Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification and analysis of a large collection of in vivo bioassay descriptions

Testing potential drug treatments in animal disease models is a decisive step of all preclinical drug discovery programs. Yet, despite the importance of such experiments for translational medicine, there have been relatively few efforts to comprehensively and consistently analyze the data produced by in vivo bioassays. This is partly due to their complexity and lack of accepted reporting standards—publicly available animal screening data are only accessible in unstructured free-text format, which hinders computational analysis. In this study, we use text mining to extract information from the descriptions of over 100,000 drug screening-related assays in rats and mice. We retrieve our dataset from ChEMBL—an open-source literature-based database focused on preclinical drug discovery. We show that in vivo assay descriptions can be effectively mined for relevant information, including experimental factors that might influence the outcome and reproducibility of animal research: genetic strains, experimental treatments, and phenotypic readouts used in the experiments. We further systematize extracted information using unsupervised language model (Word2Vec), which learns semantic similarities between terms and phrases, allowing identification of related animal models and classification of entire assay descriptions. In addition, we show that random forest models trained on features generated by Word2Vec can predict the class of drugs tested in different in vivo assays with high accuracy. Finally, we combine information mined from text with curated annotations stored in ChEMBL to investigate the patterns of usage of different animal models across a range of experiments, drug classes, and disease areas.
Compressive Sensing-Based Topology Identification for Smart Grids

Smart grid (SG) technology transforms the traditional power grid from a single-layer physical system to a cyber-physical network that includes a second layer of information. Collecting, transferring, and analyzing the huge amount of data that can be captured from different parameters in the network, together with the uncertainty that is caused by the distributed power generators, challenge the standard methods for security and monitoring in future SGs. Other important issues are the cost and power efficiency of data collection and analysis, which are highlighted in emergency situations such as blackouts. This paper presents an efficient dynamic solution for online SG topology identification (TI) and monitoring by combining concepts from compressive sensing (CS) and graph theory. In particular, the SG is modeled as a huge interconnected graph, and then using a dc power-flow model under the probabilistic optimal power flow (P-OPF), TI is mathematically reformulated as a sparse-recovery problem (SRP). This problem and challenges therein are efficiently solved using modified sparse-recovery algorithms. Network models are generated using the MATPOWER toolbox. Simulation results show that the proposed method represents a promising alternative for real-time monitoring in SGs.
Equilibrium and Welfare in Markets with Financially Constrained Arbitrageurs

We propose a multiperiod model in which competitive arbitrageurs exploit discrepancies between the prices of two identical risky assets traded in segmented markets. Arbitrageurs need to collateralize separately their positions in each asset, and this implies a financial constraint limiting positions as a function of wealth. In our model, arbitrage activity benefits all investors because arbitrageurs supply liquidity to the market. However, arbitrageurs might fail to take a socially optimal level of risk, in the sense that a change in their positions can make all investors better off. We characterize conditions under which arbitrageurs take too much or too little risk.
A Fixpoint Semantics for Ordered Logic

We develop semantics for a logic, called ordered logic (OL), which models the most important aspects of object-oriented programming languages, such as object identity, multiple inheritance and defaults. The logic is based on a partially ordered structure of logical theories, which play the role of objects. OL is non-monotonic under the natural modeltheoretic semantics. A non-deterministic procedure is defined that has all models as fixpoints. It is shown that, for a well-behaved subclass of theories, this procedure can be used to generate exactly the set of 'preferred' models, where preference is based on the lack of 'assumptions'. Classical logic programs with negation by failure are special cases of OL theories. From the above we can then derive a syntactic characterization of logic programs with stable models.
Notes on Interval Estimation of the Generalized Odds Ratio Under Stratified Random Sampling

