bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning
Authors: Zhong Xugang , Lin Yanze , Zhang Wei , Bi Qing 
Published: May 2023

Abstract
Background This study aimed at establishing more accurate predictive models based on novel machine learning algorithms, with the overarching goal of providing clinicians with effective decision-making assistance.Methods We retrospectively analyzed 283,373 breast cancer patients recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016. Multivariable logistic regression analyses were used to identify risk factors for bone metastases in breast cancer, whereas Cox proportional hazards regression analyses were used to identify prognostic factors for breast cancer with bone metastasis (BCBM). Based on the identified risk and prognostic factors, we developed diagnostic and prognostic models that incorporate six machine learning classifiers. We then used the area under the receiver operating characteristic (ROC) curve (AUC), learning curve, precision curve, calibration plot, and decision curve analysis to evaluate performance of the machine learning models.Results Univariable and multivariable logistic regression analyses showed that bone metastases were significantly associated with age, race, sex, grade, T stage, N stage, surgery, radiotherapy, chemotherapy, tumor size, brain metastasis, liver metastasis, lung metastasis, breast subtype, and PR. Univariate and multivariate Cox regression analyses revealed that age, race, marital status, grade, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, lung metastasis, breast subtype, ER, and PR were closely associated with the prognosis of BCBM. Among the six machine learning models, the XGBoost algorithm predicted the most accurate results (AUC = 0.98; AUC = 0.88). According to the Shapley additive explanations (SHAP), the most critical feature of the diagnostic model was surgery, followed by N stage. Interestingly, surgery was also the most critical feature of prognostic model, followed by liver metastasis.Conclusions Based on the XGBoost algorithm, we could effectively predict the occurrence of bone metastases in breast cancer and provide targeted references for the treatment of BCBM patients.
Dysnatremia and Subsequent Serum Sodium Level Changes Following Various Intravenous Treatments in Infants with Acute Gastroenteritis
Authors: Sakkongviseth Wattanaporn , Sommai Kanokwan , Sumboonnanonda Achra , Pattaragarn Anirut , Supavekin Suroj , Piyaphanee Nuntawan , Lomjansook Kraisoon , Thunsiribuddhichai Yarnarin , Chaiyapak Thanaporn 
Published: May 2023

Abstract
Purpose
Acute gastroenteritis (AGE) is one of the main causes of electrolyte imbalance in infants. We aimed to determine the incidence of and factors associated with early dysnatremia and establish the ideal intravenous (IV) treatment scheme.
Methods
The records of hospitalized infants aged 1–12 months with community-acquired AGE between January 2017 and March 2021 were retrospectively reviewed. Factors associated with early dysnatremia were analyzed by multivariable logistic regression analysis. Subsequent serum sodium levels 4–24 hours after IV fluid treatments, which were categorized into 3 groups, were determined in the subgroup of infants with early isonatremia.
Results
In total, 347 infants with a median age of 8.0 (5.0, 10.0) months were included. The incidence of early dysnatremia was 14.4% (hyponatremia 12.4% and hypernatremia 2.0%). Severe dehydration was associated with early dysnatremia (p = 0.048). Among 70 infants with early isonatremia, the median serum sodium change was highest in the D-5NS group, with changes of + 3 (0.5, 5), + 1 (-2, 2) and − 2.5 (-4, 2.3) mmol/L in infants who received D5-NS, D-1/2NS, and D5-1/3 − 1/5NS, respectively (p = 0.003). Four out of 39 infants (10.3%) developed hyponatremia while receiving D5-1/2NS. None of those who received D5-NS developed subsequent dysnatremia.
Conclusions
The incidence of early dysnatremia among infants with AGE was 14.4%. Severe dehydration was a strong factor associated with early dysnatremia, so electrolyte levels need to be assessed. The use of isotonic solution did not promote acquired dysnatremia. We therefore recommend that isotonic solution be considered for infants with AGE.
Changes in proprioception at different time points following anterior cruciate ligament injury or reconstruction
Authors: Zhao Yixuan , Chen Ze , Li Longfei , Wu Xipeng , Li Wei 
Published: May 2023

