Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping

Milk thistle, Silybum marianum (L.), is a well-known medicinal plant used for the treatment of liver diseases due to its high content of silymarin. The seeds contain elaiosome, a fleshy structure attached to the seeds, which is believed to be a rich source of many metabolites including silymarin. Segmentation of elaiosomes using only image analysis is difficult, and this makes it impossible to quantify the elaiosome phenotypes. This study proposes a new approach for semi-automated detection and segmentation of elaiosomes in milk thistle seed using the Detectron2 deep learning algorithm. One hundred manually labeled images were used to train the initial elaiosome detection model. This model was used to predict elaiosome from new datasets, and the precise predictions were manually selected and used as new labeled images for retraining the model. Such semi-automatic image labeling, i.e., using the prediction results of the previous stage for retraining the model, allowed the production of sufficient labeled data for retraining. Finally, a total of 6,000 labeled images were used to train Detectron2 for elaiosome detection and attained a promising result. The results demonstrate the effectiveness of Detectron2 in detecting milk thistle seed elaiosomes with an accuracy of 99.9%. The proposed method automatically detects and segments elaiosome from the milk thistle seed. The predicted mask images of elaiosome were used to analyze its area as one of the seed phenotypic traits along with other seed morphological traits by image-based high-throughput phenotyping in ImageJ. Enabling high-throughput phenotyping of elaiosome and other seed morphological traits will be useful for breeding milk thistle cultivars with desirable traits

Triple-sinusoid hedgehog lattice in a centrosymmetric Kondo metal

Superposed symmetry-equivalent magnetic ordering wave vectors can lead to topologically non-trivial spin textures, such as magnetic skyrmions and hedgehogs, and give rise to novel quantum phenomena due to fictitious magnetic fields associated with a non-zero Berry curvature of these spin textures. To date, all known spin textures are constructed through the superposition of multiple spiral orders, where spins vary in directions with constant amplitude. Recent theoretical studies have suggested that multiple sinusoidal orders, where collinear spins vary in amplitude, can construct distinct topological spin textures regarding chirality properties. However, such textures have yet to be experimentally realised. In this work, we report the observation of a zero-field magnetic hedgehog lattice from a superposition of triple sinusoidal wave vectors in the magnetically frustrated Kondo lattice CePtAl4Ge2. Notably, we also observe the emergence of anomalous electrical and thermodynamic behaviours near the field-induced transition from the zero-field topological hedgehog lattice to a non-topological sinusoidal state. These observations highlight the role of Kondo coupling in stabilising the zero-field hedgehog state in the Kondo lattice and warrant an expedited search for other topological magnetic structures coupled with Kondo coupling

Sequential Beam, User, and Power Allocation for Interference Management in 5G mmWave Networks

Small cell, mmWave, and massive Multi-Input Multi-Output (MIMO) technologies in 5G cellular networks becomes inevitable trend caused by killer applications such as holographic video which is traffic-intensive. In this paper, we study a sequential activation beam selection, user scheduling, and power allocation problem in a mmWave network with massive MIMO utilizing a physical layer preceding technique. We aim to maximize the time-Averaged utility of users with a time-Averaged transmit power constraint on top of the EdgeSON architecture, which takes advantage of both centralized and distributed characteristics. We decompose the original long-Term problem into two-Time scales in which solving the problem of choosing beam pattern is run at a slower time scale than solving user scheduling and power allocation. Then, to solve user scheduling and power allocation, we leverage the Lyapunov drift-plus-penalty framework which transforms an original long-Term average problem into per-slot modified sub-problems. Since provided per-slot problem to find a set of user scheduling and power allocation is known as NP-hard, we propose a low-complex and practical interference management algorithm, namely CRIM, by introducing two critical users with the highest interference channel gain in intra-BS and inter-BS respectively. Finally, via extensive simulations, we verify and compare the performance of the proposed algorithm and comparing algorithms in a real mmWave network environment. © 2022 IEEE

Three Steps Toward Low-Complexity: Practical Interference Management in NOMA-Based mmWave Networks

Beamforming, user scheduling and transmit power on existing interference management schemes in multi-cell mmWave networks have been independently controlled due to the high computational complexity of the problem. In this paper, we formulate a long-term utility maximization problem where beam activation, user scheduling and transmit power are incorporated in a single framework. To develop a low-complex algorithm, we first leverage the Lyapunov optimization framework to transform the original long-term problem into a series of slot-by-slot problems. Since the computational complexity to optimally solve the slot-by-slot problem is even significantly high like existing schemes, we decompose the problem into two different time scales: (i) a subproblem to find beam activation probability with a long time-scale and (ii) a subproblem to find user scheduling and power allocation with a short time-scale. Moreover, we introduce two additional gimmicks to more simplify the problem: (i) sequentially making decisions of beam activation, user scheduling, and power allocation, and (ii) considering a critical user for power allocation. Finally, via extensive simulations, we find that the proposed CRIM algorithm outperforms existing algorithms by up to 47.4% in terms of utility.TRU

The Korean Wave in Media. Seoul: Korea Press Foundation [in Korean] 해외 언론의 한류 보도 분석 연구

This is a multi-country analysis of the media coverage on the Korean wave phenomenon. It critically reviews the origin of the Korean wave in the late 1990s and analyses the ways in which this phenomenon has been reported in the mainstream media of China, the United States, Japan, UK and France. This book is an output of the research project funded by the Korea Press Foundation

First Report of Soft Rot by Pectobacterium carotovorum subsp. brasiliense on Amaranth in Korea

Amaranth has the potential for good materials related to nutrients and health benefits. There are several diseases of amaranth such as leaf blight, damping-off, and root rot. As a causal agent of soft rot disease, Pectobacterium spp. could infect various plant species. In this study, we isolated the bacterial pathogen causing soft rot of amaranth in South Korea. In Gangneung, Gangwon province during 2017, amaranth plants showed typical soft rot symptoms such as wilting, defoliation and odd smell. To isolate pathogen, the macerated tissues of contaminated amaranth were spread onto LB agar plates and purified by a single colony subculture. One ml bacterial suspension of a representative isolate was injected to the stem of five seedlings of 2-week-old amaranth with a needle. Ten mM magnesium sulfate solution was used as a negative control. 16S rDNA gene and recA gene were sequenced and compared with the reference sequences using the BLAST. In the phylogenetic tree based on 16S rDNA gene and recA gene, GSA1 strain was grouped in Pcb

Long-term survival outcome beyond the first year of for pediatric acute liver failure: A large-volume living donor liver transplantation single center study

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