Corrigendum: A community focused approach toward making healthy and affordable daily diet recommendations.

[This corrects the article DOI: 10.3389/fdata.2023.

A community focused approach toward making healthy and affordable daily diet recommendations.

Introduction: Maintaining an affordable and nutritious diet can be challenging, especially for those living under the conditions of poverty. To fulfill a healthy diet, consumers must make difficult decisions within a complicated food landscape. Decisions must factor information on health and budget constraints, the food supply and pricing options at local grocery stores, and nutrition and portion guidelines provided by government services.

On the use of real-world datasets for reaction yield prediction.

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described.

Environment and shipping drive environmental DNA beta-diversity among commercial ports.

The spread of nonindigenous species by shipping is a large and growing global problem that harms coastal ecosystems and economies and may blur coastal biogeographical patterns. This study coupled eukaryotic environmental DNA (eDNA) metabarcoding with dissimilarity regression to test the hypothesis that ship-borne species spread homogenizes port communities. We first collected and metabarcoded water samples from ports in Europe, Asia, Australia and the Americas.

Successful Aging for Community-Dwelling Older Adults: An Experimental Study with a Tablet App.

Mobile health (mHealth) technologies offer an opportunity to enable the care and support of community-dwelling older adults, however, research examining the use of mHealth in delivering quality of life (QoL) improvements in the older population is limited. We developed a tablet application (eSeniorCare) based on the Successful Aging framework and investigated its feasibility among older adults with low socioeconomic status. Twenty five participants (females = 14, mean age = 65 years) used the app to set and track medication intake reminders and health goals, and to play selected casual mobile games for 24 weeks.

Predictors of Septic Shock or Bacteremia in Children Experiencing Febrile Neutropenia Post-Chemotherapy.

Background: Febrile neutropenia (FN) is an early indicator of infection in oncology patients post-chemotherapy. We aimed to determine clinical predictors of septic shock and/or bacteremia in pediatric cancer patients experiencing FN and to create a model that classifies patients as low-risk for these outcomes.

Methods: This is a retrospective analysis with clinical data of a cohort of pediatric oncology patients admitted during July 2015 to September 2017 with FN.

Symptomatic, Presymptomatic, and Asymptomatic Transmission of SARS-CoV-2 in a University Student Population, August-November 2020.

Objectives: The impact and risk of SARS-CoV-2 transmission from asymptomatic and presymptomatic hosts remains an open question. This study measured the secondary attack rates (SARs) and relative risk (RR) of SARS-CoV-2 transmission from asymptomatic and presymptomatic index cases as compared with symptomatic index cases.

Methods: We used COVID-19 test results, daily health check reports, and contact tracing data to measure SARs and corresponding RRs among close contacts of index cases in a cohort of 12 960 young adults at the University of Notre Dame in Indiana for 103 days, from August 10 to November 20, 2020.

Predicting terrorist attacks in the United States using localized news data.

Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. To address this threat, we propose a novel feature representation method and evaluate machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model (a Random Forest aided by a novel variable-length moving average method) achieved area under the receiver operating characteristic (AUROC) of ≥ 0.

Deep Multimodal Complementarity Learning.

Complementarity plays a significant role in the synergistic effect created by different components of a complex data object. Complementarity learning on multimodal data has fundamental challenges of representation learning because the complementarity exists along with multiple modalities and one or multiple items of each modality. Also, an appropriate metric is needed for measuring the complementarity in the representation space.

SD: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance.

Comprehensively evaluating and comparing researchers' academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks often require the publication and citation data to be investigated in various manners. In this article, we present an interactive visualization framework, SD , to enable flexible data partition and composition to support various analysis requirements within a single system.

Recipe Recommendation With Hierarchical Graph Attention Network.

Recipe recommendation systems play an important role in helping people find recipes that are of their interest and fit their eating habits. Unlike what has been developed for recommending recipes using content-based or collaborative filtering approaches, the relational information among users, recipes, and food items is less explored. In this paper, we leverage the relational information into recipe recommendation and propose a graph learning approach to solve it.

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.

Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have further magnified the importance of the imbalanced data problem, especially when learning from images. Therefore, there is a need for an oversampling method that is specifically tailored to deep learning models, can work on raw images while preserving their properties, and is capable of generating high-quality, artificial images that can enhance minority classes and balance the training set.

Predicting Participant Compliance With Fitness Tracker Wearing and Ecological Momentary Assessment Protocols in Information Workers: Observational Study.

