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A Lagrangian model—the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT)—is used to quantify changes in moisture sources and paths for precipitation over North China’s Henan Province associated with tropical cyclone (TC) over the western North Pacific (WNP) during July–August of 1979–2021. During TC-active periods, an anomalous cyclone over the WNP enhances southeasterly and reduces southwesterly moisture transport to Henan. Accordingly, compared to TC-inactive periods, moisture contributions from the Pacific Ocean (PO), eastern China (EC), and the local area (Local) are significantly enhanced by 48.32% (16.73% versus 11.28%), 20.42% (9.44% versus 7.84%), and 2.89% (4.91% versus 4.77%), respectively, while moisture contributions from the Indian Ocean (IO), Southwestern China (SWC), Eurasia (EA), and the South China Sea (SCS) are significantly reduced by −31.90% (8.61% versus 12.64%), −16.27% (4.60% versus 5.50%), −8.81% (19.10% versus 20.95%), and −6.92% (12.18% versus 13.09%). Furthermore, the moisture transport for a catastrophic extreme rainfall event during 17–22 July (“21⋅7” event) influenced by Typhoon Infa is investigated. Compared to the mean state during TC-active periods, the moisture contribution from the PO was substantially increased by 126.32% (37.87% versus 16.73%), while that from IO significantly decreased by −98.26% (0.15% versus 8.61%) during the “21⋅7” event. Analyses with a bootstrap resampling method show that moisture contributions from the PO fall outside the +6σ range, for both the TC-active and TC-inactive probability distributions. Thus, the “21⋅7” event is rare and extreme in terms of the moisture contribution from the PO, with the occurrence probability being less than 1 in 1 million times. Significance Statement Henan, one of the most populated provinces in China, experienced a catastrophic extreme precipitation event in July 2021 (the “21⋅7” event), coinciding with the activity of a tropical cyclone (TC) over the western North Pacific, which helps establish the moisture channel. Using a Lagrangian model, we provide a better understanding of how moisture transport changes associated with TC for the mean state of 1979–2021, and reveal how extreme is the moisture transport for the “21⋅7” event with the bootstrap technique. It is found that during active TC periods, the moisture contribution from the Pacific Ocean (the Indian Ocean) is significantly enhanced (reduced). For every 1 000 000 six-day events, less than one instance like the “21⋅7” event should be expected.

Biological activated carbon filter backwash water (BAC FBW) removes pollutants from BAC filters in drinking water treatment plants. Characterizing the dissolved organic matter (DOM) in BAC FBW is of great significance for the recycling and treatment of backwash water. DOM pollution was monitored over a year using three-dimensional excitation–emission matrix fluorescence spectra. The dissolved organic carbon (DOC) concentration and fluorescence results showed that the DOC concentration reached the lowest value of 1.95 mg/L in the summer. The relative abundances of fluorescence components had a direct relationship with temperature variations. The soluble microbial products were enhanced greatly during cold seasons, while fulvic-acid-like and humic-acid-like compounds remained constant. Disinfection by-products (DBPs) formation potential and cytotoxicity were evaluated. The total haloacetonitriles (HANs), trihalomethanes (THMs), halonitromethanes (HNMs), and haloacetaldehyde (HALs) formation potentials ranged from 35.5 to 47.3 μg/L, 95.1 to 126 μg/L, 4.95 to 8.06 μg/L, and 43.4 to 53.0 μg/L in BAC FBW, respectively. Among all DBPs included in the calculation, the order of contribution to cytotoxicity was HANs > HALs > THMs > HNMs, respectively. Especially, HANs were much higher than the other DBPs, which should be controlled to avoid the biological risk of effluent quality.

Phased-array radar (PAR) technology can potentially provide high-quality clear-air radial velocity observations at a high spatiotemporal resolution, usually ∼1 min or less. These observations are hypothesized to partially fill the gaps in current operational observing systems with relatively coarse-resolution surface mesonet observations and the lack of high-resolution upper-air observations especially in planetary boundary layer. In this study, observing system simulation experiments are conducted to investigate the potential value of assimilating PAR observations of clear-air radial velocity to improve the forecast of convection initiation (CI) along small-scale boundary layer convergence zones. Both surface-based and elevated CIs driven by meso-γ-scale boundary layer convergence are tested. An ensemble Kalman filter method is used to assimilate synthetic surface mesonet observations and PAR clear-air radial velocity observations. Results show that assimilating only surface mesonet observations fails to predict either surface-based or elevated CI processes. Assimilating clear-air radial velocity observations in addition to surface mesonet observations can capture both surface-based and elevated CI processes successfully. Such an improvement benefits from the better analyses of boundary layer convergence, resulting from the assimilation of clear-air radial velocity observations. Additional improvement is observed with more frequent assimilation. Assimilating clear-air radial velocity observations only from the one radar results in analysis biases of cross-beam winds and CI location biases, and assimilating additional radial velocity observations from the second radar at an appropriate position can reduce these biases while sacrificing the CI timing. These results suggest the potential of assimilating clear-air radial velocity observations from PAR to improve the forecast of CI processes along boundary layer convergence zones.

