Alluvial Diagram Research Articles

PSLBI-22 Identifying plasma metabolites influencing body weight in Salmonella challenged growing pigs: A machine learning approach

Abstract Body weight (BW) is an important component of pig productivity. Understanding the metabolic factors affecting growth rate is key to guiding nutritional strategies and bringing economic benefits to pig farms. Machine learning (ML) algorithms have been used to capture patterns in large datasets and predict animal production traits based on integrated information of performance data and blood records. However, utilizing ML models and metabolomic profiling to predict BW of pigs in small datasets with challenged animals has yet to be reported. Therefore, this study investigated use of plasma metabolites in predicting BW of pigs at the growing stage. This simulation used data from gilts (n = 40; BW = 25.6 ± 2.3 kg) raised over a 28-d trial. Plasma samples and BW were collected on d 0, 14, and 28 after 8 h of fasting. Ninety plasma metabolites were identified and quantified from an untargeted metabolomics assay. The development dataset included 30, and the validation dataset contained 10 unique animals with repeated measures in time. The metabolites were log-transformed, Pareto-scaled and missing values were imputed using the minimum nonzero value method. The BW prediction model was performed in R with the caret package. The optimal hyper-parameter configuration for the random forest and neural network algorithms was evaluated based on repeated cross-validation. The final model was the random forest composed of 400 trees with 45 randomly selected predictors. Model accuracy was estimated on repeated cross-validation with 10-folds and 5 repeats and compared with the validation set based on the coefficient of determination (R2), the root mean squared error (RMSE), and Pearson correlation (r). The metabolites with greater contribution with variable importance score above 10 to the model development were extracted. Aiming to understand differences in BW categories, the model response was visualized with an alluvial diagram for the 3 variables with the greatest metabolite importance, and BW was classified as high, medium, and low. The main metabolites driving BW were lysine, oleic acid, aminomalonic acid, myo-inositol, and linoleic acid. The RMSE after cross-validation was 7.22 kg of BW (r = 0.70), and R2 = 0.39. For pigs with a high BW, the predicted BW was explained by generally less plasmatic concentrations of lysine, oleic acid, and aminomalonic acid. Lysine is the main amino acid for muscle protein accretion, whereas oleic acid is for energy substrate. Aminomalonic acid is a metabolite resulting from glycine metabolism and is important in reversing oxidative damage caused by pathogens. Therefore, decreased plasma lysine, amino malonic acid, and oleic acid may indicate faster growth and lean deposition, leading to heavier BW in pigs. Using random forests helped to connect the plasma dynamics to BW and is a promising avenue to understand the main drivers in BW of group-housed pigs.

Agile Methods in the Social Work: Research Landscape Analysis

The use of flexible methods in social work allows social workers to be more flexible, client-oriented, adaptive and responsive in a dynamic environment, respond to changes faster, achieve better social impact results, and be more coordinated in cooperation with other professionals. The article demonstrates the results of descriptive bibliometric analysis and scientific mapping (using the Biblioshiny software) of more than 750 articles and monographs indexed by Scopus. Since the appearance of the first study in 1969 and until 2000, this topic was almost not the focus of scientists; the year 2000 was determined using Reference Publication Year Spectroscopy as the key date of interest growth (the year preceding the appearance of the Manifesto for Agile Software development), since 2001, the number of publications on this topic has grown exponentially. By a Sankey plot, interdependence between top references, top authors and top keywords was summarized. According to Bradford’s law, scientific journals are structured according to the contribution to the dissemination of knowledge in the subject area. Scientists from the USA, Great Britain, China, Australia and Canada have scientific leadership in this field. The TOP-10 global and local cited documents were analyzed in detail, and “occasional” and “sore” authors were distinguished according to Lotka’s law. The most popular thematic research areas on applying flexible methods in social work are presented in visual design as a word cloud (tag cloud, weighted list) and a treemap. The analysis proved that keywords across various clusters and research sub-themes are closely interconnected. The most relevant and advanced research categories were identified by analyzing the increase in relevance and the level of subject development, as well as their trends over time. A trend toward convergence in scientific research thematic progression in scholarly literature was explored using an alluvial diagram (a longitudinal thematic map). Constructed maps of relevance degree and development degree of subtopic in documents with a focus on agile or adaptive social work methods made it possible to determine niche, emerging, and declining topics.

