Main Dataset Research Articles

Integration of Sentiment Analysis of Social Media in the Strategic Planning Process to Generate the Balanced Scorecard

Strategic planning (SP) requires attention and constant updating and is a crucial process for guaranteeing the efficient performance of companies. This article proposes a novel approach applied in a case study whereby a balanced scorecard (BSC) was generated that integrated sentiment analysis (SA) of social media (SM) and took advantage of the valuable knowledge of these sources. In this study, opinions were consolidated in the main dataset to incorporate sentiments regarding the strategic part of a restaurant in a tourist city. The proposed methodology began with the selection of the company. Information was then acquired to apply pre-processing, processing, evaluation, and validation that is capitalized in a BSC to support strategic decision-making. Python support was used in the model and comprised lexicon and machine learning approaches for the SA. The significant knowledge in the comments was automatically oriented toward the key performance indicators (KPIs) and perspectives of a BSC that were previously determined by a group of opinion leaders of the company. The methods, techniques, and algorithms of SA and SP showed that unstructured textual information can be processed and capitalized efficiently for optimal management and decision-making. The results revealed an improvement (reduced effort and time) to produce a more robust and comprehensive BSC with the support and validation of experts. Moreover, new resources and approaches were developed to implement more efficient SP. The model was based on the efficient coupling of both fields of study.

Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort

BackgroundThe progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population. Materials and methodsStarting from a public database (National Health and Nutrition Examination Survey, NHANES), representative of the American population with 7265 eligible subjects (control population n = 6828, with Fibroscan values E < 9.7 KPa; target population n = 437 with Fibroscan values E ≥ 9.7 KPa), we set up an SVM algorithm able to discriminate for individuals with liver fibrosis among the general US population. The algorithm set up involved the removal of missing data and a sampling optimization step to managing the data imbalance (only ∼ 5 % of the dataset is the target population). ResultsFor the feature selection, we performed an unbiased analysis, starting from 33 clinical, anthropometric, and biochemical parameters regardless of their previous application as biomarkers of liver diseases. Through PCA analysis, we identified the 26 more significant features and then used them to set up a sampling method on an SVM algorithm. The best sampling technique to manage the data imbalance was found to be oversampling through the SMOTE-NC. For final model validation, we utilized a subset of 300 individuals (150 with liver fibrosis and 150 controls), subtracted from the main dataset prior to sampling. Performances were evaluated on multiple independent runs. ConclusionsWe provide proof of concept of an ML clinical decision support tool for liver fibrosis diagnosis in the general US population. Though the presented ML model represents at this stage only a prototype, in the future, it might be implemented and potentially applied to program broad screenings for liver fibrosis.

Real-time detection of freezing of gait in Parkinson’s disease using multi-head convolutional neural networks and a single inertial sensor

Background:Freezing of gait (FOG) is one of the most disabling symptoms of Parkinson’s disease (PD), contributing to poor quality of life and increased risk of falls. Wearable sensors represent a valuable means for detecting FOG in the home environment. Moreover, real-time feedback has proven to help reduce the duration of FOG episodes. This work proposes a robust real-time FOG detection algorithm, which is easy to implement in stand-alone devices working in non-supervised conditions. Method:Data from three different data sets were used in this study, with two employed as independent test sets. Acceleration recordings from 118 PD patients and 21 healthy elderly subjects were collected while they performed simulated daily living activities. A single inertial sensor was attached to the waist of each subject. More than 17 h of valid data and a total number of 1110 FOG episodes were analyzed in this study. The implemented algorithm consisted of a multi-head convolutional neural network, which exploited different spatial resolutions in the analysis of inertial data. The architecture and the model parameters were designed to provide optimal performance while reducing computational complexity and testing time. Results:The developed algorithm demonstrated good to excellent classification performance, with more than 50% (30%) of FOG episodes predicted on average 3.1 s (1.3 s) before the actual onset in the main (independent) data set. Around 50% of FOG was detected with an average delay of 0.8 s (1.1 s) in the main (independent) data set. Moreover, a specificity above 88% (93%) was obtained when testing the algorithm on the main (independent) test set, while 100% specificity was obtained on healthy elderly subjects. Conclusion:The algorithm proved robust, with low computational complexity and processing time, thus paving the way to a real-time implementation in a stand-alone device that can be used in non-supervised environments.

