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General General

The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning.

In Plant phenomics (Washington, D.C.)

Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture. Traditional detection methods are time-consuming, laborious, and subjective, and image processing methods mainly rely on manually designed features that are difficult to cope with pathogen spore detection in complex scenes. Therefore, an MG-YOLO detection algorithm (Multi-head self-attention and Ghost-optimized YOLO) is proposed to detect gray mold spores rapidly. Firstly, Multi-head self-attention is introduced in the backbone to capture the global information of the pathogen spores. Secondly, we combine weighted Bidirectional Feature Pyramid Network (BiFPN) to fuse multiscale features of different layers. Then, a lightweight network is used to construct GhostCSP to optimize the neck part. Cucumber gray mold spores are used as the study object. The experimental results show that the improved MG-YOLO model achieves an accuracy of 0.983 for detecting gray mold spores and takes 0.009 s per image, which is significantly better than the state-of-the-art model. The visualization of the detection results shows that MG-YOLO effectively solves the detection of spores in blurred, small targets, multimorphology, and high-density scenes. Meanwhile, compared with the YOLOv5 model, the detection accuracy of the improved model is improved by 6.8%. It can meet the demand for high-precision detection of spores and provides a novel method to enhance the objectivity of pathogen spore detection.

Li Kaiyu, Zhu Xinyi, Qiao Chen, Zhang Lingxian, Gao Wei, Wang Yong

2023

General General

Evaluation of aerial spraying application of multi-rotor unmanned aerial vehicle for Areca catechu protection.

In Frontiers in plant science

Multi-rotor unmanned aerial vehicle (UAV) is a new chemical application tool for tall stalk tropical crop Areca catechu, which could improve deposit performance, reduce operator healthy risk, and increase spraying efficiency. In this work, a spraying experiment was carried out in two A. catechu fields with two leaf area index (LAI) values, and different operational parameters were set. Spray deposit quality, spray drift, and ground loss were studied and evaluated. The results showed that the larger the LAI of A. catechu, the lesser the coverage of the chemical deposition. The maximum coverage could reach 4.28% and the minimum 0.33%. At a flight speed of 1.5 m/s, sprayed droplets had the best penetration and worst ground loss. The overall deposition effect was poor when the flight altitudes were greater than 11.09 m and the flight speed was over 2.5 m/s. Comparing flight speed of 2.5 to 1.5 m/s, the overall distance of 90% of the total drift increased to double under the same operating parameters. This study presents reference data for UAV chemical application in A. catechu protection.

Wang Juan, Ma Chao, Chen Pengchao, Yao Weixiang, Yan Yingbin, Zeng Tiwei, Chen Shengde, Lan Yubin

2023

Areca catechu protection, LAI, aerial spray, droplet deposition, multi-rotor UAV, spray drift

Ophthalmology Ophthalmology

Development and Validation of a Diabetic Retinopathy Risk Stratification Algorithm.

In Diabetes care ; h5-index 125.0

OBJECTIVE : Although diabetic retinopathy is a leading cause of blindness worldwide, diabetes-related blindness can be prevented through effective screening, detection, and treatment of disease. The study goal was to develop risk stratification algorithms for the onset of retinal complications of diabetes, including proliferative diabetic retinopathy, referable retinopathy, and macular edema.

RESEARCH DESIGN AND METHODS : Retrospective cohort analysis of patients from the Kaiser Permanente Northern California Diabetes Registry who had no evidence of diabetic retinopathy at a baseline diabetic retinopathy screening during 2008-2020 was performed. Machine learning and logistic regression prediction models for onset of proliferative diabetic retinopathy, diabetic macular edema, and referable retinopathy detected through routine screening were trained and internally validated. Model performance was assessed using area under the curve (AUC) metrics.