Mapping change in higher-order networks with multilevel and overlapping communities

New network models of complex systems use layers, state nodes, or hyperedges to capture higher-order interactions and dynamics. Simplifying how the higher-order networks change over time or depending on the network model would be easy with alluvial diagrams, which visualize community splits and merges between networks. However, alluvial diagrams were developed for networks with regular nodes assigned to non-overlapping flat communities. How should they be defined for nodes in layers, state nodes, or hyperedges? How can they depict multilevel, overlapping communities? Here we generalize alluvial diagrams to map change in higher-order networks and provide an interactive tool for anyone to generate alluvial diagrams. We use the alluvial diagram generator in three case studies to illustrate significant changes in the organization of science, the effect of modeling network flows with memory in a citation network and distinguishing multidisciplinary from field-specific journals, and the effects of multilayer representation of a collaboration hypergraph.

Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis.

An Alluvial diagram illustrates the flow of values from one set to another. Edges (or links/connections) are the connections between nodes (or actors/ vertices). There has been an increase in the use of Alluvial deposits in medical research in recent years. However, there was no illustration of such research on the way to draw the Alluvial for the readers. Our objective was to demonstrate how to draw the Alluvial in Microsoft Excel by using 2 examples, including variable characteristics for COVID-19 patients and research achievements (RAs) on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV), and provide an easy and friendly method of drawing the Alluvial in MS Excel. Blood samples were collected and analyzed from 485 infected individuals in Wuhan, China. An operational decision tree and 2 Alluvial diagrams were shown to be capable of identifying variable characteristics in COVID-19 patients. A second example is the 100 top-cited articles downloaded from the Web of Science core collection (WoSCC) on the CEPV topic. On the Alluvial diagram, the mean citations (=citations/publications) and x-index were used to identify the top 5 members with the highest RAs in each entity (country, institute, journal, and research area). Two examples (i.e., blood samples taken from 485 infected individuals in Wuhan, China, and 100 top-cited articles on the CEPV topic) were illustrated and compared with traditional visualizations without flow relationships between nodes. The top members in entities with the x-index are U Arab Emirates (242), Jama-J. Am. Med. Assoc. (27.18), Lancet (58.34), San Francisco Va Med (178), and Chaolin Huang (189) in countries, institutes, departments, and authors, respectively. The most cited article with 1315 citations was written by Huang and his colleagues and published by Lancet in 2021. This study generates several Alluvial diagrams as demonstrations. The tutorial material and MP4 video provided in the Excel module allow readers to draw the Alluvial on their own in an easy and friendly manner.

Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study.

Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online. This study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia. We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories-emotions and influencing factors. Using cosine distance from selected seed words' embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks. Our findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41% to 39% in India. We also observed a significant change (P<.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42% of tweets coming from India and 45% of tweets from the United States represented the "vaccine_rollout" category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases. By extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions.

Tourist's engagement in eco-tourism: A review and research agenda

This study aims to provide a comprehensive and systematic review of antecedent, consequence, and intervention factors of tourists' engagement in the ecotourism context. Based on the ACI (Antecedent-Consequence-Intervention) framework, this study addresses tourist engagement from six different engagement themes. Co-word analysis of previous literature caters to the categorization of the themes. This review synthesizes 118 articles from 1993 to 2022 through content analysis. This study used an alluvial diagram to show how different engagement themes have emerged and evolved in different periods. The findings also identified the trend of ecotourism engagement in terms of theme, antecedent factor, consequence, and intervention factors. This review found that tourist engagement in ecotourism was initially centered around destination engagement, and gradually it inclined towards tourism brand engagement, social media engagement, and tourist-OTA engagement. The study also provided extensive directions for future research agendas. For example, what is the role of tourist social and ethical values in ecotourism community welfare? This study contributes to ecotourism literature by highlighting the antecedent and consequences of tourist engagement in ecotourism.

