bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biomethane produced from maize grown on peat emits more CO2 than natural gas
Authors: Evans Christopher , Rowe Rebecca , Freeman Benjamin , Rhymes Jennifer , Cumming Alex , Lloyd Isobel , Morton Daniel , Williamson Jennifer , Morrison Ross 
Published: April 2024

Abstract

Cultivation of maize for biomethane production has expanded rapidly, including on drained peat soils. The resulting soil CO2 emissions at the point of feedstock production are largely overlooked when assessing biogas climate mitigation potential. Based on field-scale flux measurements, we calculate that soil CO2 emissions from biomethane feedstock production on drained peat exceed embodied emissions for an equivalent amount of natural gas by up to a factor of three.
Lateral flow assay as radiological prognosis factor of pulmonary cryptococcosis: A single center retrospective study in China
Authors: Shi Jiejun , Chen Jianhua , Ding Qianjiang , Qian Guoqing 
Published: April 2024

Abstract

Background
Lateral flow assay (LFA) showed high sensitivity and specificity for diagnosing cryptococcosis. However, its predictive value for therapeutic efficacy of pulmonary cryptococcosis (PC) was rarely reported.
Methods
We conducted a retrospective analysis on HIV-negative PC to present the clinical profile and identify the potential predictors for radiological prognosis.
Results
All the 168 participants received antifungal therapy with a triazole agent. Of these, 84.5% experienced partial or complete absorption of pulmonary lesions. The results of the gamma test, chi-square trend test, and ordinal logistic regression all indicated that baseline LFA and changes in LFA after treatment were both predictors of imaging prognosis. The degree of radiological improvement showed a downward trend as the baseline LFA positive grade increased (P for linear-by-linear association: 0.011, Spearman correlation coefficient = -0.17; γ= -0.368, P = 0.045). Patients who experienced a decrease in LFA after therapy had a significantly better radiological prognosis than those with equal or increased LFA (linear-by-linear association, P = 0.014, Spearman correlation coefficient = 0.188; γ = 0.371, P = 0.012). Moreover, favorable outcomes were more likely in cases with lesions restricted in the right lung.
Conclusions
LFA is potential to monitor radiological outcomes of PC.
Multifactorial Analysis for Predicting Drug Resistance in Tuberculosis: Integrated Approaches to Improve Treatment
Authors: Falcão Igor W. S. , Cardoso Diego L. , Santos Albert E. C. dos , Leite Karla T. F. , Carneiro Saul R. , Moura Flávio R. T. , Pereira Ronilson W. Da S. , Pinto Lyanh V. L. , Alves André V. N. , Seruffo Marcos C. da R. 
Published: April 2024

Abstract
Tuberculosis (TB) remains a significant threat to global health, exacerbated by drug resistance, socio-economic, and environmental factors. These challenges make the treatment of the disease not only prolonged but also costly. With these issues in mind, this study introduces a multifaceted predictive model that integrates clinical, environmental, and socioeconomic factors to predict drug resistance in TB patients. Utilizing a dataset of 103,846 patient records from São Paulo, Brazil, spanning from 2006 to 2016, we applied machine learning techniques, including clustering, segmentation, and predictive modeling, to identify patterns and predictors of drug resistance. The proposed methodology enabled us to predict, analyze, and anticipate the clinical outcomes of patients in relation to TB drug resistance, with the XGBoost model achieving an accuracy of 84.65% and an F1-score of 85.34%. Moreover, the clustering of data allowed for the analysis of how comorbidities influence the likelihood of developing drug resistance in TB. Through this approach, it becomes possible to foster a better understanding of the clinical picture and diagnosis of patients, thereby facilitating more targeted and effective interventions.
PATHAK: An NLP-Based Method to Mine Gene and Function Relationships from Published Articles
Authors: Kumar Nilesh , Mukhtar M. Shahid 
Published: April 2024

