Deciphering the dynamic changes in antibodies against SARS-CoV-2 is essential for understanding the immune response in COVID-19 patients. Here we analyze the laboratory findings of 1,850 patients to ...describe the dynamic changes of the total antibody, spike protein (S)-, receptor-binding domain (RBD)-, and nucleoprotein (N)-specific immunoglobulin M (IgM) and G (IgG) levels during SARS-CoV-2 infection and recovery. The generation of S-, RBD-, and N-specific IgG occurs one week later in patients with severe/critical COVID-19 compared to patients with mild/moderate disease, while S- and RBD-specific IgG levels are 1.5-fold higher in severe/critical patients during hospitalization. The RBD-specific IgG levels are 4-fold higher in older patients than in younger patients during hospitalization. In addition, the S- and RBD-specific IgG levels are 2-fold higher in the recovered patients who are SARS-CoV-2 RNA negative than those who are RNA positive. Lower S-, RBD-, and N-specific IgG levels are associated with a lower lymphocyte percentage, higher neutrophil percentage, and a longer duration of viral shedding. Patients with low antibody levels on discharge might thereby have a high chance of being tested positive for SARS-CoV-2 RNA after recovery. Our study provides important information for COVID-19 diagnosis, treatment, and vaccine development.
The accurate identification of bird image is of great significance to protecting the ecological environment and bird species diversity. To address the issue of low recognition accuracy arising from ...the similarities in features among different bird species and the susceptibility of shallow edge features to loss, this paper proposes a fine-grained bird image classification model that incorporates hierarchical feature fusion and counterfactual feature selection. The model is based on vision transformer and builds a hierarchical feature fusion module and a counterfactual feature enhancement module. The hierarchical feature fusion module superimposes shallow features rich in fine-grained information into deep features to improve the problem of lack of edge detail information in key features. The counterfactual feature enhancement module selects distinguishing features through counterfactual intervention to reduce classification errors caused by highly similar features of different species of birds. The experimental results show that the method can achieve 91.9
%
and 91.4
%
accuracy on two available datasets, CUB-200-2011 and NABirds, respectively, which is higher than the current mainstream fine-grained bird recognition algorithms and shows excellent classification performance.
Plant roots and soil microorganisms interact with each other mainly in the rhizosphere. Changes in the community structure of the rhizosphere microbiome are influenced by many factors. In this study, ...we determined the community structure of rhizosphere bacteria in cotton, and studied the variation of rhizosphere bacterial community structure in different soil types and developmental stages using TM-1, an upland cotton cultivar (Gossypium hirsutum L.) and Hai 7124, a sea island cotton cultivar (G. barbadense L.) by high-throughput sequencing technology. Six bacterial phyla were found dominantly in cotton rhizosphere bacterial community including Acidobacteria, Actinobacteria, Bacteroidetes, Planctomycetes, Proteobacteria, and Verrucomicrobia. The abundance of Acidobacteria, Cyanobacteria, Firmicutes, Planctomycetes and Proteobacteria were largely influenced by cotton root. Bacterial α-diversity in rhizosphere was lower than that of bulk soil in nutrient-rich soil, but higher in cotton continuous cropping field soil. The β-diversity in nutrient-rich soil was greater than that in continuous cropping field soil. The community structure of the rhizosphere bacteria varied significantly during different developmental stages. Our results provided insights into the dynamics of cotton rhizosphere bacterial community and would facilitate to improve cotton growth and development through adjusting soil bacterial community structure artificially.
While lymphocytopenia is a common characteristic of coronavirus disease 2019 (COVID-19), the mechanisms responsible for this lymphocyte depletion are unclear. Here, we retrospectively reviewed the ...clinical and immunological data from 18 fatal COVID-19 cases, results showed that these patients had severe lymphocytopenia, together with high serum levels of inflammatory cytokines (IL-6, IL-8 and IL-10), and elevation of many other mediators in routine laboratory tests, including C-reactive protein, lactate dehydrogenase, α-hydroxybutyrate dehydrogenase and natriuretic peptide type B. The spleens and hilar lymph nodes (LNs) from six additional COVID-19 patients with post-mortem examinations were also collected, histopathologic detection showed that both organs manifested severe tissue damage and lymphocyte apoptosis in these six cases.
