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Fractured carbonate reservoirs are significantly developed in the eastern area of the Amu Darya Right Bank. However, their types, distributions, and fracture characteristics remain unclear. This uncertainty complicates reservoir prediction and hampers exploration and development processes. Given the strong correlation between fracture development and productivity, analyzing fractures is crucial. Comprehensive evaluation and prediction methods for fractured reservoirs are essential for advancing the oil and gas industry. Based on core and geological data analyses, it finds that these reservoirs exhibit low porosity and low to ultra-low permeability. By employing conventional logging alongside specialized methods, such as electrical imaging, nuclear magnetic resonance, and far detection logging, fractures and their effectiveness can be identified and evaluated, clarifying the characteristics of reservoir spaces. Constrained by the results from core and logging analyses, seismic single attribute analysis techniques is applied to predict fractures in the HX block of Amu Darya. To mitigate the limitations of single-attribute analysis, utilize a well-supervised BP neural network method for comprehensive fracture prediction. This multi-attribute approach increases the fracture prediction probability from less than 70%–72.7%. By integrating geological understanding and well logging, and considering the influence of lithology and structure on the reservoir, synthesize the fracture prediction results to optimally select favorable areas.

Summary: Although diverse microorganisms can remove ammonium and nitrate simultaneously, their metabolic mechanisms are not well understood. Paracoccus denitrificans R-1 showed the maximal NH4+ removal rate 9.94 mg L−1·h−1 and 2.91 mg L−1·h−1 under aerobic and anaerobic conditions, respectively. Analysis of the nitrogen balance calculation and isotope tracing experiment indicated that NH4+ was consumed through assimilation. The maximal NO3− removal rate of strain R-1 was 18.05 and 19.76 mg L−1·h−1 under aerobic and anaerobic conditions, respectively. The stoichiometric consumption ratio of acetate to nitrate was 0.902 and NO3− was reduced to N2 for strain R-1 through 15NO3− isotopic tracing experiment, which indicated a respiratory process coupled with the oxidation of electron donors. Genomic analysis showed that strain R-1 contained genes for ammonium assimilation and denitrification, which effectively promoted each other. These findings provide insights into microbial nitrogen transformation and facilitate the simultaneous removal of NH4+ and NO3− in a single reactor.

ABSTRACT In the nitrogen biogeochemical cycle, the reduction of nitrous oxide (N2O) to N2 by N2O reductase, which is encoded by nosZ gene, is the only biological pathway for N2O consumption. In this study, we successfully isolated a strain of denitrifying Paracoccus denitrificans R-1 from sewage treatment plant sludge. This strain has strong N2O reduction capability, and the average N2O reduction rate was 5.10 ± 0.11 × 10−9 µmol·h−1·cell−1 under anaerobic condition in a defined medium. This reduction was accompanied by the stoichiometric consumption of acetate over time when N2O served as the sole electron acceptor and the reduction can yield energy to support microbial growth, suggesting that microbial N2O reduction is related to the energy generation process. Genomic analysis showed that the gene cluster encoding N2O reductase of P. denitrificans R-1 was composed of nosR, nosZ, nosD, nosF, nosY, nosL, and nosZ, which was identified as that in other strains in clade I. Respiratory inhibitors test indicated that the pathway of electron transport for N2O reduction was different from that of the traditional electron transport chain for aerobic respiration. Cu2+, silver nanoparticles, O2, and acidic conditions can strongly inhibit the reduction, whereas NO3- or NH4+ can promote it. These findings suggest that modular N2O reduction of P. denitrificans R-1 is linked to the electron transport and energy conservation, and dissimilatory N2O reduction is a form of microbial anaerobic respiration.IMPORTANCENitrous oxide (N2O) is a potent greenhouse gas and contributor to ozone layer destruction, and atmospheric N2O has increased steadily over the past century due to human activities. The release of N2O from fixed N is almost entirely controlled by microbial N2O reductase activities. Here, we investigated the ability to obtain energy for the growth of Paracoccus denitrificans R-1 by coupling the oxidation of various electron donors to N2O reduction. The modular N2O reduction process of denitrifying microorganism not only can consume N2O produced by itself but also can consume the external N2O generated from biological or abiotic pathways under suitable condition, which should be critical for controlling the release of N2O from ecosystems into the atmosphere.

Background & Aims: Siglec-H is a receptor specifically expressed in mouse plasmacytoid dendritic cells (pDCs), which functions as a negative regulator of interferon-α production and plays a critical role in pDC maturation to become antigen-presenting cells. The function of pDCs in autoimmune and inflammatory diseases has been reported. However, the effect of Siglec-H expression in pDCs in liver inflammation and diseases remains unclear. Methods: Using the model of concanavalin A–induced acute liver injury (ALI), we investigated the Siglec-H/pDCs axis during ALI in BDCA2 transgenic mice and Siglec-H-/- mice. Anti-BDCA2 antibody, anti-interleukin (IL)-21R antibody, and Stat3 inhibitor were used to specifically deplete pDCs, block IL21 receptor, and inhibit Stat3 signaling, respectively. Splenocytes and purified naive CD4 T cells and bone marrow FLT3L-derived pDCs were cocultured and stimulated with phorbol myristate acetate/ionomycin and CD3/CD28 beads, respectively. Results: Data showed that specific depletion of pDCs aggravated concanavalin A–induced ALI. Remarkably, alanine aminotransferase, hyaluronic acid, and proinflammatory cytokines IL6 and tumor necrosis factor-α levels were lower in the blood and liver of Siglec-H knockout mice. This was associated with attenuation of both interferon-γ/Th1 response and Stat1 signaling in the liver of Siglec-H knockout mice while intrahepatic IL21 and Stat3 signaling pathways were upregulated. Blocking IL21R or Stat3 signaling in Siglec-H knockout mice restored concanavalin A–induced ALI. Finally, we observed that the Siglec-H-null pDCs exhibited immature and immunosuppressive phenotypes (CCR9LowCD40Low), resulting in reduction of CD4 T-cell activation and promotion of IL21+CD4 T cells in the liver. Conclusions: During T-cell-mediated ALI, Siglec-H-null pDCs enhance immune tolerance and promote IL21+CD4 T cells in the liver. Targeting Siglec-H/pDC axis may provide a novel approach to modulate liver inflammation and disease.

