Ankyrin Repeats Convey Force to Gate the NOMPC Mechanotransduction Channel.

How metazoan mechanotransduction channels sense mechanical stimuli is not well understood. The NOMPC channel in the transient receptor potential (TRP) family, a mechanotransduction channel for Drosophila touch sensation and hearing, contains 29 Ankyrin repeats (ARs) that associate with microtubules. These ARs have been postulated to act as a tether that conveys force to the channel. Here, we report that these N-terminal ARs form a cytoplasmic domain essential for NOMPC mechanogating in vitro, mechanosensitivity of touch receptor neurons in vivo, and touch-induced behaviors of Drosophila larvae. Duplicating the ARs elongates the filaments that tether NOMPC to microtubules in mechanosensory neurons. Moreover, microtubule association is required for NOMPC mechanogating. Importantly, transferring the NOMPC ARs to mechanoinsensitive voltage-gated potassium channels confers mechanosensitivity to the chimeric channels. These experiments strongly support a tether mechanism of mechanogating for the NOMPC channel, providing insights into the basis of mechanosensitivity of mechanotransduction channels.

scCorrector: a robust method for integrating multi-study single-cell data.

The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space. Specifically, we designed a Study Specific Adaptive Normalization for each study in decoder to implement these features. scCorrector substantially achieves competitive and robust performance compared with state-of-the-art methods and brings novel insights under various circumstances (e.g. various batches, multi-omics, cross-species, and development stages). In addition, the integration of single-cell data and spatial data makes it possible to transfer information between different studies, which greatly expand the narrow range of genes covered by MERFISH technology. In summary, scCorrector can efficiently integrate multi-study single-cell datasets, thereby providing broad opportunities to tackle challenges emerging from noisy resources.

scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data.

BACKGROUND: The rapid emergence of single-cell RNA-seq (scRNA-seq) data presents remarkable opportunities for broad investigations through integration analyses. However, most integration models are black boxes that lack interpretability or are hard to train. RESULTS: To address the above issues, we propose scInterpreter, a deep learning-based interpretable model. scInterpreter substantially outperforms other state-of-the-art (SOTA) models in multiple benchmark datasets. In addition, scInterpreter is extensible and can integrate and annotate atlas scRNA-seq data. We evaluated the robustness of scInterpreter in a variety of situations. Through comparison experiments, we found that with a knowledge prior, the training process can be significantly accelerated. Finally, we conducted interpretability analysis for each dimension (pathway) of cell representation in the embedding space. CONCLUSIONS: The results showed that the cell representations obtained by scInterpreter are full of biological significance. Through weight sorting, we found several new genes related to pathways in PBMC dataset. In general, scInterpreter is an effective and interpretable integration tool. It is expected that scInterpreter will bring great convenience to the study of single-cell transcriptomics.

MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm.

Effectively representing Medical Subject Headings (MeSH) headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify. In this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships), which can be constructed by the tree num. Then, five graph embedding algorithms including DeepWalk, LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed methods, we carried out the node classification and relationship prediction tasks. The results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the representation ability of vectors. Thus, it can serve as an input and continue to play a significant role in any computational models related to disease, drug, microbe, etc. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective.

Base-resolution prediction of transcription factor binding signals by a deep learning framework.

Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framework leveraging existing fully convolutional neural networks (FCN) to predict TF-DNA binding signals at the base-resolution level (named as FCNsignal). The proposed FCNsignal can simultaneously achieve the following tasks: (i) modeling the base-resolution signals of binding regions; (ii) discriminating binding or non-binding regions; (iii) locating TF-DNA binding regions; (iv) predicting binding motifs. Besides, FCNsignal can also be used to predict opening regions across the whole genome. The experimental results on 53 TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets show that our proposed framework outperforms some existing state-of-the-art methods. In addition, we explored to use the trained FCNsignal to locate all potential TF-DNA binding regions on a whole chromosome and predict DNA sequences of arbitrary length, and the results show that our framework can find most of the known binding regions and accept sequences of arbitrary length. Furthermore, we demonstrated the potential ability of our framework in discovering causal disease-associated single-nucleotide polymorphisms (SNPs) through a series of experiments.

Electron cryo-microscopy structure of the mechanotransduction channel NOMPC.

