Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mouse Genome Database (MGD): integration nexus for the laboratory mouse

The Mouse Genome Database (MGD) is the community database resource for the laboratory mouse, a key model organism for interpreting the human genome and for understanding human biology and disease (http://www.informatics.jax.org). MGD provides standard nomenclature and consensus map positions for mouse genes and genetic markers; it provides a curated set of mammalian homology records, user-defined chromosomal maps, experimental data sets and the definitive mouse 'gene to sequence' reference set for the research community. The integration and standardization of these data sets facilitates the transition between mouse DNA sequence, gene and phenotype annotations. A recent focus on allele and phenotype representations enhances the ability of MGD to organize and present data for exploring the relationship between genotype and phenotype. This link between the genome and the biology of the mouse is especially important as phenotype information grows from large mutagenesis projects and genotype information grows from large-scale sequencing projects.
BERBUDAYA MELALUI MEDIA DIGITAL

Understanding and knowing the meaning of culture that shape the culture of a nation is the first step. Indonesia as a country that has various shades of different cultures from Sabang to Merauke, the diversity of cultural patterns is a matter of pride that should be preserved and passed on to generations of recipients so that its presence can be maintained. Do not let the culture of Indonesia is claimed by other countries simply because of ignorance and indifference to the Indonesian cultural heritage. Various means have been taken to promote the culture of the archipelago in conventional media such as magazines, books and even articles. Generations who have lived in abundance world of digital media technology is the next target of inheritance. So it would be nice if steps are taken to a great start to focus more on the spread of culture and passed it along the changing times and of course without losing the identity of the nation.
Multiple Exponential Recombination for Differential Evolution

Differential evolution (DE) is a popular population-based metaheuristic approach for solving numerical optimization problems. In recent years, considerable research has been devoted to the development of new mutation strategies and parameter adaptation mechanisms. However, as one of the basic algorithmic components of DE, the crossover operation has not been sufficiently examined in existing works. Most of the main DE variants solely employ traditional binomial recombination, which has intrinsic limitations in handling dependent subsets of variables. To fill this research niche, we propose a multiple exponential recombination that inherits all the main advantages of existing crossover operators while possessing a stronger ability in managing dependent variables. Multiple segments of the involved solutions will be exchanged during the proposed operator. The properties of the new scheme are examined both theoretically and empirically. Experimental results demonstrate the robustness of the proposed operator in solving problems with unknown variable interrelations.
Assessing the generalizability of smoking studies.

We used data from the 1986 Adult Use of Tobacco Survey, 10 studies of self-quitters and seven studies of treatment seekers, to illustrate how subpopulations of smokers might differ; e.g. treatment seekers vs. self-quitters and research volunteers vs. smokers in the general population. Smoking researchers may wish to use our results to determine whether their sample is similar to the population of interest.
Candida glabrata: Review of Epidemiology, Pathogenesis, and Clinical Disease with Comparison toC. albicans

SUMMARY Until recently, Candida glabrata was considered a relatively nonpathogenic commensal fungal organism of human mucosal tissues. However, with the increased use of immunosuppressive agents, mucosal and systemic infections caused by C. glabrata have increased significantly, especially in the human immunodeficiency virus-infected population. A major obstacle in C. glabrata infections is their innate resistance to azole antimycotic therapy, which is very effective in treating infections caused by other Candida species. Candida glabrata, formerly known as Torulopsis glabrata, contrasts with other Candida species in its nondimorphic blastoconidial morphology and haploid genome. C. glabrata currently ranks second or third as the causative agent of superficial (oral, esophageal, vaginal, or urinary) or systemic candidal infections, which are often nosocomial. Currently, however, there are few recognized virulence factors of C. glabrata and little is known about the host defense mechanisms that protect against infection. Two established animal models (systemic and vaginal) have been established to study treatment, pathogenesis, and immunity. Treatment of C. glabrata infections can include azoles but often requires amphotericin B or flucytosine. This review summarizes all known clinical and experimental information about C. glabrata infections with comparisons to C. albicans as a means of contrasting the two species commonly observed and emphasizing the many recognized differences.
SESAME: European Statistics Explored via Semantic Alignment onto Wikipedia

