TOR-autophagy branch signaling via Imp1 dictates plant-microbe biotrophic interface longevity

Like other intracellular eukaryotic phytopathogens, the devastating rice blast fungus Magnaporthe (Pyricularia) oryzae first infects living host cells by elaborating invasive hyphae (IH) surrounded by a plant-derived membrane. This forms an extended biotrophic interface enclosing an apoplastic compartment into which fungal effectors can be deployed to evade host detection. M. oryzae also forms a focal, plant membrane-rich structure, the biotrophic interfacial complex (BIC), that accumulates cytoplasmic effectors for translocation into host cells. Molecular decision-making processes integrating fungal growth and metabolism in host cells with interface function and dynamics are unknown. Here, we report unanticipated roles for the M. oryzae Target-of-Rapamycin (TOR) nutrient-signaling pathway in mediating plant-fungal biotrophic interface membrane integrity. Through a forward genetics screen for M. oryzae mutant strains resistant to the specific TOR kinase inhibitor rapamycin, we discovered IMP1 encoding a novel vacuolar protein required for membrane trafficking, VATPase assembly, organelle acidification and autophagy induction. During infection, Δimp1 deletants developed intracellular IH in the first infected rice cell following cuticle penetration. However, fluorescently labeled effector probes revealed that interface membrane integrity became compromised as biotrophy progressed, abolishing the BIC and releasing apoplastic effectors into host cytoplasm. Growth between rice cells was restricted. TOR-independent autophagy activation in Δimp1 deletants (following infection) remediated interface function and cell-to-cell growth. Autophagy inhibition in wild type (following infection) recapitulated Δimp1. In addition to vacuoles, Imp1GFP localized to IH membranes in an autophagy-dependent manner. Collectively, our results suggest TOR-Imp1-autophagy branch signaling mediates membrane homeostasis to prevent catastrophic erosion of the biotrophic interface, thus facilitating fungal growth in living rice cells. The significance of this work lays in elaborating a novel molecular mechanism of infection stressing the dominance of fungal metabolism and metabolic control in sustaining long-term plant-microbe interactions. This work also has implications for understanding the enigmatic biotrophy to necrotrophy transition

MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition

Gait emotion recognition plays a crucial role in the intelligent system. Most of the existing methods recognize emotions by focusing on local actions over time. However, they ignore that the effective distances of different emotions in the time domain are different, and the local actions during walking are quite similar. Thus, emotions should be represented by global states instead of indirect local actions. To address these issues, a novel Multi Scale Adaptive Graph Convolution Network (MSA-GCN) is presented in this work through constructing dynamic temporal receptive fields and designing multiscale information aggregation to recognize emotions. In our model, a adaptive selective spatial-temporal graph convolution is designed to select the convolution kernel dynamically to obtain the soft spatio-temporal features of different emotions. Moreover, a Cross-Scale mapping Fusion Mechanism (CSFM) is designed to construct an adaptive adjacency matrix to enhance information interaction and reduce redundancy. Compared with previous state-of-the-art methods, the proposed method achieves the best performance on two public datasets, improving the mAP by 2\%. We also conduct extensive ablations studies to show the effectiveness of different components in our methods

Study on mechanical properties and microstructure of steel-polypropylene fiber coal gangue concrete

Incorporating coal gangue into the concrete matrix can realize the utilization of solid waste and reduce the use of natural aggregate. To improve the mechanical properties of coal gangue concrete, this paper designs four-level and four-factor orthogonal tests with coal gangue ceramide substitution rate, coal gangue ceramide sand substitution rate, steel fiber content, and polypropylene fiber content as independent variables. Through multidimensional data analysis of the test results, The main and secondary factors of compressive strength of hybrid fiber coal gangue concrete from strong to weak are the replacement rate of coal gangue ceramic sand, the replacement rate of coal gangue ceramic grain, the content of steel fiber and the content of polypropylene fiber. The optimal content is 30% coal gangue ceramic particle, 25∼30% coal gangue ceramic sand, 0.75∼1% steel fiber, and 0.2% polypropylene fiber. The grey prediction model GM (1, 5) is obtained, which can predict the concrete strength well within the range selected in this paper. The influence of fiber and coal gangue on the microstructure was studied by scanning electron microscopy, and the influence law of interfacial transition zone on the strength of concrete was explored, which provided a theoretical basis for the study of solid waste utilization of coal gangue

