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An artificial neural network (ANN) was applied to construct the relationship between the CO2 photocatalyst variables. A total of 147 data points from 38 research publications related to photocatalytic CO2 reduction via bismuth-based photocatalysts were used to develop, validate and test the developed model. The most important variable for the yield of the obtained product is irradiation time. The longer irradiation time the higher obtained product yield. Whereas the type of main product and band gap energy had the strongest effect on product yield in the positive and negative directions, respectively, in the Pearson correlation analysis. The ANN model was successfully tested to predict other literature datasets. The ANN model can then be used to estimate the yield of the obtained product, which reflects the CO2 photocatalytic reduction efficiency.

BackgroundHemorrhagic fever with renal syndrome (HFRS) continues to pose a significant public health threat to the population in China. Previous epidemiological evidence indicates that HFRS is climate sensitive and influenced by meteorological factors. However, past studies either focused on too-narrow geographical regions or investigated time periods that were too early. There is an urgent need for a comprehensive analysis to interpret the epidemiological patterns of meteorological factors affecting the incidence of HFRS across diverse climate zones. ObjectiveIn this study, we aimed to describe the overall epidemic characteristics of HFRS and explore the linkage between monthly HFRS cases and meteorological factors at different climate levels in China. MethodsThe reported HFRS cases and meteorological data were collected from 151 cities in China during the period from 2015 to 2021. We conducted a 3-stage analysis, adopting a distributed lag nonlinear model and a generalized additive model to estimate the interactions and marginal effects of meteorological factors on HFRS. ResultsThis study included a total of 63,180 cases of HFRS; the epidemic trends showed seasonal fluctuations, with patterns varying across different climate zones. Temperature had the greatest impact on the incidence of HFRS, with the maximum hysteresis effects being at 1 month (–19 ºC; relative risk [RR] 1.64, 95% CI 1.24-2.15) in the midtemperate zone, 0 months (28 ºC; RR 3.15, 95% CI 2.13-4.65) in the warm-temperate zone, and 0 months (4 ºC; RR 1.72, 95% CI 1.31-2.25) in the subtropical zone. Interactions were discovered between the average temperature, relative humidity, and precipitation in different temperature zones. Moreover, the influence of precipitation and relative humidity on the incidence of HFRS had different characteristics under different temperature layers. The hysteresis effect of meteorological factors did not end after an epidemic season, but gradually weakened in the following 1 or 2 seasons. ConclusionsWeather variability, especially low temperature, plays an important role in epidemics of HFRS in China. A long hysteresis effect indicates the necessity of continuous intervention following an HFRS epidemic. This finding can help public health departments guide the prevention and control of HFRS and develop strategies to cope with the impacts of climate change in specific regions.

Abstract Background Large-scale outbreaks of scrub typhus combined with its emergence in new areas as a vector-borne rickettsiosis highlight the ongoing neglect of this disease. This study aims to explore the long-term changes and regional leading factors of scrub typhus in China, with the goal of providing valuable insights for disease prevention and control. Methods This study utilized a Bayesian space–time hierarchical model (BSTHM) to examine the spatiotemporal heterogeneity of scrub typhus and analyze the relationship between environmental factors and scrub typhus in southern and northern China from 2006 to 2018. Additionally, a GeoDetector model was employed to assess the predominant influences of geographical and socioeconomic factors in both regions. Results Scrub typhus exhibits a seasonal pattern, typically occurring during the summer and autumn months (June to November), with a peak in October. Geographically, the high-risk regions, or hot spots, are concentrated in the south, while the low-risk regions, or cold spots, are located in the north. Moreover, the distribution of scrub typhus is influenced by environment and socio-economic factors. In the north and south, the dominant factors are the monthly normalized vegetation index (NDVI) and temperature. An increase in NDVI per interquartile range (IQR) leads to a 7.580% decrease in scrub typhus risk in northern China, and a 19.180% increase in the southern. Similarly, of 1 IQR increase in temperature reduces the risk of scrub typhus by 10.720% in the north but increases it by 15.800% in the south. In terms of geographical and socio-economic factors, illiteracy rate and altitude are the key determinants in the respective areas, with q-values of 0.844 and 0.882. Conclusions These results indicated that appropriate climate, environment, and social conditions would increase the risk of scrub typhus. This study provided helpful suggestions and a basis for reasonably allocating resources and controlling the occurrence of scrub typhus.

