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The objective of sound event localization and detection (SELD) is to accurately identify the temporal occurrence and spatial coordinates of a specific sound category. The existing mainstream offline methods may unintentionally introduce unfavorable future feature information during the training process, thereby potentially hindering the system's performance. The utilization of online methods can lead to improved localization accuracy to a certain extent. Nevertheless, it may result in a diminished ability with the detection capability for sound events. In this paper, a hybrid offline-online method (HOOM) is proposed that involves extracting comprehensive audio information using offline network layers, while simultaneously filtering out irrelevant future information using online network layers. Based on this method, we designed two simple sub-network architectures. The first, convolution and causal convolution alternating network (CCAN), employs regular convolution along with causal convolutions to achieve the offline and online convolution features, respectively. The second, bidirectional and unidirectional alternating network (BUAN), combines bidirectional recurrent neural networks with unidirectional recurrent neural networks, capturing the offline and online contextual sequence information, respectively. Our proposed method demonstrates a 6% improvement in localization recall on the Sony-TAU Realistic Spatial Soundscapes 2023 (STARSS23) dataset. Furthermore, compared to offline or online methods, there is a 4% overall performance improvement. On the detection and classification of acoustic scenes and events 2022 (DCASE2022) synthetic dataset, the overall performance improvement is 5%. These results indicate a significant advantage and provide a novel and robust solution for the SELD task. Propose a hybrid offline-online method for SELD. The extracted hybrid features or temporal sequences enable the acquisition of a more comprehensive range of audio information. [ABSTRACT FROM AUTHOR]

Distant supervised relation extraction has been increasingly popular in recent years, which generates datasets automatically without human intervention. However, the distant supervised assumption has the limitation that the generated datasets have inevitable labeling errors. This paper proposes the method of Hierarchical Symmetric Cross Entropy for Distant Supervised Relation Extraction (HSCERE) to alleviate the impact of the noisy labels. Specifically, HSCERE simultaneously utilizes two extractors with the same network structure for collaborative learning. This collaborative learning process guides the optimization of the extractor through a joint loss function, namely Hierarchical Symmetric Cross Entropy (HSCE). Within the HSCE loss, the predicted probability distribution of the extractors serves as the supervisory signal, guiding the optimization of the extractors on two levels to reduce the impact of noisy labels. The two levels include the internal optimization within each extractor and the collaborative optimization between extractors. Experiments on generally used datasets show that HSCERE can effectively handle noisy labels and can be incorporated into various methods to enhance their performance. [ABSTRACT FROM AUTHOR]

Purpose: A fracture of the posterior talar process is easily missed because of its hidden position. Inappropriate treatment is likely to result in complications, such as nonunion of the fracture and traumatic arthritis. This study evaluated the outcomes of arthroscopy-assisted reduction combined with robotic-assisted screw placement in the treatment of fractures of the posterior talar process. Methods: The clinical data for nine patients who underwent surgical treatment of a fracture of the posterior talar process at our institution between September 2017 and January 2021 were retrospectively reviewed. Arthroscopy-assisted reduction of the fracture was performed, and a cannulated screw was placed using three-dimensional orthopedic robotic-assisted navigation. Results: The patients (seven men, two women) had a mean age of 36.33 ± 9.77 years and were followed up for 21 ± 5.43 months. The operation time was 106.67 ± 24.5 min with blood loss of 47.78 ± 9.05 ml. Primary healing was obtained in all cases, and no patient sustained a nerve or tendon injury, had fracture nonunion, or developed talar osteonecrosis. One patient developed subtalar arthritis, for which subtalar joint fusion was performed; pain was markedly less severe after cleaning. Conclusion: Arthroscopy-assisted reduction and robotic-assisted screw placement have the advantages of visualization of fracture reduction, minimal injury, and precise screw placement in the treatment of fractures of the posterior talar process. [ABSTRACT FROM AUTHOR]

Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. On one hand, visualization can facilitate humans in data understanding through intuitive visual representation and interactive exploration. On the other hand, AI is able to learn from data and implement bulky tasks for humans. In complex data analysis scenarios, like epidemic traceability and city planning, humans need to understand large-scale data and make decisions, which requires complementing the strengths of both visualization and AI. Existing studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as VIS4AI. However, how can AI and visualization complement each other and be integrated into data analysis processes are still missing. In this paper, we define three integration levels of visualization and AI. The highest integration level is described as the framework of VIS+AI, which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces. We also summarize future directions of VIS+AI to inspire related studies. [ABSTRACT FROM AUTHOR]

Key message: We identified a quantitative trait locus, qPss3, and fine-mapped the causal locus to a 120-kb interval in maize. This locus inhibits the photoperiod sensitivity caused by ZmCCT9 and ZmCCT10, resulting in earlier flowering by 2 ~ 4 days without reduction in stalk-rot resistance in certain genotypes. Photoperiod sensitivity is a key factor affecting the adaptation of maize (Zea mays L.) to high-latitude growing areas. Although many genes associated with flowering time have been identified in maize, no gene that inhibits photoperiod sensitivity has been reported. In our previous study, we detected large differences in photoperiod sensitivity among maize inbred lines with the same photoperiod-sensitive allele at the ZmCCT10 locus. Here, we used two segregating populations with the same genetic backgrounds but different ZmCCT10 alleles to perform quantitative trait locus (QTL) analysis. We identified a unique QTL, qPss3, on chromosome 3 in the population carrying the sensitive ZmCCT10 allele. After sequential fine-mapping, we eventually delimited qPss3 to an interval of ~ 120 kb. qPss3 behaved as a dominant locus and caused earlier flowering by 2–4 days via inhibiting ZmCCT10-induced photoperiod sensitivity under long-day conditions. qPss3 also inhibited the photoperiod sensitivity induced by another flowering-related gene, ZmCCT9. For application in agriculture, an F1 hybrid heterozygous at both qPss3 and ZmCCT10 loci constitutes an optimal allele combination, showing high resistance to stalk rot without a significant delay in flowering time. Moreover, qPss3 is of great value in regulating the flowering time of tropical maize grown at high-latitude regions. [ABSTRACT FROM AUTHOR]

