Miracle-dz/UniFAR-FTUs huggingface dataset:
Personal, Social and Cultural Predictors of Intention to Misuse Prescription Stimulants among Medical Students: A Test of the Theory of Triadic Influence

Abstract Objective The misuse of prescription stimulants among students has been identified as a public health problem. To date, most research has focused on individual-level determinants of stimulant misuse, making research on the socio-cultural context of students’ misuse a priority. This study aims to test the applicability of the Theory of Triadic Influence, capturing three influence streams (personal, social and cultural) and three causational levels (ultimate, distal and proximal). Method A questionnaire on stimulant misuse was distributed among all bachelor’s and master’s students from the five Flemish medical faculties. In total, 3159 students participated (48.99% response rate). Data were analyzed using structural equation modeling. Results Multiple personal (i.e., fear of failure, procrastination, self-perceived ADHD, sensation-seeking, academic stress, controllability), social (i.e., living situation, peer endorsement, social norm) and cultural (i.e., competitive study-environment, financial worries, positive and negative expectancies, attitude) factors were identified as risk factors of misuse intention. The strongest ultimate to distal pathway was found between self-perceived ADHD and positive expectancies, meaning that students who believed they have ADHD, although not diagnosed, were more likely to have positive expectancies about stimulants. Moreover, the strongest distal to proximal pathways were found between expectancies and attitudes toward stimulant misuse (i.e., more positive and fewer negative expectancies were associated with more favorable attitudes). Finally, attitudes were most strongly related to misuse intention. Conclusions The current study shows that the TTI is an important framework to understand the risk factors of stimulant misuse among medical students. This study offers a strong basis for prevention initiatives.
Synchronized Control of Multi-motor Based on the Improved Relative Coupling Control

To solve the problem that the existed scheme of synchronized control of multi-motor is difficult to meet the requirements of high precise synchronized control and the limitation that it can not achieve proportional synchronized control,a belt PI compensative control of the improved Relative Coupling Control structure was proposed,which can apply to completive synchronized control and proportional synchronized control for multi-motor.As to the problem that the Permanent Magnet Synchronous Motor(PMSM)is a multivariable nonlinear system with strong coupling,an adaptive fuzzy sliding mode controller was designed to control the PMSM.The synchronized control simulation mode of four PMSM was set up.Compared with fixed gain compensative control structure,simulation results illustrate that this new structure has higher synchronized control accuracy.Compared with PID and conventional sliding mode control strategy,this fuzzy sliding mode control strategy has a better synchronized stability behavior and stronger robustness.
An insight into Transfemoral Prostheses: Materials, modelling, simulation, fabrication, testing, clinical evaluation and performance perspectives

ABSTRACT Introduction A transfemoral prosthesis restores any limb amputated above the knee. Designing and developing a transfemoral prosthesis that is consistent with human performance is a tough task. While prosthetic components are widely available in the market, ongoing research is being conducted to develop parts that would restore the lost capability, taking into account numerous social, economic, and technological considerations. Areas covered The present paper provides a comprehensive review about the mechanical aspects and performance of transfemoral prosthesis in recent years based on the research findings on materials, manufacturing methods, and evaluations for suitability of the prostheses. The fundamental terminologies as well as technical advancements are covered in order to impart a better knowledge in the area of Lower Limb prostheses. This review also provides a concise description on the role of computers, advanced software packages, sensors, and other hardware components for the design, fabrication, and testing of transfemoral prosthetic devices in the current environment. Expert Opinion The current state of lower limb prostheses and future research opportunities are summarized to address upcoming challenges. Based on survey of various research works, adapting modern technology may aid in the development of functional and cost-efficient prosthetic components with superior safety, comfort, and quality.
Remodeling Tumor‐Associated Neutrophils to Enhance Dendritic Cell‐Based HCC Neoantigen Nano‐Vaccine Efficiency

