Original Assignment Research Articles

Graph-Structure-Based Multigranular Belief Fusion for Human Activity Recognition.

The belief functions (BFs) introduced by Shafer in the mid of 1970s are widely applied in information fusion to model epistemic uncertainty and to reason about uncertainty. Their success in applications is however limited because of their high-computational complexity in the fusion process, especially when the number of focal elements is large. To reduce the complexity of reasoning with BFs, we can envisage as a first method to reduce the number of focal elements involved in the fusion process to convert the original basic belief assignments (BBAs) into simpler ones, or as a second method to use a simple rule of combination with potentially a loss of the specificity and pertinence of the fusion result, or to apply both methods jointly. In this article, we focus on the first method and propose a new BBA granulation method inspired by the community clustering of nodes in graph networks. This article studies a novel efficient multigranular belief fusion (MGBF) method. Specifically, focal elements are regarded as nodes in the graph structure, and the distance between nodes will be used to discover the local community relationship of focal elements. Afterward, the nodes belonging to the decision-making community are specially selected, and then the derived multigranular sources of evidence can be efficiently combined. To evaluate the effectiveness of the proposed graph-based MGBF, we further apply this new approach to combine the outputs of convolutional neural networks + attention (CNN + Attention) in the human activity recognition (HAR) problem. The experimental results obtained with real datasets prove the potential interest and feasibility of our proposed strategy with respect to classical BF fusion methods.

A time-driven simulation–optimization framework for the dynamic heterogeneous order-courier assignment problem for instant deliveries

In recent years, instant delivery services have become very popular for transporting meals to urban areas, with an extensive range of products now available to order. The platforms that offer these services rely on crowdsourced couriers who utilize their personal vehicles, resulting in heterogeneous fleets. Furthermore, the competition among companies to retain both customers and couriers is very intense, which underscores the importance of developing superior decision support systems. These systems must generate real-time assignments that meet the expectations of service providers, customers, and couriers. In this study, we designed a time-driven simulation–optimization framework that addresses the dynamic heterogeneous order-courier assignment problem and incorporates order-vehicle restrictions. The framework efficiently manages real-time order arrivals, courier movements, and positional updates while considering dynamic factors such as traffic congestion and regional speed limits for various vehicle types. Extensive testing using literature instances demonstrated the framework’s ability to satisfactorily address the defined problem. Additionally, the time-driven simulation–optimization framework was applied to a realistic case study, resulting in an approximately 4.5% reduction in the total delivery times (from the submission of the order until the delivery to the client) for all orders when compared to the original assignment.

Impact of baseline cardiovascular risk on the outcomes of intensive blood pressure intervention: a post hoc analysis of the China rural hypertension control project

BackgroundThe 2018/2023 ESC/ESH Guidelines underlined a gap how baseline cardiovascular disease (CVD) risk predicted blood pressure (BP) lowering benefits. Further, 2017 ACC/AHA Guideline and 2021 WHO Guideline recommended implementation studies about intensive BP control. Now, to bridge these guideline gaps, we conducted a post hoc analysis to validate whether the baseline CVD risk influences the effectiveness of the intensive BP control strategy, which was designed by China Rural Hypertension Control Project (CRHCP).MethodsThis is a post hoc analysis of CRHCP, among which participants were enrolled except those having CVD history, over 80 years old, or missing data. Subjects were stratified into quartiles by baseline estimated CVD risk and then grouped into intervention and usual care group according to original assignment in CRHCP. Participants in the intervention group received an integrated, multi-faceted treatment strategy, executed by trained non-physician community health-care providers, aiming to achieve a BP target of < 130/80 mmHg. Cox proportional-hazards models were used to estimate the hazard ratios of outcomes for intervention in each quartile, while interaction effect between intervention and estimated CVD risk quartiles was additionally assessed. The primary outcome comprised myocardial infarction, stroke, hospitalization for heart failure, or CVD deaths.ResultsSignificant lower rates of primary outcomes for intervention group compared with usual care for each estimated CVD risk quartile were reported. The hazard ratios (95% confidence interval) in the four quartiles (from Q1 to Q4) were 0.59 (0.40, 0.87), 0.54 (0.40, 0.72), 0.72 (0.57, 0.91) and 0.65 (0.53, 0.80), respectively (all Ps < 0.01). There’s no significant difference of hazard ratios by intervention across risk quartiles (P for interaction = 0.370). Only the relative risk of hypotension, not symptomatic hypotension, was elevated in the intervention group among upper three quartiles.ConclusionsIntensive BP lowering strategy designed by CRHCP group was effective and safe in preventing cardiovascular events independent of baseline CVD risk.Trial registrationThe trial is registered with ClinicalTrials.gov, NCT03527719.

