Analysis Of Different Data Sets Research Articles

Analysis of the emotional coloring of text using machine and deep learning methods

The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area.

GL-STGCNN: Enhancing Multi-Ship Trajectory Prediction with MPC Correction

In addressing the challenges of trajectory prediction in multi-ship interaction scenarios and aiming to improve the accuracy of multi-ship trajectory prediction, this paper proposes a multi-ship trajectory prediction model, GL-STGCNN. The GL-STGCNN model employs a ship interaction adjacency matrix extraction module to obtain a more reasonable ship interaction adjacency matrix. Additionally, after obtaining the distribution of predicted trajectories using the model, a model predictive control trajectory correction method is introduced to enhance the accuracy and reasonability of the predicted trajectories. Through quantitative analysis of different datasets, it was observed that GL-STGCNN outperforms previous prediction models with a 31.8% improvement in the average displacement error metric and a 16.8% improvement in the final displacement error metric. Furthermore, trajectory correction through model predictive control shows a performance boost of 44.5% based on the initial predicted trajectory distribution. While GL-STGCNN excels in multi-ship interaction trajectory prediction by reasonably modeling ship interaction adjacency matrices and employing trajectory correction, its performance may vary in different datasets and ship motion patterns. Future work could focus on adapting the model’s ship interaction adjacency matrix modeling to diverse environmental scenarios for enhanced performance.

Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining Particle Swarm Optimization Algorithm and Artificial Neural Network

MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments.

VASN promotes colorectal cancer progression by activating the YAP/TAZ and AKT signaling pathways via YAP.

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies. Vasorin (VASN) has been reported to be critical in tumor development and angiogenesis. However, VASN has not been reported in CRC, and its role is unclear. In this study, VASN expression is upregulated in CRC compared with the normal tissues, and VASN expression positively correlates with N stage and poor overall survival by analysis of different datasets and 32 CRC clinicopathologic samples. Overexpression of VASN significantly promotes CRC cell progression, including proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT), while knockdown of VASN inhibits CRC progression. We found that VASN was associated with the YAP/TAZ and PI3K/AKT pathways by gene set enrichment analysis (GSEA) and gene ontology (GO) analysis. Notably, western blotting, immunofluorescence staining and co-immunofluorescence (co-IP) confirmed that VASN could interact with YAP and activate the YAP/TAZ and PTEN/PI3K/AKT pathways, and knockdown of YAP reversed this effect. Importantly, our findings indicate that VASN interacts with YAP to inhibit YAP phosphorylation and stimulates CRC proliferation, migration, and invasion through activation of the YAP/TAZ-TEAD target gene CTGF and PTEN/PI3K/AKT pathways. Our results also show that knockdown of YAP reverses the cellular phenotype induced by increased VASN. In conclusion, our study reveals that VASN acts as an oncogene to stimulate tumor progression in CRC, providing new insights into the molecular mechanisms of CRC development and representing a possible novel biomarker for CRC.

Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media

When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order.

In-Depth Case Study on Artificial Neural Network Weights Optimization Using Meta-Heuristic and Heuristic Algorithmic Approach

The Meta-heuristic and Heuristic algorithms that have been introduced for deep neural network optimization is in this paper. Artificial Intelligence, and also the most used Deep Learning methods are all growing in popularity these days, thus we need faster optimization strategies for finding the results of future activities. Neural Network Optimization with Particle Swarm Optimization, Backpropagation (BP), Resilient Propagation (Rprop), and Genetic Algorithm (GA) is used for numerical analysis of different datasets and comparing each other to find out which algorithms work better for finding optimal solutions by reducing training loss. Genetic algorithm and also bio-inspired Particle Swarm Optimization is introduced in this paper. Besides, Resilient Propagation and Conventional Backpropagation algorithms which are application-specific algorithms have also been introduced. Meta-heuristic algorithms GA and PSO are a higher-level formula and problem-independent technique that may be used to a diverse number of challenges. The characteristic of Heuristic algorithms has extremely specific features that vary depending on the problem. The conventional Backpropagation (BP) based optimization, the Particle Swarm Optimization methodology, and Resilient Propagation (Rprop) are all fully presented, and how to apply these procedures in Artificial Deep Neural networks Optimization is also thoroughly described. Applied numerical simulation over several datasets proves that the Meta-heuristic algorithm Particle Swarm Optimization and also Genetic Algorithm performs better than the conventional heuristic algorithm like Backpropagation and Resilient Propagation.