Despite being a common therapy for hepatocellular carcinoma (HCC), insufficient thermal ablation can leave behind tumor residues that can cause recurrence. This is believed to augment M2 inflammatory macrophages that usually play a pro-tumorigenic role. To address this problem, we designed d-mannose-chelated iron oxide nanoparticles (man-IONPs) to polarize M2-like macrophages into the antitumor M1 phenotype. In vitro and in vivo experiments demonstrated that man-IONPs specifically targeted M2-like macrophages and accumulated in peri-ablation zones after macrophage infiltration was augmented under insufficient microwave ablation (MWA). The nanoparticles simultaneously induced polarization of pro-tumorigenic M2 macrophages into antitumor M1 phenotypes, enabling the transformation of the immunosuppressive microenvironment into an immunoactivating one. Post-MWA macrophage polarization exerted robust inhibitory effects on HCC progression in a well-established orthotopic liver cancer mouse model. Thus, combining thermal ablation with man-IONPs can salvage residual tumors after insufficient MWA. These results have strong potential for clinical translation.

Summary: Background: Ultrasonography is the most widely used technique to diagnose echinococcosis; however, challenges in using this technique and the demand on medical resources, especially in low-income or remote areas, can delay diagnosis. We aimed to develop a deep convolutional neural network (DCNN) model based on ultrasonography to identify echinococcosis and its types, especially alveolar echinococcosis. Methods: This retrospective, large-scale, multicentre study used ultrasound images from patients assessed at 84 hospitals in China, obtained between Jan 1, 2002, and Dec 31, 2021. Patients with a diagnosis of cystic echinococcosis, alveolar echinococcosis, or seven other types of focal liver lesions were included. We tested ResNet-50, ResNext-50, and VGG-16 as the backbone network architecture for a classification DCNN model and input the perinodular information from the ultrasound images. We trained and validated the DCNN model to diagnose and classify echinococcosis using still greyscale ultrasound images of focal liver lesions in four stages: differentiating between echinococcosis and other focal liver lesions (stage one); differentiating cystic echinococcosis, alveolar echinococcosis, and other focal liver lesions (stage two); differentiating cystic echinococcosis, alveolar echinococcosis, benign other focal liver lesions, and malignant focal liver lesions (stage three); and differentiating between active and transitional cystic echinococcosis and inactive cystic echinococcosis (stage four). We then tested the algorithm on internal, external, and prospective test datasets. The performance of DCNN was also compared with that of 12 radiologists recruited between Jan 15, 2022, and Jan 28, 2022, from Qinghai, Xinjiang, Anhui, Henan, Xizang, and Beijing, China, with different levels of diagnostic experience for echinococcosis and other focal liver lesions in a subset of ultrasound data that were randomly chosen from the prospective test dataset. The study is registered at ClinicalTrials.gov (NCT03871140). Findings: The study took place between Jan 1, 2002, and Dec 31, 2021. In total, to train and test the DCNN model, we used 9631 liver ultrasound images from 6784 patients (2819 [41·7%] female patients and 3943 [58·3%] male patients) from 87 Chinese hospitals. The DCNN model was trained with 6328 images, internally validated with 984 images, and tested with 2319 images. The ResNet-50 network architecture outperformed VGG-16 and ResNext-50 and was generalisable, with areas under the receiver operating characteristic curve (AUCs) of 0·982 (95% CI 0·960–0·994), 0·984 (0·972–0·992), and 0·913 (0·886–0·935) in distinguishing echinococcosis from other focal liver lesions; 0·986 (0·966–0·996), 0·962 (0·946–0·975), and 0·900 (0·872–0·924) in distinguishing alveolar echinococcosis from cystic echinococcosis and other focal liver lesions; and 0·974 (0·818–1·000), 0·956 (0·875–0·991), and 0·944 (0·844–0·988) in distinguishing active and transitional cystic echinococcosis from inactive echinococcosis in the three test datasets. Specifically, in patients with the hepatitis B or hepatitis C virus, the model could distinguish alveolar echinococcosis from hepatocellular carcinoma with an AUC of 0·892 (0·812–0·946). In identifying echinococcosis, the model showed significantly better performance compared with senior radiologists from a high-endemicity area (AUC 0·942 [0·904–0·967] vs 0·844 [0·820–0·866]; p=0·027) and improved the diagnostic ability of junior, attending, and senior radiologists before and after assistance with AI with comparison of AUCs of 0·743 (0·714–0·770) versus 0·850 (0·826–0·871); p

