XRISM reveals complex multi-temperature structures in the Abell 2029 galaxy cluster

Aim The distributions of Chinese spiders are used to form biotic regions and to infer biogeographical patterns.Location China.Methods China was initially divided into 294 quadrats of 2° latitude by 2° longitude. The distributions of 958 species of spiders were summarized for each quadrat. Subsequently, these quadrats were pooled into 28 areas based on topographical characteristics and to a lesser extent on the distributions of spiders. Parsimony analysis of endemicity (PAE) was used to classify the 28 areas based on the shared distributional patterns of spiders.Results China was found to have seven major biogeographical regions based on the distributional patterns of spiders: Western Northern region (clade B2: Tibetan Plateau and Inner Mongolia‐Xinjiang subregions), Central Northern region (clade B3), Eastern Northern region (clade B4), Central region (clade C2), Eastern Southern region (clade C3), Western Southern region (clade C4), and Central Southern region (clade C5).Main conclusions The distributional patterns of Chinese spiders correspond broadly to geological provinces. A comparison of the geological provinces and the distributional patterns of spiders reveals that the spiders occur south of the geological provinces. Furthermore, a general biogeographical classification with five natural areas is suggested as follows: Tibetan Plateau, Central Northern, Eastern Northern, Western Northern (excluding Tibetan Plateau), and Southern regions.

Coronavirus disease 2019 (COVID-19) altered healthcare utilization patterns. However, there is a dearth of literature comparing methods for quantifying the extent to which the pandemic disrupted healthcare service provision in sub-Saharan African countries. To compare interrupted time series analysis using Prophet and Poisson regression models in evaluating the impact of COVID-19 on essential health services. We used reported data from Uganda's Health Management Information System from February 2018 to December 2020. We compared Prophet and Poisson models in evaluating the impact of COVID-19 on new clinic visits, diabetes clinic visits, and in-hospital deliveries between March 2020 to December 2020 and across the Central, Eastern, Northern, and Western regions of Uganda. The models generated similar estimates of the impact of COVID-19 in 10 of the 12 outcome-region pairs evaluated. Both models estimated declines in new clinic visits in the Central, Northern, and Western regions, and an increase in the Eastern Region. Both models estimated declines in diabetes clinic visits in the Central and Western regions, with no significant changes in the Eastern and Northern regions. For in-hospital deliveries, the models estimated a decline in the Western Region, no changes in the Central Region, and had different estimates in the Eastern and Northern regions. The Prophet and Poisson models are useful in quantifying the impact of interruptions on essential health services during pandemics but may result in different measures of effect. Rigor and multimethod triangulation are necessary to study the true effect of pandemics on essential health services.

Background and Aim:To improve overall milk quality in Thailand, dairy farmers and milk collection centers employ a payment program based on milk quality (PPBMQ) for milk trade. This study aimed to determine and compare the proportion of dairy farmers receiving benefits from the PPBMQ using data from selected dairy cooperatives located in northern and central regions in Thailand.Materials and Methods:Monthly data on milk components (n=37,077), including fat, solids not fat (SNF), and somatic cell counts (SCC) were collected from the two regions in 2018 and 2019. Based on the PPBMQ, farmers were classified into benefit-gain, benefit-loss, and no-benefit groups. A mixed-effects logistic regression model was used to compare the number of farmers in northern and central regions who received monthly benefits from the PPBMQ.Results:More than 70% of dairy farmers benefited from the PPBMQ. The proportion of dairy farmers in the benefit-gain group was higher in the northern region (88.7%) than in the central region (57.1%). A high percentage of dairy farmers in the central region lost their benefits mainly due to SCC (40%) and SNF (44%).Conclusion:The PPBMQ benefited the vast majority of dairy producers in the northern region and approximately two-thirds of those in the central region. Thus, the efforts of authorities and stakeholders should be enhanced to support dairy farmers in the central region in improving milk quality.

Recognizing regional economic resilience and its influencing factors under different shocks is necessary to promote stable regional economic development. The article analyzes the regional economic resilience of 31 Chinese provinces under three kinds of shocks, namely, financial crisis, economic downturn, and COVID-19, in terms of the resistance of regional economies to shocks, and examines spatial distribution and main influencing factors. The results of the study found that: (1) The characteristics of regional economic resilience under different shocks are different. During the financial crisis, the strong resilient provinces are distributed in the central and western and northern regions; during the economic downturn, the strong resilient provinces are mainly distributed in the western and central regions; during COVID-19, the strong resilient provinces are mainly distributed in the western and eastern coastal regions. The economic resilience of each province shows significant “high-high” and “low-low” spatial clustering characteristics during the economic downturn and the COVID-19. (2) The main influencing factors of economic resilience in different shocks are different. In the financial crisis, the magnitude of the contribution of the influencing factor is leading industry (0.283) > related diversity (0.197) > foreign trade dependence (0.190); during the economic downturn, the magnitude of the contribution of the influencing factor is population density (0.464) > leading industry (0.427) > related diversity (0.285); the magnitude of the contribution of the impact factor during the COVID-19 was related diversity (0.282) > unrelated diversity (0.274) > leading industry (0.272). (3) In the interaction of impact factors, the strongest explanatory power is found in related diversity, unrelated diversity, and leading industries, which represent the industrial structure. Therefore, there is a need to adjust the industrial structure and improve the regional economic resilience from the shock itself.

