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Debates surrounding the efficacy of influenza vaccination for survival benefits persist, and there is a lack of data regarding its duration of protection. A self‐controlled case series (SCCS) and a 1:4 matched case‐control study were conducted using the National Health Interview Survey (NHIS) and public‐use mortality data from 2005 to 2018 in the United States. The SCCS study identified participants who received influenza vaccination within 12 months before the survey and subsequently died within 1 year of postvaccination. The matched case‐control study paired participants who died during the influenza season at the time of survey with four survivors. Among 1167 participants in the SCCS study, there was a 46% reduction in all‐cause mortality and a 43% reduction in cardiovascular mortality within 29–196 days of postvaccination. The greatest protection was observed during days 29–56 (all‐cause mortality: RI: 0.19; 95% CI: 0.12–0.29; cardiovascular mortality: RI: 0.28; 95% CI: 0.14–0.56). Among 626 cases and 2504 controls included in the matched case‐control study, influenza vaccination was associated with a reduction in all‐cause mortality (OR: 0.74, 95% CI: 0.60–0.92) and cardiovascular mortality (OR: 0.64, 95% CI: 0.44–0.93) during the influenza season. This study highlights the importance of influenza vaccination in reducing the risks of all‐cause and cardiovascular mortality, with effects lasting for approximately 6 months. [ABSTRACT FROM AUTHOR]

Evolutionary convergence is one of the most striking examples of adaptation driven by natural selection. However, genomic evidence for convergent adaptation to extreme environments remains scarce. Here, we assembled reference genomes of two alpine plants, Saussurea obvallata (Asteraceae) and Rheum alexandrae (Polygonaceae), with 37,938 and 61,463 annotated protein‐coding genes. By integrating an additional five alpine genomes, we elucidated genomic convergence underlying high‐altitude adaptation in alpine plants. Our results detected convergent contractions of disease‐resistance genes in alpine genomes, which might be an energy‐saving strategy for surviving in hostile environments with only a few pathogens present. We identified signatures of positive selection on a set of genes involved in reproduction and respiration (e.g., MMD1, NBS1, and HPR), and revealed signatures of molecular convergence on genes involved in self‐incompatibility, cell wall modification, DNA repair and stress resistance, which may underlie adaptation to extreme cold, high ultraviolet radiation and hypoxia environments. Incorporating transcriptomic data, we further demonstrated that genes associated with cuticular wax and flavonoid biosynthetic pathways exhibit higher expression levels in leafy bracts, shedding light on the genetic mechanisms of the adaptive "greenhouse" morphology. Our integrative data provide novel insights into convergent evolution at a high‐taxonomic level, aiding in a deep understanding of genetic adaptation to complex environments. [ABSTRACT FROM AUTHOR]

BACKGROUND Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at various growth stages and densities. This paper presents a novel deep‐learning‐based method for weed detection that recognizes vegetable crops and classifies all other green objects as weeds. RESULTS: The optimal confidence threshold values for YOLO‐v3, CenterNet, and Faster R‐CNN were 0.4, 0.6, and 0.4/0.5, respectively. These deep‐learning models had average precision (AP) above 97% in the testing dataset. YOLO‐v3 was the most accurate model for detection of vegetables and yielded the highest F1 score of 0.971, along with high precision and recall values of 0.971 and 0.970, respectively. The inference time of YOLO‐v3 was similar to CenterNet, but significantly shorter than that of Faster R‐CNN. Overall, YOLO‐v3 showed the highest accuracy and computational efficiency among the deep‐learning architectures evaluated in this study. CONCLUSION: These results demonstrate that deep‐learning‐based methods can reliably detect weeds in vegetable crops. The proposed method avoids dealing with various weed species, and thus greatly reduces the overall complexity of weed detection in vegetable fields. Findings have implications for advancing site‐specific robotic weed control in vegetable crops. [ABSTRACT FROM AUTHOR]

The flora of northern China forms the main part of the Sino‐Japanese floristic region and is located in a south–north vegetative transect in East Asia. Phylogeographic studies have demonstrated that an arid belt in this region has promoted divergence of plants in East Asia. However, little is known about how plants that are restricted to the arid belt of flora in northern China respond to climatic oscillation and environmental change. Here, we used genomic‐level data of Myripnois dioica across its distribution as a representative of northern China flora to reconstruct plant demographic history, examine local adaptation related to environmental disequilibrium, and investigate the factors related to effective population size change. Our results indicate M. dioica originated from the northern area and expanded to the southern area, with the Taihang Mountains serving as a physical barrier promoting population divergence. Genome‐wide evidence found strong correlation between genomic variation and environmental factors, specifically signatures associated with local adaptation to drought stress in heterogeneous environments. Multiple linear regression analyses revealed joint effects of population age, mean temperature of coldest quarter, and precipitation of wettest month on effective population size (Ne). Our current study uses M. dioica as a case for providing new insights into the evolutionary history and local adaptation of northern China flora and provides qualitative strategies for plant conservation. [ABSTRACT FROM AUTHOR]

