bluuebunny/crossref_metadata_2025 huggingface dataset:
Genome-scale CRISPR–Cas9 screen reveals novel regulators of B7-H3 in tumor cells
Authors: Zhao Shasha , Wang Yuelong , Yang Nian , Mu Min , Wu Zhiguo , Li Hexian , Tang Xin , Zhong Kunhong , Zhang Zongliang , Huang Cheng , Cao Ting , Zheng Meijun , Wang Guoqing , Nie Chunlai , Yang Hui , Guo Gang , Zhou Liangxue , Zheng Xi , Tong Aiping
Published: June 2022

Background

Despite advances in B7 homolog 3 protein (B7-H3) based immunotherapy, the development of drug resistance remains a major clinical concern. The heterogeneity and emerging loss of B7-H3 expression are the main causes of drug resistance and treatment failure in targeted therapies, which reveals an urgent need to elucidate the mechanism underlying the regulation of B7-H3 expression. In this study, we identified and explored the crucial role of the transcription factor SPT20 homolog (SP20H) in B7-H3 expression and tumor progression.

Methods

Here, we performed CRISPR/Cas9-based genome scale loss-of-function screening to identify regulators of B7-H3 in human ovarian cancer cells. Signaling pathways altered by SP20H knockout were revealed by RNA sequencing. The regulatory role and mechanism of SP20H in B7-H3 expression were validated using loss-of-function and gain-of-function assays in vitro. The effects of inhibiting SP20H on tumor growth and efficacy of anti-B7-H3 treatment were evaluated in tumor-bearing mice.

Results

We identified SUPT20H (SP20H) as negative and eIF4E as positive regulators of B7-H3 expression in various cancer cells. Furthermore, we provided evidence that either SP20H loss or TNF-α stimulation in tumor cells constitutively activates p38 MAPK-eIF4E signaling, thereby upregulating B7-H3 expression. Loss of SP20H upregulated B7-H3 expression both in vitro and in vivo. Additionally, deletion of SP20H significantly suppressed tumor growth and increased immune cells infiltration in tumor microenvironment. More importantly, antibody–drug conjugates targeting B7-H3 exhibited superior antitumor performance against SP20H-deficient tumors relative to control groups.

Conclusions

Activation of p38 MAPK-eIF4E signaling serves as a key event in the transcription initiation and B7-H3 protein expression in tumor cells. Genetically targeting SP20H upregulates target antigen expression and sensitizes tumors to anti-B7-H3 treatment. Collectively, our findings provide new insight into the mechanisms underlying B7-H3 expression and introduce a potential synergistic target for existing antibody-based targeted therapy against B7-H3.
Lifetime exposure to rubber dusts, fumes and N-nitrosamines and cancer mortality in a cohort of British rubber workers with 49 years follow-up
Authors: Hidajat Mira , McElvenny Damien Martin , Ritchie Peter , Darnton Andrew , Mueller William , van Tongeren Martie , Agius Raymond M , Cherrie John W , de Vocht Frank
Published: June 2022

Objectives

To quantitatively evaluate exposure-response associations between occupational exposures to rubber dust, fumes and N-nitrosamines and cancer mortality in the UK rubber industry.

Methods

Competing risk survival analyses were used to examine cancer mortality risk in a cohort of 36 441 males aged 35+ years employed in the British rubber industry in 1967, followed up to 2015 (94% mortality). Exposure measurements are based on a population-specific quantitative job-exposure matrix for rubber dust, rubber fumes and N-nitrosamines from the EU-EXASRUB project.

Results

Exposure (lifetime cumulative (LCE))-response associations were found for N-nitrosomorphiline and all cancers (subdistribution HR (SHR) 1.48, 95% CI 1.39 to 1.57) and cancers of the bladder, stomach, multiple myeloma, oesophagus, prostate and pancreas, as well as for N-nitrosodimethylamine and all cancers (SHR 2.08, 95% CI 1.96 to 2.21) and cancers of the bladder, stomach, leukaemia, multiple myeloma, prostate and liver. LCE to the N-nitrosamines sum were associated with increased risks from all cancers (SHR 1.89, 95% CI 1.78 to 2.01) and cancers of the lung, non-Hodgkin’s lymphoma and brain. LCE to rubber dust and fumes are associated with increased mortality from all cancers (rubber dust SHR 1.67, 95% CI 1.58 to 1.78; rubber fumes SHR 1.91, 95% CI 1.80 to 2.03) and cancers of the bladder, lung, stomach, leukaemia, multiple myeloma, non-Hodgkin’s lymphoma, oesophagus, prostate, pancreas and liver.

