Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Mitochondrial DNA Variation in Drug Response: A Systematic Review

Background: The triad of drug efficacy, toxicity and resistance underpins the risk-benefit balance of all therapeutics. The application of pharmacogenomics has the potential to improve the risk-benefit balance of a given therapeutic via the stratification of patient populations based on DNA variants. A growth in the understanding of the particulars of the mitochondrial genome, alongside the availability of techniques for its interrogation has resulted in a growing body of literature examining the impact of mitochondrial DNA (mtDNA) variation upon drug response. Objective: To critically evaluate and summarize the available literature, across a defined period, in a systematic fashion in order to map out the current landscape of the subject area and identify how the field may continue to advance. Methods: A systematic review of the literature published between January 2009 and December 2020 was conducted using the PubMed database with the following key inclusion criteria: reference to specific mtDNA polymorphisms or haplogroups, a core objective to examine associations between mtDNA variants and drug response, and research performed using human subjects or human in vitro models. Results: Review of the literature identified 24 articles reporting an investigation of the association between mtDNA variant(s) and drug efficacy, toxicity or resistance that met the key inclusion criteria. This included 10 articles examining mtDNA variations associated with antiretroviral therapy response, 4 articles examining mtDNA variants associated with anticancer agent response and 4 articles examining mtDNA variants associated with antimicrobial agent response. The remaining articles covered a wide breadth of medications and were therefore grouped together and referred to as “other. ” Conclusions: Investigation of the impact of mtDNA variation upon drug response has been sporadic to-date. Collective assessment of the associations identified in the articles was inconclusive due to heterogeneous methods and outcomes, limited racial/ethnic groups, lack of replication and inadequate statistical power. There remains a high degree of idiosyncrasy in drug response and this area has the potential to explain variation in drug response in a clinical setting, therefore further research is likely to be of clinical benefit.
A Novel Maximum Power Point Tracking Strategy Based on Enhanced Real-Time Adaptive Step-Size Modified Control for Photovoltaic Systems

With the development of society, the demand for energy keeps increasing. Solar energy has received widespread concern for its renewable and environmentally friendly advantages. As one of the most efficient solar energy devices, the output power of photovoltaic (PV) cells is easily affected by the external environment. In order to solve the problem of the maximum power output of PV cells, this paper proposed a maximum power point tracking (MPPT) method. Based on the online particle swarm optimization (PSO) variable step length algorithm, the pulse width modulation (PWM) control module parameters are set according to the parameters of the PV cells’ output voltage. By dynamically adjusting the output voltage step of the PV cells online, the output of the PV cells is stabilized near the maximum power point (MPP). The simulation results concluded that the method and model could accurately adjust the output voltage according to the external environment changes in real time and reduce the voltage fluctuation at the MPP, providing a new idea to solve the problem of MPPT of PV cells.
Driving With Distraction: Measuring Brain Activity and Oculomotor Behavior Using fMRI and Eye-Tracking

