A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae

IntroductionAnthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures.MethodsThis study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison.ResultsThe PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively.DiscussionThese results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day

Towards a high-intensity muon source at CiADS

The proposal of a high-intensity muon source driven by the CiADS linac, which has the potential to be one of the state-of-the-art facilities, is presented in this paper. We briefly introduce the development progress of the superconducting linac of CiADS. Then the consideration of challenges related to the high-power muon production target is given and the liquid lithium jet muon production target concept is proposed, for the first time. The exploration of the optimal target geometry for surface muon production efficiency and the investigation into the performance of liquid lithium jet target in muon rate are given. Based on the comparison between the liquid lithium jet target and the rotation graphite target, from perspectives of surface muon production efficiency, heat processing ability and target geometry compactness, the advantages of the new target concept are demonstrated and described comprehensively. The technical challenges and the feasibility of the free-surface liquid lithium target are discussed

Multi-omics data reveals the disturbance of glycerophospholipid metabolism and linoleic acid metabolism caused by disordered gut microbiota in PM2.5 gastrointestinal exposed rats

The relationships between fine particulate matter (PM2.5) exposure and health effects are complex and incompletely understood. Evidence suggests that PM2.5 exposure alters gut microbiota composition and metabolites, but the connections between these changes remain unclear. The aim of our study was to investigate how gut microbiota are involved in the systemic metabolic changes following PM2.5 gastrointestinal exposure. We used multi-omics approaches, including 16S rRNA sequencing and serum metabolomics, to identify alterations in gut microbes and metabolites of PM2.5-exposed rats. We then explored correlations between perturbed gut microbiota and metabolic changes, and conducted pathway analyses to determine critical metabolic pathways impacted by PM2.5 exposure. To verify links between gut microbiome and metabolome disruptions, we performed fecal microbiota transplantation (FMT) experiment. A total of 30 differential gut microbe taxa were identified between PM2.5 and control groups, primarily in Firmicutes, Acidobacteria, and Proteobacteria phyla. We also identified 30 differential metabolites, including glycerophospholipids, fatty acyls, amino acids and others. Pathway analysis revealed disruptions in glycerophospholipid metabolism, steroid hormone biosynthesis, and linoleic acid metabolism. Through FMT, we confirmed PM2.5 altered phosphatidylcholine and linoleic acid metabolism by changing specific gut bacteria. Our results suggest that PM2.5 gastrointestinal exposure triggers systemic metabolic changes by disrupting the gut microbiome, especially glycerophospholipid and linoleic acid metabolism pathways

Insight Into the Formation Paths of Methyl Bromide From Syringic Acid in Aqueous Bromide Solutions Under Simulated Sunlight Irradiation

Methyl bromide (CH3Br) is one of the largest natural sources of bromine in the stratosphere, where it leads to ozone depletion. This paper reported the photochemical production of CH3Br from syringic acid (SA) that has been used as an environmentally relevant model compound for terrestrially-derived dissolved organic matter. The formation of CH3Br increased with the increase of bromide ion concentration ranging from 0.8 to 80 mmol L−1. Ferric ions (Fe(III)) enhanced CH3Br production, while chloride inhibited it, with or without Fe(III). Meanwhile, methyl chloride (CH3Cl) was generated in the presence of chloride and was inhibited by Fe(III). The different effects of Fe(III) on the formation of CH3Cl and CH3Br indicate their diverse formation paths. Based on the intermediates identified by liquid chromatography-mass spectrometry and the confirmation of the formation of Fe(III)-SA complexes, it was proposed that there were two formation paths of CH3Br from SA in the bromide-enriched water under simulated sunlight irradiation. One path was via nucleophilic attack of Br− on the excited state protonation of SA; the other was via the combination of methyl radical and bromine radical when Fe(III) was present. This work suggests that the photochemical formation of CH3Br may act as a potential natural source of CH3Br in the bromide-enriched environmental matrix, and helps in better understanding the formation mechanism of CH3Br

Reversible Change in Performances of Polymer Networks via Invertible Architecture–Transformation of Cross-Links

A polymer nanoparticle network using single-chain nanoparticles (SCNPs) as cross-links is designed. The experimental and theoretical study shows that incorporating SCNPs in polymer networks leads to smaller mesh size, faster terminal relaxation time, and reduced fluctuation among cross-links, resulting in a significant increase in shear storage modulus, and enhancement in tensile stress. Notably, the reversible single-chain collapse of SCNPs under thermal stimulation enables the polymer network to undergo coherent changes between two topological states, thereby exhibiting reversible transformations between soft and stiff states. This approach and finding can effectively tailor the mechanical properties of polymer networks, potentially leading to the development of intelligent, responsive materials

Green Banana Maturity Classification and Quality Evaluation Using Hyperspectral Imaging

