Search

Dasiphora fruticosa (Rosaceae), commonly known as shrubby cinquefoil, is a flowering shrub of high ornamental value yet underutilized in East Asian landscapes. Given its broad elevational distribution range, D. fruticosa serves as an ideal model for studying genetic adaptations and speciation along elevation gradients. Here, we present a high-quality chromosome-scale assembly of D. fruticosa with a genome size of 249.23 Mb and a contig N50 length of 14.01 Mb. The genome sequence contains 32,613 protein-coding genes, of which 30,643 (93.96%) were functionally annotated. Compared to the published D. fruticosa genome sequence, our assembly demonstrates higher completeness and continuity. Furthermore, comparative genomic analyses provide insights into the phylogenetic relationship and high-altitude adaptation of D. fruticosa. Overall, our study offers a valuable genetic resource for both molecular and evolutionary research on shrubby cinquefoil. [ABSTRACT FROM AUTHOR]

The electrical power system (EPS) has been heavily stressed due to high load demand. It operates close to the total capacity limits, resulting in voltage instability that can lead to voltage collapse. In this regard, incorporating flexible alternating current transmission system (FACTS) devices and renewable energy sources (RESs) to obtain the optimum values of the generator voltage, reactive compensation, and transformer tab in optimal reactive power dispatch (ORPD) is essential in increasing the reliability and safety of the system. ORPD involves discrete and continuous variables, which are nonlinear, noncontinuous, non-convex, and complex problems. The objective functions of ORPD are reduction in active power loss (Ploss), voltage deviation, and voltage profile enhancement. This paper presents a recent advancement of the ORPD problem, mathematical formulation of the objectives function, and a summary of various metaheuristic optimization methods (single and hybrid) used to solve the ORPD problems. The hybrid method combines two or more methods to improve the demerits of one method to obtain a quality solution to a problem. This review covered incorporating FACTS devices and RESs used in solving the ORPD problem to reduce the active Ploss and improve the voltage profile in the EPS. The benefits of FACTS devices and RESs are also discussed. Also, various metaheuristic algorithms (single, modified, and hybrid) employed to solve the ORPD problem were discussed. The future direction for researchers in this field was provided to give insight into the applicability and performance. Overall, this research explores different techniques used in solving ORPD problems from the optimization point of view to incorporating RESs and FACTS devices to obtain quality solutions. Some existing methods do not guarantee an optimum solution, but incorporating RESs and FACTS devices will help attain the best solution to the problem for better power system operation to improve system reliability and voltage profile. Based on the review journal, it can be concluded that hybrid techniques offer efficient quality solutions to the ORPD problem. [ABSTRACT FROM AUTHOR]

Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) is a crop of significant interest due to its nutritional value and resilience to drought conditions. However, drought, particularly following flowering, is a major factor contributing to yield reduction. This research employed two distinct Tartary buckwheat genotypes to investigate the effects of post-anthesis drought on growth and physicochemical characteristics. The study aimed to elucidate the response of Tartary buckwheat to drought stress. The findings indicated that post-anthesis drought adversely impacted the growth, morphology, and biomass accumulation of Tartary buckwheat. Drought stress enhanced the maximum photosynthetic capacity (Fv/Fm) and light protection ability (NPQ) of the 'Xiqiao-2' genotype. In response to drought stress, 'Dingku-1' and 'Xiqiao-2' maintained osmotic balance by accumulating soluble sugars and proline, respectively. Notably, 'Xiqiao-2' exhibited elevated levels of flavonoids and polyphenols in its leaves, which helped mitigate oxidative damage caused by drought. Furthermore, rewatering after a brief drought period significantly improved plant height, stem diameter, and biomass accumulation in 'Dingku-1'. Overall, 'Xiqiao-2' demonstrated greater long-term tolerance to post-anthesis drought, while 'Dingku-1' was less adversely affected by short-term post-anthesis drought. [ABSTRACT FROM AUTHOR]

The untranslated regions (UTRs) within plant mRNAs play crucial roles in regulating gene expression and the functionality of post-translationally modified proteins by various mechanisms. These regions are vital for plants' ability to sense to multiple developmental and environmental stimuli. In this study, we conducted a genome-wide analysis of UTRs and UTR-containing genes in maize (Zea mays). Using the ZmLAZ1 family as a case study, we demonstrated that the length of 5′ UTRs could influence gene expression levels by employing GUS reporter gene assays. Although maize and arabidopsis (Arabidopsis thaliana), as well as rice (Oryza sativa), have distinct functional categories of UTR-containing genes, we observed a similar lengthwise distribution of UTRs and a recurring appearance of certain gene ontology (GO) terms between maize and rice. These suggest a potentially conserved mechanism within the Poaceae species. Furthermore, the analysis of cis-acting elements in these 5′ UTRs of the ZmLAZ1 gene family further supports the hypothesis that UTRs confer functional specificity to genes in a length-dependent manner. Our findings offer novel insights into the role of UTRs in maize, contributing to the broader understanding of gene expression regulation in plants. [ABSTRACT FROM AUTHOR]

The conservation biology field underscores the importance of understanding genetic diversity and gene flow within plant populations and the factors that influence them. This study employs Simple Sequence Repeat (SSR) molecular markers to investigate the genetic diversity of the endangered plant species Saussurea involucrata, offering a theoretical foundation for its conservation efforts. Utilizing sequencing results to screen SSR loci, we designed and scrutinized 18 polymorphic microsatellite primers across 112 samples from 11 populations in the Bayinbuluke region. Our findings reveal high genetic diversity (I = 0.837, He = 0.470) and substantial gene flow (Nm = 1.390) among S. involucrata populations (China, Xinjiang), potentially attributed to efficient pollen and seed dispersal mechanisms. Principal Coordinate Analysis (PCoA) indicates a lack of distinct genetic structuring within the Bayinbuluke populations. The cluster analysis using STRUCTURE reflected the genetic structure of S. involucrata to a certain extent compared with PCoA. The results showed that all samples were divided into four groups. To safeguard this species, we advocate for the in situ conservation of all S. involucrata populations in the area. The SSR markers developed in this study provide a valuable resource for future genetic research on S. involucrata. [ABSTRACT FROM AUTHOR]

