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Abstract Swine acute diarrhea syndrome coronavirus (SADS-CoV), an emerging Alpha-coronavirus, brings huge economic loss in swine industry. Interferons (IFNs) participate in a frontline antiviral defense mechanism triggering the activation of numerous downstream antiviral genes. Here, we demonstrated that TRIM25 overexpression significantly inhibited SADS-CoV replication, whereas TRIM25 deficiency markedly increased viral yield. We found that SADS-CoV N protein suppressed interferon-beta (IFN-β) production induced by Sendai virus (SeV) or poly(I:C). Moreover, we determined that SADS-CoV N protein interacted with RIG-I N-terminal two caspase activation and recruitment domains (2CARDs) and TRIM25 coiled-coil dimerization (CCD) domain. The interaction of SADS-CoV N protein with RIG-I and TRIM25 caused TRIM25 multimerization inhibition, the RIG-I-TRIM25 interaction disruption, and consequent the IRF3 and TBK1 phosphorylation impediment. Overexpression of SADS-CoV N protein facilitated the replication of VSV-GFP by suppressing IFN-β production. Our results demonstrate that SADS-CoV N suppresses the host IFN response, thus highlighting the significant involvement of TRIM25 in regulating antiviral immune defenses.

Abstract Cultivating rice varieties with robust blast resistance is the most effective and economical way to manage the rice blast disease. However, rice blast disease comprises leaf and panicle blast, which are different in terms of resistance mechanisms. While many blast resistant rice cultivars were bred using genes conferring resistance to only leaf or panicle blast, mining durable and effective quantitative trait loci (QTLs) for both panicle and leaf blast resistance is of paramount importance. In this study, we conducted a pangenome-wide association study (panGWAS) on 9 blast resistance related phenotypes using 414 international diverse rice accessions from an international rice panel. This approach led to the identification of 74 QTLs associated with rice blast resistance. One notable locus, qPBR1, validated in a F4:5 population and fine-mapped in a Heterogeneous Inbred Family (HIF), exhibited broad-spectrum, major and durable blast resistance throughout the growth period. Furthermore, we performed transcriptomic analysis of 3 resistant and 3 sensitive accessions at different time points after infection, revealing 3,311 differentially expressed genes (DEGs) potentially involved in blast resistance. Integration of the above results identified 6 candidate genes within the qPBR1 locus, with no significant negative effect on yield. The results of this study provide valuable germplasm resources, QTLs, blast response genes and candidate functional genes for developing rice varieties with enduring and broad-spectrum blast resistance. The qPBR1, in particular, holds significant potential for breeding new rice varieties with comprehensive and durable resistance throughout their growth period.

Magnaporthe oryzae, one of the most destructive rice pathogens, causes significant losses during the rice harvest every year. Bacillus amyloliquefaciens has been explored in many crops as a potential biocontrol agent. However, the mechanisms of B. amyloliquefaciens controled rice blast are not fully understood. Here, a biocontrol strain LM-1, isolated from a contaminated medium, was identified as B. amyloliquefaciens using morphological observation, physiological and biochemical tests, and 16S rDNA sequencing. LM-1 inhibited the growth and pathogenicity of M. oryzae and Bipolaris oryzae (Breda de Haan) Shoem. The mycelia of M. oryzae co-cultured with LM-1 were enlarged and broken by fluorescence microscopy using calcofluor white. LM-1 inhibited the mycelia of M. oryzae from producing conidia. Genes itu, srf, and fenB were detected in LM-1. Furthermore, the supernatant of LM-1 interfered with the appressorium formation of M. oryzae, blocked conidial cell death, and reduced autophagy degradation but did not affect the normal germination of rice seeds and seeding growth. Additionally, we observed hypersensitivity reactions, reactive oxygen species, and iron accumulation reduction in rice cells inoculated with supernatant. Our study reveals that LM-1 has a control effect on rice blast and affects cell wall integrity, sporulation, appressorium formation, cell death, and autophagy.

This paper proposes the D-optimal design for the additive mixture model with two-response, which is linear model with no interaction terms. The optimality was validated by using the general equivalence theorem, and the corresponding weights are found under which additive model satisfies D-optimality. In addition, relevant statistics and graphics are given to illustrate our results.

