Pathological Asymmetry-Guided Progressive Learning for Acute Ischemic Stroke Infarct Segmentation.

Quantitative infarct estimation is crucial for diagnosis, treatment and prognosis in acute ischemic stroke (AIS) patients. As the early changes of ischemic tissue are subtle and easily confounded by normal brain tissue, it remains a very challenging task. However, existing methods often ignore or confuse the contribution of different types of anatomical asymmetry caused by intrinsic and pathological changes to segmentation. Further, inefficient domain knowledge utilization leads to mis-segmentation for AIS infarcts. Inspired by this idea, we propose a pathological asymmetry-guided progressive learning (PAPL) method for AIS infarct segmentation. PAPL mimics the step-by-step learning patterns observed in humans, including three progressive stages: knowledge preparation stage, formal learning stage, and examination improvement stage. First, knowledge preparation stage accumulates the preparatory domain knowledge of the infarct segmentation task, helping to learn domain-specific knowledge representations to enhance the discriminative ability for pathological asymmetries by constructed contrastive learning task. Then, formal learning stage efficiently performs end-to-end training guided by learned knowledge representations, in which the designed feature compensation module (FCM) can leverage the anatomy similarity between adjacent slices from the volumetric medical image to help aggregate rich anatomical context information. Finally, examination improvement stage encourages improving the infarct prediction from the previous stage, where the proposed perception refinement strategy (RPRS) further exploits the bilateral difference comparison to correct the mis-segmentation infarct regions by adaptively regional shrink and expansion. Extensive experiments on public and in-house NCCT datasets demonstrated the superiority of the proposed PAPL, which is promising to help better stroke evaluation and treatment.

A new chemical derivatization reagent sulfonyl piperazinyl for the quantification of fatty acids using LC-MS/MS.

In our previous study, a chemical derivatization reagent named 5-(dimethylamino) naphthalene-1-sulfonyl piperazine (Dns-PP) was developed to enhance the chromatographic retention and the mass spectrometric response of free fatty acids (FFAs) in reversed-phase liquid chromatography coupled with electrospray ionization-mass spectrometry (RPLC-ESI-MS). However, Dns-PP exhibited strong preferences for long-chain FFAs, with limited improvement for short- or medium-chain FFAs. In this study, a new series of labeling reagents targeting FFAs were designed, synthesized, and evaluated. Among these reagents, Tmt-PP (N2, N2, N4, N4-tetramethyl-6-(4-(piperazin-1-ylsulfonyl) phenyl)-1,3,5-triazine-2,4-diamine) exhibited the best MS response and was selected for further evaluations. We compared Tmt-PP with Dns-PP and four commonly used carboxyl labeling reagents from existing studies, demonstrating the advantages of Tmt-PP. Further comparisons between Tmt-PP and Dns-PP in measuring FFAs from biological samples revealed that Tmt-PP labeling enhanced the MS response for about 80 % (30/38) of the measured FFAs, particularly for short- and medium-chain FFAs. Moreover, Tmt-PP labeling significantly improved the chromatographic retention of short-chain FFAs. To ensure accurate quantification, we developed a stable isotope-labeled Tmt-PP (i.e., d12-Tmt-PP) to react with chemical standards and serve as one-to-one internal standards (IS). The method was validated for accuracy, precision, sensitivity, linearity, stability, extraction efficiency, as well as matrix effect. Overall, this study introduced a new chemical derivatization reagent Tmt-PP (d12-Tmt-PP), providing a sensitive and accurate option for quantifying FFAs in biological samples.

Efficient Online Stream Clustering Based on Fast Peeling of Boundary Micro-Cluster.

A growing number of applications generate streaming data, making data stream mining a popular research topic. Classification-based streaming algorithms require pre-training on labeled data. Manually labeling a large number of samples in the data stream is impractical and cost-prohibitive. Stream clustering algorithms rely on unsupervised learning. They have been widely studied for their ability to effectively analyze high-speed data streams without prior knowledge. Stream clustering plays a key role in data stream mining. Currently, most data stream clustering algorithms adopt the online-offline framework. In the online stage, micro-clusters are maintained, and in the offline stage, they are clustered using an algorithm similar to density-based spatial clustering of applications with noise (DBSCAN). When data streams have clusters with varying densities and ambiguous boundaries, traditional data stream clustering algorithms may be less effective. To overcome the above limitations, this article proposes a fully online stream clustering algorithm called fast boundary peeling stream clustering (FBPStream). First, FBPStream defines a decay-based kernel density estimation (KDE). It can discover clusters with varying densities and identify the evolving trend of streams well. Then, FBPStream implements an efficient boundary micro-cluster peeling technique to identify the potential core micro-clusters. Finally, FBPStream employs a parallel clustering strategy to effectively cluster core and boundary micro-clusters. The proposed algorithm is compared with ten popular algorithms on 15 data streams. Experimental results show that FBPStream is competitive with the other ten popular algorithms.

Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.

OBJECTIVES: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods. METHODS: ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance. RESULTS: A total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84). CONCLUSIONS: The automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h. CLINICAL RELEVANCE STATEMENT: Perfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time. KEY POINTS: • The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h.

Agaricus bisporus-Derived Glucosamine Hydrochloride Regulates VEGF through BMP Signaling to Promote Zebrafish Vascular Development and Impairment Repair.

Glucosamine hydrochloride (GAH) is a natural component of glycoproteins present in almost all human tissues and participates in the construction of human tissues and cell membranes. GAH has a wide range of biological activities, particularly in anti-inflammatory and osteogenic damage repair. At present, little is known about how GAH functions in angiogenesis. To determine the role of GAH on vascular development and impairment repair, we used the inhibitors VRI, DMH1, and dorsomorphin (DM) to construct vascular-impaired models in Tg(kdrl: mCherry) transgenic zebrafish. We then treated with GAH and measured its repair effects on vascular impairment through fluorescence intensity, mRNA, and protein expression levels of vascular-specific markers. Our results indicate that GAH promotes vascular development and repairs impairment by regulating the vascular endothelial growth factor (VEGF) signaling pathway through modulation of bone morphogenetic protein (BMP) signaling. This study provides an experimental basis for the development of GAH as a drug to repair vascular diseases.

Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning.

Fossil identification is an essential and fundamental task for conducting palaeontological research. Because the manual identification of fossils requires extensive experience and is time-consuming, automatic identification methods are proposed. However, these studies are limited to a few or dozens of species, which is hardly adequate for the needs of research. This study enabled the automatic identification of hundreds of species based on a newly established fossil dataset. An available "bivalve and brachiopod fossil image dataset" (BBFID, containing >16,000 "image-label" data pairs, taxonomic determination completed) was created. The bivalves and brachiopods contained in BBFID are closely related in morphology, ecology and evolution that have long attracted the interest of researchers. We achieved >80% identification accuracy at 22 genera and ∼64% accuracy at 343 species using EfficientNetV2s architecture. The intermediate output of the model was extracted and downscaled to obtain the morphological feature space of fossils using t-distributed stochastic neighbor embedding (t-SNE). We found a distinctive boundary between the morphological feature points of bivalves and brachiopods in fossil morphological feature distribution maps. This study provides a possible method for studying the morphological evolution of fossil clades using computer vision in the future.

Bioinformatics-based analysis reveals elevated CYTL1 as a potential therapeutic target for BRAF-mutated melanoma.

Introduction: Despite many recent emerging therapeutic modalities that have prolonged the survival of melanoma patients, the prognosis of melanoma remains discouraging, and further understanding of the mechanisms underlying melanoma progression is needed. Melanoma patients often have multiple genetic mutations, with BRAF mutations being the most common. In this study, public databases were exploited to explore a potential therapeutic target for BRAF-mutated melanoma. Methods: In this study, we analyzed differentially expressed genes (DEGs) in normal tissues and melanomas, Braf wild-type and Braf mutant melanomas using information from TCGA databases and the GEO database. Subsequently, we analyzed the differential expression of CYTL1 in various tumor tissues and its effect on melanoma prognosis, and resolved the mutation status of CYTL1 and its related signalling pathways. By knocking down CYTL1 in melanoma cells, the effects of CYTL1 on melanoma cell proliferation, migration and invasion were further examined by CCK8 assay, Transwell assay and cell migration assay. Results: 24 overlapping genes were identified by analyzing DEGs common to melanoma and normal tissue, BRAF-mutated and BRAF wild-type melanoma. Among them, CYTL1 was highly expressed in melanoma, especially in BRAF-mutated melanoma, and the high expression of CYTL1 was associated with epithelial-mesenchymal transition (EMT), cell cycle, and cellular response to UV. In melanoma patients, especially BRAF-mutated melanoma patients, clinical studies showed a positive correlation between increased CYTL1 expression and shorter overall survival (OS) and disease-free survival (DFS). In vitro experiments further confirmed that the knockdown of CYTL1 significantly inhibited the migration and invasive ability of melanoma cells. Conclusion: CYTL1 is a valuable prognostic biomarker and a potentially effective therapeutic target in melanoma, especially BRAF-mutated melanoma.