Conclusions

Consistent with previous studies, N-nitrosamines exposures are associated with mortality from cancers of the bladder, lung, stomach, leukaemia, multiple myeloma, oesophagus, prostate, pancreas and liver. The long follow-up with nearly complete mortality enabled estimations of lifetime cancer mortality risk from occupational exposures in the rubber industry.
Implications of dispersal in Atlantic salmon: lessons from a demo-genetic agent-based model
Authors: Lamarins Amaïa , Hugon Florèn , Piou Cyril , Papaïx Julien , Prévost Etienne , Carlson Stephanie M. , Buoro Mathieu
Published: June 2022

Despite growing evidence of spatial dispersal and gene flow between salmonid populations, the implications of connectivity for adaptation, conservation, and management are still poorly appreciated. Here, we explore the influence of a gradient of dispersal rates on portfolio strength and eco-evolutionary dynamics in a simulated population network of Atlantic salmon ( Salmo salar) by extending a demo-genetic agent-based model to a spatially explicit framework. Our model results highlight a nonlinear relationship between dispersal rates and the stability of the metapopulation, resulting in an optimal portfolio effect for dispersal rates around 20%. At local population scale, we also demonstrate phenotypic changes induced by density-dependent effects modulated by dispersal, and a dispersal-induced increase in genetic diversity. We conclude that it is critical to account for complex interactions between dispersal and eco-evolutionary processes and discuss future avenues of research that could be addressed by such modeling approaches to more fully appreciate responses of Atlantic salmon to environmental changes and investigate management actions accordingly.
Upregulation of exonuclease 1 caused by homology-dependent repair confers cisplatin resistance to gastric cancer cells
Authors: Mao Pengfei , Wu Suxiao , Fan Yuejuan
Published: June 2022

The homology-dependent repair (HDR) pathway is involved in deoxyribonucleic acid (DNA) damage response (DDR), which is crucial to cancer cell survival after treatment with DNA damage agents, including cisplatin (CDDP). Here, we explored the interactions between exonuclease 1 ( EXO1), a core gene in the HDR pathway, and CDDP resistance in gastric cancer (GC). Using bioinformatics analysis, we identified the HDR pathway as the most amplified pathway in DDR in GC. In addition, EXO1 was the core gene in the HDR pathway and showed the most significant amplification in GC. The amplification of EXO1 resulted in higher EXO1 expression in cancerous tissues, with malignant prognostic effects. Moreover, we upregulated or downregulated EXO1 in GC cells to examine its effects on the cell malignant phenotype and CDDP resistance in vitro and in vivo. The depletion of EXO1 inhibited cell proliferatory, migratory, and invasive activities, and provided apoptosis resistance to GC cells. EXO1 expression was elevated in CDDP-resistant cells. Ectopic expression of EXO1 increased the resistance of GC cells to CDDP, while downregulation of EXO1 increased the sensitivity of GC cells. Taken together, our study indicates that the HDR pathway is an important player in CDDP resistance in GC through the regulation of EXO1.
Role of phospholipase C in catecholamine-induced increase in myocardial protein synthesis
Authors: Tappia Paramjit S. , Ramjiawan Bram , Dhalla Naranjan S.
Published: June 2022

The activation of the α1-adrenoceptor-(α1-AR) by norepinephrine results in the G-protein (Gqα) mediated increase in the phosphoinositide-specific phospholipase C (PLC) activity. The byproducts of PLC hydrolytic activity, namely, 1,2-diacylglycerol and inositol-1,4,5-trisphosphate, are important downstream signal transducers for increased protein synthesis in the cardiomyocyte and the subsequent hypertrophic response. In this article, evidence was outlined to demonstrate the role of cardiomyocyte PLC isozymes in the catecholamine-induced increase in protein synthesis by using a blocker of α1-AR and an inhibitor of PLC. The discussion was focused on the α1-AR-Gqα-PLC-mediated hypertrophic signalling pathway from the viewpoint that it may compliment the other β1-AR-Gs protein-adenylyl cyclase signal transduction mechanisms in the early stages of cardiac hypertrophy development, but may become more relevant at the late stage of cardiac hypertrophy. From the information provided here, it is suggested that some specific PLC isozymes may potentially serve as important targets for the attenuation of cardiac hypertrophy in the vulnerable patient population at-risk for heart failure.
THE EVOLUTION OF CAPABILITIES UNDERPINNING BUSINESS MODEL INNOVATION FOR SUSTAINABILITY IN LARGE INCUMBENT FIRMS
Authors: WADIN JESSICA LAGERSTEDT , BENGTSSON LARS
Published: June 2022

Many studies in traditional business model (BM) literature acknowledge that it is hard for incumbent firms to change their business models, a finding echoed in the business model innovation for sustainability (BMIfS) literature. Large incumbent firms often lack or have weak capabilities needed for sustainability-driven business model innovation. This paper addresses how capabilities for such innovation evolve in large incumbent firms. The research builds on a longitudinal case study in the energy utility industry, and on two theoretical frameworks advanced in previous literature: values-based business model innovation, and capability lifecycles. We conceptually propose a stage-based maturity model of BMIfS. Our model highlights three types of research contributions. First, we propose that the capability development underpinning BMIfS is a nonlinear process of distinct maturity stages. Second, the maturity stages have intermediate transformation junctures that the firm must go through. These junctures either change or initiate the conditions for capability development to be able to advance to the next BMIfS maturity level. Third, the shift in societal, regulatory, competitor, and customer expectations and interests triggers strategic decisions and early development of capabilities that start and push the firm into the transformation junctures.
A Machine Learning-Based Approach to Discriminating Basaltic Tectonic Settings
Authors: Liu Baoshun , Shi Junxia
Published: June 2022

