Miracle-dz/UniFAR-FTUs huggingface dataset:
Elite handcycling: a qualitative analysis of recumbent handbike configuration for optimal sports performance

Abstract Our understanding of handbike configuration is limited, yet it can be a key determinant of performance in handcycling. This study explored how 14 handcycling experts (elite handcyclists, coaches, support staff, and manufacturers) perceived aspects of recumbent handbike configuration to impact upon endurance performance via semi-structured interviews. Optimising the handbike for comfort, stability, and power production was identified as key themes. Comfort and stability were identified to be the foundations of endurance performance and were primarily influenced by the seat, backrest, headrest, and their associated padding. Power production was determined by the relationship between the athletes’ shoulder and abdomen and the trajectories of the handgrips, which were determined by the crank axis position, crank arm length, and handgrip width. Future studies should focus on quantifying the configuration of recumbent handbikes before determining the effects that crank arm length, handgrip width, and crank position have on endurance performance. Practitioner Summary: To gain a greater understanding of the impact of handbike configurations on endurance performance, the perceptions of expert handcyclists were explored qualitatively. Optimising the handbike for comfort and stability, primarily via backrest padding and power production, the position of the shoulders relative to handgrips and crank axis, were critical.
Reference Grade Characterization of Polymorphisms in Full-Length HLA Class I and II Genes With Short-Read Sequencing on the ION PGM System and Long-Reads Generated by Single Molecule, Real-Time Sequencing on the PacBio Platform

Although NGS technologies fuel advances in high-throughput HLA genotyping methods for identification and classification of HLA genes to assist with precision medicine efforts in disease and transplantation, the efficiency of these methods are impeded by the absence of adequately-characterized high-frequency HLA allele reference sequence databases for the highly polymorphic HLA gene system. Here, we report on producing a comprehensive collection of full-length HLA allele sequences for eight classical HLA loci found in the Japanese population. We augmented the second-generation short read data generated by the Ion Torrent technology with long amplicon spanning consensus reads delivered by the third-generation SMRT sequencing method to create reference grade high-quality sequences of HLA class I and II gene alleles resolved at the genomic coding and non-coding level. Forty-six DNAs were obtained from a reference set used previously to establish the HLA allele frequency data in Japanese subjects. The samples included alleles with a collective allele frequency in the Japanese population of more than 99.2%. The HLA loci were independently amplified by long-range PCR using previously designed HLA-locus specific primers and subsequently sequenced using SMRT and Ion PGM sequencers. The mapped long and short-reads were used to produce a reference library of consensus HLA allelic sequences with the help of the reference-aware software tool LAA for SMRT Sequencing. A total of 253 distinct alleles were determined for 46 healthy subjects. Of them, 137 were novel alleles: 101 SNVs and/or indels and 36 extended alleles at a partial or full-length level. Comparing the HLA sequences from the perspective of nucleotide diversity revealed that HLA-DRB1 was the most divergent among the eight HLA genes, and that the HLA-DPB1 gene sequences diverged into two distinct groups, DP2 and DP5, with evidence of independent polymorphisms generated in exon 2. We also identified two specific intronic variations in HLA-DRB1 that might be involved in rheumatoid arthritis. In conclusion, full-length HLA allele sequencing by third-generation and second-generation technologies has provided polymorphic gene reference sequences at a genomic allelic resolution including allelic variations assigned up to the field-4 level for a stronger foundation in precision medicine and HLA-related disease and transplantation studies.
Effects of exogenous lactoferrin on phenotypic profile and invasiveness of human prostate cancer cells (DU145 and LNCaP) in vitro.

AIM To investigate the biological effects of exogenous lactoferrin (LF) on phenotypic profile and invasiveness of human prostate cancer (PC) cells in vitro. MATERIALS AND METHODS Human PC cell lines (LNCaP, DU-145) were cultured with an exogenous LF at a dose corresponding to IC30. The expression levels of steroid hormone receptors (androgen receptor, estrogen receptor, progesterone receptor), Her2/neu, Ki-67, E- and N-cadherin, were monitored by immunohistochemical analysis. The levels of miRNAs were assessed using q-PCR. The invasive activity of the cells was examined in a standard invasion test. RESULTS Exogenous LF reduced expression of steroid hormone receptors (ERα and PR) and Ki-67 in both PC cell lines. The expression of E-cadherin increased significantly in LF-treated DU-145 cells. Also, we established the decrease in invasive activity upon LF treatment by 40% and 30% in DU-145 and LNCaP cells, respectively. In DU-145 cells, incubation with exogenous LF resulted in an increase in the expression of oncosuppressive (miR-133a and miR-200b) miRNAs. CONCLUSIONS Exogenous LF causes the changes in phenotypic characteristics of PC cells and levels of oncogenic and oncosuppressive miRNAs involved in the regulation of key cellular processes.
Robust Machine Learning Systems: Reliability and Security for Deep Neural Networks

