bluuebunny/crossref_metadata_2025 huggingface dataset:
IL-20 subfamily cytokines impair the oesophageal epithelial barrier by diminishing filaggrin in eosinophilic oesophagitis
Authors: Kaymak Tanay , Kaya Berna , Wuggenig Philipp , Nuciforo Sandro , Göldi Andreas ,   Swiss EoE Cohort Study Group (SEECS), Oswald Franz , Roux Julien , Noti Mario , Melhem Hassan , Hruz Petr , Niess Jan Hendrik
Published: May 2022

Objective

Disruption of the epithelial barrier plays an essential role in developing eosinophilic oesophagitis (EoE), a disease defined by type 2 helper T cell (Th2)-mediated food-associated and aeroallergen-associated chronic inflammation. Although an increased expression of interleukin (IL)-20 subfamily members, IL-19, IL-20 and IL-24, in Th2-mediated diseases has been reported, their function in EoE remains unknown.

Design

Combining transcriptomic, proteomic and functional analyses, we studied the importance of the IL-20 subfamily for EoE using patient-derived oesophageal three-dimensional models and an EoE mouse model.

Results

Patients with active EoE have increased expression of IL-20 subfamily cytokines in the oesophagus and serum. In patient-derived oesophageal organoids stimulated with IL-20 cytokines, RNA sequencing and mass spectrometry revealed a downregulation of genes and proteins forming the cornified envelope, including filaggrins. On the contrary, abrogation of IL-20 subfamily signalling inIl20R2−/−animals resulted in attenuated experimental EoE reflected by reduced eosinophil infiltration, lower Th2 cytokine expression and preserved expression of filaggrins in the oesophagus. Mechanistically, these observations were mediated by the mitogen-activated protein kinase (MAPK); extracellular-signal regulated kinases (ERK)1/2) pathway. Its blockade prevented epithelial barrier impairment in patient-derived air–liquid interface cultures stimulated with IL-20 cytokines and attenuated experimental EoE in mice.

Conclusion

Our findings reveal a previously unknown regulatory role of the IL-20 subfamily for oesophageal barrier function in the context of EoE. We propose that aberrant IL-20 subfamily signalling disturbs the oesophageal epithelial barrier integrity and promotes EoE development. Our study suggests that specific targeting of the IL-20 subfamily signalling pathway may present a novel strategy for the treatment of EoE.
Durable and dynamic hTERT immune responses following vaccination with the long-peptide cancer vaccine UV1: long-term follow-up of three phase I clinical trials
Authors: Ellingsen Espen Basmo , Aamdal Elin , Guren Tormod , Lilleby Wolfgang , Brunsvig Paal F , Mangsbo Sara M , Aamdal Steinar , Hovig Eivind , Mensali Nadia , Gaudernack Gustav , Inderberg Else Marit
Published: May 2022

Background

Therapeutic cancer vaccines represent a promising approach to improve clinical outcomes with immune checkpoint inhibition. UV1 is a second generation telomerase-targeting therapeutic cancer vaccine being investigated across multiple indications. Although telomerase is a near-universal tumor target, different treatment combinations applied across indications may affect the induced immune response. Three phase I/IIa clinical trials covering malignant melanoma, non-small cell lung cancer, and prostate cancer have been completed, with patients in follow-up for up to 8 years.

Methods

52 patients were enrolled across the three trials. UV1 was given as monotherapy in the lung cancer trial and concurrent with combined androgen blockade in the prostate cancer trial. In the melanoma study, patients initiated ipilimumab treatment 1 week after the first vaccine dose. Patients were followed for UV1-specific immune responses at frequent intervals during vaccination, and every 6 months for up to 8 years in a follow-up period. Phenotypic and functional characterizations were performed on patient-derived vaccine-specific T cell responses.

Results

In total, 78.4% of treated patients mounted a measurable vaccine-induced T cell response in blood. The immune responses in the malignant melanoma trial, where UV1 was combined with ipilimumab, occurred more rapidly and frequently than in the lung and prostate cancer trials. In several patients, immune responses peaked years after their last vaccination. An in-depth characterization of the immune responses revealed polyfunctional CD4+ T cells producing interferon-γ and tumor necrosis factor-α on interaction with their antigen.

Conclusion

Long-term immunomonitoring of patients showed highly dynamic and persistent telomerase peptide-specific immune responses lasting up to 7.5 years after the initial vaccination, suggesting a plausible functional role of these T cells in long-term survivors. The superior immune response kinetics observed in the melanoma study substantiate the rationale for future combinatorial treatment strategies with UV1 vaccination and checkpoint inhibition for rapid and frequent induction of anti-telomerase immune responses in patients with cancer.
Extracellular mitochondria drive CD8 T cell dysfunction in trauma by upregulating CD39
Authors: Tiwari-Heckler Shilpa , Lee Ghee Rye , Harbison James , Ledderose Carola , Csizmadia Eva , Melton David , Zhang Quanzhi , Junger Wolfgang , Chen Guanqing , Hauser Carl J , Otterbein Leo E , Longhi Maria Serena , Robson Simon Christopher
Published: May 2022

Rationale

The increased mortality and morbidity seen in critically injured patients appears associated with systemic inflammatory response syndrome (SIRS) and immune dysfunction, which ultimately predisposes to infection. Mitochondria released by injury could generate danger molecules, for example, ATP, which in turn would be rapidly scavenged by ectonucleotidases, expressed on regulatory immune cells.

