Identification of a broad-spectrum high-affinity peptide ligand for the purification of spike proteins.

Since the outbreak of coronavirus disease 2019, the global demand for vaccines has increased rapidly to prevent infection and protect high-risk populations. However, identifying viral mutations poses an additional challenge for chromatographic purification of vaccines and subunit vaccines. In this study, a new affinity peptide model, X1VX2GLNX3WX4RYSK, was established, and a library of 612 peptides was generated for ligand screening. Based on a multistep strategy of ligand screening, 18 candidate peptides were obtained. The top ranking peptide, LP14 (YVYGLNIWLRYSK), and two other representative peptides, LP02 and LP06, with lower rankings were compared via molecular dynamics simulation. The results revealed that peptide binding to the receptor binding domain (RBD) was driven by hydrophobic interactions and the key residues involved in the binding were identified. Surface plasmon resonance analysis further confirmed that LP14 had the highest affinity for the wild RBD (Kd=0.520 µmol/L), and viral mutation had little influence on the affinity of LP14, demonstrating its great potential as a broad-spectrum ligand for RBD purification. Finally, chromatographic performance of LP14-coupled gel-packed column verified that both wild and omicron RBDs could be purified and were eluted by 0.1 mol/L Gly-HCl buffer (pH 3.0). This research identified a broad-spectrum peptide for RBD purification based on rational design and demonstrated its potential application in the purification of RBDs from complex feedstock.

The p38 MAPK/snail signaling axis participates in cadmium-induced lung cancer cell migration and invasiveness.

To determine that p38 MAPK activation contributes to the migration and invasion of lung cancer cells caused by cadmium (Cd). A549 lung cancer cell migration and invasion were assessed using a transwell plate system, and the role of p38 was determined by knocking down p38 activity with two different inhibitors of p38. The activity of p38 was measured by western blot analysis using phospho-specific p38 antibodies and normalized to blots using antibodies directed to total p38 proteins. Snail transcripts were measured using qRT-PCR. The inhibition of p38 blocked Cd-induced migration and invasion, which correlated with an increased activation of p38 as a function of dose and time. Furthermore, Cd-induced activation of p38 MAPK controlled the increase of snail mRNA expression. The p38 MAPK/snail signaling axis was involved in Cd-induced lung cancer cell migration and invasion.

Identification of bZIP Transcription Factors That Regulate the Development of Leaf Epidermal Cells in Arabidopsis thaliana by Single-Cell RNA Sequencing.

Epidermal cells are the main avenue for signal and material exchange between plants and the environment. Leaf epidermal cells primarily include pavement cells, guard cells, and trichome cells. The development and distribution of different epidermal cells are tightly regulated by a complex transcriptional regulatory network mediated by phytohormones, including jasmonic acid, and transcription factors. How the fate of leaf epidermal cells is determined, however, is still largely unknown due to the diversity of cell types and the complexity of their regulation. Here, we characterized the transcriptional profiles of epidermal cells in 3-day-old true leaves of Arabidopsis thaliana using single-cell RNA sequencing. We identified two genes encoding BASIC LEUCINE-ZIPPER (bZIP) transcription factors, namely bZIP25 and bZIP53, which are highly expressed in pavement cells and early-stage meristemoid cells. Densities of pavement cells and trichome cells were found to increase and decrease, respectively, in bzip25 and bzip53 mutants, compared with wild-type plants. This trend was more pronounced in the presence of jasmonic acid, suggesting that these transcription factors regulate the development of trichome cells and pavement cells in response to jasmonic acid.

Insights into the cause of the Oroville dam spillway failure, 2017, California.

Earth internal seepage erosion in weathered bedrock under infrequently used hydraulic structures is often overlooked, which causes some solid particles to break away from the solid skeleton, degrading the earth's strength, and even causing unanticipated hydraulic engineering failures. The flood on the Oroville dam spillway in California in 2017 was caused by disturbed water flow due to a crack in the spillway chute caused by internal erosion in poorly weathered bedrock. The abnormal water flow of the spillway in the early stage and subsequent investigation revealed that the main reason for the accident was the insufficient weathered bedrock under the spillway chute. In this study, we formulated a coupled hydro-mechanical mechanism for internal erosion in weathered bedrock during the early stages. Using this model, we conducted an internal erosion numerical simulation at early stage, and the results showed that the physical characteristics of the weathered bedrock were degraded. Our results show the coupling analysis of quantitative computation during the early stage of internal erosion in weathered bedrock, which can provide an early warning method for the occurrence of internal erosion to avoid hydraulic disasters.