This research presents a robust model predictive controller of two microgrids (MGs) interconnected via tie‐line (TL) power. Power system operations continue to face new challenges due to the large integration of renewable energy generation with an active load demand uncertainty and the management and control of bidirectional power flow between MGs and others uncertainties and system nonlinearities due to the power system operating condition. To handle these uncertainties and system nonlinearities a robust model using predictive control scheme is presented in this paper to achieve the control of the active power between two MGs when considering three disturbances scenario models. In the design process, uncertainties and nonlinearities of the system were taken into account and contributed to the robustness of the closed‐loop system. Robust control scheme development considering power system nonlinearities and uncertainties were used to improve the online close‐loop robustness. The model helped to achieve a desired active power regulation by coordinating the MGs and active power over‐shoot and under‐shoot time were reduced outstandingly. A Comparison was made between open‐loop and close‐loop simulation results, which indicate that the model can handle robustness while settling power deviation of the interconnected system. MATLAB environment was used to solve the optimisation problem. [ABSTRACT FROM AUTHOR]

Summary: Investigating the interaction between environmental heterogeneity and local adaptation is critical for understanding the evolutionary history of a species, providing the premise for studying the response of organisms to rapid climate change. However, for most species how exactly the spatial heterogeneity promotes population divergence and how genomic variations contribute to adaptive evolution remain poorly understood.We examine the contributions of geographical and environmental variables to population divergence of the relictual, alpine herb Circaeaster agrestis, as well as the genetic basis of local adaptation using RAD‐seq and plastome data.We detected significant genetic structure with an extraordinary disequilibrium of genetic diversity among regions, and signals of isolation‐by‐distance along with isolation‐by‐resistance. The populations were estimated to begin diverging in the late Miocene, along with a possible ancestral distribution of the Hengduan Mountains and adjacent regions. Both environmental gradient and redundancy analyses revealed significant association between genetic variation and temperature variables. Genome–environment association analyses identified 16 putatively adaptive loci related mainly to biotic and abiotic stress resistance.Our genome‐wide data provide new insights into the important role of environmental heterogeneity in shaping genetic structure, and access the footprints of local adaptation in an ancient relictual species, informing future conservation efforts. [ABSTRACT FROM AUTHOR]

The increase in the price of fossil fuel due to its rarity and emissions means more integration of renewable energy sources (RESs) is required to improve economic management of the grid. This study analyses a combination of the tie‐line power between conventional power sources and the renewable energy system and its frequency deviations which are known as area control error. The minimisation of the tie‐line by optimal control is affected in such a way that the frequency and tie‐line power error can be minimised while maintaining the power balance between generation and load. The tie‐line connected to the microgrid consists of two main parts, namely the conventional source and the renewable energy source, each made up of a synchronous generator. The control application of the active power and frequency to a network is referred to as load frequency control (LFC) with the storage system as an integral part of RES. The simulation results show the performance of the proposed optimal control model in microgrid during the changing loads' condition, where the energy storage system applied to optimal control has shown a quick response to frequency deviation, which is close to 80%. [ABSTRACT FROM AUTHOR]

This study proposes a multi‐objective mathematical optimisation model which includes minimisation of the investment costs of new offshore wind farm units, investment costs of new transmission lines, fuel costs, emissions of the fossil fuel generators as well as the maximisation of incentives for new generating units. The mathematical model can be used to determine the optimal expansion planning for generation and transmission facilities with consideration of the impact of location and feed‐in tariffs for offshore wind power on the overall system capacity and efficiency. The proposed model is solved using CPLEX 12.6.3 solver in advanced interactive multidimensional modelling system and tested on the modified Garver 6‐bus test system and the IEEE 24‐bus reliability test system. Simulation results indicate that improving the incentive support scheme and minimisation of the total cost will improve the effective utilisation of offshore power. [ABSTRACT FROM AUTHOR]

The unpredictable nature of renewable power, coupled with the influx of prosumers has made accurate power forecasts in microgrids (MGs) more difficult to obtain. A direct consequence of this is the need for additional spinning reserve (SR) capacity to compensate for resulting power imbalances. Due to the economic and environmental concerns, increasing conventional generation to meet this additional SR capacity is undesirable. The aggregation of prosumer behind‐the‐meter resources for the provision of SR is proposed in this study, and a mathematical model for the proposed scheme is developed. This scheme is formulated as a constrained optimisation problem, whose solution maintains power supply and demand balance whilst reserving a virtual spinning capacity. The formulation is linearised and solved using the CPLEX 12.6.3 solver in the Advanced Interactive Multidimensional Modelling System environment. A 14‐bus MG test system is used to validate the proposed scheme, and results show the benefits of using prosumer behind‐the‐meter resources to provide ancillary services like SR. [ABSTRACT FROM AUTHOR]

