Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Probability Model for LncRNA–Disease Association Prediction Based on the Naïve Bayesian Classifier

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play crucial roles in biological processes, complex disease diagnoses, prognoses, and treatments. However, experimentally validated associations between lncRNAs and diseases are still very limited. Recently, computational models have been developed to discover potential associations between lncRNAs and diseases by integrating multiple heterogeneous biological data; this has become a hot topic in biological research. In this article, we constructed a global tripartite network by integrating a variety of biological information including miRNA–disease, miRNA–lncRNA, and lncRNA–disease associations and interactions. Then, we constructed a global quadruple network by appending gene–lncRNA interaction, gene–disease association, and gene–miRNA interaction networks to the global tripartite network. Subsequently, based on these two global networks, a novel approach was proposed based on the naïve Bayesian classifier to predict potential lncRNA–disease associations (NBCLDA). Comparing with the state-of-the-art methods, our new method does not entirely rely on known lncRNA–disease associations, and can achieve a reliable performance with effective area under ROC curve (AUCs)in leave-one-out cross validation. Moreover, in order to further estimate the performance of NBCLDA, case studies of colorectal cancer, prostate cancer, and glioma were implemented in this paper, and the simulation results demonstrated that NBCLDA can be an excellent tool for biomedical research in the future.
Label Embedding Based on Multi-Scale Locality Preservation

Label Distribution Learning (LDL) fits the situations well that focus on the overall distribution of the whole series of labels. The numerical labels of LDL satisfy the integrity probability constraint. Due to LDL's special label domain, existing label embedding algorithms that focus on embedding of binary labels are thus unfit for LDL. This paper proposes a specially designed approach MSLP that achieves label embedding for LDL by Multi-Scale Locality Preserving (MSLP). Specifically, MSLP takes the locality information of data in both the label space and the feature space into account with different locality granularity. By assuming an explicit mapping from the features to the embedded labels, MSLP does not need an additional learning process after completing embedding. Besides, MSLP is insensitive to the existing of data points violating the smoothness assumption, which is usually caused by noises. Experimental results demonstrate the effectiveness of MSLP in preserving the locality structure of label distributions in the embedding space and show its superiority over the state-of-the-art baseline methods.
Artificial Argumentation for Humans

The latest years have seen an increasing interest in the topic of Artificial Intelligence (AI), the challenges it is facing, and the recent advances it has achieved, e.g., intelligent personal assistants. Differently from the past, where research on AI was mainly confined in research labs, the topic is now attracting interest from a wider audience, including policy-makers, information technology companies, and philosophers. Alas, these advances have also raised a number of concerns on AI’s social, economic, and legal impact. Hence, the definition of design principles and automated methods to support transparent intelligent machine deliberation is highly desirable. Argumentation is important for handling conflicting beliefs, assumptions, opinions, goals, and many other mental attitudes. Argumentation pervades human intelligent behavior, and I believe that it is a mandatory element to conceive autonomous artificial machines that can exploit argumentation models and tools in the cognitive tasks they are required to carry out. Results in this area will allow reducing the gap between humans and machines towards a good AI hybrid society.
Long-term compliance, safety, and tolerability of sodium oxybate treatment in patients with narcolepsy type 1: a postauthorization, noninterventional surveillance study

Study Objectives To evaluate adherence to sodium oxybate prescribing information for indication and dosage, patients' compliance with instructions for use, safety/tolerability in routine clinical practice, and abuse potential. Methods A postauthorization, noninterventional surveillance study (NCT00244465) in patients who were prescribed sodium oxybate according to current practice by sleep disorders specialists. Patients were monitored for ≤18 months. Results Overall, 749 patients were enrolled; 730 included in the intent-to-treat population (narcolepsy type 1 n = 670, other indications n = 60). We report on patients with narcolepsy type 1 (female 47.9%, mean age 39.4 years); 495/670 (73.9%) completed the study. Median dose: at start of study 4.5 g per night, 6 g per night throughout study, in two equal doses. According to the treatment compliance checklist, 35.5 per cent of patients consumed alcohol, 19.3 per cent took the medication <2 hr after food, and 27.1 per cent did not adhere to recommended time schedule, with few associated treatment-emergent adverse events (TEAEs). Incidences of higher-than-recommended doses, difficulty in preparing doses, and abuse were low. TEAEs were reported by 67.3 per cent, most frequently headache (11.6%) and nasopharyngitis (6.4%). Discontinuation due to TEAEs: 8.8 per cent. Serious TEAEs: 6.4 per cent. There were no reports of respiratory depression. No particular safety concerns were identified in pediatric or elderly patients, or those with underlying sleep apnea. Conclusions In this large postauthorization safety study of sodium oxybate use, indication and dosage prescribing recommendations were generally followed, and most patients complied with instructions, with deviations around alcohol consumption, eating before dosing and timing. The overall safety profile was consistent with previous observations; incidence of abuse was low. Section Neurological disorders. Clinical Trial Postauthorization, noninterventional, surveillance, pharmacoepidemiology study to evaluate long-term safety, tolerability, and compliance in administration of Xyrem (sodium oxybate) oral solution in patients who receive treatment with this medication in regular clinical practice. https://clinicaltrials.gov/ct2/show/NCT00244465, ClinicalTrials.gov: NCT00244465.
A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms

