bluuebunny/crossref_metadata_2025_split huggingface dataset:
The February 2021 Cold Air Outbreak in the United States: A Subseasonal Forecast of Opportunity
Authors: Albers John R. , Newman Matthew , Hoell Andrew , Breeden Melissa L. , Wang Yan , Lou Jiale 
Published: October 2022

Abstract
The sources of predictability for the February 2021 cold air outbreak (CAO) over the central United States, which led to power grid failures and water delivery shortages in Texas, are diagnosed using a machine learning–based prediction model called a linear inverse model (LIM). The flexibility and low computational cost of the LIM allows its forecasts to be used for identifying and assessing the predictability of key physical processes. The LIM may also be run as a climate model for sensitivity and risk analysis for the same reasons. The February 2021 CAO was a subseasonal forecast of opportunity, as the LIM confidently predicted the CAO’s onset and duration four weeks in advance, up to two weeks earlier than other initialized numerical forecast models. The LIM shows that the February 2021 CAO was principally caused by unpredictable internal atmospheric variability and predictable La Niña teleconnections, with nominally predictable contributions from the previous month’s sudden stratospheric warming and the Madden–Julian oscillation. When run as a climate model, the LIM estimates that the February 2021 CAO was in the top 1% of CAO severity and suggests that similarly extreme CAOs could be expected to occur approximately every 20–30 years.
Analysis of Precipitation Data Using Innovative Trend Pivot Analysis Method and Trend Polygon Star Concept: A Case Study of Soan River Basin, Potohar Pakistan
Authors: Hussain Fiaz , Ceribasi Gokmen , Ceyhunlu Ahmet Iyad , Wu Ray-Shyan , Cheema Muhammad Jehanzeb Masud , Noor Rana Shahzad , Anjum Muhammad Naveed , Azam Muhammad , Afzal Arslan 
Published: October 2022

Abstract
The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, the environment, and water resources. In this study, the innovative trend pivot analysis method (ITPAM) and trend polygon star concept method were applied for precipitation trend detection at 11 stations located in the Soan River basin (SRB), Potohar region, Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting, and calculating monthly shifts under different trend behaviors, that is, increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows a complex nature of behavior, and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first dataset and the second dataset in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behavior in the water resources and agricultural sectors.

Significance Statement
The monthly polygonal trends with risk graphs of total monthly precipitation data depicted a clear picture of climate change effects in the Potohar region of Pakistan. The monsoonal rainfall showed a significant decreasing trend in highland stations and an increasing trend in lowland stations, indicating a shifting pattern of monsoonal rainfall from highland to lowland areas.
Atmospheric Cold Pools in the Bay of Bengal
Authors: Simoes-Sousa Iury T. , Tandon Amit , Buckley Jared , Sengupta Debasis , J Sree Lekha , Shroyer Emily , de Szoeke Simon P. 
Published: October 2022

Abstract
Atmospheric cold pools, generated by evaporative downdrafts from precipitating clouds, are ubiquitous in the Bay of Bengal. We use data from three moorings near 18°N to characterize a total of 465 cold pools. The cold pools are all dry, with a typical temperature drop of 2°C (max. 5°C) and specific humidity drop of 1 g/kg (max. 6 g/kg). Most cold pools last 1.5-3.5 hours (max. 14 hours). Cold pools occur almost every day in the North Bay from April to November, principally in the late morning, associated with intense precipitation that accounts for 80% of total rain. They increase the latent heat flux to the atmosphere by about 32 W/m2(median), although the instantaneous enhancement of latent heat flux for individual cold pools reaches 150 W/m2 . During the rainiest month (July), the cold pools occur 21% of the time and contribute nearly 14% to the mean evaporation. A composite analysis of all cold pools shows that the temperature and specific humidity anomalies are responsible for ~90% of the enhancement of sensible and latent heat flux, while variations in wind speed are responsible for the remainder. Depending on their gust front speed, the estimated height of the cold pools primarily ranges from 850 to 3200 m, with taller fronts more likely to occur during the summer monsoon season (Jun-Sep). Our results indicate that the realistic representation of cold pools in climate models is likely to be important for improved simulation of air-sea fluxes and monsoon rainfall.
Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis
Authors: Novella-Navarro Marta , Benavent Diego , Ruiz-Esquide Virginia , Tornero Carolina , Díaz-Almirón Mariana , Chacur Chafik Alejandro , Peiteado Diana , Villalba Alejandro , Sanmartí Raimon , Plasencia-Rodríguez Chamaida , Balsa Alejandro 
Published: October 2022

