bluuebunny/crossref_metadata_2025 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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Network-theoretic modeling of fluid-structure interactions
Authors: Nair Aditya G. , Douglass Samuel
Published: January 2023

The coupling interactions between deformable structures and unsteady fluid flows occur across a wide range of spatial and temporal scales in many engineering applications. These fluid-structure interactions (FSI) make it challenging to predict flow physics accurately. In the present work, two multi-layer network approaches are proposed that characterize the interactions between the fluid and structural layers for an incompressible laminar flow over a two-dimensional compliant flat plate at a 35-degrees angle of attack. In one approach, the wake vortices and bound vortexlets form the nodes of the network with the edges defined by induced velocity. In the other approach, coherent structures (fluid modes) contributing to the kinetic energy of the flow and structural modes contributing to the kinetic energy of the compliant structure constitute the network nodes. The energy transfers between the modes are extracted using a perturbation approach. The network structure of the FSI system is further simplified using the community detection algorithm in the vortical approach and by selecting dominant modes in the modal approach. Network measures are used to reveal the temporal behavior of the individual nodes within the simplified FSI system. Predictive models are then built using both data-driven and physics-based methods. We conclude by investigating the controllability of the modal interaction network. This work sets the foundation for network-theoretic reduced-order modeling of fluid-structure interactions, generalizable to other multi-physics systems.
Microscopic feature of lymph node anthracosilicosis adherent with pulmonary artery: A case report
Authors: Murakami Junichi , Tanaka Toshiki , Hoshii Yoshinobu , Hamano Kimikazu
Published: January 2023

Background; Although thoracic surgeons occasionally encounter a dense adhesion of interlobar lymph node anthoracosilicosis with the pulmonary artery. Most thoracic surgeons know forced adhesiolysis is challenging based on their experience, but the macroscopic features of such an adherent lesion remain limited.
Case presentation; During the right upper lobectomy under thoracoscopic surgery for the patient with stage IA3 primary lung adenocarcinoma, several interlobar lymph nodes were too adherent to a posterior ascending branch of the pulmonary artery to the right upper lobe. After an unplanned conversion to thoracotomy to avoid massive bleeding, the pulmonary artery branch was divided safely. Microscopic examination revealed anthracosilicosis in lymph nodes in extensive proximity to the peripheral pulmonary artery wall accompanying granulomatous inflammation. The adventitial stroma of the pulmonary artery developed into dense and borderless fibrous tissue with dust-laden macrophages.
Conclusions; Our pathological findings on lymph node anthoracosilicosis provide substantial evidence that a boundary between the lymph nodes and pulmonary artery wall developed into dense and borderless fibrous tissue, which supports that forced adhesiolysis against the boundary could cause pulmonary artery injury for thoracic surgeons.
High Glucose Mediated Apoptosis of Pericytes in the Cochlea Stria Vascularis through Mitochondrial ROS pathway
Authors: Shi Tian-feng , Zhou Zan , Jiang Wen-jun , Huang Tian-lan , Yu Miao , Yu Meng , Si Jun-qiang , Li Li
Published: January 2023

Diabetes is closely related to hearing loss, but its specific pathogenesis is not clear. Cochlear stria vascularis and pericytes play an important role in hearing. The purpose of this study was to investigate whether high glucose induces apoptosis in the cochlear stria vascularis and pericytes through oxidative stress, thus affecting hearing loss. The model of type Ⅱ diabetes was established in C57BL/6J mice. Compared with the control group, the hearing threshold of diabetic mice decreased continuously, the density of vascular stria decreased, the oxidative stress and apoptosis of various organs increased, and the level of antioxidation decreased. At the same time, after the pericytes were extracted from the stria vascularis and given in high glucose environment, the apoptosis level of pericytes increased, the content of ROS in pericytes increased, and the mitochondrial membrane potential decreased. AIF and cytochrome C in pericyte mitochondria were gradually released to the cytoplasm. On the basis of the intervention of high glucose, the release of mitochondrial AIF and cytochrome C from pericytes was alleviated and the level of pericyte apoptosis decreased by adding oxidative scavengers. It is clear that high glucose can pass through oxidative stress to induce mitochondrial apoptosis in cochlear vascular striated pericytes.
Exact solutions of flow and pressure variation inside a horizontalfilter chamber using Lie symmetry analysis
Authors: Itumeleng Thabo Joseph , Magalakwe Gabriel , Motsepa Tanki , Moleleki Letlhogonolo Daddy
Published: January 2023