Denitrification is vital to nitrogen removal and N2O release in ecosystems; in this regard, paddy soils exhibit strong denitrifying ability. However, the underlying mechanism of N2O emission from denitrification in paddy soils is yet to be elucidated. In this study, the potential N2O emission rate, enzymatic activity for N2O production and reduction, gene abundance, and community composition during denitrification were investigated using the 15N isotope tracer technique combined with slurry incubation, enzymatic activity detection, quantitative polymerase chain reaction (qPCR), and metagenomic sequencing. Results of incubation experiments showed that the average potential N2O emission rates were 0.51 ± 0.20 μmol⋅N⋅kg–1⋅h–1, which constituted 2.16 ± 0.85% of the denitrification end-products. The enzymatic activity for N2O production was 2.77–8.94 times than that for N2O reduction, indicating an imbalance between N2O production and reduction. The gene abundance ratio of nir to nosZ from qPCR results further supported the imbalance. Results of metagenomic analysis showed that, although Proteobacteria was the common phylum for denitrification genes, other dominant community compositions varied for different denitrification genes. Gammaproteobacteria and other phyla containing the norB gene without nosZ genes, including Actinobacteria, Planctomycetes, Desulfobacterota, Cyanobacteria, Acidobacteria, Bacteroidetes, and Myxococcus, may contribute to N2O emission from paddy soils. Our results suggest that denitrification is highly modular, with different microbial communities collaborating to complete the denitrification process, thus resulting in an emission estimation of 13.67 ± 5.44 g N2O⋅m–2⋅yr–1 in surface paddy soils.

Intricate associations between rhizosphere microbial communities and plants play a critical role in developing and maintaining of soil ecological functioning. Therefore, understanding the assembly patterns of rhizosphere microbes in different plants and their responses to environmental changes is of great ecological implications for dynamic habitats. In this study, a developing mid-channel bar was employed in the Yangtze River to explore the assembly processes of rhizosphere fungal communities among various plant species using high-throughput sequencing-based null model analysis. The results showed a rare significant variation in the composition and alpha diversity of the rhizosphere fungal community among various plant species. Additionally, the soil properties were found to be the primary drivers instead of plant species types. The null model analysis revealed that the rhizosphere fungal communities were primarily driven by stochastic processes (i.e., undominated processes of ecological drift), and the predominance varied with various plant species. Moreover, the assembly processes of rhizosphere fungal communities were significantly related to the changes in soil properties (i.e., soil total carbon, total nitrogen, organic matter, and pH). The co-occurrence network analysis revealed that many keystone species belonged to unclassified fungi. Notably, five network hubs were almost unaffected by the measured soil properties and aboveground plant traits, indicating the effect of stochastic processes on the rhizosphere fungal community assembly. Overall, these results will provide insights into the underlying mechanisms of fungal community assembly in the rhizosphere soils, which are significant for maintaining the functional stability of a developing ecosystem.

Canyons in carbonate depositional settings, as important elements of the source-to-sink system, remain poorly studied compared to those in siliciclastic depositional environments. The latest high-resolution three-dimensional seismic data, well logs, and core data at the eastern edge of the Precaspian Basin are used to investigate the geomorphology, infillings, and depositional process of a unique carbonate-filled canyon in the Carboniferous KT-II formation parallel to the carbonate platform, which is distinct from other slope-perpendicular canyons. The canyon has a total length of more than 52.3 km with a nearly N-S orientation and an S-shaped geometry, and the whole canyon can be divided into three segments by two knickpoints. The slope-parallel orientation of the canyon is mainly controlled by the palaeogeomorphology and reverse faults. Due to the collision of the Kazakh and European plates in the early-middle Visean (early Carboniferous), the canyon was formed in a northern tilted, elongated, and restricted palaeotopographic feature between uplifts. The development of reverse faults related to tectonic movement controlled the variations in the width of the canyon and the positions of the knickpoints. Tectonics controlled the orientation and formation of the canyon, while sedimentary processes contributed to its infilling. The well-seismic tie analysis indicated two distinct periods of the canyon fillings, Ss1 and Ss2, which were separated by a second-order sequence boundary. The lower part contained sediments supplied by both sides of the canyon through channels or gullies, and the upper part was dominated by a carbonate platform that prograded from the eastern side of the canyon. The evolution of the canyon can be subdivided into three stages. The increasing stage was mainly characterized by significant upslope erosion through headward retrogressive mass failures in the slope-parallel confined negative relief to form the canyon during the lowstand system tract of Ss1. Subsequently, in the early filling stage, the carbonate factory was productive during the highstand, and massive excess carbonate sediments were transported into the adjacent canyon by channels or gullies on both sides and deposited. The canyon was basically filled, and the morphology became much gentler. During the subsequent late filling stage, the carbonate platform was flooded again during the highstand, and the production rates of the carbonate factory greatly increased. The lateral progradation of carbonate platforms accelerated on the canyon of the early filling stage and further into the inner sag.

Abstract Anaerobic ammonium oxidation (anammox) process has been acknowledged as an environmentally friendly and time-saving technique capable of achieving efficient nitrogen removal. However, the community of nitrification process in anammox-inoculated wastewater treatment plants (WWTPs) has not been elucidated. In this study, ammonia oxidation (AO) and nitrite oxidation (NO) rates were analyzed with the incubation of activated sludge from Xinfeng WWTPs (Taiwan, China), and the community composition of nitrification communities were investigated by high-throughput sequencing. Results showed that both AO and NO had strong activity in the activated sludge. The average rates of AO and NO in sample A were 6.51 µmol L−1 h−1 and 6.52 µmol L−1 h−1, respectively, while the rates in sample B were 14.48 µmol L−1 h−1 and 14.59 µmol L−1 h−1, respectively. The abundance of the nitrite-oxidizing bacteria (NOB) Nitrospira was 0.89–4.95 × 1011 copies/g in both samples A and B, the abundance of ammonia-oxidizing bacteria (AOB) was 1.01–9.74 × 109 copies/g. In contrast, the abundance of ammonia-oxidizing archaea (AOA) was much lower than AOB, only with 1.28–1.53 × 105 copies/g in samples A and B. The AOA community was dominated by Nitrosotenuis, Nitrosocosmicus, and Nitrososphaera, while the AOB community mainly consisted of Nitrosomonas and Nitrosococcus. The dominant species of Nitrospira were Candidatus Nitrospira defluvii, Candidatus Nitrospira Ecomare2 and Nitrospira inopinata. In summary, the strong nitrification activity was mainly catalyzed by AOB and Nitrospira, maintaining high efficiency in nitrogen removal in the anammox-inoculated WWTPs by providing the substrates required for denitrification and anammox processes.

