Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning

Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture

Direct single-molecule dynamic detection of chemical reactions.

Single-molecule detection can reveal time trajectories and reaction pathways of individual intermediates/transition states in chemical reactions and biological processes, which is of fundamental importance to elucidate their intrinsic mechanisms. We present a reliable, label-free single-molecule approach that allows us to directly explore the dynamic process of basic chemical reactions at the single-event level by using stable graphene-molecule single-molecule junctions. These junctions are constructed by covalently connecting a single molecule with a 9-fluorenone center to nanogapped graphene electrodes. For the first time, real-time single-molecule electrical measurements unambiguously show reproducible large-amplitude two-level fluctuations that are highly dependent on solvent environments in a nucleophilic addition reaction of hydroxylamine to a carbonyl group. Both theoretical simulations and ensemble experiments prove that this observation originates from the reversible transition between the reactant and a new intermediate state within a time scale of a few microseconds. These investigations open up a new route that is able to be immediately applied to probe fast single-molecule physics or biophysics with high time resolution, making an important contribution to broad fields beyond reaction chemistry

Direct conversion of astrocytes into neuronal cells by drug cocktail

Direct conversion of astrocytes into neuronal cells by drug cocktail Cell Research advance online publication 2 October 2015; doi:10.1038/cr.2015.120 Dear Editor, Neurological disorder is one of the greatest threats to public health according to the World Health Organization. Because neurons have little or no regenerative capacity, conventional therapies for neurological disorders yielded poor outcomes. While the introduction of exogenous neural stem cells or neurons holds promise, many challenges still need to be tackled, including cell resource, delivery strategy, cell integration and cell maturation. Reprogramming of fibroblasts into induced pluripotent stem cells or directly into desirable neuronal cells by transcription factors (TFs) or small molecules can solve some problems, but other issues remain to be addressed, including safety, conversion efficiency and epigenetic memory [1, 2]. Astrocytes are considered to be the ideal starting candidate cell type for generating new neurons, due to their proximity in lineage distance to neurons and ability to proliferate after brain damage. Many studies have already revealed that astrocytes of the central nervous system can be reprogrammed into induced neuronal cells by virus-mediated overexpression of specific TFs in vitro and in vivo [3-6]. However, application of this virus-mediated direct conversion is still limited due to concerns on clinical safety. We have previously reported direct conversion of somatic cells into neural progenitor cells (NPCs) in vitro by cocktail of small molecules under hypoxia [7]. Here we set out to explore whether astrocytes can be induced into neuronal cells by the chemical cocktail in vitro

Improved Electric Strength and Space Charge Characterization in LDPE Composites with Montmorillonite Fillers

Space charge distribution and breakdown strength were investigated in composites of low density polyethylene (LDPE) and various contents of montmorillonite (MMT). The disperse performance of MMT in LDPE was observed with scanning electron microscopy (SEM) and X-ray diffraction. For MMT concentration of 1 wt%, the better intercalation of LDPE into MMT interlayers and the tighter interface structure between polymer-filler were observed, relative to MMT concentration of 3 and 5 wt%. Space charge profiles were obtained using the pulsed electroacoustic (PEA) method. Less space charge accumulated in the LDPE/MMT with MMT content of 1 wt% than that in pure LDPE, and space charge in the central of LDPE/MMT was much more uniformly. On the other hand, when MMT concentration was up to 3 and 5 wt%, large amounts of heterocharges were accumulated in the sample bulk. In MMT doped samples the dielectric strength increased up to a maximum at 1 wt% loading, and then decreases at 3 and 5 wt%

TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou

Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction systems. Conventional algorithms mostly follow two cascading stages: a simple General Search Unit (GSU) for fast and coarse search over tens of thousands of long-term behaviors and an Exact Search Unit (ESU) for effective Target Attention (TA) over the small number of finalists from GSU. Although efficient, existing algorithms mostly suffer from a crucial limitation: the \textit{inconsistent} target-behavior relevance metrics between GSU and ESU. As a result, their GSU usually misses highly relevant behaviors but retrieves ones considered irrelevant by ESU. In such case, the TA in ESU, no matter how attention is allocated, mostly deviates from the real user interests and thus degrades the overall CTR prediction accuracy. To address such inconsistency, we propose \textbf{TWo-stage Interest Network (TWIN)}, where our Consistency-Preserved GSU (CP-GSU) adopts the identical target-behavior relevance metric as the TA in ESU, making the two stages twins. Specifically, to break TA's computational bottleneck and extend it from ESU to GSU, or namely from behavior length $10^2$ to length $10^4-10^5$, we build a novel attention mechanism by behavior feature splitting. For the video inherent features of a behavior, we calculate their linear projection by efficient pre-computing \& caching strategies. And for the user-item cross features, we compress each into a one-dimentional bias term in the attention score calculation to save the computational cost. The consistency between two stages, together with the effective TA-based relevance metric in CP-GSU, contributes to significant performance gain in CTR prediction.Comment: Accepted by KDD 202

Orphan Nuclear Receptor Nur77 Inhibits Cardiac Hypertrophic Response to Beta-Adrenergic Stimulation.

