bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mortality from nervous system diseases and cancer in agriculture workers: a case-control study in Italy
Authors: Gariazzo Claudio , Marinaccio Alessandro , Consonni Dario , Miligi Lucia , Massari Stefania 
Published: April 2024

Abstract

Background
Population working or living in agriculture settings may experience important exposure to pesticides and other agents. Some health effects associated with them are well known (e.g. skin cancer due to solar radiation) while for others (e.g., neurological diseases and lymphoid, hematopoietic and related tissue cancers) additional epidemiological evidence is needed. We aim to investigate mortality for neurological diseases and cancer in workers employed in agriculture in Italy.
Methods
We performed a case-control study based on countrywide Italian mortality data 2005–2018 linked with National Social Insurance data to retrieve information on working histories. Adjusted cancer specific mortality odds ratios (MOR) were calculated. We modelled occupational exposure as “ever/never been employed” in agriculture, using the service sectors as reference. Analysis was stratified for gender and length of employment.
Results
About 64,000 workers employed in agriculture were analyzed in comparison with a control group of 107,000 workers of the service sector. We found elevated risk in agriculture workers for mortality from spinal muscular atrophy (MOR 1.26, 95% CI: 1.03–1.56; 261 deaths) and Parkinson’s disease (PD) (MOR 1.16, 95% CI:1.00-1.34; 742 deaths). As for cancer mortality, positive associations were found for non-follicular lymphoma (NFL) (MOR 1.59, 95% CI: 1.03–2.46; 82 deaths), multiple myeloma (MM) (MOR 1.42, 95% CI: 1.22–1.65; 546 deaths) and myeloid leukemia (ML) (MOR 1.36, 95% CI:1.16–1.60; 474 deaths), as well as for stomach (MOR 1.30, 95% CI:1.20–1.41; 1,732 deaths), prostate (MOR 2.03, 95% CI:1.85–2.24, 1,582 deaths), and brain and central nervous system cancer (MOR 1.30, 95% CI:1.13–1.50, 601 deaths). PD, NFL and ML, as well as cancers of skin, connective and soft tissue, prostate and brain were found to involve mainly men.
Conclusions
Long-term employment in agriculture was demonstrated associated with several health risks, some of which could be explained by exposure to pesticides. Although the use of the different agronomic categories of pesticides has been changed over time and some active ingredients were prohibited or limited, their health effects remain of concern for their large use, demanding for further focused investigations and preventive measures.
Thermo-acoustical analysis of sorbic acid:(food preservative) in an aqueous solution containing propylene glycol:(PG) and hexylene glycol:(HG) by using an ultrasonic technique
Authors: Firdaus Ansari Ammara , Chakraborty Nabaparna , Juglan K. C. 
Published: April 2024

Abstract

Within a temperature range of [(293.15 K) to (308.15 K)] and a pressure range of 0.1 MPa, this study analyses molecular interactions in a sorbic acid solution in water. [Sorbic acid + (propylene glycol (PG) / hexylene glycol (HG)) + water] are present in the solution at quantities of [(0.01, 0.03, 0.05) \(mol.{kg}^{-1}\)]. We use the apparatus (Anton Paar DSA 5000 M) to find the density and the speed of sound. From the data we have, we may deduce many thermo-acoustic characteristics. Among the parameters that were experimentally measured were expansibilities, thermal expansion coefficients, volumes of transfer, and partial molar volumes. An effect of sound velocity on apparent molar isentropic compression, partial molar isentropic compression, and partial partial molar isentropic compression were all three forms of transfer pressure. Pair and triplet coefficients, as well as empirical constants, have been discovered to quantify important solute-solvent interactions. ''Propylene glycol has fewer interactions with the solvent than hexylene glycol, according to their characteristics”.
Elucidating predictors of preoperative acute heart failure in elderly patients with hip fractures through machine learning and SHAP analysis: a retrospective cohort study
Authors: Yu Qili , Fu Mingming , Hou Zhiyong , Wang Zhiqian 
Published: April 2024