Background: Despite advances in the treatment of rheumatoid arthritis (RA) and the wide range of therapies available, there is a percentage of patients whose treatment presents a challenge for clinicians due to lack of response to multiple biologic and target-specific disease-modifying antirheumatic drugs (b/tsDMARDs). Objective: To develop and validate an algorithm to predict multiple failure to biological therapy in patients with RA. Design: Observational retrospective study involving subjects from a cohort of patients with RA receiving b/tsDMARDs. Methods: Based on the number of prior failures to b/tsDMARDs, patients were classified as either multi-refractory (MR) or non-refractory (NR). Patient characteristics were considered in the statistical analysis to design the predictive model, selecting those variables with a predictive capability. A decision algorithm known as ‘classification and regression tree’ (CART) was developed to create a prediction model of multi-drug resistance. Performance of the prediction algorithm was evaluated in an external independent cohort using area under the curve (AUC). Results: A total of 136 patients were included: 51 MR and 85 NR. The CART model was able to predict multiple failures to b/tsDMARDs using disease activity score-28 (DAS-28) values at 6 months after the start time of the initial b/tsDMARD, as well as DAS-28 improvement in the first 6 months and baseline DAS-28. The CART model showed a capability to correctly classify 94.1% NR and 87.5% MR patients with a sensitivity = 0.88, a specificity = 0.94, and an AUC = 0.89 (95% CI: 0.74–1.00). In the external validation cohort, 35 MR and 47 NR patients were included. The AUC value for the CART model in this cohort was 0.82 (95% CI: 0.73–0.9). Conclusion: Our model correctly classified NR and MR patients based on simple measurements available in routine clinical practice, which provides the possibility to characterize and individualize patient treatments during early stages.
EnsembleSplice: ensemble deep learning model for splice site prediction
Authors: Akpokiro Victor , Martin Trevor , Oluwadare Oluwatosin 
Published: October 2022

AbstractBackgroundIdentifying splice site regions is an important step in the genomic DNA sequencing pipelines of biomedical and pharmaceutical research. Within this research purview, efficient and accurate splice site detection is highly desirable, and a variety of computational models have been developed toward this end. Neural network architectures have recently been shown to outperform classical machine learning approaches for the task of splice site prediction. Despite these advances, there is still considerable potential for improvement, especially regarding model prediction accuracy, and error rate.ResultsGiven these deficits, we propose EnsembleSplice, an ensemble learning architecture made up of four (4) distinct convolutional neural networks (CNN) model architecture combination that outperform existing splice site detection methods in the experimental evaluation metrics considered including the accuracies and error rates. We trained and tested a variety of ensembles made up of CNNs and DNNs using the five-fold cross-validation method to identify the model that performed the best across the evaluation and diversity metrics. As a result, we developed our diverse and highly effective splice site (SS) detection model, which we evaluated using two (2) genomicHomo sapiensdatasets and theArabidopsis thalianadataset. The results showed that for of theHomo sapiensEnsembleSplice achieved accuracies of 94.16% for one of the acceptor splice sites and 95.97% for donor splice sites, with an error rate for the sameHomo sapiensdataset, 4.03% for the donor splice sites and 5.84% for theacceptor splice sites datasets.ConclusionsOur five-fold cross validation ensured the prediction accuracy of our models are consistent. For reproducibility, all the datasets used, models generated, and results in our work are publicly available in our GitHub repository here:https://github.com/OluwadareLab/EnsembleSplice
Examining the underlying processes of different dimensions of self-care behavior among persons with heart failure
Authors: Al-Hammouri Mohammed Munther , Rababah Jehad A. 
Published: October 2022

Abstract
Purpose
This study was conducted to compare how potential predictors differentially contribute to the different dimensions of self-care in persons with heart failure.

Methods
A cross-sectional design was used in this study. Purposive sampling was used to recruit a sample (N = 252) in this study. The data were collected from three major referral hospitals in Jordan. Data analysis was performed using multiple linear regression.

Results
The results showed that around 89% of our sample had insufficiency in at least one dimension of self-care. The initial regression models showed that different combinations of predictors were significantly associated with different dimensions of self-care. These models were also different in terms of the variance explained and the coefficients of the significant predictors. After the follow-up analysis of the best fit models for these dimensions, these differences were maintained.

Conclusion
Despite the similarities in the proposed variables predicting different self-care dimensions, their differences may suggest variations in the underlying processes controlling different self-care dimensions. The current study showed that seven out of the nine proposed variables predicted different dimensions of self-care either in the initial or best-fit models.
Clinical characteristics, drug resistance and death risk factors of Burkholderia cepacia infection in hematopoietic stem cell transplant patients
Authors: Jia Yan , Liu Ya , Liu Yi , Yang Kaitai , Liu Yanfeng 
Published: October 2022

Abstract
Background
Burkholderia cepacia (BC) has been detected more and more in infected patients in recent years. However, as a high-risk population, the clinical characteristics and prognosis of BC infection in hematopoietic stem cell transplantation (HSCT) patients have not been reported. The purpose of this study is to obtain data that will help fill in the gaps in this field, provide evidence for reducing the mortality rate of BC infection in HSCT patients, and guide the use of antibiotics in the future.