Peruvian Andes region has been proven in multiple studies to be one of a few regions have poor performance of many global precipitation estimations, due to its complex terrain and extreme interruption of atmospheric movement by the Andes mountain. This study provides an evaluation over two Peruvian local precipitation products PISCO and RAIN4PE, along with a regional dynamic downscaled WRF model simulation, and GPM-IMERG. The precipitation products were evaluated against local rain gauge data and used as the forcing data for CREST-VEC model to test the uncertainties of the precipitation products in a extremely dry region in Peru. This study readdress the accuracy issue of precipitation products in the Peruvian Andes region, and highlights the importance of using WRF modeling simulation to ‘fill-the-blank’ of heterogenous rain gauge distribution, when remote-sensing technologies fail to perform in this area.

High ice water content (HIWC) regions with small ice crystals, where ice water contents (IWCs) are greater than 1.5 g m-3 and median mass diameters (MMDs) less than about 300 micrometers, occur above tropical mesoscale convective systems (MCSs) and can have detrimental impacts on aircraft engines. Data collected by the French Falcon aircraft and the National Research Council of Canada Convair-580 during the 2014 and 2015 High Altitude Ice Crystals and High Ice Water Content (HAIC/HIWC) projects are revisited here along with coordinated modeling studies to investigate processes that can produce such HIWCs. In particular, data collected from 2014 in the vicinity of Darwin Australia and from 2015 in the vicinity of Cayenne French Guyana are used to determine how bulk microphysical properties (e.g., number concentration, IWC, median volume diameter) and characteristics of ice crystal size distributions (i.e., multimodal nature, parameters fit to gamma distributions for each mode) vary with environmental conditions such as temperature, vertical velocity, MCS age, distance from MCS core, and surface characteristics. It is determined that temperature and vertical velocity are the biggest controls of small ice crystals, but younger cells, stronger convective strengths and closer proximity to convective cores also increase the relative importance of small crystals.Numerical simulations conducted using the Weather Research and Forecasting model with four different bulk microphysics schemes generally reproduce the observed temperature, dew-point, and wind structure. However, comparison of regime-specific observations against properties simulated over Cayenne using a variety of existing parameterization schemes show that although the coverage and evolution of convection is well predicted, simulations overestimate the intensity and spatial extent of observed airborne X-band radar reflectivity and do not well depict the peak of observed size distributions with maximum dimensions between 0.1 and 1 mm. To explore formation mechanisms for large numbers of small ice crystals, a series of simulations varying the representation of secondary ice production (SIP) processes were conducted. Simulations including one of three SIP mechanisms separately (i.e., the Hallett–Mossop mechanism, fragmentation during ice–ice collisions, and shattering of freezing droplets) did not replicate the observed ratio of number concentration divided by IWC. However, the simulation including all three SIP processes produced HIWC regions consistent with observations in terms of number concentration and radar reflectivity, which was not replicated using the original P3 two-ice category configuration that only included the Hallett-Mossop mechanism. In summary, observations and simulations show primary ice production plays a key role in generating HIWC regions at temperatures < -40 Celsius, shattering of freezing droplets dominates ice particle production in HIWC regions between -15 and 0 Celsius during the early stage of convection, and fragmentation during ice–ice collisions dominates between -15 and 0 Celsius during the later stage of convection and between -40 and -20 Celsius over the whole convection period. This study thus shows the dominant role of SIP processes in the formation of numerous small crystals in HIWC regions. Implications for future measurement and modeling needs are discussed.

A huge variation in well performance was observed in Longmaxi shale gas wells, in Yongchuan block, Sichuan Basin. The Longmaxi shale was buried deeper than 3500 m with complex structures, in-situ stress, and natural fracture corridors, which makes well performance very uncertain. Horizontal well length was greater than 2000 m and proppant intensity was higher than 3.5 t/m. To solve these challenges in an ultradeep shale gas play, a multidisciplinary and life cycle management strategy was proposed and practiced. Initially, geology and geophysics (G&G) work using seismic data revealed the exact geological structures with folds, faults, and natural fractures. Then the study turned to 3D high-resolution geological and geomechanics modeling to identify the sweet spots and high drilling and completion risks areas. With the deep G&G understanding, the well drilling and completion design were proposed, executed, and iterated by onsite diagnosis and support to place high-quality hydraulic fractures. Finally, the production strategy was optimized to keep the flow path undamaged during its life cycle. The detailed study and post-fracture well performance indicate that the successful ultradeep shale gas development in the Longmaxi in the Sichuan Basin relies on: Placing the wells where there are fewer faults but more natural fractures Identifying the stages with high screenout and casing failure risks, thereby levering the 3D G&G model Creating the completion design by considering the screenout and casing failure risks Executing the completion design successfully by onsite diagnosis and support, leveraging on the 3D G&G model, microseismic data, and treating pressure trend Enhancing the 3D model accuracy by iterations with observation data Minimizing the propped fracture damage by adjusting the choke size during the production phase With the high-resolution quality management on well drilling, completion, and production management, the well outperformed 100% more than the neighboring wells. The geoengineering integration values were highlighted in both the in-house study and field operations, and this strategy could be duplicated to other candidates approaching their drilling, completion, and production engineering's limits under complex geological conditions.

