bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effects of social presence on cooperative trust with algorithms
Authors: Liefooghe Baptist , Min Ebelien , Aarts Henk 
Published: October 2023

AbstractAlgorithms support many processes in modern society. Research using trust games frequently reports that people are less inclined to cooperate when believed to play against an algorithm. Trust is, however, malleable by contextual factors and social presence can increase the willingness to collaborate. We investigated whether situating cooperation with an algorithm in the presence of another person increases cooperative trust. Three groups of participants played a trust game against a pre-programmed algorithm in an online webhosted experiment. The first group was told they played against another person who was present online. The second group was told they played against an algorithm. The third group was told they played against an algorithm while another person was present online. More cooperative responses were observed in the first group compared to the second group. A difference in cooperation that replicates previous findings. In addition, cooperative trust dropped more over the course of the trust game when participants interacted with an algorithm in the absence another person compared to the other two groups. This latter finding suggests that social presence can mitigate distrust in interacting with an algorithm. We discuss the cognitive mechanisms that can mediate this effect.
Association between body fat and bone mineral density in Korean adults: a cohort study
Authors: Yoon Hyunjung , Sung Eunju , Kang Jae-Heon , Kim Cheol-Hwan , Shin Hocheol , Yoo Eunsol , Kim Minyoung , Lee Mi Yeon , Shin Sujeong 
Published: October 2023

AbstractAlthough obesity was once considered protective against osteoporosis, various factors influence the relationship between fat and bone mineral density (BMD). To establish the importance of healthy body composition in decelerating declines in BMD, we conducted a study to compare the association between body fat composition and BMD in Korean adults. Using data collected from the Kangbuk Samsung Health Study from 2012 to 2019, this cohort study compared the incidence of decreased BMD among the following four groups: normal BMI and normal adiposity (NBMI-NA), normal BMI and high adiposity (NBMI-HA), overweight, and obesity. Decreased BMD was defined as a Z-score ≤  − 2.0 in premenopausal women and men < 50 years of age or a T-score <  − 1.0 in postmenopausal women and men ≥ 50 years of age. Individuals who were diagnosed with osteoporosis or compression fracture after their second visit were categorized as having decreased BMD. The incidence rate of decreased BMD in the NBMI-NA group was 3.37, and that in the NBMI-HA group was 4.81, which was the highest among all groups. After adjusting for confounding factors, NBMI-HA led to a significantly greater risk of decreased BMD compared to NBMI-NA (HR 1.47; 95% CI 1.09–1.99). Even with a normal BMI, a high BFP was associated with an increased risk of decreased BMD. Therefore, healthy body composition management, not simply BMI, is important in preventing decreased BMD.
Diagnosis and treatment outcomes of urethritis-like symptoms in young males: a retrospective cohort study
Authors: Hsu Yi-Ting , Chuang Tzu-Yu , Hsiao Jui Chang , Cheng Weiming 
Published: October 2023

AbstractThe study evaluated the prevalence of gonorrhoea and chlamydia infections and find out other non-infectious diseases in sexually active young males with urethritis-like symptoms and their treatment outcomes. We retrospectively reviewed the young adult males (aged 20–50 years) who visited our clinic with urethritis symptoms from March 2019 to April 2022. All patients underwent urinalysis, urine culture, and urinary polymerase chain reaction (PCR) testing for gonorrhoea and chlamydia. Student’s t-test and Pearson’s chi-square test were used to compare the differences between the triple-negative group (i.e., negative results in urinalysis, urine culture, and urinary PCR) and the any-positive group. Logistic regression analyses were used to evaluate the predictive factors for positive PCR results for gonorrhoea or chlamydia in patients with negative urinalysis and urine culture. Of the 365 participants with urethritis-like symptoms, 139 patients were diagnosed of gonococcal or chlamydia urethritis. Among the 202 patients with negative urinalysis and urine culture, 60 patients were diagnosed with gonorrhoea or chlamydia using PCR. Urethral discharge was an independent predictor. 142 patients with triple negative results were attributed to other non-infectious diseases. Empirical antibiotic treatment is recommended for patients with urethritis symptoms showing positive or negative urinalysis results but with urethral discharge.
Tracking SARS-CoV-2 Omicron lineages using real-time reverse transcriptase PCR assays and prospective comparison with genome sequencing
Authors: Zelyas Nathan , Pabbaraju Kanti , Croxen Matthew A. , Lynch Tarah , McCullough Emily , Murphy Stephanie A. , Shokoples Sandy , Wong Anita , Kanji Jamil N. , Tipples Graham 
Published: October 2023