Authorities such as the European Commission have recognized the need to offer a unified access to the data gathered by a wide variety of providers, such as the European Statistical Organization (Eurostat) or the European Environment Agency. Its EU Open Data Portal serves as a gateway to numerical data, statistical reports, and visualization tools. While making the data available to the users from all member states and concentrating efforts on bridging the language gap, the portal still focuses on a primarily statistical perspective. That is, numerical data are explained with general terms, only. However, the related events, people, or organizations ``causing'' or being ``affected'' by the statistical observation remain concealed to the user. In order to make statistical data better understandable, we present the SESAME system (Statistics Explored via Semantic AlignMEnt). It relies on a novel method for identifying background information and relating it with event descriptions in Wikipedia. Using SESAME, users can jointly browse numerical statistics, their explanation in general terms and - now - also directly relate it to associated Wikipedia articles.
Building a Semantic Transparency Dataset of Chinese Nominal Compounds: A Practice of Crowdsourcing Methodology

This paper describes the work which aimed to create a semantic transparency dataset of Chinese nominal compounds (SemTransCNC 1.0) by crowdsourcing methodology. We firstly selected about 1,200 Chinese nominal compounds from a lexicon of modern Chinese and the Sinica Corpus. Then through a series of crowdsourcing experiments conducted on the Crowdflower platform, we successfully collected both overall semantic transparency and constituent semantic transparency data for each of them. According to our evaluation, the data quality is good. This work filled a gap in Chinese language resources and also practiced and explored the crowdsourcing methodology for linguistic experiment and language resource construction.
Neural Models for StanceCat Shared Task at IberEval 2017

This paper describes our participation in the Stance and Gender Detection in Tweets on Catalan Independence (StanceCat) task at IberEval 2017. Our approach was focused on neural models, firstly using classical and specific model from state of the art, then we introduce a new topology of convolutional network for text classification.
Min-Cost 2-Connected Subgraphs With k Terminals

In the k-2VC problem, we are given an undirected graph G with edge costs and an integer k; the goal is to find a minimum-cost 2-vertex-connected subgraph of G containing at least k vertices. A slightly more general version is obtained if the input also specifies a subset S \subseteq V of terminals and the goal is to find a subgraph containing at least k terminals. Closely related to the k-2VC problem, and in fact a special case of it, is the k-2EC problem, in which the goal is to find a minimum-cost 2-edge-connected subgraph containing k vertices. The k-2EC problem was introduced by Lau et al., who also gave a poly-logarithmic approximation for it. No previous approximation algorithm was known for the more general k-2VC problem. We describe an O(\log n \log k) approximation for the k-2VC problem.
Monitoring training-induced fatigue in snowboard and freeski halfpipe athletes

Snowboard and freeski halfpipe are skill based, high-risk, alpine sports with high injury rates but limited published research. Information on the type and amount of training load (TL) performed and consequent fatigue from halfpipe training is important for coaches in order for them to plan training sessions and adapt to athlete energy levels, and for sport scientists to effectively assist the athletes in preparing for and recovering from training and competition. In a sport where limited research exists, initial studies should focus on observing normal training sessions and gaining coaches’ trust in sports science. Given the variability in observational studies, scientists require stable and reliable methods of observing and measuring TL in halfpipe. As part one of a two part series, this paper explores the concept of TL in halfpipe and suggests several methods of its monitoring in the field. A follow up paper discusses the presentation of fatigue and provides several possibilities for its measurement in the field for halfpipe coaches and sport scientists.
Comparison of nutrient intake by sleep status in selected adults in Mysore, India