Many unconventional resources are featured with thin laminated layers in the reservoir. Though this feature is often considered the cause of reservoir heterogeneity, the actual influence from the laminated layers can be more significant. In a comprehensive study from Sichuan shale gas, we have found that the thin laminated shell limestone layer played a very important role for the well performance. Sichuan shale gas is well known for its geological and geomechanical complexity. One of the formation sub-units, Wufeng, did not perform well enough according to its reservoir properties. Various evidence, including formation micro-image, core sample XRD and geomechanical analysis, shows that one laminated thin carbonate layer on the upper part of the Wufeng unit, often referred to as Guanyinqiao carbonate, could be the main reason for the under-performance of Wufeng unit. Due to the limited thickness, the lithology and geomechanical property are not fully well-captured or comprehended with traditional loggings and workflow. However, our new geomechanical results indicate that a much stronger stress barrier condition is in place for Wufeng, which provides a more realistic controlling mechanism of the hydraulic fractures. The following fracture modelling and reservoir numerical simulation indicates the impact from this lamina to the production is significant. The direct conclusion of this study is that the existence of the thin lamina of shell limestone can play a vital role in Sichuan shale gas reservoirs. And as a recommended practice in the same region, the geomechanical measurement need to be calibrated with high resolution formation image log, based on which both horizontal lateral landing and multistage completion strategy can be optimized. This observation may also help the understanding of other unconventional reservoirs where similar laminations of carbonate are encountered, so that the proper geo-engineering approach may apply accordingly to justify the preferred exploration and appraisal practices.

Background Radiotherapy resistance is the main cause of low tumor regression for locally advanced rectum adenocarcinoma (READ). The biomarkers correlated to radiotherapy sensitivity and potential molecular mechanisms have not been completely elucidated. Methods A mRNA expression profile and a gene expression dataset of READ (GSE35452) were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) between radiotherapy responder and non-responder of READ were screened out. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for DEGs were performed. Random survival forest analysis was used to identified hub genes by randomForestSRC package. Based on CIBERSORT algorithm, Genomics of Drug Sensitivity in Cancer (GDSC) database, Gene set variation analysis (GSVA), enrichment analysis (GSEA), nomogram, motif enrichment and non-coding RNA network analyses, the associations between hub genes and immune cell infiltration, drug sensitivity, specific signaling pathways, prognosis prediction and TF – miRNA regulatory and ceRNA network were investigated. The expressions of hub genes in clinical samples were displayed with the online Human Protein Atlas (HPA). Results In total, 544 up-regulated and 575 down-regulated DEGs in READ were enrolled. Among that, three hubs including PLAGL2, ZNF337 and ALG10 were identified. These three hub genes were significantly associated with tumor immune infiltration, different immune-related genes and sensitivity of chemotherapeutic drugs. Also, they were correlated with the expression of various disease-related genes. In addition, GSVA and GSEA analysis revealed that different expression levels of PLAGL2, ZNF337 and ALG10 affected various signaling pathways related to disease progression. A nomogram and calibration curves based on three hub genes showed excellent prognosis predictive performance. And then, a regulatory network of transcription factor (ZBTB6) - mRNA (PLAGL2) and a ceRNA network of miRNA (has-miR-133b) - lncRNA were established. Finally, the results from HPA online database demonstrated the protein expression levels of PLAGL2, ZNF337 and ALG10 varied widely in READ patients. Conclusion These findings indicated that up-regulation of PLAGL2, ZNF337 and ALG10 in READ associated with radiotherapy response and involved in multiple process of cellular biology in tumor. They might be potential predictive biomarkers for radiotherapy sensitivity and prognosis for READ.

Liver cancer remains a global health challenge with a projected incidence of over one million cases by 2025. Hepatocellular carcinoma (HCC) is a common primary liver cancer, accounting for about 90% of all liver cancer cases. The tumor microenvironment (TME) is the internal and external environment for tumor development, which plays an important role in tumorigenesis, immune escape and treatment resistance. Knowing that TME is a unique setting for HCC tumorigenesis, exploration of strategies to modulate TME has attracted increasing attention. Among them, the use of nano-delivery systems to deliver therapeutic agents to regulate TME components has shown great potential. TME-modulating nanoparticles have the advantages of protecting therapeutic agents from degradation, enhancing the ability of targeting HCC and reducing systemic toxicity. In this article, we summarize the TME components associated with HCC, including cancer-associated fibroblasts (CAFs), extracellular matrix (ECM), endothelial cells and immune cells, discuss their impact on the HCC progression, and highlight recent studies on nano-delivery systems that modulate these components. Finally, we also discuss opportunities and challenges in this field.

Summary Understanding how birds select breeding sites plays an important role in habitat protection, especially for the conservation of endangered species. With the increase in population size of the endangered Crested Ibis Nipponia nippon, its distribution range has expanded from mountain areas to plains located outside protected areas, representing a new challenge for conservation of the species. Identifying the current nesting habitat requirements is thus needed and can provide valuable information for the planning of new nature reserves. In this research, we surveyed a total of 117 nests across the whole distribution range from 2015 to 2019. We used generalized linear mixed-effects models to assess nesting preferences of the Crested Ibis in the wild. Results showed that in mountain areas, Masson pines Pinus massoniana were preferred (64.6%), whereas elms Ulmus pumila (44.9%) and aspens Populus davidiana (40.6%) were used more frequently lower down, probably because of their higher availability. In both mountain areas and plains the ibises selected tall nesting trees with larger diameter at breast height and preferred nesting rather high above ground, especially in plains where taller trees provided higher suitable nesting positions. The ibises also preferred nesting close to tree trunks, especially in mountain areas, probably for more safety from collapsing. Furthermore, in mountain areas, slope and distance to path had positive effects on nesting occurrence, and understorey coverage was avoided by nesting ibises, while these variables had little impact in plains. Our results indicate that, despite their range expansion, Crested Ibises rely on very specific habitat characteristics for nesting. We suggest relatively tall trees like elms and aspens should be preserved in plains. In addition, we highlight how selection patterns of Crested Ibises may vary, and that such variation should be addressed in conservation planning, especially in future reintroduction.