AbstractOmicron has become the dominant SARS-CoV-2 variant globally since December 2021, with distinct waves being associated with separate Omicron sublineages. Rapid detection of BA.1, BA.2, BA.4, and BA.5 was accomplished in the province of Alberta, Canada, through the design and implementation of real-time reverse transcriptase PCR assays targeting S:N501Y, S:ins214EPE, S:H69/V70, ORF7b:L11F, and M:D3N. Using the combination of results for each of these markers, samples could be designated as belonging to sublineages within BA.1, BA.2, BA.4, or BA.5. The analytical sensitivity of these markers ranged from 132 to 2229 copies/mL and in-laboratory accuracy was 98.9–100%. A 97.3% agreement using 12,592 specimens was demonstrated for the assays compared to genome sequencing. The use of these assays, combined with genome sequencing, facilitated the surveillance of SARS-CoV-2 lineages throughout a BA.5-dominated period.
Application of distributed lag models and spatial analysis for comparing the performance of the COVID-19 control decisions in European countries
Authors: Hadianfar Ali , Rastaghi Sedigheh , Tabesh Hamed , Saki Azadeh 
Published: October 2023

AbstractOver the past three years, the COVID-19 outbreak has become a major worldwide problem, affecting the health systems and economies of countries. The mean delays, the expected time to observe the average effect of the number of new cases on the number of deaths, are gold times for decision-making regarding disease control and treatment facilities to reduce the fatality rate. The interest of the present study is estimating the mean delays and adjusted fatality rates of COVID-19 with the new application of Distributed Lag Models (DLM) and their spatial distributions. The daily cases and deaths data of COVID-19 for 39 European countries was obtained from two sources; the "European Centre for Disease Prevention and Control" and the "Our World in Data" database. The mean delay and the Adjusted Fatality Rate (AFR) for each country at three-time intervals; the first and subsequent peaks before and after vaccination were estimated by the Distributed Lag Models. The spatial analysis was applied to find the spatial correlation of the mean delays and adjusted fatality rates among European countries. In the three-time intervals, the first and the subsequent peaks before vaccination, and after vaccination, the median and interquartile range of the mean delays; and AFRs were: 1.1 (0.4, 3.2); 0.024 (0.016, 0.044), 9.2 (6.2, 12.40); 0.013 (0.005, 0.020) and 7.3 (4.4, 11.0); 0.001 (0.001, 0.005), respectively. In the subsequent peaks before vaccination, the mean delays considerably increased, and the AFRs decreased for most European countries. After vaccination, the AFRs decreased considerably. Except for the first peak, the spatial correlations of AFRs were not significant among neighboring countries. Consecutive outcomes will occur with delays in outbreaks of infectious disease. Also, the fatality rates for these outcomes should be adjusted on delays. Estimating the mean delays and adjusted fatality rates by Distributed lag Models and the spatial distributions of theme in outbreaks showed that prevention and medical policies after the first peak as well as vaccination were effective to reduce the fatality rate of COVID-19, but these effects were different between countries. These results recommended policymakers and governments assign prevention and medical resources more effectively.
KCoO2-type layered nitrides Ca1−xEuxTiN2: structural stability, electrical properties and Eu coordination chemistry
Authors: Liu Junwei , Zhang Bowen , Lu Shenglin , Ming Xing , Kuang Xiaojun 
Published: October 2023