Impact of river discharge and suspended sediments on phytoplankton in the nearshore waters of the Indian southwest coast.

Over the past 30 years, Mexico’s economic liberalization has reshaped regional economic activities, resulting in a closely interconnected regional economy with strong global value chains (GVCs), particularly in the manufacturing sector. This transformation has significantly impacted regional and industrial productivity dynamics. Not all regions or industries have experienced the advantages of this integration equally. While some regions, particularly those in the north, have experienced substantial productivity gains due to their proximity to the U.S. market and deep integration into North American GVCs, others have lagged. Our paper focuses on the spatial distribution of economic activity across both space and time, also referred to as spatial dynamics. The economic activity we study is regional labor productivity within the Mexican manufacturing sector from 1993 to 2018, and our interest is in how it differs across various manufacturing segments characterized by distinct innovation processes, thereby influencing regional trajectories. To account for the diversity in innovation and technology within manufacturing, industries are classified into taxonomy groups: specialized suppliers, scale intensive, science-based, and supplier dominated. To do this, we use two complementary methods. One is modified Multifactor Partitioning (MFP), which is used in shift-share analysis and provides a role for distribution and structure effects in the measurement regional growth patterns. The other is formal spatial econometric modeling to identify potential spatial dynamics ignored in classical labor productivity decompositions. Our findings reveal notable disparities in manufacturing labor productivity growth across regions and taxonomy groups. Specialized suppliers’ industries exhibit significant regional and interaction effects in the northern and central northern regions, whereas scale-intensive industries demonstrate comparatively weaker effects in the southern and central regions. These insights are valuable for policymakers seeking to address labor productivity disparities and promote more balanced economic development.

The aim of the present work is to estimate an aggregate production function for the 20 Italian regions by emphasizing the role that agglomeration externalities (localization externalities and urbanization externalities) and spatial spillovers have in influencing the technical efficiency of the production process. To this purpose, we use the stochastic frontier approach.The results highlight the relevance and the positive impact that localization and urbanization externalities have in improving the efficiency level of the production process of Northern and Central Italian regions. Furthermore, spatial spillovers represent a source of development and growth for Northern, Central, and Southern regions. In particular, after considering spatial spillovers, we observe a reduction of the concentration and an increase of the diffusion process of efficiency among Italian regions. For some Northern and Central regions, we can observe that there are some richer regions and some poorer regions with regard to their capability to benefit from spatial spillovers.KeywordsAgglomeration externalitiesSpatial spilloversStochastic frontierTechnical efficiencyItalian regionsSpatial correlation

BACKGROUND AND OBJECTIVES:No previous study has provided a detailed description of regional variations of growth within the various regions of Saudi Arabia. Thus, we sought to demonstrate differences in growth of children and adolescents in different regions.SUBJECTS AND METHODS:The 2005 Saudi reference was based on a cross-sectional representative sample of the Saudi population of healthy children and adolescents from birth to 18 years of age. Body measurements of the length, stature, weight, head circumference and calculation of the BMI were performed according to standard recommendations. Percentile construction and smoothing were performed using the LMS (lambda, mu and sigma) methodology, followed by transformation of all individual measurements into standard deviation scores. Factors such as weight for age, height for age, weight for height, and head circumference for children from birth to 3 years, stature for age, head circumference and body mass index for children between 2-18 years of age were assessed. Subsequently, variations in growth between the three main regions in the north, southwest, and center of Saudi Arabia were calculated, with the Bonferroni: method used to assess the significance of differences between regions.RESULTS:There were significant differences in growth between regions that varied according to age, gender, growth parameter and region. The highest variation was found between children and adolescents of the southwestern region and those of the other two regions The regression lines for all growth parameters in children <3 years of age were significantly different from one region to another reaching – 0.65 standard deviation scores for the southwestern regions (P=.001). However, the difference between the northern and central regions were not significant for the head circumference and for weight for length. For older children and adolescents a significant difference was found in all parameters except between the northern and central regions in BMI in girls and head circumference in boys. Finally, the difference in head circumference of girls between southwestern and northern regions was not significant. Such variation affected all growth parameters for both boys and girls.CONCLUSION:Regional variations in growth need to be taken into consideration when assessing the growth of Saudi children and adolescents.

