Integrative omics studies revealed synergistic link between sucrose metabolic isogenes and carbohydrates in poplar roots infected by Fusarium wilt.

Advances in carbohydrate metabolism prompted its essential role in defense priming and sweet immunity during plant-pathogen interactions. Nevertheless, upstream responding enzymes in the sucrose metabolic pathway and associated carbohydrate derivatives underlying fungal pathogen challenges remain to be deciphered in Populus, a model tree species. In silico deduction of genomic features, including phylogenies, exon/intron distributions, cis-regulatory elements, and chromosomal localization, identified 59 enzyme genes (11 families) in the Populus genome. Spatiotemporal expression of the transcriptome and the quantitative real-time PCR revealed a minuscule number of isogenes that were predominantly expressed in roots. Upon the pathogenic Fusarium solani (Fs) exposure, dynamic changes in the transcriptomics atlas and experimental evaluation verified Susy (PtSusy2 and 3), CWI (PtCWI3), VI (PtVI2), HK (PtHK6), FK (PtFK6), and UGPase (PtUGP2) families, displaying promotions in their expressions at 48 and 72 h of post-inoculation (hpi). Using the gas chromatography-mass spectrometry (GC-MS)-based non-targeted metabolomics combined with a high-performance ion chromatography system (HPICS), approximately 307 metabolites (13 categories) were annotated that led to the quantification of 46 carbohydrates, showing marked changes between three compared groups. By contrast, some sugars (e.g., sorbitol, L-arabitol, trehalose, and galacturonic acid) exhibited a higher accumulation at 72 hpi than 0 hpi, while levels of α-lactose and glucose decreased, facilitating them as potential signaling molecules. The systematic overview of multi-omics approaches to dissect the effects of Fs infection provides theoretical cues for understanding defense immunity depending on fine-tuned Suc metabolic gene clusters and synergistically linked carbohydrate pools in trees.

Restoring T and B cell generation in X-linked severe combined immunodeficiency mice through hematopoietic stem cells adenine base editing.

Base editing of hematopoietic stem/progenitor cells (HSPCs) is an attractive strategy for treating immunohematologic diseases. However, the feasibility of using adenine-base-edited HSPCs for treating X-linked severe combined immunodeficiency (SCID-X1), the influence of dose-response relationships on immune cell generation, and the potential risks have not been demonstrated in vivo. Here, a humanized SCID-X1 mouse model was established, and 86.67% ± 2.52% (n = 3) of mouse hematopoietic stem cell (HSC) pathogenic mutations were corrected, with no single-guide-RNA (sgRNA)-dependent off-target effects detected. Analysis of peripheral blood over 16 weeks post-transplantation in mice with different immunodeficiency backgrounds revealed efficient immune cell generation following transplantation of different amounts of modified HSCs. Therefore, a large-scale infusion of gene-corrected HSCs within a safe range can achieve rapid, stable, and durable immune cell regeneration. Tissue-section staining further demonstrated the restoration of immune organ tissue structures, with no tumor formation in multiple organs. Collectively, these data suggest that base-edited HSCs are a potential therapeutic approach for SCID-X1 and that a threshold infusion dose of gene-corrected cells is required for immune cell regeneration. This study lays a theoretical foundation for the clinical application of base-edited HSCs in treating SCID-X1.

Redirected Walking On Omnidirectional Treadmill.

