bluuebunny/crossref_metadata_2025 huggingface dataset:
PINN-Based Method for Predicting Flow Field Distribution of the Tight Reservoir after Fracturing
Authors: Pu Jun , Song Wenfang , Wu Junlai , Gou Feifei , Yin Xia , Long Yunqian
Published: April 2022

The physical-informed neural network (PINN) model can greatly improve the ability to fit nonlinear data with the incorporation of prior knowledge, which endows traditional neural networks with interpretability. Considering the seepage law in the tight reservoir after hydraulic fracturing, a model based on PINN and two-dimensional seepage physical equations was proposed, which can effectively predict the flow field distribution of the tight reservoir after fracturing. Firstly, the dataset was obtained based on physical and numerical models of the tight reservoirs developed by volume fracturing. Furthermore, coupling the neural networks and the two-dimensional unsteady seepage equation, a PINN model was developed to predict the flow field distribution of the tight reservoir. Finally, a systematic study was performed concerning the noise corruption levels, training iterations, and training sample size that affect the prediction results of PINN models. Besides, a comparison between PINN and traditional deep neural networks (DNN) was presented. The results show that the DNN model was not only sensitive to noisy data but also more vulnerable to overfitting as the training iterations increase. In addition, the prediction accuracy cannot be guaranteed when the samples are inadequate (<500). In contrast, the PINN model was less affected by noise and training iterations and thus indicates greater stability. Moreover, the PINN model outperforms the DNN model in the case of inadequate samples attributing to prior knowledge. This study confirms that the adopted PINN model can provide algorithmic support for the accurate prediction of flow field distribution of the tight reservoirs.
Study of Complexity Systems in Public Health for Evaluating the Correlation between Mental Health and Age-Related Demographic Characteristics: A General Health Study
Authors: Haghi Fereshte , Goli Shadi , Rezaei Rana , Akhormi Fatemeh , Eskandari Fatemeh , Isfahani Zeinab Nasr
Published: April 2022

The main objective of this study is to evaluate the quality of nurses’ work lives and mental health during outbreaks. We also use the General Health Questionnaire–28 and Walton’s QWL technique to assess the association between these two and their dimensions with demographic variables and each other. First, 165 nurses from COVID-19 medical centers in Iran filled surveys for this research. In an SPSS program, the data were examined. There was a strong link between mental health and age-related demographic factors. There was no evidence of a link between the quality of nurses’ work life and their psychological health. However, there was a strong link between somatic symptoms and fair and appropriate compensation, as well as constitutionalism. The worst situations for work life quality were linked to the whole living area dimension. In contrast, the worst conditions for mental health were linked to the somatic symptoms dimension.
Application of Improved Deep Belief Network Model in 3D Art Design
Authors: Ye Zilin
Published: April 2022

In recent years, driven by the high-speed computing performance of computers and massive data on the Internet, deep nervine networks with highly abstract feature extraction and classification capabilities have been widely used in 3D art design and other fields, and a large number of breakthrough results have emerged. 3D art design is a research hotspot in the field of computer vision, which has broad application prospects and practical application value. Aiming at the problems of slow convergence and long training time of traditional deep belief network in the process of data feature expression, this paper proposes an unsupervised learning algorithm, namely adaptive deep belief network, and applies it to 3D art design. Its linear correction unit has good sparsity, which can improve the network performance well. The deep belief network DBN is formed by stacking the restricted Boltzmann machine RBM. The recognition research of 3D art design by optimizing the wavelet deep belief network can effectively improve the recognition rate and recognition speed of handwritten character recognition and achieve good results.
Indigenous-Based Adult Education Learning Material Development: Integration, Practical Challenges, and Contextual Considerations in Focus
Authors: Assefa Yalalem , Mohammed Shireen Jamal
Published: April 2022

Indigenous perspectives hold promising opportunities for education and practice. It is possible to entertain indigenous knowledge into adult education through modelling, guided practice, and application approaches. However, there are several limiting factors to do so. Today, many indigenous knowledge systems and practices are at risk of becoming extinct because of rapidly changing natural environments and fast pacing economic, political, and cultural changes on a global scale. Considering this, therefore, this study is aimed at exploring the integration, existing practical challenges of indigenous based adult education, and forward contextual consideration from the perspectives of learning material development. In doing so, a case study research design was employed. Date was collected from adult education experts, adult literacy centre coordinators and adult learners who were drawn by using purposive sampling technique. Data were obtained through interview and FGD. Thematic analysis was placed as the centre of data analysis method. As a result, the integration of indigenous knowledge into educational programs especially in adult education has been passed with many ups and downs. The inclusion was found at a substandard level due to a lack of gratefulness and value regarding what indigenous knowledge might bring to educational practice and how it might be enacted into learning materials. Although the integration has been challenged with many barriers, fortunately, many opportunities which need to be considered are emerging. Context-based working strategies and guidelines have to be advanced by governments aim at mobilizing community support for indigenous based education development and handover to generations through revitalizing all forms of educational provision within the local and global market context.
The Improved Nonparametric Regression Model for the IoT Link Load Balancing Control Algorithm
Authors: Xue Qingping
Published: April 2022