Constrained Symmetric Non-Negative Matrix Factorization with Deep Autoencoders for Community Detection

Recently, community detection has emerged as a prominent research area in the analysis of complex network structures. Community detection models based on non-negative matrix factorization (NMF) are shallow and fail to fully discover the internal structure of complex networks. Thus, this article introduces a novel constrained symmetric non-negative matrix factorization with deep autoencoders (CSDNMF) as a solution to this issue. The model possesses the following advantages: (1) By integrating a deep autoencoder to discern the latent attributes bridging the original network and community assignments, it adeptly captures hierarchical information. (2) Introducing a graph regularizer facilitates a thorough comprehension of the community structure inherent within the target network. (3) By integrating a symmetry regularizer, the model’s capacity to learn undirected networks is augmented, thereby facilitating the precise detection of symmetry within the target network. The proposed CSDNMF model exhibits superior performance in community detection when compared to state-of-the-art models, as demonstrated by eight experimental results conducted on real-world networks.

An Algorithm for Solving Three-dimensional Assignment Problem

This article presents a algorithm for solving Three-dimensional assignment problem. Firstly, decompose the three-dimensional cubic matrix corresponding to the three-dimensional assignment problem into multiple two-dimensional planar matrices, and obtain that the assignment problems corresponding to these two-dimensional planar matrices have the same feasible solution as the original three-dimensional assignment problem. Then, the leading principal submatrix algorithm is used to solve each two-dimensional assignment problem. The characteristic of the leading principal submatrix algorithm is that each operation only needs to consider the local (leading principal submatrix) of the assignment matrix of the two-dimensional assignment matrix, without considering the entire assignment matrix. Starting from the first-order leading principal submatrix of the assignment matrix, Through the same solution transformation, the row minimum element of the leading principal submatrix of each order of the assignment matrix is found step by step, and the optimal solution of the two-dimensional assignment problems are obtained. Finally, by comparing the optimal solutions of these two-dimensional assignment problems, the optimal solutions of the original three-dimensional assignment problems are obtained. This algorithm can find the optimal solution for three-dimensional assignment problems in a patterned manner, facilitating computer programming and handling assignment problems with a large number of people and tasks.

An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers’ attitudes

Group multiple criteria sorting (MCS) has become a trend in dealing with a variety of practical problems. During the process of managing group MCS, it is critical to reduce conflicts among decision-makers (DMs). Given the key role of DMs’ attitudes in affecting consensus level, this study aims to propose a novel consensus-based approach to solve group MCS problems considering DMs’ attitudes with flexible expression linguistic distribution assessments (LDAs) that can capture massive DMs’ qualitative preferences. To achieve this goal, first, a minimum adjustment-based optimization model is built to guide individuals in revising their preferences, and a maximum assignment interval-based optimization model is constructed to derive consistent and possible assignments of each alternative while maintaining the accuracy levels of the original assignments. An attitudinal consensus index is then defined to measure the group consensus level, by which group DMs’ attitudes can be well considered in MCS problems. A sophisticated adaptive feedback adjustment mechanism is also developed and inserted into the consensus model, which provides support for consensus-reaching based on the advantages of both types of adaptive feedback adjustment mechanism strategies. Afterwards, to generate more straightforward and scientific assignment solutions, this study proposes a minimum information loss-based optimization model to identify the final categories of each alternative. Finally, an illustrative example for evaluating livable cities, followed by sensitivity and comparative analyses, is presented to demonstrate the applicability and advantages of the proposed MCS approach.

