bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dysregulated lymphocyte antigen receptor signaling in common variable immunodeficiency with granulomatous lymphocytic interstitial lung disease
Authors: Lui Victor , Ghosh Tusharkanti , Rymaszewski Amy , Chen Shaoying , Baxter Ryan , Kong Daniel , Ghosh Debashis , Routes John , Verbsky James , Hsieh Elena Wen-Yuan 
Published: January 2023

Abstract
Purpose A subset of common variable immunodeficiency (CVID) patients either present with or develop autoimmune and lymphoproliferative complications, such as granulomatous lymphocytic interstitial lung disease (GLILD), a major cause of morbidity and mortality in CVID. While a myriad of phenotypic lymphocyte derangements has been associated with and described in GLILD, defects in T and B cell antigen receptor (TCR/BCR) signaling pathways in CVID and CVID with GLILD (CVID/GLILD) remain undefined, which hinders the discovery of biomarkers for disease monitoring, prognostic prediction, and personalized medicine approaches.
Methods To identify perturbations of immune cell subsets and TCR/BCR downstream signal transduction, we applied mass cytometry analysis to peripheral blood mononuclear cells (PBMCs) from healthy control participants (HC), CVID, and CVID/GLILD patients.
Results Patients with CVID, regardless of GLILD status, had increased frequency of HLADR+ CD4+ T cells, CD57+ CD8+ T cells, and CD21lo B cells when compared to healthy controls. Within these cellular populations in CVID/GLILD patients, engagement of T or B cell antigen receptors resulted in discordant downstream signaling responses compared to CVID. In CVID/GLILD patients, CD21lo B cells showed perturbed BCR-mediated protein kinase B and extracellular signal-regulated kinase activation, while HLADR+ CD4+ T cells and CD57+ CD8+ T cells displayed disrupted TCR-mediated activation of kinases most proximal to the antigen receptor.
Conclusion Lymphocyte populations from CVID/GLILD patients exhibit an activated phenotype and discordant antigen receptor signaling. These findings contribute to our understanding of the mechanisms of immune dysregulation in CVID with GLILD.
Validity and reliability of the Integrated Palliative care Outcome Scale (IPOS) for non-cancer patients
Authors: Ishii Yoko , Ito Nao , Matsumura Yuko , Aoyama Maho , Kohara Izumi , Murai Kunihiko , Takeuchi Kazuhisa , Yokoyama Takako , Miyashita Masako , Miyashita Mitsunori 
Published: January 2023

Abstract

Purpose This study assessed the validity and reliability of the Integrated Palliative Care Outcome Scale (IPOS) for non-cancer patients. Methods We recruited 223 non-cancer patients receiving palliative care and their healthcare providers (222) across two home care facilities and two hospitals for a cross-sectional study. We assessed the construct validity and known-group validity of the IPOS for this cohort by dividing them into stable and non-stable groups based on disease status. The weighted kappa and interclass correlation coefficients were assessed to ascertain reliability. Results The IPOS scores were significantly higher for the ‘non-stable’ group (worsening condition group) measured in the palliative care phase than for the ‘stable’ group (p < 0.001). Concerning validity, Spearman’s correlations between similar IPOS and Edmonton Symptom Assessment System items ranged from 0.61 to 0.94. Regarding reliability, the weighted kappa coefficients ranged from 0.53 to 0.81 for patients and from 0.58 to 0.90 for healthcare providers. For inter-rater reliability between patients and healthcare providers, the weighted kappa coefficients for each item ranged from 0.05 to 0.42 for physical symptoms, 0.03–0.25 for emotional symptoms, and 0.05–0.15 for communication/practical issues. Conclusion This study confirmed the validity and reliability of IPOS for non-cancer patients requiring palliative care. However, the inter-rater reliability indicates poor agreement between patients and healthcare providers. This highlights the discrepancies between the patients’ and the healthcare providers’ assessments and the importance of the patient’s assessment.
Analysing the implementation of a didactic sequence based on peer assessment: reflections on the development of evaluative judgement in higher education
Authors: García Elena Cano , Bellowa Lyda Halbaut , Gironelli Ludmila Martins , Molins Laia Lluch 
Published: January 2023