Nuclear bodies protect phase separated proteins from degradation in stressed proteome.

RNA-binding proteins (RBPs) containing intrinsically disordered domains undergo liquid-liquid phase separation to form nuclear bodies under stress conditions. This process is also connected to the misfolding and aggregation of RBPs, which are associated with a series of neurodegenerative diseases. However, it remains elusive how folding states of RBPs changes upon the formation and maturation of nuclear bodies. Here, we describe SNAP-tag based imaging methods to visualize the folding states of RBPs in live cells via time-resolved quantitative microscopic analyses of their micropolarity and microviscosity. Using these imaging methods in conjunction with immunofluorescence imaging, we demonstrate that RBPs, represented by TDP-43, initially enters the PML nuclear bodies in its native state upon transient proteostasis stress, albeit it begins to misfolded during prolonged stress. Furthermore, we show that heat shock protein 70 co-enters the PML nuclear bodies to prevent the degradation of TDP-43 from the proteotoxic stress, thus revealing a previously unappreciated protective role of the PML nuclear bodies in the prevention of stress-induced degradation of TDP-43. In summary, our imaging methods described in the manuscript, for the first time, reveal the folding states of RBPs, which were previously challenging to study with conventional methods in nuclear bodies of live cells. This study uncovers the mechanistic correlations between the folding states of a protein and functions of nuclear bodies, in particular PML bodies. We envision that the imaging methods can be generally applied to elucidating the structural aspects of other proteins that exhibit granular structures under biological stimulus.

M3W: Multistep Three-Way Clustering.

Three-way clustering has been an active research topic in the field of cluster analysis in recent years. Some efforts are focused on the technique due to its feasibility and rationality. We observe, however, that the existing three-way clustering algorithms struggle to obtain more information and limit the fault tolerance excessively. Moreover, although the one-step three-way allocation based on a pair of fixed, global thresholds is the most straightforward way to generate the three-way cluster representations, the clusters derived from a pair of global thresholds cannot exactly reveal the inherent clustering structure of the dataset, and the threshold values are often difficult to determine beforehand. Inspired by sequential three-way decisions, we propose an algorithm, called multistep three-way clustering (M3W), to address these issues. Specifically, we first use a progressive erosion strategy to construct a multilevel structure of data, so that lower levels (or external layers) can gather more available information from higher levels (or internal layers). Then, we further propose a multistep three-way allocation strategy, which sufficiently considers the neighborhood information of every eroded instance. We use the allocation strategy in combination with the multilevel structure to ensure that more information is gradually obtained to increase the probability of being assigned correctly, capturing adaptively the inherent clustering structure of the dataset. The proposed algorithm is compared with eight competitors using 18 benchmark datasets. Experimental results show that M3W achieves superior performance, verifying its advantages and effectiveness.

Insights into plastic biodegradation: community composition and functional capabilities of the superworm (Zophobas morio) microbiome in styrofoam feeding trials.

Plastics are inexpensive and widely used organic polymers, but their high durability hinders biodegradation. Polystyrene, including extruded polystyrene (also known as styrofoam), is among the most commonly produced plastics worldwide and is recalcitrant to microbial degradation. In this study, we assessed changes in the gut microbiome of superworms (Zophobas morio) reared on bran, polystyrene or under starvation conditions over a 3 weeks period. Superworms on all diets were able to complete their life cycle to pupae and imago, although superworms reared on polystyrene had minimal weight gains, resulting in lower pupation rates compared to bran reared worms. The change in microbial gut communities from baseline differed considerably between diet groups, with polystyrene and starvation groups characterized by a loss of microbial diversity and the presence of opportunistic pathogens. Inferred microbial functions enriched in the polystyrene group included transposon movements, membrane restructuring and adaptations to oxidative stress. We detected several encoded enzymes with reported polystyrene and styrene degradation abilities, supporting previous reports of polystyrene-degrading bacteria in the superworm gut. By recovering metagenome-assembled genomes (MAGs) we linked phylogeny and functions and identified genera including Pseudomonas, Rhodococcus and Corynebacterium that possess genes associated with polystyrene degradation. In conclusion, our results provide the first metagenomic insights into the metabolic pathways used by the gut microbiome of superworms to degrade polystyrene. Our results also confirm that superworms can survive on polystyrene feed, but this diet has considerable negative impacts on host gut microbiome diversity and health.

Three families of Asgard archaeal viruses identified in metagenome-assembled genomes.