Retrospective analysis of clinical characteristics and outcomes of patients with carcinoma of unknown primary from three tertiary centers in Australia.

Carcinoma of unknown primary (CUP) remains an important tumor entity and a disproportionate cause of cancer mortality. Little is known about the contemporary clinical characteristics, treatment patterns, and outcomes of CUP patients based on updated international classification guidelines. We evaluated a contemporary CUP cohort to provide insight into current clinical practice and the impact of tissue of origin assignment, site-specific and empirical therapy in a real-world setting. We conducted a retrospective cohort study of CUP patients, as defined by the updated European Society of Medical Oncology (ESMO) 2023 guidelines, across three tertiary referral centers in Australia between 2015 and 2022. We analyzed clinical characteristics, treatment patterns, and survival outcomes using the Kaplan-Meier method and Cox regression proportional hazard model between favorable and unfavorable risk groups. We identified a total of 123 CUP patients (n = 86 unfavorable, n = 37 favorable risk as per the 2023 ESMO guidelines). Sixty-four patients (52%) were assigned a tissue of origin by the treating clinician. Median progression free survival (PFS) was 6.8 (95% confidence interval (CI) 5.1-12.1) months and overall survival (OS) 10.2 (95% CI 6.0-18.5) months. Unfavorable risk (hazard ratio [HR] 2.9, p = 0.006), poor performance status (HR 2.8, p < 0.001), and non-squamous histology (HR 2.5, p < 0.05) were associated with poor survival outcome. A total of 70 patients (57%) proceeded to systemic therapy. In patients with non-squamous histology and unfavorable risk, site-specific therapy compared to empirical chemotherapy did not improve outcome (median OS 8.2 vs. 11.8 months, p = 0.7). In this real-world cohort, CUP presentations were heterogenous. Overall survival and rates of systemic treatment were poor. Poor performance status and unfavorable risk were associated with worse survival. For most patients, site-specific therapy did not improve survival outcome. Improved and timely access to diagnostic tests and therapeutics for this group of patients is urgently required.

High performance Legionella pneumophila source attribution using genomics-based machine learning classification.

Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.

AI-generated vs. student-crafted assignments and implications for evaluating student work in nursing: an exploratory reflection.

Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial intelligence-powered language model that can generate a unique outputs, in reponse to a user's textual request. This has raised concerns related to academic integrity in nursing education as students may use the platform to generate original assignment content. Subsequently, the objective of this quality improvement project were to explore and identify effective strategies that educators can use to discern AI-generated papers from student-written submissions. Four nursing students were requested to submit two versions a Letter to the Editor assignement; one assignment that was written by the student; the other, exclusively generated by ChatGPT-3.5. AI-generated assignments were typically grammatically well-written, but some of the scholarly references used were outdated, incorrectly cited, or at times completely fabricated,. Additionally, the AI-generated assignments lacked detail and depth. Nursing educators should possess an understanding of the capabilities of ChatGPT-like technologies to further enhance nursing students' knowledge development and to ensure academic integrity is upheld.

Total Synthesis of Nicrophorusamide A and Structural Disproof of the Proposed Noursamycin A.

Total synthesis of the proposed noursamycin A has been accomplished, which disproves the original structural assignments. The synthetic strategy described herein has also been employed in the first total synthesis of nicrophorusamide A, a cyclopeptide that is structurally related to noursamycin A.