Aim A phylogeographical study of the widespread but phylogenetically isolated East Asian endemic tree species Tetracentron sinense (Trochodendraceae) was performed to evaluate whether and how Pleistocene and pre-Pleistocene climate changes helped to influence current phylogeographical patterns, and to describe the current patterns of genetic diversity and their implications for conservation. Location Southwestern and central subtropical China. Methods Sequences of four chloroplast spacer regions were obtained from 157 individuals of T. sinense. A haplotype network was constructed using tcs. Genetic diversity and differentiation, spatial analysis of molecular variance ( SAMOVA) and analysis of molecular variance ( AMOVA) were used to test for genetic structure. beast was used to estimate the divergence times between haplotypes. Historical demographic expansion was tested using pairwise mismatch distribution analysis. Results Of the 21 recovered haplotypes, three were widely distributed, but most were restricted to particular regions. Populations with high haplotype diversity were located in western Hubei, southern Sichuan and southern Chongqing. The two earliest-diverging haplotypes were found in southwestern China. The haplotype distribution of T. sinense demonstrated significant phylogeographical structure ( NST > GST; P < 0.05). The best partitioning of genetic diversity by SAMOVA ( K = 5) produced groups that matched the main tcs-derived clades. Two independent range expansions within SAMOVA-derived groups 2 and 3 were dated to approximately 399 and 311 ka, respectively. The time to the most recent common ancestor of all haplotypes was 9.6 (95% highest posterior density: 27.0-2.2) Ma, but most of the haplotype diversity appeared during the Quaternary. Main conclusions The extant distribution of T. sinense is likely to have been shaped by both pre-Quaternary and Pleistocene climate changes. Southwestern China may have served as an important refugium for T. sinense throughout the Neogene, while the species also occupied multiple refugia during the late Pleistocene glacial periods. Populations of T. sinense were resolved into five allopatric groups, between which there is apparently no seed movement. [ABSTRACT FROM AUTHOR]

Cerebral malaria ( CM) is a neurological syndrome often occurring in severe malaria. Although CM is known as an immunopathology in brain tissue mediated by excessive proinflammatory cytokines, the immunoregulatory mechanism is poorly understood. Here, we investigated the role of IL-10-producing regulatory B ( Breg) cells in modulating CM development in a murine model of Plasmodium berghei ANKA infection. We observed that blood-stage P. berghei induced expansion of IL-10-producing Breg cells in C57 BL/6 mice. Adoptive transfer of IL-10+ Breg cells to P. berghei infected mice significantly reduced the accumulation of NK and CD8+ T cells and hemorrhage in brain tissue, and improved the survival of the mice compared with control groups, although parasitemia levels were not altered. Treatment of Breg-cell recipient mice with anti- IL-10 receptor m Ab blocked the protective effect of Breg cells. Adoptive transfer of CD4+ CD25+ Treg cells failed to prevent CM in infected mice. Spleen cells from Breg-cell recipient mice produced increased levels of IL-10 in vitro. Cell co-culture showed that purified IL-10+ B cells, but not IL-10− B cells, promoted IL-10 production by CD4+ T cells. These results demonstrate that IL-10-producing Breg cells may represent an important mechanism for controlling the immunopathology and prevention of CM associated with P. berghei infection. [ABSTRACT FROM AUTHOR]

Modulation and suppression of the immune response of the host by nematode parasites have been reported extensively and the cysteine protease inhibitor ( CPI or cystatin) is identified as one of the major immunomodulators. In the present study, we cloned and produced recombinant CPI protein from the murine nematode parasite Heligmosomoides polygyrus (r Hp- CPI) and investigated its immunomodulatory effects on dendritic cell ( DC) function and immune responses in mice. Bone-marrow-derived CD11c+ DC ( BMDC) that were exposed to r Hp- CPI during the differentiation stage showed reduced MHC- II molecule expression compared with BMDC that were generated in normal culture conditions. The BMDC generated in the presence of r Hp- CPI also exhibited reduced expression of CD40, CD86 and MHC- II molecules and reduced interleukin-6 and tumour necrosis factor-α cytokine production when stimulated with Toll-like receptor ligand Cp G. Activation of BMDC generated in normal conditions induced by lipopolysaccharide and Cp G was also suppressed by r Hp- CPI, as shown by reduced co-stimulatory molecule expression and cytokine production. Furthermore, BMDC treated with r Hp- CPI before ovalbumin ( OVA) antigen pulsing induced a weaker proliferation response and less interferon-γ production of OVA-specific CD4+ T cells compared with BMDC without r Hp- CPI pre-treatment. Adoptive transfer of r Hp- CPI-treated and OVA-loaded BMDC to mice induced significantly lower levels of antigen-specific antibody response than the BMDC loaded with antigen alone. These results demonstrated that the CPI from nematode parasites is able to modulate differentiation and activation stages of BMDC. It also interferes with antigen and MHC- II molecule processing and Toll-like receptor signalling pathway, resulting in functionally deficient DC that induce a suboptimum immune response. [ABSTRACT FROM AUTHOR]