Muscle density, but not size, is independently associated with cognitive health in older adults with hip fractures.

Emerging evidence indicates a complex interplay between skeletal muscle and cognitive function. Despite the known differences between muscle quantity and quality, which can be measured via computed tomography (CT), the precise nature of their associations with cognitive performance remain underexplored. To investigate the links between muscle size and density and cognitive impairment (CI) in the older adults with hip fractures, we conducted a post hoc, cross-sectional analysis within a prospective cohort study on 679 patients with hip fractures over 65. Mini-Mental State Examination (MMSE) and routine hip CT imaging were utilized to assess cognition function and muscle characteristics in older adults with hip fractures. The CT scans provided data on cross-sectional area and attenuation for the gluteus maximus (G.MaxM) and the combined gluteus medius and minimus (G.Med/MinM). Participants were categorized into CI and non-CI groups based on education levels and MMSE scores. Multivariate logistic regressions, propensity score (PS) methods, and subgroup analysis were employed to analyze associations and validate findings. This study included 123 participants (81.6 ± 6.8 years, 74% female) with CI and 556 participants (78.5 ± 7.7 years, 72% female) without. Compared to the non-CI group, muscle parameters, especially density, were significantly lower in the CI group. Specifically, G.Med/Min muscle density, but not size was robustly associated with CI (odds ratio (OR) = 0.77, 95% confidence interval = 0.62-0.96, P = 0.02), independent of other medical situations. Sensitivity analysis corroborated that G.Med/Min muscle density was consistently lower in the CI group than the non-CI group, as evidenced in the PS matched (P = 0.024) and weighted cohort (P = 0.033). Enhanced muscle parameters, particularly muscle density in the G.Med/MinM muscle, correlate with a lower risk of CI. Muscle density demonstrates a stronger association with cognitive performance than muscle size, highlighting its potential as a key focus in future cognitive health research.

Sagittal support rather than medial cortical support matters in geriatric intertrochanteric fracture: A finite element analysis study.

Hip fracture, increasing exponentially with age, is osteoporosis's most severe clinical consequence. Intertrochanteric fracture, one of the main types of hip fracture, is associated with higher mortality and morbidity. The current research hotspots lay in improving the treatment effect and optimizing the secondary stability after intertrochanteric fracture surgery. Cortex buttress reduction is a widely accepted method for treating intertrochanteric fracture by allowing the head-neck fragment to slide and rigidly contact the femoral shaft's cortex. Medial cortical support is considered a more effective option in treating young patients. However, osteo-degenerations features, including bone weakness and cortical thickness thinning, affect the performance of cortex support in geriatric intertrochanteric fracture treatment. Literature focusing on the age-specific difference in cortex performance in the fractured hip is scarce. We hypothesized that this osteo-19 degenerative feature affects the performance of cortex support in treating intertrochanteric fractures between the young and the elderly. We established twenty models for the old and the young with intertrochanteric fractures and performed static and dynamic simulations under one-legged stance and walking cycle conditions. The von Mises stress and displacement on the femur, proximal femoral nail anti-rotation (PFNA) implant, fracture plane, and the cutting volume of cancellous bone of the femur were compared. It was observed that defects in the anterior and posterior cortical bone walls significantly increase the stress on the PFNA implant, the displacement of the fracture surface, and cause a greater volume of cancellous bone to be resected. We concluded that ensuring the integrity and alignment of the anterior and posterior cortical bones is essential for elderly patients, and sagittal support is recommended. This finding suggests that the treatment method for intertrochanteric fracture may differ, considering the patient's age difference.

Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.

Risk prediction of second hip fracture by bone and muscle density of the hip varies with time after first hip fracture: A prospective cohort study.