What a Genderful World – Thinking through and making of an exhibition

On 10 October 2019 the exhibition What a Genderful World opened in the Tropenmuseum in Amsterdam. The central premise was that all people experience inhabiting a body that moves, lives and breathes in a gendered world. The article firstly examines the process of making this a ‘gender' exhibition. In order to refrain from labelling, the exhibition team departed from the original assignment that aimed at an exhibition about ‘women’. Asking questions of what makes a woman or a man or any other gender identity in relation to the museum's historically and geographically diverse collections, revealed the necessity to do justice to the multiple layers involved in a discussion about gender. Ideas around gender are shaped and expressed differently through time and space, but other intersectional aspects including race, religion, socio-economic class, age or sexual orientation may well be equally important. Hence, this section discusses the means by which this huge and complex topic is made accessible in the exhibition to a broad audience. Finally, the article takes the reader on a journey through the various areas of the exhibition framed around the following questions: (1) What do you think? (2) Does your body determine your gender? (3) How do you become a gender? (4) How ought you to behave? (5) Gender without borders? (6) Who has power here? (7) Playful with gender? In so doing, the article addresses the theoretical framework underlying the choice of certain interactives and the privileging of design elements. The article will conclude with a critical reflection on the museum's role in the societal debate around gender.

MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile

BackgroundIdentification of pathogenic bacteria from clinical specimens and evaluating their antimicrobial resistance (AMR) are laborious tasks that involve in vitro cultivation, isolation, and susceptibility testing. Recently, a number of methods have been developed that use machine learning algorithms applied to the whole-genome sequencing data of isolates to approach this problem. However, making AMR assessments from more easily available metagenomic sequencing data remains a big challenge.ResultsWe present the Metagenomic Sequencing to Antimicrobial Resistance (MGS2AMR) pipeline, which detects antibiotic resistance genes (ARG) and their possible organism of origin within a sequenced metagenomics sample. This in silico method allows for the evaluation of bacterial AMR directly from clinical specimens, such as stool samples. We have developed two new algorithms to optimize and annotate the genomic assembly paths within the raw Graphical Fragment Assembly (GFA): the GFA Linear Optimal Path through seed segments (GLOPS) algorithm and the Adapted Dijkstra Algorithm for GFA (ADAG). These novel algorithms improve the sensitivity of ARG detection and aid in species annotation. Tests based on 1200 microbiome samples show a high ARG recall rate and correct assignment of the ARG origin. The MGS2AMR output can further be used in many downstream applications, such as evaluating AMR to specific antibiotics in samples from emerging intestinal infections. We demonstrate that the MGS2AMR-derived data is as informative for the entailing prediction models as the whole-genome sequencing (WGS) data. The performance of these models is on par with our previously published method (WGS2AMR), which is based on the sequencing data of bacterial isolates.ConclusionsMGS2AMR can provide researchers with valuable insights into the AMR content of microbiome environments and may potentially improve patient care by providing faster quantification of resistance against specific antibiotics, thereby reducing the use of broad-spectrum antibiotics. The presented pipeline also has potential applications in other metagenome analyses focused on the defined sets of genes.6MP9Kjm5NAthB1ioNbPxBHVideo

Prediction of American Society of Anesthesiologists Physical Status Classification from preoperative clinical text narratives using natural language processing

BackgroundElectronic health records (EHR) contain large volumes of unstructured free-form text notes that richly describe a patient’s health and medical comorbidities. It is unclear if perioperative risk stratification can be performed directly from these notes without manual data extraction. We conduct a feasibility study using natural language processing (NLP) to predict the American Society of Anesthesiologists Physical Status Classification (ASA-PS) as a surrogate measure for perioperative risk. We explore prediction performance using four different model types and compare the use of different note sections versus the whole note. We use Shapley values to explain model predictions and analyze disagreement between model and human anesthesiologist predictions.MethodsSingle-center retrospective cohort analysis of EHR notes from patients undergoing procedures with anesthesia care spanning all procedural specialties during a 5 year period who were not assigned ASA VI and also had a preoperative evaluation note filed within 90 days prior to the procedure. NLP models were trained for each combination of 4 models and 8 text snippets from notes. Model performance was compared using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Shapley values were used to explain model predictions. Error analysis and model explanation using Shapley values was conducted for the best performing model.ResultsFinal dataset includes 38,566 patients undergoing 61,503 procedures with anesthesia care. Prevalence of ASA-PS was 8.81% for ASA I, 31.4% for ASA II, 43.25% for ASA III, and 16.54% for ASA IV-V. The best performing models were the BioClinicalBERT model on the truncated note task (macro-average AUROC 0.845) and the fastText model on the full note task (macro-average AUROC 0.865). Shapley values reveal human-interpretable model predictions. Error analysis reveals that some original ASA-PS assignments may be incorrect and the model is making a reasonable prediction in these cases.ConclusionsText classification models can accurately predict a patient’s illness severity using only free-form text descriptions of patients without any manual data extraction. They can be an additional patient safety tool in the perioperative setting and reduce manual chart review for medical billing. Shapley feature attributions produce explanations that logically support model predictions and are understandable to clinicians.

