Population Datasets Research Articles

Considerations for using population frequency data in germline variant interpretation: Cancer syndrome genes as a model.

Aggregate population genomics data from large cohorts are vital for assessing germline variant pathogenicity. However, there are no specifications on how sequencing quality metrics should be considered, and whether exome-derived and genome-derived allele frequencies should be considered in isolation. Germline genome sequence data were simulated for nine read-depths to identify a minimum acceptable read-depth for detecting variants. gnomAD exome-derived and genome-derived datasets were assessed for read-depth, for six key cancer genes selected for variant curation by ClinGen expert panels. Non-Finnish European allele frequency (AF) or filter AF of coding variants in these genes, assigned into frequency bins using modified ACMG-AMP criteria, was compared between exome-derived and genome-derived datasets. A 30X read-depth achieved acceptable precision and recall for detection of substitutions, but poor recall for small insertions/deletions. Exome-derived and genome-derived datasets exhibited low read-depth for different gene exons. Individual variants were mostly assigned to non-divergent AF bins (>95%) or filter AF bins (>97%). Two major bin divergences were resolved by applying the minimal acceptable read-depth threshold. These findings show the importance of assessing read-depth separately for population datasets sourced from different short-read sequencing technologies before assigning a frequency-based ACMG-AMP classification code for variant interpretation.

Residential mobility and spatial sorting in Stockholm 1990-2014: the changing importance of housing tenure and income

In this paper an analysis of residential mobility and sorting by income and housing tenure in Stockholm is carried out. The study contrasts two periods: one (1990-2001) characterised by recovery after the economic crisis in 1991-1992 and one (2002-2014) characterised by large changes to the housing stock composition. With the use of a longitudinal full population data set, it is shown that the socioeconomic composition of movers is relatively stable over time, while the economic sorting of movers increases. People with low income are increasingly likely to move into low-income neighbourhoods, whether they move into the rental or owner segments of the housing market. Those who own their housing unit and mid- to high-income earners increasingly avoid moving to low-income neighbourhoods. The paper concludes that the increasingly owner-dominated housing market in Stockholm contributes to stronger socio-spatial residential patterns through the economic sorting of movers across the whole income scale.

Massively parallel sequencing of 25 autosomal STRs including SE33 in four population groups for forensic applications

The introduction of massively parallel sequencing (MPS) in forensic investigation enables sequence-based large-scale multiplexing beyond size-based analysis using capillary electrophoresis (CE). For the practical application of MPS to forensic casework, many population studies have provided sequence data for autosomal short tandem repeats (STRs). However, SE33, a highly polymorphic STR marker, has little sequence-based data because of difficulties in analysis. In this study, 25 autosomal STRs were analyzed, including SE33, using an in-house MPS panel for 350 samples from four populations (African–American, Caucasian, Hispanic, and Korean). The barcoded MPS library was generated using a two-step PCR method and sequenced using a MiSeq System. As a result, 99.88% genotype concordance was obtained between length- and sequence-based analyses. In SE33, the most discordances (eight samples, 0.08%) were observed because of the 4 bp deletion between the CE and MPS primer binding sites. Compared with the length-based CE method, the number of alleles increased from 332 to 725 (2.18-fold) for 25 autosomal STRs in the sequence-based MPS method. Notably, additional 129 unique alleles, a 4.15-fold increase, were detected in SE33 by identifying sequence variations. This population data set provides sequence variations and sequence-based allele frequencies for 25 autosomal STRs.

Revealing environmentally driven population dynamics of an Arctic diatom using a novel microsatellite PoolSeq barcoding approach.

Ecological stability under environmental change is determined by both interspecific and intraspecific processes. Particularly for planktonic microorganisms, it is challenging to follow intraspecific dynamics over space and time. We propose a new method, microsatellite PoolSeq barcoding (MPB), for tracing allele frequency changes in protist populations. We successfully applied this method to experimental community incubations and field samples of the diatom Thalassiosira hyalina from the Arctic, a rapidly changing ecosystem. Validation of the method found compelling accuracy in comparison with established genotyping approaches within different diversity contexts. In experimental and environmental samples, we show that MPB can detect meaningful patterns of population dynamics, resolving allelic stability and shifts within a key diatom species in response to experimental treatments as well as different bloom phases and years. Through our novel MPB approach, we produced a large dataset of populations at different time-points and locations with comparably little effort. Results like this can add insights into the roles of selection and plasticity in natural protist populations under stable experimental but also variable field conditions. Especially for organisms where genotype sampling remains challenging, MPB holds great potential to efficiently resolve eco-evolutionary dynamics and to assess the mechanisms and limits of resilience to environmental stressors.

