bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-rigid Interest Object Extraction Using Minimum Convex Coverage
Authors: Yang Xubing , Zhang Yao , Zhang Li , Fan Xijian , Ye Qiaolin , Fu Liyong 
Published: October 2023

Abstract
Interest object (IO) extraction, or called region of interest (ROI) selection, is a fundamental task in remote sensing (RS), computer vision, and machine learning. Due to the non-rigidity, e.g., shapelessness and color-uncertainty, of interest objects like fires, smokes, clouds, or top-view tree canopies in a RS image, an unideal results usually occurs in IO detection tasks because the model-training is carried on the pixels drawn from an inaccurate ROI, e.g., a bounding-box or convex region. Although it is simple, it fails to capture a complete interest object or sub-object, especially at the requirement of high-precision. In this work, we focus on the non-convex ROI and develop a method for the accurate pixel extraction. To speed pixel extraction, the given non-convex ROI is first decomposed into a minimum convex coverage, and then the ROI pixels can be quickly extracted out from the coverage by using convex operation. For the decomposition , we provide two strategies, named inner decomposition and outer decomposition. Computationally, batch-decision for pixel selection and logical operation for the decomposition are also presented to speed extraction. Theoretically , the existence of minimum coverage for both decompositions is proved. Finally, extensive experimental verifications are carried out on public and our collected RS images, and the results show the superiority of our method in terms of ease of use, rapidity and pixel-precision.
Screening cataract patients in a public hospital for a multifocal intraocular lens implantation
Authors: Halkiadakis Ioannis , Ntravalias Thomas , Kollia Elpida , Chatzistefanou Klio , Kandarakis Stelios , Patsea Eleni 
Published: October 2023

Abstract
Purpose
To identify the rate and characteristics of cataract surgery candidates suitable for multifocal intraocular lens (MIOL) implantation among patients undergoing preoperative evaluation in a public hospital.
Materials and Methods
Screening was performed based on patient’s medical record, comprehensive ophthalmic examination, optical biometry (Zeiss, IOL Master 700) and Optical Coherence Tomography (Heidelberg, OCT Spectralis) in accordance with the relative indications and contraindications as mentioned in pertinent literature.
Results
The study evaluated 1200 consecutive patients. Four hundred and thirty -two patients (36%) were not eligible for bilateral surgery and were excluded from the study. Of 768 patients included in the present study 346 (45.1%) were considered suitable candidates. Four hundred twenty-two patients (54.9%) had reasons of exclusion in one or both eyes. Among them 121 (28.7%) were excluded because of retinal disease, 120 (28.4%) due to regular astigmatism (> 1.0 D of corneal astigmatism), 32 (7.5%) due to pseudoexfoliation or zonular instability, 30 (7.1%) because of glaucoma or ocular hypertension while in 90 (21.3%) patients there were multiple reasons of exclusion. Suitable candidates were significantly younger (70 and 75 years respectively (p < 0.001). After being informed about potential risks and benefits of MOIL implantation 212 of 346 (83.8%) eligible patients would consent for it.
Conclusion
Close to half of cataract patients in a public hospital are eligible for MOIL implantation and the majority would procced with the operation. The most prevalent contraindication is macular disease.
Deep Variational Autoencoders for Population Genetics
Authors: Geleta Margarita , Montserrat Daniel Mas , Giro-i-Nieto Xavier , Ioannidis Alexander G. 
Published: October 2023

Abstract
Motivation: Modern biobanks provide numerous high-resolution genomic sequences of diverse populations. These datasets enable a better understanding of genotype-phenotype interactions with genome-wide association studies (GWAS) and power a new personalized precision medicine with polygenic risk scores (PRS). In order to account for diverse and admixed populations, new algorithmic tools are needed in order to properly capture the genetic composition of populations. Here we explore deep learning techniques, namely variational autoencoders (VAEs), to process genomic data from a population perspective. We hope this work will encourage the adoption of deep neural networks in the population genetics community.
Results: In this paper, we show the power of VAEs for a variety of tasks relating to the interpretation, classification, simulation, and compression of genomic data with several worldwide whole genome datasets from both humans and canids and evaluate the performance of the proposed applications with and without ancestry conditioning. The unsupervised setting of autoencoders allows for the detection and learning of granular population structure and inferring of informative latent factors. The learned latent spaces of VAEs are able to capture and represent differentiated Gaussian-like clusters of samples with similar genetic composition on a fine-scale from single nucleotide polymorphisms (SNPs), enabling applications in dimensionality reduction, data simulation, and imputation. These individual genotype sequences can then be decomposed into latent representations and reconstruction errors (residuals) which provide a sparse representation useful for lossless compression. We show that different population groups have differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, its effect on compression across populations, its relation to historical migrations, and we show how to introduce autoencoders into existing compression pipelines.
FericipXT-Coated PEGylated Rutile TiO2 Nanoparticles in Drug Delivery: In-vitro Assessment of Imatinib Release
Authors: Bhullar Shilpy , Goyal Navdeep , Gupta Shikha 
Published: October 2023