Treating patients with complex diseases or co-existing conditions by polypharmacy (i.e., the use of drug combination) is very common. However, due to drug-drug interactions, polypharmacy often results in unpredictable side effects, which may endanger patients' life. Moreover, since adverse drug reactions are rare, discovering polypharmacy side effects only from sparse drug-drug interactions remains challenging. Thus, it is necessary to explore the knowledge of polypharmacy side effects from the interaction network with complex relationships. In this paper, we propose a novel multi-modal graph convolutional neural network (M2GCN) for link prediction in multi-modal networks which consist of protein-protein interactions, drug-protein interactions and drug-drug interactions. Specifically, we first propose a propagation strategy to perform graph aggregations on each subgraph. Then we leverage consistency regularization to align the consistency across different subgraphs. Finally, referring to the DistMult method, we use the embeddings obtained above to calculate the probability of side effects between drugs. Experimental results on benchmark dataset show that our method significantly outperforms the compared network embedding models. [ABSTRACT FROM AUTHOR]

Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities. [ABSTRACT FROM AUTHOR]

Notwithstanding the great progress on deep convolutional neural networks (CNNs) has been made during last decade, the representation ability may still be restricted and it usually needs more epochs to converge in training, due to the information loss caused by the up-/down-sampling operations. In this paper, we propose a general deep feature recovery layer, termed Low-rank Deep Feature Recovery (LDFR), to enhance the representation of convolutional features by seamlessly integrating the low-rank recovery into CNNs, which can be easily extended to all CNNs-based models. To be specific, to recover the lost useful information, LDFR learns the low-rank projections to embed feature maps onto a low-rank subspace based on the selected informative convolutional feature maps. Such operation can ensure all the convolutional feature maps to be reconstructed easily to recover the underlying subspace, with more useful detailed information discovered, e.g., the strokes of characters or the texture information of clothes. To make the learnt low-rank subspaces more powerful for feature recovery, we design a fusion strategy to obtain a generalized subspace, which averages over all learnt subspaces in each LDFR layer, so that the convolutional features in test phase can be recovered effectively via low-rank embedding. We also present a fast version of LDFR, called FLDFR, to speedup the optimization of LDFR by flattening all feature maps of batch images to recover the lost information. Extensive simulations on several image datasets show that the existing CNN models equipped with our LDFR layers can obtain better performance. [ABSTRACT FROM AUTHOR]

Analysis of human interaction is one important research topic of human motion analysis. It has been studied either using first-person vision (FPV) or third-person vision (TPV). However, the joint learning of both types of vision has so far attracted little attention. One of the reasons is the lack of suitable datasets that cover both FPV and TPV. In addition, existing benchmark datasets of either FPV or TPV have several limitations, including the limited number of samples, participant subjects, interaction categories, and modalities. In this work, we contribute a large-scale human interaction dataset, namely FT-HID dataset. FT-HID contains pair-aligned samples of first-person and third-person visions. The dataset was collected from 109 distinct subjects and has more than 90K samples for three modalities. The dataset has been validated by using several existing action recognition methods. In addition, we introduce a novel multi-view interaction mechanism for skeleton sequences, and a joint learning multi-stream framework for first-person and third-person visions. Both methods yield promising results on the FT-HID dataset. It is expected that the introduction of this vision-aligned large-scale dataset will promote the development of both FPV and TPV, and their joint learning techniques for human action analysis. [ABSTRACT FROM AUTHOR]

Fusarium ear rot (FER) is a common disease of maize that can seriously affect the yield and quality of maize. Resistance to FER is highly quantitative, largely under polygenic control with minor effects, and strongly influenced by environmental conditions. In this study, two F2:3 populations with 220 and 152 lines were developed from PHW43 × 8107 (PH81) and Zong31 × DTMA165 (ZODT) for QTL mapping and genomic selection (GS). Artificial inoculation was performed to evaluate FER resistance in four environments, Gongzhuling 2019, Shunyi 2019, Xinxiang 2019, and Xinxiang 2020. For both populations, we constructed a relatively high-density linkage map. Each F2:3 population showed a lower average FER severity than the natural population tested before. Significant genotypic variance, environment variance, and genotype × environment interaction variance were detected in both populations by combined ANOVA analysis. The combined broad-sense heritability of the FER severity was estimated as 0.60 and 0.67 in PH81 and ZODT, respectively. In PH81, seven QTL were identified on chromosomes 1, 4, 6, 7, and 10, four of which were detected in Xinxiang 2019 environment together explaining 24.15% of the total phenotypic variation. The QTL qPBLUE–7–1 on bin7.01 explaining the highest phenotypic variation (6.14–8.60%) was consistently identified across Xinxiang 2019 environment and the best linear unbiased estimators. Eight QTL were identified in ZOTD, explaining 7.11% (qZBLUE–6–1) to 13.29% (qZS19–10–1) of phenotypic variation. Two candidate genes were identified in bin 6.05, of which GRMZM2G066153 encodes an enzyme involving the defense of pathogens in many plants. Genomic best linear unbiased prediction (GBLUP) showed that 500 SNP markers were enough for F2:3 populations to achieve reasonably good prediction. The prediction accuracy in F2:3 populations could be improved by integrating QTL information into the GBLUP model and by using the extended GBLUP model with additive and dominance effects included. [ABSTRACT FROM AUTHOR]