Machine learning is commonly being used in almost all the areas that involve advanced data analytics and intelligent control. From applications like Natural Language Processing (NLP) to autonomous driving are based upon machine learning algorithms. An increasing trend is observed in the use of Deep Neural Networks (DNNs) for such applications. While the slight inaccuracy in applications like NLP does not have any severe consequences, it is not the same for other safety-critical applications, like autonomous driving and smart healthcare, where a small error can lead to catastrophic effects. Apart from high-accuracy DNN algorithms, there is a significant need for robust machine learning systems and hardware architectures that can generate reliable and trustworthy results in the presence of hardware-level faults while also preserving security and privacy. This paper provides an overview of the challenges being faced in ensuring reliable and secure execution of DNNs. To address the challenges, we present several techniques for analyzing and mitigating the reliability and security threats in machine learning systems.
Testing Untestable Neural Machine Translation: An Industrial Case

Neural Machine Translation (NMT) has shown great advantages and is becoming increasingly popular. However, in practice, NMT often produces unexpected translation failures in its translations. While reference-based black-box system testing has been a common practice for NMT quality assurance during development, an increasingly critical industrial practice, named in-vivo testing, exposes unseen types or instances of translation failures when real users are using a deployed industrial NMT system. To fill the gap of lacking test oracles for in-vivo testing of NMT systems, we propose a new methodology for automatically identifying translation failures without reference translations. Our evaluation conducted on real-world datasets shows that our methodology effectively detects several targeted types of translation failures. Our experiences on deploying our methodology in both production and development environments of WeChat (a messenger app with over one billion monthly active users) demonstrate high effectiveness of our methodology along with high industry impact.
Holistic CNN Compression via Low-Rank Decomposition with Knowledge Transfer

Convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks, which are extremely powerful to deal with massive training data by using tens of millions of parameters. However, CNNs often cost significant memory and computation consumption, which prohibits their usage in resource-limited environments such as mobile or embedded devices. To address the above issues, the existing approaches typically focus on either accelerating the convolutional layers or compressing the fully-connected layers separatedly, without pursuing a joint optimum. In this paper, we overcome such a limitation by introducing a holistic CNN compression framework, termed LRDKT, which works throughout both convolutional and fully-connected layers. First, a low-rank decomposition (LRD) scheme is proposed to remove redundancies across both convolutional kernels and fully-connected matrices, which has a novel closed-form solver to significantly improve the efficiency of the existing iterative optimization solvers. Second, a novel knowledge transfer (KT) based training scheme is introduced. To recover the accumulated accuracy loss and overcome the vanishing gradient, KT explicitly aligns outputs and intermediate responses from a teacher (original) network to its student (compressed) network. We have comprehensively analyzed and evaluated the compression and speedup ratios of the proposed model on MNIST and ILSVRC 2012 benchmarks. In both benchmarks, the proposed scheme has demonstrated superior performance gains over the state-of-the-art methods. We also demonstrate the proposed compression scheme for the task of transfer learning, including domain adaptation and object detection, which show exciting performance gains over the state-of-the-arts. Our source code and compressed models are available at https://github.com/ShaohuiLin/LRDKT.
Face Sketch Synthesis by Multidomain Adversarial Learning

Given a training set of face photo-sketch pairs, face sketch synthesis targets at learning a mapping from the photo domain to the sketch domain. Despite the exciting progresses made in the literature, it retains as an open problem to synthesize high-quality sketches against blurs and deformations. Recent advances in generative adversarial training provide a new insight into face sketch synthesis, from which perspective the existing synthesis pipelines can be fundamentally revisited. In this paper, we present a novel face sketch synthesis method by multidomain adversarial learning (termed MDAL), which overcomes the defects of blurs and deformations toward high-quality synthesis. The principle of our scheme relies on the concept of “interpretation through synthesis. ” In particular, we first interpret face photographs in the photodomain and face sketches in the sketch domain by reconstructing themselves respectively via adversarial learning. We define the intermediate products in the reconstruction process as latent variables, which form a latent domain. Second, via adversarial learning, we make the distributions of latent variables being indistinguishable between the reconstruction process of the face photograph and that of the face sketch. Finally, given an input face photograph, the latent variable obtained by reconstructing this face photograph is applied for synthesizing the corresponding sketch. Quantitative comparisons to the state-of-the-art methods demonstrate the superiority of the proposed MDAL method.
Highly Synergistic Effects of Melittin with Conventional Antibiotics Against Multidrug-Resistant Isolates of Acinetobacter baumannii and Pseudomonas aeruginosa.

