Reintroducing face-to-face support alongside remote support to form a hybrid stop smoking service in England: a formative mixed methods evaluation.

BACKGROUND: During the COVID-19 pandemic, United Kingdom (UK) stop smoking services had to shift to remote delivery models due to social distancing regulations, later reintroducing face-to-face provision. The "Living Well Smokefree" service in North Yorkshire County Council adopted a hybrid model offering face-to-face, remote, or a mix of both. This evaluation aimed to assess the hybrid approach's strengths and weaknesses and explore potential improvements. METHODS: Conducted from September 2022 to February 2023, the evaluation consisted of three components. First, qualitative interviews involved 11 staff and 16 service users, analysed thematically. Second, quantitative data from the QuitManager system that monitored the numbers and proportions of individuals selecting and successfully completing a 4-week quit via each service option. Third, face-to-face service expenses data was used to estimate the value for money of additional face-to-face provision. The qualitative findings were used to give context to the quantitative data via an "expansion" approach and complementary analysis. RESULTS: Overall, a hybrid model was seen to provide convenience and flexible options for support. In the evaluation, 733 individuals accessed the service, with 91.3% selecting remote support, 6.1% face-to-face, and 2.6% mixed provision. Remote support was valued by service users and staff for promoting openness, privacy, and reducing stigma, and was noted as removing access barriers and improving service availability. However, the absence of carbon monoxide monitoring in remote support raised accountability concerns. The trade-off in "quantity vs. quality" of quits was debated, as remote support reached more users but produced fewer carbon monoxide-validated quits. Primarily offering remote support could lead to substantial workloads, as staff often extend their roles to include social/mental health support, which was sometimes emotionally challenging. Offering service users a choice of support options was considered more important than the "cost-per-quit". Improved dissemination of information to support service users in understanding their options for support was suggested. CONCLUSIONS: The hybrid approach allows smoking cessation services to evaluate which groups benefit from remote, face-to-face, or mixed options and allocate resources accordingly. Providing choice, flexible provision, non-judgmental support, and clear information about available options could improve engagement and match support to individual needs, enhancing outcomes.

A Novel Algorithm for Improving the Prehospital Diagnostic Accuracy of ST-Segment Elevation Myocardial Infarction.

INTRODUCTION: Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting. METHODS: A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics. RESULTS: There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r2 = 0.90) and specificity (r2 = 0.85) with increasing number of criteria. CONCLUSIONS: Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.

Outcomes of patients with active cancers and pre-existing cardiovascular diseases infected with SARS-CoV-2.

OBJECTIVE: To determine the impact of acute SARS-CoV-2 infection on patient with concomitant active cancer and CVD. METHODS: The researchers extracted and analyzed data from the National COVID Cohort Collaborative (N3C) database between January 1, 2020, and July 22, 2022. They included only patients with acute SARS-CoV-2 infection, defined as a positive test by PCR 21 days before and 5 days after the day of index hospitalization. Active cancers were defined as last cancer drug administered within 30 days of index admission. The "Cardioonc" group consisted of patients with CVD and active cancers. The cohort was divided into four groups: (1) CVD (-), (2) CVD ( +), (3) Cardioonc (-), and (4) Cardioonc ( +), where (-) or ( +) denotes acute SARS-CoV-2 infection status. The primary outcome of the study was major adverse cardiovascular events (MACE), including acute stroke, acute heart failure, myocardial infarction, or all-cause mortality. The researchers analyzed the outcomes by different phases of the pandemic and performed competing-risk analysis for other MACE components and death as a competing event. RESULTS: The study analyzed 418,306 patients, of which 74%, 10%, 15.7%, and 0.3% had CVD (-), CVD ( +), Cardioonc (-), and Cardioonc ( +), respectively. The Cardioonc ( +) group had the highest MACE events in all four phases of the pandemic. Compared to CVD (-), the Cardioonc ( +) group had an odds ratio of 1.66 for MACE. However, during the Omicron era, there was a statistically significant increased risk for MACE in the Cardioonc ( +) group compared to CVD (-). Competing risk analysis showed that all-cause mortality was significantly higher in the Cardioonc ( +) group and limited other MACE events from occurring. When the researchers identified specific cancer types, patients with colon cancer had higher MACE. CONCLUSION: In conclusion, the study found that patients with both CVD and active cancer suffered relatively worse outcomes when they had acute SARS-CoV-2 infection during early and alpha surges in the United States. These findings highlight the need for improved management strategies and further research to better understand the impact of the virus on vulnerable populations during the COVID-19 pandemic.

