Miracle-dz/UniFAR-FTUs huggingface dataset:
Epigenetic and Transcriptional Control of the Opioid Prodynorphine Gene: In-Depth Analysis in the Human Brain

Neuropeptides serve as neurohormones and local paracrine regulators that control neural networks regulating behavior, endocrine system and sensorimotor functions. Their expression is characterized by exceptionally restricted profiles. Circuit-specific and adaptive expression of neuropeptide genes may be defined by transcriptional and epigenetic mechanisms controlled by cell type and subtype sequence-specific transcription factors, insulators and silencers. The opioid peptide dynorphins play a critical role in neurological and psychiatric disorders, pain processing and stress, while their mutations cause profound neurodegeneration in the human brain. In this review, we focus on the prodynorphin gene as a model for the in-depth epigenetic and transcriptional analysis of expression of the neuropeptide genes. Prodynorphin studies may provide a framework for analysis of mechanisms relevant for regulation of neuropeptide genes in normal and pathological human brain.
NSS_A_307996 703..712

1Department of Psychiatry, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangzhou, People’s Republic of China; 3Department of Psychiatry, The Third People’s Hospital of Qinghai Province, Xining, People’s Republic of China; 4Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 5GuangdongHong Kong-Macao Greater Bay Area Center for Brain Science and BrainInspired Intelligence, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China Introduction: The prevalence rate and related factors of insomnia remained unknown after the COVID-19 epidemic had been under control. Therefore, we conducted this survey to investigate the prevalence rate and related factors of insomnia symptoms in the Chinese general public after the COVID-19 had been initially control. Methods: An online survey was conducted among Chinese citizens through the JD Health APP. The questionnaire was used for collecting demographic data and self-designed questions related to the COVID-19 outbreak. Insomnia Severity Index, Patient Health Questionnaire-9, Somatic Symptom Scale-8 and Impact of Events Scale-Revised were used for measuring psychological symptoms. To examine the associations of sociodemographic and psychological factors with insomnia symptoms, a binary logistic regression was used. Results: In total, there were 14,894 eligible participants, and 4601 (30.9%) participants were found to have insomnia symptoms. The regression model revealed that a higher risk of insomnia symptoms was associated with being over the age of 40 years, having history of psychiatric disorders, smoking, having infected friends or colleagues, having depressive or somatic symptoms, experiencing psychological distress and feeling estranged from family members. Meanwhile a lower risk of insomnia symptoms was associated with being female, having closer family relationships, not feeling alienated from others and being satisfied with the available information. Conclusion: In our study, 30.9% of the participants in the general public reported insomnia symptoms after the COVID-19 epidemic had been initially controlled. When providing precise interventions for insomnia, extra attention should be paid to the individuals who are male, elderly and smokers, and those with psychiatric disorder history, with infected friends or colleagues, with psychological symptoms and with poor social support.
An Ontology-based Sentiment Analysis Model towards Classification of Drug Reviews

Due to the abundance of information that can be obtained through careful analysis of such data, the need to analyze user-generated data on the internet has recently gained significance. Mining and analyzing such data have become an important aspect for the companies to understand the opinion of the people on specific drug information. The purpose of this research project is to use deep learning models to analyze the feelings of patient reviews to determine the polarity of opinions expressed in reviews that may be positive or negative. All positive relation instances are combined Minto one class using a binary Support Vector Machine. Logistic regression and Long short-term memory (LSTM) networks are used for sentiment classification of the drug review collected from the Drug Reviews dataset. Logistic regression can be used for the prediction of a group membership. The power of prediction with the given features can be identified using the LSTM classification. The accuracy of 80% is achieved with LSTM classification method.
Continuous Latent Search for Combinatorial Optimization

Combinatorial optimization problems are notoriously hard because they often require enumeration of the exponentially large solution space. Both classical solving techniques and machine learning-based approaches usually address combinatorial optimization problems by manipulating solutions in their original discrete form. In contrast, we propose a framework that consists of reparametrizing the original discrete solution space into a continuous latent space in which the problem can be (approximately) solved by running continuous optimization methods. We achieve this by learning a surrogate function that is shaped to correlate with the original objective when the latent solution is decoded back to the original solution space. We show that this approach can learn efﬁcient solution strategies and is useful as a primal heuristic inside the widely-used open-source solver SCIP.
Demystifying the System Vulnerability Stack: Transient Fault Effects Across the Layers