Physical model exact solutions improve crucial industrial production processes by giving operatorsand designers a greater grasp of how systems operate in practice. The current casestudy aims to find exact momentum and pressure variation solutions during the unsteadystatefiltration process to advance fluid purification. Lie symmetry analysis is used to transforma system of PDEs representing the flow and pressure variation into solvable ODEswithout changing the dynamics of the case study. The velocity (momentum) and pressuresolutions are then determined by integrating the obtained solvable ODEs. The effects ofphysical parameters resulting from the process dynamics are examined using the exact solutionsacquired to identify the parameter combination that results in the maximum permeatesoutflow. Graphical representations of the flow velocity and pressure are presented and analysedas well as the skin friction tabular. The results indicate that as time evolves, permeateoutflow decreases because internal momentum, work done, and pressure diminish over timedue to the clustering of particles. In addition, low wave speed, small porosity, more chamberspace, high injection rate, and stronger magnetic effects are ideal for minimising the effectsof no-slip condition at the bottom wall during operation.
Analysis of recovery cases of COVID19 of densely affected countries using LSTM deep learning model
Authors: HAQUE MD ERSHADUL , Hoque Samiul , Paul Manoranjan
Published: January 2023

The present world is badly affected by the novel coronavirus (COVID-19). Using medical kits to identify coronavirus-affected persons is very slow. What happens in the next, nobody knows. The world is facing an erratic problem and doesn't know what will happen in near future. This paper is trying to make a prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data from 258 regions, their latitude and longitude, and the number of death in 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect in extracting highly essential features for time series data (TSD) analysis. There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.
Image classification method of underground coal destruction type based on texture feature fusion
Authors: Lv Fu , Han Xiaotian , Feng Yongan , Guo Zhihao
Published: January 2023

A coal image dataset that can reflect the destruction type and a classification method that focuses on the texture features of the tectonic coal are needed for the study of coal destruction type image classification based on deep learning. Coal photographs of various sorts of destruction are taken downhole using high-definition explosion-proof cameras, and a destruction coal image dataset is created. By combining the texture elements of the destruction coal, a deep learning classification model of the destruction type that is tailored to the downhole coal photos is created. This model is based on an upgraded bilinear neural network model. First, the destruction coal's texture feature images are built using the inter-class variations in image textures; next, an improved bilinear neural network is built using ResNet with its average pooling layer; finally, the original image and the resulting texture feature maps are learned separately on the improved bilinear neural network for features; The average pooling layers of the original and texture map bilinear networks are then connected on the proposed adaptive classification module based on decision fusion, channel weights are extracted, and weights are applied to the feature outputs of the two parallel bilinear networks to achieve the best fusion classification results. The algorithm obtains classification accuracy of 99.98%, 99.95%, and 99.99% on the texture-related public datasets KTH-TIPS, KTH-TIPS-2b, and UIUC, respectively, and 99.52% on the tectonic coal destruction type image dataset.
Opioid crisis in rural and urban counties from 2010 to 2018: in light of the index of relative rurality
Authors: Sun Feinuo
Published: January 2023