This study focused on the changes in the composition and immune evolution in milk from birth to 144 h postpartum and the genes associated with the colostrum yield of Hu sheep. Twelve Hu sheep, which were bred carefully under animal health standards and have a litter size of two kids and similar gestation length (149 ± 1 days), were used. Lambs were transferred into their own cots to avoid interference. The compositional content (i.e., fat, protein, and lactose) and some other properties, including daily colostrum yield, DM, and SNF, were determined. In addition, immunity molecules (IgG, IgA, and IgM concentrations) received remarkable attention. The DM, SNF, fat, and protein contents were higher in the first days postpartum and then dropped quickly from the time of birth to 144 h postpartum. However, the lactose content displayed an increasing pattern and reached normal milk percentage at 48 h. The highest IgG (103.17 mg/mL), IgA (352.82 μg/mL), and IgM (2.79 mg/mL) colostrum concentrations were observed at partum, decreased quickly, and finally stabilized. The change law of concentration of IgA and IgM in colostrum are the same with IgG. Furthermore, the whole-genome resequencing was performed, and a missense variant locus in the

Planetary boundary layer (PBL) schemes parameterize unresolved turbulent mixing within the PBL and free troposphere (FT). Previous studies reported that precipitation simulation over the Amazon in South America is quite sensitive to PBL schemes and the exact relationship between the turbulent mixing and precipitation processes is, however, not disentangled. In this study, regional climate simulations over the Amazon in January-February 2019 are examined at process level to understand the precipitation sensitivity to PBL scheme. The focus is on two PBL schemes, the Yonsei University (YSU) scheme, and the asymmetric convective model v2 (ACM2) scheme, which show the largest difference in the simulated precipitation. During daytime, while the FT clouds simulated by YSU dissipate, clouds simulated by ACM2 maintain because of enhanced moisture supply due to the enhanced vertical moisture relay transport process: 1) vertical mixing within PBL transports surface moisture to the PBL top, and 2) FT mixing feeds the moisture into the FT cloud deck. Due to the thick cloud deck over Amazon simulated by ACM2, surface radiative heating is reduced and consequently the convective available potential energy (CAPE) is reduced. As a result, precipitation is weaker from ACM2. Two key parameters dictating the vertical mixing are identified, p, an exponent determining boundary layer mixing and λ, a scale dictating FT mixing. Sensitivity simulations with altered p, λ, and other treatments within YSU and ACM2 confirm the precipitation sensitivity. The FT mixing in the presence of clouds appears most critical to explain the sensitivity between YSU and ACM2.

As the leading cause of disability worldwide, low back pain is commonly caused by biomechanical and catabolic disruptions to key structures of the spine, such as intervertebral discs and facet joints. To date, accurate, noninvasive detection of microdestruction within these tissues remains an elusive goal. Here, we report an

Cryptochrome 1 (cry1), the core regulator of the circadian clock, is essential for ontogeny and mammalian reproduction. Unlike in other tissues, the cry1 gene have noncircadian functions in spermatogenesis, which implies the unique role of cry1 gene in the development of testis. The role of cry1 during the puberty has not been described yet. This study aimed to explore the relationship between cry1 expression and spermatogenic cell numbers. We analyzed testicular tissues from Hu sheep aged 0–180 days by hematoxylin and eosin staining, measured cry1 and cell proliferation regulatory factors (bricd5, tnfrsf21, cdk1) expression by quantitative real-time PCR and characterized the transcription factor in the 5′ flanking region of cry1 gene. The data revealed that the number of spermatocytes and early spermatocytes increased rapidly from 90 to 120 dpp (day postpartum). Correspondingly, there was a marked variation in the cry1 and cell proliferation related genes (bricd5, tnfrsf21, cdk1) mRNA expression in the testes from the age of 90 days to 180 days (p

The rhizosphere microbiome plays an important role in plant growth and behavior during phytoremediation. This work aims to explore how hyperaccumulating plant performance (shoot biomass and Cd uptake) and rhizosphere bacterial community are driven by plant development under a Cd concentration gradient and their relationship during plant growth. A pot experiment was conducted using the Cd hyperaccumulator Sedum plumbizincicola grown in soil spiked with 0, 5 and 20 mg Cd kg−1 for 13 weeks. Plant performance (shoot biomass and Cd uptake) was determined and the rhizosphere bacterial community was examined by weekly Illumina sequencing. Biomarker genera were identified by the Random Forest model and the functions were predicted by PICRUSt2. The rhizospheres bacterial community tended to stabilize from the 6th or 7th week regardless of the soil Cd concentration, coinciding with the vigorous growth stage of the hyperaccumulator. Biomarker genera Paenibacillus and Streptacidiphilus enriched in the stable stage showed significant positive correlations with Cd uptake. In addition, some predicted gene abundances of enzymes related to plant growth promotion by the rhizosphere bacterial community were influenced to various degrees by plant development and soil Cd concentration. There exists a stable bacterial community in the rhizosphere of S. plumbizincicola during phytoremediation and biomarker taxa enriched at this stage were positively correlated with Cd uptake. Selecting microorganisms that match the developmental stages might further enhance phytoremediation. This provides some theoretical basis for modulating rhizosphere microbes at the appropriate time during phytoextraction to optimize the phytoremediation technique.

