bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hyponatremia Is an Overlooked Sign of Trouble Following Total Joint Arthroplasty
Authors: Baker Colin M. , Goh Graham S. , Tarabichi Saad , Sherman Matthew B. , Khan Irfan A. , Parvizi Javad 
Published: March 2023

Background:
Hyponatremia is a common electrolyte abnormality in arthroplasty patients. This issue, underrecognized by surgeons, can impact the postoperative course of patients. There are, however, little data on the implications of sodium disturbances following total joint arthroplasty (TJA). The primary aims of this study were to (1) report the rate of hyponatremia following TJA, and (2) examine the impact of hyponatremia on the perioperative course of TJA patients.

Methods:
This was a retrospective analysis of 3,071 primary and revision TJAs performed between 2015 and 2017. Based on preoperative and postoperative sodium values (pre-post), patients were classified into 4 groups: normonatremic-normonatremic (Group 1), normonatremic-hyponatremic (Group 2), hyponatremic-normonatremic (Group 3), and hyponatremic-hyponatremic (Group 4). Primary end points were length of stay (LOS), postoperative discharge, in-hospital complications, and 90-day readmissions.

Results:
The distribution of cases was 84.6% Group 1, 9.4% Group 2, 2.1% Group 3, and 3.8% Group 4. Overall, 13.2% of patients had hyponatremia after TJA. Older age, hip arthroplasty, general anesthesia, higher Charlson Comorbidity Index, congestive heart failure, revision surgery, and history of stroke, liver disease, and chronic kidney disease were risk factors for postoperative hyponatremia. Patients with postoperative hyponatremia (Groups 2 and 4) had greater likelihoods of having a 90-day complication and non-home discharge and greater LOS.

Conclusions:
Postoperative hyponatremia was a relatively common occurrence in patients undergoing TJA, and was associated with greater LOS, complications, and non-home discharge. Surgeons should identify patients at risk for developing sodium abnormalities in order to optimize these patients and avoid increased resource utilization.

Level of Evidence:
Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
"Inflation in Yemen: Analysis of Economic Factors and Strategic Interventions"
Authors: Hassan Ramzi Abdullah 
Published: March 2023

Abstract
This research paper aims to analyze the electronic services market in Yemen. The study used data collected from various banks in Yemen over 11 years (2011–2021). The data were analyzed using multiple comparisons by the Dunnett T3 test, which revealed significant differences in the mean values of electronic services offered by different banks. The results of the study showed that there is a clear difference in the electronic services offered by different banks in Yemen. The study found that the electronic services offered by the bank of Al-Amal had the highest mean value, while the electronic services offered by the bank of Al-Khair had the lowest mean value. Additionally, the study revealed a significant increase in the mean values of electronic services offered by all banks from 2011 to 2021. The results of this study can be used to inform policies and strategies for developing electronic services in Yemen and to guide banks in their efforts to improve the quality of their services.
Heterogenization of marine catch assemblages over the past four decades in China
Authors: Ma Zhiyuan , Zhu Jiangling 
Published: March 2023

Abstract
Marine fishery biodiversity is crucial for sustainable food supply to billions of people, yet it has been severely damaged by a long history of overexploitation. Although local marine species diversity in response to anthropogenic change has been intensively studied, however, the trajectories of marine catch assemblages at larger temporal and spatial scales remain elusive. Here, we use data collected from the China Fishery Statistical Yearbook that spans 37 years (1983-2019) to systemically and quantitatively assess the trajectory change of marine catch assemblages and the drivers. We found that both species taxa and the structure has undergone two distinguish periods (1983-2002 versus 2003-2019), and the composition of marine catch experienced rapid temporal heterogenization for both occurrence and abundance-based dissimilarity over about past four decades, but the rate of the change has slowed in the latter two decades. Importantly, the fish species with a smaller size and living in the upper water may drive the heterogenization in the earlier two decades. Conversely, the fish with a bigger size and living in the deeper water may drive the heterogenization in the latter two decades. These findings can help to identify species that urgently need more effective fishing strategies, and incorporate them into future marine conservation and fisheries management decisions to rebuild sustainable marine fishery stocks.
Determinants of time-to-recovery from hypertension by application of Weibull-Inverse Gaussian shared frailty model
Authors: Yihuna yeshambel , Takele Nigist , Muluneh Essey 
Published: March 2023