Abstract

Background
Acute heart failure has become a significant challenge in elderly patients with hip fractures. Timely identification and assessment of preoperative acute heart failure have become key factors in reducing surgical risks and improving outcomes.
Objective
This study aims to precisely predict the risk of acute heart failure in elderly patients with hip fractures before surgery through machine learning techniques and SHapley Additive exPlanations (SHAP), providing a scientific basis for clinicians to optimize patient management strategies and reduce adverse events.
Methods
A retrospective study design was employed, selecting patients admitted for hip surgery in the Department of Geriatric Orthopedics at the Third Hospital of Hebei Medical University from January 2018 to December 2022 as research subjects. Data were analyzed using logistic regression, random forests, support vector machines, AdaBoost, XGBoost, and GBM machine learning methods combined with SHAP analysis to interpret relevant factors and assess the risk of acute heart failure.
Results
A total of 2,631 patients were included in the final cohort, with an average age of 79.3 ± 7.7. 33.7% of patients experienced acute heart failure before surgery. A predictive model for preoperative acute heart failure in elderly hip fracture patients was established through multivariate logistics regression: Logit(P) = -2.262–0.315 × Sex + 0.673 × Age + 0.556 × Coronary heart disease + 0.908 × Pulmonary infection + 0.839 × Ventricular arrhythmia + 2.058 × Acute myocardial infarction + 0.442 × Anemia + 0.496 × Hypokalemia + 0.588 × Hypoalbuminemia, with a model nomogram established and an AUC of 0.767 (0.723–0.799). Predictive models were also established using five machine learning methods, with GBM performing optimally, achieving an AUC of 0.757 (0.721–0.792). SHAP analysis revealed the importance of all variables, identifying acute myocardial infarction as the most critical predictor and further explaining the interactions between significant variables.
Conclusion
This study successfully developed a predictive model based on machine learning that accurately predicts the risk of acute heart failure in elderly patients with hip fractures before surgery. The application of SHAP enhanced the model's interpretability, providing a powerful tool for clinicians to identify high-risk patients and take appropriate preventive and therapeutic measures in preoperative management.
Design, Motion-planning, and Manufacturing of Custom-shaped Tools for 5-axis Super Abrasive Machinig of a Turbomachinery Blade Type Component
Authors: Martinez-Aguirre Maialen , Gómez Gaizka , Bo Pengbo , Barton Michael , González-Barrio Haizea , Calleja-Ochoa Amaia , de Lacalle Luis Norberto López 
Published: April 2024

Abstract

Turbomachinery components are evolving towards increasing complexity to achieve enhanced efficiency. Turbine blades are being designed using free-form surfaces and generated by Non-Uniform Rational B-Splines (NURBS). Custom-shaped tools are presented as a solution to improve the productivity of super abrasive machining processes for complex geometries, meeting the high standards required for rotating turbomachinery components. The adaptability and flexibility of these new tool concepts are specifically designed to better fit the target of freeform surfaces. This paper presents a blade type component finished with a custom-shaped tool designed through an optimization process that simultaneously optimizes both the shape of the tool and its motion. The proposed method with SAM finishing using a custom-shaped tool is compared against a standard tool and traditional machining process. The result obtained on the blade test case show that the custom-shaped tools need fewer paths, yet produce more accurate surface finish.
High-affinity mirror-image monobody targeting MCP-1 generated via TRAP display and chemical protein synthesis
Authors: Murakami Hiroshi , Hayashi Gosuke , Naito Toshinori , Miura Sayaka , Iwamoto Naoya , Usui Yusuke , Nonaka Motohiro , Oishi Shinya 
Published: April 2024

Abstract

Engineered protein scaffolds function as binders against various target molecules with high affinity and specificity comparable to conventional IgG antibodies. However, biologically produced protein drugs are generally degraded by proteases and often exhibit immunogenicity. To increase protease resistance and decrease immunogenicity of peptides and proteins, mirror-image peptide/protein binders consisting of D-amino acids have been developed so far mainly through the mirror-image phage display technique. Here, we develop a mirror-image protein binder derived from a monobody, one of the most promising protein scaffolds, using two notable technologies: chemical protein synthesis and TRAP (transcription-translation coupled with association of puromycin linker) display, an improved and streamlined version of mRNA display. A sequential workflow of initial screening followed by affinity maturation, facilitated by TRAP display, generates an L-monobody with high affinity (KD = 1.3 nM) against the pharmaceutically important monocyte chemoattractant protein-1 (MCP-1) D-enantiomer. By symmetry, the chemically synthesized D-monobody demonstrates strong and enantio-selective binding against L-MCP-1 and possesses pharmaceutically favorable properties such as resistance to proteolytic degradation, minimal immune response, and a potent inhibitory effect on MCP-1 binding to its cell membrane receptor. The production of a high-affinity mirror-image monobody elevates the value of mirror-image peptide/protein binders as a new modality in drug discovery.
Prognostic signature based on PANoptosis in clear cell renal cell carcinoma
Authors: Zhang Jiayu , Li Dongquan , Gao Jie , Xu Aoyu , Rao Dapang 
Published: April 2024