Insomnia has become a major public health issue in recent times. Although quality of sleep is affected by environmental, psychophysiological, and pharmacological factors, diet and nutrient intake also contribute to sleep problems. This study investigated the association between nutrient intake and co-morbid symptoms associated with sleep status among selected adults. Subjects in this study included 87 men and women aged 21-45 years. Presence of insomnia was assessed using the Insomnia Screening Questionnaire, and dietary intake was measured over three consecutive days by dietary survey. Descriptive analysis, ANOVA, and Chi-Square tests were performed to compute and interpret the data. Approximately 60% of the participants were insomniacs. People with insomnia consumed significantly lesser quantities of nutrients as compared to normal sleepers. Differences in intakes of energy, carbohydrates, folic acid, and B12 were highly significant (P < 0.002). Further, intakes of protein, fat, and thiamine were significantly different (P < 0.021) between insomniacs and normal sleepers. The nutrient intake pattern of the insomniacs with co-morbid symptoms was quite different from that of the normal sleepers. Based on these results, it is probable that there is an association between nutrition deficiency, co-morbid symptoms, and sleep status. More studies are required to confirm these results.
Adaptive Normalized Risk-Averting Training for Deep Neural Networks

This paper proposes a set of new error criteria and a learning approach, called Adaptive Normalized Risk-Averting Training (ANRAT) to attack the non-convex optimization problem in training deep neural networks without pretraining. Theoretically, we demonstrate its effectiveness based on the expansion of the convexity region. By analyzing the gradient on the convexity index $\lambda$, we explain the reason why our learning method using gradient descent works. In practice, we show how this training method is successfully applied for improved training of deep neural networks to solve visual recognition tasks on the MNIST and CIFAR-10 datasets. Using simple experimental settings without pretraining and other tricks, we obtain results comparable or superior to those reported in recent literature on the same tasks using standard ConvNets + MSE/cross entropy. Performance on deep/shallow multilayer perceptron and Denoised Auto-encoder is also explored. ANRAT can be combined with other quasi-Newton training methods, innovative network variants, regularization techniques and other common tricks in DNNs. Other than unsupervised pretraining, it provides a new perspective to address the non-convex optimization strategy in training DNNs.
An in vivo canine study to assess granulomatous responses in the MedStream Programmable Infusion System (TM) and the SynchroMed II Infusion System®.

OBJECTIVE A rare, but consistently reported complication of intrathecal (IT) delivery of opioids via programmable implantable infusion pumps is aseptic granuloma formation around the tip of the IT catheter. In the current study, the incidence and severity of IT granuloma formation was assessed for the MedStream Programmable Infusion and SynchroMed II Infusion pumps when delivering saline, baclofen, or morphine at varying concentrations and daily doses. DESIGN Randomized study with six groups: 1) MedStream with 0.9% saline; 2) SynchroMed II with 0.9% saline; 3) MedStream with morphine sulfate; 4) SynchroMed II with morphine sulfate; 5) MedStream with baclofen; or 6) SynchroMed II with baclofen. The groups receiving morphine received either low or high concentrations (6 or 12.5 mg/mL) delivered in rates resulting in low to high (1.25-12 mg/day) daily doses. Animals receiving baclofen began at low dose/low concentration and underwent stepwise increases in concentration and dose to a maximum of 2 mg/mL/day. Animals receiving saline received a constant flow rate through either a silicone or a polyurethane IT catheter. SETTING The study was set as in vivo laboratory experiment. SUBJECTS 52 (21-32 kg) canines. INTERVENTIONS Delivering IT saline, baclofen, or morphine through SynchroMed II and MedStream Programmable Infusion pumps. OUTCOME MEASURES The incidence and severity of IT catheter tip granuloma formation was assessed with paraffin-embedded spinal cord sections by a pathologist without knowledge of treatment and compared between drug type, drug concentration, daily dose, and pump type. RESULTS Granuloma formation occurred only in animals receiving morphine, with zero incidence in baclofen or saline infusion, regardless of catheter type. Granuloma incidence increased with increasing morphine concentration or daily dose (one [12.5%] low dose/low concentration, 3 [37.5%] low dose/high concentrations, 4 [50%] high dose/low concentration, and 7 [100%] high dose/high concentration). A granulomatous response to morphine was observed in 10 (63%) animals with SynchroMed II pumps and 5 (33%) animals with MedStream pumps, P = 0.156. CONCLUSIONS In this in vivo canine model of IT drug delivery, catheter tip granuloma formation was associated with higher concentrations and daily doses of morphine infusion while none were seen with baclofen or saline, and was not associated with catheter type. For both pumps, granulomas were only produced in the presence of morphine infusion. These results suggest that the MedStream Programmable Infusion System has a granuloma safety profile at least equivalent to that of the SynchroMed II pump.
Stereoscopic families of permutations, and their applications

