bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative transcriptome and metabolome analyses identified the mode of sucrose degradation as a metabolic marker for early vegetative propagation in bulbs of Lycoris
Authors: Ren Zi‐Ming , Zhang Dong , Jiao Chen , Li Dan‐Qing , Wu Yun , Wang Xiu‐Yun , Gao Cong , Lin Ye‐Fan , Ruan Yong‐Ling , Xia Yi‐Ping
Published: August 2022

Vegetative propagation (VP) is an important practice for production in many horticultural plants. Sugar supply constitutes the basis of VP in bulb flowers, but the underlying molecular basis remains elusive. By performing a combined sequencing technologies coupled with ultra‐high performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry approach for metabolic analyses, we compared two Lycoris species with contrasting regeneration rates: high‐regeneration Lycoris sprengeri and low‐regeneration Lycoris aurea. A comprehensive multi‐omics analyses identified both expected processes involving carbohydrate metabolism and transcription factor networks, as well as the metabolic characteristics for each developmental stage. A higher abundance of the differentially expressed genes including those encoding ethylene responsive factors was detected at bulblet initiation stage compared to the late stage of bulblet development. High hexose‐to‐sucrose ratio correlated to bulblet formation across all the species examined, indicating its role in the VP process in Lycoris bulb. Importantly, a clear difference between cell wall invertase (CWIN)‐catalyzed sucrose unloading in high‐regeneration species and the sucrose synthase‐catalyzed pathway in low‐regeneration species was observed at the bulblet initiation stage, which was supported by findings from carboxyfluorescein tracing and quantitative real‐time PCR analyses. Collectively, the findings indicate a sugar‐mediated model of the regulation of VP in which high CWIN expression or activity may promote bulblet initiation via enhancing apoplasmic unloading of sucrose or sugar signals, whereas the subsequent high ratio of hexose‐to‐sucrose likely supports cell division characterized in the next phase of bulblet formation.
Novel identification and modulation of the mechanosensitive Piezo1 channel in human myometrium
Authors: Barnett Scott D. , Asif Hazik , Buxton Iain L. O.
Published: August 2022

Approximately 10% of US births deliver preterm before 37 weeks of completed gestation. Premature infants are at risk for life‐long debilitating morbidities and death, and spontaneous preterm labour explains 50% of preterm births. In all cases existing treatments are ineffective, and none are FDA approved. The mechanisms that initiate preterm labour are not well understood but may result from dysfunctional regulation of quiescence mechanisms. Human pregnancy is accompanied by large increases in blood flow, and the uterus must enlarge by orders of magnitude to accommodate the growing fetus. This mechanical strain suggests that stretch‐activated channels may constitute a mechanism to explain gestational quiescence. Here we identify for the first time that Piezo1, a mechanosensitive cation channel, is present in the uterine smooth muscle and microvascular endothelium of pregnant myometrium. Piezo is downregulated during preterm labour, and stimulation of myometrial Piezo1 in an organ bath with the agonist Yoda1 relaxes the tissue in a dose‐dependent fashion. Further, stimulation of Piezo1 while inhibiting protein kinase A, AKT, or endothelial nitric oxide synthase mutes the negative inotropic effects of Piezo1 activation, intimating that actions on the myocyte and endothelial nitric oxide signalling contribute to Piezo1‐mediated contractile dynamics. Taken together, these data highlight the importance of stretch‐activated channels in pregnancy maintenance and parturition, and identify Piezo1 as a tocolytic target of interest.

image

Key points

Spontaneous preterm labour is a serious obstetric dilemma without a known cause or effective treatments.

Piezo1 is a stretch‐activated channel important to muscle contractile dynamics.

Piezo1 is present in the myometrium and is dysregulated in women who experience preterm labour.

Activation of Piezo1 by the agonist Yoda1 relaxes the myometrium in a dose‐dependent fashion, indicating that Piezo1 modulation may have therapeutic benefits to treat preterm labour.
Electron density and temperature of dual-frequency capacitively coupled argon plasma in two-dimensional distribution obtained and studied in experiment
Authors: Wu Jidun , Xiao Xijian , Wang Yanfei , Huang Xiaojiang , Dai Lu
Published: August 2022

Behaviors of plasma in spatial distributions are vital for understanding physical mechanisms of low pressure capacitively coupled discharges. However, due to the lack of simple and reliable diagnostic techniques, few experimental studies have focused on two-dimensional distributions of plasma. Recently, we have developed a new method for rapid two-dimensional plasma diagnosis. With this method, the dual-frequency capacitively coupled plasma, an important plasma commonly used in etching, is systematically studied. Three circumstances are presented here: In case I, the plasma discharge was driven by a radio-frequency source; in case II, two radio-frequency sources were applied to one electrode; and in case III, such sources were applied to two separate electrodes. Results show that both the emission intensity and electron density in case III are higher than those in case II, and because the absolute value of the DC self-bias voltage is smaller, the structure of axial discharge is more symmetric in case III. For the asymmetrically capacitive discharges (cases I and II), the position of the maximum axial electron density is close to the powered electrode due to the presence of the DC self-bias voltage. As the low-frequency power increases or gas pressure decreases, the position of the maximum axial electron density moves gradually toward the grounded electrode because of the enlarged thickness of the sheath. However, high-frequency power has a limited influence on the position of the maximum axial electron density. The radial distribution of plasma is more uniform when plasma density is relatively low. For a higher plasma density situation, the center-peaked density profile results from the higher power deposition at the electrode center and/or more local charged particle dynamics. In addition, under the same discharge conditions, a lower electrode gap is favorable for the occurrence of the α-γ mode transition.
The Stochastic Assembly of Nitrobacter winogradskyi-Selected Microbiomes with Heterotrophs from Sewage Sludge or Grassland Soil
Authors: Laanbroek Hendrikus J. , Cassman Noriko A. , Keijzer Rosalinde M. , Kuramae Eiko E.
Published: August 2022