Redirected walking (RDW) and omnidirectional treadmill (ODT) are two effective solutions to the natural locomotion interface in virtual reality. ODT fully compresses the physical space and can be used as the integration carrier of all kinds of devices. However, the user experience varies in different directions of ODT, and the premise of interaction between users and integrated devices is a good match between virtual and real objects. RDW technology uses visual cues to guide the user's location in physical space. Based on this principle, combining RDW technology with ODT to guide the user's walking direction through visual cues can effectively improve user experience on ODT and make full use of various devices integrated on ODT. This paper explores the novel prospects of combining RDW technology with ODT and formally puts forward the concept of O-RDW (ODT-based RDW). Two baseline algorithms, i.e., OS2MD (ODT-based steer to multi-direction), and OS2MT (ODT-based steer to multi-target), are proposed to combine the merits of both RDW and ODT. With the help of the simulation environment, this paper quantitatively analyzes the applicable scenarios of the two algorithms and the influence of several main factors on the performance. Based on the conclusions of the simulation experiments, the two O-RDW algorithms are successfully applied in the practical application case of multi-target haptic feedback. Combined with the user study, the practicability and effectiveness of O-RDW technology in practical use are further verified.

Strolling in Room-Scale VR: Hex-Core-MK1 Omnidirectional Treadmill.

The natural locomotion interface is critical to the development of many VR applications. For household VR applications, there are two basic requirements: natural immersive experience and minimized space occupation. The existing locomotion strategies generally do not simultaneously satisfy these two requirements well. This article presents a novel omnidirectional treadmill (ODT) system named Hex-Core-MK1 (HCMK1). By implementing two kinds of mirror-symmetrical spiral rollers to generate the omnidirectional velocity field, this proposed system is capable of providing real walking experiences with a full-degree of freedom in an area as small as 1.76 m 2, while delivering great advantages over several existing ODT systems in terms of weight, volume, latency and dynamic performance. Compared with the sizes of Infinadeck and HCP, the two best motor-driven ODTs so far, the 8 cm height of HCMK1 is only 20% of Infinadeck and 50% of HCP. In addition, HCMK1 is a lightweight device weighing only 110 kg, which provides possibilities for further expanding VR scenarios, such as terrain simulation. The system latency of HCMK1 is only 9ms. The experiments show that HCMK1 can deliver a starting acceleration of 16.00 m/s 2 and a braking acceleration of 30.00 m/s 2.

Graph Regularized Structured Output SVM for Early Expression Detection With Online Extension.

In this study, a graph regularized algorithm for early expression detection (EED), called GraphEED, is proposed. EED is aimed at detecting the specified expression in the early stage of a video. Existing EED detectors fail to explicitly exploit the local geometrical structure of the data distribution, which may affect the prediction performance significantly. According to manifold learning, the data in real-world applications are likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. The proposed graph Laplacian consists of two parts: 1) a k -nearest neighbor graph is first constructed to encode the geometrical information under the manifold assumption and 2) the entire expressions are regarded as the must-link constraints since they all contain the complete duration information and it is shown that this can also be formulated as a graph regularization. GraphEED is to have a detection function representing these graph structures. Even with the inclusion of the graph Laplacian, the proposed GraphEED has the same computational complexity as that of the max-margin EED, which is a well-known learning-based EED, but the detection performance has been largely improved. To further make the model appropriate in large-scale applications, with the technique of online learning, the proposed GraphEED is extended to the so-called online GraphEED (OGraphEED). In OGraphEED, the buffering technique is employed to make the optimization practical by reducing the computation and storage cost. Extensive experiments on three video-based datasets have demonstrated the superiority of the proposed methods in terms of both effectiveness and efficiency.

Genome-Wide Survey and Expression Analyses of Hexokinase Family in Poplar (Populus trichocarpa).