Abstract

Hypertension is a major public health problem that is responsible for morbidity and mortality. In Ethiopia hypertension is becoming a double burden due to urbanization. The aim of the study is to identify factors that affect time-to-recovery from hypertension. In this study, a retrospective study design was used and the data was collected in the patient’s chart from September 2016 to January 2018. Accelerated failure time, and parametric shared frailty models were employed to identify factors associated with the recovery time of hypertension patients. Eighty one percent of sampled patients were recovered to normal condition and nineteen percent of patients were censored observations. The median survival time of hypertensive patients to attain normal condition was 13 months. Weibull- inverse Gaussian shared frailty model which was found to be the best model for predicting recovery time of hypertension patients. Unobserved heterogeneity in residences as estimated by the Weibull-Inverse Gaussian shared frailty model was θ = 0.385 (p-value = 0.00). Predictor age, systolic blood pressure, related disease, creatine, blood urea nitrogen, and the interaction between blood urea nitrogen and age were the determinant factors of the recovery status of patients. Therefore, health-care providers give great attention and priorities those identified factors and forward frequent counseling about reducing hypertension disease.
Associations between the average blastomere number of all embryos on day 3 and the live birth rate in the transfer cycle: a single-centre, retrospective study
Authors: Xu Hongyi , Qi Tiange , Tian Jiarong , Chen Xin , Zhang Xin , Fu Kui , Liao Huiqi , Jiang Shengfang , Zhang Ying , Zhang Changjun 
Published: March 2023

Abstract
Research question: What are the influencing factors of the average blastomere number of all embryos in D3 patients and the association with a live birth rate in the transfer cycle?
Methos: A total of 7420 fresh embryo transferred cycles of patients who underwent IVF-ET in the Reproductive Medicine Center, Renmin Hospital, Hubei University of Medicine from January 2016 to December 2021 were selected retrospective observed.
Results: According to the average blastomeres number in all embryos on day 3 following oocyte retrieval, the patients were split into five groups: 5.00 group (747 cases), 5.01-6.00 group (1763 cases), 6.01-7.00 group (3052 patients), 7.01-8.00 group (1646 cases), and >8 groups (212 patients). The average number of blastomere on D3 was negatively associated (P <0.05) with infertility, duration infertility, male factors infertility, E2 value on hCG day, and ICSI insemination method. The live birth rate(LBR) of the groups 5.00 and 5.01-6.00 was considerably lower than that of the other groups, according to multivariate regression analysis（P＜0.001). Further additional curve fitting was utilized to demonstrate that the LBR increased significantly as the number of blastomeres grew, increasing a value of 20% for every different blastomere number when the average number of blastomeres on D3＜5.7 (P<0.001). On the other hand, when the number≥5.7 (P=0.258), the live birth rate no longer increases.
Conclusion: An average blastomere number between 7.01 and 8.00 is associated with higher LBR in D3 fresh embryo transfer cycle, when the average blastomeres in D3 embryos are less than 5.7 to reduce the live birth rate of the transfer cycle.
A comprehensive evaluation of the technical, economic, and environmental aspects of utilizing a PV-RO desalination process in a coastal region of Andhra Pradesh
Authors: Ananthasai Somasi , Srichan Kondamudi 
Published: March 2023

Abstract
This paper puts forward a systematic study on the desalination plant feasibility at Chinni Rushikonda, Visakhapatnam, Andhra Pradesh, India. The water samples are collected from Rushikonda beach and tested in the nearby testing facility, National Collateral Management Services Limited. The salinity and TDS are within the limits and are encouraging for a Reverse Osmosis plant. A mathematical model of RO system is analyzed with different recovery ratio and feed salinity. Based on the energy requirements, the solar PV system is estimated and designed. An economic and environmental analysis is conducted to understand the capital cost, operating cost, profit, and CO2 emissions of the system. The analysis would examine the practicality and effectiveness of the system, along with the associated costs and benefits of implementing it. Additionally, the study would investigate the potential environmental impacts and carbon footprint of the PV-RO desalination process, with a focus on its sustainability and long-term viability.
Proteome analysis of splenic responses to Marek’s disease virus strain GX0101 infection
Authors: Wang Chuan , Liu Yuanzi , Yang Yuze , Teng Man , Wan Xuerui , Wu Zixiang , Zhang Zhao 
Published: March 2023