Renewed incision and complex response of the North Fork Toutle River following the eruption of Mount St. Helens in 1980

The upper North Fork Toutle River (NFTR) has been evolving in a complex way since being disrupted by a 2.5-km3 debris avalanche deposit during the catastrophic eruption of Mount St. Helens in Washington State, USA in 1980. In this study, we improved the alluvial phase space diagram (APSD), which classifies channel adjustment into different stages based on bed elevation and cross-sectional area changes, and applied it to the NFTR at annual time scale in the 1980s. The results showed that in the early 1980s, the upstream narrow single-threaded reaches were degraded, while the downstream wide anabranching reaches were aggraded, with the middle transitional reach acting as the hinge zone. Gradually, the upstream channel reaches armoured, widened, bed slope and excess stream power decreased in the late 1980s, and quasi-equilibrium occurred locally and during short time periods. Sediment load decreased at the lower reaches, resulting in bed degradation and a transition from sediment sink to source. This reversal seemed to have triggered renewed incision, which migrated upstream. The renewed incision was characterised by Stage 1/2 (bed incision with limited bank erosion) followed by Stage 2 (degradation and widening) in the improved APSD. The degradation rate accelerated in the 1990s, especially due to the big flood in 1996, and then slowed down after ∼2000. Channel adjustment rates of the renewed incision were much smaller compared to those of the initial rapid degradation. The change rates of cross-sectional area and valley width decreased by ∼58% and ∼85%, respectively. The comparison between this study and existing studies highlights the significant role played by the time scale, at which the APSD is applied to study channel morphology adjustment.

Citation analysis of the 100 top-cited articles on discectomy via endoscopy since 2011 using alluvial diagrams: bibliometric analysis

BackgroundPercutaneous endoscopic lumbar discectomy (PELD) is synonymous with percutaneous endoscopic transforaminal discectomy (PETD) and percutaneous endoscopic interlaminar discectomy (PEID). PEID has gained increasing recognition for its small incision, quick recovery, short hospital stay, and equivalent clinical outcome to open surgery. Numerous articles related to PEID have been published in the literature. However, which countries, journals, subject categories, and articles have ultimate influence remains unknown. The study aimed to (1) display influential entities in 100 top-cited PEID-related articles (T100PEID) on the alluvial diagram and (2) investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations.MethodsT100PEID data can be found since 2011 in the PubMed and Web of Science (WOS) databases. Using alluvial diagrams, citation analysis was conducted to compare the dominant entities. We used social network analysis (SNA) to classify MeSH terms and research areas extracted from PubMed and WOS. The difference in article citations across subject categories and the predictive power of MeSH terms on article citations in T100 PEID were examined using one-way analysis of variance (ANOVA) and regression analysis.ResultsA total of 81% of T100PEID is occupied by the top three countries (the US, China, and South Korea). There was an overall T100PEID impact factor of 41.3 (IF = citations/100). Articles were published in Spine (Phila Pa 1976) (23%; IF = 41.3). Six subject categories were classified using the SNA. The most cited article authored by D Scott Kreiner from Ahwatukee Sports and Spine in the US state of Phoenix had 123 citations in PubMed. The network characteristics of T100PEID are displayed on the alluvial diagram. No difference was found in article citations among subject categories (F = 0.813, p = 0.543). The most frequently occurring MeSH term was surgery. MeSH terms were evident in the prediction power of the number of article citations (F = 15.21; p < 0 .001).ConclusionWe achieved a breakthrough by displaying the T100PEID network characteristics on the alluvial plateau. The MeSH terms can be used to classify article subject categories and predict T100PEID citations. The alluvial diagram can be applied to bibliometrics on 100 top-cited articles in future studies.

Half Of Adolescents Never Met Physical Activity Guidelines Over A Nine Week Period

PURPOSE: Consistency in physical activity is crucial to acquire maximum health benefits. The current physical activity (PA) recommendation for adolescents is to accrue 420 minutes of PA a week. The purpose of this study was to examine proximal changes in adolescent physical activity. METHODS: Adolescents in a freshman high school health class answered Exercise Vital Sign (EVS) questions over the course of nine weeks. Minutes of PA per week was calculated by multiplying days of PA by minutes of PA. Participants were then categorized as meeting (≥420 minutes/week) or not meeting (<420 minutes per week) current PA guidelines. An alluvial diagram was created to perform exploratory visual analysis of the magnitude and variability in meeting and not meeting PA trajectories across the four time points. RESULTS: Of the twenty-five who participated, 21 had EVS data at four points in time. The average PA across all time points was 312 ± 272 minutes per week. PA between the time points ranged from 245-355 minutes per week. 14% of adolescents always met PA recommendations and 48% never met PA recommendations. 10% of adolescents missed the recommended PA only once, 24% missed twice, and 5% missed PA recommendations three times. From Time Point 1 to Time Point 4 the percentage of adolescents who did not meet PA recommendations were 57%, 71%, 71%, and 62%, respectfully. DISCUSSION: The benefits of regular, consistent exercise include improved mood, physical fitness and sleep quality as well as a decreased risk of chronic diseases. Adolescence is a key developmental stage because developed health behaviors often translate into adulthood. A large majority (86%) did not meet PA guidelines at least once during the study. There may be differences between those who never meet and those who sometimes meet the recommended PA levels. Individual Trajectories of Meeting and Not Meeting PA Guidelines over Four points in Time