Hepatocellular carcinoma (HCC) commonly emerges in an immunologically “cold” state, thereafter protects it away from cytolytic attack by tumor‐infiltrating lymphocytes, resulting in poor response to immunotherapy. Herein, an acidic/photo‐sensitive dendritic cell (DCs)‐based neoantigen nano‐vaccine has been explored to convert tumor immune “cold” state into “hot”, and remodel tumor‐associated neutrophils to potentiate anticancer immune response for enhancing immunotherapy efficiency. The nano‐vaccine is constructed by SiPCCl2‐hybridized mesoporous silica with coordination of Fe(III)‐captopril, and coating with exfoliated membrane of matured DCs by H22‐specific neoantigen stimulation. The nano‐vaccines actively target H22 tumors and induce immunological cell death to boost tumor‐associated antigen release by the generation of excess 1O2 through photodynamic therapy, which act as in situ tumor vaccination to strengthen antitumor T‐cell response against primary H22 tumor growth. Interestingly, the nano‐vaccines are also home to lymph nodes to directly induce the activation and proliferation of neoantigen‐specific T cells to suppress the primary/distal tumor growth. Moreover, the acidic‐triggered captopril release in tumor microenvironment can polarize the protumoral N2 phenotype neutrophils to antitumor N1 phenotype for improving the immune effects to achieve complete tumor regression (83%) in H22‐bearing mice and prolong the survival time. This work provides an alternative approach for developing novel HCC immunotherapy strategies.
Enhanced antibacterial, antioxidant and anticancer activity of caffeic acid by simple acid-base complexation with spermine/spermidine

Abstract Caffeic acid (CA) is a naturally occurring plant-derived polyphenol possessing diverse biological properties. However, the poor water-solubility of CA restricts its widespread applications. On the other hand, biogenic amines such as spermine and spermidine are natural constituents in eukaryotes. In this work, we present water-soluble complexes of CA with spermine and spermidine by exploiting the acid-base interaction. Four different compositions have been prepared by varying the CA to amine ratios, whose chemical structures have been probed in detail using Fourier-transform infrared spectroscopy (FT-IR) and nuclear magnetic resonance (NMR) studies that have revealed the acid-base interaction between the constituent precursors. The obtained acid-base complexes at their native pH values have shown enhanced antibacterial and antioxidant activities than pristine CA. Further, the CA-polyamine complexes have shown high anticancer performances in the concentration range that is compatible with the normal cell lines. GRAPHICAL ABSTRACT
Adaptive Exponential Synchronization of Complex Networks With Nondifferentiable Time-Varying Delay

In recent years, the adaptive exponential synchronization (AES) problem of delayed complex networks has been extensively studied. Existing results rely heavily on assuming the differentiability of the time-varying delay, which is not easy to verify in reality. Dealing with nondifferentiable delay in the field of AES is still a challenging problem. In this brief, the AES problem of complex networks with general time-varying delay is addressed, especially when the delay is nondifferentiable. A delay differential inequality is proposed to deal with the exponential stability of delayed nonlinear systems, which is more general than the widely used Halanay inequality. Next, the boundedness of the adaptive control gain is theoretically proved, which is neglected in much of the literature. Then, the AES criteria for networks with general delay are established for the first time by using the proposed inequality and the boundedness of the control gain. Finally, an example is given to demonstrate the effectiveness of the theoretical results.
Improving older adults’ ability to follow instructions: benefits of actions at encoding and retrieval in working memory

ABSTRACT The ability to follow instructions is critical for learning new skills and may support successful aging. Recent evidence indicates a close link between following instructions and working memory, and that action-based processing at encoding and retrieval can improve this ability. In this study, we examined the ability to follow instructions and the benefits of action-based processing in young and older adults. In Experiment 1, participants were presented with spoken or silent demonstrated instructions, then recalled them by oral repetition or physical enactment. Older adults produced fewer correct responses in all conditions. Both age groups were better at recalling demonstrated than spoken instructions in the verbal but not the enacted recall condition. Older adults also benefited from enacted recall relative to verbal recall, but to a smaller extent than younger adults. In Experiment 2, the additional benefit of dual modalities (spoken instructions with simultaneous demonstration) relative to single modality presentation (spoken instructions, or silent demonstration) was examined. Both age groups showed superior performance in dual modality conditions relative to spoken instructions when using verbal recall. These findings suggest that although following instruction ability appears to decline with age, older adults can still benefit from action at encoding and retrieval.
A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

Abstract Objective To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects. Material and Methods We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features. Results We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 “other headache” clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2–8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles. Discussion Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data. Conclusion We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.
Ethnic disparities in immunisation: analyses of zero-dose prevalence in 64 low- and middle-income countries

