Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavioral Phenotyping of Juvenile Long-Evans and Sprague-Dawley Rats: Implications for Preclinical Models of Autism Spectrum Disorders

The laboratory rat is emerging as an attractive preclinical animal model of autism spectrum disorder (ASD), allowing investigators to explore genetic, environmental and pharmacological manipulations in a species exhibiting complex, reciprocal social behavior. The present study was carried out to compare two commonly used strains of laboratory rats, Sprague-Dawley (SD) and Long-Evans (LE), between the ages of postnatal day (PND) 26–56 using high-throughput behavioral phenotyping tools commonly used in mouse models of ASD that we have adapted for use in rats. We detected few differences between young SD and LE strains on standard assays of exploration, sensorimotor gating, anxiety, repetitive behaviors, and learning. Both SD and LE strains also demonstrated sociability in the 3-chamber social approach test as indexed by spending more time in the social chamber with a constrained age/strain/sex matched novel partner than in an identical chamber without a partner. Pronounced differences between the two strains were, however, detected when the rats were allowed to freely interact with a novel partner in the social dyad paradigm. The SD rats in this particular testing paradigm engaged in play more frequently and for longer durations than the LE rats at both juvenile and young adult developmental time points. Results from this study that are particularly relevant for developing preclinical ASD models in rats are threefold: (i) commonly utilized strains exhibit unique patterns of social interactions, including strain-specific play behaviors, (ii) the testing environment may profoundly influence the expression of strain-specific social behavior and (iii) simple, automated measures of sociability may not capture the complexities of rat social interactions.
Method-centered digital communities on protocols.io for fast-paced scientific innovation

The Internet has enabled online social interaction for scientists beyond physical meetings and conferences. Yet despite these innovations in communication, dissemination of methods is often relegated to just academic publishing. Further, these methods remain static, with subsequent advances published elsewhere and unlinked. For communities undergoing fast-paced innovation, researchers need new capabilities to share, obtain feedback, and publish methods at the forefront of scientific development. For example, a renaissance in virology is now underway given the new metagenomic methods to sequence viral DNA directly from an environment. Metagenomics makes it possible to “see” natural viral communities that could not be previously studied through culturing methods. Yet, the knowledge of specialized techniques for the production and analysis of viral metagenomes remains in a subset of labs. This problem is common to any community using and developing emerging technologies and techniques. We developed new capabilities to create virtual communities in protocols.io, an open access platform, for disseminating protocols and knowledge at the forefront of scientific development. To demonstrate these capabilities, we present a virology community forum called VERVENet. These new features allow virology researchers to share protocols and their annotations and optimizations, connect with the broader virtual community to share knowledge, job postings, conference announcements through a common online forum, and discover the current literature through personalized recommendations to promote discussion of cutting edge research. Virtual communities in protocols.io enhance a researcher’s ability to: discuss and share protocols, connect with fellow community members, and learn about new and innovative research in the field. The web-based software for developing virtual communities is free to use on protocols.io. Data are available through public APIs at protocols.io.
Behavior of the oscillating microbubble clusters trapped in focused ultrasound field

Microbubbles (MBs) are widely used in clinical site as contrast agents in ultrasound imaging due to its eminent responsiveness to several MHz band. In recent years, ultrasound drug delivery system (DDS) using MBs are widely researched to achieve selective medication to the targeted diseased part. In the condition of high number density of bubbles to enhance the performance of DDS, it is essentially important to understand interaction between bubbles and establish a control method of bubble clusters. In previous works, we can manipulate MBs using focused ultrasound, forming a bubbles cluster at the focus of the ultrasound field. The purpose of this study is to clarify scaling behavior of bubble clusters with various physical properties, since such scaling parameters is highly relating various bubble parameters which have not been measured in other methods.
Intrusion Detection for Web Applications (Short Version)

Intrusion detection systems (IDS) are usually classified into two categories: misuseand anomaly detection systems. Misuse detection is based on signatures; it is precise but can only accommodate already known attacks. Unlike this, anomaly detection models a system’s usual behavior and is able to detect new attacks, but some major challenges remain to be solved in this field, in particular the improvement of the detection process and the reduction of false alarms. On the application/service level, several misuse detection systems exist and work, but only one anomaly detection system is known to be efficient for now. In this short paper, we propose a Web learning-based anomaly detection system based on this system, and resulting from the junction of academic research in several fields, which we improved. The system analyzes HTTP requests as logged by most of the Web servers; it exclusively relates to the queries containing attributes. The analysis process implements a multi-model statistical approach. A Bayesian network is used as decision process, specifying six states (one normal state and five attack states) at the classification node. The system is improved after each log analysis thanks to a technique of alarm clustering, which allows filtering false positive. Compared to traditional anomaly detection systems, the system we present globally gains in sensitivity (each step of the process reduces the number of false positive to be dealt with) and in specificity (if an attack is detected, its type is immediately specified). Moreover, a co-operation feature (alarm correlation) with other systems is proposed for distributed intrusion detection. To date, the system has only been partially implemented but the preliminary experiments in real environment show encouraging results.
A concept analysis of undergraduate nursing students speaking up for patient safety in the patient care environment.