Abstract
Competency-based designs promote the development of knowledge, skills and attitudes that enable professionals to develop adaptive experience, preparing them through learning activities and authentic assessment, for the future acquisition of new content. This requires the development of evaluative judgement, so that the quality of one’s own work processes can be assessed autonomously and with critical judgement. This paper presents the design of an educational experience with students of Galenic Pharmacy (N = 339) during the 2021–2022 academic year, the objective of which, when giving presentations on ways of administering medicines, was not only the learning of content but also the development of competencies. A complex task with iterative deliverables is proposed in which peer assessment is the key to the development of evaluative judgement. The results show the positive effects of peer assessment, the growth of feedback provided from loop to loop and the development of critical judgement. However, engagement with the process has been erratic and the focus of feedback has not been sufficiently centred on content. Improved assessment literacy would probably be necessary both for teachers, in order to be able to establish criteria more aligned with their competencies, and for students to be able to attach greater formative value to these practices and engage with the learning process itself, and thus be able to continue this autonomous and self-regulated learning throughout life.
New energy power system security and stability assessment based on Apirori and DEMATEL-AEW- dynamic weighted cloud model
Authors: Maihemuti Saniye , Wang Weiqing , Wang Haiyun , Wu Jiahui , Muhedaner Muladi 
Published: January 2023

Abstract

With the rapid development of new energy in China, the large-scale grid connection of new energy continues to rise, and the operation safety of new energy power systems is facing a severe test. Therefore, correct identification and assessment of security risks become an important prerequisite to effectively improve the operation safety level of the new energy power systems. In order to accurately and effectively complete the security and stability assessment of the new energy power system operation, a new energy power system operation security assessment model based on the fuzzy DEMATEL-AEW cloud model was built. Firstly, the paper collects the index set that affects the safe and stable operation of the new energy power system, and selects 15 indexes that have a great impact on the safe and stable operation of the system through the association rules mining algorithm (Apriori), and establishes the evaluation index system for the safe and stable operation of the new energy power system. Then, the fuzzy decision-making laboratory analysis method (DEMATEL) is used to determine the interaction between the evaluation indicators, draw the network structure diagram between the evaluation indicators, and determine the subjective weight of each evaluation indicator. The objective weight of each evaluation index is determined by using the anti-entropy weight (AEW) method, and the comprehensive weight of the evaluation index is calculated by using the dynamic weighting of cooperative game theory; Finally, the weight results are combined with the evaluation layer of cloud model to evaluate the safety of all levels of safety assessment indicators, and the simulation analysis of the example is completed through MATLAB. The results show that the operation security status level of the power system is between average and good, not only taken some effective measures to control some risk factors, but also normal inspection and monitoring are arranged; At the same time, carried out the comparative analysis of the simulation results in the security assessment of power system operation to verify the feasibility and accuracy of evaluation methods, and has guiding significance for the security assessment of power system.
Automated LVO Detection and Collateral Scoring on CTA using a 3D Self- Configuring Object Detection Network: A Multi-Center Study
Authors: Bagcilar Omer , Alis Deniz , Alis Ceren , Seker Mustafa Ege , Yergin Mert , Ustundag Ahmet , Hikmet Emil , Tezcan Alperen , Polat Gokhan , Akkus Ahmet Tugrul , Alper Fatih , Velioglu Murat , Yildiz Omer , Selcuk Hakan Hatem , Oksuz Ilkay , Kizilkilic Osman , Karaarslan Ercan 
Published: January 2023

