Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying daily changes in circRNAs and circRNA-associated-ceRNA networks in the rat pineal gland

Circular RNAs (circRNAs) are a new class of covalently closed circular RNA molecules that are involved in many biological processes. However, information about circRNAs in the pineal gland, particularly that of rats, is limited. To establish resources for the study of the rat pineal gland, we performed transcriptome analysis of the pineal glands during the day and night. In this study, 1413 circRNAs and 1989 miRNAs were identified in the pineal gland of rats during the night and day using the Illumina platform. Forty differentially expressed circRNAs and 93 differentially expressed miRNAs were obtained, among which 20 circRNAs and 37 miRNAs were significantly upregulated during the day and 20 circRNAs and 56 miRNAs were significantly upregulated during the night. As circRNAs have been reported to work as miRNA sponges, we predicted 15940 interactions among 40 circRNAs, 93 miRNAs and 400 mRNAs with differential diurnal expression using miRanda and TargetScan to build a ceRNA regulatory network in the rat pineal gland. The diurnal expression profile of circRNAs in the rat pineal gland may provide additional information about the role of circRNAs in regulating changes in melatonin circadian rhythms. The analyzed data reported in this study will be an important resource for future studies to elucidate the altered physiology of circRNAs in diurnal rhythms.
Differentiation of the Age-Crime Curve Trajectory by Types of Crime

The observed relationship between age and criminal behavior in the general population is widely regarded as one of the more venerable findings in the field of criminology (Fagan & Western, 2005; Sweeten, Steinberg & Piquero, 2013). This phenomenon, in which crime rates have been consistently observed to rise through adolescence, peak in the mid to late 20’s and decline steadily with age, has been routinely referred to as the Age-Crime Curve (ACC). While this broad correspondence between age and crime has been widely observed, the particular sub-components and co-variants of this relationship are less well understood and have come under increasing investigation (Pratt & Cullen, 2005). The current study continued in this progression by examining the ACC arrest trajectories of various property versus violent crimes, as well as the relative pattern of specific crimes within those broad, categorical trajectories using state-wide arrest data, over the course of 7 years (2007-2013) compiled by the California State Department of Justice (CSDOJ). Our most fundamental finding was that Property and Violent crimes, in toto, conformed to the classic description of the ACC. Chi-Square analyses showed that Burglary and Robbery showed significantly different trajectories from their Property and Violent counterparts, respectively. Burglary arrests actually go down from the <20 category to the 20-29 age category. Although not as dramatically steep as Burglary, Robbery also showed a decline from the earliest (<20) to the next category (20-39). These findings challenge the invariance hypothesis of the ACC. Future researchers may want to include all forms of arrests in investigating ACC trajectories as well as analyze the data with different categorizations of age, or with no age categorizations at all; examining more nuanced relationships between aging and criminal conduct.
A Dynamic Malware Detection Mechanism Based on Deep Learning

Summary Static malware analysis cannot identify malware that uses encryption or shell technology. Traditional dynamic malware analysis has fingerprints, such as using hooks to monitor function calls, which can be recognised and tampered by malware. To address this issue, this paper proposes a dynamic malware detection mechanism based on the cloud environment. Malware is running at the guest level while malware monitoring is conducted at the hypervisor level, therefore malware execution and monitoring environments are isolated. The breakpoint injection technology is utilised to capture the kernel function calls so that malware behaviours, such as processes, file access, registries and system services, can be monitored and the log is generated. The log is processed to extract four dimensions of information which is utilised as the input for the deep learning network. The deep learning network, trained by a large number of samples, can recognise and output the malware types at an accuracy as high as 97.3%.
Tracking the Impact and Evolution of AI: The AIcollaboratory

Artiﬁcial Intelligence (AI) is omnipresent today, but, what is AI capable of doing and where are its boundaries? Bench-marks, competitions and challenges are behind much of the recent progress in AI, but the dynamics of rushing breakthroughs at the expense of massive data and compute has led to a more complex AI landscape, in terms of what can be achieved and how. As a result, policy makers and other stakeholders have no way of assessing what AI systems can do today and in the future. In this paper, we identify a series of problems to track and understand what AI is capable of. Then we present the AIcollaboratory , a data-driven framework to collect and explore data about AI results, progress and ultimately capabilities, which is being developed in the context of AI Watch, the European Commission knowledge service to monitor the development, uptake and impact of AI in Europe.
Image Stabilization using Practical Information from Aerial View