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]

Credit risk prediction is a crucial task for financial institutions. The technological advancements in machine learning, coupled with the availability of data and computing power, has given rise to more credit risk prediction models in financial institutions. In this paper, we propose a stacked classifier approach coupled with a filter-based feature selection (FS) technique to achieve efficient credit risk prediction using multiple datasets. The proposed stacked model includes the following base estimators: Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB). Furthermore, the estimators in the Stacked architecture were linked sequentially to extract the best performance. The filter- based FS method that is used in this research is based on information gain (IG) theory. The proposed algorithm was evaluated using the accuracy, the F1-Score and the Area Under the Curve (AUC). Furthermore, the Stacked algorithm was compared to the following methods: Artificial Neural Network (ANN), Decision Tree (DT), and k-Nearest Neighbour (KNN). The experimental results show that stacked model obtained AUCs of 0.934, 0.944 and 0.870 on the Australian, German and Taiwan datasets, respectively. These results, in conjunction with the accuracy and F1-score metrics, demonstrated that the proposed stacked classifier outperforms the individual estimators and other existing methods. [ABSTRACT FROM AUTHOR]

Background and objective: The effectiveness of goal-directed fluid therapy (GDFT) in promoting postoperative recovery remains unclear, the aim of this study was to evaluate the effect of GDFT on length of hospital stay and postoperative recovery of GI function in patients undergoing major abdominal oncologic surgery. Methods: In this randomized, double- blinded, controlled trial, adult patients scheduled for elective major abdominal surgery with general anesthesia, were randomly divided into the GDFT protocol (group G) or conventional fluid therapy group (group C). Patients in group C underwent conventional fluid therapy based on mean arterial pressure (MAP) and central venous pressure (CVP) whereas those in group G received GDFT protocol associated with the SVV less than 12% and the cardiac index (CI) was controlled at a minimum of 2.5 L/min/m2. The primary outcomes were the length of hospital stay and postoperative GI function. Results: One hundred patients completed the study protocol. The length of hospital stay was significantly shorter in group G compared with group C [9.0 ± 5.8 days versus 12.0 ± 4.6 days, P = 0.001]. Postoperative gastrointestinal dysfunction (POGD) occurred in two of 50 patients (4%) in group G and 16 of 50 patients (32%) in the control group (P < 0.001). GDFT significantly also shorten time to first flatus by 11 h (P = 0.009) and time to first tolerate oral diet by 2 days (P < 0.001). Conclusions: Guided by SVV and CI, the application of GDFT has the potential to expedite postoperative recovery of GI function and reduce hospitalization duration after major abdominal surgery. Trial registration: This study was registered on www.clinicaltrials.gov on 07/05/2019 with registration number: NCT03940144. [ABSTRACT FROM AUTHOR]

The underlying mechanism of chronic hepatitis B virus (HBV) functional cure by interferon (IFN), especially in patients with low HBsAg and/or young ages, is still unresolved due to the lack of surrogate models. Here, we generate a type I interferon receptor humanized mouse (huIFNAR mouse) through a CRISPR/Cas9-based knock-in strategy. Then, we demonstrate that human IFN stimulates gene expression profiles in huIFNAR peripheral blood mononuclear cells (PBMCs) are similar to those in human PBMCs, supporting the representativeness of this mouse model for functionally analyzing human IFN in vivo. Next, we reveal the tissue-specific gene expression atlas across multiple organs in response to human IFN treatment; this pattern has not been reported in healthy humans in vivo. Finally, by using the AAV-HBV model, we test the antiviral effects of human interferon. Fifteen weeks of human PEG-IFNα2 treatment significantly reduces HBsAg and HBeAg and even achieves HBsAg seroconversion. We observe that activation of intrahepatic monocytes and effector memory CD8 T cells by human interferon may be critical for HBsAg suppression. Our huIFNAR mouse can authentically respond to human interferon stimulation, providing a platform to study interferon function in vivo. PEG-IFNα2 treatment successfully suppresses intrahepatic HBV replication and achieves HBsAg seroconversion. There is increasing evidence that treatment of hepatitis B with interferon alpha can be beneficial. Here, Wang et al, present a type 1 interferon receptor humanized mouse model and characterize it as a platform in which to study interferon function in vivo. [ABSTRACT FROM AUTHOR]

Storage systems are needed to boost the reliability of intermittent solar and wind resources in power networks. Rather than focus on one storage system or one hybrid energy storage system (HESS), this work models the operation of six HESS configurations in a Renewable Energy (RE) based grid-tied network. The objective is to minimise the daily operational costs of the microgrid while prolonging the storage lifetime by considering storage degradation costs. The influence of fixed tariffs and time-of-use (TOU) tariffs on the optimal operational of the HESS configurations have also been investigated; as well as deferrable demand satisfaction, charge-discharge pattern of different HESS and availability of the power-dense storage system within the microgrid. Results show that the lead-acid battery and hydrogen fuel cell (HFC) HESS incurs the highest operational costs, while the supercapacitor-lead-acid battery HESS incurs the lowest operational costs. The supercapacitor-lead acid battery and the supercapacitor-HFC HESS incur the highest annual storage degradation costs. The grid expenses were seen to be the same for all HESS under each tariff scheme. Lastly, decreasing the minimum storage level further by 10% from the 30% in the base case, led to an increase in the number of hours of availability of the power-dense storage system of five of the six HESS. These results have given a deeper understanding to the operation of HESS systems and can inform better decision making of the suitable HESS to be deployed in different operating scenarios. [ABSTRACT FROM AUTHOR]