Previous studies have documented the rural-urban disparities of the opioid crisis, but rarely consider the heterogeneities within rural and urban areas, nor the changing dynamics of the rural-urban boundaries. Taking advantage of a continuous measure of rurality, this study separates counties into “becoming-more-rural” and “becoming-more-urban” groups according to the change of rurality between 2000 and 2010, and compares the determinants of the development of prescription opioid-, heroin-, and synthetic opioid-related opioid mortality in the two groups of counties. The analysis uses longitudinal data of 2010–2018 from a variety of national datasets such as the confidential Multiple-Cause of Death data, the U.S. Opioid Dispensing Rate Maps, and the American Community Survey. The results show that while increasing rurality does not predict increasing prescription opioid- and synthetic opioid-involved mortality, the growth of heroin-involved mortality could be a severe issue for becoming-more-rural counties that have already observed heroin overdose deaths. The demographic groups that have higher risk of opioid overdoses may be different in different areas, for example increasing males and people engaged in manual labor occupations are associated with increasing prescription opioid-involved mortality for becoming-more-rural counties but in becoming-more-urban areas, heroin-involved mortality correlates to a higher percentage of Hispanics. Concentrations of socioeconomically disadvantaged populations like veterans and people in poverty increase opioid overdose deaths particularly for becoming-more-urban counties, while healthcare services shown more beneficial to becoming-more-rural areas. The findings suggest that the programs and regulations to reduce opioid-involved mortality should consider the changing nature of counties in the degree of rurality.
Attitude and Motivation of Medical Professors in the Use of Virtual Simulation Technology of Radiotherapy in Clinical Teaching Context in Chongqing Medical University
Authors: Chen Dan , Wu Shunlong , Yang Haiying , Liao Kui
Published: January 2023

Virtual simulation technology of radiotherapy has completely changed the way we teach and learn. In the context of medical school education, the technology bridges the gap between abstract knowledge and ordinary life. Technology advances the possibilities available to mankind and are essential to medical professors in the teaching. However, educators are faced with the challenge of guiding students through this transition and ensuring they don’t lose valuable time using their devices. The rise of technology in learning has also carried some disadvantages with it, some of which might be overlooked. This study aimed to determine the attitude and motivation of medical professors to adopt virtual simulation technology of radiotherapy in clinical teaching context. Descriptive statistics, t-test analysis, one-way ANOVA, Pearson-r correlation, and chi-squared test were used in this study. It is recommended to upskill the technological and pedagogical knowledge and skills of faculty member, crucial especially in the context of a post-pandemic education.
Minimization of Cancer Cells with ATS Fuzzy Controller: An Application to Androgen Deprivation therapy
Authors: Dubey Priya , Kumar Surendra
Published: January 2023

Androgen deprivation therapy, also known as hormone therapy, is a standard treatment for prostate cancer, but its 5-year survival rate is only 57 percent. Moreover, the prolonged therapy duration results in increased medication toxicity and drug resistance. A solution to this problem is adaptive therapy, which applies chemotherapy or immunotherapy after hormone therapy is withdrawn. In the proposed work, the idea of adaptive therapy is used by applying nonlinear control algorithms to the proposed ADT model and making the drug dosage based on the control laws that result from applying these algorithms. The overall objective is to reduce the cancer cells to zero in a minimum amount of time to reduce prolonged exposure to drugs. For this, a super-twisting sliding mode controller (STSMC) is applied to reduce the number of androgenindependent (AI) and androgen-dependent (AD) cells. Next, an active control algorithm (ATS)-based Takagi-Sugeno fuzzy controller is introduced and compared to the STSMC design. The ATS fuzzy controller has significantly reduced the therapy duration to two months and is also globally asymptotically stable. Using the Linear Matrix Inequality (LMI) algorithm and the YALMIP toolbox, the controller has been built. Theoretical outcomes are validated using MATLAB and Simulink.
The Histopathological Assesments of Effecting Fe and Zn Heavy Metals in Cerebrum and Cerebellum Tissues of Rats
Authors: Şenol Nurgül , ŞAHİN Melda
Published: January 2023

