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Diabetic nephropathy (DN) is a serious complication of diabetes mellitus whose expand process is linked with the fibrosis, renal hypertrophy and inflammation. The current study was to formulate and optimize the nano-formulation of crocetin (CT-PLGA-NPs) against Streptozotocin-induced renal nephropathy in rats. Double emulsion evaporation technique was used for the preparation of CT-PLGA-NPs. CT-PLGA-NPs were scrutinized for polydispersity index, size, gastric stability, entrapment, drug-loading capacity and in-vitro drug release and in vivo preclinical study. Single intraperitoneal injection of streptozotocin (STZ) (55 mg/kg) and rats were divided into different group. Renal function and metabolic parameters of urine and serum were estimated. Fibrotic protein, renal pro-inflammatory cytokines and degree of renal damage expression were also determined. We also estimated the fibronectin, type IV collagen and transforming growth factor-β1 for a possible mechanism of action. Crocetin supplement (10 mg/kg) and CT-PLGA-NPs exhibited the accumulation of the drug in kidney and liver of diabetic rats. Crocetin reduced the BGL and enhanced plasma insulin and body weight. Dose dependent treatment of crocetin significantly (p < .001) down-regulated the expression of renal tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin (IL)-1β (IL-1β) and Monocyte Chemoattractant Protein-1 (MCP-1). Crocetin significantly (p < .001) altered the expression of fibronectin, type IV collagen, and transforming growth factor-β1 (TGF-1β). Crocetin significantly (p < .001) down-regulated the protein kinase C activity and the expression of nuclear factor κB (NF-κB) p65 activity and protein production in renal tissue. On the basis of the available result, we can conclude that nano-formulation of crocetin could attenuate the diabetic nephropathy via antifibrotic and anti-inflammatory effect.

Helical structures have attracted considerable attention due to their inherent optical chirality. Here, we report a unique type of 3D Janus plasmonic helical nanoaperture with direction-controlled polarization sensitivity, which is simply fabricated via the one-step grayscale focused ion beam milling method. Circular dichroism in transmission of as large as 0.72 is experimentally realized in the forward direction due to the spin-dependent mode coupling process inside the helical nanoaperture. However, in the backward direction, the nanoaperture acquires giant linear dichroism in transmission of up to 0.87. By encoding the Janus metasurface with the two nanoaperture enantiomers having specified rotation angles, direction-controlled polarization-encrypted data storage is demonstrated for the first time, where a binary quick-response code image is displayed in the forward direction under the circularly polarized incidence of a specified handedness, while a distinct grayscale image is revealed in the backward direction under linearly polarized illumination with a specified azimuthal angle. We envision that the proposed Janus helical nanoapertures will provide an appealing platform for a variety of applications, which will range from multifunctional polarization control, enantiomer sensing, data encryption and decryption to optical information processing.Data storage: Helical holes for polarized encryptionTiny helical apertures etched in gold could act as unit cells for data storage technologies based on the manipulation of polarized light. Helical nanostructures are suited to storing binary data because they exist in two forms, left- and right-handed helixes. Xiaodong Yang, Jie Gao and Yang Chen at Missouri University of Science and Technology used ion beam milling to carve arrays of helter-skelter-shaped helical ‘nanoapertures’ in 180-nanometer-thick gold films. Their samples allow selective transmission of certain types of polarized light, while blocking others. Moreover, this polarization sensitivity depends on the direction of the incoming light, meaning that when light is directed in one direction the array produces binary images such as QR codes, while in the other direction it can reproduce grayscale photographs. This opens exciting possibilities for data encryption and optical information processing.

Bearings are very important components in mechanical equipment, and detecting bearing failures helps ensure healthy operation of mechanical equipment and can prevent catastrophic accidents. Most of the well-established detection methods do not take into account the correlation between signals and are difficult to accurately identify those fault samples that have a low degree of failure. To address this problem, we propose a graph neural network-based bearing fault detection (GNNBFD) method. The method first constructs a graph using the similarity between samples; secondly the constructed graph is fed into a graph neural network (GNN) for feature mapping, and the samples outputted by the GNN network fuse the feature information of their neighbors, which is beneficial to the downstream detection task; then the samples mapped by the GNN network are fed into base detector for fault detection; finally, the results determined by the integrated base detector algorithm are determined, and the top n samples with the highest outlier scores are the faulty samples. The experimental results with five state-of-the-art algorithms on publicly available datasets show that the GNNBFD algorithm improves the AUC by 6.4% compared to the next best algorithm, proving that the GNNBFD algorithm is effective and feasible.