Abstract
Purpose:To investigate the changes in 30° and 60° position sense in patients with anterior cruciate ligament (ACL) injury at different time points after injury and reconstruction.
Methods：Patients were divided into six groups according to time after ACL injury and reconstruction: group A (ACL injury 1.5–6 months), group B (ACL injury 6–12 months), group C (ACL injury >12 months), group D (postoperative ACL reconstruction 1–6 months), group E (postoperative ACL reconstruction >6 months), and group F consisting of 14 healthy adults (control group). The ability of the affected leg to reproduce the same joint position during knee flexion was tested using active joint position sense assays to assess proprioception in both the lower extremities of the patient or between the patient and the control group.
Results:Proprioception decreased rapidly during the early stages of ACL injury. Significant difference in the affected side at 30° compared to the healthy side (Group A: 4.70(4.78,9.00) vs 4.15(3.35,6.13), P=0.03; Group B: 2.90(0.48,4.56) vs 8.30(4.18,10.43), P=0.001; Group E: 6.25(2.55,11.60) vs 9.60(3.90,12.73), P=0.009). However, no significant differences were detected for a double lower limb contrast of 60°(Group A: 5.1(1.00,8.00) vs 3.00(0.75,3.55), P=0.044). Finally, significant differences were found between patients on the affected side and healthy subjects (P<0.01).
Conclusion: ACL injury had a greater impact on the patient's 30° position sense, with only a small impact for 60°. Further, the early and middle proprioception recovery stages after ACL injury were the best before surgery. Finally, proprioception recovery training should be performed soon after injury.
Transplantation of thermosensitive hydrogel encapsulated miR-216a-5p overexpressing mesenchymal stem cells for functional recovery of spinal cord injury
Authors: Dou Zhi , He Liangliang , Yue Jianning , Zhao Wenxing , Yang Liqiang 
Published: May 2023

Abstract
Spinal cord injury (SCI) is one of the most incapacitating conditions, with neuropathic pain (NP) as a comorbidity that is the most difficult to treatment. In recent years, microRNAs (miRNAs) have been studied more thoroughly in SCI. In this study, based on the construction of the SCI rat model, miR-216a-5p expression was reduced in SCI rats through experimental validation analysis. And human mesenchymal stem cells (hMSCs) overexpressing miR-216a-5p were loaded by constructing thermosensitive hydrogels and transplanted into SCI rats. It was found that the constructed hydrogel system exhibited temperature-dependent phase change behavior and biodegradability, and rats with spinal cord injury showed considerable behavioral recovery and significantly reduced cellular mortality after transplantation. Mechanistic studies revealed that sustained release of miR-216a-5p was able to bind to the 3'UTR of NUDT21 and involve in the regulation of recovery after SCI through modulation of NUDT21, thus greatly increasing recovery after spinal cord injury. Thus, thermosensitive hydrogel-encapsulated human mesenchymal stem cells overexpressing miR-216a-5p provide a microenvironmentally responsive and noninvasive pathway for recovery from spinal cord injury, we hypothesized that miR-216a-5p could be a potential biomarker for neuropathic pain.
The clarity and adaptability of instructions preventing the spread of the Covid-19 virus. Perceptions of staff in care of older people in Sweden and their association with individual factors and organisational factors regarding the psychosocial work environment. A cross-sectional study
Authors: Hammar Lena Marmstål , Alam Moudud , Eklund Caroline , Boström Anne-Marie , Löwenmark Annica 
Published: May 2023