Identifying the weak buses in power system networks is crucial for planning and operation since most generators operate close to their operating limits, resulting in generator failures. This work aims to identify the critical/weak node and reduce the system's power loss. The line stability index ( L m n ) and fast voltage stability index (FVSI) were used to identify the critical node and lines close to instability in the power system networks. Enhanced particle swarm optimization (EPSO) was chosen because of its ability to communicate with better individuals, making it more efficient to obtain a prominent solution. EPSO and other PSO variants minimized the system's actual/real losses. Nodes 8 and 14 were identified as the critical nodes of the IEEE 9 and 14 bus systems, respectively. The power loss of the IEEE 9 bus system was reduced from 9.842 MW to 7.543 MW, and for the IEEE 14 bus system, the loss was reduced from 13.775 MW of the base case to 12.253 MW for EPSO. EPSO gives a better active power loss reduction and improves the node's voltage profile than other PSO variants and algorithms in the literature. This suggests the feasibility and suitability of EPSO to improve the grid voltage quality. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the changes in oocytes at the transcriptome level after applying continuous microvibrational mechanical stimulation to human immature oocytes during in vitro maturation. Methods: The discarded germinal-vesicle stage (GV) oocytes with no fertilization value after oocytes retrieval in assisted reproduction cycles were collected. Part of them was stimulated with vibration (n = 6) at 10 Hz for 24 h after obtaining informed consent; the other was cultured in static condition (n = 6). Single-cell transcriptome sequencing was used to detect the differences in oocyte transcriptome compared with the static culture group. Results: The applied 10-Hz continuous microvibrational stimulation altered the expression of 352 genes compared with the static culture. Gene Ontology (GO) analysis suggested that the altered genes were mainly enriched with 31 biological processes. The mechanical stimulation upregulated 155 of these genes and downregulated 197 genes. Among them, the genes related to mechanical signaling, such as protein localization to intercellular adhesion (DSP and DLG-5) and cytoskeleton (DSP, FGD6, DNAJC7, KRT16, KLHL1, HSPB1, MAP2K6), were detected. DLG-5, which was related to protein localization to intercellular adhesion, was selected for immunofluorescence experiments based on the transcriptome sequencing results. The protein expression of DLG-5 in the microvibration-stimulated oocytes was higher than that in the static culture oocytes. Conclusions: Mechanical stimulation affects the transcriptome during oocyte maturation, causing the express changes in intercellular adhesion and cytoskeleton-related genes. We speculate that the mechanical signal may be transmitted to the cell through DLG-5 protein and cytoskeleton-related protein to regulate cellular activities. [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]

With the rapid developments in electronic commerce and digital payment technologies, credit card transactions have increased significantly. Machine learning (ML) has been vital in analyzing customer data to detect and prevent fraud. However, the presence of redundant and irrelevant features in most real-world credit card data degrades the performance of ML classifiers. This study proposes a hybrid feature-selection technique consisting of filter and wrapper feature-selection steps to ensure that only the most relevant features are used for machine learning. The proposed method uses the information gain (IG) technique to rank the features, and the top-ranked features are fed to a genetic algorithm (GA) wrapper, which uses the extreme learning machine (ELM) as the learning algorithm. Meanwhile, the proposed GA wrapper is optimized for imbalanced classification using the geometric mean (G-mean) as the fitness function instead of the conventional accuracy metric. The proposed approach achieved a sensitivity and specificity of 0.997 and 0.994, respectively, outperforming other baseline techniques and methods in the recent literature. [ABSTRACT FROM AUTHOR]

Future energy planning relies on understanding how much energy is produced and consumed. In response, this study developed a multihybrid adaptive neuro-fuzzy inference system (ANFIS) for students' residences, using the University of Johannesburg residence, South Africa as a case study. The model input variables are wind speed, temperature, and humidity, with the output being the equivalent energy consumption for the student housing. While the particle swarm optimization (PSO) technique is versatile and widely used, it falls short by exhibiting premature convergence. To address this problem, the velocity update equation of the original PSO algorithm is modified by incorporating a dynamic linear decreasing inertia weight, which improves the PSO algorithm's convergence behaviour and aids both local and global search. Following that, the modified PSO (MPSO) is used to optimize the ANFIS parameters for the best model prediction. A comparative analysis is conducted between the MPSO, the original PSO, and six other hybrid models using a dataset division of 70% for training and 30% for testing. Performance evaluation was carried out using three well-known performance benchmarks: root mean square error (RMSE), mean absolute deviation (MAD), and coefficient of variation (RCoV). The experimental results show that the performance of the proposed MPSO-ANFIS outperformed other methods with the least values of the RMSE (1.8928 KWh), MAD (1.5051 KWh), and RCoV (0.1370), respectively. Furthermore, when compared to the PSO-ANFIS, the MPSO-ANFIS demonstrated improvements in RMSE, MAD, and RCoV with 1.58%, 2.11%, and 5.23%, respectively. Based on the results, it can be concluded that the MPSO-ANFIS provides better prediction accuracy which is vital for strategic energy planning. [ABSTRACT FROM AUTHOR]

Part of the widely discussed problem in electrical power systems is the optimal reactive power dispatch (ORPD) due to its reliability and economical operation of electrical power systems. The ORPD is a complex and nonlinear optimization problem. The pathfinder algorithm (PFA) is a newly developed algorithm that inspires the group movement of prey with a leader called a pathfinder when hunting for food. The inertia weight is added to the PFA and is called an improved pathfinder algorithm (IPFA) to support the proper random work of the swarm to avoid the decrease in searchability of the PFA. The IPFA was proposed in this work to diminish the active power loss while improving the voltage profile. The IPFA was validated on the IEEE 30 and 118 bus systems along with particle swarm optimization (PSO) and the teaching–learning-based optimizer (TLBO). The proposed IPFA provides the best result as the losses of the IEEE 30 and 118 test systems were reduced to 16.035 and 115.048 MW from the initial base of 17.89 and 132.86 MW, respectively. The losses of PSO and the TLBO were 16.1568 and 16.1607 MW for the IEEE 30 bus system, respectively, while for the IEEE 118 bus system, the PSO provided 117.9129 MW and the TLBO provided 118.0524 MW. The two test systems' reduction percentages (%) were 10.37% and 13.41%, respectively. The results were compared with those of other algorithms in the literature, and the IPFA provided a superior result, thereby suggesting the superiority of IPFA methods in diminishing the power loss and improving the system's voltage profile. [ABSTRACT FROM AUTHOR]