hybridization assays illustrated that SARS-CoV-2 viral RNA accumulates in these tissues, and transmission electronic microscopy confirmed that coronavirus-like particles were visible in the LNs. SARS-CoV-2 Spike and Nucleocapsid protein (NP) accumulated in the spleens and LNs, and the NP antigen restricted in angiotensin-converting enzyme 2 (ACE2) positive macrophages and dendritic cells (DCs). Furthermore, SARS-CoV-2 triggered the transcription of
,
and
genes in infected primary macrophages and DCs
, and SARS-CoV-2-NP
macrophages and DCs also manifested high levels of IL-6 and IL-1β, which might directly decimate human spleens and LNs and subsequently lead to lymphocytopenia
. Collectively, these results demonstrated that SARS-CoV-2 induced lymphocytopenia by promoting systemic inflammation and direct neutralization in human spleen and LNs.
In the era of remote sensing big data, the intelligent interpretation of remote sensing images is a key technology for mining the value of remote sensing big data and promoting a number of major ...applications, mainly including land cover classification and extraction. Among these, the rapid extraction of open-pit mining areas plays a vital role in current practices for refined mineral resources development and management and ecological–environmental protection in China. However, existing methods are not accurate enough for classification, not fine enough for boundary extraction, and poor in terms of multi-scale adaptation. To address these issues, we propose a new semantic segmentation model based on Transformer, which is called Segmentation for Mine—SegMine—and consists of a Vision Transformer-based encoder and a lightweight attention mask decoder. The experimental results show that SegMine enhances the network’s ability to obtain local spatial detail information and improves the problem of disappearing small-scale object features and insufficient information expression. It also better preserves the boundary details of open-pit mining areas. Using the metrics of mIoU, precision, recall, and dice, experimental areas were selected for comparative analysis, and the results show that the new method is significantly better than six other existing major Transformer variants.
Quickly obtaining fine-scale mining area types information in large-scale scenes is significant for dynamically detecting mineral resources. Currently, mining area types recognition methods encounter ...challenges such as low recognition accuracy and difficulty detecting small mining areas. To address these issues, this article proposes a stepwise top-down mining area types recognition framework. The framework consists of two steps. First, a GF-5 spectral index named the Normalized Difference Mining Area Index (NDMAI) is constructed to obtain the rough position of the mining area quickly. Then, the identification network of Mine Types with Transformer (Mitformer) is proposed for accurate type recognition of the candidate mining area regions. Mitformer combines a multiscale feature enhancement module and a decoder based on multilevel skip connections, which achieves a sufficient fusion of features at each layer of deep feature maps and adds the skip connections between low-level and high-level feature maps, thus, can improve the accuracy of types identification and the detection rate of small-scale mining areas. Moreover, this framework can effectively avoid misclassification caused by different objects with similar spectra to the maximum extent possible. This article selects two independent study areas with a large spatial extent, respectively, in Hebei Province and Anhui Province. The imagery utilized for these regions is obtained from Chinese GF-2 and GF-5 satellites. Multiple experiments are conducted to verify the superiority of NDMAI and Mitformer and the effectiveness of this framework. The experimental results illustrate that this framework can provide adequate technical support for the dynamic detection of mineral resources.
