Spatial Analysis of the Ecogeographic Diversity of Wild Creeping Cucumber (Melothria pendula L.) for In Situ and Ex Situ Conservation in Mexico.

Melothria pendula L., a wild relative of cucurbit crops, is also used for food and as a medicinal plant in Mexico. The objective of this study was to ecogeographically characterize the known populations of M. pendula in Mexico, determining its adaptive range and possible sites for in situ and ex situ conservation. To achieve this goal, we compiled a dataset of 1270 occurrences of M. pendula from herbarium and botanical databases and individual observations. Adaptive scenarios were generated through the development of an ecogeographic land characterization (ELC) map, preceded by the identification of abiotic variables influencing the species' distribution. Eleven bioclimatic, edaphic, and geophysical variables were found to be important for the species' distribution. The ELC map obtained contained 21 ecogeographic categories, with 14 exhibiting the presence of M. pendula. By analyzing ecogeographic representativeness, 111 sites of high interest were selected for the efficient collection of M. pendula in Mexico. Eight high-priority hotspots for future in situ conservation of M. pendula were also identified based on their high ecogeographic diversity, with only three of these hotspots located within protected natural areas. In this study, ecogeographic approaches show their potential utility in conservation prioritization when genetic data are scarce, a very common condition in crop wild relatives.

Development and application of a GIS tool in the design of surface water quality monitoring networks: A micro-watershed-based approach.

The design of a representative surface water quality monitoring network is vital for accurately capturing the dynamics of water bodies and variability in pollution across a catchment. The representativeness of a surface water monitoring network refers to how well it reflects the characteristics of all monitored surface water bodies. In this study, using a micro-watershed-based approach, a Geographic Information System (GIS) tool (Surface Water Quality Monitoring Point Locations ANalysis (SWQM_PLAN)) has been developed to optimize the design of surface water quality monitoring networks. In the first stage of the two-stage study, a digital elevation model and minimum watershed area size were taken as input parameters and micro-watersheds with defined upstream-downstream relations were created. In the second stage, input parameters including land use data, pollution sources, and micro-watershed data, along with specific criteria, were used to identify the basins and determine the optimal locations for surface water monitoring stations. The developed GIS tool was then applied to evaluate the existing surface water monitoring network in the Gediz River Basin, designed by the Republic of Türkiye, Ministry of Agriculture and Forestry. The tool assessed the effectiveness if the existing monitoring network in terms of assessing agricultural pollution and provided potential revision suggestions to enhance the effectiveness of implemented pollution reduction measures.

Similarity analyses of causative viruses for chronic obstructive pulmonary disease and asthma exacerbations : Author.

BACKGROUND: The representativeness of cohort studies compared to nationwide data is a major concern. This study evaluated the similarity and seasonality of causative respiratory viruses for chronic obstructive pulmonary disease (COPD) and asthma exacerbations between retrospective multicenter cohort study and nationwide data. METHODS: We compared data from the retrospective multicenter cohort study with Korean Influenza and Respiratory Surveillance System data between 2015 and 2018. Correlation, dynamic time warping (DTW), and seasonal autoregressive integrated moving average (SARIMA) analyses were performed. RESULTS: Spearman correlation coefficients [ρ] indicated very strong (respiratory syncytial virus [RSV] [ρ = 0.8458] and influenza virus [IFV] [ρ = 0.8272]), strong (human metapneumovirus [HMPV] [ρ = 0.7177] and parainfluenza virus [PIV] [ρ = 0.6742]), and moderate (rhinovirus [RV] [ρ = 0.5850] and human coronavirus [HCoV] [ρ = 0.5158]) correlations. DTW analyses showed moderate (PIV) and high (IFV, RSV, and HMPV) synchronicity between the two datasets, while RV and HCoV showed low synchronicity. SARIMA analyses revealed 12-month seasonality for IFV, RSV, PIV, and HMPV. The peak season was winter for RSV and IFV, spring to summer for PIV, and spring for HMPV. CONCLUSIONS: This was the first study to report the synchronicity between a retrospective multicenter cohort study of viruses that can cause COPD or asthma exacerbations and nationwide surveillance system data.

An in-depth analysis of data reduction methods for sustainable deep learning.