Coherent terahertz laser feedback interferometry for hydration sensing in leaves.

The response of terahertz to the presence of water content makes it an ideal analytical tool for hydration monitoring in agricultural applications. This study reports on the feasibility of terahertz sensing for monitoring the hydration level of freshly harvested leaves of Celtis sinensis by employing a imaging platform based on quantum cascade lasers and laser feedback interferometry. The imaging platform produces wide angle high resolution terahertz amplitude and phase images of the leaves at high frame rates allowing monitoring of dynamic water transport and other changes across the whole leaf. The complementary information in the resulting images was fed to a machine learning model aiming to predict relative water content from a single frame. The model was used to predict the change in hydration level over time. Results of the study suggest that the technique could have substantial potential in agricultural applications.

Outcomes of Patients with Active Cancers and Pre-Existing Cardiovascular Diseases Infected with SARS-CoV-2.

Objective To determine the impact of acute SARS-CoV-2 infection on patient with concomitant active cancer and CVD. Methods The researchers extracted and analyzed data from the National COVID Cohort Collaborative (N3C) database between January 1, 2020, and July 22, 2022. They included only patients with acute SARS-CoV-2 infection, defined as a positive test by PCR 21 days before and 5 days after the day of index hospitalization. Active cancers were defined as last cancer drug administered within 30 days of index admission. The "Cardioonc" group consisted of patients with CVD and active cancers. The cohort was divided into four groups: (1) CVD (-), (2) CVD (+), (3) Cardioonc (-), and (4) Cardioonc (+), where (-) or (+) denotes acute SARS-CoV-2 infection status. The primary outcome of the study was major adverse cardiovascular events (MACE), including acute stroke, acute heart failure, myocardial infarction, or all-cause mortality. The researchers analyzed the outcomes by different phases of the pandemic and performed competing-risk analysis for other MACE components and death as a competing event. Results The study analyzed 418,306 patients, of which 74%, 10%, 15.7%, and 0.3% had CVD (-), CVD (+), Cardioonc (-), and Cardioonc (+), respectively. The Cardioonc (+) group had the highest MACE events in all four phases of the pandemic. Compared to CVD (-), the Cardioonc (+) group had an odds ratio of 1.66 for MACE. However, during the Omicron era, there was a statistically significant increased risk for MACE in the Cardioonc (+) group compared to CVD (-). Competing risk analysis showed that all-cause mortality was significantly higher in the Cardioonc (+) group and limited other MACE events from occurring. When the researchers identified specific cancer types, patients with colon cancer had higher MACE. Conclusion In conclusion, the study found that patients with both CVD and active cancer suffered relatively worse outcomes when they had acute SARS-CoV-2 infection during early and alpha surges in the United States. These findings highlight the need for improved management strategies and further research to better understand the impact of the virus on vulnerable populations during the COVID-19 pandemic.

Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy.