Introduction Driving motor vehicles is a complex task that depends heavily on how visual stimuli are received and subsequently processed by the brain. The potential impact of distraction on driving performance is well known and poses a safety concern – especially for individuals with cognitive impairments who may be clinically unfit to drive. The present study is the first to combine functional magnetic resonance imaging (fMRI) and eye-tracking during simulated driving with distraction, providing oculomotor metrics to enhance scientific understanding of the brain activity that supports driving performance. Materials and Methods As initial work, twelve healthy young, right-handed participants performed turns ranging in complexity, including simple right and left turns without oncoming traffic, and left turns with oncoming traffic. Distraction was introduced as an auditory task during straight driving, and during left turns with oncoming traffic. Eye-tracking data were recorded during fMRI to characterize fixations, saccades, pupil diameter and blink rate. Results Brain activation maps for right turns, left turns without oncoming traffic, left turns with oncoming traffic, and the distraction conditions were largely consistent with previous literature reporting the neural correlates of simulated driving. When the effects of distraction were evaluated for left turns with oncoming traffic, increased activation was observed in areas involved in executive function (e.g., middle and inferior frontal gyri) as well as decreased activation in the posterior brain (e.g., middle and superior occipital gyri). Whereas driving performance remained mostly unchanged (e.g., turn speed, time to turn, collisions), the oculomotor measures showed that distraction resulted in more consistent gaze at oncoming traffic in a small area of the visual scene; less time spent gazing at off-road targets (e.g., speedometer, rear-view mirror); more time spent performing saccadic eye movements; and decreased blink rate. Conclusion Oculomotor behavior modulated with driving task complexity and distraction in a manner consistent with the brain activation features revealed by fMRI. The results suggest that eye-tracking technology should be included in future fMRI studies of simulated driving behavior in targeted populations, such as the elderly and individuals with cognitive complaints – ultimately toward developing better technology to assess and enhance fitness to drive.
Measuring Teacher Buy-in for the Texas Pay-for-Performance Program

Teacher buy-in is a critical component for the success of any educational reform, especially one involving evaluation and compensation. We report on an instrument developed to measure teacher buy-in for district-developed designation plans associated with a state pay-for-performance (PFP) program, and teacher responses. We used modern test theory to investigate the instrument’s psychometric properties, a procedure often missing from research reports of self-designed surveys. A sample of 3,001 elementary, middle school, and high school teachers in Texas school districts participated in the survey. Our results suggest satisfactory reliability of the instrument and adequate discriminant validity in measuring distinct but related aspects of teacher buy-in. In addition, we found that teacher support for PFP as instantiated in their particular districts was generally high, but still buy-in levels varied significantly among different teacher groupings, pointing the way for future developers of pay-for-performance schemes to improve or maximize their acceptance.
DNA Functional Nanomaterials for Controlled Delivery of Nucleic Acid-Based Drugs

Nucleic acid-based drugs exhibited great potential in cancer therapeutics. However, the biological instability of nucleic acid-based drugs seriously hampered their clinical applications. Efficient in vivo delivery is the key to the clinical application of nucleic acid-based drugs. As a natural biological macromolecule, DNA has unique properties, such as excellent biocompatibility, molecular programmability, and precise assembly controllability. With the development of DNA nanotechnology, DNA nanomaterials have demonstrated significant advantages as delivery vectors of nucleic acid-based drugs by virtue of the inherent nucleic acid properties. In this study, the recent progress in the design of DNA-based nanomaterials for nucleic acid delivery is summarized. The DNA nanomaterials are categorized according to the components including pure DNA nanomaterials, DNA-inorganic hybrid nanomaterials, and DNA-organic hybrid nanomaterials. Representative applications of DNA nanomaterials in the controlled delivery of nucleic acid-based drugs are exemplified to show how DNA nanomaterials are rationally and exquisitely designed to address application issues in cancer therapy. At the end of this study, the challenges and future development of DNA nanomaterials are discussed.
Deficiency of Intellectual Disability-Related Gene Brpf1 Attenuated Hippocampal Excitatory Synaptic Transmission and Impaired Spatial Learning and Memory Ability