Development of a Natural Language Processing System to Identify Clinical Documentation of Electronic Cigarette Use.

Electronic Nicotine Delivery Systems (ENDS) use has increased substantially in the United States since 2010. To date, there is limited evidence regarding the nature and extent of ENDS documentation in the clinical note. In this work we investigate the effectiveness of different approaches to identify a patient's documented ENDS use. We report on the development and validation of a natural language processing system to identify patients with explicit documentation of ENDS using a large national cohort of patients at the United States Department of Veterans Affairs.

A High Crystalline Perylene-Based Hydrogen-Bonded Organic Framework for Enhanced Photocatalytic H2O2 Evolution.

Hydrogen-bonded organic frameworks (HOFs) are a kind of crystalline porous material that have shown great potential for photocatalysis on account of their mild synthesis conditions and high crystallinity. Perylene-based photocatalysts have great potential for photocatalytic H2O2 production due to their excellent photochemical stability and broad spectral absorption. In this work, we designed and synthesized a high crystalline perylene-based HOF (PTBA) and an amorphous analog sample PTPA for photocatalytic H2O2 evolution. Under visible light irradiation, PTBA shows a higher photocatalytic H2O2 production rate of 2699 µmol g-1 h-1 than PTPA (2176 µmol g-1 h-1) and an apparent quantum yield (AQY) of 2.96% at 500 nm. The enhanced photocatalytic performance of PTBA is attributed to the promotion of the separation and transfer of photocarriers due to its high crystallinity. This work provides a precedent for the application of HOFs in the field of photocatalytic H2O2 generation.

FLS2-RBOHD module regulates changes in the metabolome of Arabidopsis in response to abiotic stress.

Through crosstalk, FLAGELLIN SENSITIVE 2 (FLS2) and RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD) are involved in regulating the homeostasis of cellular reactive oxygen species (ROS) and are linked to the metabolic response of plants toward both biotic and abiotic stress. In the present study, we examined the metabolome of Arabidopsis seedlings under drought and salt conditions to better understand the potential role of FLS2 and RBOHD-dependent signaling in the regulation of abiotic stress response. We identified common metabolites and genes that are regulated by FLS2 and RBOHD, and are involved in the response to drought and salt stress. Under drought conditions, D-aspartic acid and the expression of associated genes, such as ASPARAGINE SYNTHASE 2 (ASN2), increased in both fls2 and robed/f double mutants. The accumulation of amino acids, carbohydrates, and hormones, such as L-proline, D-ribose, and indoleacetaldehyde increased in both fls2 and rbohd/f double mutants under salt conditions, as did the expression of related genes, such as PROLINE IMINOPEPTIDASE, PHOSPHORIBOSYL PYROPHOSPHATE SYNTHASE 5, and NITRILASE 3. Collectively, these results indicate that the FLS2-RBOHD module regulates plant response to drought and salt stress through ROS signaling by adjusting the accumulation of metabolites and expression of genes related to metabolite synthesis.

A deep learning approach for medication disposition and corresponding attributes extraction.

OBJECTIVE: This article summarizes our approach to extracting medication and corresponding attributes from clinical notes, which is the focus of track 1 of the 2022 National Natural Language Processing (NLP) Clinical Challenges(n2c2) shared task. METHODS: The dataset was prepared using Contextualized Medication Event Dataset (CMED), including 500 notes from 296 patients. Our system consisted of three components: medication named entity recognition (NER), event classification (EC), and context classification (CC). These three components were built using transformer models with slightly different architecture and input text engineering. A zero-shot learning solution for CC was also explored. RESULTS: Our best performance systems achieved micro-average F1 scores of 0.973, 0.911, and 0.909 for the NER, EC, and CC, respectively. CONCLUSION: In this study, we implemented a deep learning-based NLP system and demonstrated that our approach of (1) utilizing special tokens helps our model to distinguish multiple medications mentions in the same context; (2) aggregating multiple events of a single medication into multiple labels improves our model's performance.

The comprehensive risk assessment of the Tangjiashan landslide dam incident, China.