Dust grains absorb half of the radiation emitted by stars throughout the history of the universe, re-emitting this energy at infrared wavelengths1-3. Polycyclic aromatic hydrocarbons (PAHs) are large organic molecules that trace millimetre-size dust grains and regulate the cooling of interstellar gas within galaxies4,5. Observations of PAH features in very distant galaxies have been difficult owing to the limited sensitivity and wavelength coverage of previous infrared telescopes6,7. Here we present James Webb Space Telescope observations that detect the 3.3 µm PAH feature in a galaxy observed less than 1.5 billion years after the Big Bang. The high equivalent width of the PAH feature indicates that star formation, rather than black hole accretion, dominates infrared emission throughout the galaxy. The light from PAH molecules, hot dust and large dust grains and stars are spatially distinct from one another, leading to order-of-magnitude variations in PAH equivalent width and ratio of PAH to total infrared luminosity across the galaxy. The spatial variations we observe suggest either a physical offset between PAHs and large dust grains or wide variations in the local ultraviolet radiation field. Our observations demonstrate that differences in emission from PAH molecules and large dust grains are a complex result of localized processes within early galaxies.

VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation.

Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.

A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background.

It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m2 m-2) (root mean square error (RMSE): 0.23 to 0.89 m2 m-2), including for sparse canopy (LAI less than 0.3 m2 m-2) grown on different soil types (RMSE: 0.02 to 0.25 m2 m-2). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.

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.

From Prototype to Inference: A Pipeline to Apply Deep Learning in Sorghum Panicle Detection.

Head (panicle) density is a major component in understanding crop yield, especially in crops that produce variable numbers of tillers such as sorghum and wheat. Use of panicle density both in plant breeding and in the agronomy scouting of commercial crops typically relies on manual counts observation, which is an inefficient and tedious process. Because of the easy availability of red-green-blue images, machine learning approaches have been applied to replacing manual counting. However, much of this research focuses on detection per se in limited testing conditions and does not provide a general protocol to utilize deep-learning-based counting. In this paper, we provide a comprehensive pipeline from data collection to model deployment in deep-learning-assisted panicle yield estimation for sorghum. This pipeline provides a basis from data collection and model training, to model validation and model deployment in commercial fields. Accurate model training is the foundation of the pipeline. However, in natural environments, the deployment dataset is frequently different from the training data (domain shift) causing the model to fail, so a robust model is essential to build a reliable solution. Although we demonstrate our pipeline in a sorghum field, the pipeline can be generalized to other grain species. Our pipeline provides a high-resolution head density map that can be utilized for diagnosis of agronomic variability within a field, in a pipeline built without commercial software.

Angiotensin-Converting Enzyme 2 Expression and Severity of SARS-CoV-2 Infection.

Angiotensin-converting enzyme 2 (ACE2), first discovered in 2000, serves as an important counterregulatory enzyme to the angiotensin II-mediated vasoconstrictive, pro-inflammatory, and pro-fibrotic actions of the renin-angiotensin system (RAS). Conversion of angiotensin II to the peptide angiotensin 1-7 (ANG 1-7) exerts protective vasodilatory, anti-inflammatory, and anti-fibrotic actions through interaction with the MasR receptor. There are many important considerations when noting the role of ACE2 in the pathogenesis and sequelae of COVID-19 infection. ACE2, in the role of COVID-19 infection, was recognized early in 2020 at the beginning of the pandemic as a cell membrane-bound and soluble binding site for the viral spike protein facilitating entering into tissue cells expressing ACE2, such as the lungs, heart, gut, and kidneys. Mechanisms exist that alter the magnitude of circulating and membrane-bound ACE2 (e.g., SARS-CoV-2 infection, viral variants, patient characteristics, chronic disease states, and the degree of cell surface expression of ACE2) and the influence these mechanisms have on the severity of disease and associated complications (e.g., respiratory failure, systemic inflammatory response syndrome, acute myocarditis, acute kidney injury). Several medications alter the ACE2 receptor expression, but whether these medications can influence the course of the disease and improve outcomes is unclear. In this review, we will discuss what is known about the interrelation of SARS-CoV-2, ACE2 and the factors that may contribute to the variability of its expression and potential contributors to the severity of COVID-19 infection.

Disseminated Mycobacterium chelonae infection complicating rheumatoid arthritis: A case report and review of the literature.