In the process of aerobic nitrification, nitrite-oxidizing bacteria together with ammonia-oxidizing microorganisms convert mineral nitrogen from its most reduced appearance, i.e., ammonium, into its most oxidized form, i.e., nitrate. Because the form of mineral nitrogen has large environmental implications, nitrite-oxidizing bacteria such as
Nitrobacter winogradskyi
play a central role in the global biogeochemical nitrogen cycle.
Elimination of LRVs Elicits Different Responses in
Leishmania
spp.
Authors: Saura Andreu , Zakharova Alexandra , Klocek Donnamae , Gerasimov Evgeny S. , Butenko Anzhelika , Macedo Diego H. , Servienė Elena , Zagirova Diana , Meshcheryakova Anastasia , Rogozin Igor B. , Serva Saulius , Kostygov Alexei Yu. , Yurchenko Vyacheslav
Published: August 2022

Leishmania
spp. represent human pathogens that cause leishmaniasis, a widespread parasitic disease with mild to fatal clinical manifestations. Some strains of leishmaniae bear leishmaniaviruses (LRVs), and this has been shown to aggravate disease course.
Infant Gut Microbial Metagenome Mining of α-
l
-Fucosidases with Activity on Fucosylated Human Milk Oligosaccharides and Glycoconjugates
Authors: Moya-Gonzálvez Eva M. , Peña-Gil Nazaret , Rubio-del-Campo Antonio , Coll-Marqués José M. , Gozalbo-Rovira Roberto , Monedero Vicente , Rodríguez-Díaz Jesús , Yebra María J.
Published: August 2022

α-
l
-Fucosyl residues are frequently present in many relevant glycans, such as human milk oligosaccharides (HMOs), histo-blood group antigens (HBGAs), and epitopes on cell surface glycoconjugate receptors. These fucosylated glycans are involved in a number of mammalian physiological processes, including adhesion of pathogens and immune responses.
Improving Suspected Pulmonary Infection Diagnosis by Bronchoalveolar Lavage Fluid Metagenomic Next-Generation Sequencing: a Multicenter Retrospective Study
Authors: Jin Xiao , Li Juan , Shao Mingyue , Lv Xuedong , Ji Ningfei , Zhu Yehan , Huang Mao , Yu Feichao , Zhang Changwen , Xie Lixu , Huang Jianling , Chen Sixi , Zhu Changjun , Lv Minjie , Feng Ganzhu
Published: August 2022

Pulmonary infection is a heterogeneous and complex infectious disease with high morbidity and mortality worldwide. In clinical practice, a considerable proportion of the etiology of pulmonary infection is unclear, microbiological diagnosis being challenging. Metagenomic next-generation sequencing detects all nucleic acids in a sample in an unbiased manner, revealing the microbial community environment and organisms and improving the microbiological detection and diagnosis of infectious diseases in clinical settings.
ORGANISATIONAL DIGITAL TRANSFORMATION OF SMEs—DEVELOPMENT AND APPLICATION OF A DIGITAL TRANSFORMATION MATURITY MODEL FOR BUSINESS MODEL TRANSFORMATION
Authors: PETZOLT SOPHIE , HÖLZLE KATHARINA , KULLIK OLIVER , GERGELEIT WIEBKE , RADUNSKI ANNE
Published: August 2022

One of the most challenging difficulties for incumbent organisations, especially small- and medium-sized enterprises (SMEs), is to manage digital transformation driven by technological change. Incumbent organisations’ responses to digital transformation have been extensively studied in the current literature. However, most research neglects digital transformation in SMEs. There are hardly any valid developed measures for the maturity of digital transformation. We present a holistic digital transformation maturity model based on an extensive literature review, qualitative computer-assisted data analysis, and empirical findings. The digital transformation maturity model focuses on small- and medium-sized enterprises’ unique features and characteristics. We proved the practical applicability and relevance of the digital transformation maturity model in an extensive study involving various organisations, particularly German SMEs ([Formula: see text]= 310). Organisations can use this model to assess themselves initially and, through this process, gain a comprehensive understanding of the multiple forms of digital transformation.
High-resolution 3D geological modelling of heterogeneity in poorly exposed glacial deposits using sedimentary and glaciotectonic architectural element analysis: a case example from Sellafield in west Cumbria, UK
Authors: Smith Nicholas T. , Merritt Jonathan W. , Phillips Emrys R.
Published: August 2022

