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The study of causal relationship of investor sentiment on stock prices has got more and more attention. This paper examines the impact of investor sentiment on investment behavior and thus on stock prices in four different countries including two developed countries (United States and United Kingdom), and two developing countries (China and India). Due to different levels of development, different countries have different measures of consumer sentiment. Therefore, the author enumerates in detail the measures of sentiment index for each studied country. By comparing representatives of investor indices, the index accuracy was analyzed as a function of market spacing. In addition, the differences between the conclusions of developing countries and developed countries are also compared. Meanwhile, the mutual influence of investor sentiment was summarized among developed countries. The empirical findings show that the study about exploring the causal relationship of the investor sentiment on the stock prices plays an important role in formulating detailed investor strategies. In other words, investors who are familiar with the impact of emotions on stock prices can be more successful in stock investing and their investment risk can be reduced than those who are not.

The timely, rapid, and accurate near real-time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of armyworm would lead to severe yield losses. Therefore, the potential of machine learning algorithms for identifying the armyworm infected areas automatically and accurately by multispectral unmanned aerial vehicle (UAV) dataset is explored in this study. The study area is in Beicuizhuang Village, Langfang City, Hebei Province, which is the main corn-producing area in the North China Plain.Firstly, we identified the optimal combination of image features by Gini-importance and the comparation of four kinds of machine learning methods including Random Forest (RF), Multilayer Perceptron (MLP), Naive Bayesian (NB) and Support Vector Machine (SVM) was done. And RF was proved to be the most potential with the highest Kappa and OA of 0.9709 and 0.9850, respectively. Secondly, the armyworm infected areas and healthy corn areas were predicted by an optimized RF model in the UAV dataset, and the armyworm incidence levels were classified subsequently. Thirdly, the relationship between the spectral characteristics of different bands and pest incidence levels within the Sentinel-2 and UAV images were analyzed, and the B3 in UAV images and the B6 in Sentinel-2 image were less sensitive for armyworm incidence levels. Therefore, the Sentinel-2 image was used to monitor armyworm in two towns.The optimized dataset and RF model are effective and reliable, which can be used for identifying the corn damage by armyworm using UAV images accurately and automatically in field-scale. © 2022 Society of Chemical Industry.

The development of multimetallic Pt-based nanostructures as high-performance cathodic electrocatalysts to be used in water-splitting devices for hydrogen generation is the focus of increasing attention. In this study, a family of hierarchical architectures constructed from dendritic quaternary PtFeRuRh, ternary PtFeRh or PtFeRu and binary PtFe nanoalloys are achieved via a general liquid-phase strategy for hydrogen evolution in alkaline electrolyte. Among them, the PtFeRuRh nanoalloys exhibit the lowest overpotential (20.0 mV at 10 mA cm−2) and Tafel slope (21.1 mV dec−1). At a potential of −0.07 V, the mass activity of the PtFeRuRh nanoalloy is 7.04 A mgPt−1, it is 6.97 times that of commercial Pt/C. The dendritic PtFeRuRh nanoalloys exhibit a negligible decrease in activity after 20 h of continuous testing at 10 mA cm−2/100 mA cm−2 and 3000 cyclic voltammetry cycles. In a practical application, the cell voltage of a PtFeRhRu (−) || IrO2 (+) couple is 1.568 V at 10 mA cm−2 with almost 100% faradaic efficiency. The turnover frequency of the PtFeRhRu electrocatalyst at 70 mV was 78.5 s−1, which is 11.71 times as large as that of commercial Pt/C (6.7 s−1).