BACKGROUND Morbidity and mortality due to multidrug-resistant (MDR) bacteria are of great concern in burn patients. In this critical condition, synergism between antimicrobial peptides and conventional antibiotics would be a promising strategy. Accordingly, this study aimed to determine the therapeutic value of melittin as a natural peptide by examining its synergistic effect with conventional antibiotics against MDR isolates of Acinetobacter baumannii and Pseudomonas aeruginosa. MATERIALS AND METHODS Fifteen clinical isolates for each kind of bacteria were collected from burn patients. Antibiotic susceptibility of all isolates was evaluated by disk diffusion method. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration for melittin, colistin, doripenem, doxycycline, and ceftazidime were also examined. Fractional inhibitory concentration (FIC) of melittin in combination with the antibiotics was determined for six MDR isolates. The cytotoxicity of melittin in combination with the antibiotics was examined on a normal human cell line. RESULTS The geometric means of MIC (GMMIC) for melittin and doripenem after combination were reduced to 61.5- and 51.5-fold, respectively, against MDR A. baumannii isolates. These reductions for melittin-doripenem and melittin-ceftazidime against MDR P. aeruginosa isolates were (63.5 and 58)-fold and (16 and 11)-fold, respectively. FIC for melittin-doripenem against A. baumannii and FIC for melittin-doripenem and melittin-ceftazidime against P. aeruginosa strains were ≤0.5. This issue caused a decrease of up to 104-, 68-, and 17-fold, respectively, in the cytotoxicity of melittin. CONCLUSION In conclusion, the synergism of melittin at its nontoxic dose with doripenem and ceftazidime could be of great therapeutic value as a topical drug against burn infections caused by MDR bacteria.
National perioperative outcomes of flap coverage for pressure ulcers from 2005 to 2015 using American College of Surgeons National Surgical Quality Improvement Program

Background Complication rates after flap coverage for pressure ulcers have been high historically. These patients have multiple risk factors associated with poor wound healing and complications including marginal nutritional status, prolonged immobilization, and a high comorbidities index. This study utilizes the National Surgical Quality Improvement Program (NSQIP) to examine perioperative outcomes of flap coverage for pressure ulcers. Methods Data from the NSQIP database (2005–2015) for patient undergoing flap coverage for pressure ulcers was identified. Demographic, perioperative information, and complications were reviewed. One-way analysis of variance and Pearson chi-square were used to assess differences for continuous variables and nominal variables, respectively. Multivariate logistic regression was performed to identify independent risk factors for complications. Results There were 755 cases identified: 365 (48.3%) sacral ulcers, 321 (42.5%) ischial ulcers, and 69 (9.1%) trochanteric ulcers. Most patients were older male, with some degree of dependency, neurosensory impairment, high functional comorbidities score, and American Society of Anesthesiologists class 3 or above. The sacral ulcer group had the highest incidence of septic shock and bleeding, while the trochanteric ulcer group had the highest incidence of superficial surgical site infection. There was an overall complication rate of 25% at 30-day follow-up. There was no statistical difference in overall complication among groups. Total operating time, diabetes, and non-elective case were independent risk factors for overall complications. Conclusions Despite patients with poor baseline functional status, flap coverage for pressure ulcer patients is safe with acceptable postoperative complications. This type of treatment should be considered for properly selected patients.
Long Non-coding RNAs: A New Regulatory Code for Osteoporosis

Osteoporosis is a metabolic bone disease characterized by a decrease in bone mass and degradation of the bone microstructure, which increases bone fragility and fracture risk. However, the molecular mechanisms of osteoporosis remain unclear. Long non-coding RNAs (lncRNAs) have become important epigenetic regulators controlling the expression of genes and affecting multiple biological processes. Accumulating evidence of the involvement of lncRNAs in bone remolding has increased understanding of the molecular mechanisms underlying osteoporosis. This review aims to summarize recent progress in the elucidation of the role of lncRNAs in bone remodeling, and how it contributes to osteoblast and osteoclast function. This knowledge will facilitate the understanding of lncRNA roles in bone biology and shed new light on the modulation and potential treatment of osteoporosis.
Recent advances in the biological treatment of mood disorders

Abstract Welcome to the first module in our Continuing Professional Development Section (CPD). CPD is now a key element in the clinical activity of all health professionals and a cornerstone of good clinical governance throughout mental health services. This section of the Irish Journal of Psychological Medicine will provide CPD modules dedicated to key topics in mental health care. In order to assist learning and self-assessment, multiple choice questions will be provided at the end of each module. This module and its multiple choice questions are available online on the website of the Irish Journal of Psychological Medicine (www.ijpm.org). The CPD policy of the College of Psychiatry of Ireland indicates that psychiatrists who participate in suitable online learning which fits the criteria for CPD may claim CPD points under the Personal CPD category (up to a maximum of 5 points per year). We are confident that this CPD Section of the Irish Journal of Psychological Medicine will prove to be a valuable resource for consultant psychiatrists, psychiatric trainees and all journal readers. We welcome feedback from readers and, especially, any suggestions for topics to be covered in future CPD modules. Suggestions should be emailed to: psychological@medmedia.ie
Rubella Virus Strain-Associated Differences in the Induction of Oxidative Stress Are Independent of Their Interferon Activation