Considering a mixture model with qualitative factors, the R-optimal design problem is investigated when the levels of the qualitative factor interact with the mixture factors. In this paper, the conditions for R-optimality of designs with mixture and qualitative factors are presented. General analytical expressions are also derived for the decision function under the R-optimal designs, in order to verify that the resulting designs satisfy the general equivalence theorem. In addition, the relative efficiency of the R-optimal design is discussed.

High-salinity and blast disease are two major stresses that cause dramatic yield loss in rice production. GF14 (14-3-3) genes have been reported to play important role in biotic and abiotic stresses in plants. However, the roles of OsGF14C remain unknown. To understand the functions and regulatory mechanisms of OsGF14C in regulating salinity tolerance and blast resistance in rice, we have conducted OsGF14C-overexpressing transgenic experiments in the present study. Our results showed that overexpression of OsGF14C enhanced salinity tolerance but reduced blast resistance in rice. The enhanced salinity tolerance is related to the reduction of methylglyoxal and Na+ uptake instead of exclusion or compartmentation and the negative role of OsGF14C in blast resistance is associated with the suppression of OsGF14E, OsGF14F and PR genes. Our results together with the results from the previous studies suggest that the lipoxygenase gene LOX2 which is regulated by OsGF14C may play roles in coordinating salinity tolerance and blast resistance in rice. The current study for the first time revealed the possible roles of OsGF14C in regulating salinity tolerance and blast resistance in rice, and laid down a foundation for further functional study and crosstalk regulation between salinity and blast resistance in rice.

Abstract Background Although panicle blast is more destructive to yield loss than leaf blast in rice, the cloned genes that function in panicle blast resistance are still very limited and the molecular mechanisms underlying panicle blast resistance remain largely unknown. Results In the present study, we have confirmed that the three Oxalate oxidase (OXO) genes, OsOXO2, OsOXO3 and OsOXO4 from a blast-resistant cultivar BC10 function in panicle blast resistance in rice. The expression of OsOXO2, OsOXO3 and OsOXO4 were induced by panicle blast inoculation. Subcellular localization analysis revealed that the three OXO proteins are all localized in the nucleus and cytoplasm. Simultaneous silencing of OsOXO2, OsOXO3 and OsOXO4 decreased rice resistance to panicle blast, whereas the OsOXO2, OsOXO3 and OsOXO4 overexpression rice plants individually showed enhanced panicle blast resistance. More H2O2 and higher expression levels of PR genes were observed in the overexpressing plants than in the control plants, while the silencing plants exhibited less H2O2 and lower expression levels of PR genes compared to the control plants. Moreover, phytohormone treatment and the phytohormone signaling related gene expression analysis showed that panicle blast resistance mediated by the three OXO genes was associated with the activation of JA and ABA signaling pathways but suppression of SA signaling pathway. Conclusion OsOXO2, OsOXO3 and OsOXO4 positively regulate panicle blast resistance in rice. The OXO genes could modulate the accumulation of H2O2 and expression levels of PR gene in plants. Moreover, the OXO genes mediated panicle blast resistance could be regulated by ABA, SA and JA, and may be associated with the activation of JA and ABA signaling pathways but suppression of the SA signaling pathway.

Abstract Background The rice (Oryza sativa) gene Xa7 has been hypothesized to be a typical executor resistance gene against Xanthomonas oryzae pv. oryzae (Xoo), and has conferred durable resistance in the field for decades. Its identity and the molecular mechanisms underlying this resistance remain elusive. Results Here, we filled in gaps of genome in Xa7 mapping locus via BAC library construction, revealing the presence of a 100-kb non-collinear sequence in the line IRBB7 compared with Nipponbare reference genomes. Complementary transformation with sequentially overlapping subclones of the BACs demonstrated that Xa7 is an orphan gene, encoding a small novel protein distinct from any other resistance proteins reported. A 27-bp effector binding element (EBE) in the Xa7 promoter is essential for AvrXa7-inducing expression model. XA7 is anchored in the endoplasmic reticulum membrane and triggers programmed cell death in rice and tobacco (Nicotiana benthamiana). The Xa7 gene is absent in most cultivars, landraces, and wild rice accessions, but highly homologs of XA7 were identified in Leersia perrieri, the nearest outgroup of the genus Oryza. Conclusions Xa7 acts as a trap to perceive AvrXa7 via EBEAvrXa7 in its promoter, leading to the initiation of resistant reaction. Since EBEAvrXa7 is ubiquitous in promoter of rice susceptible gene SWEET14, the elevated expression of which is conducive to the proliferation of Xoo, that lends a great benefit for the Xoo strains retaining AvrXa7. As a result, varieties harboring Xa7 would show more durable resistance in the field. Xa7 alleles analysis suggests that the discovery of new resistance genes could be extended beyond wild rice, to include wild grasses such as Leersia species.