The geochemical characteristics of magmatic rocks can distinguish the tectonic setting of magma formation and their geochemical signatures are discriminated by using the whole-rock geochemical data. As a new attempt of artificial intelligence technology in geochemistry, the machine learning discrimination method is gradually complementary to the classical discriminative graphical method. However, the feature selection of high-dimensional data and the determination of many unknown parameters are the two main factors affecting the classification accuracy of the algorithm. In this paper, a particle swarm optimized support vector machine (PSO-SVM) model is established to classify the tectonic environments of basaltic rocks in the GEOROC database. The model mainly relies on the powerful search capability of the particle swarm algorithm to find the best parameter combination selected by the SVM based on experience to improve the accuracy. In this study, based on the basalt samples in the database and the confusion matrix, the performance of PSO-SVM model is evaluated by simulation experiments. The results show that the model proposed in this paper is more effective in distinguishing the basaltic tectonic environments, with an accuracy of more than 90%. Therefore, compared with the traditional discriminant map method, the machine learning method based on the fusion of two algorithms performs better in the tectonic environment classification problems.
Transfer of water and contaminants in the Chalk unsaturated zone – underground quarry of Saint-Martin-le-Nœud
Authors: Valdes Danièle , Chen Ningxin , Dumont Marc , Marlin Christelle , Blanchoud Hélène , Guérin Roger , Guillemoteau Julien , Alliot Fabrice , Nespoulet Romane , Aubry Emmanuel , Rouelle Maryse , Fauchard Cyrille , Gombert Philippe , Ribstein Pierre
Published: June 2022

The aim of this study is to understand the water and contaminant (nitrate and atrazine) transfer in the unsaturated zone (UZ) of Chalk. For this, the underground quarry of Saint-Martin-le-Nœud is an exceptional site because it permits entry to the aquifer at the limit between the UZ and the saturated zone (SZ). It provides direct access to the water table: underground lakes and the output of the UZ (percolation water at the ceiling). The thicknesses of the UZ and the clay-with-flints (CwF) layer that overlie the Chalk, vary along the 1200 m length of the quarry.

From 2012, the chemical evolution and the flow variability of groundwater are characterized for 16 sites with different UZ properties. Chalk groundwater has highly spatially variable hydrodynamic behaviour and geochemical properties. A peak of contaminants is observed in the UZ around 15–20 m depth, with differing behaviours of nitrate and atrazine. The downward matrix water velocity is estimated to be from 0.3 to over 0.72 m a
−1
, and the water table is mainly composed of ‘old’ water resulting from transfer through the matrix. A thick CwF layer modifies (1) the transfer processes: surface water is stored in a sort of ‘near-surface perched groundwater’, the infiltration is concentrated by preferential pathways; and (2) water quality: pesticides degradation processes occur in the perched groundwater.
Consent Verification Monitoring
Authors: Robol Marco , Breaux Travis D. , Paja Elda , Giorgini Paolo
Published: June 2022

Advances in personalization of digital services are driven by low-cost data collection and processing, in addition to the wide variety of third-party frameworks for authentication, storage, and marketing. New privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act, increasingly require organizations to explicitly state their data practices in privacy policies. When data practices change, a new version of the policy is released. This can occur a few times a year, when data collection or processing requirements are rapidly changing. Consent evolution raises specific challenges to ensuring GDPR compliance. We propose a formal consent framework to support organizations, data users, and data subjects in their understanding of policy evolution under a consent regime that supports both the retroactive and non-retroactive granting and withdrawal of consent. The contributions include (i) a formal framework to reason about data collection and access under multiple consent granting and revocation scenarios, (ii) a scripting language that implements the consent framework for encoding and executing different scenarios, (iii) five consent evolution use cases that illustrate how organizations would evolve their policies using this framework, and (iv) a scalability evaluation of the reasoning framework. The framework models are used to verify when user consent prevents or detects unauthorized data collection and access. The framework can be integrated into a runtime architecture to monitor policy violations as data practices evolve in real time. The framework was evaluated using the five use cases and a simulation to measure the framework scalability. The simulation results show that the approach is computationally scalable for use in runtime consent monitoring under a standard model of data collection and access and practice and policy evolution.
Extreme Partial-Sum Quantization for Analog Computing-In-Memory Neural Network Accelerators
Authors: Kim Yulhwa , Kim Hyungjun , Kim Jae-Joon
Published: June 2022

In Analog Computing-in-Memory (CIM) neural network accelerators, analog-to-digital converters (ADCs) are required to convert the analog partial sums generated from a CIM array to digital values. The overhead from ADCs substantially degrades the energy efficiency of CIM accelerators so that previous works attempted to lower the ADC resolution considering the distribution of the partial sums. Despite the efforts, the required ADC resolution still remains relatively high. In this article, we propose the data-driven partial sum quantization scheme, which exhaustively searches for the optimal quantization range with little computational burden. We also report that analyzing the characteristics of the partial sum distributions at each layer gives an additional information to further reduce the ADC resolution compared to previous works that mostly used the characteristics of the partial sum distributions of the entire network. Based on the finer-level data-driven approach combined with retraining, we present a methodology for extreme partial-sum quantization. Experimental results show that the proposed method can reduce the ADC resolution to 2 to 3 bits for CIFAR-10 dataset, which is the smaller ADC bit resolution than any previous CIM-based NN accelerators.
Organoid Imaging: Seeing Development and Function
Authors: Keshara Rashmiparvathi , Kim Yung Hae , Grapin-Botton Anne
Published: June 2022

