bluuebunny/crossref_metadata_2025_split huggingface dataset:
Soft ordered double quantitative approximations based three-way decisions and their applications
Authors: Santos-García Gustavo , Ali Abbas , Rehman Noor 
Published: November 2022

AbstractThe classical theory of rough set was established by Pawlak, which mainly focusses on the approximation of sets characterized by a single equivalence relation over the universe. However, most of the current single granulation structure models cannot meet the user demand or the target of solving problems. Multigranulation rough sets approach can better deal with the problems, where data might be spread over various locations. In this article, we present the idea of soft preference and soft dominance relation for the development of soft dominance rough set in an incomplete information system. Subsequently, several important structural properties and results of the proposed model are carefully analyzed. After employing soft dominance based rough set approach to it for any times, we can only get six different sets at most in an incomplete information system. That is to say, every rough set in a universe can be approximated by only six sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Based on soft dominance relation, we introduce logical disjunction/conjunction soft dominance optimistic/pessimistic multigranulation decision theoretic rough approximations in an incomplete information. Meanwhile, to measure the uncertainty of soft dominance optimistic/pessimistic multigranulation decision theoretic rough approximation and some of their interesting properties are examined. Thereafter, a novel multi attribute with multi decision making problem approach based on logical disjunction/conjunction soft dominance optimistic/pessimistic multigranulation decision theoretic rough sets approach are developed to solve the selection of medicine to treat the coronavirus disease (COVID-19). The basic principle and the detailed steps of the decision making model (algorithms) are presented in detail. To demonstrate the applicability and potentiality of the proposed model, we present a practical example of a medical diagnosis is given to validate the practicality of the technique.
Brain tumor classification based on neural architecture search
Authors: Chitnis Shubham , Hosseini Ramtin , Xie Pengtao 
Published: November 2022

AbstractBrain tumor is a life-threatening disease and causes about 0.25 million deaths worldwide in 2020. Magnetic Resonance Imaging (MRI) is frequently used for diagnosing brain tumors. In medically underdeveloped regions, physicians who can accurately diagnose and assess the severity of brain tumors from MRI are highly lacking. Deep learning methods have been developed to assist physicians in detecting brain tumors from MRI and determining their subtypes. In existing methods, neural architectures are manually designed by human experts, which is time-consuming and labor-intensive. To address this problem, we propose to automatically search for high-performance neural architectures for classifying brain tumors from MRIs, by leveraging a Learning-by-Self-Explanation (LeaSE) architecture search method. LeaSE consists of an explainer model and an audience model. The explainer aims at searching for a highly performant architecture by encouraging the architecture to generate high-fidelity explanations of prediction outcomes, where explanations’ fidelity is evaluated by the audience model. LeaSE is formulated as a four-level optimization problem involving a sequence of four learning stages which are conducted end-to-end. We apply LeaSE for MRI-based brain tumor classification, including four classes: glioma, meningioma, pituitary tumor, and healthy, on a dataset containing 3264 MRI images. Results show that our method can search for neural architectures that achieve better classification accuracy than manually designed deep neural networks while having fewer model parameters. For example, our method achieves a test accuracy of 90.6% and an AUC of 95.6% with 3.75M parameters while the accuracy and AUC of a human-designed network—ResNet101—is 84.5% and 90.1% respectively with 42.56M parameters. In addition, our method outperforms state-of-the-art neural architecture search methods.
Transgenerational effects of lambda-cyhalothrin on Musca domestica L. (Diptera: Muscidae)
Authors: Ghramh Hamed A. , Sadiq Nauman , Naqqash Muhammad Nadir , Abid Allah Ditta , Shahzad Sohail , Saeed Shafqat , Iqbal Naeem , Khan Khalid Ali 
Published: November 2022