Emergency Information Communication Structure by Using Multimodel Fusion and Artificial Intelligence Algorithm.

With the development of The Times, social events are increasing, and emergency management has gradually become the main helper to solve the crisis in the public domain. By observing the current situation of many countries and regions, we can find that various types of public crises often occur in many countries and regions in the world, which have severely affected people's daily life, lives, and property. Through long-term research and analysis, it can be known that the emergency management mechanism currently established in China has certain shortcomings. The communication problem of emergency information is likely to cause the emergency work to not proceed smoothly. In addition, problems in the communication channels of emergency information are likely to cause problems in the cooperation of various departments when people carry out emergency management work, and the efficiency of the government in dealing with problems will also be reduced in real scenarios. In order to improve the efficiency of emergency information management, this paper aims at the various problems existing and facing in the construction of emergency management system. On this basis, the integration of various relevant emergency information management plan models is analyzed and sorted out, and based on the research and integration of the development of artificial intelligence algorithms. The main research results of emergency information management at home and abroad are comprehensively studied and evaluated. Finally, a QG algorithm based on more model fusion is developed. In the process of analysis, this article uses artificial intelligence algorithms to build a prediction model of multiple modes and collects the data needed to build the model by random extraction. Through the analysis of different data sets, it is used as the basic training data for prediction. Through comprehensive analysis, the model constructed in this paper can promote the sharing of emergency information among departments to a certain extent.

Integrative species delimitation of Selaginella labordei and closely related species: Uncovering the mysterious identity of S. jugorum and S. tibetica, and description of a new species

AbstractThe taxonomy of Selaginella has been historically problematic due to the indistinguishable morphological characters within species complexes. The S. labordei group contains (S. subg. Stachygynandrum) 4–7 species, distributed from the Himalayas to eastern China. The taxonomy of this group was problematic due to few collections that were available to delimitate S. jugorum and S. tibetica, and large variation of the widely distributed S. labordei in different ecological conditions. Here, we re‐evaluate the species delimitation of the S. labordei group using morphological, molecular and cytological data. A total of 55 individuals representing five species of the S. labordei group were investigated using two nuclear markers (26S rDNA, pgiC), one plastid marker (rbcL), as well as 43 plastid‐coding sequences obtained from assembled plastid genomes. Five species are recognized based on the phylogenetic analysis of nuclear genes and plastid‐coding sequences. The plastid genomes phylogeny showed a good resolution at lower taxonomic levels. All the individuals of the broadly defined S. labordei formed a monophyletic group in our analyses of different datasets. However, samples of S. chrysocaulos were strongly supported as non‐monophyletic and were divided into two clades. One clade of S. chrysocaulos is described as a new species, Selaginella parachrysocaulos sp. nov., which is a cryptic species morphologically almost indistinguishable from S. chrysocaulos but with the microspores covered by a honeycomb‐like network microstructure. The nuclear pgiC phylogeny and flow cytometry evidence indicate that some individuals of S. parachrysocaulos may be derived from polyploidization. Based on molecular and morphological evidence, S. sichuanica and S. daozhenensis are treated as synonymous to S. labordei, and S. hengduanshanicola to S. tibetica. Our results uncover the previously mysterious affinities of S. jugorum and S. tibetica, which are members of the S. labordei group.

Comparative Analyses of 3,654 Plastid Genomes Unravel Insights Into Evolutionary Dynamics and Phylogenetic Discordance of Green Plants.