Worker-Churn-Based Task Assignment With Context-LSTM in Spatial Crowdsourcing

The pervasiveness of GPS-enabled devices and wireless communication technologies flourish the market of Spatial Crowdsourcing (SC), which consists of location-based tasks and requires workers to be at specific locations physically to complete them. In this work, we study the problem of worker-churn-based task assignment in SC, where tasks are assigned by considering workers' churn. In particular, we aim to maximize the total rewards of task assignments based on the worker churn prediction. To solve the problem, we propose a two-phase framework, which consists of a worker churn prediction and a task assignment phase. In the first phase, we use an LSTM-based model to extract latent feelings of workers based on historical data and then estimate idle time intervals of workers. In the assignment phase, we design an efficient greedy algorithm and a Kuhn-Munkras-based algorithm that can achieve the optimal task assignment. To improve the accuracy of the idle time interval estimation for workers, we adopt a context-dependent LSTM model, which involves interactions between inputs and their context. We further optimize the original task assignment framework by proposing a travel distance optimization strategy to reduce the overall travel distance. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.

1H NMR-based approach to determine the geographical origin and cultivation method of roasted coffee

A comprehensive study of 603 roasted arabica coffee samples using NMR fingerprinting and multivariate data analysis was performed to differentiate coffee samples according to their geographical origin and cultivation method. Both lipophilic and hydrophilic coffee metabolites were recorded using 1H NMR spectroscopy, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was applied. Coffee samples were fist differentiated according to their continents of origin followed by discrimination of coffee samples from Brazil, Ethiopia, and Colombia from coffee samples originating from another continent. Discrimination of coffee samples according to their continent of origin and additional assignment to the countries Brazil and Ethiopia were successful. However, an unambiguous separation of Colombian coffee samples from coffee samples of another continent (other than South America) was not possible. Also, differentiation of organically and conventionally produced coffee samples by using 1H NMR and PCA-LDA was not achieved.

Absolute configuration reassignment of nectamazin A: Implications to related neolignans

The ability of nature to produce structurally complex molecules makes the determination of the absolute configuration of natural products a challenging task. Although extensive NMR analysis generally allows for the reliable assignment of relative configurations, the assignments of absolute stereochemistry are commonly performed by empirical comparisons of optical rotation (OR) and/or electronic circular dichroism (ECD) data obtained for related molecules. Such an approach, however, may lead to misassignments and consequent error propagations. Herein, we present the case of the bicyclo(3.2.1)octane neolignan named (+)-nectamazin A. This compound was first reported in 2009 from Nectandra amazonum Nees. (Lauraceae) and had its absolute configuration determined as 7R,8S,3′S,4′R,5′S by means of experimental ECD spectroscopy. Our chemical studies on Ocotea aciphylla (Lauraceae) led to the isolation of (+)-nectamazin A. The extensive analysis of OR, ECD, and vibrational CD data aided by quantum chemical calculations, however, indicated (+)-nectamazin A to have the 7S,8R,3′R,4′S,5′R absolute configuration, in conflict with the configuration reported in the literature. The cause of the original incorrect assignment of (+)-nectamazin A derives from the direct comparison of experimental OR and ECD data obtained for structurally related molecules with different chromophoric systems. As an alternative, VCD spectroscopy is presented as a more reliable and sensitive technique to stereochemical investigations of bicyclo(3.2.1)octane neolignans.