Evolutionary algorithms (EAs) have been widely applied to solve multi-objective optimization problems. In contrast to great practical successes, their theoretical foundations are much less developed, even for the essential theoretical aspect, i.e., running time analysis. In this paper, we propose a general approach to estimating upper bounds on the expected running time of multi-objective EAs (MOEAs), and then apply it to diverse situations, including bi-objective and many-objective optimization as well as exact and approximate analysis. For some known asymptotic bounds, our analysis not only provides their leading constants, but also improves them asymptotically. Moreover, our results provide some theoretical justification for the good empirical performance of MOEAs in solving multi-objective combinatorial problems.
Quantifying Evolution of Short and Long-Range Correlations in Chinese Narrative Texts across 2000 Years

We investigate how short and long-range word length correlations evolve in Chinese narrative texts. The results show that, for short-range word length correlations, no significant linear evolutionary trend was found. But for long-range correlations, there are two opposite tendencies for two different regimes: the Hurst exponent of small-scale (box size ranges from 10 to 100) word length correlations decreases over time, and the exponent of large-scale (box size ranges from 101 to 1000) shows an increasing tendency. The increase of word length is corroborated as an essential regularity of word evolution in written Chinese. Further analyses show that a significant correlation coefficient is obtained between Hurst exponents from the small-scale correlations and mean word length across time. These indicate that word length correlation evolution possesses different self-adaptive mechanisms in terms of different scales of distances between words. We speculate that the increase of word length and sentence length in written Chinese may account for this phenomenon, in terms of both the social-cultural aspects and the self-adapting properties of language structures.
In vivo CYP3A4 activity does not predict the magnitude of interaction between itraconazole and tacrolimus from an extended release formulation

The magnitude of interaction between the CYP3A4 substrate tacrolimus and various CYP3A4 inhibitors is highly unpredictable. We investigated whether an individual's baseline in vivo CYP3A4 activity, assessed using the oral midazolam (MDZ) probe, could be used to predict the magnitude of drug–drug interaction between tacrolimus and the potent CYP3A4 inhibitor itraconazole. In a prospective single‐arm open‐label study, 16 healthy volunteers were administered single doses of MDZ and tacrolimus before and after a 4‐day course of itraconazole. Itraconazole treatment resulted in a 9.0‐fold decrease in MDZ apparent oral clearance (CL/F) and a 3.3‐fold decrease in tacrolimus CL/F (P < 0.001 for each). MDZ CL/F and tacrolimus CL/F were positively correlated both at baseline (r = 0.582, P = 0.018) and after itraconazole (r = 0.811, P < 0.001). Furthermore, baseline MDZ CL/F was positively correlated to the fold change in MDZ CL/F resulting from CYP3A4 inhibition (r = 0.759, P = 0.001). However, no predictors of change in tacrolimus CL/F resulting from CYP3A4 inhibition were identified, including baseline MDZ CL/F (P = 0.453), baseline tacrolimus CL/F (P = 0.759) and fold change in MDZ CL/F between both phases (P = 0.274). In conclusion, baseline oral MDZ clearance does not predict the magnitude of interaction between tacrolimus and itraconazole.
H9N2 influenza viruses from Bangladesh: Transmission in chicken and New World quail

The H9N2 influenza viruses that have become established in Bangladeshi live poultry markets possess five gene segments of the highly pathogenic H7N3 avian influenza virus. We assessed the replication, transmission, and disease potential of three H9N2 viruses in chickens and New World quail. Each virus replicated to high titers and transmitted by the airborne route to contacts in both species. Infected chickens showed no disease signs, and the viruses differed in their disease potential in New World quail. New World quail were more susceptible than chickens to H9N2 viruses and shed virus after airborne transmission for 10 days. Consequently, New World quail are a potential threat in the maintenance and spread of influenza virus in live poultry markets.
Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG-Based Brain-Computer Interface: A Comprehensive Study