Organoids are miniaturized and simplified versions of an organ produced in vitro from stem or progenitor cells. They are used as a model system consisting of multiple cell types forming an architecture relevant to the organ and carrying out the function of the organ. They are a useful tool to study development, homeostasis, regeneration, and disease. The imaging of organoids has become a pivotal method to visualize and understand their self-organization, symmetry breaking, growth, differentiation, and function. In this review, we discuss imaging methods, how to analyze these images, and challenges in organoid research.
Data Management Platform of Forest Ecological Station Based on Internet of Things and Big Data Sensor
Authors: Sun Ping’an
Published: June 2022

In order to solve the problems of low data storage efficiency and poor retrieval performance in forest ecological station, a method of a forest ecological station data management platform based on Internet of Things and big data sensor is proposed. The framework method designs the prepartition algorithm to ensure the uniform distribution of data in the cluster. According to the characteristics of ecological data, the RowKey is scientifically designed to realize the rapid retrieval of ecological data. The Elasticsearch index fragment placement strategy based on index data and server performance evaluation is designed, and the packaging and merging strategy based on data site and time correlation is proposed to improve the storage efficiency. The results are as follows: when the scale of structured data is 108, the retrieval time of the system is 1.045 s, which is 3.99 times faster than that of the original HBase. When the scale of unstructured data is 107, the packaging small image strategy based on data site and time correlation is 1.15 times higher than that of the sequence file and 1.79 times higher than that of the original HBase. In the case of 104 concurrent users, the number of queries per second after optimization is 1.88 times higher than the original, the throughput per second is 1.74 times higher than that before optimization, and the system response time is 69.5% lower than that before optimization. The results show that the proposed scheme has significantly improved the performance in the aspects of cluster load balancing and massive structured and unstructured data retrieval efficiency and system throughput, and provide the necessary theoretical basis and technical implementation for the storage and management of forest ecological data.
Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes
Authors: Bao Xiaoju , Qiu Jingyue , Xuan Qin , Ye Xinming
Published: June 2022

Background. Exercise is a regular behavioral activity that not only helps to lose weight but also reduces the risk of cardiovascular and cerebrovascular diseases. Diabetes is a common disease that plagues human health. It is shown that regular exercise can improve the insulin sensitivity of diabetic patients and have an important function in adjuvant therapy. Methods. We downloaded the GSE101931 dataset from the Gene Expression Omnibus (GEO) database, 10 samples were obtained from the GSE101931 dataset, including 5 before exercise and 5 postexercise samples, and GEO2R was used to screen the differentially expressed genes (DEGs) exhibited by a heat map. Then, the enrichment analysis of DEGs in Gene Ontology (GO) function was analyzed by Metascape, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of DEGs was also analyzed by gene set enrichment analysis (GSEA). Next, the protein-protein interaction (PPI) network maps were drawn, and the hub genes were identified through Metascape. Finally, the expressions of the hub genes in the dataset were analyzed. Results. Totally, 116 upregulated DEGs and 1017 downregulated DEGs were identified from these data. These DEGs were mainly enriched in the platelet-derived growth factor receptor signaling pathway and mRNA processing. Then, the GSEA analysis showed that 6 KEGG pathways were associated with postexercise prediabetic samples, namely, ABC transporters, focal adhesion, MAPK signaling pathway, prion diseases, melanogenesis, and gap junction. Afterward, three hub genes (HSPA8, STIP1, and HSPH1) were highly expressed after exercise through the box plot analysis. Conclusion. A myriad of research results confirms that there is a certain connection between exercise and diabetes, which provides a favorable basis for emerging exercise into the treatment of diabetic patients.
Financial Risk Evaluation of Digital Currency Based on CART Algorithm Blockchain
Authors: Zhao Aping
Published: June 2022

In recent years, digital currencies based on blockchain technology have brought about a boom in the research of encrypted digital currencies with the design concept of peer-to-peer trading and decentralization, but due to the lack of supervision and obvious speculative characteristics of the digital currency market, the sharp fluctuations in the market can easily trigger investor sentiment fluctuations, which in turn will lead to social instability and even financial system risks. In this paper, through the analysis of the financial risk source factors of blockchain digital currency, the evaluation index is established, the risk evaluation index system is constructed, and then the CART classification algorithm is used to analyze it and evaluate and test the model. The characteristic factor structure obtained by the CART algorithm in this paper better explains the financial risk characteristics of blockchain digital currency and gives relatively reliable identification results. The results show that the CART decision tree classification method is effective and has a high accuracy rate, which classifies the financial risks of blockchain digital currency, and the method has excellent adaptability and matchability for the classification of risk problems.
The Efficacy of Ultrasound-Guided Superior Laryngeal Nerve Block as an Adjuvant to General Anesthesia during Suspension Laryngoscopy Vocal Cord Polypectomy
Authors: Zhou Yu , Chen Bin , Xiong Yiqiang , Yu Xiangdi
Published: June 2022