Patients with monoallelic bromodomain and PHD finger-containing protein 1 (BRPF1) mutations showed intellectual disability. The hippocampus has essential roles in learning and memory. Our previous work indicated that Brpf1 was specifically and strongly expressed in the hippocampus from the perinatal period to adulthood. We hypothesized that mouse Brpf1 plays critical roles in the morphology and function of hippocampal neurons, and its deficiency leads to learning and memory deficits. To test this, we performed immunofluorescence, whole-cell patch clamp, and mRNA-Seq on shBrpf1-infected primary cultured hippocampal neurons to study the effect of Brpf1 knockdown on neuronal morphology, electrophysiological characteristics, and gene regulation. In addition, we performed stereotactic injection into adult mouse hippocampus to knock down Brpf1 in vivo and examined the learning and memory ability by Morris water maze. We found that mild knockdown of Brpf1 reduced mEPSC frequency of cultured hippocampal neurons, before any significant changes of dendritic morphology showed. We also found that Brpf1 mild knockdown in the hippocampus showed a decreasing trend on the spatial learning and memory ability of mice. Finally, mRNA-Seq analyses showed that genes related to learning, memory, and synaptic transmission (such as C1ql1, Gpr17, Htr1d, Glra1, Cxcl10, and Grin2a) were dysregulated upon Brpf1 knockdown. Our results showed that Brpf1 mild knockdown attenuated hippocampal excitatory synaptic transmission and reduced spatial learning and memory ability, which helps explain the symptoms of patients with BRPF1 mutations.
Real-Time Task Allocation of Heterogeneous Unmanned Aerial Vehicles for Search and Prosecute Mission

In recent years, the Internet of Things (IoT) has developed rapidly after the era of computers and smart phones, which is expected to be applied to cities to improve the quality of life and realize the intelligence of smart cities. In particular, with the outbreak of coronavirus disease 2019 (COVID-19) last year, in order to reduce contact, some IoT devices, such as robots, unmanned aerial vehicles (UAVs), and unmanned vehicles, have played a great role in temperature monitoring, goods delivery, and so on. In this paper, we study the real-time task allocation problem of heterogeneous UAVs searching and delivering goods in the city. Considering the resource requirement of task and resource constraints of the UAV, when the resource of a single UAV cannot meet the requirement of the task, we propose a method of forming a UAV coalition based on contract net protocol. We analyze the coalition formation problem from two aspects: mission completion time and UAV’s energy consumption. Firstly, the mathematical model is established according to the optimization objective and condition constraints. Then, according to the established mathematical model, different coalition formation algorithms are proposed. To minimize the mission completion time, we propose a two-stage coalition formation algorithm. Aiming at minimizing the UAV’s energy consumption, it is transformed into a zero-one integer programming problem, which can be solved by the existing solver. Then, considering both mission completion time and energy consumption, we propose a coalition formation algorithm based on a resource tree. Finally, we design some simulation experiments and compare with the task allocation algorithm based on resource welfare. The simulation results show that our proposed algorithms are feasible and effective.
Retrospective Observational Study of Daytime Add-On Administration of Zopiclone to Difficult-to-Treat Psychiatric Inpatients With Unpredictable Aggressive Behavior, With or Without EEG Dysrhythmia

Managing violent behavior is a particularly challenging aspect of hospital psychiatric care. Available pharmacological interventions are often unsatisfactory. Aim: To assess the effectiveness and safety of daytime zopiclone add-on administration in violent and difficult-to-treat psychiatric inpatients. Methods: Chart review of inpatients treated with daytime zopiclone, between 2014 and 2018, with up to 12 weeks follow-up. Effectiveness was retrospectively assessed with the Clinical Global Impression rating scale (CGI) and the frequency and severity of aggressive incidents recorded with the Staff Observation Aggression Scale-Revised (SOAS-R). Results: Forty-five (30 male, 15 female) cases, 18–69 years age range, average (SD) baseline CGI-S score of 5.4 (1.0), and a variety of diagnoses. Sixty-nine percent showed CGI-S improvement of any degree. For patients with at least one aggressive incident within 7 days prior to initiation of zopiclone (N = 22), average (SD) SOAS-R-Severity LOCF to baseline change was −3.5 (2.7) P < 0.0001. Most patients reported no side effects; 24% reported one or more side effects, and 11% discontinued zopiclone due to sedation (4), insomnia (1) or slurred speech (1). No SAEs were recorded. Zopiclone maximum daily dose correlated with CGI-S baseline-to-LOCF change (rho = −0.5, P = 0.0003). The ROC AUC of zopiclone maximum daily dose and improvement on CGI-S was 0.84 (95% CI 0.70–0.93, P < 0.0001). The ROC AUC of zopiclone maximum daily dose and SOAS-R-N improvement was 0.80 (95% CI 0.58–0.92; P = 0.0008) and maximum Youden's index value was achieved at a dose of >30 mg. Conclusions: Zopiclone doses >30 mg daily achieved the best anti-aggressive effect.
CircCDYL Acts as a Tumor Suppressor in Wilms’ Tumor by Targeting miR-145-5p

