Edge effect compensation for citrus canker lesion detection due to light source variation – a hyperspectral imaging application

The spread of citrus canker has become one of the most important challenges faced by Florida Fresh Market citrus is affecting the export of citrus fruits to several international markets including European countries.  Previous studies have shown that automated detection systems can help detect citrus canker infected fruit and could assist in eliminating the detected fruit from shipment to closed markets.  Most automated detection systems use some form of machine vision with artificial light sources.  However, when capturing images of spherical objects, non-uniform illumination results in an edge blackening effect resulting in higher misclassification rate.  The basic objective of this research was to implement a compensation algorithm to eliminate the edge effect when capturing hyperspectral image of citrus fruits.  The algorithm originally developed by Gomez et al. 2007, was adapted for the purpose of canker detection.  The image was corrected for spatial variations (flat field correction) caused by intensity of light source as well as geometrical variation caused by the spherical geometry of the citrus fruit.  In this study, the geometric correction was accomplished by constructing a 3-D digital elevation model (DEM) of the fruit from its 2-D image.  This DEM provided the geometric properties of the fruit X, Y, and Z coordinates which were exploited in the course of estimating the geometric correction factor for each pixel.  The corrected image portrayed a more uniform brightness of the citrus fruit surface throughout.  Tests were conducted on 10 orange samples (five marketable and five cankerous) to validate the results of the algorithm which demonstrated that the geometric correction resulted in uniform intensity of radiation throughout the fruit surface thus reducing the within class variation.   Keywords: edge effect compensation, hyperspectral imaging, canker, spatial correction, geometric correctio

Sample-efficient benchmarking of multi-photon interference on a boson sampler in the sparse regime

Verification of a quantum advantage in the presence of noise is a key open problem in the study of near-term quantum devices. In this work, we show how to assess the quality of photonic interference in a linear optical quantum device (boson sampler) by using a maximum likelihood method to measure the strength at which various noise sources are present in the experiment. This allows us to use a sparse set of samples to test whether a given boson sampling experiment meets known upper bounds on the level of noise permissible to demonstrate a quantum advantage. Furthermore, this method allows us monitor the evolution of noise in real time, creating a valuable diagnostic tool. Finally, we observe that sources of noise in the experiment compound, meaning that the observed value of the mutual photon indistinguishability, which is the main imperfection in our study, is an effective value taking into account all sources of error in the experiment

Construction and validation of a cuproptosis-related lncRNA signature as a novel and robust prognostic model for colon adenocarcinoma

BackgroundCuproptosis, a newly identified form of programmed cell death, is thought to play a role in tumorigenesis. Long non-coding RNAs (lncRNAs) are reported to be associated with tumor progression and prognosis in colon adenocarcinoma (COAD). However, the role and prognostic value of cuproptosis-related lncRNAs in COAD remains unknown. This study is devoted to constructing and validating a cuproptosis-related lncRNA signature that can predict COAD patient outcomes using bioinformatics methods.MethodsThe COAD mRNA and lncRNA expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and 2,567 cuproptosis-related lncRNAs were obtained. A 10 cuproptosis-related-lncRNA prognostic signature was then constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model and patients were divided into high- and low-risk groups. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a nomogram were employed to evaluate the predictive power of the signature. The immune characteristics and drug sensitivity were also investigated based on the signature. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model. In vitro experiments were conducted to validate the expression of the ten lncRNAs during cuproptosis.ResultsThe high-risk group was associated with shorter overall survival (OS) time in COAD patients (p<0.001). Multivariate Cox regression indicated that a high-risk score was an independent risk factor for poor prognosis (p<0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 3 years: 0.879). Gene Ontology (GO) enrichment analysis revealed that the signature was highly correlated with the immune response in biological processes. The immune function, the score of the immune cells, and the expression of immune checkpoints were significantly different between the two risk groups. Three drugs, LAQ824, FH535, YM155, were found to be more sensitive in the high-risk group. Finally, the expression levels of the ten lncRNAs comprising the signature were tested by qRT-PCR.ConclusionA ten-cuproptosis-related lncRNA signature was constructed that provided promising insights into personalized prognosis and drug selection among COAD patients

