Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing domain knowledge and risk of bias of a large-scale general artificial intelligence model in mental health

Background With a rapidly expanding gap between the need for and availability of mental health care, artificial intelligence (AI) presents a promising, scalable solution to mental health assessment and treatment. Given the novelty and inscrutable nature of such systems, exploratory measures aimed at understanding domain knowledge and potential biases of such systems are necessary for ongoing translational development and future deployment in high-stakes healthcare settings. Methods We investigated the domain knowledge and demographic bias of a generative, AI model using contrived clinical vignettes with systematically varied demographic features. We used balanced accuracy (BAC) to quantify the model’s performance. We used generalized linear mixed-effects models to quantify the relationship between demographic factors and model interpretation. Findings We found variable model performance across diagnoses; attention deficit hyperactivity disorder, posttraumatic stress disorder, alcohol use disorder, narcissistic personality disorder, binge eating disorder, and generalized anxiety disorder showed high BAC (0.70 ≤ BAC ≤ 0.82); bipolar disorder, bulimia nervosa, barbiturate use disorder, conduct disorder, somatic symptom disorder, benzodiazepine use disorder, LSD use disorder, histrionic personality disorder, and functional neurological symptom disorder showed low BAC (BAC ≤ 0.59). Interpretation Our findings demonstrate initial promise in the domain knowledge of a large AI model, with performance variability perhaps due to the more salient hallmark symptoms, narrower differential diagnosis, and higher prevalence of some disorders. We found limited evidence of model demographic bias, although we do observe some gender and racial differences in model outcomes mirroring real-world differential prevalence estimates.
Transcriptomic Complexity of Culm Growth and Development in Different Types of Moso Bamboo

Moso bamboo is capable of both sexual and asexual reproduction during natural growth, resulting in four distinct types of culms: the bamboo shoot-culm, the seedling stem, the leptomorph rhizome, and a long-ignored culm—the outward-rhizome. Sometimes, when the outward rhizomes break through the soil, they continue to grow longitudinally and develop into a new individual. However, the roles of alternative transcription start sites (aTSS) or termination sites (aTTS) as well as alternative splicing (AS) have not been comprehensively studied for their development. To re-annotate the moso bamboo genome and identify genome-wide aTSS, aTTS, and AS in growing culms, we utilized single-molecule long-read sequencing technology. In total, 169,433 non-redundant isoforms and 14,840 new gene loci were identified. Among 1311 lncRNAs, most of which showed a positive correlation with their target mRNAs, one-third of these IncRNAs were preferentially expressed in winter bamboo shoots. In addition, the predominant AS type observed in moso bamboo was intron retention, while aTSS and aTTS events occurred more frequently than AS. Notably, most genes with AS events were also accompanied by aTSS and aTTS events. Outward rhizome growth in moso bamboo was associated with a significant increase in intron retention, possibly due to changes in the growth environment. As different types of moso bamboo culms grow and develop, a significant number of isoforms undergo changes in their conserved domains due to the regulation of aTSS, aTTS, and AS. As a result, these isoforms may play different roles than their original functions. These isoforms then performed different functions from their original roles, contributing to the transcriptomic complexity of moso bamboo. Overall, this study provided a comprehensive overview of the transcriptomic changes underlying different types of moso bamboo culm growth and development.
Coupling Between Hippocampal Parenchymal Fraction and Cortical Grey Matter Atrophy at Different Stages of Cognitive Decline

Background: Hippocampal atrophy is a significant brain marker of pathology in Alzheimer’s disease (AD). The hippocampal parenchymal fraction (HPF) was recently developed to better assess the hippocampal volumetric integrity, and it has been shown to be a sensitive measure of hippocampal atrophy in AD. Objective: To investigate the clinical relevance of hippocampal volumetric integrity as measured by the HPF and the coupling between the HPF and brain atrophy during AD progression. Methods: We included data from 143 cognitively normal (CN), 101 mild cognitive impairment (MCI), and 125 AD participants. We examined group differences in the HPF, associations between HPF and cognitive ability, and coupling between the HPF and cortical grey matter volume in the CN, MCI, and AD groups. Results: We observed progressive decreases in HPF from CN to MCI and from MCI to AD, and increases in the asymmetry of HPF, with the lowest asymmetry index (AI) in the CN group and the highest AI in the AD group. There was a significant association between HPF and cognitive ability across participants. The coupling between HPF and cortical regions was observed in bilateral hippocampus, parahippocampal gyrus, temporal, frontal, and occipital regions, thalamus, and amygdala in CN, MCI, and AD groups, with a greater involvement of temporal, occipital, frontal, and subcortical regions in MCI and AD patients, especially in AD patients. Conclusion: This study provides novel evidence for the neuroanatomical basis of cognitive decline and brain atrophy during AD progression, which may have important clinical implications for the prognosis of AD.
The influence of professional attitude, welfare, self-sustaining development, and job satisfaction on teacher performance

