Directional electronic tuning of Ni nanoparticles by interfacial oxygen bridging of support for catalyzing alkaline hydrogen oxidation.

Metallic nickel (Ni) is a promising candidate to substitute Pt-based catalysts for hydrogen oxidation reaction (HOR), but huge challenges still exist in precise modulation of the electronic structure to boost the electrocatalytic performances. Herein, we present the use of single-layer Ti3C2Tx MXene to deliberately tailor the electronic structure of Ni nanoparticles via interfacial oxygen bridges, which affords Ni/Ti3C2Tx electrocatalyst with exceptional performances for HOR in an alkaline medium. Remarkably, it shows a high kinetic current of 16.39 mA cmdisk-2 at the overpotential of 50 mV for HOR [78 and 2.7 times higher than that of metallic Ni and Pt/C (20%), respectively], also with good durability and CO antipoisoning ability (1,000 ppm) that are not available for conventional Pt/C (20%) catalyst. The ultrahigh conductivity of single-layer Ti3C2Tx provides fast transmission of electrons for Ni nanoparticles, of which the uniform and small sizes endow them with high-density active sites. Further, the terminated -O/-OH functional groups on Ti3C2Tx directionally capture electrons from Ni nanoparticles via interfacial Ni-O bridges, leading to obvious electronic polarization. This could enhance the Nids-O2p interaction and weaken Nids-H1s interaction of Ni sites in Ni/Ti3C2Txenabling a suitable H-/OH-binding energy and thus enhancing the HOR activity.

Landscape Heterogeneity Explains the Genetic Differentiation of a Forest Bird across the Sino-Himalayan Mountains.

Mountains are the world's most important centers of biodiversity. The Sino-Himalayan Mountains are global biodiversity hotspot due to their extremely high species richness and endemicity. Ample research investigated the impact of the Qinghai-Tibet Plateau uplift and Quaternary glaciations in driving species diversification in plants and animals across the Sino-Himalayan Mountains. However, little is known about the role of landscape heterogeneity and other environmental features in driving diversification in this region. We utilized whole genomes and phenotypic data in combination with landscape genetic approaches to investigate population structure, demography, and genetic diversity in a forest songbird species native to the Sino-Himalayan Mountains, the red-billed leiothrix (Leiothrix lutea). We identified 5 phylogeographic clades, including 1 in the East of China, 1 in Yunnan, and 3 in Tibet, roughly consistent with differences in song and plumage coloration but incongruent with traditional subspecies boundaries. Isolation-by-resistance model best explained population differentiation within L. lutea, with extensive secondary contact after allopatric isolation leading to admixture among clades. Ecological niche modeling indicated relative stability in the extent of suitable distribution areas of the species across Quaternary glacial cycles. Our results underscore the importance of mountains in the diversification of this species, given that most of the distinct genetic clades are concentrated in a relatively small area in the Sino-Himalayan Mountain region, while a single shallow clade populates vast lower-lying areas to the east. This study highlights the crucial role of landscape heterogeneity in promoting differentiation and provides a deep genomic perspective on the mechanisms through which diversity hotspots form.

Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning.

The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

MOTIVATION: Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. In addition, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics. RESULTS: In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. AVAILABILITY AND IMPLEMENTATION: Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC.

MARS: a motif-based autoregressive model for retrosynthesis prediction.