Perceiver CPI: a nested cross-attention network for compound-protein interaction prediction.

Compound-protein interaction (CPI) plays an essential role in drug discovery and is performed via expensive molecular docking simulations. Many artificial intelligence-based approaches have been proposed in this regard. Recently, two types of models have accomplished promising results in exploiting molecular information: graph convolutional neural networks that construct a learned molecular representation from a graph structure (atoms and bonds), and neural networks that can be applied to compute on descriptors or fingerprints of molecules. However, the superiority of one method over the other is yet to be determined. Modern studies have endeavored to aggregate information that is extracted from compounds and proteins to form the CPI task. Nonetheless, these approaches have used a simple concatenation to combine them, which cannot fully capture the interaction between such information. We propose the Perceiver CPI network, which adopts a cross-attention mechanism to improve the learning ability of the representation of drug and target interactions and exploits the rich information obtained from extended-connectivity fingerprints to improve the performance. We evaluated Perceiver CPI on three main datasets, Davis, KIBA and Metz, to compare the performance of our proposed model with that of state-of-the-art methods. The proposed method achieved satisfactory performance and exhibited significant improvements over previous approaches in all experiments. Perceiver CPI is available at https://github.com/dmis-lab/PerceiverCPI. Supplementary data are available at Bioinformatics online.

Introduction

Introduction

Comprehensive bioinformatics analysis and molecular validation of lncRNAs-mediated ceRNAs network in schizophrenia

AimsThe present study aimed to investigate how Schizophrenia (SCZ)-specific long non-coding RNAs (lncRNAs) served as competing endogenous RNAs (ceRNAs) to modulate the biological functions and pathways involved in the pathogenesis of SCZ. Main methodsMicroarray dataset (GSE54913) was obtained from Gene Expression Omnibus (GEO) database. Differently expressed (DE) lncRNAs and mRNAs were identified by “limma” package. The binding miRNAs of lncRNAs and target mRNAs of shared miRNAs were predicted by miRcode, miRDB, miRTarbase and targetscan databases. Following the ceRNAs theory, interaction network was established and visualized with the cytoscape. Functional enrichment analysis uncovered the concentrated functions and signaling pathways that may be associated with SCZ progression. Protein-protein interaction (PPI) analysis was utilized to determine hub genes. Quantitative real-time PCR (qRT-PCR) and receiver operating characteristic curve (ROC) were performed to evaluate the expression and diagnostic value of ceRNAs members, respectively. Key findingsDElncRNAs and DEmRNAs were initially screened from GSE54913 to construct the SCZ-related ceRNAs network with 42 nodes and 53 edges. Functional enrichment analysis revealed that ceRNAs members appeared to be highly correlated with transcription factor activation, cell replication and tumor-related pathways. Once validated, a significant ceRNAs subnetwork was proposed as being implicated in the pathogenesis of SCZ. ROC analysis indicated that SCZ-related ceRNAs members may be sensitive diagnostic biomarkers for SCZ. SignificanceThe significant SCZ-related ceRNAs subnetworks (lncRNA-C2orf48A/hsa-miR-20b-5p,-17-5p/KIF23, FOXJ2) may represent promising predictive and diagnostic biomarkers and provide novel insights into the mechanism by which lncRNAs act as microRNA sponges and contribute to the pathogenesis of SCZ.