Genome-wide analysis of genes encoding core components of the ubiquitin system in soybean (\u3ci\u3eGlycine max\u3c/i\u3e) reveals a potential role for ubiquitination in host immunity against soybean cyst nematode

Background: Ubiquitination is a major post-translational protein modification that regulates essentially all cellular and physiological pathways in eukaryotes. The ubiquitination process typically involves three distinct classes of enzymes, ubiquitin-activating enzyme (E1), ubiquitin-conjugating enzyme (E2) and ubiquitin ligase (E3). To date, a comprehensive identification and analysis of core components comprising of the whole soybean (Glycine max) ubiquitin system (UBS) has not been reported. Results: We performed a systematic, genome-wide analysis of genes that encode core members of the soybean UBS in this study. A total of 1431 genes were identified with high confidence to encode putative soybean UBS components, including 4 genes encoding E1s, 71 genes that encode the E2s, and 1356 genes encoding the E3-related components. Among the E3-encoding genes, 760 encode RING-type E3s, 124 encode U-box domain-containing E3s, and 472 encode F-box proteins. To find out whether the identified soybean UBS genes encode active enzymes, a set of genes were randomly selected and the enzymatic activities of their recombinant proteins were tested. Thioester assays indicated proteins encoded by the soybean E1 gene GmUBA1 and the majority of selected E2 genes are active E1 or E2 enzymes, respectively. Meanwhile, most of the purified RING and U-box domain-containing proteins displayed E3 activity in the in vitro ubiquitination assay. In addition, 1034 of the identified soybean UBS genes were found to express in at least one of 14 soybean tissues examined and the transcript level of 338 soybean USB genes were significantly changed after abiotic or biotic (Fusarium oxysporum and Rhizobium strains) stress treatment. Finally, the expression level of a large number of the identified soybean UBS-related genes was found significantly altered after soybean cyst nematode (SCN) treatment, suggesting the soybean UBS potentially plays an important role in soybean immunity against SCN. Conclusions: Our findings indicate the presence of a large and diverse number of core UBS proteins in the soybean genome, which suggests that target-specific modification by ubiquitin is a complex and important part of cellular and physiological regulation in soybean. We also revealed certain members of the soybean UBS may be involved in immunity against soybean cyst nematode (SCN). This study sets up an essential foundation for further functional characterization of the soybean UBS in various physiological processes, such as host immunity against SCN

Enhancing Landslide Susceptibility Modelling Through a Novel Non-landslide Sampling Method and Ensemble Learning Technique

In recent years, several catastrophic landslide events have been observed throughout the globe, threatening to lives and infrastructures. To minimize the impact of landslides, the need of landslide susceptibility map is important. The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). The Zigui-Badong section of the Three Gorges Reservoir area (TGRA) in China was considered in the present study. Twelve influencing factors were selected as inputs for LSM, and the relationship between each causal factor and landslide spatial development was quantitatively analyzed. A total of 179 landslides have been used in the present study. About 70% of the landslide pixels were randomly considered for training, and the remaining 30% were used for validation. Logistic Regression (LR) model was applied to produce an initial susceptibility map, and the non-landslide samples were selected within the classified low-susceptibility zone. Subsequently, two ML classifiers – the Classification and Regression Tree (CART), and the Multi-Layer Perceptron (MLP), and four coupling models – the CART-Bagging, CART-Boosting, MLP-Bagging, and MLP-Boosting, were utilized for LSM. Finally, the receiver operating characteristics (ROC) curve and statistical analysis were applied for accuracy assessment. The results show that altitude and distance to rivers were the main causal factors of landslides in the study area. The LR-MLP-Boosting performed the best with an accuracy of 0.986 followed by the LR-CART-Bagging, LR-CART-Boosting, and LR-MLP-Bagging. Accuracy comparisons demonstrate that ensemble learning algorithm can notably enhance the LSM performance of ML classifiers, and the Boosting algorithm marginally outperforms the Bagging algorithm. Moreover, the LR model can effectively constrain the selection range of non-landslide samples. The non-landslide sampling method constrained by LR yields higher quality samples compared to raditional random sampling method with no constraints, which develops a more excellent LSM

Molecular understanding of the catalytic consequence of ketene intermediates under confinement