Risk assessment for landslide dams is very important to avoid unanticipated landslide failure and calamity. Recognition of the risk of landslide dams associated with changing influencing factors is to identify the risk grade and provide early warning of oncoming failure, while quantitative risk analysis of landslide dams due to many influencing factors changing in spatiotemporal domain is currently lacking. We applied the model to analyze the risk level of the Tangjiashan landslide dam caused by the Wenchuan Ms 8.0 earthquake. The risk evaluation, obtained according to the analysis of the influencing factors located in the risk assessment grade criteria, clearly shows that the risk reaches a higher level at that moment. Our analysis shows that the risk level of landslide dams can be quantitatively analyzed with our assessment method. Our results suggest that the risk assessment system can be an effective measure to dynamically predict the risk level and provide a sufficient early warning of the oncoming hazard by analyzing the variables of influencing factors at different times.

Improved cotton yield: Can we achieve this goal by regulating the coordination of source and sink?

Cotton is one of the major cash crops globally. It is characterized by determinate growth and multiple fruiting, which makes the source-sink contradiction more obvious. Coordination between source and sink is crucial for normal growth, yield, and quality of cotton. Numerous studies reported how the assimilate transport and distribution under varying environmental cues affected crop yields. However, less is known about the functional mechanism underlying the assimilate transport between source and sink, and how their distribution impacts cotton growth. Here, we provided an overview of the assimilate transport and distribution mechanisms , and discussed the regulatory mechanisms involved in source-sink balance in relation to cotton yield. Therefore, this review enriched our knowledge of the regulatory mechanism involved in source-sink relationship for improved cotton yield.

PIN1 regulates epidermal cells development under drought and salt stress using single-cell analysis.

Over the course of evolution, plants have developed plasticity to acclimate to environmental stresses such as drought and salt stress. These plant adaptation measures involve the activation of cascades of molecular networks involved in stress perception, signal transduction and the expression of stress related genes. Here, we investigated the role of the plasma membrane-localized transporter of auxin PINFORMED1 (PIN1) in the regulation of pavement cells (PCs) and guard cells (GCs) development under drought and salt stress conditions. The results showed that drought and salt stress treatment affected the development of PCs and GCs. Further analysis identified the different regulation mechanisms of PIN1 in regulating the developmental patterns of PCs and GCs under drought and salt stress conditions. Drought and salt stress also regulated the expression dynamics of PIN1 in pif1/3/4/5 quadruple mutants. Collectively, we revealed that PIN1 plays a crucial role in regulating plant epidermal cells development under drought and salt stress conditions, thus contributing to developmental rebustness and plasticity.

Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia.

Background: Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging. Objective: The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit. Methods: We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19-related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country. Results: Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19-related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits. Conclusions: This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches.

Interstitial lung disease independently associated with higher risk for COVID-19 severity and mortality: A meta-analysis of adjusted effect estimates.

OBJECTIVE: The aim of this study was to address the association between interstitial lung disease and the risk for severity and mortality among patients with coronavirus disease 2019 (COVID-19). METHODS: The electronic databases of PubMed, Web of Science and EMBASE were systematically searched. The pooled effect size with 95 % confidence interval (CI) was computed by a random-effects meta-analysis model. Heterogeneity test, sensitivity analysis, subgroup analysis, meta-regression analysis, Begg's test and Egger's test were performed. RESULTS: A total of sixteen eligible studies with 217,260 COVID-19 patients were enrolled in this meta-analysis. The findings based on adjusted effect estimates indicated that pre-existing interstitial lung disease was significantly associated with higher risk for COVID-19 severity (pooled effect = 1.34 [95 % CI: 1.16-1.55]) and mortality (pooled effect = 1.26 [95 % CI: 1.09-1.46]). Consistent results were observed in the subgroup analysis stratified by sample size, age, the percentage of male patients, study design, setting, the methods for adjustment and the factors for adjustment. The results of meta-regression demonstrated that sample size, age and region might be the potential sources of heterogeneity. Sensitivity analysis exhibited that our results were stable and robust. No publication bias was observed in Egger's test and Begg's test. CONCLUSION: This meta-analysis on the basis of adjusted effect estimates demonstrated that pre-existing interstitial lung disease was independently associated with significantly higher risk for COVID-19 severity and mortality.

Research strategies for single-cell transcriptome analysis in plant leaves.

The recent and continuous improvement in single-cell RNA sequencing (scRNA-seq) technology has led to its emergence as an efficient experimental approach in plant research. However, compared with single-cell research in animals and humans, the application of scRNA-seq in plant research is limited by several challenges, including cell separation, cell type annotation, cellular function analysis, and cell-cell communication networks. In addition, the unavailability of corresponding reliable and stable analysis methods and standards has resulted in the relative decentralization of plant single-cell research. Considering these shortcomings, this review summarizes the research progress in plant leaf using scRNA-seq. In addition, it describes the corresponding feasible analytical methods and associated difficulties and problems encountered in the current research. In the end, we provide a speculative overview of the development of plant single-cell transcriptome research in the future.

