bluuebunny/crossref_metadata_2025_split huggingface dataset:
Facial trustworthiness dampens own-gender bias in emotion recognition
Authors: Bagnis Arianna , Colonnello Valentina , Russo Paolo Maria , Mattarozzi Katia 
Published: August 2023

AbstractPrevious research suggests that emotion recognition is influenced by social categories derived by invariant facial features such as gender and inferences of trustworthiness from facial appearance. The current study sought to replicate and extend these findings by examining the intersection of these social categories on recognition of emotional facial expressions. We used a dynamic emotion recognition task to assess accuracy and response times in the happiness and anger categorization displayed by female and male faces that differed in the degree of facial trustworthiness (i.e., trustworthy- vs. untrustworthy-looking faces). We found that facial trustworthiness was able to modulate the own-gender bias on emotion recognition, as responses to untrustworthy-looking faces revealed a bias towards ingroup members. Conversely, when faces look trustworthy, no differences on emotion recognition between female and male faces were found. In addition, positive inferences of trustworthiness lead to faster recognition of happiness in females and anger in males, showing that facial appearance was able to influence also the intersection between social categories and specific emotional expressions. Together, these results suggest that facial appearance, probably due to the activation of approach or avoidance motivational systems, is able to modulate the own-gender bias on emotion recognition.
Cavity-based lymphomas: challenges and novel concepts. A report of the 2022 EA4HP/SH lymphoma workshop
Authors: Di Napoli Arianna , Soma Lori , Quintanilla-Martinez Leticia , de Leval Laurence , Leoncini Lorenzo , Zamò Alberto , Ng Siok-Bian , Ondrejka Sarah L. , Climent Fina , Wotherspoon Andrew , Dirnhofer Stefan 
Published: August 2023

AbstractThe 2022 European Association for Haematopathology/Society for Hematopathology lymphoma workshop session on cavity-based lymphomas included sixty-eight cases in seven sections. The disease entities discussed include primary effusion lymphomas (PEL), extracavitary primary effusion lymphomas and confounding entities (ECPEL), HHV8-negative B-lineage lymphomas-effusion based (EBV-negative, EBV-positive, and plasmablastic types), diffuse large B-cell lymphoma associated with chronic inflammation, fibrin-associated diffuse large B-cell lymphoma (FA-DLBCL), breast implant-associated anaplastic large cell lymphoma (BIA-ALCL), and other lymphomas presenting as an effusion. All entities above are discussed; however, three are delved into greater detail given the challenges with classification: ECPEL, HHV8-negative effusion-based lymphomas, and FA-DLBCL. Cases exemplifying the diagnostic difficulty in differentiating ECPEL from HHV8-positive diffuse large B-cell lymphoma and germinotropic lymphoproliferative disorder were discussed. The more recently recognized effusion-based HHV8-negative large B-cell lymphoma is explored, with several cases submitted raising the question if this subset should be carved out as a specific entity, and if so, what should be the refining diagnostic criteria. Case submissions to the FA-DLBCL section yielded one of the largest case series to date, including classic cases, cases furthering the discussion on disease sites and prognosis, as well as novel concepts to be considered in this entity. The 2022 EA4HP/SH workshop cases allowed for further confirmation of the characteristics of some of the more historically accepted cavity-based lymphomas, as well as further inquiry and debate on relatively new or evolving entities.
Hospital admissions associated with dehydration in childhood kidney transplantation
Authors: Le Page Amelia K. , Johnstone Lilian M. , Kausman Joshua Y. 
Published: August 2023

Abstract
Background
Paediatric kidney transplant recipients may be at a particular risk of dehydration due to poor kidney concentrating capacity and illness associated with poor fluid intake or losses. In this population, creatinine rise may be more likely with relatively mild dehydration, which may trigger hospital admission. This study describes hospital admissions in the first 12 months after transplantation with diagnosis of graft dysfunction associated with dehydration due to illness or poor fluid intake. We assess risk factors for these admissions.

Methods
Data was extracted from medical records of patients transplanted in two tertiary children hospitals. Following descriptive analysis, multiple failure regression analyses were used to identify factors associated with admission for acute kidney allograft dysfunction associated with dehydration.