Geography; Microbiome Geografía; Microbioma Geografia; Microbioma The development of biomarkers for inflammatory bowel disease (IBD) diagnosis would be relevant in a generalized context. However, intercontinental investigation on these microbial biomarkers remains scarce. We examined taxonomic microbiome variations in IBD using published DNA shotgun metagenomic data. For this purpose, we used sequenced data from our previous Spanish Crohn’s disease (CD) and ulcerative colitis (UC) cohort, downloaded sequence data from a Chinese CD cohort, and downloaded taxonomic and functional profiling tables from a USA CD and UC cohort. At the global level, geographical location and disease phenotype were the main explanatory covariates of microbiome variations. In healthy controls (HC) and UC, geography turned out to be the most important factor, while disease intestinal location was the most important one in CD. Disease severity correlated with lower alpha-diversity in UC but not in CD. Across geography, alpha-diversity was significantly different independently of health status, except for CD. Despite recruitment from different countries and with different disease severity scores, CD patients may harbor a very similar microbial taxonomic profile. Our study pointed out that geographic location, disease activity status, and other environmental factors are important contributing factors in microbiota changes in IBD. We therefore strongly recommend taking these factors into consideration for future IBD studies to obtain globally valid and reproducible biomarkers. Z.X. received a fellowship from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Action, Innovative Training Network: FunHoMic; grant number 812969. Chaysavanh Manichanh received grants from the Instituto de Salud Carlos III/FEDER (PI17/00614; PI20/00130).

Background: Research into the association between the microbiome and SARS-CoV-2 infection and acute respiratory distress syndrome remains unclear. Here we examined the dynamics of the gut and respiratory microbiomes in severe COVID-19 cases in need of mechanical ventilation in the intensive care unit (ICU). Methods: We recruited 76 critically ill patients (48 with COVID-19 and 32 without COVID-19) and 17 healthy controls (HCs) and monitored them for up to 4 weeks. We analyzed the bacterial and fungal taxonomic profiles and loads of 232 gut and respiratory samples and we measured blood levels of Interleukin 6, IgG, and IgM in COVID-19 patients. Results: At ICU admission, bacterial composition and load in the gut and respiratory samples were altered in critically ill patients compared to HCs. Six bacterial species could discriminate ICU-COV(+) from ICU-COV(-) cases. During their time in the ICU, all patients experienced increased bacterial and fungal loads, decreased bacterial richness, and progressive changes in bacterial and fungal taxonomic profiles. In ICU-COV(+) patients, Pseudomonas was found positively and Streptococcus negatively correlated with the length of ICU stay. Respiratory bacterial signatures, associated with disease severity and inflammation markers, were predictive of death and a fecal dysbiosis score, associated negatively with PaO2/FiO2 ratio, could be used as an additional indicator for ICU admission. Conclusion: We propose using a high-throughput sequencing approach as an alternative to traditional isolation techniques to monitor the ICU-COV(+) patient infection., Zixuan Xie received a fellowship from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Action, Innovative Training Network: FunHoMic; grant number 812969. Chaysavanh Manichanh received a grant from the Instituto de Salud Carlos III/FEDER (PI17/00614; PI20/00130)

This paper proposes an elite coded particle swarm optimization (CEPSO) unmanned aerial vehicles (UAVs) search target motion for target detection in the shortest time. According to Bayesian theory, the problem of moving object searched is transformed into the optimization of cost function, which is used to describe the probability of moving objects being found. In order to better fit the problem, a coded elite particle swarm optimization algorithm is used to encode the search trajectory of the UAV as the movement path of the particle in the process of generation and evolution. This coding method uses the cognitive properties and social learning properties of particle swarms to generate better solutions. At the same time, in order to increase the diversity of particle swarms, a method of elite particle perturbation is used, which greatly improves the detection probability. In order to verify the superiority of the algorithm in this paper, a large number of simulation experiments are carried out on the existing methods. The experimental results show that compared with PSO, the detection probability of the proposed CEPSO is improved by 6.9%, and the time performance is improved by 8.5%. It also compares with other meta-heuristic algorithms, including differential evolution (DE), artificial bee colony (ABC), harmony search (HS), biogeographic-based optimization (BBO), invasive weed optimization (IWO), teaching-learning-based Optimization (TLBO). The experimental results show that the algorithm proposed in this paper has better detection accuracy and less detection time.