Hexokinase (HXK) family proteins exert critical roles in catalyzing hexose phosphorylation, sugar sensing, and modulation of plant growth and stress adaptation. Nevertheless, a large amount remains unknown about the molecular profile of HXK enzymes in Populus trichocarpa, a woody model tree species. A genome-wide survey of HXK-encoding genes, including phylogenies, genomic structures, exon/intron organization, chromosomal distribution, and conserved features, was conducted, identifying six putative HXK isogenes (PtHXK1-6) in the Populus genome. The evolutionary tree demonstrated that 135 homologous HXKs between 17 plant species were categorized into four major subfamilies (type A, B, C, and D), clustering one plastidic (PtHXK3) and five mitochondrial PtHXKs grouped into type A and B, respectively. The in silico deduction prompted the presence of the conserved sugar-binding core (motif 4), phosphorylation sites (motif 2 and 3), and adenosine-binding domains (motif 7). The transcriptomic sequencing (RNA-seq) and the quantitative real-time PCR (qRT-PCR) assays revealed that three isogenes (PtHXK2, 3, and 6) were abundantly expressed in leaves, stems, and roots, while others appeared to be dominantly expressed in the reproductive tissues. Under the stress exposure, PtHXK2 and 6 displayed a significant induction upon the pathogenic fungi (Fusarium solani) infection and marked promotions by glucose feeding in roots. In contrast, the PtHXK3 and 6 are ABA-responsive genes, following a dose-dependent manner. The comprehensive analyses of the genomic patterns and expression profiling provide theoretical clues and lay a foundation for unraveling the physiological and signaling roles underlying the fine-tuned PtHXKs responding to diverse stressors.

Efficient Unsupervised Dimension Reduction for Streaming Multiview Data.

Multiview learning has received substantial attention over the past decade due to its powerful capacity in integrating various types of information. Conventional unsupervised multiview dimension reduction (UMDR) methods are usually conducted in an offline manner and may fail in many real-world applications, where data arrive sequentially and the data distribution changes periodically. Moreover, satisfying the requirements of high memory consumption and expensive retraining of the time cost in large-scale scenarios are difficult. To remedy these drawbacks, we propose an online UMDR (OUMDR) framework. OUMDR aims to seek a low-dimensional and informative consensus representation for streaming multiview data. View-specific weights are also learned in this article to reflect the contributions of different views to the final consensus presentation. A specific model called OUMDR-E is developed by introducing the exclusive group LASSO (EG-LASSO) to explore the intraview and interview correlations. Then, we develop an efficient iterative algorithm with limited memory and time cost requirements for optimization, where the convergence of each update is theoretically guaranteed. We evaluate the proposed approach in video-based expression recognition applications. The experimental results demonstrate the superiority of our approach in terms of both effectiveness and efficiency.

A Deep Fourier Neural Network for Seizure Prediction Using Convolutional Neural Network and Ratios of Spectral Power.

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods usually adopt handcrafted features and manual parameter setting. The over-reliance on the expertise of specialists may lead to weak exploitation of features and low popularization of clinical application. This paper proposes a novel parameterless patient-specific method based on Fourier Neural Network (FNN), where the Fourier transform and backpropagation learning are synthesized to make the predictor more efficient and practical. The employment of FNN is the first attempt in the field of seizure prediction due to its automatic extraction of immanent spectra in epileptic signals. Despite the self-adaptive superiority of FNN, we introduce Convolutional Neural Network (CNN) to further improve its search capability in high-dimensional feature spaces. The study also develops a multi-layer module to estimate spectral power ratios of raw recordings, which optimizes the prediction by enhancing feature diversity. Based on these modules, this paper proposes a two-channel deep neural network: Fourier Ratio Convolutional Neural Network (FRCNN). To demonstrate the reliability of the model, we explain the mathematical meaning of hidden-layer neurons in FRCNN theoretically. This approach is evaluated on both intracranial and scalp EEG datasets. It shows that the predictor achieved a sensitivity of 91.2% and a false prediction rate (FPR) of 0.06[Formula: see text]h[Formula: see text] across intracranial subjects and a sensitivity of 85.4% and an FPR of 0.14[Formula: see text]h[Formula: see text] over scalp subjects. The results indicate that FRCNN enables the convenience of epilepsy treatments while preserving a high degree of precision. In the end, a detailed comparison with the previous methods demonstrates that FRCNN has achieved higher performance and generalization ability.

Transcriptomic Profiling of Populus Roots Challenged with Fusarium Reveals Differential Responsive Patterns of Invertase and Invertase Inhibitor-Like Families within Carbohydrate Metabolism.