Abstract
Understanding the intricacies of genes function within biological systems is paramount for scientific advancement and medical progress. Owing to evolving landscape of this research and the complexity of biological processes, however, this task presents challenges. We introduce PATHAK, a natural language processing (NLP)-based method that mines relationships between genes and their functions from published scientific articles. PATHAK utilizes a pre-trained Transformer language model to generate sentence embeddings from a vast dataset of scientific documents. This enables the identification of meaningful associations between genes and their potential functional annotations. Our approach is adaptable and applicable across diverse scientific domains. Applying PATHAK to over 5,000 research articles focused on Arabidopsis thaliana, we demonstrate its efficacy in elucidating gene function relationships. This method promises to significantly advance our understanding of gene functionality and potentially accelerate discoveries in the context of plant development, growth and stress responses in plants and other systems.
Medication Adherence and Associated Factors Among Chronic Heart Failure Patients on Follow Up in North Shewa Public Hospitals, Oromia Region, Ethiopia
Authors: Hussein Dursa , Jima Abyot Kebede , Geleta Leta Adugna , Gashaw Ketema , Girma Derara , Ibrahim Seifu Mohammed , Lakew Meron Seyoum , Kumbe Bedo Megersa , Oyato Befekadu Tesfaye , Siyum Getu , Senbate Addis Adugna 
Published: April 2024

Abstract

Background
Chronic heart failure affects around 26 million people globally. World Health Organization data shows only about half of chronically ill patients in developed countries adhere to recommended medication, with even lower rates in developing countries. Medication adherence is critical for managing chronic heart failure affects symptoms, delaying disease progression, and preventing hospitalizations. However, poor adherence leads to increased re-hospitalizations, morbidity, mortality, and healthcare costs.
Objective
To assess medication adherence and its associated factors among chronic heart failure patients on follow-up at North Shewa public hospitals, Oromia Region, Ethiopia, in 2023.
Methods
An institutional-based cross-sectional study design was conducted from March 1 to April 30 2023 G.C. A total of 603 sample size were selected consecutively among those coming for chronic OPD after being proportionally allocated to five hospitals in the zone. Data were collected using an interviewer-administered questionnaire and a medical chart review. Data were entered into Epi-data version 3.1, then exported to SPSS version 26 for analysis. Variables with a P value < 0.25 in the bivariate analysis were included in the multivariate logistic regression model. The degree of association was expressed using an adjusted odds ratio (AOR) with a 95% confidence interval (CI) at a P value < 0.05
Result
Among 603 patients, 56% had optimal medication adherence with 95% CI: (52.1, 60). Being able to read and write (AOR: 2.20; 95% CI: 1.34, 3.61), having a secondary education (AOR: 1.97; 95% CI: 1.06, 3.67), having community-based health insurance (AOR: 1.82; 95% CI: 1.22, 2.71), not having co-morbidity (AOR: 1.82; 95% CI: 1.18, 2.52), taking a number of drugs < 2 (AOR: 2.11; 95% CI: 1.20, 2.45), not adding salt when cooking (AOR: 1.72; 95% CI: 1.20, 2.45), and asking a doctor or nurse without fear (AOR: 1.87; 95% CI: 1.03, 3.40) were factors associated with medication adherence among CHF.
Conclusion
This study found 56% of chronic heart failure patients had optimal medication adherence. Factors associated with higher adherence included higher education, community health insurance, lack of comorbidities, fewer medications, avoiding added salt, and comfort communicating with providers. Health professionals should provide education to strengthen medication adherence.
Design and Analysis of a Dual-Port Multiband RF-to-DC Converter for IoT Energy Harvesting Applications
Authors: Tariq Bismah , Amjad Muhammad , Al-Jaloud Khaled A. , Tahir Farooq A. , Malik Waqar Ahmad , Hussain Rifaqat 
Published: April 2024