Plants are affected by changes in light and adaptation mechanisms can affect secondary metabolite synthesis. In this study, the physiological response and regulation of the coumarin biosynthetic pathway of

Microbial communities are closely related to plant performance and numerous studies have shown their involvement with the growth and development of host plants, resistance to pathogen invasion and adaptation to environmental stress. Here we described in detail the ecological process of the microbial community assembly in hyperaccumulator plant Sedum plumbizincicola. We divided the microbiota into four ecological compartments (bulk soil, rhizosphere, root endosphere and aboveground endosphere). The results showed that host selection strongly controlled the aggregation of microbial community. So that microbes occupied different niches from the bulk soil to the aboveground endosphere, and bacterial diversity and network complexity decreased gradually. Soil types were the second influencing factor, especially for the microbial community in the root endosphere. The SourceTracker analysis further confirmed the vertical migration of microbes from bulk soil to aboveground endosphere. In addition, under the condition of heavy metal pollution, the microbial community of S. plumbizincicola tended to form a microbial pool dominated by Proteobacteria and Actinobacteria. Ellin6067, Sphingomonas, Ralstonia, SC-I-84_uncultured bacterium, Burkholderiaceae_Undibacterium and Pedosphaeraceae_uncultured bacterium etc. were identified as the vital biomarker taxa. Among these genera, the relative abundance of last three was significantly positively correlated with the activation and transfer of cadmium, and they mainly enriched in paddy soil. This study provides evidence for the mechanism by which the microbial community assembly occurs and experience for regulating the microbial community and increasing the accumulation efficiency of potentially toxic metals in S. plumbizincicola.

In recent years, with the integration and development of artificial intelligence technology and geology, traditional geological prospecting has begun to change to intelligent prospecting. Intelligent prospecting mainly uses machine learning technology to predict the prospecting target area by mining the correlation between geological variables and metallogenic characteristics, which usually requires a large amount of data for training. However, there are some problems in the actual research, such as fewer geological sample data and irregular mining features, which affect the accuracy and reliability of intelligent prospecting prediction. Taking the Pangxidong study area in Guangdong Province as an example, this paper proposes a deep learning framework (SKT) for prospecting target prediction based on selective knowledge transfer and carries out intelligent prospecting target prediction research based on geochemical data in Pangxidong. The irregular features of different scales in the mining area are captured by dilation convolution, and the weight parameters of the source network are selectively transferred to different target networks for training, so as to increase the generalization performance of the model. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved, which can effectively alleviate the problems of a small number of geological samples and irregular features of mining areas in prospecting prediction.

Rapid economic development and industrialization may include environmentally harmful human activities that cause heavy-metal accumulation in soils, ultimately threatening the quality of the soil environment and human health. Therefore, accurate identification of pollution sources is an important weapon in efforts to control and prevent pollution. The self-organizing map (SOM) method is widely used in pollution source identification because of its capacity for visualization of high-dimensional data. The SOM ignores the graph structure relationship among chemical elements in soils; the SOM analysis of pollution sources has high uncertainty. Here, we propose a new analysis method, i.e., the graph convolutional self-organizing map (GCSOM), which uses a graph convolutional network (GCN) to extract the graph structure relationship among the chemical elements in soils, then performs data visualization using an SOM. We compared the performances of GCSOM and SOM, then assessed the pollution source characteristics of trace metal(loid)s (TMs, mostly heavy metals) in Jiangmen City using the GCSOM. Our experimental results showed that the GCSOM is superior to the SOM for identification of TM sources, while the TMs in the soil of Jiangmen originate from three main sources: agricultural activities (mainly in Taishan City, Jiangmen), traffic emissions (mainly in Xinhui and Pengjiang Districts), and industrial activities (mainly in Xinhui District). The risk assessment indicated that the risk of all TMs was within threshold.

Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefore, the identification of MCI is a challenging task. The current functional brain network (FBN) analysis to predict human brain tissue structure is a new method emerging in recent years, which provides sensitive and effective medical biomarkers for the diagnosis of neurological diseases. Therefore, to address this challenge, we propose a novel Deep Spatiotemporal Attention Network (DSTAN) framework for MCI recognition based on brain functional networks. Specifically, we first extract spatiotemporal features between brain functional signals and FBNs by designing a spatiotemporal convolution strategy (ST-CONV). Then, on this basis, we introduce a learned attention mechanism to further capture brain nodes strongly correlated with MCI. Finally, we fuse spatiotemporal features for MCI recognition. The entire network is trained in an end-to-end fashion. Extensive experiments show that our proposed method significantly outperforms current baselines and state-of-the-art methods, with a classification accuracy of 84.21%.