Rubella virus (RV) infection impacts cellular metabolic activity in a complex manner with strain-specific nutritional requirements. Here we addressed whether this differential metabolic influence was associated with differences in oxidative stress induction and subsequently with innate immune response activation. The low passaged clinical isolates of RV examined in this study induced oxidative stress as validated through generation of the reactive oxygen species (ROS) cytoplasmic hydrogen peroxide and mitochondrial superoxide. The addition of the cytoplasmic and mitochondrial ROS scavengers N-acetyl-l-cysteine and MitoTEMPO, respectively, reduced RV-associated cytopathogenicity and caspase activation. While the degree of oxidative stress induction varied among RV clinical isolates, the level of innate immune response and interferon-stimulated gene activation was comparable. The type III IFNs were highly upregulated in all cell culture systems tested. However, only pre-stimulation with IFN β slightly reduced RV replication indicating that RV appears to have evolved the ability to counteract innate immune response mechanisms. Through the data presented, we showed that the ability of RV to induce oxidative stress was independent of its capacity to stimulate and counteract the intrinsic innate immune response.
A Validated GC-MS Method for the Determination of Genotoxic Impurities in Divalproex Sodium Drug Substance.

A specific GC-MS method has been developed, optimized and validated for the determination of five genotoxic impurities namely Methyl bromide (Me.-Br), Ethyl bromide (Et.-Br), Isopropyl bromide (Ipr.-Br), n-Propyl bromide (n-Pr.-Br) and n-Butyl bromide (n-But.-Br) in Divalproex sodium (DPS) drug substance. Chromatographic separation of five genotoxic impurities was achieved on DB-1 column (30 m × 0.32 mm, 3.0 μm), consists of 100% dimethyl polysiloxane as stationary phase and passing helium carrier gas. The mass fragments (m/z) were selected for the quantification of Me. -Br (m/z 94), Et. -Br (m/z 108), Ipr. -Br (m/z 122), n-Pr. -Br (m/z 122) and n-But. -Br (m/z 136). Bromide ion (m/z 79) was the qualifier ion for the analytes [(Me.-Br), (Et.-Br), (Ipr.-Br), (n-Pr.-Br) and (n-But.-Br)]. The performance of the method was assessed by evaluating the specificity, linearity, sensitivity, precision and accuracy experiments. The established limit of detection and limit of quantification values for the genotoxic impurities were in the range of 0.005-0.019 μg mL-1. The correlation coefficient values of the linearity experiment were in the range of 0.9947-0.9983. The average recoveries for the accuracy were in the range of 97.6-111.3%. The results proved that the method is suitable for the determination of Me. -Br, Et. -Br, Ipr. -Br, n-Pr. -Br and n-But. -Br contents in divalproex sodium.
Opinion Formation Threshold Estimates from Different Combinations of Social Media Data-Types

Passive consumption of a quantifiable amount of social media information related to a topic can cause individuals to form opinions. If a substantial amount of these individuals are motivated to take action from their recently established opinions, a movement or public opinion shift can be induced independent of the information's veracity. Given that social media is ubiquitous in modern society, it is imperative that we understand the threshold at which social media data results in opinion formation. The present study estimates population opinion formation thresholds by querying 2222 participants about the number of various social media data-types (i.e., images, videos, and/or messages) that they would need to passively consume to form opinions. Opinion formation is assessed across three dimensions, 1) data-type(s), 2) context, and 3) source. This work provides a theoretical basis for estimating the amount of data needed to influence a population through social media information.
Learning and engagement in a gamified course: Investigating the effects of student characteristics

The current study investigated college students' experiences of a gamified informatics course. We surveyed 139 students aged 18 – 31 years ( M = 20 years, SD = 1.5) enrolled in an undergraduate informatics course focused on social networking technologies. Surveys were conducted at 3 time points during the course (beginning, middle, and end). Overall, we found positive trends with respect to students' perceptions of gamification's impact on their learning, achievement, and engagement in the course material. Although students who played and identified variously with recreational games were more alike than not, we did uncover one notable difference with respect to how students' gaming frequency impacted their engagement in the course. Nongamers expressed somewhat less motivation to do well in the course than frequent gamers. For all other measures of engagement, however, nongamers appeared to be equally engaged by the gamified format of the course as gamers. There were virtually no differences between male and female students' perceptions of gamification. This study contributes new insight into the conditions under which gamification suc-ceeds or fails in educational settings. These insights will be useful to designers and instructors of gamified learning environments as they seek to engage and support a variety of learners.