Abstract

The proliferation of Internet of Things (IoT) devices has led to an increasing demand for efficient and sustainable energy sources to power these autonomous systems. Radio Frequency (RF) energy harvesting has emerged as a promising solution, capitalizing on the omnipresent RF signals in our environment. This article presents the design and analysis of a Dual-Port Multiband RF-to-DC Converter using a multi-stage Cockcroft Walton Voltage Multiplier (CWVM) topology, which is tailored to meet the specific needs of IoT energy harvesting applications. Impedance matching is achieved by the implementation of L-network and π-network using distributed elements. The proposed design enables simultaneous energy harvesting across six bands from 0.87 to 2.5 GHz. It exhibits peak efficiency of 66% and 62% at 10kΩ and 18kΩ, respectively. The multiband feature enhances the energy acquisition versatility. The key parameters, including impedance matching, output voltage, and conversion efficiency, are simultaneously analyzed and optimized for all the frequency bands to achieve the desired performance. The proposed design performance is compared with the recent converter designs in literature, which shows that this research contributes to the ongoing development of energy-efficient solutions for IoT devices, addressing the critical need for prolonged autonomy of IoT devices and reduced reliance on traditional power sources.
Assessing Model Validity Using KL Divergence and Beta-Stacy Process for Right Censored Data
Authors: Labadi Luai Al 
Published: April 2024

Abstract

Model validation constitutes a crucial component of statistical analysis, with heightened challenges encountered when addressing censored data. This paper introduces a novel approach that integrates the Kullback-Leibler divergence, the beta-Stacy process, and relative belief ratio inferences to form a comprehensive method for model validation. Significantly, this method is applicable to both complete and right-censored data. Theoretical results related to this approach are presented, accompanied by a computational algorithm for implementation. The effectiveness of the approach is illustrated through a series of data analysis examples, highlighting its good performance.
MSC 2020 62N03, 62F15.
An Intellectual Hybrid Machine Learning Model for Effective Breast Cancer Diagnosis
Authors: Nallamala Sri Hari , Mishra Pragnyaban , Koneru Suvarna Vani , Chakrabarti Prasun , Chakrabarti Tulika , Shanmuganathan Vimal , Margala Martin 
Published: April 2024

Abstract

Cancer was characterized as an illness brought about by an uncontrolled division of irregular cells in a piece of the body. Breast Cancer is characterized as a harmful development or tumor coming about because of an uncontrolled division of cells in the breast. Breast cancer is one of the most dangerous types of cancer after the lung cancer. The breast cancer is also considering as critical one like as skin cancer. Since, decades, women are suffering due to this breast cancer and also most of times majority percentage of them are dying due to non-identification at early stages and with the severity of the disease. Therefore, most of the doctors, scientists, researchers are did a major research and contributed for diagnosing the breast cancer at early stages and also curing process of the breast cancer. Here, in our research, we are also going to be diagnosing the breast cancer at early stages with the available less symptoms or data sets in an effective way to serve the needy people and finally trying to decrease the mortality rate by recovering them from the breast cancer. In this work, we have taken the most popular breast cancer datasets, i.e. Wisconsin Breast Cancer Datasets (i.e. Original and Diagnostic) as input to our proposed classifier to obtain the effective accuracy of classification which are nearly 98.97% and 97.73% respectively.
Sodium Dithionite-Assisted Gold Nanoparticle Synthesis and BSA Conjugation
Authors: Natarajan Belliraj , Krishnasamy Ilamurugu , Palaniyappan Kumaresan , Rajendran Gobinath , Natarajan Balakrishnan 
Published: April 2024

Abstract

The wet chemical method has long been a widely accepted approach for synthesizing gold nanoparticles (AuNPs). Trisodium citrate (Na3C6H5O7) has traditionally served as a common reducing agent in the colloidal gold nanoparticle synthesis (known as the Turkevich method), effectively converting citrate ions into bulk AuCl4 in ionic form. In contrast, sodium dithionite (Na2S2O4), an inexpensive reducing agent, acts on precursor materials by reducing their functional groups. In our current investigation, we explored the reducing capacity of sodium ions within the dithionite compound, leading to the formation of unique-sized ionic gold nanoparticles. The synthesized gold nanoparticles exhibited a strong surface plasmon resonance (PR) band at 531 nm in UV spectroscopy at a concentration of 1 mM. Additionally, the Raman spectrum of sodium dithionite-derived gold nanoparticles displayed intense bands at 523.4, 547, 593.4, and 1027 cm-1. The FT-IR spectrum revealed a weak intensity peak at 3934.78 and 3838.34 cm-1, with medium intensity observed at 3390 and 3367.71 cm-1. Furthermore, high-resolution transmission electron microscopy (HR-TEM) analysis indicated that the gold nanoparticles exhibited anisotropic shapes, with average diameters of 3.24 nm and 8.5 nm, respectively. However, conjugated with bovine serum albumin (BSA), the gold nanoparticles demonstrated maximum absorbance at a concentration of 400 µg/ml. Notably, BSA-conjugated gold nanoparticles exhibited strong surface plasmon resonance peaks at 537 nm under acidic conditions. This proposed synthesis protocol, utilizing dithionite as a reducing agent and BSA protein conjugation, may yield uniform-sized colloidal Au nanoparticles suitable for biomedical applications.
Density Functional Theory Study of Interaction between Oxazepam and Alginic Acid as a Nanocarrier
Authors: Shahi Masoumeh , Azarakhshi Fatemeh , Sahraei Mahya , Khorshidi Negar 
Published: April 2024

