Miracle-dz/UniFAR-FTUs huggingface dataset:
Cytogenetic Effects of Chronic Methylphenidate Treatment and Chronic Social Stress in Adults with Attention-Deficit/Hyperactivity Disorder

Abstract Introduction: Methylphenidate (MPH) is widely used to treat childhood and adult attention-deficit/hyperactivity disorder (ADHD). However, there are still safety concerns about side effects in long-term treatment. The aim of this study was to assess cytogenetic effects of chronic MPH treatment in adult ADHD and to find out if chronic social stress is attenuated by medication and to investigate whether chronic psychosocial stress leads to mutagenic effects by itself. Methods: Lymphocytes for micronucleus assay and saliva samples for cortisol measurement were collected from adult ADHD patients and healthy controls. Stress exposure of the last 3 months was assessed by TICS (Trier Inventory for Chronic Stress). Results: We could not detect an influence of MPH treatment on cytogenetic markers. ADHD patients displayed significantly higher chronic stress levels measured by TICS compared to healthy controls which were influenced by duration of MPH treatment. ADHD patients also showed significantly lower basal cortisol levels. Discussion: We could corroborate that there are neither cytogenetic effects of chronic stress nor of chronic MPH intake even after several years of treatment.
The MeSHSim package

MeSH(Medical Subject Headings) is a vocabulary thesaurus, being controlled by NLM(National Library of Medicine) to index MEDLINE documents. MeSH consists of a set of description terms, which are organized in a hierarchical structure(called MeSH trees), where more general terms appear at nodes closer to the root and more specific terms appear at nodes closer to leaves(Nelson et al., 2004). Each MeSH node is represented by a tree number, which indicates the postion of the node in MeSH tree. MeSH headings are term names or identifiers. Each MEDLINE document is manually annotated with a set of (usually 10-15) MeSH headings, including around three to five major headings, representing main topics of corresponding document. Besides, MeSH is also used for indexing the NLM-produced database including cataloging of books and audiovisuals. Computing semantic similarities between two MeSH headings as well as two documents (one document having a set of MeSH headings) has been proved very useful to improve the performance of many biomedical text mining tasks, such as retrieval (Rada et al., 1989; Blott et al., 2003), indexing (Névéol et al., 2006) and clustering (Zhu et al., 2009; Gu et al., 2013).
Smoking cessation guidelines for health professionals: an update

This paper updates the evidence base and key recommendations of the Health Education Authority (HEA) smoking cessation guidelines for health professionals published in Thorax in 1998. The strategy for updating the evidence base makes use of updated Cochrane reviews supplemented by individual studies where appropriate. This update contains additional detail concerning the effectiveness of interventions as well as comments on issues relating to implementation. The recommendations include clarification of some important issues addressed only in general terms in the original guidelines. The conclusion that smoking cessation interventions delivered through the National Health Service are an extremely cost effective way of preserving life and reducing ill health remains unchanged. The strategy recommended by the guidelines involves: (1) GPs opportunistically advising smokers to stop during routine consultations, giving advice on and/or prescribing effective medications to help them and referring them to specialist cessation services; (2) specialist smokers' services providing behavioural support (in groups or individually) for smokers who want help with stopping and using effective medications wherever possible; (3) specialist cessation counsellors providing behavioural support for hospital patients and pregnant smokers who want help with stopping; (4) all health professionals involved in smoking cessation encouraging and assisting smokers in use of nicotine replacement therapies (NRT) or bupropion where appropriate. The key points of clarification of the previous guidelines include: (1) primary health care teams and hospitals should create and maintain readily accessible records on the current smoking status of patients; (2) GPs should aim to advise smokers to stop, and record having done so, at least once a year; (3) inpatient, outpatient, and pregnant smokers should be advised to stop as early as possible and the advice recorded in the notes in a readily accessible form; (4) there is currently little scientific basis for matching individual smokers to particular forms of NRT; (5) NHS specialist smokers' clinics should be the first point of referral for smokers wanting help beyond what can be provided through brief advice from the GP; (6) help from trained health care professionals specialising in smoking cessation such as practice nurses should be available for smokers who do not have access to specialist clinics; (7) the provision of specialist NHS smokers' clinics should be commensurate with demand; this is currently one or two full time clinics or their equivalent per average sized health authority, but demand may rise as publicity surrounding the services increases.
Set-valued consensus for distributed clock synchronization