In recent years, deep learning has gained popularity for its ability to solve complex classification tasks. It provides increasingly better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference processes. In this context, data reduction can help reduce energy consumption when training a deep learning model. In this paper, we present up to eight different methods to reduce the size of a tabular training dataset, and we develop a Python package to apply them. We also introduce a representativeness metric based on topology to measure the similarity between the reduced datasets and the full training dataset. Additionally, we develop a methodology to apply these data reduction methods to image datasets for object detection tasks. Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model.

Perception-action coupling in anticipation research: a classification and its application to racket sports.

Anticipation is key to performance in many sports. By definition, anticipation as a perceptual-cognitive process is meant to inform action and help athletes reduce potential motor costs under spatiotemporal pressure. Anticipation research has repeatedly been criticized for neglecting action and raised the need for predominant testing under conditions of perception-action coupling (PAC). To the best of our knowledge, however, there is a lack of explicit criteria to characterize and define PAC conditions. This can lead to blurred terminology and may complicate interpretation and comparability of PAC conditions and results across studies. Here, we make a first proposal for a 7-level classification of PAC conditions with the defining dimensions of stimulus presentation and response mode. We hope this classification may constitute a helpful orientation for study planning and reporting in research on anticipation. Further, we illustrate the potential utilization of the PAC classification as a template for experimental protocol analysis in a review on anticipation in racket sports. Analysis of N = 115 studies reported in N = 91 articles confirms an underrepresentation of representative PAC conditions and reveals little change in PAC approaches over more than 40 years of research in that domain. We discuss potential reasons for these findings, the benefits of adopting the proposed PAC classification and reiterate the call for more action in anticipation research.

Considering challenges for the new Alzheimer's drugs: Clinical, population, and health system perspectives.

Recent approvals of amyloid immunotherapy drugs for early Alzheimer's disease (AD) have been highly controversial. In this piece, we consider challenges from the clinical, population health, and health systems perspectives to the role that the new AD drugs might be expected to play, now and in the future, in alleviating the morbidity caused by AD in the population. Clinically, short-term effects are small, adverse events are frequent, treatment regimens are burdensome, and, crucially, long-term effects are unknown. At a population level, there is always likely to be a trade-off between breadth of access and magnitude of benefit for any given individual. At a health system level, roll out of treatment even for only narrowly-defined patient groups will involve considerable resources to identify and treat eligible patients, with profound opportunity costs. Our considered view on current evidence is that there are challenges from each perspective to imagining a foreseeable future in which amyloid immunotherapy significantly alleviates AD morbidity at scale. HIGHLIGHTS: Recent approvals of Alzheimer's drugs have met with excitement but also controversy. Trial effects are small, adverse effects concerning, and long-term effects unknown. Results from trial cohorts may not generalize to broader, more complex patients. Significant resource requirements of eligibility assessment and drug administration. Use in "presymptomatic" populations is not supported by current evidence.

A survey of the representativeness and usefulness of wastewater-based surveillance systems in 10 countries across Europe in 2023.

Wastewater-based surveillance (WBS) has become a widespread method to monitor transmission of SARS-CoV-2 and other human pathogens in Europe. We conducted a survey about WBS systems' objectives, approaches, representativeness and usefulness in 10 invited European countries in 2023, i.e. Austria, Belgium, Denmark, Finland, Greece, Hungary, Italy, Luxembourg, the Netherlands and Norway. All countries completed the study questionnaire about their SARS-CoV-2 WBS systems, and shared information about WBS of other pathogens as deemed relevant. SARS-CoV-2 WBS systems primarily monitored national and subnational trends (population coverage: 25-99%), and a majority (8/10) also tracked variant distribution. Nine of 10 countries reported that their SARS-CoV-2 WBS systems were representative of their population and all countries remarked that the findings were valuable for public health decision-making. Results were shared with relevant public health authorities and published via dedicated websites and/or dashboards. WBS systems of other pathogens were mostly in the early stages, with some countries implementing pilots. Notable exceptions were the well-established poliovirus surveillance systems in Finland, Italy and the Netherlands. This study brings understanding the diverse landscape of WBS in Europe, offering insights for future developments and collaborations. Furthermore, it highlights the need for further integration of WBS into other European surveillance systems.

Genomic representativeness and chimerism in large collections of SAGs and MAGs of marine prokaryoplankton.

