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The coronavirus disease 2019 (COVID-19) pandemic poses an enormous threat to public health worldwide. Many retrospective studies and case reports to date have shown associations between severe COVID-19 and diseases of multi-organs. However, the research on the causal mechanisms behind this phenomenon is neither extensive nor comprehensive. We conducted a proteome-wide Mendelian randomization (MR) study using summary statistics from a Genome-Wide Association Studies (GWAS) of severe COVID-19 and diseases related to seven organs: lung, spleen, liver, heart, kidney, testis, and thyroid, based on the European ancestry. The primary analytical method used is the radial inverse variance-weighted (radial IVW) method, supplemented with the inverse variance-weighted (IVW), weighted-median (WM), MR-Egger methods. Our findings have confirmed the association between severe COVID-19 and multiple organ-related diseases, such as Hypothyroidism, strict autoimmune (HTCBSA), Thyroid disorders (TD), and Graves’ disease (GD). And we have also identified certain proteins that are associated with organ-related diseases, such as Superoxide Dismutase 2 (SOD2) and TEK Receptor Tyrosine Kinase (TEK), which are also considered potential drug targets. Phenotype scanning and sensitivity analyses were implemented to consolidate the results for Mendelian randomization. This study provides a compelling foundation for investigating COVID-19 caused diseases in future studies.

Study region: The Yellow River basin (YRB) Study focus: Runoff plays an important role in ecology and hydrology, which can be affected by climate change and human activities. The fractional vegetation cover can reflect the influence of human activities on underlying surface to some extent. However, the attribution research on future runoff changes is not sufficient. Using the CMIP6 global climate model with Delta-downscaling, Budyko equation, and empirical orthogonal function method, the effects of climate change and underlying surface change on runoff in the YRB from 2022 to 2100 are analyzed in this paper. New hydrological insights for the region: The trend of runoff increased uniformly on the YRB except for the upstream of the YRB and some areas of the source region of the YRB. Runoff is negatively correlated with potential evapotranspiration and underlying surface change but positively correlated with precipitation, according to the Budyko equation. And the increase in the concentration of emissions will amplify the impact of climate change particularly precipitation on runoff. In most areas of the basin, precipitation changes will be the dominant factor in runoff changes between 2022 and 2100, and their contribution rate will increase over time. On the source region of the YRB, however, the potential evapotranspiration dominated runoff changes. This study will help formulate appropriate soil and water conservation and ecological protection strategies in ecologically vulnerable areas of the YRB in the context of climate change.

Abstract Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.

Increasing climate change makes vegetation dynamic. At the same time, dynamic changes in vegetation not only have a feedback effect on climate change, but also affect the hydrological cycle process. Therefore, understanding the vegetation change and its response to climate change is a priority for predicting future climate change and studying the impact of vegetation change on the hydrological cycle. In this study, the Yellow River Basin in China is the study area. Based on the analysis of the evolution characteristics of meteorological elements and fractional vegetation cover (FVC), the delta downscaling Coupled Model Intercomparison Project Phase 6 (CMIP6) models are optimized. The empirical orthogonal function (EOF) and singular value decomposition (SVD) methods are used to investigate the impact of climate change on vegetation in the Yellow River Basin. The results show that: (1) in the four scenarios (SSP126, SSP245, SSP370, and SSP585), FVC in the Yellow River Basin from 2022 to 2100 shows an increasing trend, SSP370 (0.017 10a–1) > SSP126 (0.014 10a–1) > SSP245 (0.0087 10a–1) > SSP585 (0.0086 10a–1). Spatially, FVC in most regions of the Yellow River Basin show an increasing trend under the four scenarios, and the degraded areas are concentrated in a small part of the Yellow River headwaters. (2) There is a significant positive correlation between FVC and precipitation (Pre) and temperature (Tem) under four scenarios in the Yellow River Basin from 2022 to 2100. Under the same scenario, the annual average temperature can be considered as the dominant factor of FVC change in the Yellow River Basin. Under different scenarios, the impact of climate change on FVC under the high emission scenarios is greater than that under the low emission scenarios. This study will help to better understand the response of vegetation to climate change and provide a scientific basis for formulating ecological protection measures to cope with future climate change in the Yellow River Basin.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Formalin‐fixed, paraffin‐embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT)‐SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B‐cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh‐frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.