Ca1−xEuxTiN2 solid solution shows limited substitution, and EuTiN2 is inaccessible due to much higher formation energy showing a preferred orthorhombic phase, revealing the different coordination chemistry of Eu ions in nitrides compared to oxides.
Electrochemical trifluoroalkylation/annulation for the synthesis of CF3-functionalized tetrahydroquinolines and dihydroquinolinones
Authors: Wu Hao-Zeng , Teng Zhong-Shan , Ke Yu-Xin , Zou Yu , Gao Ping , Li Yue , Zhou Cheng-He , Zang Zhong-Lin 
Published: October 2023

This paper demonstrates tandem trifluoromethylation/C(sp2)–H annulation using Langlois’ reagent as the CF3 source for the electrochemical synthesis of functionalized tetrahydroquinolines and dihydroquinolinones.
The one‐bit diffusion for distributed beamforming over cell free wireless networks
Authors: Mostafapour Ehsan , Ghobadi Changiz , Nourinia Javad , Ezzati Nematollah 
Published: October 2023

AbstractThis paper presents an investigation into the theoretical performance of diffusion adaptive networks within a distributed beam coordination application in a cell‐free communication network that utilizes coarsely quantized data. This analysis assumed data pulses with 1, 2, and 3 bits, and based on this assumption, simulations and theoretical analyses of distributed beamforming are conducted. The consistency between the simulation results and theoretical findings validated the accuracy of the calculations. Additionally, the distributed quantization aware least mean square (DQA‐LMS) algorithm was introduced for distributed beam coordination, demonstrating its superior performance in scenarios involving coarsely quantized data. Convergence conditions were provided and the results through various simulations were presented, including the array network mean square deviation (MSD), mean square error (MSE), and beamforming diagrams.
A new fuzzy decoupled sliding mode control of flexible joint robotic manipulators based on the finite‐time observer in the presence of chaos with experimental validation
Authors: Nezhad Abdollah Hasan , Noiey Abolfazl Ranjbar , Soltanpour Mohammad Reza , Veysi Mohammad 
Published: October 2023

AbstractThis paper presents a new fuzzy non‐singular fast terminal decoupled sliding mode control based on the finite‐time extended state observer for trajectory tracking control of flexible joint robot manipulators under chaos and uncertainty. First, a suitable chaotic reference is used in a synchronization mechanism to create chaos in the dynamics. Then, the essential modifications made to the conventional decoupled sliding mode control are introduced. A new coupling variable and a non‐singular fast terminal sliding surface are proposed in combination with an extended state observer. A complete convergence analysis is also performed for all the sliding surfaces. Furthermore, two fuzzy systems are designed in such a way that not only they can eliminate the chattering phenomenon but can also improve the convergence time without increasing the control input in a voltage‐based manner. The mathematical proof guarantees the finite‐time asymptotic stability of the closed‐loop control system under the proposed scheme for a class of under‐actuated systems in the presence of structured and un‐structured uncertainties. Finally, some simulations and experimental implementations as hardware‐in‐the‐loop are performed to evaluate the proposed method's performance. The proposed controller is an effective method, as confirmed by the numerical simulation results and experimental validation.
Optimal classification tree for frequency security assessment of power systems with inverter‐based resources reinforcement
Authors: Li Yalong , Sun Yanying , Dan Yangqing , Sun Feifei 
Published: October 2023