In recent years, crowd counting has increasingly drawn attention due to its widespread applications in the field of computer vision. Most of the existing methods rely on datasets with scarce labeled images to train networks. They are prone to suffer from the over-fitting problem. Further, these existing datasets usually just give manually labeled annotations related to the head center position. This kind of annotation provides limited information. In this paper, we propose to exploit virtual synthetic crowd scenes to improve the performance of the counting network in the real world. Since we can obtain people masks easily in a synthetic dataset, we first learn to distinguish people from the background via a segmentation network using the synthetic data. Then we transfer the learned segmentation priors from synthetic data to real-world data. Finally, we train a density estimation network on real-world data by utilizing the obtained people masks. Our experiments on two crowd counting datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Searching for rare astronomical objects based on spectral data is similar to finding needles in a haystack owing to their rarity and the immense data volume gathered from large astronomical spectroscopic surveys. In this paper, we propose a novel automated approximate nearest neighbor search method based on unsupervised hashing learning for rare spectra retrieval. The proposed method employs a multilayer neural network using autoencoders as the local compact feature extractors. Autoencoders are trained with a non-gradient learning algorithm with graph Laplace regularization. This algorithm also simplifies the tuning of network architecture hyperparameters and the learning control hyperparameters. Meanwhile, the graph Laplace regularization can enhance the robustness by reducing the sensibility to noise. The proposed model is data-driven; thus, it can be viewed as a general-purpose retrieval model. The proposed model is evaluated in experiments and real-world applications where rare O-type stars and their subclass are retrieved from the dataset obtained from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (Guo Shoujing Telescope). The experimental and application results show that the proposed model outperformed the baseline methods, demonstrating the effectiveness of the proposed method in rare spectra retrieval tasks. [ABSTRACT FROM AUTHOR]

Trajectory prediction is a fundamental and challenging task for numerous applications, such as autonomous driving and intelligent robots. Current works typically treat pedestrian trajectories as a series of 2D point coordinates. However, in real scenarios, the trajectory often exhibits randomness, and has its own probability distribution. Inspired by this observation and other movement characteristics of pedestrians, we propose a simple and intuitive movement description called a trajectory distribution, which maps the coordinates of the pedestrian trajectory to a 2D Gaussian distribution in space. Based on this novel description, we develop a new trajectory prediction method, which we call the social probability method. The method combines trajectory distributions and powerful convolutional recurrent neural networks. Both the input and output of our method are trajectory distributions, which provide the recurrent neural network with sufficient spatial and random information about moving pedestrians. Furthermore, the social probability method extracts spatio-temporal features directly from the new movement description to generate robust and accurate predictions. Experiments on public benchmark datasets show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time. They are common in multimedia applications. Anomaly detection is one of the essential tasks in analyzing these networks though it is not well addressed. In this paper, we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic networks. We conclude features of rare categories and two types of anomalies of rare categories. Then we present a novel rare category detection method, called DIRAD, to detect rare category candidates with anomalies. We develop a prototype system called iNet, which integrates two major visualization components, including a glyph-based rare category identifier, which helps users to identify rare categories among detected substructures, a major view, which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex attributes. Evaluations, including an algorithm performance evaluation, a case study, and a user study, are conducted to test the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

Although there are many visualization authoring tools, often the design process is confirmatory in that the user either selects and refines pre-built visualization templates or manually constructs the visualization following the standard visual mapping pipelines. During this process, an amount of time is spent on the interactive specification of visual forms or visual encoding configurations. In this paper, we present a novel visualization design scheme that provides operational guidance for progressive refinements when designing a visualization. The key idea is an evolutional operation model that models the design flowchart as a composition of a sequence of operations based on the current design and recommends available evolutions in the design space. One distinct advantage of our approach is that it facilitates discovering design subtasks relevant to the visualizations and enables the composition of a visualization based on the evolutional operations. As a demonstration of the concept, we implement a prototype and test its efficiency on a set of challenging design examples. Case studies verify that the proposed scheme effectively enhances the creativity of designs and releases the user from having to interpret their data and design intention with toolkit-specific abstractions. [ABSTRACT FROM AUTHOR]

Existing unsupervised visual odometry (VO) methods either match pairwise images or integrate the temporal information using recurrent neural networks over a long sequence of images. They are either not accurate, time-consuming in training or error accumulative. In this paper, we propose a method consisting of two camera pose estimators that deal with the information from pairwise images and a short sequence of images, respectively. For image sequences, a transformer-like structure is adopted to build a geometry model over a local temporal window, referred to as transformer-based auxiliary pose estimator (TAPE). Meanwhile, a flow-to-flow pose estimator (F2FPE) is proposed to exploit the relationship between pairwise images. The two estimators are constrained through a simple yet effective consistency loss in training. Empirical evaluation has shown that the proposed method outperforms the state-of-the-art unsupervised learning-based methods by a large margin and performs comparably to supervised and traditional ones on the KITTI and Malaga dataset. [ABSTRACT FROM AUTHOR]