Adjusting for unmeasured confounding in survival causal effect using validation data

Unmeasured confounding is an important problem in observational studies, which brings a great challenge to eliminate or reduce bias. A large main data set with unmeasured confounders and a smaller validation data set with detailed information on these confounders are combined to estimate the survival causal effect. The initial estimator based on the small validation data set under the ignorable treatment assignment and the error-prone estimator based on the large main data set are both obtained by the doubly robust method. Then, the proposed estimator is obtained by leveraging the correlation between the initial estimator and the error-prone estimator. The large sample theory of the proposed estimator is established. Simulation studies are conducted to show the good performance of the proposed method. A real data of breast cancer from the cBio Cancer Genomics Portal is analyzed to illustrate the proposed method.

Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis.

To develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS). This retrospective study included 674 patients with 674 breast lesions. The data, a main and an independent datasets, were divided into three cohorts. Cohort 1 (80% of the main dataset; n = 448) was analyzed by logistic regression analysis to identify risk factors and establish the predictive model. The area under the receiver operating characteristic curve (AUC) was analyzed in Cohort 2 (20% of the main dataset; n = 119) to validate and in Cohort 3 (the independent dataset; n = 107) to evaluate the predictive model. Multivariable regression analysis revealed nine independent breast cancer risk factors, including age > 40 years; ill-defined margin, heterogeneity, rich blood flow, and abnormal axillary lymph nodes on US; enhanced area enlargement, contrast agent retention, and irregular shape on CEUS; mean SWE higher than the cutoff value (P < 0.05 for all). The diagnostic performance of the model was good, with AUC values of 0.847, 0.857, and 0.774 for Cohorts 1, 2, and 3, respectively. The model increased the diagnostic specificity (from 31% to 81.3% and 7.3% to 73.1% in cohorts 2 and 3, respectively) without a significant loss in sensitivity (from 100.0% to 90.1% and 100.0% to 81.8% in cohorts 2 and 3, respectively). The multi-parameter US-based model showed good performance in breast cancer diagnosis, improving specificity without a significant loss in sensitivity. Using the model could reduce unnecessary biopsies and guide clinical diagnosis and treatment.

A case study of a real-time internet of things system for site-specific potato crop management in El-Salhia Area-Egypt

The site-specific management is the technology that considers the natural variability within the same field of factors related to crop growth to improve its management practices such that the agricultural treatments are varied for field's small production zones saving resources and environment, and improving crop quality and size. Since site-specific decisions are not far from the Fourth Industrial Revolution and the concept of processes automation, this work addresses improving the process of spatial variability analysis and thus supporting management decisions by developing a system—entitled EGYPADS—based on the Internet of Things and its enabling technologies. EGYPADS automates data collection, zones delineation according to their land suitability evaluation, and maps generation. The paper addresses a case study of potato crop in a specific area in Egypt, El-Salhia, in which eighty-five sites were chosen as main dataset for the modeling process during different stages of crop growth. Three management zones were recognized of the selected field based on the differentiation in their land suitability characteristics, representing about 5%, 65%, and 30% of the total area, respectively. The structure, screens, and services of EGYPADS are described in this paper. EGYPADS offered services include: management zones delineation using absolute and virtual coordinates, Land Suitability Assessment (LSA), data entry from field in real-time as well as from excel sheets, saving maps in suitable format for variable rate application, real-time and historical data processing, centralized management, and flexible formulation of events and related actions. The implementation of EGYPADS was verified. The system dynamically produces non-contiguous isobands, each representing a specific range of parameter values, and can be properly exported for use by other programs or smart machinery. It was proven that EGYPADS supports more than one land with different geometry, area, location, and number of nodes. EGYPADS was compared with the traditional LSA method, and was found to produce similar management zones.

ACP_MS: prediction of anticancer peptides based on feature extraction.

Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc.

Dual-branch self-attention network for pedestrian attribute recognition

• A dual-branch self-attention network is proposed for pedestrian attribute recognition. • A second-order self-attention module is introduced to exploit global information of a pedestrian with reduced feature dimensions. • Extensive experiments on three public datasets demonstrate the effectiveness of the proposed method. Pedestrian attribute recognition (PAR) is still a challenging task in real surveillance scenes, where the difficulties, such as occlusion, complex background, and varying views, degrade the recognition accuracy. To fully exploit attribute correlation and regional context, we propose a dual-branch self-attention network for PAR: (1) For the attribute branch, the second-order self-attention module (SO-SAM) is first introduced to derive the second-order feature maps; they are then fused with the first-order information to learn unique features for each attribute using the constrained loss function. (2) For the context branch, multiple adaptive visual tokens and a group of multi-head context self-attention modules (C-SAM) are exploited to describe the image and explore the relationships between different regions. The experimental results on three main public benchmarks, RAP, PA100K, and PETA datasets, demonstrate the effectiveness of the proposed method.