This paper addresses the clock synchronization problem in a wireless sensor network (WSN) and proposes a distributed solution that consists of a form of consensus, where agents are able to exchange data representing intervals or sets. The solution is based on a centralized algorithm for clock synchronization, proposed by Marzullo, that determines the smallest interval that is in common with the maximum number of measured intervals. We first show how to convert such an algorithm into a problem involving only operations on sets, and then we convert it into a set-valued consensus. The solution is valid for more general scenarios where agents have uncertain measures of e.g. the position of an object detected by a vision system, a temperature in a room, but it will be applied to the case where a set of uncertain time values are propagated through a WSN. Under suitable joint conditions on the communication connectivity and bounded agent failure, we prove the correctness of the algorithm that indeed allows the network agents to consent on the value of a unique global time.
The Structure of CcmP, a Tandem Bacterial Microcompartment Domain Protein from the β-Carboxysome, Forms a Subcompartment Within a Microcompartment

Background: CcmP is a hypothetical protein conserved among all β-cyanobacteria. Results: CcmP is a β-carboxysome component; it forms a bilayered shell protein. Conclusion: CcmP may facilitate flux of larger metabolites across the carboxysome shell. Significance: It is the first structure of a β-carboxysome tandem BMC domain protein; phylogenetically, it represents a new type of microcompartment building block. The carboxysome is a bacterial organelle found in all cyanobacteria; it encapsulates CO2 fixation enzymes within a protein shell. The most abundant carboxysome shell protein contains a single bacterial microcompartment (BMC) domain. We present in vivo evidence that a hypothetical protein (dubbed CcmP) encoded in all β-cyanobacterial genomes is part of the carboxysome. We show that CcmP is a tandem BMC domain protein, the first to be structurally characterized from a β-carboxysome. CcmP forms a dimer of tightly stacked trimers, resulting in a nanocompartment-containing shell protein that may weakly bind 3-phosphoglycerate, the product of CO2 fixation. The trimers have a large central pore through which metabolites presumably pass into the carboxysome. Conserved residues surrounding the pore have alternate side-chain conformations suggesting that it can be open or closed. Furthermore, CcmP and its orthologs in α-cyanobacterial genomes form a distinct clade of shell proteins. Members of this subgroup are also found in numerous heterotrophic BMC-associated gene clusters encoding functionally diverse bacterial organelles, suggesting that the potential to form a nanocompartment within a microcompartment shell is widespread. Given that carboxysomes and architecturally related bacterial organelles are the subject of intense interest for applications in synthetic biology/metabolic engineering, our results describe a new type of building block with which to functionalize BMC shells.
Computational Prediction of miRNA Genes from Small RNA Sequencing Data

Next-generation sequencing now for the first time allows researchers to gage the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. microRNAs (miRNAs) are 22 nucleotide small RNAs (sRNAs) that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq), which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here, we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field.
Modified MUF and EDF algorithms for overloaded soft real time systems

This paper proposes static and dynamic mechanisms by which MUF scheduling algorithm can be made more effective in overloaded soft real time systems (such as multimedia) using the concept of Random Dropping (RD) and negative laxity check. With introduction of RD, both algorithms result in higher values of DMTR as compared to without RD. Using negative laxity check and appropriate random dropping, EDF gives better results as compared to MUF across all tasks - important and less important.
Using Approximated Auditory Roughness as a Pre-Filtering Feature for Human Screaming and Affective Speech AED

Detecting human screaming, shouting, and other verbal manifestations of fear and anger are of great interest to security Audio Event Detection (AED) systems. The Internet of Things (IoT) approach allows wide-covering, powerful AED systems to be distributed across the Internet. But a good feature to prefilter the audio is critical to these systems. This work evaluates the potential of detecting screaming and affective speech using Auditory Roughness and proposes a very light-weight approximation method. Our approximation uses a similar amount of Multiple Add Accumulate (MAA) compared to short-term energy (STE), and at least 10× less MAA than MFCC. We evaluated the performance of our approximated roughness on the Mandarin Affective Speech corpus and a subset of the Youtube AudioSet for screaming against other low-complexity features. We show that our approximated roughness returns higher accuracy.
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level features for CMBs, and hence achieve a much better detection accuracy. To further improve the detection performance while reducing the computational cost, we propose a cascaded framework under 3D CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN) strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with traditional sliding window strategy, the proposed 3D FCN strategy can remove massive redundant computations and dramatically speed up the detection process. We constructed a large dataset with 320 volumetric MR scans and performed extensive experiments to validate the proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74 false positives per subject, outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. The proposed method, in principle, can be adapted to other biomarker detection tasks from volumetric medical data.
Adaptive Scheduling of Spectrum Sensing Periods in Cognitive Radio Networks