Methods
Electronic medical records of patients with BC infection who underwent HSCT in Xiangya Hospital of Central South University from September 1, 2015 to August 31, 2021 were collected. At the same time, 1:1 case–control matching was conducted according to gender, age and disease type. Comparisons between patients with/without BC infection and respiratory failure were made respectively, and the sensitivity of BC to five clinically commonly used antibiotics was also evaluated. Univariate and multivariate analyses were performed to identify independent risk factors for death.

Results
The most common site of BC infection in HSCT patients was the lung (75%). Although BC infection rate (3.74%) and antibiotic resistance were not significant, it was closely associated with a higher risk of death (P = 0.022), which even further increased to 90.9% when combined with respiratory failure (P = 0.008). Procalcitonin > 10 µg/L (HR = 40.88, 95% CI 6.51–256.63, P = 0.000) and septic shock (HR = 4.08, 95% CI 1.02–16.33, P = 0.047) were two independent risk factors for death.

Conclusion
HSCT patients with BC infection are in critical condition, and the management of respiratory infection should be especially strengthened to improve the prognosis of these patients.
Palliative care symptoms, concerns and well-being of older people with frailty and complex care needs upon hospital discharge: a cross-sectional study
Authors: de Nooijer Kim , Van Den Noortgate Nele , Pype Peter , Van den Block Lieve , Pivodic Lara 
Published: October 2022

Abstract
Background
Little is known about the nature and intensity of palliative care needs of hospitalised older people. We aimed to describe the palliative care symptoms, concerns, and well-being of older people with frailty and complex care needs upon discharge from hospital to home, and to examine the relationship between palliative care symptoms and concerns, and well-being.

Methods
Cross-sectional study using baseline survey data of a pilot randomised controlled trial. Hospital staff identified patients (≥ 70 years) about to be discharged home, with a clinical frailty score of 5 to 7 and complex needs based on physician-assessment. Patients completed structured interviews, using the Integrated Palliative Care Outcome Scale (IPOS), ICEpop CAPability measure for supportive care (ICECAP-SCM) and IPOS Views on Care quality of life item. We calculated descriptive statistics.

Results
We assessed 37 older people with complex needs (49% women, mean age 84, standard deviation 6.1). Symptoms rated as causing severe problems were weakness (46%) and poor mobility (40%); 75% reported that their family felt anxious at least occasionally. Of the 17 IPOS items, 41% of patients rated five or more symptoms as causing severe problems, while 14% reported that they were not severely affected by any symptom. 87% expressed feeling supported. There was a negative correlation between symptoms (IPOS) and well-being (ICECAP); r = -0.41.

Conclusion
We identified a large variety of symptoms experienced by older people identified as having frailty and complex needs upon hospital discharge. Many were severely affected by multiple needs. This population should be considered for palliative care follow-up at home.
Building the plane while it’s flying: implementation lessons from integrating a co-located exercise clinic into oncology care
Authors: Kennedy Mary A. , Bayes Sara , Newton Robert U. , Zissiadis Yvonne , Spry Nigel A. , Taaffe Dennis R. , Hart Nicolas H. , Galvão Daniel A. 
Published: October 2022

Abstract
Background
Despite its therapeutic role during cancer treatment, exercise is not routinely integrated into care and implementation efforts are largely absent from the literature. The aim of this study was to evaluate a strategy to integrate the workflow of a co-located exercise clinic into routine care within a private oncology setting in two clinics in the metropolitan region of Western Australia.

Methods
This prospective evaluation utilised a mixed methods approach to summarise lessons learned during the implementation of an integrated exercise workflow and supporting implementation plan. Data collection was informed by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework. Reports detailing utilisation of the exercise service and its referral pathways, as well as patient surveys and meeting minutes documenting the implementation process informed the evaluation.

Results
The co-located exercise service achieved integration into routine care within the clinical oncology setting. Patient utilisation was near capacity (reach) and 100% of clinicians referred to the service during the 13-month evaluation period (adoption). Moreover, ongoing adaptations were made to improve the program (implementation) and workflows were integrated into standard operating practices at the clinic (maintenance). The workflow performed as intended for ~70% of exercise participants (effectiveness); however, gaps were identified in utilisation of the workflow by both patients and clinicians.