Abstract
The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object detection network called nnDetection was used to detect LVO and assess collateralization on CTA scans using a multi-task 3D object detection approach. The model was trained on single-phase CTA scans of 2425 patients at five centers, and its performance was evaluated on an external test set of 345 patients from another center. Ground-truth labels for the presence of LVO and collateral scores were provided by three radiologists. The nnDetection model achieved a diagnostic accuracy of 98.26% (95% CI, 96.25–99.36%) in identifying LVO, correctly classifying 339 out of 345 CTA scans in the external test set. The DL-based collateral scores had a kappa of 0.80, indicating good agreement with the consensus of the radiologists. These results demonstrate that the self-configuring 3D nnDetection model can accurately detect LVO on single-phase CTA scans and provide semi-quantitative collateral scores, offering a comprehensive approach for automated stroke diagnostics in patients with LVO.
Automated Machine Learning (AutoML) Can Predict 90-Day Mortality after Gastrectomy for Cancer
Authors: SenthilKumar Gopika , Madhusudhana Sharadhi , Flitcroft Madelyn A. , Sheriff Salma A. , Thalji Samih , Merrill Jennifer R. , Clarke Callisia N. , Maduekwe Ugwuji N. , Tsai Susan , Christians Kathleen K. , Gamblin T. Clark , Kothari Anai N. 
Published: January 2023

Abstract
Early post-operative mortality risk prediction is crucial for clinical management of gastric cancer. This study aims to predict 90-day mortality in gastric cancer patients undergoing gastrectomy using automated machine learning (AutoML), optimize models for pre-operative prediction, and identify factors influential in prediction. National Cancer Database was used to identify stage I–III gastric cancer patients undergoing gastrectomy between 2004–2016. 26 features were used to train predictive models using H2O.ai AutoML. Performance on validation cohort was measured. In 39108 patients, 90-day mortality rate was 8.8%. The highest performing model was an ensemble (AUC = 0.77); older age, nodal ratio, and length of inpatient stay (LOS) following surgery were most influential for prediction. Removing the latter two parameters decreased model performance (AUC 0.71). For optimizing models for pre-operative use, models were developed to first predict node ratio or LOS, and these predicted values were inputted for 90-day mortality prediction (AUC of 0.73–0.74). AutoML performed well in predicting 90-day mortality in a larger cohort of gastric cancer patients that underwent gastrectomy. These models can be implemented pre-operatively to inform prognostication and patient selection for surgery. Our study supports broader evaluation and application of AutoML to guide surgical oncologic care.
The impact of mesorectal fat area on long-term outcomes following proctectomy for lower rectal cancer
Authors: Shioi Ikuma , Yamaoka Yusuke , Shiomi Akio , Kagawa Hiroyasu , Hino Hitoshi , Manabe Shoichi , Chen Kai , Nanishi Kenji , Notsu Akifumi 
Published: January 2023

Abstract
Background
The mesorectal fat area (MFA) at the tip of the ischial spines on magnetic resonance imaging has been used to characterize mesorectal morphology. Recent studies reported that a larger MFA correlated with difficulties in rectal cancer surgery. However, the relationship between MFA and prognosis in rectal cancer remains unclear. This study evaluated the impact of MFA on long-term outcomes following robotic proctectomy for rectal cancer.
Methods
Patients who underwent robotic proctectomy for lower rectal cancer from December 2011 to December 2016 were enrolled. Cox regression analysis was performed to determine variables associated with relapse-free survival (RFS). Patients were divided into groups based on the MFA, and long-term outcomes were compared.
Results
Of 230 patients, 173 (75.3%) were male. The median age was 63 years, and median MFA was 19.7 cm2. In multivariate analysis, smaller MFA (hazard ratio [HR], 0.92; 95% confidence interval [CI], 0.87–0.98; p < 0.01), p/yp stage II (HR, 4.34; 95% CI, 1.71–11.03; p < 0.01), and p/yp stage III (HR, 6.02; 95% CI, 2.19–16.56; p < 0.01) were independently associated with worse RFS. In the median follow-up period of 60.8 months, patients with MFA < 20.7 cm2 had a significantly lower 5-year RFS rate (72.6%) than those with MFA ≥ 20.7cm2 (87.0%).
Conclusions
Smaller MFA was associated with worse RFS in patients undergoing robotic proctectomy for lower rectal cancer. MFA is considered to be a prognostic factor in rectal cancer.
Resource Allocation Technique among Mobile Virtual VCN operators
Authors: Agarwal Vartika , Sharma Sachin 
Published: January 2023