Three-dimensional modelling of superficial geology is challenging, particularly for complex industrial sites such as the Sellafield nuclear site, Cumbria, UK. A lithostratigraphic approach is sufficient to show distribution of key units where only a general understanding of hydrogeology is required. For many sites, however, a much higher-resolution understanding is needed to better characterize heterogeneity and identify potential pathways for contaminant transport within safety case development and remediation planning. Models that focus on often thin, laterally discontinuous, heterogeneous sedimentary units are beset with issues including (1) a lack of exposure coupled with a dearth of data gathered by low-intrusive and intrusive means, (2) structural, stratigraphic and sedimentological complexities making correlation between data points difficult and (3) uncertainties associated with data quality. However, these problems may be overcome by combining land-system-based sedimentary and glaciotectonic architectural analysis of local field analogues with the creation of a portfolio of generic unit-bounding surface geometries. We test this through the development of a high-resolution 3D geological model of Sellafield nuclear site, where exposures are sparse, but shallow site-investigation boreholes are many. The results demonstrate that land-system-based sedimentary and glaciotectonic architectural element analysis can be applied to capture substantial subsurface heterogeneity resulting from dynamic glacial processes.
The Core Industry Manufacturing Process of Electronics Assembly Based on Smart Manufacturing
Authors: Chen Rongli , Chen Xiaozhong , Wang Lei , Li Jianxin
Published: August 2022

This research takes a case study approach to show the development of a diverse adoption and product strategy distinct from the core manufacturing industry process. It explains the development status in all aspects of smart manufacturing, via the example of ceramic circuit board manufacturing and electronic assembly, and outlines future smart manufacturing plans and processes. The research proposed two experiments using artificial intelligence and deep learning to demonstrate the problems and solutions regarding methods in manufacturing and factory facilities, respectively. In the first experiment, a Bayesian network inference is used to find the cause of the problem of metal residues between electronic circuits through key process and quality correlations. In the second experiment, a convolutional neural network is used to identify false defects that were overinspected during automatic optical inspection. This improves the manufacturing process by enhancing the yield rate and reducing cost. The contributions of the study built in circuit board production. Smart manufacturing, with the application of a Bayesian network to an Internet of Things setup, has addressed the problem of residue and redundant conductors on the edge of the ceramic circuit board pattern, and has improved and prevented leakage and high-frequency interference. The convolutional neural network and deep learning were used to improve the accuracy of the automatic optical inspection system, reduce the current manual review ratio, save labor costs, and provide defect classification as a reference for preprocess improvement.
Heterogeneous Big Data Parallel Computing Optimization Model using MPI/OpenMP Hybrid and Sensor Networks
Authors: Yin Fei , Shi Feng
Published: August 2022

For the heterogeneous big data parallel computing model, two levels of parallelism between nodes are not considered, resulting in low efficiency of heterogeneous big data parallel computing and bandwidth to send and receive information, high communication overhead, long model running time and small computational volume. In the paper, we propose an optimization model of heterogeneous big data parallel computing based on a hybrid Multi Point Interface (MPI)/Open Multi-Processing (OpenMP) and Sensor Networks. First, the processor characteristics of heterogeneous big data architecture is analyzed, the parallel tasks among processors are divided, collect the heterogeneous big data to be computed and cluster them, and use the processing results as the input items of the model. Then, a parallel load balancing mechanism is established to optimally divide the parallel computing load of heterogeneous big data, and a parallel computing optimization program is written by combining the hybrid programming mode of MPI and OpenMP and using the hybrid MPI/OpenMP, and finally, the parallel computing optimization of heterogeneous big data is realized by optimizing the parallel communication and determining the model parameters. The results show that the proposed model has a communication bandwidth of 510Mbps, a computational volume of 1.16GB, a model runtime of 24s, and an improved network bandwidth utilization of 93%, which can effectively reduce the communication overhead, and improve the efficiency of parallel computing and bandwidth sending and receiving information in sensor networks, and shorten the model running time.
Analyzing Variations of Everyday Japanese Conversations Based on Semantic Labels of Functional Expressions
Authors: Chiba Yuya , Higashinaka Ryuichiro
Published: August 2022

To achieve effective dialogue processing, the kinds of daily conversations people have must be clarified. Unfortunately, the characteristics of everyday conversations remain insufficiently investigated. In recent years, the Corpus of Everyday Japanese Conversation (CEJC) was developed, which is a large-scale corpus constructed by recording everyday Japanese conversations. In this article, we investigate the linguistic variations of everyday conversations in a multitude of situations using CEJC. We conducted factor analysis of it using the semantic categories of functional expressions that represent such subjective information as modality, thoughts, and communicative intention in addition to various tenses and facts. Our analysis identified seven factors that characterize everyday conversations and suggests that they are expressed by a combination of a dialogue’s purpose (e.g., “Explanation” and “Suggestion”) and its manners (e.g., “Politeness” and “Involvement”). We also analyzed the BTSJ–Japanese natural conversation corpus with transcripts and recordings and the Nagoya University conversational corpus and confirmed the generalizability of these factors.
Classification of Layout vs. Relational Tables on the Web: Machine Learning with Rendered Pages
Authors: Haider Waqar , Yesilada Yeliz
Published: August 2022