Abstract Background Avr-Pita was the first effector identified in the blast fungus (Magnaporthe oryzae)–rice (Oryza sativa) pathosystem. However, the molecular mechanism underlying its effects on the host plant has remained a long-standing mystery. Results Here, we report that ectopically expressing Avr-Pita in rice enhances susceptibility to M. oryzae and suppresses pathogen-associated molecular pattern (PAMP)-triggered defense responses. Avr-Pita targets the host mitochondria and interacts with the cytochrome c oxidase (COX) assembly protein OsCOX11, a key regulator of mitochondrial reactive oxygen species (ROS) metabolism in rice. Overexpressing Avr-Pita or OsCOX11 increased COX activity and decreased ROS accumulation triggered by the fungal PAMP chitin. OsCOX11-overexpressing plants showed increased susceptibility to M. oryzae, whereas OsCOX11-knockdown plants showed resistance to M. oryzae. Conclusions Taken together, these findings suggest that the fungal pathogen M. oryzae delivers the effector Avr-Pita to the host plant, where it enhances COX activity thus decreasing ROS accumulation. Therefore, this effector suppresses host innate immunity by perturbing ROS metabolism in the mitochondria.

Abstract Multiple haplotypes at the same rice blast R-gene locus share extremely high sequence similarity, which makes the gene diagnostic method using molecular markers less effective in differentiation from one another. The composition and distribution pattern of deployed R genes/haplotypes in elite rice varieties has not been extensively analyzed. In this study, we employed PCR amplification and sequencing approach for the diagnosis of R-gene haplotypes in 54 Chinese elite rice varieties. A varied number of functional and nonfunctional haplotypes of 4 target major R-gene loci, i.e., Pi2/9, Pi5, Pik, and Pib, were deduced by referring to the reference sequences of known R genes. Functional haplotypes accounted for relatively low frequencies for the Pi2/9 (15%) and Pik (9%) loci but for relatively high frequencies for the Pi5 (50%) and Pib (54%) loci. Intriguingly, significant frequencies of 33%, 39%, 46% of non-functional haplotypes at the Pi2/9, Pik, and Pib loci, respectively, with traceable original donors were identified, suggesting that they were most likely unintentionally spread by using undesirable donors in various breeding programs. In the case of Pi5 locus, only a single haplotype, i.e., Pi5 was identified. The reactions of 54 rice varieties to the differential isolates were evaluated, which showed a good correlation to the frequency of cognate avirulence (Avr) genes or haplotypes in the differential isolates. Four R genes, i.e., Pi2, Piz-t, Pi50, and Pikm were found to contribute significantly to the resistance of the elite rice varieties. Other two genes, Pi9 and Pikh, which were not utilized in rice varieties, showed promising values in breeding durable resistance due to their high resistance frequencies to the contemporary rice blast population. The sequence-based molecular diagnosis provided a promising approach for the identification and verification of haplotypes in different R-gene loci and effective R genes valuable for breeding durable rice resistance to rice blast.

Integrated motor-transmission (IMT) system is prone to suffer from torsional oscillations due to its fast dynamic behavior and weak damping characteristics. Meanwhile, signal communication between sensors, controllers and actuators via controller area network (CAN) that introduces time-varying delays, could also stimulate torsional oscillations in IMT system. Thus, this paper is intended to develop a robust controller to suppress these oscillations for IMT system during vehicle speed tracking. Considering the coupling effects of CAN-induced time-varying delays and event-driven manner of the controller nodes, as well as possible sampling period change, a delay-free discrete time model is built for IMT system by using polytopic inclusion approach and system augmentation technique. Based on this model, an energy-to-peak performance based robust controller is then developed for IMT system over CAN. It can achieve stable as well as good torsional oscillations suppression performance in spite of CAN-induced time-varying delays, sampling period change and also measurement noises. Finally, with a detailed CAN model developed by using SimEvent, conventional proportional-integral (PI) controller and energy-to-peak controller that designed with fixed sampling period are utilized in the comparative tests to show the effectiveness as well as performance of proposed torsional oscillations controller.

