A Segmentation-Guided Deep Learning Framework for Leaf Counting

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes

Discriminative attention-augmented feature learning for facial expression recognition in the wild

Facial expression recognition (FER) in-the-wild is challenging due to unconstraint settings such as varying head poses, illumination, and occlusions. In addition, the performance of a FER system significantly degrades due to large intra-class variation and inter-class similarity of facial expressions in real-world scenarios. To mitigate these problems, we propose a novel approach, Discriminative Attention-augmented Feature Learning Convolution Neural Network (DAF-CNN), which learns discriminative expression-related representations for FER. Firstly, we develop a 3D attention mechanism for feature refinement which selectively focuses on attentive channel entries and salient spatial regions of a convolution neural network feature map. Moreover, a deep metric loss termed Triplet-Center (TC) loss is incorporated to further enhance the discriminative power of the deeply-learned features with an expression-similarity constraint. It simultaneously minimizes intra-class distance and maximizes inter-class distance to learn both compact and separate features. Extensive experiments have been conducted on two representative facial expression datasets (FER-2013 and SFEW 2.0) to demonstrate that DAF-CNN effectively captures discriminative feature representations and achieves competitive or even superior FER performance compared to state-of-the-art FER methods

A segmentation-guided deep learning framework for leaf counting

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this paper, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted on the CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. Experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes

Leaf image based plant disease identification using transfer learning and feature fusion

With the continuing changes in the structure of plant and cultivation patterns, new diseases are constantly appearing on the leaves of plant, exacerbating the threat to food security and agricultural production in many areas of the world. Thus, a rapid and accurate recognition of various diseases in plant will not only significantly reduce unnecessary planting costs, but also alleviate the economic losses and environmental pollution caused by incorrect disease diagnosis. Recent advances in deep learning have improved the performance in recognizing plant leaf diseases. In this paper, we present a general framework for recognizing plant diseases. Firstly, we propose a deep feature descriptor based on transfer learning to obtain a high-level latent feature representation. Then, we integrate the deep features with traditional handcrafted features by feature fusion to capture the local texture information in plant leaf images. In addition, centre loss is incorporated to further enhance the discriminative ability of the fused feature. The centre loss simultaneously minimizes intra-class distance and maximizes inter-class distance to learn both compact and separate features. Extensive experiments have been conducted on three publicly available datasets (two Apple Leaf datasets and one Coffee Leaf dataset) to validate the effectiveness of proposed method. The propose method achieves 99.79%, 92.59% and 97.12% classification accuracies on the three datasets, respectively. The experiment results demonstrate that the proposed method effectively captures the discriminative feature representation for distinguishing plant leaf diseases

Curcumin Alleviates Hepatic Ischemia-Reperfusion Injury by Inhibiting Neutrophil Extracellular Traps Formation

Background Hepatic ischemia-reperfusion injury (IRI) is a common innate immune-mediated sterile inflammatory response in liver transplantation and liver tumor resection. Neutrophil extracellular traps (NETs) can aggravate liver injury and activates innate immune response in the process of liver IRI. However, Curcumin (Cur) can reverse this damage and reduce NETs formation. Nevertheless, the specific regulatory mechanism is still unclear in liver IRI. This study aimed to explore the potential mechanisms that how does Cur alleviate hepatic IRI by inhibits NETs production and develop novel treatment regimens. Methods We established a hepatic IRI model by subjecting C57BL/6J mice to 60 min of ischemia, followed by reperfusion for 2 h, 6 h, 12 h, and 24 h respectively. Subsequently, we were separated into 5 groups, namely the I/R group, Cur group, DNase-1 group, Cur + DNase1 group and sham operation group. Serum alanine aminotransferase (ALT) and aspartate transaminase (AST), Hematoxylin-eosin staining, immunofluorescence, and TUNEL analysis were applied to assess liver injury degree and NETs levels. Western blot assay was used to detect the protein levels of apoptosis-related proteins and MEK pathway proteins. Results Cur could alleviate hepatic IRI by inhibiting the generation of NETs via suppressing the MEK/ERK pathway. In addition, this study also revealed that DNase-1 is vital for alleviating hepatic IRI by reducing the generation of NETs. Conclusions Cur combined with DNase-1 was more effective than the two drugs administered alone in alleviating hepatic IRI by inhibiting the generation of NETs. These results also suggested that curcumin combined with DNase-1 was a potential therapeutic strategy to mitigate hepatic IRI

Flexible Liquid Crystal Polymer Technologies from Microwave to Terahertz Frequencies

With the emergence of fifth-generation (5G) cellular networks, millimeter-wave (mmW) and terahertz (THz) frequencies have attracted ever-growing interest for advanced wireless applications. The traditional printed circuit board materials have become uncompetitive at such high frequencies due to their high dielectric loss and large water absorption rates. As a promising high-frequency alternative, liquid crystal polymers (LCPs) have been widely investigated for use in circuit devices, chip integration, and module packaging over the last decade due to their low loss tangent up to 1.8 THz and good hermeticity. The previous review articles have summarized the chemical properties of LCP films, flexible LCP antennas, and LCP-based antenna-in-package and system-in-package technologies for 5G applications, although these articles did not discuss synthetic LCP technologies. In addition to wireless applications, the attractive mechanical, chemical, and thermal properties of LCP films enable interesting applications in micro-electro-mechanical systems (MEMS), biomedical electronics, and microfluidics, which have not been summarized to date. Here, a comprehensive review of flexible LCP technologies covering electric circuits, antennas, integration and packaging technologies, front-end modules, MEMS, biomedical devices, and microfluidics from microwave to THz frequencies is presented for the first time, which gives a broad introduction for those outside or just entering the field and provides perspective and breadth for those who are well established in the field

Genome evolution and diversity of wild and cultivated potatoes

Potato (Solanum tuberosum L.) is the world’s most important non-cereal food crop, and the vast majority of commercially grown cultivars are highly heterozygous tetraploids. Advances in diploid hybrid breeding based on true seeds have the potential to revolutionize future potato breeding and production1–4. So far, relatively few studies have examined the genome evolution and diversity of wild and cultivated landrace potatoes, which limits the application of their diversity in potato breeding. Here we assemble 44 high-quality diploid potato genomes from 24 wild and 20 cultivated accessions that are representative of Solanum section Petota, the tuber-bearing clade, as well as 2 genomes from the neighbouring section, Etuberosum. Extensive discordance of phylogenomic relationships suggests the complexity of potato evolution. We find that the potato genome substantially expanded its repertoire of disease-resistance genes when compared with closely related seed-propagated solanaceous crops, indicative of the effect of tuber-based propagation strategies on the evolution of the potato genome. We discover a transcription factor that determines tuber identity and interacts with the mobile tuberization inductive signal SP6A. We also identify 561,433 high-confidence structural variants and construct a map of large inversions, which provides insights for improving inbred lines and precluding potential linkage drag, as exemplified by a 5.8-Mb inversion that is associated with carotenoid content in tubers. This study will accelerate hybrid potato breeding and enrich our understanding of the evolution and biology of potato as a global staple food crop