The orphan nuclear receptor Nur77 plays critical roles in cardiovascular diseases, and its expression is markedly induced in the heart after beta-adrenergic receptor (β-AR) activation. However, the functional significance of Nur77 in β-AR signaling in the heart remains unclear. By using Northern blot, Western blot, and immunofluorescent staining assays, we showed that Nur77 expression was markedly upregulated in cardiomyocytes in response to multiple hypertrophic stimuli, including isoproterenol (ISO), phenylephrine (PE), and endothelin-1 (ET-1). In a time- and dose-dependent manner, ISO increases Nur77 expression in the nuclei of cardiomyocytes. Overexpression of Nur77 markedly inhibited ISO-induced cardiac hypertrophy by inducing nuclear translocation of Nur77 in cardiomyocytes. Furthermore, cardiac overexpression of Nur77 by intramyocardial injection of Ad-Nur77 substantially inhibited cardiac hypertrophy and ameliorated cardiac dysfunction after chronic infusion of ISO in mice. Mechanistically, we demonstrated that Nur77 functionally interacts with NFATc3 and GATA4 and inhibits their transcriptional activities, which are critical for the development of cardiac hypertrophy. These results demonstrate for the first time that Nur77 is a novel negative regulator for the β-AR-induced cardiac hypertrophy through inhibiting the NFATc3 and GATA4 transcriptional pathways. Targeting Nur77 may represent a potentially novel therapeutic strategy for preventing cardiac hypertrophy and heart failure

3D Alternating Direction TV-Based Cone-Beam CT Reconstruction with Efficient GPU Implementation

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In order to improve the performance for iterative reconstruction, an efficient IIR algorithm for cone-beam computed tomography (CBCT) with GPU implementation has been proposed in this paper. In the first place, an algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction. The applied proximal technique avoids the horrible pseudoinverse computation of big matrix which makes the proposed algorithm applicable and efficient for CBCT imaging. The iteration for this algorithm is simple but convergent. The simulation and real CT data reconstruction results indicate that the proposed algorithm is both fast and accurate. The GPU implementation shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy. The runtime for the new 3D algorithm is about 6.8 seconds per loop with the image size of 256×256×256 and 36 projections of the size of 512×512

Notch1 Pathway Protects against Burn-Induced Myocardial Injury by Repressing Reactive Oxygen Species Production through JAK2/STAT3 Signaling

Oxidative stress plays an important role in burn-induced myocardial injury, but the cellular mechanisms that control reactive oxygen species (ROS) production and scavenging are not fully understood. This study demonstrated that blockade of Notch signaling via knockout of the transcription factor RBP-J or a pharmacological inhibitor aggravated postburn myocardial injury, which manifested as deteriorated serum CK, CK-MB, and LDH levels and increased apoptosis in vitro and in vivo. Interruption of Notch signaling increased intracellular ROS production, and a ROS scavenger reversed the exacerbated myocardial injury after Notch signaling blockade. These results suggest that Notch signaling deficiency aggravated postburn myocardial injury through increased ROS levels. Notch signaling blockade also decreased MnSOD expression in vitro and in vivo. Notably, Notch signaling blockade downregulated p-JAK2 and p-STAT3 expression. Inhibition of JAK2/STAT3 signaling with AG490 markedly decreased MnSOD expression, increased ROS production, and aggravated myocardial injury. AG490 plus GSI exerted no additional effects. These results demonstrate that Notch signaling protects against burn-induced myocardial injury through JAK2/STAT3 signaling, which activates the expression of MnSOD and leads to decreased ROS levels

A Novel Chromone Derivative with Anti-Inflammatory Property via Inhibition of ROS-Dependent Activation of TRAF6-ASK1-p38 Pathway

The p38 MAPK signaling pathway plays a pivotal role in inflammation. Targeting p38 MAPK may be a potential strategy for the treatment of inflammatory diseases. In the present study, we show that a novel chromone derivative, DCO-6, significantly reduced lipopolysaccharide (LPS)-induced production of nitric oxide, IL-1β and IL-6, decreased the levels of iNOS, IL-1β and IL-6 mRNA expression in both RAW264.7 cells and mouse primary peritoneal macrophages, and inhibited LPS-induced activation of p38 MAPK but not of JNK, ERK. Moreover, DCO-6 specifically inhibited TLR4-dependent p38 activation without directly inhibiting its kinase activity. LPS-induced production of intracellular reactive oxygen species (ROS) was remarkably impaired by DCO-6, which disrupted the formation of the TRAF6-ASK1 complex. Administering DCO-6 significantly protected mice from LPS-induced septic shock in parallel with the inhibition of p38 activation and ROS production. Our results indicate that DCO-6 showed anti-inflammatory properties through inhibition of ROS-dependent activation of TRAF6-ASK1-p38 pathway. Blockade of the upstream events required for p38 MAPK action by DCO-6 may provide a new therapeutic option in the treatment of inflammatory diseases