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From December 2019, an outbreak of unusual pneumonia was reported in Wuhan with many cases linked to Huanan Seafood Market that sells seafood as well as live exotic animals. We investigated two patients who developed acute respiratory syndromes after independent contact history with this market. The two patients shared common clinical features including fever, cough, and multiple ground-glass opacities in the bilateral lung field with patchy infiltration. Here, we highlight the use of a low-input metagenomic next-generation sequencing (mNGS) approach on RNA extracted from bronchoalveolar lavage fluid (BALF). It rapidly identified a novel coronavirus (named 2019-nCoV according to World Health Organization announcement) which was the sole pathogens in the sample with very high abundance level (1.5% and 0.62% of total RNA sequenced). The entire viral genome is 29,881 nt in length (GenBank MN988668 and MN988669, Sequence Read Archive database Bioproject accession PRJNA601736) and is classified into β-coronavirus genus. Phylogenetic analysis indicates that 2019-nCoV is close to coronaviruses (CoVs) circulating in Rhinolophus (Horseshoe bats), such as 98.7% nucleotide identity to partial RdRp gene of bat coronavirus strain BtCoV/4991 (GenBank KP876546, 370 nt sequence of RdRp and lack of other genome sequence) and 87.9% nucleotide identity to bat coronavirus strain bat-SL-CoVZC45 and bat-SL-CoVZXC21. Evolutionary analysis based on ORF1a/1b, S, and N genes also suggests 2019-nCoV is more likely a novel CoV independently introduced from animals to humans.

When simulating groundwater flow in unconfined and convertible aquifers using a groundwater model with the block‐centered finite‐difference approach, such as MODFLOW, it frequently encounters drying and rewetting of cells. Although many drying and rewetting simulation methods have been proposed in the past, balancing simulation accuracy and convergence capability all at once is difficult. MODFLOW‐2005, which has second‐order accuracy, employs a trial‐and‐error method, but it suffers from computational instability when large quantities of grid cells are dried. MODFLOW‐NWT adopts the upstream‐weighting approach and Newton iteration method to ensure the stability of the drying and rewetting simulations. However, the upstream‐weighting approach has only first‐order accuracy, and the Newton iteration method is complex to implement because it necessitates the establishment of an additional Jacobian matrix. The methods employed by MODFLOW‐NWT are also available in MODFLOW 6, therefore it inherits both the strengths and weaknesses of MODFLOW‐NWT. In this study, a new method, Picard iteration‐based always active cell (PAAC), is proposed. Similar to MODFLOW‐NWT, the PAAC method also uses dry cells as active cells. The PAAC method, however, does not use the upstream‐weighting approach and has second‐order accuracy. Moreover, it ensures good convergence stability even under the Picard iteration method. In addition to discussing the algorithm, five cases were used to comprehensively compare the simulation effects of the PAAC method with MODFLOW‐2005 and MODFLOW‐NWT, including an analytical solution, repeated drying‐rewetting of multi‐layer grids, pumping well problem, perched aquifer problem and a nearly dry single‐layer grid, which verified the practicability of the PACC method. Key Points A new physically‐based method to simulating the drying‐rewetting problems of groundwater model, Picard iteration‐based always active cell The new method performed robust convergence even with the Picard iteration method and a general PCG solver The new method achieved second‐order accuracy

Dew is a significant water resource in arid desert areas. However, information regarding dew is scarce because it is difficult to measure due to the harsh environment of locations such as Gurbantunggut Desert, China. In this study, a non-destructive field experiment was conducted from 2015 to 2018 at a desert test station located in the western edge of the Gurbantunggut Desert, using a calibrated leaf wetness sensor (LWS) to measure dew yield. The results are as follows: (1) Dew formed after sunset with the atmospheric temperature gradually dropping and evaporated after sunrise with the temperature increasing in the second morning. (2) Dew was featured as ‘high frequency and low yield’. The average daily dew yield during dew days was 0.10 mm with a daily maximum of 0.62 mm, while dew days accounted for 44% of the total monitoring days, with a monthly maximum of 25 days. Compared with rainfall, dew days were two times as frequent as rainy days, while the average annual dewfall (12.21 mm) was about 1/11th of the average annual rainfall (134.6 mm), which indicates the dew contribution to regional water balance is about 9%. (3) March–April and October–November are the main periods of dew occurrence in this region because accumulated snow begins to melt slowly in March–April, providing sufficient vapor for dew formation, and the air temperature difference between day and night in October–November is the highest in the year, meaning that the temperature drops rapidly at night, making it easier to reach the dewpoint for vapor condensation. (4) Daily dew yield (D) was positively correlated to relative humidity (RH) and the difference between soil temperature at 10 cm below the ground and surface soil temperature (Tss), and negatively correlated to wind speed (V), air temperature (Ta), surface soil temperature (Ts), cloud cover (N), dewpoint temperature (Td) and the difference between air temperature and dewpoint temperature (Tad). It should be noted that the measured values of all factors above were the average value of the overnight period. The multivariate regression equation, D = −0.705 + 0.011 × RH − 0.006 × N − 0.01 × V, can estimate the daily dew yield with the thresholds of the parameters, i.e., RH > 70%, N < 7 (oktas) and V < 6 m/s.