Abstract

Alginic acid is a hydrophilic linear polysaccharide that is of interest due to its unique physicochemical and biological properties. In this study, the interaction between Oxazepam and Alginic acid as a drug nanocarrier has been studied using Density Functional Theory (DFT) at the M06-2X/6–31 + G* level of theory. Here, the DFT results showed that the non-covalent interaction between Oxazepam and Alginic acid affects the electronic properties, chemical shift tensors, electronic potential of the molecule and atomic charges. Natural Bond Orbital (NBO) analysis showed that Oxazepam molecule as electron donor and Alginic acid play the role of electron acceptor in Oxazepam/Alginic acid complex. The electronic spectrum of the Oxazepam/Alginic acid complex was calculated to investigate the effect of Oxazepam drug absorption on Alginic acid on the maximum wavelength. Quantum Theory of Atoms in Molecules (QTAIM) analysis showed that the main driving force in complex formation is the non-covalent interaction between Alginic acid and Oxazepam. This result led to the possibility of using Alginic acid to deliver Oxazepam to diseased cells.
Timely vaccination and its associated factors among parents with children aged from 0-23 months in Wolaita Zone Public hospitals, southern Ethiopia, 2024: a facility-based cross-sectional study
Authors: Hardido Temesgen Geta , Atinafu Bizuayehu , Elifios Eshetu 
Published: April 2024

Abstract

Background
In Ethiopia, vaccination is mentioned as the vital preventive measure of undue childhood mortality from measles, pneumonia, diarrheal diseases, and other VPDs. Although there are studies conducted on full vaccine coverage, there is relatively no data available about timely vaccination and its related factors in southern Ethiopia. Thus, this study assessed timely vaccination and related factors in children who age from 0 to 23 months in the study area.
Methods and material:
An institution-based cross-sectional study was undertaken in Wolaita zone Public hospitals, from December 2023, G.C to January 2024, G.C. A total of 376 study subjects were randomly selected through systematic sampling method among parents with children who are 0–23 months old. Data were gathered by questionnaire with a structure which was based on mother’s recalling as well as vaccination cards. A basic questionnaire was completed by respondents and the responses were entered into Epidata Version 4.6, which was later transferred to SPSS Version 23 for analysis. Data were entered into Epidata Version 4.6 and exported to SPSS Version 23 for analysis. An adjusted odds ratio (AOR) along with a 95% confidence interval at a P-value less than 0.05 was used to declare significance level.
Results
A total of 376 study participants responded to the interview with 100% response rate. This study found that 269 children (71.5%) were timely vaccinated and 107 children (28.5%) had not got their children vaccinated timely. The independent variables, like time to reach the health facility[AOR = 5.7; 95% CI (1.81,17.55), place of delivery[AOR = 4.91; 95% CI (2.85, 10.83)], ANC follow-up[AOR = 8; 95% CI (5.81, 25.01)], attitude[AOR = 2.5; 95% CI (1.23,4.3)] and knowledge [AOR = 3.45; 95% CI (1.61, 10.62)] on the vaccine time, significantly attributed to timely vaccination.
Conclusion
71.5% of study participants vaccinated their children on the national recommended vaccination schedule. The research also revealed statistical correlation between time taken from home to health facility, antenatal care follow up, place of delivery, maternal attitude and knowledge of the prompt vaccination and vaccination timeline. For the all stake holders, highly strategic interventions like promoting maternal knowledge about the benefits versus risks of timely vaccination, community-based vaccination programs or campaigns, routine supervision, and counseling during antenatal visits are very effective ways of improving timely vaccination.
Revisiting the Titius-Bode law: planetary distances with advanced data analysis for better physical significance
Authors: Ray Saibal , Sanyal Aritra , Sharma U. K. , Sen B. 
Published: April 2024