Global 1-km present and future hourly anthropogenic heat flux

Numerical weather prediction models are progressively used to downscale future climate in cities at increasing spatial resolutions. Boundary conditions representing rapidly growing urban areas are imperative to more plausible future predictions. In this work, 1-km global anthropogenic heat emission (AHE) datasets of the present and future are constructed. To improve present AHE maps, 30 arc-second VIIRS satellite imagery outputs such as nighttime lights and night-fires were incorporated along with the LandScanTM population dataset. A futuristic scenario of AHE was also developed while considering pathways of radiative forcing (i.e. representative concentration pathways), pathways of social conditions (i.e. shared socio-economic pathways), a 1-km future urbanization probability map, and a model to estimate changes in population distribution. The new dataset highlights two distinct features; (1) a more spatially-heterogeneous representation of AHE is captured compared with other recent datasets, and (2) consideration of future urban sprawls and climate change in futuristic AHE maps. Significant increases in projected AHE for multiple cities under a worst-case scenario strengthen the need for further assessment of futuristic AHE.

The Temporal Hyper-Morbidity of Asthma and Attention Deficit Disorder: Implications for Interpretation Based on Comparison of Prospective and Cross-Sectional Population Samples.

ObjectiveThe purpose of this study was to test the hypothesis that a significant temporal relationship exists between asthma and attention deficit hyperactivity disorder (ADHD). MethodsThe population dataset consisted of 95,846,511 physician diagnoses for 768,460 (46% male) individuals spanning 1993–2010. Four groups were labeled as having neither Asthma nor ADHD, Asthma only, ADHD only, or both Asthma and ADHD and formed the basis of calculating the odds ratios for each sex describing the association of Asthma and ADHD by age, and, in addition, a prospective sample age (<5 years) between 1993 and 1996 was utilized to evaluate the temporal association between Asthma and ADHD. ResultsThere was a significant relationship between ADHD and Asthma within the age strata of the sample, one from the cohort and two from the whole sample. When both ADHD and Asthma were diagnosed in the same patients, the age was younger in both cross-sectional and prospective cohort samples. ADHD arose significantly more often after Asthma in the cross-sectional samples stratified on age and in the prospective cohort sample. Conclusion The results are consistent with previous literature where ADHD has been linked to allergic diseases, such as asthma.

Paleostress reconstruction of faults recorded in the Nied\u017awiedzia Cave (Sudetes): insights into Alpine intraplate tectonic of NE Bohemian Massif

Brittle structures identified within the largest karstic cave of the Sudetes (the Niedźwiedzia Cave) were studied to reconstruct the paleostress driving post-Variscan tectonic activity in the NE Bohemian Massif. Individual fault population datasets, including local strike and dip of fault planes, striations, and Riedel shear, enabled us to discuss the orientation of the principal stresses tensor. The (meso) fault-slip data analysis performed both with Dihedra and an inverse method revealed two possible main opposing compressional regimes: (1) NE–SW compression with the formation of strike-slip (transpressional) faults and (2) WNW–ESE horizontal compression related to fault-block tectonics. The (older) NE-SW compression was most probably associated with the Late Cretaceous–Paleogene pan-regional basin inversion throughout Central Europe, as a reaction to ongoing African-Iberian-European convergence. Second WNW–ESE compression was active as of the Middle Miocene, at the latest, and might represent the Neogene–Quaternary tectonic regime of the NE Bohemian Massif. Exposed fault plane surfaces in a dissolution-collapse marble cave system provided insights into the Meso-Cenozoic tectonic history of the Earth’s uppermost crust in Central Europe, and were also identified as important guiding structures controlling the origin of the Niedźwiedzia Cave and the evolution of subsequent karstic conduits during the Late Cenozoic.