A Meta-Analysis of the Effects of Some Factors on Teachers’ Classroom Management Skills

The purpose of this study is to examine the effect of personal, professional and organizational factors on teacher’s classroom management skills in the Turkish context. Meta-analyses were performed to find out the effect sizes and understand the trend in substantive findings across classroom management skills studies. Data consisted of articles, master theses, and dissertations which were retrieved from the Turkish Higher Education Council (THEC) National Theses Center, ULAKBIM (a database for articles indexed by TUBITAK-ULAKBIM), THEC Academic, and Google Scholar databases. The main dataset in this study consisted of 59 independent studies. Findings revealed that the effect of personality traits of teachers, professional, and organizational factors on teachers’ classroom management skills was found at medium level. Emotional traits of teachers’, sub-dimension of personality traits, produced a greater effect size than cognitive skills and personality type. Teachers, who feel satisfied with their job, work in a positive school climate, get motivated by their top managers and share common values and culture with their colleagues, tend to use CMSs more effectively. While occupational burnout, low teaching performance, and poor organizational communication have a negative but significant effect, job satisfaction, school climate, organizational motivation, shared organizational values and rules have a moderately positive and significant effect on teachers’ CMSs.

NARRATIVES ABOUT MENSTRUATION IN TURKEY: SELF-SHAME, SUPPRESSION AND SILENCE

In this study, we explore how women perceive menstruation in Turkey. The general discourse in Turkey defines menstruation as an adverse and even dirty biological process mainly described as “being ill” and/or making women unable to worship until becoming clean again. Experiences of menstruation also shape women’s identities to a certain extent and create adverse feelings as a fundamental part of womanhood in Turkey. The experiences of the participants reflect the dynamic interactions among being suppressed, feeling self-shame, and being silent. The main dataset of this study was collected from December 2021 to February 2022 in Turkey via 20 online in-depth interviews conducted with a semi-structured questionnaire.

Predicting the potential human lncRNA-miRNA interactions based on graph convolution network with conditional random field.

Long non-coding RNA (lncRNA) and microRNA (miRNA) are two typical types of non-coding RNAs (ncRNAs), their interaction plays an important regulatory role in many biological processes. Exploring the interactions between unknown lncRNA and miRNA can help us better understand the functional expression between lncRNA and miRNA. At present, the interactions between lncRNA and miRNA are mainly obtained through biological experiments, but such experiments are often time-consuming and labor-intensive, it is necessary to design a computational method that can predict the interactions between lncRNA and miRNA. In this paper, we propose a method based on graph convolutional neural (GCN) network and conditional random field (CRF) for predicting human lncRNA-miRNA interactions, named GCNCRF. First, we construct a heterogeneous network using the known interactions of lncRNA and miRNA in the LncRNASNP2 database, the lncRNA/miRNA integration similarity network, and the lncRNA/miRNA feature matrix. Second, the initial embedding of nodes is obtained using a GCN network. A CRF set in the GCN hidden layer can update the obtained preliminary embeddings so that similar nodes have similar embeddings. At the same time, an attention mechanism is added to the CRF layer to reassign weights to nodes to better grasp the feature information of important nodes and ignore some nodes with less influence. Finally, the final embedding is decoded and scored through the decoding layer. Through a 5-fold cross-validation experiment, GCNCRF has an area under the receiver operating characteristic curve value of 0.947 on the main dataset, which has higher prediction accuracy than the other six state-of-the-art methods.

