bluuebunny/crossref_metadata_2025_split huggingface dataset:
Olfactory and Cognitive Decrements in 1991 Gulf War Veterans
Authors: Chao Linda 
Published: December 2023

Abstract
Background
Prominent symptoms of Gulf War illness (GWI), the disorder related to military service in the 1991 Gulf War (GW), include fatigue, pain, and cognitive dysfunction. Although anosmia is not a typical GWI symptom, anecdotally some veterans reported losing their sense smell shortly after the war. Because olfactory deficit is a prodromal symptom of neurodegenerative diseases like Parkinson’s and Alzheimer’s disease, and because we previously reported suggestive evidence that deployed GW veterans may be at increased risk for Mild Cognitive Impairment (MCI) and dementia, the current study examined the relationship between olfactory and cognitive function in deployed GW veterans.
Methods
Eighty deployed GW veterans (mean age: 59.9 ± 7.0; 4 female) were tested remotely with the University of Pennsylvania Smell Identification Test (UPSIT) and the Montreal Cognitive Assessment (MoCA). Veterans also completed self-report questionnaires about their health and deployment-related exposures and experiences. UPSIT and MoCA data from age-matched healthy controls (HC) were downloaded from the Parkinson’s Progression Markers Initiative (PPMI) study for comparison.
Results
GW veterans had a mean UPSIT score of 27.8 ± 6.3 (range 9–37) and a mean MoCA score of 25.3 ± 2.8 (range 19–30). According to age- and sex-specific normative data, 31% of GW veterans (vs. 8% PPMI HCs) had UPSIT scores below the 10th percentile. Nearly half (45%) of GW veterans (vs. 8% PPMI HCs) had MoCA scores below the cut-off for identifying MCI. Among GW veterans, but not PPMI HCs, there was a positive correlation between UPSIT and MoCA scores (Spearman’s ρ = 0.39, p < 0.001). There was no significant difference in UPSIT or MoCA scores between GW veterans with and without history of COVID and with and without Kansas GWI exclusionary conditions.
Conclusions
We found evidence of olfactory and cognitive deficits and a significant correlation between UPSIT and MoCA scores in a cohort of 80 deployed GW veterans. Because impaired olfactory function has been associated with increased risk for MCI and dementia, it may be prudent to screen aging, deployed GW veterans with smell identification tests so that hypo- and anosmic veterans can be followed longitudinally and offered targeted neuroprotective therapies as they become available.
Implementing an electronic gate keeping (EGK) intervention at a rural academic tertiary hospital in South Africa over a two-year period: A cost effectiveness analysis
Authors: Mayekiso Zoliswa , Oladimeji Kelechi Elizabeth , Estrada Guillermo Alfredo Pulido , Hongoro Charles , Apalata Teke 
Published: December 2023

Abstract
Background
Many countries, including South Africa, are developing, and testing new methods to reduce the excessive demand for medical laboratory tests for patient investigations to reduce the burden of rising laboratory costs on national budgets. The objective of this study was to compare the cost effectiveness of an electronic gate keeping intervention implemented at a rural academic tertiary hospital in the Eastern Cape province, South Africa.
Methods
A cost effectiveness analysis (CEA) was performed in this cross-sectional study, taking only direct costs associated with implementing EGK into account. The incremental cost effectiveness ratio (ICER) was calculated over a 48-months period using the World Health Organization (WHO) threshold recommendation. According to the WHO-CHOICE threshold recommendation, for a cost to be considered ‘very cost effective,' it must be one time the Gross Domestic Product Per Capita (GDPPC) of the country in question. South Africa's GDPPC in 2021 was USD 7,055. A t-test was also used to investigate statistical differences in costs and number of tests performed 24 months prior to the intervention and during the intervention period of 24 months. The statistical level of significance was set at 0.05.
Results
The results showed that implementing EGK resulted in lower costs - $515,114.96 - and 212 fewer tests, resulting in an ICER of USD 2,430.00 which is < 1 times GDPPC of South Africa in 2021. Also, the pre-intervention period had significantly higher mean costs than the EGK intervention period (M = 69,831.14, SD = 11,059.39 vs. M = 48,368.01, SD = 4,505.96; t 30.43 = 8.81, p < 0.01). Similarly, the number of laboratory tests showed a statistically significant difference in the mean number of tests performed pre the intervention and during the intervention (M = 26,946.58 SD = 4,330.76 vs. M = 18,116.25 SD = 1,695.71; t 29.890 = 9.30, p < 0.01).
Conclusions
EGK implementation at the rural academic tertiary hospital was very cost effective, based on (WHO)-CHOICE criteria. However, the results are grounded in conditions at the selected hospital; more evidence is needed to evaluate the cost effectiveness of EGK in South Africa from a societal perspective.
Hardware Architecture Optimization for High-Frequency Zeroing and LFNST in H.266/VVC Based on FPGA
Authors: Zhang Junxiang , Sheng Qinghua , Pan Rui , Wang Jiawei , Qin Kuan , Huang Xiaofang , Niu Xiaoyan 
Published: December 2023