The Monarch butterfly eastern population (Danaus plexippus) is in decline primarily due to habitat loss. Current habitat restoration programs focus on re-establishing milkweed, the primary food resource for Monarch caterpillars, in the central United States of America. However, individual components of the Monarch life cycle function as part of an integrated whole. Here we develop the MOBU-SDyM, a migration-wide systems dynamics model of the Monarch butterfly migratory cycle to explore alternative management strategies’ impacts. Our model offers several advances over previous efforts, considering complex variables such as dynamic temperature-dependent developmental times, dynamic habitat availability, and weather-related mortality across the entire range. We first explored whether the predominant focus of milkweed restoration in the mid-range of the Monarch’s migration could be overestimating the Monarch’s actual habitat requirements. Second, we examined the robustness of using the recommended 1.2–1.6 billion milkweed stems as a policy objective when accounting for factors such as droughts, changes in temperature, and the stems’ effective usability by the Monarchs. Third, we used the model to estimate the number and distribution of stems across the northern, central, and southern regions of the breeding range needed to reach a self-sustainable long-term Monarch population of six overwintering hectares. Our analysis revealed that concentrating milkweed growth in the central region increases the size of the overwintering colonies more so than equivalent growth in the south region, with growth in the northern region having a negligible effect. However, even though simulating an increase in milkweed stems in the south did not play a key role in increasing the size of the overwintering colonies, it plays a paramount role in keeping the population above a critically small size. Abiotic factors considerably influenced the actual number of stems needed, but, in general, our estimates of required stems were 43–91% larger than the number of stems currently set as a restoration target: our optimal allocation efforts were 7.35, 92, and 0.15% to the south, central, and northern regions, respectively. Systems dynamics’ analytical and computational strengths provided us with new avenues to investigate the Monarch’s migration as a complex biological system and to contribute to more robust restoration policies for this unique species.

Survey of bioavailable selenium in Sweden with the moose ( Alces alces L.) as monitoring animal

A large scale survey of diseased legume plants (mainly clover and alfalfa in the Fabaceae family) was conducted from 2009 to 2013 in four Economic Regions of Russia, Northern (Arkhangelsk and Vologda oblast), Central (Moscow oblast), Volga (Samara oblast) and West Siberian (Novosibirsk oblast). The majority of infected clover plants exhibited symptoms typical of clover phyllody (CPh), clover yellow edge (CYE), or clover proliferation (CP), and infected alfalfa plants exhibited symptoms typical of alfalfa witches’-broom (AWB). Of 161 symptomatic plants from 22 different legume species, 103 tested positive for phytoplasma infection. Phytoplasmas belonging to four groups and six subgroups were detected, of which 31.1% were group 16SrI, with the majority belonging to subgroup 16SrI-C- (causal agent of CPh disease), two belonging to 16SrI-B and two group 16SrI phytoplasmas not identified to the subgroup level;47.6% were group 16SrIII, with the majority belonging to subgroup 16SrIII-B or 16SrIII-B variant (causal agent of CYE disease), and one strain belonging to16SrIII-F; 8.7% were subgroup 16SrVI-A (causal agent of CP and AWB diseases); 9.7% were subgroup 16SrXII-A (causal agent of AWB disease); and 2.9% were mixed infected with subgroups 16SrIII-B and 16SrI-C. The predominant phytoplasma species detected varied by region. In the Northern and Central Regions, the majority of the phytoplasmas detected belonged to subgroups 16SrI-C and 16SrIII-B. In the West Siberian and the Volga Regions, the phytoplasmas predominately detected belonged to subgroups 16SrVI-A and 16SrXII-A, respectively. Subgroup 16SrIII-F was detected in a single plant in the West Siberian Region and a mixed infection of 16SrIII-B and 16SrI-C was detected in three plants, one in the Northern Region and two in the Central Region. Eleven species of insects of the order Hemiptera, suborder Auchenorrhyncha, were collected from leguminous plants in the Moscow oblast of the Central Region. Euscelis incisus and Aphrodes bicinctus were the most prevalent species and may be potential phytoplasma vectors in the Central Region.