This paper works on the oscillation of electrified powertrain system in an integrated manner where combined mechanical-electric-network effects are all taking into consideration, and a robust oscillation controller is proposed to suppress the effects-caused oscillation and to maintain the system stability. An integrated model is developed in which driving motor, drivetrain and communication network are all included. Thus, torque ripples in the driving motor, nonlinear gear backlash as well as driveshaft flexibility in the drivetrain and network-induced delays that may cause powertrain system oscillation can be all considered. In order to dealing with the coupling effects of network-induced delays and event-driven manner of the controller nodes, a delay-free discrete model is further built via polytopic inclusion approach and system augmentation technique. An energy-to-peak performance based robust controller is proposed to ensure the torsional oscillation damping as well as vehicle speed tracking performance. During backlash mode, as the driving motor and the load are decoupled, a sliding mode compensator is further adopted to restrain the torsional oscillation. The stability of electrified powertrain control system is ensured by using Lyapunov theory, and the controller gain is obtained by solving a set of linear matrix inequalities (LMIs). Comparative simulation tests are carried out by using Matlab/Simulink in which a delicate controller area network (CAN) model is developed via SimEvent. The combined mechanical-electric-networked effects on torsional oscillation are demonstrated during the simulation tests, while the performance of the proposed controller is well verified.

Cancer cell-derived exosomes regulate tumor growth and progression. However, the effects of exosomes and its contents on head and neck squamous cell carcinoma (HNSCC) and its underlying mechanisms remain unclear. Here, we found HNSCC displayed a dysregulation of exosomes biogenesis. miR-185-3p was one of the most upregulated exosome-derived miRNAs in HNSCC. Functional assay showed that RAB25 is a direct downstream target of miR-185-3p. miR-185-3p/RAB25 signaling controlled tumor progression and correlated with disease prognosis. Targeting miR-185-3p/RAB25 significantly inhibited tumor growth and promoted drug response to chemotherapy. To conclude, the findings demonstrate exosomal miR-185-3p promotes tumor growth by mediating RAB25 that could be effectively targeted for HNSCC treatment.

Swine enteric disease is the predominant cause of morbidity and mortality, and viral species involved in swine enteric disease include rotaviruses and coronaviruses, among others. Awareness of the circulating porcine rotavirus group C (PoRVC) in pig herds is critical to evaluate the potential impact of infection. At present, due to the lack of disease awareness and molecular diagnostic means, the research on RVC infection in China is not well-studied. In this study, diarrhea samples collected from pig farms were detected positive for RVC by PCR, and the full-length RVC was not previously reported for Chinese pig farms. This rotavirus strain was designated as RVC/Pig/CHN/JS02/2018/G6P6. A natural recombination event was observed with breakpoints at nucleotides (nt) 2509 to 2748 of the VP2 gene. Phylogenetic analysis based on nsp1 revealed that a new branch A10 formed. Collectively, our data suggest a potentially novel gene recombination event of RVC in the VP2 gene. These findings provide a new insight into the evolution of the rotavirus.