Physiological maturity of bananas is of vital importance in determination of their quality and marketability. This study assessed, with the use of a Vis/NIR hyperspectral imaging (400–1000 nm), the feasibility in differentiating six maturity levels (maturity level 2, 4, and 6 to 9) of green dwarf banana and characterizing their quality changes during maturation. Spectra were extracted from three zones (pedicel, middle and apex zone) of each banana finger, respectively. Based on spectra of each zone, maturity identification models with high accuracy (all over 91.53% in validation set) were established by partial least squares discrimination analysis (PLSDA) method with raw spectra. A further generic PLSDA model with an accuracy of 94.35% for validation was created by the three zones’ spectra pooled to omit the effect of spectra acquisition position. Additionally, a spectral interval was selected to simplify the generic PLSDA model, and an interval PLSDA model was built with an accuracy of 85.31% in the validation set. For characterizing some main quality parameters (soluble solid content, SSC; total acid content, TA; chlorophyll content and total chromatism, ΔE*) of banana, full-spectra partial least squares (PLS) models and interval PLS models were, respectively, developed to correlate those parameters with spectral data. In full-spectra PLS models, high coefficients of determination (R2) were 0.74 for SSC, 0.68 for TA, and fair of 0.42 as well as 0.44 for chlorophyll and ΔE*. The performance of interval PLS models was slightly inferior to that of the full-spectra PLS models. Results suggested that models for SSC and TA had an acceptable predictive ability (R2 = 0.64 and 0.59); and models for chlorophyll and ΔE* (R2 = 0.34 and 0.30) could just be used for sample screening. Visualization maps of those quality parameters were also created by applying the interval PLS models on each pixel of the hyperspectral image, the distribution of quality parameters in which were basically consistent with the actual measurement. This study proved that the hyperspectral imaging is a useful tool to assess the maturity level and quality of dwarf bananas

Green Banana Maturity Classification and Quality Evaluation Using Hyperspectral Imaging

Physiological maturity of bananas is of vital importance in determination of their quality and marketability. This study assessed, with the use of a Vis/NIR hyperspectral imaging (400–1000 nm), the feasibility in differentiating six maturity levels (maturity level 2, 4, and 6 to 9) of green dwarf banana and characterizing their quality changes during maturation. Spectra were extracted from three zones (pedicel, middle and apex zone) of each banana finger, respectively. Based on spectra of each zone, maturity identification models with high accuracy (all over 91.53% in validation set) were established by partial least squares discrimination analysis (PLSDA) method with raw spectra. A further generic PLSDA model with an accuracy of 94.35% for validation was created by the three zones’ spectra pooled to omit the effect of spectra acquisition position. Additionally, a spectral interval was selected to simplify the generic PLSDA model, and an interval PLSDA model was built with an accuracy of 85.31% in the validation set. For characterizing some main quality parameters (soluble solid content, SSC; total acid content, TA; chlorophyll content and total chromatism, ΔE*) of banana, full-spectra partial least squares (PLS) models and interval PLS models were, respectively, developed to correlate those parameters with spectral data. In full-spectra PLS models, high coefficients of determination (R2) were 0.74 for SSC, 0.68 for TA, and fair of 0.42 as well as 0.44 for chlorophyll and ΔE*. The performance of interval PLS models was slightly inferior to that of the full-spectra PLS models. Results suggested that models for SSC and TA had an acceptable predictive ability (R2 = 0.64 and 0.59); and models for chlorophyll and ΔE* (R2 = 0.34 and 0.30) could just be used for sample screening. Visualization maps of those quality parameters were also created by applying the interval PLS models on each pixel of the hyperspectral image, the distribution of quality parameters in which were basically consistent with the actual measurement. This study proved that the hyperspectral imaging is a useful tool to assess the maturity level and quality of dwarf bananas

Profiling and initial validation of urinary microRNAs as biomarkers in IgA nephropathy

Background. MicroRNAs (miRNAs) have been found in virtually all body fluids and used successfully as biomarkers for various diseases. Evidence indicates that miRNAs have important roles in IgA nephropathy (IgAN), a major cause of renal failure. In this study, we looked for differentially expressed miRNAs in IgAN and further evaluated the correlations between candidate miRNAs and the severity of IgAN.Methods. Microarray and RT-qRCR (real-time quantitative polymerase chain reaction) were sequentially used to screen and further verify miRNA expression profiles in urinary sediments of IgAN patients in two independent cohorts. The screening cohort consisted of 32 urine samples from 18 patients with IgAN, 4 patients with MN (membranous nephropathy), 4 patients with MCD (minimal changes disease) and 6 healthy subjects; the validation cohort consisted of 102 IgAN patients, 41 MN patients, 27 MCD patients and 34 healthy subjects. The renal pathological lesions of patients with IgAN were evaluated according to Lee’s grading system and Oxford classification.Results. At the screening phase, significance analysis of microarrays analysis showed that no miRNA was differentially expressed in the IgAN group compared to all control groups. But IgAN grade I–II and III subgroups (according to Lee’s grading system) shared dysregulation of two miRNAs (miR-3613-3p and miR-4668-5p). At the validation phase, RT-qPCR results showed that urinary level of miR-3613-3p was significantly lower in IgAN than that in MN, MCD and healthy controls (0.47, 0.44 and 0.24 folds, respectively, all P < 0.01 by Mann–Whitney U test); urinary level of miR-4668-5p was also significantly lower in IgAN than that in healthy controls (0.49 fold, P < 0.01). Significant correlations were found between urinary levels of miR-3613-3p with 24-hour urinary protein excretion (Spearman r = 0.50, P = 0.034), eGFR (estimated glomerular filtration rate) (r = − 0.48, P = 0.043) and Lee’s grades (r = 0.57, P = 0.014). Similarly, miR-4668-5p was significantly correlated with eGFR (r = − 0.50, P = 0.034) and Lee’s grades (r = 0.57, P = 0.013). For segmental glomerulosclerosis according to Oxford classification, patients scored as S0 had significantly lower levels of urinary miR-3613-3p and miR-4668-5p than those scored as S1 (0.41 and 0.43 folds, respectively, all P < 0.05).Conclusions. The expression profile of miRNAs was significantly altered in urinary sediments from patients with IgAN. Urinary expression of miR-3613-3p was down-regulated in patients with IgAN. Moreover, urinary levels of both miR-3613-3p and miR-4668-5p were correlated with disease severity. Further studies are needed to explore the roles of miR-3613-3p and miR-4668-5p in the pathogenesis and progression of IgA nephropathy