The AP2/ERF (APETALA2/Ethylene−Responsive element binding factor) family genes play crucial roles in plant growth and development, and responses to environmental factors; however, this family has not been characterized in Diospyros species. In Diospyros, the diploid Oily persimmon (D. oleifera, 2n = 2x = 30) has been released with complete genome assembly, which makes it possible for genome-wide gene family identification and exploration of molecular function in cultivated persimmon (D. kaki, 2n = 6x = 90). Here, we identified the AP2/ERF family in Oily persimmon for the first time and investigated its classification, main physicochemical properties, structural characteristic, chromosome distribution, gene replication and collinearity, cis-factor binding sites deduction, GO term annotation, and PPI interaction, as well as its expression profiles in different tissue and under the treatment of polyamines. A total of 157 AP2/ERF genes, including four subfamilies (AP2, RAV, Soloist, and ERF), were identified with distribution on all 15 chromosomes. DkAP2/ERF gene expression patterns were extensive and diverse. They were detected expression in every examined tissue, with the highest number of DkAP2/ERF genes expressed in the root. DkAP2/ERF gene expression analysis in adventitious root generation and elongation of polyamines showed their different responses to the action of polyamines, and more pairs of DkAP2/ERF genes with high correlation in gene expression were obtained. In addition, some DkAP2/ERF genes were detected remarkably correlated with genes related to polyamine synthesis and cell metabolism, including S-adenosyl-L-methionine Decarboxyla2 (SAMDC2), D-type cyclin1 (CYCD1), and D-type cyclin2 (CYCD2) genes,. indicating that DkAP2/ERF genes may play a synergistic role in adventitious root development This study was the first to analyze the AP2/ERF gene comprehensively in Diospyros on a genome−wide scale and will provide insights into the application of adventitious root formation in cultivated persimmon. [ABSTRACT FROM AUTHOR]

Asexual lineages are perceived to be short-lived on evolutionary timescales. Hence, reports for exceptional cases of putative 'ancient asexuals' usually raise questions about the persistence of such species. So far, there have been few studies to solve the mystery in plants. The monotypic Kingdonia dating to the early Eocene, contains only K. uniflora that has no known definitive evidence for sexual reproduction nor records for having congeneric sexual species, raising the possibility that the species has persisted under strict asexuality for a long period of time. Here, we analyze whole genome polymorphism and divergence in K. uniflora. Our results show that K. uniflora is characterized by high allelic heterozygosity and elevated πN/πS ratio, in line with theoretical expectations under asexual evolution. Allele frequency spectrum analysis reveals the origin of asexuality in K. uniflora occurred prior to lineage differentiation of the species. Although divergence within K. uniflora individuals exceeds that between populations, the topologies of the two haplotype trees, however, fail to match each other, indicating long-term asexuality is unlikely to account for the high allele divergence and K. uniflora may have a recent hybrid origin. Phi-test shows a statistical probability of recombination for the conflicting phylogenetic signals revealed by the split network, suggesting K. uniflora engages in undetected sexual reproduction. Detection of elevated genetic differentiation and premature stop codons (in some populations) in genes regulating seed development indicates mutational degradation of sexuality-specific genes in K. uniflora. This study unfolds the origin and persistence mechanism of a plant lineage that has been known to reproduce asexually and presents the genomic consequences of lack of sexuality. [ABSTRACT FROM AUTHOR]

This study aimed to understand the intention and correlation of receiving and recommending influenza vaccine (IV) among healthcare workers (HCWs) in China during the 2022/2023 season using the behavior and social drivers (BeSD) tools. A self-administered electronic survey collected 17,832 participants on a media platform. We investigated the willingness of IV and used multivariate logistic regression analysis to explore its associated factors. The average scores of the 3Cs' model were compared by multiple comparisons. We also explored the factors that potentially correlated with recommendation willingness by partial regression. The willingness of IV was 74.89% among HCWs, and 82.58% of the participants were likely to recommend it to others during this season. Thinking and feeling was the strongest domain independently associated with willingness. All domains in BeSD were significantly different between the hesitancy and acceptance groups. Central factors in the 3Cs model were significantly different among groups (p < 0.01). HCWs' willingness to IV recommendation was influenced by their ability to answer related questions (r  =  0.187, p  <  0.001) after controlling for their IV willingness and perceived risk. HCWs' attitudes towards IV affect their vaccination and recommendation. The BeSD framework revealed the drivers during the decision-making process. Further study should classify the causes in detail to refine HCWs' education. [ABSTRACT FROM AUTHOR]

Electricity has become one of the most essential components of establishing a quality standard of living in any country. Consequently, considerable work has been focused on designing a sophisticated load frequency control (LFC) system. However, in light of limited resources and real-world challenges, computationally based control algorithms that are more effective and less expensive remain critically needed. Thus, this paper employs a modified honey badger algorithm (HBA) in conjunction with the concepts of Lévy flight and inertia weight to optimize the parameters of a new cascaded two-degree-of-freedom fractional-PID structure coupled with a proportional derivative (2DOF + FOPIDN)-PD controller to solve LFC problems in an interconnected power system (IPS) comprising conventional and renewable energy sources (RES). The proposed control technique is applied to a two-area IPS under diverse load conditions and in the presence of nonlinear elements and electronic devices. The proposed method is evaluated with respect to a range of performance metrics, such as settling time, undershoots, and error criteria values. The collective performance of the established control scheme indicated that the suggested control approach provides excellent reliability under various load condition scenarios, sensitivity tests, and perturbations, proving the system's efficacy and dependability. [ABSTRACT FROM AUTHOR]

This cross-sectional study evaluated the long-term health effects of coronavirus disease 2019 (COVID-19) in Jianghan District (Wuhan, China). The results showed that 61.4% of COVID-19 patients reported at least one symptom and 8.8% had depressive symptoms at the 17-month follow-up. The proportion of patients with chest radiographic abnormalities in Fangcang shelter hospitals and designated COVID-19 hospitals was 31.6% and 41.1%, respectively, and the proportion of patients with impaired pulmonary diffusion capacity in these hospitals was 52.8% and 60.9%, respectively. Female sex (odds ratio [OR] = 1.48, 95% confidence interval [CI]: 1.16–1.88), severe disease (OR = 1.46, 95% CI: 1.01–2.10) and a higher number of initial symptoms (OR = 1.31, 95% CI: 1.23–1.40) were associated with the development of sequelae symptoms at 17 months. This study involving community-dwelling COVID-19 adults may help determine the long-term effects of COVID-19 during the first pandemic wave. Nonetheless, larger follow-up studies are needed to characterize the post-COVID-19 condition. [ABSTRACT FROM AUTHOR]

Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens' quality of life and ensure an organization's seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive clustering (SC), fuzzy c-means (FCM), and other key parameters such as clustering radius (CR), input and output membership functions, and the number of clusters. Using renowned statistical metrics, the best sub-models for each clustering technique were selected. The outcome showed that the ANFIS-based FCM technique produced the best results with five clusters, with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Variation (RCoV), Coefficient of Variation of the Root Mean Square Error (CVRMSE), and Mean Absolute Percentage Error (MAPE) being 1137.6024, 898.5070, 0.0586, 11.5727, and 9.3122, respectively. The FCM clustering technique is recommended for usage in ANFIS models that employ similar time series data due to its accuracy and speed. [ABSTRACT FROM AUTHOR]

In the real applications, we found that it is difficult to achieve good control performance through manually tuning proportional–integral (PI) parameters of phase locked loop (PLL) and speed-loop of Luenberger observer (LO) for the PMSM sensorless control system. Therefore, this paper is to use the particle swarm optimization (PSO) algorithm to optimize the PI parameters of PLL and speed-loop of Luenberger observer of the system. Firstly, the ranges of PLL parameters are obtained by analyzing the PLL subsystem stability. Then, the ranges of PI parameters of PLL and speed-loop are set based on theoretical estimation and empirical values. The control system model is realized in MATLAB/Simulink that considers the constraints such as the saturation. The integral time absolute error is the objective function, and the PSO with different topologies is used to optimize the PI parameters. The simulation and experimental results show that the proposed method is feasible, and the optimized parameters can effectively improve the precision of position estimation and speed estimation. Moreover, the simulations and experiments are carried out to verify the robustness of the proposed method, and the results show that the optimized system can achieve good performance when there are uncertainties or disturbances. [ABSTRACT FROM AUTHOR]

Quadrature phase shift keying (QPSK) is a digital modulation technique that transmits data at a constant frequency whilst varying the phases of the carrier signal. QPSK is one of the fundamental modulation schemes for orthogonal frequency division multiplexing systems (OFDM). It is a stable modulation technique with good spectral efficiency. However, during transmission, the carrier signal can undergo numerous phase changes. This creates phase ambiguity problems at the receiver end. This results in inter-symbol interference (ISI) and a high bit error rate (BER). In this paper, the wind-driven optimization was incorporated into the genetic algorithm (GA) as its population selection function. This hybrid algorithm was used to determine the phase assignments for the QPSK. The developed QPSK was implemented on an OFDM network and the message signal was recovered at more than 92% accuracy in a noisy Rayleigh fading channel and 100% accuracy in a noiseless channel. The enhancements greatly mitigated phase ambiguity and bit errors. [ABSTRACT FROM AUTHOR]

Ni-rich layered cathode materials for lithium-ion batteries have received widespread attention due to their large capacity and low cost; however, the structural stability of the material needs to be improved. Herein, F-doped and undoped cathode materials prepared with an advanced co-precipitation method were used to measure the effect of F doping on the material. Compared to the undoped sample, the F-doped cathode materials exhibited an improved rate performance, because the porous structure of F-doped cathode materials is favorable for the infiltration of the electrolyte and the material, and the F-doped cathode material has a larger (003) crystal plane and a smaller Li+ migration barrier energy. This simple F-doping treatment strategy provides a promising way to improve the performance of Ni-rich layered cathode materials for lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Loss of selection pressure in the presence of many objectives is one of the pertinent problems in evolutionary optimization. Therefore, it is difficult for evolutionary algorithms to find the best-fitting candidate solutions for the final Pareto optimal front representing a multi-objective optimization problem, particularly when the solution space changes with time. In this study, we propose a multi-objective algorithm called enhanced dynamic non-dominated sorting genetic algorithm III (E-dyNSGA-III). This evolutionary algorithm is an improvement of the earlier proposed dyNSGA-III, which used principal component analysis and Euclidean distance to maintain selection pressure and integrity of the final Pareto optimal front. E-dyNSGA-III proposes a strategy to select a group of super-performing mutated candidates to improve the selection pressure at high dimensions and with changing time. This strategy is based on an earlier proposed approach on the use of mutated candidates, which are randomly chosen from the mutation and crossover stages of the original NSGA-II algorithm. In our proposed approach, these mutated candidates are used to improve the diversity of the solution space when the rate of change in the objective function space increases with respect to time. The improved algorithm is tested on RPOOT problems and a real-world hydrothermal model, and the results show that the approach is promising. [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]

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously based on the MOP neighborhood information. However, the MOEA/D strategy tends to produce a distributed optimization that is not of good quality in some problems with complex Pareto optimal front, such as problems with a long tail and sharp peak, common in real-world situations. This paper proposes an improved MOEA/D to enhance the distributed optimization quality and minimize its complexity while accelerating the optimization to get a better solution. The improved method is achieved by incorporating a Hybrid Differential Evolution/Particle Swarm Optimization algorithm and a hybrid operator based on nondominated sorting and crowding distance algorithm. This incorporation takes place in the mutation generator and initial population part of the original MOEA/D algorithm. Simulations and comparisons are carried out based on some MOP benchmark functions to verify the proposed method's performance. The experimental results show that the proposed method achieves better performance compared to other algorithms. Furthermore, the proposed method is also applied to optimize the multiobjective wave energy converter model to maximize power per year and minimize cost per unit power. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) plays a vital role in electrical power networks. However, power loss reduction, voltage profile improvement, friendly environment, and reliability are all benefits of DG units. In this research work, a worthwhile methodology is recommended for optimal allocation of traditional (gas turbine) and renewable energy sources that are based on distributed generators which include solar and wind in the distribution system. The major objective of the research paper is the minimization of real, reactive power losses and emissions produced during the application of these conventional sources. Originally, the best locations to place this DG are identified using the concept of water, energy, and food algorithm (WEFA). The number and sizes of these renewable energy sources selected (wind and solar) are determined by applying the concepts of the Dragonfly Algorithm. The Weibull and beta distribution functions are modeled to extract the exact position to fix our DGs to minimize losses within the distribution network. To assess the performance of WEF five different cases scenario considered are DG capacity, Location of Bus, voltage profile, maximum power loss as well as utilization rate. The proposed WEF Algorithm is tested on the IEEE standard 33-bus system. The simulated results were compared with others found in literature and found to be better in terms of power loss reductions. [ABSTRACT FROM AUTHOR]