Purpose: Predictors of 'imminent' risk of second hip fracture are unknown. The aims of the study were to explore strength of hip areal bone mineral density (aBMD), and muscle area and density for predicting second hip fracture at different time intervals. Methods: Data of the Chinese Second Hip Fracture Evaluation were analyzed, a longitudinal study to evaluate the risk of second hip fracture (of the contralateral hip) by using CT images obtained immediately after first hip fracture. Muscle cross-sectional area and density were measured of the gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) and aBMD of the proximal femur at the contralateral unfractured side. Patients were followed up for a median time of 4.5 years. Separate Cox models were used to predict second hip fracture risk at different time intervals after first event adjusted for age, sex, BMI and diabetes. Results: The mean age of subjects with imminent (within 1st or 2nd year) second hip fracture was 79.80 ± 5.16 and 81.56 ± 3.64 years. In the 1st year after the first hip fracture, femoral neck (FN) aBMD predicted second hip fracture (HR 5.88; 95 % CI, 1.32-26.09). In the remaining years of follow-up after 2nd year, muscle density predicted second hip fracture (G.MaxM HR 2.13; 95 % CI, 1.25-3.65,G.Med/MinM HR 2.10; 95 % CI, 1.32-3.34). Conclusions: Our results show that femoral neck aBMD is an important predictor for second hip fracture within the first year and therefore suggest supports the importance concept of early and rapid-acting bone-active drugs to increase hip BMD. In addition, the importance of muscle density predicting second hip fracture after the second year suggest post hip fracture rehabilitation and exercise programs could also be important to reduce muscle fatty infiltration.

Associations of Quantitative and Qualitative Muscle Parameters With Second Hip Fracture Risk in Older Women: A Prospective Cohort Study.

Older women with a first hip fracture exhibit heightened susceptibility and incidence of second fracture and potentially severe consequences. This prospective study was to compare the predictive power of qualitative and quantitative muscle parameters for a second hip fracture in older women with a first hip fracture. A total of 206 subjects were recruited from the longitudinal Chinese Second Hip Fracture Evaluation study. Hip computed tomography (CT) scans were obtained immediately after the first fracture. Muscle fat infiltration was assessed according to the Goutallier classification qualitatively. Quantitative parameters included cross-sectional area and density of gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) muscles. CT X-ray absorptiometry was used to measure the areal bone mineral density (aBMD) of the contralateral femur. Cox proportional hazards models were used to compute hazard ratios (HR) of second hip fracture risk. The mean age of subjects was 74.9 (±9.5) years at baseline. After 4.5 years, 35 had a second hip fracture, 153 without a second hip fracture, and 18 died. Except for the combined G.MinM Goutallier grade 3 and 4 groups before adjustment for covariates (HR = 5.83; 95% confidence interval [CI] 1.49-22.83), there were no significant HRs for qualitative classification to predict a second hip fracture. Among quantitative metrics, after adjustment for covariates, G.Med/MinM density was significant in the original (HR = 1.44; CI 1.02-2.04) and competing risk analyses (HR = 1.46; CI 1.02-2.07). After additional adjustment for femoral neck (FN) aBMD, G.Med/MinM density remained borderline significant for predicting a second hip fracture in competing risk analysis (HR = 1.43; CI 0.99-2.06; p = 0.057). Our study revealed that Goutallier classification was less effective than quantitative muscle metrics for predicting hip second fracture in this elderly female cohort. After adjustment for FN aBMD, G.Med/MinM density is a borderline independent predictor of second hip fracture risk. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Rapid online plant leaf area change detection with high-throughput plant image data.

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

Orthogeriatric co-managements lower early mortality in long-lived elderly hip fracture: a post-hoc analysis of a prospective study.

OBJECTIVE: To evaluate the clinical effectiveness of orthogeriatric co-management care in long-lived elderly hip fracture patients (age ≥ 90). METHODS: Secondary analysis was conducted in long-lived hip fracture patients between 2018 to 2019 in 6 hospitals in Beijing, China. Patients were divided into the orthogeriatric co-management group (CM group) and traditional consultation mode group (TC group) depending on the management mode. With 30-day mortality as the primary outcome, multivariate regression analyses were performed after adjusting for potential covariates. 30-day mobility and quality of life were compared between groups. RESULTS: A total of 233 patients were included, 223 of whom completed follow-up (125 in CM group, 98 in TC group). The average age was 92.4 ± 2.5 years old (range 90-102). The 30-day mortality in CM group was significantly lower than that in TC group after adjustments for (2.4% vs. 10.2%; OR = 0.231; 95% CI 0.059 ~ 0.896; P = 0.034). The proportion of patients undergoing surgery and surgery performed within 48 h also favored the CM group (97.6% vs. 85.7%, P = 0.002; 74.4% vs. 24.5%, P < 0.001; respectively). In addition, much more patients in CM group could walk with or without aids in postoperative 30 days than in the TC group (87.7% vs. 60.2%, P < 0.05), although differences were not found after 1-year follow-up. And there was no significant difference in total cost between the two groups (P > 0.05). CONCLUSIONS: For long-lived elderly hip fracture patients, orthogeriatric co-management care lowered early mortality, improved early mobility and compared with the traditional consultation mode.