Table mining on the web is an open problem, and none of the previously proposed techniques provides a complete solution. Most research focuses on the structure of the HTML document, but because of the nature and structure of the web, it is still a challenging problem to detect relational tables. Web Content Accessibility Guidelines (WCAG) also cover a wide range of recommendations for making tables accessible, but our previous work shows that these recommendations are also not followed; therefore, tables are still inaccessible to disabled people and automated processing. We propose a new approach to table mining by not looking at the HTML structure, but rather, the rendered pages by the browser. The first task in table mining on the web is to classify relational vs. layout tables, and here, we propose two alternative approaches for that task. We first introduce our dataset, which includes 725 web pages with 9,957 extracted tables. Our first approach extracts features from a page after being rendered by the browser, then applies several machine learning algorithms in classifying the layout vs. relational tables. The best result is with Random Forest with the accuracy of 97.2% (F1-score: 0.955) with 10-fold cross-validation. Our second approach classifies tables using images taken from the same sources using Convolutional Neural Network (CNN), which gives an accuracy of 95% (F1-score: 0.95). Our work here shows that the web’s true essence comes after it goes through a browser and using the rendered pages and tables, the classification is more accurate compared to literature and paves the way in making the tables more accessible.
Double Attention Convolutional Neural Network for Sequential Recommendation
Authors: Chen Qi , Li Guohui , Zhou Quan , Shi Si , Zou Deqing
Published: August 2022

The explosive growth of e-commerce and online service has led to the development of recommender system. Aiming to provide a list of items to meet a user’s personalized need by analyzing his/her interaction

1

history, recommender system has been widely studied in academic and industrial communities. Different from conventional recommender systems, sequential recommender systems attempt to capture the pattern of users’ sequential behaviors and the evolution of users’ preferences. Most of the existing sequential recommendation models only focus on user interaction sequence, but neglect item interaction sequence. An item interaction sequence also contains rich contextual information for capturing the item’s dynamic characteristic, since an item’s dynamic characteristic can be reflected by the users who interact with it in a period. Furthermore, existing dual sequential models use the same method to handle the user interaction sequence and item interaction sequence, and do not consider their different characteristics. Hence, we propose a novel

D
ouble
A
ttention
C
onvolution
N
eural
N
etwork (DACNN)

, which incorporates user interaction sequence and item interaction sequence into an integrated neural network framework. DACNN leverages the strength of attention mechanism to capture the temporary suitability and adopts CNN to extract local sequential features. Experimental evaluations on the real datasets show that DACNN outperforms the baseline approaches.
Investigation of Dielectric, Ferroelectric and Conduction Behavior of Dy3+ Substituted SrBi2Ta2O9 Bismuth Layer Structured Ceramics
Authors: Sahu G. K. , Behera S. , Senthil V. , Badapanda T.
Published: August 2022

The present manuscript explores the impact of Dy doping in the Tantalum based bismuth layer structured ferroelectrics with chemical composition of Sr(Bi1-xDyx)2Ta2O9 (where x = 0.00, 0.025, 0.05, 0.075 and 0.10) prepared by mixed oxide process. X-ray diffraction study of all the ceramics implement orthorhombic phase without any secondary phase. The polycrystalline nature and grain distribution in the materials is studied from scanning electron microscope study. The temperature dependent dielectric performance of Dy doped SBT ceramics at selected frequencies indicates diffuse order phase transitions with reduction in transition temperature (Tc) and relative permittivity with doping level. The residual polarization and coercive field reduce with doping. The conduction mechanism was analyzed using the frequency and temperature domain impedance spectroscopy for all composition. The electrical contribution from both grains and grain boundary in the doped ceramics in the reported temperatures is confirmed from the Nyquist plots and the non-Debye type of relaxation mechanism is manifested from the depressed semicircles in all of them. The ac conductivities variation with frequencies at the studied temperatures follow Jonscher’s power law and the fitting parameters suggests that the conduction mechanism obey the correlated barrier-hopping model.
Effect and Related Mechanism of Platelet‐Rich Plasma on the Osteogenic Differentiation of Human Adipose‐Derived Stem Cells
Authors: Chen Jufang , Zhang Yuanyuan , Liu Mingjin , Zhou Zongyuan , Li Qianwen , Huang Tianjiao , Yue Yang , Tian Yulou
Published: August 2022