Background: Early screening for colorectal cancer (CRC) is essential to improve its prognosis. Liquid biopsies are increasingly being considered for diagnosing cancer due to low invasiveness and high reproducibility. In addition, circulating extracellular vesicles (crEVs, extracellular vesicles isolated from plasma) expressing tumour-specific proteins are potential biomarkers for various cancers. Here, we present a data-independent acquisition (DIA)-mass spectrometry (MS)-based diagnostic method for liquid biopsies. Methods: Extracellular vesicles (EVs) were isolated from culture supernatants of human CRC cell lines, and plasma of patients with CRC at different tumour stages, by overnight ultracentrifugation coupled with sucrose density gradient centrifugation. Tumour-specific EV proteins were prioritized using Tandem Mass Tag (TMT)-based shotgun proteomics and phosphoproteomics. The results were verified in a second independent cohort and a mouse tumour-bearing model using Western blotting (WB). The candidate biomarkers were further validated in a third cohort by DIA-MS. Finally, the DIA-MS methodology was accelerated to permit high-throughput detection of EV biomarkers in another independent cohort of patients with CRC and healthy controls. Results: High levels of total and phosphorylated fibronectin 1 (FN1) in crEVs, haptoglobin (HP), S100A9 and fibrinogen α chain (FGA) were significantly associated with cancer progression. FGA was the most dominant biomarker candidate. Analysis of the human CRC cell lines and the mouse model indicated that FGA+ crEVs were likely released by CRC cells. Furthermore, fast DIA-MS and parallel reaction monitoring (PRM)-MS both confirmed that FGA+ crEVs could distinguish colon adenoma with an area of curve (AUC) in the receiver operating characteristic (ROC) curve of 0.949 and patients with CRC (AUC of ROC is 1.000) from healthy individuals. The performance outperformed conventional tumour biomarkers. The DIA-MS quantification of FGA+ crEVs among three groups agreed with that from PRM-MS. Conclusion: DIA-MS detection of FGA+ crEVs is a potential rapid and non-invasive screening tool to identify early stage CRC. Abbreviations: FGA: fibrinogen α chain; CRC: colorectal cancer; crEVs: circulating extracellular vesicles; EV: extracellular vesicles;MS: mass spectrometry; WB: Western blotting; ROC: receiver operating characteristic; PRM: Parallel Reaction Monitoring; GPC1: Glypican-1; GO: Gene ontology; TEM: transmission electron microscopy; FN1: Fibronectin 1; HP: haptoglobin; TMT: Tandem Mass Tag; LC-MS/MS: liquid chromatography coupled to tandem mass spectrometry; DIA: data-independent acquisition; DDA: data-dependent acquisition; CiRT: Common internal Retention Time standards;AGC: Automatic gain control; AUC: area under curve.

To confirm phytoplasma infection, samples of pomegranate (Punica granatum L.) plants showing symptoms of fasciation were collected from an orchard located in Tai'an, Shandong Province, China. A fragment of approximately 1.2 kb was amplified with universal primers targeting the phytoplasma 16S rRNA gene from symptomatic pomegranate plants, while no fragment was obtained from healthy plants. The phytoplasma associated with the disease was designated as pomegranate fasciation (PoF). Two representative phytoplasma 16S rDNA gene sequences (PoF-Ch01 and PoF-Ch02) had 100% nucleotide sequence identity. The 16S rDNA sequence of PoF-Ch01 and PoF-Ch02 showed the highest similarity (99.6%) to that of ‘P. granatum’ phytoplasma isolate AY-PG, which belong to 16SrI-B. Further phylogenetic analysis showed that PoF-Ch01 and PoF-Ch02 belonged to a cluster of 16SrI subgroup members. In silico RFLP analysis indicated that PoF-Ch01 shared the highest similarity coefficient of 0.97 with reference strains of 16SrI-B, M and N. Actual RFLP analysis of both enzymes BstUI and BfaI confirmed that of the virtual RFLP analysis. Combining these results, we concluded that PoF was a member of the ‘Candidatus Phytoplasma asteris’ group (16SrI), and has very close relationship with 16SrI-B subgroup. Keywords: Punica granatum, fasciation disease, RFLP analysis, 16SrI group

