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Abstract Background Frailty is common in patients undergoing cardiac surgery and is associated with poorer postoperative outcomes. Ultrasound examination of skeletal muscle morphology may serve as an objective assessment tool as lean muscle mass reduction is a key feature of frailty. Methods This study investigated the association of ultrasound-derived muscle thickness, cross-sectional area, and echogenicity of the rectus femoris muscle (RFM) with preoperative frailty and predicted subsequent poor recovery after surgery. Eighty-five patients received preoperative RFM ultrasound examination and frailty-related assessments: Clinical Frailty Scale (CFS) and 5-m gait speed test (GST5m). Association of each ultrasound measurement with frailty assessments was examined. Area under receiver-operating characteristic curve (AUROC) was used to assess the discriminative ability of each ultrasound measurement to predict days at home within 30 days of surgery (DAH30). Results By CFS and GST5m criteria, 13% and 34% respectively of participants were frail. RFM cross-sectional area alone demonstrated moderate predictive association for frailty by CFS criterion (AUROC: 0.76, 95% CI: 0.66–0.85). Specificity improved to 98.7% (95% CI: 93.6%-100.0%) by utilising RFM cross-sectional area as an ‘add-on’ test to a positive gait speed test, and thus a combined muscle size and function test demonstrated higher predictive performance (positive likelihood ratio: 40.4, 95% CI: 5.3–304.3) for frailty by CFS criterion than either test alone (p

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Future changes in tropical cyclone properties are an important component of climate change impacts and risk for many tropical and midlatitude countries. In this study we assess the performance of a multimodel ensemble of climate models, at resolutions ranging from 250 to 25 km. We use a common experimental design including both atmosphere-only and coupled simulations run over the period 1950-2050, with two tracking algorithms applied uniformly across the models. There are overall improvements in tropical cyclone frequency, spatial distribution, and intensity in models at 25 km resolution, with several of them able to represent very intense storms. Projected tropical cyclone activity by 2050 generally declines in the South Indian Ocean, while changes in other ocean basins are more uncertain and sensitive to both tracking algorithm and imposed forcings. Coupled models with smaller biases suggest a slight increase in average TC 10 m wind speeds by 2050.

Introduction Protein malnutrition is associated with higher risks of postoperative complications, mortality, prolonged postoperative stays in hospital, slower physical and mental recovery after surgery and lower subsequent health-related quality of life. To reduce the risk of postoperative morbidity and mortality, nutritional prehabilitation programmes have been developed recently to build up patient’s nutritional reserve to withstand the stress of surgery. The intervention involves nutritional screening and counselling, and increasing dietary protein intake in protein-malnourished patients in the several weeks before surgery. However, there are few well-conducted preoperative studies to examine the effect of increasing dietary protein intake on the quality of recovery of malnourished patients after elective cardiac surgery.Method and analysis This randomised controlled trial of malnourished patients undergoing major elective cardiac surgery will compare the quality of postoperative recovery in patients with or without nutritional prehabilitation. One hundred and thirty-two patients will be randomised to receive nutritional prehabilitation (target-adjusted whey protein powder supplementation and an individualised 1 hour session/week counselling by a dietician 1 month before operation date) or standard care (no nutritional prehabilitation). Primary outcomes will be the quality of recovery after surgery (15-item Quality of Recovery) on the third postoperative day. Secondary outcomes will include days (alive and) at home within 30 days, changes in the WHO Disability Assessment Schedule 2.0, changes in health-related quality of life (EQ-5D) and Cardiac Postoperative Morbidity Survey. An outcomes assessor will be blinded to the treatment allocation. Appropriate univariate analyses, generalised estimating equations and multiple regressions will be performed for intention-to-treat and per-protocol analyses.Ethics and dissemination The Joint CUHK-NTEC Clinical Research Ethics Committee approved the study protocol (CREC Ref. No.: 2021.703 T). The findings will be presented at scientific meetings, peer-reviewed journals and to study participants.Trial registration number ChiCTR2200057463.