In order to improve the load balance control effect of the Internet of Things link, this paper combines the nonparametric regression model to improve the load balancing algorithm of the Internet of Things link. Moreover, this paper proposes a load balancing research strategy based on data plane data flow, which is aimed at improving the load balancing problem of data flow in the link. The data center network uses a multilayer fat tree topology to store information in the flow table corresponding to the switch for data flow processing and forwarding operations. In addition, this paper constructs a load balancing model for intelligent Internet of Things link and verifies the model in this paper through experimental research. The research results show that the load balancing control algorithm for Internet of Things link based on the nonparametric regression model proposed in this paper can effectively improve the internal scheduling of the Internet of Things link system and promote the load balancing effect.
Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network
Authors: Zeb Asim , Wakeel Sonia , Rahman Taj , Khan Inayat , Uddin M. Irfan , Niazi Badam
Published: April 2022

Wireless sensor network is widely used in different IoT-enabled applications such as health care, underwater sensor networks, body area networks, and various offices. A sensor node may face operational difficulties due to low computing capacity. Moreover, mobility has become an open challenge in the healthcare wireless body area network that is highly affected by message loss due to topological manipulation. In this article, an enhanced version of the well-known algorithm MT-MAC is proposed, namely DT-MAC, to ensure successful message delivery. It considers node handover mechanism among virtual clusters to ensure network integrity and also uses the concept of minimum connected dominating set for network formation to achieve efficient energy utilization. It is then compared with well-known algorithms such as MT-MAC. The simulation results show that an increase in little latency of roughly 3 percent in using the proposed protocol improves the MT-MAC's packet delivery by 13–17 percent and the response time by around 15 percent. Therefore, the algorithm is best fitted for real-time applications where the high packet delivery and response time are required.
Posterior Rectus Sheath Defect with Interparietal Hernia Causing a Small Bowel Obstruction
Authors: Vegas Austin , Shea Caroline , Sparzak Paul
Published: April 2022

Posterior rectus sheath hernias are exceptionally rare, with around twelve reported cases to date. This case report examines a 38-year-old female who demonstrated symptoms of intermittent small bowel obstruction five days following an abdominal hysterectomy. The patient was diagnosed via CT to have a small bowel obstruction within the rectus abdominis. Exploratory laparotomy determined the etiology to be an interparietal hernia through a posterior rectus wall defect, which was repaired with primary closure. Postoperatively, the patient was again unable to tolerate food. Repeat CT showed concern for repeat SBO, though symptoms subsided without intervention. The patient had no complaints during her follow up at one month. This report was aimed at building upon the few reported cases as well as enumerating potential risk factors that may allow for the consideration of this diagnosis in the future.
CircINTS4 Facilitates Chemoresistance of TNBC by Competitively Binding miR-129-5p/POM121 Axis
Authors: Tang Qian , Zhou Feidu , Yang Chuanguang , Dai Jue , Li Jintao , He Yanxian
Published: April 2022

Objectives. To detect the expression of circular RNA (circRNA) circINTS4 in triple-negative breast cancer (TNBC) and to analyze the relationship between the expression of circRNA circINTS4 and the clinicopathological characteristics and chemotherapy resistance of patients with TNBC. Methods. Bioinformatics was used to predict that circINTS4 and POM121 could bind to miR-129-5p, and dual luciferase reporter genes proved that circINTS4 could bind to miR-129-5p and miR-129-5p could bind to POM121. RNA immunoprecipitation (RIP) and RNA pull-down experiments confirmed that circINTS4 binds to miR-129-5p. The correlation among circINTS4, miR-129-5p, and POM121 was detected by qRT-PCR. Results. In ADR-resistant TNB cells, circINTS4 was significantly up-regulated, miR-129-5p was down-regulated, and POM121 protein expression was significantly up-regulated. Experimental results showed that circINTS4 knockdown inhibited proliferation, migration, invasion, and autophagy. Knocking down miR-129-5p or overexpression of POM121 reversed the inhibitory effect of sh-circints4 on the development of ADR-resistant TNBC cells. In addition, CIRCINTS4 regulates POM121 expression by sponge-adsorbed miR-129-5p. CIRCINTS4 knockdown prevents ADR-resistant tumor growth by regulating the miR-129-5p/POM121 axis in vivo. Conclusions. CircRNA circINTS4 may act as the ceRNA of miR-129-5p to regulate the expression of target gene POM121, thereby promoting the progress of TNBC molecular mechanism and providing scientific basis for circINTS4 as a new molecular target for clinical diagnosis and drug resistance therapy of TNBC.
Quantitative Evaluation of Well Performance Affected by Fracture Density and Fracture Connectivity in Fractured Tight Reservoirs
Authors: Fang Wenchao , Qin Xuejie , Liu Chuanxi , Wang Ruyue , Pu Jun , Jiang Hanqiao , He Weiling
Published: April 2022