In this paper, we revisit the system vulnerability stack for transient faults. We reveal severe pitfalls in widely used vulnerability measurement approaches, which separate the hardware and the software layers. We rely on microarchitecture level fault injection to derive very tight full-system vulnerability measurements. For our architectural and microarchitectural measurements, we employ GeFIN, a state-of-the-art fault injector built on top of the gem5 simulator, while for software level measurements we employ the LLFI fault injector. Analyzing two different Arm ISAs and two different microarchitectures for each ISA, we quantify the sources and the magnitude of error of architecture and software level vulnerability evaluation methods, which aim to reproduce the effects of hardware faults. We show that widely applied methodologies for system resilience evaluation fail to capture important fault manifestation and propagation aspects and lead to misleading findings, which report opposite vulnerability results than a comprehensive cross-layer analysis. To justify the validity of our findings we employ a state-of-the-art software-based fault tolerance technique and evaluate its impact at all layers through a case study. Our evaluation shows that although higher-level methods can report significant vulnerability improvements (up to 3.8x vulnerability reduction), the actual cross-layer vulnerability of the protected system can be degraded (increased) by up to 30% for the selected benchmarks. Our analysis firmly suggests that only accurate methodologies for full-system vulnerability evaluation of a microprocessor can guide informed transient faults protection decisions either at the hardware or at the software layer.
Sentimental Analysis - A Survey of Some Existing Studies

Sentimental Analysis is a process of computing and categorizing the expressed opinions of people about certain event, subject or product as positive, negative or neutral. The major objective of Sentimental Analysis is to help data-driven decisions using insights from replies in social media, surveys and product reviews. Sentiment Analysis can be done with words, sentences, documents, features or aspects, concepts, phrases, links, clauses and implications. Recently, there has been a lot of attention on sentiment analysis especially from researchers in the fields of text mining and natural language processing. But due to extreme absence of annotated datasets which are used to train models in various domains, the accuracy of sentiment analysis has been hindered. Many types of research have been done to confront the challenge and enhance sentiment analysis classification. Sentiment analysis is important as it helps in identifying the emotional and attitude states of people. Positive or negative feelings of people can be expressed in different ways. This research article talks about, in subtle terms, the different ways to deal with sentiment analysis mostly in Machine Learning, Lexicon-based, Hybrid and Ontology-based approaches. This research article gives point by point perspective of the distinctive applications and challenges of Sentiment Analysis.
Potential impact and mechanism of Long Non-coding RNAs on cancer and associated T cells

The discovery of many aberrant expressions of long non-coding RNAs (lncRNAs) in various cancers has focused attention on the effects of lncRNA on cancer cells themselves, including cell proliferation, growth inhibition, cell migration, cell immortality, vascular regeneration and cell viability. But with the increasing role of immunotherapy in cancer therapy, a large number of studies have revealed that the regulatory role of lncRNAs in immunity such as differentiation of immune cells can also influence the development and progression of cancer. In particular, recent publications have suggested that lncRNAs play critical roles in T-lymphocyte activation, proliferation, differentiation, function, apoptosis and metabolism. To elucidate the actual functions of lncRNAs at the molecular level of cancer pathogenesis, we summarize some of the current lncRNA regulatory mechanisms associated with T cell to discuss their effects in cancer in the hope of providing potential cancer therapeutic targets or cancer biomarkers. However, we all know that the differentiation and function of T cells is an extremely complex process that involves the expression and regulation of multiple lncRNAs. As a result, more regulatory mechanisms of lncRNAs need to be further studied.
Quantifying Server Memory Frequency Margin and Using It to Improve Performance in HPC Systems

To maintain strong reliability, memory manufacturers label server memories at much slower data rates than the highest data rates at which they can still operate correctly for most (e.g., 99.999%+ of) accesses; we refer to the gap between these two data rates as memory frequency margin. While many prior works have studied memory latency margins in a different context of consumer memories, none has publicly studied memory frequency margin (either for consumer or server memories). To close this knowledge gap in the public domain, we perform the first public study to characterize frequency margins in commodity server memory modules. Through our large-scale study, we find that under standard voltage and cooling, they can operate 27% faster, on average, without error(s) for 99.999%+ of accesses even at high temperatures.The current practice of conservatively operating server memory is far from ideal; it slows down 99.999%+ of accesses to benefit the <0.001% of accesses that would be erroneous at a faster data rate. An ideal system should only pay this reliability tax for the <0.001% of accesses that actually need it.Towards unleashing ideal performance, our second contribution is performing the first exploration on exploiting server memory frequency margin to maximize performance. We focus on High-Performance Computing (HPC) systems, where performance is paramount. We propose exploiting HPC systems’ abundant free memory in the common case to store copies of every data block and operate the copies unreliably fast to speedup common-case accesses; we use the safely-operated original blocks for recovery when the unsafely-operated copies become corrupted. We refer to our idea as Heterogeneously-accessed Dual Module Redundancy (Hetero-DMR). Hetero-DMR improves node-level performance by 18%, on average across two CPU memory hierarchies and six HPC benchmark suites, while weighted by different frequency margins and different levels of memory utilization. We also use a real system to emulate the speedup of Hetero-DMR over a conventional system; it closely matches simulation. Our system-wide simulations show applying Hetero-DMR to an HPC system provides 1.4x average speedup on job turnaround time. To facilitate adoption, Hetero-DMR also rigorously preserves system reliability and works for commodity DIMMs and CPU-memory interfaces.
Hetero-ViTAL: A Virtualization Stack for Heterogeneous FPGA Clusters