Abstract

The Titius-Bode law, initially proposed as an empirical relationship describing planetary distances from the Sun, has been a subject of both fascination and scrutiny in the field of celestial mechanics. Over time, variations and modifications to the original law have emerged, aiming to enhance its predictive accuracy and explanatory power. This research paper presents a comparative analysis of the original Titius-Bode law and a modified version proposed in recent literature. The study assesses the predictive performance and goodness of fit of both laws using statistical metrics such as mean squared error (MSE) and R 2 score. Additionally, graphical representations of observed and predicted distances for various planet positions are analyzed to discern patterns and deviations. The findings reveal significant differences between the original and modified laws in terms of predictive accuracy and model fit. While the original law demonstrates superior performance in capturing the observed data patterns and providing accurate predictions, the modified law exhibits limitations, potentially stemming 1 from over-fitting or simplifications. The implications of these findings for understanding planetary dynamics and the broader field of celestial mechanics are discussed, highlighting the importance of rigorous model evaluation and refinement in advancing our understanding of the cosmos. Furthermore, this study introduces a novel polynomial equation derived from observational data, offering insights into potential refinements of existing models and avenues for future research.
Challenges of Depth Estimation for Transparent Objects
Authors: Weibel Jean-Baptiste , Thalhammer Stefan , Vincze Markus 
Published: April 2024

Abstract

Transparent objects and surfaces are pervasive in man-made environments and need to be considered in any vision system.
Accurate depth data is a key factor for the reliability of such systems, requiring methods tailored for transparency to overcome the sensing shortcomings.
However, the current state-of-the-art methods to predict the depth of such objects are not yet reliable enough to ensure safe operation of autonomous systems in arbitrary complex environments.
In order to better understand and improve upon existing solutions, we evaluate the performance of a variety of depth estimation methods.
Doing so, we disentangle the different factors impacting their performance.
Among our findings, neural radiance fields offer the best accuracy, but are very sensitive to the number of images used to understand the scene, and do not benefit from any level of object understanding to help them fill in the gaps.
Genomic mechanisms of resistance to tyrosine kinase inhibitors in HER2 amplified breast cancer
Authors: Parsons Heather , Messer Conor , Santos Katheryn , Weiss Jakob , Merrell David , Danysh Brian , Hughes Melissa , Kirkner Greg , Patel Ashka , Hess Julian , Sendrick Kerry , Stewart Chip , Grant Elizabeth , Schlueter-Kuck Kristy , Grinshpun Albert , Wagle Nikhil , Veeraraghavan Jamunarani , Leone José , Freedman Rachel , Metzger Otto , Schiff Rachel , Winer Eric , Tolaney Sara , Rimawi Mothaffar , Krop Ian , Getz Gad , Lin Nancy 
Published: April 2024

Abstract

Though there has been substantial progress in the development of anti-HER2 therapies to treat HER2-positive metastatic breast cancer (MBC) within the past two decades, most patients still experience disease progression and cancer-related death. HER2-directed tyrosine kinase inhibitors (TKIs) can be highly effective therapies for patients with HER2-positive MBC, however, an understanding of resistance mechanisms is needed to better inform treatment approaches. We performed whole exome sequencing on 111 patients with 73 tumor biopsies and 120 cell-free DNA (cfDNA) samples to assess mechanisms of resistance. In 11/26 patients with acquired resistance, we identified alterations in previously characterized genes, such as PIK3CA and ERBB2 that could explain treatment resistance. Mutations in growing subclones identified potential novel mechanisms of resistance in 5/26 patients and included alterations in ESR1, FGFR2, and FGFR4. Additional studies are needed to assess the functional role and clinical utility of these alterations in driving resistance.