Miracle-dz/UniFAR-FTUs huggingface dataset:
The provision of healthcare services to older LGBT adults in the Nordic countries: a scoping review

Abstract Objectives Our objectives were to examine what is known about the provision of healthcare services to older LGBT adults in the Nordic countries, identify knowledge gaps, map implications of this research for the education of healthcare professionals and delivery of healthcare, and identify key future research priorities to advance policy and practice for older LGBT adults in this region. Design We conducted searches in nine databases. Peer-reviewed articles and PhD theses published in and after 2002 written in English, Norwegian, Swedish or Danish languages were included. 41 studies met our inclusion criteria. However, only eight of these studies focused specifically on older LGBT adults. Therefore, to answer all research questions, five book chapters about older groups were also included. Results There were few studies from countries other than Sweden and few quantitative studies. Bisexual people represented a neglected group in research. The studies included showed that healthcare personnel lack knowledge on LGBT issues, particularly about older LGBT adults and non-binary gender identification. Older LGBT adults frequently reported being met with cis- and heteronormative expectations in healthcare encounters. For transgender people, access to medical treatment has been managed by gatekeepers influenced by a binary understanding of gender. Conclusions Relevant measures to enhance practices are increased attention on LGBT issues in education; training of healthcare professionals; measures at the institutional level; and ensuring that transgender people identifying as non-binary receive the same quality of care as individuals identifying in a binary way. KEY POINTS To advance healthcare practices for older LGBT adults in Nordic countries, it is vital to review the available knowledge. There are few studies about older LGBT adults and few studies from Nordic countries besides Sweden. Studies indicate that healthcare personnel lack knowledge on LGBT issues and that older LGBT adults are met with cis- and heteronormative expectations. Studies suggest increased attention to LGBT issues in education, training of healthcare personnel, institutional level measures, and that transgender people who identify as non-binary receive the same quality of care as others.
5-Aminolevulinic acid/sodium ferrous citrate improves the quality of heat-stressed bovine oocytes by reducing oxidative stress

A high temperature-humidity index during summer has deleterious effects on mitochondrial function, reducing oocyte developmental competence. 5-Aminolevulinic acid (5-ALA) and sodium ferrous citrate (SFC) are both known to support mitochondrial function and have strong anti-oxidant and anti-apoptotic activities. This study aimed to determine the mechanism of action of 5-ALA/SFC on oocyte quality. Bovine oocytes were collected from medium-sized follicles during summer (July–September, temperature-humidity index:76.6), cultured with 0, 1, 2, 4, and 8 µM 5-ALA with SFC at a molar ratio of 1:0.125, fertilized, and cultured for 10 days. The addition of 8/1 µM 5-ALA/SFC had a deleterious effect on oocyte cleavage rate in comparison with control oocytes, but did not affect the blastocyst rate, while 1/0.125 µM 5-ALA/SFC had a significantly higher increase in blastocyst rate than 8/1 µM 5-ALA/SFC. The addition of 1/0.125 and 2/0.25 µM 5-ALA/SFC improved oocyte quality by increasing the mitochondrial distribution pattern and metaphase-II oocytes, reducing reactive oxygen species and upregulating nuclear factor erythroid-2-related factor 2, heme oxygenase-1, and superoxide dismutase-1 in oocytes, and nuclear factor erythroid-2-related factor 2 and mitochondrial transcription factor A in cumulus cells. These results indicate that 1/0.125 and 2/0.25 µM 5-ALA/SFC may support oocyte quality and developmental competence and provide anti-oxidant actions in cumulus-oocyte complexes.
Anti–myelin‐associated glycoprotein neuropathy: Where do we stand?