Understanding the relationship between petroleum recovery and characteristics of hydraulic fracture network is a key component of economic development of tight reservoirs. Owing to the limitations inherent in current reservoir simulators, optimization of fracture network has been simply focused on the parameters of fracture conductivity, fracture number, aperture, and so on. Deeper insight into the effect of decisive parameters, such as fracture density and fracture connectivity on the well production in tight reservoirs, is now required to maximize the petroleum recovery. In this work, a newly developed discrete fracture simulator is applied to comprehensively study the effect of fracture density and fracture connectivity in tight reservoirs. Conceptual models with different fracture densities and different fracture connectivity are firstly designed and simulated to explore how these two parameters affect the reservoir behavior and establish the equations for effect measurement. Then, we simulate models with different well placement strategies and a fixed set of natural fractures to determine the optimal strategy. Finally, simulations are performed on a field-scale reservoir with three long fractured horizontal wells. Results demonstrate that increases in either fracture density or fracture connectivity can significantly improve well production. However, an optimal value exists considering the economic profit. Compared to the fracture density, fracture connectivity plays a more important role in affecting the well production. In a tight reservoir with abundant natural fractures, making the horizontal well parallel to the direction of natural fractures is determined to be the optimal well placement strategy. The heterogeneous distribution of remaining oil in real tight oil reservoirs is mainly caused by the heterogeneous distribution of fracture density and fracture connectivity.
Response Surface Models Using the Wavelet Technique for Reservoir Inflow Prediction
Authors: Bashir Adnan , Shehzad Muhammad Ahmed , Khan Aamna , Asghar Muhammad Nabeel , Aslam Muhammad , Aldallal Ramy , Kilai Mutua , Mohamed Mohamed S.
Published: April 2022

Reliable streamflow prediction is vital to improving river operations, flood avoidance, water supply, and water resources management. Recently, response surface models have been launched in reservoir inflow prediction due to their potential to model composite nonlinear behaviour. Authors develop a hybrid model, wavelet quadratic response surface for reservoir inflow prediction in Chenab river basin, Pakistan. Wavelet transform has extensive applications in the field biomedical, engineering, and hydrology. Discrete wavelet transform technique discloses the structure of nonstationary signals. A proper and careful selection of mother wavelet ensure the best performance of wavelet transform. The choice of a suitable wavelet function participates in implementing the wavelet function used in response surface based models for reliable prediction. The performance of the proposed model is checked on different performance indices for model evaluation. The new developed model, wavelet quadratic response surface, depicts excellent results than other studied models.
Weighted Gene Coexpression Network Analysis Identifies TBC1D10C as a New Prognostic Biomarker for Breast Cancer
Authors: Qiao Huiying , Lv Rong , Pang Yongkui , Yao Zhibing , Zhou Xi , Zhu Wei , Zhou Wenqing
Published: April 2022

Background. Immune checkpoint inhibitors are a promising therapeutic strategy for breast cancer (BRCA) patients. The tumor microenvironment (TME) can downregulate the immune response to cancer therapy. Our study is aimed at finding a TME-related biomarker to identify patients who might respond to immunotherapy. Method. We downloaded raw data from several databases including TCGA and MDACC to identify TME hub genes associated with overall survival (OS) and the progression-free interval (PFI) by WGCNA. Correlations between hub genes and either tumor-infiltrating immune cells or immune checkpoints were conducted by ssGSEA. Result. TME-related green and black modules were selected by WGCNA to further screen hub genes. Random forest and univariate and multivariate Cox regressions were applied to screen hub genes (MYO1G, TBC1D10C, SELPLG, and LRRC15) and construct a nomogram to predict the survival of BRCA patients. The

C

-index for the nomogram was 0.713. A DCA of the predictive model revealed that the net benefit of the nomogram was significantly higher than others and the calibration curve demonstrated a good performance by the nomogram. Only TBC1D10C was correlated with both OS and the PFI (both

p

values < 0.05). TBC1D10C also had a high positive association with tumor-infiltrating immune cells and common immune checkpoints (PD-1, CTLA-4, and TIGIT). Conclusion. We constructed a TME-related gene signature model to predict the survival probability of BRCA patients. We also identified a hub gene, TBC1D10C, which was correlated with both OS and the PFI and had a high positive association with tumor-infiltrating immune cells and common immune checkpoints. TBC1D10C may be a new biomarker to select patients who may benefit from immunotherapy.
Time Series Based Data Explorer and Stream Analysis for Anomaly Prediction
Authors: Yin Xiao-Xia , Miao Yuan , Zhang Yanchun
Published: April 2022