This study aims to identify: i) The effect of teacher welfare, self-sustaining development, job satisfaction, and teacher performance on the teaching professional attitude; ii) The effect of teacher welfare on self-development, job satisfaction, and teacher performance; iii) The effect of self-sustaining development on the professional attitude, welfare, job satisfaction, and teacher performance of teachers; and iv) The effect of teacher job satisfaction on teaching performance. This study was quantitative research with a correlational type. The sample was 155 vocational high school teachers in Central Java, Indonesia. Data was collected through a questionnaire technique. The data analysis technique used structural equation modeling with the linear structural relationship type. The study revealed that the teacher's professional attitude has an effect on teacher welfare but not on self-development, job satisfaction, or teacher performance. Teacher welfare has an effect on independent development but has no effect on teacher performance. Hence, independent development does not affect job satisfaction but affects teacher performance. Furthermore, job satisfaction has no effect on teacher performance. These findings can be used as study material for future researchers to analyze the research variables or other variables, such as leadership style, work motivation, work discipline, and others.
Central Production of Two-Pseudoscalar Meson Systems at the COMPASS Experiment at CERN

COMPASS is a fixed-target experiment at the CERN SPS which recorded a data set with an incident proton beam impinging on a liquid hydrogen target in order to study the central production of light scalar mesons. We select events with two protons and two pseudo-scalar mesons in the final state and decompose their angular distribution in terms of partial-wave amplitudes. Fits to the mass-dependence of these amplitudes are used to determine the Breit-Wigner parameters of scalar resonances.
Proactive Personality and Construction Worker Safety Behavior: Safety Self-Efficacy and Team Member Exchange as Mediators and Safety-Specific Transformational Leadership as Moderators

Research on the correlation between personality traits and safety behaviors has been thoroughly explored in previous literature. However, most of these studies are based on explaining the relationship between the Big Five personality traits and safety behavior, with few explaining the relationship between proactive personality and safety behavior. This study relies on trait activation theory, social cognitive theory, and social exchange theory to understand the relationship between proactive personality and safety behavior (safety participation and safety compliance) by using safety self-efficacy and team member exchange as mediating variables and safety-specific transformational leadership as moderating variables. Method: Considering the issue of common method bias, a multi-source and multi-stage data collection research design was used to collect 287 valid questionnaires from construction workers in 10 construction projects and apply regression analysis for hypothesis testing. Conclusions: Research results indicated that proactive personality positively and significantly influenced construction workers’ safety behaviors, while safety self-efficacy and team member exchange partially mediated the relationship between proactive personality and safety behaviors. In addition, safety-specific transformational leadership enhanced the positive relationship between proactive personality and safety behavior. These findings enrich the research on the correlation between personality traits and safety behaviors of construction workers in a safety context.
Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis

Background: prosthetic loosening after hip and knee arthroplasty is one of the most common causes of joint arthroplasty failure and revision surgery. Diagnosis of prosthetic loosening is a difficult problem and, in many cases, loosening is not clearly diagnosed until accurately confirmed during surgery. The purpose of this study is to conduct a systematic review and meta-analysis to demonstrate the analysis and performance of machine learning in diagnosing prosthetic loosening after total hip arthroplasty (THA) and total knee arthroplasty (TKA). Materials and Methods: three comprehensive databases, including MEDLINE, EMBASE, and the Cochrane Library, were searched for studies that evaluated the detection accuracy of loosening around arthroplasty implants using machine learning. Data extraction, risk of bias assessment, and meta-analysis were performed. Results: five studies were included in the meta-analysis. All studies were retrospective studies. In total, data from 2013 patients with 3236 images were assessed; these data involved 2442 cases (75.5%) with THAs and 794 cases (24.5%) with TKAs. The most common and best-performing machine learning algorithm was DenseNet. In one study, a novel stacking approach using a random forest showed similar performance to DenseNet. The pooled sensitivity across studies was 0.92 (95% CI 0.84–0.97), the pooled specificity was 0.95 (95% CI 0.93–0.96), and the pooled diagnostic odds ratio was 194.09 (95% CI 61.60–611.57). The I2 statistics for sensitivity and specificity were 96% and 62%, respectively, showing that there was significant heterogeneity. The summary receiver operating characteristics curve indicated the sensitivity and specificity, as did the prediction regions, with an AUC of 0.9853. Conclusions: the performance of machine learning using plain radiography showed promising results with good accuracy, sensitivity, and specificity in the detection of loosening around THAs and TKAs. Machine learning can be incorporated into prosthetic loosening screening programs.
Intercomparison of the Averaged Induced Electric Field in Learning-Based Human Head Models Exposed to Low-Frequency Magnetic Fields

