bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptive Particle Swarm Optimized XGBoost Ensemble Algorithm for Online Credit Scoring
Authors: Museba Tinofirei 
Published: May 2022

Abstract
Determining the credit worthiness of an individual for a loan approval is a challenging task for most financial institutions. To assess the credibility of an individual, a credit score model is often used and it encompasses factors such payment consistency of customers and their behaviors and their previous behaviors. Due to its easy interpretation and outstanding prediction performance for both static and time-varying, evolving domains, machine learning has gained a lot of reputation over statistical approaches. For this reason, machine learning has gained much attention in the automation of the design of effective and efficient credit scoring techniques over the years. Credit scoring task is also an ephemeral scenario as most of the variables are likely to drift overtime, making the use of stream mining suitable for detecting and adapting to changes in the underlying data distribution. Given a problem in such time-varying and evolving environment, credit scoring models that were once robust may become suboptimal if the behavior of customers change over time as their earnings get eroded by changes in interest rates, natural disasters and increases in pay as you earn, rendering the current credit score model outdated. In credit scoring, many of the variables tend to drift over time resulting in an ephemeral scenario that requires online machine learning algorithms tailored for learning in time-varying and evolving environments where the target concept changes. Since machine learning algorithms can learn incrementally, they are able to detect learn changes that occur in the data. In this paper, we propose an online Decision Trees based eXtreme Gradient Boosting (XGBoost) credit scoring model that is based on a heterogeneous adaptive particle swarm optimization where behaviors change at each iteration by randomly selecting new behaviors from the behavior pool. The prediction performance of our approach is evaluated first as a batch learning algorithm and secondly as a data steam learning algorithm using a variety of validation schemes for traditional batch learning and the Kolmogorov-Smirnov and Population Stability index metrics. Each behavior consists of a pair containing a position update and a velocity update. Decision Trees can easily sift through credit data characterized by a high dimensional curse and a complex correlation. The behavior of our proposed approach is evaluated for both static and dynamic domains since customer variables tend to drift over time. Empirical experiments conducted showed that our proposed approach performs comparatively well in both static and dynamic environments on four datasets.
Plasmon Resonances of Graphene-Assisted Core-Bishell Nanoparticles
Authors: Fares Hesham , Ahmed Moustafa , Moustafa Samar 
Published: May 2022

Abstract
We study the Localized Surface Plasmon Resonance (LSPR) in graphene-assisted core-bishell nanoparticles which consists of a graphene layer (outer shell) wrapped around a metal shell and either a dielectric or metal core. Small nanoparticles with a size much smaller than the wavelength of incident light are assumed, and the quasi-static approximation is applied to develop analytic equations to describe the absorption, scattering, extinction efficiencies in the core-bishell nanoparticles. The proposed nanostructures exhibit two LSPRs; one is in the visible range and corresponds to a plasmon mode of the core-inner shell composite while the second lies in the near infrared and is induced by the graphene plasmons excited at the outer shell. Interestingly, the latter LSPR of graphene has an ultra-narrow bandwidth and can be tuned in the NIR by altering the physical parameters of graphene, such as the Fermi energy and the number of graphene layers. Therefore, the LSPR peak of graphene is promising for medical applications. In addition, the LSPR of graphene can be tuned to the visible range near the position of the first LSPR, resulting in two narrow linewidth peaks. These resonance peaks could be beneficial for high-sensitive LSPR-based sensors.
Unraveling the genetics of polyamine metabolism in barley for senescence-related crop improvement
Authors: Tanwar Umesh Kumar , Stolarska Ewelina , Paluch-Lubawa Ewelina , Mattoo Autar K. , Arasimowicz-Jelonek Magdalena , Sobieszczuk-Nowicka Ewa 
Published: May 2022