Summary: In the biological nitrogen cycle, nitrite oxidation is performed by nitrite oxidation bacteria, of which Nitrospira is widespread and diverse. Communities of Nitrospira were collected at 25–1500 m depths in the South China Sea. Phylogenetic diversity, community composition, and environmental factors were investigated using high-throughput sequencing targeting the nxrB gene and statistical analyses. The community composition of Nitrospira varied spatially and by depth. Among the 24 OTUs with relatively high abundance, 70% were unclassified and not affiliated with the known Nitrospira genus, suggesting a previously unrecognized high diversity of marine Nitrospira. Five known Nitrospira genera were detected, of which the common marine Nitrospira marina was not the dominant species, whereas Candidatus Nitrospira lenta and Candidatus Nitrospira defluvii dominated in shallow habitats. Comammox Candidatus Nitrospira nitrosa was discovered in the marine ecosystem. The niche differentiation of versatile Nitrospira species was mainly shaped by nitrate, temperature, and DO.

Abstract Lactic acid bacteria (LAB) are a phylogenetically diverse group with the ability to convert soluble carbohydrates into lactic acid. Many LAB have a long history of safe use in fermented foods and are recognized as food-grade microorganisms. LAB are also natural inhabitants of the human intestinal tract and have beneficial effects on health. Considering these properties, LAB have potential applications as biotherapeutic vehicles to delivery cytokines, antigens and other medicinal molecules. In this review, we summarize the development of, and advances in, genome manipulation techniques for engineering LAB and the expected future development of such genetic tools. These methods are crucial for us to maximize the value of LAB. We also discuss applications of the genome-editing tools in enhancing probiotic characteristics and therapeutic functionalities of LAB.

Succession of microbial and plant communities is crucial for the development and the stability of soil ecological functions. The relative role of plant communities and environmental disturbance in shaping the microbial community in a newly established habitat remains unclear. In this study, a mid-channel bar (MCB) exposed to an environmental disturbance gradient in the Yangtze River was studied to explore the effects of such disturbance and plant community traits on the succession of the soil microbial community. Bulk and rhizospheric soils were collected from the MCB and classified according to their level of exposure to environmental disturbance: head, central and tail. These subsequently underwent high-throughput sequencing and interdomain ecological network (IDEN) analysis to identify and characterize the predominant microbial groups present in the soils at each disturbance level. Furthermore, at each site, the presence and distribution of the plant community was also noted. The present study demonstrated that both bulk soil nutrients and plant community exhibited significant spatial distribution dependent on the level of disturbance and this influenced the composition of the microbial community. In less eroded parts of the MCB, i.e., the central, nutrients accumulated, promoting growths of plants. This in turn encouraged a more diverse microbial community, dominated by the bacterial genus Pseudarthrobacter. Plant showed a stronger association with bulk soil microbial communities compared to rhizosphere soil microbial communities. Particularly, Triarrhena sacchariflora and Hemarthria altissima, present in sites of low disturbance, exhibiting a more extensive plant-microbe association. They thus played a key role in shaping the soil microbial community. In general, however, plant species did not directly determine the composition of the bacterial community, but instead altered the nutritive state of the soil to promote microbial growth. Such findings are of significant value for conservation practices of newly formed ecosystems, which requires an integrated understanding of the role of environmental disturbance and plants on soil microbial community assemblage.

Denitrification is an important pathway for nitrogen sink and N2O emissions, but little is known about the ecological distribution of key functional genes of denitrification and their potential N2O emissions in marine sediments. In this study, we analyzed the abundance, ecological distribution, and diversity of key functional genes (nir and nosZ) for denitrification in the northern South China Sea (SCS) surface sediments. Our results showed that the gene abundances varied from 105 to 108 and from 106 to 107 copies·g-1 for the nirS and nirK, respectively. The nosZ II/nosZ I gene abundance ratios were 1.28–9.88 in shallow-sea and deep-sea sediments, suggesting that the nosZ II gene should play a dominant role in N2O reduction in the northern SCS sediments. Moreover, the significantly higher abundance ratios of nir/nosZ in deep-sea surface sediments implied that there might be stronger N2O emissions potential in deep-sea sediments than in shallow-sea sediments. The ecological distribution profiles of the nirS, nosZ I, and nosZ II gene communities varied with water depth, and denitrification genes in shallow-sea and deep-sea sediments differed in their sensitivity to environmental factors. Water temperature was the major factor affecting both the abundance and the community distribution of the nirS gene in deep-sea sediments. Nitrate was the major factor shaping the community of nosZ I and nosZ II genes in shallow-sea sediments. Our study provides a pattern of ecological distribution and diversity for the nir and nosZ genes and emphasizes the role of these key functional genes in potential N2O emissions of the northern SCS surface sediments.