All over the world, time series-based anomaly prediction plays a vital role in all walks of life such as medical monitoring in hospitals and climate and environment risks. In the present study, a survey on the methods and techniques for time series data mining and proposes is carried, in order to solve a brand-new problem, time series progressive anomaly prediction. In terms of contents, the first part sketches out the methods that have captured most of the interest of researchers, which include an overview of abnormal prediction problems, a summary of main characteristics of anomaly prediction, and an introduction of anomaly prediction methodology in literature. The second part focuses on the future research trends on the phase/staged abnormal prediction of time series, where a novel time series compression method and a corresponding similarity measure will be designed, which can be explored subsequently. Finally, the related challenges to take this trend are mentioned. It is hoped that this paper can provide a profound understanding of anomaly prediction for the time series-based data mining research field.
Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning
Authors: Du XiangXi , Liu MuYun , Sun YanHua
Published: April 2022

The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative medicine and tissue damage restoration. Firstly, based on the relevant theories of stem cell image segmentation, digital twins (DTs), and lightweight deep learning, a new phase contrast microscope is introduced through the research of optical microscope. Secondly, the results of DTs method and phase contrast imaging principle are compared in stem cell image segmentation and detection. Finally, a lightweight deep learning model is introduced in the segmentation and tracking of stem cell image to observe the gray value and mean value before and after stem cell image movement and stem cell division. The results show that phase contrast microscope can increase the phase contrast and amplitude difference of stem cell image and solve the problem of stem cell image segmentation to a certain extent. The detection results of DTs method are compared with phase contrast imaging principle. It indicates that not only can DTs method make the image contour more accurate and clearer, but also its accuracy, recall, and F1 score are 0.038, 0.024, and 0.043 higher than those of the phase contrast imaging method. The lightweight deep learning model is applied to the segmentation and tracking of stem cell image. It is found that the gray value and mean value of stem cell image before and after movement and stem cell division do not change significantly. Hence, the application of DTs and lightweight deep learning methods in the segmentation, detection, and tracking of stem cell image has great reference significance for the development of biology and medicine.
Capecitabine Monotherapy as Palliative Treatment for Patients with Recurrent/Metastatic Nasopharyngeal Cancer
Authors: Alshamsan Bader , Gad Ahmed Mostafa , Alata Maaz Kamal , Alzahrani Mashari , Elhassan Tusneem , Atallah Jean Paul
Published: April 2022

Background. Numerous chemotherapeutic agents have antitumor activity in recurrent/metastatic (R/M) nasopharyngeal cancer (NPC). Evidence of capecitabine’s effectiveness as monotherapy is limited. Capecitabine tolerability in solid malignancies has ethnic and geographical variability. We investigated capecitabine’s tolerability and identified potential prognostic factors for clinical outcomes in R/M NPC. Methods. A consecutive retrospective cohort of patients who received capecitabine as the first recurrence, second- or third-line monotherapy for metastatic NPC (2011–2019) was reviewed concerning patient characteristics, pathological features, treatment outcomes, and toxicity. Results. Fifty-one patients were eligible (median age at diagnosis: 42 [35.5–52.5] years). Most patients (78.4%) tolerated a standard oral dose of 1,250 mg/m2 capecitabine (2 weeks on/1 week off) in a 3-week cycle. The objective response rate was 49%, and the disease control rate was 66.7%, with a median response duration of 6.2 months. Hand-foot syndrome (HFS) was associated with a higher objective response rate (odds ratio, 5.1 [95% confidence interval: 1.18–21.98];

P
=
0.02

). The median follow-up duration was 17.8 (interquartile range: 7.8–30.4) months. The median (95% confidence interval) progression-free survival and overall survival were 6.6 (4.3–8.8) and 32.7 (25.9–39.5) months, respectively. HFS (

P
=
0.02

), better performance status (

P
=
0.02

), and absence of brain metastasis (

P
=
0.04

) were associated with prolonged progression-free survival. Conclusion. Capecitabine monotherapy is effective and well-tolerated as a palliative treatment for R/M NPC. Despite the lower incidence of HFS in our patients, it remained a favorable prognostic factor for objective response and progression-free survival.
Application of PRP in Chloasma: A Meta-Analysis and Systematic Review
Authors: Deng Tinghan , Cheng Fengrui , Guo Shanshan , Cheng Hongbin , Wu Jingping
Published: April 2022