Abstract
Marek's disease virus (MDV) strain GX0101 was the first reported field strain of recombinant gallid herpesvirus type 2 (GaHV-2). However, the splenic proteomics study of MDV strain GX0101 infected chicken was still unclear. In this study, GX0101 was used to infect the chicken spleen in order to analyze the splenic proteomics of the chicken after GX0101 infection. The viral genome was extracted from blood samples of chickens after 7 days post infection (dpi), 14 dpi, 21 dpi, 30 dpi, 45 dpi, 60 dpi and 90 dpi to extract the viral genome. qPCR results showed that the expression levels of MDV related genes meq and gB in blood peaked at 45 dpi. The heart, liver and spleen were respectively dissected 30 dpi and 45 dpi, and hematoxylin-eosin (HE) staining indicated that the normal organizational structure of the internal organs of the chicken in the experimental group at 45 dpi was destroyed. Among them, the spleen structure was severely damaged, and the lymphocytes in the white medulla were significantly reduced. Mass spectrometry (MS) together with liquid chromatography and Label-free technique were further used to analyze the difference of proteomics of spleen tissue between the experimental group and the control group at 30 dpi and 45 dpi. Proteomic analysis revealed 1660 and 1244 differentially expressed proteins (DEPs) 30 dpi and 40 dpi in the spleen tissues, respectively, compared with the uninfected spleen tissues. According to GO analysis, these DEPs were involved in organelle organization, cellular component biogenesis, cellular component assembly, anion binding, small molecule binding, metal ion binding, cation binding, cytosol, nuclear part and other processes. KEGG analysis indicated the following pathways were linked to MDV-induced inflammation, apoptosis and tumor, including Wnt, Hippo, AMPK, cAMP, Notch, TGF-β, PI3K-Akt, Rap1, Ras, Calcium, NF-κB, PPAR, cGMP-PKG, Apoptosis, VEGF, mTOR, FoxO, TNF, JAK-STAT, MAPK, Prion disease, T cell receptor, B cell receptor. We found 674 DEPs from analyzed KEGG-enriched pathways, and these proteins were linked to MDV infection in spleen tissue. This study offered helpful data for understanding the response mechanism of MDV in spleen.
Flood susceptibility zonation map using remote sensing and XGboost, Random Forest, Nearest neighbor models in GIS: a case study Tebessa city, Algeria.
Authors: TALBI Sarra , GUERZOULI Lazhar , FEZZAI Soufiane 
Published: March 2023

Abstract
Predictive mapping of flooding zone occurrence at the regional scale was performed for the urban perimeter of Tebessa City, Algeria, using remote sensing by Landsat to detect the past effect of flood occurrences of the last 10 years and estimate the future potential flood disaster. Machine learning ML was instrumental in predicting the flooding zones of Tebessa City through the utilization of three models: XGboost, Random Forest, and Nearest neighbor models. To generate the Flood susceptibility zonation map of Tebessa City, approximately 495 flood locations, and 490 Non-flood locations were selected as a training data according to the data set recorded by The direction of civil protection of the wilaya of Tebessa, and about 15% of them were used as a validation set. To predict the 54,945 locations as a test dataset; The estimated accuracy values of prediction rates using the Accuracy score method for XGboost, Random Forest classified, and Nearest neighbor models were 93.92%, 93.91%, and 93.24%, respectively. Geospatial databases relevant to flood zone occurrence (hydrographic factors, topographic factors, climatic factors, and human factors) were analyzed in a geographic information system environment GIS.
Giant spin Hall effect in AB-stacked MoTe2/WSe2 bilayers
Authors: Tao Zui , Shen Bowen , Zhao Wenjin , Hu Nai Chao , Li Tingxin , Jiang Shengwei , Li Lizhong , Watanabe Kenji , Taniguchi Takashi , MacDonald Allan , Shan Jie , Mak Kin Fai 
Published: March 2023