AbstractThe hormetic effect may cause disease control measures to fail due to inadequate treatment of human disease vectors such as houseflies. Age-stage, two-sex life table is used for accurate estimation of the hermetic impacts on insects as it allows to study sub-lethal or transgenerational effects. Pyrethroids insecticides are primarily used for the management of houseflies. This study used lambda-cyhalothrin (a pyrethroid insecticide) to quantify its transgenerational impacts on houseflies. Life table parameters of a progeny of adult houseflies exposed to LC10, LC30, and LC50 of lambda-cyhalothrin were computed. Statistically higher fecundity (71.31 per female) was observed in control treatment, while it was the adults exposed to LC50 recorded the lowest progeny. Significantly higher values for intrinsic rate of growth (r), limiting rate of growth (λ), and net reproductive rate (Ro) (0.16, 1.16, and 31.38 per day, respectively) were recorded for the control treatment of the study. Contrarily, lower values for λ, Ro, and r were (0.10, 1.10, and 9.24 per day, respectively) were noted in the LC50 treatment. Decreased population parameters suggest that lambda-cyhalothrin can be successfully used in indoor environments to control houseflies.
Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19
Authors: Kogan Yuri , Robinson Ari , Itelman Edward , Bar-Nur Yeonatan , Jakobson Daniel Jorge , Segal Gad , Agur Zvia 
Published: November 2022

AbstractOur study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the model's prediction accuracy showed ROC AUC of 0.8 and 0.79 for prediction scopes of 48 or 96 h, respectively; external validation showed ROC AUC of 0.7 and 0.73 for the same prediction scopes. Results indicate the feasibility of predicting the forthcoming deterioration of non-severe COVID-19 patients by eight routinely collected blood parameters, including neutrophil, lymphocyte, monocyte, and platelets counts, neutrophil-to-lymphocyte ratio, CRP, LDH, and D-dimer. A prospective clinical study and an impact assessment will allow implementation of this model in the clinic to improve care, streamline resources and ease hospital burden by timely focusing the medical attention on potentially deteriorating patients.
Prediction of the disease course in Friedreich ataxia
Authors: Hohenfeld Christian , Terstiege Ulrich , Dogan Imis , Giunti Paola , Parkinson Michael H. , Mariotti Caterina , Nanetti Lorenzo , Fichera Mario , Durr Alexandra , Ewenczyk Claire , Boesch Sylvia , Nachbauer Wolfgang , Klopstock Thomas , Stendel Claudia , Rodríguez de Rivera Garrido Francisco Javier , Schöls Ludger , Hayer Stefanie N. , Klockgether Thomas , Giordano Ilaria , Didszun Claire , Rai Myriam , Pandolfo Massimo , Rauhut Holger , Schulz Jörg B. , Reetz Kathrin 
Published: November 2022

AbstractWe explored whether disease severity of Friedreich ataxia can be predicted using data from clinical examinations. From the database of the European Friedreich Ataxia Consortium for Translational Studies (EFACTS) data from up to five examinations of 602 patients with genetically confirmed FRDA was included. Clinical instruments and important symptoms of FRDA were identified as targets for prediction, while variables such as genetics, age of disease onset and first symptom of the disease were used as predictors. We used modelling techniques including generalised linear models, support-vector-machines and decision trees. The scale for rating and assessment of ataxia (SARA) and the activities of daily living (ADL) could be predicted with predictive errors quantified by root-mean-squared-errors (RMSE) of 6.49 and 5.83, respectively. Also, we were able to achieve reasonable performance for loss of ambulation (ROC-AUC score of 0.83). However, predictions for the SCA functional assessment (SCAFI) and presence of cardiological symptoms were difficult. In conclusion, we demonstrate that some clinical features of FRDA can be predicted with reasonable error; being a first step towards future clinical applications of predictive modelling. In contrast, targets where predictions were difficult raise the question whether there are yet unknown variables driving the clinical phenotype of FRDA.
Organotypic sinonasal airway culture systems are predictive of the mucociliary phenotype produced by bronchial airway epithelial cells
Authors: Delhove Juliette , Alawami Moayed , Macowan Matthew , Lester Susan E. , Nguyen Phan T. , Jersmann Hubertus P. A. , Reynolds Paul N. , Roscioli Eugene 
Published: November 2022