Owing to the inherent heterogeneity and plasticity of fibroblasts, they are considered as the conventional biological resources for basic and clinical medical research. Thus, it is essential to generate knowledge about the establishment of fibroblast cultures and the effects of cryopreservation processes on their biological characteristics. Since the pig (Sus scrofa) possesses numerous genetic, physiological, and anatomical similarities with humans, porcine fibroblasts are naturally regarded as useful analogues of human fibroblasts. Nonetheless, less attention has been given to the alterations in viability and gene expression of cryopreserved porcine fibroblasts. In this study, we aimed to obtain fibroblasts from porcine ear skin and evaluate the effects of cryopreservation on the cell survival, proliferation, and gene expression profiles of the fibroblasts by trypan-blue-staining assay, Cell Counting kit-8 (CCK-8) assay, and RNA-sequencing analysis, respectively. Our results suggested that morphologically stable fibroblast cultures can be constructed from pig-ear skin. The post-thaw survival rate of the cryopreserved fibroblasts at 0 h and 24 h was over 90%. The proliferative activity of the cryopreserved fibroblasts was similar to that of the non-cryopreserved fibroblasts after 7 days of in vitro culture, which suggested that cryopreservation did not influence the viability. The RNA-sequencing analysis indicated that this should be attributed to the 867 differentially expressed genes (DGEs) identified, which are involved in molecular process related to cell recovery and survival after cryo-stimulation. In addition, eight important DEGs BMP2, GDF15, EREG, AREG, HBEGF, LIF, IL-6, and HOX-7 could potentially be applied to improve the efficiency of fibroblast cryopreservation, but comprehensive and systematic studies on understanding the underlying mechanisms responsible for their modulatory roles are urgently needed.

Background Traumatic brain injury (TBI) is a major cause of disability worldwide, without definitive and effective intervention. Dexmedetomidine (DEX) has a neuroprotective effect against TBI; however, the detailed mechanism underlying this effect remains unclear. Methods Ten male Sprague Dawley rats were used to establish a TBI model. The rats were randomly divided into two groups: the TBI group (TBI, control group) and the DEX treatment group (DEX). The next day, the neurological function of the rats were evaluated by the modified neurological severity score (mNSS). Then, the rats were sacrificed, and RNA sequencing was performed to identify differentially expressed messenger RNAs (mRNAs) and microRNAs (miRNAs) in brain tissue samples. Additionally, we performed a bioinformatics analysis to explore the candidate genes and pathways that might play important roles in DEX-induced neuroprotection. The most significantly differentially expressed miRNAs and possible hub genes were validated by quantitate reverse transcription-polymerase chain reaction (qRT-PCR) using more samples. Results In the DEX group, 517 mRNAs (352 up-regulated and 165 down-regulated) and 35 miRNAs (18 up-regulated and 17 down-regulated) were differentially expressed compared to the TBI group. Gene Ontology analysis revealed the up-regulated mRNAs to be significantly enriched in microtubule-based movement or processes, microtubule and tubulin binding. Kyoto Encyclopedia of Genes and Genomes analysis showed that these up-regulated mRNAs were significantly enriched in the B-cell receptor signaling pathway as well as the cell cycle pathway. Also, Lyn and Cdk1 were found to be associated with the B-cell receptor signaling and cell cycle pathways, respectively. Furthermore, the down-regulated miRNAs were significantly enriched in cellular components, although no significant Gene Ontology terms or KEGG pathways were found for the down-regulated mRNAs or up-regulated miRNAs. Conclusions Differentially expressed mRNAs and miRNAs were identified after the administration of DEX in a TBI rat model. The B-cell receptor signaling pathway and the cell cycle pathway might be involved in the neuroprotective effect of DEX against TBI, Lyn and Cdk1 might be hub genes.

Circular RNAs (circRNAs) function as efficient microRNA (miRNA) sponges that regulate gene expression in the pathogenesis of many human malignancies. However, their roles in cervical adenocarcinoma remain largely unknown. In this study, we aimed to seek novel circRNAs that regulate cervical adenocarcinoma carcinogenesis and to explore their regulatory mechanisms as well as clinical significance. We identified that 24 circRNAs were differentially expressed in cervical adenocarcinoma tissues by RNA sequencing. Among them, circEYA1 was the most significantly downregulated circRNA in cervical adenocarcinoma. In cervical adenocarcinoma cells, circEYA1 overexpression led to suppression of cell viability and colony formation, promotion of apoptosis, and a decrease of the xenograft tumor growth. The mechanism underlying these observations is that circEYA1 functioned as a sponge of miR-582-3p and abrogated its suppression of CXCL14 expression. Consistently, miR-582-3p inhibition phenocopied the biological effects of circEYA1 overexpression in cervical adenocarcinoma cells. Moreover, miR-582-3p overexpression reversed the suppressive behaviors of circEYA1 in vitro and in vivo. In addition, the expression, correlation, and clinical diagnostic value of circEYA1/miR-582-3p/CXCL14 were confirmed in 198 clinical cervical tissue samples. In summary, our findings highlight a novel tumor suppressive role of circEYA1 in cervical adenocarcinoma tumorigenesis and may provide a potential diagnostic marker and therapeutic target for patients with cervical adenocarcinoma., Graphical Abstract, Xu et al. explored the expression profile of circRNAs in cervical adenocarcinoma. circEYA1 functions as a sponge of miR-582-3p to abolish the suppressive effect of miR-582-3p on CXCL14 and then inhibits cell viability. The findings may provide a potential diagnostic marker and therapeutic target for patients with cervical adenocarcinoma.