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions. [ABSTRACT FROM AUTHOR]

Emergency medical services (EMS) clinicians are tasked with early fluid resuscitation for patients with sepsis. Traditional methods for prehospital fluid delivery are limited in speed and ease-of-use. We conducted a comparative effectiveness study of a novel rapid infusion device for prehospital fluid delivery in suspected sepsis patients. This pre-post observational study evaluated a hand-operated, rapid infusion device in a single large EMS system from July 2021-July 2022. Prior to device deployment, EMS clinicians completed didactic and simulation-based device training. Data were extracted from the EMS electronic health record. Eligible patients included adults with suspected sepsis treated by EMS with intravenous fluids. The primary outcome was the proportion of patients receiving goal fluid volume (at least 500 mL) prior to hospital arrival. Secondary outcomes included in-hospital mortality, disposition, and length of stay. Multivariable logistic regression was used to compare outcomes between 6-month pre- and post-implementation periods (July-December 2021 and February-July 2022, respectively), adjusting for patient demographics, abnormal prehospital vital signs, and EMS transport interval. Of 1,180 eligible patients (552 in the pre-implementation period; 628 in the post-implementation period), the mean age was 72 years old, 45% were female, and 25% were minority race-ethnicity. Median (interquartile range) fluid volume (in mL) increased between the pre- and post-implementation periods (600 [400,1,000] and 850 [500–1,000], respectively). Goal fluid volume was achieved in 70% of pre-implementation patients and 82% of post-implementation patients. In adjusted analysis, post-implementation patients were significantly more likely to receive goal fluid volume than pre-implementation patients (adjusted odds ratio (aOR) 2.00, 95% confidence interval (CI) 1.51–2.66). Pre-post in-hospital mortality was not significantly different (aOR 0.91, 95% CI 0.59–1.39). In a single EMS system, sepsis education and introduction of a rapid infusion device was associated with achieving goal fluid volume for suspected sepsis. Further research is needed to assess the clinical effectiveness of infusion device implementation to improve sepsis patient outcomes. [ABSTRACT FROM AUTHOR]

A current trend towards the sustainable production of valuable chemicals consists in coupling nuclear energy with the chemical industry. Here, the authors propose to use γ radiation to transform glycerol (a waste product of biorefineries) into high-value solketal.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green–red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

Rapidly developing economies of countries in hot climates face the risk of a significant increase in CO2 emissions. This study developed strategies for low energy cooling and ventilation for Indian residences (LECaVIR). Ventilation and cooling techniques were developed and tested for India’s four climatic zones. The analysis shows that natural ventilation is possible in typical residential buildings for about 20–40% of the year. Using an enhanced natural ventilation mode with appropriately sized openable windows and controls, the total number of hours for which natural ventilation is able to offer satisfactory conditions for occupants can be extended by a further 13 percentage points, leading to a potential reduction of 46% in the mechanical cooling hours for residences. Dynamic thermal simulation models, coupled with control software, were used to test the most promising natural ventilation strategies as part of a mixed-mode approach to ensure year-round comfort at minimal energy cost. The simulation shows that energy savings of up to 55% are possible. 'Practice relevance' This paper demonstrates that it is possible to design and control cooling and ventilation strategies that make significant use of natural ventilation, despite hot climates. The paper contains details of innovative control algorithms that were tested using real designs for Indian residences where poor ventilation and overheating are common problems, often leading to the excessive use of inefficient, portable air-conditioning systems. Practitioners will also benefit from guidance on how to use dynamic thermal simulation coupled with control software to quantify the energy and thermal comfort performance of mixed-mode ventilation and cooling strategies.

Wheat occupies a special role in global food security since, in addition to providing 20% of our carbohydrates and protein, almost 25% of the global production is traded internationally. The importance of wheat for food security was recognised by the Chief Agricultural Scientists of the G20 group of countries when they endorsed the establishment of the Wheat Initiative in 2011. The Wheat Initiative was tasked with supporting the wheat research community by facilitating collaboration, information and resource sharing and helping to build the capacity to address challenges facing production in an increasingly variable environment. Many countries invest in wheat research. Innovations in wheat breeding and agronomy have delivered enormous gains over the past few decades, with the average global yield increasing from just over 1 tonne per hectare in the early 1960s to around 3.5 tonnes in the past decade. These gains are threatened by climate change, the rapidly rising financial and environmental costs of fertilizer, and pesticides, combined with declines in water availability for irrigation in many regions. The international wheat research community has worked to identify major opportunities to help ensure that global wheat production can meet demand. The outcomes of these discussions are presented in this paper.

Abstract Reconstruction of the B 0 → D ∗− τ + ν τ angular distribution is complicated by the strongly biasing effect of losing the neutrino information from both the B and τ decays. In this work, a novel method for making unbiased measurements of the angular coefficients while preserving the model independence of the angular technique is demonstrated. The twelve angular functions that describe the signal decay, in addition to background terms, are modelled in a multidimensional fit, using template probability density functions that encapsulate all resolution and acceptance effects. Sensitivities at the LHCb and Belle II experiments are estimated, and sources of systematic uncertainty are discussed, notably in the extrapolation to a measurement of R(D ∗ ).