Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject ...to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.
The detection and identification of plant diseases is crucial for an appropriate and targeted application of plant protection measures in crop production. Recently, intensive research has been ...conducted to develop innovative and technology-based optical methods for plant disease detection. In contrast to common visual rating and detection methods, optical sensors are able to measure pathogen-induced changes in the plant physiology non-invasively and objectively. Several studies showed that especially hyperspectral sensors are valuable tools for disease detection, identification and quantification on different scales from the tissue to the canopy level. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors on different scales for disease detection and plant protection are discussed and evaluated. The advantages and disadvantages on each particular scale, as well as the impact of external factors, such as: light, wind, viewing angle, for measurements in laboratories, greenhouses and fields, are critically assessed in order to support researchers and agriculture technicians. Additionally, a comprehensive literature review about the use of hyperspectral sensors on these different scales for plant disease detection reflects the possibilities of non-invasive measurement systems. This highlights advantages of hyperspectral sensors when investigating plant–pathogen interactions through multiple examples. By some approaches, detection before visible symptoms appear is feasible. The potential of hyperspectral sensors as a tool for disease identification and quantification, based on disease characteristic changes in the plants spectral signature, is discussed as well. The review is concluded with an overview on different data analysis methods, which are required to extract key information from gathered hyperspectral datasets.
This work explored the possibility of using hyperspectral microscope imaging (HMI) technique coupled with advanced chemometric methods to evaluate the cell wall microstructure and physiochemical ...properties of ‘Korla’ fragrant pear disease caused by Alternaria alternata. The physicochemical characteristics such as SSC, firmness and L* value of pears undergo successive decreases and the microstructure of the cell wall breaks down during the process of pathogen infection. Principal component analysis was applied on the HMI of pear tissues at different infected stages, which could clearly visualize the distribution of pigment, carbohydrate compounds and structural changes in parenchyma cells. Further, partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and convolutional neural network (CNN) model coupled with selected spectral variables, and HMI features were used to identify the diseased ‘Korla’ fragrant pears. The CNN model based on the fused data showed the best discrimination between healthy and diseased pears (96.72%) and provided a satisfactory discrimination accuracy of 94.74% in successfully identifying the diseased diameter of 1.56 mm after 1 d of storage. This study indicated the HMI combined with CNN has great potential in detecting the early stages of pear infection and provides a possible method for monitoring fruit quality and safety.
•HMI with deep learning were applied for early disease detection in korla pears.•Early diseased pears showed successive decreases in physicochemical characteristic.•PCA visualized the cell wall structural changes in pear at the cellular scale.•The spectra and texture feature fusion data improved the model performance.•The HMI combined with CNN has great potential in early detection of disease.
Abstract
Pine wilt disease (PWD) is caused by the pine wilt nematode and is a tremendous threat to coniferous forests. Remote sensing, particularly hyperspectral remote sensing, has been utilized to ...identify PWD. However, most studies have focused on distinguishing the spectra between infected and healthy pine trees and ignored further visualization of spectral symptoms, which could greatly improve the pre-visual diagnosis of PWD. This research used the false color feature maps (FCFMs) synthesized using the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) calculated from selected feature bands to analyze the changes in the spectral and image dimensions of the hyperspectral data. Our main findings were (1) the confirmed feature bands were 440, 550, 672, 752, 810, and 958 nm; and (2) NDVI (810, 440), NDVI (810, 672), NDVI (550, 672), RVI (810, 550), RVI (810, 672), and RVI (550, 672) were suitable to synthesize the FCFMs. As PWD developed, the color of the infected needles changed from blue and white to red on the NDVI-based feature maps and from blue to red on the RVI-based feature maps. Importantly, the color changes were captured by the FCFMs when the symptoms were not visible on the true color images, indicating the ability to identify PWD during the early infection stage.
