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

A Dual-Fluorescent Composite of Graphene Oxide and Poly(3-Hexylthiophene) Enables the Ratiometric Detection of Amines

A composite prepared by grafting a conjugated polymer, poly(3-hexylthiophene) (P3HT), to the surface of graphene oxide was shown to result in a dual-fluorescent material with tunable photoluminescent properties. Capitalizing on these unique features, a new class of graphene-based sensors that enables the ratiometric fluorescence detection of amine-based pollutants was developed. Moreover, through a detailed spectroscopic study, the origin of the optical properties of the aforementioned composite was studied and was found to be due to electronic decoupling of the conjugated polymer from the GO. The methodology described herein effectively overcomes a long-standing challenge that has prevented graphene based composites from finding utility in sensing and related applications.Meng, Dongli, Shaojun Yang, Dianming Sun, Yi Zeng, Jinhua Sun, Yi Li, Shouke Yan, Yong Huang, Christopher W. Bielawski, and Jianxin Geng. "A dual-fluorescent composite of graphene oxide and poly (3-hexylthiophene) enables the ratiometric detection of amines." Chemical Science 5, no. 8 (Apr., 2014): 3130-3134.Chemistr

miR-181a increases FoxO1 acetylation and promotes granulosa cell apoptosis via SIRT1 downregulation.

Oxidative stress impairs follicular development by inducing granulosa cell (GC) apoptosis, which involves enhancement of the transcriptional activity of the pro-apoptotic factor Forkhead box O1 (FoxO1). However, the mechanism by which oxidative stress promotes FoxO1 activity is still unclear. Here, we found that miR-181a was upregulated in hydrogen peroxide (

Precise influence evaluation in complex networks

Evaluating node influence is fundamental for identifying key nodes in complex networks. Existing methods typically rely on generic indicators to rank node influence across diverse networks, thereby ignoring the individualized features of each network itself. Actually, node influence stems not only from general features but the multi-scale individualized information encompassing specific network structure and task. Here we design an active learning architecture to predict node influence quantitively and precisely, which samples representative nodes based on graph entropy correlation matrix integrating multi-scale individualized information. This brings two intuitive advantages: (1) discovering potential high-influence but weak-connected nodes that are usually ignored in existing methods, (2) improving the influence maximization strategy by deducing influence interference. Significantly, our architecture demonstrates exceptional transfer learning capabilities across multiple types of networks, which can identify those key nodes with large disputation across different existing methods. Additionally, our approach, combined with a simple greedy algorithm, exhibits dominant performance in solving the influence maximization problem. This architecture holds great potential for applications in graph mining and prediction tasks

MFA-DVR: Direct Volume Rendering of MFA Models

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for large-scale volumetric data, and uniform representation of both structured and unstructured data. In this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured and unstructured volumetric datasets. We demonstrate improved rendering quality using MFA-DVR on both synthetic and real datasets through a comparative study. We show that MFA-DVR not only generates more faithful volume rendering than using local filters but also performs faster on high-order interpolations on structured and unstructured datasets. MFA-DVR is implemented in the existing volume rendering pipeline of the Visualization Toolkit (VTK) to be accessible by the scientific visualization community