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Established hedgerows of native plants on the borders of crop fields provide a variety of ecosystem service benefits in agricultural landscapes. However, their influence on weed communities is not well understood, and there are concerns that hedgerows could contribute to weed infestations on farms. To address this research gap, we examined the role of established hedgerows of native California plants on weed abundance (weed numbers and cover) and weed species richness in field borders, and in adjacent crops, in large-scale, monocropping systems compared with conventionally managed field borders (i.e., no hedgerows). Across 20 farm sites in California’s Central Valley, hedgerows on orchard crop borders reduced weed numbers by 66%, weed species richness by 59%, and weed cover by 74%. On annual field crop borders, hedgerows reduced weed numbers by 71%, weed species richness by 60%, and weed cover by 70%. In orchards, hedgerows also reduced weed intrusion into the adjacent crop interior, with significantly lower weed cover to the first tree row (area directly underneath the trees), weed species richness to the 10-m tree row, and weed numbers to the 10-m avenue (area between the tree rows). Yearly management practices and associated costs for weed control in established hedgerows were significantly less than for conventionally managed field borders. This study highlights the effectiveness of native hedgerows as a sustainable nature-based solution for reducing weed pressure and management inputs on farms.
The gut microbiome is impacted by certain types of dietary fibre. However, the type, duration and dose needed to elicit gut microbial changes and whether these changes also influence microbial metabolites remain unclear. This study investigated the effects of supplementing healthy participants with two types of non-digestible carbohydrates (resistant starch (RS) and polydextrose (PD)) on the stool microbiota and microbial metabolite concentrations in plasma, stool and urine, as secondary outcomes in the Dietary Intervention Stem Cells and Colorectal Cancer (DISC) Study. The DISC study was a double-blind, randomised controlled trial that supplemented healthy participants with RS and/or PD or placebo for 50 d in a 2 × 2 factorial design. DNA was extracted from stool samples collected pre- and post-intervention, and V4 16S rRNA gene sequencing was used to profile the gut microbiota. Metabolite concentrations were measured in stool, plasma and urine by high-performance liquid chromatography. A total of fifty-eight participants with paired samples available were included. After 50 d, no effects of RS or PD were detected on composition of the gut microbiota diversity (alpha- and beta-diversity), on genus relative abundance or on metabolite concentrations. However, Drichlet’s multinomial mixture clustering-based approach suggests that some participants changed microbial enterotype post-intervention. The gut microbiota and fecal, plasma and urinary microbial metabolites were stable in response to a 50-d fibre intervention in middle-aged adults. Larger and longer studies, including those which explore the effects of specific fibre sub-types, may be required to determine the relationships between fibre intake, the gut microbiome and host health.
Characterised by the extensive use of obsidian, a blade-based tool inventory and microblade technology, the late Upper Palaeolithic lithic assemblages of the Changbaishan Mountains are associated with the increasingly cold climatic conditions of Marine Isotope Stage 2, yet most remain poorly dated. Here, the authors present new radiocarbon dates associated with evolving blade and microblade toolkits at Helong Dadong, north-east China. At 27 300–24 100 BP, the lower cultural layers contain some of the earliest microblade technology in north-east Asia and highlight the importance of the Changbaishan Mountains in understanding changing hunter-gatherer lifeways in this region during MIS 2.
Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.
Methods:
We conducted a PubMed search using “SDOH” and “EHR” Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.
Results:
Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.
Discussion:
Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.
Disparities in the recruitment of minority populations in research are well-documented. However, the degree of participation and retention of minorities following enrollment is less known, particularly in decentralized studies. Although decentralized clinical research methods may allow researchers to engage broader study populations with less participation burden, they may present different retention challenges. To evaluate racial and ethnic differences in the degree of participation after enrollment in a decentralized study, we analyzed data from a cohort of patients with inflammatory bowel diseases following COVID-19 immunization.
Methods:
We compared by race and ethnicity the following post-enrollment participation metrics: response to > 50% of follow-up surveys, donation of a blood sample for antibody testing, consent to use of bio samples for future research, and withdrawal prior to study completion.
Results:
Overall, we observed higher levels of post-enrollment study participation among non-Hispanic White (NHW) participants as compared to Black or Hispanic participants: 95% of NHW participants completed follow-up versus 87% of Black participants and 91% of Hispanic participants, 73% of NHW participants provided bio samples versus 64% Black participants and 67% Hispanic participants, and 65% of NHW participants provided consent for future research versus 62% of Black participants and 52% of Hispanic participants.
Conclusions:
Our findings demonstrate that the degree of study participation after enrollment in this decentralized study differed by race and ethnicity, indicating that attention to diversity, equity, and inclusion is needed not only in clinical research recruitment but also throughout study administration.
Olfactory function declines during normal aging; however, accelerated olfactory decline is observed in neurodegenerative diseases, such as Alzheimer’s disease (AD). Moreover, olfactory deficits in pre-clinical AD are associated with future cognitive decline. Odor identification and memory deficits have been consistently reported in early stage AD indicating its potential sensitivity to AD pathophysiology in olfactory and limbic structures, yet few studies of olfaction have incorporated structural measures in a well-characterized cohort of older adults. In the current study we examined the association between odor identification impairment, cognition, and medial temporal lobe (MTL) sub-regions in cognitively unimpaired and impaired older adults.