Abstract

To reduce the hardware implementation resource consumption of the two-dimensional transform component in H.266 VVC, a unified hardware structure is proposed that supports full-size Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and full-size Low-Frequency Non-Separable Transform (LFNST). This paper presents an area-efficient hardware architecture for two-dimensional transforms based on a general Regular Multiplier (RM) and a high-throughput hardware design for LFNST in the context of H.266/VVC. The first approach utilizes the high-frequency zeroing characteristics of VVC and the symmetric properties of the DCT-II matrix, allowing the RM-based architecture to use only 256 general multipliers in a fully pipelined structure with a parallelism of 16. The second approach optimizes the transpose operation of the input matrix for LFNST in a parallelism of 16 architecture, aiming to save storage and logic resources.
Are the two lone pairs of electrons on oxygen atom of carbonyl group in HCHO identical!
Authors: Ghosh Safalla Kr. , Ray Umasankar 
Published: December 2023

Abstract
Different opinions about the nature of two lone pairs on oxygen atom of carbonyl group in HCHO promote the present authors to undertake the study on this matter. Here opinions of different text books as stated by the authors are represented in brief and systematic manner to conclude finally that carbonyl oxygen may be sp hybridized with two different types of lone pairs as supported by M.O. theory and uv spectral data [one lone pair on py orbital and another lone pair on sp hybrid orbital] on oxygen atom.
Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma
Authors: Zhou Quanwei , Guo Youwei , Yan Xuejun , Jiang Xingjun , Du Can , Ke Yiquan 
Published: December 2023

Abstract
Background: The growth of glioma is frequently accompanied by a hypoxic microenvironment, which contributes to its progression and unfavorable prognosis. Nevertheless, the clinical implications of hypoxia have not been extensively investigated.
Methods: A comprehensive collection of over 3000 glioma samples was acquired from publicly available databases. The identification of a hypoxia-cluster was accomplished through the utilization of Consensus Cluster Plus across six cohorts. Subsequently, a hypoxia-derived signature was formulated by employing 100 machine learning algorithms on the expression profiles of 11 genes within six glioma cohorts. To further investigate the expression of these 11 genes and certain hub molecules, RT-qPCR and IHC were conducted in an in-house cohort.
Results：Two hypoxia-associated subtypes (C1 and C2) were identified in glioma, exhibiting notable prognostic and molecular disparities. Subtype C2 gliomas demonstrated an abundance of hypoxia-related pathways, increased presence of immune and stromal cells, elevated expression of immune checkpoint genes and receptors, and a more unfavorable prognosis compared to those in C1. Employing a machine learning-based integrative approach, we established an 11-gene signature capable of predicting distant clinical outcomes across six cohorts. This signature was subsequently validated through RT-qPCR analysis in an in-house cohort, and effectively categorizes patients into high-risk and low-risk groups, demonstrating consistent and robust performance in predicting both overall and relapse-free survival. Moreover, the risk score exhibits significantly higher accuracy compared to conventional clinical variables, molecular characteristics, and 100 previously published signatures. Glioma in the high-risk group exhibits elevated levels of CD163, PD1, HIF1A, and PD-L1 expression.
Conclusion: Ultimately, we developed a novel hypoxia-related classification that provides the potential to guide treatment decisions and a reliable prognostic tool.
Meeting a need for anti-racism curricula in medical residency programs: A pilot study and results for implementation and future research
Authors: Gertz Alida M , Smith Michele , Thomas Davon , Ti Angeline , Vamos Cheryl , Bohn Joe 
Published: December 2023