The plastid organelle is essential for many vital cellular processes and the growth and development of plants. The availability of a large number of complete plastid genomes could be effectively utilized to understand the evolution of the plastid genomes and phylogenetic relationships among plants. We comprehensively analyzed the plastid genomes of Viridiplantae comprising 3,654 taxa from 298 families and 111 orders and compared the genomic organizations in their plastid genomic DNA among major clades, which include gene gain/loss, gene copy number, GC content, and gene blocks. We discovered that some important genes that exhibit similar functions likely formed gene blocks, such as the psb family presumably showing co-occurrence and forming gene blocks in Viridiplantae. The inverted repeats (IRs) in plastid genomes have doubled in size across land plants, and their GC content is substantially higher than non-IR genes. By employing three different data sets [all nucleotide positions (nt123), only the first and second codon positions (nt12), and amino acids (AA)], our phylogenomic analyses revealed Chlorokybales + Mesostigmatales as the earliest-branching lineage of streptophytes. Hornworts, mosses, and liverworts forming a monophylum were identified as the sister lineage of tracheophytes. Based on nt12 and AA data sets, monocots, Chloranthales and magnoliids are successive sister lineages to the eudicots + Ceratophyllales clade. The comprehensive taxon sampling and analysis of different data sets from plastid genomes recovered well-supported relationships of green plants, thereby contributing to resolving some long-standing uncertainties in the plant phylogeny.

A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease

Due to recent advances in computer technology and the accessibility of large datasets, deep learning has become at the forefront of artificial intelligence and on various tasks, especially those related to image classification and modelling, its performance is often equal to or even better than human appreciation. Ophthalmology has always been in an ideal position to employ one of CNN’s most popular deep learning algorithms to evaluate vast volumes of data from these tests since it is one of the health practices that focuses extensively on imaging. Glaucoma is one among the situations in which CNN can benefit from the enormous quantity of data collected by tests that assess the anatomy as well as function of the optic nerve and macula. We discussed the recommended use of CNN algorithm for specific glaucoma scenarios, such as fundus photography screening and diagnosis and detection of the course of glaucoma through OCT imaging modality. The purpose of this article is not only to critically examine and discuss the latest applications of CNN models in glaucoma but also to focus on the challenges associated with developing such models for screening, diagnosis, and progress detection. After a brief overview of the clinical practices and their comparison with conventional clinical methods, we discussed training and validation of CNN algorithm and how it was developed and why it is particularly suitable for glaucoma. The following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to detection of the glaucoma disease using conventional and nonconventional approaches; (ii) it covers a very different outlook of the glaucoma disease by providing in-depth discussions of the existing works at different granularity levels, that is, from primary to mediatory to the severe level; (iii) this state-of-the-art review covers each article in the following dimensions: the computer-based approach to tomographic model; analysis of different datasets; and summarizing the literature review in a disciplined way by mentioning the research gap concluded with discussion on future work.

Occupational inequalities during the pandemic: epidemiological evidence

COVID-19 infection risks at work as well as psychosocial risks following the fundamental changes in the economy during the pandemic were equally distributed across the workforce. Obviously, certain occupations (e.g. health care) had particular high health risks but apart from that it has also been hypothesised that workers in precarious employment or with low occupational positions are also disproportionally affected. This contribution summarizes the epidemiological evidence on the association between occupational inequalities and COVID-19 related burden of disease. It will also comment on the possible mechanisms linking different aspects of precarious work with health during the pandemic - including social protection and occupational safety and health policies at the company level. The presentation relies on an unsystematic literature review conducted in Spring 2021 and on the results of statistical analyses of different datasets including hospitalisation data, official data on COVID-19 cases and a large German cohort study. Briefly, preliminary results suggest occupational inequalities in a) infection risks, b) risk of severe disease in COVID-19 patients, and c) non-COVID-19 outcomes such as depression or anxiety. It has to be noted, howewer, that the number of empirical studies on that issue is surprisingly low - in particular in regard to direct infection risks. The reasons for this lack of data and the possible implications for a timely policy responses have to be discussed.