Abstract

Background: PANoptosis, a recently identified pathway of cell death, entails interplay and coordination among pyroptosis, apoptosis, and necroptosis mechanisms. Yet, the prognostic implications of PANoptosis-related genes (PRGs) in clear cell renal cell carcinoma (ccRCC) remain largely unexplored.
Methods: In this study, nineteen PANoptosis-related genes (PRGs) were identified from prior research, and clinical data of patients with clear cell renal cell carcinoma (ccRCC) were retrieved from TCGA. In the training cohort, we conducted univariate Cox, Lasso, and multivariate Cox regression analyses to identify prognostic PANoptosis-related genes (PRGs) and subsequently develop a prognostic PRGs signature. The prognostic signature was validated using both the testing cohort and the entire cohort. Furthermore, a nomogram was developed to predict prognosis at various clinicopathological stages and risk scores. Additionally, a network comprising 6 prognosis-related differentially expressed genes (PRDEGs) and 5 predicted functional partners was constructed.
Results: We utilized univariate Cox analysis to assess the prognostic significance of the 19 PANoptosis-related genes (PRGs). Subsequently, we employed both the least absolute shrinkage and selection operator (Lasso) and multivariate Cox analysis to further evaluate the prognostic value of these PRGs. We established a prognostic risk model consisting of six PRDEGs: TAB2, TAB3, TNFAIP3, CASP7, AIM2, and TRADD. Kaplan-Meier (KM) analysis and time-related receiver operating characteristic (ROC) curve analysis were performed to evaluate overall survival and compare high- and low-risk groups. Incorporating clinicopathological characteristics, the risk model was validated as a novel independent prognostic factor for clear cell renal cell carcinoma through univariate and multivariate Cox regression analyses. Furthermore, the nomogram demonstrated reliable predictive ability.
Conclusion: The signature comprising six PRDEGs showed notable prognostic significance for ccRCC. These genes hold promise as potential therapeutic targets in clinical settings.
Crafting Pixels in Wood: Understanding the Interplay of Technologies and Visual Perception in Wooden Photo Engraving
Authors: Jurek M. , Wagnerová R. , Šafář M. 
Published: April 2024

Abstract

This paper delves into the realm of laser engraving of photographs on wooden surfaces and explores the differences between mathematical and human perception. Wide quality study was used to, thoroughly explore the results attainable by 27 different realisations. The evaluation process involves both objective analyses and subjective assessments from hundreds of ratings of 60 evaluators, aiming to unravel the intricate interplay between technologies and visual perception. The study also poses the intriguing question of whether the image most accurately transferred onto a wooden surface is also the most favourably perceived by potential users.
Reconstructing Contact and Potential Interbreeding Geographical Zone for Neanderthals and Anatomically Modern Humans
Authors: Heydari-Guran Saman , Yosefi Masood , Kafash Anoshe , Ghasidian Elham 
Published: April 2024

Abstract

Whilst the admixture of the Neanderthal and anatomically modern human is precisely proven, due to the shortage of fossils and absence of appropriate DNA, the timing and geography of it, are the subjects of questions. In this study, we applied ecological niche modelling (maximum entropy approach) and GIS to reconstruct Neanderthal and modern human palaeodistribution and identify their contact and potential interbreeding zone during the marine isotope stage 5. We used climatic variables characterising environmental conditions of marine isotope stage 5 ca. 120 to 80 thousand years ago (Kyr) along with topography and coordinates of Neanderthal and modern human archaeological sites to build each species palaeodistribution. Overlapping the models showed that Zagros Mountains was a contact and potential interbreeding zone for the two sub-human species. We believe that Zagros Mountains acted as a corridor connected the Palearctic/Afro-Arabian realms, facilitated northward dispersal of anatomically modern human and southward dispersal of Neanderthal during marine isotope stage 5. Our analyses are comparable with archaeological and genetic evidence collected during the last decades.
A novel sub-network level ensemble deep neural network with regularized loss function to improve prediction performance
Authors: Toosifar Jalil , Forghani Yahya , Hosseini Seyyed Abed 
Published: April 2024