Abstract
The spin Hall effect (SHE), in which electrical current generates transverse spin current, plays an important role in spintronics for the generation and manipulation of spin-polarized electrons 1–7. The phenomenon originates from spin-orbit coupling. In general, stronger spin-orbit coupling favors larger SHEs but shorter spin relaxation times and diffusion lengths 1,4–7. To achieve both large SHEs and long-range spin transport in a single material has remained a challenge. Here we demonstrate a giant intrinsic SHE in AB-stacked MoTe2/WSe2 moiré bilayers by direct magneto optical imaging. Under moderate electrical currents with density < 1 A/m, we observe spin accumulation on transverse sample edges that nearly saturates the spin density. We also demonstrate long-range spin Hall transport and efficient non-local spin accumulation limited only by the device size (about 10 µm). The gate dependence shows that the giant SHE occurs only near the Chern insulating state, and at low temperatures, it emerges after the quantum anomalous Hall breakdown. Our results demonstrate moiré engineering of Berry curvature and large SHEs for potential spintronics applications.
Spatio-Temporal Analysis of Pre-earthquake Scenario to Forecasting the Earthquake Magnitude
Authors: M Senthil Kumar , Natarajan Venkatanathan 
Published: March 2023

Abstract
Although many earthquake precursors have been identified to forecast earthquakes, even in this modern era, forecasting earthquakes in the short term remains a challenging task because of the heterogeneous nature of earthquakes. This study mainly focused on how pre-earthquake scenarios like pre-shocks and atmospheric parameters such as OLR are influenced by the earthquake. The Himalayan belt is one of the most at-risk areas during a continental-continental collision, we decided to focus on earthquakes in that area for our study. For this, we investigated the spatiotemporal analysis of the pre-earthquake scenario to identify the most vulnerable seismic risk zone and to forecast the magnitude of the earthquake. We found there is an energy distribution concentration toward the epicentre of the earthquake. Furthermore, the study also found that abnormal changes in atmospheric parameters could occur several days before an earthquake, which could potentially serve as an early warning system for seismic activity. We observed that on certain days, the OLR spread moved from one location to another in the direction of, or across, the epicentre. This allows us to determine the epicentre of the earthquake with relative ease. This research highlights the importance of monitoring both seismic and atmospheric activity to improve earthquake forecasting and preparedness. As a result of our research, we may be able to identify the most vulnerable seismic zone, locate the epicentre using spatial analysis, and forecast the magnitude of the earthquake using the temporal technique.
Deep learning-based algorithm versus physician judgement for myopathy and neuropathy diagnosis based on needle electromyography findings
Authors: Yoo Ilhan , Yoo Jaesung , Kim Dongmin , Youn Ina , Kim Hyodong , Youn Michelle , Won Jun Hee , Cho Woosup , Myong Youho , Kim Sehoon , Yu Ri , Kim Sung-Min , Kim Kwangsoo , Lee Seung-Bo , Kim Keewon 
Published: March 2023

Abstract
Electromyography is a valuable diagnostic tool for diagnosing patients with neuromuscular diseases; however, it has possible drawbacks including diagnostic accuracy and a time- and effort-intensive process. To overcome these limitations, we developed a deep learning-based electromyography diagnosis system and compared its performance with that of six physicians. This study included 58 participants who underwent electromyography and were subsequently confirmed to have myopathy or neuropathy or to be in a normal state at single tertiary centre. We developed a one-dimensional convolutional neural network and Divide-and-Vote algorithms for diagnosing patients. Diagnostic results from our deep learning model were compared with those of six physicians with experience in performing and interpreting electromyography. The accuracy, sensitivity, specificity, and positive predictive value of the deep learning model were 0.875, 0.820, 0.904, and 0.820, respectively, whereas those of the physicians were 0.694, 0.537, 0.773, and 0.524, respectively. The area under the receiver operating characteristic curves of the deep learning model was also better than those of the averaged results of the six physicians. Thus, deep learning could play a key role in diagnosing patients with neuromuscular diseases.
Machine Learning and Bioinformatics Approaches to Identify the Candidate Biomarkers in Severe Asthma
Authors: Zhang Fuying , zhu jiabao , Lei Mingsheng 
Published: March 2023