Results
Of 92 children, 42% had at least 1 dehydration admission in the 12 months following transplantation. Almost half of the dehydration admissions were due to poor fluid intake, which accounted for 1/5 of all unplanned hospital admissions. Target fluid intake at first discharge of > 100 ml/kg/day was associated with dehydration admissions of all types (hazard ratio (HR) 2.04 (95% CI 1.13–3.68)). Teen age was associated with poor fluid intake dehydration admissions (HR 4.87 (95% CI 1.19–19.86)), which were more frequent in mid-summer. Use of enteric feeding tube, which correlated with age under 4, associated with contributing illness dehydration admissions (HR 2.18 (95% CI 1.08–4.41)).

Conclusions
Dehydration admissions in the 12 months following childhood kidney transplantation are common. Highlighted admission risk factors should prompt further study into optimal fluid intake prescription and hydration advice given to children, teenagers, and their carers following kidney transplantation. Use of an enteric feeding tube may not protect patients from admission with dehydration associated with contributing illness.

Graphical abstract
Oculometric measures as a tool for assessment of clinical symptoms and severity of Parkinson’s disease
Authors: Reiner Johnathan , Franken Liron , Raveh Eitan , Rosset Israel , Kreitman Rivka , Ben-Ami Edmund , Djaldetti Ruth 
Published: August 2023

Abstract
Abnormalities of oculometric measures (OM) are widely described in people with Parkinson's disease (PD). However, knowledge of correlations between abnormal OM, disease severity and clinical assessment in PD patients is still lacking. To evaluate these correlations, PD patients (215 patients, mean age 69 ± 9.1 years, 79 females) with severe (H&Y > 3) and mild to moderate (H&Y ≤ 2) disease, and 215 age-matched healthy subjects were enrolled. All patients were evaluated using MDS-UPDRS and an oculometric test using computer vision and deep learning algorithms. Comparisons of OM between groups and correlations between OM and MDS-UPDRS scores were calculated. Saccadic latency (ms) was prolonged in patients with severe compared with mild to moderate disease (pro-saccades: 267 ± 69 vs. 238 ± 53, p = 0.0011; anti-saccades: 386 ± 119 vs. 352 ± 106, p = 0.0393) and in patients with mild to moderate disease versus healthy subjects (pro-saccades: 238 ± 53 vs. 220 ± 45, p = 0.0003; anti-saccades: 352 ± 106 vs. 289 ± 71, p < 0.0001). Error rate (%) was higher among patients with severe (64.06 ± 23.08) versus mild to moderate disease (49.84 ± 24.81, p = 0.0001), and versus healthy subjects (49.84 ± 24.81 vs. 28.31 ± 21.72, p = 0.00001). Response accuracy (%) was lower for patients with severe (75.66 ± 13.11) versus mild to moderate disease (79.66 ± 13.56, p = 0.0462), and versus healthy subjects (79.66 ± 13.56 vs. 90.27 ± 8.79, p < 0.0001). Pro- and anti-saccadic latency, error rate and accuracy were correlated with MDS-UPDRS scores (r = 0.32, 0.28, 0.36 and -0.30, respectively, p < 0.0001) and similar correlations were found with its axial subscore (R = 0.38, 0.29, 0.44, and -0.30, respectively, p < 0.0001). Several OM were different in patients under levodopa treatment. OM worsened as PD severity increases, and were correlated with MDS-UPDRS scores. Using OM can be implemented for PD patients’ assessment as a tool to follow disease progression.
Applying a Lens of Temporality to Better Understand Voice About Unethical Behaviour
Authors: Brooks Sarah , Richmond John , Blenkinsopp John 
Published: August 2023