Objective

To determine the association between circulating mitochondria, purinergic signalling and immune dysfunction after trauma.

Methods

We tested the impact of hepatocyte-derived free mitochondria on blood-derived and lung-derived CD8 T cells in vitro and in experimental mouse models in vivo. In parallel, immune phenotypic analyses were conducted on blood-derived CD8 T cells obtained from trauma patients.

Results

Isolated intact mitochondria are functional and generate ATP ex vivo. Extracellular mitochondria perturb CD8+T cells in co-culture, inducing select features of immune exhaustion in vitro. These effects are modulated by scavenging ATP, modelled by addition of apyrase in vitro. Injection of intact mitochondria into recipient mice markedly upregulates the ectonucleotidase CD39, and other immune checkpoint markers in circulating CD8+T cells. We note that mice injected with mitochondria, prior to instilling bacteria into the lung, exhibit more severe lung injury, characterised by elevated neutrophil influx and by changes in CD8+T cell cytotoxic capacity. Importantly, the development of SIRS in injured humans, is likewise associated with disordered purinergic signalling and CD8 T cell dysfunction.

Conclusion

These studies in experimental models and in a cohort of trauma patients reveal important associations between extracellular mitochondria, aberrant purinergic signalling and immune dysfunction. These pathogenic factors with immune exhaustion are linked to SIRS and could be targeted therapeutically.
Space charge effects for collective instabilities in circular machines
Authors: Burov Alexey
Published: May 2022

Coulomb fields of charged particle beams in circular machines determine, together with wake fields, modes of the collective beam oscillations, for both transverse and longitudinal degrees of freedom. Recent progress in these two areas of beam dynamics is discussed.
Calculations of GDR parameters for deformed nuclei using LogitBoost classifier and artificial neural network
Authors: Üncü Yiğit Ali , Danışman Taner , Özdoğan Hasan
Published: May 2022

Photo-nuclear interactions are important for investigating fundamental nuclear physics phenomena. The photo-absorption cross-section energy curve displays a wide resonance called giant dipole resonance (GDR) until 30 MeV. First, spherical and deformed nuclei have been determined by using LogitBoost classifier, and then GDR parameters for deformed nuclei have been estimated by using an artificial neural network (ANN) via Levenberg–Marquardt algorithm which has been selected for the training section. In the last step, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] reaction cross-sections have been computed by using GDR parameters obtained ANN estimations. The mean square error, root mean square error, and [Formula: see text] are evaluated as the best performance of ANN estimates. Photo-neutron cross-section results have been compared with experimental data from the literature. Consequently, it has been found that ANN algorithms can be used to determine the GDR parameters for deformed nuclei in the lack of experimental data of photo-absorption reaction.
Investigation of effective stress imposed on flexible single-crystalline semiconductor nanomembrane electronics under bending conditions
Authors: Yang Xiaodong , Liu Jiali , Huang Zhiyuan , Ma Zhenqiang , Qin Guoxuan
Published: May 2022

Flexible electronics have gained attention with their unique ability to withstand stress deformations. However, stress makes significant impact on the electrical performance of the flexible devices. It is commonly believed that the stress imposed on the flexible device is only related to the bending curvature. Our study shows that the actual stress imposed on the device is not only related to the bending radius but also closely related to the device dimension. In this study, the effective stress of the device layer as well as the stress distributions are investigated, with the fundamental flexible diodes as representative examples. Flexible single-crystalline silicon [Formula: see text]-[Formula: see text]-[Formula: see text] diodes are fabricated and measured under different bending curvature conditions. Then the dominant affecting factor of the flexible diodes with bending stress is extracted. 3D finite-element method (3D-FEM) stress distribution modeling is also employed to analyze the stress distributions and demonstrate that the effective stress of the device layer is closely and proportionally related to the device dimension. Furthermore, theoretical calculations are conducted to verify the FEM results and quantitatively show the relation between the stress and the dimension of the flexible devices. The analysis provides a guideline in the design of flexible electronics under bending conditions.
Comparison between three distinct perceptions to the new solitary solutions of the generalized Hirota–Satsuma coupling KDV system
Authors: Zahran Emad H. M. , Guner Ozkan , Bekir Ahmet
Published: May 2022