With field-programmable gate arrays (FPGAs) being widely deployed into data centers, an efficient virtualization support is required to fully unleash the potential of cloud FPGAs. Nevertheless, existing FPGA virtualization solutions only support a homogeneous FPGA cluster comprising identical FPGA devices. Representative work such as ViTAL provides sufficient system support for scale-out acceleration and improves the overall resource utilization through a fine-grained spatial sharing. While these existing solutions (including ViTAL) can efficiently virtualize a homogeneous cluster, it is hard to extend them to virtualizing a heterogeneous cluster which comprises multiple types of FPGAs. We expect the future cloud FPGAs are likely to be more heterogeneous due to hardware rolling upgrade.In this paper, we rethink FPGA virtualization from ground up and propose Hetero-ViTAL to virtualize heterogeneous FPGA clusters. We identify the conflicting requirements of runtime management and offline compilation when designing the abstraction for a heterogeneous cluster, which is also the fundamental reason why the single-level abstraction as proposed in ViTAL (and other prior works) cannot be trivially extended to the heterogeneous case. To decouple these conflicting requirements, we provide a two-level system abstraction in Hetero-ViTAL. Specifically, the high-level abstraction is FPGA-agnostic and provides a simple and homogeneous view of the FPGA resources to simplify the runtime management. On the contrary, the low-level abstraction is FPGA-specific and exposes sufficient spatial resource constraints to the compilation framework to ensure the mapping quality. Rather than simply adding a layer on top of the single-level abstraction as proposed in ViTAL and other prior work, we judiciously determine how much hardware details should be exposed at each level to balance the management complexity, mapping quality and compilation cost. We then develop a compilation framework to map applications onto this two-level abstraction with several optimization techniques to further improve the mapping quality. We also provide a runtime management policy to alleviate the fragmentation issue, which becomes more severe in a heterogeneous cluster due to the distinct resource capacities of diverse FPGAs.We evaluate Hetero-ViTAL on a custom-built FPGA cluster and demonstrate its effectiveness using machine learning and image processing applications. Results show that Hetero-ViTAL reduces the average response time (a critical metric for QoS) by 79.2% for a heterogeneous cluster compared to the non-virtualized baseline. When virtualizing a homogeneous cluster, Hetero-ViTAL also reduces the average response time by 42.0% compared with ViTAL due to a better system design.
The Effect of Oil and Non-Oil Exports on Economic Growth in Iran

The present study aims to investigate the relationship of oil, non-oil exports, capital formation and economic growth in Iran. Empirical results show that there is positive significant relationship between these variables and economic growth. Thus, findings of this study recommend that government should create an enabling environment that would facilitate non-oil exports and augment capital formation. © 2014 Elixir All rights reserved. Elixir Economics 68 (2014) 22149-22151 Economics Available online at www.elixirpublishers. com (Elixir International Journal)
Biological Nitrogen Removal in a Photosequencing Batch Reactor with an Algal-Nitrifying Bacterial Consortium and Anammox Granules

This study demonstrates the feasibility of combining microalgae, ammonia-oxidizing bacteria (AOB), and Anammox in a photosequencing batch reactor. Alternating light and dark periods were applied to achieve biological nitrogen removal without mechanical aeration or external electron donor addition. This process is termed ALGAMMOX (algal anaerobic ammonium oxidation) and differs from the SHARON–Anammox process in that oxygen is generated during light periods through microalgal photosynthesis, replacing mechanical aeration. Results from bench-scale ALGAMMOX experiments with high-ammonia strength wastewater (COD/TN from 1 to 3) showed that influent ammonia was converted to nitrite during light periods at a rate of 7.0 mg of NH4+-N L–1 h–1. Nitrite was subsequently reduced by an average of 82% during the dark (anoxic) periods due to Anammox activity. Further studies are needed to optimize the system to maximize nitrogen removal rates and to assess long-term process stability.
Determinants of adherence to COVID-19 preventive behaviours in Canada: Results from the iCARE Study