Abstract Background Rice blast caused by Magnaporthe oryzae (M. oryzae) is one of the most destructive diseases in rice production. Development of resistant varieties through pyramiding of resistant (R) genes is considered as an effective strategy to cope with the disease. However, is it really essential to pyramid more R genes in a specific ecological regions? To answer this question, a set of rice improved lines were developed in this study. Afterwards, the blast disease resistance and agronomic traits of the recurrent parent (RP), donor parents (DPs) and improved lines were investigated. Results We developed seven improved lines, comprising three monogenic lines, three two-gene pyramids and one three-gene pyramid, by introgression of R gene(s) into a common genetic background using marker-assisted backcross breeding (MABB). Based on 302 SSR markers, the recurrent genome of the seven improved lines reached a range of 89.1 to 95.5%, with the average genome recovery of 92.9%. The pathogenicity assays inoculated with 32 different blast isolates under artificial conditions showed that the resistance spectrum of all the improved lines was significantly broadened. The assays further showed that the two-gene pyramids and the three-gene pyramid exhibited wider resistance spectrum than the monogenic lines. At natural nurseries, the three monogenic lines still showed high ratios of infected panicles, whereas the two-gene pyramids and the three-gene pyramid showed high level of panicle blast resistance. However, the two-gene pyramid R504 reached the similar resistance effect of the three-gene pyramid R507 considering resistance spectrum under artificial conditions and panicle blast resistance under field conditions. Generally, the improved lines showed comparable agronomic traits compared with the recurrent parent (RP), but the three-gene pyramid showed reduced grain yield per plant. Conclusions All the improved lines conferred wider resistance spectrum compared with the RP. Yet, the three monogenic lines did not work under field conditions of the two nurseries. Given the similar performances on the main agronomic traits as the RP, the two-gene pyramids have achieved the breeding goals of broad resistance spectrum and effective panicle blast resistance. Whereas, the three-gene pyramid harboring Pi2, Pi46 and Pita seems superfluous considering its reduced yield, although it also showed displayed high level of blast resistance. Thus, rational use of R genes rather than stacking more R genes is recommended to control the disease.

In this paper, a novel strategy for the importance analysis of structural performance models under both aleatory and epistemic uncertainties is presented. Random variables and fuzzy numbers are adopted for the representation of the two types of uncertainty respectively. Based on the statistical moments of the model outputs, two categories of importance measures are proposed under a hybrid framework composed of probability theory and fuzzy logic. The first category is for the input factors with aleatory uncertainty, while the other is for the input factors with epistemic uncertainty. In order to depict the credibility of the important measures of the random factors, a stability indicator is further introduced. Under the hybrid framework, the statistical moments of the performance outputs are fuzzy membership functions instead of deterministic values. Therefore, the importance measures are defined based on the area differences between conditional and unconditional membership functions. For the estimation of the proposed importance measures and stability indicator, a uniform discretization of the fuzzy membership function is first performed to combine the fuzzy factors with the random samples. Then, the Monte Carlo simulation (MCS) and Gorman and Seo's three-point estimates (GSP) are employed as uncertainty propagation methods to address the statistical moments of the performance outputs. Finally, the proposed importance measures and stability indicator are studied through two numerical examples by MCS and GSP comparatively for demonstrating their benefits in stability, applicability, and efficiency.

This paper proposed a braking torque controller via two-time-scale design with a sliding mode for electric vehicles with four in-wheel motors. According to the different changing rates between the vehicle and wheel motion during the braking process, the design of the braking controller is carried out in two steps. In the first step, a nominal braking controller is developed over the slow-time scale without considering the tire-road friction. Then, a tire-road friction observer is adopted in the fast-time scale to recover the performance of the nominal braking controller. Owning to the high nonlinearity and complexity of the braking system, a sliding mode surface is further added in the nominal braking controller to ensure the stability and robustness of the proposed braking controller. A braking supervisor is adopted to enable the proposed braking controller, which is based on the wheel slip as well as vehicle speed condition. And a torque allocation scheme is presented for the coordination between the regenerative braking system and the friction braking system in each wheel. Co-simulation is conducted using MATLAB/Simulink and CarSim. The effectiveness of proposed controller under different braking conditions is fully validated. A delicate controller area network (CAN) bus model is developed via SimEvent, by which the robust performance of proposed braking controller against CAN-induced time-varying delays is also investigated.

Rice false smut disease, caused by Ustilaginoidea virens, is an important grain disease in rice production worldwide. An F2 population of 176 plants was used to conduct quantitative trait locus (QTL) mapping for rice false smut disease resistance. A total of 360 simple sequence repeat (SSR) markers were screened for polymorphism on the parents, susceptible and resistant bulks, which yielded SSR markers linked to false smut disease resistance gene. Bulk segregant analysis located the resistance gene to chromosome 5. QTL analysis also identified two QTLs on chromosome 5, the phenotypic variations explained by these two QTLs are 7.3% and 16.4%. The qRFSr-5.1 was about 0.8 centimorgans (cM) to SSR marker RM18451 accounting for 7.3% of phenotypic variation, and qRFSr-5.2 about 1.1 cM to SSR marker M72 accounting for 16.4%. The SSR markers linked to rice false smut disease resistance genes can be used for marker-assisted selection and also they serve as landmarks for cloning false smut disease resistance genes in the future. Keywords: QTL mapping, Rice, Rice false smut disease, Ustilaginoidea virens