Objective. Human adipose‐derived stem cells (hADSCs) are ideal seed cells for the regeneration of alveolar bone defects. Platelet‐rich plasma (PRP), which is rich in growth factors, promotes tissue repair. The purpose of the present study was to investigate whether PRP promotes the osteogenic differentiation of hADSCs and to perform high‐throughput sequencing to explore the possible mechanism. Methods. hADSCs were divided into the three following groups: CON group, OM group, and PRP group. Osteogenesis was detected by Alizarin Red staining on day 14. Total RNA was extracted from the OM and PRP groups for high‐throughput sequencing. The target genes of the differentially expressed osteogenic‐related miRNAs were predicted, and combined miRNA/mRNA analysis was then performed. The mRNA and protein expression levels of hsa‐miR‐212‐5p, type 1 cannabinoid receptor (CNR1), alkaline phosphatase (ALP), Runx2, osteocalcin (OCN), and collagen 1 A1 (COL1A1) in the OM and PRP groups were detected by qRT–PCR and Western blot analyses. The binding between hsa‐miR‐212‐5p and CNR1 was detected by a dual‐luciferase reporter assay. Results. Both the OM and PRP groups exhibited enhanced proliferation of hADSCs, and the differences at 48 h and 72 h were statistically significant (P < 0.05). The PRP group had significantly more calcium nodules than the CON group (P < 0.05). Through high‐throughput sequencing analysis, differential miRNA and mRNA expression profiles were obtained. During hADSC osteogenesis, the expression of hsa‐miR‐212‐5p was downregulated, and the expression of CNR1 was upregulated. hsa‐miR‐212‐5p was found to bind directly to the 3′ UTR of CNR1. Conclusions. The present findings indicated that downregulation of hsa‐miR‐212‐5p and upregulation of CNR1 may be involved in the process by which PRP promotes the osteogenic differentiation of hADSCs.
A Meta‐Analysis on Clinical Outcomes of Ceftolozane versus Piperacillin in Combination with Tazobactam in Patients with Complicated Urinary Tract Infections
Authors: Saeed Muhammad Waqas , Gillani Syed Wasif , Mahmood Rana Kamran , Vippadapu Prasanna , Hussain Mian Waqar , Rathore Hassaan Anwer
Published: August 2022

Objective. To evaluate efficacy and adverse events of ceftolozane/tazobactam in complicated UTI including acute pyelonephritis. Method. Databases that include PubMed, Embase, Scopus, and TRIP were searched. All randomized controlled trials and cohort studies were considered for the study. Statistical analysis was done using a fixed effects model, and results were expressed in proportion for dichotomous data and risk ratio for continuous data with 95% confidence intervals (CI). Results. A clinical cure of ceftolozane/tazobactam was found to be 92% with 95% CI of 90‐94 while that of piperacillin/tazobactam was only 78% (95% CI, 74‐82) in patients with complicated UTI. Microbiological eradication was still higher in the ceftolozane/tazobactam group (83%, 95% CI 81‐88) when compared with piperacillin/tazobactam (63% 95% CI, 58.77‐65.2). Ceftolozane/tazobactam was more effective in the treatment of complicated urinary tract infections other than acute pyelonephritis as compared to piperacillin/tazobactam (RR = 1.21, 95% CI, 1.07‐1.23). Serious adverse events were found comparable in both groups (RR = 1.15, 95% CI, 0.64‐2.09). Conclusion. The analysis showed that ceftolozane/tazobactam has better clinical outcomes including cure rates and low resistance for the treatment of complicated urinary tract infection.
Research on the Impact of Entrepreneurship Education on Employee Creativity from the Perspective of Career Environment Based on an Intermediary Model
Authors: Wang Heli , Jiao Runkai , Li Feifei
Published: August 2022

Based on the human capital theory and creativity component theory, this study empirically examines the direct effect of entrepreneurship education on employees’ environment protection creativity in the workplace and the dual mediating effect of boundary‐free mental model and organizational mobility preference based on 266 valid sample data. The results show that entrepreneurship and environmental protection education received in colleges and universities can significantly promote the improvement of employees’ environment protection creativity. Borderless mental model and organizational mobility preference play an intermediary role between them. The impact of entrepreneurship education on creativity is expanded from college students to employees through the bridge of borderless career attitude, which effectively verifies the lag effect of entrepreneurship education in colleges and universities and the dual intermediary effect of borderless mental model and organizational mobility preference. It further expands the research on the impact of entrepreneurship education in colleges and universities and has certain theoretical value.
Double-Balanced Loss for Imbalanced Colorectal Lesion Classification
Authors: Yu Chang , Sun Wei , Xiong Qilin , Gao Junbo , Qu Guoqiang
Published: August 2022

Colorectal cancer has a high incidence rate in all countries around the world, and the survival rate of patients is improved by early detection. With the development of object detection technology based on deep learning, computer-aided diagnosis of colonoscopy medical images becomes a reality, which can effectively reduce the occurrence of missed diagnosis and misdiagnosis. In medical image recognition, the assumption that training samples follow independent identical distribution (IID) is the key to the high accuracy of deep learning. However, the classification of medical images is unbalanced in most cases. This paper proposes a new loss function named the double-balanced loss function for the deep learning model, to improve the impact of datasets on classification accuracy. It introduces the effects of sample size and sample difficulty to the loss calculation and deals with both sample size imbalance and sample difficulty imbalance. And it combines with deep learning to build the medical diagnosis model for colorectal cancer. Experimentally verified by three colorectal white-light endoscopic image datasets, the double-balanced loss function proposed in this paper has better performance on the imbalance classification problem of colorectal medical images.
The IncRNA SCIRT Promotes the Proliferative, Migratory, and Invasive Properties of Cervical Cancer Cells by Upregulating MMP-2/-9
Authors: Guan Chunfeng , Wang Beibei , Dong Qixiu
Published: August 2022