[Image: see text] Neutral ketene is a crucial intermediate during zeolite carbonylation reactions. In this work, the roles of ketene and its derivates (viz., acylium ion and surface acetyl) associated with direct C–C bond coupling during the carbonylation reaction have been theoretically investigated under realistic reaction conditions and further validated by synchrotron radiation X-ray diffraction (SR-XRD) and Fourier transformed infrared (FT-IR) studies. It has been demonstrated that the zeolite confinement effect has significant influence on the formation, stability, and further transformation of ketene. Thus, the evolution and the role of reactive and inhibitive intermediates depend strongly on the framework structure and pore architecture of the zeolite catalysts. Inside side pockets of mordenite (MOR), rapid protonation of ketene occurs to form a metastable acylium ion exclusively, which is favorable toward methyl acetate (MA) and acetic acid (AcOH) formation. By contrast, in 12MR channels of MOR, a relatively longer lifetime was observed for ketene, which tends to accelerate deactivation of zeolite due to coke formation by the dimerization of ketene and further dissociation to diene and alkyne. Thus, we resolve, for the first time, a long-standing debate regarding the genuine role of ketene in zeolite catalysis. It is a paradigm to demonstrate the confinement effect on the formation, fate, and catalytic consequence of the active intermediates in zeolite catalysis

High-Mobility Semiconducting Polymers With Different Spin Ground States

Organic semiconductors with high-spin ground states are fascinating because they could enable fundamental understanding on the spin-related phenomenon in light element and provide opportunities for organic magnetic and quantum materials. Although high-spin ground states have been observed in some quinoidal type small molecules or doped organic semiconductors, semiconducting polymers with high-spin at their neutral ground state are rarely reported. Here we report three high-mobility semiconducting polymers with different spin ground states. We show that polymer building blocks with small singlet-triplet energy gap (ΔES-T) could enable small ΔES-T gap and increase the diradical character in copolymers. We demonstrate that the electronic structure, spin density, and solid-state interchain interactions in the high-spin polymers are crucial for their ground states. Polymers with a triplet ground state (S = 1) could exhibit doublet (S = 1/2) behavior due to different spin distributions and solid-state interchain spin-spin interactions. Besides, these polymers showed outstanding charge transport properties with high hole/electron mobilities and can be both n- and p-doped with superior conductivities. Our results demonstrate a rational approach to obtain high-mobility semiconducting polymers with different spin ground states

Involvement of Actin-Regulating Factor Cofilin in the Inclusion Body Formation and RNA Synthesis of Human Parainfluenza Virus Type 3 via Interaction With the Nucleoprotein

Human parainfluenza virus type 3 (HPIV3) is one of the primary pathogens that causing severe respiratory tract diseases in newborns and infants. It could induce inclusion bodies (IBs) in infected cells. Comprised of viral nucleoprotein (N) and phosphoprotein (P), as well as some cellular factors, HPIV3 IBs are unique platform for efficient viral synthesis. Although several studies have demonstrated the formation of IBs, little is known about cellular proteins involved in HPIV3 IBs formation. By quantitative real-time PCR assays after cytochalasin D treatment, we found actin microfilaments of the cytoskeleton were indispensible for HPIV3 RNA synthesis. Using co-immunoprecipitation and immunofluorescence assays, an actin-modulating protein, cofilin was found to involve in the IBs formation through interaction with the N protein in N–P induced IBs complex. Viral IBs formation reduced upon RNA interference knockdown of cellular cofilin, thus viral RNA synthesis and protein expression level were also suppressed. What’s more, the inactive form of cofilin, p-cofilin was increased after HPIV3 infection, and phosphorylation of cofilin was required for interacting with N–P complex and IBs formation. We further identified that the regions in cofilin interacting with N protein lies in the C-terminus. Our findings for the first time to state that cellular cofilin involves in HPIV3 IBs and interaction with N is critical for cofilin to aid IBs formation and enhancing viral RNA synthesis

GPU Acceleration of Melody Accurate Matching in Query-by-Humming

With the increasing scale of the melody database, the query-by-humming system faces the trade-offs between response speed and retrieval accuracy. Melody accurate matching is the key factor to restrict the response speed. In this paper, we present a GPU acceleration method for melody accurate matching, in order to improve the response speed without reducing retrieval accuracy. The method develops two parallel strategies (intra-task parallelism and inter-task parallelism) to obtain accelerated effects. The efficiency of our method is validated through extensive experiments. Evaluation results show that our single GPU implementation achieves 20x to 40x speedup ratio, when compared to a typical general purpose CPU's execution time