Background: Studies that use ecological momentary assessments (EMAs) or wearable sensors to track numerous attributes, such as physical activity, sleep, and heart rate, can benefit from reductions in missing data. Maximizing compliance is one method of reducing missing data to increase the return on the heavy investment of time and money into large-scale studies.

Objective: This paper aims to identify the extent to which compliance can be prospectively predicted from individual attributes and initial compliance.

Discrete Heart Rate Values or Continuous Streams? Representation, Variability, and Meaningful Use of Vital Sign Data.

Documentation and review of patient heart rate are a fundamental process across a myriad of clinical settings. While historically recorded manually, bedside monitors now provide for the automated collection of such data. Despite the availability of continuous streaming data, patients' charts continue to reflect only a subset of this information as snapshots recorded throughout a hospitalization.

Examining Response to Negative Life Events Through Fitness Tracker Data.

Negative life events, such as the death of a loved one, are an unavoidable part of life. These events can be overwhelmingly stressful and may lead to the development of mental health disorders. To mitigate these adverse developments, prior literature has utilized measures of psychological responses to negative life events to better understand their effects on mental health.

Unified Representation of Twitter and Online News Using Graph and Entities.

To improve consumer engagement and satisfaction, online news services employ strategies for personalizing and recommending articles to their users based on their interests. In addition to news agencies' own digital platforms, they also leverage social media to reach out to a broad user base. These engagement efforts are often disconnected with each other, but present a compelling opportunity to incorporate engagement data from social media to inform their digital news platform and vice-versa, leading to a more personalized experience for users.

Network analysis of ballast-mediated species transfer reveals important introduction and dispersal patterns in the Arctic.

Rapid climate change has wide-ranging implications for the Arctic region, including sea ice loss, increased geopolitical attention, and expanding economic activity resulting in a dramatic increase in shipping activity. As a result, the risk of harmful non-native marine species being introduced into this critical region will increase unless policy and management steps are implemented in response. Using data about shipping, ecoregions, and environmental conditions, we leverage network analysis and data mining techniques to assess, visualize, and project ballast water-mediated species introductions into the Arctic and dispersal of non-native species within the Arctic.

Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance.

Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine sport medical examinations, which included electrocardiography at rest.

HONEM: Learning Embedding for Higher Order Networks.

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and, as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network, that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher order dependencies in the network.

Higher-order patterns of aquatic species spread through the global shipping network.

The introduction and establishment of nonindigenous species (NIS) through global ship movements poses a significant threat to marine ecosystems and economies. While ballast-vectored invasions have been partly addressed by some national policies and an international agreement regulating the concentrations of organisms in ballast water, biofouling-vectored invasions remain largely unaddressed. Development of additional efficient and cost-effective ship-borne NIS policies requires an accurate estimation of NIS spread risk from both ballast water and biofouling.

Biomedical Knowledge Graphs Construction From Conditional Statements.

Conditions play an essential role in biomedical statements. However, existing biomedical knowledge graphs (BioKGs) only focus on factual knowledge, organized as a flat relational network of biomedical concepts. These BioKGs ignore the conditions of the facts being valid, which loses essential contexts for knowledge exploration and inference.

Unraveling Complexity about Childhood Obesity and Nutritional Interventions: Modeling Interactions Among Psychological Factors.

As the global prevalence of childhood obesity continues to rise, researchers and clinicians have sought to develop more effective and personalized intervention techniques. In doing so, obesity interventions have expanded beyond the traditional context of nutrition to address several facets of a child's life, including their psychological state. While the consideration of psychological features has significantly advanced the view of obesity as a holistic condition, attempts to associate such features with outcomes of treatment have been inconclusive.

Examining the weekend effect across ICU performance metrics.

Background: Known colloquially as the "weekend effect," the association between weekend admissions and increased mortality within hospital settings has become a highly contested topic over the last two decades. Drawing interest from practitioners and researchers alike, a sundry of works have emerged arguing for and against the presence of the effect across various patient cohorts. However, it has become evident that simply studying population characteristics is insufficient for understanding how the effect manifests.

Social network structure is predictive of health and wellness.

Social networks influence health-related behavior, such as obesity and smoking. While researchers have studied social networks as a driver for diffusion of influences and behavior, it is less understood how the structure or topology of the network, in itself, impacts an individual's health behavior and wellness state. In this paper, we investigate whether the structure or topology of a social network offers additional insight and predictability on an individual's health and wellness.