BACKGROUND: Single amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) are the predominant sources of information about the coding potential of uncultured microbial lineages, but their strengths and limitations remain poorly understood. Here, we performed a direct comparison of two previously published collections of thousands of SAGs and MAGs obtained from the same, global environment. RESULTS: We found that SAGs were less prone to chimerism and more accurately reflected the relative abundance and the pangenome content of microbial lineages inhabiting the epipelagic of the tropical and subtropical ocean, as compared to MAGs. SAGs were also better suited to link genome information with taxa discovered through 16S rRNA amplicon analyses. Meanwhile, MAGs had the advantage of more readily recovering genomes of rare lineages. CONCLUSIONS: Our analyses revealed the relative strengths and weaknesses of the two most commonly used genome recovery approaches in environmental microbiology. These considerations, as well as the need for better tools for genome quality assessment, should be taken into account when designing studies and interpreting data that involve SAGs or MAGs. Video Abstract.

The Hand, Foot, and Mouth Disease Sentinel Surveillance System in South Korea: Retrospective Evaluation Study.

Background: South Korea has implemented a hand, foot, and mouth disease (HFMD) surveillance system since 2009 to monitor incidence trends and identify disease burden. This nationwide surveillance involves a network of approximately 100 pediatric clinics that report all probable and confirmed HFMD cases. Following the COVID-19 pandemic, infectious disease surveillance systems must be evaluated to ensure the effective use of limited public health resources. Objective: This study aimed to evaluate the HFMD sentinel surveillance system in South Korea from 2017 to 2022, focusing on the transition period after the COVID-19 pandemic. Methods: We retrospectively reviewed the HFMD sentinel surveillance system from the Korea Disease Control and Prevention Agency using systematic guidelines for public health surveillance system evaluation developed by the US Centers for Disease Control and Prevention. We assessed the system's overall performance in 5 main factors: timeliness, stability, completeness, sensitivity, and representativeness (ie, the age and geographic distribution of sentinels). We rated these factors as weak, moderate, or good. Results: Our study showed that the completeness, sensitivity, and age representativeness of the HFMD surveillance performance were temporarily reduced to moderate levels from 2020 to 2021 and recovered in 2022, while the timeliness and geographic representativeness were maintained at a good level throughout the study period. The stability of the surveillance was moderate from 2017 to 2021 and weak in 2022. Conclusions: This is the first study to evaluate the HFMD surveillance system after the acute phase of the COVID-19 pandemic. We identified a temporarily reduced level of performance (ie, completeness, sensitivity, and age-specific representativeness) during the acute phase of the pandemic and good performance in 2022. Surveillance system evaluation and maintenance during public health emergencies will provide robust and reliable data to support public health policy development. Regular staff training programs and reducing staff turnover will improve HFMD surveillance system stability.

Race and ethnicity in multiple sclerosis phase 3 clinical trials: A systematic review.

BACKGROUND: Distinctive differences in multiple sclerosis (MS) have been observed by race and ethnicity. We aim to (1) assess how often race and ethnicity were reported in clinical trials registered on ClinicalTrials.gov, (2) evaluate whether the population was diverse enough, and (3) compare with publications. METHODS: We included phase 3 clinical trials registered with results on ClinicalTrials.gov between 2007 and 2023. When race and/or ethnicity were reported, we searched for the corresponding publications. RESULTS: Out of the 99 included studies, 56% reported race and/or ethnicity, of which only 26% of those primarily completed before 2017. Studies reporting race or ethnicity contributed to a total of 33,891 participants, mainly enrolled in Eastern Europe. Most were White (93%), and the median percentage of White participants was 93% (interquartile range (IQR) = 86%-98%), compared to 3% for Black (IQR = 1%-12%) and 0.2% for Asian (IQR = 0%-1%). Four trials omitted race and ethnicity in publications and even when information was reported, some discrepancies in terminology were identified and categories with fewer participants were often collapsed. CONCLUSION: More efforts should be done to improve transparency, accuracy, and representativeness, in publications and at a design phase, by addressing social determinants of health that historically limit the enrollment of underrepresented population.

Developing survey weights to ensure representativeness in a national, matched cohort study: results from the children and young people with Long Covid (CLoCk) study.

