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Background Over the past two decades, preventive chemotherapy (PC) with praziquantel (PZQ) is the major strategy for controlling schistosomiasis in Senegal. The objective of this analysis was to update the endemicity of schistosomiasis at community level for better targeting mass treatment with PZQ in Senegal. Methods Demographic and epidemiological data from 1610 community health areas were analyzed using the schistosomiasis community data analysis tool of Expanded Special Project for Elimination of Neglected Tropical Diseases which developed by World Health Organization/Africa Office (WHO/AFRO). The tool uses a WHO/AFRO decision tree for areas without epidemiological data to determine whether mass treatment should be continued at community level. Descriptive analysis was performed. Results Overall, the endemicity of 1610 community health areas were updated based on the data from the district endemicity (33.5%) and the form of Join request for selected PC medicine (40.5%). Up to 282 (17.5%) and 398 (24.7%) of community health areas were classified as moderate and high endemicity. 41.1% of communities were non endemic. High endemicity was more important in Tambacounda, Saint Louis, Matam, Louga and Kedougou. A change in endemicity category was observed when data was disagregted from district level to community level. Implementation units classified non endemic were more important at community level ( n  = 666) compared to district level ( n  = 324). Among 540 areas previously classified high endemic at district level, 392 (72.6%) remained high prevalence category, while 92 (17.0%) became moderate, 43 (8.0%) low and 13 (2.4%) non-endemics at community level. Number of implementation units requiring PC was more important at district level (1286) compared to community level (944). Number of school aged children requiring treatment was also more important at district level compared to community level. Conclusions The analysis to disaggregate data from district level to community level using the WHO/AFRO schistosomiasis sub-district data optimization tool provide an update of schistosomiasis endemicity at community level. This study has allowed to better target schistosomiasis interventions, optimize use of available PZQ and exposed data gaps.

Effective vegetation classification schemes identify the processes determining species assemblages and support the management of protected areas. They can also provide a framework for ecological research. In the tropics, elevation-based classifications dominate over alternatives such as river catchments. Given the existence of floristic data for many localities, we ask how useful floristic data are for developing classification schemes in species-rich tropical landscapes and whether floristic data provide support for classification by river catchment. We analyzed the distribution of vascular plant species within 141 plots across an elevation gradient of 130 to 3200 m asl within La Amistad National Park. We tested the hypothesis that river catchment, combined with elevation, explains much of the variation in species composition. We found that annual mean temperature, elevation, and river catchment variables best explained the variation within local species communities. However, only plots in high-elevation oak forest and Paramo were distinct from those in lowand mid-elevation zones. Beta diversity did not significantly differ in plots grouped by elevation zones, except for low-elevation forest, although it did differ between river catchments. None of the analyses identified discrete vegetation assemblages within mid-elevation (700–2600 m asl) plots. Our analysis supports the hypothesis that river catchment can be an alternative means for classifying tropical forest assemblages in conservation settings.

Human mobility datasets, such as traffic flow data, reveal the connections between urban spaces. A novel framework is proposed to explore the spatial association between urban commercial and residential spaces via consumption travel flows in Shanghai. A social network analysis and a community detection method are employed using taxi trajectory data during the daytime to validate the framework. The machine learning-based approach, such as the community detection method, can overcome the limitation regarding spatial uncertainty and spatial effects. The empirical findings suggest that people’s commercial activities are sensitive to the power of accessible commercial centers and travel distances. The high-level commercial centers would contribute to the monocentric structure in the outer urban region based on consumption flows. In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people’s mobility based on the trajectory big data, which is promising in future urban research.

The chapter examines the simultaneous analysis of two, or possibly several data tables by direct or indirect comparisons, also called direct and indirect gradient analyses. Canonical analysis may be asymmetric (i.e. redundancy analysis, canonical correspondence analysis, and linear discriminant analysis) or symmetric (i.e. canonical correlation analysis, co-inertia analysis, and Procrustes analysis. The chapter includes discussion of the following topics: redundancy analysis (RDA, simple RDA, statistics in simple RDA, redundancy statistic, algebra of simple RDA, biplot, triplot, partial RDA, statistics in partial RDA, tests of significance in partial RDA, and variation partitioning by RDA), canonical correspondence analysis (CCA, algebra of CCA, and partial CCA), analysis of community composition data with RDA and CCA (classical RDA, classical CCA, transformation-based RDA, tb-RDA, and distance-based RDA, db-RDA), linear discriminant analysis (LDA, discriminant functions, identification function, algebra of LDA, confusion table, classification table, and statistics in LDA), canonical correlation analysis (CCorA, algebra of CCorA, and statistics in CCorA), co-inertia analysis (CoIA, algebra of CoIA, multiple factor analysis), Procrustes analysis (Proc, orthogonal Proc, asymmetric Proc, symmetric Proc, and generalized Proc), uses of canonical correlation, Procrustes and co-inertia analyses, and canonical analysis of community composition data. Numerical methods are illustrated with real ecological applications, drawn from the literature. The chapter ends on a description of relevant software implemented in the R language; it also cites some commercially available statistical packages and programs from researchers.

ABSTRACT A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-‘microeco’ as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco). An integrated and powerful R package-microeco was developed for researchers to perform data mining of amplicon sequencing in microbial community ecology.