Background The Sustainable Development Goals (SDGs) recommend stratification of health indicators by ethnic group, yet there are few studies that have assessed if there are ethnic disparities in childhood immunisation in low and middle income countries (LMICs). Methods We identified 64 LMICs with standardized national surveys carried out since 2010, which provided information on ethnicity or a proxy variable and on vaccine coverage; 339 ethnic groups across the 64 countries were identified after excluding those with fewer than 50 children in the sample and countries with a single ethnic group. Lack of vaccination with diphtheria-pertussis-tetanus (DPT) vaccine, a proxy for no access to routine vaccination or zero-dose status, was the outcome of interest. Differences among ethnic groups were assessed using a chi-squared test for heterogeneity. Additional analyses controlled for household wealth, maternal education and urban-rural residence. Findings The median gap between the highest and lowest zero-dose prevalence ethnic groups in all countries was equal to 10 percentage points (interquartile range 4-22; range 1 to 84) and the median ratio was 3.3 (interquartile range 1.8-6.7; range 1.1-30.4). In 35 of the 64 countries, there was significant heterogeneity in zero-dose prevalence among the ethnic groups. In most countries, adjustment for wealth, education and residence made little difference to the ethnic gaps, but in four countries (Angola, Benin, Nigeria, and Philippines) the high-low ethnic gap decreased by over 15 pp after adjustment. Children belonging to a majority group had 29% lower prevalence of zero-dose compared to the rest of the sample. Interpretation Statistically significant ethnic disparities in child immunisation were present in over half of the countries studied. Such inequalities have been seldom described in the published literature. Regular analyses of ethnic disparities are essential for monitoring trends, targeting resources and assessing the impact of health interventions to ensure zero-dose children are not left behind in the Sustainable Development Goals era.
The effect of competition between health opinions on epidemic dynamics

Past major epidemic events showed that when an infectious disease is perceived to cause severe health outcomes, individuals modify health behavior affecting epidemic dynamics. To investigate the effect of this feedback relationship on epidemic dynamics, we developed a compartmental model that couples a disease spread framework with competition of two mutually exclusive health opinions (health-positive and health-neutral) associated with different health behaviors. The model is based on the assumption that individuals switch health opinions based on exposure to opinions of others through interpersonal communications. To model opinion switch rates, we considered a family of functions and identified the ones that allow health opinions to co-exist. In the disease-free population, either the opinions cannot co-exist and one of them is always dominating (monobelief equilibrium) or there is at least one stable co-existence of opinions equilibrium. In the latter case, there is multistability between the co-existence equilibrium and the two monobelief equilibria. When two opinions co-exist, it depends on their distribution whether the infection can invade. If presence of the infection leads to increased switching to a health-positive opinion, the epidemic burden becomes smaller than indicated by the basic reproduction number. Additionally, a feedback between epidemic dynamics and health opinion dynamics may result in (sustained) oscillatory dynamics and a switch to a different stable opinion distribution. Our model captures feedback between spread of awareness through social networks and infection dynamics and can serve as a basis for more elaborate individual-based models.
Phenome-wide Association Analysis of Substance Use Disorders in a Deeply Phenotyped Sample

Background: Substance use disorders (SUDs) are associated with a variety of co-occurring psychiatric disorders and other SUDs, which partly reflects genetic pleiotropy. Polygenic risk scores (PRS) and phenome-wide association studies (PheWAS) are useful in evaluating pleiotropic effects. The comparatively low prevalence of SUDs and lack of detailed information available in electronic health records limits their informativeness for such analyses. Methods: We used the deeply-phenotyped Yale-Penn sample [(N=10,610; 46.3% African ancestry (AFR), 53.7% European ancestry (EUR)], recruited for genetic studies of substance dependence, to examine pleiotropy for 4 major substance-related traits: alcohol use disorder (AUD), opioid use disorder (OUD), smoking initiation (SMK), and lifetime cannabis use (CAN). The sample includes both affected and control subjects interviewed using the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA), a comprehensive psychiatric interview. Results: In AFR individuals PRS for AUD, and in EUR individuals PRS for AUD, OUD, and SMK, were associated with their respective primary DSM diagnoses. These PRS were also associated with additional phenotypes involving the same substance. PheWAS analyses of PRS in EUR individuals identified associations across multiple phenotypic domains, including phenotypes not commonly assessed in PheWAS analyses, such as family environment and early childhood experiences. Conclusions: Smaller, deeply-phenotyped samples can complement large biobank genetic studies with limited phenotyping by providing greater phenotypic granularity. These efforts allow associations to be identified between specific features of disorders and genetic liability for SUDs, which help to inform our understanding of the pleiotropic pathways underlying them.
Are Metal Fans Angrier than Jazz Fans? A Genre-Wise Exploration of the Emotional Language of Music Listeners on Reddit