Background and Aims Intestinal microbiota dysbiosis is implicated in Crohn’s disease [CD] and may play an important role in triggering postoperative disease recurrence [POR]. We prospectively studied faecal and mucosal microbial recolonisation following ileocaecal resection to identify the predictive value of recurrence-related microbiota. Methods Mucosal and/or faecal samples from 121 CD patients undergoing ileocaecal resection were collected at predefined time points before and after surgery. Ileal biopsies were collected from 39 healthy controls. POR was defined by a Rutgeerts score ≥i2b. The microbiota was evaluated by 16S rRNA sequencing. Prediction analysis was performed using C5.0 and Random Forest algorithms. Results The mucosa-associated microbiota in CD patients was characterised by a depletion of butyrate-producing species (false discovery rate [FDR] Conclusions Microbiota recolonisation after ileocaecal resection is different between recurrence and non-recurrence patients, with Fusobacteria as the most prominent player driving early POR. These bacteria involved in the early recolonisation and POR represent a promising therapeutic strategy in the prevention of disease recurrence.

China is facing dual challenges of air pollution and climate change. By using city-level data, we comprehensively assessed air quality and CO

Introduction The FunHoMic project is a Marie Skłodowska-Curie Innovative Training Network comprising 13 PhD students, 8 academic partners and 3 industry partnersaimingto understand the interplay between fungi, hostsand microbiota to improve prevention and treatment of fungal infections. Importance About 2 billion people suffer fungal infections, which have a mortality rate close to that of malaria or breast cancer. Candida albicans has a high clinical and economic burden, making it of particular interest to the FunHoMic project. 70% of women experience at least one episode of vulvovaginal candidiasis (“thrush”) during their lifetime; 8% suffer recurring infections. C. albicans may live as a commensal but can cause symptoms when the fungus-host-microbiota equilibrium is disrupted. Infections by C. albicans have a significant clinical impact, with fatalities in severe cases. Many factors are associated with C. albicans infections; intensive care, neutropenic and diabetic patients are most at risk of systemic infection. Rising antifungal drug resistance has led to certain C. albicans infections having no treatment option. Aim The FunHoMic consortium combines projectson fungal pathogenesis, immunology, microbial ecology and’omics technologies to understand and exploit interactions between fungus, host and microbiota. Identification of novel bio markers on the fungal side such as genetic polymorphisms or on the host side such asmicrobiota profiles, metabolites and/or immune markers can lead to patient classification based on relative risk of infection. This could be the beginning of personalised management for fungal infections using preventive or therapeutic interventions like new antifungals, immune modulators or Live Biotherapeutic Products (LBPs). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the MarieSklodowska-Curie grant agreement No 812969.

With the large-scale optimization problems in the real world becoming more and more complex, they also require different optimization algorithms to keep pace with the times. Particle swarm optimization algorithm is a good tool that has been proved to deal with various optimization problems. Conventional particle swarm optimization algorithms learn from two particles, namely, the best position of the current particle and the best position of all particles. This particle swarm optimization algorithm is simple to implement, simple, and easy to understand, but it has a fatal defect. It is hard to find the global optimal solution quickly and accurately. In order to deal with these defects of standard particle swarm optimization, this paper proposes a particle swarm optimization algorithm (SHMPSO) based on the hybrid strategy of seed swarm optimization (using codes available from https://gitee.com/mr-xie123234/code/tree/master/). In SHMPSO, a subpopulation coevolution particle swarm optimization algorithm is adopted. In SHMPSO, an elastic candidate-based strategy is used to find a candidate and realize information sharing and coevolution among populations. The mean dimension learning strategy can be used to make the population converge faster and improve the solution accuracy of SHMPSO. Twenty-one benchmark functions and six industries-recognized particle swarm optimization variants are used to verify the advantages of SHMPSO. The experimental results show that SHMPSO has good convergence speed and good robustness and can obtain high-precision solutions.