Fusarium solani (Fs) is one of the notorious necrotrophic fungal pathogens that cause root rot and vascular wilt, accounting for the severe loss of Populus production worldwide. The plant-pathogen interactions have a strong molecular basis. As yet, the genomic information and transcriptomic profiling on the attempted infection of Fs remain unavailable in a woody model species, Populus trichocarpa. We used a full RNA-seq transcriptome to investigate the molecular interactions in the roots with a time-course infection at 0, 24, 48, and 72 h post-inoculation (hpi) of Fs. Concomitantly, the invertase and invertase inhibitor-like gene families were further analyzed, followed by the experimental evaluation of their expression patterns using quantitative PCR (qPCR) and enzyme assay. The magnitude profiles of the differentially expressed genes (DEGs) were observed at 72 hpi inoculation. Approximately 839 genes evidenced a reception and transduction of pathogen signals, a large transcriptional reprogramming, induction of hormone signaling, activation of pathogenesis-related genes, and secondary and carbohydrate metabolism changes. Among these, a total of 63 critical genes that consistently appear during the entire interactions of plant-pathogen had substantially altered transcript abundance and potentially constituted suitable candidates as resistant genes in genetic engineering. These data provide essential clues in the developing new strategies of broadening resistance to Fs through transcriptional or translational modifications of the critical responsive genes within various analyzed categories (e.g., carbohydrate metabolism) in Populus.

Seizure Prediction in EEG Signals Using STFT and Domain Adaptation.

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional approaches commonly collect training and testing samples from the same patient due to inter-individual variability. However, the challenging problem of domain shift between various subjects remains unsolved, resulting in a low conversion rate to the clinic. In this work, a domain adaptation (DA)-based model is proposed to circumvent this issue. The short-time Fourier transform (STFT) is employed to extract the time-frequency features from raw EEG data, and an autoencoder is developed to map these features into high-dimensional space. By minimizing the inter-domain distance in the embedding space, this model learns the domain-invariant information, such that the generalization ability is improved by distribution alignment. Besides, to increase the feasibility of its application, this work mimics the data distribution under the clinical sampling situation and tests the model under this condition, which is the first study that adopts the assessment strategy. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can minimize the domain gap effectively compared with previous approaches.

Event-Triggered Adaptive Fault Tolerant Control for a Class of Uncertain Nonlinear Systems.

This paper considers an adaptive fault-tolerant control problem for a class of uncertain strict feedback nonlinear systems, in which the actuator has an unknown drift fault and the loss of effectiveness fault. Based on the event-triggered theory, the adaptive backstepping technique, and Lyapunov theory, a novel fault-tolerant control strategy is presented. It is shown that an appropriate comprise between the control performance and the sensor data real-time transmission consumption is made, and the fault-tolerant tracking control problem of the strict feedback nonlinear system with uncertain and unknown control direction is solved. The adaptive backstepping method is introduced to compensate the actuator faults. Moreover, a new adjustable event-triggered rule is designed to determine the sampling state instants. The overall control strategy guarantees that the output signal tracks the reference signal, and all the signals of the closed-loop systems are convergent. Finally, the fan speed control system is constructed to demonstrate the validity of the proposed strategy and the application of the general systems.

Fisher Information Matrix of Unipolar Activation Function-Based Multilayer Perceptrons.

The multilayer perceptrons (MLPs) are widely used in many fields, however, singularities in the parameter space may seriously influence the learning dynamics of MLPs and cause strange learning behaviors. Given that the singularities are the subspaces of the parameter space where the Fisher information matrix (FIM) degenerates, the FIM plays a key role in the study of the singular learning dynamics of the MLPs. In this paper, we obtain the analytical form of the FIM for unipolar activation function-based MLPs where the input subjects to the Gaussian distribution with general covariance matrix and the unipolar error function is chosen as the activation function. Then three simulation experiments are taken to verify the validity of the obtained results.

Early Expression Detection via Online Multi-Instance Learning With Nonlinear Extension.