Participants and Methods:
We enrolled 140 participants (age=72.25±6.54, 56% female, years of education=16.30±2.63, 82% Caucasian, 15% Black/AA, 3% Multiracial) from the Penn Alzheimer’s Disease Research Center Clinical Cohort. Participants completed the Sniffin’ Sticks Odor Identification Test (SS-OIT), cognitive testing (NACC UDS2 or UDS3 and additional cognitive tests), and MRI scans (3T Siemens MAGNETOM Prisma MRI scanner). For the SS-OIT, participants were presented with 16 odorants using felt-tipped pen dispensers and asked to identify each odor from four multiple-choice options. Scores range from 0 to 16. Additionally, cognitive domains were created by averaging z-scores from tests within each domain: attention, memory, language, executive function, and visuospatial. This cohort was divided into participants with unimpaired cognition (n=96) and impaired cognition (MCI, dementia; n=44) using established normative data and consensus diagnosis. Linear regressions were performed to examine the association between SS-OIT score, each cognitive domain, and MTL measurements for unimpaired and impaired groups. For all analyses, we controlled for age, race, sex, education, smoking status, and hypertension and additionally for MOCA score and intracranial volume with MTL measurements.
Results:
In the unimpaired group, SS-OIT significantly associated with language (p<.05). In the impaired group, SS-OIT significantly associated with language and memory (p<.05). In the unimpaired group, SS-OIT significantly associated with right anterior hippocampal volume (p<.05). In the impaired group, significant associations were found between SS-OIT and right anterior hippocampal volume (p<.05) and left hippocampal mean thickness (p<.05). Additionally, SS-OIT significantly associated with left and right entorhinal cortex volume (p<.05) and mean thickness (p<.05).
Conclusions:
This study reveals that lower odor identification performance is related to lower performance on measures of cognition and atrophy in MTL sub-regions in unimpaired and impaired older adults. Our findings support prior results demonstrating relationships between olfactory function, cognition, and MTL sub-regions. Specifically, olfactory function and episodic memory have been shown to follow similar patterns of decline in the course of AD, potentially reflecting AD pathology in shared regions of the MTL subserving episodic memory and olfactory function. Our findings demonstrate that reductions in both cortical thickness and grey matter volume of MTL regions are linked to olfactory deficits in individuals at risk for Alzheimer’s dementia. Future steps will include the analysis of longitudinal cognitive and imaging indices and the incorporation of fluid biomarker data.
Chapter 5 gives an extended empirical example of the Benford agreement procedure for assessing the validity of social science data. The example uses country-level data collected and estimated by the Sea Around Us organization on the dollar values of reported and unreported fish landings from 2010 to 2016. We report Benford agreement analyses for the Sea Around Us data (1) by reporting status, (2) by decade, (3) for a large fishing region of 22 West African countries, and (4) foreach of the 22 individual countries in West Africa.
Chapter 4 begins with a discussion of the types and kinds of data most suitable for an analysis that uses the Benford probability distribution. Next we describe an R computer program – program Benford – designed to evaluate observed data for agreement with the Benford probability distribution; and we give an example of output from the program using a typical dataset. We then move to an overview of our workflow of Benford agreement analyses where we outline our process for assessing the validity of data using Benford agreement analyses. We end the chapter with a discussion of the concept of Benford validity, which we will employ in subsequent chapters.
Chapter 7 takes a closer look at some of the Sea Around Us fish-landings data that we assessed for Benford agreement in Chapter 5. We chose these data because of the mixed agreement findings among them: while the full dataset and several sets of subgroups indicated that the data exhibited Benford validity, when we analyzed West African countries individually, a number of them were found to have unacceptable Benford agreement and therefore problematic Benford validity. We present ways in which researchers can assess the impact of unacceptable Benford agreement on their analyses.
Chapter 3 describes and illustrates the Benford probability distribution. A brief summary of the origin and evolution of the Benford distribution is drawn and the development and assessment of various measures of goodness of fit between an empirical distribution and the Benford distribution are described and illustrated. These masures are Pearson’s chi-squared, Wilks’ likelihood-ratio, Hardy and Ramanujan’s partition theory, Fisher’s exact test, Kuiper’s measure, Tam Cho and Gaines’ d measure, Cohen’s w measure, and Nigrini’s MAD measure.
Chapter 6 provides a second empirical example of the Benford agreement procedure: here we analyze new daily COVID-19 cases at the US state level and at the global level across nations. Both the state-level and the global analyses consider time as a variable. Specifically we examine, (1) for the United States, new reports of COVID-19 between January 22, 2020 and November 16, 2021 at the state level, and (2) for the cross-national data, new reports of COVID-19 between February 24, 2020 and January 13, 2022. At the state level, we report Benford agreement analyses for (1) the full dataset, (2) cases grouped alphabetically, (3) cases grouped regionally, (4) cases grouped by days of the week, and (5) cases grouped by their governor’s party (Republican or Democratic). We then turn our Benford agreement analysis to global cross-national COVID-19 data to assess whether Benford agreement of COVID-19 varies across countries.
This chapter gives an overview of the remainder of the book. We first provide commonsense and social science examples of reliability and validity, two necessary conditions that data must posses to have trustworthy conclusions based upon it. We next introduce Benford’s law and offer a brief overview of other social science studies that have employed it to check the accuracy of their data. We then turn to an overview of our Benford agreement analysis procedure and introduce the concept of Benford validity. The chapter concludes with a plan for the remainder of the book.