scholarly journals High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics

2021 ◽  
Vol 9 ◽  
Author(s):  
Alexander Gerovichev ◽  
Achiad Sadeh ◽  
Vlad Winter ◽  
Avi Bar-Massada ◽  
Tamar Keasar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
High Throughput ◽  
Well Being ◽  
Ecosystem Functions ◽  
High Tech ◽  
Pest Species ◽  
Agricultural Pests ◽  
Sticky Traps ◽  
Flying Insects

Ecology documents and interprets the abundance and distribution of organisms. Ecoinformatics addresses this challenge by analyzing databases of observational data. Ecoinformatics of insects has high scientific and applied importance, as insects are abundant, speciose, and involved in many ecosystem functions. They also crucially impact human well-being, and human activities dramatically affect insect demography and phenology. Hazards, such as pollinator declines, outbreaks of agricultural pests and the spread insect-borne diseases, raise an urgent need to develop ecoinformatics strategies for their study. Yet, insect databases are mostly focused on a small number of pest species, as data acquisition is labor-intensive and requires taxonomical expertise. Thus, despite decades of research, we have only a qualitative notion regarding fundamental questions of insect ecology, and only limited knowledge about the spatio-temporal distribution of insects. We describe a novel high throughput cost-effective approach for monitoring flying insects as an enabling step toward “big data” entomology. The proposed approach combines “high tech” deep learning with “low tech” sticky traps that sample flying insects in diverse locations. As a proof of concept we considered three recent insect invaders of Israel’s forest ecosystem: two hemipteran pests of eucalypts and a parasitoid wasp that attacks one of them. We developed software, based on deep learning, to identify the three species in images of sticky traps from Eucalyptus forests. These image processing tasks are quite difficult as the insects are small (<5 mm) and stick to the traps in random poses. The resulting deep learning model discriminated the three focal organisms from one another, as well as from other elements such as leaves and other insects, with high precision. We used the model to compare the abundances of these species among six sites, and validated the results by manually counting insects on the traps. Having demonstrated the power of the proposed approach, we started a more ambitious study that monitors these insects at larger spatial and temporal scales. We aim at building an ecoinformatics repository for trap images and generating data-driven models of the populations’ dynamics and morphological traits.

scholarly journals Detection of insect health with deep learning on near-infrared sensor data

2021 ◽  
Author(s):  
Emily Nicole Bick ◽  
Sam Edwards ◽  
Henrik Hjarvard De Fine Licht
Keyword(s):  
Deep Learning ◽  
Health Status ◽  
Optical Sensors ◽  
Near Infrared ◽  
Rapid Assessment ◽  
Sensor Data ◽  
Agricultural Pests ◽  
Monitoring Methods ◽  
Require Time ◽  
Flying Insects

Conventional monitoring methods for disease vectors, pollinators or agricultural pests require time-consuming trapping and identification of individual insects. Automated optical sensors that detect backscattered near-infrared modulations created by flying insects are increasingly used to identify and count live insects, but do not inform about the health status of individual insects. Here we show that deep learning in trained convolutional neural networks in conjunction with sensors is a promising emerging method to detect infected insects. Health status was correctly determined in 85.6% of cases as early as two days post infection with a fungal pathogen. The ability to monitor insect health in real-time potentially has wide-reaching implications for preserving pollinator biodiversity and the rapid assessment of disease carrying individuals in vector populations.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

Human potential in the system of social and economic development: problems and prospects of Vinnytsia region

2020 ◽  
pp. 71-80
Author(s):  
Olesya Tomchuk
Keyword(s):  
Economic Development ◽  
Human Development ◽  
Rural Areas ◽  
Structural Changes ◽  
Agricultural Sector ◽  
Positive Impact ◽  
Well Being ◽  
Human Potential ◽  
High Tech ◽  
Social And Economic Development

The article highlights the problems and prospects of human development, which is the basis for the long-term strategies of social and economic growth of different countries and regions at the present stage. Submitting strategies of this type provides an opportunity to focus on individual empowerment and to build a favorable environment for effective management decisions in the field of forming, maintaining, and restoring human potential. The analysis of the Vinnytsia region human potential dynamics in the regional system of social and economic development factors was carried out. Application of generalized assessment of the regional human development index components allowed the identification of the main trends that characterize the formation of human potential of the territory, including the reproduction of the population, social environment, comfort and quality of life, well-being, decent work, and education. The article emphasizes that despite some positive changes in the social and economic situation of the region and in assessing the parameters of its human development level relative to other regions of Ukraine, Vinnytsia region is now losing its human potential due to negative demographic situation and migration to other regions and countries. The main reason for such dynamics is proven to be related to the outdated structure of the region's economy, the predominance of the agricultural sector, the lack of progressive transformations in the development of high-tech fields of the economy. An important factor is the low level of urbanization of the region, which leads to the spread of less attractive working conditions and less comfortable living conditions. The key factors that cause the growth of urbanization in the region have been identified, including the significant positive impact of the transport and social infrastructure expansion, the lack of which in rural areas leads to a decrease in the level and comfort of life. Without progressive structural changes in the economy and the resettlement system, the loss of human potential will continue.