Background. In the current study, we assessed the effect of the ultrasound-guided internal branch of the upper laryngeal nerve (USG-guided iSLN) block combined with general anesthesia on perioperative sore throat (POST), cough, hoarseness of voice, intraoperative hemodynamic changes, and the quality of early recovery for the patients undergoing suspension laryngoscopy vocal cord polypectomy (SLVCP). Methods. This was a randomized controlled trail. Eighty patients, aged from 18 to 70 years old, ASA I ∼ II, scheduled for polypectomy of the vocal cord by using a laryngoscope, were randomized into 2 groups (n = 40 each) using a random number table. Patients in group C received general anesthesia (GA), whereas those in group S received USG-guided iSLN block bilaterally (37.5 mg of 0.375% ropivacaine, 5 ml each side) combined with GA. The primary outcome was the quality of patients’ recovery using the Quality of Recovery Questionnaire (QoR-9). The secondary outcomes were postoperative cough, sore throat, hoarseness of voice, and hemodynamic changes in both groups at corresponding time points. The adverse reactions such as postoperative chocking, or aspiration, and dyspnea was recorded as well. Results. The QoR-9 scores of patients in group C were lower than those of group S at time points of D1∼D2 (

P
<
0.05

). Patients in group S had a significantly lower incidence of perioperative cough than those in group C in the early postoperative period (1 hour after extubation) (

P
<
0.05

), the scores of sore throat were lower in group S than those in group C (

P
<
0.05

), the incidence of postoperative hoarseness was increased in group S than that in group C at the time points of 30 min, 2 h, and 4 h after extubation (

P
<
0.05

); however, the incidence of postoperative hoarseness was decreased in group S than that in group C at the time point of 24 h after extubation (

P
<
0.05

). MAP and HR of group S was lower than those of group C at time points of T1∼T4 (

P
<
0.05

). No serious adverse events were observed in both groups. Conclusion. The study found that the application of ultrasound guided superior laryngeal nerve block combined with general anesthesia for the patients undergoing SLVCP could effectively promote the quality of early recovery. Clinical trial registration: This trial is registered with NCT05309174. The date of registration: March 12th 2021.Trial registry name: The Study of Bilateral Upper Laryngeal Nerve Block for Supporting the Removal of Vocal Cord Polyps Under Laryngoscopy.
Effect of Maxwell Velocity and Smoluchowski Temperature Jump Slip Conditions on Tangent Hyperbolic Nanofluid
Authors: Atif S. M. , Emadifar Homan , Günerhan H. , Oyelami F. H.
Published: June 2022

In this numerical investigation, three-dimensional tangent hyperbolic nanofluid flow past a stretching surface has been analyzed under the Smoluchowski thermal and Maxwell velocity slip conditions at the boundary. For the modification of the flow fields, the magnetic effect has also been incorporated. Nonlinear thermal radiation and Joule heating have also been included to improve heat transmission. The modelled PDEs have been turned into nonlinear ODEs after the application of the appropriate transformation. MATLAB bvp4c algorithm is utilized to achieve the solution of the problem. The code’s legitimacy is accomplished by repeating previously published results in the literature. Efforts are made to discuss the effects of the important parameters on velocities, energy profile, and concentration distribution in detail via graphs and tables. Based on our simulations, an enhancement in the slip parameter leads both velocities to decline. The ratio parameter causes a decline in velocity in the horizontal direction whereas it increases in the vertical direction. The heat transfer rate is boosted as the temperature ratio parameter is increased.
Optimization of UAV Cooperative Path Planning Mathematical Model Based on Personalized Multigroup Sparrow Search Algorithm in Complex Environment
Authors: Yan Shaoqiang , Liu Weidong , Yang Ping , Wu Fengxuan , Zhu Donglin , Chen Gang
Published: June 2022

Sparrow search algorithm has the problem of redundancy of convergence speed due to its fast convergence speed, and it is easy to fall into local optimum in multimodal environment. To solve the above problem, this paper presents a personalized multipopulation sparrow search algorithm (MPSSA). By introducing multiple population mechanisms to reduce the probability of falling into the local optimum due to single-population search, by using a personalized subpopulation strategy to improve the personalized differences of subpopulations and balance the exploratory ability of algorithm development, then by using weighted center-of-gravity communication strategy to improve the quality of communication between populations, and finally by using dimension by dimension dynamic reverse learning to improve the accuracy of search. The superiority of MPSSA is validated by comparing the benchmark function and CEC2017. Finally, the algorithm solves the problem of poor quality due to the dimension increase of the UAV cooperative track. MPSSA helps the UAV to quickly plan a better and stable track group to ensure the UAV to complete the cooperative task safely and stably.
Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations
Authors: Zhu Hong , Yu Yunkai , Xie Meiyi
Published: June 2022