Abstract

In recent years, deep neural networks have been widely used and have achieved great success in various fields such as image recognition, natural language processing, and robotics. One method to increase the accuracy and generalization of neural networks is the use of a group of neural networks, ensemble learning, and aggregation of their outputs. The main idea of this paper is ensemble learning at the layer or block level of the network. This is because each neuron, or each layer, or each block of the network can be considered as a network, and ensemble learning can be performed at the neuron, layer, or block level. In fact, a large neural network composed of a large number of sub-networks (layers or blocks) can be considered as a combination of a large number of small sub-neural networks, and improving the performance of each sub-network can contribute to the overall improvement of the network performance. In other words, a layer of a network or, more generally, a sub-network of a network has the task of extracting or detecting the features of the input data. The more reliable this detection is, the more it can contribute to improving the model's performance. Ensemble learning at the layer or sub-network level can help increase the generalization performance of the layer or sub-network. Better performance of the layers or sub-networks can also lead to better performance of the entire network. The results of testing the proposed method on standard datasets for image classification show an improvement of 0.08 to 7.05 percent in the performance of the proposed model compared to traditional models.
Design of a 40 MW Grid-Connected Solar Photovoltaic Power Plant for a School in Patenga
Authors: Rahman Md. Arifur , Islam Mazharul , Islam Md Torikul 
Published: April 2024

Abstract

Bangladesh, as a populous South Asian nation, grapples with the dual challenge of meeting escalating energy demands while tackling environmental degradation and reducing dependency on fossil fuels. In response to the imperative of sustainable development and the pressing global issue of climate change, the adoption of renewable energy sources has emerged as a critical strategy. This paper explores the viability and potential of solar photovoltaic (PV) power plants as a solution to Bangladesh’s energy challenges, with a specific focus on the Patenga region.Situated advantageously for solar energy harnessing, Patenga offers a promising locale for solar PV power generation. Through the establishment of a 40 MW solar PV power plant, this study proposes to address the energy requirements of the South Patenga City Corporation High School while also contributing surplus energy to the national grid. The geographic coordinates of Patenga (latitude: 22.235128°N, longitude: 91.806085°E) along the Bay of Bengal present an ideal coastal setting for solar energy exploitation.Detailed analysis reveals the economic viability and environmental benefits of the proposed solar PV project. With a projected payback period of seven years and an estimated revenue exceeding Tk 9 billion, the project promises profitability and sustainability. Leveraging solar energy not only ensures a stable and cost-effective electricity supply for the school but also aligns with the United Nations Sustainable Development Goals (SDGs), particularly in advancing industry, innovation, infrastructure, decent work, and economic growth.The feasibility study conducted through platforms such as the Global Solar Atlas, PVGIS, and PVsyst underscores the viability of solar PV power generation in Patenga. Utilizing advanced software tools, including PVGIS for optimization, the project design aims for maximum efficiency and reliability. By harnessing solar energy, the school endeavors to reduce its carbon footprint, promote green energy adoption, and contribute to climate change mitigation efforts.
Applying novel random redistribution method in predicting malignant drivers for non-tumor diseases from transferring survival information
Authors: Zhou Xiaobei , Wan Jing , Lv Na , Huang Huihui , Zou Renlong , Liu Wensu 
Published: April 2024