Objective. The morbidity and mortality of cervical cancer (CC) rank the fourth-most common among cancers in females, seriously threatening women’s health and affecting their quality of life. However, the molecular mechanism of CC development remains poorly understood. This study investigates the role of lncRNA SCIRT in the development of CC. Methods. The expression profile of long noncoding RNA stem cell inhibitory RNA transcript (lncRNA SCIRT) in CC (n = 34), tumor-adjacent tissue, and CC cell culture was determined through fluorescence quantitative PCR. The knockdown /overexpressed lncRNA SCIRT vectors were constructed and transfected into cells, and the effects of knockdown or overexpression of lncRNA SCIRT on the proliferative, invasive, and migratory properties of CC cells were determined through Cell Counting Kit-8 (CCK-8), colony forming, and Transwell experiments. Western blot was employed to determine the knockdown/overexpression efficiency of SCIRT and its role on the expression of proteins (e-cadherin, n-cadherin, vimentin, matrix metalloproteinase (MMP)-9 and MMP-2) in CC cells. Finally, SCIRT knockdown on the proliferative ability for CC cells was determined through tumorigenic experiment in nude mice. Results. LncRNA SCIRT was highly expressed in CC tissues and cells, and significantly linked with clinical/pathology-based characteristics of patients, including Federation Internationale of Gynecologie and Obstetrigue (FIGO) stage, tumor dimensions, and lymph-node metastasis. SCIRT knockdown markedly reduced CC proliferative, colony forming, and invasive properties, while overexpressing SCIRT promoted the proliferative and invasive properties of CC. Western blotting analysis demonstrated that SCIRT knockdown upregulated e-cadherin and downregulated n-cadherin, vimentin, MMP-9, and MMP-2. Meanwhile, overexpressing SCIRT of lncRNA SCIRT had the opposite effect. Tumorigenic experiment showed that SCIRT knockdown could markedly reduce CC proliferative property the nude mouse. Conclusion. LncRNA SCIRT was highly expressed in CC clinical cases. Knockdown/overexpressing SCIRT affected CC proliferative/invasive properties. Hence, lncRNA SCIRT is a promising drug-target and a new biological diagnostic molecule for CC patients.
Green Nanotechnology: Recent Research on Bioresource-Based Nanoparticle Synthesis and Applications
Authors: Alqarni Laila S. , Alghamdi Maha D. , Alshahrani Aisha A. , Nassar Amr M.
Published: August 2022

In the last decades, the idea of green nanotechnology has been expanding, and researchers are developing greener and more sustainable techniques for synthesizing nanoparticles (NPs). The major objectives are to fabricate NPs using simple, sustainable, and cost-effective procedures while avoiding the use of hazardous materials that are usually utilized as reducing or capping agents. Many biosources, including plants, bacteria, fungus, yeasts, and algae, have been used to fabricate NPs of various shapes and sizes. The authors of this study emphasized the most current studies for fabricating NPs from biosources and their applications in a wide range of fields. This review addressed studies that cover green techniques for synthesizing nanoparticles of Ag, Au, ZnO, CuO, Co3O4, Fe3O4, TiO2, NiO, Al2O3, Cr2O3, Sm2O3, CeO2, La2O3, and Y2O3. Also, their applications were taken under consideration and discussed.
Dimensional Analysis of Regional Environmental Planning Based on NPP/VIIRS Lighting Data
Authors: Yan Haizhang
Published: August 2022

The actual status of the current regional environmental planning is difficult to obtain through traditional statistical methods, and it is necessary to use remote-sensing identification technology to collect information. Based on the NPP/VIIRS technology, this study uses the NPP/VIIRS technology to identify and analyze China’s regional environment. Moreover, on this basis, this study conducts a dimensional analysis of regional environmental planning, verifies the feasibility of the technology, and promotes the development of the technology in environmental planning. In addition, this study links light intensity and carbon emissions based on night-light data and traditional energy consumption data. Finally, from the perspective of time and space continuity, new solutions and research methods are provided for many problems existing in traditional carbon emission research, which in turn provides a solid scientific foundation and theoretical basis for the formulation and implementation of carbon emission reduction strategies. The research results show that the method proposed in this study has certain effects.
Artificial Potential Field-Based Multi-UAV Formation Control and Target Tracking
Authors: Song He , Hu Shaolin , Jiang Wenqiang , Guo Qiliang , Zhu Ming
Published: August 2022

To simultaneously achieve space formation flight and target tracking of multiple unmanned aerial vehicles (UAVs) and solve the rotation buffeting problem of the UAV, a robust formation control and target tracking algorithm is proposed. The artificial potential function consisting of formation control term and target tracking term is established, and its convergence is proved. The sliding mode control method with the saturation function is established, and a sufficient condition for sliding mode to occur is analysed. Finally, the numerical simulation is conducted for the proposed algorithm, and the simulation result is analysed. The results show that the proposed algorithm can quickly achieve the formation flight and target tracking of multi-UAVs and improve the tracking performance; meanwhile, it can effectively weaken the rotation buffeting and improve the robustness.
Improved Deep Learning Method for Intelligent Analysis of Sports Training Posture
Authors: Yan Xianyou
Published: August 2022