A deep learning-based concept for high throughput image flow cytometry

2021 ◽  
Vol 118 (12) ◽  
pp. 123701
Author(s):  
Julie Martin-Wortham ◽  
Steffen M. Recktenwald ◽  
Marcelle G. M. Lopes ◽  
Lars Kaestner ◽  
Christian Wagner ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
High Throughput ◽  
Image Flow

scholarly journals Biodiversity and Health in the Urban Environment

2021 ◽  
Author(s):  
Melissa R. Marselle ◽  
Sarah J. Lindley ◽  
Penny A. Cook ◽  
Aletta Bonn
Keyword(s):  
Public Health ◽  
Human Health ◽  
Urban Policy ◽  
Policy Research ◽  
Well Being ◽  
Ecosystem Functions ◽  
Natural Environments ◽  
Positive Health ◽  
Causal Pathways ◽  
Health And Well Being

Abstract Purpose of review Biodiversity underpins urban ecosystem functions that are essential for human health and well-being. Understanding how biodiversity relates to human health is a developing frontier for science, policy and practice. This article describes the beneficial, as well as harmful, aspects of biodiversity to human health in urban environments. Recent findings Recent research shows that contact with biodiversity of natural environments within towns and cities can be both positive and negative to human physical, mental and social health and well-being. For example, while viruses or pollen can be seriously harmful to human health, biodiverse ecosystems can promote positive health and well-being. On balance, these influences are positive. As biodiversity is declining at an unprecedented rate, research suggests that its loss could threaten the quality of life of all humans. Summary A key research gap is to understand—and evidence—the specific causal pathways through which biodiversity affects human health. A mechanistic understanding of pathways linking biodiversity to human health can facilitate the application of nature-based solutions in public health and influence policy. Research integration as well as cross-sector urban policy and planning development should harness opportunities to better identify linkages between biodiversity, climate and human health. Given its importance for human health, urban biodiversity conservation should be considered as public health investment.

scholarly journals Quantifying Fetal Reprogramming for Biomarker Development in the Era of High-Throughput Sequencing

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 329
Author(s):  
Fu-Sheng Chou ◽  
Krystel Newton ◽  
Pei-Shan Wang
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Well Being ◽  
Placental Insufficiency ◽  
Definitive Treatment ◽  
Optimal Timing ◽  
Hypertensive Disorders ◽  
Clinical Tools ◽  
Fetal Reprogramming ◽  
Offspring Health

Gestational hypertensive disorders continue to threaten the well-being of pregnant women and their offspring. The only current definitive treatment for gestational hypertensive disorders is delivery of the fetus. The optimal timing of delivery remains controversial. Currently, the available clinical tools do not allow for assessment of fetal stress in its early stages. Placental insufficiency and fetal growth restriction secondary to gestational hypertensive disorders have been shown to have long-term impacts on offspring health even into their adulthood, becoming one of the major focuses of research in the field of developmental origins of health and disease. Fetal reprogramming was introduced to describe the long-lasting effects of the toxic intrauterine environment on the growing fetus. With the advent of high-throughput sequencing, there have been major advances in research attempting to quantify fetal reprogramming. Moreover, genes that are found to be differentially expressed as a result of fetal reprogramming show promise in the development of transcriptional biomarkers for clinical use in detecting fetal response to placental insufficiency. In this review, we will review key pathophysiology in the development of placental insufficiency, existing literature on high-throughput sequencing in the study of fetal reprogramming, and considerations regarding research design from our own experience.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

A Deep Learning Model for Detection and Tracking in High-Throughput Images of Organoid

2021 ◽  
pp. 104490
Author(s):  
Xuesheng Bian ◽  
Gang Li ◽  
Cheng Wang ◽  
Weiquan Liu ◽  
Xiuhong Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Learning Model ◽  
Detection And Tracking ◽  
Deep Learning Model
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