Identification of macrophage related gene in colorectal cancer patients and their functional roles

BackgroundRecent scientific research has enabled the identification of macrophages related-genes (MaRG), which play a key role in the control of the immune microenvironment in many human cancers. However, the functional role of MaRGs in human tumors is ill-defined. Herein, we aimed at bioinformatically exploring the molecular signatures of MaRGs in colorectal cancer.MethodsA list of MaRGs was generated and their differential expression was analyzed across multiple datasets downloaded from the publicly available functional genomics database Gene Expression Omnibus. The weighted gene co-expression network analysis (WGCNA) was also applied to identify the partner genes of these MaRGs in colorectal cancer.ResultsAfter integration of the results from analyses of different datasets, we found that 29 differentially expressed MaRGs (DE-MaRGs) could be considered as CRC-related genes as obtained from the WGCNA analysis. These genes were functionally involved in positive regulation of DNA biosynthetic process and glutathione metabolism. Protein–protein interaction network analysis indicated that PDIA6, PSMA1, PRC1, RRM2, HSP90AB1, CDK4, MCM7, RFC4, and CCT5 were the hub MaRGs. The LASSO approach was used for validating the 29 MaRGs in TCGA-COAD and TCGA-READ data and the results showed that ten among the 29 genes could be considered as MaRGs significantly involved in CRC. The maftools analysis showed that MaRGs were mutated at varying degrees. The nomogram analysis indicated the correlation of these MaRGs with diverse clinical features of CRC patients.ConclusionsConclusively, the present disclosed a signature of MaRGs as potential key regulators involved in CRC pathogenesis and progression. These findings contribute not only to the understanding of the molecular mechanism of CRC pathogenesis but also to the development of adequate immunotherapies for CRC patients.

COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients.

BackgroundThe COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments.MethodsElectronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined.ResultsNeither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events.ConclusionAdministration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

Antibodies purification from human plasma using fractionation, chromatography and gel electrophoresis assisted by multivariate analysis of complimentary absorption and fluorescence spectra

Employing simple precipitation (fractionation) using Cohn method and weak anion exchange chromatography with DEAE resin, antibodies such as Immunoglobulin G are purified from human plasma. Fractions are eluted from column in four different regions depending on washing NaCl concentrations. Absorbance and excitation-emission fluorescence spectral data are measured for separated chromatographic fractions and analyzed using Multivariate Curve Resolution- Alternating Least Squares (MCR-ALS) and Parallel Factor Analysis (PARAFAC) techniques. Resolved concentration and spectral profiles provided information about existing components in each fraction. Protein and non-protein components are distinguished considering their resolved pure spectra and information from the two applied spectroscopic techniques is complementary. A number of components displayed both fluorescence and absorbance signals. When concentration of component (protein or non-protein) in sample is low and no significant absorbance signal is observed, sensitive fluorescence is useful to recognize the component and for non-fluorescent components absorbance spectra are utilized. Electrophoresis is utilized for separation of proteins in each fraction and showed that one distinguished protein from fluorescence and/or absorbance data can be a group of proteins with similar pure spectra and retention volume. Results showed presence of two protein in the first region (IgM and IgA), a group of proteins in second region (IgM, α-globulin, and IgG), a pure protein in third region (IgG), and a group of β-globulin proteins in fifth region. It is well and clearly shown that multivariate analysis of different data sets with complementary information is necessary for better interpretation of such technically simple and biochemically complicated systems.

Comprehensive Review on Facemask Detection Techniques in the Context of Covid-19

The outbreak of Coronavirus Disease 2019 (Covid-19) had an enormous impact on humanity. Till May 2021, almost 172 million people have been affected globally due to the contagious spread of Covid-19. Although the distribution of vaccines has been started, the worldwide mass distribution is yet to happen. According to the World Health Organization (WHO), wearing a facemask can reduce the contagious spread of Covid-19 significantly. The governments of different countries have recommended implementing the “no mask, no service” method to impede the spread of Covid-19. However, even the improper wearing of a facemask can obstruct the goal and lead to the spread of the virus. Therefore, to ensure public safety, a system for monitoring facemasks on faces, commonly known as a facemask detection algorithm, is essential for overcoming this crisis. The facemask detection algorithms are part of the object detection algorithms which are used to detect objects in an image. Among the various object detection algorithms, deep learning showed tremendous performance in facemask detection for its excellent feature extraction capability than the traditional machine learning algorithms. However, there remains a lot of scope for future research to build an efficient facemask detection system. Therefore, this study aims to draw attention to the researchers by providing a narrative and meta-analytic review on all the published works related to facemask detection in the context of Covid-19. Because facemask detection algorithms are run mainly by adopting object detection algorithms, this paper also explores the progress of object detection algorithms over the last few decades. A comprehensive analysis of different datasets used in facemask detection techniques by many studies has been explored. The performance comparison among these algorithms is discussed in narrative and meta-analytic approaches. Finally, this study concludes with a discussion of some of the major challenges and future scope in the related field.