Significant association between anemia and higher risk for COVID-19 mortality: A meta-analysis of adjusted effect estimates.

OBJECTIVE: This study aimed to evaluate whether there was a significant relationship between anemia and the risk for mortality among coronavirus disease 2019 (COVID-19) patients by a quantitative meta-analysis based on the adjusted effect estimates. METHODS: A systematic search was conducted in electronic databases to identify all published literature. A random-effects meta-analysis model was used to estimate the pooled effect size and 95% confidence interval (CI). Heterogeneity test, Begg's test, subgroup analysis and meta-regression were performed. RESULTS: Twenty-three articles with 573,928 COVID-19 patients were included in the quantitative meta-analysis. There was a significant association between anemia and an elevated risk of COVID-19 mortality (pooled effect size = 1.47, 95% CI [1.30-1.67]). We observed this significant association in the further subgroup analyses by age, proportion of males, sample size, study design, region and setting. Sensitivity analysis exhibited that our results were reliable. Begg's test showed that there was no publication bias. Meta-regression indicated that the tested variables might not be the source of heterogeneity. CONCLUSION: Our meta-analysis based on risk factors-adjusted effect estimates indicated that anemia was independently associated with a significantly elevated risk for mortality among COVID-19 patients.

The potential ecological risk assessment of soil heavy metals using self-organizing map.

Heavy metal pollution control zoning aiming at the health maintenance of watershed soil ecosystem has become an important means of soil environmental protection and governance. Based on the self-organizing map (SOM), this study classifies the data sets of eight heavy metals such as Co, Cd, Zn, Cr, Cu, Pb, Ni, and Tl in 354 samples, calculates the potential ecological risk value of soil heavy metals in combination with the potential Hakansom Risk index (HRI), and uses the geographic information system (GIS) for visualization. In the research results, SOM has divided five soil ecological risk categories. The highest average ecological risk value of 85.95 is found in cluster IV, which is clustered and distributed in urban development areas in the upper reaches of the river. The average ecological risk values of cluster I and cluster V are relatively close at 79.64 and 79.19, respectively. Cluster I and cluster V are distributed in the north of the river in a linear and cluster manner, respectively, and are located on a concave bank with a relatively gentle slope. The average ecological risk of soil pollution in cluster II is 77.59, which is linearly distributed on both banks of the river. The ecological risk of soil pollution in cluster III is the lowest (74.39), mainly scattered in the south of rivers with less human activities. The study further identified the environmental factors that affect the soil ecological risk value in different cluster units and put forward the classified and differentiated management and control strategies for different cluster units. The research shows that SOM can cluster the data sets of heavy metals with high sensitivity and low threshold through competitive learning to effectively provide the distribution information of abnormal soil ecological risk areas. This information is helpful for urban environmental management departments and planning departments to take targeted management and recovery measures to avoid the health risks related to soil heavy metal pollution.

Identification of the Regulators of Epidermis Development under Drought- and Salt-Stressed Conditions by Single-Cell RNA-Seq.

As sessile organisms, plants constantly face challenges from the external environment. In order to meet these challenges and survive, plants have evolved a set of sophisticated adaptation strategies, including changes in leaf morphology and epidermal cell development. These developmental patterns are regulated by both light and hormonal signaling pathways. However, our mechanistic understanding of the role of these signaling pathways in regulating plant response to environmental stress is still very limited. By applying single-cell RNA-Seq, we determined the expression pattern of PHYTOCHROME INTERACTING FACTOR (PIF) 1, PIF3, PIF4, and PIF5 genes in leaf epidermal pavement cells (PCs) and guard cells (GCs). PCs and GCs are very sensitive to environmental stress, and our previous research suggests that these PIFs may be involved in regulating the development of PCs, GCs, and leaf morphology under environmental stress. Growth analysis showed that pif1/3/4/5 quadruple mutant maintained tolerance to drought and salt stress, and the length to width ratio of leaves and petiole length under normal growth conditions were similar to those of wild-type (WT) plants under drought and salt treatment. Analysis of the developmental patterns of PCs and GCs, and whole leaf morphology, further confirmed that these PIFs may be involved in mediating the development of epidermal cells under drought and salt stress, likely by regulating the expression of MUTE and TOO MANY MOUTHS (TMM) genes. These results provide new insights into the molecular mechanism of plant adaptation to adverse growth environments.