Background. Chloasma is a common skin pigment disorder. Treatment of chloasma has been challenging, often unsatisfactory, and difficult to avoid recurrence. PRP is a new treatment for chloasma, but there is no consensus on its use. Lingyun Zhao’s team recently reported a systematic evaluation and meta-analysis of the efficacy and safety of PRP in the treatment of chloasma, which is consistent with our ideas, but we will elaborate on the application of PRP in chloasma from a deeper and more comprehensive perspective. Before we started this study, we had registered with Prospero as CRD42021233721. Methods. The authors searched the public medical network, MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, ScienceDirect, Scopus, and Science Network. The clinical trials registry ClinicalTrials.gov databases were searched for relevant publications to June 2021. The results showed the area and severity of chloasma (MASI) or revised MASI (mMASI) score. Results. Three RCTs, one nonrandomized controlled study, and four were prospective before and after self-controlled studies met the inclusive criteria. Intradermal PRP injections significantly improved chloasma as indicated by the significant decrease MASI (average balance -6.71, 95% CI -8.99 to -4.33) and mMASI scores (average balance -2.94, 95% CI -4.81 to -1.07). The adverse reactions were mild, and there were no significant long-term adverse events. Conclusive. The data can reflect the effectiveness and safety of PRP therapy for chloasma. RCTs are needed to determine effective treatment parameters, and long-term follow-up should be included to better clarify the efficacy and side effects of PRP in treating chloasma.
An Analytical Study of Speech Pathology Detection Based on MFCC and Deep Neural Networks
Authors: Zakariah Mohammed , B Reshma , Ajmi Alotaibi Yousef , Guo Yanhui , Tran-Trung Kiet , Elahi Mohammad Mamun
Published: April 2022

Diseases of internal organs other than the vocal folds can also affect a person’s voice. As a result, voice problems are on the rise, even though they are frequently overlooked. According to a recent study, voice pathology detection systems can successfully help the assessment of voice abnormalities and enable the early diagnosis of voice pathology. For instance, in the early identification and diagnosis of voice problems, the automatic system for distinguishing healthy and diseased voices has gotten much attention. As a result, artificial intelligence-assisted voice analysis brings up new possibilities in healthcare. The work was aimed at assessing the utility of several automatic speech signal analysis methods for diagnosing voice disorders and suggesting a strategy for classifying healthy and diseased voices. The proposed framework integrates the efficacy of three voice characteristics: chroma, mel spectrogram, and mel frequency cepstral coefficient (MFCC). We also designed a deep neural network (DNN) capable of learning from the retrieved data and producing a highly accurate voice-based disease prediction model. The study describes a series of studies using the Saarbruecken Voice Database (SVD) to detect abnormal voices. The model was developed and tested using the vowels /a/, /i/, and /u/ pronounced in high, low, and average pitches. We also maintained the “continuous sentence” audio files collected from SVD to select how well the developed model generalizes to completely new data. The highest accuracy achieved was 77.49%, superior to prior attempts in the same domain. Additionally, the model attains an accuracy of 88.01% by integrating speaker gender information. The designed model trained on selected diseases can also obtain a maximum accuracy of 96.77% (

cordectomy
×
healthy

). As a result, the suggested framework is the best fit for the healthcare industry.
How to Decide Approaches and Procedures for Early and Advanced Gastric Cancer?
Authors: Izumi Daisuke , Nunobe Souya
Published: April 2022

In the 6th edition of the Japanese Gastric Cancer Treatment Guidelines, laparoscopic surgery is recommended as one of the standard treatments for cStage I. On the other hand, the recommendation of robot-assisted surgery for gastric cancer was also added, albeit not conclusively, to perform it for cStage I gastric cancer. Conversely, laparoscopic surgery for cStage II/III is not recommended, and several randomized controlled trials (RCTs) are being conducted in East Asia to expand the indication for advanced gastric cancer. Although laparoscopic surgery and robot-assisted surgery are now recommended in the Guidelines for Early-Stage Gastric Cancer, each institution should set its own criteria for indications according to its level of proficiency and try to provide high-quality treatment. For advanced gastric cancer, although there is no solid evidence for laparoscopic or robot-assisted surgery, the reality is that it is already being performed in facilities with ample experience. New evidence is expected to be reported in the future, based on which the recommendations may change.
Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable
Authors: Huang Hanqiao , Cheng Haoyu , Song Ruijia , Sun Gonghao , Fang Yangwang , Huang Guan
Published: April 2022

This problem of intelligent switched fault detection filter design is investigated in this article. Firstly, the mode-dependent average dwell time (MDADT) method is applied to generate the time-dependent switching signal for switched systems with all subsystems unstable. Afterwards, the switched fault detection filter is proposed for the generation of residual signal, which consists of dynamics-based filter and learning-based filter. The MDADT method and multiple Lyapunov function (MLF) method are employed to guarantee the stability and prescribed attenuation performance. The parameters of dynamics-based filter are given by solving a series of linear matrix inequalities. To improve the transient performance, the deep reinforcement learning is introduced to design learning-based filter in the framework of actor-critic. The output of learning-based filter can be viewed as uncertainties of dynamics-based filter. The deep deterministic policy gradient algorithm and nonfragile control are adopted to guarantee the stability of algorithm and compensate the external disturbance. Finally, simulation results are given to illustrate the effectiveness of the method in the paper.
Expression Profiles of HOXC6 Predict the Survival of Glioblastoma Patients and Correlate with Cell Cycle
Authors: Li Zhen-Hang , Ma Yue , Zhou Yan , Huang Zhen-Hua
Published: April 2022