Abstract Background WRKY proteins are one of the largest gene families and are well-known for their regulatory roles in many aspects of plant development, including plant response to both biotic and abiotic stresses. Although the roles of WRKY proteins in leaf blast resistance have been well-documented in rice, their functions in panicle blast, the most destructive type of blast disease, are still largely unknown. Results Here, we identified that the transcription of OsWRKY67 was strongly activated by leaf and panicle blast infection. OsWRKY67 is ubiquitously expressed and sub-localized in the nucleus. Rice plants overexpressing OsWRKY67 showed quantitatively enhanced resistance to leaf blast, panicle blast and bacterial blight. In contrast, silencing of OsWRKY67 increased the susceptibility to blast and bacterial blight diseases. RNA-seq analysis indicated that OsWRKY67 induces the transcription of a set of defense-related genes including the ones involved in the salicylic acid (SA)-dependent pathway. Consistent with this, the OsWRKY67-overexpressing plants accumulated higher amounts of endogenous SA, whereas lower endogenous SA levels were observed in OsWRKY67-silenced plants relative to wild-type Nipponbare plants before and after pathogen attack. Moreover, we also observed that OsWRKY67 directly binds to the promoters of PR1a and PR10 to activate their expression. Conclusions These results together suggest the positive role of OsWRKY67 in regulating rice responses to leaf blast, panicle blast and bacterial blight disease. Furthermore, conferring resistance to two major diseases makes it a good target of molecular breeding for crop improvement in rice.

This paper proposes a hierarchical braking torque control system design over controller area network (CAN) for electric vehicle with four in-wheel motors. Based on nonlinear tyre-road friction estimation, an adaptive braking torque control scheme is devised in the upper level controller to simultaneously regulate the wheel slip ratio for both front and rear wheels. While in the lower-level controller, a frequency-depended braking torque allocator is developed to dynamically distribute the braking torques command from upper level controller into friction braking torque and regenerative braking torque. A braking supervisor is established to enable or disable proposed hierarchical braking controller according to wheel slip ratio as well as vehicle speed condition. To avoid undesired discontinuities in braking torque signal when proposed braking controller is enabled abruptly, an initial value of the estimated tyre-road friction is rescaled additionally. Dedicated CAN bus module is built by using SimEvent toolbox, by which vehicle states as well as control signals can be transmitted and shared. Co-simulation by using MATLAB/Simulink and CarSim is conducted to demonstrate the effectiveness of the proposed hierarchical braking torque controller. Random CAN-induced delays are considered in the braking control process, by which the robustness of the proposed hierarchical braking controller is further verified.

Although 14-3-3 proteins have been reported to be involved in responses to biotic stresses in plants, their functions in rice blast, the most destructive disease in rice, are largely unknown. Only GF14e has been confirmed to negatively regulate leaf blast. We report that GF14b is highly expressed in seedlings and panicles during blast infection. Rice plants overexpressing GF14b show enhanced resistance to panicle blast but are susceptible to leaf blast. In contrast, GF14b-silenced plants show increased susceptibility to panicle blast but enhanced resistance to leaf blast. Yeast one-hybrid assays demonstrate that WRKY71 binds to the promoter of GF14b and modulates its expression. Overexpression of GF14b induces expression of jasmonic acid (JA) synthesis-related genes but suppresses expression of salicylic acid (SA) synthesis-related genes. In contrast, suppressed GF14b expression causes decreased expression of JA synthesis-related genes but activation of SA synthesis-related genes. These results suggest that GF14b positively regulates panicle blast resistance but negatively regulates leaf blast resistance, and that GF14b-mediated disease resistance is associated with the JA- and SA-dependent pathway. The different functions for 14-3-3 proteins in leaf and panicle blast provide new evidence that leaf and panicle blast resistance are controlled by different mechanisms.