In this paper, we describe a practical method for stabilizing an image sequence captured by a wide-angle camera mounted on a helicopter. For our application, an adjustment of 3-D rotation of the image is effective, and an appropriate camera pose estimation method is required. We demonstrate that the method using vanishing point is most suitable to our purpose. Since no information about it can be found in the image, we adopt other features that have similar properties as the vanishing point, and we prove that they are adequate for the stabilization even though the estimation is not strictly correct. We also show an actual algorithm that can avoid a problem that the de-rotated image may include non-picturized area.
Posture Related Musculoskeletal Disorders (MSDs) among Computer Users in Higher Education Sectors of Malaysia

Introduction: Computer usage has become an indispensable tool in the official set up of all the workplaces in the current era. Predominance of musculoskeletal disorders (MSDs) in relation to habitual posture during work is the utmost problem of modern society. Teaching staff stand out amongst a group of workers exposed to occupational MSDs. The objective of this study was to identify the prevalence and risk factors of musculoskeletal disorders in relation to posture and computer ergonomics at workplace among the college and university staff in Petaling Jaya, Malaysia. Methods: This cross-sectional quantitative study was conducted from August 2019-October 2019, among 419 volunteers by using a self-administered survey questionnaire. Descriptive and bivariate statistics were used for the analyses of multiple variables. The association between demographic characteristics, computer ergonomics and prevalence of musculoskeletal pains were analyzed through Chi-square test. Results: 55.8 % respondents (n=234) reported neck pain (NP), (n=196) 46.8% shoulder pain (SP) and (n=308) 73.5% low back pain (LBP) respectively. A significant relationship between desktop computer usage and musculoskeletal pains in LBP (P=0.036) and SP (P=0.023) was observed. Significant association of head posture was found with NP (P=0.002), SP (P=0.042) and LBP (P=0.001), correspondingly. Discussion: Habitual postures were significantly associated with musculoskeletal pains while using computer. Conclusion: This study proved with precession that higher prevalence rate of musculoskeletal disorders was undoubtedly influenced by prolonged sitting, awkward postures at workstation, and repetitive movements of shoulders and hands. Further synchronous studies are vital to limit the modern era of musculoskeletal disorders
A Concentrated Mix of Verbascoside and Echinacoside Induces Whole Biological Photoprotection of Skin Tissue

Edwige Ranouille1, Emilie Bony1, Isabelle Renimel-Krolikiewicz1, Jean-Yves Berthon1 and Edith Filaire2,3,4 1 Greentech, Biopôle Clermont-Limagne, 63360 Saint Beauzire, France 2 CIAMS, Université Paris-Sud, Université Paris-Saclay, 91405 Orsay Cedex, France 3 CIAMS, Université d‘Orléans, 45067, Orléans, France 4 Université Clermont Auvergne, UMR 1019 INRA-UcA, UNH (Human Nutrition Unity), ECREIN Team, 63000 Clermont-Ferrand, France
Single Document Viewpoint Summarization based on Triangle Identification in Dependency Graph

: The task of viewpoint summarization is to extract a set of keywords or key phrases from a given machine readable document so that the main idea of the document can be automatically summarized. Most of the well-known viewpoint summarization approaches utilizes supervised learning technique with a collection of corpus as training data. In this paper, we propose a novel algorithm, called “TriangleIdent” for key sentence extraction from single document. A dependency graph is built for the given electronic document based on co-occurrence relations as well as syntactic dependency relations among words of the given document. The set of nodes represents words or phrases with high frequency. The set of edges represents dependency relations. The set of key-sentences can be highlighted in each paragraph, and extracted for summarizing the document based on counting the number of triangles in a sentence. For measuring the strength of connectivity, the clustering coefficient is employed as a metric due to the characteristics of triangle. The more triangles contains in a sentence, the more important of the sentence. The set of sentences containing abundant triangles is extracted for summarizing the main idea of a document. The results verify the high competitiveness of the proposed strategy in extracting key-sentences among state-of-the-art approaches.
Interval Privacy: A New Data Privacy Framework