MOTIVATION: Retrosynthesis is a critical task in drug discovery, aimed at finding a viable pathway for synthesizing a given target molecule. Many existing approaches frame this task as a graph-generating problem. Specifically, these methods first identify the reaction center, and break a targeted molecule accordingly to generate the synthons. Reactants are generated by either adding atoms sequentially to synthon graphs or by directly adding appropriate leaving groups. However, both of these strategies have limitations. Adding atoms results in a long prediction sequence that increases the complexity of generation, while adding leaving groups only considers those in the training set, which leads to poor generalization. RESULTS: In this paper, we propose a novel end-to-end graph generation model for retrosynthesis prediction, which sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants. Given that chemically meaningful motifs fall between the size of atoms and leaving groups, our model achieves lower prediction complexity than adding atoms and demonstrates superior performance than adding leaving groups. We evaluate our proposed model on a benchmark dataset and show that it significantly outperforms previous state-of-the-art models. Furthermore, we conduct ablation studies to investigate the contribution of each component of our proposed model to the overall performance on benchmark datasets. Experiment results demonstrate the effectiveness of our model in predicting retrosynthesis pathways and suggest its potential as a valuable tool in drug discovery. AVAILABILITY AND IMPLEMENTATION: All code and data are available at https://github.com/szu-ljh2020/MARS.

Stretchable ionic-electronic bilayer hydrogel electronics enable in situ detection of solid-state epidermal biomarkers.

Continuous and in situ detection of biomarkers in biofluids (for example, sweat) can provide critical health data but is limited by biofluid accessibility. Here we report a sensor design that enables in situ detection of solid-state biomarkers ubiquitously present on human skin. We deploy an ionic-electronic bilayer hydrogel to facilitate the sequential dissolution, diffusion and electrochemical reaction of solid-state analytes. We demonstrate continuous monitoring of water-soluble analytes (for example, solid lactate) and water-insoluble analytes (for example, solid cholesterol) with ultralow detection limits of 0.51 and 0.26 nmol cm-2, respectively. Additionally, the bilayer hydrogel electrochemical interface reduces motion artefacts by a factor of three compared with conventional liquid-sensing electrochemical interfaces. In a clinical study, solid-state epidermal biomarkers measured by our stretchable wearable sensors showed a high correlation with biomarkers in human blood and dynamically correlated with physiological activities. These results present routes to universal platforms for biomarker monitoring without the need for biofluid acquisition.

Single-cell analysis of CD4+ tissue residency memory cells (TRMs) in adult atopic dermatitis: A new potential mechanism.

The pathophysiology of atopic dermatitis (AD) is complex. CD4+ T cells play an essential role in the development of lesions in AD. However, the underlying mechanism remains unclear. In the present study, we investigated the differentially expressed genes (DEGs) between adult AD lesioned and non-lesioned skin using two datasets from the Gene Expression Omnibus (GEO) database. 62 DEGs were shown to be related to cytokine response. Compared to non-lesioned skin, lesioned skin showed immune infiltration with increased numbers of activated natural killer (NK) cells and CD4+ T memory cells (p < 0.01). We then identified 13 hub genes with a strong association with CD4+ T cells using weighted correlation network analysis. Single-cell analysis of AD detected a novel CD4+ T subcluster, CD4+ tissue residency memory cells (TRMs), which were verified through immunohistochemistry (IHC) to be increased in the dermal area of AD. The significant relationship between CD4+ TRM and AD was assessed through further analyses. FOXO1 and SBNO2, two of the 13 hub genes, were characteristically expressed in the CD4+ TRM, but down-regulated in IFN-γ/TNF-α-induced HaCaT cells, as shown using quantitative polymerase chain reaction (qPCR). Moreover, SBNO2 expression was associated with increased Th1 infiltration in AD (p < 0.05). In addition, genes filtered using Mendelian randomization were positively correlated with CD4+ TRM and were highly expressed in IFN-γ/TNF-α-induced HaCaT cells, as determined using qPCR and western blotting. Collectively, our results revealed that the newly identified CD4+ TRM may be involved in the pathogenesis of adult AD.

Increasingly amplified stimulation mediated by TaNAC69-B is crucial for the leaf senescence in wheat.