Secondary ice production (SIP) is an important physical phenomenon that results in an increase in the ice particle concentration and can therefore have a significant impact on the evolution of clouds. In this study, idealized simulations of a mesoscale convective system (MCS) were conducted using a high-resolution (250 m horizontal grid spacing) mesoscale model and a detailed bulk microphysics scheme in order to examine the impacts of SIP on the microphysics and dynamics of a simulated tropical MCS. The simulations were compared to airborne in situ and remote sensing observations collected during the “High Altitude Ice Crystals – High Ice Water Content” (HAIC-HIWC) field campaign in 2015. It was found that the observed high ice number concentration can only be simulated by models that include SIP processes. The inclusion of SIP processes in the microphysics scheme is crucial for the production and maintenance of the high ice water content observed in tropical convection. It was shown that SIP can enhance the strength of the existing convective updrafts and result in the initiation of new updrafts above the melting layer. Agreement between the simulations and observations highlights the impacts of SIP on the maintenance of tropical MCSs in nature and the importance of including SIP parameterizations in models.

Bolted joints are commonly used for assembling carbon fiber/resin composite structures. Since drilling may generate defects at hole edges which affect mechanical properties, it is of great engineering significance to develop proper repair methods to restore the mechanical properties of the defective parts. However, there are few studies on hole edge defects and their repair methods. Therefore, a novelty short fiber filling repair method was proposed to repair defective holes in this study. The mechanical properties of intact, defective and repaired countersunk composite joints were compared and investigated. Experimental tensile tests showed that defective joints had lower initial stiffness and failure loads compared to intact joints, while the mechanical properties were effectively restored after repair. Three-dimensional finite element models were also established to analyze the damage process of the joints. Results of numerical modelling were consistent with the experimental results. The simulations showed that changes in contact behaviors and local deformations caused by hole edge defects led to the low initial stiffness and stiffness transition point of the joint, while this phenomenon was reduced after repair. Additionally, despite different joint types, laminate failure mainly occurred around the hole and countersink.

Phased-array radar (PAR) technology offers the flexibility of sampling the storm and clear-air regions with different update times. As such, the radial velocity from clear-air regions, typically with a lower signal-to-noise ratio, can be measured more accurately. In this work, observing system simulation experiments are conducted to explore the potential value of assimilating clear-air radial velocity observations to improve numerical prediction of supercell thunderstorms. Synthetic PAR observations of a splitting supercell are assimilated at different life cycle stages using an ensemble Kalman filter. Results show that assimilating environmental clear-air radial velocity can reduce wind errors in the near-storm environment and within the precipitation region. Improvements in the forecast are seen at different stages, especially for the forecast after 30 min. After assimilating clear-air radial velocity observations, the probabilities of updraft helicity and precipitation within the corresponding swaths of the truth simulation increase up to 30%–40%. Additional diagnostics suggest that the more accurate track forecast, stronger vertical motion, and better-maintained supercell can be attributed to the better analysis and prediction of the mean environmental winds and linear and nonlinear dynamic forces. Consequently, assimilating clear-air radial velocity produces accurate storm structure (rotating updrafts), updraft size, and storm track, and improves the surface accumulated precipitation forecast. The performance of forecasts with a higher frequency of assimilating clear-air radial velocity does not show systematic improvement. These results highlight the potential of assimilating clear-air radial velocity observations to improve numerical weather prediction forecasts of supercell thunderstorms.

Marine flavobacteria are specialists for polysaccharide degradation. They dominate in habitats enriched with polysaccharides, but are also prevalent in pelagic environments where polysaccharides are less available. These niches are likely occupied by distinct lineages, but evolutionary processes underlying their niche differentiation remain elusive. Here, genomic analyses and physiological assays indicate that the sister flavobacteria lineages Leeuwenhoekiella and Nonlabens likely explore polysaccharide-rich macroalgae and polysaccharide-poor pelagic niches respectively. Phylogenomic analyses inferred that the niche separation likely occurred anciently and coincided with increased sequence evolutionary rate in Nonlabens compared with Leeuwenhoekiella. Further analyses ruled out the known mechanisms likely driving evolutionary rate acceleration, including reduced selection efficiency, decreased generation time and increased mutation rate. In particular, the mutation rates were determined using an unbiased experimental method, which measures the present-day populations and may not reflect ancestral populations. These data collectively lead to a new hypothesis that an ancestral and transient mutation rate increase resulted in evolutionary rate increase in Nonlabens. This hypothesis was supported by inferring that gains and losses of genes involved in SOS response, a mechanism known to drive transiently increased mutation rate, coincided with evolutionary rate acceleration. Our analyses highlight the evolutionary mechanisms underlying niche differentiation of flavobacteria lineages.

We have witnessed great success of Single Image Super-Resolution (SISR) with convolutional neural networks (CNNs) in recent years. However, most existing Super-Resolution (SR) networks fail to utilize the multi-scale features of low-resolution (LR) images to further improve the representation capability for more accurate SR. In addition, most of them do not exploit the hierarchical features across networks for the final reconstruction. In this paper, we propose a novel multi-scale feature fusion residual network (MSFFRN) to fully exploit image features for SISR. Based on the residual learning, we propose a multi-scale feature fusion residual block (MSFFRB) with multiple intertwined paths to adaptively detect and fuse image features at different scales. Furthermore, the outputs of each MSFFRB and the shallow features are used as the hierarchical features for global feature fusion. Finally, we recover the high-resolution image based on the fused global features. Extensive experiments on four standard benchmarks demonstrate that our MSFFRN achieves better accuracy and visually pleasing than the current state-of-the-art methods.