Rhizosphere fungal communities exert important influencing forces on plant growth and health. However, information on the dynamics of the rhizosphere fungal community structure of the worldwide ...economic crop cotton (Gossypium spp.) is limited. In the present study, next-generation sequencing of nuclear ribosomal internal transcribed spacer-1 (ITS1) was performed to characterize the rhizosphere fungal communities of G. hirsutum cv. TM-1 (upland cotton) and G. barbadense cv. Hai 7124 (island cotton). The plants were grown in field soil (FS) that had been continuously cropped with cotton and nutrient-rich soil (NS) that had not been cropped. The fungal species richness, diversity, and community composition were analyzed and compared among the soil resources, cotton genotypes, and developmental stages. We found that the fungal community structures were different between the rhizosphere and bulk soil and the difference were significantly varied between FS and NS. Our results suggested that cotton rhizosphere fungal community structure variation may have been primarily influenced by the interaction of cotton roots with different soil resources. We also found that the community composition of the cotton rhizosphere fungi varied significantly during different developmental stages. In addition, we observed fungi that was enriched or depleted at certain developmental stages and genotypes in FS and NS, and these insights can lay a foundation for deep research into the dynamics of pathogenic fungi and nutrient absorption of cotton roots. This research illustrates the characteristics of the cotton rhizosphere fungal communities and provides important information for understanding the potential influences of rhizosphere fungal communities on cotton growth and health.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Research on ecological corridor extraction methods has made some progress and has been gradually applied to the planning and construction of regional ecological corridors, which play a role in ...biodiversity conservation efforts. However, the factors affecting species migration in ecological environments are very complex, especially anthropogenic disturbances, typically including noise pollution. Their effects on species habitats, reproduction, predation, and other activities are currently underestimated. In this paper, we propose an algorithm for superposition analysis of multiple road impacts and construct an ecological corridor extraction method that considers landscape pattern, habitat quality, remote sensing ecology, and road traffic resistance to address the shortcomings of current ecological corridor extraction methods that underestimate the potential impacts of road traffic. An extraction of ecological corridors was completed in Wuhan, and a quantitative comparative analysis was conducted from multiple perspectives. The results show that the improved method was effective, with the proportion of ecological corridors not re-identified due to road traffic impacts being 0.45% and the proportion of ecological corridors with significant changes in spatial location, represented by regions far from roads or high road network density, being 22.15% in the whole of Wuhan.
Coronavirus disease 2019 (COVID-19) is a respiratory disorder caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which had rapidly spread all over the world and caused public ...health emergencies in the past two years. Although the diagnosis and treatment for COVID-19 have been well defined, the immune cell characteristics and the key lymphocytes subset alterations in COVID-19 patients have not been thoroughly investigated. The levels of immune cells including T cells, B cells, and natural killer (NK) cells in 548 hospitalized COVID-19 patients, and 30 types of lymphocyte subsets in 125 hospitalized COVID-19 patients admitted to Wuhan Huoshenshan Hospital of China were measured using flow cytometry. The relationship between lymphocytes subsets with the cytokine interleukin-6 (IL-6) and the characteristics of lymphocyte subsets in single-cell RNA sequencing (scRNA-seq) data obtained from peripheral blood mononuclear cells (PBMCs) were also analysed in COVID-19 patients. In this study, we found that patients with critical COVID-19 infection exhibited an overall decline in lymphocytes including CD4.sup.+ T cells, CD8.sup.+ T cells, total T cells, B cells, and NK cells compared to mild and severe patients. However, the number of lymphocyte subsets, such as CD21.sup.low CD38.sup.low B cells, effector T4 cells, and PD1.sup.+ depleted T8 cells, was moderately increased in critical COVID-19 patients compared to mild cases. Notably, except for effector memory T4 cells, plasma blasts and Tregs, the number of all lymphocyte subsets was markedly decreased in COVID-19 patients with IL-6 levels over 30-fold higher than those in healthy cases. Moreover, scRNA-seq data showed obvious differences in the distribution and numbers of lymphocyte subsets between COVID-19 patients and healthy persons, and subsets-specific marker genes of lymphocyte subsets including CD4, CD19, CCR7, and IL7R, were markedly decreased in COVID-19 patients compared with those in healthy cases. A comprehensive decrease in immune cell and lymphocyte subsets in critical COVID-19 patients, and peripheral lymphocyte subset alterations showed a clear association with clinical characteristics.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Research on the potential accessibility of medical services has made great progress, but there is a large gap between the analysis results and the actual feelings of residents. With the refinement of ...urban management, the need for actual accessibility calculations reflecting the current status of medical service levels is becoming stronger. In modern society, as people work and live at an increasingly fast pace, people increasingly focus on time saving. However, in addition to travel time and distance, personal perceptions of medical facilities and access habits also influence residents’ choice of specific hospitals for medical treatment. With the combined effect of these factors, the actual status of accessibility of medical facility services is formed. In order to improve estimates of the actual accessibility and narrow the gap with residents’ subjective perceptions, this study leverages realistic data, such as real-time navigation prediction data that approximates residents’ actual travel time to hospitals and information on residents’ subjective behaviors in choosing specific hospitals for medical treatment. Finally, a new approach is proposed to further improve the existing Gaussian two-step floating catchment area (Ga2SFCA) method by fully respecting the important effects of distance cost and time cost, and combining them by using a weighted mean.
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