Video-based facial expression recognition has received substantial attention over the past decade, while early expression detection (EED) is still a relatively new and challenging problem. The goal of EED is to identify an expression as quickly as possible after the expression starts and before it ends. This timely ability has many potential applications, ranging from human-computer interaction to security. The max-margin early event detector (MMED) is a well-known ranking model for early event detection. It can achieve competitive EED performance but suffers from several critical limitations: 1) MMED lacks flexibility in extracting useful information for segment comparison, which leads to poor performance in exploring the ranking relation between segment pairs; 2) the training process is slow due to the large number of constraints, and the memory requirement is also usually hard to satisfy; and 3) MMED is linear in nature, and hence may not be appropriate for data in a nonlinear feature space. To overcome these limitations, we propose an online multi-instance learning (MIL) framework for EED. In particular, the MIL technique is first introduced to generalize MMED, resulting in the proposed MIL-based EED (MIED), which is more general and flexible than MMED, since various instance construction and combination strategies can be adopted. To accelerate the training process, we reformulate MIED in the online setting and develop online multi-instance learning framework for EED (OMIED). To further exploit the nonlinear structure of the data distribution, we incorporate the kernel methods in OMIED, which results in the proposed online kernel multi-instance learning for early expression detection. Experiments on two popular and one challenging video-based expression data sets demonstrate both the efficiency and effectiveness of the proposed methods.

Bone fracture and the interaction between bisphosphonates and proton pump inhibitors: a meta-analysis.

OBJECTIVE: Recent studies suggested an increased risk of fractures with interaction between bisphosphonates (BPs) and proton pump inhibitors (PPIs). We performed a meta-analysis of fractures between patients taking BPs/PPIs and those taking BPs only. METHODS: We conducted a PubMed database and Ovid database search, as well as Cochrane Library search (up to July 2014) for studies assessing the association between fractures and BPs or/and PPIs. We performed random effects meta-analysis of odds ratios (OR) according to fracture type and conducted subgroup analyses by race and BP subtypes. Heterogeneity was assessed using Q statistics and I(2) statistic. RESULTS: After study selection, 4 unique studies (5 comparisons) including 57259 patients were available for this meta-analysis. Pooled analysis of overall fracture risk of BP+PPI group versus BP group showed a significant increase in risk of fractures (OR = 1.52, P = 0.025), with substantial heterogeneity. However, heterogeneity was drastically reduced in subgroup of Asian (I(2) = 24% and P = 0.251), and fracture risk showed a significant increase (OR = 1.75, P = 0.026). In contrast, heterogeneity was little eliminated in subgroup of European, and fracture risk was no statistical difference (OR = 1.42, P = 0.068). Three studies including 4 comparisons reported on spine fracture were included in the pooled analysis demonstrating an increased spine fracture risk associated with BP/PPI interaction (OR = 1.60, 95% CI 1.13-2.26, P = 0.008, I(2) = 58.6%). CONCLUSIONS: This meta-analysis suggests that there is an interaction associated with increased fracture risk (particularly for spine and Asian race) between BP and PPI use. Clinicians should carefully evaluate such risk factors for osteoporosis in patients taking BPs, before routinely prescribing PPIs, and make a careful judgment as to whether PPIs may be safe for patients at high risk of fractures.

Natural gradient learning algorithms for RBF networks.

Radial basis function (RBF) networks are one of the most widely used models for function approximation and classification. There are many strange behaviors in the learning process of RBF networks, such as slow learning speed and the existence of the plateaus. The natural gradient learning method can overcome these disadvantages effectively. It can accelerate the dynamics of learning and avoid plateaus. In this letter, we assume that the probability density function (pdf) of the input and the activation function are gaussian. First, we introduce natural gradient learning to the RBF networks and give the explicit forms of the Fisher information matrix and its inverse. Second, since it is difficult to calculate the Fisher information matrix and its inverse when the numbers of the hidden units and the dimensions of the input are large, we introduce the adaptive method to the natural gradient learning algorithms. Finally, we give an explicit form of the adaptive natural gradient learning algorithm and compare it to the conventional gradient descent method. Simulations show that the proposed adaptive natural gradient method, which can avoid the plateaus effectively, has a good performance when RBF networks are used for nonlinear functions approximation.