It is crucial to monitor the status of aquaculture objects in recirculating aquaculture systems (RASs). Due to their high density and a high degree of intensification, aquaculture objects in such systems need to be monitored for a long time period to prevent losses caused by various factors. Object detection algorithms are gradually being used in the aquaculture industry, but it is difficult to achieve good results for scenes with high density and complex environments. This paper proposes a monitoring method for Larimichthys crocea in a RAS, which includes the detection and tracking of abnormal behavior. The improved YOLOX-S is used to detect Larimichthys crocea with abnormal behavior in real time. Aiming to solve the problems of stacking, deformation, occlusion, and too-small objects in a fishpond, the object detection algorithm used is improved by modifying the CSP module, adding coordinate attention, and modifying the part of the structure of the neck. After improvement, the AP50 reaches 98.4% and AP50:95 is also 16.2% higher than the original algorithm. In terms of tracking, due to the similarity in the fish’s appearance, Bytetrack is used to track the detected objects, avoiding the ID switching caused by re-identification using appearance features. In the actual RAS environment, both MOTA and IDF1 can reach more than 95% under the premise of fully meeting real-time tracking, and the ID of the tracked Larimichthys crocea with abnormal behavior can be maintained stably. Our work can identify and track the abnormal behavior of fish efficiently, and this will provide data support for subsequent automatic treatment, thus avoiding loss expansion and improving the production efficiency of RASs.

Summary: The significant primary production by ammonia-oxidizing archaea (AOA) in the ocean was reported, but the carbon fixation process of AOA and its community composition along the water depth remain unclear. Here, we investigated the abundance, community composition, and potential carbon fixation of AOA in water columns of the South China Sea. Higher abundances of the amoA and accA genes of AOA were found below the euphotic zone. Similarly, higher carbon fixation potential of AOA, evaluated by the ratios of amoA to accA gene, was also observed below euphotic zone and the ratios increased with increasing water depth. The vertical niche differentiation of AOA was further evidenced, with the dominant genus shifting from Nitrosopelagicus in the epipelagic zone to uncultured genus in the meso- and bathypelagic zones. Our findings highlight the higher carbon fixation potential of AOA in deep water and the significance of AOA to the ocean carbon budget.

High-yielding oil wells were recently found in the first member of Paleogene Shahejie Formation, the Binhai area of Qikou Sag, providing an example of medium- and deep-buried high-quality reservoirs in the central part of a faulted lacustrine basin. By using data of cores, cast thin sections, scanning electron microscope and physical property tests, the sedimentary facies, physical properties and main control factors of the high-quality reservoirs were analyzed. The reservoirs are identified as deposits of slump-type sub-lacustrine fans, which are marked by muddy fragments, slump deformation structure and Bouma sequences in sandstones. They present mostly medium porosity and low permeability, and slightly medium porosity and high permeability. They have primary intergranular pores, intergranular and intragranular dissolution pores in feldspar and detritus grains, and structural microcracks as storage space. The main factors controlling the high quality reservoirs are as follows: (1) Favorable sedimentary microfacies of main and proximal distributary gravity flow channels. The microfacies with coarse sediment were dominated by transportation and deposition of sandy debris flow, and the effect of deposition on reservoir properties decreases with the increase of depth. (2) Medium texture maturity. It is shown by medium-sorted sandstones that were formed by beach bar sediment collapsing and redepositing, and was good for the formation of the primary intergranular pores. (3) High content of intermediate-acid volcanic rock detritus. The reservoir sandstone has high content of detritus of various components, especially intermediate-acid volcanic rock detritus, which is good for the formation of dissolution pores. (4) Organic acid corrosion. It was attributed to hydrocarbon maturity during mesodiagenetic A substage. (5) Early-forming and long lasting overpressure. A large-scale overpressure compartment was caused by under-compaction and hydrocarbon generation pressurization related to thick deep-lacustrine mudstone, and is responsible for the preservation of abundant primary pores. (6) Regional transtensional tectonic action. It resulted in the structural microcracks.

The oxidation of nitrite to nitrate as the second step of nitrification is vital for the global nitrogen cycle, and the genus Nitrospira is the most widespread nitrite-oxidizing bacteria (NOB) in diverse natural ecosystems. However, our understanding of Nitrospira distribution and their response to dynamic environmental parameters is still limited in the subterranean estuary (STE), a special environment of the mixed zone between land and sea. In this study, Nitrospira communities were collected within 5–10 cm depth intervals of sediments with the physiochemical gradients along the transections of the Daya Bay (DYB) from the landward member to the seaward member. The abundance, community structure of Nitrospira, and their potentially influencing environmental factors were investigated using Q-PCR, the high throughput sequencing targeting Nitrospira nxrB gene, and biostatistics analyses. The abundance of Nitrospira showed uniform distribution along the transections, but significantly decreasing variations were observed from the surface (20 cm) of sediments. The community diversity and structure of Nitrospira also displayed a remarkable vertical distribution in the DYB (STE), although no significant differences were observed along the transections of STE. Nitrospira marina (N. marina) predominated in the sediments, Candidatus Nitrospira bockiana (Ca. N. bockiana), and Candidatus Nitrospira defluvii (Ca. N. defluvii) were the main Nitrospira species, and Candidatus Nitrospira lenta (Ca. N. lenta) and Nitrospira japonica (N. japonica) also existed with relatively low abundance in the DYB (STE). These findings revealed that Nitrospira species (Ca. N. defluvii, Ca. N. lenta, and N. japonica) derived from activated sludge was also widespread in natural habitats and deduced that the STE may be affected by the pollution derived from terrigenous human activities. The statistical analysis combined with the STE dynamic variation indicated that dissolved organic carbon (DOC), salinity, and ammonium along the sediment depths attributed to the vertical community distribution of the Nitrospira species. In summary, the vertical distribution of Nitrospira and their response to the dynamic physicochemical parameters imply their important role in the nitrite oxidation of the STE and provide insights into the niche differentiation and diversely physiologic metabolism of NOB.