Anatomical human models have been widely used in the assessment of induced field strength for low-frequency (LF) electromagnetic field exposure. One bottleneck is the assignment of a single electrical conductivity to all the voxels of the corresponding tissue. This simplification is known to cause computational artifact; therefore, a large reduction factor was considered in international guidelines and standards. Recently, head models with nonuniform conductivities generated using deep learning networks were proposed, and the effect on the reduction of staircasing artifacts was demonstrated. If the effectiveness of the models is confirmed for different models and codes, it would be useful to derive the relationship between the internal and external field strengths needed for setting the exposure limit. The Subcommittee 6 of the IEEE International Committee on Electromagnetic Safety Technical Committee 95 launched a working group to conduct the first intercomparison study of the induced electric field in learning-based head models exposed to LF magnetic fields. Seven international research groups have cooperated in this joint study. The highest relative difference (RD) in averaged electric fields was 23%, which is attributable to the difference caused the by scalar potential finite difference (SPFD) method and finite element method. Except for one group, the RDs in the $100^{th}$ and $99^{th}$ percentile values of the averaged electric field using the SPFD method with different solvers and codes were below 1%, indicating that the uncertainty due to different codes is sufficiently small under the same exposure scenarios. The findings would be informative for future revision of exposure limits and reduction factors in the exposure standard, which is closely related to computational uncertainty.
Intersections of the Left and Right Essential Spectra of 2 × 2 Upper Triangular Operator Matrices

In this paper, perturbations of the left and right essential spectra of 2 × 2 upper triangular operator matrix MC are studied, where MC=(AC0B) is an operator acting on the Hilbert space H ⊕ K. For given operators A and B, the sets ∩C∈B(K,H)σle(MC) and ∩C∈B(K,H)σre(MC) are determined, where σle(T) and σre(T) denote, respectively, the left essential spectrum and the right essential spectrum of an operator T. 2000 Mathematics Subject Classification 47A10, 47A55.
Jóvenes con derecho a migrar. De la problematización al reconocimiento del sujeto

Este artículo analiza los derechos que se les reconocen a los/as adolescentes y jóvenes migrantes en los diferentes momentos de su estancia en el país de recepción y las representaciones e imaginarios existentes sobre ellos, que se legitiman e institucionalizan en discursos políticos, normativas y prácticas. Estas representaciones contrastan con sus propias narrativas que reflejan su capacidad de agencia, de interpretación de su situación y de elaboración y ejecución de un proyecto vital. La metodología cualitativa y participativa empleada ha permitido recoger su voz y sus narrativas en toda su complejidad y riqueza. A partir de sus relatos se puede observar su resiliencia ante las situaciones de dificultad, así como las acciones de resistencia, individuales y colectivas, que articulan para interpelar los dispositivos de control y afrontar los mecanismos de opresión presentes en los contextos de recepción. Se evidencia así la importancia de reconocer la emergencia del actor colectivo, que se construye en los espacios de autorganización de la lucha migrante.
Evidence of Diversification and Leverage in the Performance of Brazilian and Mexican Family Businesses

Abstract This study identifies evidence of the influence of diversification and leverage on the financial performance of Brazilian and Mexican family businesses. It analyzes 102 Brazilian and 71 Mexican publicly traded family companies. Data analysis uses ordinary least squares regression in Stata. The results indicate that Brazilian family businesses have a higher return on assets when diversifying their products or services. When diversifying international markets, Brazilian companies present a lower return on assets and return on equity. For Mexican companies, international diversification derives a higher return on assets and return on equity. In addition, results show that leverage moderates the relationship between diversification and performance both for Brazilian and Mexican family businesses. The study contributes to the current literature by investigating that diversification improves business performance and that leverage is a significant element in intensifying the benefits of this strategy in the performance of family businesses. The study also emphasizes that diversification can be useful to address market difficulties and imperfections in unstable scenarios, such as when it is targeted to planned performance and considers financial conservatism in family companies.
The Potential of Surveillance Data for Dengue Risk Mapping: An Evaluation of Different Approaches in Cuba