Coded aperture snapshot spectral imaging (CASSI) compresses tens to hundreds of spectral bands of the hyperspectral image (HSI) to a 2-D compressive measurement. For spatially or spectrally rich scenes, the compressive measurement provided by a single snapshot CASSI may not be sufficient. By taking multiple snapshots of the same scene, multishot CASSI leads to a less ill-posed inverse reconstruction problem, making the CASSI system more suitable for spatially or spectrally rich HSI. Considering the strong spectral correlation of HSI and the directional characteristics of mask shifting in multishot CASSI, the mode-1 tensor fibered rank (TFR) minimization is presented for its reconstruction in this article. Specifically, the mode-1 TFR is derived from the tensor singular value decomposition (t-SVD) to the mode-1 t-SVD, and the mode-1 TFR minimization is reduced to a mode-1 tensor nuclear norm minimization problem, to achieve more accurate HSI characterization in multishot CASSI reconstruction. The primal-dual algorithm (PDA) is applied to solve the objective optimization problem, which is flexible. Experimental results on the CAVE, Cuperite, and Urban datasets demonstrate the effectiveness of the proposed method.

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

Abstract Bismuth oxyhalides (BiOX) including BiOCl, BiOBr, and BiOI, were well synthesized using solvothermal technique and then used in the aqueous phase photooxidation of glycerol as a catalyst. The as-synthesized BiOBr could achieve the highest glycerol transformation of around 85.6% in 8 h under ultraviolet light (UV) irradiation among as-synthesized BiOXs. Moreover, the BiOBr/TiO2 heterojunction was also prepared through an ethylene glycol-assisted solvothermal process. This new BiOBr/TiO2 heterostructure exhibited excellent photocatalytic activity (97.4%) for the oxidation of glycerol compared with pure BiOBr (74%) under ultraviolet light irradiation at 6 h. This obtained behavior was confirmed by more produced OH• radicals of BiOBr/TiO2.

The aqueous-phase glycerol photocatalytic oxidation under ultraviolet irradiation was studied to reduce the fossil energy or electricity usage. Here, the glycerol is transformed into several valuable products over the new Bi2O3/TiO2 composite heterostructure which was well produced by employing maleic acid as an organic binder. The prepared α-Bi2O3/TiO2 composites revealed higher photocatalytic activity for the conversion of glycerol (88.7%) compared with TiO2 (69.7%) under ultraviolet irradiation at 8 h which could be attributed to their relative positions of the energy bands. Nevertheless, all the Bi2O3/TiO2 heterostructures had the same photocatalytic transformation of glycerol and gave similar distribution of generated valuable products. Moreover, the photocatalytic activity of the β-Bi2O3/TiO2 heterostructure had been compared to those of pure TiO2, α-Bi2O3, β-Bi2O3 and α-Bi2O3/TiO2 for the photooxidation of glycerol under the same conditions.

Plant architecture strongly influences rice grain yield. We report the cloning and characterization of the LTA1 gene, which simultaneously controls tiller angle and yield of rice. LTA1 encodes a chloroplast-localized protein with a conserved YbaB DNA-binding domain, and is highly expressed in photosynthetic tissues including leaves and leaf sheaths. Disrupting the function of LTA1 leads to large tiller angle and yield reduction of rice. LTA1 affects the gravity response by mediating the distribution of endogenous auxin, thereby regulating the tiller angle. An lta1 mutant showed abnormal chloroplast development and decreased chlorophyll content and photosynthetic rate, in turn leading to reduction of rice yield. Our findings shed light on the genetic basis of tiller angle and provide a potential gene resource for the improvement of plant architecture and rice yield.

A process-based carbon isotope biogeochemistry model substantially reduces uncertainty in regional and global estimates of the stable carbon isotopic composition of methane emissions from wetlands and suggests rising atmospheric concentrations are due to increased microbial emissions.

The present experiment was carried out to analyze the longitudinal changes in milk microorganisms. For this purpose, milk samples were collected from 12 healthy cows (n = 96; six primiparous cows and six multiparous cows) at eight different time points. The characteristics and variations in microbial composition were analyzed by 16S rRNA gene high-throughput sequencing. In the primiparous group, higher and more stable alpha diversity was observed in transitional and mature milk compared with the colostrum, with no significant difference in alpha diversity at each time point in the multiparous group. Proteobacteria, Firmicutes, Bacteroidota, and Actinobacteriota were the most dominant phyla, and Pseudomonas, UCG-005, Acinetobacter, Vibrio, Lactobacillus, Bacteroides, Serratia, Staphylococcus, and Glutamicibacter were the most dominant genera in both primiparous and multiparous cow milk. Some typically gut-associated microbes, such as Bacteroides, UCG-005, and Rikenellaceae_RC9_gut_group, etc., were enriched in the two groups. Biomarker taxa with the day in time (DIM) were identified by a random forest algorithm, with Staphylococcus showing the highest degree of interpretation, and the difference in milk microbiota between the two groups was mainly reflected in 0 d–15 d. Additionally, network analysis suggested that there were bacteria associated with the total protein content in milk. Collectively, our results disclosed the longitudinal changes in the milk microbiota of primiparous and multiparous cows, providing further evidence in dairy microbiology.