Disease resistance is essential for reliable maize production. In a long-term tug-of-war between maize and its pathogenic microbes, naturally occurring resistance genes gradually accumulate and play a key role in protecting maize from various destructive diseases. Recently, significant progress has been made in deciphering the genetic basis of disease resistance in maize. Enhancing disease resistance can now be explored at the molecular level, from marker-assisted selection to genomic selection, transgenesis technique, and genome editing. In view of the continuing accumulation of cloned resistance genes and in-depth understanding of their resistance mechanisms, coupled with rapid progress of biotechnology, it is expected that the large-scale commercial application of molecular breeding of resistant maize varieties will soon become a reality. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and is currently incurable. Amyloid β protein (Aβ) deposition is the main pathogenesis of AD, and many studies have shown that Aβ accumulation is toxic to neurons, leading to the inflammatory reaction, neuronal apoptosis, and neurofibrillary tangles. Thus, reducing Aβ levels might be a potential therapeutic strategy for AD. Liquiritigenin (LG), a dihydroflavone monomer compound extracted from natural plant licorice, has a variety of biological activities such as antioxidant, anti-tumor, anti-inflammatory and anti-virus. However, the exact function of LG in the pathogenesis of AD is elusive. Here, we reported that LG could significantly attenuate neuronal apoptosis in Aβ-induced N2A cells and APP/PS1 transgenic mice. Our in vivo and in vitro studies revealed that LG could alleviate the inflammation response, reflected by the reduction of NLRP3 and cleaved caspase-1. Meanwhile, we also found that LG was able to shift M1 type microglia towards M2 type microglia in Aβ-induced BV2 cells and AD mice. Furthermore, LG could reduce the Aβ levels by decreasing APP processing and accelerating Aβ clearance in AD mice. More importantly, daily treatment of LG (30 mg/kg day) for 90 days dramatically ameliorated the spatial learning and memory of AD mice. Taken together, these results suggest that LG can reduce the Aβ levels by regulating the M1/M2 transformation of microglia, thereby reversing memory decline during AD development, suggesting that LG may be a potential therapeutic agent for treating AD. [ABSTRACT FROM AUTHOR]

RGB-D based action recognition is attracting more and more attention in both the research and industrial communities. However, due to the lack of training data, pre-training based methods are popular in this field. This paper presents a review of the concept of dynamic maps for RGB-D based human motion recognition using pretrained models in image domain. The dynamic maps recursively encode the spatial, temporal and structural information contained in the video sequence into dynamic motion images simultaneously. They enable the usage of Convolutional Neural Network and its pretained models on ImageNet for 3D human motion recognition. This simple, compact and effective representation achieves state-of-the-art results on various gesture/action/activities recognition datasets. Based on the review of previous methods using this concept upon different modalities (depth, skeleton or RGB-D data), a novel encoding scheme is developed and presented in this paper. The improved method generates effective flow-guided dynamic maps, and they could select the high motion window and distinguish the order among the frames with small motion. The improved flow-guided dynamic maps achieve state-of-the-art results on the large Chalearn LAP IsoGD and NTU RGB+D datasets. [ABSTRACT FROM AUTHOR]

We report on the state-of-the-art of software visualization. To ensure reproducibility, we adopted the Systematic Literature Review methodology. That is, we analyzed 1440 entries from IEEE Xplore and ACM Digital Library databases. We selected 105 relevant full papers published in 2013–2019, which we classified based on the aspect of the software system that is supported (i.e., structure, behavior, and evolution). For each paper, we extracted main dimensions that characterize software visualizations, such as software engineering tasks, roles of users, information visualization techniques, and media used to display visualizations. We provide researchers in the field an overview of the state-of-the-art in software visualization and highlight research opportunities. We also help developers to identify suitable visualizations for their particular context by matching software visualizations to development concerns and concrete details to obtain available visualization tools. [ABSTRACT FROM AUTHOR]

Maize rough dwarf disease (MRDD), caused by various species of the genus Fijivirus, threatens maize production worldwide. We previously identified a quantitative locus qMrdd1 conferring recessive resistance to one causal species, rice black-streaked dwarf virus (RBSDV). Here, we show that Rab GDP dissociation inhibitor alpha (RabGDIα) is the host susceptibility factor for RBSDV. The viral P7-1 protein binds tightly to the exon-10 and C-terminal regions of RabGDIα to recruit it for viral infection. Insertion of a helitron transposon into RabGDIα intron 10 creates alternative splicing to replace the wild-type exon 10 with a helitron-derived exon 10. The resultant splicing variant RabGDIα-hel has difficulty being recruited by P7-1, thus leading to quantitative recessive resistance to MRDD. All naturally occurring resistance alleles may have arisen from a recent single helitron insertion event. These resistance alleles are valuable to improve maize resistance to MRDD and potentially to engineer RBSDV resistance in other crops. Maize rough dwarf disease threatens its production. Here, the authors show that a helitron transposon insertion in the Rab GDP dissociation inhibitor alpha leads to recessive viral resistance by affecting its interaction with viral P7-1 protein and that all naturally occurring alleles come from a single mutation event after domestication. [ABSTRACT FROM AUTHOR]