The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature selection. After the optimized features are chosen, the proposed detection engine uses the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems. [ABSTRACT FROM AUTHOR]

Three-dimensional profilometry tends to be less effective at measuring discontinuous surfaces. To overcome this problem, an optimized profilometry based on fringe projection is proposed in this paper. Due to the limitation of the shooting angle, there are projection blind spots on the surface of discontinuous objects. Since the noises and unwrapping errors are always localized at the projection blind spots, an algorithm is designed to determine the blind spots automatically with the light intensity difference information. Besides, in order to improve the measurement accuracy, a processing scheme is introduced to deal with the local height distortion introduced by the dual-frequency grating profilometry. Lots of measurement tests on various surfaces are carried out to assess the optimized profilometry, and experimental results indicate that the modified profilometry system works more robust with high reliability and accuracy in measuring different kinds of surfaces, especially discontinuous ones. [ABSTRACT FROM AUTHOR]

Esophageal squamous-cell carcinoma (ESCC), one of the most prevalent and lethal malignant disease, has a complex but unknown tumor ecosystem. Here, we investigate the composition of ESCC tumors based on 208,659 single-cell transcriptomes derived from 60 individuals. We identify 8 common expression programs from malignant epithelial cells and discover 42 cell types, including 26 immune cell and 16 nonimmune stromal cell subtypes in the tumor microenvironment (TME), and analyse the interactions between cancer cells and other cells and the interactions among different cell types in the TME. Moreover, we link the cancer cell transcriptomes to the somatic mutations and identify several markers significantly associated with patients' survival, which may be relevant to precision care of ESCC patients. These results reveal the immunosuppressive status in the ESCC TME and further our understanding of ESCC. Esophageal squamous-cell carcinomas (ESCC) have poor prognosis, and detailed molecular profiles are necessary to identify prognostic markers. Here the authors analyse 60 ESCC patient samples using scRNA-seq, TCR-seq and genomics; they find mucosal immunity markers associated with survival and immunosuppressive microenvironments. [ABSTRACT FROM AUTHOR]

Purpose: Storage systems are deemed to be unable to provide revenue commensurate with the resources invested in them, thus discouraging their incorporation within power networks. In prosumer microgrids, storage systems can provide revenue from reduced grid consumption, energy arbitraging or when serving as back-up power. This study aims to examine stacking these revenue streams with the aim of making storage systems financially viable for inclusion in prosumer microgrids. Design/methodology/approach: With the aim of reducing self-consumption and maximising revenue, the prosumer microgrid incorporating hybrid energy storage systems (HESS) and solar PV power is solved using the CPLEX solver of the Advanced Interactive Multidimensional Modeling Software (AIMMS). The financial analysis of the results is carried out to provide the payback periods of different system configurations of the prosumer microgrid. Findings: The findings reveal that the payback period of the three HESS when minimising grid expenses during self-consumption alone and when compared with stacked revenue streams shows an improvement from 4.8–11.2 years to 2.4–6.6 years. With stacked HESS revenues, the supercapacitor-lithium ion battery HESS gave the shortest payback period of 2.31 years when solar PV power is at 75% penetration level. Originality/value: Existing literature has considered revenue streams of storage systems at the electrical power transmission and distribution levels, but not for prosumer microgrids in particular. This study has captured these benefits and verified the profitability of stacking revenue from HESS to prosumer microgrids, using a case study. [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]

The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one dimensional in nature, suffer from poor aggregate and load signal feature localization necessitating a larger training dataset spanning very long time periods and normally require signal formatting and pre-processing. We use Gramian angular summation fields to transform the power series into a reduced image dataset that contains a rich set of localized signal features. A computer vision approach allows us to capture as much information as possible, and then propose an image-based mains load recognition system with high performance. In this paper for the entire recognition system, we use convolutional neural networks that very well adapted to vision recognition. The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network. To test the proposed system, some simulations and comparisons are carried out and the results show that our easier to handle method can achieve acceptable performance. [ABSTRACT FROM AUTHOR]

Background: Ischemic stroke is a serious cardiovascular disease with high morbidity and mortality rates that affects millions of people worldwide.Currently, the only therapy with proven efficacy for acute ischemic stroke is alteplase, however, it still has many shortcomings and limitations. Therefore,we screen new compounds from traditional Chinese medicine to explore their efficacy against ischemic reperfusion injury. Procyanidins, a natural productextracted from grapes seed, which have been shown can ameliorate cerebral ischemic injury. However, the underlying mechanism is still not very clear. Theaim of this study was to investigate the effect of procyanidins on middle cerebral artery occlusion/reperfusion (MCAO/R)-mediated cerebral ischemic injuryand its underlying possible mechanisms. Methods: SD rats were used to evaluate the effect of procyanidins on MCAO/R induced cerebral ischemic injury in vivo. Histological analysis was used toassess neuronal apoptosis. Cell signaling was assayed by Western blot. Results: In this study, we found that procyanidins can significantly ameliorate the middle cerebral artery occlusion/reperfusion (MCAO/R)-mediatedneurological deficits, and relieved brain edema, cerebral infarction volume, histopathological damage and apoptosis in rats. In addition, procyanidins canalso markedly inhibit MCAO/R and oxygen-glucose deprivation/reoxygenation (OGD/R)-mediated activation of TLR4-p38-NF-κB-NLRP3 signalingpathway in vitro and in vivo. Moreover, procyanidins can inhibit MCAO/R and OGD/R-induced the production of inflammatory cytokines such asinterleukin-1β (IL-1β) in vitro and in vivo. Besides, treatment with TLR4 inhibitor (Cli-095) in BV2 cell also shows the same effect. Conclusion: Altogether, these data suggested that procyanidins exerted a potential neuroprotective effect may by inhibit the TLR4-p38-NF-κB-NLRP3signaling pathway in the brain in MCAO/R rats. [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]