Liquid-phase dehydration of glycerol to acrolein was investigated with solid acid catalysts, including H-ZSM-5, H3PO4-modified H-ZSM-5, H3PW12O40·14H2O and Cs2.5H0.5PW12O40, in the presence of sulfolane ((CH2)4SO2) as a dispersing agent under atmospheric pressure N2 in a batch reactor. High weak-acidity H-ZSM-5, high temperatures and high-boiling-point sulfolane improved the activity and selectivity for the production of acrolein through suppressing the formation of polymers and coke and promoting the diffusion of glycerol and products. Brønsted acid sites were soundly demonstrated to be responsible for dehydration of glycerol to acrolein by infrared spectroscopy of pyridine adsorption. Brønsted weak acid sites favored the selectivity to acrolein. Combined catalytic and temperature-programmed desorption of ammonia studies revealed that the selectivity to acrolein increased as the weak-acidity increased over the ZSM-5-based catalysts. The ZSM-5-based catalysts produced a higher selectivity to acrolein, while the heteropolyacids resulted in a higher selectivity to polymers and coke.

Antimicrobial peptides (AMPs), as an important part of the innate immune system of an organism, is a kind of promising drug candidate for novel antibiotics due to their unique antibacterial mechanism. However, the discovery of novel AMPs is facing a great challenge due to the complexity of systematic experiments and the poor predictability of antimicrobial activity. Here, a novel and comprehensive screening system, the Multiple Descriptor Multiple Strategy (MultiDS), was proposed based on 59 physicochemical and structural parameters, three strategies, and four algorithms for the mining of α-helical AMPs. This approach was applied to mine the encrypted peptide antibiotics from the global human genome, including introns and exons. A library of approximately 70 billion peptides with 15–25 amino acid residues was screened by the MultiDS system and generated a list of peptides with the Multiple Descriptor Index (MD index) scores, which was the core part of the MultiDS system. Sixty peptides with top MD scores were chemically synthesized and experimentally tested their antimicrobial activity against 10 kinds of Gram-positive bacteria, Gram-negative bacteria (including drug-resistant pathogens). A total of fifty-nine out of 60 (98.3%) peptides exhibited antimicrobial activity (MIC ≤ 64 μg/mL), and 24 out of 60 (40%) peptides showed high activity (MIC ≤ 2 μg/mL), validating the MultiDS system was an effective and predictive screening tool with high hit rate and superior antimicrobial activity. For further investigation, AMPs S1, S2, and S3 with the highest MD scores were used to treat the skin infection mouse models in vivo caused by Escherichia coli, drug-resistance Escherichia coli, and Staphylococcus aureus, respectively. All of S1, S2, and S3 showed comparable therapeutic effects on promoting infection healing to or even better than the positive drug levofloxacin. A mechanism study discovered that rapid bactericidal action was caused by cell membrane disruption and content leakage. The MultiDS system not only provides a high-throughput approach that allows for the mining of candidate AMPs from the global genome sequence but also opens up a new route to accelerate the discovery of peptide antibiotics.

The aerial parts of Agastache rugosa are rich in essential oils containing monoterpenoids, phenylpropanoids, and aromatic compounds. These are used as herbs, perfume plants, and ornamental plants. Based on the difference in the constituents of the essential oil, A. rugosa is divided into pulegone and estragole chemotypes, but the mechanism of key metabolite biosynthesis in these two A. rugosa chemotypes remains unclear. In this study, we compared the morphological differences, metabolite constituents, and transcriptomic data between the two chemotypes of A. rugosa. Monoterpenoid was the main compound in the pulegone chemotype, and phenylpropanoid was the main compound in the estragole chemotype; however, limonene was detected in both chemotypes. Furthermore, 46 genes related to pulegone and estragole biosynthesis were identified. Limonene synthase, limonene-3-hydroxylase, and isopiperitenol dehydrogenase were upregulated in the pulegone chemotype, while phenylalanine ammonia-lyase, 4-coumarate: CoA ligase, CYP73A, coumaroyl-aldehyde dehydrogenase, and eugenol synthase were downregulated in the pulegone chemotype. We identified chavicol methyl transferase and limonene-3-hydroxylase in A. rugosa. This work not only provides the difference in morphology and metabolites in pulegone and estragole chemotypes, but also offers a molecular mechanism of volatile oil biosynthesis, which could be a basis for specialized metabolites in specialized chemotypes.