Avocado (Persea americana) is a crop that is second in importance in Florida behind citrus with a wholesale value of $35 million and represents approximately 60% of the tropical fruit crop acreage. ...Laurel wilt (LW) is a lethal disease that has spread rapidly along the southeastern seaboard of the United States affecting commercial avocado production. This article evaluates the spatial and spectral requirements for quick and accurate detection of LW. Spectral data from healthy (H), Phytophthora root rot (PPR) and LW leaves were analyzed using ANOVA and two neural networks, multilayer perceptron (MLP) and radial basis function (RBF). The most effective wavelengths were identified and the filters were updated to a MCA-6 Tetracam camera (580–10nm, 650–10nm, 740–10nm, 750–10nm, 760–10nm and 850–40nm). Then, the MCA camera was used to take multispectral aerial images from a helicopter at three altitudes (180, 250 and 300m) in an avocado field with trees at different stages of LW development, early, intermediate and late. The analyses were conducted based upon 2-class and 4-class systems. The 2-class system was designed to differentiate H and LW trees sufficient to identify trees for removal and the 4-class system was used to differentiate H plants and the three stages of LW development. Aerial image analysis proved the utility of the selected filters for successful identification of LW, even for trees in early stage of disease development with minimal symptoms. The ideal flight altitude of 250m (15.3cm pixel size) was selected according to the M-values and biological parameters such as canopy size and orchard size. The optimum VIs determined by higher M-values were TCARI760–650 as well as GNDVI, NIR/G, Redge/G and VIGreen using any of the bands related to Redge (740 and 750nm) or NIR regions (760 and 850nm). Results reported on the utility of the 2-class and 4-class systems using the above VIs to discriminate LW; however it would be more convenient to develop the algorithm based on the 4-class system (H, early, intermediate and late). The early detection of LW through the methodology proposed in this research could allow farmers to control the movement of this disease through proper management strategies.
•Spatial and spectral requirements for early laurel wilt detection were evaluated.•The effective wavelengths to separate laurel wilt were identified.•The ideal flight altitude was 250m (15.3cm pixel size).•The optimum VIs were TCARI760–650, GNDVI, NIR/G, Redge/G and VIGreen.•It should be more convenient to develop the algorithm based on a 4 class system.
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•Synthesis and surface-functionalized of Fe2C@PEG, Fe2C@NH2 and Fe2C@COOH.•TEM, FTIR and LC‐MS/MS Spectrophotometry were used to characterized NPs and proteins.•Total 119 proteins, 57 ...were LMW proteins of 82 % up-regulated and 17 down-regulated.•Surface coating help molecular, cellular & biological role for cell signaling process.•A STRING algorithm was used to study functional interaction network of proteins.
Harvesting the low molecular weight (LMW) proteins from the cellular exudates is a big challenge for early disease detection. Here, we introduce a unique probe composed of surface-functionalized Fe2C NPs with different functional groups to harvest, identify and profile differentially expressed biomarker proteins. Three different functionalization of Fe2C NPs with Fe2C@NH2, Fe2C@COOH and Fe2C@PEG enabled to harvest 119 differentially expressed proteins from HeLa cell exudates. Among these proteins, 57 were LMW which 82.46 % were up-regulated and 17.54 % were down-regulated. The Fe2C@NH2 were able to separate 60S ribosomal proteins L7a, and L11, and leucine-rich repeat-containing protein 59. These proteins play a vital role in the maturation of large subunit ribosomal ribonucleic acid, mRNA splicing via spliceosome and cancer cell inhibitor, respectively. While, Fe2C@COOH identifies the 60S ribosomal protein types L7, 40S ribosomal protein S11, and 60S ribosomal protein L24. These proteins were important for large ribosomal subunit biogenesis, translational initiation, and assembly of large subunit precursor of pre-ribosome. Finally, the Fe2C@PEG extracted 40S ribosomal protein S2, splicing factor, arginine/serine-rich and 40S ribosomal protein S4, X isoform which were responsible for nonsense-mediated decay, oligodendrocyte differentiation and multicellular organism development. Thus, these results help us in defining oncogenic biomarkers for early disease detection.
Fungal leaf diseases cause economically important damage to crop plants. Protective treatments help producers to secure good quality crops. In contrast, curative treatments based on visually ...detectable symptoms are often riskier and less effective because diseased crop plants may develop disease symptoms too late for curative treatments. Therefore, early disease detection prior symptom development would allow an earlier, and therefore more effective, curative management of fungal diseases. Using a five-lens multispectral imager, spectral reflectance of green, blue, red, near infrared (NIR, 840 nm), and rededge (RE, 720 nm) was recorded in time-course experiments of detached tomato leaves inoculated with the fungus
and mock infection solution. Linear regression models demonstrate NIR and RE as the two most informative spectral data sets to differentiate pathogen- and mock-inoculated leaf regions of interest (ROI). Under controlled laboratory conditions, bands collecting NIR and RE irradiance showed a lower reflectance intensity of infected tomato leaf tissue when compared with mock-inoculated leaves. Blue and red channels collected higher intensity values in pathogen- than in mock-inoculated ROIs. The reflectance intensities of the green band were not distinguishable between pathogen- and mock infected ROIs. Predictions of linear regressions indicated that gray mold leaf infections could be identified at the earliest at 9 h post infection (hpi) in the most informative bands NIR and RE. Re-analysis of the imagery taken with NIR and RE band allowed to classify infected tissue.
African Swine Fever (ASF) has emerged as a disease of great concern to swine producers and government disease control agencies because of its severe consequences to animal health and the pig ...industry. Early detection of an ASF introduction is considered essential for reducing the impact of the disease. Risk-based surveillance approaches have been used as enhancements to early disease epidemic detection systems in livestock populations. Such approaches may consider the role wildlife plays in hosting and transmitting a disease. In this study, a method is presented to estimate and map the risk of introducing ASF into the domestic pig population through wild boar intermediate hosts. It makes use of data about hunted wild boar, rest areas along motorways connecting ASF affected countries to Switzerland, outdoor piggeries, and forest cover. These data were used to compute relative wild boar abundance as well as to estimate the risk of both disease introduction into the wild boar population and disease transmission to domestic pigs. The way relative wild boar abundance was calculated adds to the current state of the art by considering the effect of beech mast on hunting success and the probability of wild boar occurrence when distributing relative abundance values among individual grid cells. The risk of ASF introduction into the domestic pig population by wild boar was highest near the borders of France, Germany, and Italy. On the north side of the Alps, areas of high risk were located on the unshielded side of the main motorway crossing the Central Plateau, which acts as a barrier for wild boar. Estimating the risk of disease introduction into the domestic pig population without the intermediary of wild boar suggested that dispersing wild boar may play a key role in spreading the risk to areas remote from motorways. The results of this study can be used to focus surveillance efforts for early disease detection on high risk areas. The developed method may also inform policies to control other diseases that are transmitted by a direct contact from wild boar to domestic pigs.
This study investigated physiological and behavioral responses associated with the onset of neonatal calf diarrhea (NCD) in calves experimentally infected with rotavirus and assessed the suitability ...of these responses as early disease indicators. The suitability of infrared thermography (IRT) as a noninvasive, automated method for early disease detection was also assessed. Forty-three calves either (1) were experimentally infected with rotavirus (n = 20) or (2) acted as uninfected controls (n = 23). Health checks were conducted on a daily basis to identify when calves presented overt clinical signs of disease. In addition, fecal samples were collected to verify NCD as the cause of illness. Feeding behavior was recorded continuously as calves fed from an automated calf feeder, and IRT temperatures were recorded once per day across 5 anatomical locations using a hand-held IRT camera. Lying behavior was recorded continuously using accelerometers. Drinking behavior at the water trough was filmed continuously to determine the number and duration of visits. Respiration rate was recorded once per day by observing flank movements. The effectiveness of inoculating calves with rotavirus was limited because not all calves in the infected group contracted the virus; further, an unexpected outbreak of Salmonella during the trial led to all calves developing NCD, including those in the healthy control group. Therefore, treatment was ignored and instead each calf was analyzed as its own control, with data analyzed with respect to when each calf displayed clinical signs of disease regardless of the causative pathogen. Milk consumption decreased before clinical signs of disease appeared. The IRT temperatures were also found to change before clinical signs of disease appeared, with a decrease in shoulder temperature and an increase in side temperature. There were no changes in respiration rate or lying time before clinical signs of disease appeared. However, the number of lying bouts decreased and lying bout duration increased before and following clinical signs of disease. There was no change in the number of visits to the water trough, but visit duration increased before clinical signs of disease appeared. Results indicate that milk consumption, IRT temperatures of the side and shoulder, number and duration of lying bouts, and duration of time spent at the water trough show potential as suitable early indicators of disease.
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•New therapies are improving care, but early diagnosis remains critical in the effective treatment of most diseases.•Identity of nanoparticles in biological milieu is derived from the ...biomolecular corona adsorbed on their surface.•Biomolecular corona is affected by individual changes in concentration and structure of plasma proteins as those produced by clinical manifestations.•Principal component analysis of biomolecular corona datasets will be a turning point in early detection of diseases.
Today, early disease detection (EDD) is a matter of more importance than ever in medicine. Upon interaction with human plasma, nanoparticles are covered by proteins leading to formation of a biomolecular corona (BC). As the protein patterns of patients with conditions differ from those of healthy subjects, current research into technologies based on the exploitation of personalized BC patterns could be a turning point for early disease detection. Here, we present a framework based on principal component analysis of large personalized BC datasets. We comment on how principal component analysis of personalized BC data is a fundamental step towards turning the output of basic research into fast, safe and inexpensive technologies with superior prediction ability than current methods.
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