Structural analysis and evolutionary exploration based on the research topic network of a field: a case in high-frequency trading

This study aims to systematically analyze the distribution dynamics of research topics and uncover the development state of the research in the specific field, which will provide a practical reference for developing professional subject knowledge services in the era of big data. The research topic network is constructed and analyzed using methods and tools of scientometrics. Basic statistics on network characteristics are performed to reveal the research status. Community detection, node ordering, and other steps are conducted to generate the evolutionary alluvial diagram. Then, relevant results are analyzed to explore the knowledge structure of the specific field and evolutionary context of research topics. Visualization analysis on the network structure of the latest period is executed to distinguish related concepts and predict the research trends. Taking high-frequency trading (HFT) as a case, this study achieves diversified scientometrics analysis of the research topic network and multi-dimensional evolution exploration of the relevant research topics in the specific field, which obtaining some knowledge insights. (1) Six major topics in HFT: liquidity &amp; market microstructure, market efficiency, financial market, incomplete market, cointegration &amp; price discovery, and event study. (2) The research focus about markets gradually transferred from international to emerging, meanwhile continuous attention to volatility/risk related issues. (3) The emphasis will change from theory to practice, technologies (big data, etc.) and theories (behavioral finance, etc.) will have more interaction with HFT. An effective research idea is proposed to reveal the knowledge structure of field and analyze the evolutionary context of research topics, which demonstrating the knowledge insights.

Trends and Hotspots in Nanoparticles for the Targeted Delivery of Nucleic Acids: A Ten-Year Bibliometric Study.

Nanoparticles for the gene therapy field have seen remarkable progress over the last 10 years; however, low delivery efficiency and other reasons impede the clinical translation of nanocarriers. Therefore, a summary of hotspots and trends in this field is needed to promote further research development. In this research, from 2011 to 2021, 1,221 full records and cited references of Web of Science–indexed manuscripts regarding nanoparticle-targeted delivery systems have been analyzed by CiteSpace, VOSviewer, and MapEquation. In these software, keywords co-occurrence networks, alluvial diagram, co-citation networks, and structural variation analysis were carried out to emphasize the scientific community’s focus on nanomedicine of targeted delivering of nucleic acids. Keywords such as transfection efficiency, tumor cell, membrane antigen, and siRNA delivery were highlighted in the density map from VOSviewer. In addition, an alluvial flow diagram was constructed to detect changes in concepts. In the co-citation network, cluster 1 (exosomes) and cluster 17 (genome editing) were new research fields, and the efforts in modifying nanoparticles were revealed in the structural variation analysis. Aptamer and SELEX (systematic evolution of ligands by exponential enrichment) represented a helpful system in targeted delivery. These results indicated that the transfection efficiency of nanocarriers required continuous improvements. With the approval of several nucleic acid drugs, a new content of nanoparticle carriers is to introduce gene-editing technology, especially CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR–associated protein 9). In addition, exosomes have great potential as targeted nanoparticles. By mapping the knowledge domains of nanomedicine in targeted delivering of nucleic acids, this study analyzed the intellectual structure of this domain in the recent 10 years, highlighting classical modifications on nanoparticles and estimating future trends for researchers and decision-makers interested in this field.

Mapping the scientific structure and evolution of renewable energy for sustainable development.