Abstract
Study presents a facile synthesis strategy for magnetic field-responsive PEGylated iron-supplement-coated rutile TiO2 nanoparticles (NPs) for stimuli-responsive drug delivery. Imatinib, an anticancer drug, was successfully loaded onto the NPs, and its in-vitro release was investigated under different pH conditions. XRD analysis confirmed the successful synthesis of PEGylated iron supplement-coated rutile titania NPs. HR-TEM studies revealed increased NP size due to coating, PEGylation, and drug-loading. FTIR spectra confirmed the drug loading onto the NPs, while DLS provided hydrodynamic diameter and polydispersity index, indicating appropriate NP synthesis. The PEGylated NPs exhibited negative Zeta Potential, indicating high stability. In-vitro drug-release studies demonstrated controlled release with maximum efficiency under acidic conditions. Hemolysis assay confirmed the safety and biocompatibility of PEGylated NPs. All drug-loaded nanoformulations followed the Peppas-Sahlin model, suggesting Fickian diffusion and Case II relaxation mechanism of drug release. These NPs have potential for targeted delivery and controlled release of chemotherapeutics, minimizing side effects.
An artificial visual neuron with multiplexed rate and time-to-first-spike coding
Authors: Zhu Bowen , Li Fanfan , Li Dingwei , Wang Chuanqing , Liu Guolei , Wang Rui , Ren Huihui , Tang Yingjie , Wang Yan , Chen Yitong , Liang Kun , Huang Qi , Sawan Mohamad , Qiu Min , Wang Hong 
Published: October 2023

Abstract

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.
Evolution of protective SARS-CoV-2-specific B- and T-cell responses upon vaccination and Omicron breakthrough infection
Authors: Ahmed Mohamed I.M. , Einhauser Sebastian , Peiter Clemens , Senninger Antonia , Baranov Olga , Eser Tabea M. , Huth Manuel , Olbrich Laura , Castelletti Noemi , Rubio-Acero Raquel , Carnell George , Heeney Jonathan , Kroidl Inge , Held Kathrin , Wieser Andreas , Janke Christian , Hoelscher Michael , Hasenauer Jan , Wagner Ralf , Geldmacher Christof 
Published: October 2023

Abstract

Vaccine breakthrough infections with SARS-CoV-2 Omicron induced a higher level of protection compared to triple vaccination and contributed to herd immunity on a population level. To address the underlying immunological mechanisms, we studied the evolution of SARS-CoV-2-specific antibody and Tcell responses during vaccination and upon breakthrough infection in Bavarian residents between February 2021 and December 2022. Further, we investigated the temporal distance between completed vaccination and break-through infection, as well as any occurring re-infection. Each vaccination significantly increased peak neutralization titers against Wuhan, Delta, and Omicron BA.5 with simultaneous increases in circulating spike-specific Tcell frequencies. After vaccination, Omicron BA.5 neutralization titers were most significantly associated with a reduced hazard rate for SARS-CoV-2 infection, also when accounting for spikespecific Tcell responses. Yet, 97% of triple vaccinees became SARS-CoV-2 infected, often within a few months after their third vaccination. Breakthrough infections further boosted neutralization magnitude and breadth, broadened virusspecific Tcell responses to non-vaccine-encoded antigens and protected with an efficiency of 88% from further infections by December 2022. This effect was then assessed by utilizing mathematical modelling, which accounted for time-dependent infection risk in Bavaria, as well as the antibody and Tcell concentration at any time point after breakthrough infection. Our findings suggest that cross-variant protective hybrid immunity induced by vaccination and breakthrough infection was an important contributor to the reduced virus transmission observed in Bavaria in late 2022 and thereafter.
Increasing risks of extreme salt intrusion events across European estuaries in a warming climate
Authors: Lee Jiyong , Biemond Bouke , de Swart Huib , Dijkstra Henk A. 
Published: October 2023