With the prevailing use of smartphones and location-based services, vast amounts of trajectory data are collected and used for many applications. When trajectory data are sent to a third-party research institute for analytical applications, the privacy of users would be severely disclosed. For example, the relationship between users will be revealed from the correlation between trajectories. In this paper, we propose a method for releasing trajectory datasets without revealing correlation between trajectories, called RDPT. In RDPT, we first quantify the trajectory correlation and convert the problem of protecting trajectory correlations into reducing the trajectory similarities of users and preserving the utility of the perturbed trajectories. Based on the insight, we model a multi-objective optimization problem and solve the problem with the particle swarm optimization algorithm modified to satisfy differential privacy. Then we generate synthetic trajectories and the correlations between trajectories are reduced. We conduct extensive experiments on three real trajectory datasets. The experimental results show that RDPT achieves almost equivalent data utility to and better privacy than the existing methods.
A Framework for Green Interior Design and Simulation Using Immersive VR Technology
Authors: Yang Haoran , Xiao Zhigao , Deng HanSong
Published: June 2022

The green decoration indoor environment design method has become the main trend in recent years with the rising awareness of environmental protection. The green design strategy emphasizes the reduction of production materials, packaging materials, recycling of waste products, and material recycling. Generally, green interior design is a kind of cognition of friendly treatment to the environment. Considering the future use and development of space to adopt flexible strategies, the design process should incorporate the possibility of future changes. The flexible strategies can reduce the future part of the change and can redecorate waste. In the process of creativity and design, the design objectives should be clearly defined, and the frequency of modification should be reduced. This research introduces a framework for green interior design and simulation using immersive VR technology. The concept of regeneration is used to achieve maximum utilization and minimum damage. It improves the efficiency of using resources; on the other hand, it reduces the impact of interior design on human health and environment.
Application of Wavelet Packet and Fuzzy Algorithm in Power System Short Circuit Fault Classification
Authors: Qiu Guangping
Published: June 2022

Short-circuit fault, also known as a lateral fault, is one of the common types of faults in power transmission lines. This paper proposes an accurate method for identifying short-circuit fault types for power transmission line systems. According to the three-phase A, B, and C values, short-circuit faults can be subdivided into ten classes. An MDL power system with controllable short-circuit fault types is designed and tested. The method of using wavelet packet to analyze short-circuit fault waveforms and using the fuzzy controller to analyze eigenvectors is also proposed. Wavelet packet analysis makes up for the defect that wavelet transform usually only decomposes low frequencies and retains high frequency. Therefore, more accurate frequency band information and eigenvectors are obtained through wavelet packet transform, and then, the fuzzy controller is used to identify the eigenvectors, which can effectively detect and discriminate short-circuit fault types. Accurate identification of short-circuit fault types can improve transmission lines’ overall operational efficiency and promote the power system’s development.
Dynamical Behaviour of Conformable Time-Fractional Coupled Konno-Oono Equation in Magnetic Field
Authors: Elbrolosy M. E. , Elmandouh A. A.
Published: June 2022

Our aim is to construct distinct types of novel traveling wave solutions for the nonlinear conformable time-fractional coupled Konno–Oono equation in a magnetic field utilizing the qualitative analysis of the dynamical system that corresponds to the Hamiltonian system. The influences of fractional-order derivative on the wave solutions in addition to equation parameters on the waveform are investigated. Furthermore, He’s variational technique has been applied to seek some new solutions. 2D and 3D graphics of the obtained solutions are presented to clarify the physical and mathematical meaning of the waves.
Changes in Mitochondria-Related Gene Expression upon Acupuncture at LR3 in the D-Galactosamine-Induced Liver Damage Rat Model
Authors: Lee Yu-Mi , Choi Dong-Hee , Cheon Min-Woo , Kim Jae Gwan , Kim Jeong-Sang , Shin Myung-Geun , Kim Hye-Ran , Youn Daehwan
Published: June 2022

Hepatic diseases, such as hepatonecrosis, hepatitis, and hepatocirrhosis, are associated with mitochondrial dysfunction and increased reactive oxygen species generation and inflammation, ultimately leading to liver failure. In this study, we examined if acupuncture at LR3 can affect mitochondria-related gene expression in a liver damage model of experimentally induced acute liver failure (ALF). ALF was induced by the intraperitoneal injection of D-galactosamine (D-GalN) in experimental rats, who then received either sham (ALF), manual acupuncture (MA), electroacupuncture (EA), or silymarin (PC, positive control) treatment. Liver tissues were extracted from experimental and untreated control rats for histopathological analysis and expression profiling of genes involved in mitochondrial function. Of the 168 mitochondria-related genes profiled, two genes belonging to the solute-carrier transporter family (Slc25a15 and Slc25a25) and Ndufb7 were upregulated. Gamma-glutamylcysteine synthetase was more downregulated in MA than ALF. Furthermore, MA reversed D-GalN-induced inflammatory cell infiltration, destruction of hepatic cell plates, and increase in the levels of the proinflammatory cytokine TNF-α. MA at LR3 can reduce the risk of D-GalN-induced ALF by inducing the expression of metabolic and inflammation-related genes and regulating proinflammatory factor production in hepatic mitochondria.
p38 MAPK Endogenous Inhibition Improves Neurological Deficits in Global Cerebral Ischemia/Reperfusion Mice
Authors: Hou Kun , Xiao Zhi-cheng , Dai Hai-Long
Published: June 2022

