoClique, to address the hub problem and improve the efficiency of frequent correlated-Quasi-Cliques mining. Meanwhile, we exploit several effective techniques to prune the search space. An extensive experimental evaluation on real databases demonstrates that our algorithm outperforms previous methods.
Label Correlation Mixture Model: A Supervised Generative Approach to Multilabel Spoken Document Categorization

Multilabel categorization, which is more difficult but practical than the conventional binary and multiclass categorization, has received a great deal of attention in recent years. This paper proposes a novel probabilistic generative model, label correlation mixture model (LCMM), to depict the multiply labeled documents, which can be used for multilabel spoken document categorization as well as multilabel text categorization. In LCMM, labels and topics have the one-to-one correspondences. The LCMM consists of two important components: 1) a label correlation model and 2) a multilabel conditioned document model. The label correlation model formulates the generating process of labels where the dependences between the labels are taken into account. We also propose an efficient algorithm for calculating the probability of generating an arbitrary subset of labels. The multilabel conditioned document model can be regarded as a supervised label mixture model, in which labels for a document are known. Each label is characterized by distributions over words. For the parameter learning of the multilabel conditioned document model, in addition to maximum-likelihood estimation, a discriminative approach based on the minimum classification error rate training is proposed. To evaluate LCMM, extensive multilabel categorization experiments are conducted on a spoken document data set and three standard text data sets. The experimental results in comparison with other competitive methods demonstrate the effectiveness of LCMM.
Panther: a specification system for graphical controls

An experimental graphical control specification system, called Panther, has been written in C for UNIX®-based applications. Unlike similar systems, which focus on combining interaction techniques, Panther allows the specification of low-level interactions by invoking user-selectable subroutines for input-device transitions. A Panther interface is specified in a textual table as a set of hierarchically nested regions. Regions can model any control device, such as menu buttons, slider-bars, switches, alphanumeric displays, or even combinations of other regions. Panther does not rely on special hardware, extensive software, or interprocess communication.
The effect of leadership styles on professional learning community

The main aim of this study was to investigate the effect of school administratorsâ€™ leadership styles on professional learning community in those schools under jurisdiction of the Office of Secondary Educational Service Area 27, Thailand. A total of 3,012 samples consisted of school administrators and teachers were selected from a population of 27,459. A survey quantitative method was employed using questionnaire which was constructed by researchers as an instrument to collect data. Data was analyzed using descriptive and inferential statistics like frequency, percentage, mean score, standard deviation, correlation Pearson Product Moment Coefficient and stepwise multiple regression analysis. The results of the study indicated that all five leadership styles of school administrator were at high level particularly supportive leadership style. However reward and punishment leadership style was found to be the most unpopular leadership style. On the other hand, all the aspects of professional learning community were found to be at high level too. The highest mean score was learning enhancement and information technology whereas the lowest mean score was team empowerment. Implementation of professional learning community was significantly affected by three types of leadership style at significant level of 0.01. The significant predictors of professional learning community were reward and punishment, directive, and charismatic leadership styles. The multiple regression coefficient was 0.836 and the predictive power was 69.9 per cent. Finally, this paper also provided recommendations and suggestions for future research.
Biologically Derived Soft Conducting Hydrogels Using Heparin-Doped Polymer Networks

The emergence of flexible and stretchable electronic components expands the range of applications of electronic devices. Flexible devices are ideally suited for electronic biointerfaces because of mechanically permissive structures that conform to curvilinear structures found in native tissue. Most electronic materials used in these applications exhibit elastic moduli on the order of 0.1–1 MPa. However, many electronically excitable tissues exhibit elasticities in the range of 1–10 kPa, several orders of magnitude smaller than existing components used in flexible devices. This work describes the use of biologically derived heparins as scaffold materials for fabricating networks with hybrid electronic/ionic conductivity and ultracompliant mechanical properties. Photo-cross-linkable heparin–methacrylate hydrogels serve as templates to control the microstructure and doping of in situ polymerized polyaniline structures. Macroscopic heparin-doped polyaniline hydrogel dual networks exhibit impedances as low as Z = 4.17 Ω at 1 kHz and storage moduli of G′ = 900 ± 100 Pa. The conductivity of heparin/polyaniline networks depends on the oxidation state and microstructure of secondary polyaniline networks. Furthermore, heparin/polyaniline networks support the attachment, proliferation, and differentiation of murine myoblasts without any surface treatments. Taken together, these results suggest that heparin/polyaniline hydrogel networks exhibit suitable physical properties as an electronically active biointerface material that can match the mechanical properties of soft tissues composed of excitable cells.
Nonlinear Regression of Functional MRI Data: An Item Recognition Task Study