Contrary to the well-documented incidence and severity of Citrus variegated chlorosis (CVC) throughout the sweet orange groves of Sao Paulo state (Brazil), the incidence and distribution of its causal agent, Xylella fastidiosa, is not well known. Sweet orange samples selected according to variety and age were collected from commercial groves in northern, central and southern regions of Sao Paulo state and analysed visually for the presence of symptoms and by PCR for the presence of X. fastidiosa. A decreasing gradient of both infected and symptomatic trees from northern to southern regions of the state was observed. However, there was a higher incidence of latent infection (symptomless trees) in the southern (32%) than in the northern and central regions (16%). The survey disclosed an increasing gradient of CVC and X. fastidiosa incidence according to the age of the trees. Infected trees younger than two years were observed only in the central region. CVC symptom expression is related to environmental conditions and the incidence of symptomatic trees does not correspond with that of trees infected by X. fastidiosa.

In the urgent context of global climate change and carbon neutrality goals, effective carbon balance regulation is critical for achieving temperature control targets. Metropolitan areas encounter significant challenges in carbon emission reduction, energy transition advancement, and enhancement of sequestration capabilities. However, traditional carbon balance analysis methods have limitations in capturing dynamic changes and guiding precise regulation. Therefore, this study developed a dynamic–static classification system for carbon balance based on the Ecological Support Coefficient (ESC) and the Economic Contributive Coefficient (ECC). This system examined carbon emissions and carbon sequestration in China’s 14 national-level metropolitan areas from 2000 to 2020. The results showed that: (1) Carbon emissions showed an increasing trend, exhibiting a spatial distribution with higher levels in the north, moderate levels in the central region, and the lowest levels in the southeast. In contrast, carbon sequestration exhibited a spatial pattern with higher levels in the east, moderate levels in the central region, and lower levels in the west. (2) Static classification revealed that the ECC and ESC of metropolitan areas in the central and northern regions were relatively weaker than those in other regions. Dynamic classification further showed an upward trend in the economic and ecological capabilities of these central and northern metropolitan areas. In contrast, metropolitan areas along the coast and within the Yangtze River Economic Belt needed to optimize their economic–ecological coordination efficiency. Although southern coastal metropolitan areas demonstrated robust economic vitality, they encountered significant ecological support pressures. (3) Economic development level and ecological environmental quality were the predominant factors in metropolitan area classification. Regions with a higher ECC tended to exhibit an enhanced ESC, while regions with a stronger ESC prioritized economic growth. This classification system provided a solid scientific basis for formulating differentiated low-carbon transformation strategies, thereby supporting high-quality development in China’s metropolitan areas while maintaining a dynamic balance between economic and ecologic objectives. Moreover, it offered both theoretical foundations and practical guidance for optimizing sustainable development pathways in similar metropolitan areas globally.

Spatial and temporal patterns of seagrass habitat use by fishes at the Ryukyu Islands, Japan

This study evaluates drought evolution across Vietnam’s seven climatic sub-regions from 1985 to 2099 using the one-month Standardized Precipitation Evapotranspiration Index (SPEI) derived from the 10-km resolution CMIP6-VN dataset. Three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) were analyzed for near-future (2015–2039), mid-century (2040–2069), and far-future (2070–2099) periods, with potential evapotranspiration estimated via the Hargreaves-Samani method. Mann-Kendall trend tests and signal-to-noise ratio (SNR) analyses assessed drought intensity, frequency, and severity. Temperature projections indicate consistent warming, with national averages increasing by 1.4°C (SSP1-2.6), 2.6°C (SSP2-4.5), and 4.5°C (SSP5-8.5) by century's end relative to the 1985–2014 baseline, with northern regions experiencing enhanced warming (up to 5.1°C in the Northeast) compared to southern regions (4.0°C in the Southeast). Precipitation is projected to increase across all scenarios, with SSP2-4.5 showing the largest rise (7.2% by 2070–2099), though with greater uncertainty (±20%) compared to temperature projections. Despite rising temperature and precipitation, drought tendencies vary non-linearly due to amplified evapotranspiration outpacing precipitation gains, with northern and central regions showing prolonged drying under SSP1-2.6 early-century, while southern regions stabilize due to balanced wet-dry cycles. Drought analysis reveals dynamic hydroclimatic variability: near-term (2015–2039) drying trends are most pronounced under SSP1-2.6 with extended moderate droughts, while mid-century (2040–2069) shifts to wetter conditions across all SSPs but most evidently in SSP1-2.6; late-century projections show modest SPEI increases with stronger wetting in northern regions under low-emission SSP1-2.6, contrasted by higher inter-model uncertainty and potential intensification in SSP5-8.5 due to amplified evapotranspiration-precipitation feedbacks. Drought intensity increases by up to 22.7% in southern Vietnam by 2015–2039 under SSP2-4.5, while frequency and severity exhibit non-monotonic trends, with notable increases in central regions (up to 15.7 months/event under SSP2-4.5). These findings underscore the need for adaptive irrigation and water management strategies to mitigate intensified drought impacts on Vietnam’s agriculture, particularly in central and northern regions.