Abstract

In cancer research, genomic survival analysis is commonly used to identify potential targets or subtyping models. However, there is limited survival information available for some non-tumor diseases, such as hypertension. In this manuscript, we propose a novel approach to address this issue by leveraging survival data from tumors to identify malignant drivers in genomic data for non-tumor diseases. Our approach begins by identifying common genes that may be implicated in both tumor and non-tumor diseases. We calculate similarities between feature genes using co-occurrence analysis in PubMed and also gene expression levels accopanied with disease stage information in transcriptome data. To assess the similarities and rank the genes, we have developed a customized metric. From the higher-ranking genes, we select potential malignant drivers based on calculated hazard ratios greater than 1 and log-rank p values less than 0.05. The validation was applied on 5 different diseases and afterwards the predicted malignant drivers were validated by accordance with reported information, gene ontology and protein-protein interaction analysis. Predicted log-rank p value and hazard ratio on known markers for target disease were also compared. The high accordance for our prediction with public information for all 5 diseases confirmed that our methods were effective in predicting malignant drivers and consequently gaining clinical and research significance.
Optimizing Energy-Conserving Sleep Strategies for NB-IoT Devices in Coordinated 5G Networks within Smart Environments
Authors: Jenitha J. , Hema L. K. , Regilan S. 
Published: April 2024

Abstract

An increased interest in energy-efficient communication protocols to extend battery life and improve network scalability has resulted from the fast growth of Internet of Things (IoT) devices, especially Narrowband IoT (NB-IoT) devices. In this study, we suggest a unique method of sleep scheduling using machine learning techniques for NB-IoT networks. Our method seeks to optimize energy usage while maintaining responsive connection by dynamically adjusting the sleep patterns of NB-IoT devices depending on anticipated network activity levels. Using machine learning algorithms trained on historical data gathered from NB-IoT devices and base stations, the suggested process entails developing a prediction model. In order to produce real-time estimates of future network demand, the model analyzes a variety of input parameters, including as the surrounding environment, traffic patterns, and the closeness of the device to the base station. The sleep scheduling mechanism, which coordinates the sleep-wake cycles of NB-IoT devices to coincide with expected periods of low activity, is informed by these forecasts. We illustrate the efficacy of our machine learning-based sleep scheduling technique in attaining noteworthy energy savings while maintaining network performance, utilizing comprehensive simulations and real-world tests. We are able to strike a compromise between energy economy and responsiveness by cleverly scheduling sleep, which keeps NB-IoT devices operational for their monitoring and control functions while preserving battery life. Our study addresses the increasing need for sustainable IoT solutions in smart city settings and beyond by advancing energy-efficient communication protocols for NB-IoT networks. The suggested method opens the door for more effective and durable IoT ecosystems by providing useful advice and insights for applying machine learning-based sleep scheduling algorithms in actual IoT installations.
Herbolario v. 1: Building Database for Medicinal Plants Used by Ethnolinguistic Groups in the Philippines to Support Indigenous Knowledge Conservation
Authors: Plang Yalaira A. , Tanalgo Krizler C. , Cruz Kier Celestial Dela , Rubio Meriam M. , Hilario-Husain Bona Abigail , Respicio Jeaneth Magelen V. , Lidasan Asraf K. , Abdullah Sumaira S. , Fabrero Gerald Vince N. , Ele Renee Jane A. , Murray Sedra A. , Saliling Yvonne V. , Macatabon Radji A. , Casim Lothy F. , Reyes Jamaica L. Delos , Agduma Angelo Rellama 
Published: April 2024

Abstract

The significance of open and accessible biodiversity data is increasingly receiving attention among ecologists and conservation biologists. However, there is a scarcity on the information focusing on indigenous knowledge of biodiversity particularly in developing economies where such knowledge is highly concentrated. To address this gap, we introduced the Herbolario v. 1: A Database of Medicinal Plants Used in the 21st Century by Ethnolinguistic Groups in the Philippines, aiming to consolidate, digitally store, and make data publicly available following the principle of FAIR data-sharing. Herbolario v. 1 presents a comprehensive FAIR database focusing on the diversity and distribution of ethnomedicinal plants used by 35 ethnolinguistic groups in the Philippines. The database includes 13, 402 interaction records of ethnolinguistic groups of 796 plant (and algae) species from 160 families and 68 orders. This initiative offers a vital platform for preserving and disseminating indigenous knowledge and protecting traditional practices and cultural heritage. Additionally, incorporating this knowledge into broader biodiversity databases will improve the comprehensiveness and accuracy of global biodiversity assessments and conservation strategies. Furthermore, by making such data accessible, it can stimulate interdisciplinary research and foster cooperation among traditional knowledge holders, scientists, and policymakers.