Abstract
Background: Racism contributes to health disparities and is a serious threat to public health. Teaching physicians about racism, how to address it in medical practice, and developing quality, sustainable curricula are essential.
Objective: This study aimed to (1) describe the experience of racism and anti-racism teaching in residency programs, and elicit recommendations, and (2) use data formative research to develop recommendations for residencies when considering the creation, implementation, and evaluation of anti-racism curricula.
Methods: From May to July 2023, 20 faculty/residents were recruited via convenience sampling for key informant interviews conducted via Microsoft Teams. Interviews were audio recorded, transcribed, and coded, and themes were developed deductively using implementation science frameworks and inductively via a grounded-theory approach.
Results: Eighty percent (20/25) of participants approached were interviewed. Seventy-five percent (15/20) answered a brief optional demographic survey. Seven themes emerged: (1) Racism in medicine is ubiquitous; (2) anti-racism teaching in medicine ranges widely; (3) Sustainability strategies should be multifaceted and include recruitment, resources allocation, and outcome measures; (4) Resources are widely available and accessible; (5) Outcomes and metrics should include resident- faculty-, patient- and system-focused outcomes; (6) Curricular strategies should be multilayered, longitudinal, and woven into the curriculum; and (7) Other considerations including self-reflection and discomfort are necessary .
Conclusions: This work is the first to qualitatively examine perspectives of key stakeholders invested in anti-racism curricula for residents. Utilizing models for implementation of anti-racism curricula for residents can guide design, implementation, and evaluation of curricula to facilitate effectiveness and sustainability of such efforts.
Psychometric characteristic of scientific literacy test among primary school pupils
Authors: Kovarik Petr , Kubiatko Milan 
Published: December 2023

Abstract
Scientific literacy is a term which is often mentioned in the context of contemporary problems and in the current society in general. The main aims of the research was to determine psychometric characteristics of scientific literacy test and to find out the literacy level in natural sciences of 8 and 9 grades pupil and whether it was possible to use a test tool composed from published TIMSS 2019 tasks. Primary-school pupils from 8 and 9 grades (n = 112) participated in the research. The test tool composed of 12 tasks which had been included in the TIMSS 2019 assessment. Descriptive and inductive statistical methods were used for data analysis. From the statistical perspective, 9-grade pupils were more successful than 8-grade pupils. From the gender perspective, boys were statistically more successful. The study ends by suggesting options for further research and application in educational practice.
Management of Left Atrial Myxoma in Pregnant Women: A Case Series
Authors: Liu Yanli , Wang Haiping , Huang Huanlei , Han Fengzhen , Zhuang Jian , Ou Yanqiu , Lin Yanyan , Zhang Weina 
Published: December 2023

Abstract
Background Left atrial myxoma during pregnancy is rare. We present three cases in order to aid in the management.
Case report Three cases of left atrial myxoma during pregnancy were presented in this article. Three patients all received multidisciplinary team work and acquired good outcomes. The case 1 had no symptoms and delivered before traditional cardiac surgery. The case 2 and case 3 undergone totally endoscopic minimally invasive cardiac surgery during pregnancy. The case 3 maintained pregnancy to term and gave birth to a healthy baby via vaginal delivery . No relapse of the tumor was observed.
Conclusions The management of left atrial myxoma during pregnancy ought to be individualized and combined with the gestational age. If the diagnosis was made in the first two trimesters of pregnancy, totally endoscopic minimally invasive cardiac surgery during pregnancy would be an optimal choose. The patients can benefit from the multidisciplinary team work.
Global Adaptive Histogram Feature Network for Automatic Segmentation of Infection Regions in CT Images
Authors: Min Xinren , Liu Yang , Zhou Shengjing , Zhang Li , Gong Xiaojun , Yang Dongshan , Huang Huihua , Wang Menghao , Yang Rui , Zhong Mingyang 
Published: December 2023