Abstract
Estuaries are the semi-enclosed transition zones between river and open sea, where saline and fresh water mix. Over the last decade, many estuaries worldwide have faced increased salt intrusion as a result of human activities and a changing climate, leading to lower food production and reduced accessibility to drinking water. Despite its socio-economic importance, our current projections on the statistics of future salt intrusion are limited to case studies in certain regions. Here, we show that, compared to present-day conditions, river discharge in the summer months is projected to be reduced by 10 - 60 % in 22 major European river basins at the end of the 21st century under a high CO2 emission scenario. We find that the reduced future river discharge in the summer months, in turn, increases salt intrusion lengths by 10 - 30 % in 8 representative European estuaries at low and mid latitudes. Our analysis further indicates that the representative European estuaries are projected to experience more than eight times more frequent extreme salt intrusion events at the end of the century as compared to present day. Hence, urgent and drastic mitigation measures are required to guarantee sustainable future freshwater supplies in low-lying coastal regions.
The Effective Followership Scale for faculty: Development and initial validation
Authors: Kusumowardhani Retno P. Arum , Handoyo Seger , Fajrianthi Fajrianthi , Ardi Rahkman 
Published: October 2023

Abstract
The followership among faculty in higher education institutions is inherently different from those among members of business organizations and other non-academic organizations. This article describes developing and validating the Effective Followership Scale (EFS), the first measure of followership behaviors for faculty, based on a theoretically and empirically grounded definition of followership. This article includes four studies. Study 1 was a qualitative analysis aimed at empirically confirming the definition of followership and developing an initial pool of items. Study 2 identified the initial factor structure using exploratory factor analysis. In Study 3, the confirmatory factor analysis was used to confirm the factor structure and to obtain evidence of convergent and discriminant validity, and the internal consistency reliability was analyzed. Finally, in Study 4, the scale validity was evaluated using criterion-related validity. A four-factor structure of the scale was supported by exploratory factor analysis and confirmatory factor analysis of two independent samples. The EFS scores were significantly correlated with individual performance measures. The analyses presented herein show that the scale has good psychometric properties and is suitable for research purposes.
The Effect of Ethical Knowledge, Religiosity and Moral Reasoning on Accounting Student’s Perception of Creative Accounting Practices
Authors: Pratiwi Dhika Ayu , Fajar Muhammad Andryzal 
Published: October 2023

ABSTRACTThis study aims to knows the effect of ethical knowledge, religiosity and moral reasoning on the accounting student’s perception of creative accounting practices. The population uses accounting students of Yogyakarta State University (UNY) and Universitas Muhammadiyah Yogyakarta (UMY). The sample were 147 respondents. Data analysis is based on multiple linear regression analysis. The results showed that ethical knowledge positively influences the perception of accounting students regarding creative accounting practices, religiosity does not affect the perception of accounting students regarding creative accounting practices and moral reasoning has a positive effect on accounting students' perceptions about creative accounting practices. Additional testing for different test show ethical knowledge has significant different whereas religiosity and moral reasoning no significant different between accounting student’s UNY and UMY.Keywords: Knowledge of Ethics, Religiosity, Moral Reasoning, Creative Accounting PracticesABSTRAKPenelitian ini bertujuan mengetahui pengaruh pengetahuan etika, religiusitas dan moral reasoning terhadap persepsi mahasiswa akuntansi mengenai praktik creative accounting. Populasi menggunakan mahasiswa Prodi Akuntansi Universitas Negeri Yogyakarta (UNY) dan Universitas Muhammadiyah Yogyakarta (UMY). Sampel sebesar 147 responden. Analisis data menggunakan analisis regresi linear berganda. Hasil penelitian menunjukkan pengetahuan dan etika berpengaruh positif terhadap Persepsi mahasiswa akuntansi mengenai praktik creative accounting, Religiusitas tidak berpengaruh terhadap persepsi mahasiswa akuntansi mengenai praktik creative accounting dan moral reasoning berpengaruh positif terhadap Persepsi mahasiswa akuntansi mengenai praktik creative accounting. Tes tambahan untuk uji beda menunjukkan pengetahuan etika mempunyai perbedaan signifikan sedangkan religiusity dan moral Reasoning tidak mempunyai perbedaan signifikan antara mahasiswa prodi akuntansi di UNY dan UMY.Kata Kunci: Pengetahuan Etika, Religiusitas, Moral Reasoning Praktik, Akuntansi Kreatif
Characterizing the Patterns of Electronic Health Record–Integrated Secure Messaging Use: Cross-Sectional Study
Authors: Baratta Laura R , Harford Derek , Sinsky Christine A , Kannampallil Thomas , Lou Sunny S 
Published: October 2023