BACKGROUND: Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP. METHODS: Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall 'envisioned population' survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census. RESULTS: Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings. CONCLUSIONS: Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings.

Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation.

BACKGROUND: In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near-real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic's importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities. OBJECTIVE: The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies. METHODS: To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission. RESULTS: We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days. CONCLUSIONS: The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting.

Recruitment, data collection, participation rate, and representativeness of the international cross-sectional PRICOV-19 study across 38 countries.

BACKGROUND: Recruitment for surveys has been a great challenge, especially in general practice. METHODS: Here, we reported recruitment strategies, data collection, participation rates (PR) and representativeness of the PRICOV-19 study, an international comparative, cross-sectional, online survey among general practices (GP practices) in 37 European countries and Israel. RESULTS: Nine (24%) countries reported a published invitation; 19 (50%) had direct contact with all GPs/GP practices; 19 (50%) contacted a sample of GPs /GP practices; and 7 (18%) used another invitation strategy. The median participation rate was 22% (IQR = 10%, 28%). Multiple invitation strategies (P-value 0.93) and multiple strategies to increase PR (P-value 0.64) were not correlated with the PR. GP practices in (semi-) rural areas, GP practices serving more than 10,000 patients, and group practices were over-represented (P-value < 0.001). There was no significant correlation between the PR and strength of the primary care (PC) system [Spearman's r 0.13, 95% CI (-0.24, 0.46); P-value 0.49]; the COVID-19 morbidity [Spearman's r 0.19, 95% CI (-0.14, 0.49); P-value 0.24], or COVID-19 mortality [Spearman's r 0.19, 95% CI (-0.02, 0.58); P-value 0.06] during the three months before country-specific study commencement. CONCLUSION: Our main contribution here was to describe the survey recruitment and representativeness of PRICOV-19, an important and novel study.

Assessing inclusion and representativeness on digital platforms for health education: Evidence from YouTube.

BACKGROUND: Studies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for underrepresented demographics. We study issues of bias in inclusion and representativeness in the context of healthcare information disseminated via videos on the YouTube social media platform, a widely used online channel for multi-media rich information. With one in three US adults using the Internet to learn about a health concern, it is critical to assess inclusivity and representativeness regarding how health information is disseminated by digital platforms such as YouTube. METHODS: Leveraging methods from fair machine learning (ML), natural language processing and voice and facial recognition methods, we examine inclusivity and representativeness of video content presenters using a large corpus of videos and their metadata on a chronic condition (diabetes) extracted from the YouTube platform. Regression models are used to determine whether presenter demographics impact video popularity, measured by the video's average daily view count. A video that generates a higher view count is considered to be more popular. RESULTS: The voice and facial recognition methods predicted the gender and race of the presenter with reasonable success. Gender is predicted through voice recognition (accuracy = 78%, AUC = 76%), while the gender and race predictions use facial recognition (accuracy = 93%, AUC = 92% and accuracy = 82%, AUC = 80%, respectively). The gender of the presenter is more significant for video views only when the face of the presenter is not visible while videos with male presenters with no face visibility have a positive relationship with view counts. Furthermore, videos with white and male presenters have a positive influence on view counts while videos with female and non - white group have high view counts. CONCLUSION: Presenters' demographics do have an influence on average daily view count of videos viewed on social media platforms as shown by advanced voice and facial recognition algorithms used for assessing inclusion and representativeness of the video content. Future research can explore short videos and those at the channel level because popularity of the channel name and the number of videos associated with that channel do have an influence on view counts.

Racial, Ethnic, and Geographic Diversity in Population Neuroscience.

In this chapter, we consider lack of racial, ethnic, and geographic diversity in research studies from a public health perspective in which representation of a target population is critical. We review the state of the research field with respect to racial, ethnic, and geographic diversity in study participants. We next focus on key factors which can arise from the lack of diversity and can negatively impact external validity. Finally, we argue that the public's health, and future research, will ultimately be served by approaches from both recruitment and representation science and population neuroscience, and we close with recommendations from these two fields to improve diversity in studies.

Revisiting representativeness heuristic classic paradigms: Replication and extensions of nine experiments in Kahneman and Tversky (1972).