The emerging public awareness and government regulations of data privacy motivate new paradigms of collecting and analyzing data transparent and acceptable to data owners. We present a novel concept of data privacy and corresponding data formats, mechanisms, and tradeo ↵ s for privatizing data before they are collected and analyzed. The privacy, named Interval Privacy, enforces the raw data conditional distribution on the privatized data to be the same as its unconditional distribution over a nontrivial support set. The proposed privatizing mechanism is based on interval censoring techniques, where a set of points is recorded as a set of intervals containing them. To our best knowledge, this is the ﬁrst work that advocates the use of random intervals to mask raw data and systematically enhance data privacy. A unique feature of interval mechanisms is that they obfuscate the truth but not distort it. Various theoretical aspects of the proposed privacy are studied. Moreover, in the context of supervised learning, we propose a novel method such that existing supervised learning algorithms designed for point-valued data could be directly applied to learn from interval-valued data.
A Novel Approach in Evaluating Efficiency of Basketball Players

All the way to the nineties of the 20th century, the sports economics and management was regarded as a hobby for a minority of economists and managers who were primarily involved in other areas of research and business. Modern sports, however, value the significance of the management and economy as impact of sports on the world economy. This has coincided with an equivalent rise in the volume of economic literature devoted to the study of sport and because of that more and more attention is paid to the analysis of sports teams and athletes.
A Survey on Grey Wolf Optimizer

—the following survey describes the basics of greywolf-optimization and significant modifications done in the past few years. We divided the review of previous work into three parts; in the first part, we explained the work in which new parameters are defined. In the second part, we explained the work in which new concepts are proposed and control parameters are estimated. In the third part, we explained the work in which GWO is combined with other nature-inspired algorithms
Impact of Piriformospora indica on growth and yield parameters of groundnut [Arachis hypogea (L.)]

Piriformospora indica an axenically cultivable mycelium of recently defined family sebacinaceae enhances several growths promotional and several other biochemical traits with significant output by establishing stable associations with a wide range of host plant. The aim of the current study was to study the impact of P. indica with Arachis hypogaea (L.) roots in a pot culture conditions. The forty five days old in vitro P. indica treated and controlled plants transplanted to pot culture experiment. The root colonization was confirmed by microscopy and PCR assay. The considerable positive effects on the different growth and yield parameters of host plant have been recorded. Two growth parameters plant height (32.53±0.7cm) and total leaf area (5.92±0.37 cm/plant) significantly influenced by P. indica inoculation with the host plant compared to rest of the parameters under present investigation. All the yield parameters viz. number pods plant (17.31±2.3), number of seeds plant (37.36±2.4), hundred seed weight (69.16±7.9 g/seed), shelling percentage (67.33±3.6 %) and pod yield (19.4±2.2 g plant) found significant over controlled plants.
Molecular Simulations in Activated Carbons for Carbon Monoxide Removal From Wet Mixture of Hydrogen and Carbon Monoxide

Activated carbons are porous materials with a high surface area which are widely used in gases separation and catalysis. This work focuses on the understanding of the separation characteristics of activated carbons for purifying hydrogen from the wet mixture of hydrogen and carbon monoxide (in hundreds of ppm). This study would build a basic insight of the separation mechanism of the activated carbon and determine whether activated carbons are suitable for CO removal from the mixture. Systematic Grand Canonical Monte Carlo (GCMC) simulations have been carried out to assess the adsorption properties and selectivity of the activated carbon to the gaseous mixture with different gas phase compositions, temperatures, and pressures. The calculated adsorption isotherms, both for single and multi-component gas mixture, and isosteric heats of adsorption on the activated carbon showed reasonably good agreement with the experimental data available in the literature.
Additive regression for non-Euclidean responses and predictors