Abstract

We explored the polyamine (PA) metabolic pathway genes in barley (Hv) to understand plant development and stress adaptation in Gramineae crops with emphasis on leaf senescence. Bioinformatics and functional genomics tools were utilized for genome-wide identification, comprehensive gene features, evolution, development and stress effects on the expression of the polyamine metabolic pathway gene families (PMGs). Three S-adenosylmethionine decarboxylases (HvSAMDCs), two ornithine decarboxylase, one arginine decarboxylase, one spermidine synthase (HvSPDS), two spermine synthases (HvSPMSs), five copper amine oxidases (HvCuAO) and seven polyamine oxidases (HvPAOs) members of PMGs were identified and characterized in barley. All the genes except for HvCuAO3 were found distributed on all chromosomes of barley. The phylogenetic and comparative assessment revealed that PA metabolic pathway is highly conserved in plants and the prediction of nine H. vulgare miRNAs (hvu-miR) target sites, 18 protein-protein interactions and 961 putative CREs in the promoter region were discerned. Gene expression of HvSAMDC3, HvCuAO7, HvPAO4 and HvSPMS1 were apparent at every developmental stage. SPDS/SPMS gene family was found to be the most responsive to induced leaf senescence. This study provides a reference for the functional investigation of the molecular mechanism(s) that regulate polyamine in plants as a tool for future breeding decision management systems.
Characteristics of donor vessels and cerebral blood flow in the chronic phase after combined revascularization surgery for moyamoya disease
Authors: Mamiya Takashi , Araki Yoshio , Taoka Toshiaki , Fujita Naotoshi , Yokoyama Kinya , Uda Kenji , Muraoka Shinsuke , Kanamori Fumiaki , Takayanagi Kai , Ishii Kazuki , Nishihori Masahiro , Izumi Takashi , Kato Katsuhiko , Saito Ryuta 
Published: May 2022

Abstract

This study aimed to analyze whether the development of donor vessels after combined revascularization surgery for moyamoya disease (MMD) was related to cerebral blood flow (CBF) changes. We retrospectively reviewed the charts of 11 adult (12 hemispheres) and 13 pediatric (19 hemispheres) patients who underwent combined revascularization in our department. The total vessel cross-sectional area (TVA) was the sum of the cross-sectional areas of the superficial temporal, middle meningeal, and deep temporal arteries imaged using time-of-flight magnetic resonance angiography. The ipsilateral relative CBF (RCBF) on the brain surface in the craniotomy area was calculated by single-photon emission computed tomography. The preoperative and postoperative ratios of the TVA and RCBF were defined as ΔTVA and ΔRCBF, respectively. Finally, we investigated the correlation between ΔTVA and ΔRCBF in adults and children. The TVA and RCBF showed a significant increase after surgery regardless of the age group. However, there was no significant correlation between ΔTVA and ΔRCBF in either the adult or pediatric groups. The adult group had significantly higher ΔRCBF values than the pediatric group (p < 0.01, r = −0.44), but the pediatric group had higher ΔTVA values than the adult group (p = 0.06). An increase in CBF was detected around the craniotomy area during the chronic phase after combined revascularization for MMD. However, there was no correlation between the increase in CBF and the measurable total donor vessel area changes.
Is overestimation of one’s hand hygiene compliance affected by how it is measured? A cross-sectional survey of physicians and nurses in surgical clinics of six nonuniversity hospitals in Germany
Authors: Lamping Jonas , Tomsic Ivonne , Stolz Maike , Krauth Christian , Chaberny Iris F. , von Lengerke Thomas 
Published: May 2022