In this paper, we will implement a comparison between three different perceptions to the behavior of the exact solution to the generalized Hirota–Satsuma coupling Korteweg–de Vries System (GHSCKDVS). The proposed model plays a vital role in different branches of physics and applied mathematics. Specially, this model describes dispersive waves as the shallow water waves in hydrodynamics and it has various types of solutions like soliton solutions and traveling wave solutions. These three various visions to the behavior of the exact solution are established via three various techniques that have three different mathematical analyses. These three important different techniques are the Paul–Painlevé approach method (PPAM) examined previously to extract the exact solutions for many nonlinear partial differential equations (NLPDE) and achieved good results, the Ricatti–Bernolli Sub-ODE method (RBSODM) which is one of the famous ansatze approaches that doesn’t surrender to the balance rule, treats the balance rule fails and realizes impressive forms of the exact solutions. Many new various types of soliton solutions as hyperbolic and trigonometric function solutions, represented by the M-shaped, W-shaped, kink, anti-kink and dark soliton solutions have been established via these two methods. Moreover, we will document the identical numerical solutions for all achieved exact solutions realized by the two methods mentioned above via the famous numerical variational iteration method (VIM) that usually gives good results. The powerful of our achieved numerical solutions related to that their initial conditions emerged from the extracted exact solutions.
Discussion of “The Evolution of Environmental Reporting in Europe: The Role of Financial and Non-Financial Regulation”
Authors: Charitou Andreas
Published: May 2022

Although regulation on corporate social and environmental reporting in Europe has grown in recent years, there has been limited empirical research on the level, quality, and economic consequences of this regulated disclosure. Barbu, Feleaga and Ferrat (2022) advance our understanding of the impact of European Union legislation on the quantity of corporate environmental disclosures in Europe, showing that regulations improve the level of environmental disclosure. Future research on environmental reporting should provide more in-depth economic intuition and address issues related to differences in cross-country analyses, as well as investigate in more depth both the level and quality of increased disclosure on social and environmental issues, the factors influencing this type of disclosure, and any economic consequences.
Discourse Behavior of Older Adults Interacting with a Dialogue Agent Competent in Multiple Topics
Authors: Razavi S. Zahra , Schubert Lenhart K. , van Orden Kimberly , Ali Mohammad Rafayet , Kane Benjamin , Hoque Ehsan
Published: May 2022

We present a conversational agent designed to provide realistic conversational practice to older adults at risk of isolation or social anxiety, and show the results of a content analysis on a corpus of data collected from experiments with elderly patients interacting with our system. The conversational agent, represented by a virtual avatar, is designed to hold multiple sessions of casual conversation with older adults. Throughout each interaction, the system analyzes the prosodic and nonverbal behavior of users and provides feedback to the user in the form of periodic comments and suggestions on how to improve. Our avatar is unique in its ability to hold natural dialogues on a wide range of everyday topics—27 topics in three groups, developed in collaboration with a team of gerontologists. The three groups vary in “degrees of intimacy,” and as such in degrees of cognitive difficulty for the user. After collecting data from nine participants who interacted with the avatar for seven to nine sessions over a period of 3 to 4 weeks, we present results concerning dialogue behavior and inferred sentiment of the users. Analysis of the dialogues reveals correlations such as greater elaborateness for more difficult topics, increasing elaborateness with successive sessions, stronger sentiments in topics concerned with life goals rather than routine activities, and stronger self-disclosure for more intimate topics. In addition to their intrinsic interest, these results also reflect positively on the sophistication and practical applicability of our dialogue system.
Experience Replay-based Deep Reinforcement Learning for Dialogue Management Optimisation
Authors: Malviya Shrikant , Kumar Piyush , Namasudra Suyel , Tiwary Uma Shanker
Published: May 2022

Dialogue policy is a crucial component in task-oriented Spoken Dialogue Systems (SDSs). As a decision function, it takes the current dialogue state as input and generates appropriate system’s response. In this paper, we explore the reinforcement learning approaches to solve this problem in an Indic language scenario. Recently, Deep Reinforcement Learning (DRL) has been used to optimise the dialogue policy. However, many DRL approaches are not sample-efficient. Hence, particular attention is given to actor-critic methods based on off-policy reinforcement learning that utilise the Experience Replay (ER) technique for reducing the bias and variance to achieve high sample efficiency. ER based actor-critic methods, such as Advantage Actor-Critic Experience Replay (A2CER) are proven to deliver competitive results in gaming environments that are fully observable and have a very small action-set. While, in SDSs, the states are not fully observable and often have to deal with the large action space. Describing the limitations of traditional methods, i.e., value-based and policy-based methods, such as high variance, low sample-efficiency, and often converging to local optima, we firstly explore the use of A2CER in dialogue policy learning. It is shown to beat the current state-of-the-art deep learning methods for SDS. Secondly, to handle the issues of early-stage performance, we utilise a demonstration corpus to pre-train the models prior to on-line policy learning. We thus experiment with the A2CER on a larger action space and find it significantly faster than the current state-of-the-art. Combining both approaches, we present a novel DRL based dialogue policy optimisation method, A2CER and its effectiveness for a task-oriented SDS in the Indic language.
Sirtuins in Epigenetic Silencing and Control of Gene Expression in Model and Pathogenic Fungi
Authors: Zhao Guolei , Rusche Laura N.
Published: May 2022