Myelin‐associated glycoprotein (MAG) is a transmembrane glycoprotein concentrated in periaxonal Schwann cell and oligodendroglial membranes of myelin sheaths that serves as an antigen for immunoglobulin M (IgM) monoclonal antibodies. Individuals who harbor anti‐MAG antibodies classically develop a progressive autoimmune peripheral neuropathy characterized clinically by ataxia, distal sensory loss, and gait instability, and electrophysiologically by distally accentuated conduction velocity slowing. Although off‐label immunotherapy is common, there are currently no proven effective disease‐modifying therapeutics, and most patients experience slow accumulation of disability over years and decades. The typically slowly progressive nature of this neuropathy presents unique challenges when trying to find effective anti‐MAG therapeutic agents. Drug development has also been hampered by the lack of validated outcome measures that can detect clinically meaningful changes in a reasonable amount of time as well as by the lack of disease activity biomarkers. In this invited review, we provide an update on the state of clinicometric outcome measures and disease activity biomarkers in anti‐MAG neuropathy. We highlight the insensitivity of widely used existing clinicometric outcome measures such as the Inflammatory Neuropathy Cause and Treatment (INCAT) disability score as well as the INCAT sensory subscore in anti‐MAG neuropathy, referencing the two previous negative randomized controlled clinical trials evaluating rituximab. We then discuss newly emerging candidate therapeutic agents, including tyrosine kinase inhibitors and enhanced B‐cell–depleting agents, among others. We conclude with a practical approach to the evaluation and management of anti‐MAG neuropathy patients.
Detection of Parkinson’s disease with keystroke data

Abstract Parkinson’s disease (PD) is one of the most widespread neurological disorders associated with nerve damage without definitive treatment. Impairments, such as trembling and slowing down in hand movements are among the first symptoms. For this purpose, in this study, machine learning (ML)-based models were developed by using keyboard keystroke dynamics. According to patients’ drug use status, disease severity, and gender, we created 14 different sub-datasets and extracted 378 features using raw keystroke data. We developed alternative models with Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) algorithms. We further used Minimum Redundancy Maximum Relevance (mRmR), RELIEF, sequential forward selection (SFS), and RF feature selection methods to investigate prominent features in distinguishing PD. We developed ML models that jointly use the most popular features of selection algorithms (feature ensemble [FE]) and an ensemble classifier by combining multiple ML algorithms utilizing majority vote (model ensemble [ME]). With 14 different sets, FE and ME models provided accuracy (Acc.) in the range of 91.73 − 100% and 81.08 − 100%, respectively.
Prevalence of amyotrophic lateral sclerosis in the United States, 2018

Abstract Objective To estimate prevalent ALS cases in the United States for calendar year 2018. Methods The National ALS Registry (Registry) compiled data from national administrative databases (from the Centers for Medicare and Medicaid Services, the Veterans Health Administration, and the Veterans Benefits Administration) and enrollment data voluntarily submitted through a web portal (www.cdc.gov/als). We used log-linear capture-recapture (CRC) model-based methodology to estimate the number of cases not ascertained by the Registry. Results The Registry identified 21,655 cases of ALS in 2018, with an age-adjusted prevalence of 6.6 per 100,000 U.S. population. When CRC methods were used, an estimated 29,824 cases were identified, for an adjusted prevalence of 9.1 per 100,000 U.S. population. The demographics of cases of ALS did not change from previous year’s reports. ALS continues to impact Whites, males, and persons over 50 years of age more so than other comparison groups. The results from the present report suggest case ascertainment for the Registry has improved, with the estimate of missing prevalent cases decreasing from 44% in 2017 to 27% in in 2018. Discussion Consistent with previous estimates that used CRC, ALS prevalence in the United States is about 29,824 cases per year.
See No Evil: Information Chains and Reciprocity in Teams

Transparency in teams can facilitate cooperation. We study contribution decisions by agents when previous decisions can be observed. We find that an information chain, in which each agent directly observes only the decision of her immediate predecessor, is at least as effective as a fully-transparent protocol in inducing cooperation under increasing returns to scale. In a comparable social dilemma, the information chain leads to high cooperation both in early movers when compared to a non-transparent protocol and in late movers when compared to a fully-transparent protocol. we conclude that information chains facilitate cooperation by balancing positive and negative reciprocity.
Gelatin Zymography Can Be Performed on Fixed Brain Tissue