Association Between Hip Bone Mineral Density and Mortality Risk After Hip Fracture: A Prospective Cohort Study.

Factors related to mortality after osteoporotic hip fracture (HF) have been investigated intensively, except for proximal femoral bone mineral density (BMD), which is also the primary cause of osteoporosis. In this study, we aimed to investigate the association of hip BMD with mortality risk after HF. Four hundred and eleven elderly patients with HF in Beijing, China, were included and prospectively followed up with a median time of 3 years. At baseline, quantitative CT technique (QCT) was used to measure areal BMD (aBMD) of the unaffected hip. Areal BMDs of the total hip (TH), femoral neck (FN), trochanter (TR), and intertrochanter were analyzed with postoperative mortality as the primary outcome. A total of 394 patients (78.59 ± 7.59 years, 75.4% female) were included in our final analysis, with 86 (82.23 ± 7.00 years, 81.4% female) dead. All hip bone densities demonstrated a significant association with mortality risks in the unadjusted model, but only TR aBMD remained significantly correlated after adjusting for all covariates. Compared to the lower TR aBMD group, the higher TR aBMD group yielded significantly lower death risks (HR 0.21 95% CI 0.05-0.9, P = 0.036). Higher survival probabilities were observed for higher TH and TR aBMD in survival analysis (P < 0.001). Hip BMD, especially TR BMD assessed by QCT, is an independent risk factor for postoperative mortality following HF. QCT may present a promising avenue for opportunistic analysis in immobilized patients, providing valuable information for early detection and personalized interventions to enhance patient outcomes.

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage.

The number of leaves at a given time is important to characterize plant growth and development. In this work, we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images. The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages (150,000 images with over 2 million labels). The realism of the images was then improved using domain adaptation methods before training deep learning models. The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset, collecting measurements from 5 countries obtained under different environments, growth stages, and lighting conditions with different cameras (450 images with over 2,162 labels). Among the 6 combinations of deep learning models and domain adaptation techniques, the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance (R2 = 0.94, root mean square error = 8.7). Complementary studies show that it is essential to simulate images with sufficient realism (background, leaf texture, and lighting conditions) before applying domain adaptation techniques. Furthermore, the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips. The method is claimed to be self-supervised since no manual labeling is required for model training. The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems. The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection.

Muscle size and density are independently associated with death after hip fracture: A prospective cohort study.

BACKGROUND: Mortality following hip fracture is high and incompletely understood. We hypothesize that hip musculature size and quality are related to mortality following hip fracture. This study aims to investigate the associations of hip muscle area and density from hip CT with death following hip fracture as well as assess the dependence of this association on time after hip fracture. METHODS: In this secondary analysis of the prospectively collected CT images and data from the Chinese Second Hip Fracture Evaluation, 459 patients were enrolled between May 2015 and June 2016 and followed up for a median of 4.5 years. Muscle cross-sectional area and density were measured of the gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) and aBMD of the proximal femur. The Goutallier classification (GC) was used for qualitatively assessing muscle fat infiltration. Separate Cox models were used to predict mortality risk adjusted for covariates. RESULTS: At the end of the follow-up, 85 patients were lost, 81 patients (64% women) had died, and 293 (71% women) survived. The mean age of non-surviving patients at death (82.0 ± 8.1 years) was higher than that of the surviving patients (74.4 ± 9.9 years). The Parker Mobility Score and the American Society of Anesthesiologists scores of the patients that died were respectively lower and higher compared to the surviving patients. Hip fracture patients received different surgical procedures, and no significant difference in the percentage of hip arthroplasty was observed between the dead and the surviving patients (P = 0.11). The cumulative survival was significantly lower for patients with low G.MaxM area and density and low G.Med/MinM density, independent of age and clinical risk scores. The GC grades were not associated with the mortality after hip fracture. Muscle density of both G.MaxM (adj. HR 1.83; 95% CI, 1.06-3.17) and G.Med/MinM (adj. HR 1.98; 95% CI, 1.14-3.46) was associated with mortality in the 1st year after hip fracture. G.MaxM area (adj. HR 2.11; 95% CI, 1.08-4.14) was associated with mortality in the 2nd and later years after hip fracture. CONCLUSION: Our results for the first time show that hip muscle size and density are associated with mortality in older hip fracture patients, independent of age and clinical risk scores. This is an important finding to better understand the factors contributing to the high mortality in older hip fracture patients and to develop better future risk prediction scores that include muscle parameters.