Seoul orthohantavirus (SEOV) has been identified as one of the main causative agents of hemorrhagic fever with renal syndrome (HFRS) in China. The virus was found circulating in rodent populations in almost all provinces of the country, reflecting the wide distribution of HFRS. Here, using the direct immunofluorescence assay (DFA) and real-time quantitative reverse transcription PCR (qRT-PCR) approach, we performed screening in 1784 small mammals belonging to 14 species of three orders captured in the main areas of HFRS endemicity in Yunnan province (southwestern China) and identified 37 SEOV-positive rats (36 Rattus norvegicus and 1 Rattus tanezumi ). A 3-year surveillance of HFRS epidemics and dynamics of rodent reservoir density and virus prevalence implied a potential correlation between them. The subsequent meta-transcriptomic sequencing and phylogenetic analyses revealed three SEOV variants, among which two are completely novel. The ancestral character state reconstruction (ACSR) analysis based on both novel variants and documented strains from 5 continents demonstrated that SEOV appeared to originate near the southwestern area (Yunnan-Kweichow Plateau) of China, then could spread to other regions and countries by their rodent carriers, resulting in a global distribution today. In summary, these data furthered the understanding regards genetic diversity and the potential origin for SEOV. However, the expanding endemic foci in the province suggest that the virus is spreading over a wider region and is much more diverse than previous depicted, which means that increased sampling is necessary.

Regional persistent extreme cold events are meteorological disasters that cause serious harm to people’s lives and production; however, they are very difficult to predict. Low-temperature weather systems and their effects have a significant low-frequency oscillation period (10–20 d and 30–60 d). This paper uses deep learning to analyze the extended-range time scale and predict regional persistent extreme cold events. The dominant low-frequency oscillation components of cold events are obtained via wavelet transform and Butterworth filtering. The low-frequency oscillation component is decomposed via empirical orthogonal function decomposition to extract the main spatial mode and time coefficient. A convolutional neural network is used to establish the correlation between large-scale circulations and the time coefficient of the low-frequency oscillation component of the lowest temperature. The proposed deep learning model exhibits good prediction accuracy for regional persistent extreme cold events with low-frequency oscillations.

Postprocess correction is essential to improving the model forecasting result, in which machine learning methods play more and more important roles. In this study, three machine learning (ML) methods of Linear Regression, LSTM-FCN and LightGBM were used to carry out the correction of temperature forecasting of an operational high-resolution model GRAPES-3km. The input parameters include 2 m temperature, relative humidity, local pressure and wind speed forecasting and observation data in Shaanxi province of China from 1 January 2019 to 31 December 2020. The dataset from September 2018 was used for model evaluation using the metrics of root mean square error (RMSE), average absolute error (MAE) and coefficient of determination (R2). All three machine learning methods perform very well in correcting the temperature forecast of GRAPES-3km model. The RMSE decreased by 33%, 32% and 40%, respectively, the MAE decreased by 33%, 34% and 41%, respectively, the R2 increased by 21.4%, 21.5% and 25.2%, respectively. Among the three methods, LightGBM performed the best with the forecast accuracy rate reaching above 84%.