Gelatin zymography is widely used to detect gelatinase activity, which is performed on unfixed tissue because it is assumed that fixation inactivates enzymes. However, using fixed tissues has several advantages over using fresh tissues for such prevention of tissue decay, thereby preserving the proteins as well as the morphology and structure of the specimens. In this study, we investigated the effects of the four commonly used fixatives (ethanol, acetone, zinc-based fixative (ZBF), and paraformaldehyde (PFA)) on the gelatinolytic activity in mouse brain tissue. Multiple protocols were employed to extract proteins from the fixed brain tissue. Western blotting and in-gel zymography (IGZ) were used to detect the gelatinase proteins and gelatinolytic activity of the extractions, respectively. In situ zymography (ISZ) revealed that ethanol, acetone, ZBF, and short-time PFA fixation did not inhibit gelatinolytic activity. Neither 1% Triton + 1 M NaCl nor 10% DMSO + 1 M NaCl was effective in extracting proteins from ethanol-, acetone-, ZBF-, or PFA-fixed brain tissues. However, 8 M urea + 4% CHAPS effectively extracted gelatinase proteins from ethanol- and acetone-fixed tissues while retaining the gelatinolytic activity. 2% SDS effectively extracted gelatinase proteins from ethanol-, acetone-, and ZBF-fixed tissues while retaining the gelatinolytic activity. Although 2% SDS + heating extracted gelatinase proteins from ethanol-, acetone-, ZBF-, and even long-term PFA-fixed tissues, the gelatinolytic activity was not retained. Our findings suggest that both ISZ and IGZ can be performed on fixed brain tissue, which is anticipated to be an improvement over the conventionally used gelatin zymography methods. (J Histochem Cytochem 71: 481–493, 2023)
Exploring Correlations Between Pain and Deformity in Idiopathic Scoliosis With Validated Self-reported Pain Scores, Radiographic Measurements, and Trunk Surface Topographic Measurements

Background: Up to 75% of patients with idiopathic scoliosis (IS) report back pain, but the exact contributors are unclear. This study seeks to assess how pain correlates with demographics, radiographic and surface topographic (ST) measurements, and patient-reported outcome measures (PROMs) in patients with IS. Methods: Patient-Reported Outcome Measurement Information System (PROMIS) Pain Interference (PI) and Scoliosis Research Society revised (SRS-22r) pain domain from an IRB approved prospectively collected registry containing patients 11 to 21 years old with IS were correlated (Spearman coefficients) with measurements from whole-body EOS radiography and ST scanning, PROMIS 1.0 PROMs, Trunk Appearance Perception Scale (TAPS), and SRS-22r domains. SRS-22r and PROMIS-PI were also compared between different sex, scoliosis severities, and primary curve locations with Mann-Whitney U or Kruskal-Wallis tests, and if significant differences were found, included with the 5 highest univariate correlated variables into stepwise multivariate linear regression models (P<0.05 to enter, P>0.1 to remove) predicting SRS-22r pain and PROMIS-PI. Results: One hundred and forty-nine patients (14.5 ± 2.0 y, body mass index 20.6 ± 4.1 kg/m2, 96 (64%) female, mean major coronal curve 40 ± 19 deg, range: 10 deg, 83 deg) reported mean PROMIS-PI of 42.2 ± 10.0 and SRS-22r pain of 4.4 ± 0.6. SRS-22r self-image was the most correlated variable with both SRS-22r pain (rho=0.519) and PROMIS-PI (rho=−0.594). Five variables, none of which were ST or radiographic measures, strongly predicted SRS pain domain (R=0.711, R2=0.505, N=138). Two variables (SRS-22r self-image and SRS-22r function) were utilized by a model correlated with PROMIS-PI (R=0.687, R2=0.463, N=124). Conclusions: SRS-22r function and self-image domains were more strongly correlated with SRS-22r pain and PROMIS-PI than any radiographic or ST measurements. Level of Evidence: Level II—retrospective study.
Tisagenlecleucel: CAR-T cell therapy for adult patients with relapsed or refractory follicular lymphoma