Mechanosensory transduction for senses such as proprioception, touch, balance, acceleration, hearing and pain relies on mechanotransduction channels, which convert mechanical stimuli into electrical signals in specialized sensory cells. How force gates mechanotransduction channels is a central question in the field, for which there are two major models. One is the membrane-tension model: force applied to the membrane generates a change in membrane tension that is sufficient to gate the channel, as in the bacterial MscL channel and certain eukaryotic potassium channels. The other is the tether model: force is transmitted via a tether to gate the channel. The transient receptor potential (TRP) channel NOMPC is important for mechanosensation-related behaviours such as locomotion, touch and sound sensation across different species including Caenorhabditis elegans, Drosophila and zebrafish. NOMPC is the founding member of the TRPN subfamily, and is thought to be gated by tethering of its ankyrin repeat domain to microtubules of the cytoskeleton. Thus, a goal of studying NOMPC is to reveal the underlying mechanism of force-induced gating, which could serve as a paradigm of the tether model. NOMPC fulfils all the criteria that apply to mechanotransduction channels and has 29 ankyrin repeats, the largest number among TRP channels. A key question is how the long ankyrin repeat domain is organized as a tether that can trigger channel gating. Here we present a de novo atomic structure of Drosophila NOMPC determined by single-particle electron cryo-microscopy. Structural analysis suggests that the ankyrin repeat domain of NOMPC resembles a helical spring, suggesting its role of linking mechanical displacement of the cytoskeleton to the opening of the channel. The NOMPC architecture underscores the basis of translating mechanical force into an electrical signal within a cell.

A learning-based method to predict LncRNA-disease associations by combining CNN and ELM.

BACKGROUND: lncRNAs play a critical role in numerous biological processes and life activities, especially diseases. Considering that traditional wet experiments for identifying uncovered lncRNA-disease associations is limited in terms of time consumption and labor cost. It is imperative to construct reliable and efficient computational models as addition for practice. Deep learning technologies have been proved to make impressive contributions in many areas, but the feasibility of it in bioinformatics has not been adequately verified. RESULTS: In this paper, a machine learning-based model called LDACE was proposed to predict potential lncRNA-disease associations by combining Extreme Learning Machine (ELM) and Convolutional Neural Network (CNN). Specifically, the representation vectors are constructed by integrating multiple types of biology information including functional similarity and semantic similarity. Then, CNN is applied to mine both local and global features. Finally, ELM is chosen to carry out the prediction task to detect the potential lncRNA-disease associations. The proposed method achieved remarkable Area Under Receiver Operating Characteristic Curve of 0.9086 in Leave-one-out cross-validation and 0.8994 in fivefold cross-validation, respectively. In addition, 2 kinds of case studies based on lung cancer and endometrial cancer indicate the robustness and efficiency of LDACE even in a real environment. CONCLUSIONS: Substantial results demonstrated that the proposed model is expected to be an auxiliary tool to guide and assist biomedical research, and the close integration of deep learning and biology big data will provide life sciences with novel insights.

Comparative transcriptome analyses reveal genes related to pigmentation in the petals of a flower color variation cultivar of Rhododendron obtusum.

BACKGROUND: Rhododendron is an important woody ornamental plant, and breeding varieties with different colors is a key research goal. Although there have been a few reports on the molecular mechanisms of flower colors and color patterning in Rhododendron, it is still largely unknown what factors regulate flower pigmentation in Rhododendron. METHODS AND RESULTS: In this study, the flower color variation cultivar 'Yanzhi Mi' and the wild-type (WT) cultivar 'Dayuanyangjin' were used as research objects, and the pigments and transcriptomes of their petals during five flower development stages were analyzed and compared. The results showed that derivatives of cyanidin, peonidin and pelargonidin might be responsible for the pink color of mutant petals and that the S2 stage was the key stage of flower color formation. In total, 412,910 transcripts and 2780 differentially expressed genes (DEGs) were identified in pairwise comparisons of WT and mutant petals. GO and KEGG enrichment analyses of the DEGs showed that 'DNA-binding transcription factor activity', 'Flavonoid biosynthesis' and 'Phenylpropanoid biosynthesis' were more active in mutant petals. Early anthocyanin pathway candidate DEGs (CHS3-CHS6, CHI, F3Hs and F3'H) were significantly correlated and were more highly expressed in mutant petals than in WT petals in the S2 stage. An R2R3-MYB unigene (TRINITY_DN55156_c1_g2) was upregulated approximately 10.5-fold in 'Yanzhi Mi' petals relative to 'Dayuanyangjin' petals in the S2 stage, and an R2R3-MYB unigene (TRINITY_DN59015_c3_g2) that was significantly downregulated in 'Yanzhi Mi' petals in the S2 stage was found to be closely related to Tca MYB112 in cacao. CONCLUSIONS: Taken together, the results of the present study could shed light on the molecular basis of anthocyanin biosynthesis in two Rhododendron obtusum cultivars and may provide a genetic resource for breeding varieties with different flower colors.

iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.

Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Most methods, such as k-mer and PSSM, based on the analysis of high-throughput expression data have the tendency to think functionally similar nucleic acid lack direct linear homology regardless of positional information and only quantify nonlinear sequence relationships. However, in many complex diseases, the sequence nonlinear relationship between the pathogenic nucleic acid and ordinary nucleic acid is not much different. Therefore, the analysis of positional information expression can help to predict the complex associations between circRNA and disease. To fill up this gap, we propose a new method, named iCDA-CGR, to predict the circRNA-disease associations. In particular, we introduce circRNA sequence information and quantifies the sequence nonlinear relationship of circRNA by Chaos Game Representation (CGR) technology based on the biological sequence position information for the first time in the circRNA-disease prediction model. In the cross-validation experiment, our method achieved 0.8533 AUC, which was significantly higher than other existing methods. In the validation of independent data sets including circ2Disease, circRNADisease and CRDD, the prediction accuracy of iCDA-CGR reached 95.18%, 90.64% and 95.89%. Moreover, in the case studies, 19 of the top 30 circRNA-disease associations predicted by iCDA-CGR on circRDisease dataset were confirmed by newly published literature. These results demonstrated that iCDA-CGR has outstanding robustness and stability, and can provide highly credible candidates for biological experiments.

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.

BACKGROUND: The interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive. Accurate and efficient computational methods can assist and accelerate the study of ncRNA-protein interactions. RESULTS: In this work, we develop a stacking ensemble computational framework, RPI-SE, for effectively predicting ncRNA-protein interactions. More specifically, to fully exploit protein and RNA sequence feature, Position Weight Matrix combined with Legendre Moments is applied to obtain protein evolutionary information. Meanwhile, k-mer sparse matrix is employed to extract efficient feature of ncRNA sequences. Finally, an ensemble learning framework integrated different types of base classifier is developed to predict ncRNA-protein interactions using these discriminative features. The accuracy and robustness of RPI-SE was evaluated on three benchmark data sets under five-fold cross-validation and compared with other state-of-the-art methods. CONCLUSIONS: The results demonstrate that RPI-SE is competent for ncRNA-protein interactions prediction task with high accuracy and robustness. It's anticipated that this work can provide a computational prediction tool to advance ncRNA-protein interactions related biomedical research.

Prediction of drug-target interactions from multi-molecular network based on LINE network representation method.

BACKGROUND: The prediction of potential drug-target interactions (DTIs) not only provides a better comprehension of biological processes but also is critical for identifying new drugs. However, due to the disadvantages of expensive and high time-consuming traditional experiments, only a small section of interactions between drugs and targets in the database were verified experimentally. Therefore, it is meaningful and important to develop new computational methods with good performance for DTIs prediction. At present, many existing computational methods only utilize the single type of interactions between drugs and proteins without paying attention to the associations and influences with other types of molecules. METHODS: In this work, we developed a novel network embedding-based heterogeneous information integration model to predict potential drug-target interactions. Firstly, a heterogeneous multi-molecuar information network is built by combining the known associations among protein, drug, lncRNA, disease, and miRNA. Secondly, the Large-scale Information Network Embedding (LINE) model is used to learn behavior information (associations with other nodes) of drugs and proteins in the network. Hence, the known drug-protein interaction pairs can be represented as a combination of attribute information (e.g. protein sequences information and drug molecular fingerprints) and behavior information of themselves. Thirdly, the Random Forest classifier is used for training and prediction. RESULTS: In the results, under the five-fold cross validation, our method obtained 85.83% prediction accuracy with 80.47% sensitivity at the AUC of 92.33%. Moreover, in the case studies of three common drugs, the top 10 candidate targets have 8 (Caffeine), 7 (Clozapine) and 6 (Pioglitazone) are respectively verified to be associated with corresponding drugs. CONCLUSIONS: In short, these results indicate that our method can be a powerful tool for predicting potential drug-target interactions and finding unknown targets for certain drugs or unknown drugs for certain targets.