Abstract
Background
One barrier to hand hygiene compliance is overestimation of one’s own performance. Overconfidence research shows that overestimation tends to be higher for difficult tasks, which suggests that the magnitude of overestimation also depends on how it is assessed. Asking health care workers to self-report compliance using a single item is common but difficult since responses must be based on aggregation across the “5 Moments of Hand Hygiene” (WHO-5). We tested the hypothesis that self-reported overall compliance based on one item is higher than that based on WHO-5-items. Additionally, we compared both overall and WHO-5-specific self-reported compliance rates with direct observations.
Methods
In the WACH trial (German Clinical Trials Register [DRKS] ID: DRKS00015502), a questionnaire survey was conducted among physicians and nurses in nine surgical clinics (general/visceral surgery or orthopedics/trauma surgery) of six nonuniversity hospitals. Self-reported compliance was assessed both by a single item and WHO-5-items using percentage scales, and compared among each other and to direct observations. Relative frequencies of the WHO-5 indications used to calculate the WHO-5-based self-reported overall compliance rate were estimated from a systematized review (first author). In analysis, t-tests, chi²-tests and multiple linear regressions were used.
Results
Ninety-three physicians (response rate: 28.4%) and 225 nurses (30.4%) participated. Significant compliance differences between physicians and nurses were found for direct observations (overall and for WHO-5 except “after patient contact”) and were in favor of nurses, while no such differences were found for self-reports (except “after body fluid exposure” in favor of physicians). Across the WHO-5, overestimation showed inverse correlations with observed compliance (physicians: r = − 0.88, = .049; nurses: r = − 0.81, p = 0.093). Support for the hypothesis that self-reported overall compliance based on one item is higher than that based on WHO-5 items was found for physicians (M = 87.2 vs. 84.1%, p = 0.041; nurses: 84.4 vs. 85.5%, p = 0.296). Exploratory analyses showed that this effect was confined to orthopedic/trauma surgeons (89.9 vs. 81.7%, p = 0.006).
Conclusion
Results indicate stronger hand hygiene overestimation among physicians and are consistent with their commonly lower compliance. For practice, results indicate that overestimation is a psychological mechanism that is relevant to professional hand hygiene promotion.
Frequency and Reasons for Unplanned Transfer to the Primary Acute Care Service of Inpatient Rehabilitation Glioblastoma Multiforme Patients
Authors: Fu Jack B. , Gupta Ekta , Morishita Shinichiro , Andersen Clark R. , Ngo-Huang An , Engle Jessica P. , Guo Ying 
Published: May 2022

Abstract
Purpose: To determine the percentage of and factors associated with unplanned transfer to the primary acute care service of glioblastoma multiforme acute rehabilitation inpatients.Methods: Retrospective review of glioblastoma multiforme acute rehabilitation inpatients admitted 4/1/2016-3/31/2020 at a National Cancer Institute Comprehensive Cancer CenterResults: 139 consecutive admissions of unique glioblastoma multiforme acute rehabilitation inpatients were analyzed. Fifteen patients (10.7%, 95% confidence interval 6.5%-17.1%) were transferred back to the primary acute care service for unplanned reasons. The most common reasons for transfer back were neurosurgical complication 6/15(40%), neurologic decline due to mass effect 4/15(26.7%), and pulmonary embolism 2/15(13.3%). Older age (p=0.010), infection prior to acute inpatient rehabilitation transfer (p=0.020), and lower Activity Measure of Post-Acute Care 6 Clicks - Basic Mobility scores (p=0.048) were significantly associated with transfer to the primary acute care service. Patients who returned to the primary acute care service had significantly lower overall survival than patients who did not transfer off (log-rank test p=0.001). Conclusion: Acute inpatient physiatrists should closely monitor patients for neurosurgical and neurologic complications. The variables significantly associated with return to the primary acute care service may help identify patients at increased risk for medical complications who may require closer observation.
COL11A1 as a potential prognostic target for oral squamous cell carcinoma Short title：Prognostic target for oral squamous cell carcinoma
Authors: Di Yong-bin , Bao Yang , Guo Jie , Liu Wei , Zhang Su-xin , Zhang Guan-hua , Li Tian-ke 
Published: May 2022