Objective: Key to slowing the spread of SARS-Cov-2 is adherence to preventive behaviours promoted through government policies, which may be influenced by policy awareness, attitudes and concerns about the virus and its impacts. This study assessed determinants of adherence to major coronavirus preventive behaviours, including demographics, attitudes and concerns, among Canadians during the first pandemic wave. Methods: As part of the iCARE study (www.iCAREstudy.com), we weighted data from two population-based, online surveys (April and June, 2020) of Canadian adults. Questions tapped into behaviour change constructs. Multivariate regression models identified determinants of adherence. Results: Data from 6,008 respondents (51% female) were weighted for age, sex, and province. Awareness of government policies was high at both time points (80-99%), and adherence to prevention behaviours was high in April (87.5%-93.5%) but decreased over time, particularly for avoiding social gatherings (68.1%). Adherence was worse among men, those aged 25 and under, and those currently working. Aligned with the Health Beliefs Model, perceptions of the importance of prevention behaviours and the nature of peoples COVID-19-related concerns were highly predictive of adherence. Interestingly, health and social/economic concerns predicted better adherence, but having greater personal financial concerns predicted worse adherence at both time points. Conclusion: Adherence to COVID-19 prevention behaviours was worse among men, younger adults, and workers, and deteriorated over time. Perceived importance of prevention behaviours measures and health and social/economic concerns predicted better adherence, but personal financial concerns predicted worse adherence. Results have implications for tailoring policy and communication strategies during subsequent pandemic waves.
Firm Differences: Skill Sorting and Software

Recent research shows that much recent rise in wage inequality comes from growing differences between firms, especially sorting of skilled workers to high-paying firms. This paper explores the role of proprietary software in these changes. Using job ad data, we find that proprietary software is strongly associated with firm wage fixed effects and also with firm skills. Software accounts for half or more of skill sorting across firms. Moreover, both skill sorting and firm wage effects are greater for larger firms. The huge growth in proprietary software helps explain the growth in skill sorting that increases wage inequality.
The Impact of Corporate Social Responsibility on the Corporate Financial Performance: Evidence from Russian and Dutch Companies

Abstract. This research paper aims to investigate the impact of Corporate Social Responsibility (CSR) reporting on Russian and Dutch companies’ financial performance (CFP). The theoretical framework we use is the stakeholder theory. The study applies regression analysis to obtain quantitative evidence of CSR-CFP relations. Companies’ CSR involvement is measured by reputation index - CSRhub. Return on equity ratio has been chosen as a measure of corporate financial performance. Our findings demonstrate a weak positive correlation between CSR and companies’ ROE. Moreover, the research shows the existence of a higher CSR impact on Russian companies’ financial performance comparing to the Dutch one. The results may become useful for corporate management while assessing a financial return from CSR strategies. This work also provides an alternative way for a quantitative operationalization of companies’ social performance, which is especially important for further statistical researches.
Scene-Agnostic Multi-Microphone Speech Dereverberation

Neural networks (NNs) have been widely applied in speech processing tasks, and, in particular, those employing microphone arrays. Nevertheless, most existing NN architectures can only deal with fixed and position-specific microphone arrays. In this paper, we present an NN architecture that can cope with microphone arrays whose number and positions of the microphones are unknown, and demonstrate its applicability in the speech dereverberation task. To this end, our approach harnesses recent advances in deep learning on set-structured data to design an architecture that enhances the reverberant log-spectrum. We use noisy and noiseless versions of a simulated reverberant dataset to test the proposed architecture. Our experiments on the noisy data show that the proposed scene-agnostic setup outperforms a powerful scene-aware framework, sometimes even with fewer microphones. With the noiseless dataset we show that, in most cases, our method outperforms the position-aware network as well as the state-of-the-art weighted linear prediction error (WPE) algorithm.
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection

Traditional toxicity detection models have focused on the single utterance level without deeper understanding of context. We introduce CONDA, a new dataset for in-game toxic language detection enabling joint intent classification and slot filling analysis, which is the core task of Natural Language Understanding (NLU). The dataset consists of 45K utterances from 12K conversations from the chat logs of 1.9K completed Dota 2 matches. We propose a robust dual semantic-level toxicity framework, which handles utterance and token-level patterns, and rich contextual chatting history. Accompanying the dataset is a thorough in-game toxicity analysis, which provides comprehensive understanding of context at utterance, token, and dual levels. Inspired by NLU, we also apply its metrics to the toxicity detection tasks for assessing toxicity and game-specific aspects. We evaluate strong NLU models on CONDA, providing fine-grained results for different intent classes and slot classes. Furthermore, we examine the coverage of toxicity nature in our dataset by comparing it with other toxicity datasets.