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

Leaf-Counting in Monocot Plants Using Deep Regression Models.

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models.

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography.

BACKGROUND: The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI. RESULTS: In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with [Formula: see text] and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with [Formula: see text] and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the [Formula: see text] and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively. CONCLUSIONS: Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.

Modified chair method: an easy and efficient reduction method without medication for anterior shoulder dislocation.

BACKGROUND: Various maneuvers have been introduced to address anterior shoulder dislocations. Chair method allows the patient to sit comfortably and feel less pain during the reduction procedure. However, the rarity of comparative studies led to a lack of evidence to popularize. The present study aimed to introduce a modified chair (MOC) reduction method for anterior shoulder dislocation and explore its effectiveness compared with the traditional Hippocratic approach. METHODS: This is a single-center retrospective study of 257 patients with anterior shoulder dislocation from September 2020 and July 2021. Patients were divided into two groups according to the reduction method they received (either the Hippocratic method or the MOC method). Success rate, reduction time, visual analog scale (VAS) pain score, satisfaction level, and a new indicator, pain index (reduction time (s)* VAS/ 10), were compared. RESULTS: One hundred sixteen patients (43 females, 73 males) underwent the Hippocratic method, and 141 (65 females, 76 males) MOC method. A significantly higher success rate was seen in the MOC group (96.5%(136/141) vs. 84.5%(98/116) in the Hippocratic group; OR 5, 95%CI 1.79 ~ 13.91; p = 0.002). Pain index of the patients in the MOC group was much lower than that in the Hippocratic group (3.20 (2.10, 4.53) vs. 36.70 (22.40, 47.25), p <  0.001). The reduction time, VAS pain score, and satisfaction level also favored the MOC method. CONCLUSIONS: The MOC method is an easy and efficient reduction method with minimum assistance for anterior shoulder dislocations. Physicians can skillfully perform this procedure with the help of their body weight. The MOC method could be attempted for shoulder dislocations in the emergency department.

Variation in morpho-physiological and metabolic responses to low nitrogen stress across the sorghum association panel.

BACKGROUND: Access to biologically available nitrogen is a key constraint on plant growth in both natural and agricultural settings. Variation in tolerance to nitrogen deficit stress and productivity in nitrogen limited conditions exists both within and between plant species. However, our understanding of changes in different phenotypes under long term low nitrogen stress and their impact on important agronomic traits, such as yield, is still limited. RESULTS: Here we quantified variation in the metabolic, physiological, and morphological responses of a sorghum association panel assembled to represent global genetic diversity to long term, nitrogen deficit stress and the relationship of these responses to grain yield under both conditions. Grain yield exhibits substantial genotype by environment interaction while many other morphological and physiological traits exhibited consistent responses to nitrogen stress across the population. Large scale nontargeted metabolic profiling for a subset of lines in both conditions identified a range of metabolic responses to long term nitrogen deficit stress. Several metabolites were associated with yield under high and low nitrogen conditions. CONCLUSION: Our results highlight that grain yield in sorghum, unlike many morpho-physiological traits, exhibits substantial variability of genotype specific responses to long term low severity nitrogen deficit stress. Metabolic response to long term nitrogen stress shown higher proportion of variability explained by genotype specific responses than did morpho-pysiological traits and several metabolites were correlated with yield. This suggest, that it might be possible to build predictive models using metabolite abundance to estimate which sorghum genotypes will exhibit greater or lesser decreases in yield in response to nitrogen deficit, however further research needs to be done to evaluate such model.