Leaf senescence involves massive multidimensional alterations, such as nutrient redistribution, and is closely related to crop yield and quality. No apical meristem, Arabidopsis transcription activation factor, and Cup-shaped cotyledon (NAC)-type transcription factors integrate various signals and modulate an enormous number of target genes to ensure the appropriate progression of leaf senescence. However, few leaf senescence-related NACs have been functionally characterized in wheat. Based on our previous RNA-sequencing (RNA-seq) data, we focused on a NAC family member, TaNAC69-B, which is increasingly expressed during leaf senescence in wheat. Overexpression of TaNAC69-B led to precocious leaf senescence in wheat and Arabidopsis, and affected several agricultural traits in transgenic wheat. Moreover, impaired expression of TaNAC69-B by virus-induced gene silencing retarded the leaf senescence in wheat. By RNA-seq and quantitative real-time polymerase chain reaction analysis, we confirmed that some abscisic acid (ABA) biosynthesis genes, including AAO3 and its ortholog in wheat, TraesCS2B02G270600 (TaAO3-B), were elevated by the overexpression of TaNAC69-B. Consistently, we observed more severe ABA-induced leaf senescence in TaNAC69-B-OE wheat and Arabidopsis plants. Furthermore, we determined that TaNAC69-B bound to the NAC binding site core (CGT) on the promoter regions of AAO3 and TaAO3-B. Moreover, we confirmed elevated ABA levels in TaNAC69-B-OE wheat lines. Although TaNAC69-B shares 39.83% identity (amino acid) with AtNAP, TaNAC69-B did not completely restore the delayed leaf senescence in the atnap mutant. Collectively, our results revealed a positive feedback loop, consisting of TaNAC69-B, ABA biosynthesis and leaf senescence, that is essential for the regulation of leaf senescence in wheat.

Genome-wide identification and expression analysis of histone deacetylase and histone acetyltransferase genes in response to drought in poplars.

BACKGROUND: Histone deacetylases (HDACs) and histone acetyltransferases (HATs) are involved in plant growth and development as well as in response to environmental changes, by dynamically regulating gene acetylation levels. Although there have been numerous reports on the identification and function of HDAC and HAT in herbaceous plants, there are fewer report related genes in woody plants under drought stress. RESULTS: In this study, we performed a genome-wide analysis of the HDAC and HAT families in Populus trichocarpa, including phylogenetic analysis, gene structure, conserved domains, and expression analysis. A total of 16 PtrHDACs and 12 PtrHATs were identified in P. trichocarpa genome. Analysis of cis-elements in the promoters of PtrHDACs and PtrHATs revealed that both gene families could respond to a variety of environmental signals, including hormones and drought. Furthermore, real time quantitative PCR indicated that PtrHDA906 and PtrHAG3 were significantly responsive to drought. PtrHDA906, PtrHAC1, PtrHAC3, PtrHAG2, PtrHAG6 and PtrHAF1 consistently responded to abscisic acid, methyl jasmonate and salicylic acid under drought conditions. CONCLUSIONS: Our study demonstrates that PtrHDACs and PtrHATs may respond to drought through hormone signaling pathways, which helps to reveal the hub of acetylation modification in hormone regulation of abiotic stress.

Conscious but not thinking-Mind-blanks during visuomotor tracking: An fMRI study of endogenous attention lapses.