Cerebral ischemia/reperfusion (I/R) injury is a complex pathophysiological process that can lead to neurological function damage and the formation of cerebral infarction. The p38 MAPK pathway has attracted considerable attention in cerebral I/R injury (IRI), but little research has been carried out on its direct role in vivo. In this study, to observe the effects of p38 MAPK endogenous inhibition on cerebral IRI, p38 heterozygous knockdown (p38KI/+) mice were used. We hypothesized that p38 signaling might be involved in I/R injury and neurological damage reduction and that neurological behavioral deficits improve when p38 MAPK is inhibited. First, we examined the neurological damage and neurological behavioral deficit effects of I/R injury in WT mice. Cerebral I/R injury was induced by the bilateral common carotid artery occlusion (BCCAO) method. The cerebral infarction area and volume were assessed and analyzed by 2,3,5-triphenyltetrazolium chloride (TTC) staining. p38 MAPK and caspase-3 were detected by western blotting. Neuronal apoptosis was measured using TUNEL staining. Neurological deficits were detected by behavioral testing. Furthermore, to assess whether these neuroprotective effects occurred when p38 MAPK was inhibited, p38 heterozygous knockdown (p38KI/+) mice were used. We found that p38 MAPK endogenous inhibition rescued hippocampal cell apoptosis, reduced ischemic penumbra, and improved neurological behavioral deficits. These findings showed that p38 MAPK endogenous inhibition had a neuroprotective effect on IRI and that p38 MAPK may be a potential therapeutic target for cerebral IRI.
Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization
Authors: Han Songyue , Huang Wei , Ma DaWei , Guo JiLian , He Hang
Published: June 2022

To improve the contradiction between the surge of business demand and the limited resources of MEC, firstly, the “cloud, fog, edge, and end” collaborative architecture is constructed with the scenario of smart campus, and the optimization model of joint computation offloading and resource allocation is proposed with the objective of minimizing the weighted sum of delay and energy consumption. Second, to improve the convergence of the algorithm and the ability to jump out of the bureau of excellence, chaos theory and adaptive mechanism are introduced, and the update method of teaching and learning optimization (TLBO) algorithm is integrated, and the chaos teaching particle swarm optimization (CTLPSO) algorithm is proposed, and its advantages are verified by comparing with existing improved algorithms. Finally, the offloading success rate advantage is significant when the number of tasks in the model exceeds 50, the system optimization effect is significant when the number of tasks exceeds 60, the model iterates about 100 times to converge to the optimal solution, the proposed architecture can effectively alleviate the problem of limited MEC resources, the proposed algorithm has obvious advantages in convergence, stability, and complexity, and the optimization strategy can improve the offloading success rate and reduce the total system overhead.
Designing a National Music System for a Smart Concert Hall Using Neural Network and Wireless Internet of Things
Authors: Zhang Mingjie
Published: June 2022

With the fast advancement of science and technology, numerous music systems have emerged. However, there are few folk music in the music industry, for example, pop music, which leads to the inability of folk music lovers to find their favorite music. For this problem, this paper develops a national system of smart concert hall based on a neural network and wireless Internet of things (IoT). It establishes the architecture model of the wireless IoT system of the concert hall by describing the neural network, neuron model, and BP neural network model in detail. Besides, this paper develops the smart music national system based on this model, completes the functional module design of the national music system, and uses the music attribute recommendation model. Finally, it analyzes the scoring performance of the national music system. The results show that when the number of recommended folk music songs is less than 20, the lowest MAE is 0.73.
A Computation Complexity Reduction of the Size Decision Algorithm in 3D-HEVC Depth Map Intracoding
Authors: Hamout Hamza , Elyousfi Abderrahmane
Published: June 2022

A new video format called a multiview video plus depth map is recently designed as the most efficient 3D video representation. 3D-high efficiency video coding (3D-HEVC) is the international standard for 3D video coding finalized in 2016. In 3D-HEVC intracoding, the depth map is an essential component, in which the depth map intraprediction occupies more than 85% of the overall intraencoding time. In 3D-HEVC design, a highly flexible quadtree coding unit partitioning is adopted, in which one coding tree unit is partitioned recursively into a prediction unit (PU) from 64 × 64 down to 4 × 4. This highly flexible partitioning provides more accurate prediction signals and thereby achieves better intradepth map compression efficiency. However, performing all depth map intraprediction modes for each PU level achieves high depth map intracoding efficiency, but it results in a substantial computational complexity increase. This paper proposes an amelioration of the previously proposed depth map PU size decision using an efficient homogeneity determination. The resulting experiences show that the proposed method can significantly save the computational complexity with a negligible loss of intracoding efficiency.
Construction of Lymph Node Metastasis-Related Prognostic Model and Analysis of Immune Infiltration Mode in Lung Adenocarcinoma
Authors: Li Wujin , Ou Debin , Zhang Jiguang , Ye Mingfan
Published: June 2022