AbstractFrequency security is the premise of realizing the net‐zero transition. A novel assessment scheme is proposed to quantify the frequency security levels of transmission systems under different inverter‐based resources (IBRs) and their control parameter combinations. A novel system frequency dynamic model is proposed as a parametric optimization method, where the saturation of generators, energy storage systems, and renewable energy sources is incorporated as differential algebra equations. This problem is further reformulated as a mixed‐integer linear programming problem to generate a sufficient amount of data under different IBR integration and control parameters. The frequency security assessment problem is formulated as a data‐driven multivariate classification problem, which is solved by the optimal classification tree (OCT) algorithm with better interptretionability. Simulations are conducted on a transmission system. Numerical results indicate that the proposed system frequency dynamic model can capture the frequency dynamic under different IBR reinforcement plans and the OCT can realize accurate classification of the synthetic data regarding frequency security.
Israel and the global synthetic biology ecosystem
Authors: Dorfan Yuval , Zeevi Aviv , Reinitz Gita , Mualem Magi , Shacham‐Diamand Yosi 
Published: October 2023

AbstractThe field of synthetic biology emerged a few decades ago, following some key works of researchers in the USA, Europe, and the Far East. It reached Israel through academia and a few years later it finally got the attention of industry, venture capitals, and government authorities, especially the Israeli Innovation Authority, hoping to encourage entrepreneurs to establish startups in this field. Here we provide an overview of the activity of the field of synthetic biology in Israel, including historical notes, current strategy, prospects and developments, and further insight that are relevant to any stakeholders in the synthetic biology field.
Making cities smarter for an inclusive green transition towards a long‐term sustainable development: A critical literature review
Authors: Hatem Faten Mostafa 
Published: October 2023

AbstractThis review critically approaches the literature on smart cities while describing the significance of more value‐based rationality and more reflexive practice for constructing smart cities, rethinking how human experiences are approached to improve it to be more balanced and engaging. This transition establishes a sense of place in the city necessary to enhance people's attitudes and overall well‐being. As the vision of smart cities promotes them as more liveable cities while focusing on achieving more efficient services, the review clarifies the need to improve the ability of smart cities to produce more engaging experiences to achieve long‐term sustainable development, planning and governance as part of their green transition. The authors promote innovative approaches to realising agendas of citizen engagement and sustainability by clarifying the potential of interdisciplinary cooperation among art, place and technology. This will help redefine progress in city development from merely enhancing basic functions to improving the human experience.
The mediating role of perceived brand authenticity between brand experience and brand love: a cross-cultural perspective
Authors: Rodrigues Clarinda , Brandão Amélia , Billore Soniya , Oda Tetsuhisa 
Published: October 2023

AbstractThis research investigates the mediating role of perceived brand authenticity (PBA) between brand experience (BE) and brand love (BL) of global high-tech brands Apple and Samsung. A quantitative study was conducted in Japan, India, and Portugal. The research found evidence that PBA is a multidimensional, reflective-formative higher-order construct composed of two lower-order components namely PBA Core and PBA Peripheral. The findings also contribute to understand how consumers are impacted by different BE and PBA dimensions using the lens of consumer culture theory and how BL is formed as a social-cultural phenomena. Finally, the study demonstrates for the first time that relationship intensity and self-authenticity moderate the effect of BE on PBA. Although limited to three countries and high-tech brands, the findings are of relevance to global brands by raising awareness that culture plays a key role in how consumers perceive authentic brand experiences and how passionate feelings for global brands can be strengthened.
Atmospheric muons at PeV energies in radio neutrino detectors
Authors: Pyras L. , Glaser C. , Hallmann S. , Nelles A. 
Published: October 2023

Abstract
Experiments seeking to detect radio emission stemming from neutrino interactions will soon
reach sensitivities that bring a detection within reach. Since experiments like RNO-G or
the future IceCube-Gen2 target more than an order of magnitude more effective volume than existing
experiments, the renewed and detailed study of rare backgrounds is needed. In this paper, we study
the potential background from energy losses of highly energetic atmospheric muons. Due to both
limited experimental measurements and limited modeling in hadronic interaction models, the
expected event rate is subject to large uncertainties. Here, we estimate rate predictions and
their uncertainties for different models and instrumental parameters. We also study possible
routes towards mitigation of the muon background, such as parent air shower detection, and
illustrate what is needed to make the first measurement of the prompt muon flux at energies above
10 PeV.
Hemophagocyte morphology and hypertriglyceridemia correlate with diagnosis of hemophagocytic lymphohistiocytosis in patients with bone marrow hemophagocytes
Authors: Yan Mingfei , Beck Rose C , Gadde Ramya 
Published: October 2023

Abstract

Objectives
To investigate laboratory and bone marrow findings that can help predict a diagnosis of hemophagocytic lymphohistiocytosis (HLH) for patients who have demonstrated hemophagocytes (HPCs) in the bone marrow.