The integration of renewable energy and sustainable development (RE&SD) can help overcome existing obstacles and create opportunities for renewable energy deployment to achieve sustainable development goals. In view of the limited research on science mapping and visualization analyses of RE&SD, this study sought to determine the scientific structure and evolution based on longitudinal and mapping change analysis. As an entity in the knowledge base, keyword and subject were considered essential information of documents. The co-word network was generated using SciMAT to reveal the dynamic aspects of the scientific research in the five subperiods. The thematic evolutionary analysis identified two main RE&SD thematic areas, with the current research hotspots that involved technological, environmental, sustainable energy innovation, and sustainable biofuel contributions. The alluvial diagram using MapEquation revealed significant structural changes from subject data. Clusters of subjects continued to grow, and more interdisciplinary integration was undergoing. This study provides a systematic study of RE&SD research, and the future research of RE&SD may inevitably consider renewable energy investment and renewable energy perspective approaches to achieve sustainable development goals.

Dramatic decline of two freshwater killifishes, main anthropogenic drivers and appropriate conservation actions

Freshwater fish biodiversity loss in the Mediterranean Basin is regarded as among the highest globally, with long-term population data sets required to discern long-term population trends of threatened species, in order to design appropriate conservation interventions. In this study, we assessed the population trends of two threatened freshwater fishes, Valencia letourneuxi and Valencia robertae, employing the most recent and largest compiled database to date (16 populations over 14 years). We applied the innovative methodology of the Living Planet Index (LPI) to assess the average rate of change over time across a set of V. letourneuxi and V. robertae populations in Greece. The LPI application revealed a dramatic decline of both species, with V. letourneuxi declining by 97.7% and V. robertae by 91.0%. Beta regression showed that water pollution, eutrophication and alien Eastern mosquitofish Gambusia holbrooki’ presence were the three best fitting predictors of the decline of V. letourneuxi and V. robertae populations. Based on the above, we outline the conservation measures urgently required to revert the near collapse of the populations of the two species. Conservation actions include the strict protection of the their lowland spring habitats, habitat improvement through changes in water management and agricultural practices, mosquitofish invasion prevention and mosquitofish impact mitigation measures, as well as translocation actions and captive breeding. Lastly, the association patterns of the proposed conservation actions with anthropogenic pressures and their expected outcomes were analysed through an alluvial diagram, providing insights on the scale of pressures mitigated by conservation actions and on their conservation benefits.

Portraying the Pandemic: Analysis of Textual-Visual Frames in German News Coverage of COVID-19 on Twitter

ABSTRACT The novel coronavirus disease (SARS-CoV-2/COVID-19) resulted in a severe global pandemic in 2020. Amid the unprecedented health crisis, visuals play important roles in media framing and health crisis communication. This article provides one of the first analyses of textual-visual news concerning COVID-19 posted by two leading German newspapers on Twitter. Our objective is to integrate texts and visuals into framing analysis to better understand the multimodal news presentation of health crises. A quantitative content analysis of 2479 tweets examined nine news frames, among which politics and instructions, update information, economic impact and social impact presented more thematic salience. The semiotic signs of indexical, symbolic and iconic appeared in news visuals and served evidential, performative and illustrative functions. The results also detected recurrent news visuals in the color-clustered patterns. Notably, an alluvial diagram revealed the connections between textual frames and visual signs across three crisis phases. In recognizing the diverse roles of visuals in constructing mediated reality, this article demonstrates the value in the multimodal framing of health crises and suggests expanding our focus to risk cultures for future international comparative research.

Impact of cardiac magnetic resonance for defining the etiology of an acute non-ischemic troponin rise depending on the pre-test diagnostic suspicion