High ice water content (HIWC) regions in tropical deep convective clouds, composed of high concentrations of small ice crystals, were not reproduced by Weather Research and Forecasting (WRF) model simulations at 1 km horizontal grid spacing using four different bulk microphysics schemes (i.e., the WRF single‐moment 6‐class microphysics scheme (WSM6), the Morrison scheme and the Predicted Particle Properties (P3) scheme with one- and two-ice options) for conditions encountered during the High Altitude Ice Crystals (HAIC) and HIWC experiment. Instead, overestimates of radar reflectivity and underestimates of ice number concentrations were realized. To explore formation mechanisms for large numbers of small ice crystals in tropical convection, a series of quasi-idealized WRF simulations varying the model resolution, aerosol profile, and representation of secondary ice production (SIP) processes are conducted based on an observed radiosonde released at Cayenne during the HAIC-HIWC field campaign. The P3 two-ice category configuration, which has two “free” ice categories to represent all ice-phase hydrometeors, is used. Regardless of the horizontal grid spacing or aerosol profile used, without including SIP processes the model produces total ice number concentrations about 2 orders of magnitude less than observed at −10 ∘C and about an order of magnitude less than observed at −30 ∘C but slightly overestimates the total ice number concentrations at −45 ∘C. Three simulations including one of three SIP mechanisms separately (i.e., the Hallett–Mossop mechanism, fragmentation during ice–ice collisions, and shattering of freezing droplets) also do not replicate observed HIWCs, with the results of the simulation including shattering of freezing droplets most closely resembling the observations. The simulation including all three SIP processes produces HIWC regions at all temperature levels, remarkably consistent with the observations in terms of ice number concentrations and radar reflectivity, which is not replicated using the original P3 two-ice category configuration. This simulation shows that primary ice production plays a key role in generating HIWC regions at temperatures <-40 ∘C, shattering of freezing droplets dominates ice particle production in HIWC regions at temperatures between −15 and 0 ∘C during the early stage of convection, and fragmentation during ice–ice collisions dominates at temperatures between −15 and 0 ∘C during the later stage of convection and at temperatures between −40 and −20 ∘C over the whole convection period. This study confirms the dominant role of SIP processes in the formation of numerous small crystals in HIWC regions.

Plants are affected by changes in light and adaptation mechanisms can affect secondary metabolite synthesis. In this study, the physiological response and regulation of the coumarin biosynthetic pathway of Angelica dahurica to different light intensities (natural light (CK), shade rate 50% (L1), shade rate 70% (L2), and shade rate 90% (L3)) were examined. The chlorophyll content, level of the enzymes of the antioxidant system, extent of lipid peroxidation, and concentrations of the osmoregulatory solute levels were determined in potted plants. Root transcriptome under different light intensities was sequenced using high-throughput technology, and differentially expressed genes (DEGs) related to coumarin biosynthesis were analyzed by quantitative real-time PCR (qRT-PCR). With increasing shade, Chl a, Chl b, Chl a + b, and Chl a/b content increased, while the Chl a/b ratio decreased. The antioxidant enzyme system activity and extent of membrane lipid peroxidation increased. The soluble protein (SP) and proline (Pro) content decreased with the reduction in the light intensity, and soluble sugar (SS) content was found to be highest at 50% shade. The RNA-seq analysis showed that 9388 genes were differentially expressed in the L3 group (7561 were upregulated and 1827 were downregulated). In both the L1 and L2 groups, DEGs were significantly enriched in “Ribosome biosynthesis”; meanwhile, in the L3 group, the DEGs were significantly enriched in “Amino and ribonucleotide sugar metabolism” in KEGG metabolic pathway analysis. Additionally, 4CL (TRINITY_DN40230_c0_g2) and COMT (TRINITY_DN21272_c0_g1) of the phenylpropanoid metabolic pathway were significantly downregulated in the L3 group. In conclusion, A. dahurica grew best under 50% shade and the secondary-metabolite coumarin biosynthetic pathway was inhibited by 90% shade, affecting the yield and quality of medicinal compounds.

In recent years, with the development of geological prospecting from shallow ore to deep and hidden ore, the difficulty of prospecting is increasing day by day, so the application of computer technology and new methods of geological and mineral exploration is paid more and more attention. The mining and prediction of geological prospecting information based on deep learning have become the frontier field of earth science. However, as a deep artificial intelligence algorithm, deep learning still has many problems to be solved in the big data mining and prediction of geological prospecting, such as the small number of training samples of geological and mineral images, the difficulty of building deep learning network models, and the universal applicability of deep learning models. In this paper, the training samples and convolutional neural network models suitable for geochemical element data mining are constructed to solve the above problems, and the model is successfully applied to the prediction research of gold, silver, lead and zinc polymetallic metallogenic areas in South China. Taking the Pangxidong research area in the west of Guangdong Province as an example, this paper carries out prospecting target prediction research based on a 1:50000 stream sediment survey original data. Firstly, the support vector machine (SVM) model and statistical method were used to determine the ore-related geochemical element assemblage. Secondly, the experimental data of geochemical elements were augmented and a dataset was established. Finally, ResNet-50 neural network model is used for data training and prediction research. The experimental results show that the areas numbered 9, 29, 38, 40, 95, 111, 114, 124, 144 have great metallogenic potential, and this method would be a promising tool for metallogenic prediction. By applying the ResNet-50 neural network in metallogenic prediction, it can provide a new idea for the future exploration of mineral resources. In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of B area, another deposit research area in South China. The results show that 100% of the prediction area obtained by using the proposed method covers the known ore deposit area. This model also provides method support for further delineating the prospecting target area in study area B.