AbstractThe relationship between time and voice about unethical behaviour has been highlighted as a key area for exploration within the voice and silence field (Morrison Annual Review of Organizational Psychology and Organizational Behavior 10:79–107, 2023). Previous studies have made only modest progress in this area, so we present a temporal lens which can act as a guide for others wishing to better understand the role of time and voice. Applying the concept of theory adaptation (Jaakkola AMS Review 10:18–26, 2020), a method which attempts to build on a given field through the application of a new theoretical lens, we begin by reviewing what is known in relation to voice about unethical behaviour specifically. Then we introduce two temporal frameworks, one suggested by Ancona, Okhuysen, and Perlow (Ancona et al. The Academy of Management Review 26:645–663, 2001a; Ancona et al. The Academy of Management Review 26:512–529, 2001b) as a useful way of analysing time in organisations, and a second one by Bansal, Anna, and Wood, (Bansal et al. Academy of Management Review 43:217–241, 2018) focusing on the way organisations include voice into their temporal rhythm. We then draw conclusions about the role of time in relation to voice about unethical behaviour and identify three insights; a) it takes time for voices to generate evidence for unethical behaviour, b) perceptions of unethical behaviour change over time, and c) it is most difficult to voice about unethical behaviour at the time it is most needed. Our recommendations for future avenues of research based on these insights recommend new research designs better suited to explore the relationship between voice and time and a focus on how the formality of voice mechanisms shapes the timing of voice.
Is hiring fast a good sign? The informativeness of job vacancy duration for future firm profitability
Authors: Chen Ciao-Wei , Li Laura Yue 
Published: August 2023

AbstractJob vacancy duration reflects the time a firm spends searching, selecting, and hiring for a job opening. Capturing vacancy duration using the creation and deletion dates of job postings by US public firms, we examine the informativeness of vacancy duration for future firm profitability. We find that while firms that quickly fill low-skill job vacancies exhibit higher future profitability, firms that take more time to fill high-skill jobs exhibit higher future profitability. Our cross-sectional analyses across the benefits and costs of candidate selection and performance expectations suggest that the informativeness of vacancy duration comes from its reflection of firms’ hiring strategies. That is, firms expecting higher profitability recruit more intensively to avoid the opportunity cost associated with vacancies for low-skill jobs and to ensure the selection of high-quality workers for high-skill jobs. Further analyses show that the implication of job vacancy duration for future profitability is not incorporated timely in the capital markets, as evidenced by pessimistic analyst forecasts and positive earnings announcement returns in future quarters for firms with short (long) durations for low-skill (high-skill) jobs. These results demonstrate the informativeness of job vacancy duration for firm profitability and advance the understanding of firms’ hiring strategies.
The Effect of Short-Term Air Pollutants Exposure on Daily Mortality Among Elderly Individuals in China: A Systematic Review and Meta-Analysis
Authors: Liu Cong , Jia Fengwei , Ji Mengmeng , Qu Guofeng , Ye Chunping , Cheng Jiali , Schwingel Andiara , Gao Rong 
Published: August 2023

AbstractTo systematically review the literature regarding the effect of air pollutants on the excess mortality risk among the elderly Chinese. PubMed, Scopus, Web of Science (WOS), and Chinese National Knowledge Infrastructure (CNKI) electronic databases were searched. A standardized data extraction form was used to collect methodological and outcome variables from each eligible study. The effect value was combined with a fixed- or random-effect model, and sensitivity analysis and publication bias test were performed. A total of 26 articles on air pollutants and excess risk (ER) of mortality in the Chinese elderly were identified. Among them, 16, 14, 8, and 8 articles contained PM2.5, PM10, SO2, and NO2 data, respectively. The meta-analysis results showed that the excess risk of mortality in the Chinese elderly associated with per 10 µg/m3 increase in PM2.5, PM10, SO2, and NO2 concentrations was 1.01%, 0.54%, 1.06%and 1.35%, respectively. Sufficient evidence supports that the increase in air pollutants (PM2.5, PM10, SO2, and NO2) will escalate the mortality of the elderly in China. Future research can study two or more air pollution models and the impact of air pollution on elderly mortality rates under different temperatures, duration of exposure, seasons and regions.
Methods for estimating insulin resistance from untargeted metabolomics data
Authors: Hsu Fang-Chi , Palmer Nicholette D. , Chen Shyh-Huei , Ng Maggie C. Y. , Goodarzi Mark O. , Rotter Jerome I. , Wagenknecht Lynne E. , Bancks Michael P. , Bergman Richard N. , Bowden Donald W. 
Published: August 2023

AbstractContextInsulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming.ObjectiveDevelop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data.DesignInsulin Resistance Atherosclerosis Family Study (IRASFS).SettingCommunity based.ParticipantsMexican Americans (MA) and African Americans (AA).Main outcomeEstimation models for measures of insulin resistance, i.e. SI and HOMA-IR.ResultsLeast Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression were used to build insulin resistance estimation models from 1274 metabolites combined with clinical data, e.g. age, sex, body mass index (BMI). Metabolite data were transformed using three approaches, i.e. inverse normal transformation, standardization, and Box Cox transformation. The analysis was performed in one MA recruitment site (San Luis Valley, Colorado (SLV); N = 450) and tested in another MA recruitment site (San Antonio, Texas (SA); N = 473). In addition, the two MA recruitment sites were combined and estimation models tested in the AA recruitment sample (Los Angeles, California; N = 495). Estimated and empiric SI were correlated in the SA (r2 = 0.77) and AA (r2 = 0.74) testing datasets. Further, estimated and empiric SI were consistently associated with BMI, low-density lipoprotein cholesterol (LDL), and triglycerides. We applied similar approaches to estimate HOMA-IR with similar results.ConclusionsWe have developed a method for estimating insulin resistance with metabolomics data that has the potential for application to a wide range of biomedical studies and conditions.
They Were the Best and the Worst of Times: Reflections Illuminate Emerging Adults’ Sexual Experiences
Authors: Turner George , Pelts Michael , Frabotta Richelle , Paceley Megan S. 
Published: August 2023

Abstract
Introduction
Human sexuality courses are offered at most universities. Students often bring a breadth of experiences and a need for emotional processing of these narratives into these courses as it relates to course content. The purpose of this study was to expand research exploring emerging adults’ sexuality narratives to be inclusive of positive stories.

Methods
Guided by a semi-structured questionnaire, 113 students from two USA universities reflected in 2017 on their sexual history using an established educational activity asking students about their “best” and “worst” sexual experiences.

Results
Qualitative analysis resulted in five themes describing their “Best” and four themes describing their “Worst” sexual experiences and one theme with dual meaning. “Best of” was highlighted by student themes of romance, beyond intercourse, emotional intimacy, listening, and safety. “Worst of” was highlighted by themes of physical health factors, alcohol and/or substance use/misuse factors, exploitation and victimization, and feelings of shame. The dual meaning theme of firsts emerged in both best of and worst of.

Conclusions
Findings suggest that human sexuality courses must consider students’ lived experiences. Further, we propose a call-to-action for clinically trained mental health professionals, such as social workers, as sexuality educators.

Policy Implications
Universities should incorporate sexual health education policies that acknowledge students who bring a collection of sexuality stories to learning spaces. Centering a student’s sexual voice also would support campus programs, policies, and interventions that benefit students. Finally, policies that guide professional accreditation of social work university programs to include sexual wellness content in the curricula are warranted.
A novel adaptive parameter strategy differential evolution algorithm and its application in midcourse guidance maneuver decision-making
Authors: Xie Lei , Wang Yuan , Tang Shangqin , Huang Changqiang , Li Yintong , Dong Kangsheng , Song Ting 
Published: August 2023

AbstractIn this paper, a novel Adaptive Parameter Strategy Differential Evolution (APSDE) algorithm is proposed to overcome the parameters dependence and avoid local optima. The Parameter Update Mechanism (PUM), which has three different strategies, is used to reduce the dependence on parameters of DE. The Adaptive Proportion Adjustment Mechanism (APAM) is used to balance the proportion of PUM strategies in different development terms of exploitation and exploration, and the Random Restart Mechanism (RRM) is used to improve population diversity when exploitation is in stagnation. The proposed algorithm is verified in the CEC2018 test functions and the results show that APSDE has good abilities of exploitation, exploration, convergence, and stability. Secondly, Midcourse Guidance Maneuver Decision-making (MGMD) in Beyond Visual Range (BVR) air combat is studied and transformed into a single objective variational optimization problem, a MGMD system based on APSDE is established. Finally, the simulation of MGMD is carried out. The APSDE ranks first in the typical MGMD scenario experiment. In the adaptive Midcourse guidance confrontation, the winning rate of APSDE is 54%, and the statistical results show that the APSDE has an excellent MGMD ability.
Imperceptible graph injection attack on graph neural networks
Authors: Chen Yang , Ye Zhonglin , Wang Zhaoyang , Zhao Haixing 
Published: August 2023