In a smart home, the nonintrusive load monitoring recognition scheme normally achieves high appliance recognition performance in the case where the appliance signals have widely varying power levels and signature characteristics. However, it becomes more difficult to recognize appliances with equal or very close power specifications, often with almost identical signature characteristics. In literature, complex methods based on transient event detection and multiple classifiers that operate on different hand crafted features of the signal have been proposed to tackle this issue. In this paper, we propose a deep learning approach that dispenses with the complex transient event detection and hand crafting of signal features to provide high performance recognition of close tolerance appliances. The appliance classification is premised on the deep multilayer perceptron having three appliance signal parameters as input to increase the number of trainable samples and hence accuracy. In the case where we have limited data, we implement a transfer learning-based appliance classification strategy. With the view of obtaining an appropriate high performing disaggregation deep learning network for the said problem, we explore individually three deep learning disaggregation algorithms based on the multiple parallel structure convolutional neural networks, the recurrent neural network with parallel dense layers for a shared input, and the hybrid convolutional recurrent neural network. We disaggregate a total of three signal parameters per appliance in each case. To evaluate the performance of the proposed method, some simulations and comparisons have been carried out, and the results show that the proposed method can achieve promising performance. [ABSTRACT FROM AUTHOR]

Background: Plastome-scale data have been prevalent in reconstructing the plant Tree of Life. However, phylogenomic studies currently based on plastomes rely primarily on maximum likelihood inference of concatenated alignments of plastid genes, and thus phylogenetic discordance produced by individual plastid genes has generally been ignored. Moreover, structural and functional characteristics of plastomes indicate that plastid genes may not evolve as a single locus and are experiencing different evolutionary forces, yet the genetic characteristics of plastid genes within a lineage remain poorly studied. Results: We sequenced and annotated 10 plastome sequences of Gentianeae. Phylogenomic analyses yielded robust relationships among genera within Gentianeae. We detected great variation of gene tree topologies and revealed that more than half of the genes, including one (atpB) of the three widely used plastid markers (rbcL, atpB and matK) in phylogenetic inference of Gentianeae, are likely contributing to phylogenetic ambiguity of Gentianeae. Estimation of nucleotide substitution rates showed extensive rate heterogeneity among different plastid genes and among different functional groups of genes. Comparative analysis suggested that the ribosomal protein (RPL and RPS) genes and the RNA polymerase (RPO) genes have higher substitution rates and genetic variations among plastid genes in Gentianeae. Our study revealed that just one (matK) of the three (matK, ndhB and rbcL) widely used markers show high phylogenetic informativeness (PI) value. Due to the high PI and lowest gene-tree discordance, rpoC2 is advocated as a promising plastid DNA barcode for taxonomic studies of Gentianeae. Furthermore, our analyses revealed a positive correlation of evolutionary rates with genetic variation of plastid genes, but a negative correlation with gene-tree discordance under purifying selection. Conclusions: Overall, our results demonstrate the heterogeneity of nucleotide substitution rates and genetic characteristics among plastid genes providing new insights into plastome evolution, while highlighting the necessity of considering gene-tree discordance into phylogenomic studies based on plastome-scale data. [ABSTRACT FROM AUTHOR]

Investigating the biogeographical disjunction of East Asian and North American flora is key to understanding the formation and dynamics of biodiversity in the Northern Hemisphere. The small Cupressaceae genus Thuja, comprising five species, exhibits a typical disjunct distribution in East Asia and North America. Owing to obscure relationships, the biogeographical history of the genus remains controversial. Here, complete plastomes were employed to investigate the plastome evolution, phylogenetic relationships, and biogeographic history of Thuja. All plastomes of Thuja share the same gene content arranged in the same order. The loss of an IR was evident in all Thuja plastomes, and the B-arrangement as previously recognized was detected. Phylogenomic analyses resolved two sister pairs, T. standishii-T. koraiensis and T. occidentalis-T. sutchuenensis, with T. plicata sister to T. occidentalis-T. sutchuenensis. Molecular dating and biogeographic results suggest the diversification of Thuja occurred in the Middle Miocene, and the ancestral area of extant species was located in northern East Asia. Incorporating the fossil record, we inferred that Thuja likely originated from the high-latitude areas of North America in the Paleocene with a second diversification center in northern East Asia. The current geographical distribution of Thuja was likely shaped by dispersal events attributed to the Bering Land Bridge in the Miocene and subsequent vicariance events accompanying climate cooling. The potential effect of extinction may have profound influence on the biogeographical history of Thuja. [ABSTRACT FROM AUTHOR]

Investigating the biogeographical disjunction of East Asian and North American flora is key to understanding the formation and dynamics of biodiversity in the Northern Hemisphere. The small Cupressaceae genus Thuja, comprising five species, exhibits a typical disjunct distribution in East Asia and North America. Owing to obscure relationships, the biogeographical history of the genus remains controversial. Here, complete plastomes were employed to investigate the plastome evolution, phylogenetic relationships, and biogeographic history of Thuja. All plastomes of Thuja share the same gene content arranged in the same order. The loss of an IR was evident in all Thuja plastomes, and the B-arrangement as previously recognized was detected. Phylogenomic analyses resolved two sister pairs, T. standishii-T. koraiensis and T. occidentalis-T. sutchuenensis, with T. plicata sister to T. occidentalis-T. sutchuenensis. Molecular dating and biogeographic results suggest the diversification of Thuja occurred in the Middle Miocene, and the ancestral area of extant species was located in northern East Asia. Incorporating the fossil record, we inferred that Thuja likely originated from the high-latitude areas of North America in the Paleocene with a second diversification center in northern East Asia. The current geographical distribution of Thuja was likely shaped by dispersal events attributed to the Bering Land Bridge in the Miocene and subsequent vicariance events accompanying climate cooling. The potential effect of extinction may have profound influence on the biogeographical history of Thuja. [ABSTRACT FROM AUTHOR]