In this study, two polymorphs of bismuth oxide were synthesized via the solution-based route for the aqueous phase glycerol transformation to high valuable compounds with low energy photocatalysis in the presence of hydrogen peroxide as the electron acceptor. The results showed that the hydrogen peroxide was deficient to produce OH•radicals in tetragonal bismuth oxide (β-Bi2O3) photocatalyst because it decomposed very fast to H2O and O2and unoxidized glycerol, resulting in a low glycerol conversion at almost 6.6 times comparing with that of monoclinic bismuth oxide (α-Bi2O3) photocatalyst. Also, more valuable products could be produced with α-Bi2O3 photocatalyst system. In addition, the possible mechanisms of hydrogen peroxide on the low energy photocatalytic performances of both bismuth oxide polymorphs were discussed.

Developing effective catalysts based on earth abundant elements is critical for electrochemical CO2 reduction. Here, the authors prepare a manganese single atom catalyst with high CO2 reduction performance, which can be attributed to the Mn-N3 site embedded in graphitic carbon nitride.

Abstract Background Hypertension is the most common chronic disease and is the leading cause of morbidity and mortality. Its screening, diagnosis, and management depend heavily on accurate blood pressure (BP) measurement. It is recommended that the diagnosis of hypertension should be confirmed or corroborated by out-of-office BP values, measured using ambulatory BP monitoring (ABPM) and home BP monitoring (HBPM). When office BP is used, automated office BP (AOBP) measurement method, which automatically provides an average of 3–5 BP readings, should be preferred. This study aimed to describe the BP measurement methods commonly used by doctors in primary care in Hong Kong, to screen, diagnose, and manage hypertensive patients. Methods In this cross-sectional survey, all doctors registered in the Hong Kong “Primary Care Directory” were mailed a questionnaire, asking their preferred BP-measuring methods to screen, diagnose, and manage hypertensive patients. Furthermore, we also elicited information on the usual number of office BP or HBPM readings obtained, to diagnose or manage hypertension. Results Of the 1738 doctors included from the directory, 445 responded. Manual measurement using a mercury or aneroid device was found to be the commonest method to screen (63.1%), diagnose (56.4%), and manage (72.4%) hypertension. There was a significant underutilisation of ABPM, with only 1.6% doctors using this method to diagnose hypertension. HBPM was used by 22.2% and 56.8% of the respondents to diagnose and manage hypertension, respectively. A quarter (26.7%) of the respondents reported using only one in-office BP reading, while around 40% participants reported using ≥12 HBPM readings. Doctors with specialist qualification in family medicine were more likely to use AOBP in clinics and to obtain the recommended number of office BP readings for diagnosis and management of hypertension. Conclusion Primary Care doctors in Hong Kong prefer to use manual office BP values, measured using mercury or aneroid devices, to screen, diagnose, and manage hypertension, highlighting a marked underutilisation of AOBP and out-of-office BP measuring techniques, especially that of ABPM. Further studies are indicated to understand the underlying reasons and to minimise the gap between real-life clinical practice and those recommended, based on scientific advances. Trial registration Clinicaltrial.gov; ref. no.: NCT03926897 .

Monoterpenoids are the main components of plant essential oils and the active components of some traditional Chinese medicinal herbs like Mentha haplocalyx Briq., Nepeta tenuifolia Briq., Perilla frutescens (L.) Britt and Pogostemin cablin (Blanco) Benth. Pulegone reductase is the key enzyme in the biosynthesis of menthol and is required for the stereoselective reduction of the Δ2,8 double bond of pulegone to produce the major intermediate menthone, thus determining the stereochemistry of menthol. However, the structural basis and mechanism underlying the stereoselectivity of pulegone reductase remain poorly understood. In this study, we characterized a novel (−)-pulegone reductase from Nepeta tenuifolia (NtPR), which can catalyze (−)-pulegone to (+)-menthone and (−)-isomenthone through our RNA-seq, bioinformatic analysis in combination with in vitro enzyme activity assay, and determined the structure of (+)-pulegone reductase from M. piperita (MpPR) by using X-ray crystallography, molecular modeling and docking, site-directed mutagenesis, molecular dynamics simulations, and biochemical analysis. We identified and validated the critical residues in the crystal structure of MpPR involved in the binding of the substrate pulegone. We also further identified that residues Leu56, Val282, and Val284 determine the stereoselectivity of the substrate pulegone, and mainly contributes to the product stereoselectivity. This work not only provides a starting point for the understanding of stereoselectivity of pulegone reductases, but also offers a basis for the engineering of menthone/menthol biosynthetic enzymes to achieve high-titer, industrial-scale production of enantiomerically pure products.