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Cardiac magnetic resonance (CMR) is determinant for defining the final diagnosis in patients with an acute troponin rise of non-ischemic etiology. However, depending on the pre-test diagnostic suspicion, the usefulness of CMR to improve the final diagnosis may vary. Alluvial diagrams are not commonly applied to medicine fields, however, these charts offer a unique opportunity to visualize the changes in diagnosis after a specific test. Purpose The aim of the study was to define in which patients CMR may be more efficient for defining the final diagnosis of an acute rise of troponin. To reach our objective we applied for the first time an alluvial diagram to a cohort of patients undergoing CMR after an acute rise of troponins. Methods All consecutive patients admitted during a 2-year-period in our tertiary hospital with an acute non-ischemic troponin rise who underwent CMR were retrospectively included in the study. In patients with a suspicion of myocardial infarction, ischemic etiology was previously ruled out with an invasive coronary angiogram with concomitant intravascular imaging when necessary. Based on patients´ clinical characteristics, wall motion abnormalities, ECG findings and cardiac biomarkers pattern a pre-CMR preliminary diagnostic suspicion was made: suspicion of Takotsubo syndrome, suspicion of myocarditis, non-ST-elevation troponin rise without a specific diagnostic suspicion or ST-elevation troponin rise without a specific diagnostic suspicion. Based on CMR findings a final diagnosis was obtained. The changes between pre-CMR and post-CMR diagnosis were analyzed using an alluvial diagram. Results A total of 64 patients were included. Thirty patients (47%) underwent coronary angiography which ruled out obstructive lesions. Previous to CMR, a high suspicion of Takostubo syndrome and myocarditis was present in 25 (39%) and 14 (22%) patients, respectively. Despite invasive angiogram, 1 ST-elevation troponin rise and 20 non-ST elevation troponin rise underwent CMR without a specific diagnostic suspicion. The diagnostic changes after CMR are presented in Figure 1. Takotsubo syndrome was confirmed in the 96% of patients with a high suspicion of Takotsubo syndrome and myocarditis was confirmed in all the patients with a high suspicion of myocarditis. Among patients without a specific suspicion of disease a final diagnosis was reached in 71% of them. Specifically, Takotsubo syndrome, myocardial damage, myocarditis and myocardial infarction were diagnosed in 25%, 10%, 30% and 10% of the patients, respectively. Conclusions For the first time an alluvial diagram describes the impact of CMR for defining the etiology of an acute non-ischemic rise of troponin. Based on our results, CMR has a confirmatory role in patients with a high suspicion of Takotsubo syndrome and myocarditis, whilst CMR findings provide a final diagnosis in the 71% of patients without a previous specific etiologic suspicion. Abstract Figure.

The Dark Side of Pyroptosis of Diffuse Large B-Cell Lymphoma in B-Cell Non-Hodgkin Lymphoma: Mediating the Specific Inflammatory Microenvironment.

Background: Diffuse large B-cell lymphoma (DLBCL) is a common aggressive B-cell non-Hodgkin lymphoma (B-NHL). While combined chemotherapy has improved the outcomes of DLBCL, it remains a highly detrimental disease. Pyroptosis, an inflammatory programmed cell death, is considered to have both tumor-promoting and tumor-suppressing effects. The role of pyroptosis in DLBCL has been gradually appreciated, but its value needs further investigation. Methods: We analyzed mutations and copy number variation (CNV) alterations of pyroptosis-related genes (PRGs) from The Cancer Genome Atlas (TCGA) cohort and evaluated the differences in expression in normal B cells and DLBCL patients in two Gene Expression Omnibus (GEO) datasets (GSE12195 and GSE56315). Based on the expression of 52 PRGs, we divided 421 DLBCL patients from the GSE31312 dataset into distinct clusters using consensus clustering. The Kaplan-Meier method was used to prognosis among the three clusters, and GSVA was used to explore differences in the biological functions. ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used to analyze the tumor immune microenvironment (TME) in different clusters. A risk score signature was developed using a univariate survival analysis and multivariate regression analysis, and the reliability and validity of the signature were verified. By combining the signature with clinical factors, a nomogram was established to predict the prognosis of DLBCL patients. The alluvial diagram and correlation matrix were used to explore the relationship between pyroptosis risk score, clinical features and TME. Results: A large proportion of PRGs are dysregulated in DLBCL and associated with the prognosis. We found three distinct pyroptosis-related clusters (cluster A, B, and C) that differed significantly with regard to the prognosis, biological process, clinical characteristics, chemotherapeutic drug sensitivity, and TME. Furthermore, we developed a risk score signature that effectively differentiates high and low-risk patients. The nomogram combining this signature with several clinical indicators showed an excellent ability to predict the prognosis of DCBCL patients. Conclusions: This work demonstrates that pyroptosis plays an important role in the diversity and complexity of the TME in DLBCL. The risk signature of pyroptosis is a promising predictive tool. A correct and comprehensive assessment of the mode of action of pyroptosis in individuals will help guide more effective treatment.

Longitudinal bibliometric analysis applied to home care services

The aim of this study is to present a new methodology to explore a field of research and exercise this technique to find good mathematical models to solve the problem of territorial alignment applied to health services. For this purpose we show a methodology that combines three methods of analysis: social network analysis, longitudinal analysis, and mapping change analysis. In this paper, we applied the mapping change method, originally used in large networks, to small and medium ones, and used the Tabu search scheme instead of simulated annealing. Finally, to highlight the significant changes over time of keywords networks, an alluvial diagram is used to show the significance clusterings through the subperiods studied. The work reports on the most relevant authors on the subject and the most widely used mathematical models applied to solve the problem.