Abstract
We applied deep learning techniques in lung CT image diagnosis of COVID-19 for accurate segmentation of disease diagnosis. We propose a new deep learning framework, GAHFNet, specifically designed for automatic segmentation of COVID-19 lung CT images. GAHFNet outperforms other traditional and the state-of-the-art methods in various evaluation metrics, demonstrating the effectiveness and the efficiency of the proposed method in this task. This article discusses the limitations of current diagnostic methods, such as RT-PCR, and highlights the advantages of deep learning, including its ability to automatically learn features and handle complex lesion morphology and texture. Furthermore, the proposed method addresses the challenges in lung CT image segmentation, such as the complex image structure and difficulties of distinguishing COVID-19 pneumonia lesions from other pathologies. We provide the detailed description of the proposed GAHFNet. Finally, comprehensive experiments are carried out to evaluate the performance of GAHFNet, demonstrating that GAHFNet is able to facilitate the application of artificial intelligence in COVID-19 diagnosis and achieve accurate automatic segmentation of infected areas in COVID-19 lung CT images.
Early Implant Placement with Immediate loading in the Mandibular Anterior Region – A Rapid Solution to Edentulism
Authors: Rijal Arjun Hari , Dhami Bhageshwar , Ghimire Pratistha , Humagain Manoj , Lamichhane Simant 
Published: December 2023

Abstract
The aim of this article is to present the case of an early implant placement with immediate loading in the mandibular anterior region as a rapid solution to edentulism. A 40-years-old healthy male patient reported with a chief complaint of loosening of tooth in lower front region of jaw. On intra-oral examination, there was mobile tooth with respect to 41. The mobile tooth was extracted, and early implant placement was done along with Bio-oss bone grafts to fill the jumping distance with no barrier membrane. Immediate provisionalisation was done on early placed dental implants. After 5 months of the healing period, the final implant-level impressions were made and the provisional crown was replaced with the final zirconia crown. This case report demonstrates satisfactory esthetic and functional outcomes along with various other advantages.
Considered Covariance Approach to Assessing the Impact of Collector Orbital and Data Measurement Biases on Geolocation Performance
Authors: Lerner Gerald M 
Published: December 2023

Abstract

A Gauss-Newton estimator with considered biases is developed and applied to the problem of geolocating a mobile communications signal with an overhead network of receivers. Monte Carlo (MC) simulations are used to validate the model implementation, and to examine practical issues —incorporating navigation errors on the collectors and other biases into the covariance data, analyzing measurement data and validating algorithm performance. The use of considered covariance biases dramatically improves containment of the n-dimensional geolocation solution, position and velocity, and provides a mechanism to identify, model and estimate the impact of physical biases on the time and frequency data. Approaches to improving the convergence of the algorithm, estimating measurement noise, identifying biases on measurement types and collectors and reporting geolocation data to consumers are described. The incorporation of sensor navigation data covariance into the geolocation algorithm is shown to significantly improve the validity of geolocation uncertainty data.
A Fuzzy Logic Scheme Based on Population and Spread Rate for the Management of Vaccination during Pandemics
Authors: Kareem Abdul , Varuna  
Published: December 2023

Abstract
The pandemics like COVID-19 cause a massive shock to the global economy and its impacts are huge and endure across all domains of life. Effectively managing the limited vaccine supply is crucial in the fight against pandemics. A central issue in the management of pandemic vaccination is the allocation of vaccines from the central government to state authorities. The objective of this research was to make use of a fuzzy logic scheme for the management of vaccination to the local state authorities by a central Government based on population and spread rate. The proposed scheme utilizes a fuzzy logic inference system taking into account on population and spread rate to infer the vaccination rate. This scheme is in contrast to conventional approaches that often consider either a state's population or spread rate as the sole basis for vaccine allocation. The Covid-19 data of 6 southern states of India during the first week of October 2020 collected from the database maintained by the Ministry of Health and Family Welfare of Government of India was used for the verification of the proposed scheme. The proposed scheme was implemented using MATLAB/SIMULINK software and compared with the conventional schemes, one based on population and another based on spread rate. The results show that the proposed scheme ensures that sufficient doses of vaccines are allotted to the states on priority where spread rate is more and vaccines are not wasted in states where spread rate is less. At the same time, all states are eventually allotted sufficient vaccine doses to halt transmission. The proposed scheme ensures that sufficient vaccines are distributed in a quick, effective and unbiased way, and enhances the fight against pandemics.
NTFS+: An Enhanced NTFS File System Against Ransomware Invasions
Authors: Gu Yuanyuan , Kang Qian 
Published: December 2023