Near infrared hyperspectral imaging of blends of conventional and waxy hard wheats

Recent development of hard winter waxy (amylose-free) wheat adapted to the North American climate has prompted the quest to find a rapid method that will determine mixture levels of conventional wheat in lots of identity preserved waxy wheat. Previous work documented the use of conventional near infrared (NIR) reflectance spectroscopy to determine the mixture level of conventional wheat in waxy wheat, with an examined range, through binary sample mixture preparation, of 0–100% (weight conventional / weight total). The current study examines the ability of NIR hyperspectral imaging of intact kernels to determine mixture levels. Twenty-nine mixtures (0, 1, 2, 3, 4, 5, 10, 15, …, 95, 96, 97, 98, 99, 100%) were formed from known genotypes of waxy and conventional wheat. Two-class partial least squares discriminant analysis (PLSDA) and statistical pattern recognition classifier models were developed for identifying each kernel in the images as conventional or waxy. Along with these approaches, conventional PLS1 regression modelling was performed on means of kernel spectra within each mixture test sample. Results indicated close agreement between all three approaches, with standard errors of prediction for the better preprocess transformations (PLSDA models) or better classifiers (pattern recognition models) of approximately 9 percentage units. Although such error rates were slightly greater than ones previously published using non-imaging NIR analysis of bulk whole kernel wheat and wheat meal, the HSI technique offers an advantage of its potential use in sorting operations

Near infrared hyperspectral imaging of blends of conventional and waxy hard wheats

Recent development of hard winter waxy (amylose-free) wheat adapted to the North American climate has prompted the quest to find a rapid method that will determine mixture levels of conventional wheat in lots of identity preserved waxy wheat. Previous work documented the use of conventional near infrared (NIR) reflectance spectroscopy to determine the mixture level of conventional wheat in waxy wheat, with an examined range, through binary sample mixture preparation, of 0–100% (weight conventional / weight total). The current study examines the ability of NIR hyperspectral imaging of intact kernels to determine mixture levels. Twenty-nine mixtures (0, 1, 2, 3, 4, 5, 10, 15, …, 95, 96, 97, 98, 99, 100%) were formed from known genotypes of waxy and conventional wheat. Two-class partial least squares discriminant analysis (PLSDA) and statistical pattern recognition classifier models were developed for identifying each kernel in the images as conventional or waxy. Along with these approaches, conventional PLS1 regression modelling was performed on means of kernel spectra within each mixture test sample. Results indicated close agreement between all three approaches, with standard errors of prediction for the better preprocess transformations (PLSDA models) or better classifiers (pattern recognition models) of approximately 9 percentage units. Although such error rates were slightly greater than ones previously published using non-imaging NIR analysis of bulk whole kernel wheat and wheat meal, the HSI technique offers an advantage of its potential use in sorting operations

Detection of Citrus Greening Using Microscopic Imaging

Citrus greening reduces fruit production and quality and will likely result in rapid tree decline and death. Because citrus greening symptoms are usually observed on the leaf surface, detection of citrus greening leaf symptoms can significantly aid in scouting for infected trees and managing the disease, thus reducing its spread and minimizing losses for citrus growers. This article presents the microscopic image analysis using color co-occurrence method to differentiate citrus leaves with eight conditions: greening blotchy mottle, green islands, iron deficiency, manganese deficiency, zinc deficiency, young flush leaves and normal mature leaves. Thirty-nine statistical features were extracted from transformed hue (H), saturation (S), and intensity (I) images using the color co-occurrence method for each leaf sample. The number of extracted texture features was reduced by a stepwise discriminant analysis. A discriminant function based on a measure of the generalized squared distance was used for classification. Three classification models were performed using (1) all leaf conditions, (2) all conditions except young flush leaves and (3) all conditions except young flush leaves and blotchy mottle. The three classification models obtained accuracies of 86.67 %, 95.60 % and 97.33 %, respectively. The overall performance was demonstrated in a confusion matrix. The model HSI_14, which used all conditions except young flush and blotchy mottle, resulted in the best accuracy for positive (96.67 %) and negative (97.5 %) symptoms