Shift in transcriptional landscape of human right ventricle in chronic thromboembolic pulmonary arterial hypertension

Abstract Background Chronic thromboembolic pulmonary hypertension (CTEPH) is a sub group of pulmonary hypertension (PH). CTEPH is characterized by the existence of thromboemboli and vascular remodeling in pulmonary vessels. The effect of increase in pulmonary artery pressures causes right ventricle (RV) hypertrophy and dilatation and finally leads to right heart failure and death. Surgical intervention in operable patients makes the CTEPH as an only curable and unique form of ph. Pulmonary endarterectomy (PEA) is the surgical procedure to remove the thromboembolic clots from the pulmonary vasculature, which restores RV function back to normal with significant improvements in cardiovascular magnetic resonance. Purpose The aim of this study is to use transcriptomic profiling to identify signaling pathways, master regulators, and potentially new biomarkers that specifically indicate the effect of PEA on the RV of patients with chronic thromboembolic pulmonary hypertension. Results RNA -sequencing (RNA-seq) was performed on RV biopsies obtained from CTEPH patients at PEA baseline (before PEA surgery) and the results were compared with those from RV biopsies obtained during follow-up evaluation. Bioinformatic analysis of RNA-seq data identified 2799 genes (n=14, −0.585 ≤ Log2 fold change ≥0.585, FDR ≤0.05) differentially regulated between the PEA baseline and follow-up sample groups. The great number of genes (2799) differentially expressed after PEA surgery in CTEPH patients confirms a major shift in the transcriptional landscape of RV in these patients. To further identify potential biomarker candidates from the large pool of 2799 differentially expressed genes (DEGs), extensive bioinformatic analysis of different data sets shortlisted 250 DEGs that were functionally associated with cardiovascular development or disease. The findings of this study reveal prominent transcriptional changes that occur in response to PEA. Gene ontology enrichment and pathway analysis confirmed altered regulation of hypoxia-inducible factor 1 (HIF-1) signaling, advanced glycation end products and their receptors (AGE-RAGE), mitogen-activated protein kinase (MAPK) signaling, hippo signaling, the Janus kinase/ signal transducers and activators of transcription (Jak-STAT) signaling pathway, and proteoglycans after PEA compared with before PEA. Conclusion Comparison of the results of RNA-seq analysis of RV biopsies of CTEPH patients, pre and post PEA, revealed a major shift in the transcriptional landscape of these patients after reducing the pressure overload of the RV by PEA. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): German Research Foundation (DFG)

Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning

Abstract Analyzing consistency of different land-cover data is significant to reasonably select land-cover data for regional development and resource survey. Existing consistency analysis of different datasets mainly focused on the phenomena of spatial consistency regional distribution or accuracy comparison to provide guidelines for choosing the land-cover data. However, few studies focused on the hidden inconsistency distribution rules of different datasets, which can provide guidelines not only for users to properly choose them but also for producers to improve their mapping strategies. Here, we zoned the Sindh province of Pakistan by the Terrestrial Ecoregions of the World as a case to analyze the inconsistency patterns of the following three datasets: GlobeLand30, FROM-GLC, and regional land cover (RLC). We found that the inconsistency of the three datasets was relatively low in areas having a dominant type and also showing homogeneity characteristics in remote sensing images. For example, cropland of the three datasets in the ecological zoning of Northwestern thorn scrub forests showed high consistency. In contrast, the inconsistency was high in areas with strong heterogeneity. For example, in the southeast of the Thar desert ecological zone where cropland, grassland, shrubland, and bareland were interleaved and the surface cover complexity was relatively high, the inconsistency of the three datasets was relatively high. We also found that definitions of some types in different classification systems are different, which also increased the inconsistency. For example, the definitions of grassland and bareland in GlobeLand30 and RLC were different, which seriously affects the consistency of these datasets. Hence, producers can use the existing land-cover products as reference in ecological zones with dominant types and strong homogeneity. It is necessary to pay more attention on ecological zoning with complex land types and strong heterogeneity. An effective way is standardizing the definitions of complex land types, such as forest, shrubland, and grassland in these areas.