Abstract
Background
Oral squamous cell carcinoma (OSCC) is a malignant tumor of epithelial origin. Explore correlation between COL11A1 and prognosis of OSCC, provide a new direction for treatment and prognosis of patients.
Methods
Bioinformatics was used to screen and identify role of COL11A1 on OSCC. 200 patients with oral squamous cell carcinoma were recruited. Clinical and follow-up data were recorded and COL11A1 expression levels were tested. Pearson chi-square test and Spearman correlation coefficient were used to analyze relationship between prognosis and related parameters in patients with oral squamous cell carcinoma. Univariate and multivariate Logistic regression, univariate and multivariate Cox proportional risk regression were used for further analysis, survival curve was drawn.
Results
Through bioinformatics analysis, OSCC patients with higher expression of COL11A1 have poor overall survival compare with OSCC patients with lower expression of COL11A1 (HR = 1.32, P = 0.047). Pearson chi-square test showed that age (P = 0.011), tumor grade (P = 0.023), COL11A1 (P < 0.001) was significantly correlated with prognosis of oral squamous cell carcinoma. Spearman correlation coefficient analysis showed that age (ρ = 0.179, P = 0.011), tumor grade (ρ = 0.161, P = 0.023), COL11A1 (ρ = 0.561, P < 0.001) were significantly correlated with prognosis of patients with oral squamous cell carcinoma. Univariate Logistic regression analysis showed age (OR = 2.102, 95%CI: 1.180–3.746, P = 0.012), tumor grade (OR = 1.919, 95%CI: 1.093–3.372, P = 0.023) and COL11A1 (OR = 12.775, 95%CI: 6.509–25.071, P < 0.001). Multivariate Logistic regression analysis showed that COL11A1 (OR = 12.066, 95%CI: 6.042–24.096, P < 0.001) was significantly associated with prognosis of patients with oral squamous cell carcinoma. Univariate Cox regression analysis showed that age (HR = 1.592, 95%CI: 1.150–2.205, P = 0.005), tumor grade (HR = 1.460, 95%CI: 1.067–1.999, P = 0.018) and COL11A1 (HR = 1.848, 95%CI: 1.340–2.548, P < 0.001) were significantly correlated with survival time of oral squamous cell carcinoma patients. Multivariate Cox regression analysis showed that tumor grade (HR = 1.466, 95%CI: 1.064–2.020, P = 0.019) and COL11A1 (HR = 1.645, 95%CI: 1.164–2.325, P = 0.005) were significantly correlated with survival time of oral squamous cell carcinoma patients.
Conclusion
COL11A1 is significantly correlated with occurrence of oral squamous cell carcinoma.
Predicting treatment-resistance from first-episode psychosis using routinely collected clinical information: development and external validation study
Authors: Osimo Emanuele , Perry Benjamin , Mallikarjun Pavan , Pritchard Megan , Lewis Jonathan , Katunda Asia , Murray Graham , Perez Jesus , Jones Peter , Cardinal Rudolf , Howes Oliver , Upthegrove Rachel , Khandaker Golam 
Published: May 2022

Abstract
Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia (TRS), but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for TRS of routinely collected, objective biomedical predictors at FEP onset, and to externally validate the model in a separate clinical sample of people with FEP. We developed and externally validated two risk prediction models to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels, and lymphocyte counts in forced-entry logistic regression models. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index, and random glucose levels. The models were developed using data from two UK psychosis early intervention services (EIS) and externally validated in another UK EIS. Model performance was assessed via discrimination and calibration. We developed the models in 785 patients, and validated externally in 1,110 patients. Both models predicted clozapine use well at internal validation (forced-entry: C 0.70; 95%CI 0.63,0.76; LASSO: 0.69; 95%CI 0.63,0.77). At external validation, discrimination performance reduced (forced-entry: 0.63; 0.58,0.69; LASSO: 0.64; 0.58,0.69) but recovered after re-estimation of the lymphocyte predictor (C: 0.67; 0.62,0.73). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualisation tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.
Deep learning architectures for recognizing ontology concepts from scientific literature
Authors: Devkota Pratik , Mohanty Somya D. , Manda Prashanti 
Published: May 2022

Abstract
Background Annotating scientific literature with ontology concepts is a critical task in biology and several other domains for knowledge discovery. Ontology based annotations can power large-scale comparative analyses in a wide range of applications ranging from evolutionary phenotypes to rare human diseases to the study of protein functions. Computational methods that can tag scientific text with ontology terms have included lexical/syntactic methods, traditional machine learning, and most recently, deep learning. Results Here, we present state of the art deep learning architectures based on Gated Recurrent Units for annotating text with ontology concepts. We use the Colorado Richly Annotated Full Text Corpus (CRAFT) as a gold standard for training and testing. We explore a number of additional information sources including NCBI's BioThesauraus and Unified Medical Language System (UMLS) to augment information from CRAFT for increasing prediction accuracy. Our best model results in a 0.84 F1 and semantic similarity. Conclusion The results shown here underscore the impact for using deep learning architectures for automatically recognizing ontology concepts from literature. The augmentation of the models with biological information beyond that present in the gold standard corpus shows a distinct improvement in prediction accuracy.
Should we reduce distal femoral valgus cut by two degrees on radiograpchic measurement to avoid overcorrection of alignment in TKA? —A comparison study with radiographic VCA and real 3D-CT VCA
Authors: Liu LiMing , Lei Kai , Guo Lin , Chen Xin , Yang Pengfei , Fu Dejie , Xiong Ran , Yang Liu 
Published: May 2022