Abstract
Background Older people in residential care had the highest mortality rates, followed by those who received home care during the COVID-19 pandemic in Sweden. Staff working in the care of older persons assumed responsibility for preventing the spread of the virus in spite of lacking the prerequisites and training to do so. This study aimed to investigate how staff in the care of older people described their psychosocial work environment during the COVID-19 pandemic; it also aimed to examine the perceived clarity of the instructions and the staff’s ability to follow the instructions to prevent the spread of the virus and its association with individual factors and organisational factors regarding the psychosocial work environment.Methods A cross-sectional web survey was conducted for the data collection. The staff’s perceptions of their psychosocial environment were analysed using descriptive statistics. The association between organisational and individual factors, as well as the degree of clarity of the instructions and the staff’s ability to follow them, were assessed using multivariate (ordinal) regression analysis.Results The main findings were that perceptions of the clarity and adaptability of the instructions were primarily correlated with organisational factors, as higher responses (positive) for the subscales focusing on positive challenges, role clarity, control, support and encouragement in leadership at work were associated with the belief that the instructions were clear. Similarly, those indicating high job demands and high individual learning demands were less likely to report that the instructions were clear.Conclusions High job demands and individual learning demands were demonstrated to decrease the staffs understanding and adoption of instructions. These findings are significant on an organisational level, since the work environment must be prepared for potential future pandemics to promote quality improvement and to generally increase patient safety as well as the health of staff.
Pulse-Poled Porous BaTiO3 Ceramic for Piezocatalysis Applications
Authors: Gaur Akshay , Porwal Chirag , Boukhris Imed , Mahmoud Z. , Chauhan Vishal Singh , Vaish Rahul 
Published: May 2023

Abstract
The porous ceramic BaTiO3 pellets were synthesized by using polymethyl methacrylate (PMMA) as a pore former in 0–30 wt% proportion through solid route reaction method. Further, the synthesized porous BaTiO3 ceramic pellets were poled via pulse poling; a non traditional poling technique and their piezoactivity were examined by means of piezovoltage. There was significant enhancement in piezoelectric coefficient (d33) of porous BaTiO3 ceramic pellets using pulse poling technique. Later on, in the present study a quantitative analysis was done through comparision by degrading an organic dye by using poled/ unpoled porous BaTiO3 sample.
Response of Meiyu Process Considering the Temporal and Spatial Characteristics of GNSS PWV
Authors: Ke Fuyang , Zhao Pan , Yu Wei , Hong Guan , Tian Jianing , Ming lulu 
Published: May 2023

Abstract
This study explores the spatiotemporal characteristics of Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) and its relationship with the Meiyu process. Using multiple sources of atmospheric PWV data and meteorological information, the study quantitatively analyzes PWV's spatiotemporal characteristics and its association with the onset and withdrawal of the Meiyu season. The research findings are as follows: (1)PWV's spatiotemporal evolution provides indications for the Meiyu season. The daily variation of water vapor content generally follows a camelback shape. Before the Meiyu season begins, PWV exhibits an upward trend with content below 40mm. After the onset of the Meiyu season, PWV gradually accumulates during the early Meiyu season with content exceeding 50mm, accompanied by rainfall. In the late Meiyu season, water vapor releases, leading to a decrease in PWV content. After the Meiyu season ends, PWV gradually declines but remains relatively high, linked to moisture transport during the Jianghuai flood season. (2)Anomaly analysis reveals that water vapor activity is highest during the Meiyu season, showing good correspondence with special Meiyu years. This provides new insights for monitoring and forecasting abnormal Meiyu events. (3)Spatially, PWV distribution during the Meiyu season exhibits a pattern of more water vapor in southern regions and less in northern areas. This pattern is influenced by the stronger atmospheric water storage capacity in low-latitude areas and the gradual weakening of monsoon water vapor during northward and westward transport.
The Power of Progressive Active Learning in Floorplan Images for Energy Assessment
Authors: Al-Turki Dhoyazan , Kyriakou Marios , Basurra Shadi , Gaber Mohamed Medhat , Abdelsamea Mohammed M. 
Published: May 2023