The goal of this study was to investigate the homeobox (HOX) gene expression status and its prognostic value in glioblastoma multiforme (GBM) and to uncover the biological processes related to its expression. The prognostic value of HOX genes in GBM was systematically investigated by a genome-wide analysis of HOX gene expression profiles in GBM patient samples in The Cancer Genome Atlas (TCGA) project (microarray dataset) and validation datasets. Using the differentially expressed gene (DEG) analysis and a Cox regression model, we discovered that the HOXC6 could stratify patients into significantly different survival (

p
=
0.0012

, log-rank test) groups in the training cohort. TCGA RNA-seq and GSE16011 datasets were used for validation. Multivariate Cox and stratification analysis indicated that HOXC6 was an independent prognostic factor after adjusting for other clinical covariates. Bioinformatic analysis suggested that the HOXC6 might be involved in the cell cycle-related biological processes and pathways that are well established in the context of glioblastoma tumorigenesis. We further explored the bioinformatic implications by gene set enrichment analysis (GSEA). Tumor cell biology experiments verified the role of HOXC6 in proliferation and cell cycle progression. In conclusion, HOXC6 might be a candidate biomarker gene for individual treatment optimization of glioblastoma. HOXC6 expression has a significant prognostic value and is related to the cell cycle process in glioblastoma.
Improving Medical Q&A Matching by Augmenting Dual-Channel Attention with Global Similarity
Authors: Li Shi , Yao Yaohan
Published: April 2022

The emergence of online medical question-answer communities has helped to balance the supply of medical resources. However, the dramatic increase in the number of patients consulting online resources has resulted in a large number of repetitive medical questions, significantly reducing the efficiency of doctors in answering these questions. To improve the efficiency of online consultations, a large number of deep learning methods have been used for medical question-answer matching tasks. Medical question-answer matching involves identifying the best answer to a given question from a set of candidate answers. Previous studies have focused on representation-based and interaction-based question-answer pairs, with little attention paid to the effect of noise words on matching. Moreover, only local-level information was used for similarity modeling, ignoring the importance of global-level information. In this paper, we propose a dual-channel attention with global similarity (DCAG) framework to address the above issues in question-answer matching. The introduction of a self-attention mechanism assigns a different weight to each word in questions and answers, reducing the noise of “useless words” in sentences. After the text representations were obtained through the dual-channel attention model, a gating mechanism was introduced for global similarity modeling. The experimental results on the cMedQA v1.0 dataset show that our framework significantly outperformed existing state-of-the-art models, especially those using pretrained BERT models for word embedding, improving the top-1 accuracy to 75.6%.
RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm
Authors: Nia Raheleh Ghouchan Nezhad Noor , Jalali Mehrdad
Published: April 2022

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems.
Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model
Authors: Teng Yuanyang , Li Yicun , Wu Xiaobo
Published: April 2022

This study focuses on the volatility prediction and option volatility investment. By investigating the traditional Volatility Prediction Model and machine learning algorithms, this study tries to merge these two aspects together. This work setup a bridge of previous financial studies and machine learning studies by proposing an algorithm integrating neural network and three traditional volatility models, called “Quantile based neural network and model integration combination algorithm.” The algorithm effectively lowers the volatility prediction error (measured by root of mean square error, shorted for RMSE: 0.319724) and beat the Wavenet (RMSE: 0.44) which is the benchmark and surpasses integrated model (RMSE: 0.348346) in test set. In terms of option investment strategy, this paper constructs a CSI 300 index option portfolio which hedges the underlying asset price risk and exposes the volatility risk. Then propose the “Option strategy of volatility prediction with dynamic thresholds.” With the new algorithm above, the strategy improves the return‐risk ratio in test set (measured by Sharpe ratio: 1.99–2.07).
Microgrid Data Security Sharing Method Based on Blockchain under Internet of Things Architecture
Authors: Shang Jian , Guan Runmin , Tong Yuhao
Published: April 2022

The efficient and secure data sharing mechanism can support the microgrid to achieve more accurate business control, while the current data processing methods have the problems of large computing overhead and low data sharing security. Aiming at the current problems, this paper proposes a microgrid data sharing method based on blockchain technology based on the processing mode of cloud-edge-terminal architecture. Firstly, the elliptic curve encryption algorithm is used on the edge side to encrypt the data collected by the terminal equipment reliably, so as to improve the security and efficiency of microgrid key management. Then, in the cloud, the Reputation-Evaluation Practical Byzantine Fault Tolerant mechanism (REPBFT) based on smart contract and reputation evaluation can effectively manage the data sharing of edge computing devices, avoid the waste of network computing resources, and further improve the efficiency of microgrid data sharing. The simulation results show that when the number of edge devices reaches 25, the calculation and communication overhead of the proposed method are 63.46 ms and 2.66 KB, respectively, and when the processing data reaches 1024 KB, the security of the microgrid system is still 95%, which can realize safe and reliable data sharing and interaction, and can stably support the optimal operation of the microgrid.
Exploratory Study of NPC-0501 Trial: Optimal Cisplatin Dose of Concurrent and Induction/Adjuvant Chemotherapy for Locoregionally Advanced Nasopharyngeal Carcinoma
Authors: Ng Wai-Tong , Choi Cheuk-Wai , But Barton , Ngan Roger K.C. , Tung Stewart , Cheng Ashley C. , Kwong Dora L.W. , Lu Tai-Xiang , Chan Anthony T.C. , Yiu Harry , Lee Sarah , Wong Frank , Yuen Kam-Tong , Chappell Richard J. , Lee Anne W.M.
Published: April 2022