Asgardarchaeota harbour many eukaryotic signature proteins and are widely considered to represent the closest archaeal relatives of eukaryotes. Whether similarities between Asgard archaea and eukaryotes extend to their viromes remains unknown. Here we present 20 metagenome-assembled genomes of Asgardarchaeota from deep-sea sediments of the basin off the Shimokita Peninsula, Japan. By combining a CRISPR spacer search of metagenomic sequences with phylogenomic analysis, we identify three family-level groups of viruses associated with Asgard archaea. The first group, verdandiviruses, includes tailed viruses of the class Caudoviricetes (realm Duplodnaviria); the second, skuldviruses, consists of viruses with predicted icosahedral capsids of the realm Varidnaviria; and the third group, wyrdviruses, is related to spindle-shaped viruses previously identified in other archaea. More than 90% of the proteins encoded by these viruses of Asgard archaea show no sequence similarity to proteins encoded by other known viruses. Nevertheless, all three proposed families consist of viruses typical of prokaryotes, providing no indication of specific evolutionary relationships between viruses infecting Asgard archaea and eukaryotes. Verdandiviruses and skuldviruses are likely to be lytic, whereas wyrdviruses potentially establish chronic infection and are released without host cell lysis. All three groups of viruses are predicted to play important roles in controlling Asgard archaea populations in deep-sea ecosystems.

Automatic Video Analysis Framework for Exposure Region Recognition in X-Ray Imaging Automation.

The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer.

A novel regression method for the analysis of multireader multicase-free-response receiver operating characteristics studies.

In diagnostic radiology, the multireader multicase (MRMC) design and the free-response receiver operating characteristics (FROC) method are often used in combination. The cross-correlated data generated by the MRMC-FROC study leads to difficulties in the corresponding analysis, and the need to include covariates in the model further complicates the subsequent analysis. In this paper, we propose a regression approach based on three new measures with good interpretability. The correlation structure of the original test results is taken directly into account in the estimation procedure. The proposed method also allows the inclusion of continuous or discrete covariates. Consistent and asymptotically normal estimators are derived for the new measures. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that the regression approach performs well under a wide range of scenarios. We also apply the proposed regression approach to a diagnostic study of computer-aided diagnosis in lung cancer.

Characterization of the Human Oropharyngeal Microbiomes in SARS-CoV-2 Infection and Recovery Patients.

Respiratory tract microbiome is closely related to respiratory tract infections, while characterization of oropharyngeal microbiome in recovered coronavirus disease 2019 (COVID-19) patients is not studied. Herein, oropharyngeal swabs are collected from confirmed cases (CCs) with COVID-19 (73 subjects), suspected cases (SCs) (36), confirmed cases who recovered (21), suspected cases who recovered (36), and healthy controls (Hs) (140) and then completed MiSeq sequencing. Oropharyngeal microbial α-diversity is markedly reduced in CCs versus Hs. Opportunistic pathogens are increased, while butyrate-producing genera are decreased in CCs versus Hs. The classifier based on eight optimal microbial markers is constructed through a random forest model and reached great diagnostic efficacy in both discovery and validation cohorts. Notably, the classifier successfully diagnosed SCs with positive IgG antibody as CCs and is demonstrated from the perspective of the microbiome. Importantly, several genera with significant differences gradually increase and decrease along with recovery from COVID-19. Forty-four oropharyngeal operational taxonomy units (OTUs) are closely correlated with 11 clinical indicators of SARS-CoV-2 infection and Hs based on Spearman correlation analysis. Together, this research is the first to characterize oropharyngeal microbiota in recovered COVID-19 cases and suspected cases, to successfully construct and validate the diagnostic model for COVID-19 and to depict the correlations between microbial OTUs and clinical indicators.

Alterations in the human oral and gut microbiomes and lipidomics in COVID-19.

OBJECTIVE: To characterise the oral microbiome, gut microbiome and serum lipid profiles in patients with active COVID-19 and recovered patients; evaluate the potential of the microbiome as a non-invasive biomarker for COVID-19; and explore correlations between the microbiome and lipid profile. DESIGN: We collected and sequenced 392 tongue-coating samples, 172 faecal samples and 155 serum samples from Central China and East China. We characterised microbiome and lipid molecules, constructed microbial classifiers in discovery cohort and verified their diagnostic potential in 74 confirmed patients (CPs) from East China and 37 suspected patients (SPs) with IgG positivity. RESULTS: Oral and faecal microbial diversity was significantly decreased in CPs versus healthy controls (HCs). Compared with HCs, butyric acid-producing bacteria were decreased and lipopolysaccharide-producing bacteria were increased in CPs in oral cavity. The classifiers based on 8 optimal oral microbial markers (7 faecal microbial markers) achieved good diagnostic efficiency in different cohorts. Importantly, diagnostic efficacy reached 87.24% in the cross-regional cohort. Moreover, the classifiers successfully diagnosed SPs with IgG antibody positivity as CPs, and diagnostic efficacy reached 92.11% (98.01% of faecal microbiome). Compared with CPs, 47 lipid molecules, including sphingomyelin (SM)(d40:4), SM(d38:5) and monoglyceride(33:5), were depleted, and 122 lipid molecules, including phosphatidylcholine(36:4p), phosphatidylethanolamine (PE)(16:0p/20:5) and diglyceride(20:1/18:2), were enriched in confirmed patients recovery. CONCLUSION: This study is the first to characterise the oral microbiome in COVID-19, and oral microbiomes and lipid alterations in recovered patients, to explore their correlations and to report the successful establishment and validation of a diagnostic model for COVID-19.