Abstract Background Turnip mosaic virus (TuMV) is one of the most widespread and economically important virus infecting both crop and ornamental species of the family Brassicaceae. TuMV isolates can be classified to five phylogenetic lineages, basal-B, basal-BR, Asian-BR, world-B and Orchis. Results To understand the genetic structure of TuMV from radish in China, the 3′-terminal genome of 90 TuMV isolates were determined and analyzed with other available Chinese isolates. The results showed that the Chinese TuMV isolates from radish formed three groups: Asian-BR, basal-BR and world-B. More than half of these isolates (52.54%) were clustered to basal-BR group, and could be further divided into three sub-groups. The TuMV basal-BR isolates in the sub-groups I and II were genetically homologous with Japanese ones, while those in sub-group III formed a distinct lineage. Sub-populations of TuMV basal-BR II and III were new emergent and in a state of expansion. The Chinese TuMV radish populations were under negative selection. Gene flow between TuMV populations from Tai’an, Weifang and Changchun was frequent. Conclusions The genetic structure of Turnip mosaic virus population reveals the rapid expansion of a new emergent lineage in China.

In this paper, the voltage characteristics of short air arc with high average current density arc root were studied at atmospheric pressure. Cu-W electrodes with diameter of 5-14mm and gap length of 0.2-3 mm were used in the experimental study. The experimental current varies from 1,000A to 16,600 A. The average current density of the arc roots in the experiment was up to 245 A/mm2. In the scope of this study, the arc voltage is a function of the average current density of arc root when the electrode gap length is fixed. For same average current density of arc root, the arc voltage is approximately the same even if the experimental currents are different. And the arc voltage increases with the increment of the average current density of arc root. Theoretical analysis and experimental results show that the electrode fall voltage almost doesn’t change with the average current density of arc root. However the arc column voltage increases with the increment of the average current density of arc root.

Bamboo and rattan are widely grown for manufacturing, horticulture, and agroforestry. Bamboo and rattan production might help reduce poverty, boost economic growth, mitigate climate change, and protect the natural environment. Despite progress in research, sufficient molecular and genomic resources to study these species are lacking. We launched the Genome Atlas of Bamboo and Rattan (GABR) project, a comprehensive, coordinated international effort to accelerate understanding of bamboo and rattan genetics through genome analysis. GABR includes 2 core subprojects: Bamboo-T1K (Transcriptomes of 1000 Bamboos) and Rattan-G5 (Genomes of 5 Rattans), and several other subprojects. Here we describe the organization, directions, and status of GABR. [ABSTRACT FROM AUTHOR]

Meiosis is a special type of cell division process necessary for the sexual reproduction of all eukaryotes. The ever expanding meiosis research calls for an effective and specialized database that is not readily available yet. To fill this gap, we have developed a knowledge database MeioBase (http://meiosis.ibcas.ac.cn), which is comprised of two core parts, Resources andTools. In the Resources part, a wealth of meiosis data collected by curation and manual review from published literatures and biological databases are integrated and organized into various sections, such as Cytology, Pathway, Species, Interaction, and Expression. In theTools part, some useful tools have been integrated into MeioBase, such as Search, Download, Blast, Comparison, My Favorites, Submission, and Advice. With a simplified and efficient web interface, users are able to search against the database with gene model IDs or keywords, and batch download the data for local investigation. We believe that MeioBase can greatly facilitate the researches related to meiosis. [ABSTRACT FROM AUTHOR]