RESULTS : The study cohort (N = 276,794) was 51.9% male and 42.1% White. Mean (±SD) age at baseline was 60.0 (±13.1) years. A machine learning XGBoost algorithm was effective in identifying patients who developed proliferative diabetic retinopathy (AUC 0.86; 95% CI, 0.86-0.87), diabetic macular edema (AUC 0.76; 95% CI, 0.75-0.77), and referable retinopathy (AUC 0.78; 95% CI, 0.78-0.79). Similar results were found using a simpler nine-covariate logistic regression model: proliferative diabetic retinopathy (AUC 0.82; 95% CI, 0.80-0.83), diabetic macular edema (AUC 0.73; 95% CI, 0.72-0.74), and referable retinopathy (AUC 0.75; 95% CI, 0.75-0.76).

CONCLUSIONS : Relatively simple logistic regression models using nine readily available clinical variables can be used to rank order patients for onset of diabetic eye disease and thereby more efficiently prioritize and target screening for at risk patients.

Tarasewicz Dariusz, Karter Andrew J, Pimentel Noel, Moffet Howard H, Thai Khanh K, Schlessinger David, Sofrygin Oleg, Melles Ronald B

2023-Mar-17

General General

CEO's functional experience and firm performance based on text mining.

In PloS one ; h5-index 176.0

The impact of a chief executive officer's (CEO's) functional experience on firm performance has gained the attention of many scholars. However, the measurement of functional experience is rarely disclosed in the public database. Few studies have been conducted on the comprehensive functional experience of CEOs. This paper used the upper echelons theory and obtained deep-level curricula vitae (CVs) data through the named entity recognition technique. First, we mined 15 consecutive years of CEOs' CVs from 2006 to 2020 from Chinese listed companies. Second, we extracted information throughout their careers and automatically classified their functional hierarchy. Finally, we constructed breadth (functional breadth: functional experience richness) and depth (functional depth: average tenure and the hierarchy of function) for empirical analysis. We found that a CEO's breadth is significantly negatively related to firm performance, and the quadratic term is significantly positive. A CEO's depth is significantly positively related to firm performance, and the quadratic term is significantly negative. The research results indicate a u-shaped relationship between a CEO's breadth and firm performance and an inverted u-shaped relationship between their depth and firm performance. The study's findings extend the literature on factors influencing firm performance and CEOs' functional experience. The study expands from the horizontal macro to the vertical micro level, providing new evidence to support the recruitment and selection of high-level corporate talent.

Huang Xiaohong, Liu Jiangwei, Min Liangyu, Zeng Qianqian, Zhang Jun, Zhang Xiaorong

2023

Public Health Public Health

Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV.

In The Journal of infectious diseases ; h5-index 82.0

Cognitive disorders are prevalent in people with HIV (PWH) despite antiretroviral therapy. Given the heterogeneity of cognitive disorders in PWH in the current era and evidence that these disorders have different etiologies and risk factors, scientific rationale is growing for using data-driven models to identify biologically defined subtypes (biotypes) of these disorders. Here, we discuss the state of science using machine learning to understand cognitive phenotypes in PWH and their associated comorbidities, biological mechanisms, and risk factors. We also discuss methods, example applications, challenges, and what will be required from the field to successfully incorporate machine learning in research on cognitive disorders in PWH. These topics were discussed at the National Institute of Mental Health meeting on "Biotypes of CNS Complications in People Living with HIV" held in October 2021. These ongoing research initiatives seek to explain the heterogeneity of cognitive phenotypes in PWH and their associated biological mechanisms to facilitate clinical management and tailored interventions.

Mukerji Shibani S, Petersen Kalen J, Pohl Kilian M, Dastgheyb Raha M, Fox Howard S, Bilder Robert M, Brouillette Marie-Josée, Gross Alden L, Scott-Sheldon Lori A J, Paul Robert H, Gabuzda Dana

2023-Mar-17

HIV, HIV-associated neurocognitive disorders, cognitive impairment, deep learning, machine learning

General General

Constraining nonlinear time series modeling with the metabolic theory of ecology.

In Proceedings of the National Academy of Sciences of the United States of America

Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a "metabolic time step," our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.

Munch Stephan B, Rogers Tanya L, Symons Celia C, Anderson David, Pennekamp Frank

2023-Mar-21

empirical dynamic modeling, physical–biological interactions, population dynamics, thermal biology