Additive regression is studied in a very general setting where both the response and predictors are allowed to be non-Euclidean. The response takes values in a general separable Hilbert space, whereas the predictors take values in general semimetric spaces, which covers a very wide range of nonstandard response variables and predictors. A general framework of estimating additive models is presented for semimetric space-valued predictors. In particular, full details of implementation and the corresponding theory are given for predictors taking values in Hilbert spaces and/or Riemannian manifolds. The existence of the estimators, convergence of a backfitting algorithm, rates of convergence and asymptotic distributions of the estimators are discussed. The finite sample performance of the estimators is investigated by means of two simulation studies. Finally, three data sets covering several types of nonEuclidean data are analyzed to illustrate the usefulness of the proposed general approach.
Development of Novel Measures for Alzheimer's Disease Prevention Trials (NoMAD)

Introduction: Assessment of cognition and everyday function is essential in clinical trials for Alzheimers disease (AD). Two novel measures of cognition (No Practice Effects (NPE) cognitive battery and Miami Computerized Functional Assessment Scale (CFAS)) were designed to have robust psychometric properties and reduced practice and ceiling effects. This study aims to evaluate if the NPE and CFAS demonstrate stronger psychometric properties and reduced practice effects compared with established measures, including the Preclinical Alzheimer Cognitive Composite (PACC), Alzheimers Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), and Functional Activities Questionnaire (FAQ). Methods: This parallel group, four-site study will randomize 320 cognitively intact adults aged 60 to 85 years to novel or well-established measures of cognition and function. All participants will receive assessments at baseline (week 0), week 12, and week 52, as well as a brain MRI scan and Apolipoprotein E genetic test at study entry. Analyses will determine psychometric properties of the NPE and CFAS, compare the sensitivity of measures to AD risk markers, and identify cognitive domains within the NPE. Discussion: Practice effects have been a major limitation of Alzheimers disease clinical trials that typically assess cognitive changes over serial assessments. Detection of functional impairment in cognitively normal individuals with biomarkers for Alzheimers disease requires instruments sensitive to very subtle functional changes. This study is intended to support the validation of two new composite measures, the NPE battery and the CFAS, which may advance clinical testing of interventions for individuals across the spectrum of early stage Alzheimers disease.
Nanofibrous nerve guidance conduits decorated with decellularized matrix hydrogel facilitate peripheral nerve injury repair

Rationale: Peripheral nerve injury (PNI) is a great challenge for regenerative medicine. Nerve autograft is the gold standard for clinical PNI repair. Due to its significant drawbacks, artificial nerve guidance conduits (NGCs) have drawn much attention as replacement therapies. We developed a combinatorial NGC consisting of longitudinally aligned electrospun nanofibers and porcine decellularized nerve matrix hydrogel (pDNM gel). The in vivo capacity for facilitating nerve tissue regeneration and functional recovery was evaluated in a rat sciatic nerve defect model. Methods: Poly (L-lactic acid) (PLLA) was electrospun into randomly oriented (PLLA-random) and longitudinally aligned (PLLA-aligned) nanofibers. PLLA-aligned were further coated with pDNM gel at concentrations of 0.25% (PLLA-aligned/0.25% pDNM gel) and 1% (PLLA-aligned/1% pDNM gel). Axonal extension and Schwann cells migration were evaluated by immunofluorescence staining of dorsal root ganglia cultured on the scaffolds. To fabricate implantable NGCs, the nanofibrous scaffolds were rolled and covered with an electrospun protection tube. The fabricated NGCs were then implanted into a 5 mm sciatic nerve defect model in adult male Sprague-Dawley rats. Nerves treated with NGCs were compared to contralateral uninjured nerves (control group), injured but untreated nerves (unstitched group), and autografted nerves. Nerve regeneration was monitored by an established set of assays, including T2 values and diffusion tensor imaging (DTI) derived from multiparametric magnetic resonance imaging (MRI), histological assessments, and immunostaining. Nerve functional recovery was evaluated by walking track analysis. Results: PLLA-aligned/0.25% pDNM gel scaffold exhibited the best performance in facilitating directed axonal extension and Schwann cells migration in vitro due to the combined effects of the topological cues provided by the aligned nanofibers and the biochemical cues retained in the pDNM gel. Consistent results were obtained in animal experiments with the fabricated NGCs. Both the T2 and fractional anisotropy values of the PLLA-aligned/0.25% pDNM gel group were the closest to those of the autografted group, and returned to normal much faster than those of the other NGCs groups. Histological assessment indicated that the implanted PLLA-aligned/0.25% pDNM gel NGC resulted in the largest number of axons and the most extensive myelination among all fabricated NGCs. Further, the PLLA-aligned/0.25% pDNM gel group exhibited the highest sciatic nerve function index, which was comparable to that of the autografted group, at 8 weeks post-surgery. Conclusions: NGCs composed of aligned PLLA nanofibers decorated with 0.25% pDNM gel provided both topological and biochemical guidance for directing and promoting axonal extension, nerve fiber myelination, and functional recovery. Moreover, T2-mapping and DTI metrics were found to be useful non-invasive monitoring techniques for PNI treatment.
Pharmacological strategies to prevent haemodynamic changes after intubation in parturient women with hypertensive disorders of pregnancy: A network meta-analysis