Projected plastic waste loss scenarios between 2000 and 2030 into the largest freshwater-lake system in Southeast Asia

Freshwater plastic pollution is critically understudied in Southeast Asia (SEA). Recent modelling studies indicate that SEA rivers contribute vast quantities of plastic to the world’s oceans, however, these fail to capture the complexity of individual systems. We determine the volume of mismanaged plastic waste (MPW) entering Tonle Sap Basin (TSB)—the largest freshwater lake–river system in SEA, between 2000 and 2030. Using economic, population and waste data at provincial and national levels, coupled with high resolution population and flood datasets, we estimate that ca. 221,700 tons of plastic entered between 2000 and 2020, and 282,300 ± 8700 tons will enter between 2021 and 2030. We demonstrate that policy interventions can reduce MPW up to 76% between 2021 and 2030. The most-stringent scenario would prevent 99% of annual MPW losses by 2030, despite substantially higher waste volumes and population. If successfully implemented, Cambodia will prevent significant losses in natural capital, material value and degradation in TSB worth at least US$4.8 billion, with additional benefits for the Mekong River and South China Sea.

Hybrid Incompatibility of the Plant Immune System: An Opposite Force to Heterosis Equilibrating Hybrid Performances.

Hybridization is a core element in modern rice breeding as beneficial combinations of two parental genomes often result in the expression of heterosis. On the contrary, genetic incompatibility between parents can manifest as hybrid necrosis, which leads to tissue necrosis accompanied by compromised growth and/or reduced reproductive success. Genetic and molecular studies of hybrid necrosis in numerous plant species revealed that such self-destructing symptoms in most cases are attributed to autoimmunity: plant immune responses are inadvertently activated in the absence of pathogenic invasion. Autoimmunity in hybrids predominantly occurs due to a conflict involving a member of the major plant immune receptor family, the nucleotide-binding domain and leucine-rich repeat containing protein (NLR; formerly known as NBS-LRR). NLR genes are associated with disease resistance traits, and recent population datasets reveal tremendous diversity in this class of immune receptors. Cases of hybrid necrosis involving highly polymorphic NLRs as major causes suggest that diversified R gene repertoires found in different lineages would require a compatible immune match for hybridization, which is a prerequisite to ensure increased fitness in the resulting hybrids. In this review, we overview recent genetic and molecular findings on hybrid necrosis in multiple plant species to provide an insight on how the trade-off between growth and immunity is equilibrated to affect hybrid performances. We also revisit the cases of hybrid weakness in which immune system components are found or implicated to play a causative role. Based on our understanding on the trade-off, we propose that the immune system incompatibility in plants might play an opposite force to restrict the expression of heterosis in hybrids. The antagonism is illustrated under the plant fitness equilibrium, in which the two extremes lead to either hybrid necrosis or heterosis. Practical proposition from the equilibrium model is that breeding efforts for combining enhanced disease resistance and high yield shall be achieved by balancing the two forces. Reverse breeding toward utilizing genomic data centered on immune components is proposed as a strategy to generate elite hybrids with balanced immunity and growth.

Evaluation of adverse effects of particulate matter on human life

Particulate matter (PM2.5) has a severe impact on human health. The concentration of PM2.5, related to air-quality changes, may be associated with perceptible effects on people's health. In this study, computer intelligence was used to assess the negative effects of PM2.5. The input data, used for the evaluation, were grid definitions (shape-file), PM2.5, air-quality data, incidence/prevalence rates, a population dataset, and the (Krewski) health-impact function. This paper presents a local (Pakistan) health-impact assessment of PM2.5 in order to estimate the long-term effects on mortality. A rollback-to-a-standard scenario was based on the PM2.5 concentration of 15 μg m−3. Health benefits for a population of about 73 million people were calculated. The results showed that the estimated avoidable mortality, linked to ischemic heart disease and lung cancer, was 2,773 for every 100,000 people, which accounts for 2,024,290 preventable deaths of the total population. The total cost, related to the above mortality, was estimated to be US $ 1,000 million. Therefore, a policy for a PM2.5-standard up to 15 μg m−3 is suggested.

Human Consequences of Multiple Nuclear Detonations in New Delhi (India): Interdisciplinary Requirements in Triage Management.