Transmembrane channel-like (tmc) gene regulates Drosophila larval locomotion.

Drosophila larval locomotion, which entails rhythmic body contractions, is controlled by sensory feedback from proprioceptors. The molecular mechanisms mediating this feedback are little understood. By using genetic knock-in and immunostaining, we found that the Drosophila melanogaster transmembrane channel-like (tmc) gene is expressed in the larval class I and class II dendritic arborization (da) neurons and bipolar dendrite (bd) neurons, both of which are known to provide sensory feedback for larval locomotion. Larvae with knockdown or loss of tmc function displayed reduced crawling speeds, increased head cast frequencies, and enhanced backward locomotion. Expressing Drosophila TMC or mammalian TMC1 and/or TMC2 in the tmc-positive neurons rescued these mutant phenotypes. Bending of the larval body activated the tmc-positive neurons, and in tmc mutants this bending response was impaired. This implicates TMC's roles in Drosophila proprioception and the sensory control of larval locomotion. It also provides evidence for a functional conservation between Drosophila and mammalian TMCs.

A Defensive Kicking Behavior in Response to Mechanical Stimuli Mediated by Drosophila Wing Margin Bristles.

Mechanosensation, one of the fastest sensory modalities, mediates diverse behaviors including those pertinent for survival. It is important to understand how mechanical stimuli trigger defensive behaviors. Here, we report that Drosophila melanogaster adult flies exhibit a kicking response against invading parasitic mites over their wing margin with ultrafast speed and high spatial precision. Mechanical stimuli that mimic the mites' movement evoke a similar kicking behavior. Further, we identified a TRPV channel, Nanchung, and a specific Nanchung-expressing neuron under each recurved bristle that forms an array along the wing margin as being essential sensory components for this behavior. Our electrophysiological recordings demonstrated that the mechanosensitivity of recurved bristles requires Nanchung and Nanchung-expressing neurons. Together, our results reveal a novel neural mechanism for innate defensive behavior through mechanosensation. SIGNIFICANCE STATEMENT: We discovered a previously unknown function for recurved bristles on the Drosophila melanogaster wing. We found that when a mite (a parasitic pest for Drosophila) touches the wing margin, the fly initiates a swift and accurate kick to remove the mite. The fly head is dispensable for this behavior. Furthermore, we found that a TRPV channel, Nanchung, and a specific Nanchung-expressing neuron under each recurved bristle are essential for its mechanosensitivity and the kicking behavior. In addition, touching different regions of the wing margin elicits kicking directed precisely at the stimulated region. Our experiments suggest that recurved bristles allow the fly to sense the presence of objects by touch to initiate a defensive behavior (perhaps analogous to touch-evoked scratching; Akiyama et al., 2012).

Pseudofulvibacter marinus sp. nov., isolated from seawater.

A novel Gram-stain-negative, rod-shaped, non-spore-forming, non-flagellated, strictly aerobic strain, designated RZW2-1T, was isolated from coastal seawater of the Yellow Sea in China (35.475° N 119.613° E). The organism grew optimally at 24 °C, at pH 7.0 and in the presence of 3.0 % (w/v) NaCl. The strain requires seawater or artificial seawater for growth and NaCl alone does not support growth. Strain RZW2-1T contained MK-6 as the only respiratory quinone and iso-C15 : 0, iso-C15 : 1 G and 10-methyl C16 : 0 and/or iso-C17 : 1ω9c as the dominant fatty acids. The polar lipids of strain RZW2-1T were four unidentified phospholipids (PL1-PL4), two unknown lipids (L1, L2) and one unidentified aminolipid (AL1). The DNA G+C content of strain RZW2-1T was 32 mol%. Phylogenetic analysis based on 16S rRNA gene sequences showed that the novel strain was most closely related to the type strain of the only described species of genus Pseudofulvibacter, Pseudofulvibacter geojedonensis YCS-9T, with 95.1 % 16S rRNA gene sequence similarity. On the basis of polyphasic analyses, strain RZW2-1T represents a novel species of the genus Pseudofulvibacter, for which the name Pseudofulvibacter marinus sp. nov. is proposed. The type strain is RZW2-1T ( = JCM 30826T = MCCC 1K00695T).