As a result of climate change and human activities, water resources in the Xiangjiang River Basin (XRB) are subject to seasonal and regional shortages. However, previous studies have lacked assessment of the spatiotemporal evolution of water yield in the XRB at seasonal and monthly scales and quantitative analysis of the driving forces of climate change and land use on water-yield change. Quantitative evaluation of water yield in the XRB is of great significance for optimizing water-resource planning and allocation and maintaining ecological balance in the basin. In this paper, the seasonal water-yield InVEST model and modified Morris sensitivity analysis were combined to study the characteristics of monthly water yield in the XRB. Seventeen attributes were identified using the Budyko framework. The results show that: (1) the water yield of the XRB showed an increase trend from northeast to southwest from 2006 to 2020; (2) the transfer-in of unused land, grassland, woodland and farmland as well as the transfer-out of water and construction land have positive effects on the increase in water yield, and the change to construction land has the greatest impact on water yield; (3) water yield is positively correlated with NDVI and precipitation and negatively correlated with potential evapotranspiration; (4) climate change and land-use change contributed to water-yield changes of 67.08% and 32.92%, respectively.

In traditional machine learning algorithms, more labeled data are required in model training for better classification accuracy. However, collecting labeled data is an expensive task for real applications. In this paper, a transfer support vector machine (TSVM) technique is introduced to tackle this problem with application to the fault diagnosis of delta 3D printers. Transfer component analysis is first proposed to capture shared features for representing the source and target domains, by cross-domain feature extraction from less labeled data in the source domain and massive unlabeled data in the target domain. Support vector machine is then applied to the fault diagnosis using the transferred features of attitude signals. In the experiments, fault classification rate achieves 83.79% using only 6.7% of the dataset for model training. Compared with peer methods, the results show that the present TSVM exhibits the best performance in extracting the cross-domain feature and improves the accuracy of the 3D printer fault diagnosis.

Following the rapid development and the maturity of blockchain (BC) and the Internet of Things (IoT) technologies, this paper proposes two applications of a BC-IoT transaction model in the accounting field. The BC-IoT transaction model is capable of automatically collecting, uploading, and recording all relevant data in the enterprise transaction process under the premise of satisfying certain assumptions. This model does not require manual intervention at any point in the process; no data recorded on the distributed ledgers can be tampered with or revoked. The data collected using this model can be used as information on events, providing data support for the events approach to basic accounting theory. This practice responds to the important insight of sorter, who proposes the provision of accounting information in real time and on demand to substantially improve the usefulness of accounting information. This paper also identifies that BC technology can significantly improve the relevance, faithful representation, timeliness, comparability, and other aspects of accounting information quality.

Denitrification and anammox occur widely in aquatic ecosystems serving vital roles in nitrogen pollution removal. However, small waterbodies are sensitive to external influences; stormwater runoff carrying nutrients and oxygen, flows into waterbodies resulting in a disruption of geochemical and microbial processes. Nonetheless, little is known about how these short-term external inputs affect the microbial processes of nitrogen removal in small waterbodies. To investigate the effects of NO3−, NH4+, dissolved oxygen (DO) and organic C on microbial nitrogen removal in pond sediments, regulation experiments have been conducted using slurry incubation experiments and 15N tracer techniques in this study. It was demonstrated the addition of NO3− (50 to 800 μmol L−1) significantly promoted denitrification rates, as expected by Michaelis-Menten kinetics. Ponds with higher NO3− concentrations in the overlying water responded more greatly to NO3− additions. Moreover, N2O production was also promoted by such an addition of NO3−. Denitrification was significantly inhibited by the elevation of DO concentration from 0 to 2 mg L−1, after which no significant increase in inhibition was observed. Denitrification rates increased when organic C was introduced. Due to the abundant NH4+ in pond sediments, the addition demonstrated little influence on nitrogen removal. Moreover, anammox rates showed no significant changes to any amendment.

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

BackgroundUreteral stenosis after percutaneous microwave ablation (MWA) of renal tumor is a rare but severe complication, and its risk factors are not apparent.PurposeThis study aimed to investigate the risk factors for stenosis of ureter after MWA treatment of the renal tumor that is a rare complication.Materials and MethodsData of 211 patients who underwent MWA for the treatment of renal tumor were retrospectively analyzed from September 2006 to August 2019. Demographic characteristics, clinical features, ablation parameters, and outcomes were analyzed to find out the potential risk factors of this complication. P < 0.05 is considered significant.ResultsSix of 211 patients developed ureter stenosis, and the rate of this complication is 2.84%. The median time of emergence of hydronephrosis was 226 (range, 3–390) days. Univariate analysis shows the distance between ureter and tumor (P = 0.225) or ablation zone (P = 0.089) is not related to this complication. Postoperative urine routine (red blood cell, P = 0.001; white blood cell, P = 0.035) and R.E.N.A.L. score (P < 0.001) is related to this complication. But after multivariate logistic analysis, only R.E.N.A.L. score (P = 0.004) is associated with this complication. The location and growth pattern of tumor and the energy of ablation were not related to this complication independently.ConclusionThe stenosis of the ureter after MWA of renal tumor is not associated with the tumor size, location, or the distance between the ureter and tumor and ablation site independently. But R.E.N.A.L. score is associated with ureter stenosis after MWA for the treatment of renal tumor, which combines the information of location, depth, and size of tumor. Preoperative evaluation of the tumor is necessary for avoiding ureter stenosis. Further studies should focus on these risk factors of this complication.