Anticancer Peptides Prediction: A Deep Learning Approach

Anticancer peptides play a vital role in the treatment of cancer, due to that it has gained a lot of attention. Several machine learning and deep learning algorithms were developed for the prediction of anticancer peptides. Machine learning algorithms involves features extraction from the dataset and then model is trained to make predictions. In machine learning algorithms features extraction and the training of the model takes a lot of time and efforts, this is a complex process for biologists and biochemists. On the other hand deep learning algorithms require a large amount of dataset for training and accurate predictions. This study has proposed a deep learning algorithm which can be trained on smaller dataset because it uses hyperparameter optimization framework for the accurate predictions of anticancer peptides. The deep learning model has outperformed all the other algorithms and achieved the optimal 99% Acc and 0.982 MCC on Main dataset, 98% Acc and 0.972 MCC on Alternative dataset. The code is available at Github for validation purposes [33].

Y- chromosomal STR Variation in Arab, Soran and Behdinan Kurds population in Kurdistan region of Iraq

Background: Over than eight tenant groups make up Kurdistan region of Iraq local populations. Overall majority of the inhabitants are Muslim Kurds, followed by Yezidi Kurds. Alternative groups including Armenians, Assyrian, Chaldea Syriacs as well as a little minority of Arab and Turkmen individuals. Methods: A total of 60 unrelated males from three population groups in Kurdistan region of Iraq Arabs, Soran and Behdinan kurd were successfully analyzed for ten Y- chromosome STRs (DYS19, DYS390, DYS393, DYS437, DYS439, DYS447, DYS460, DYS461, DYS481, DYS576). Whole DNA has been extracted from the blood samples using DNA extraction kit. PCR products were run on 8% polyacrylamide gel with a 50bp DNA ladder marker to size the bands for each sample. silver staining was used to identify the DNA bands. Power Marker V3.25 software was used successfully to determine a variety of genetic parameters which include total allele number, allele frequency, gene diversity and polymorphic information content (PIC). Phylogenetic tree was constructed by MEGA-X software. Results: The total number of alleles identified in the three populations was 155. The size of the alleles ranged from 112bp to 245bp. The DYS19 had the highest diversity (GD: 0.941), whereas DYS393 locus had the lowest value among all (GD: 0.813). The Dendrogram split the populations into two main clusters: Arabs in one cluster, whereas Soran and Behdinan in another cluster. Conclusions: This study validates that the discrimination potential of high-resolution Y-STR typing and supports the main datasets on the samples from Kurdistan region of Iraq. The comparison of two group of Kurds and Arab datasets offers an intriguing total pattern of Kurd groups. Meaning that Sorans are genetically closer to the Behdinan population.

Missing voices in GRI standards? Distinct material concerns of Latin American stakeholders revealed by COVID-19

PurposeThis paper aims to explore the extent to which global reporting initiative (GRI) standards reflect the material concerns of stakeholders in developing countries, with particular reference to Latin America.Design/methodology/approachThe main dataset was a sample of 120 media articles that discussed corporate conduct related to COVID-19 from both developing (Chile, Mexico and Peru) and developed (Australia, UK and the USA) countries. Concerns evident from those articles were compared and then mapped to applicable GRI standards to identify relevant disclosures and gaps. Findings were triangulated by drawing on two additional datasets: Latin American GRI-related academic literature (in Spanish) and submissions to GRI standards.FindingsMedia analysis reveals significant differences between developing and developed country concerns, as well as gaps in GRI disclosure requirements in relation to customers, labour standards and corporate interactions with non-government organisations and governments. Analysis of Latin American literature corroborates the concerns raised in media articles regarding employment. Additionally, it points out country-specific issues and calls for increased reporting of corruption. Analysis of the GRI standards development process reveals marked underrepresentation of developing countries, which may contribute to the observed deficiencies in the GRI standards.Originality/valueThis paper contributes to the (surprisingly rare) research concerning the quality of GRI standards and responds to calls for greater attention to developing countries in the SEA literature by showing that GRI standards may not fully meet the needs of users in the developing country context of Latin America. The paper also contributes to practice via specific recommendations for improvement to GRI standards and the standard-setting process and provides a summary of the key findings from Spanish-language Latin American literature.