Fungi, including yeasts, molds, and mushrooms, proliferate on decaying matter and then adopt quiescent forms once nutrients are depleted. This review explores how fungi use sirtuin deacetylases to sense and respond appropriately to changing nutrients. Because sirtuins are NAD+-dependent deacetylases, their activity is sensitive to intracellular NAD+ availability. This allows them to transmit information about a cell's metabolic state on to the biological processes they influence. Fungal sirtuins are primarily known to deacetylate histones, repressing transcription and modulating genome stability. Their target genes include those involved in NAD+ homeostasis, metabolism, sporulation, secondary metabolite production, and virulence traits of pathogenic fungi. By targeting different genes over evolutionary time, sirtuins serve as rewiring points that allow organisms to evolve novel responses to low NAD+ stress by bringing relevant biological processes under the control of sirtuins.
More Than the Face: Representations of Bodies in the Inferior Temporal Cortex
Authors: Vogels Rufin
Published: May 2022

Visual representations of bodies, in addition to those of faces, contribute to the recognition of con- and heterospecifics, to action recognition, and to nonverbal communication. Despite its importance, the neural basis of the visual analysis of bodies has been less studied than that of faces. In this article, I review what is known about the neural processing of bodies, focusing on the macaque temporal visual cortex. Early single-unit recording work suggested that the temporal visual cortex contains representations of body parts and bodies, with the dorsal bank of the superior temporal sulcus representing bodily actions. Subsequent functional magnetic resonance imaging studies in both humans and monkeys showed several temporal cortical regions that are strongly activated by bodies. Single-unit recordings in the macaque body patches suggest that these represent mainly body shape features. More anterior patches show a greater viewpoint-tolerant selectivity for body features, which may reflect a processing principle shared with other object categories, including faces.
Inverse Source Problem for Sobolev Equation with Fractional Laplacian
Authors: Phuong Nguyen Duc , Nguyen Van Tien , Long Le Dinh
Published: May 2022

In this paper, we are interested in the problem of determining the source function for the Sobolev equation with fractional Laplacian. This problem is ill-posed in the sense of Hadamard. In order to edit the instability instability of the solution, we applied the fractional Landweber method. In the theoretical analysis results, we show the error estimate between the exact solution and the regularized solution by using an a priori regularization parameter choice rule and an a posteriori regularization parameter choice rule. Finally, we investigate the convergence of the source function when fractional order

β
⟶

1

+

.
Maximum Power Point Tracking of PV Grids Using Deep Learning
Authors: Rafeeq Ahmed K. , Sayeed Farrukh , Logavani K. , Catherine T. J. , Ralhan Shimpy , Singh Mahesh , Prabu R. Thandaiah , Subramanian B. Bala , Kassa Adane
Published: May 2022

In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. This deep learning model aims to maximise the output power from the solar grids when the panels are connected with the boost converter under different variable load conditions. BPNN-DL enables the prediction of reference voltage at different weather conditions for severing the various output power that ensures maximum power with stable output voltage. The proposed BPNN-DL is tested under different conditions to estimate the robustness of the modules under internal/external interferences. The results of the simulation show that the proposed method achieves maximum output power from each panel compared with existing methods in terms of regression analysis on training, testing, and validation.
CT-ML: Diagnosis of Breast Cancer Based on Ultrasound Images and Time-Dependent Feature Extraction Methods Using Contourlet Transformation and Machine Learning
Authors: Hajipour Khire Masjidi Behnam , Bahmani Soufia , Sharifi Fatemeh , Peivandi Mohammad , Khosravani Mohammad , Hussein Mohammed Adil
Published: May 2022

Breast diseases are a group of diseases that appear in different forms. An entire group of these diseases is breast cancer. This disease is one of the most important and common diseases in women. A machine learning system has been trained to identify specific patterns using an algorithm in a machine learning system to diagnose breast cancer. Therefore, designing a feature extraction method is essential to decrease the computation time. In this article, a two-dimensional contourlet is utilized as the input image based on the Breast Cancer Ultrasound Dataset. The sub-banded contourlet coefficients are modeled using the time-dependent model. The features of the time-dependent model are considered the leading property vector. The extracted features are applied separately to determine breast cancer classes based on classification methods. The classification is performed for the diagnosis of tumor types. We used the time-dependent approach to feature contourlet sub-bands from three groups of benign, malignant, and health control test samples. The final feature of 1200 ultrasound images used in three categories is trained based on k-nearest neighbor, support vector machine, decision tree, random forest, and linear discrimination analysis approaches, and the results are recorded. The decision tree results show that the method’s sensitivity is 87.8%, 92.0%, and 87.0% for normal, benign, and malignant, respectively. The presented feature extraction method is compatible with the decision tree approach for this problem. Based on the results, the decision tree architecture with the highest accuracy is the more accurate and compatible method for diagnosing breast cancer using ultrasound images.
Structural Characteristics and Tectonic Evolution of the Wunansha Uplift in the South Yellow Sea Basin, China
Authors: Zhang Tian , Zhu Weilin , Fu Qiang , Fu Xiaowei
Published: May 2022