A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry

In the construction industry, it is difficult to predict occupational accidents because various accident characteristics arise simultaneously and organically in different types of work. Furthermore, even when analyzing occupational accident data, it is difficult to deduce meaningful results because the data recorded by the incident investigator are qualitative and include a wide variety of data types and categories. Recently, numerous studies have used machine learning to analyze the correlations in such complex construction accident data; however, heretofore the focus has been on predicting severity with various variables, and several limitations remain when deriving the correlations between features from various variables. Thus, this paper proposes a data processing procedure that can efficiently manipulate accident data using optimal machine learning techniques and derive and systematize meaningful variables to rationally approach such complex problems. In particular, among the various variables, the most influential variables are derived through methods such as clustering, chi-square, Cramer’s V, and predictor importance; then, the analysis is simplified by optimally grouping the variables. For accident data with optimal variables and elements, a predictive model is constructed between variables, using a support vector machine and decision-tree-based ensemble; then, the correlation between the dependent and independent variables is analyzed through an alluvial flow diagram for several cases. Therefore, a new processing procedure has been introduced in data preprocessing and accident prediction modelling to overcome difficulties from complex and diverse construction occupational accident data, and effective accident prevention is possible by deriving correlations of construction accidents using this process.

Defining non-indigenous fish assemblage types in Mediterranean rivers: Network analysis and management implications

The design and implementation of appropriate management actions to tackle the spread and negative impacts of non-indigenous fish species (NIFS) in freshwater ecosystems still remains a complex task. In an effort to address the limitations of current managerial approaches, our study develops and applies a classification framework to define non-indigenous fish assemblage types (FATs) in Mediterranean riverine ecosystems and identifies the linkage with various regional, local, biotic and abiotic environmental factors. This framework contributes to a pre-invasion stage screening and to the design of effective type-specific post-invasion management actions dealing with specific NIFS assemblages. A network analysis was applied in order to extract NIFS community level information from 393 samples obtained from 51 river basins of Greece, while a multivariate ordination analysis was conducted to detect the factors best explaining the structure and distribution of FATs. The association patterns of the study's inputs and outcomes are illustrated through an alluvial diagram, providing insights across different spatial scales. In total, five FATs were generated where the major NIFS contributors of average similarity within each modularity class revealed the key indicator species (Gambusia-FAT; Carassius/Lepomis-FAT; Pseudorasbora-FAT; Salmonids-FAT and Carp-FAT). Overall, the identified FATs varied spatially, indicating different community structure, mainly based on the diverse habitat preferences and life-history traits of indicator species. Alien FATs were mainly distributed within large and transboundary rivers, while Translocated and Salmonids FATs mostly occupied ecoregions with relatively depauperate faunas and often in biodiversity hotspots. The results of this study can identify conservation priorities within FATs, inform specific-type post-invasion management actions tackling NIFS, while in addition may provide valuable information for protecting high-priority water bodies before invasion.

WASH and Gender: a critical review of the literature and implications for gender-transformative WASH research

Abstract The connections between gender and water, sanitation, and hygiene (WASH) are profound, and the sector is beginning to explore the integration of gender-transformative principles into WASH programming and research. Gender-transformative approaches challenge inequalities and move beyond an instrumentalist approach to gender in development interventions. Through a critical review of academic empirical studies, this paper explores the last decade of WASH-gender literature (2008–2018). Trends were visualised using an alluvial diagram. The reviewed literature was underpinned by a diversity of disciplines, yet was dominated by women-focused, water-focused studies. Although the studies addressed many important gender considerations, few studies engaged with transformational aspects of gender equality. The majority of the studies were based in rural sub-Saharan Africa and South Asia, indicating opportunity to explore contextual dynamics in other areas of the global south. Lastly, the studies primarily focus on women of productive age; only a few studies touched on gender dynamics relevant for a diversity of women, and men and boys were mostly absent. Insights from this analysis can inform future studies at the intersection of WASH and gender. Researchers and practitioners are encouraged to include a diversity of voices, reflect on the strengths and limitations of research disciplines, and incorporate gender-transformative concepts.