[Prediction the Soluble Solid Content in Sugarcanes by Using Near Infrared Hyperspectral Imaging System].

In order to explore the feasibility of prediction soluble solid contents (SSC) in sugarcane stalks by using near infrared hyperspectral imaging techniques, two hundred and forty sugarcane stalks which come from three different varieties were studied. After obtaining the raw hyperspectral images of sugarcane stalks, the spectral information and textural features were discussed respectively. The prediction models were established by using partial least squares regression (PLSR), principal components regression (PCR) and least squares support vector machines (LS-SVM) algorithms. Besides, three different selected wavelengths algorithms such as successive projection (SPA) algorithms, intervals partial least squares (iPLS) algorithms and uninformation variables elimination (UVE) algorithm were analyzed after building partial least squares regression model. The results indicate that partial least squares regression model based on spectral features can be an steady model to predict SSC and the correlation coefficient (R2) of calibration sets and prediction sets are 0.879, 0.843. The root mean square errors of calibration sets and prediction sets are 0.644, 0.742 respectively. The obtained 105 wavelengths which were selected by UVE algorithm are effective spectral features. The R2 results of calibration sets and prediction sets of its PLSR model are 0.860, 0.813. The root mean square errors of calibration sets and prediction sets are 0.693, 0.810 respectively

GraphCPIs: A novel graph-based computational model for potential compound-protein interactions.

Identifying proteins that interact with drug compounds has been recognized as an important part in the process of drug discovery. Despite extensive efforts that have been invested in predicting compound-protein interactions (CPIs), existing traditional methods still face several challenges. The computer-aided methods can identify high-quality CPI candidates instantaneously. In this research, a novel model is named GraphCPIs, proposed to improve the CPI prediction accuracy. First, we establish the adjacent matrix of entities connected to both drugs and proteins from the collected dataset. Then, the feature representation of nodes could be obtained by using the graph convolutional network and Grarep embedding model. Finally, an extreme gradient boosting (XGBoost) classifier is exploited to identify potential CPIs based on the stacked two kinds of features. The results demonstrate that GraphCPIs achieves the best performance, whose average predictive accuracy rate reaches 90.09%, average area under the receiver operating characteristic curve is 0.9572, and the average area under the precision and recall curve is 0.9621. Moreover, comparative experiments reveal that our method surpasses the state-of-the-art approaches in the field of accuracy and other indicators with the same experimental environment. We believe that the GraphCPIs model will provide valuable insight to discover novel candidate drug-related proteins.

Predicting the Sequence Specificities of DNA-Binding Proteins by DNA Fine-Tuned Language Model With Decaying Learning Rates.

DNA-binding proteins (DBPs) play vital roles in the regulation of biological systems. Although there are already many deep learning methods for predicting the sequence specificities of DBPs, they face two challenges as follows. Classic deep learning methods for DBPs prediction usually fail to capture the dependencies between genomic sequences since their commonly used one-hot codes are mutually orthogonal. Besides, these methods usually perform poorly when samples are inadequate. To address these two challenges, we developed a novel language model for mining DBPs using human genomic data and ChIP-seq datasets with decaying learning rates, named DNA Fine-tuned Language Model (DFLM). It can capture the dependencies between genome sequences based on the context of human genomic data and then fine-tune the features of DBPs tasks using different ChIP-seq datasets. First, we compared DFLM with the existing widely used methods on 69 datasets and we achieved excellent performance. Moreover, we conducted comparative experiments on complex DBPs and small datasets. The results show that DFLM still achieved a significant improvement. Finally, through visualization analysis of one-hot encoding and DFLM, we found that one-hot encoding completely cut off the dependencies of DNA sequences themselves, while DFLM using language models can well represent the dependency of DNA sequences. Source code are available at: https://github.com/Deep-Bioinfo/DFLM.

Investigation of gauze and medical bottle co-pyrolysis on the product formation, reactivity, and reaction pathway of char, liquid oil, and gas.