Magneto-Transport Properties of Kagome Magnet TmMn6_6Sn6_6

Kagome magnet usually hosts nontrivial electronic or magnetic states drawing great interests in condensed matter physics. In this paper, we report a systematic study on transport properties of kagome magnet TmMn$_6$Sn$_6$. The prominent topological Hall effect (THE) has been observed in a wide temperature region spanning over several magnetic phases and exhibits strong temperature and field dependence. This novel phenomenon due to non-zero spin chirality indicates possible appearance of nontrival magnetic states accompanying with strong fluctuations. The planar applied field drives planar Hall effect(PHE) and anistropic magnetoresisitivity(PAMR) exhibiting sharp disconnections in angular dependent planar resistivity violating the empirical law. By using an effective field, we identify a magnetic transition separating the PAMR into two groups belonging to various magnetic states. We extended the empirical formula to scale the field and temperature dependent planar magnetoresistivity and provide the understandings for planar transport behaviors with the crossover between various magnetic states. Our results shed lights on the novel transport effects in presence of multiple nontrivial magnetic states for the kagome lattice with complicated magnetic structures

Tongluo Zhitong Prescription Alleviates Allodynia, Hyperalgesia, and Dyskinesia in the Chronic Constriction Injury Model of Rats

Neuropathic pain is common in clinical practice. Exploration of new drug therapeutics has always been carried out for more satisfactory effects and fewer side-effects. In the present study, we aimed to investigate effects of Tongluo Zhitong Prescription (TZP), a compounded Chinese medicine description, on neuropathic pain model of rats with chronic constriction injury (CCI). The CCI model was established by loosely ligating sciatic nerve with catgut suture, proximal to its trifurcation. The static and dynamic allodynia, heat hyperalgesia, mechanical allodynia, cold allodynia, and gait were assessed. Our results showed that TZP alleviated CCI-induced static and dynamic allodynia, suppressed heat hyperalgesia and cold and mechanical allodynia, and improved gait function. These results suggest that TZP could alleviate neuropathic pain. Further experiments are needed to explore its mechanisms

Rotavirus nonstructural protein 1 antagonizes innate immune response by interacting with retinoic acid inducible gene I

<p>Abstract</p> <p>Background</p> <p>The nonstructural protein 1 (NSP1) of rotavirus has been reported to block interferon (IFN) signaling by mediating proteasome-dependent degradation of IFN-regulatory factors (IRFs) and (or) the β-transducin repeat containing protein (β-TrCP). However, in addition to these targets, NSP1 may subvert innate immune responses via other mechanisms.</p> <p>Results</p> <p>The NSP1 of rotavirus OSU strain as well as the IRF3 binding domain truncated NSP1 of rotavirus SA11 strain are unable to degrade IRFs, but can still inhibit host IFN response, indicating that NSP1 may target alternative host factor(s) other than IRFs. Overexpression of NSP1 can block IFN-β promoter activation induced by the retinoic acid inducible gene I (RIG-I), but does not inhibit IFN-β activation induced by the mitochondrial antiviral-signaling protein (MAVS), indicating that NSP1 may target RIG-I. Immunoprecipitation experiments show that NSP1 interacts with RIG-I independent of IRF3 binding domain. In addition, NSP1 induces down-regulation of RIG-I in a proteasome-independent way.</p> <p>Conclusions</p> <p>Our findings demonstrate that inhibition of RIG-I mediated type I IFN responses by NSP1 may contribute to the immune evasion of rotavirus.</p