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Using data from the Human Connectome Project, a single holistic multivariate analysis identified one strong mode of population co-variation: subjects were predominantly spread along a single ‘positive-negative’ axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of functional brain connectivity. We investigated the relationship between individual subjects' functional connectomes and 280 behavioral and demographic measures in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation: subjects were predominantly spread along a single 'positive-negative' axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.

The All of Us Research Program (All of Us) is a national effort to accelerate health research by exploring the relationship between lifestyle, environment, and genetics. It is set to become one of the largest research efforts in U.S. history, aiming to build a national resource of data from at least one million participants. All of Us aims to address the need for more diversity in research and set the stage for that diversity to be leveraged in precision medicine research to come. This paper describes how the program assessed demographic characteristics of participants who have enrolled in other U.S. biomedical research cohorts to better understand which groups are traditionally represented or underrepresented in biomedical research. We 1) reviewed the enrollment characteristics of national cohort studies like All of Us, and 2) surveyed the literature, focusing on key diversity categories essential to the program's enrollment aims. Based on these efforts, All of Us emphasizes enrollment of racial and ethnic minorities, and has formally designated the following additional groups as historically underrepresented: individuals-with inadequate access to medical care; under the age of 18 or over 65; with an annual household income at or below 200% of the federal poverty level; who have a cognitive or physical disability; have less than a high school education or equivalent; are intersex; identify as a sexual or gender minority; or live in rural or non-metropolitan areas. Research accounting for wider demographic variability is critical. Only by ensuring diversity and by addressing the very barriers that limit it, can we position All of Us to better understand and tackle health disparities.

Reconstructing species’ demographic histories is a central focus of molecular ecology and evolution. Recently, an expanding suite of methods leveraging either the sequentially Markovian coalescent (SMC) or the site-frequency spectrum has been developed to reconstruct population size histories from genomic sequence data. However, few studies have investigated the robustness of these methods to genome assemblies of varying quality. In this study, we first present an improved genome assembly for the Tasmanian devil using the Chicago library method. Compared with the original reference genome, our new assembly reduces the number of scaffolds (from 35,975 to 10,010) and increases the scaffold N90 (from 0.101 to 2.164 Mb). Second, we assess the performance of four contemporary genomic methods for inferring population size history (PSMC, MSMC, SMC++, Stairway Plot), using the two devil genome assemblies as well as simulated, artificially fragmented genomes that approximate the hypothesized demographic history of Tasmanian devils. We demonstrate that each method is robust to assembly quality, producing similar estimates of Ne when simulated genomes were fragmented into up to 5,000 scaffolds. Overall, methods reliant on the SMC are most reliable between ∼300 generations before present (gbp) and 100 kgbp, whereas methods exclusively reliant on the site-frequency spectrum are most reliable between the present and 30 gbp. Our results suggest that when used in concert, genomic methods for reconstructing species’ effective population size histories 1) can be applied to nonmodel organisms without highly contiguous reference genomes, and 2) are capable of detecting independently documented effects of historical geological events.

Phylogeographic methods can help reveal the movement of genes between populations of organisms. This has been widely done to quantify pathogen movement between different host populations, the migration history of humans, and the geographic spread of languages or gene flow between species using the location or state of samples alongside sequence data. Phylogenies therefore offer insights into migration processes not available from classic epidemiological or occurrence data alone. Phylogeographic methods have however several known shortcomings. In particular, one of the most widely used methods treats migration the same as mutation, and therefore does not incorporate information about population demography. This may lead to severe biases in estimated migration rates for data sets where sampling is biased across populations. The structured coalescent on the other hand allows us to coherently model the migration and coalescent process, but current implementations struggle with complex data sets due to the need to infer ancestral migration histories. Thus, approximations to the structured coalescent, which integrate over all ancestral migration histories, have been developed. However, the validity and robustness of these approximations remain unclear. We present an exact numerical solution to the structured coalescent that does not require the inference of migration histories. Although this solution is computationally unfeasible for large data sets, it clarifies the assumptions of previously developed approximate methods and allows us to provide an improved approximation to the structured coalescent. We have implemented these methods in BEAST2, and we show how these methods compare under different scenarios.