A stereoscopic family of permutations maps an m-dimensional mesh into several 1-dimensional lines, in a way that jointly preserves distance information. Specifically, consider any two points and denote their distance on the m-dimensional mesh by d. Then the distance between their images, on the line on which these images are closest together is O(d/sup m/). We initiate a systematic study of stereoscopic families of permutations. We show a construction of these families that involves the use of m+1 images. We also show that under some additional restrictions (namely adjacent points on the image lines originate at points which are not too far away on the mesh), three images are necessary in order to construct such a family for the 2-dimensional mesh. We present two applications for stereoscopic families of permutations. One application is an algorithm for routing on the mesh that guarantees delivery of each packet within a number of steps that depends upon the distance between this packet's source and destination, but is independent of the size of the mesh. Our algorithm is exceptionally simple, involves no queues, and can be used in dynamic settings in which packets are continuously generated. Another application is an extension of the construction of nonexpansive hash functions of Linial and Sasson (STOC 96) from the case of one dimensional metrics to arbitrary dimensions.
Perception-based Visualization of High-Dimensional Medical Images Using Distance Preserving Dimensionality Reduction

A method for visualizing high dimensional medical image data is proposed. The method operates on images in which each pixel contains a high dimensional vector, e.g. a time activity curve (TAC) in a dynamic positron emission tomography (dPET) image, or a tensor, as is the case in diffusion tensor magnetic resonance images (DTMRI). A nonlinear mapping reduces the dimensionality of the data to achieve two goals: Distance preservation and embedding into a perceptual color space. We use multidimensional scaling distance preserving mapping to render similar pixels (e.g. DT or TAC pixels) with perceptually similar colors. The 3D CIELAB perceptual color space is adopted as the range of the distance preserving mapping, with a final similarity transform mapping colors to a maximum gamut size. Similarity between pixels is determined analytically as geodesics on the manifold of pixels or approximated using manifold learning techniques. In particular, dissimilarity between DTMRI pixels is evaluated via a LogEuclidean Riemannian metric respecting the manifold of the rank 3, 2nd order positive semi-definite DTs. Dissimilarity between TACs is approximated via ISOMAP. We demonstrate our approach via artificial highdimensional data, as well as clinical DTMRI and dPET images. Our results demonstrate the effectiveness of our approach in capturing, in a perceptually meaningful way, important structures in the data.
Tyrosine Nitration of PA700 Links Proteasome Activation to Endothelial Dysfunction in Mouse Models with Cardiovascular Risk Factors