Abstract
Ransomware poses a significant threat to digital security, evolving rapidly with sophisticated encryption and data exfiltration tactics. This study introduces NTFS+, an enhanced version of the New Technology File System (NTFS), designed to combat these emerging ransomware threats. NTFS+ integrates machine learning, specifically Generative Adversarial Networks, to detect abnormal file system activities indicative of ransomware attacks. The system's core architecture features minifilter drivers that analyze file access patterns in real-time, enabling proactive response to potential threats. Evaluation of NTFS+ demonstrates its efficacy in accurately distinguishing between benign and ransomware activities, with a focus on minimizing false positives and negatives. Despite its effectiveness, challenges such as zero-day ransomware attacks highlight areas for future improvement, including enhanced learning capabilities and cloud integration. NTFS+ represents a significant advancement in file system-based ransomware mitigation, offering a scalable, adaptable, and robust solution for modern cybersecurity needs.
Multivariate spatio-temporal modeling of drought prediction using graph neural network
Authors: Yu Jiaxin , Ma Tinghuai , Jia Li , Rong Huan , Su Yuming , Wahab Mohamed Magdy Abdel 
Published: December 2023

Abstract

Drought is a serious natural disaster that causes huge losses to various regions of the world. To effectively cope with this disaster, we need to use drought indices to classify and compare the drought conditions of different regions. We can take appropriate measures according to the category of drought to mitigate the impact of drought. Recently, deep learning models have shown promising results in this domain. However, few of these models consider the relationships between different areas, which limits their ability to capture the complex spatio-temporal dynamics of droughts. In this study, we propose a novel multivariate spatio-temporal sensitive network (MSTSN) for drought prediction, which incorporates both geographical and temporal knowledge in the network and improves its predictive power. We obtained the standardized precipitation evapotranspiration index and meteorological data from the climatic research unit dataset, covering the period from 1961 to 2018. This is the first deep learning method that embeds geographical knowledge in drought prediction. We also provide a solid foundation for comparing our method with other deep learning baselines and evaluating their performance. Experiments show that our method consistently outperforms the existing state-of-the-art methods on various metrics, validating the effectiveness of geospatial and temporal information.
Integrating water quality monitoring and ecological assessment for wetland classification and risk evaluation in the Kirala Kele wetland, Sri Lanka
Authors: Nikawela N. D. L. , Aththanayake A. M. N. S. , Bellanthudawa B. K. A. , Wijetunga S. 
Published: December 2023

Abstract
We conducted the present study to assess the spatial and temporal variations of selected physicochemical water quality parameters, to quantify the level of ecological risks of disturbances occurring, and to understand the relationship between the selected disturbances observed and physicochemical parameters of surface water of sampling sites of the Kirala Kele wetland. We selected sampling sites to reflect different land uses in the Kirala Kele wetland using purposive sampling and collected triplicated surface water samples to assess quality of water. The sampling sites of wetland characterization and risk assessment were followed using the protocol by Escom. We found that there was a significant temporal variation of pH, electrical conductivity, water temperature, and dissolved oxygen content among sampling sites (p &gt; 0.05, One-Way ANOVA), however, spatial variation of electrical conductivity and dissolved oxygen was significantly higher in site 9, site 10, and site 11, respectively (p &gt; 0.05, One-Way ANOVA). Site 2, site 5, site 10, and site 11 showed a category B level in wetland characterization and risk assessment highlighting the few levels of modifications and largely natural status of sites. The study demonstrates the applicability of this model for rapid assessment of wetland characteristics and risks to sustainably conserve and manage wetlands.