Authentication of Remote IoT Users Based on Deeper Gait Analysis of Sensor Data

In IoT based systems, authentication of users and devices is a major challenge where the traditional authentication mechanisms such as login-password are no longer supportable. It requires continuous authentication methods to ensure authenticity of the users and devices specifically for long sessions. The authentication through the IoT-Sensors based data has already gained the attention of a considerable number of researchers as it does not require direct involvement of the users and provide an extra layer of security and user privacy. Due to the instability of IoT-Sensor data, the authentication techniques need to extract a large number of features to produce high-accurate results. Moreover, the limited capabilities of computation, communication, storage, and the small battery power of IoT devices further makes its implementation hard. In this paper, we have introduced a user authentication framework for remote IoT users based on unique walking patterns to extract gait-related features with minimum data samples and cycle length. For an in-depth analysis of the distinctive features of the sensor-based gait profiles, we have applied an ECG signal processing technique. The proposed approach applies to a diverse range of IoT devices such as cellphones, smartwatches, and wearable sensors. The main contribution of the proposed approach is the deeper gait analysis with the least number of features and data for authentication purposes. We have introduced an authentication algorithm for feature comparison. The machine learning models have been applied to the gait based profiles for validation of the proposed approach. The detailed experimental analysis of different data sets has achieved an accuracy of 94% and an equal error rate (EER) of 6% higher than the existing approaches.

Weighted Gene Co-Expression Network Analysis Identified Cancer Cell Proliferation as a Common Phenomenon During Perineural Invasion.

PurposePerineural invasion (PNI) is the neoplastic invasion of nerves by cancer cells, a process that may prove to be another metastatic route besides direct invasion, lymphatic spread, and vascular dissemination. Given the increasing incidence and association with poor prognosis, revealing the pathogenesis of perineural invasion is of great importance.Materials and methodsFour datasets related to PNI were downloaded from the Gene Expression Omnibus database and used to construct weighted gene co-expression network analysis (WGCNA). The intersection of potential pathways obtained from further correlation and enrichment analyses of different datasets was validated by the coculture model of Schwann cells (SCs), flow cytometry and immunohistochemistry (IHC).ResultsGSE7055 and GSE86544 datasets were brought into the analysis for there were some significant modules related to PNI, while GSE103479 and GSE102238 datasets were excluded for insignificant differences. In total, 13,841 genes from GSE86544 and 10,809 genes from GSE7055 were used for WGCNA. As a consequence, 19 and 26 modules were generated, respectively. The purple module of GSE86544 and the dark gray module of GSE7055 were positively correlated with perineural invasion. Further correlation and enrichment analyses of genes from the two modules suggested that these genes were mainly enriched in cell cycle processes; especially, the terms S/G2/M phase were enriched. Three kinds of cells grew vigorously after coculture with SCs ex vivo. The Ki67 staining of the cervical cancer samples revealed that the Ki67 index of cancer cells surrounding nerves was higher than of those distant ones.ConclusionOur work has identified cancer cell proliferation as a common response to neural cancerous microenvironments, proving a foundation for cancer cell colonization and metastasis.

Deterministic Machine Learning Cluster Analysis of Research Data: using R Programming

This review paper clearly discusses the compression between various types of cluster analysis of different data sets were explained sufficiently. Although there is large gap between the way of analysis of collected data and its cluster categorization research data using r programming. Its primary purpose is to explain the simplest way of clustering analysis whose data structure were wide scattered using R software whose outputs were sufficiently explain with various inter-mediate output and graphical interpretation to reach the conclusion of analysis. Therefore, this paper presents easiest way of clustering when data sets with large dimensions with multivariate analysis and its strengths for data analysis using R programming.