Human activities have greatly influenced the natural nitrogen cycle, causing dramatic degradation of ecosystem function. Net anthropogenic nitrogen input (NANI) is an important factor contributing to the impact of human activities on the regional nitrogen cycle. Here, we analyzed the temporal and spatial variation of NANI in the Pearl River Basin of China between 1986 to 2015, and found that the total amount of NANI significantly increased from 3,362.25 kg N km-2 yr-1 to 8,071.15 kg N km-2 yr-1. Application of nitrogen fertilizers was the largest component of NANI in the Basin, accounting for 55.53% in the total NANI, followed by food/feed net nitrogen input (21.26%), atmospheric nitrogen deposition (12.95%), and crop nitrogen fixation (10.26%). Over the last three decades, nitrogen inputs from atmospheric nitrogen deposition have become the second largest source of NANI due to rapid industrialization and urbanization in the region. Regression analysis showed that the rapid growth of both GDP and population density were the main contributors to the increase of NANI. In addition, the increase in the number of red tides in the Pearl River Estuary was strongly correlated with NANI discharge (R2 = 0.90, p

Denitrification, an important process in microbial mediated nitrogen cycle, plays important roles in nitrogen loss in estuarine sediments. However, the function of denitrifiers in the estuarine subsurface sediments remained poorly understood. In this study, we analyzed the potential activity, abundance and community structure of nirS-type denitrifiers using 15N-labeled incubation quantitative-PCR and high throughput sequencing techniques in sediment cores from Pearl River Estuary (PRE). Results showed that subsurface sediments had nearly same level denitrification potential activity compare to surface sediments, although the abundance of nirS gene decreased sharply from surface to bottom in sediment cores. Meanwhile, nirS gene abundance exhibit significant temporal variations, which is consistent with denitrification potential activity. Moreover, the community structure and diversity of nirS-type denitrifiers in sediment cores exhibited remarkable temporal shift pattern. For spatial variation, no significant difference was observed of denitrifiers community structure in each sediment core from the surface to the subsurface, while there were significant different diversity characteristic among different cores. Redundancy analysis (RDA) showed that multiple environmental factors including salinity, pH, oxidation-reduction potential, nutrient content and organic substances synergistically shaped the diversity and distribution of nirS-type denitrifers in PRE sediments. Our results showed that nirS-type denitrifers played important roles in the nitrogen removal in subsurface sediments of PRE.

The identification of anammox bacteria is mostly relied on PCR with various marker genes. However, the community composition revealed by different marker genes and whether the marker genes influence the resulted community composition remain unclear. We compared the community structure of anammox bacteria in enriched and natural environments revealed by 16S rRNA and functional genes (hzo, hzsA and hzsB) from public database and published papers. The genus of Ca. Scalindua showed the lowest similarities with other genera, especially for the hzsA gene (66.9%-68.6%). The 16S rRNA gene is the most commonly used marker gene in natural habitats with 151 out 221 papers in total. The anammox bacterial community composition is distributed according to the source of habitat regardless the use of various marker genes. The role of marker gene is limited with explanatory of 5.4% for variance of community composition, versus 20.5% of habitat. The effect of marker gene is mainly acted on freshwater habitat, which shows significant different community composition revealed by 16S rRNA and hzo, with Ca. Brocadia and Ca. Jettenia as dominant genus, respectively.

Recently identified broadly neutralizing antibodies (bnAbs) show great potential for clinical interventions against HIV-1 infection. However, resistant strains may impose substantial challenges. Here, we report on the identification and characterization of a panel of HIV-1 strains with broad and potent resistance against a large number of bnAbs, particularly those targeting the CD4-binding site (CD4bs). Site-directed mutagenesis revealed that several key epitope mutations facilitate resistance and are located in the inner domain, loop D, and β23/loop V5/β24 of HIV-1 gp120. The resistance is largely correlated with binding affinity of antibodies to the envelope trimers expressed on the cell surface. Our results therefore demonstrate the existence of broadly resistant HIV-1 strains against CD4bs neutralizing antibodies. Treatment strategies based on the CD4bs bnAbs must overcome such resistance to achieve optimal clinical outcomes.

Subterranean estuaries (STEs) have been recognized as important ecosystems for the exchange of materials between the land and sea, but the microbial players of biogeochemical processes have not been well examined. In this study, we investigated the bacterial and archaeal communities within 10 cm depth intervals of a permeable sediment core (100 cm in length) collected from a STE located at Gloucester Point (GP-STE), VA, United States. High throughput sequencing of 16S rRNA genes and subsequent bioinformatics analyses were conducted to examine the composition, diversity, and potential functions of the sediment communities. The community composition varied significantly from the surface to a depth of 100 cm with up to 13,000 operational taxonomic units (OTUs) based on 97% sequence identities. More than 95% of the sequences consisted of bacterial OTUs, while the relative abundances of archaea, dominated by Crenarchaea, gradually increased with sediment core depth. Along the redox gradients of GP-STE, differential distribution of ammonia- and methane-oxidizing, denitrifying, and sulfate reducing bacteria was observed as well as methanogenic archaea based on predicted microbial functions. The aerobic-anaerobic transition zone (AATZ) had the highest diversity and abundance of microorganisms, matching with the predicted functional diversity. This indicates the AATZ as a hotspot of biogeochemical processes of STEs. The physical and geochemical gradients in different depths have attributed to vertical stratification of microbial community composition and function in the GP-STE.