Attention lapses (ALs) are complete lapses of responsiveness in which performance is briefly but completely disrupted and during which, as opposed to microsleeps, the eyes remain open. Although the phenomenon of ALs has been investigated by behavioural and physiological means, the underlying cause of an AL has largely remained elusive. This study aimed to investigate the underlying physiological substrates of behaviourally identified endogenous ALs during a continuous visuomotor task, primarily to answer the question: Were the ALs during this task due to extreme mind-wandering or mind-blanks? The data from two studies were combined, resulting in data from 40 healthy non-sleep-deprived subjects (20M/20F; mean age 27.1 years, 20-45). Only 17 of the 40 subjects were used in the analysis due to a need for a minimum of two ALs per subject. Subjects performed a random 2-D continuous visuomotor tracking task for 50 and 20 min in Studies 1 and 2, respectively. Tracking performance, eye-video, and functional magnetic resonance imaging (fMRI) were recorded simultaneously. A human expert visually inspected the tracking performance and eye-video recordings to identify and categorise lapses of responsiveness as microsleeps or ALs. Changes in neural activity during 85 ALs (17 subjects) relative to responsive tracking were estimated by whole-brain voxel-wise fMRI and by haemodynamic response (HR) analysis in regions of interest (ROIs) from seven key networks to reveal the neural signature of ALs. Changes in functional connectivity (FC) within and between the key ROIs were also estimated. Networks explored were the default mode network, dorsal attention network, frontoparietal network, sensorimotor network, salience network, visual network, and working memory network. Voxel-wise analysis revealed a significant increase in blood-oxygen-level-dependent activity in the overlapping dorsal anterior cingulate cortex and supplementary motor area region but no significant decreases in activity; the increased activity is considered to represent a recovery-of-responsiveness process following an AL. This increased activity was also seen in the HR of the corresponding ROI. Importantly, HR analysis revealed no trend of increased activity in the posterior cingulate of the default mode network, which has been repeatedly demonstrated to be a strong biomarker of mind-wandering. FC analysis showed decoupling of external attention, which supports the involuntary nature of ALs, in addition to the neural recovery processes. Other findings were a decrease in HR in the frontoparietal network before the onset of ALs, and a decrease in FC between default mode network and working memory network. These findings converge to our conclusion that the ALs observed during our task were involuntary mind-blanks. This is further supported behaviourally by the short duration of the ALs (mean 1.7 s), which is considered too brief to be instances of extreme mind-wandering. This is the first study to demonstrate that at least the majority of complete losses of responsiveness on a continuous visuomotor task are, if not due to microsleeps, due to involuntary mind-blanks.

DEMOC: a deep embedded multi-omics learning approach for clustering single-cell CITE-seq data.

Advances in single-cell RNA sequencing (scRNA-seq) technologies has provided an unprecedent opportunity for cell-type identification. As clustering is an effective strategy towards cell-type identification, various computational approaches have been proposed for clustering scRNA-seq data. Recently, with the emergence of cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), the cell surface expression of specific proteins and the RNA expression on the same cell can be captured, which provides more comprehensive information for cell analysis. However, existing single cell clustering algorithms are mainly designed for single-omic data, and have difficulties in handling multi-omics data with diverse characteristics efficiently. In this study, we propose a novel deep embedded multi-omics clustering with collaborative training (DEMOC) model to perform joint clustering on CITE-seq data. Our model can take into account the characteristics of transcriptomic and proteomic data, and make use of the consistent and complementary information provided by different data sources effectively. Experiment results on two real CITE-seq datasets demonstrate that our DEMOC model not only outperforms state-of-the-art single-omic clustering methods, but also achieves better and more stable performance than existing multi-omics clustering methods. We also apply our model on three scRNA-seq datasets to assess the performance of our model in rare cell-type identification, novel cell-subtype detection and cellular heterogeneity analysis. Experiment results illustrate the effectiveness of our model in discovering the underlying patterns of data.

Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey.

Advances in high-throughput experimental technologies promote the accumulation of vast number of biomedical data. Biomedical link prediction and single-cell RNA-sequencing (scRNA-seq) data imputation are two essential tasks in biomedical data analyses, which can facilitate various downstream studies and gain insights into the mechanisms of complex diseases. Both tasks can be transformed into matrix completion problems. For a variety of matrix completion tasks, matrix factorization has shown promising performance. However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issues, various matrix factorization methods have emerged recently. In this paper, we present a comprehensive review on such matrix factorization methods and their usage in biomedical link prediction and scRNA-seq data imputation. Moreover, we select representative matrix factorization methods and conduct a systematic empirical comparison on 15 real data sets to evaluate their performance under different scenarios. By summarizing the experimental results, we provide general guidelines for selecting matrix factorization methods for different biomedical matrix completion tasks and point out some future directions to further improve the performance for biomedical link prediction and scRNA-seq data imputation.

scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning.