It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of a brain-computer interface (BCI) and diagnosis of brain diseases in clinical research. Therefore, for BCI and clinical applications, it is very important to remove/reduce these artifacts before EEG signal analysis. Although, EOG-based methods are simple and fast for removing artifacts but their performance, meanwhile, is highly affected by the bidirectional contamination process. Some studies emphasized that the solution to this problem is low-pass filtering EOG signals before using them in artifact removal algorithm but there is still no evidence on the optimal low-pass frequency limits of EOG signals. In this study, we investigated the optimal EOG signal filtering limits using state-of-the-art artifact removal techniques with fifteen artificially contaminated EEG and EOG datasets. In this comprehensive analysis, unfiltered and twelve different low-pass filtering of EOG signals were used with five different algorithms, namely, simple regression, least mean squares, recursive least squares, REGICA, and AIR. Results from statistical testing of time and frequency domain metrics suggested that a low-pass frequency between 6 and 8 Hz could be used as the most optimal filtering frequency of EOG signals, both to maximally overcome/minimize the effect of bidirectional contamination and to achieve good results from artifact removal algorithms. Furthermore, we also used BCI competition IV datasets to show the efficacy of the proposed framework on real EEG signals. The motor-imagery-based BCI achieved statistically significant high-classification accuracies when artifacts from EEG were removed by using 7 Hz low-pass filtering as compared to all other filterings of EOG signals. These results also validated our hypothesis that low-pass filtering should be applied to EOG signals for enhancing the performance of each algorithm before using them for artifact removal process. Moreover, the comparison results indicated that the hybrid algorithms outperformed the performance of single algorithms for both simulated and experimental EEG datasets.
On the Mechanism of Magnesium Storage in Micro- and Nano-Particulate Tin Battery Electrodes

This study reports on the electrochemical alloying-dealloying properties of Mg2Sn intermetallic compounds. 119Sn Mössbauer spectra of β-Sn powder, thermally alloyed cubic-Mg2Sn, and an intermediate MgSn nominal composition are used as references. The discharge of a Mg/micro-Sn half-cell led to significant changes in the spectra line shape, which is explained by a multiphase mechanism involving the coexistence of c-Mg2Sn, distorted Mg2−δSn, and Mg-doped β-Sn. Capacities and capacity retention were improved by using nanoparticulate tin electrodes. This material reduces significantly the diffusion lengths for magnesium and contains surface SnO and SnO2, which are partially electroactive. The half-cell potentials were suitable to be combined versus the MgMn2O4 cathodes. Energy density and cycling properties of the resulting full Mg-ion cells are also scrutinized.
A Fast Local Search Algorithm for Minimum Weight Dominating Set Problem on Massive Graphs

The minimum weight dominating set (MWDS) problem is NP-hard and also important in many applications. Recent heuristic MWDS algorithms can hardly solve massive real world graphs effectively. In this paper, we design a fast local search algorithm called FastMWDS for the MWDS problem, which aims to obtain a good solution on massive graphs within a short time. In this novel local search framework, we propose two ideas to make it effective. Firstly, we design a new fast construction procedure with four reduction rules to cut down the size of massive graphs. Secondly, we propose the three-valued two-level configuration checking strategy to improve local search, which is interestingly a variant of configuration checking (CC) with two levels and multiple values. Experiment results on a broad range of massive real world graphs show that FastMWDS finds much better solutions than state of the art MWDS algorithms.
Generalization Bounds for Regularized Pairwise Learning

Pairwise learning refers to learning tasks with the associated loss functions depending on pairs of examples. Recently, pairwise learning has received increasing attention since it covers many machine learning schemes, e.g., metric learning, ranking and AUC maximization, in a unified framework. In this paper, we establish a unified generalization error bound for regularized pairwise learning without either Bernstein conditions or capacity assumptions. We apply this general result to typical learning tasks including distance metric learning and ranking, for each of which our discussion is able to improve the state-of-the-art results.
A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.
Prenatal Exposure to Stressful Life Events and Infant Breastfeeding.

BACKGROUND Of the various barriers to breastfeeding, limited information is available on the relationship between prenatal stress and breastfeeding. This study investigates the association between prenatal stressful life event (SLE) exposure and breastfeeding initiation postpartum. MATERIALS AND METHODS Using Rhode Island Pregnancy Risk Assessment Monitoring System data from 2012 to 2014, SLE was defined as self-report of prenatal exposure to 14 predefined life events such as job loss or illness. Exposure to SLE was categorized by number and type of events. Multivariable logistic regression was performed to assess the relationship between SLE and breastfeeding initiation. Results accounted for complex survey design and were adjusted for maternal and infant characteristics (age, race, ethnicity, insurance, delivery type, parity, gestational age, birth weight for gestational age, and neonatal intensive care unit admission). RESULTS Among 3,353 respondents, 86% reported breastfeeding initiation, 74% reported exposure to ≥1 SLE, and 17% reported exposure to ≥4 SLE. Decreased odds of breastfeeding initiation were associated with prenatal exposure to ≥4 SLE (adjusted odds ratio [aOR] 0.67; 95% confidence interval [CI]: 0.48-0.95), emotional stressors (aOR 0.77; 95% CI: 0.61-0.98), and traumatic stressors (aOR 0.68; 95% CI: 0.50-0.91). CONCLUSION This study underscores the impact of exposure to prenatal SLE on breastfeeding initiation among postpartum women. Findings may assist providers in identifying at-risk women for anticipatory guidance to improve breastfeeding rates.
The Fungal Cyp51 Inhibitor VT-1129 Is Efficacious in an Experimental Model of Cryptococcal Meningitis