We consider a cognitive radio (CR) network that makes opportunistic use of a set of channels licensed to some primary network. During its operation, the CR network is required to carry out spectrum sensing to detect active primary users, thereby avoiding interfering with them. However, spectrum sensing can cause negative effects to the performance of the CR network, as all CR communication has to be postponed during channel sensing. This paper focuses on adaptively scheduling the spectrum sensing periods so that negative impacts to the performance of the CR network are minimized. We first consider the case when CR nodes always have data to transmit and experience time- varying channels. Based on knowledge of channel conditions, the sensing periods are adaptively scheduled to maximize the spectrum efficiency of the CR operation. We then consider the case when CR nodes experience both stochastic data arrival and time-varying channels. By treating each sensing period as a 'virtual sensing packet', we convert the problem of joint data-transmission/spectrum-sensing scheduling into a standard queueing model. Based on that, efficient scheduling algorithms that take into account channel and queueing conditions of the CR network to achieve good quality of service are proposed.
Predictive Mining of Time Series Data in Astronomy

We discuss the development of a Java toolbox for astronomical time series data. Rather than using methods conventional in astronomy (e.g., power spectrum and cross-correlation analysis) we employ rule discovery techniques commonly used in analyzing stock-market data. By clustering patterns found within the data, rule discovery allows one to build pre- dictive models, allowing one to forecast when a given event might occur or whether the occurrence of one event will trigger a second. We have tested the toolbox and accompanying display tool on datasets (represent- ing several classes of objects) from the RXTE All Sky Monitor. We use these datasets to illustrate the methods and functionality of the toolbox. We also discuss issues that can come up in data analysis as well as the possible future development of the package.
An asymptotically capacity-achieving scheme for the Gaussian multiple-access relay channel

We study the multiple access relay channel (MARC) with relay-destination cooperation (MARC-RDC). This channel resembles the uplink transmission in heterogeneous network where two user equipments (UEs) communicate with macro-cell base station (BS) through small-cell BS. We propose a coding scheme where the transmission is carried over B blocks and each UE performs superposition block Markov encoding. The relay (small-cell BS) first jointly decodes both UEs information using sliding window decoding over two transmission blocks and then forwards these information to the destination (macro-cell BS) coherently with UEs. The destination quantizes its received signal in each block and forwards the quantization index to the relay. The destination then decodes both UEs information using backward decoding. For this scheme, we derive the achievable rate region and compare it with existing schemes and the cut-set bound. Results show that relay-destination cooperation enlarges the rate region as the destination power increases. We further show that the proposed scheme asymptotically achieves the capacity by reaching the cut-set bound when the destination power approaches infinity and the ratio of one UE-destination to UE-relay link amplitudes is equal to that of the other UE. These results make the proposed scheme appealing for deployment in 5G cellular networks.
Scholarly communication and possible changes in the context of social media: A Finnish case study

Purpose – The focus of this paper is to study the influence of social media on scholarly communication. The aim is to provide an overview of researchers' use of Web 2.0 techniques, and discuss a possible change of information behaviors in the context of scholarly communication.Design/methodology/approach – A web survey was distributed to a targeted sample of university staff (professors, teachers, researchers, and doctoral students). SPSS was utilized as a main tool to synthesize and analyze data, and present the results.Findings – Web 2.0 tools are well‐known to researchers. Most researchers are familiar with blogs, wikis, social networks, multimedia sharing, and online document. Social media provide a convenient environment for scholarly communication. Depending on different aims within the scholarly communication process, researchers choose appropriate modes of communication in their research work.Research limitations/implications – A combination of content analysis with survey and/or interviews may hi.
Determination of properties of composite materials from the Lamb wave propagation: probabilistic, interval, and fuzzy approaches