Cadmium (Cd) is one of heavy metals harmful to human health. As rice is the main staple food in Asia and Cd is easily contaminated in rice, the molecular regulation of Cd accumulation should be explored. In this study, a recombinant inbred population derived from Xiang 743/Katy was grown in Cd-polluted fields and used to map the quantitative trait loci (QTLs) for Cd accumulation in rice grains. We identified seven QTLs distributed on chromosomes 2, 3, 6, 7, 8 and 10. These QTLs displayed phenotypic variances of 58.50% and 40.59% in 2014 and 2015, respectively. Two QTLs, qCd-2 and qCd-7, were identified in both the two years. qCd-2 was detected on the interval of RM250–RM207 on chromosome 2, with an LOD of 2.51 and a phenotypic contribution of 13.75% in 2014, and an LOD of 3.35 and a phenotypic contribution of 14.16% in 2015. qCd-7 co-localized with the cloned qCdT7 on chromosome 7 and may represent the correct candidate. The other five QTLs were detected only in one year. To further confirm the effects of qCd-2, a residual heterozygous line designated as RHL945, with a heterozygous interval of RM263–RM207 on chromosome 2, was selected from the recombinant inbred population and used to develop an F2 population consisting of 155 individual plants. By incorporating further simple sequence repeat markers into the segmental linkage map of the target region, qCd-2 was delimited in the interval of RM5404–RM3774, with an LOD value of 4.38 and a phenotypic contribution of 15.52%. These results reflected the genetic regulation of grain Cd in rice and paved the way for the future cloning of qCd-2. Keywords: cadmium, recombinant inbred line, quantitative trait locus, rice, simple sequence repeat

We propose a robust and accurate camera pose determination method based on geometric optimization search using the Internet of Things (IoT). The central idea is to (1) obtain image information through Internet of Things technology, (2) obtain the first pose by minimizing the error function, and (3) use the geometric relationship and constraint condition to obtain the appropriate attitude angles as a new initial value for the next iteration calculation. The features of this method are as follows. First, this method can deal with a large amount of uncertain data, such as in the case of any shooting angle, in the case of any reference point, and in the case of a small number of feature points. Finally, because of using Internet of Things technology, our method can quickly complete data processing and transmission. Compared to state-of-the-art methods, the experimental results show that our approach performs well on both synthetic and real data and can be used to provide accurate and stable data for subsequent applications.

The high productivity in the US Corn Belt is largely enabled by the consumption of millions of tons of manufactured fertilizer. Excessive application of nitrogen (N) fertilizer has been pervasive in this region, and the unrecovered N eventually escaped from croplands in forms of nitrous oxide (N _2 O) emission and N leaching. Mitigating these negative impacts is hindered by a lack of practical information on where to focus and how much mitigation potential to expect. At a large scale, process-based crop models are the primary tools for predicting variables required by decision making, but their applications are prohibited by expensive computational and data storage costs. To overcome these challenges, we built a series of metamodels to learn the key mechanisms regarding the carbon (C) and N cycle from a well-validated process-based biogeochemical model, ecosys . The trained metamodel captures over 98% of the variability of the ecosys simulated outputs for 99 randomly selected counties in Iowa, Illinois, and Indiana. To identify hotspots with high mitigation potential, we introduce net societal benefit (NSB) as an indicator for synthesizing the loss in yield and social benefits through emissions and pollutants avoided. Our results show that reducing N fertilizer by 10% leads to 9.8% less N _2 O emissions and 9.6% less N leaching at the cost of 4.9% more SOC depletion and 0.6% yield reduction over the study region. The estimated total annual NSB is $395 M (uncertainty ranges from $114 M to $1271 M), including $334 from social benefits (uncertainty ranges from $46 M to $1076 M), $100 M from saving fertilizer (uncertainty ranges from $13 M to $455 M), and −$40 M due to yield changes (uncertainty ranges from −$261 M to $69 M). For the median scenario, we noted that 20% of the study area accounts for nearly 50% of the NSB, and thus represent hotspot locations for targeted mitigation. Although the uncertainty range suggests that developing such a high-resolution framework is not yet settled and the scenario based estimations are not appropriate to inform the management practices for individual farmers, our efforts shed light on the new generation of analytical tools for life cycle assessment.