Motion recognition based on human bones has attracted extensive attention in recent years because of its simplicity and robustness. Considering the causality of human movement, this paper proposes an improved deep learning method for posture analysis in sports training. In order to deal with the complex situation of calculating joint torques as weights, the edge weights and convolution weights of bone maps are used as auxiliary information networks according to the causality of joint distribution. Thus, the stronger driving force of joint weights in the neural network is improved, the low importance of joint attention is reduced, and the high importance of joint attention is enhanced. Experiments on three public motion recognition datasets show that the proposed method can distinguish similar motions effectively compared with the mainstream methods. Besides, experiments on a challenging UCF (University of Central Florida) sports dataset show that the proposed method can effectively enhance the motion features and improve the accuracy of recognition.
Different Intestinal Microbiota with Growth Stages of Three‐Breed Hybrid Pig
Authors: Long Cheng-xing , Wu Jie-qi , Tan Zhou-jin , Wang Sheng-ping
Published: August 2022

The changes of intestinal microbiota are closely related to the growth and development of animals. The current study is aimed at exploring the composition of the microbial community of pigs at different growth stages. Fresh fecal samples of three‐breed hybrid pigs at three developmental stages (60, 120, and 180 days of age) were collected. The microbial composition was analyzed based on the 16S rDNA gene of bacteria Illumina NovaSeq sequencing platform. The results showed that the intestinal microbiota of pigs was distributed in 22 phyla, 46 classes, 84 orders, 147 families, and 287 genera. Firmicutes, Bacteroides, Spirochaetae, Proteobacteria, and Actinobacteria were the dominant phyla. Lactobacillus, Streptococcus, SMB53, Oscillospira, and Prevotella were the dominant genera. Among them, the abundance of Lactobacillus and SMB53 increased first and then decreased, while the change of Oscillospira was opposite. In addition, the abundance of Streptococcus increased while that of Prevotella decreased gradually. Moreover, with the increase of time and body weight, the microbial diversity showed a decreasing trend. In conclusion, the intestinal microbial composition of the three‐breed hybrid pigs was relatively stable during the fattening stage, but there were significant differences in abundance.
Analysis of the Influencing Factors of Immunological Nonresponders in Wuhan, China
Authors: Lei Enze , Jin Shuna , Ni Wei , Feng Manlin , Luo Yanhe , Ruan Lianguo , Xiao Mingzhong , Liu Jianzhong
Published: August 2022

Objective. CD4+ cell recovery is hampered in some human immunodeficiency virus (HIV)-infected patients, despite a successful highly active antiretroviral therapy (HAART) with suppressed viral replication. We investigated the factors that might have hindered the CD4+ cell recovery in these patients. Methods. In this retrospective study, we collected the data of all immune nonresponders (INRs) in Wuhan, China, until the end of 2020. A linear model was constructed based on the data from 220 patients with baseline and follow-up records. The response variables in this study were the CD4+ cell count increase. The predictor variables considered in this study were those factors likely to affect the CD4+ cell recovery. Results. Our findings revealed that the plasma HIV-1 viral load of all patients was suppressed and 87.3% patients’ CD4+ cells was increased after more than one year of the HAART treatment. In addition, their last follow-up showed a significant reduction in complications. In our results, the body mass index (BMI), number of months since HIV diagnosis to HAART start, and nonuse of co-trimoxazole were negatively correlated with the increase in CD4+ cells (

P
<
0.05

). However, there were positive associations between serum creatinine levels and CD4+ cell recovery (

P
<
0.05

). Further stratified analyses indicated that the associations between HAART replacement or creatinine usage and CD4+ cell growth were only observed in those participants with a BMI <18.5 (

P
<
0.05

). Conclusions. An early initiation of HAART and co-trimoxazole preventive therapy (CPT) can promote immune reconstitution. BMI and serum creatinine can serve as monitoring indicators of immune reconstitution prognosis after the HAART.
Confidence Region Identification and Contour Detection in MRI Image
Authors: Ejaz Khurram , Arif Muhammad , Rahim Mohd Shafry Mohd , Izdrui Diana , Craciun Daniela Maria , Geman Oana
Published: August 2022

Tumour region extraction (RE) method identifies the area of interest in MR imaging as it also highlights tumour boundaries. Some other intensities are existing, they are not visible but have their existence in region, and this region is called growing region. Such region is to be tumour region. Due to the variation of intensities in MRI images, tumour visibility becomes uncleared. Tumour intensity variations (tumour tissues) mix with normal brain tissues. In the light of above circumstance, tumour growing region becomes challenge. The goal of work is to extract the region of interest with confidence. The objective of the study is to develop the region of interest of brain tumour MRI image method by using confidence score for identifying the variation of intensity. The significance of work is based on identification of region of interest (tumour region). Confidence score is measured through pattern of intensities of MRI image. Similar patterns of brain tumour intensities are identified. Each pattern of intensities is adjusted with certain scale, and then biggest blob is analysed. Various biggest area blobs are combined, and resultant biggest blob is formed. In fact, resultant area blob is a combination of different patterns. Each pattern is assigned with particular colour. These colours highlight the growing region. Further, a contour is detected around the tumour boundaries. With combination of region scale fitting and contour detection (CD), tumour boundaries are further separated from normal tissues. Hence, the confidence score (CS) is formed from CD. CS is further converted to confidence region (CR). Conversion to CR is performed though confidence interval (CI). CI is based on defined conditions. In such conditions, different probabilities are considered. Probability identifies the region. Source of region formation is pixels; these pixels highlight tumour core significantly. This CR is obtained through checking standard deviation and statistical evaluation using confidence interval. Hence, region-of-interest pixels are identifying the CR. CR is evaluated through 97% Dice over index (DOI), 94% Jacquard, MSE 1.24, and PSNR 17.45. Value of testing parameter from benchmark study was JI, DOI, and MSE, PSNR : JI was 31.5%, DOI was 47.3%, MSE was 2.5 dB, and PSNR was 40 dB. The parameters are measured for the complex images; contribution parameter classifies the mean pixel values and deviating pixel values, and the classification of the pixel value is like to be termed as intensities. Mentioned classification extracts the variation of intensity pixels accurately; then, algorithm is highlighting the region as compared to the normal tumour cells.
Youth Sports Special Skills’ Training and Evaluation System Based on Machine Learning
Authors: Yao Jing , Li Yanze
Published: August 2022