Context: Communities, funding agencies, and institutions are increasingly involving community stakeholders as partners in research. Community stakeholders can provide firsthand knowledge and insight, thereby increasing research relevance and feasibility. Despite the greater emphasis and use of community-academic partnerships (CAP) across multiple disciplines, definitions of partnerships and methodologies vary greatly, and no systematic reviews consolidating this literature have been published. The purpose of this article, then, is to facilitate the continued growth of this field by examining the characteristics of CAPs and the current state of the science, identifying the facilitating and hindering influences on the collaborative process, and developing a common term and conceptual definition for use across disciplines. Methods: Our systematic search of 6 major literature databases generated 1,332 unique articles, 50 of which met our criteria for inclusion and provided data on 54 unique CAPs. We then analyzed studies to describe CAP characteristics and to identify the terms and methods used, as well as the common influences on the CAP process and distal outcomes. Findings: CAP research spans disciplines, involves a variety of community stakeholders, and focuses on a large range of study topics. CAP research articles, however, rarely report characteristics such as membership numbers or duration. Most studies involved case studies using qualitative methods to collect data on the collaborative process. Although various terms were used to describe collaborative partnerships, few studies provided conceptual definitions. Twenty-three facilitating and hindering factors influencing the CAP collaboration process emerged from the literature. Outcomes from the CAPs most often included developing or refining tangible products. Conclusions: Based on our systematic review, we recommend using a single term, community-academic partnership, as well as a conceptual definition to unite multiple research disciplines. In addition, CAP characteristics and methods should be reported more systematically to advance the field (eg, to develop CAP evaluation tools). We have identified the most common influences that facilitate and hinder CAPs, which in turn should guide their development and sustainment.

The stochastic block model (SBM) and its variants have been a popular tool for analyzing large network data with community structures. In this article, we develop an efficient network cross-validation (NCV) approach to determine the number of communities, as well as to choose between the regular stochastic block model and the degree corrected block model (DCBM). The proposed NCV method is based on a block-wise node-pair splitting technique, combined with an integrated step of community recovery using sub-blocks of the adjacency matrix. We prove that the probability of under-selection vanishes as the number of nodes increases, under mild conditions satisfied by a wide range of popular community recovery algorithms. The solid performance of our method is also demonstrated in extensive simulations and two data examples. Supplementary materials for this article are available online.

Given that an estimated 0.6% of the U.S. population is transgender (trans) and that large health disparities for this population have been documented, government and research organizations are increasingly expanding measures of sex/gender to be trans inclusive. Options suggested for trans community surveys, such as expansive check-all-that-apply gender identity lists and write-in options that offer maximum flexibility, are generally not appropriate for broad population surveys. These require limited questions and a small number of categories for analysis. Limited evaluation has been undertaken of trans-inclusive population survey measures for sex/gender, including those currently in use. Using an internet survey and follow-up of 311 participants, and cognitive interviews from a maximum-diversity sub-sample (n = 79), we conducted a mixed-methods evaluation of two existing measures: a two-step question developed in the United States and a multidimensional measure developed in Canada. We found very low levels of item missingness, and no indicators of confusion on the part of cisgender (non-trans) participants for both measures. However, a majority of interview participants indicated problems with each question item set. Agreement between the two measures in assessment of gender identity was very high (K = 0.9081), but gender identity was a poor proxy for other dimensions of sex or gender among trans participants. Issues to inform measure development or adaptation that emerged from analysis included dimensions of sex/gender measured, whether non-binary identities were trans, Indigenous and cultural identities, proxy reporting, temporality concerns, and the inability of a single item to provide a valid measure of sex/gender. Based on this evaluation, we recommend that population surveys meant for multi-purpose analysis consider a new Multidimensional Sex/Gender Measure for testing that includes three simple items (one asked only of a small sub-group) to assess gender identity and lived gender, with optional additions. We provide considerations for adaptation of this measure to different contexts.

There is increasing availability and use of unstructured and semi‐structured citizen science data in biodiversity research and conservation. This expansion of a rich source of ‘big data’ has sparked numerous research directions, driving the development of analytical approaches that account for the complex observation processes in these datasets. We review outstanding challenges in the analysis of citizen science data for biodiversity monitoring. For many of these challenges, the potential impact on ecological inference is unknown. Further research can document the impact and explore ways to address it. In addition to outlining research directions, describing these challenges may be useful in considering the design of future citizen science projects or additions to existing projects. We outline challenges for biodiversity monitoring using citizen science data in four partially overlapping categories: challenges that arise as a result of (a) observer behaviour; (b) data structures; (c) statistical models; and (d) communication. Potential solutions for these challenges are combinations of: (a) collecting additional data or metadata; (b) analytically combining different datasets; and (c) developing or refining statistical models. While there has been important progress to develop methods that tackle most of these challenges, there remain substantial gains in biodiversity monitoring and subsequent conservation actions that we believe will be possible by further research and development in these areas. The degree of challenge and opportunity that each of these presents varies substantially across different datasets, taxa and ecological questions. In some cases, a route forward to address these challenges is clear, while in other cases there is more scope for exploration and creativity.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective. SYNOPSIS COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus. COVID‐19 Disease Map was built by over 20 independent biocuration teams and harmonised using systems biology standards. Biocuration efforts were assisted by the systematic use of text‐ and AI‐assisted mining of relevant bioinformatic databases and platforms. Case studies illustrate the applications of the map for visual exploration and computational analysis of SARS‐CoV‐2 pathways in combination with omic data. The map is an open‐access effort, with all content and code shared in public repositories. Graphical Abstract COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus.