scholarly journals Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

2021 ◽  
Vol 13 (10) ◽  
pp. 1975
Author(s):  
Lin Wang ◽  
Yuzhen Zhou ◽  
Qiao Hu ◽  
Zhenghong Tang ◽  
Yufeng Ge ◽  
...  
Keyword(s):  
Early Detection ◽  
Early Development ◽  
Spatial Resolution ◽  
Early Stage ◽  
Semantic Segmentation ◽  
Development Stage ◽  
Unmanned Aircraft ◽  
Economic Losses ◽  
Juniperus Virginiana ◽  
Segmentation Algorithms

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.

scholarly journals Pathogenetic aspects of rabbits’ experimental infection caused by bovine leukemia virus

2020 ◽  
Vol 22 (100) ◽  
pp. 16-22
Author(s):  
T. A. Romanishina ◽  
D. V. Feschenko ◽  
G. O. Rinyak ◽  
V. V. Honcharenko ◽  
A. A. Macibora ◽  
...  
Keyword(s):  
Blood Serum ◽  
Bovine Leukemia Virus ◽  
Viral Infections ◽  
Early Stage ◽  
Leukemia Virus ◽  
Clinical Signs ◽  
Development Stage ◽  
The Body ◽  
Interspecies Transmission ◽  
Economic Losses

Bovine leukemia virus (BLV) is an infectious disease of cattle, causing high economic losses worldwide, especially in the field of dairy farming. There is no common vision on the problem of interspecies transmission of BLV. Therefore, a detailed study of the etiologic relationship between leukemia in cattle and other animal species is relevant. Various laboratory animal models provide insight into the pathogenesis of viral infections. The article presents the research results of two series rabbits’ intravenous infection with bovine leukemia virus (BLV) using the culture antigen FLK-BLV and the blood of rabbits with clinical, hematological and immunological signs of viral tumor growth. Blood from all animals was taken from the ear vein after 14, 21, 30 days, and then monthly for six months: to study the morphological parameters of blood and to determine the titer of antibodies to BLV. Blood serum for the presence of antibodies to BLV was examined using a diagnostic kit for the indication of animals infected with the leukemia virus in an immunodiffusion reaction produced by LLC “SRE Veterinary Medicine”, Kharkiv. It was found that the stage of the BLV provirus in the blood leukogram of infected animals was characterized by pronounced lymphocytosis on the 21st day of the experiment. The highest concentration of antibodies to BLV in the blood serum was found on the 90th day after the administration of the virus-containing material, which disappeared from the blood on the 150–180th day after infection. In experimental rabbits, after five months for thirty days, in the absence of antibodies to leukemia in the blood serum, multiple tumors of a dense consistency began to develop throughout the body. Such clinical signs and changes in the of rabbits’ blood of the experimental group are characteristic of serologically positive cows on the hematological development stage of leukemic process and correlate with the results of domestic and foreign authors. The presence of a large number of lymphoblasts, as well as leukolysis cells, in the histological preparation of lymph nodes, lungs, heart and the accumulation of lymphocytes’ immature forms around the interlobular vessels of the liver, which were found in pathohistological studies of the experimental rabbits’ organs, may indicate the development of the leukemia process on early stage in them. The results obtained indicate the ability of BLV to overcome successfully the interspecies barrier upon parenteral ingestion of heterologous individuals from infected lymphocytes and in the form of a culture antigen.

scholarly journals Can we assess innovative bio-based chemicals in their early development stage? A comparison between early-stage and life cycle assessments

2019 ◽  
Vol 230 ◽  
pp. 137-149 ◽  
Author(s):  
Cora Fernandez-Dacosta ◽  
Pim N.H. Wassenaar ◽  
Ivana Dencic ◽  
Michiel C. Zijp ◽  
Ana Morao ◽  
...  
Keyword(s):  
Life Cycle ◽  
Early Development ◽  
Early Stage ◽  
Development Stage ◽  
Life Cycle Assessments

scholarly journals Low Light/Darkness as Stressors of Multifactor-Induced Senescence in Rice Plants

2021 ◽  
Vol 22 (8) ◽  
pp. 3936
Author(s):  
Ahmed G. Gad ◽  
Habiba ◽  
Xiangzi Zheng ◽  
Ying Miao
Keyword(s):  
Leaf Senescence ◽  
Crop Yields ◽  
Crop Improvement ◽  
Light Response ◽  
Development Stage ◽  
Regulatory Mechanisms ◽  
Economic Losses ◽  
Low Light ◽  
Chlorophyll Breakdown ◽  
Premature Leaf Senescence

Leaf senescence, as an integral part of the final development stage for plants, primarily remobilizes nutrients from the sources to the sinks in response to different stressors. The premature senescence of leaves is a critical challenge that causes significant economic losses in terms of crop yields. Although low light causes losses of up to 50% and affects rice yield and quality, its regulatory mechanisms remain poorly elucidated. Darkness-mediated premature leaf senescence is a well-studied stressor. It initiates the expression of senescence-associated genes (SAGs), which have been implicated in chlorophyll breakdown and degradation. The molecular and biochemical regulatory mechanisms of premature leaf senescence show significant levels of redundant biomass in complex pathways. Thus, clarifying the regulatory mechanisms of low-light/dark-induced senescence may be conducive to developing strategies for rice crop improvement. This review describes the recent molecular regulatory mechanisms associated with low-light response and dark-induced senescence (DIS), and their effects on plastid signaling and photosynthesis-mediated processes, chloroplast and protein degradation, as well as hormonal and transcriptional regulation in rice.

scholarly journals Semi-Automatic Method for Early Detection of Xylella fastidiosa in Olive Trees Using UAV Multispectral Imagery and Geostatistical-Discriminant Analysis

2020 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
Annamaria Castrignanò ◽  
Antonella Belmonte ◽  
Ilaria Antelmi ◽  
Ruggiero Quarto ◽  
Francesco Quarto ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Early Detection ◽  
Plant Pathogens ◽  
Early Stage ◽  
Xylella Fastidiosa ◽  
Mediterranean Countries ◽  
Parametric Classification ◽  
Aerial Vehicle ◽  
Olive Groves ◽  
Olive Trees

Xylella fastidiosa subsp. pauca (Xfp) is one of the most dangerous plant pathogens in the world. Identified in 2013 in olive trees in south–eastern Italy, it is spreading to the Mediterranean countries. The bacterium is transmitted by insects that feed on sap, and causes rapid wilting in olive trees. The paper explores the use of Unmanned Aerial Vehicle (UAV) in combination with a multispectral radiometer for early detection of infection. The study was carried out in three olive groves in the Apulia region (Italy) and involved four drone flights from 2017 to 2019. To classify Xfp severity level in olive trees at an early stage, a combined method of geostatistics and discriminant analysis was implemented. The results of cross-validation for the non-parametric classification method were of overall accuracy = 0.69, mean error rate = 0.31, and for the early detection class of accuracy 0.77 and misclassification probability 0.23. The results are promising and encourage the application of UAV technology for the early detection of Xfp infection.

scholarly journals Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 325
Author(s):  
Christopher Walker ◽  
Tuan-Minh Nguyen ◽  
Shlomit Jessel ◽  
Ayesha B. Alvero ◽  
Dan-Arin Silasi ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Detection ◽  
Predictive Value ◽  
Early Stage ◽  
Ca 125 ◽  
Healthy Controls ◽  
Classification Models ◽  
Serum Samples ◽  
Protein Biomarker ◽  
Biomarker Panel

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10−9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

Health Insurance Status as a Predictor of Mode of Colon Cancer Detection but Not Stage at Diagnosis: Implications for Early Detection

2021 ◽  
pp. 003335492199917
Author(s):  
Lindsey A. Jones ◽  
Katherine C. Brewer ◽  
Leslie R. Carnahan ◽  
Jennifer A. Parsons ◽  
Blase N. Polite ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Health Insurance ◽  
Early Detection ◽  
Late Stage ◽  
Early Stage ◽  
Insurance Status ◽  
Stage At Diagnosis ◽  
Health Insurance Status ◽  
Percentage Point Increase ◽  
Point Increase

Objective For colon cancer patients, one goal of health insurance is to improve access to screening that leads to early detection, early-stage diagnosis, and polyp removal, all of which results in easier treatment and better outcomes. We examined associations among health insurance status, mode of detection (screen detection vs symptomatic presentation), and stage at diagnosis (early vs late) in a diverse sample of patients recently diagnosed with colon cancer from the Chicago metropolitan area. Methods Data came from the Colon Cancer Patterns of Care in Chicago study of racial and socioeconomic disparities in colon cancer screening, diagnosis, and care. We collected data from the medical records of non-Hispanic Black and non-Hispanic White patients aged ≥50 and diagnosed with colon cancer from October 2010 through January 2014 (N = 348). We used logistic regression with marginal standardization to model associations between health insurance status and study outcomes. Results After adjusting for age, race, sex, and socioeconomic status, being continuously insured 5 years before diagnosis and through diagnosis was associated with a 20 (95% CI, 8-33) percentage-point increase in prevalence of screen detection. Screen detection in turn was associated with a 15 (95% CI, 3-27) percentage-point increase in early-stage diagnosis; however, nearly half (47%; n = 54) of the 114 screen-detected patients were still diagnosed at late stage (stage 3 or 4). Health insurance status was not associated with earlier stage at diagnosis. Conclusions For health insurance to effectively shift stage at diagnosis, stronger associations are needed between health insurance and screening-related detection; between screening-related detection and early stage at diagnosis; or both. Findings also highlight the need to better understand factors contributing to late-stage colon cancer diagnosis despite screen detection.

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

scholarly journals Integration of early disease-resistance phenotyping, histological characterization, and transcriptome sequencing reveals insights into downy mildew resistance in impatiens

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ze Peng ◽  
Yanhong He ◽  
Saroj Parajuli ◽  
Qian You ◽  
Weining Wang ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Downy Mildew ◽  
Transcriptome Sequencing ◽  
Early Stage ◽  
Economic Losses ◽  
Potential Candidate ◽  
Early Disease ◽  
True Leaf ◽  
Cotyledon Stage ◽  
Wide Range

AbstractDowny mildew (DM), caused by obligate parasitic oomycetes, is a destructive disease for a wide range of crops worldwide. Recent outbreaks of impatiens downy mildew (IDM) in many countries have caused huge economic losses. A system to reveal plant–pathogen interactions in the early stage of infection and quickly assess resistance/susceptibility of plants to DM is desired. In this study, we established an early and rapid system to achieve these goals using impatiens as a model. Thirty-two cultivars of Impatiens walleriana and I. hawkeri were evaluated for their responses to IDM at cotyledon, first/second pair of true leaf, and mature plant stages. All I. walleriana cultivars were highly susceptible to IDM. While all I. hawkeri cultivars were resistant to IDM starting at the first true leaf stage, many (14/16) were susceptible to IDM at the cotyledon stage. Two cultivars showed resistance even at the cotyledon stage. Histological characterization showed that the resistance mechanism of the I. hawkeri cultivars resembles that in grapevine and type II resistance in sunflower. By integrating full-length transcriptome sequencing (Iso-Seq) and RNA-Seq, we constructed the first reference transcriptome for Impatiens comprised of 48,758 sequences with an N50 length of 2060 bp. Comparative transcriptome and qRT-PCR analyses revealed strong candidate genes for IDM resistance, including three resistance genes orthologous to the sunflower gene RGC203, a potential candidate associated with DM resistance. Our approach of integrating early disease-resistance phenotyping, histological characterization, and transcriptome analysis lay a solid foundation to improve DM resistance in impatiens and may provide a model for other crops.

scholarly journals Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1574
Author(s):  
Shabana Urooj ◽  
Satya P. Singh ◽  
Areej Malibari ◽  
Fadwa Alrowais ◽  
Shaeen Kalathil
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Differential Evolution ◽  
Early Stage ◽  
Selection Process ◽  
Parameter Tuning ◽  
Wavelet Neural Network ◽  
Tuning Parameter

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

Targets, triggers, and directionality in non-local and local place assimilation in child and adult language

2014 ◽  
Vol 50 (3) ◽  
pp. 273-307
Author(s):  
Mi-Hui Cho ◽  
Shinsook Lee
Keyword(s):  
Longitudinal Study ◽  
Early Development ◽  
Early Stage ◽  
Decisive Role ◽  
Diary Study ◽  
Consonant Harmony ◽  
Stage Of Development ◽  
Place Assimilation ◽  
Korean Child ◽  
Non Local

Abstract Data collected from one Korean child in a longitudinal diary study present novel patterns of consonant harmony in that labials, coronals, and velars can be triggers and targets of both progressive and regressive non-local place assimilation in an early stage of development. The same child also shows some cases of local regressive place assimilation. In another study where 4 children's data were gathered from a naturalistic longitudinal study, local regressive place assimilation as well as conso-nant harmony is witnessed regardless of place features. In adult Korean, however, only coronal to labial/velar and labial to velar local regressive assimilation occurs. This paper argues that the non-local and local place assimilation is connected and shows that the connection can be accounted for in terms of different constraint rankings within the Optimality-theoretic framework. More specifically, it is shown that the Ident-Onset(place) constraint plays a decisive role even in the early stage of acquisition, unlike child English, accounting for the predominant regressive assimilation. Also, the Agree-Place constraint is exploded into two sub-constraints in Stage 3, capturing the asymmetrical behavior of assimilation. Further, the unranking of place features in early development gradually evolves to the fixed ranking which reflects the universal markedness hierarchy in adult Korean.

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