Diet is recognised as the main driver of changes in gut microbiota. However, linking habitual dietary intake to microbiome composition and activity remains a challenge, leaving most microbiome studies with little or no dietary information. To fill this knowledge gap, we conducted two consecutive studies (n = 84: a first pilot study (n = 40) to build a web-based, semi-quantitative simplified FFQ (sFFQ) based on three 24-h dietary recalls (24HRs), a second study (n = 44) served to validate the newly developed sFFQ using three 24HRs as reference method and to relate gut microbiome profiling (16S rRNA gene) with the extracted dietary and lifestyle data. Relative validation analysis provided acceptable classification and agreement for 13 out of 24 (54%) food groups and 20 out of 29 nutrients (69%) based on intraclass correlation coefficient, cross-classification, Spearman’s correlation, Wilcoxon test, and Bland–Altman. Microbiome analysis showed that higher diversity was positively associated with age, vaginal birth, and intake of fruit. In contrast, microbial diversity was negatively associated with BMI, processed meats, ready-to-eat meals, sodium, and saturated fat. Our analysis also revealed a correlation between food groups or nutrients and microbial composition. Overall, we provide the first dietary assessment tool to be validated and correlated with microbiome data for population studies.

Candida albicans is a major fungal pathogen of humans. It exists as a commensal in the oral cavity, gut or genital tract of most individuals, constrained by the local microbiota, epithelial barriers and immune defences. Their perturbation can lead to fungal outgrowth and the development of mucosal infections such as oropharyngeal or vulvovaginal candidiasis, and patients with compromised immunity are susceptible to life-threatening systemic infections. The importance of the interplay between fungus, host and microbiota in driving the transition from C. albicans commensalism to pathogenicity is widely appreciated. However, the complexity of these interactions, and the significant impact of fungal, host and microbiota variability upon disease severity and outcome, are less well understood. Therefore, we summarise the features of the fungus that promote infection, and how genetic variation between clinical isolates influences pathogenicity. We discuss antifungal immunity, how this differs between mucosae, and how individual variation influences a person's susceptibility to infection. Also, we describe factors that influence the composition of gut, oral and vaginal microbiotas, and how these affect fungal colonisation and antifungal immunity. We argue that a detailed understanding of these variables, which underlie fungal-host-microbiota interactions, will present opportunities for directed antifungal therapies that benefit vulnerable patients., The complexity and variability of FunHoMic interactions between the fungal pathogen, its human host and the Microbiota strongly influence the development and outcomes of the superficial and systemic Candida albicans infections that plague human health worldwide.

Background: Monitoring armyworm (Mythimna separata Walker) damage in crops requires timely, rapid and accurate observations to avoid severe yield losses. Results: The Random Forest (RF) classifier was more effective at automatically and accurately monitoring armyworm damage compared with Support Vector Machine (SVM), Multilayer Perceptron Classifier (MLPC) and Naive Bayes Classifier (NB) classifiers. Furthermore, the incorporation of an Unmanned Aerial Vehicle (UAV) image-generated digital surface model improved the performance of the RF classifier, increasing the F-score from 0.985 and 0.970 to 0.997 and 0.994, and increasing the Kappa coefficient from 0.955 to 0.990. In addition, we found that Band 3 (735 nm) of the UAV image and Band 6 (740 nm) of a coincident Sentinel-2 image were not sensitive to an armyworm infestation in this study. Conclusions: We developed an accurate algorithm for the automated identification of armyworm-damaged corn plants using UAV images at the field scale. The study also indicated the feasibility of the developed method for monitoring corn armyworm damage at regional scale when combined with Sentinel-2 images.