Dexterous Identification of Carcinoma through ColoRectalCADx with Dichotomous Fusion CNN and UNet Semantic Segmentation.

Human colorectal disorders in the digestive tract are recognized by reference colonoscopy. The current system recognizes cancer through a three-stage system that utilizes two sets of colonoscopy data. However, identifying polyps by visualization has not been addressed. The proposed system is a five-stage system called ColoRectalCADx, which provides three publicly accessible datasets as input data for cancer detection. The three main datasets are CVC Clinic DB, Kvasir2, and Hyper Kvasir. After the image preprocessing stages, system experiments were performed with the seven prominent convolutional neural networks (CNNs) (end-to-end) and nine fusion CNN models to extract the spatial features. Afterwards, the end-to-end CNN and fusion features are executed. These features are derived from Discrete Wavelet Transform (DWT) and Vector Support Machine (SVM) classification, that was used to retrieve time and spatial frequency features. Experimentally, the results were obtained for five stages. For each of the three datasets, from stage 1 to stage 3, end-to-end CNN, DenseNet-201 obtained the best testing accuracy (98%, 87%, 84%), ((98%, 97%), (87%, 87%), (84%, 84%)), ((99.03%, 99%), (88.45%, 88%), (83.61%, 84%)). For each of the three datasets, from stage 2, CNN DaRD-22 fusion obtained the optimal test accuracy ((93%, 97%) (82%, 84%), (69%, 57%)). And for stage 4, ADaRDEV2-22 fusion achieved the best test accuracy ((95.73%, 94%), (81.20%, 81%), (72.56%, 58%)). For the input image segmentation datasets CVC Clinc-Seg, KvasirSeg, and Hyper Kvasir, malignant polyps were identified with the UNet CNN model. Here, the loss score datasets (CVC clinic DB was 0.7842, Kvasir2 was 0.6977, and Hyper Kvasir was 0.6910) were obtained.

Analysis of data consistency identifies measurement abnormality in Howells' craniometric test data set

AbstractHowells' craniometric data set is the largest publicly available craniometric data set on the internet and has been widely used in craniometric methods development. The data consists of a main data set of 2524 human crania from 28 populations and an additional “test” data set of 524 crania. Up to 82 measurements were recorded from those crania. We studied the data consistency between the main and test data sets for potential combined usage of the two. We found that the two data sets can be separated clearly via Uniform Manifold Approximation and Projection, suggesting some data inconsistency between the two. To further investigate the cause, we split the two data sets into six continental groups (African, Austro‐Melanesian, East Asian, European, Native American, and Polynesian) and tested the distribution difference between the two data sets for each of the groups. We found that the measures of simotic chord (WNB) and simotic subtense (SIS) are significantly and abnormally larger in the test data set than in the main data set. After removing the two measures, the two data sets are broadly comparable. We further showed the evidence that missing decimal points likely caused the abnormality.

Data and Knowledge for Overtaking Scenarios in Autonomous Driving

Abstract Autonomous driving has become one of the most popular research topics within artificial intelligence. An autonomous vehicle is understood as a system that combines perception, decision-making, planning, and control. All of those tasks require that the vehicle collects surrounding data in order to make a good decision and action. In particular, the overtaking manoeuvre is one of the most critical actions of driving. The process involves lane changes, acceleration and deceleration actions, and estimation of the speed and distance of the vehicle in front or in the lane in which it is moving. Despite the amount of work available in the literature, just a few handle overtaking manoeuvres and, because overtaking can be risky, no real-world dataset is available. This work contributes in this area by presenting a new synthetic dataset whose focus is the overtaking manoeuvre. We start by performing a review of the state of the art in autonomous driving and then explore the main datasets found in the literature (public and private, synthetic and real), highlighting their limitations, and suggesting a new set of features whose focus is the overtaking manoeuvre.