By selecting typical seismic sections to carry out detailed structural interpretation, the structural style features of the Wunansha Uplift in the South Yellow Sea basin were systematically combined, and the compressional structures (imbricate, opposite/back thrust, and Y-shaped structures), strike-slip faults (positive flower-shaped faults), and extensional normal faults (listric-shaped normal faults) were identified. On this basis, combined with the characteristics of the regional stress field and the background of deep geodynamics, the genetic mechanism and structural evolution of the structural style in the Wunansha Uplift were defined. The stress mechanism of the compressional structures originated from the initial high-speed and low-angle NW subduction of the paleo-Pacific plate during the early Yanshanian movement in the Early Jurassic. The regional strike-slip fault was mainly a positive flower structure with compression and torsion characteristics, and its stress mechanism originated from sinistral shear caused by the Early Cretaceous low-angle NNW subduction of the paleo-Pacific plate. The Tan-Lu fault in eastern China also had sinistral shear characteristics in this period. The extensional normal fault was characterized by a listric shape, which developed along the northern boundary of the Wunansha Uplift, that is, the connection between the Wunansha Uplift and the Southern Depression of the South Yellow Sea basin. The stress mechanism was derived from the transition of the paleo-Pacific plate from low-angle to high-angle subduction during the late Yanshanian movement in the Late Cretaceous. Simultaneously, the tectonic stress system in eastern China also changed from compressional to tensional.
Four Equivalent Relations between MCP and CP and Its Implication in Quantum and Information Theory
Authors: Wan Xiao-long
Published: May 2022

(Norraw) modal propositional logic (MCP) tries to be a strict model for quantum and information theory, but it has serious difficulties in syntax, semantics, and metaphysics. According to the guiding definition of minimal hidden variables, this study presents four equivalences between MCP and classical propositional logic (CP). It concludes that MCP is CP containing minimal syntactic hidden variables, modal axiom represents the classification of CP formulas, and possible world is CP formula as assignment background, which establishes a simpler and unified basis for quantum information theory. The black modal of classical propositional logic (BCP), as an assisted discovery method, reveals that modal nature is actually to express the interdependence between things and their environment in a mathematical way, which can express the holism, dialectics, and uncertainty of metaphysics, and use mark hiding (e.g., f (x) is a function of x, f () is the functional expression, functional-e for short, and “□” is, in fact, a cluster of functional-es in which superscript and subscript are hidden) that does not affect the effectiveness of reasoning to express a more simplified, efficient, and consistent artificial intelligence.
Numerical Simulation, Analysis, and Fabrication of MEMS-Based Solid Ag and Cu Microneedles for Biomedical Applications
Authors: Tariq Nimra , Ashraf Muhammad Waseem , Tayyaba Shahzadi , Herrera-May Agustín L. , Delgado-Alvarado Enrique
Published: May 2022

Microelectromechanical system (MEMS)-based devices have gained attention recently due to their beneficial biomedical applications. MEMS-based devices like microneedles have set new trends in drug delivery, vaccination, skin, and eye treatment. Different materials like metals, sugars, polymers, and silicon have been used for fabrication. Various techniques have been used for their fabrication, including laser ablation, lithography, injection molding, and additive manufacturing. The tip diameter of different micron ranges has been achieved. The strength and stiffness of the microneedle’s tip have always been important in fabricating microneedles so that it does not break on insertion. This research paper presents a comparison between silver (Ag) and copper (Cu) solid microneedles by performing numerical analysis using the fuzzy approach, structural simulation, and fabrication. Firstly, structural simulation has been performed in ANSYS software to test the strength of silver (Ag) and copper (Cu) microneedles separately. The purpose is to compare the stress effect and fracture limit of both microneedles. The results collected from the simulation provide valuable target and prediction facts to fabricate improved designs of the solid Ag and Cu microneedles. Then, fuzzy-based numerical analysis has been performed in MATLAB software for both microneedles separately. In this numerical analysis, the effect on the range of microneedle tip diameter and cone length has been observed by varying input voltage and time. Finally, fabrication has been performed using a novel economical technique such as electrochemical etching. Electrochemical etching is a very low-cost and clean room-free technique as compared to other techniques used for the fabrication of microneedles. The fabrication technique adopted in this work is the same for both silver and copper microneedles. The scanning electron microscopy (SEM) characterization has been performed for both fabricated microneedle tips. The tip of the fabricated solid Ag and Cu microneedle has been then coated with drugs using the dip-coating method. The coated solid Ag and Cu microneedle’s tip has been then characterized again using SEM. The numerical results calculated from the fuzzy analysis have been then compared with fabrication results. The fuzzy analysis gives the simulated size of the microneedle’s tip for 5.05 μm silver and 5.12 μm copper which have very close approximation with the experimental values from the SEM micrographs which also give the values of the cone length from 400 to 500 μm and the tip size from 5 to 6 μm for the time of 10–15 minutes, whose values were optimized by the fuzzy analysis. The results of this research provide valuable benchmark and prediction data to fabricate improved designs of the silver solid microneedles for drug delivery and other biomedical applications.
Diagnosis of Nonperitonealized Colorectal Cancer with Computerized Tomography Image Features under Deep Learning
Authors: Wang Xiaohong , Guo Changyi , Zha Yufeng , Xu Kai , Liu Xiaochao
Published: May 2022