The identification of differentially expressed genes between different cell groups is a crucial step in analyzing single-cell RNA-sequencing (scRNA-seq) data. Even though various differential expression analysis methods for scRNA-seq data have been proposed based on different model assumptions and strategies recently, the differentially expressed genes identified by them are quite different from each other, and the performances of them depend on the underlying data structures. In this paper, we propose a new ensemble learning-based differential expression analysis method, scDEA, to produce a more stable and accurate result. scDEA integrates the P-values obtained from 12 individual differential expression analysis methods for each gene using a P-value combination method. Comprehensive experiments show that scDEA outperforms the state-of-the-art individual methods with different experimental settings and evaluation metrics. We expect that scDEA will serve a wide range of users, including biologists, bioinformaticians and data scientists, who need to detect differentially expressed genes in scRNA-seq data.

Nondestructive measurement of terahertz optical thin films by machine learning based on physical consistency.

Optical scattering measurement is one of the most commonly used methods for non-contact online measurement of film properties in industrial film manufacturing. Terahertz photons have low energy and are non-ionizing when measuring objects, so combining these two methods can enable online nondestructive testing of thin films. In the visible light band, some materials are transparent, and their thickness and material properties cannot be measured. Therefore, a method based on physical consistency modeling and machine learning is proposed in this paper, which realizes the method of obtaining high-precision thin film parameters through single-frequency terahertz wave measurement, and shows good performance. Through the experimental measurement of organic material thin films, it is proved that the proposed method is an effective terahertz online detection technology with high precision and high throughput.

LncRNA RASAL2-AS1 promotes METTL14-mediated m6A methylation in the proliferation and progression of head and neck squamous cell carcinoma.

BACKGROUND: Long non-coding RNAs (lncRNAs) are key regulators of the 6-methyladenosine (m6A) epigenetic modification, playing a role in the initiation and progression of tumors. However, the regulatory mechanisms in head and neck squamous cell carcinoma (HNSCC) remain elusive. In this study, we investigated the molecular regulatory mechanisms of the lncRNA RASAL2-AS1 in the occurrence and development of HNSCC tumors. METHODS: A bioinformatics analysis was conducted to analyze the expression level of RASAL2-AS1 in HNSCC and normal tissues. RASAL2-AS1 mRNA and protein levels were detected using RT-PCR and Western blotting. Wound healing, transwell assays, flow cytometry, M6A dot blot, and RNA immunoprecipitation experiments were conducted to explore the regulatory role of the RASAL2-AS1 and downstream targets METTL14/LIS1 signaling pathway in HNSCC. Immunohistochemical examination was conducted to evaluate the expression of METTL14 and LIS1 in HNSCC and normal tissues. A tumor xenograft model of BALB/c nude mice was established to assess the impact of RASAL2-AS1 on cell proliferation and growth. RESULTS: RASAL2-AS1 high expression in HNSCC and cells deteriorated with survival rates of HNSCC. RASAL2-AS1 overexpression in HNSCC accelerated cell migration, colony formation, cell proliferation, cell cycle in S stage, while RASAL2-AS1 knockdown in HNSC cells inhibited cell cycle in G1 stage. After silencing METTL14, the above effects induced by overexpression of the RASAL2-AS1 were reversed. RASAL2-AS1 overexpression prompted LIS1 expression, whereas RASAL2-AS1 silencing reduced LIS1 levels in HNSCC cells, which was confirmed by immunohistological staining. Results demonstrated elevated expression of METTL14 or LIS1 in tongue cancer tissues. Overexpression of RASAL2-AS1 promoted tumor weight and tumor volume, which was counteracted by pcDNA3.1 RASAL2-AS1 plus silencing METTL14 and METTL14 and LIS1 were significantly decreased. CONCLUSION: Our study highlights the functional importance of the LncRNA RASAL2-AS1 in HNSCC and might assist in the development of a prognostic stratification and therapeutic approach. Which regulates HNSCC with the dependence of m6a manner.

The association of HBV infection and head and neck cancer: a systematic review and meta-analysis.