The 'Fuji' line includes many varieties with a similar genetic background and consistent inducement factors with epigenetic occurrence, thus it may be considered an ideal candidate for epigenetic research. In this study, 91 bud mutations of 'Fuji' apple were used as the test materials. Using the genetic variation within 'Fuji' as the control, the characteristics of epigenetic variation at different levels in both varieties and mutant groups were examined. The results showed that: (1) the global genomic DNA methylation level of the 91 bud mutants of 'Fuji' ranged from 29.120%-45.084%, with an average of 35.910%. Internal cytosine methylation was the main DNA methylation pattern. Regarding the variation of methylation patterns of 'Fuji' mutants, the vast majority of loci maintained the original methylation pattern existed in 'Fuji'. CHG methylation variation was the main type of variation; (2) the variation in methylation patterns between the mutant groups was greater than that of methylation levels. Among these patterns, the variation in CHG methylation patterns (including CHG hypermethylation and CHG demethylation) was expected to be dominant. The observed variation in methylation levels was more important in the Color mutant group; however, the variation in methylation patterns was more obvious in both the early maturation and Spur mutant groups. Moreover, the range of variation in the Early-maturation group was much wider than that in the Spur mutant group; (3) epigenetic diversity and genetic diversity were both low between the mutant groups. In the 'Fuji' mutant groups, there was few correlation between genetic and epigenetic variation, and epigenetic differentiation resulted in more loci with moderate or greater differentiation; (4) the purifying selection seemed to play a major role in the differentiation of different groups of 'Fuji' mutants (65.618%), but epigenetic diversity selection still occurred at nearly 35% of loci. Sixteen epigenetic outlier loci were detected. [ABSTRACT FROM AUTHOR]

Modern power systems are increasingly becoming more complex and thus become vulnerable to voltage collapse due to constant increase in load demand and introduction of new operation enhancement technologies. In this study, an approach which is based on network structural properties of a power system is proposed for the identification of critical nodes that are liable to voltage instability. The proposed Network Structurally Based Closeness Centrality (NSBCC) is formulated based on the admittance matrix between the interconnection of load to load nodes in a power system. The vertex (node) that has the highest value of NSBCC is taken as the critical node of the system. To demonstrate the significance of the concept formulated, the comparative analysis of the proposed NSBCC with the conventional techniques such as Electrical Closeness Centrality (ECC), Closeness Voltage Centrality (CVC) and Modal Analysis is performed. The effectiveness of all the approaches presented is tested on both IEEE 30 bus and the Southern Indian 10-bus power systems. Results of simulation obtained show that the proposed NSBCC could serve as valuable tool for rapid real time voltage stability assessment in a power system. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be applied to the recovery of precious metals in WEEE and waste catalyst. The extraction of precious metals is of significant importance for recycling valuable precious metal resources, however, most of the current methods for extraction are inefficient, energy-intensive, or environmentally unfriendly. Herein, we report a new chemical dissolution system for precious metals employing the trichloroisocyanuric acid (TCCA) as an oxidant and tributyl monomethyl ammonium chloride (N4441Cl) as a complexant. The leaching yield was achieving 100 wt% for the metals of Au, Pd, Cu, and Ag at 25 °C, and the average dissolution rate of gold was reaching 375 mg/h, 107.1 times higher than that of traditional cyanide method. Based on the analysis of UV-Vis and XPS results, a possible reaction mechanism was proposed: the precious metals were probably oxidized by TCCA, then the formed precious metal ions coordinate with N4441Cl strongly to promote the dissolution process. Applied to gold dissolution from ores, waste electronic and electrical equipment (WEEE) and waste catalysts, the leaching yield of the TCCA-N4441Cl coordinative composite reached 97, 100, and 100 wt%, respectively, demonstrating that this method is not only efficient and environmentally friendly, but also with great adaptability and high potential for real applications. [ABSTRACT FROM AUTHOR]

Purpose. To identify the characteristics of the physical and mental health status of patients with pituitary adenomas, explore the postoperative reversibility of impaired health status, and assess the impact of clinical characteristics, hormone levels, anxiety, depression, and disease stigma on health status. Methods. We prospectively enrolled 147 and 138 patients with nonfunctioning and secretory pituitary adenomas, respectively. Health status was evaluated in 8 domains using the 36-item Short-Form Health Survey before and 3 months after transsphenoidal surgery. The Self-Rating Anxiety Scale, the Self-Rating Depression Scale, and the Stigma Scale for Chronic Illness were used to assess the psychological status. Results. Compared with the healthy population reference values, general physical and mental health, social functioning, and role limitations due to physical and psychological health problems were all found to be significantly impaired in the adenoma patients. Health status was worse in patients with adrenocorticotropic hormone- (ACTH-) secreting and growth hormone- (GH-) secreting adenomas than in patients with nonfunctioning adenomas. Among the patients, 11.6% had anxiety and 30.9% had depression. Higher scores for anxiety, depression, and disease stigma; older age; higher body mass index; and tumor recurrence were independent risk factors for health status impairment in at least one domain. Physical function impairment and role limitations caused by physical health problems became worse after surgery, whereas the mental component of health status remained the same. Conclusion. Health status was impaired in patients with pituitary adenomas, especially secretory adenomas. Physical function and role limitations were worse 3 months after surgery than before surgery. Mental problems, old age, obesity, and tumor recurrence reduced health status. [ABSTRACT FROM AUTHOR]

The relationship between ζ-potential and surface defects of micro-nano sized hexagram-like α-Al2O3 particles was investigated in this paper. The 2D α-Al2O3 particles was prepared by the phase transformation of gibbsite precursor by the following steps during calcination in air: Al(OH)3 → AlOOH→a-Al2O3 → θ-Al2O3 → α-Al2O3. Resulting from the varied intensities of surface defects, as-prepared α-Al2O3 samples have different Eg values around 4.5 eV, which is much narrower than that of bulk α-Al2O3. While benefiting from the interface reaction between surface > AlOH0 and water (>AlOH0 + OH−⇋ > AlO− + H2O), as-prepared α-Al2O3 suspensions can reach their specific pHE values after stirring for 8 h at room temperature. And the measured IEP values of Al2O3-1250°C-2 h, Al2O3-1250°C-3 h, and Al2O3–1300 °C-3 h, which remain 2D morphology, are 7.96, 8.67, and 8.86, respectively. This study is significant and useful for making the industrial applications of α-Al2O3 valid. [ABSTRACT FROM AUTHOR]