Although quantitative trait loci (QTL) underpin many desirable agronomic traits, their incorporation into crop plants through marker-assisted selection is limited by the low predictive value of markers on phenotypic performance. Here we used candidate defense response (DR) genes to dissect quantitative resistance in rice using recombinant inbred (RI) and advanced backcross (BC) populations derived from a blast-resistant cultivar, Sanhuangzhan 2 (SHZ-2). Based on DNA profiles of DR genes, RI lines were clustered into two groups corresponding to level of resistance. Five DR genes, encoding putative oxalate oxidase, dehydrin, PR-1, chitinase, and 14-3-3 protein, accounted for 30.0, 23.0, 15.8, 6.7, and 5.5% of diseased leaf area (DLA) variation, respectively. Together, they accounted for 60.3% of the DLA variation and co-localized with resistance QTL identified by interval mapping. Average phenotypic contributions of oxalate oxidase, dehydrin, PR-1, chitinase, and 14-3-3 protein in BC lines were 26.1, 19.0, 18.0, 11.5, and 10.6%, respectively, across environments. Advanced BC lines with four to five effective DR genes showed enhanced resistance under high disease pressure in field tests. Our results demonstrate that the use of natural variation in a few candidate genes can solve a long-standing problem in rice production and has the potential to address other problems involving complex traits.

How does the visual system encode natural scenes? What are the basic structures of natural scenes? In current models of scene perception, there are two broad feature representations, global and local representations. Both representations are useful and have some successes; however, many observations on human scene perception seem to point to an intermediate-level representation. In this paper, we proposed natural scene structures, i.e., multi-scale spatial concatenations of local features, as an intermediate-level representation of natural scenes. To compile the natural scene structures, we first sampled a large number of multi-scale circular scene patches in a hexagonal configuration. We then performed independent component analysis on the patches and classified the independent components into a set of clusters using the K-means method. Finally, we obtained a set of natural scene structures, each of which is characterized by a set of dominant clusters of independent components. We examined a range of statistics of the natural scene structures, compiled from two widely used datasets of natural scenes, and modeled their spatial arrangements at larger spatial scales using adjacency matrices. We found that the natural scene structures include a full range of concatenations of visual features in natural scenes, and can be used to encode spatial information at various scales. We then selected a set of natural scene structures with high information, and used the occurring frequencies and the eigenvalues of the adjacency matrices to classify scenes in the datasets. We found that the performance of this model is comparable to or better than the state-of-the-art models on the two datasets. These results suggest that the natural scene structures are a useful intermediate-level representation of visual scenes for our understanding of natural scene perception.

Human and many other animals can detect, recognize, and classify natural actions in a very short time. How this is achieved by the visual system and how to make machines understand natural actions have been the focus of neurobiological studies and computational modeling in the last several decades. A key issue is what spatial-temporal features should be encoded and what the characteristics of their occurrences are in natural actions. Current global encoding schemes depend heavily on segmenting while local encoding schemes lack descriptive power. Here, we propose natural action structures, i.e., multi-size, multi-scale, spatial-temporal concatenations of local features, as the basic features for representing natural actions. In this concept, any action is a spatial-temporal concatenation of a set of natural action structures, which convey a full range of information about natural actions. We took several steps to extract these structures. First, we sampled a large number of sequences of patches at multiple spatial-temporal scales. Second, we performed independent component analysis on the patch sequences and classified the independent components into clusters. Finally, we compiled a large set of natural action structures, with each corresponding to a unique combination of the clusters at the selected spatial-temporal scales. To classify human actions, we used a set of informative natural action structures as inputs to two widely used models. We found that the natural action structures obtained here achieved a significantly better recognition performance than low-level features and that the performance was better than or comparable to the best current models. We also found that the classification performance with natural action structures as features was slightly affected by changes of scale and artificially added noise. We concluded that the natural action structures proposed here can be used as the basic encoding units of actions and may hold the key to natural action understanding.

To develop effective learning algorithms for continuous prediction of cursor movement using EEG signals is a challenging research issue in brain-computer interface (BCI). In this paper, we propose a novel statistical approach based on expectation-maximization (EM) method to learn the parameters of a classifier for EEG-based cursor control. To train a classifier for continuous prediction, trials in training data-set are first divided into segments. The difficulty is that the actual intention (label) at each time interval (segment) is unknown. To handle the uncertainty of the segment label, we treat the unknown labels as the hidden variables in the lower bound on the log posterior and maximize this lower bound via an EM-like algorithm. Experimental results have shown that the averaged accuracy of the proposed method is among the best.