Effective in-site treatment of medical waste has become a weak link in hospitals. Pyrolysis technology is a treatment method for medical waste that can enable rapid disposal in hospital settings and relieve environmental pressure, while also producing high-value products and reducing disposal costs. In this work, the effects of feedstock ratio and temperature on product yield and components of gauze (GA) and medical bottles (MB) co-pyrolysis have been investigated. The higher yield of solid products was obtained by co-pyrolysis of GA and MB at 400 ℃. With the addition of MB and an increase in temperature for the co-pyrolysis of GA and MB in a similar ratio, the pyrolysis oil and gas yields gradually increased. According to GC-MS analysis, co-feeding 75% MB to GA improved the alcohol content from 33.21% to a maximum yield of 59.8% at a pyrolysis temperature of 700 ℃. The content of aliphatic hydrocarbon reached 38.68% when the pyrolysis temperature and MB addition ratio were 700 °C and 75%, respectively. The GC data shows that the main gas components of co-pyrolysis of GA/MB were CH4 and H2, while the pyrolysis of pure GA or MB resulted in CO or CO2. Additionally, the solid carbon products obtained have an excellent pore structure. This strategy can benefit medical waste control and resource utilization for the low-cost disposal of medical waste and the acquisition of high-value resource products.

Genome-Wide Analysis of the Rhododendron AP2/ERF Gene Family: Identification and Expression Profiles in Response to Cold, Salt and Drought Stress.

The AP2/ERF gene family is one of the most conserved and important transcription factor families mainly occurring in plants with various functions in regulating plant biological and physiological processes. However, little comprehensive research has been conducted on the AP2/ERF gene family in Rhododendron (specifically, Rhododendron simsii), an important ornamental plant. The existing whole-genome sequence of Rhododendron provided data to investigate the AP2/ERF genes in Rhododendron on a genome-wide scale. A total of 120 Rhododendron AP2/ERF genes were identified. The phylogenetic analysis showed that RsAP2 genes were classified into five main subfamilies, AP2, ERF, DREB, RAV and soloist. Cis-acting elements involving plant growth regulators, response to abiotic stress and MYB binding sites were detected in the upstream sequences of RsAP2 genes. A heatmap of RsAP2 gene expression levels showed that these genes had different expression patterns in the five developmental stages of Rhododendron flowers. Twenty RsAP2 genes were selected for quantitative RT-PCR experiments to clarify the expression level changes under cold, salt and drought stress treatments, and the results showed that most of the RsAP2 genes responded to these abiotic stresses. This study generated comprehensive information on the RsAP2 gene family and provides a theoretical basis for future genetic improvement.

Contribution of reaction of atmospheric amine with sulfuric acid to mixing particle formation from clay mineral.

During dust storm, mineral particle is frequently observed to be mixed with anthropogenic pollutants (APs) and forms mixing particle which arises more complex influences on regional climate than unmixed mineral particle. Even though mixing particle formation mechanism received significant attention recently, most studies focused on the heterogeneous reaction of inorganic APs on single composition of mineral. Here, the heterogeneous reaction mechanism of amine (a proxy of organic APs) with sulfuric acid (SA) on kaolinite (Kao, a proxy of mineral dust), and its contribution to mixing particle formation are investigated under variable atmospheric conditions. Two heterogeneous reactions of Kao-SA-amine and Kao-H2O-SA-amine in absence/presence of water were comparably investigated using combined theoretical and experimental methods, respectively. The contribution from such two heterogeneous reactions to mixing particle formation was evaluated, respectively, exploring the effect of methyl groups (1-3 -CH3), relative humidity (RH) (11-100%) and temperature (220-298.15 K). Water was observed to play a significant role in promoting heterogeneous reaction of amines with SA on Kao surface, reducing formation energy of mixing particle containing ammonium salt converted by SA. Moreover, the promotion effect from water is enhanced with the increasing RH and the decreasing temperature. For methylamine and dimethylamine containing 1-2 -CH3, the heterogeneous reaction of Kao-H2O-SA-amine contributes more to mixing particle formation. However, for trimethylamine containing 3 -CH3, the heterogeneous reaction of Kao-SA-amine is the dominant source to mixing particle formation. For mixing particle generated from the above two heterogeneous reactions, ammoniums salts are supposed to be predominant components which is of strong hygroscopicity and further leads to significant influence on climate by altering radiative forcing of mixed particle and participating in the cloud condensation nuclei and ice nuclei.