It is not rare to encounter the patient response on the ordinal scale in a randomized clinical trial (RCT). Under the assumption that the generalized odds ratio (GOR) is homogeneous across strata, we consider four asymptotic interval estimators for the GOR under stratified random sampling. These include the interval estimator using the weighted-least-squares (WLS) approach with the logarithmic transformation (WLSL), the interval estimator using the Mantel-Haenszel (MH) type of estimator with the logarithmic transformation (MHL), the interval estimator using Fieller's theorem with the MH weights (FTMH) and the interval estimator using Fieller's theorem with the WLS weights (FTWLS). We employ Monte Carlo simulation to evaluate the performance of these interval estimators by calculating the coverage probability and the average length. To study the bias of these interval estimators, we also calculate and compare the noncoverage probabilities in the two tails of the resulting confidence intervals. We find that WLSL and MHL can generally perform well, while FTMH and FTWLS can lose either precision or accuracy. We further find that MHL is likely the least biased. Finally, we use the data taken from a study of smoking status and breathing test among workers in certain industrial plants in Houston, Texas, during 1974 to 1975 to illustrate the use of these interval estimators.
Estimation of the mean of functional time series and a two‐sample problem

Summary. The paper is concerned with inference based on the mean function of a functional time series. We develop a normal approximation for the functional sample mean and then focus on the estimation of the asymptotic variance kernel. Using these results, we develop and asymptotically justify testing procedures for the equality of means in two functional samples exhibiting temporal dependence. Evaluated by means of a simulation study and application to a real data set, these two‐sample procedures enjoy good size and power in finite samples.
Logic Programming with Graph Automorphism: Integrating nauty with Prolog (Tool Description)*

Abstract This paper presents the pl-nauty library, a Prolog interface to the nauty graph-automorphism tool. Adding the capabilities of nauty to Prolog combines the strength of the “generate and prune” approach that is commonly used in logic programming and constraint solving, with the ability to reduce symmetries while reasoning over graph objects. Moreover, it enables the integration of nauty in existing tool-chains, such as SAT-solvers or finite domain constraints compilers which exist for Prolog. The implementation consists of two components: pl-nauty, an interface connecting nauty's C library with Prolog, and pl-gtools, a Prolog framework integrating the software component of nauty, called gtools, with Prolog. The complete tool is available as a SWI-Prolog module. We provide a series of usage examples including two that apply to generate Ramsey graphs.
Heart Rate Adjustment of Exercise‐Induced ST Segment Depression: Improved Risk Stratification in the Framingham Offspring Study

BackgroundSimple heart rate adjustment of ST segment depression during exercise (ΔST/HR index) and the pattern of ST depression as a function of heart rate during exercise and recovery (the rate-recovery loop) have been shown to improve the ability of the exercise electrocardiogram to detect the presence of coronary heart disease (CHD), but the performance of these methods for the prediction of future coronary events remains to be examined. Methods and ResultsWe compared the ΔST/HR index and the rate-recovery loop with standard electrocardiographic criteria for prediction of CHD events in 3,168 asymptomatic men and women in the Framingham Offspring Study who underwent treadmill exercise electrocardiography and who, at entry, were free of clinical and electrocardiographic evidence of CHD. After a mean follow-up of 4.3 years, there were 65 new CHD events: four sudden deaths, 24 new myocardial infarctions, and 37 incident cases of angina pectoris. When a Cox proportional hazards model with adjustment for age and sex was used, a positive exercise electrocardiogram by standard criteria (≥.0.1 mV horizontal or downsloping ST segment depression) was not predictive of new CHD events (x2=0.40, p =0.52). In contrast, stratification according to the presence or absence of a positive AST/HR index (≥.1.6 μV/beat/min) and a positive (counterclockwise) rate-recovery loop was associated with CHD event risk (x2=9.45, p< 0.1) and separated subjects into three groups with varying risks of coronary events: high risk, when both tests were positive (relative risk 3.6; 95% confidence interval, 2.4-5.4); intermediate risk, when either the ΔST/HR index or the rate-recovery loop was positive (relative risk, 1.9; 95% confidence interval, 1.3-2.8); and low risk, when both tests were negative. After multivariate adjustment for age, sex, smoking, total cholesterol level, fasting glucose level, diastolic blood pressure, and electrocardiographic evidence of left ventricular hypertrophy, the combined AST/HR index and rate-recovery loop criteria remained predictive of coronary events (X2=5.45, p = 0.02). ConclusionsHeart rate adjustment of ST segment depression by the AST/HR index and the rate-recovery loop during exercise electrocardiography can improve prediction of future coronary events in asymptomatic men and women. (Circulation 1991;83:866–874)
What Happens in a Moment