This study aimed to explore the value of abdominal computerized tomography (CT) three‐dimensional reconstruction using the dense residual single‐axis super‐resolution algorithm in the diagnosis of nonperitonealized colorectal cancer (CC). 103 patients with nonperitonealized CC (the lesion was located in the ascending colon or descending colon) were taken as the research subjects. The imagological tumor (T) staging, the extramural depth (EMD) of the cancer tissues, and the extramural vascular invasion (EMVI) grading were analyzed. A dense residual single‐axis super‐resolution network model was also constructed for enhancing CT images. It was found that the CT images processed using the algorithm were clear, and the peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM) were 33.828 dB and 0.856, respectively. In the imagological T staging of CC patients, there were 17 cases in the T3 stage and 68 cases in the T4 stage. With the EMD increasing, the preoperative carcinoembryonic antigen (CEA) highly increased, and the difference was statistically significant (P < 0.05). The postoperative hospital stays of patients were also different with different grades of EMVI. The hospital stay of grade 1 patients (19.45 days) was much longer than that of grade 2 patients (13.19 days), grade 3 patients (15.36 days), and grade 4 patients (14.36 days); the differences were of statistical significance (P < 0.05). It was suggested that CT images under the deep learning algorithm had a high clinical value in the evaluation of T staging, EMD, and EMVI for the diagnosis of CC.
A Fuzzy Neural Network-Based System for Alleviating Students’ Boredom in English Learning
Authors: Yang Liuhui , Wu Xiuying
Published: May 2022

In order to explore students’ boredom in English learning, a recognition algorithm based on fuzzy neural network is proposed. The algorithm selects Gaussian membership function and initializes the clustering center obtained by fuzzy c-means algorithm to the center of Gaussian function, and the width of Gaussian function is obtained by the membership and center of fuzzy c-means clustering algorithm. In the construction of base classifier, diversity strategy is adopted to increase its diversity and complementarity. In the selection of base classifiers, the combination of contour coefficient and clustering algorithm is used to determine the number of classifiers to be fused, and the inconsistency measurement method is used to evaluate their differences. In the combination strategy, we learn from the Bayesian thought and dynamically adapt the weight of learning base classifier based on its a priori probability and class conditional probability. The experimental results show that 10 times of cross-validation are carried out, respectively, and the accuracy of each algorithm is given. The algorithm based on tree structure obviously has better performance, followed by the rule-based algorithm, and finally the fuzzy neural network algorithm based on neural network, while the accuracy of SVM and logistic regression algorithm LR is lower. It is proved that the fuzzy neural network can effectively identify students’ boredom in English learning.
A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA
Authors: Li Hanwen , Chen Shaohua , Mi Hua
Published: May 2022

Background. Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein‐protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and “CancerSubtypes” package of R software for transcriptomics can help identify biomarkers related to BLCA prognosis. This will have significant implications for prevention and treatment. Method. BLCA data were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database (GSE13507). GSVA analysis converted the gene expression matrix to the gene set expression matrix. “CancerSubtypes” classified patients into three subtypes and established a prognostic model based on differentially expressed gene sets (DEGSs) among the three subtypes. For genes from prognosis‐related DEGSs, functional and pathway enrichment analyses and PPI network analysis were carried out. The Human Protein Atlas (HPA) database was used for validation. Finally, the proportion of tumor‐infiltrating immune cells (TIICs) was determined using the CIBERSORT algorithm. Results. In total, 414 tumor samples and 19 adjacent‐tumor samples were obtained from TCGA, with 145 samples belonging to subtype A, 126 samples belonging to subtype B, and 136 samples belonging to subtype C. Then, we identified 83 DEGSs and constituted a prognostic signature with two of them: “GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.” Finally, five subnets of two PPI networks were established, and nine core proteins were obtained: CDH2, COL1A1, EIF2S2, PSMA3, NAA10, DNM1L, TUBA4A, KIF11, and KIF23. The HPA database confirmed the expression of the nine core proteins in BLCA tissues. Furthermore, EIF2S2, PSMA3, DNM1L, and TUBA4A could be novel BLCA prognostic biomarkers. Conclusions. In this study, we discovered two gene sets linked to BLCA prognosis. PPI analysis confirmed the network’s core proteins, and several newly discovered biomarkers of BLCA prognosis were identified.
Microbiological and Physicochemical Characterization of Irrigation Water in Market Gardening in Korhogo
Authors: Kambire Ollo , Yao Konan Mathurin , Aka Natchia , Bamba Karidja Emilie , Koffi-Nevry Rose
Published: May 2022