Objective: This network meta-analysis (NMA) aimed to determine the relative efficacy and safety of pharmacological strategies used to mitigate haemodynamic instability by intubation for general anaesthesia in hypertensive parturient women undergoing caesarean section. Methods: We considered randomised controlled studies comparing the effects of pharmacological strategies used to alleviate haemodynamic instability during intubation in parturient women with hypertensive disorders of pregnancy. The primary endpoints were maximum blood pressure and heart rate after intubation, and secondary endpoints were the Apgar scores at 1 and 5 min. NMA allowed us to combine direct and indirect comparisons between strategies. Results: Twelve studies evaluating nine pharmacological strategies in 619 patients were included. According to the surface under the cumulative ranking curve, the maximal mean arterial pressure was lowest for high-dose remifentanil (99.4%) followed by nitroglycerin (73.6%) and labetalol (60.9%). The maximal heart rate was lowest for labetalol (99.9%) followed by high dose of remifentanil (81.2%) and fentanyl (61.6%). Apgar score at 1 min was higher with low-dose than with high-dose remifentanil (mean difference, 0.726; 95% confidence interval, 0.056 to 1.396; I2=0.0%). Conclusions: High-dose remifentanil produces minimum blood pressure changes, while labetalol is most effective in maintaining normal heart rate in parturient women with hypertensive disorders of pregnancy during caesarean section under general anaesthesia.
Effects of Chlorpheniramine Maleate on Catheter-Related Bladder Discomfort in Patients Undergoing Ureteroscopic Stone Removal: A Randomized Double-Blind Study

Catheter-related bladder discomfort (CRBD) associated with intraoperative urinary catheterization is a distressing symptom during recovery from anesthesia. Anticholinergics have been used to manage CRBD. Chlorpheniramine maleate (CPM) is a first-generation antihistamine, which also has anticholinergic effects. This study was undertaken to evaluate the efficacy of CPM in preventing CRBD. Seventy-six adults (19-65 years old) with American Society of Anesthesiologists physical status I, II, or III of either sex, undergoing elective ureteroscopic stone removal under general anesthesia were randomized into one of two groups (each n = 38). Group C (control) received a placebo, and group CPM received 8 mg of intravenous CPM before the induction of anesthesia. CRBD was assessed upon arrival in the post-anesthetic care unit at 0, 1, 2, and 6 h. The severity of CRBD was graded as none, mild, moderate, and severe. Tramadol was administered when the severity of CRBD was more than moderate. The incidence rate and overall severity of CRBD did not differ between the groups at any of the time points (р > 0.05). The incidence of moderate CRBD was higher in group C than in group CPM only at 0 h (26.3% vs. 5.3%, р = 0.025). However, fewer patients in the CPM group required rescue tramadol to relieve CRBD after surgery (31.6% vs. 60.5%, р = 0.011). CPM administration before the induction of anesthesia had little effect on the incidence and severity of CRBD after surgery, but it reduced the administration of tramadol required to control CRBD postoperatively.