Recently, engineering high performance Pd-based nanocatalysts for alkaline ethanol fuel cells has attracted wide attention. Here, we report a one-pot synthesis of low-palladium ternary palladium-copper-cobalt (PdCuCo) alloy nanowires (NWs) with a diameter of ∼4.0 nm to improve the mass activity (MA) of ethanol electrooxidation in alkaline medium. The MA (7.45 A mg-1) of the Pd31Cu61Co8 NWs is 8.5-fold and 12.4-fold that of commercial Pd black and Pd/C, respectively. The reaction mechanism of the EOR and the reasons for the activity enhancement on Pd31Cu61Co8 NWs are elucidated based on the results of in situ Fourier transform infrared spectroscopy and structure characterization. Besides the electronic effect and surface defect sites, the coexistence of surface Cu and Co that have high capacities to activate water to produce reactive oxygen species is another key factor. This study shows an example of how to design low-palladium ternary PdCuCo NWs as improved anode electrocatalysts for alkaline direct ethanol fuel cells with high mass activity.

Recently, the preparation of a high-performance one-dimensional alloy nanostructure for fuel cells has been given increasing attention due to its smart-structure merits and electronic effect triggered by alloying different kinds of metals at the nanoscale. In this study, unique ternary PtCuCo nanochains assembled with small polyhedra are first achieved and used as high-performance anode electrocatalysts toward methanol oxidation at elevated temperature (60 °C) that is closer to the operating temperature of direct methanol fuel cells than room temperature. The specific activity/mass activity of Pt45Cu35Co20 one-dimensional nanochains can reach up to 18.24 mA cm-2/4.19 A mg-1Pt that is 9.25/10.47 times that of commercial Pt black in sulfuric acid medium. After a 3600 s durability test, the remaining current density of Pt45Cu35Co20 one-dimensional nanochains is 73.3 times that of commercial Pt black. The structure characterizations show that the high density of surface active sites, d-band center of the Pt downshift, moderate strain effect, and synergetic effect are jointly responsible for the enhanced electrocatalytic performance of one-dimensional ternary PtCuCo nanochains.

Superhydrophobic-superoleophilic ceramic membranes with chemical, thermal and mechanical robustness are promising for efficient water-in-oil emulsion separation. Here, we report a facile approach to fabricate superhydrophobic-superoleophilic SiC membranes via ZnO nanosphere (NS) deposition and n-octyltriethoxysilane surface grafting. ZnO NSs were grown onto SiC grains by chemical solution deposition method to form micro-nano hierarchical structures for the membrane surfaces. By tuning the precursor concentration, the surface structures of SiC-25, SiC-50 and SiC-75 membranes (C(Zn2+) = 25, 50 and 75 mM) were tailored with the increasing density of ZnO NSs. We found that the surfaces of SiC-50 and SiC-75 membrane were superhydrophobic-superoleophilic. Compared with pristine and simply grafted SiC membranes, the three SiC membranes with ZnO NS and grafting modification displayed excellent water rejection (>99.0%) and superior oil flux. The SiC-50 membrane displayed the optimal performance with steady-state flux was up to ~1000 L m-2 h-1 under 1 bar for 500 ppm water-in-hexane emulsion. The effects of surface structure and wettability on water-in-oil emulsion separation performance is revealed. We also confirmed the stability of the superhydrophobicity for the SiC-75 membrane. This work offers new insights into constructing robust superhydrophobic-superoleophilic ceramic membranes for high-performance water removal from oil products.

Transcription initiates the cascade of gene expression and is often assumed to play a predominant role in determining how much gene products are ultimately expressed. The relationship between mRNA levels and protein levels has been studied extensively to reveal the degrees of transcriptional and post-transcriptional regulation of protein expression. The extent to which transcription globally controls the differential expression of non-coding RNAs, however, is poorly defined. MicroRNAs (miRNAs) are a class of small, non-coding RNAs whose biogenesis involves transcription followed by extensive processing. Here, using hundreds of datasets produced from the ENCODE (Encyclopedia of DNA Elements) project we calculated the correlations between transcriptional activity and mature miRNA expression in diverse human cells, human tissues, and mouse tissues. While correlations vary among samples, most correlation coefficients are small. Interestingly, excluding miRNAs that were discovered later or weighting miRNA expression improves the correlations. Our results suggest that transcription contributes only modestly to differential miRNA expression at the genome-wide scale in mammals.