Background
Communication among health care professionals is essential for the delivery of safe clinical care. Secure messaging has rapidly emerged as a new mode of asynchronous communication. Despite its popularity, relatively little is known about how secure messaging is used and how such use contributes to communication burden.

Objective
This study aims to characterize the use of an electronic health record–integrated secure messaging platform across 14 hospitals and 263 outpatient clinics within a large health care system.

Methods
We collected metadata on the use of the Epic Systems Secure Chat platform for 6 months (July 2022 to January 2023). Information was retrieved on message volume, response times, message characteristics, messages sent and received by users, user roles, and work settings (inpatient vs outpatient).

Results
A total of 32,881 users sent 9,639,149 messages during the study. Median daily message volume was 53,951 during the first 2 weeks of the study and 69,526 during the last 2 weeks, resulting in an overall increase of 29% (P=.03). Nurses were the most frequent users of secure messaging (3,884,270/9,639,149, 40% messages), followed by physicians (2,387,634/9,639,149, 25% messages), and medical assistants (1,135,577/9,639,149, 12% messages). Daily message frequency varied across users; inpatient advanced practice providers and social workers interacted with the highest number of messages per day (median 19). Conversations were predominantly between 2 users (1,258,036/1,547,879, 81% conversations), with a median of 2 conversational turns and a median response time of 2.4 minutes. The largest proportion of inpatient messages was from nurses to physicians (972,243/4,749,186, 20% messages) and physicians to nurses (606,576/4,749,186, 13% messages), while the largest proportion of outpatient messages was from physicians to nurses (344,048/2,192,488, 16% messages) and medical assistants to other medical assistants (236,694/2,192,488, 11% messages).

Conclusions
Secure messaging was widely used by a diverse range of health care professionals, with ongoing growth throughout the study and many users interacting with more than 20 messages per day. The short message response times and high messaging volume observed highlight the interruptive nature of secure messaging, raising questions about its potentially harmful effects on clinician workflow, cognition, and errors.
Perspectives of Patients With Chronic Diseases on Future Acceptance of AI–Based Home Care Systems: Cross-Sectional Web-Based Survey Study
Authors: Wang Bijun , Asan Onur , Mansouri Mo 
Published: October 2023

Background
Artificial intelligence (AI)–based home care systems and devices are being gradually integrated into health care delivery to benefit patients with chronic diseases. However, existing research mainly focuses on the technical and clinical aspects of AI application, with an insufficient investigation of patients’ motivation and intention to adopt such systems.

Objective
This study aimed to examine the factors that affect the motivation of patients with chronic diseases to adopt AI-based home care systems and provide empirical evidence for the proposed research hypotheses.

Methods
We conducted a cross-sectional web-based survey with 222 patients with chronic diseases based on a hypothetical scenario.

Results
The results indicated that patients have an overall positive perception of AI-based home care systems. Their attitudes toward the technology, perceived usefulness, and comfortability were found to be significant factors encouraging adoption, with a clear understanding of accountability being a particularly influential factor in shaping patients’ attitudes toward their motivation to use these systems. However, privacy concerns persist as an indirect factor, affecting the perceived usefulness and comfortability, hence influencing patients’ attitudes.

Conclusions
This study is one of the first to examine the motivation of patients with chronic diseases to adopt AI-based home care systems, offering practical insights for policy makers, care or technology providers, and patients. This understanding can facilitate effective policy formulation, product design, and informed patient decision-making, potentially improving the overall health status of patients with chronic diseases.
Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study
Authors: Watase Teruhisa , Omiya Yasuhiro , Tokuno Shinichi 
Published: October 2023

Background
In Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores.

Objective
This feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients’ voices to distinguish moderate illness from mild illness at a significant level.

Methods
We conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (α<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy.

Results
Participants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants’ attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant (P<.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/.

Conclusions
The proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care.