A Case Study in Sharing Marine eDNA Metabarcoding Data to OBIS

Metabarcoding of DNA collected from an environmental sample (eDNA) is increasingly employed in marine biodiversity surveys, with the ability to target taxa from microbes to plankton to large vertebrates depending on the molecular markers used. These techniques are often the only viable method to detect certain taxonomic groups, and therefore provide observations that are currently under-represented on existing biodiversity data platforms, such as the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility (GBIF). Some of the reasons for this disconnect include the unique data structures inherent to eDNA datasets, the complexities of combining marine observation data and environmental data (De Pooter et al. 2017), and the minimal availability of documented examples. Here we present a detailed case study on the preparation of marine metabarcoding survey data for publication to OBIS. This data comes from the 2021 Gulf of Mexico Ecosystems and Carbon Cycle (GOMECC) cruise led by the National Oceanic and Atmospheric Administration (NOAA), employing 17 coastal-offshore transects across the Gulf of Mexico and the Atlantic Ocean. Metabarcoding libraries targeted bacteria and archaea with the 16S rRNA marker and eukaryotes with the 18S rRNA marker. Amplicon sequence variants (ASVs) were inferred for each marker, then taxonomy was assigned to these ASVs using the open-sourced reference databases PR2 5.0.1 and SILVA 138.1. OBIS requires that taxonomic assignments are converted to the World Register of Marine Species (WoRMS) nomenclature, which can be particularly challenging for marine bacteria and archaea as these taxa are underrepresented on WoRMS. Three tables—the per-sample ASV observation counts, assigned taxonomy of the ASV sequences, and sample collection data—were then converted to the DNA derived extension for Darwin Core (Abarenkov et al. 2023) using a combination of new and OBIS-provided Python scripts (LaScala-Gruenewald et al. 2021). This workflow is available at the NOAA Omics Data Management Guide*1, which will also host links to NOAA Omics datasets. Some of the key challenges to navigate when preparing metabarcoding data for OBIS include: developing a sample collection data template, converting taxonomic assignments to WoRMS nomenclature while preserving the original assignments, and recording the complex methodological and bioinformatic processes involved in data generation in order to be reproducible. developing a sample collection data template, converting taxonomic assignments to WoRMS nomenclature while preserving the original assignments, and recording the complex methodological and bioinformatic processes involved in data generation in order to be reproducible. We describe our workflow for tackling these challenges, with the aim of fostering discussion on best practices for publishing marine eDNA data to biodiversity data platforms. This work is part of a larger effort across NOAA ’Omics to develop a comprehensive bioinformatics platform and data management framework for marine eDNA and microbiome data.

Effects of metformin and intensive lifestyle interventions on the incidence of kidney disease in adults in the DPP/DPPOS

AimsWe analyzed the incidence of kidney disease in the Diabetes Prevention Program Outcomes Study (DPPOS) by originally randomized treatment group assignment: Intensive Lifestyle (ILS), Metformin (MET) or Placebo (PLB). MethodsThe current analyses used a time-to-event approach in which the primary outcome was kidney disease, ascertained as urine albumin-to-creatinine ratio (ACR) ≥ 3.39 mg/mmol (30 mg/g) or eGFR <45 mL/min/1.73m2, with confirmation required at the next visit, or adjudicated end-stage kidney disease (ESKD). ResultsAt a median of 21 years following randomization in DPP, diabetes development was reduced in both the ILS (HR 0.73 [95%CI = 0.62, 0.85]) and MET groups (HR 0.85 [0.73, 0.99]) compared to the PLB group. Although risk for developing the primary kidney disease outcome was higher among those with incident diabetes compared to those without (HR 1.81 [1.43, 2.30]), it did not differ by intervention groups (ILS vs. PLB 1.02 (0.81, 1.29); MET vs. PLB 1.08 (0.86, 1.35). There was a non-significant metformin by age interaction (p = 0.057), with metformin being beneficial for kidney disease in the younger but potentially harmful in the older participants. ConclusionsDevelopment of kidney disease was increased in participants who developed diabetes but did not differ by original treatment group assignment. Clinical trial registrationsDiabetes Prevention Program (DPP) Clinical trial reg. no. NCT00004992DPP Outcomes Study (DPPOS) Clinical trial reg. no. NCT0038727