There has been evidence for the very brief, temporal quantization of perceptual experience at regular intervals below 100 ms for several decades. We briefly describe how earlier studies led to the concept of “psychological moment” of between 50 and 60 ms duration. According to historical theories, within the psychological moment all events would be processed as co-temporal. More recently, a link with physiological mechanisms has been proposed, according to which the 50–60 ms psychological moment would be defined by the upper limit required by neural mechanisms to synchronize and thereby represent a snapshot of current perceptual event structure. However, our own experimental developments also identify a more fine-scaled, serialized process structure within the psychological moment. Our data suggests that not all events are processed as co-temporal within the psychological moment and instead, some are processed successively. This evidence questions the analog relationship between synchronized process and simultaneous experience and opens debate on the ontology and function of “moments” in psychological experience.
Load balancing for D2D-based relay communications in heterogeneous network

To increase the number of accommodated users in the cellular heterogeneous network (HetNet), we propose an energy efficient load balancing strategy for device-to-device (D2D) based relay communications. In a HetNet, user could sent data to an adjacent uncongested femtocell through the D2D-based relay communications rather than wait for the response from the congested macrocell. Specifically, the proposed strategy manages resources by taking into account both the cross-tier and the co-tier interference, and solves the maximum transmission rate problem based on D2D communications in heterogeneous network. Simulation results show that, with the guarantee of the performance of the pre-existing users, the heterogeneous network can accommodate more users and eventually achieve a higher throughput and better energy efficiency, thus the performance is significantly improved.
A method of limiting performance loss of CNNs in noisy environments

Convolutional Neural Network (CNN) recognition rates drop in the presence of noise. We demonstrate a novel method of counteracting this drop in recognition rate by adjusting the biases of the neurons in the convolutional layers according to the noise conditions encountered at runtime. We compare our technique to training one network for all possible noise levels, dehazing via preprocessing a signal with a denoising autoencoder, and training a network specifically for each noise level. Our system compares favorably in terms of robustness, computational complexity and recognition rate.
Sildenafil Inhibits the Proliferation of Cultured Human Endothelial Cells

The proliferation of endothelial cells plays a crucial role in the development of intraplaque angiogenesis (IPA). IPA is a major source of intraplaque hemorrhage and therefore contributes to the destabilization of atherosclerotic plaques. Therefore, the aim of the present study was to examine, whether sildenafil inhibits endothelial cell growth. The proliferation of human endothelial cells derived from umbilical cord veins (HUVEC) was examined on DNA level by measurements of (3H)-thymidine incorporation. Cell viability was analyzed using trypan blue staining. The proliferation of cultured human endothelial cells was significantly decreased by 1 μmol/l (-48.4%) and 10 μmol/l (-89.6%) sildenafil (n=10, p<0.05). This was not a cytotoxic effect, because cell viability was only reduced at sildenafil concentrations of 50 μmol/l or greater. In addition sildenafil significantly reduced endothelial proliferation induced by bFGF (n=10, p<0.05). The presented results demonstrate an antiangiogenic effect of sildenafil that might be useful in the prevention of atherosclerotic plaque vascularization.
Teleoperation based on the hidden robot concept