Several sources of water are used by farmers without concern for quality, which can have consequences on the health of the consumer of market garden products. The aim of this study is to evaluate the microbiological and physicochemical qualities of irrigation water. Microorganisms were counted using the membrane filtration and incorporation into the agar methods. The physicochemical parameters were measured using multiparameter and spectrophotometric methods. The average values of the physicochemical parameters are between 6.46 and 6.9 (pH), 27.15 and 29.9°C (temperature), 170 and 760 μS/cm (electrical conductivity), 70 and 380 mg/L (total dissolved solids), 3.85 and 77.59 mg/L (nitrates), and between 0.13 and 2.35 mg/L for ammonium. Irrigation water in market gardening is highly contaminated by microorganisms. Loads ranging from 3.64 to 4.35 log10 cfu/100 mL, 2.44 to 3.31 log10 cfu/100 mL, 2.44 to 2.9 log10 cfu/100 mL, and 2.07 to 3.63 log10 cfu/100 mL were obtained for total coliforms, E. coli, fecal enterococci, and sulphite-reducing clostridia, respectively. Mean loadings ranging from 4.95 to 5.98 log10 cfu/100 mL, 1.8 to 2.08 log10 cfu/100 mL, and 1.5 to 1.98 log10 cfu/100 mL were obtained for mesophilic aerobic germs, moulds, and yeasts, respectively. Four different mould strains were identified in irrigation water. These strains belong to the genus Aspergillus. Shallows water was more contaminated with microorganisms. These results show that water should be treated before being used for irrigation; market garden products must be properly washed and disinfected before consumption.
An Implementation of Modified Blowfish Technique with Honey Bee Behavior Optimization for Load Balancing in Cloud System Environment
Authors: Rani Preeti , Singh Prem Narayan , Verma Sonia , Ali Nasir , Shukla Prashant Kumar , Alhassan Musah
Published: May 2022

Cloud computing is a system that allows data to be saved in the cloud on a virtual worker. Outsiders and virtual machines in the cloud worker supplier played a critical part in efficiently storing and accessing information. Security, access control, and load balancing are critical challenges in cloud engineering. In the past, various solutions for adjusting cloud load have been proposed. Operator-based burden adjustment calculation surpassed all other offered CPU use, cost, and idle time strategies. The productivity of the specialist-based load adjustment computation decreased when any of the client hubs changed regions. Experimental outcomes show that Modified-HBB-LB performs better than the existing load balancing strategies such as HBB-LB, DLB, FCFS, WRR, HDLB, and FIFO by achieving the load balance of the complete system. The Modified-HBB-LB technique reduces the number of migrations tasks (30%, 25% and 20%) as compared to HDLB, DLB, and HBB-LB. The proposed Modified-HBB-LB technique maintains the 3-5% higher performance levels on makespan, completion, and response time as compared to existing comparative techniques.
Seismic Performance Analysis of Fabricated Concrete Beam-Column Joints Based on Intelligent Finite Element Analysis
Authors: Yang Chen , Li Xiancheng
Published: May 2022

In order to improve the seismic performance analysis effect of prefabricated concrete beam-column joints, this article uses intelligent finite element analysis technology to analyze the seismic performance of prefabricated concrete beam-column joints. Moreover, this article conducts in-depth research on the shear bearing capacity of the plastic hinge area so as to improve the accuracy of the calculation of the shear bearing capacity of the plastic hinge area. In addition, this article conducts finite element analysis of integral frame joints, uses finite element software to carry out numerical simulation of frame joints, and compares and analyzes the experimental results in the literature. Further, this article proposes an improvement of a prefabricated frame joint, performs finite element analysis on it, and compares and analyzes the numerical simulation results of concrete joints. The analysis results show that the finite element analysis model proposed in this article has high accuracy in the seismic performance analysis of prefabricated concrete beam-column joints, which meets the actual needs of the seismic performance analysis of modern prefabricated concrete beam-column joints.
Phytochemistry and Biological Properties of Salvia verbenaca L.: A Comprehensive Review
Authors: Mrabti Hanae Naceiri , El Menyiy Naoual , Charfi Saoulajan , Saber Mohammed , Bakrim Saad , Alyamani Reema A. , Rauf Abdur , Ali Ahmed M. H. , Abdallah Emad M. , El Omari Naserddine , Bouyahya Abdelhakim , Assaggaf Hamza
Published: May 2022

The family Lamiaceae contains several plants used in traditional medicine to fight against different diseases. Salvia verbenaca L. (S. verbenaca) is one of the Lamiaceae species distributed around the Mediterranean regions. This plant exhibits different bioactive properties, including antibacterial, anticancer, antioxidant, antileishmanial, antidiabetic, immunomodulatory, and wound healing. This review was conducted to revise previous studies on S. verbenaca addressing its botanical description, geographical distribution, and phytochemical, pharmacological, and toxicological properties. Moreover, the main pharmacological actions of S. verbenaca major compounds were well investigated. Literature reports have revealed that S. verbenaca possesses a pivotal role in medicinal applications. The findings of this work noted that S. verbenaca was found to be rich in chemical compound classes such as terpenoids, phenolics, fatty acids, sterols, and flavonoids. Numerous studies have found that S. verbenaca essential oils and extracts have a wide range of biological effects. These results support the potential pharmacological properties of S. verbenaca and its traditional uses. This analysis can constitute a scientific basis for further refined studies on its pure secondary metabolites. Therefore, the outcome of the present work may support the perspective of identifying new therapeutical applications with detailed pharmacological mechanisms of S. verbenaca to prevent the development of some diseases such as neurodegenerative disorders. However, toxicological investigations into S. verbenaca are needed to assess any potential toxicity before it can be further used in clinical studies.
Improvement of Steam Injection Efficiency in Horizontal Well with the Semi-Analytical Model: A Case Study of Liaohe Oilfield Steam Injection Well
Authors: Zhang He , Zhou Xiaofeng , Zhang Ao
Published: May 2022