The human casualties from simulated nuclear detonation scenarios in New Delhi, India are analyzed, with a focus on the distribution of casualties in urban environments and the theoretical application of a nuclear-specific triage system with significant innovation in interdisciplinary disaster management applicable generally to urban nuclear detonation medical response. Model estimates of nuclear war casualties employed ESRI’s ArcGIS 9.3, blast and prompt radiation were calculated using the Defense Nuclear Agency’s WE program, and fallout radiation was calculated using the Defense Threat Reduction Agency’s (DTRA’s) Hazard Prediction and Assessment Capability (HPAC) V404SP4, as well as custom GIS and database software applications. ESRI ArcGISTM programs were used to calculate affected populations from the Oak Ridge National Laboratory’s LandScanTM 2007 Global Population Dataset for areas affected by thermal, blast and radiation data. Trauma, thermal burn, and radiation casualties were thus estimated on a geographic basis for New Delhi, India for single and multiple (six) 25 kt detonations and a single 1 mt (1000 kt) detonation. Major issues related to the emergency management of a nuclear incident are discussed with specific recommendations for improvement. The consequences for health management of thermal burn and radiation patients is the worst, as burn patients require enormous resources to treat, and there will be little to no familiarity with the treatment of radiation victims. Of particular importance is the interdisciplinary cooperation necessary for such a large-scale emergency response event, which would be exemplified by efforts such as the application of a Nuclear Global Health Workforce.

GENDER AND MULTIDIMENSIONAL POVERTY IN LESOTHO

This study applies the Alkire &amp; Foster (2011) approach to estimate gender gaps in multidimensional poverty in Lesotho using 2016 population and housing census dataset. Results show that when using three-dimensions; education, health and living-standards, gender-gaps observed in multidimensional poverty are above 20% in all age groups except for adults implying that females are more vulnerable compared to males. However; when adding the fourth dimension, economic activity that captures employment status leads to larger estimates of male poverty than female. We conclude that overall, poverty seems to be feminized in Lesotho and on average the rural population suffers more from deprivation in all the indicators considered. Two strong policy implications arise from our findings i.e. reduction of the rural-urban poverty gap and gender gap. It is recommended that some serious protective measures to combat poverty should be taken in rural areas to address equality issues to comprehend the Sustainable Development Goals.

The utility of built environment geospatial data for high-resolution dasymetric global population modeling

Several global gridded population data sets are available at unprecedented high-resolution, including recent releases at 100-m, 30-m, and 10-m resolution. These data sets are the result of the application of advanced methods to disaggregate census population counts from administrative units and facilitated by the proliferation of increasingly high-resolution spatial information pertaining to the built environment (e.g. built-up and building footprint data). Accordingly, these gridded population data are increasingly dependent on a single ancillary data set to inform the distribution of populations across space. Our study tests several combinations of binary masking variables (land areas, all building footprints, residential building footprints) and density variables (building footprint areas, building volumes) derived from characteristics of the built environment at 20× and 8000× downscaling using a flexible equation for high-resolution global dasymetric population modeling. The assessment is applied in New York City, where large spatial heterogeneities exist across confined geographic areas. Results confirm that the performance of the model generally improves as: (i) the binary masking variable becomes increasingly limiting; and, (ii) the density variable becomes more pronounced. However, application requires careful consideration due to their propensity to amplify both positive results and errors.

Deep learning identifies partially overlapping subnetworks in the human social brain

Complex social interplay is a defining property of the human species. In social neuroscience, many experiments have sought to first define and then locate ‘perspective taking’, ‘empathy’, and other psychological concepts to specific brain circuits. Seldom, bottom-up studies were conducted to first identify explanatory patterns of brain variation, which are then related to psychological concepts; perhaps due to a lack of large population datasets. In this spirit, we performed a systematic de-construction of social brain morphology into its elementary building blocks, involving ~10,000 UK Biobank participants. We explored coherent representations of structural co-variation at population scale within a recent social brain atlas, by translating autoencoder neural networks from deep learning. The learned subnetworks revealed essential patterns of structural relationships between social brain regions, with the nucleus accumbens, medial prefrontal cortex, and temporoparietal junction embedded at the core. Some of the uncovered subnetworks contributed to predicting examined social traits in general, while other subnetworks helped predict specific facets of social functioning, such as the experience of social isolation. As a consequence of our population-level evidence, spatially overlapping subsystems of the social brain probably relate to interindividual differences in everyday social life.

Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds

The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-factor values according to a land-use/land-cover (LULC) map from field surveys is a typical traditional approach. However, this approach may have limitations caused by the difficulty and cost in conducting field surveys and updating the LULC map regularly, thus significantly affecting the feasibility of multi-temporal analysis of soil erosion. To address this issue, this study uses data mining to build a random forest (RF) model between eight geospatial factors and the C-factor for the Shihmen Reservoir watershed in northern Taiwan for multi-temporal estimation of soil loss. The eight geospatial factors were collected or derived from remotely sensed images taken in 2004, a digital elevation model, and related digital maps. Due to the memory size limitation of the R software, only 4% of the total data points (population dataset) in each C-factor class were selected as the sample dataset (input dataset) for analysis using the stratified random sampling method. Seventy percent of the input dataset was used to train the RF model, and the other 30% was used to test the model. The results show that the RF model could capture the trend of vegetation recovery and soil loss reduction after the destructive event of Typhoon Aere in 2004 for multi-temporal analysis. Although the RF model was biased by the majority class’s large sample size (C = 0.01 class), the estimated soil erosion rate was close to the measurement obtained by the erosion pins installed in the watershed (90.6 t/ha-year). After the model’s completion, we furthered our aim to address the input dataset’s imbalanced data problem to improve the model’s classification performance. An ad-hoc down-sampling of the majority class technique was used to reduce the majority class’s sampling rate to 2%, 1%, and 0.5% while keeping the other minority classes at a 4% sample rate. The results show an improvement of the Kappa coefficient from 0.574 to 0.732, the AUC from 0.780 to 0.891, and the true positive rate of all minority classes combined from 0.43 to 0.70. However, the overall accuracy decreases from 0.952 to 0.846, and the true positive rate of the majority class declines from 0.99 to 0.94. The best average C-factor was achieved when the sampling rate of the majority class was 1%. On the other hand, the best soil erosion estimate was obtained when the sampling rate was 2%.

Effect of improved cookstove intervention on childhood acute lower respiratory infection in Northwest Ethiopia: a cluster-randomized controlled trial

BackgroundHousehold air pollution exposure is linked with over 3.5 million premature deaths every year, ranking highest among environmental risk factors globally. Children are uniquely vulnerable and sensitive to the damaging health effects of household air pollution which includes childhood acute lower respiratory infection (ALRI). The use of improved cookstoves has been widely encouraged to reduce these health burdens. It is, however, unclear as to whether it is possible to prevent household air pollution-related disease burdens with biomass-fuelled improved cookstove intervention and the evidence regarding its child health effect still attracts wide debate. Therefore, we investigated the child health effect of improved baking stove intervention compared with the continuation of the open burning traditional baking stove.MethodsA cluster-randomized controlled trial was conducted to assess the health effect of improved baking stove intervention. A total of 100 clusters were randomly allocated to both arms at a 1:1 ratio, and a total of four follow-up visits were carried out within 1 year immediately after the delivery of the intervention to all households allocated into the intervention arm. Data were analyzed in SPSS-22, and the intervention effect was estimated using a Generalized Estimating Equations modeling approach among the intention-to-treat population.ResultsA total of 5508 children were enrolled in the study across 100 randomly selected clusters in both arms, among which data were obtained from a total of 5333 participants for at least one follow-up visit which establishes the intention-to-treat population dataset. The intervention was not found to have a statistically significant effect on the longitudinal childhood ALRI with an estimated odds ratio of 0.95 (95% CI: 0.89–1.02). Nevertheless, the longitudinal change in childhood ALRI was significantly associated with age, baseline childhood ALRI, location of cooking quarter, secondary stove type and frequency of baking event measured at baseline.ConclusionsWe found no evidence that an intervention comprising biomass-fuelled improved baking stove reduced the risk of childhood ALRI compared with the continuation of an open burning traditional baking stove. Therefore, effective cooking solutions are needed to avert the adverse health effect of household air pollution, particularly, childhood ALRI.Trial registrationThe trial was registered on August 2, 2018 at clinical trials.gov registry database (registration identifier number: NCT03612362).