A stable solidified product of Cs was generated by pressing/sintering method with the addition of allophane. Experiments were conducted to further explore the properties of solidified products. Results indicated that the hardness of sintered product increased with increasing calcination temperature. A duration time of 3 h led to the volatilization of Cs. The leaching rate of Cs, Si and Al in sintered products was evaluated in different leachates with different days. A strong leaching resistance in deionized water was obtained. These results indicated that the solidification of silica-based ammonium molybdophosphate that absorbed Cs was stabilized after the addition of allophane. [ABSTRACT FROM AUTHOR]

Key message: The elite ZmCCT haplotypes which have no transposable element in the promoter could enhance maize resistance to Gibberella stalk rot and improve yield-related traits, while having no or mild impact on flowering time. Therefore, they are expected to have great value in future maize breeding programs. Abstract: A CCT domain-containing gene, ZmCCT, is involved in both photoperiod response and stalk rot resistance in maize. At least 15 haplotypes are present at the ZmCCT locus in maize germplasm, whereas only three of them are found in Chinese commercial maize hybrids. Here, we evaluated ZmCCT haplotypes for their potential application in corn breeding. Nine resistant ZmCCT haplotypes that have no CACTA-like transposable element in the promoter were introduced into seven elite maize inbred lines by marker-assisted backcrossing. The resultant 63 converted lines had 0.7-5.1 Mb of resistant ZmCCT donor segments with over 90% recovery rates. All converted lines tested exhibited enhanced resistance to maize stalk rot but varied in photoperiod sensitivity. There was a close correlation between the hybrids and their parental lines with respect to both resistance performance and photoperiod sensitivity. Furthermore, in a given hybrid A5302/83B28, resistant ZmCCT haplotype could largely improve yield-related traits, such as ear length and 100-kernel weight, resulting in enhanced grain yield. Of nine resistant ZmCCT haplotypes, haplotype H5 exhibited excellent performance for both flowering time and stalk rot resistance and is thus expected to have potential value in future maize breeding programs. [ABSTRACT FROM AUTHOR]

Key message: A quantitative trait locus qRfg3 imparts recessive resistance to maize Gibberella stalk rot. qRfg3 has been mapped into a 350-kb interval and could reduce the disease severity index by ~26.6%. Abstract: Gibberella stalk rot, caused by the fungal pathogen Fusarium graminearum, severely affects maize yield and grain quality worldwide. To identify more resistance quantitative trait loci (QTLs) against this disease, we analyzed a recombinant inbred line (RIL) population derived from a cross between resistant H127R and susceptible C7-2 inbred lines. Within this population, maize resistance to Gibberella stalk rot had high broad-sense heritability. A major QTL, qRfg3, on chromosome 3 was consistently detected across three field trials, accounting for 10.7-19.4% of the total phenotypic variation. Using a progeny-based sequential fine-mapping strategy, we narrowed qRfg3 down to an interval of ~350 kb. We further demonstrated that qRfg3 is a recessive resistance locus to Gibberella stalk rot that reduced the disease severity index by ~26.6%. Both the gene location and recessive genetic mode distinguish qRfg3 from other stalk rot resistance loci. Hence, qRfg3 is valuable as a complement to existing resistance QTLs to improve maize resistance to Gibberella stalk rot. [ABSTRACT FROM AUTHOR]

Mesh geometry can be used to model both object shape and details. If texture maps are involved, it is common to let mesh geometry mainly model object shapes and let the texture maps model the most object details, optimising data size and complexity of an object. To support efficient object rendering and transmission, model simplification can be applied to reduce the modelling data. However, existing methods do not well consider how object features are jointly represented by mesh geometry and texture maps, having problems in identifying and preserving important features for simplified objects. To address this, we propose a visual saliency detection method for simplifying textured 3D models. We produce good simplification results by jointly processing mesh geometry and texture map to produce a unified saliency map for identifying visually important object features. Results show that our method offers a better object rendering quality than existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a user model of subjective quality assessment on virtual viewpoint image (VVI) for free-viewpoint video system. VVIs are rendered through neighbor viewpoint color and depth images, and it is a new type of image that generated for human-computer interaction (HCI) in free-viewpoint video system. In this system, a natural scene is captured by multi-viewpoint cameras, and users can view the scene from any desired viewpoint, regardless the real or virtual one. The subjective quality of VVIs is crucial for the quality of experiences for HCI, because the magnitude of VVI is much greater than the real. In order to find the user model of VVI quality assessment, we organize three sets of stimuli, including Symmetric Stimuli, Asymmetric Stimuli Part I and Part II, to reveal the psychological responses of participants. A psychometric function is consequently obtained to determine the relationship between stimulus and psychological responses. Further discussions on the factors of distortion level, gender, age and academic background are examined to find the influence on the user model. We find that the distortion level of neighbor viewpoint color images has the dominant impact on the user model, while other factors contribute little. [ABSTRACT FROM AUTHOR]