Purpose:

The current recommendation for patients with locoregionally advanced nasopharyngeal carcinoma (NPC) is cisplatin-based induction chemotherapy (IC) or adjuvant chemotherapy (AC) plus concurrent chemoradiotherapy (CRT). However, data on the optimal platinum doses for each phase of combined regimens are lacking.

Experimental Design:

742 patients with NPC in the NPC-0501 trial treated with CRT plus IC/AC and irradiated with intensity-modulated radiotherapy (IMRT) were analyzed. The optimal platinum dose to achieve the best overall survival (OS) in the concurrent and induction/adjuvant phases was studied.

Results:

Evaluation of the whole series shows the optimal platinum dose was 160 mg/m2 in the concurrent and 260 mg/m2 in the induction/adjuvant phase. Repeating the analyses on 591 patients treated with cisplatin throughout (no replacement by carboplatin) confirmed the same results. The cohort with optimal platinum doses in both phases had better OS than the cohort suboptimal in both phases (stage III: 90% vs. 75%; stage IVA–B: 80% vs. 56%, at 5-year). Multivariable analyses confirmed optimal platinum doses in both phases versus suboptimal dose in each phase are significant independent factors for OS, with HR of 0.61 [95% confidence interval (CI), 0.41–0.91] and 0.67 (95% CI, 0.48–0.94), respectively. Treatment sequence was statistically insignificant after adjusting for platinum doses.

Conclusions:

Both concurrent and IC/AC are needed for locoregionally advanced NPC, even for patients irradiated by IMRT; the concurrent platinum dosage could be set at ≥160 mg/m2 when coupled with adequate induction/adjuvant dosage at ≥260 mg/m2 (or at least ≥240 mg/m2). To achieve these optimal dosages, IC-CRT at conventional fractionation is favored.
Redefining CD56 as a Biomarker and Therapeutic Target in Multiple Myeloma
Authors: Cottini Francesca , Rodriguez Jose , Hughes Tiffany , Sharma Nidhi , Guo Ling , Lozanski Gerard , Liu Bei , Cocucci Emanuele , Yang Yiping , Benson Don
Published: April 2022

Multiple myeloma cells aberrantly express surface antigens compared with normal plasma cells. Among others, CD56 is present at variable levels in approximately 70% of patients with multiple myeloma; however, very little is known about CD56 role in multiple myeloma. We demonstrated that patients with multiple myeloma with more than 10% of CD56-expressing clonal multiple myeloma cells have inferior clinical outcomes. By gain-of and loss-of function models, we revealed that CD56 promotes multiple myeloma cell growth, survival, and adhesion to stromal cells. These protumoral effects are induced by the activation of the RSK2/CREB1 signaling pathway, with increased mRNA and protein levels of the anti-apoptotic genes BCL2 and MCL1. Consequently, the genomic and pharmacological inhibition of RSK2 or CREB1 specifically induced multiple myeloma cell death in CD56-expressing multiple myeloma cells. Finally, we observed that CD56 signaling decreases CRBN expression, reducing responses to lenalidomide. RSK2 or CREB1 inhibition increased CRBN levels and were synergic with lenalidomide in inducing cell death, especially in CD56-expressing multiple myeloma cells. In conclusion, our findings demonstrate that CD56 promotes multiple myeloma cell growth, and pave the way to novel therapies based on targeting CD56, along with the use of CD56 as a predictive biomarker for multiple myeloma therapies.

Implications:

Multiple myeloma is an incurable, genetically heterogeneous disease, without available tailored therapeutic approaches. CD56 signaling promotes multiple myeloma growth and adhesion, by activating CREB1 target genes, MCL1 and BCL2. Inhibition of CREB1 alone or in combination with lenalidomide is an unexplored synthetic lethal approach in CD56-expressing patients with multiple myeloma.
The Impact of the COVID-19 Pandemic on Surgeons’ and Surgical Residents’ Caseload, Surgical Skills, and Mental Health in Kuwait
Authors: Alsafran Salman , Albloushi Dalia , Quttaineh Danah , Alfawaz Abdullah A. , Alkhamis Ahmed , Alkhayat Ali , Alsejari Maha , Alsabah Salman
Published: April 2022