BACKGROUND: Hepatitis B virus (HBV) infections is an important public health problem worldwide and closely affect extrahepatic cancer. Several recent studies have investigated the relationship between HBV infection and head and neck cancer (HNC), but their findings were inconsistent.In order to address the limitations of small sample sizes, we conducted a meta-analysis to assess the association between HBV and HNC. METHODS: We systematically searched PubMed, Web of Science, Embase, Scopus, Cochrane Library, and China National Knowledge Infrastructure from inception to August 2023. Original articles published as a case-control or cohort study were included. HBV infection was identified by HBsAg, HBV DNA or ICD codes. Review articles, meeting abstracts, case reports, communications, editorials and letters were excluded, as were studies in a language other than English or Chinese. According to the MOOSE guidelines, frequencies reported for all dichotomous variables were extracted by two reviewers independently. Similarly, the outcomes of OR, RR or HR, and 95% CIs after adjusting for age and gender were collected. RESULTS: Thirteen relevant studies and 58,006 patients with HNC were included. Our analysis revealed a positive correlation between HBV and HNC (OR = 1.50; 95% CI: 1.28-1.77). After adjusting for age and gender, the similar result (OR = 1.30; 95% CI: 1.10-1.54) was obtained. Subgroup analysis further demonstrated a significant association between HBV infection and oral cancer (OR = 1.24; 95% CI: 1.05-1.47), as well as nasopharyngeal carcinoma (OR = 1.41; 95% CI: 1.26-1.58). However, due to the limited number of studies included, the statistical significance was not reached for cancer of the oropharynx (OR = 1.82; 95% CI: 0.66-5.05), hypopharynx (OR = 1.33; 95% CI: 0.88-2.00), and larynx (OR = 1.25; 95% CI: 0.69-2.24) after adjusting for age and gender. When excluding the interference of HIV/HCV, smoking and alcohol use, the final outcome (OR = 1.17; 95% CI: 1.01-1.35) got the same conclusion. CONCLUSIONS: Our study confirmed a positive relationship between HNC, specifically oral cancer and nasopharyngeal carcinoma, and HBV infection. However, further investigation is required at the molecular level to gather additional evidence in HNC.

Seasonality of respiratory syncytial virus infection in children hospitalized with acute lower respiratory tract infections in Hunan, China, 2013-2022.

BACKGROUND: In China, respiratory syncytial virus (RSV) infections traditionally occur during the spring and winter seasons. However, a shift in the seasonal trend was noted in 2020-2022, during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: This study investigated the seasonal characteristics of RSV infection in children hospitalized with acute lower respiratory tract infections (ALRTIs). The RSV epidemic season was defined as RSV positivity in > 10% of the hospitalized ALRTI cases each week. Nine RSV seasons were identified between 2013 and 2022, and nonlinear ordinary least squares regression models were used to assess the differences in year-to-year epidemic seasonality trends. RESULTS: We enrolled 49,658 hospitalized children diagnosed with ALRTIs over a 9-year period, and the RSV antigen-positive rate was 15.2% (n = 7,566/49,658). Between 2013 and 2022, the average onset and end of the RSV season occurred in week 44 (late October) and week 17 of the following year, respectively, with a typical duration of 27 weeks. However, at the onset of the COVID-19 pandemic, the usual spring RSV peak did not occur. Instead, the 2020 epidemic started in week 32, and RSV seasonality persisted into 2021, lasting for an unprecedented 87 weeks before concluding in March 2022. CONCLUSIONS: RSV seasonality was disrupted during the COVID-19 pandemic, and the season exhibited an unusually prolonged duration. These findings may provide valuable insights for clinical practice and public health considerations.

Copper Catalyst-Promoted Regioselective Multicomponent Cascade Cyclization of 3-Aza-1,5-enynes with Sulfur Dioxide and Cycloketone Oxime Esters to Access Cyanoalkylsulfonylated 1,2-Dihydropyridines.