To better guide dengue prevention and control efforts, the use of routinely collected data to develop risk maps is proposed. For this purpose, dengue experts identified indicators representative of entomological, epidemiological and demographic risks, hereafter called components, by using surveillance data aggregated at the level of Consejos Populares (CPs) in two municipalities of Cuba (Santiago de Cuba and Cienfuegos) in the period of 2010–2015. Two vulnerability models (one with equally weighted components and one with data-derived weights using Principal Component Analysis), and three incidence-based risk models were built to construct risk maps. The correlation between the two vulnerability models was high (tau > 0.89). The single-component and multicomponent incidence-based models were also highly correlated (tau ≥ 0.9). However, the agreement between the vulnerability- and the incidence-based risk maps was below 0.6 in the setting with a prolonged history of dengue transmission. This may suggest that an incidence-based approach does not fully reflect the complexity of vulnerability for future transmission. The small difference between single- and multicomponent incidence maps indicates that in a setting with a narrow availability of data, simpler models can be used. Nevertheless, the generalized linear mixed multicomponent model provides information of covariate-adjusted and spatially smoothed relative risks of disease transmission, which can be important for the prospective evaluation of an intervention strategy. In conclusion, caution is needed when interpreting risk maps, as the results vary depending on the importance given to the components involved in disease transmission. The multicomponent vulnerability mapping needs to be prospectively validated based on an intervention trial targeting high-risk areas.
Applications of Multiwavelets to Image Compression

(ABSTRACT) Methods for digital image compression have been the subject of much study over the past decade. Advances in wavelet transforms and quantization methods have produced algorithms capable of surpassing the existing image compression standards like the Joint Photographic Experts Group (JPEG) algorithm. For best performance in image compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogo-nality and symmetry. However, the design possibilities for wavelets are limited because they cannot simultaneously possess all of these desirable properties. The relatively new field of multiwavelets shows promise in removing some of the limitations of wavelets. Multiwavelets offer more design options and hence can combine all desirable transform features. The few previously published results of multiwavelet-based image compression have mostly fallen short of the performance enjoyed by the current wavelet algorithms. This thesis presents new multiwavelet transform methods and measurements that verify the potential benefits of multiwavelets. Using a zerotree quantization scheme modified to better match the unique decomposition properties of multiwavelets, it is shown that the latest multiwavelet filters can give performance equal to, or in many cases superior to, the current wavelet filters. The performance of multiwavelet packets is also explored for the first time and is shown to be competitive to that of wavelet packets in some cases. The wavelet and multiwavelet filter banks are tested on a much wider range of images than in the usual literature, providing a better analysis of the benefits and drawbacks of each. Acknowledgments I would like to thank my advisor, Dr. Bell. Without the technical and financial assistance she provided I could not have completed this thesis. Her suggestions and corrections for this thesis have improved it enormously. She always helped me keep sight of the " big picture " and focus on those areas that needed the most attention. My thanks also go to my wife, Kim, whose help in proofreading this thesis is much appreciated, and Dr. Brian Woerner for agreeing to serve on my committee at the last minute. I would like to thank a number of people from the Virginia Tech physics department. In particular, I am indebted to professors Dale Long, John Ficenec, and Joseph Slawny for their support and guidance in my days as a physics student. I am also grateful for the friendship and assistance provided by my colleagues in physics, Mike Kleder and Zoltan Toroczkai. Without the support of …
Patients’ Satisfaction With Pharmaceutical Care Services Provided During COVID Pandemic: Experience From Greece

Introduction Private pharmacies can contribute to the health care system through primary care. Purpose The purpose of this study is to determine patients' expectations of pharmaceutical care services during covid pandemic in order to measure the level of patient satisfaction provided by the Greek healthcare system. Also, it is important to identify the associated factors that might affect patient satisfaction. Material and Method The sample of the study consisted of 168 customers of pharmacies in Athens. A patient satisfaction survey was conducted at health facilities operating in Athens. Data regarding socio-demographic characteristics and parameters that measure patients' expectations and satisfaction were collected through a closed-ended questionnaire that had been tested for validity and reliability. The patient’s point of view was evaluated based on their expectation and perception of the pharmaceutical care services they had received. Data were entered into SPSS version 22 (IBM Corp, Armonk, NY ), and descriptive statistics, cross-tabs, and binary logistic regressions were utilized. P < 0.05 was used to declare association. Results About 89.3% of the participants were insured in the Greek health system. The main reason for visiting the pharmacy was the purchase of medicines and products (95.2%), vaccinations (19.6%), and consulting services for first aid (17.3%). The pharmacist was rated for his courtesy, willingness, friendliness, and reliability. Only 48.2% of participants knew that the pharmacy provided primary care services during the pandemic. The most common services provided were blood pressure measurement and intramuscular injections. Around 64.2% of them were fully satisfied. Pharmacists in primary care teams are uniquely positioned to facilitate practice expansion and make medicine a trusted resource for physicians, as well as improve health outcomes for patients. Conclusions The pharmacy has a leading role in health care due to easy access, and fast and immediate service. Patient-clients in Greek society trust their pharmacist as a health professional. Further research is suggested to ensure that through the delivery of health services by pharmacies, the cost of primary care could be lower.