Circular RNAs (circRNA) have been reported to exert evident functions in many human carcinomas. However, the possible mechanisms concerning the circRNA in various tumors are still elusive. In this research, we analyzed the expression profile and biological functions of circular RNA CDYL (circCDYL, circBase ID: hsa_circ_0008285) in Wilms’ tumor. Here, miRNA and gene expression were examined by real-time PCR in Wilms’ tumor tissues and cell lines. The functions of circCDYL and its potential targets to influence cell proliferation, migration, and invasion in Wilms’ tumor cells were determined by biological functional experiments in vitro and in vivo. We predicted and analyzed potential miRNA targets through online bioinformatic tools. To validate the interactions between circCDYL and its targets, we performed RNA fluorescence in situ hybridization, biotin-coupled miRNA capture assay, and biotin-coupled probe pull-down assay. Tight junction protein l (TJP1) was proved to be the target gene of the predicted miRNA by dual-luciferase reporter assay. The expression level of TJP1 in Wilms’ tumor cells was identified via Western blot. We showed that circCDYL was downregulated in WT tissue compared with adjacent non-tumor tissue. Upregulation of circCDYL could reduce cell proliferation, migration, and invasion. Mechanically, circCDYL, functioning as a miRNA sponge, decreased the expression level of miR-145-5p and TJP1 3′UTR was validated as the target of miR-145-5p, facilitating the circCDYL/miR-145-5p/TJP1 axis. In conclusion, our study suggested circCDYL as a novel biomarker and therapeutic target for WT treatment.
Epsilon Best Arm Identification in Spectral Bandits

We propose an analysis of Probably Approximately Correct (PAC) identification of an ε-best arm in graph bandit models with Gaussian distributions. We consider finite but potentially very large bandit models where the set of arms is endowed with a graph structure, and we assume that the arms’ expectations μ are smooth with respect to this graph. Our goal is to identify an arm whose expectation is at most ε below the largest of all means. We focus on the fixed-confidence setting: given a risk parameter δ, we consider sequential strategies that yield an ε-optimal arm with probability at least 1−δ. All such strategies use at least T ∗ R,ε(μ) log(1/δ) samples, where R is the smoothness parameter. We identify the complexity term T ∗ R,ε(μ) as the solution of a min-max problem for which we give a game-theoretic analysis and an approximation procedure. This procedure is the key element required by the asymptotically optimal Track-andStop strategy.
Associations Between COVID-19 Symptoms and Psychological Distress

Background: Hospital isolation for COVID-19 may cause significant psychological stress. The association between COVID-19 symptoms and psychological symptoms has not been systematically studied. We investigated the effects of telephonic intervention on the relationship between psychological symptoms and COVID-19 symptoms at the time of hospitalization and 1 week later. Method: We screened 461 patients with COVID-19 for psychiatric symptoms from February 29, 2020, to January 3, 2021. In total, 461 patients were evaluated 2 days after admission, and 322 (69.8%) were followed 1 week later. To assess anxiety and depressive symptoms, the Hospital Anxiety and Depression Scale (HADS) was administered to patients once per week. The Insomnia Severity Index (ISI) and item 9 of the Beck Depression Inventory (BDI-9) were used weekly to assess insomnia and suicidal ideation. Results: Of 461 enrolled patients, we observed clinically meaningful psychological anxiety symptoms (in 75/16.3% of patients), depression (122/26.5%), insomnia (154/33.4%), and suicidal ideation (54/11.7%). Commonly reported COVID-19 symptoms are cough/sputum/sneezing (244, 52.9%), headache/dizziness (98, 21.3%), myalgia (113, 24.5%), and sore throat (89, 19.3%). Compared to baseline, significant improvements were found in anxiety, depression, and suicidal ideation at 1 week. No significant group differences in ISI score were observed. Conclusions: COVID-19 symptoms at baseline had a significant and persistent negative impact on anxiety and depression at admission and at 1 week after hospitalization. Early intervention is essential to improve the outcomes of patients with mental illness.
Solving Partially Observable Stochastic Shortest-Path Games