Overlaying classical teleoperation control schemes based on a bilateral master-slave coupling, a teleoperation architecture designed in a general teleworking context is proposed. In this scheme, the executing machine is perceptually and functionally hidden to the operator by means of an intermediate functional representation between a real remote world and man. As any executing machine, and more particularly a robot, will be replaced by man, the image of the robot will not appear in the intermediate representation. This principle is thus named: "the hidden robot concept. " In this approach, the teleoperation problem is divided into two main parts: 1) choosing the appropriate intermediate representation and determining its interaction and relation with man and 2) building the relations and transformations between the intermediate representation and the real remote environment. The constituents of this teleoperator are outlined in this paper and an experiment validating this concept is presented.
Guide for Current Nutrigenetic, Nutrigenomic, and Nutriepigenetic Approaches for Precision Nutrition Involving the Prevention and Management of Chronic Diseases Associated with Obesity

Chronic diseases, including obesity, are major causes of morbidity and mortality in most countries. The adverse impacts of obesity and associated comorbidities on health remain a major concern due to the lack of effective interventions for prevention and management. Precision nutrition is an emerging therapeutic approach that takes into account an individual's genetic and epigenetic information, as well as age, gender, or particular physiopathological status. Advances in genomic sciences are contributing to a better understanding of the role of genetic variants and epigenetic signatures as well as gene expression patterns in the development of diverse chronic conditions, and how they may modify therapeutic responses. This knowledge has led to the search for genetic and epigenetic biomarkers to predict the risk of developing chronic diseases and personalizing their prevention and treatment. Additionally, original nutritional interventions based on nutrients and bioactive dietary compounds that can modify epigenetic marks and gene expression have been implemented. Although caution must be exercised, these scientific insights are paving the way for the design of innovative strategies for the control of chronic diseases accompanying obesity. This document provides a number of examples of the huge potential of understanding nutrigenetic, nutrigenomic, and nutriepigenetic roles in precision nutrition.
Selective NFAT targeting in T cells ameliorates GvHD while maintaining antitumor activity

Significance Patients suffering from hematologic malignancies might have no alternative to eliminating their morbid immune system. Although lethal irradiation or chemotherapy largely eradicates tumor cells, it also destroys the hematopoietic system, which necessitates transplantation of bone marrow stem cells from a healthy donor. T cells within the graft attack remaining tumor cells. This graft-versus-leukemia (GvL) effect is highly appreciated, because it prevents tumor relapses. Coevally, however, T cells attack nonmalignant tissue of the host with life-threating consequences called graft-versus-host disease (GvHD). We found that absence of the transcription factors NFAT (nuclear factor of activated T cells) in T cells prevents harmful GvHD, but preserves the valuable GvL. Therefore, instead of broad immune suppression, we propose to target NFAT specifically during allogenic stem cell transplantation. Graft-versus-host disease (GvHD) is a life-threatening immunological complication after allogenic hematopoietic stem cell transplantation (allo-HCT). The intrinsic graft-versus-leukemia (GvL) effect, however, is the desirable curative benefit. Patients with acute GvHD are treated with cyclosporine A (CsA) or tacrolimus (FK506), which not only often causes severe adverse effects, but also interferes with the anticipated GvL. Both drugs inhibit calcineurin, thus at first suppressing activation of the nuclear factor of activated T cells (NFAT). Therefore, we explored the specific contribution of individual NFAT factors in donor T cells in animal models of GvHD and GvL. Ablation of NFAT1, NFAT2, or a combination of both resulted in ameliorated GvHD, due to reduced proliferation, target tissue homing, and impaired effector function of allogenic donor T cells. In contrast, the frequency of Foxp3+ regulatory T (Treg) cells was increased and NFAT-deficient Tregs were fully protective in GvHD. CD8+ T-cell recall response and, importantly, the beneficial antitumor activity were largely preserved in NFAT-deficient effector T cells. Thus, specific inhibition of NFAT opens an avenue for an advanced therapy of GvHD maintaining protective GvL.
Replication and persistence of simian immunodeficiency virus variants after passage in macaque lymphocytes and established human cell lines