Yunnan province in China shares its borders with three neighboring countries: Myanmar, Vietnam, and Laos. The region is characterized by a diverse climate and is known to be a suitable habitat for various arthropods, including midges which are notorious for transmitting diseases which pose significant health burdens affecting both human and animal health. A total of 431,100 midges were collected from 15 different locations in the border region of Yunnan province from 2015 to 2020. These midges were divided into 37 groups according to the collection year and sampling site. These 37 groups of midges were then homogenized to extract nucleic acid. Metatranscriptomics were used to analyze their viromes. Based on the obtained cytochrome C oxidase I gene (COI) sequences, three genera were identified, including one species of Forcipomyia, one species of Dasyhelea, and twenty-five species of Culicoides. We identified a total of 3199 viruses in five orders and 12 families, including 1305 single-stranded positive-stranded RNA viruses (+ssRNA) in two orders and seven families, 175 single-stranded negative-stranded RNA viruses (−ssRNA) in two orders and one family, and 1719 double-stranded RNA viruses in five families. Six arboviruses of economic importance were identified, namely Banna virus (BAV), Japanese encephalitis virus (JEV), Akabane virus (AKV), Bluetongue virus (BTV), Tibetan circovirus (TIBOV), and Epizootic hemorrhagic disease virus (EHDV), all of which are capable, to varying extents, of causing disease in humans and/or animals. The survey sites in this study basically covered the current distribution area of midges in Yunnan province, which helps to predict the geographic expansion of midge species. The complexity and diversity of the viral spectrum carried by midges identified in the study calls for more in-depth research, which can be utilized to monitor arthropod vectors and to predict the emergence and spread of zoonoses and animal epidemics, which is of great significance for the control of vector-borne diseases.

Background The global pork industry is continuously affected by infectious diseases that can result in large-scale mortality, trade restrictions, and major reductions in production. Nevertheless, the cause of many infectious diseases in pigs remains unclear, largely because commonly used diagnostic tools fail to capture the full diversity of potential pathogens and because pathogen co-infection is common. Results We used a meta-transcriptomic approach to systematically characterize the pathogens in 136 clinical cases representing different disease syndromes in pigs, as well as in 12 non-diseased controls. This enabled us to simultaneously determine the diversity, abundance, genomic information, and detailed epidemiological history of a wide range of potential pathogens. We identified 34 species of RNA viruses, nine species of DNA viruses, seven species of bacteria, and three species of fungi, including two novel divergent members of the genus Pneumocystis . While most of these pathogens were only apparent in diseased animals or were at higher abundance in diseased animals than in healthy animals, others were present in healthy controls, suggesting opportunistic infections. Importantly, most of the cases examined here were characterized by co-infection with more than two species of viral, bacterial, or fungal pathogens, some with highly correlated occurrence and abundance levels. Examination of clinical signs and necropsy results in the context of relevant pathogens revealed that a multiple-pathogen model was better associated with the data than a single-pathogen model was. Conclusions Our data demonstrate that most of the pig diseases examined were better explained by the presence of multiple rather than single pathogens and that infection with one pathogen can facilitate infection or increase the prevalence/abundance of another. Consequently, it is generally preferable to consider the cause of a disease based on a panel of co-infecting pathogens rather than on individual infectious agents. -CFtrnVrpn8o8P42pZ9u6v Video abstract

Ticks, an arthropod known for transmitting various pathogens such as viruses, bacteria, and fungi, pose a perpetual public health concern. A total of 2,570 ticks collected from Nujiang Prefecture in Yunnan Province between 2017 and 2022 were included in the study. Through the meta-transcriptomic sequencing of four locally distributed tick species, we identified 13 RNA viruses belonging to eight viral families, namely, Phenuiviridae , Nairoviridae , Peribunyaviridae , Flaviviridae , Chuviridae , Rhabdoviridae , Orthomyxoviridae , and Totiviridae . The most prevalent viruses were members of the order Bunyavirales , including three of Phenuiviridae , two were classified as Peribunyaviridae , and one was associated with Nairoviridae . However, whether they pose a threat to human health still remains unclear. Indeed, this study revealed the genetic diversity of tick species and tick-borne viruses in Nujiang Prefecture based on COI gene and tick-borne virus research. These data clarified the genetic evolution of some RNA viruses and furthered our understanding of the distribution pattern of tick-borne pathogens, highlighting the importance and necessity of monitoring tick-borne pathogens.

Improved twin support vector machine (ITSVM) is a binary classification model with strong theoretical interpretation. Compared with Twin bound support vector machine (TWBSVM), it avoids the matrix inverse operation. However, the disadvantage of slow speed is exposed during the training process. Thus authors are motivated to employ safe screening method to reduce the scale of dual ITSVM and accelerate its computational speed. More specifically, the proposed method is to identify redundant points in advance and eliminate them before actually solving the problem. If safe screening method is directly used to accelerate ITSVM, it will be inevitable to bring matrix inverse operation during the screening process. In this case, a screening method which is distinct from existing safe screening method is devised to avoid calculating inverse matrix. Meanwhile an improved dual coordinate descent method (DCDM) is employed to accelerate ITSVM. Experiments on eleven real data sets are conducted to demonstrate the effectiveness of the proposed acceleration method.