Abstract
BackgroundRestoration of target alignment is one of the main goals of total knee arthroplasty (TKA). Radiograph is the world-wide accepted method to obtain valgus cut angle (VCA) which determine distal femoral cut before TKA, although its accuracy is questionable. The aim of this study was to investigate the deviation between radiographic VCA and real 3D-CT VCA in order to adjust the radiograph-based preoperative plan for better alignment target.MethodsA retrospective collection of 360 patients with constitutional varus and knee osteoarthritis. Both radiographic VCA and 3D VCA (CT based) were measured and the difference between them were calculated. Then corrected the radiographic VCA value according to the direction of deviation, each time by 1°, to ensure that the target alignment was within 0°-3° varus range. Finally, it was investigated whether age, gender, side, BMI, and preoperative alignment had an effect on the deviation of radiographic VCA to verify whether the correction was widely applicable.Results More than 91% of the radiographic VCA were overestimated and one third were overestimated by more than 1° (P < 0.001). Reducing the radiographic VCA by 2° could ensure that more than 96% of the target alignment was within 0°-3° varus range. The deviations were not statistically correlated with age, gender, BMI, side, and preoperative alignment (P >0.05).ConclusionsUsing the radiographic VCA reduced by 2° as a reference for distal femoral resection could ensure that more than 96% of patients with constitutional varus had a TKA target alignment in 0°-3° varus range.Level of evidence Ⅲ.
Systemic immune-inflammation index predicts postoperative delirium in elderly patients after surgery: A retrospective cohort study
Authors: Song Yuxiang , Luo Yungen , Zhang Faqiang , Ma Yulong , Lou Jingsheng , Li Hao , Liu Yanhong , Mi Weidong , Cao Jiangbei 
Published: May 2022

Abstract
BackgroundPostoperative delirium (POD) is a common complication among elderly patients after surgery. It is unclear whether the systemic immune-inflammation index (SII) can be a predictor of POD. We explored the prognostic value of the SII in predicting POD in elderly patients undergoing nonneurosurgery and noncardiac surgery in a large retrospective cohort.MethodsWe enrolled elderly patients undergoing nonneurosurgery and noncardiac surgery between January 2014 and August 2019. Univariate and multivariate logistic regression analyses were performed to explore the correlation between POD and the SII value as both a continuous and categorical variable. Then, propensity score matching (PSM) analysis was applied to eliminate the confounding effect of covariates and prove our results. Subgroup analyses were then performed to discover the association between the SII and POD in different subgroups.ResultsA total of 29608 patients with a median age of 70 years (IQR: 67-74) were enrolled in the retrospective cohort. The cut-off value of the SII was 650, which was determined by the receiver operating characteristic (ROC) curve. The ORs of an SII value>650 was 2.709 (95% CI:2.373-3.092, P<0.001), 1.615 (95% CI:1.384-1.882, P<0.001), 1.855 (95% CI:1.602-2.146, P<0.001), and 1.302 (95% CI:1.106-1.531, P=0.001) for prediction of POD in univariate model and three multivariate regression models. After PSM, the OR of an SII value>650 was 1.301 (95% CI: 1.062-1.598, P=0.011). The subgroup analysis indicated that the SII indicates a significantly increased risk of POD in patients with Hb<130 g/L, 4*109/L<WBC≤10*109/L, albumin <39 g/L, or a duration of MAP<60 mmHg ≥5 min. The SII was found to be a useful prognostic predictor of POD for patients with different ages, sexes, and ASA classifications.ConclusionsThe SII had a predictive value for POD in patients undergoing nonneurosurgery and noncardiac surgery. As an index generated from routine blood tests, the SII has advantages regarding cost and time. After further validation, the SII may provide a new option for POD prediction.