As China’s largest heavy oil field, Liaohe oilfield encounters many problems after long-term thermal recovery, including high water percentage at early production stage, steam channelling and water cresting. To solve the above problems, the steam flow behavior and thermal characteristics in horizontal well should be understood. In this paper, a semi-analytical model is proposed to investigate the thermal properties and heat losses along the steam injection horizontal wellbore. The flow pattern is determined by the modified Beggs-Brill method and different conservation equations are incorporated to calculate the steam pressure, steam temperature and steam quality. The model shows good consistency with the field data of the horizontal steam injection well in Liaohe oilfield. The sensitivity analysis shows that the steam quality along the wellbore is strongly affected by the steam pressure, steam injection rate and steam quality at the heel. Generally, the steam injection rate does not have significant effect on steam pressure, steam temperature and steam quality. The model is then used to optimize the injection parameters of Well XH27 to improve the steam injection efficiency. After optimization, more steam is injected to the formation to heat the region near the wellbore, which leads to a higher production rate. In summary, this study presents a semi-analytical model to calculate the thermal properties of steam and the model can be used to optimize steam injection process for field application.
Heat Transfer Analysis of the MHD Stagnation Point Flow of a Non‐Newtonian Tangent Hyperbolic Hybrid Nanofluid past a Non‐Isothermal Flat Plate with Thermal Radiation Effect
Authors: Dawar Abdullah , Islam Saeed , Alshehri Ahmed , Bonyah Ebenezer , Shah Zahir
Published: May 2022

Heat transfer phenomena are used in a variety of industries, including chemical devices, shipbuilding, power plants, electronic devices, and medicinal plants. Propylene glycol, engine oil, water, and ethylene glycol are common single‐phase heat transfer liquids used in a variety of industries, including chemical process industries and thermal power plants. Therefore, the authors are interested in investigating the magnetohydrodynamic flow of a water‐based hybrid nanofluid containing ferrous and graphene oxide nanoparticles past a flat plate. The stagnation points, as well as the effects of magnetic field and thermal radiation are taken into account in this analysis. The non‐Newtonian tangent hyperbolic flow, which is laminar and incompressible, is also considered to investigate the non‐Newtonian behavior of the hybrid nanofluid flow. The proposed model has been solved analytically with the help of HAM. The convergence of HAM is shown with the help of figure. The hydrothermal characteristics of hybrid nanofluid flow past a nonisothermal flat plate at a stagnation point are affected by the necessary parameters. The results show that the boosting volume fractions of the ferrous and graphene oxide nanoparticles have significantly reduced the velocity field, while the thermal field has increased with the augmenting volume fractions of the ferrous and graphene oxide nanoparticles. The increasing power‐law index has augmented the viscosity of the non‐Newtonian hybrid nanofluid flow due to which the velocity field escalated. However, this impact is opposite for the thermal field. Due to the direct relation between the Weissenberg number and relaxation time, the greater Weissenberg number has reduced the velocity profile, while increased the thermal field.
Supplementation of High Velocity Nasal Insufflation with a Nonrebreather Mask for Severe Hypoxemic Respiratory Failure in Adult Patients with COVID-19
Authors: Whittle Jessica S. , Sethi Jigme , Volakis Leonithas I. , Greenberg Jeremy
Published: May 2022

The unique clinical features of COVID-19-related acute hypoxemic respiratory failure, as well as the widespread impact leading to resource strain, have led to reconsiderations of classic approaches to respiratory support. HFNO includes high flow nasal cannula (HFNC) and high velocity nasal insufflation (HVNI). There are currently no widely accepted criteria for HFNO failure. We report a series of three patients who experienced COVID-19-related acute severe hypoxemic respiratory failure. Each patient was initially managed with HVNI and had a ROX

index
<
3.85

, suggesting HFNO failure was likely. They were subsequently managed with a nonrebreather mask (NRM) overlying and in combination with HVNI at maximal settings and were able to be managed without the need for invasive mechanical ventilation.
Analytical Computation of Sandy Slope Stability under the Groundwater Seepage and Earthquake Conditions
Authors: Lu Yulin , Chen Xiaoran , Wang Li
Published: May 2022

Groundwater and earthquake are two of the factors behind the occurrence of slope instability. This paper focuses on the computation of the safety factor acting on a sandy slope when subjected to groundwater seepage and earthquake load. Based on the theory of limit equilibrium and a pseudodynamic method of analysis, a general solution for the dynamic factor of sandy slope containing two load conditions is proposed. In the solution, the effects of parameters such as water level, seismic acceleration coefficients, slope angle, and soil internal friction angle on the slope stability have been discussed. In order to evaluate the validity of the present formulation, the dynamic factors of safety computed by the present method are compared with those given by other cases. It is found that the results of the present method are in good agreement with those of the previous method. The results indicate that the dynamic of safety factor increases linearly with an increase in the soil internal friction angle but decreases with the slope angle and groundwater level and that the effects of direction and magnitude of the seismic inertia forces on the dynamic safety factor of sandy slope are different at different times.