AbstractDifferentiated air–liquid interface models are the current standard to assess the mucociliary phenotype using clinically-derived samples in a controlled environment. However, obtaining basal progenitor airway epithelial cells (AEC) from the lungs is invasive and resource-intensive. Hence, we applied a tissue engineering approach to generate organotypic sinonasal AEC (nAEC) epithelia to determine whether they are predictive of bronchial AEC (bAEC) models. Basal progenitor AEC were isolated from healthy participants using a cytological brushing method and differentiated into epithelia on transwells until the mucociliary phenotype was observed. Tissue architecture was assessed using H&E and alcian blue/Verhoeff–Van Gieson staining, immunofluorescence (for cilia via acetylated α-tubulin labelling) and scanning electron microscopy. Differentiation and the formation of tight-junctions were monitored over the culture period (day 1–32) by quantifying trans-epithelial electrical resistance. End point (day 32) tight junction protein expression was assessed using Western blot analysis of ZO-1, Occludin-1 and Claudin-1. Reverse transcription qPCR-array was used to assess immunomodulatory and autophagy-specific transcript profiles. All outcome measures were assessed using R-statistical software. Mucociliary architecture was comparable for nAEC and bAEC-derived cultures, e.g. cell density P = 0.55, epithelial height P = 0.88 and cilia abundance P = 0.41. Trans-epithelial electrical resistance measures were distinct from day 1–14, converged over days 16–32, and were statistically similar over the entire culture period (global P < 0.001). This agreed with end-point (day 32) measures of tight junction protein abundance which were non-significant for each analyte (P > 0.05). Transcript analysis for inflammatory markers demonstrated significant variation between nAEC and bAEC epithelial cultures, and favoured increased abundance in the nAEC model (e.g. TGFβ and IL-1β; P < 0.05). Conversely, the abundance of autophagy-related transcripts were comparable and the range of outcome measures for either model exhibited a considerably more confined uncertainty distribution than those observed for the inflammatory markers. Organotypic air–liquid interface models of nAEC are predictive of outcomes related to barrier function, mucociliary architecture and autophagy gene activity in corresponding bAEC models. However, inflammatory markers exhibited wide variation which may be explained by the sentinel immunological surveillance role of the sinonasal epithelium.
A new catalytic site functioning in antigen cleavage by H34 catalytic antibody light chain
Authors: Hifumi Emi , Nonaka Tamami , Taguchi Hiroaki , Uda Taizo 
Published: November 2022

AbstractThe cleavage reactions of catalytic antibodies are mediated by a serine protease mechanism involving a catalytic triad composed of His, Ser, and Asp residues, which reside in the variable region. Recently, we discovered a catalytic antibody, H34 wild type (H34wt), that is capable of enzymatically cleaving an immune-check point PD-1 peptide and recombinant PD-1; however, H34wt does not contain His residues in the variable region. To clarify the reason behind the catalytic features of H34wt and the amino acid residues involved in the catalytic reaction, we performed site-directed mutagenesis focusing on the amino acid residues involved in the cleavage reaction, followed by catalytic activity tests, immunological reactivity evaluation, and molecular modeling. The results revealed that the cleavage reaction by H34wt proceeds through the action of a new catalytic site composed of Arg, Thr, and Gln. This new scheme differs from that of the serine protease mechanism of catalytic antibodies.
Reduced susceptibility of western corn rootworm (Diabrotica virgifera virgifera LeConte) populations to Cry34/35Ab1-expressing maize in northeast Nebraska
Authors: Reinders Jordan D. , Meinke Lance J. 
Published: November 2022