Stable isotope ratios in wood show little potential for sub-country origin verification in Central Africa

Origin verification of timber is essential to expose origin fraud and reduce illegal timber trade. A promising forensic method for origin verification is based on stable isotope ratios in wood, but large-scale studies that test local and regional variation to apply the method at a sub-country scale are lacking.We investigated the isotopic variation in wood in Central Africa for two economically important timbers (Azobé and Tali). We measured wood δ18O, δ2H, δ13C and δ34S at 17 locations in the main timber exporting countries (Cameroon, Republic of the Congo, Gabon). This is the first study to quantify both local and regional variation as well as species differences. We applied a novel statistical technique, Quantile Regression Forests, to develop spatial predictions of isotopic composition based on gridded covariables (isoscapes). This is a powerful method to develop isoscapes based on non-normally distributed and correlating covariables.We found limited potential for origin assignment with stable isotope ratios in Central Africa, because the local variability of values for all four studied isotopes was generally higher than regional variation. This led to low site classification success (Azobé: 32.1%, Tali: 20.5%) and large uncertainty in the isoscapes. The limited origin signal can be attributed to low variation in the driving environmental factors in the region. The isotope ratios did differ between the three countries, country-level assignment was good (Azobé: 79.4%, Tali: 61.7%) and considerably higher than site-level values. Lastly, we found a significant species and site effect, stressing species-specific reference datasets might be needed for future isotope tracing studies.These results show that the four isotopes applied here have limited potential for sub-country origin assignment of timber in this region (Cameroon, Republic of the Congo, Gabon). Nevertheless, the variation at large spatial scales resulted in promising accuracy of country-level assignment. These findings contribute to our understanding of forensic timber tracing methods and can help to identify the most appropriate method for a tracing question at hand.

Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades.

Stable isotopes are an important tool to uncover animal migration. Geographic natal assignments often require categorizing the spatial domain through a nominal approach, which can introduce bias given the continuous nature of these tracers. Stable isotopes predicted over a spatial gradient (i.e., isoscapes) allow a probabilistic and continuous assignment of origin across space, although applications to marine organisms remain limited. We present a new framework that integrates nominal and continuous assignment approaches by (1) developing a machine-learning multi-model ensemble classifier using Bayesian model averaging (nominal); and (2) integrating nominal predictions with continuous isoscapes to estimate the probability of origin across the spatial domain (continuous). We applied this integrated framework to predict the geographic origin of the Northwest Atlantic mackerel (Scomber scombrus), a migratory pelagic fish comprised of northern and southern components that have distinct spawning sites off Canada (northern contingent) and the US (southern contingent), and seasonally overlap in the US fished regions. The nominal approach based on otolith carbon and oxygen stable isotopes (δ13C/δ18O) yielded high contingent classification accuracy (84.9%). Contingent assignment of unknown-origin samples revealed prevalent, yet highly varied contingent mixing levels (12.5-83.7%) within the US waters over four decades (1975-2019). Nominal predictions were integrated into mackerel-specific otolith oxygen isoscapes developed independently for Canadian and US waters. The combined approach identified geographic nursery hotspots in known spawning sites, but also detected geographic shifts over multi-decadal time scales. This framework can be applied to other marine species to understand migration and connectivity at a high spatial resolution, relevant to management of unit stocks in fisheries and other conservation assessments.