Radical cascade cyclization via the cracking of alkenyl C-H has emerged as an attractive and remarkable tool for the rapid construction of ring frameworks with endocyclic double bonds. We developed a cascade reaction of 3-aza-1,5-enynes with sulfur dioxide and cycloketone oxime esters to access cyanoalkylsulfonylated 1,2-dihydropyridines, which can be easily converted to pyridine derivatives. This protocol involves radical addition to the C≡C bond and 6-endo cyclization and features high regioselectivity and a broad substrate scope.

Drought responses and carbon allocation strategies of poplar with different leaf maturity.

Leaf characteristics can reflect the adaptation of trees to drought stress. However, the effect of leaf maturity on drought stress has been neglected, leading to uncertainty in inferring individual tree responses to drought from leaves. The allocation strategy of photosynthetic carbon between leaf organs (fully expanded young and old leaves) under drought stress remains unclear. Poplar is a diverse and widespread tree species in arid and semi-arid regions. Here, three poplar genotypes (Populus cathayana, P. × euramericana 'Nanlin 895', and P. alba × P. tremula var. glandulosa) were selected and exposed to different watering regimes. The responses and carbon allocation strategies of leaves with different maturity to drought were investigated using a combination of leaf traits and 13 C pulse labelling technique. The results showed that (1) fully expanded young leaves had better osmotic regulation and antioxidant capacity than aged leaves under drought stress. (2) Aged leaves acted as a carbon source during water deficit, where their photosynthetic products were transferred and supplied to upper young leaves to promote stronger photosynthesis in young leaves to acquire resources for tree growth. This study highlights that the effect of leaf maturity should be considered in the future when investigating the effects of drought on woody plants, especially for continuously growing tree species. Therefore, our study not only demonstrates the existence of leaf-age-dependent responses to drought in poplar but also provides new insights into carbon allocation at the leaf level.

Identification of the C1qDC gene family in grass carp (Ctenopharyngodon idellus) and the response of C1qA, C1qB, and C1qC to GCRV infection in vivo and in vitro.

Proteins from the C1q domain-containing (C1qDC) family recognize self-, non-self-, and altered-self ligands and serves as an initiator molecule for the classical complement pathway as well as recognizing immune complexes. In this study, C1qDC gene family members were identified and analyzed in grass carp (Ctenopharyngodon idellus). Members of the C1q subfamily were cloned, and their response to infection with the grass carp virus was investigated. In the grass carp genome, 54 C1qDC genes and 67 isoforms have been identified. Most were located on chromosome 3, with 52 shared zebrafish homologies. Seven substantially differentially expressed C1qDC family genes were identified in the transcriptomes of cytokine-induced killer (CIK) cells infected with grass carp reovirus (GCRV), all of which exhibited sustained upregulation. The opening reading frames of grass carp C1qA, C1qB, and C1qC, belonging to the C1q subfamily, were determined to be 738, 732, and 735 base pairs, encoding 245, 243, and 244 amino acids with molecular weights of 25.81 kDa, 25.63 kDa and 26.16 kDa, respectively. Three genes were detected in the nine collected tissues, and their expression patterns were similar, with the highest expression levels observed in the spleen. In vivo after GCRV infection showed expression trends of C1qA, C1qB, and C1qC in the liver, spleen, and kidney. An N-type pattern in the liver and kidney was characterized by an initial increase followed by a decrease, with the highest expression occurring during the recovering period, and a V-type pattern in the spleen with the lowest expression levels during the death period. In vitro, after GCRV infection showed expression trends of C1qA, C1qB, and C1qC, and this gradually increased within the first 24 h, with a notable increase observed at the 24 h time point. After CIK cells incubation with purified recombinant proteins, rC1qA, rC1qB, and rC1qC for 3 h, followed by GCRV inoculation, the GCRV replication indicated that rC1qC exerted a substantial inhibitory effect on viral replication in CIK cells after 24 h of GCRV inoculation. These findings offer valuable insights into the structure, evolution, and function of the C1qDC family genes and provide a foundational understanding of the immune function of C1q in grass carp.