The applications of compressive sensing (CS) in the field of information security have captured a great deal of researchers' attention in the past decade. To supply guidance for researchers from a comprehensive perspective, this paper, for the first time, reviews CS in information security field from two aspects: theoretical security and application security. Moreover, the CS applied in image cipher is one of the most widespread applications, as its characteristics of dimensional reduction and random projection can be utilized and integrated into image cryptosystems, which can achieve simultaneous compression and encryption of an image or multiple images. With respect to this application, the basic framework designs and the corresponding analyses are investigated. Specifically, the investigation proceeds from three aspects, namely, image ciphers based on chaos and CS, image ciphers based on optics and CS, and image ciphers based on chaos, optics, and CS. A total of six frameworks are put forward. Meanwhile, their analyses in terms of security, advantages, disadvantages, and so on are presented. At last, we attempt to indicate some other possible application research topics in future.

In 2007, a novel bunyavirus was found in Henan Province, China and named fever, thrombocytopenia and leukocytopenia syndrome virus (FTLSV); since then, FTLSV has been found in ticks and animals in many Chinese provinces. Human-to-human transmission has been documented, indicating that FTLSV should be considered a potential public health threat. Determining the historical spread of FTLSV could help curtail its spread and prevent future movement of this virus.To examine the pattern of FTLSV evolution and the origin of outbreak strains, as well to examine the rate of evolution, the genome of 12 FTLSV strains were sequenced and a phylogenetic and Bayesian phylogeographic analysis of all available FTLSV sequences in China were performed. Analysis based on the FTLSV L segment suggests that the virus likely originated somewhere in Huaiyangshan circa 1790 (95% highest probability density interval: 1756-1817) and began spreading around 1806 (95% highest probability density interval: 1773-1834). Analysis also indicates that when FTLSV arrived in Jiangsu province from Huaiyangshan, Jiangsu Province became another source for the spread of the disease. Bayesian factor test analysis identified three major transmission routes: Huaiyangshan to Jiangsu, Jiangsu to Liaoning, and Jiangsu to Shandong. The speed of FTLSV movement has increased in recent decades, likely facilitated by modern human activity and ecosystem changes. In addition, evidence of RNA segment reassortment was found in FTLSV; purifying selection appears to have been the dominant force in the evolution of this virus.Results presented in the manuscript suggest that the Huaiyangshan area is likely be the origin of FTLSV in China and identified probable viral migration routes. These results provide new insights into the origin and spread of FTLSV in China, and provide a foundation for future virological surveillance and control.

Simultaneous detection of enteric viruses that cause similar symptoms (e.g. hand, foot and mouth disease) is essential to the prevention of outbreaks and control of infections. In this study, a novel PCR-Mass assay combining multiplex polymerase chain reaction (PCR) with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was developed and used for simultaneous detection of eight distinct human enteric viruses. Enteric viral isolates and standard viral RNAs were examined to determine the sensitivity and specificity of the PCR-Mass assay. Clinical performance was evaluated with a total of 101 clinical specimens from patients suspected of having hand, foot and mouth disease (HFMD). The results were compared to those of previous analyses using real-time RT-PCR. The identification of specific viruses and clinical specimens shows that the PCR-Mass assay performed as well as or better than standard methods with respect to indicating the presence of multiplex pathogens in a single specimen.

Since 2007, many cases of fever, thrombocytopenia and leukopenia syndrome (FTLS) have emerged in Henan Province, China. Patient reports of tick bites suggested that infection could contribute to FTLS. Many tick-transmitted microbial pathogens were tested for by PCR/RT-PCR and/or indirect immunofluorescence assay (IFA). However, only 8% (24/285) of samples collected from 2007 to 2010 tested positive for human granulocytic anaplasmosis (HGA), suggesting that other pathogens could be involved. Here, we used an unbiased metagenomic approach to screen and survey for microbes possibly associated with FTLS. BLASTx analysis of deduced protein sequences revealed that a novel bunyavirus (36% identity to Tehran virus, accession: HQ412604) was present only in sera from FTLS patients. A phylogenetic analysis further showed that, although closely related to Uukuniemi virus of the Phlebovirus genus, this virus was distinct. The candidate virus was examined for association with FTLS among samples collected from Henan province during 2007-2010. RT-PCR, viral cultures, and a seroepidemiologic survey were undertaken. RT-PCR results showed that 223 of 285 (78.24%) acute-phase serum samples contained viral RNA. Of 95 patients for whom paired acute and convalescent sera were available, 73 had serologic evidence of infection, with 52 seroconversions and 21 exhibiting a 4-fold increase in antibody titer to the virus. The new virus was isolated from patient acute-phase serum samples and named Henan Fever Virus (HNF virus). Whole-genome sequencing confirmed that the virus was a novel bunyavirus with genetic similarity to known bunyaviruses, and was most closely related to the Uukuniemi virus (34%, 24%, and 29% of maximum identity, respectively, for segment L, M, S at maximum query coverage). After the release of the GenBank sequences of SFTSV, we found that they were nearly identical (>99% identity). These results show that the novel bunyavirus (HNF virus) is strongly correlated with FTLS.