Oxidative stress is believed to cause endothelial dysfunction, an early event and a hallmark in cardiovascular diseases (CVD) including hypertension, diabetes, and dyslipidemia. However, the targets for oxidative stress-mediated endothelial dysfunction in CVD have not been completely elucidated. Here we report that 26S proteasome activation by peroxynitrite (ONOO−) is a common pathway for endothelial dysfunction in mouse models of diabetes, hypertension, and dyslipidemia. Endothelial function, assayed by acetylcholine-induced vasorelaxation, was impaired in parallel with significantly increased 26S proteasome activity in aortic homogenates from streptozotocin (STZ)-induced type I diabetic mice, angiotensin-infused hypertensive mice, and high fat-diets -fed LDL receptor knockout (LDLr−/−) mice. The elevated 26S proteasome activities were accompanied by ONOO−-mediated PA700/S10B nitration and increased 26S proteasome assembly and caused accelerated degradation of molecules (such as GTPCH I and thioredoxin) essential to endothelial homeostasis. Pharmacological (administration of MG132) or genetic inhibition (siRNA knockdown of PA700/S10B) of the 26S proteasome blocked the degradation of the vascular protective molecules and ablated endothelial dysfunction induced by diabetes, hypertension, and western diet feeding. Taken together, these results suggest that 26S proteasome activation by ONOO−-induced PA700/S10B tyrosine nitration is a common route for endothelial dysfunction seen in mouse models of hypertension, diabetes, and dyslipidemia.
Effects of storage temperature and time on qualitative and quantitative detection of cytomegalovirus in blood specimens by shell vial culture and PCR

Cytomegalovirus (CMV) infectious titers and DNA levels were determined by quantitative shell vial culture and quantitative-competitive PCR with blood samples from 10 renal transplant recipients with active CMV infection. Blood samples were stored at either room temperature or 4 degrees C and were processed at intervals of 0, 6, 24, 48, and 72 h. All samples were culture and PCR positive at baseline. Whereas the sensitivity of shell vial culture progressively declined, with only 55% positive at 24 h and 10% positive at 48 h, all samples remained PCR positive at all time points. Furthermore, the infectious titer diminished by 83 to 91% by 24 h compared to that at baseline (P < 0.0001), but quantitative DNA levels did not decline over time. Storage temperature had no significant effect on either infectious titer or DNA levels.
Clustering Time Series with Hidden Markov Models and Dynamic Time Warping

Given a source of time series data, such as the stock market or the monitors in an intensive care unit, there is often utility in determining whether there are qualitatively di erent regimes in the data and in characterizing those regimes. For example, one might like to know whether the various indicators of a patient's health measured over time are being produced by a patient who is likely to live or one that is likely to die. In this case, there is a priori knowledge of the number of regimes that exist in the data (two), and the regime to which any given time series belongs can be determined post hoc (by simply noting whether the patient lived or died). However, these two pieces of information are not always present. Consider a system that produces multivariate, realvalued time series by selecting one of K hidden Markov models (HMMs), generating data according to that model, and periodically transitioning to a di erent HMM. Only the time series produced by the system are observable. In particular, K and the identity of the HMM generating any given time series are not observable. Given a set of time series produced by such a system, this paper presents a method for automatically determining K, the number of generating HMMs, and for learning the parameters of those HMMs. An initial estimate of K is obtained by unsupervised clustering of the time series using dynamic time warping (DTW) to assess similarity. In addition to producing an estimate of K, this process yields an initial partitioning of the data. As later sections will explain, DTW is related to HMM training algorithms but is weaker in several respects. Therefore, the clusters based on DTW are likely to contain mistakes. These initial clusters serve as input to a process that trains one HMM on each cluster and iteratively moves time series between clusters based on their likelihoods given the various HMMs. Ultimately the process converges to a nal clustering of the data and a generative model (the HMMs) for each of the clusters. The remainder of the paper is organized as follows. Section 2 brie y reviews HMMs and the algorithms used to induce HMMs from data. Section 3 describes dynamic time warping and its use as a distance measure between multivariate time series for the purpose of unsupervised clustering. Section 4 describes an algorithm that uses DTW to bootstrap the process of tting HMMs to data containing multiple regimes. Section 5 presents the results of experiments with this approach using arti cial data, and section 6 concludes and points to future work.