Abstract
Floorplan energy assessments present a highly efficient method for evaluating the energy efficiency of residential properties without requiring physical presence. By employing computer modeling, accurate determination of the building’s heat loss or gain can be achieved, enabling planners and homeowners to devise energy-efficient renovation or redevelopment plans. However, the creation of an AI model for floorplan element detection necessitates the manual annotation of a substantial collection of floorplans, which poses a daunting task. This paper introduces a novel active learning model designed to detect and annotate the primary elements within floorplan images, aiming to assist energy assessors in automating the analysis of such images—an inherently challenging problem due to the time-intensive nature of the annotation process. Our active learning approach initially trained on a set of 500 annotated images and progressively learned from a larger dataset comprising 4,500 unlabeled images. This iterative process resulted in an mean average precision score of 0.833, precision score of 0.972, and recall score of 0.950. The annotated dataset will be made publicly available under a Creative Commons license.
The Effect PSO/ABC, ABC/BFO, PSO/BFO and CS/BFO Hybrid Techniques on Power System Buses for Voltage Stability Margin Improvement
Authors: Ikeli ndubuisi , Ashigwuike Chinemezu 
Published: May 2023

Abstract
This paper presents and investigates new approaches inspired by nature to determine the optimal placement of UPFC for enhancing voltage stability margin of power system network. Voltage collapse phenomenon has remained an operational problem yet to be solved by power system engineers. To improve the voltage stability of the power system network, new optimisation techniques viz: PSO/ABC, ABC/BFO, PSO/BFO AND CS/BFO hybrids are investigated for their performance on power system buses for voltage stability improvement.
The performance of the proposed hybrid methods are examined on the practical Nigerian 56 - bus network for voltage stability improvement. The simulation results has shown that ABC/BFO technique is better than
BFO/PSO by 3.44% in improving voltage stability of margin of power system, PSO/ABC gives additional improvement of 4.07%, totalling 7.51% and lastly BFO/CS technique improved voltage stability margin of power system further by 5% totalling 12.51%. Also this research revealed that the smaller the gamma value the better the UPFC contribution to voltage stability enhancement. BFO/CS technique yielded the smallest gamma values of 0. 74532 and 0.830913 in both Nigerian practical network and IEEE – 14 bus network as shown in the analysis.
Policy frameworks to maximize sustainability benefits of bioenergy systems
Authors: Thornley Patricia , Roeder Mirjam 
Published: May 2023

Abstract

Background Bioenergy is a significant contributor to renewable power generation, renewable transport fuel and renewable heat. However, the deployed capacity significantly lags identified potential and has not seen the same rapid response to policy stimuli observed in the solar and wind sectors. This work analyses the historical trajectory of UK bioenergy development to discern potential underpinning reasons for that.Results It is noted that the technology landscape is arguably more complex than in other renewables, with multiple feedstocks, pre-treatment and conversion technologies involved in potentially hundreds of different pathways/combinations; not all of these pathways/combinations deliver greenhouse gas reductions, and most have other impacts (positive and negative) that go beyond energy and greenhouse gas balances to interact with atmospheric, aquatic, land, economic and social systems. We apply a risk management approach to show how disaggregation of the system can support more appropriate decision-making and provide greater resilience to the inherent variability associated with natural, land-based systems.Conclusions It is concluded that disaggregation of bioenergy systems into 3 sub-systems allows management of the most significant risks to be placed with the parties most able to deal with them and that a simple, semi-quantitative assessment of the performance of each sub-system facilitates an effective ranking of the “best” use of biomass in line with policy objectives; supporting effective decision making about priority feedstocks, technologies and demand sectors.
An adsorption isotherm identification method based on CNN-LSTM neural network
Authors: Liu Kaidi , Xie Xiaohan , Yan Juanting , Zhang Sizong , Zhang Hui 
Published: May 2023