Kahneman and Tversky showed that when people make probability judgements, they tend to ignore relevant statistical information (e.g., sample size) and instead rely on a representativeness heuristic, whereby subjective probabilities are influenced by the degree to which a target is perceived as similar to (representative of) a typical example of the relevant population, class or category. Their article has become a cornerstone in many lines of research and has been used to account for various biases in judgement and decision-making. Despite the impact this article has had on theory and practice, there have been no direct replications. In a pre-registered experiment (N = 623; Amazon MTurk on CloudResearch), we conducted a replication and extensions of nine problems from Kahneman and Tversky's 1972 article. We successfully replicated eight out of the nine problems. We extended the replication by examining the consistency of heuristic responses across problems and by examining decision style as a predictor of participants' use of the representativeness heuristic. Materials, data, and code are available on: https://osf.io/nhqc4/.

Representativeness of a national, probability-based panel survey of COVID-19 isolation practices-United States, 2020-2022.

The U.S. Centers for Disease Control and Prevention (CDC) received surveillance data on how many people tested positive for SARS-CoV-2, but there was little information about what individuals did to mitigate transmission. To fill the information gap, we conducted an online, probability-based survey among a nationally representative panel of adults living in the United States to better understand the behaviors of individuals following a positive SARS-CoV-2 test result. Given the low response rates commonly associated with panel surveys, we assessed how well the survey data aligned with CDC surveillance data from March, 2020 to March, 2022. We used CDC surveillance data to calculate monthly aggregated COVID-19 case counts and compared these to monthly COVID-19 case counts captured by our survey during the same period. We found high correlation between our overall survey data estimates and monthly case counts reported to the CDC during the analytic period (r: +0.94; p < 0.05). When stratified according to demographic characteristics, correlations remained high. These correlations strengthened our confidence that the panel survey participants were reflective of the cases reported to CDC and demonstrated the potential value of panel surveys to inform decision making.

Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation.

In this work, we study various hybrid models of entropy-based and representativeness sampling techniques in the context of active learning in medical segmentation, in particular examining the role of UMAP (Uniform Manifold Approximation and Projection) as a technique for capturing representativeness. Although UMAP has been shown viable as a general purpose dimension reduction method in diverse areas, its role in deep learning-based medical segmentation has yet been extensively explored. Using the cardiac and prostate datasets in the Medical Segmentation Decathlon for validation, we found that a novel hybrid combination of Entropy-UMAP sampling technique achieved a statistically significant Dice score advantage over the random baseline (3.2% for cardiac, 4.5% for prostate), and attained the highest Dice coefficient among the spectrum of 10 distinct active learning methodologies we examined. This provides preliminary evidence that there is an interesting synergy between entropy-based and UMAP methods when the former precedes the latter in a hybrid model of active learning.

Examining the quality and population representativeness of linked survey and administrative data: guidance and illustration using linked 1958 National Child Development Study and Hospital Episode Statistics data.

Introduction: Recent years have seen an increase in linkages between survey and administrative data. It is important to evaluate the quality of such data linkages to discern the likely reliability of ensuing research. Evaluation of linkage quality and bias can be conducted using different approaches, but many of these are not possible when there is a separation of processes for linkage and analysis to help preserve privacy, as is typically the case in the UK (and elsewhere). Objectives: We aimed to describe a suite of generalisable methods to evaluate linkage quality and population representativeness of linked survey and administrative data which remain tractable when users of the linked data are not party to the linkage process itself. We emphasise issues particular to longitudinal survey data throughout. Methods: Our proposed approaches cover several areas: i) Linkage rates, ii) Selection into response, linkage consent and successful linkage, iii) Linkage quality, and iv) Linked data population representativeness. We illustrate these methods using a recent linkage between the 1958 National Child Development Study (NCDS; a cohort following an initial 17,415 people born in Great Britain in a single week of 1958) and Hospital Episode Statistics (HES) databases (containing important information regarding admissions, accident and emergency attendances and outpatient appointments at NHS hospitals in England). Results: Our illustrative analyses suggest that the linkage quality of the NCDS-HES data is high and that the linked sample maintains an excellent level of population representativeness with respect to the single dimension we assessed. Conclusions: Through this work we hope to encourage providers and users of linked data resources to undertake and publish thorough evaluations. We further hope that providing illustrative analyses using linked NCDS-HES data will improve the quality and transparency of research using this particular linked data resource.