We quantify the source contributions to surface PM2.5 (fine particulate matter) pollution over North China from January 2013 to 2015 using the GEOS-Chem chemical transport model and its adjoint with improved model horizontal resolution (1/4° × 5/16°) and aqueous-phase chemistry for sulfate production. The adjoint method attributes the PM2.5 pollution to emissions from different source sectors and chemical species at the model resolution. Wintertime surface PM2.5 over Beijing is contributed by emissions of organic carbon (27% of the total source contribution), anthropogenic fine dust (27%), and SO _2 (14%), which are mainly from residential and industrial sources, followed by NH _3 (13%) primarily from agricultural activities. About half of the Beijing pollution originates from sources outside of the city municipality. Adjoint analyses for other cities in North China all show significant regional pollution transport, supporting a joint regional control policy for effectively mitigating the PM2.5 air pollution.

Since ancient times, calligraphy and meteorology have had an inseparable relationship. Wang Xizhi's Prologue to the Collection of Poems Composed at the Orchid Pavilion records the beautiful scenery of mild wind and bright sun, as well as the relaxed and joyful mood of people in such weather. Su Shi's Cold Food Calligraphy Copybook records the scenery of solar terms and the author's psychological changes during these solar terms through calligraphy. The Quick, Snow and Clear Time Calligraphy Copybook also reflects the grandeur of snowy days and the customs of literati recording weather and sharing it with friends. In Sun Quoting's Shupu, it is clearly stated that the third element of the "five harmony and five obedience" refers to the clear sky, humid air, and pleasant climate, and excellent climatic conditions are conducive to writing. [ABSTRACT FROM AUTHOR]

[Objectives] The effects of stabilizers such as sodium carboxymethyl cellulose, pectin, sodium alginate and xanthan gum on the stability of mango juice with skin were discussed using mango as the raw material. [Methods] The formula and stability of mango beverage with skin were studied using mango juice with skin as the raw material. Four stabilizers, including pectin, sodium alginate and sodium carboxymethyl cellulose, were added to improve the stability of the juice. Based on single factor experiments and a response surface experiment, the stability model of mango juice was established to determine the best compound stabilizer. [Results] According to an orthogonal experiment on the stability of mango juice with skin, the optimum technical parameters were as follows: sodium carboxymethyl cellulose 0. 20%, xanthan gum 0. 08%, sodium alginate 0. 18%, and pectin 0. 14%, with which the optimal suspension stability was 69. 12%. The optimum technical parameters from the response surface experiment were as follows: sodium carboxymethyl cellulose 0.199%, pectin 0. 118 5%, sodium alginate 0.163%, and xanthan gum 0. 077%, with which the suspension stability was 70. 32%. It was found that the stability of mango juice with skin obtained by the response surface experiment was better than that by the orthogonal experiment, and the order of factors affecting the stability of mango was sodium alginate ( C) > sodium carboxymethyl cellulose (A) > pectin ( D) > xanthan gum ( B). [Conclusions] The formula of compound stabilizer for mango juice was optimized by the orthogonal experiment and the response surface method, providing a theoretical basis for the actual production of mango juice with skin. [ABSTRACT FROM AUTHOR]

Meteorological comment is a kind of rational, thoughtful and knowledgeable argumentation form that communicators use mass media tools or carriers to directly express their own wishes on the newly occurred or discovered meteorological news facts, problems and phenomena. At present, the writing of meteorological comment has received special attention. Its deep background includes not only the international background of global attention to climate change, but also the domestic background of China's proposed carbon peak and carbon neutralization targets, and more importantly, the scientific and technological background of the frequent occurrence of extreme meteorological disasters caused by climate change and its early warning and disaster reduction. The prospect of great importance attached to the writing of meteorological comments is that a new subject, namely the grammatology of meteorological comment, is bound to emerge, and the construction of the grammatology of meteorological comment has its own unique requirements. [ABSTRACT FROM AUTHOR]

Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode high-dimensional features in the Hamming space with similarity preservation between instances. However, most existing methods learn hash functions in manifold-based approaches. Those methods capture the local geometric structures (i.e., pairwise relationships) of data, and lack satisfactory performance in dealing with real-world scenarios that produce similar features (e.g., color and shape) with different semantic information. To address this challenge, in this work, we propose an effective unsupervised method, namely, Jointly Personalized Sparse Hashing (JPSH), for binary representation learning. To be specific, first, we propose a novel personalized hashing module, i.e., Personalized Sparse Hashing (PSH). Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space. In addition, we deploy sparse constraints for different personalized subspaces to select important features. We also collect the strengths of the other clusters to build the PSH module with avoiding over-fitting. Then, to simultaneously preserve semantic and pairwise similarities in our proposed JPSH, we incorporate the proposed PSH and manifold-based hash learning into the seamless formulation. As such, JPSH not only distinguishes the instances from different clusters but also preserves local neighborhood structures within the cluster. Finally, an alternating optimization algorithm is adopted to iteratively capture analytical solutions of the JPSH model. We apply the proposed representation learning algorithm JPSH to the similarity search task. Extensive experiments on four benchmark datasets verify that the proposed JPSH outperforms several state-of-the-art unsupervised hashing algorithms. [ABSTRACT FROM AUTHOR]

Synthesis parameters, including different templates, initial Si/Cr molar ratios and temperature, have a detectable influence on the actual Cr content and structure property of mesoporous chromium-incorporated MSU-x molecular sieves that are prepared by nonionic poly-(ethylene oxide) surfactant templating under acidic conditions. Higher concentrations of chromium in the source gel solution and lower aging temperatures can increase the actual Cr content but reduce the short-range order of the Cr-MSU-x product .

Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gases (GHGs) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or constraints of model parameters, and to leverage recent advances in machine learning (ML) new method is needed to unlock the 'black box' to overcome its limitations due to low interpretability, out-of- sample failure and massive data demand. In this study, we developed a first of its kind knowledge-guided machine learning model for agroecosystems (KGML-ag), by incorporating biogeophysical/chemical domain knowledge from an advanced PB model, ecosys, and tested it by simulating daily N2O fluxes with real observed data from mesocosm experiments. The Gated Recurrent Unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including: 1) Using initials of intermediate variables (IMVs) instead of time series as model input to reduce data demand; 2) Building hierarchical structures to explicitly estimate IMVs for further N2O prediction; 3) Using multitask learning to balance the simultaneous training on multiple variables; and 4) Pretraining with millions of synthetic data generated from ecosys and fine tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2 = 0.81, and RMSE = 3.6 mg N m-2 day-1 from cross-validation). Importantly KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML- ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes. [ABSTRACT FROM AUTHOR]

With the advent of the era of short video, people can receive more information easy to be read and understood in a short time. Meteorological communicators should seize this opportunity, dig out characteristics and make innovations. In this paper, the first award-winning excellent short weather video in Hubei Province is taken as an example, to analyze some characteristics of short weather video, and some innovative development strategies are proposed. [ABSTRACT FROM AUTHOR]

When Taiwan's famous poet and painter Xi Murong answered a Nankai University student's question that " is value more important or technology more important in the process of saving ecology", she said, "if our heart is greedy, the fate of civilization is terrible". Xi Murong called for the removal of "A Foolish Old Man Tries to Remove Two Mountains" from Chinese textbooks of the secondary school. The " red" interpretation of B Foolish Old Man Tries to Remove Two Mountains by Mao Zedong should be regarded as a special "metaphor" in special era and circumstances. Of course, it has its own special value. In a sense, it is a "masterpiece" of " A Foolish Old Man Tries to Remove Two Mountains". However, later the founding of the PRC, especially after the reform and opening up, the story of "A Foolish Old Man Tries to Remove Two Mountains" still has been retained in Chinese textbooks of secondary school as a revolutionary " Bible", and it is necessary to re-rational view. The opinions of experts and scholars are integrated, and the ecological concept of media should le people-oriented, green, low-carbon, comprehensive and harmonious sustainable communication. Based on the theory of media ecology, A Foolish Old Man Tries to Remove Two Mountains is considered. It is thought that the current communication of B Foolish Old Man Tries to Remove Two Mountains has encountered unprecedented ecological dilemma: first, the plight of political ecology; second, the plight of scientific ecology; third, the dilemma of educational ecology. Xi Jinping, general secretary sad well, "we need invaluable assets, a well as clean water and green mountains", to keep beautiful homesickness for ourselves and green mountains and clean water for the future generations. This is the true meaning of deeply reflecting on the story of "A Foolish Old Man Tries to Remove Two Mountains". [ABSTRACT FROM AUTHOR]