The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

The SARS-CoV-2 B.1.1.529 (Omicron) variant contains 15 mutations of the receptor-binding domain (RBD). How Omicron evades RBD-targeted neutralizing antibodies requires immediate investigation. Here we use high-throughput yeast display screening 1 , 2 to determine the profiles of RBD escaping mutations for 247 human anti-RBD neutralizing antibodies and show that the neutralizing antibodies can be classified by unsupervised clustering into six epitope groups (A–F)—a grouping that is highly concordant with knowledge-based structural classifications 3 – 5 . Various single mutations of Omicron can impair neutralizing antibodies of different epitope groups. Specifically, neutralizing antibodies in groups A–D, the epitopes of which overlap with the ACE2-binding motif, are largely escaped by K417N, G446S, E484A and Q493R. Antibodies in group E (for example, S309) 6 and group F (for example, CR3022) 7 , which often exhibit broad sarbecovirus neutralizing activity, are less affected by Omicron, but a subset of neutralizing antibodies are still escaped by G339D, N440K and S371L. Furthermore, Omicron pseudovirus neutralization showed that neutralizing antibodies that sustained single mutations could also be escaped, owing to multiple synergetic mutations on their epitopes. In total, over 85% of the tested neutralizing antibodies were escaped by Omicron. With regard to neutralizing-antibody-based drugs, the neutralization potency of LY-CoV016, LY-CoV555, REGN10933, REGN10987, AZD1061, AZD8895 and BRII-196 was greatly undermined by Omicron, whereas VIR-7831 and DXP-604 still functioned at a reduced efficacy. Together, our data suggest that infection with Omicron would result in considerable humoral immune evasion, and that neutralizing antibodies targeting the sarbecovirus conserved region will remain most effective. Our results inform the development of antibody-based drugs and vaccines against Omicron and future variants. A high-throughput yeast display platform is used to analyse the profiles of mutations in the SARS-CoV-2 receptor-binding domain (RBD) that enable escape from antibodies, and suggests that most anti-RBD antibodies can be escaped by the Omicron variant.

Background Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas. Results In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors. Conclusions These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.

Inflammation, nutritional status, and depression interact complexly, impacting health outcomes. This study investigates their associations with all-cause and cardiovascular mortality in middle-aged and elderly individuals with osteoporosis. Using NHANES data from 2007 to 2023, the study applied Cox regression models and restricted cubic spline plots to assess the effects of ALI (Advanced Lung Cancer Inflammation Index) and PHQ-9 scores on mortality outcomes in osteoporosis patients. Subgroup, threshold, and mediation analyses were also conducted. The study included 862 cases of all-cause mortality and 211 cardiovascular deaths. Higher ALI was associated with reduced mortality risk, while higher PHQ-9 scores indicated increased mortality risk. Combined analysis showed that osteoporosis patients with high ALI and no depressive symptoms had the lowest mortality risk. Restricted cubic spline and threshold analyses revealed a linear negative correlation between ALI and mortality risk and a nonlinear positive correlation between PHQ-9 scores and mortality risk. Subgroup analysis showed gender, physical activity, and sleep status influenced the interaction between ALI/PHQ-9 and mortality risk. Causal mediation analysis with bootstrapping found that ALI mediated 3.9% of the effect of osteoporosis on all-cause mortality and 5.6% on cardiovascular mortality, while PHQ-9 scores mediated 6.6% of cardiovascular mortality. A significant negative correlation exists between ALI and mortality risk in osteoporosis patients, while PHQ-9 scores correlate positively. Favorable nutrition and inflammation, coupled with the absence of depression, help reduce mortality risks.

Inflammation, nutritional status, and depression interact complexly, impacting health outcomes. This study investigates their associations with all-cause and cardiovascular mortality in middle-aged and elderly individuals with osteoporosis. Using NHANES data from 2007 to 2023, the study applied Cox regression models and restricted cubic spline plots to assess the effects of ALI (Advanced Lung Cancer Inflammation Index) and PHQ-9 scores on mortality outcomes in osteoporosis patients. Subgroup, threshold, and mediation analyses were also conducted. The study included 862 cases of all-cause mortality and 211 cardiovascular deaths. Higher ALI was associated with reduced mortality risk, while higher PHQ-9 scores indicated increased mortality risk. Combined analysis showed that osteoporosis patients with high ALI and no depressive symptoms had the lowest mortality risk. Restricted cubic spline and threshold analyses revealed a linear negative correlation between ALI and mortality risk and a nonlinear positive correlation between PHQ-9 scores and mortality risk. Subgroup analysis showed gender, physical activity, and sleep status influenced the interaction between ALI/PHQ-9 and mortality risk. Causal mediation analysis with bootstrapping found that ALI mediated 3.9% of the effect of osteoporosis on all-cause mortality and 5.6% on cardiovascular mortality, while PHQ-9 scores mediated 6.6% of cardiovascular mortality. A significant negative correlation exists between ALI and mortality risk in osteoporosis patients, while PHQ-9 scores correlate positively. Favorable nutrition and inflammation, coupled with the absence of depression, help reduce mortality risks.

Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

Crop yield is related to national food security and economic performance, and it is therefore important to estimate this parameter quickly and accurately. In this work, we estimate the yield of winter wheat using the spectral indices (SIs), ground-measured plant height (H), and the plant height extracted from UAV-based hyperspectral images (HCSM) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random Forest (RF). The SIs, H, and HCSM were used as input values, and then the PLSR, ANN, and RF were trained using regression techniques. The three different regression techniques were used for modeling and verification to test the stability of the yield estimation. The results showed that: (1) HCSM is strongly correlated with H (R2 = 0.97); (2) of the regression techniques, the best yield prediction was obtained using PLSR, followed closely by ANN, while RF had the worst prediction performance; and (3) the best prediction results were obtained using PLSR and training using a combination of the SIs and HCSM as inputs (R2 = 0.77, RMSE = 648.90 kg/ha, NRMSE = 10.63%). Therefore, it can be concluded that PLSR allows the accurate estimation of crop yield from hyperspectral remote sensing data, and the combination of the SIs and HCSM allows the most accurate yield estimation. The results of this study indicate that the crop plant height extracted from UAV-based hyperspectral measurements can improve yield estimation, and that the comparative analysis of PLSR, ANN, and RF regression techniques can provide a reference for agricultural management.