INTRODUCTION: Rheumatoid arthritis (RA) may predispose patients to opportunistic infections-either from innate immune dysregulation, or as a result of immunosuppressant use to treat the RA. Particularly concerning opportunistic infections are those caused by non-tuberculous mycobacterial (NTM) organisms, the incidence of which has been increasing in epidemiological studies. Despite this, guidelines on the management of patients with RA who develop NTM infections are scarce, particularly with respect to immunosuppressant regimen modulation and duration of antibiotic therapy. CASE REPORT: Herein, we present a case of disseminated Mycobacterium chelonae infection, manifesting as arthralgia and cutaneous nodules. DISCUSSION: In addition, a review of the literature was conducted to Preferred Reporting Items for Systemic Reviews and Meta-Analyses guidelines to identify similar cases in the literature-revealing that all RA-associated M. Chelonae infections occurred in immunosuppressed patients (the majority with corticosteroids or tumor necrosis factor inhibitors), and considerable heterogeneity in management approaches. Further research regarding risk factors, preventative approaches and best management of such NTM infections in vulnerable patients with RA is required in order to establish consensus guidelines and consistency.

Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting.

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. Data competitions have a rich history in plant phenotyping, and new outdoor field datasets have the potential to embrace solutions across research and commercial applications. We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions. We analyze the winning challenge solutions in terms of their robustness when applied to new datasets. We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.

Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning.

High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation, which requires accurate estimation of leaf area index (LAI). This study developed a hybrid method to train the random forest regression (RFR) models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images. The RFR models were evaluated on both (i) subsets from the synthetic datasets and (ii) observed data from two field experiments (i.e., Exp16, Exp19). Given the parameter ranges and soil reflectance are well calibrated in synthetic training data, RFR models can accurately predict LAI from canopy reflectance captured in field conditions, with systematic overestimation for LAI<2 due to background effect, which can be addressed by applying background correction on original reflectance map based on vegetation-background classification. Overall, RFR models achieved accurate LAI prediction from background-corrected reflectance for Exp16 (correlation coefficient (r) of 0.95, determination coefficient (R 2) of 0.90~0.91, root mean squared error (RMSE) of 0.36~0.40 m2 m-2, relative root mean squared error (RRMSE) of 25~28%) and less accurate for Exp19 (r =0.80~0.83, R 2 = 0.63~0.69, RMSE of 0.84~0.86 m2 m-2, RRMSE of 30~31%). Additionally, RFR models correctly captured spatiotemporal variation of observed LAI as well as identified variations for different growing stages and treatments in terms of genotypes and management practices (i.e., planting density, irrigation, and fertilization) for two experiments. The developed hybrid method allows rapid, accurate, nondestructive phenotyping of the dynamics of LAI during vegetative growth to facilitate assessments of growth rate including in breeding program assessments.

Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to introduce biological constraints in a synthetic training dataset. In addition to the comparison of two datasets without and with biological constraints, we also investigated the effects of observation geometry, retrieval method, and wavelength range on estimation accuracy of four crop traits (leaf area index, leaf chlorophyll content, leaf dry matter, and leaf water content) of wheat. The theoretical analysis demonstrated potential advantages of adding biological constraints in synthetic training datasets as well as the capability of deep learning. Additionally, the predictive models were validated on real unmanned aerial vehicle-based multispectral images collected from wheat plots contrasting in canopy structure. The predictive model trained over a synthetic dataset with biological constraints enabled the prediction of leaf water content from using wavelengths in the visible to near infrared range based on the correlations between crop traits. Our findings presented the potential of the proposed conceptual framework in simultaneously retrieving multiple crop traits from canopy reflectance for applications in precision agriculture and plant breeding.

Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum.