Introduction: The onset of the COVID-19 pandemic resulted in governments implementing new regulations to divert healthcare resources, which in return led to the postponement of elective and semi-elective surgical procedures. Therefore, many surgeons and as well as surgeons in training reported feeling redundant, which eventually resulted in psychological distress. This study aimed to assess the sociodemographic differences in the psychological impact resulting from the COVID-19 pandemic and outline the effect it had on surgical training. Methods: This is a cross-sectional study conducted in Kuwait. Data were collected by distributing a questionnaire electronically to surgeons and surgeons in training. The survey included questions aimed at assessing both the effect of the pandemic on surgical training and the psychological impact it had on surgeons, assessing the latter using the Depression, Anxiety and Stress Scale-21 screening tool. Results: The response rate for the study was 52%, with the majority being junior male surgeons. A majority of surgeons in training reported postponement of their scheduled academic teaching sessions (78.9%) and pre-assigned surgical rotations (65.8%). In terms of the psychological impact of the pandemic, a majority of the participants reported an element of depression and stress, 61.2% and 55%, respectively, while approximately half, 48.1%, had symptoms associated with anxiety. Conclusion: The COVID-19 pandemic had a negative association with the psychological well-being of a significant proportion of surgeons and associated surgical training programs.
Interplay between the Conserved Pore Residues Thr-91 and His-209 Controls Formate Translocation through the FocA Channel
Authors: Kammel Michelle , Trebbin Oliver , Sawers Robert Gary
Published: April 2022

The formate channel A (FocA) belongs to the formate-nitrite transporter (FNT) family, members of which permeate small monovalent anions. FocA from Escherichia coli translocates formate/formic acid bi-directionally across the cytoplasmic membrane during fermentative growth. Two residues are particularly well-conserved within the translocation pores of FNTs: threonine-91 and histidine-209, based on E. coli FocA numbering. These residues are located at the tips of two broken transmembrane helices and control anion passage. H209 is the only charged residue within the pore and interacts with T91. Here, we addressed the role of the T91-H209 interaction network in the permeation of formate in vivo through FocA by performing an extensive amino acid-exchange study. Monitoring changes in intracellular formate using a formate-responsive fdhFP::lacZ reporter system revealed that T91 is essential for the ability of FocA to translocate formate bi-directionally. Only exchange for serine was partially tolerated, indicating that the hydroxyl group of T91 is mechanistically important. Substitution of H209 with N or Q was previously shown to convert FocA into a formate efflux channel. We show here that residue exchanges A, I, and T at this position resulted in a similar phenotype. Moreover, efflux function was confirmed for these FocA variants by measuring excreted formate in the culture medium. Substitution of bulky or charged residues for H209 prevented bi-directional formate passage. Studies using hypophosphite, a toxic analogue of formate taken up by FocA, and which causes impaired growth, confirmed that T91 and H209 substitutions essentially abolished, or drastically reduced, FocA’s translocation activity, as shown by effects on growth rate. The exceptions were T91S- and T91Y-exchange variants that retained partial ability to take up inhibitory hypophosphite. Together, our findings indicate that T91 is essential for formate permeation in both directions; however, it is particularly important to allow anion efflux. Moreover, H209 is essential for formate uptake by FocA, strongly suggesting that protonation-deprotonation of this residue plays a role in formate uptake. Finally, our results substantiate the premise that efflux and influx of formate by FocA are mechanistically distinct processes that are controlled by the interplay between T91 and H209.
Três dimensões para compreender as mediações comunicacionais do consumo na contemporaneidade
Authors: Perez Clotilde , Trindade Eneus
Published: April 2022

O artigo atualiza a reflexão teórico-metodológica em comunicação para situar os fenômenos do consumo e sua pertinência ao campo. A condição comunicativa da mediação sígnica no consumo é o ponto de partida, respaldado na semiótica de Peirce, situando estes fenômenos nas dimensões estética, ética e lógica. Busca-se observar essas interfaces nas realidades socioculturais em que tais fenômenos ocorrem. Assim, o consumo é visto como mediação comunicacional que articularia instâncias das dinâmicas socioculturais, baseado no mapa das mediações. Tal modelo é discutido em suas intermedialidades implicadas à dinâmica interacional das mercadorias com os consumidores para a constituição de vínculos de sentidos na vida cultural.
Fé, vida e morte: representações imagéticas de uma América espanhola crente
Authors: Gross Jacson
Published: April 2022

O pequeno ensaio fotográfico a seguir, apresenta algumas fotografias capturadas na cidade de Lima no Peru em março de 2020, o que não sabíamos, é que este seria um dos últimos meses antes da pandemia que mudaria nossos olhares, sentimentos e comportamentos. Com a intenção de causar estranhamento, as fotografias apresentam desde aspectos cotidianos da religiosidade local, até outros muito delicados e impactantes ao olhar externo, como o corredor dos suicidas, no maior cemitério local, ou as tumbas de bruxas, reverenciadas mesmo depois de mortas no mesmo cemitério.