Music forms a big part of our identity and as such, people with a shared preference for certain kinds of music may also share similar traits. In this study, we explore differences in the emotional language of fan communities of different music genres. In focusing on Reddit, we analyze the utterances on online community forums of different music genres using lexicon-based sentiment (emotion) analysis. Upon clustering Subreddit forums, we obtained two clusters: forums discussing genres like Rock, RnB, Country, and Jazz were found to have a higher abundance of positively valenced emotions and a lower amount of negatively valenced emotions. Likewise, Subreddits discussing genres like Metal, Punk, and Rap had a lower amount of positively valenced emotions and a higher abundance of negatively valenced emotion. We observed a high correlation between counts in lyrics of a genre and counts in a fan community for the emotions of anger, disgust, fear, and joy. In sum, we found differences in the emotional language of fan utterances by genre, and these could be partially attributed to the emotions contained in the lyrics.
What Musical Knowledge Does Self-Attention Learn ?

Since their conception for NLP tasks in 2017, Transformer neural networks have been increasingly used with compelling results for a variety of symbolic MIR tasks including music analysis, classiﬁcation and generation. Although the concept of self-attention between words in text can intuitively be transposed as a relation between musical objects such as notes or chords in a score, it remains relatively unknown what kind of musical relations precisely tend to be captured by self attention mechanisms when applied to musical data. Moreover, the principle of self-attention has been elaborated in NLP to help model the “meaning” of a sentence while in the musical domain this concept appears to be more subjective. In this explorative work, we open the music transformer black box looking to identify which aspects of music are actually learnt by the self-attention mechanism. We apply this approach to two MIR probing tasks : composer classiﬁcation and cadence identiﬁcation.
Combining Shallow and Deep Representations for Text-Pair Classification

Text-pair classification is the task of determining the class relationship between two sentences. It is embedded in several tasks such as paraphrase identification and duplicate question detection. Contemporary methods use fine-tuned transformer encoder semantic representations of the classification token in the text-pair sequence from the transformer’s final layer for class prediction. However, research has shown that earlier parts of the network learn shallow features, such as syntax and structure, which existing methods do not directly exploit. We propose a novel convolution-based decoder for transformer-based architecture that maximizes the use of encoder hidden features for text-pair classification. Our model exploits hidden representations within transformer-based architecture. It outperforms a transformer encoder baseline on average by 50% (relative F1-score) on six datasets from the medical, software engineering, and open-domains. Our work shows that transformer-based models can improve text-pair classification by modifying the fine-tuning step to exploit shallow features while improving model generalization, with only a slight reduction in efficiency.
A Systematic Review of Long‐Distance Triathlon Musculoskeletal Injuries

Abstract The distribution of injuries affecting long-distance triathletes is yet to be fully understood. A systematic review was performed of the clinical literature to determine the epidemiology of musculoskeletal injuries affecting long-distance triathletes. Searched databases in Feb 2020 were PubMed, Medline, EMBASE, EMCARE, and CINHAL databases. Published observational research articles related to the incidence or prevalence of musculoskeletal injuries in long-distance triathletes (competing at “Ironman” full distance or greater), written in the English language and not restricted by age or gender or date were eligible. Of the 975 studies identified on the initial search, six studies met the inclusion criteria for analysis. The mean age (SD) of the long-distance triathletes in these studies was 35.1 (2.7) and the range was 21-68 years. Overuse injuries were most frequent with the incidence range of 37-91%, and acute injury incidence range was 24-27%. The knee and spine were the most frequent location of injury. Running and cycling were the most frequently affected disciplines. Elite athletes had a lower incidence of overuse injury (37%). The highest acute injury incidence (27%) was recorded in non-elite athletes. The quality of the studies was relatively poor with only one study satisfying >50% of the quality assessment tool questions and only two studies were prospective, the rest were retrospective cross-sectional studies. Overall, there is a lack of literature reporting on musculoskeletal injuries in long-distance triathletes. Overuse injuries, particularly in the knee, are the most frequently reported, running and cycling are the most frequent disciplines associated. Long-distance triathletes may have a lower incidence of both overuse and acute injuries.