ABSTRACT Introduction Tisagenlecleucel (tisa-cel) is an anti CD19 CAR-T therapy that has demonstrated clinical activity in R/R large B-cell lymphoma and R/R B-cell acute lymphoblastic leukemia. It showed particularly high efficacy in R/R follicular lymphoma (FL) with a manageable toxicity profile. The pivotal ELARA study in R/R FL confirmed these findings and led to the FDA approval of tisa-cel in R/R FL after two lines of systemic therapies. Areas covered We start with an introduction of FL and the current treatment landscape with emphasis on the R/R setting. We review the role of CAR-T in R/R FL with focus on currently available products. We describe the ELARA study at a high level to give a perspective of the patient population that was treated. Finally, we discuss aspects related to product selection and whether bispecific antibodies will challenge the role of CAR-T in FL given their similar efficacy. Expert opinion Tisa-cel is a highly effective therapy for heavily pretreated R/R FL with a toxicity profile that is low grade and manageable. Durable remissions (including high-risk patients) are seen in the pivotal ELARA study. Clinicians should consider early referral of R/R FL patients for assessment and discussion.
Large Language Models for Software Engineering: A Systematic Literature Review

Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a systematic literature review on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes. We collect and analyze 229 research papers from 2017 to 2023 to answer four key research questions (RQs). In RQ1, we categorize different LLMs that have been employed in SE tasks, characterizing their distinctive features and uses. In RQ2, we analyze the methods used in data collection, preprocessing, and application highlighting the role of well-curated datasets for successful LLM for SE implementation. RQ3 investigates the strategies employed to optimize and evaluate the performance of LLMs in SE. Finally, RQ4 examines the specific SE tasks where LLMs have shown success to date, illustrating their practical contributions to the field. From the answers to these RQs, we discuss the current state-of-the-art and trends, identifying gaps in existing research, and flagging promising areas for future study.
Dynamic Strategy Chain: Dynamic Zero-Shot CoT for Long Mental Health Support Generation

Long counseling Text Generation for Mental health support (LTGM), an innovative and challenging task, aims to provide help-seekers with mental health support through a comprehensive and more acceptable response. The combination of chain-of-thought (CoT) prompting and Large Language Models (LLMs) is employed and get the SOTA performance on various NLP tasks, especially on text generation tasks. Zero-shot CoT prompting is one of the most common methods in CoT prompting. However, in the LTGM task, Zero-shot CoT prompting can not simulate a counselor or provide personalized strategies without effective mental health counseling strategy prompts. To tackle this challenge, we propose a zero-shot Dynamic Strategy Chain (DSC) prompting method. Firstly, we utilize GPT2 to learn the responses written by mental health counselors and dynamically generate mental health counseling strategies tailored to the help-seekers' needs. Secondly, the Zero-shot DSC prompting is constructed according to mental health counseling strategies and the help-seekers' post. Finally, the Zero-shot DSC prompting is employed to guide LLMs in generating more human-like responses for the help-seekers. Both automatic and manual evaluations demonstrate that Zero-shot DSC prompting can deliver more human-like responses than CoT prompting methods on LTGM tasks.
Clothoid Curve-based Emergency-Stopping Path Planning with Adaptive Potential Field for Autonomous Vehicles