AIM An analysis of the concept of nursing students speaking up for patient safety in the workplace. BACKGROUND 'Speaking up' is assertive communication in clinical situations that requires action through questions or statements of opinion or information with appropriate persistence and is linked to patient safety. Previously, the concept of speaking up has focused on the registered or experienced practitioners, there is minimal discussion relating to student nurses. Analysis of the elements of students speaking up will identify the key elements that will give understanding to their position and experiences. DESIGN A concept analysis. DATA Literature included publications between 1970-2015 from, MEDLINE, CINHAL, PUBMED and SCOPUS. Search terms included patient safety AND speaking up; AND pre-registration/undergraduate nursing students, patient advocate, error reporting, organizational silence, whistleblowing and clinical placement/practicum. METHODS The Walker and Avant concept analysis model was modified and used to examine the literature. RESULTS Nursing students speaking up behaviour is influenced by individual and contextual factors that differ from those influencing more experienced colleagues. Motivators and barriers to voicing concerns include moral and ethical beliefs, willingness and confidence to speak up in the workplace. Students' subordinate and often vulnerable position creates additional tensions and challenges that impact their decisions and actions. CONCLUSION This concept analysis provides a clear definition of 'speaking up' in relation to nursing students. The analysis will facilitate understanding and operationalization of the concept applied to learning and teaching, practice and research.
Non‐THC cannabinoids inhibit prostate carcinoma growth in vitro and in vivo: pro‐apoptotic effects and underlying mechanisms

Cannabinoid receptor activation induces prostate carcinoma cell (PCC) apoptosis, but cannabinoids other than Δ9‐tetrahydrocannabinol (THC), which lack potency at cannabinoid receptors, have not been investigated. Some of these compounds antagonize transient receptor potential melastatin type‐8 (TRPM8) channels, the expression of which is necessary for androgen receptor (AR)‐dependent PCC survival.
Current methods of sedation in dental patients - a systematic review of the literature

Objetive The main objective of this systematic literature review is to identify the safest and most effective sedative drugs so as to ensure successful sedation with as few complications as possible. Study Design A systematic literature review of the PubMed MEDLINE database was carried out using the key words “conscious sedation,” “drugs,” and “dentistry. ” A total of 1,827 scientific articles were found, and these were narrowed down to 473 articles after applying inclusion and exclusion criteria. These 473 studies were then individually assessed for their suitability for inclusion in this literature review. Results A total of 21 studies were selected due to their rigorous study design and conduciveness to further, more exhaustive analysis. The selected studies included a total of 1,0003 patients classified as ASA I or II. Midazolam was the drug most frequently used for successful sedation in dental surgical procedures. Ketamine also proved very useful when administered intranasally, although some side effects were observed when delivered via other routes of administration. Both propofol and nitrous oxide (N2O) are also effective sedative drugs. Conclusions Midazolam is the drug most commonly used to induce moderate sedation in dental surgical procedures, and it is also very safe. Other sedative drugs like ketamine, dexmedetomidine and propofol have also been proven safe and effective; however, further comparative clinical studies are needed to better demonstrate which of these are the safest and most effective. Key words:Conscious sedation, drugs, dentistry.
A Fog-Based Healthcare Framework for Chikungunya

Chikungunya virus is an infection which is transmitted to humans by the bite of Aedes agypti and Aedes albopictis mosquitoes. It causes deterioration in health leading to multiorgan failure. Diagnostic tests for this virus are not easily available and affordable in developing countries. The medical technologies are not so efficient in diagnosing and preventing the outbreak of Chikungunya virus. Healthcare services based on a fog-cloud technology are emerging as a proactive and effective solution to provide remote detection and monitoring of users. A fog assisted healthcare system can be used to diagnose users infected by Chikungunya virus in an initial state of their infection so that proper treatment can be given to them on time to enable fast recovery. In this paper, a fog assisted cloud-based healthcare system is designed to diagnose and prevent the outbreak of this virus. Initially, a decision tree is used to classify the category of user’s infection depending on his/her health symptoms and diagnostic alerts are immediately generated on the user’s mobile phone from fog layer. Furthermore, the state of Chikungunya virus outbreak is represented using temporal network analysis (TNA) on cloud layer using proximity data. Various outbreak metrics from TNA graph are calculated which indicate the probability to receive or transmit Chikungunya infection to human beings. Results depict that J48 decision tree classifier has higher accuracy and lower response time in determining the level of Chikungunya virus infection as compared to other classification algorithms. Moreover, alert generation based on real-time healthcare data further enhances the utility of the proposed system.
Amperometric Hydrogen Peroxide Biosensor Based on Horseradish Peroxidase-labeled Nano-Au Colloids Immobilized on Poly(2,6-pyridinedicarboxylic acid) Layer by Cysteamine