Nowadays, microblogging platforms such as Twitter and Weibo can also be seen as a good media to present reviews about topics. More and more people tend to share their thoughts through various microblogging sites. For example, when a TV show is being shown, the users would like to share and discuss their opinions about it on these platforms. However, one phenomenon is the popularity of the TV usually varies for different regions due to the cultural differences, custom and some other factors. Predicting whether a TV show will be popular at certain locations is then desirable. In this paper, a location-aware TV show recommendation scheme is proposed. By incorporating the social network information of users from different locations, a location-based user profile is obtained. Then, the scheme conducts prediction of TV show popularity for different regions based on the profile and similar shows. For a new TV show, the popularity of the similar shows is utilized to get the initial location-show matrix. Then, the location profiles and physical distance are used as regularizer into the collaborative filtering framework to further refine the prediction. A TV show dataset with location-aware social network information has been collected. Experiments have been conducted on real data and encouraging results have been achieved. [ABSTRACT FROM AUTHOR]

Head smut is a soil-borne systemic maize disease caused by the causal pathogen Sporisorium reiliana. ZmWAK is the gene within the major QTL qHSR1 conferring resistance to head smut disease. ZmWAK has been transferred by marker-assisted backcrossing into elite inbred lines with high-yield potential but susceptible to head smut. The converted inbred lines, together with their hybrids, were grown in 2012 and 2013 to investigate resistance performance, yield per plot, and yield-related traits. ZmWAK-mediated resistance to maize head smut follows a dominant genetic model, and a single ZmWAK allele could achieve high disease resistance to prevent yield loss from S. reiliana infection. Genetically, ZmWAK itself had no detectable negative impacts on yield-related traits. Instead, ZmWAK could indirectly improve most yield components, particularly ear-related traits, in symptomless plants by reducing the amount of endophytic S. reiliana. Overall, ZmWAK is beneficial to both head smut resistance and yield-related traits in maize. [ABSTRACT FROM AUTHOR]

Head smut is a systemic disease in maize caused by the soil-borne fungus Sporisorium reilianum that poses a grave threat to maize production worldwide. A major head smut quantitative resistance locus, qHSR1, has been detected on maize chromosome bin2.09. Here we report the map-based cloning of qHSR1 and the molecular mechanism of qHSR1-mediated resistance. Sequential fine mapping and transgenic complementation demonstrated that ZmWAK is the gene within qHSR1 conferring quantitative resistance to maize head smut. ZmWAK spans the plasma membrane, potentially serving as a receptor-like kinase to perceive and transduce extracellular signals. ZmWAK was highly expressed in the mesocotyl of seedlings where it arrested biotrophic growth of the endophytic S. reilianum. Impaired expression in the mesocotyl compromised ZmWAK-mediated resistance. Deletion of the ZmWAK locus appears to have occurred after domestication and spread among maize germplasm, and the ZmWAK kinase domain underwent functional constraints during maize evolution. [ABSTRACT FROM AUTHOR]

Gray leaf spot (GLS), caused by the causal fungal pathogen Cercospora zeae- maydis, is one of the most serious foliar diseases of maize worldwide. In the current study, a highly resistant inbred line Y32 and a susceptible line Q11 were used to produce segregating populations for both genetic analysis and QTL mapping. The broad-sense heritability ( H) for GLS resistance was estimated to be as high as 0.85, indicating that genetic factors played key roles in phenotypic variation. In initial QTL analysis, four QTL, located on chromosomes 1, 2, 5, and 8, were detected to confer GLS resistance. Each QTL could explain 2.53-23.90 % of the total phenotypic variation, predominantly due to additive genetic effects. Two major QTL, qRgls1 and qRgls2 on chromosomes 8 and 5, were consistently detected across different locations and replicates. Compared to the previous results, qRgls2 is located in a 'hotspot' for GLS resistance; while, qRgls1 does not overlap with any other known resistance QTL. Furthermore, the major QTL- qRgls1 was fine-mapped into an interval of 1.4 Mb, flanked by the markers GZ204 and IDP5. The QTL- qRgls1 could enhance the resistance percentages by 19.70-61.28 %, suggesting its usefulness to improve maize resistance to GLS. [ABSTRACT FROM AUTHOR]

Head smut, one of the most devastating diseases in maize, causes severe yield losses worldwide. Resistance to head smut has been proven to be a quantitative inherited trait. In our previous study, a major resistance quantitative trait locus (named qHSR1) was detected on maize chromosome 2 (bin 2.09), and a number of molecular markers have been developed in the qHSR1 region. Here, we report the marker-assisted introgression of qHSR1 to improve maize resistance to head smut. The 10 maize inbred lines, namely Ji853, 444, 98107, 99094, Chang7-2, V022, V4, 982, 8903, and 8902, which have high yield potential but are susceptible to head smut, were selected for resistance improvement. Each of the 10 high-yielding lines was crossed with a donor parent Ji1037 that is completely resistant to head smut, followed by five generations of backcrossing to the respective recurrent parent. Marker-assisted foreground selection was conducted to identify qHSR1. Recombinant selection was carried out in the fourth backcross (BC4) generation by using the flanking markers to reduce the size of the Ji1037 donor segment carrying qHSR1. Background selection was performed in the BC5 generation with genome-wide SSR markers to select the line with the highest recovery rate of the recurrent parent genome. Self-pollination was conducted twice for the BC5 plant with both the shortest qHSR1 region and the highest recovery rate to obtain converted inbred lines harboring qHSR1. The 10 converted inbred lines all showed substantial improvement in resistance to head smut. Furthermore, the hybrids prepared from the converted lines also showed significant increase in resistance to head smut, while remaining mostly unchanged for other agronomic traits. [ABSTRACT FROM AUTHOR]