AbstractIn recent years, Graph Neural Networks (GNNs) have achieved excellent applications in classification or prediction tasks. Recent studies have demonstrated that GNNs are vulnerable to adversarial attacks. Graph Modification Attack (GMA) and Graph Injection Attack (GIA) are commonly attack strategies. Most graph adversarial attack methods are based on GMA, which has a clear drawback: the attacker needs high privileges to modify the original graph, making it difficult to execute in practice. GIA can perform attacks without modifying the original graph. However, many GIA models fail to take care of attack invisibility, i.e., fake nodes can be easily distinguished from the original nodes. To solve the above issue, we propose an imperceptible graph injection attack, named IMGIA. Specifically, IMGIA uses the normal distribution sampling and mask learning to generate fake node features and links respectively, and then uses the homophily unnoticeability constraint to improve the camouflage of the attack. Our extensive experiments on three benchmark datasets demonstrate that IMGIA performs better than the existing state-of-the-art GIA methods. As an example, IMGIA shows an improvement in performance with an average increase in effectiveness of 2%.
Biosignals meet Adaptive Systems
Authors: Schultz Tanja , Maedche Alexander 
Published: August 2023

AbstractThis paper presents a perspective on Biosignal-Adaptive Systems (BAS) which automatically adapt to user needs by continuously interpreting their biosignals and by providing transparent feedback, thereby keeping the user in the loop. The major hallmark of the described BAS is the low latency with which biosignals are processed, interpreted, and applied to perform rapid system adaptation, providing the user in the loop with immediate feedback on the BAS’s understanding of his or her condition. In contrast to explicit user input or the interpretation of observable behavior, the rapid system adaptation relies on biosignals, which in context of a concrete application can be interpreted as implicit signals of user needs. Recently, great advances have been made in sensor integration into smart devices, making it possible to collect vasts amounts of multimodal biosignal data. Furthermore, powerful machine learning methods enable rapidly processing and learning from such data. We argue that the time has come to harness the full spectrum of low-latency processing of biosignals to understand user needs and to apply this information to deliver adaptive systems accordingly. However, this will just be the beginning: real-time signal processing in combination with ubiquitous devices that are always connected to huge processing and storage capacities allow systems to provide users (and bystanders) with instant and transparent feedback and adaptations for the recognized needs. In the future, such systems could run 24/7 to assist users @home, @work, and @play from the cradle to the grave. Thus, BAS must be human-centric to curate data, archive information, learn from experience, and adapt to individual users throughout their lifespan. In this position paper, we present the concept of BAS with its key building blocks, provide selected examples of BAS from our research, and articulate selected challenges for future research.
Comparing quantum and classical machine learning for Vector Boson Scattering background reduction at the Large Hadron Collider
Authors: Cugini Davide , Gerace Dario , Govoni Pietro , Perego Aurora , Valsecchi Davide 
Published: August 2023

AbstractWe report on a consistent comparison between techniques of quantum and classical machine learning applied to the classification of signal and background events for the Vector Boson Scattering processes, studied at the Large Hadron Collider installed at the CERN laboratory. Quantum machine learning algorithms based on variational quantum circuits are run on freely available quantum computing hardware, showing very good performances as compared to deep neural networks run on classical computing facilities. In particular, we show that such kind of quantum neural networks is able to correctly classify the targeted signal with an Area Under the characteristic Curve (AUC) that is very close to the one obtained with the corresponding classical neural network, but employing a much lower number of resources, as well as less variable data in the training set. Albeit giving a proof-of-principle demonstration with limited quantum computing resources, this work represents one of the first steps towards the use of near term and noisy quantum hardware for practical event classification in High Energy Physics experiments.