Levofloxacin increases apoptosis of rat annulus fibrosus cells via the mechanism of upregulating MMP-2 and MMP-13.

Levofloxacin was previously reported to induce apoptosis of rat annulus fibrosus (AF) cells by upregulating active caspase-3 and matrix metalloproteinase (MMP)-3 expression in vitro. However, the effects of levofloxacin on rat AF cells, as well as the related mechanism, have not been revealed completely. The purpose of this study was to further explore the changes in extracellular matrix and MMPs of rat AF cells based on levofloxacin-induced apoptosis. AF cells isolated from rat AF regions were cultured in monolayers and treated with levofloxacin in a dose- and time-dependent manner. To determine the cytotoxic effects of levofloxacin, inverted phase-contrast microscopy was used to perform morphological observation of apoptotic cells. The mRNA expression levels of MMP-2, -9 and -13 were quantified by reverse transcription and real-time quantitative polymerase chain reaction (RT-qPCR). Protein level of MMP-2 and MMP-13 were determined by western blot. The results showed that levofloxacin induced marked AF cell apoptosis, which was observed by inverted phase-contrast microscopy, and indicated by the increased expression of active caspase-3. Both RT-qPCR and western blot revealed that MMP-2 and MMP-13 expression were upregulated by levofloxacin treatment in a time- and dose-dependent manner. Moreover, cellular binding to type I collagen was found to be decreased by levofloxacin. In conclusion, the results above suggest that the possible cytotoxic effects of levofloxacin on AF cells in vitro may be attributed to the decreased cell binding to type I collagen and up-regulated expression of MMP-2 and MMP-13.

Dynamics of learning near singularities in radial basis function networks.

The radial basis function (RBF) networks are one of the most widely used models for function approximation in the regression problem. In the learning paradigm, the best approximation is recursively or iteratively searched for based on observed data (teacher signals). One encounters difficulties in such a process when two component basis functions become identical, or when the magnitude of one component becomes null. In this case, the number of the components reduces by one, and then the reduced component recovers as the learning process proceeds further, provided such a component is necessary for the best approximation. Strange behaviors, especially the plateau phenomena, have been observed in dynamics of learning when such reduction occurs. There exist singularities in the space of parameters, and the above reduction takes place at the singular regions. This paper focuses on a detailed analysis of the dynamical behaviors of learning near the overlap and elimination singularities in RBF networks, based on the averaged learning equation that is applicable to both on-line and batch mode learning. We analyze the stability on the overlap singularity by solving the eigenvalues of the Hessian explicitly. Based on the stability analysis, we plot the analytical dynamic vector fields near the singularity, which are then compared to those real trajectories obtained by a numeric method. We also confirm the existence of the plateaus in both batch and on-line learning by simulation.

Dynamics of learning near singularities in layered networks.

We explicitly analyze the trajectories of learning near singularities in hierarchical networks, such as multilayer perceptrons and radial basis function networks, which include permutation symmetry of hidden nodes, and show their general properties. Such symmetry induces singularities in their parameter space, where the Fisher information matrix degenerates and odd learning behaviors, especially the existence of plateaus in gradient descent learning, arise due to the geometric structure of singularity. We plot dynamic vector fields to demonstrate the universal trajectories of learning near singularities. The singularity induces two types of plateaus, the on-singularity plateau and the near-singularity plateau, depending on the stability of the singularity and the initial parameters of learning. The results presented in this letter are universally applicable to a wide class of hierarchical models. Detailed stability analysis of the dynamics of learning in radial basis function networks and multilayer perceptrons will be presented in separate work.