Abstract
Context: Adsorption isotherm is integral to comprehending the adsorption mechanism and catalytic processes. Despite the well-established research on isotherm classification methods, existing techniques for isotherm identification are hampered by inefficiency, human interference, insufficient feature information extracting and the fact that specific types isotherm of identification can be achieved. To overcome these limitations, an end-to-end isotherm identification method based on a CNN-LSTM neural network is proposed, which employs a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory network (LSTM) to extract the features of adsorption isotherm. Additionally, dataset is constructed for training and validating the neural network using various theoretical adsorption isotherm equations, thereby eliminating the need for time-consuming and expensive repetitive experiments. The results indicate that the model achieves identification accuracy of 100% on both the training and validation sets constructed using theoretical adsorption equations. The model's F1-score on the test set, which consists of five categories actual adsorption isotherms, is 88.85%, and there has been a 20% enhancement in the mean precision of isotherm identification compared to the traditional method. All these results demonstrate that the proposed method can accurately identify adsorption isotherm.
Method: Pycharm was used as the experimental and testing platform, python 3.9 was used as the programming language, Tensorflow 2.11.0 and Keras 2.10.0 were used to train and test the CNN-LSTM model, numpy 1.21.5 and scipy 1.81 were used to generate the train and validation dataset.
DBP2Vec: Predicting DNA-binding proteins directly using pre-trained protein language model
Authors: Wei Chao , Ye Zhiwei , Wang Panru , Lan Wuyang 
Published: May 2023

Abstract
DNA-binding proteins (DBPs) play a crucial role in numbers of biological processes and have received wide attention in recent years. Meanwhile, the rapid development of sequencing technologies lead to the explosive growth of new protein sequences, it is highly desired to develop a fast and accurate method for DNA-binding proteins prediction. Experimental methods such as chromatin immunoprecipitation on microarray (ChIP-chip) and X-ray crystallography are highly accurate but expensive and time-consuming. To address this issue, many computational methods have been proposed, they usually exploit multiple information about protein sequence, e.g., sequence composition information, physicochemical properties, evolutionary information, structural information, etc. Despite the effectiveness of these approaches, they heavily depend on prior biological knowledge and undergo a very complex process of feature extraction. In view of these shortcomings, here, we present a novel method, named DBP2Vec, to predict DNA-binding proteins directly from pre-trained protein language model (e.g., ESM-1b) which effectively encode biological properties without any prior knowledge by virtue of deep representation learning (e.g., BERT) on large protein sequences databases (e.g., UniParc). Tests on two benchmark datasets (e.g., PDB296, UniSwiss-Tst) and one newly constructed DBPs dataset demonstrate that our proposed method significantly outperforms existing state-of-the-art methods.
Application of groundwater quality index (GWQI) by Deterministic and Probabilistic Approaches to Assess Effect of Land Use Change on Groundwater Quality in the northern county of Iran: non-carcinogenic risk assessment
Authors: Faraji Hossein , Javanian Mostafa , Mohammadpour Amin , Chavoshi Ebrahim , Rezaei Fatemeh , Badeenezhad Ahmad , Nikbakht Hossein-Ali 
Published: May 2023

Abstract
This study aimed to evaluate the groundwater quality, land use changes, and non-carcinogenic risk caused by F−, and NO3 – ions in Babol county's drinking water. A total of 228 samples were collected from 38 drinking water wells between 2016–2021, and analyzed for various chemical parameters to determine the groundwater quality index (GWQI). The results obtained from the deterministic approach indicated that 74.6% of the water sources exhibited good quality, with a GWQI value range of 49.91 to 107.38. However, the Probabilistic Approach revealed that only 0.01% of the water samples demonstrated excellent quality, while the majority (98.37%) were classified as having good quality, with a smaller proportion (1.6%) considered poor. The qualitative zoning of GWQI showed that part of the studied area on the shores of the Caspian Sea and the north of the region is in an unfavorable condition. Principal component analysis (PCA) on parameters of chemicals showed three factors that account for 71.55% of the total variance in the groundwater quality data set. The land use results showed that in residential areas where the population is dense, a higher concentration of NO3− is observed. The study also found that the non-carcinogenic risk caused by F− and NO3− was below the risk limit (HQ < 1) for all age groups, except for infants in the wet season where 18.4% of the HQF values were greater than 1. The results showed that NO3− concentration had the highest effect on the non-carcinogenic risk in all age groups. This study highlights the importance of monitoring and managing land use changes to maintain the quality of drinking water in Babol County.