The Potential Field (PF)-based path planning method is widely adopted for autonomous vehicles (AVs) due to its real-time efficiency and simplicity. PF often creates a rigid road boundary, and while this ensures that the ego vehicle consistently operates within the confines of the road, it also brings a lurking peril in emergency scenarios. If nearby vehicles suddenly switch lanes, the AV has to veer off and brake to evade a collision, leading to the"blind alley"effect. In such a situation, the vehicle can become trapped or confused by the conflicting forces from the obstacle vehicle PF and road boundary PF, often resulting in indecision or erratic behavior, even crashes. To address the above-mentioned challenges, this research introduces an Emergency-Stopping Path Planning (ESPP) that incorporates an adaptive PF (APF) and a clothoid curve for urgent evasion. First, we design an emergency triggering estimation to detect the"blind alley"problem by analyzing the PF distribution. Second, we regionalize the driving scene to search the optimal breach point on the road PF and the final stopping point for the vehicle by considering the possible motion range of the obstacle. Finally, we use the optimized clothoid curve to fit these calculated points under vehicle dynamics constraints to generate a smooth emergency avoidance path. The proposed ESPP-based APF method was evaluated by conducting the co-simulation between MATLAB/Simulink and CarSim Simulator in a freeway scene. The simulation results reveal that the proposed method shows increased performance in emergency collision avoidance and renders the vehicle safer, in which the duration of wheel slip is 61.9% shorter, and the maximum steering angle amplitude is 76.9% lower than other potential field-based methods.
Can LLM Replace Stack Overflow? A Study on Robustness and Reliability of Large Language Model Code Generation

Recently, large language models (LLMs) have shown an extraordinary ability to understand natural language and generate programming code. It has been a common practice for software engineers to consult LLMs when encountering coding questions. Although efforts have been made to avoid syntax errors and align the code with the intended semantics, the reliability, and robustness of the code generation from LLMs have not yet been thoroughly studied. The executable code is not equivalent to reliable and robust code, especially in the context of real-world software development. For example, the misuse of APIs in the generated code could lead to severe problems, such as resource leaks, program crashes, etc. Existing code evaluation benchmarks and datasets focus on crafting small tasks such as programming questions in coding interviews, which, however, deviates from the problem that developers would ask LLM for real-world coding help. To fill the missing piece, in this work, we propose a dataset RobustAPI for evaluating the reliability and robustness of code generated by LLMs. We collect 1208 coding questions from Stack Overflow on 18 representative Java APIs. We summarize the common misuse patterns of these APIs and evaluate them on current popular LLMs. The evaluation results show that even for GPT-4, 62% of the generated code contains API misuses, which would cause unexpected consequences if the code is introduced into real-world software.
A Novel Loss Function Utilizing Wasserstein Distance to Reduce Subject-Dependent Noise for Generalizable Models in Affective Computing

Emotions are an essential part of human behavior that can impact thinking, decision-making, and communication skills. Thus, the ability to accurately monitor and identify emotions can be useful in many human-centered applications such as behavioral training, tracking emotional well-being, and development of human-computer interfaces. The correlation between patterns in physiological data and affective states has allowed for the utilization of deep learning techniques which can accurately detect the affective states of a person. However, the generalisability of existing models is often limited by the subject-dependent noise in the physiological data due to variations in a subject's reactions to stimuli. Hence, we propose a novel cost function that employs Optimal Transport Theory, specifically Wasserstein Distance, to scale the importance of subject-dependent data such that higher importance is assigned to patterns in data that are common across all participants while decreasing the importance of patterns that result from subject-dependent noise. The performance of the proposed cost function is demonstrated through an autoencoder with a multi-class classifier attached to the latent space and trained simultaneously to detect different affective states. An autoencoder with a state-of-the-art loss function i.e., Mean Squared Error, is used as a baseline for comparison with our model across four different commonly used datasets. Centroid and minimum distance between different classes are used as a metrics to indicate the separation between different classes in the latent space. An average increase of 14.75% and 17.75% (from benchmark to proposed loss function) was found for minimum and centroid euclidean distance respectively over all datasets.
An Observer-Based Reinforcement Learning Solution for Model-Following Problems

This paper introduces a novel model-free solution for a multi-objective model-following control problem, utilizing an observer-based adaptive learning approach. The goal is to regulate model-following error dynamics and optimize process variables simultaneously. Integral reinforcement learning is employed to adapt three key strategies, including observation, closed-loop stabilization, and reference trajectory tracking. Implementation uses an approximate projection estimation method under mild conditions on learning parameters.