Heavy metals left intensively to the environment cause different healthy problems on people by entering the food chain heavy metals. Our aim is to provide determinig effect of in tissues especially in central nervous system as a result of consuming cause foods included heavy metals and water. Five groups were constituded by using 35 adult male Wistar-Albino sexual rats. Hematoxylin-eosin (H&E) staining was applied to determine the histological sides of the damages of heavy metals in cerebrum and cerebellum tissues and effects of given juglone (5-hydroxy-1,4-naphthoquinone) for reducing these damages. Besides, immunohistochemical TUNNEL method was applied to determine DNA damages in cell. Density of damage in cerebrum of Fe and Fe + Juglone groups were higher than in the control group. Apoptotic cells number was decreased in the group consisting of juglone. There was little damage and less apoptotic cells in treated Zn group. In the cerebellum similar cerebrum results were obtained. There were much degeneration Fe and Fe + Juglone groups cerebellum. Apoptotic cells were increased Fe groups. Fe/600 ppm. doses heavy metals have created a toxic effect on the cerebrum and cerebellum tissues.
A novel geometry optimization approach for multi-recess hydrostatic bearing pad operating in static and low-speed conditions using CFD simulation
Authors: Michalec Michal , Ondra Martin , Svoboda Martin , Chmelík Jiří , Zeman Petr , Svoboda Petr , Jackson Robert
Published: January 2023

This study investigates a new two-parameter method for estimating optimal hydrostatic bearing pad proportions. The design of a hydrostatic bearing pad is limited to simple geometry using analytical equations or one-parameter optimization based on experimental data. In this study, 3D static CFD model results were verified using analytical results and experimental data on a hydrostatic bearing testing device. The obtained CFD results for load and pressure show a deviation within 5.2% compared to the experimentally obtained results and the literature. Using the proposed novel two-parameter optimisation, the energetic loss was reduced by 30% compared to the classical one-parameter approach. This methodology allows versatile and effective design of optimal hydrostatic bearings operating in low-speed conditions to achieve minimum energetic loss.
Effects of the COVID-19 pandemic on general health and malaria control in Ghana: A qualitative study with mothers and health care professionals
Authors: Heuschen Anna-Katharina , Abdul-Mumin Alhassan , Abubakari Abdulai , Agbozo Faith , Lu Guangyu , Jahn Albrecht , Müller Olaf
Published: January 2023

Background
COVID-19 has severely impacted health systems and the management of non-COVID-19 diseases, including malaria, globally. The pandemic has hit sub-Saharan Africa less than expected; even considering large underreporting, the direct COVID-19 burden was minor compared to the Global North. However, the indirect effects of the pandemic, for example, on socio-economic inequality and health care systems, may have been more disruptive. Following a quantitative analysis of routine surveillance data of 2020 compared to 2015 to 2019 from northern Ghana, which showed significant reductions in overall outpatient department visits and malaria cases, this qualitative study aims to provide further explanations to those quantitative findings.
Methods
In the Northern Region of Ghana, 72 participants, consisting of 18 health care professionals (HCP) and 54 mothers of children under the age of five, were recruited in urban and rural districts. Data were collected using focus group discussions with women that were pregnant during the first phase of the pandemic, women that had already under five children by then, and through key informant interviews with HCP.
Results
Three main themes occurred. The first theme - general effects of the pandemic - includes impacts on finances, food security, health service provision as well as education and hygiene. Many women lost their jobs, which increased their dependance on males, children had to drop out of school, and families had to develop strategies to cope with food shortages and considering migration. HCPs had problems to reach the communities, suffered stigmatization and were often barely protected against the virus. The second theme - effects of the pandemic on health seeking - includes fear of infection, lack of COVID-19 testing capacities, changed behavior of HCPs, and reduced access to clinics and treatment due to reduced opening hours as well as patients’ and HCPs’ fear of getting infected with COVID-19. The third theme – effects of the pandemic on malaria – includes disruptions of malaria preventive measures and reduced access to and acceptance of chemoprophylaxis as the side effects mimic COVID-19 symptoms. Clinical discrimination between malaria and COVID-19 symptoms was difficult and HCPs observed increases in severe malaria cases in health facilities due to late reporting.
Conclusion
The COVID-19 pandemic has had large collateral impacts on mothers, children and HCPs. In addition to overall negative effects on families and communities, access to and quality of health services was severely impaired, including serious implications for malaria prevention and treatment.
Tbx6/Ripply mechanism as dynamic-to-static converter in somite segmentation
Authors: Yabe Taijiro , Uriu Koichiro , Takada Shinji
Published: January 2023