A classical item-recognition task was used to examine human verbal working memory in an event-related functional MRI (fMRI) study. A highly flexible experimental design incorporated a broad variation of memory load and delay time. This design allows for only three or four repetitions per stimulus condition. In a first step, linear regression analysis was applied to locate functional activation in the fMRI data. As a second step, the time course of the hemodynamic response (HR) was analyzed using nonlinear regression, which served to quantify the dependency between HR shape properties and stimulation conditions in several regions-of-interest. On the basis of this study, a closer description of the frontoparietal network involved in verbal working memory was possible.
The complexities of bacterial-fungal interactions in the mammalian gastrointestinal tract

Eduardo Lopez-Medina and Andrew Y. Koh 1 Department of Pediatrics, Universidad del Valle and Centro de Estudios en Infectologia Pediatrica, Cali, Colombia. 2 Departments of Pediatrics and Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA. * Corresponding Authors: Andrew Y. Koh, Department of Pediatrics and Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA; Tel: +1 214 648-3896; Fax +1 214 648-3122; E-mail: Andrew.koh@utsouthwestern.edu
Contribution of Type IV Pili to the Virulence of Aeromonas salmonicida subsp. salmonicida in Atlantic Salmon (Salmo salar L.)

ABSTRACT Aeromonas salmonicida subsp. salmonicida, a bacterial pathogen of Atlantic salmon, has no visible pili, yet its genome contains genes for three type IV pilus systems. One system, Tap, is similar to the Pseudomonas aeruginosa Pil system, and a second, Flp, resembles the Actinobacillus actinomycetemcomitans Flp pilus, while the third has homology to the mannose-sensitive hemagglutinin pilus of Vibrio cholerae. The latter system is likely nonfunctional since eight genes, including the gene encoding the main pilin subunit, are deleted compared with the orthologous V. cholerae locus. The first two systems were characterized to investigate their expression and role in pathogenesis. The pili of A. salmonicida subsp. salmonicida were imaged using atomic force microscopy and Tap- and Flp-overexpressing strains. The Tap pili appeared to be polar, while the Flp pili appeared to be peritrichous. Strains deficient in tap and/or flp were used in live bacterial challenges of Atlantic salmon, which showed that the Tap pilus made a moderate contribution to virulence, while the Flp pilus made little or no contribution. Delivery of the tap mutant by immersion resulted in reduced cumulative morbidity compared with the cumulative morbidity observed with the wild-type strain; however, delivery by intraperitoneal injection resulted in cumulative morbidity similar to that of the wild type. Unlike the pili of other piliated bacterial pathogens, A. salmonicida subsp. salmonicida type IV pili are not absolutely required for virulence in Atlantic salmon. Significant differences in the behavior of the two mutant strains indicated that the two pilus systems are not redundant.
The effect of Aerobic Training on Serotonin and Tryptophan Hydroxylase of Prefrontal Cortex in type 2 Diabetic Rats

Background & Aims: Type 2 diabetes (T2D) is a self-management disease and depression is a common problem related to it. One of the causes of depression is serotonin (5-HT) depleted. The enzyme tryptophan hydroxylase (TPH) is known as limiting enzyme in the production of 5-HT in the brain. Aerobic exercise also has proven benefits in treating and reducing the incidence of chronic diseases such as diabetes. Thus, in this study, we examine the effect of aerobic training on 5-HT and TPHof prefrontal cortex in type 2 diabetic rats. Materials & Methods: This study is experimental and post-test. 30 rats were randomly divided into 3 groups: 1healthy control 2diabetic control and 3exercise diabetic. Groups 2 and 3 received streptozotocin (37mg/kg) by intraperitoneal injection two weeks after the high-fat diet Diabetic training 5 times a week for 8 weeks run on a treadmill with duration and intensity that in the final weeks were 55 min / d and 26 m / min , respectively. 24 hours after the last exercise the prefrontal cortex of mice tissue samples of all groups were extracted and 5-HT (μg/g) and TPHconcentration was measured respectively by Elisa and Western Blotting from prefrontal cortex tissue samples. To evaluate the differences between the group of design, analysis of variance (ANOVA) and Tukey post hoc test at the significance level was less than 0.05 were used. Results: Statistical analysis showed that 5-HT levels in the diabetic group were significantly lower than the healthy control group (P=0.001) and exercise diabetic (P=0.009) and average 5-HT between control group and exercise diabetic has no significant difference. TPH results show that the average diabetic groups were significantly lower than the healthy control group (P=0.000). The results showed that the amount of TPH in the exercise diabetic group was significantly higher than the diabetic control group (P=0.000). Conclusion: In this study, diabetes reduces 5-HT in the prefrontal cortex. Some studies have shown that inflammation in type 2 increases the cytokines IL-6 and TNF-α, and these cytokines by increasing the activity of indolamine 2, 3 dioxygenase in the brain alters the metabolism of tryptophan and reduces the production of 5-HT. Chronic activity reduces systemic and tissue inflammation, thus increasing 5-HT in the brain. The reduction of TPH due to diabetes can also be the factors that affect in the decrease of prefrontal