Background. Lung adenocarcinoma (LUAD) is a major cause for global cancer-related deaths. Research reports demonstrate that lymph node metastasis (LNM) is pertinent to the survival rate of LUAD patients, and crux lies in the lack of biomarkers that could distinguish patients with LNM. We aimed to verify the LNM-related prognostic biomarkers in LUAD. Methods. We firstly accessed the expression data of mRNA from The Cancer Genome Atlas (TCGA) database and then obtained samples with LNM (N+) and without LNM (N-). Differential expression analysis was conducted to acquire differentially expressed genes (DEGs). Univariate-LASSO-multivariate Cox regression analyses were performed on DEGs to build a risk model and obtain optimal genes. Afterwards, effectiveness and independence of risk model were assessed based on TCGA-LUAD and GSE31210 datasets. Moreover, a nomogram was established combining clinical factors and riskscores. Nomogram performance was measured by calibration curves. The infiltration abundance of immune cells was scored with CIBERSORT to explore the differences between high- and low-risk groups. Lastly, gene set enrichment analysis (GSEA) was used to investigate differences in immune features between the two risk groups. Results. Nine optimal feature genes closely related to LNM in LUAD were identified to construct a risk model. Prognostic ability of the risk model was verified in independent databases. Patients were classified into high- and low-risk groups in accordance with their median riskscores. CIBERSORT score displayed differences in immune cell infiltration like T cells CD4 memory resting between high/low-risk groups. LNM-related genes may also be closely relevant to immune features. Additionally, GSEA indicated that differential genes in the two risk groups were enriched in genes related to immune cells. Conclusion. This research built a risk model including nine optimal feature genes, which may be potential biomarkers for LUAD.
Single-Input Single-Output System with Multiple Time Delay PID Control Methods for UAV Cluster Multiagent Systems
Authors: Yan Li , Ahmad Mohd Wazih , Jawarneh Malik , Shabaz Mohammad , Raffik R. , Kishore Kakarla Hari
Published: June 2022

Multiagent has become a multidomain intersecting hot issue as artificial intelligence technology has advanced in the industrial sector. Multiagent system creation control has lately undergone a lot of academic research, and it has got applications in a wide range of fields, including drag reduction, monitoring, telecommunications relay, and searching. A matrix theory using algebraic graph theory for automatic control principle learning is proposed based on multiagent system collaborative control strategy method to study the application of PID control method in human-machine cluster multi-intelligence system. The method first presents the determination of the stability set of the low-order controller parameters for the multidelay single-input single-output system with complex coefficients; through matrix theory, the multi-intelligence system is decomposed into multiple subsystems and the problem is transformed into subsystem stability analysis problem; thus, the complexity of the system is reduced. It has been proved that the power system device adopted by the UAV hardware platform can make the UAV flight time up to 24.7 min and additional load up to 1.5 kg. Based on the simulation analysis of PID control algorithm, the PID parameters of UAV are adjusted, which improves the parameter tuning efficiency and enhances the response speed of UAV to error and the stability of motion. The experimental findings suggest that the UAV hardware platform in the UAV control system has high dynamic performance. Good PID settings allow the UAV to respond to control orders quickly and correctly; also, the data received by the sensor decrease the complexity of operating several UAVs at the same time and minimize the operators’ burden.
Direction of Arrival Estimation for Coherent Signals’ Method Based on LSTM Neural Network
Authors: Han-Trong Thanh , Ngo Duc Nam , Tran Van Hung , Pham-Viet Hung
Published: June 2022

Radio direction finding system is a system that determines the direction or coordinates of radio signal sources. The main function of this system is to determine the direction of arrival (DOA) of an incident radio wave. DOA information plays an important role in array signal processing and has many applications in communications, radar, seismic survey, etc. In this study, we propose a method to estimate the DOA by using the simulated signal dataset obtained at the linear antenna array (ULA) and the suitable Long Short-Term Memory (LSTM) network model. The performance of the method is evaluated based on the root mean square error (RMSE) parameter and then is compared with 2 other algorithms, multiple signal classification (MUSIC) and deep neural network (DNN) in different cases such as deviation of incoming signals, variation of signal-to-noise ratio (SNR), and coherent incoming signals. The obtained results have shown that the proposed method has significantly improved accuracy compared to other methods.
Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network
Authors: Zhang Min
Published: June 2022

Rockburst is one of the main disasters in railway tunnel construction. In order to accurately predict the rockburst intensity level of the railway tunnel, the rock stress coefficient

σ

θ

/

σ

c

, rock brittleness coefficient

σ

c

/

σ

t

, and elastic energy index

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et

are used as evaluation indexes of rockburst intensity, and a BP neural network rockburst prediction model based on hybrid particle swarm optimization algorithm is proposed. First, 90 groups of existing rockburst examples are selected as the basic data of the mode based on the research results at home and abroad. Then, the BP neural network is improved by using particle swarm optimization (PSO) combined with the simulated annealing algorithm. The results are obtained from the training data. Based on hybrid PSO-BP neural network, the prediction model of rockburst intensity is obtained. Finally, the model is applied to the actual railway tunnel project to verify. The results show that the model takes into account individual optimization and global optimization and can correctly and effectively predict the rockburst grade of the railway tunnel, which provides a new method for rockburst prediction of the railway tunnel.