Cryptococcal meningitis is a significant cause of morbidity and mortality in immunocompromised patients. VT-1129 is a novel fungus-specific Cyp51 inhibitor with potent in vitro activity against Cryptococcus species. ABSTRACT Cryptococcal meningitis is a significant cause of morbidity and mortality in immunocompromised patients. VT-1129 is a novel fungus-specific Cyp51 inhibitor with potent in vitro activity against Cryptococcus species. Our objective was to evaluate the in vivo efficacy of VT-1129 against cryptococcal meningitis. Mice were inoculated intracranially with Cryptococcus neoformans. Oral treatment with VT-1129, fluconazole, or placebo began 1 day later and continued for either 7 or 14 days, and brains and plasma were collected on day 8 or 15, 1 day after therapy ended, and the fungal burden was assessed. In the survival study, treatment continued until day 10 or day 28, after which mice were monitored off therapy until day 30 or day 60, respectively, to assess survival. The fungal burden was also assessed in the survival arm. VT-1129 plasma and brain concentrations were also measured. VT-1129 reached a significant maximal survival benefit (100%) at a dose of 20 mg/kg of body weight once daily. VT-1129 at doses of ≥0.3 mg/kg/day and each dose of fluconazole significantly reduced the brain tissue fungal burden compared to that in the control after both 7 and 14 days of dosing. The fungal burden was also undetectable in most mice treated with a dose of ≥3 mg/kg/day, even ≥20 days after dosing had stopped, in the survival arm. In contrast, rebounds in fungal burden were observed with fluconazole. These results are consistent with the VT-1129 concentrations, which remained elevated long after dosing had stopped. These data demonstrate the potential utility of VT-1129 to have a marked impact in the treatment of cryptococcal meningitis.
Neural Networks Incorporating Unlabeled and Partially-labeled Data for Cross-domain Chinese Word Segmentation

Most existing Chinese word segmentation (CWS) methods are usually supervised. Hence, large-scale annotated domain-specific datasets are needed for training. In this paper, we seek to address the problem of CWS for the resource-poor domains that lack annotated data. A novel neural network model is proposed to incorporate unlabeled and partially-labeled data. To make use of unlabeled data, we combine a bidirectional LSTM segmentation model with two character-level language models using a gate mechanism. These language models can capture co-occurrence information. To make use of partially-labeled data, we modify the original cross entropy loss function of RNN. Experimental results demonstrate that the method performs well on CWS tasks in a series of domains.
Nutritional Intake in Elite Cross-Country Skiers During Two Days of Training and Competition.

This study investigated the energy, macronutrient, and fluid intakes, as well as hydration status (urine specific gravity), in elite cross-country skiers during a typical day of training (Day 1) and a sprint skiing competition the following day (Day 2). A total of 31 (18 males and 13 females) national team skiers recorded their food and fluid intakes and urine specific gravity was measured on Days 1 and 2. In addition, the females completed the Low Energy Availability in Females Questionnaire to assess their risk of long-term energy deficiency. Energy intake for males was 65 ± 9 kcal/kg on Day 1 versus 58 ± 9 kcal/kg on Day 2 (p = .002) and for females was 57 ± 10 on Day 1 versus 55 ± 5 kcal/kg on Day 2 (p = .445). Carbohydrate intake recommendations of 10-12 g·kg-1·day-1 were not met by 89% of males and 92% of females. All males and females had a protein intake above the recommended 1.2-2.0 g/kg on both days and a postexercise protein intake above the recommended 0.3 g/kg. Of the females, 31% were classified as being at risk of long-term energy deficiency. In the morning of Day 1, 50% of males and 46% of females were dehydrated; on Day 2, this was the case for 56% of males and 38% of females. In conclusion, these data suggest that elite cross-country skiers ingested more protein and less carbohydrate than recommended and one third of the females were considered at risk of long-term energy deficiency. Furthermore, many of the athletes were dehydrated prior to training and competition.