Semi-automatic mapping of pre-census enumeration areas and population sampling frames

Enumeration Areas (EAs) are the operational geographic units for the collection and dissemination of census data and are often used as a national sampling frame for various types of surveys. In many poor or conflict-affected countries, EA demarcations are incomplete, outdated, or missing. Even for countries that are stable and prosperous, creating and updating EAs is one of the most challenging yet essential tasks in the preparation for a national census. Commonly, EAs are created by manually digitising small geographic units on high-resolution satellite imagery or physically walking the boundaries of units, both of which are highly time, cost, and labour intensive. In addition, creating EAs requires considering population and area size within each unit. This is an optimisation problem that can best be solved by a computer. Here, for the first time, we produce a semi-automatic mapping of pre-defined census EAs based on high-resolution gridded population and settlement datasets and using publicly available natural and administrative boundaries. We demonstrate the approach in generating rural EAs for Somalia where such mapping is not existent. In addition, we compare our automated approach against manually digitised EAs created in urban areas of Mogadishu and Hargeysa. Our semi-automatically generated EAs are consistent with standard EAs, including having identifiable boundaries for field teams to follow on the ground, and appropriate sizing and population for coverage by an enumerator. Furthermore, our semi-automated urban EAs have no gaps, in contrast, to manually drawn urban EAs. Our work shows the time, labour and cost-saving value of automated EA delineation and points to the potential for broadly available tools suitable for low-income and data-poor settings but applicable to potentially wider contexts.

Biomass-fuelled improved cookstove intervention to prevent household air pollution in Northwest Ethiopia: a cluster randomized controlled trial

BackgroundHousehold air pollution from biomass fuels burning in traditional cookstoves currently appeared as one of the most serious threats to public health with a recent burden estimate of 2.6 million premature deaths every year worldwide, ranking highest among environmental risk factors and one of the major risk factors of any type globally. Improved cookstove interventions have been widely practiced as potential solutions. However, studies on the effect of improved cookstove interventions are limited and heterogeneous which suggested the need for further research.MethodsA cluster randomized controlled trial study was conducted to assess the effect of biomass-fuelled improved cookstove intervention on the concentration of household air pollution compared with the continuation of an open burning traditional cookstove. A total of 36 clusters were randomly allocated to both arms at a 1:1 ratio, and improved cookstove intervention was delivered to all households allocated into the treatment arm. All households in the included clusters were biomass fuel users and relatively homogenous in terms of basic socio-demographic and cooking-related characteristics. Household air pollution was determined by measuring the concentration of indoor fine particulate, and the effect of the intervention was estimated using the Generalized Estimating Equation.ResultsA total of 2031 household was enrolled in the study across 36 randomly selected clusters in both arms, among which data were obtained from a total of 1977 households for at least one follow-up visit which establishes the intention-to-treat population dataset for analysis. The improved cookstove intervention significantly reduces the concentration of household air pollution by about 343 μg/m3 (Ḃ = − 343, 95% CI − 350, − 336) compared to the traditional cookstove method. The overall reduction was found to be about 46% from the baseline value of 859 (95% CI 837–881) to 465 (95% CI 458–472) in the intervention arm compared to only about 5% reduction from 850 (95% CI 828–872) to 805 (95% CI 794–817) in the control arm.ConclusionsThe biomass-fuelled improved cookstove intervention significantly reduces the concentration of household air pollution compared to the traditional method. This suggests that the implementation of these cookstove technologies may be necessary to achieve household air pollution exposure reductions.Trial registrationThe trial project was retrospectively registered on August 2, 2018, at the clinical trials.gov registry database (https://clinicaltrials.gov/) with the NCT03612362 registration identifier number.

Changes of vegetation and its forces driving in the Aral Sea Basin of Central Asia

Global change brings great uncertainty to the fragile ecological environment of arid area. In order to understand the driving role of climatic factors and socio-economic activity on changes, the distribution and change of vegetation in the Aral Sea basin were examined using data from remote sensing, population data sets from 2000 to 2015, transfer matrix, and the centre of gravity model. The salient results of the analysis were as follows. (1) Although the index increased slightly in the past 20 years overall, it fluctuated greatly over that time. From 2000 to 2015, the NDVI decreased in approximately 62% of the area; increased in 24%; and remained unchanged in 14%. (2) From 2000 to 2015, the geographic centre of the area under forest land to the northeast, marking the advancement of urbanization upstream. The geographic centre of grassland moved to the west. (3) The overall impact of precipitation on vegetation was greater than that of temperature. Areas showing a strong correlation were mostly concentrated in forest land; the impact of precipitation on grasslands was weak. (4) The distribution of vegetation was adversely affected by the increase in population and in GDP. The present study is of particular significance to the restoration and reconstruction of the Aral Sea basin ecosystem.

What the obesity epidemic does not need: A cancel culture

What the obesity epidemic does not need: A cancel culture