Recent advances in the use of summed probability distribution (SPD) of calibrated 14C dates have opened new possibilities for studying prehistoric demography. The degree of correlation between climate change and population dynamics can now be accurately quantified, and divergences in the demographic history of distinct geographic areas can be statistically assessed. Here we contribute to this research agenda by reconstructing the prehistoric population change of Jomon hunter-gatherers between 7,000 and 3,000 cal BP. We collected 1,433 14C dates from three different regions in Eastern Japan (Kanto, Aomori and Hokkaido) and established that the observed fluctuations in the SPDs were statistically significant. We also introduced a new non-parametric permutation test for comparing multiple sets of SPDs that highlights point of divergences in the population history of different geographic regions. Our analyses indicate a general rise-and-fall pattern shared by the three regions but also some key regional differences during the 6th millennium cal BP. The results confirm some of the patterns suggested by previous archaeological studies based on house and site counts but offer statistical significance and an absolute chronological framework that will enable future studies aiming to establish potential correlation with climatic changes.

Ensuring that protected areas (PAs) maintain the biodiversity within their boundaries is fundamental in achieving global conservation goals. Despite this objective, wildlife abundance changes in PAs are patchily documented and poorly understood. Here, we use linear mixed effect models to explore correlates of population change in 1,902 populations of birds and mammals from 447 PAs globally. On an average, we find PAs are maintaining populations of monitored birds and mammals within their boundaries. Wildlife population trends are more positive in PAs located in countries with higher development scores, and for larger-bodied species. These results suggest that active management can consistently overcome disadvantages of lower reproductive rates and more severe threats experienced by larger species of birds and mammals. The link between wildlife trends and national development shows that the social and economic conditions supporting PAs are critical for the successful maintenance of their wildlife populations. Protected areas are intended to safeguard wildlife, but their effectiveness has at times been questioned. Barnes, Craigie, and colleagues show that protected areas do offer refuge—maintaining their bird and mammal abundances—but with greater success for larger-bodied species and in more developed nations.

This study extends the current body of work on dehumanization by evaluating the social, psychological, and demographic correlates of blatant disregard for immigrants. Participants (n = 468) were randomly assigned to read a scenario where 1) an immigrant or 2) an immigrant and their child were caught illegally crossing the southern border of the United States, and then rated how long they should spend in jail if convicted. Participants reported that they would sentence the immigrant to more jail time than the immigrant and child. Those who sent immigrants to jail for more time also viewed them as socially distant and less human, described immigration in impersonal terms, and endorsed other social harms unrelated to immigration (e.g., the death penalty for convicted murderers). Crucially, endorsed social harms accounted for explained variance beyond simply holding conservative views. We position these data within the current literature on dehumanization theory and immigration issues.