The metameric pattern of somites is created based on oscillatory expression of clock genes in presomitic mesoderm. However, the mechanism for converting the dynamic oscillation to a static pattern of somites is still unclear. Here, we provide evidence that Ripply/Tbx6 machinery is a key regulator of this conversion. Somite boundary determination by Ripply1/Ripply2-mediated removal of Tbx6 protein leads to cessation of clock gene expression in zebrafish embryos. On the other hand, activation of ripply1/ripply2 mRNA and protein expression is periodically regulated by clock oscillation in conjunction with an Erk signaling gradient. Whereas Ripply protein decreases rapidly in embryos, Ripply-triggered Tbx6 suppression persists long enough to complete somite boundary formation. Mathematical modeling shows that a molecular network based on results of this study can reproduce dynamic-to-static conversion in somitogenesis. Furthermore, simulations with this model suggest that sustained suppression of Tbx6 caused by Ripply is crucial in this conversion.
Effect of Selected Factors on Features of Short-Term Social Isolation of Horses: A Pilot Study
Authors: JASTRZĘBSKA EWA , WIŚNIEWSKA ANNA , WILK IZABEL , PRÓCHNIAK TOMASZ , JANCZAREK IWONA
Published: January 2023

This study analysed the effect of the sex of the horses as well as the type of their maintenance and use at the centre on the behavioural and physiological expression of short-term social isolation, which was assessed on the basis of heart and respiratory rates. A total of 27 clinically healthy horses were examined. The horses had social contact of various durations, were used with different regularity and were not equally accustomed to isolation. An isolation test was conducted. The heart and respiratory rates were measured at rest and after isolation, and the differences between the values were calculated. Moreover, points were assigned for the effect of isolation. The behaviour in subsequent stages of isolation was also observed. It was found that the sex of the horses, the period spent daily in the paddock in the herd, the regularity of use and habituation to staying in isolation can all be classified as factors that have no effect on the heart rate, respiratory rate or behaviour during short-term isolation. It was suggested that the resting respiratory rate and the effect of isolation could be classified as features that are potentially useful in assessing horses’ adaptability to short-term social isolation.
Experiences of Mothers of Multiple Children with Autism Spectrum Disorder: An Interpretative Phenomenological Analysis Study
Authors: ŞENGÜL ERDEM Hatice
Published: January 2023

Introduction: This study aimed to investigate the experiences of mothers of multiple children with autism spectrum disorder (ASD).Method: Nine mothers of multiple children with ASD participated in the research, designed as an interpretative phenomenological analysis study, and data were collected through semi-structured interviews. In the data analysis, the interpretative phenomenological analysis method was employed to reveal the mothers’ personal statements and emotional reflections in depth.Findings: As a result of the analysis, three main themes were reached, the center of life: Being a mother of two children with autism, gradually increasing isolation, and empowerment attempts. In line with the findings, mothers continue their lives by putting their children with ASD at the center, and they become increasingly lonely in social life. Mothers experience difficulty in maintaining their daily lives, have intense worries about the future, think that they lack the support resources they need, and feel that having multiple children with ASD causes transformations in their relationships with their spouses and their social networks, which will increase their loneliness. Furthermore, mothers develop various strategies to empower themselves. These strategies have emerged as religious beliefs, comparing their children with each other or with children with more severe disabilities, and focusing on the minor achievements of their children.Discussion: There are few studies on the subject, and the experiences of mothers of multiple children with ASD are similar to the findings of research investigating the experiences of mothers of multiple children with disabilities or mothers of a child with ASD.Conclusion and Recommendations: Whereas mothers of multiple children with ASD experience intense social isolation and put the “motherhood role” at the center of their lives, on the one side, they feel obliged to empower themselves and develop some strategies to this end, on the other side. According to the study findings, it is suggested that arrangements are needed to facilitate the lives of mothers of multiple children with ASD and the experiences of mothers should be examined with studies to be performed with a larger number of participants.