We study the two-player zero-sum extension of the partially observable stochastic shortest-path problem where one agent has only partial information about the environment. We formulate this problem as a partially observable stochastic game (POSG): given a set of target states and negative rewards for each transition, the player with imperfect information maximizes the expected undiscounted total reward until a target state is reached. The second player with the perfect information aims for the opposite. We base our formalism on POSGs with one-sided observability (OS-POSGs) and give the following contributions: (1) we introduce a novel heuristic search value iteration algorithm that iteratively solves depth-limited variants of the game, (2) we derive the bound on the depth guaranteeing an arbitrary precision, (3) we propose a novel upper-bound estimation that allows early terminations, and (4) we experimentally evaluate the algorithm on a pursuit-evasion game.
Research on Machine Translation of Deep Neural Network Learning Model Based on Ontology

To align different ontologies, it is necessary to find effective ways to achieve interoperability of information in the context of the Semantic Web. The development of accurate and reliable techniques to automatically perform this task, it is becoming more and more crucial as overlap between ontologies grows proportionally. In order to solve the problem that traditional machine translation cannot meet the needs of users because of the slow translation speed. According to the characteristics of Ontology's domain knowledge concept system, deep neural network learning model based machine translation method is proposed. Through the experimental design, we examine the translation time and BLEU score and other indicators. After junior translators use the tools, the translation time is reduced by 34.0% and the BLEU score increases by 7.59; after the senior translators use the tools, the translation time is reduced by 11.3%, and the BLEU score is increased by 1.67. Analysis of the experimental results shows that the essence of this method is to complement translation skills, so it is more effective for junior translators who are not good enough in translation skills. The machine translation method based on deep neural network learning can significantly improve the quality and efficiency of translation.
Ten Years of BabelNet: A Survey

The intelligent manipulation of symbolic knowledge has been a long-sought goal of AI. However, when it comes to Natural Language Processing (NLP), symbols have to be mapped to words and phrases, which are not only ambiguous but also language-specific: multilinguality is indeed a desirable property for NLP systems, and one which enables the generalization of tasks where multiple languages need to be dealt with, without translating text. In this paper we survey BabelNet, a popular wide-coverage lexical-semantic knowledge resource obtained by merging heterogeneous sources into a unified semantic network that helps to scale tasks and applications to hundreds of languages. Over its ten years of existence, thanks to its promise to interconnect languages and resources in structured form, BabelNet has been employed in countless ways and directions. We first introduce the BabelNet model, its components and statistics, and then overview its successful use in a wide range of tasks in NLP as well as in other fields of AI.
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning

Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e.g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes. To tackle these challenges, we propose Spatial-Temporal Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime spatial-temporal patterns as well as the underlying category-wise crime semantic relationships. In specific, to handle spatial-temporal dynamics under the long-range and global context, we design a graph-structured message passing architecture with the integration of the hypergraph learning paradigm. To capture category-wise crime heterogeneous relations in a dynamic environment, we introduce a multi-channel routing mechanism to learn the time-evolving structural dependency across crime types. We conduct extensive experiments on two real-word datasets, showing that our proposed ST-SHN framework can significantly improve the prediction performance as compared to various state-of-the-art baselines. The source code is available at https://github.com/akaxlh/ST-SHN.