The entry-glide guidance strategy for hypersonic vehicles that can satisfy both terminal and path constraints is investigated in this paper. We propose a quasi-equilibrium glide adaptive guidance methodology based on the quasi-equilibrium glide condition (QEGC), which innovatively utilizes the quasi-equilibrium glide phenomenon in lifting entry. With the aid of QEGC, both range and terminal velocity can be predicted analytically with high precision. The path constraints are converted into angle of attack constraints, which has been difficult to realize by using traditional predictive guidance methods. The algorithm is independent of the standard trajectory. All the guidance commands, including the bank angle and the angle of attack, are calculated analytically in real time, which endows the algorithm with sufficient adapbility. The results of a CAV-H vehicle guidance test show that the algorithm leads the vehicle along a quasi-equilibrium glide trajectory satisfying both the terminal and path constraints and has sufficient flexibility for occasional mission changes. Furthermore, the robustness of the guidance algorithm under disturbances is validated through a Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Stalk rot is one of the most devastating diseases in maize worldwide. In our previous study, two QTLs, a major qRfg1 and a minor qRfg2, were identified in the resistant inbred line '1145' to confer resistance to Gibberella stalk rot. In the present study, we report on fine-mapping of the minor qRfg2 that is located on chromosome 1 and account for ~8.9% of the total phenotypic variation. A total of 22 markers were developed in the qRfg2 region to resolve recombinants. The progeny-test mapping strategy was developed to accurately determine the phenotypes of all recombinants for fine-mapping of the qRfg2 locus. This fine-mapping process was performed from BCF to BCF generations to narrow down the qRfg2 locus into ~300 kb, flanked by the markers SSRZ319 and CAPSZ459. A predicted gene in the mapped region, coding for an auxin-regulated protein, is believed to be a candidate for qRfg2. The qRfg2 locus could steadily increase the resistance percentage by ~12% across different backcross generations, suggesting its usefulness in enhancing maize resistance against Gibberella stalk rot. [ABSTRACT FROM AUTHOR]

Vernalization, a period of low temperature to induce transition from vegetative to reproductive state, is an important environmental stimulus for many cool season grasses. A key gene in the vernalization pathway in grasses is the VRN1 gene. The objective of this study was to identify causative polymorphism(s) at the VRN1 locus in perennial ryegrass ( Lolium perenne) for variation in vernalization requirement. Two allelic Bacterial Artificial Chromosome clones of the VRN1 locus from the two genotypes Veyo and Falster with contrasting vernalization requirements were identified, sequenced, and characterized. Analysis of the allelic sequences identified an 8.6-kb deletion in the first intron of the VRN1 gene in the Veyo genotype which has low vernalization requirement. This deletion was in a divergent recurrent selection experiment confirmed to be associated with genotypes with low vernalization requirement. The region surrounding the VRN1 locus in perennial ryegrass showed microcolinearity to the corresponding region on chromosome 3 in Oryza sativa with conserved gene order and orientation, while the micro-colinearity to the corresponding region in Triticum monococcum was less conserved. Our study indicates that the first intron of the VRN1 gene, and in particular the identified 8.6 kb region, is an important regulatory region for vernalization response in perennial ryegrass. [ABSTRACT FROM AUTHOR]

Isolation and mapping of genome-wide resistance ( R) gene analogs (RGAs) is of importance in identifying candidate(s) for a particular resistance gene/QTL. Here we reported our result in mapping totally 228 genome-wide RGAs in maize. By developing RGA-tagged markers and subsequent genotyping a population consisting of 294 recombinant inbred lines (RILs), 67 RGAs were genetically mapped on maize genome. Meanwhile, in silico mapping was conducted to anchor 113 RGAs by comparing all 228 RGAs to those anchored EST and BAC/BAC-end sequences via tblastx search ( E-value < 10−20). All RGAs from different mapping efforts were integrated into the existing SSR linkage map. After accounting for redundancy, the resultant RGA linkage map was composed of 153 RGAs that were mapped onto 172 loci on maize genome, and the mapped RGAs accounted for approximate three quarters of the genome-wide RGAs in maize. The extensive co-localizations were observed between mapped RGAs and resistance gene/QTL loci, implying the usefulness of this RGA linkage map in R gene cloning via candidate gene approach. [ABSTRACT FROM AUTHOR]

Apparent amylose content (AAC), gel consistency (GC), and gelatinization temperature (GT) are recognized as the most important determinants of rice eating and cooking qualities. The contributions of major starch-synthesis genes to these three traits have been investigated in the three consecutive experiments. In an initial QTL mapping with 130 doubled haploid (DH) lines, derived from an inter-subspecific cross of ‘Nanjing11’/‘Balilla’, the major QTLs responsible for AAC, GC, and GT coincided with the Wx (granule-bound starch synthase gene), Wx, and Sss IIa (soluble starch synthase gene) loci, respectively. In the second experiment, contributions of the major starch-synthesis genes to AAC, GC, and GT variations were estimated by using a multiple linear regression analysis. As shown, the Wx locus was a principal determinant for both AAC and GC, and could account for 58.5% and 38.9% of the phenotypic variations, respectively; while the Sss IIa locus was associated with GT, and could explain 25.5% of the observed variation. Eventually, a F2 population consisting of 501 individuals, derived from an inter-subspecific cross of the two sticky rice varieties ‘Suyunuo’ and ‘Yangfunuo 4’, was examined with gene-tagged markers. In the absence of the Wx gene, none of the starch-synthesis genes investigated could dominate the GC variation, however, the Sss IIa locus could also explain 25.1% of the GT variation. In summary, the Wx locus dominates the AAC variation, and meanwhile plays a major role in the GC variation. The Sss IIa locus is a major factor in explaining the GT variation. Apart from the major genes, other genetic factors may also contribute to the GC/GT variations. [ABSTRACT FROM AUTHOR]