Nowadays, most sports training adopts such a mode, that is, the coach guides and observes the movements of the trainer on the side, or uses the camera to collect the data information in the training video. After the training, the trainer needs to experience and comprehend it according to the coach’s guidance and suggestions, which leads to a poor training effect to a certain extent, and the training movements are not so standardized. The most important thing is that the physical condition of the trainer will change at any time during the exercise. However, the traditional training mode cannot perceive and predict it at all, and it may even lead to the training volume being too large for the athletes to bear, which also causes irreversible effects on the body. Based on this, this paper aims to use the relevant theories and technologies of machine learning to build a system for the training and evaluation of sports-specific skills. This paper automatically determines and guides the movements of the trainer, which greatly improves the training efficiency. The movements are also more standardized, and the workload of the coaches is also reduced. In addition, the system includes explained functions. It uses holographic projection to play various videos of training and guidance, which brings convenience to trainers to observe and understand specific sports behaviors. Finally, the data results of the experimental subjects were analyzed to verify the effectiveness and feasibility of this training evaluation system, in which their special scores increased by 1.51% compared with the previous ones.
Investigation in Gas Carburizing of AISI 4140, EN36, and 16MnCr5 Steels Using the Grey Incidence-Based Taguchi (GIBT) Method
Authors: Arumparithy G. L. , Adalarasan R. , Santhanakumar M. , Balaji N. , Asres Yalew
Published: August 2022

Gas carburizing is an effective surface treatment process for improving the hardness and wear resistance of different class of steels. The study reports an application of grey-incidence based Taguchi (GIBT) method in gas carburizing of case-hardening steels like AISI 4140, EN36, and 16MnCr5 which are widely employed in precision levers, transmission shafts, and pinions. Carburizing trials are performed using Taguchi’s L9 orthogonal array by varying the design parameters like carburizing temperature, soaking time, and tempering temperature. Surface hardness (SH), diffusion depth (DD), and wear loss (WL) are studied as process responses at the completion of various carburizing trials with replications. Optimal design variables are identified using grey incidence grade as a performance index in the GIBT method. The contribution of individual parameters is also studied using the analysis of variance (ANOVA). Microscopic examination and SEM images of the treated surface are also studied after validating the method of GIBT.
The Validity of Digital Media Communication of Platforms
Authors: Yu Baoying , Li Haigang , Ma Chengming
Published: August 2022

The status of Politics and Ideology curriculum is becoming more and more important in university education. The Politics and Ideology curriculum is closely related to both the development of the country and the nation and the quality of university students. Due to the progress of technology, new media has become an important information collection channel for college students, which has an important impact on college students’ thoughts and life. Situation, problems, environment, politics, and ideology work need to deal with new challenges. It also brings new perspectives, new ideas, and new models. This study focuses on how new media technology affects the college students’ Politics and Ideology learning, studies the timeliness of new media on political education, and analyzes the path and countermeasures to improve the new media communication with the theoretical basis of communication, Politics and Ideology curriculum, and other disciplines. The AIDMA model of the validity of new media communication is constructed and the validity of the digital media communication is studied by means of literature research and questionnaire survey. The results show that the use of new media to attract archive users is clearly feasible and has advantages that the traditional media technology cannot achieve. The fast and effective speed of information dissemination can affect the behavior of profile users in the model. In the political education of colleges and universities, the influence of digital media is used to continuously improve the quality of educators, improve the literacy of students in using new media, optimize educational content, innovate educational carriers, optimize educational environment, and improve the new media management mechanism, etc.
Study on the Evaluation of Exercise Effect in Physical Education Teaching under the Application of Random Forest Model
Authors: Li Jinpeng , Wang Le
Published: August 2022

Physical education is a school-based topic that emphasizes the development of physical fitness and the capacity to do and enjoy everyday physical activities. Children also get the skills they need to participate in a wide range of activities. Engaging classes, skilled P.E. teachers, proper instructional hours, and student evaluation should all be part of an effective physical education program. The basics of physical education start with exercises. A remarkable change can be observed when individuals practice exercises in their physical education. In this research work, exercise requirement in physical education teaching is analyzed under students of certain groups under different dimensions. The work analysis is performed by implementing a random forest algorithm and comparison with the support vector machine and fuzzy set based on hesitation algorithms. The performance is evaluated based on teaching and learning, evaluation, and exercises with the support of all the equipment. In all these categories, the proposed random forest algorithm has achieved 98%, 98%, and 43%, respectively.