AbstractThe western corn rootworm (WCR;Diabrotica virgifera virgiferaLeConte) is a significant pest of maize (Zea maysL.) across the United States Corn Belt. Transgenic maize hybrids expressing insecticidal proteins derived fromBacillus thuringiensis(Bt) have been used to manage WCR since 2003. Widespread resistance to Cry3Bb1 (and associated cross-resistance to mCry3A and eCry3.1Ab) has placed increased selection pressure on Cry34/35Ab1 in single-protein and pyramided transgenic maize hybrids. Data on the susceptibility of Nebraska WCR populations to Cry34/35Ab1 has not been published since 2015 and plant-based bioassays conducted in 2017–2018 confirmed resistance to Cry3Bb1 + Cry34/35Ab1 maize, suggesting resistance to Cry34/35Ab1 has evolved in the Nebraska landscape. Therefore, plant-based bioassays were conducted on F1progeny of WCR populations collected from northeast Nebraska in 2018 and 2019. Larval survival and development were used to classify resistance to Cry34/35Ab1 in each WCR population. Bioassays confirmed incomplete resistance to Cry34/35Ab1 maize in 21 of 30 WCR populations; 9 of 30 WCR populations remained susceptible to Cry34/35Ab1. Collectively, results indicate that northeast Nebraska WCR populations were in the initial stages of resistance evolution to Cry34/35Ab1 during 2018–2019. Appropriate resistance management strategies are needed to mitigate resistance and preserve efficacy of rootworm-active products containing Cry34/35Ab1.
Identification and in silico analysis of noval alteration Arg420Gly in KIT proto oncogene among acute myeloid leukemia patients
Authors: Akram Afia Muhammad , Hassan Mubashir , Chaudhary Asma , Hayat Sikandar , Ali Qurban , Hussain Taha , Zafar Amjad , Javed Muhammad Arshad 
Published: November 2022

AbstractA number of studies have reported frequent incidence of c-kit gene mutations in association with core binding factor acute myeloid leukemia (CBF-AML). These genetic changes have become important prognostic predictors in patients with abnormal karyotype. Aim of this study was the detection of nucleotide alterations in newly diagnosed acute myeloid leukemia patients for three exons of c-kit gene, including cytogenetically normal patients. Thirty-one de novo AML patients were screened for any possible variations in exon 8, 11 and 17 sequences of c-kit proto-oncogene leading to amino acid substitutions or frame shift. Sanger sequencing method was employed followed by sequence analysis. Mutation data was then correlated with clinical and hematological parameters of patients and prognostic significance of genetic changes was assessed as well. The computational tools were then used to further understand the extent of damage caused by these mutations to c-kit protein. Fifteen (48.4%) mutant patients were observed with single, double or multiple mutations in one, two or all three exons studied. The analysis revealed eight new alterations which were not reported previously. Significant variation among mutant and non-mutant group of patients was observed with respect to FAB subtypes (x2 = 12.524, p = 0.029), Spleen size (x2 = 4.288, p = 0.038) and Red blood cell count (x2 = 8.447, p = 0.007). The survival analysis indicates poor overall and event free survival outcomes in mutant individuals. Furthermore, the in silico analysis suggests that changes in nucleotide sequences can possibly damage the protein structure and effect it’s function. This study emphasizes the need to consider screening of c-kit gene alterations not only in CBF-AML but in cytogenetically normal AML patients as well. In current investigation the effect of mutation Arg420Gly on structure and function of c-kit protein was investigated, as this was the most observed substitution in present cohort. Various bioinformatics tools and techniques were employed, which determined that Arg420Gly is possibly non-pathogenic mutation.
Word Learning as a Window to Memory and Rehabilitation Outcomes in Traumatic Brain Injury
Authors: Morrow Emily L. , Duff Melissa C. 
Published: November 2022

Purpose:
The purpose of this viewpoint is to advocate for increased study of word learning abilities and word learning interventions in traumatic brain injury (TBI).

Method:
We describe the word learning process and the unique opportunities afforded by studying each component and stage. Building on discussions at the 2022 International Cognitive-Communication Disorders Conference, we describe how word learning may underlie a variety of functional outcomes after TBI, making it a promising target for rehabilitation. Finally, we discuss principles that may guide study in this critical area to advance outcomes after TBI for children and adults.

Results:
Word learning is a dynamic and iterative process taking place over time and in multiple stages. Thus, studying word learning affords the opportunity to parse the relative contributions of multiple memory systems to different phases and components of the process. However, single–timepoint designs are insufficient to capture the full word learning process, which occurs over time and across contexts. Word learning also presents an opportunity to assess the contributions of behavioral and lifestyle factors (e.g., sleep and exercise) to different memory phases. Understanding these interactions could drive clinical interventions aimed at improving memory through manipulable external behaviors.