A sensitive hydrogen peroxidase (H_2O_2) amperometric sensor based on horseradish peroxidase (HRP)-labeled nano-Au colloids has been proposed. Nano-Au colloids were immobilized by the thiol group of cysteamine, which was associated with the carboxyl groups of poly(2,6-pyridinedicarboxylic acid) (PPDA). With the aid of the hydroquinone, the sensor displayed excellent electrocatalytical response to the reduction of H_2O_2. Compared with the non-Au-colloid modified electrode, i.e. , PPDA/HRP, the Au-colloid modified electrode exhibited better performance characteristics, including stability, reproducibility, sensitivity and accuracy. The biosensor shows a linear response to H_2O_2 in the range of 3.0 × 10^–7–2 × 10^–3 M. The detection limit was 1.0 × 10^–7 M.
Robust 1-Bit Compressed Sensing via Hinge Loss Minimization

We study the problem of estimating a structured high-dimensional signal x0 ∈ ℝn from noisy 1-bit Gaussian measurements. Our recovery approach is based on a simple convex program which uses the hinge loss function as data fidelity term. While such a risk minimization strategy is typically applied in classification tasks, its capacity to estimate a specific signal vector is largely unexplored. In contrast to other popular loss functions considered in signal estimation, which are at least locally strongly convex, the hinge loss is just piecewise linear, so that its "curvature energy" is concentrated in a single point. It is therefore somewhat unexpected that we can still prove very similar types of recovery guarantees for the hinge loss estimator, even in the presence of strong noise. More specifically, our error bounds show that stable and robust reconstruction of x0 can be achieved with the optimal approximation rate O(m−1/2) in terms of the number of measurements m. Moreover, we permit a wide class of structural assumptions on the ground truth signal, in the sense that x0 can belong to an arbitrary bounded convex set K ⊂ ℝn. For the proofs of our main results we invoke an adapted version of Mendelson’s small ball method that allows us to establish a quadratic lower bound on the error of the first order Taylor approximation of the empirical hinge loss function.
Multi-word Structural Topic Modelling of ToR Drug Marketplaces

Topic Modelling (TM) is a widely adopted generative model used to infer the thematic organization of text corpora. When document-level covariate information is available, so-called Structural Topic Modelling (STM) is the state-of-the-art approach to embed this information in the topic mining algorithm. Usually, TM algorithms rely on unigrams as the basic text generation unit, whereas the quality and intelligibility of the identified topics would significantly benefit from the detection and usage of topical phrasemes. Following on from previous research, in this paper we propose the first iterative algorithm to extend STM with n-grams, and we test our solution on textual data collected from four well-known ToR drug marketplaces. Significantly, we employ a STM-guided n-gram selection process, so that topic-specific phrasemes can be identified regardless of their global relevance in the corpus. Our experiments show that enriching the dictionary with selected n-grams improves the usability of STM, allowing the discovery of key information hidden in an apparently "mono-thematic" dataset.
Evidence for scale up: the differentiated care research agenda