Methods
A total of 57 cases from 48 patients with HPCs present on bone marrow examination were included. The numbers and morphologic characteristics of HPCs with ingested nucleated cells (nHPC) were counted. Pertinent medical history, relevant laboratory values, and flow cytometry data at the time of bone marrow biopsy were collected.

Results
A total of 24 patients fulfilled diagnostic criteria for HLH, and the remaining 24 patients did not. By using HLH-2004 cutoffs, only hypertriglyceridemia (≥265 mg/dL) was significantly associated with HLH diagnosis. The HLH cases more frequently had nHPC-ingesting granulocytic cells (gHPC) (75.9% vs 24.1%, P = .009). The percentage of gHPC to all nHPC was also significantly higher in HLH cases (median, 15.4% vs 0%; P = .0002). Both triglyceride level (area under the curve [AUC]  = 0.88, P &lt; .0001) and gHPC percentage (AUC = 0.81, P = .0005) were significant in predicting HLH diagnosis. Finally, no overt immunophenotypic abnormality was noted for 19 HLH cases with available flow cytometry data.

Conclusions
The presence of hypertriglyceridemia and more frequent gHPC has predictive value for HLH diagnosis in patients with bone marrow HPC.
MSI-XGNN: an explainable GNN computational framework integrating transcription- and methylation-level biomarkers for microsatellite instability detection
Authors: Cao Yang , Wang Dan , Wu Jin , Yao Zhanxin , Shen Si , Niu Chao , Liu Ying , Zhang Pengcheng , Wang Quannian , Wang Jinhao , Li Hua , Wei Xi , Wang Xinxing , Dong Qingyang 
Published: October 2023

Abstract
Microsatellite instability (MSI) is a hypermutator phenotype caused by DNA mismatch repair deficiency. MSI has been reported in various human cancers, particularly colorectal, gastric and endometrial cancers. MSI is a promising biomarker for cancer prognosis and immune checkpoint blockade immunotherapy. Several computational methods have been developed for MSI detection using DNA- or RNA-based approaches based on next-generation sequencing. Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play critical roles in the development and progression of cancer. We here developed MSI-XGNN, a new computational framework for predicting MSI status using bulk RNA-sequencing and DNA methylation data. MSI-XGNN is an explainable deep learning model that combines a graph neural network (GNN) model to extract features from the gene-methylation probe network with a CatBoost model to classify MSI status. MSI-XGNN, which requires tumor-only samples, exhibited comparable performance with two well-known methods that require tumor-normal paired sequencing data, MSIsensor and MANTIS and better performance than several other tools. MSI-XGNN also showed good generalizability on independent validation datasets. MSI-XGNN identified six MSI markers consisting of four methylation probes (EPM2AIP1|MLH1:cg14598950, EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436 and TSC22D2:cg15048832) and two genes (RPL22L1 and MSH4) constituting the optimal feature subset. All six markers were significantly associated with beneficial tumor microenvironment characteristics for immunotherapy, such as tumor mutation burden, neoantigens and immune checkpoint molecules such as programmed cell death-1 and cytotoxic T-lymphocyte antigen-4. Overall, our study provides a powerful and explainable deep learning model for predicting MSI status and identifying MSI markers that can potentially be used for clinical MSI evaluation.
Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction
Authors: Liang Jing , Li Zong-Wei , Sun Ze-Ning , Bi Ying , Cheng Han , Zeng Tao , Guo Wei-Feng 
Published: October 2023

Abstract
Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.