One of the most efficient non-destructive techniques for finding hidden faults in a plate is the use of ultrasonic Lamb waves. These waves are reflected by faults, and from this reflection, we can locate the faults. For that, we need to know how the Lamb waves propagate. Their propagation is determined by the dynamic elastic constants C'/sub pq/, so we must find these constants. These constants cannot be measured directly; instead, we measure the dependence of the speed of frequency c(f), and we must reconstruct C'/sub pq/ from the measured values of c(f). In this paper, we show how this can be done in the presence of probabilistic, interval, and fuzzy uncertainty.
GENETIC VARIANCE OF SEXUALLY SELECTED TRAITS IN WAXMOTHS: MAINTENANCE BY GENOTYPE X ENVIRONMENT INTERACTION

Abstract. — . –When traits experience directional selection, such as that imposed by sexual selection, their genetic variance is expected to diminish. Nonetheless, theory and findings from sexual selection predict and demonstrate that male traits favored by female choice retain substantial amounts of additive genetic variance. We explored this dilemma through an ecological genetic approach and focused on the potential contributions of genotype X environment interaction (GEI) to maintenance of additive genetic variance for male signal characters in the lesser waxmoth, Achroia grisella (Lepidoptera: Pyralidae). We artificially selected genetic variants for two male signal characters, signal rate (SR) and peak amplitude (PA), that influence female attraction and then examined the phenotypic plasticity of these variants (high‐ and low‐SR and high‐ and low‐PA lines) under a range of environmental conditions expected in natural populations.
Effectiveness of a Simple Internet-Based Disaster Triage Educational Tool Directed Toward Latin-American EMS Providers

Background. A previous survey demonstrated a lack of standardization related to disaster triage among Latin-American providers. Objective. To assess the effectiveness of a short Internet-based educational intervention in disaster andmass-casualty triage. Using three Spanish Internet emergency medical services (EMS) forums, Latin-American providers were invited to participate in the study. The tool consisted of two educational modules: an introduction to disaster triage module anda START (simple triage andrapid treatment) module. Pre- andpostintervention tests were administered, each consisting of five standardized scenarios. Factorial analysis was used to measure the weight of each scenario. The first andfifth scenarios were identical for intraclass correlation. Skill retention was assessed through a one-month follow-up survey. Statistical analysis was performed using chi-square andFisher's exact test. A total of 55 EMS providers participated in the study. Five of 55 (9.1%) participants correctly answered four or more scenarios on the pretest intervention, compared with 53 of 55 (96.4%) on the posttest [p < 0.001, relative risk 10.60 (95% CI 4.59–24.49)]. Similar findings were obtained for those accurately triaging all five scenarios, with zero of 55 (0%) in the pretest compared with 49 of 55 in the posttest (p < 0.001). Follow-up at one month was 69%. Four or more scenarios were correctly answered at follow-up by 34 of 38 (89.5%) respondents. No significant difference was noted compared with the immediate postcourse survey (p = 0.18). Although initial ability of the cohort to accurately triage patients was suboptimal, a short Internet-based educational tool significantly impacted the cohort's ability to perform triage in a simulated patient environment. This improvement was maintained after one month. Key words. disaster; traum; triage; Internet; education; prehospital; Latin America.
Parameter-Independent Online Compensation Scheme for Dead Time and Inverter Nonlinearity in IPMSM Drive Through Waveform Analysis

A simple parameter-independent online compensation scheme for the dead time and inverter nonlinearity in an interior-permanent-magnet-synchronous-motor drive through waveform analysis is presented. The effectiveness of the proposed scheme is verified through the simulations and experiments using the DSP TMS320F28335. Without requiring any additional hardware, the proposed scheme can effectively compensate the dead time and inverter nonlinearity even in the presence of the parameter uncertainty.
Omnidirectional Circularly Polarized Antenna with High Gain in Wide Bandwidth

A novel omnidirectional circularly polarized (CP) slot array antenna with high gain is proposed, which is based on the coaxial cylinder structure, and the orthogonal slots radi‐ ated the circular polarization wave around the cylinder. Further, the improved dual cir‐ cularly polarized (CP) omnidirectional antenna based on slot array in coaxial cylinder structure is presented too, and two ports are assigned in its two side as left hand circu‐ larly polarized (LHCP) port and right hand circularly polarized (RHCP) port, respec‐ tively. The simulation and experiment results show their novelty and good performance of omnidirectional circular polarization with about 5 dBi gain in 5.2–5.9 GHz.