Abstract
Resource allocation has been a guaranteed approach to provide users, spectrum from different network operators. In this research, we examine the use of Mobile virtual VCN operator (MVVO) to allow spectrum sharing. Specifically, we introduce a spectrum dividing framework between three VCN operators. We execute fairness by confirming that each VCN operators receive spectrum from another VCN operators for its user at reasonable charge. We formulate a Markov decision process to determine user scheduling between three VCN operators. Simulation result shows that virtual network operator takes 9 seconds for collecting information about user and network operator. Our numerical result validate that this scheme can achieve up to 90% improvement in terms of user satisfaction rate.
Title of the manuscript: Lynch-like syndrome with germline WRN mutation in Bulgarian patient with synchronous endometrial and ovarian cancer
Authors: Kamburova Zornitsa Bogomilova , Dimitrova Polina Damyanova , Dimitrova Diana Strateva , Kovacheva Katya Stefanova , Popovska Savelina Lubenova , Nikolova Slavena Enkova 
Published: January 2023

Abstract
Background: Synchronous endometrial and ovarian cancer (SEOC) accounts for 50-70% of all synchronous gynecology cancers in women. Approximately 14% of SEOC cases are caused by Lynch syndrome (LS). The widespread introduction of "universal screening" at LS (all cases with CRC and all EC cases diagnosed before age 60 should be tested for MMR deficiency) has led to an increasing number of suspected LS cases- MMR-deficient tumors without germline mutation in the MMR genes. These cases are attributed to the so-called Lynch-like syndrome (LLS).
Case presentation: We present a case of LLS with a detected germline, likely pathogenic variant in the WRN gene. The proband was a woman diagnosed with SEOC at the age of 51 years. Histology of both tumors (endometrium and ovary) was endometroid and showed loss of MLH1 and PMS protein expression. Genetic testing by next generation sequencing (NGS) detected a germline mutation (in the heterozygous state) in the WRN gene - c.4109del, p.(Asn1370ThrfsTer23) in the proband.
Conclusions: The presented case contributes to the etiology of LLS and confirms the need for specific genetic testing, together with genetic counseling, in hereditary cancer syndromes. The use of combined information from clinicians, pathologists, genetic counselors, and data from NGS testing for cancer predisposition, clinical surveillance, and follow-up management in women with gynecology cancers, especially SEOC, could be improved.
Prediction of the concentration of plant growth regulators for somatic embryogenesis and regeneration of Hyoscyamus niger using Box-Behnken design of response surface methodology
Authors: Ahmadpour Roghayeh , Zanjani Bahram Maleki , Garoosi Ghasem-ali , Haddad Raheem , Farjaminezhad Reza 
Published: January 2023

Abstract
Optimizing of the in vitro culture condition, somatic embryogenesis, and plant regeneration is the first step in transformation. In this study, the response surface methodology was used to optimize and predict the concentrations of TDZ, NAA, BAP, and kinetin for callus formation, and regeneration by indirect somatic embryogenesis of Hyoscyamus niger. The results proved that TDZ is more efficient than NAA for the regeneration of H. niger. By using different concentrations of TDZ, BAP, and kinetin, the highest percentage of callusing (100.00%), callus fresh weight (1207.75 mg/explant), percentage of somatic embryogenesis (92.66%) and shooting (93.30%), and the number of the shoot (7.75 per callus) was recorded from petiole explants on MS medium containing 0.25 mg/L TDZ and 1 mg/L BAP, leaf explants by using 0.5 mg/L TDZ and 1 mg/L kinetin, petiole explants with addition 0.5 mg/L TDZ, leaf explants by using 0.50 mg/L TDZ and 1 mg/L kinetin, and petiole explants with addition 0.25 mg/L TDZ and 1 mg/L BAP, respectively. Also, the highest percentage of callusing (100.00%), callus fresh weight (3273.64 mg/explant), percentage of somatic embryogenesis (100%), shooting (100.00%), and the number of the shoot (8.61 per callus) were predicted by culturing of petiole explant on the MS medium containing 0.11 mg/L TDZ and 1.253 mg/L BAP, leaf explant by using 0.52 mg/L TDZ, petiole explant by using 0.533 mg/L TDZ and 1 mg/L BAP, leaf explant with addition 0.18 mg/L TDZ and 2.89 mg/L BAP and petiole explant by using 0.1 mg/L TDZ and 3 mg/L BAP.
Integrated Document Segmentation and Region Identification- Textual, Equation and Graphical
Authors: Thiyam Jennil , Singh Sanasam Ranbir , Kumar Prabin 
Published: January 2023