Associations between adverse childhood experiences (ACEs) and chronic diseases among middle-aged or older Chinese individuals have not been well documented. In addition, whether demographic and socioeconomic characteristics modify any such associations has been underexplored. To examine associations between ACEs and subsequent chronic diseases and to assess whether age, sex, educational level, annual per capita household expenditure level, and childhood economic hardship modify these associations. This population-based cross-sectional study used data from the China Health and Retirement Longitudinal Study (CHARLS), a survey of residents aged 45 years or older in 28 provinces across China; specifically, the study used data from the CHARLS life history survey conducted from June 1 to December 31, 2014, and a CHARLS follow-up health survey conducted from July 1 to September 30, 2015. The study population included 11 972 respondents aged 45 years or older who had data on at least 1 of 14 specified chronic diseases and information on all 12 of the ACE indicators included in this study. Data analysis was performed from December 1 to 30, 2020. Any of 12 ACEs (physical abuse, emotional neglect, household substance abuse, household mental illness, domestic violence, incarcerated household member, parental separation or divorce, unsafe neighborhood, bullying, parental death, sibling death, and parental disability), measured by indicators on a questionnaire. The number of ACEs per participant was summed and categorized into 1 of 5 cumulative-score groups: 0, 1, 2, 3, and 4 or more. Hypertension, dyslipidemia, diabetes, heart disease, stroke, chronic lung disease, asthma, liver disease, cancer, digestive disease, kidney disease, arthritis, psychiatric disease, and memory-related disease were defined by self-reported physician diagnoses or in combination with health assessment and medication data. Multimorbidity was defined as the presence of 2 or more of these 14 chronic diseases. Logistic regression models were used to assess associations of the 12 ACEs with the 14 chronic diseases and with multimorbidity. Modification of the associations by demographic and socioeconomic characteristics was assessed by stratified analyses and tests for interaction. Of the 11 972 individuals included (mean [SD] age, 59.85 [9.56] years; 6181 [51.6%] were females), 80.9% had been exposed to at least 1 ACE and 18.0% reported exposure to 4 or more ACEs. Compared with those without ACE exposure, participants who experienced 4 or more ACEs had increased risks of dyslipidemia, chronic lung disease, asthma, liver disease, digestive disease, kidney disease, arthritis, psychiatric disease, memory-related disease, and multimorbidity. The estimated odds ratios (ORs) ranged from 1.27 (95% CI, 1.02-1.59) for dyslipidemia to 2.59 (95% CI, 2.16-3.11) for digestive disease. A dose-response association was also observed between the number of ACEs and the risk of most of the chronic diseases (excluding hypertension, diabetes, and cancer) (eg, chronic lung disease for ≥4 ACEs vs none: OR, 2.01; 95% CI, 1.59-2.55; P < .001 for trend) and of multimorbidity (for individuals among the overall study population with ≥4 ACEs vs none: OR, 2.03; 95% CI, 1.70-2.41; P < .001 for trend). The demographic or socioeconomic characteristics of age, sex, educational level, annual per capita household expenditure level, or childhood economic hardship were not shown to significantly modify the associations between ACEs and multimorbidity. In this population-based, cross-sectional study of adults in China, exposure to ACEs was associated with higher risks of chronic diseases regardless of demographic and socioeconomic characteristics during childhood or adulthood. These findings suggest a need to prevent ACEs and a need for a universal life-course public health strategy to reduce potential adverse health outcomes later in life among individuals who experience them.

Despite strides in characterizing human history from genetic polymorphism data, progress in identifying genetic signatures of recent demography has been limited. Here we identify very recent fine-scale population structure in North America from a network of over 500 million genetic (identity-by-descent, IBD) connections among 770,000 genotyped individuals of US origin. We detect densely connected clusters within the network and annotate these clusters using a database of over 20 million genealogical records. Recent population patterns captured by IBD clustering include immigrants such as Scandinavians and French Canadians; groups with continental admixture such as Puerto Ricans; settlers such as the Amish and Appalachians who experienced geographic or cultural isolation; and broad historical trends, including reduced north-south gene flow. Our results yield a detailed historical portrait of North America after European settlement and support substantial genetic heterogeneity in the United States beyond that uncovered by previous studies. Genetic data has led to great advances in our understanding of human evolution and dispersal, but information on more recent events is limited. Here, the authors analyse genotypes from 770,000 US individuals to map the fine-scale population structure of North America after European settlement.

Purpose Necrotising soft-tissue infections (NSTI) are characterised by necrosis, fast progression, and high rates of morbidity and mortality, but our knowledge is primarily derived from small prospective studies and retrospective studies. Methods We performed an international, multicentre, prospective cohort study of adults with NSTI describing patient’s characteristics and associations between baseline variables and microbiological findings, amputation, and 90-day mortality. Results We included 409 patients with NSTI; 402 were admitted to the ICU. Cardiovascular disease [169 patients (41%)] and diabetes [98 (24%)] were the most common comorbidities; 122 patients (30%) had no comorbidity. Before surgery, bruising of the skin [210 patients (51%)] and pain requiring opioids [172 (42%)] were common. The sites most commonly affected were the abdomen/ano-genital area [140 patients (34%)] and lower extremities [126 (31%)]. Monomicrobial infection was seen in 179 patients (44%). NSTI of the upper or lower extremities was associated with monomicrobial group A streptococcus (GAS) infection, and NSTI located to the abdomen/ano-genital area was associated with polymicrobial infection. Septic shock [202 patients (50%)] and acute kidney injury [82 (20%)] were common. Amputation occurred in 22% of patients with NSTI of an extremity and was associated with higher lactate level. All-cause 90-day mortality was 18% (95% CI 14–22); age and higher lactate levels were associated with increased mortality and GAS aetiology with decreased mortality. Conclusions Patients with NSTI were heterogeneous regarding co-morbidities, initial symptoms, infectious localisation, and microbiological findings. Higher age and lactate levels were associated with increased mortality, and GAS infection with decreased mortality.