A Two-Photon Fluorescent Probe for the Visual Detection of Peroxynitrite in Living Cells and Zebrafish.

Peroxynitrite (ONOO-), as an important reactive oxygen species (ROS), holds great potential to react with a variety of biologically active substances, leading to the occurrence of various diseases such as cancer and neurodegenerative diseases. In this work, we developed a novel mitochondria-localized fluorescent probe, HDBT-ONOO-, which was designed as a mitochondria-targeting two-photon fluorescence probe based on 1,8-naphthylimide fluorophore and the reactive group of 4-(bromomethyl)-benzene boronic acid pinacol ester. More importantly, the probe exhibited good biocompatibility, sensitivity, and selectivity, enabling its successful application in imaging the generation of intracellular and extracellular ONOO-. Furthermore, exogenous and endogenous ONOO- products in live zebrafish were visualized. It is greatly expected that the designed probe can serve as a useful imaging tool for clarifying the distribution and pathophysiological functions of ONOO- in cells and zebrafish.

Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize.

The root-associated microbiome (rhizobiome) affects plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here, we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize (Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We analyze the maize rhizobiome in terms of 150 abundant and consistently reproducible microbial groups and we show that the abundance of many root-associated microbes is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in -N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association study was conducted using the abundance of microbial groups as rhizobiome traits, and n=622 plant loci were identified that are linked to the abundance of n=104 microbial groups in the maize rhizosphere. In 62/104 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. We provide comprehensive datasets about the three-way interaction of host genetics, microbe abundance, and plant performance under two N treatments to facilitate targeted experiments toward harnessing the full potential of root-associated microbial symbionts in maize production.

Flexible Self-Powered Weak Light Detectors Based on ZnO/CsPbBr3/γ-CuI Heterojunctions.

Halide perovskites (HPs) with marvelous optical and electrical properties are regarded as one of the competitive candidates for building next-generation photodetectors (PDs). However, combining their excellent properties with satisfactory long-term robustness is still challenging, ultimately limiting the practical applications of HP-based PDs. Herein, a high vacuum deposition system is employed to fabricate flexible self-powered PDs with a ZnO/CsPbBr3/γ-CuI structure, which shows excellent stability and outstanding performance in weak light detection. Benefiting from the improved crystallinity and optimized device structure, a high detectivity of 8.1 × 1013 Jones and a rapid response speed (rise/decay time of 3.9/1.8 µs) are obtained in this self-powered device. Furthermore, the unencapsulated device exhibits intriguing environmental stability and mechanical flexibility. The photocurrent remains unchanged after 7000 s of continuous operation or 100 bending cycles. Furthermore, a 15 × 15 PD array is fabricated as an image sensor. A high contrast image of the target object can be obtained owing to the high sensitivity and uniformity of the self-powered PDs. These results demonstrate the feasibility and practicality of the ZnO/CsPbBr3/γ-CuI heterojunction for applications in weak light detection and image formation.

Quasi-two-dimensional α-molybdenum oxide thin film prepared by magnetron sputtering for neuromorphic computing.

Two-dimensional (2D) layered materials have attracted intensive attention in recent years due to their rich physical properties, and shown great promise due to their low power consumption and high integration density in integrated electronics. However, mostly limited to mechanical exfoliation, large scale preparation of the 2D materials for application is still challenging. Herein, quasi-2D α-molybdenum oxide (α-MoO3) thin film with an area larger than 100 cm2 was fabricated by magnetron sputtering, which is compatible with modern semiconductor industry. An all-solid-state synaptic transistor based on this α-MoO3 thin film is designed and fabricated. Interestingly, by proton intercalation/deintercalation, the α-MoO3 channel shows a reversible conductance modulation of about four orders. Several indispensable synaptic behaviors, such as potentiation/depression and short-term/long-term plasticity, are successfully demonstrated in this synaptic device. In addition, multilevel data storage has been achieved. Supervised pattern recognition with high recognition accuracy is demonstrated in a three-layer artificial neural network constructed on this α-MoO3 based synaptic transistor. This work can pave the way for large scale production of the α-MoO3 thin film for practical application in intelligent devices.