The scale up of antiretroviral therapy (ART) to more than 18 million people living with HIV (PLHIV) [1], primarily in lowand middle-income countries, is one of history’s greatest public health achievements. Life expectancy among PLHIV on ART has improved worldwide [2], and in some settings hardest hit by the epidemic, rapid gains have been seen. For example, in KwaZulu-Natal, South Africa, life expectancy has increased three times faster than the previous highest recorded increase – seen in Japan as it recovered from World War II [3]. Advances in treatment over the past decade combined with increased access to care have transformed HIV into a manageable, chronic disease [4]. HIV has led the way in providing a platform that could lead to the successful management of chronic diseases in resource-limited settings [5,6]. We now live in an era where it is recommended that all PLHIV initiate ART as soon as possible following diagnosis [7], and consequently the size of the potential treatment cohort has almost doubled, from 18.2 million to 36.7 million [1,8]. However, global funding for the epidemic remains flat or declining in present-day value [9,10]. With the cost of antiretroviral drugs having decreased 100-fold in the past decade [11], further cost reductions are dependent on making “efficiency gains” within the healthcare system. However, what about the client – the person living with HIV? While there is evidence of high rates of attrition after starting ART [12], among those who do stay on treatment, high levels of viral suppression are achieved [13,14]. Yet, as the world pushes towards 90–90–90 targets, the resources being utilized to achieve the current successes will be further stretched and known challenges, like rates of attrition, may be exacerbated. Business as usual will not be enough to meet global treatment goals. However, much of the success of ART scale up to date has been attributed to its simple, one-size-fits-all approach [15] in which most PLHIV receive facility-based care from professional healthcare cadres at frequent intervals regardless of their context, clinical characteristics or subpopulation. Differentiated care or differentiated service delivery is defined as “a client-centred approach that simplifies and adapts HIV services across the cascade, in ways that both serve the needs of PLHIV better and reduce unnecessary burdens on the health system” [16]; it is an attempt to maintain a public health approach while acknowledging that people’s needs change over time. The majority of published evidence of differentiated care has been limited to ART delivery for stable adults in high-prevalence settings in sub-Saharan Africa. Largely pioneered by the medical aid agency Médecins Sans Frontières (MSF), four innovative service delivery models have emerged in response to context-specific client needs and health systems challenges met in different countries [17]:
Tacrolimus in preventing transplant rejection in Chinese patients – optimizing use

Tacrolimus is a product of fermentation of Streptomyces, and belongs to the family of calcineurin inhibitors. It is a widely used immunosuppressive drug for preventing solid-organ transplant rejection. Compared to cyclosporine, tacrolimus has greater immunosuppressive potency and a lower incidence of side effects. It has been accepted as first-line treatment after liver and kidney transplantation. Tacrolimus has specific features in Chinese transplant patients; its in vivo pharmacokinetics, treatment regimen, dose and administration, and adverse-effect profile are influenced by multiple factors, such as genetics and the spectrum of primary diseases in the Chinese population. We reviewed the clinical experience of tacrolimus use in Chinese liver- and kidney-transplant patients, including the pharmacology of tacrolimus, the immunosuppressive effects of tacrolimus versus cyclosporine, effects of different factors on tacrolimus metabolism on Chinese patients, personalized medicine, clinical safety profile, and patient satisfaction and adherence. This article provides guidance for the rational and efficient use of tacrolimus in Chinese organ-transplant patients.
Towards Gamification Analytics - Requirements for Monitoring and Adapting Gamification Designs

Application and gamification data contain valuable information about users and their behavior. This data can be used to measure the success of gamification projects, to analyze user behavior, and to continuously improve gamification designs. However, to support the process of gamification analyses, appropriate tool support is missing. Moreover, the specific requirements for methods and tools have not been studied yet and remain unclear. Therefore, this paper presents a theoretical model of 22 requirements that can be used to assess existing solutions or construct novel methods and tools for gamification analytics. The presented model is validated based on comprehensive expert interviews.
Metrics for Deep Generative Models

Neural samplers such as variational autoencoders (VAEs) or generative adversarial networks (GANs) approximate distributions by transforming samples from a simple random source---the latent space---to samples from a more complex distribution represented by a dataset. While the manifold hypothesis implies that the density induced by a dataset contains large regions of low density, the training criterions of VAEs and GANs will make the latent space densely covered. Consequently points that are separated by low-density regions in observation space will be pushed together in latent space, making stationary distances poor proxies for similarity. We transfer ideas from Riemannian geometry to this setting, letting the distance between two points be the shortest path on a Riemannian manifold induced by the transformation. The method yields a principled distance measure, provides a tool for visual inspection of deep generative models, and an alternative to linear interpolation in latent space. In addition, it can be applied for robot movement generalization using previously learned skills. The method is evaluated on a synthetic dataset with known ground truth; on a simulated robot arm dataset; on human motion capture data; and on a generative model of handwritten digits.
Achieving high-resolution video using scalable capture, processing and display

New video applications are becoming possible with the advent of several enabling technologies: multicamera capture, increased PC bus bandwidth, multicore processors, and advanced graphics cards. We present a commercially-available multicamera system and a software architecture that, coupled with industry trends, create a situation in which video capture, processing, and display are all increasingly scalable in the number of video streams. Leveraging this end-to-end scalability, we introduce a novel method of generating high-resolution, panoramic video. While traditional point-based mosaicking requires significant image overlap, we gain significant advantage by calibrating using shared observations of lines to constrain the placement of images. Two non-overlapping cameras do not share any scene points; however, seeing different parts of the same line does constrain their spatial alignment. Using lines allows us to reduce overlap in the source images, thereby maximizing final mosaic resolution. We show results of synthesizing a 6 megapixel video camera from 18 smaller cameras, all on a single PC and at 30 Hz.