Abstract

Background
Severe asthma is defined as a persistent increase in airway inflammation despite the use of systemic glucocorticoids, targeted biologic therapies. Early prediction of severe asthma is challenging due to the lack of valuable biomarkers. The aim of this study was to identify crucial differentially expressed genes (DEGs) associated with severe asthma through approaches of bioinformatics analysis.
Methods
Three datasets GSE130499, GSE43142 and GSE43696 were derived from the GEO expression database. Two datasets (GSE130499 and GSE43142) were merged, and batch effects were removed by using the "SVA" package. Afterwards, the differentially expressed genes (DEGs) were analyzed with the "limma" package. Next, DEGs were functionally enriched and pathway analyzed in the online analysis website DAVID, then DEGs were analyzed again by two machine learning algorithms (LASSO and SVM-RFE) to obtain the candidate biomarkers, and the diagnostic validity of the biomarkers was assessed using subject operating characteristic (ROC) curves, and finally the results were further validated through the GSE43696.
Results
Total of 73 gene differential expression genes were identified in severe asthma and normal control. After screening with two machine learning algorithms, LASSO and SVM-RFE, three genes (BCL3, DDIT4 and S100A14) were recognized as biomarkers of asthma and had good diagnostic effect. Among them, BCL3 transcript level was down-regulated in severe asthma, while S100A14 and DDIT4 transcript levels were up-regulated.
Conclusions
In this study, we identified three differentially expressed genes (BCL3, DDIT4 and S100A14) of diagnostic significance that may be involved in the development of severe asthma and proposed new insights into the underlying mechanisms.
Room-temperature ultrastrong exciton-plasmon coupling in WS2 monolayer synthesized with a multi-singular metasurface
Authors: Wu Tingting , Wang Chongwu , Hu Guangwei , Wang Zhixun , Zhao Jiaxin , Wang Zhe , Chaykun Ksenia , Liu Lin , Chen Mengxiao , Li Dong , Xiong Qihua , Shen Ze , Gao Huajian , Garcia-Vidal Francisco , Wei Lei , Wang Qi Jie , Luo Yu 
Published: March 2023

Abstract
Monolayer semiconductors exemplified by two-dimensional transition-metal dichalcogenides (TMDs) have promised next-generation atomically thin optoelectronics. Boosting their interactions with light is vital for practical applications, especially in the quantum regime where ultrastrong interaction is highly demanded but not yet realized. Here we report ultrastrong exciton-plasmon coupling in a tungsten disulfide (WS2) monolayer loaded with a multi-singular plasmonic metasurface at room temperature. Different from seeking perfect metals or high-quality resonators, we create a unique type of cold-etched metasurface with multiple singularities. Multiple plasmonic hotspots that support tightly confined gap plasmons fit into the coherent area of WS2 excitons and thus trigger the ultrastrong exciton-plasmon coupling with a normalized coupling strength of 0.104. As an application demonstration, we showed dispersive polariton-enhanced and tunable second-order nonlinearities in the WS2 monolayer. Our findings reveal room-temperature extreme light-matter interactions in TMD monolayers for future applications in nonlinear optics, quantum information processing, laser physics and others.
Distinct neural mechanisms for heading retrieval and context recognition in the hippocampus during spatial reorientation
Authors: Gagliardi Celia , Normandin Marc , Keinath Alexandra , Julian Joshua , Lopez Matthew , Ramos-Alvarez Manuel , Epstein Russell , Muzzio Isabel 
Published: March 2023

Abstract

Reorientation, the process of regaining one’s bearings after becoming lost, requires identification of a spatial context (context recognition) and recovery of heading direction within that context (heading retrieval). We previously showed that these processes rely on the use of features and geometry, respectively. Here, we examine reorientation behavior in a task that creates contextual ambiguity over a long timescale to demonstrate that mice learn to combine both featural and geometric cues to recover heading with experience. At the neural level, most CA1 neurons persistently align to geometry, and this alignment predicts heading behavior. However, a small subset of cells shows feature-sensitive place field remapping, which serves to predict context. Efficient heading retrieval and context recognition require integration of featural and geometric information in the active network through rate changes. These data illustrate how context recognition and heading retrieval are coded in CA1 and how these processes change with experience.