Conserved domains or motifs shared by most known resistance ( R) genes have been extensively exploited to identify unknown R-gene analogs (RGAs). In an attempt to isolate all potential RGAs from the maize genome, we adopted the following three methods: modified amplified fragment length polymorphism (AFLP), modified rapid amplification of cDNA ends (RACE), and data mining. The first two methods involved PCR-based isolations of RGAs with degenerate primers designed based on the conserved NBS domain; while the third method involved mining of RGAs from the maize EST database using full-length R-gene sequences. A total of 23 and 12 RGAs were obtained from the modified AFLP and RACE methods, respectively; while, as many as 109 unigenes and 77 singletons with high homology to known R-genes were recovered via data-mining. Moreover, R-gene-like ESTs (or RGAs) identified from the data-mining method could cover all RACE-derived RGAs and nearly half AFLP-derived RGAs. Totally, the three methods resulted in 199 non-redundant RGAs. Of them, at least 186 were derived from putative expressed R-genes. RGA-tagged markers were developed for 55 unique RGAs, including 16 STS and 39 CAPS markers. [ABSTRACT FROM AUTHOR]

A single-particle microbeam facility has been constructed at the Key Laboratory of Ion Beam Bioengineering (LIBB), Chinese Academy of Sciences (CAS). The system was designed to deliver a defined numbers of hydrogen ions, produced by a van de Graaff accelerator, in an energy range of 2.0–3.0 MeV, into an area smaller than that of the nucleus of an individual living cell. The beam is collimated by a borosilicate glass capillary that forms the beam-line exit. An integrated computer program recognizes the cells and locates them one by one over the microbeam exit for irradiation. We present technical details of the CAS-LIBB microbeam facility, particularly on the collimator, hardware, control program, as well as cell irradiation protocols available. Various factors contributing to the targeting and positioning precision are discussed along with accuracy measurement results. [ABSTRACT FROM AUTHOR]

New Guinea impatiens (Impatiens hawkeri) is an economically important floral crop, however, little work has been conducted to further our understanding of the genetics of this crop. In this study, we used amplified fragment length polymorphism (AFLP) technology to investigate the level of polymorphism present among 41 commercial cultivars of New Guinea impatiens, study their genetic relatedness, and assess the genetic diversity in this material. An efficient DNA extraction protocol was developed, and a total of 48 EcoRI and MseI primer combinations were used for PCR amplification. Amplification products were then subjected to polyacrylamide gel electrophoresis. The AFLP analysis showed that all 41 cultivars generated between 73 and 130 scoreable polymorphic bands per primer combination. Gower's Genetic Dissimilarity estimates for the entire set of cultivars ranged between 0.940 and 0.488. A dendogram was generated from these dissimilarity data that revealed four groupings among these 41 cultivars. The implications of these results on genotypic variation, genetic relationships, and genetic diversity in New Guinea impatiens will be discussed. [ABSTRACT FROM AUTHOR]

The availability of high-density anchored markers is a prerequisite for reliable construction of a deep coverage BAC contig, which leads to creation of a sequence-ready map in the target chromosomal region. Unfortunately, such markers are not available for most plant species, including woody perennial plants. Here, we report on an efficient approach to build a megabase-size sequence-ready map in the apple genome for the Vf region containing apple scab resistance gene(s) by targeting AFLP-derived SCAR markers to this specific genomic region. A total of 11 AFLP-derived SCAR markers, previously tagged to the Vf locus, along with three other Vf-linked SCAR markers have been used to screen two apple genome BAC libraries. A single BAC contig which spans the Vf region at a physical distance of approximately 1,100 kb has been constructed by assembling the recovered BAC clones, followed by closure of inter-contig gaps. The contig is ∼ 4 ×deep, and provides a minimal tiling path of 16 contiguous and overlapping BAC clones, thus generating a sequence-ready map. Within the Vf region, duplication events have occurred frequently, and the Vf locus is restricted to the ca. 290 kb region covered by a minimum of three overlapping BAC clones. [ABSTRACT FROM AUTHOR]

By using the isoschizomers Hpa II and Msp I which display differential sensitivity to cytosine methylation, a modified amplified fragment length polymorphism (AFLP) technique has been developed to investigate DNA methylation profiles in eukaryotic organisms. Genomic DNA was digested with a mixture of EcoR I and one of the isoschizomers, and ligated to oligonucleotide adapters. After two rounds of selective PCR amplification, followed by DNA separation on a Long Ranger gel electrophoresis, a subset of restriction fragments can be displayed on an X-ray film. Comparison of AFLP banding patterns between Hpa II and Msp I revealed the extent of DNA methylation. The technique has been successfully applied in this study to investigate DNA methylation profiles of apple (Malus domestica cv. Gala) genomic DNA extracted from leaves of field-grown adult trees and in vitro-grown shoot cultures. The results showed that up to 25 percent of AFLP bands were derived from methylated sequences, and among those, a few bands unique to either adult trees or in vitro shoots were observed. These results demonstrated that this protocol is effective in identifying methylated DNA profiles. [ABSTRACT FROM AUTHOR]