In lentivirus infections, there are typically few cells in the host that harbor the provirus. For this reason, molecular clones of human and simian immunodeficiency viruses (HIV and SIV) are generally derived after passage and amplification of the virus in cell culture. To determine whether SIV variants that persist in culture are similar to the variants that predominate in the host, we examined the proviral sequence of the SIV envelope (env) gene before and after cocultivation of lymphocytes from a macaque with AIDS with naive macaque lymphocytes or human cell lines. Many of the predominant variants in the monkey replicated and persisted in macaque lymphocytes and CEMx174 cells in culture, but a more limited population of variants replicated in C8166 cells. Passage of virus, harvested after 4 weeks of coculture, onto naive cells further demonstrated that the majority of proviruses detected by polymerase chain reaction were also viral variants that were expressed and packaged into infectious virions.
On Private Similarity Search Protocols

In a private similarity search (PSS) protocol, a client receives from the database the entry, closest to her query, without either the client or the database getting to know more information than necessary. PSS protocols have potentially wide application in areas like bioinformatics, where precise queries might be impossible. We show that the previously proposed PSS protocols by Du and Atallah have serious weaknesses; in particular, some of their protocols can be broken by a semi-honest third party who observes a relatively small amount of traffic. In several cases, we show that even maximally securified versions of these protocols— when used as proposed by Du and Atallah—are not private in the sense, needed in the practice. We propose a few protocols that are better from the privacy viewpoint, but none of the proposed protocols is really efficient.
Peer and parent-related loneliness in Irish adolescents

Using a cross-sectional design, adolescent loneliness was explored in a sample of 1090 Irish adolescents (396 male and 688 female) drawn from the 2nd year and transition/5th year of 23 secondary schools from 8 counties across the Irish Republic. The Louvain Loneliness Scale for Children and Adolescents (LLCA) was administered to this sample and the results from the 4 sub-scales (Parent-Related and Peer-Related Loneliness, A version to and Affinity for Aloneness) compared across the age-groups and by gender. A significant drop in parent and peer-related loneliness across adolescence was found suggesting that early adolescence is a time of greater vulnerability for loneliness.
Recurrence statistics of great earthquakes

We investigate the sequence of great earthquakes over the past century. To examine whether the earthquake record includes temporal clustering, we identify aftershocks and remove those from the record. We focus on the recurrence time, defined as the time between two consecutive earthquakes. We study the variance in the recurrence time and the maximal recurrence time. Using these quantities, we compare the earthquake record with sequences of random events, generated by numerical simulations, while systematically varying the minimal earthquake magnitude Mmin. Our analysis shows that the earthquake record is consistent with a random process for magnitude thresholds 7.0≤Mmin≤8.3, where the number of events is larger. Interestingly, the earthquake record deviates from a random process at magnitude threshold 8.4≤Mmin≤8.5, where the number of events is smaller; however, this deviation is not strong enough to conclude that great earthquakes are clustered. Overall, the findings are robust both qualitatively and quantitatively as statistics of extreme values and moment analysis yield remarkably similar results.
Channel-dependent GMM and Multi-class Logistic Regression models for language recognition

This paper describes two new approaches to spoken language recognition. These were both successfully applied in the NIST 2005 Language Recognition Evaluation. The first approach extends the Gaussian mixture model technique with channel dependency, which results in actual detection costs (CDET) of 0.095 in NIST LRE-2005, and which should be compared to a traditional 2-gender dependency of GMM language models achieving 0.120. The second approach is a multi-class logistic regression system, which operates similarly to a support vector machine (SVM), but can be trained for all languages simultaneously. This new approach resulted in a CDET of 0.198. The joint TNO-Spescom Datavoice (TNO-SDV) submission to NIST LRE-2005 contained two more systems and obtained a result of 0.0958