Abstract
With the advancement in the world of digitization, storing information in the form of scanned copies, images, etc. becomes a new normal. This new normal leads to the need for a system that can extract accurate information from the scanned documents or images with respect to every component they may have, such as textual, graphical, etc. The first step in extracting document information is to segment the document layout: divide the document into textual and non-textual regions of interest. There have been various studies over document layout segmentation, and this study observed that the majority of the existing studies face one common challenge, i.e., accurate segmentation of graphical components with sparsely clustered pixels such as flowcharts, block diagrams, etc. The study addresses it with a two-tier feedback-based framework. The first tier segments and classifies the textual and mathematical equation components, while the second tier segments and classifies the graphical regions using the feedback information from the first tier. The information provided by the first tier is the regional information of the equation and textual components to get a different copy of the original input document image in such a way that most of the foreground pix-els are part of graphical regions. The proposed framework outperforms various existing studies (when evaluated against multiple datasets).
BERT-PG: A two-branch associative feature gated filtering network for aspect sentiment classification
Authors: Wang Jiamei , Wu Wei , Ren Jiansi 
Published: January 2023

Abstract
Aspect sentiment classification is an important branch of sentiment classification that has gained increasing attention recently. Existing aspect sentiment classification methods typically use different network branches to encode context and aspect words separately, and then use an attention mechanism to capture their associations. This attention-based approach cannot completely ignore the contexts unrelated to the current aspect words, which brings noise interference. In this paper, a gated filtering network based on BERT is suggested as a solution to this issue. We employ BERT to encode the text semantics of contexts and sentence pairs consisting of context and aspect words respectively, and to extract lexical features as well as associative features of context and aspect words. Based on this, we designed a gating module that, unlike the attention mechanism, uses association features to precisely filter irrelevant contexts. Additionally, because the BERT network parameters are so big, there is a tendency to over-fitting during training. To effectively combat this problem, we developed a loss function with a threshold. We carried out extensive experiments using three benchmark datasets to verify the performance of our proposed model. According to the experimental results, the model outperforms other reference models on all three datasets, proving that our approach is effective in improving the presentation of aspect sentiment classification in comparison to other cutting-edge sentiment classification methods.
Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm
Authors: Su Yuzhen , Ye Weichuan , Yang Kai , Li Meng , Xiao Qingtai 
Published: January 2023

Abstract
Aiming at the problems of conventional linear regression and traditional neural network models in predicting the quality of electrolytic copper, such as poor fitting effect and low prediction accuracy, the quality of machine learning algorithms based on the maximum information coefficient, Random Forest algorithm, support vector machine, and correlation vector machine is established. The prediction model is used to reveal the non-linear fluctuation law of electrolytic copper quality under the influence of multiple factors. Taking the electrolytic copper production data of a copper industry company as an example, based on 19 related factors affecting the quality of electrolytic copper and 5 electrolytic copper quality control index systems, the maximum information coefficient is first used to analyze the non-linear correlation between the influencing factors. Secondly, the Random Forest algorithm is used to clarify the main control factors that affect the quality of electrolytic copper. Finally, the particle swarm optimization support vector machine and correlation vector machine are used to establish the quality prediction model of electrolytic copper. The results show that the RF algorithm is used to characterize the complex nonlinear relationship between the quality of electrolytic copper and the quality of electrolytic copper, and the five main controlling factors of electrolytic copper are clarified, which is confirmed by the nonlinear correlation analysis based on MIC. For all 19 influencing factors, the RVM model is very similar in prediction accuracy to the PSO-LSSVM model (the gaps are 4.45% and 14.16%, respectively), and are better than the conventional multiple linear regression model and the traditional neural network model. Quality prediction model of electrolytic copper based on RF-RVM model, the prediction error of the test data set is smaller than that of the RVM model (the lowest error index value is less than 5%). The research results have certain reference value for electrolytic copper quality control.