Agaricus bisporus-Derived Glucosamine Hydrochloride Facilitates Skeletal Injury Repair through Bmp Signaling in Zebrafish Osteoporosis Model.

Glucosamine hydrochloride (GAH), one of the most basic and important derivatives of chitin, is obtained by hydrolysis of chitin in concentrated hydrochloric acid. At present, little is known about how GAH functions in skeletal development. In this report, we demonstrate that GAH, extracted from the cell wall of Agaricus bisporus, acts in a dose-dependent manner to promote not only cartilage and bone development in larvae but also caudal fin regeneration in adult fish. Furthermore, GAH treatment causes a significant increase in expression of bone-related marker genes, indicating its important role in promoting skeletal development. We show that in both larval and adult osteoporosis models induced by high iron osteogenic defects are significantly ameliorated after treatment with GAH, which regulates expression of a series of bone-related genes. Finally, we demonstrate that GAH promotes skeletal development and injury repair through bone morphogenetic protein (Bmp) signaling, and it works at the downstream of the receptor level. Taken together, our findings not only provide a strong research foundation and strategy for the screening of natural osteoporosis drugs and product development using a zebrafish model but also establish the potential for the development of Agaricus bisporus-derived GAH as a new drug for osteoporosis treatment.

Recoding of stop codons expands the metabolic potential of two novel Asgardarchaeota lineages.

Asgardarchaeota have been proposed as the closest living relatives to eukaryotes, and a total of 72 metagenome-assembled genomes (MAGs) representing six primary lineages in this archaeal phylum have thus far been described. These organisms are predicted to be fermentative heterotrophs contributing to carbon cycling in sediment ecosystems. Here, we double the genomic catalogue of Asgardarchaeota by obtaining 71 MAGs from a range of habitats around the globe, including the deep subsurface, brackish shallow lakes, and geothermal spring sediments. Phylogenomic inferences followed by taxonomic rank normalisation confirmed previously established Asgardarchaeota classes and revealed four additional lineages, two of which were consistently recovered as monophyletic classes. We therefore propose the names Candidatus Sifarchaeia class nov. and Ca. Jordarchaeia class nov., derived from the gods Sif and Jord in Norse mythology. Metabolic inference suggests that both classes represent hetero-organotrophic acetogens, which also have the ability to utilise methyl groups such as methylated amines, with acetate as the probable end product in remnants of a methanogen-derived core metabolism. This inferred mode of energy conservation is predicted to be enhanced by genetic code expansions, i.e., stop codon recoding, allowing the incorporation of the rare 21st and 22nd amino acids selenocysteine (Sec) and pyrrolysine (Pyl). We found Sec recoding in Jordarchaeia and all other Asgardarchaeota classes, which likely benefit from increased catalytic activities of Sec-containing enzymes. Pyl recoding, on the other hand, is restricted to Sifarchaeia in the Asgardarchaeota, making it the first reported non-methanogenic archaeal lineage with an inferred complete Pyl machinery, likely providing members of this class with an efficient mechanism for methylamine utilisation. Furthermore, we identified enzymes for the biosynthesis of ester-type lipids, characteristic of bacteria and eukaryotes, in both newly described classes, supporting the hypothesis that mixed ether-ester lipids are a shared feature among Asgardarchaeota.

Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model.

The current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing dynamic models. In this paper, a novel stochastic model was proposed aiming to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We found that: (1) instead of aberration, there was a remarkable amount of asymptomatic virus carriers, (2) a virus carrier with symptoms was approximately twice more likely to pass the disease to others than that of an asymptomatic virus carrier, (3) the transmission rate reduced significantly since the implementation of control measures in Mainland China, and (4) it was expected that the epidemic outbreak would be contained by early March in the selected provinces and cities in China.