Conclusion
Integration of exercise into standard oncology care is possible, but it requires the ongoing commitment of multiple stakeholders across an organisation. The integrated workflow and supporting implementation plan greatly improved utilisation of the co-located exercise service, demonstrating the importance of targeted implementation planning. However, challenges regarding workflow fidelity within and across sites limited its success highlighting the complexities inherent in integrating exercise into clinical oncology care in a real-world setting.
Initial psychometric evaluation of the Portuguese version of the Liverpool Osteoarthritis in Dogs
Authors: Alves J. C. , Jorge P. , Santos A. 
Published: October 2022

Abstract
Background
Osteoarthritis (OA) is the most commonly diagnosed joint disease in companion animals, and proper tools are necessary to assess patients and response to treatment. We aimed to perform an initial psychometric evaluation of the Portuguese version of the Liverpool Osteoarthritis in Dogs (LOAD).Fifty Police working dogs with bilateral hip OA were assessed in a prospective, randomized, double-blinded study. Canine handlers, who were native Portuguese speakers, completed a copy of the translated version of the LOAD. Their results were compared with those of fifty sound dogs. Construct validity was evaluated by assessing differences between OA and sound animals with the Mann-Whitney test. Further evaluation was performed with the Kaiser-Meyer-Olkin measure of sampling adequacy, Eigenvalue, and scree-plot analysis. Internal consistency was tested with Cronbach’s α.

Results
A significant difference was observed between OA and sound dogs (p < 0.01), indicating construct validity. Two factors accounted for 81.5% of the total variance. Cronbach’s α was 0.96, and a high inter-item correlation was observed, raging from 0.76 to 0.95, showing strong internal consistency. We presented criterion and construct validity of the Portuguese version of the LOAD, which is valid for use in the Portuguese language. It is an additional stage in providing a broader number of clinicians with an accessible tool to evaluate dogs with osteoarthritis. Further studies are required.
Severe malaria
Authors: White Nicholas J. 
Published: October 2022

AbstractSevere malaria is a medical emergency. It is a major cause of preventable childhood death in tropical countries. Severe malaria justifies considerable global investment in malaria control and elimination yet, increasingly, international agencies, funders and policy makers are unfamiliar with it, and so it is overlooked. In sub-Saharan Africa, severe malaria is overdiagnosed in clinical practice. Approximately one third of children diagnosed with severe malaria have another condition, usually sepsis, as the cause of their severe illness. But these children have a high mortality, contributing substantially to the number of deaths attributed to ‘severe malaria’. Simple well-established tests, such as examination of the thin blood smear and the full blood count, improve the specificity of diagnosis and provide prognostic information in severe malaria. They should be performed more widely. Early administration of artesunate and broad-spectrum antibiotics to all children with suspected severe malaria would reduce global malaria mortality.
AXL, along with PROS1, is overexpressed in papillary thyroid carcinoma and regulates its biological behaviour
Authors: Wei Mingze , Wang Yizeng , Liu Yuanchao , Li Dongyang , He Xianghui 
Published: October 2022

Abstract
Background
AXL, a TAM tyrosine kinase receptor, plays an essential role in the pathogenesis of various solid tumours. This study explores the role of AXL and its ligand PROS1 in the generation and biological behaviour of papillary thyroid cancer (PTC).

Methods
The expression levels of AXL in PTC cancer tissue were analysed using immunohistochemistry (IHC) staining. The expression levels of AXL in PTC and normal thyroid cell lines were analysed using real-time quantitative polymerase chain reaction (RT-qPCR). CCK-8 was used to assess the proliferation of the PTC cell line with and without the effect of the AXL inhibitor (R428). Scratching assays played a role in evaluating the cell migration rate.

Results
PROS1 and AXL were expressed in TPC-1, B-CPAP, and Nthy-Ori 3–1 cells at different levels. Expression was significantly higher in PTC cell lines (TPC-1 and B-CPAP) than in the normal thyroid cell line (Nthy-Ori 3–1) (p < 0.05). In addition, AXL expression in PTC tissues was significantly higher than in adjacent normal tissues (p < 0.05). CCK-8 experiments confirmed that R428 suppresses the proliferation of PTC cell lines in a dose-dependent manner, with an increase in concentration from 0.5 to 4 μM, decreasing the inhibitory effect (p < 0.01). In addition, R428 inhibited PTC cell line migration to different degrees in a range of concentrations from 0.5 to 2 μM compared to control cells (p < 0.01).

Conclusion
PROS1 and its downstream receptor AXL expression were significantly higher in PTC than in normal thyroid cells. AXL expression was also higher in human PTC tissues than in normal thyroid tissues. Inhibiting the PROS1-AXL-mediated TAM signaling pathway via the AXL blocker R428 suppressed the proliferation and migration of human PTC cells, highlighting the role of this cascade in human PTC development and progression.