Conclusions:
Word learning is key to success in functional spheres across the life span. The importance of words to daily life remains after TBI, even as the memory systems that support word learning are disrupted. The empirical study of word learning and rehabilitation of word learning deficits in TBI presents a promising new direction in understanding the breadth of neurogenic cognitive-communication disorders and an opportunity to explore a potential driver of functional outcome and impactful rehabilitation target.
Thermal stability of piezoelectricity in polylactide polymers and related piezoelectric minimotor application
Authors: Zhang Jinxi , Wang Yonggui , Tian Junyuan , Xu Xiangchen , Wang Weidong , Ren Kailiang 
Published: November 2022

AbstractPiezoelectric polymers have been widely used in a variety of applications, including tactile sensors, energy harvesting, polymer actuators, and biological devices. In this investigation, we fabricated the PLLA (poly(L‐lactic Acid)‐based bimorph structures for piezoelectric motor applications. First, a strain measurement setup was established to measure the strain of the PLLA and PDLA film at different temperatures. The effective piezoelectric constants d14 calculated for the PLLA and PDLA films were 10.2 pC/N and 9.4 pC/N, respectively. Furthermore, the PLLA films showed a strong thermostability of piezoelectricity at 130°C. The PLLA bimorph minimotor showed the maximum load of 1.2 g and the maximum torque of 0.019 cN·m. The minimotors were capable of rotating a plastic hemisphere container in the clockwise and counterclockwise directions at 65 V voltage. Due to the light weight, low driving voltage, and thermal stability, the PLLA/PDLA motors show a great promise in future Braille display, robots, and mini‐digital camera applications.
DC‐DC parallel legs linked high gain converter for microgrid applications
Authors: Bhaskar M.S. , Subramaniam Umashankar , Selvam Sivakumar , Almakhles Dhafer , Padmanaban Sanjeevikumar , Muhibbullah M. 
Published: November 2022

AbstractFor microgrid operations, this article suggests a DC‐DC Parallel Legs Linked High Gain Converter. To realize high voltage gain, the recommended converter is transformer‐free and developed with n‐parallel legs. One active switch, one capacitor, one inductor, and one diode make up each leg of the boost converter. The operating principle, characteristic waveforms, and steady‐state study are provided for continuous and discontinuous conduction modes. High voltage gain, low voltage and low current stress on switches, compact size inductors, and low voltage capacitors are the important features of the recommended converter. The suggested converter achieves a large voltage gain without the demand of high‐frequency transformers or voltage multiplier stages. Furthermore, it works on two separate duty cycles, allowing for a wide variety of duty cycle functioning. The suggested converter is compared to a newly discussed converter, and the fundamentals of reactive component design and semiconductor device selection are discussed. The experimental findings are shown, demonstrating that the suggested converter's theoretical analysis and functioning are correct.
Genes associated with diagnosis and prognosis of Burkitt lymphoma
Authors: Doughan Albert , Salifu Samson Pandam 
Published: November 2022

AbstractBurkitt lymphoma (BL) is one of the most aggressive forms of non‐Hodgkin's lymphomas that affect children and young adults. The expression of genes and other molecular markers during carcinogenesis can be the basis for diagnosis, prognosis and the design of new and effective drugs for the management of cancers. The aim of this study was to identify genes that can serve as prognostic and therapeutic targets for BL. We analysed RNA‐seq data of BL transcriptome sequencing projects in Africa using standard RNA‐seq analyses pipeline. We performed pathway enrichment analyses, protein–protein interaction networks, gene co‐expression and survival analyses. Gene and pathway enrichment analyses showed that the differentially expressed genes are involved in tube development, signalling receptor binding, viral protein interaction, cell migration, external stimuli response, serine hydrolase activity and PI3K‐Akt signalling pathway. Protein–protein interaction network analyses revealed the genes to be highly interconnected, whereas module analyses revealed 25 genes to possess the highest interaction score. Overall survival analyses delineated six genes (ADAMTSL4, SEMA5B, ADAMTS15, THBS2, SPON1 and THBS1) that can serve as biomarkers for prognosis for BL management.