Sorghum, a genetically diverse C4 cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation (V cmax), phosphoenolpyruvate (PEP) carboxylation (V pmax), and electron transport (J max), quantified using a C4 photosynthesis model, were evaluated in two field-grown training sets (n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V cmax (R 2 = 0.83), V pmax (R 2 = 0.93), J max (R 2 = 0.76), SLN (R 2 = 0.82), and LMA (R 2 = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V cmax, V pmax, J max, SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 (n = 875 plots including 650 genotypes) and 2020 (n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V cmax and two QTL for J max. Candidate genes within 200 kb of the V cmax QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se. J max QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.

Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments.

Increasing grain number through fine-tuning duration of the late reproductive phase (LRP; terminal spikelet to anthesis) without altering anthesis time has been proposed as a genetic strategy to increase yield potential (YP) of wheat. Here we conducted a modelling analysis to evaluate the potential of fine-tuning LRP in raising YP in irrigated mega-environments. Using the known optimal anthesis and sowing date of current elite benchmark genotypes, we applied a gene-based phenology model for long-term simulations of phenological stages and yield-related variables of all potential germplasm with the same duration to anthesis as the benchmark genotypes. These diverse genotypes had the same duration to anthesis but varying LRP duration. Lengthening LRP increased YP and harvest index by increasing grain number to some extent and an excessively long LRP reduced YP due to reduced time for canopy construction for high biomass production of pre-anthesis phase. The current elite genotypes could have their LRP extended for higher YP in most sites. Genotypes with a ratio of the duration of LRP to pre-anthesis phase of about 0.42 ensured high yields (≥95% of YP) with their optimal sowing and anthesis dates. Optimization of intermediate growth stages could be further evaluated in breeding programmes to improve YP.

Comparison of 3-factor versus 4-factor prothrombin complex concentrate for emergent warfarin reversal: a systematic review and meta-analysis.

BACKGROUND: Patients requiring emergent warfarin reversal (EWR) have been prescribed three-factor prothrombin complex concentrate (PCC3) and four-factor prothrombin complex concentrate (PCC4) to reverse the anticoagulant effects of warfarin. There is no existing systematic review and meta-analysis of studies directly comparing PCC3 and PCC4. METHODS: The primary objective of this systematic review and meta-analysis was to determine the effectiveness of achieving study defined target INR goal after PCC3 or PCC4 administration. Secondary objectives were to determine the difference in safety endpoints, thromboembolic events (TE), and survival during the patients' hospital stay. Random-effects meta-analysis models were used to estimate the odds ratios (OR), and heterogeneity associated with the outcomes. The Newcastle-Ottawa Scale was used to assess study quality, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. RESULTS: Ten full-text manuscripts and five abstracts provided data for the primary and secondary outcomes. Patients requiring EWR had more than three times the odds of reversal to goal INR when they were given PCC4 compared to PCC3 (OR = 3.61, 95% CI: 1.97-6.60, p < 0.001). There was no meaningful clinical association or statistically significant result between PCC4 and PCC3 groups in TE (OR = 1.56, 95% CI: 0.83-2.91, p = 0.17), or survival during hospital stay (OR = 1.34, 95% CI: 0.81-2.23, p = 0.25). CONCLUSION: PCC4 is more effective than PCC3 in meeting specific predefined INR goals and has similar safety profiles in patients requiring emergent reversal of the anticoagulant effects of warfarin.

A wiring diagram to integrate physiological traits of wheat yield potential.

As crop yields are pushed closer to biophysical limits, achieving yield gains becomes increasingly challenging and will require more insight into deterministic pathways to yields. Here, we propose a wiring diagram as a platform to illustrate the interrelationships of the physiological traits that impact wheat yield potential and to serve as a decision support tool for crop scientists. The wiring diagram is based on the premise that crop yield is a function of photosynthesis (source), the investment of assimilates into reproductive organs (sinks) and the underlying processes that enable expression of both. By illustrating these linkages as coded wires, the wiring diagram can show connections among traits that may not have been apparent, and can inform new research hypotheses and guide crosses designed to accumulate beneficial traits and alleles in breeding. The wiring diagram can also serve to create an ever-richer common point of reference for refining crop models in the future.