Health assessment and risk factor identification represent the premise and foundation of scientific management and ecological restoration of urban lakes. Based on in-depth understanding of the nature–society duality of urban lakes, a framework for evaluating urban lake health was constructed, including four modules, namely, establishing an index system; determining the index weight; identifying risk factors; and a comprehensive lake health evaluation. Employing this framework, we evaluated Lianshi Lake, Beijing, classifying the lake condition as “sub-healthy”. Based on the evaluation data, we identified the health risk factors of the lake. We applied standard difference rate (SDR) and risk degree (Rd) (safe degree (Sd)) concepts, and classified the indices of risk areas employing the Pareto analysis method. Finally, we identified the lake residence period, landscape connectivity, and eutrophication as the major risk factors in Lianshi Lake. Three factors constitute the basis of ecosystem health and are key targets of ecological restoration: the lake residence period represents the hydrological and hydrodynamic characteristics of the lake; landscape connectivity is described from an ecological perspective, and represents the integrity of the lake ecosystem; and the eutrophication states describe the water quality characteristics and represent the availability of lake water. The results contribute to decision-making for comprehensive urban lake management.

Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time−frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique was used to visualize the output features for checking the effectiveness of the proposed algorithm in feature extraction. The results showed that the proposed GTL could improve classification rates under various working loads. Compared with other domain adaptation algorithms, the proposed method exhibited not only higher accuracy but faster convergence speed as well.

Anaerobic ammonium oxidation (anammox) has been proven to be an important nitrogen removal process in terrestrial ecosystems, particularly paddy soils. However, the contribution of anammox in acidic red soils to nitrogen loss has not been well-documented to date. Here, we investigated the activity, abundance, and distribution of anammox bacteria in red soils collected from nine provinces of Southern China. High-throughput sequencing analysis showed that Candidatus Brocadia dominates the anammox bacterial community (93.03% of sequence reads). Quantification of the hydrazine synthase gene (hzsB) and anammox 16S rRNA gene indicated that the abundance of anammox bacteria ranged from 6.20 × 106 to 1.81 × 109 and 4.81 × 106 to 4.54 × 108 copies per gram of dry weight, respectively. Contributions to nitrogen removal by anammox were measured by a 15N isotope-pairing assay. Anammox rates in red soil ranged from 0.01 to 0.59 nmol N g−1 h−1, contributing 16.67–53.27% to N2 production in the studied area, and the total amount of removed nitrogen by anammox was estimated at 2.33 Tg N per year in the natural red soils of southern China. Pearson correlation analyses revealed that the distribution of anammox bacteria significantly correlated with the concentration of nitrate and pH, whereas the abundance and activity of anammox bacteria were significantly influenced by the nitrate and total nitrogen concentrations. Our findings demonstrate that Candidatus Brocadia dominates anammox bacterial communities in acidic red soils and plays an important role in nitrogen loss of the red soil in Southern China.

An autonomous remote clock control system is proposed to provide time synchronization and frequency syntonization for satellite to satellite or ground to satellite time transfer, with the system comprising on-board voltage controlled oven controlled crystal oscillators (VC-OCXOs) that are disciplined to a remote master atomic clock or oscillator. The synchronization loop aims to provide autonomous operation over extended periods, be widely applicable to a variety of scenarios and robust. A new architecture comprising the use of frequency division duplex (FDD), synchronous time division (STDD) duplex and code division multiple access (CDMA) with a centralized topology is employed. This new design utilizes dual one-way ranging methods to precisely measure the clock error, adopts least square (LS) methods to predict the clock error and employs a third-order phase lock loop (PLL) to generate the voltage control signal. A general functional model for this system is proposed and the error sources and delays that affect the time synchronization are discussed. Related algorithms for estimating and correcting these errors are also proposed. The performance of the proposed system is simulated and guidance for selecting the clock is provided.

The extrusion process with a large extrusion ratio (36:1) has a great effect on microstructure refinement and strength improvement of the Mg-10Gd-2Y-0.5Zn-0.3Zr alloy. The tensile yield strength, ultimate tensile strength, and elongation of the extruded alloy are 306MPa, 410MPa, and 16.3%, respectively. The causes of strength improvement of the extruded alloy are discussed in detail. The grain refinement is a main strengthening source, contributing ~67MPa to the tensile yield strength of the extruded alloy. Dense precipitation of long period stacking ordered (LPSO) and β′ phases on the matrix and transformation of texture type in the extrusion process also partly increase the strength. In addition, a small number of {10 1 ¯ 2} twins during tensile test is another factor improving the strength of the extruded alloy.

Due to the advantages of wide coverage and continuity, remotely sensed data are widely used for large-scale drought monitoring to compensate for the deficiency and discontinuity of meteorological data. However, few studies have focused on the capability of various remotely sensed drought indices (RSDIs) to represent the spatio⁻temporal variations of meteorological droughts. In this study, five RSDIs, namely the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Modified Temperature Vegetation Dryness Index (MTVDI), and Normalized Vegetation Supply Water Index (NVSWI), were calculated using monthly Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The monthly NDVI and LST data were filtered by the Savitzky⁻Golay (S-G) filtering method. A meteorological station-based drought index represented by the Standardized Precipitation Evapotranspiration Index (SPEI) was compared with the RSDIs. Additionally, the dimensionless Skill Score (SS) method was adopted to identify the spatiotemporally optimal RSDIs for presenting meteorological droughts in the Yellow River basin (YRB) from 2000 to 2015. The results indicated that: (1) RSDIs revealed a decreasing drought trend in the overall YRB consistent with the SPEI except for in winter, and different variations of seasonal trends spatially; (2) the optimal RSDIs in spring, summer, autumn, and winter were VHI, TCI, MTVDI, and VCI, respectively, and the average correlation coefficient between the RSDIs and the SPEI was 0.577 (α = 0.05); and (3) different RSDIs have time lags of zero⁻three months compared with the meteorological drought index.