Heart failure is a complex clinical syndrome characterized by insufficient cardiac function. In addition to abnormalities intrinsic to the heart, dysfunction of other organs and dysregulation of systemic factors greatly affect the development and consequences of heart failure. Here we show that the heart and kidneys function cooperatively in generating an adaptive response to cardiac pressure overload. In mice subjected to pressure overload in the heart, sympathetic nerve activation led to activation of renal collecting-duct (CD) epithelial cells. Cell-cell interactions among activated CD cells, tissue macrophages and endothelial cells within the kidney led to secretion of the cytokine CSF2, which in turn stimulated cardiac-resident Ly6C macrophages, which are essential for the myocardial adaptive response to pressure overload. The renal response to cardiac pressure overload was disrupted by renal sympathetic denervation, adrenergic β2-receptor blockade or CD-cell-specific deficiency of the transcription factor KLF5. Moreover, we identified amphiregulin as an essential cardioprotective mediator produced by cardiac Ly6C macrophages. Our results demonstrate a dynamic interplay between the heart, brain and kidneys that is necessary for adaptation to cardiac stress, and they highlight the homeostatic functions of tissue macrophages and the sympathetic nervous system.

25Progress in remote sensing and robotic technologies decreases the hardware costs of 26 phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, 27 and present a trade-off between investment and manpower costs. We then discuss the structure 28 of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and 29 infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) 30 major costs arise from plant handling and manpower; (ii) the total costs per pot/microplot are 31 similar in robotized platform or field experiments with drones, hand-held or robotized ground 32 vehicles; (iii) the cost of vehicles carrying sensors represents only 5-26% of the total costs. These 33 conclusions depend on the context, in particular for labor cost, the quantitative demand of 34 phenotyping and the number of days available for phenotypic measurements due to climatic 35 constraints. Data analysis represents 10-20% of total cost if pipelines have already been 36 developed. A trade-off exists between the initial high cost of pipeline development and labor cost 37 of manual operations. Overall, depending on the context and objectives, "cost-effective" 38 phenotyping may involve either low investment ("affordable phenotyping"), or initial high 39 investments in sensors, vehicles and pipelines that result in higher quality and lower operational 40 costs. 41 Highlights 42 -New technologies considerably reduce the costs of sensors and automated vehicles 43 -Low investment in sensors, vehicles or pipelines present trade-offs with labor costs 44 -Plant/plot handling and labor costs represent the major proportion of costs in phenotyping 45 experiments 46 -The costs of high-throughput experiments in the field and in automated platforms is similar 47 regardless of vehicles 48 -The development of software applications (e.g. imaging, phenotypic analyses, models, 49 information system) is a major part of costs 50 51 52 54 I Imaging techniques with a range of hardware costs 55 1.1 Handheld phenotyping technologies 56 1.2 Aerial imaging for large-scale phenotyping 57 1.3 Imaging with ground vehicles 58 1.4 Environmental measurements 59 II Costs associated with image capture represent a fraction of the overall cost of phenotyping 60 2.1 A method for calculating costs in field and greenhouse platforms 61 2.2 A high cost for plant management 62 2.3 Investing in appropriate environmental characterization results in comparatively low cost 63 for a high return 64 2.4 Imaging costs: a trade-off between investment and labor costs 65 2.4.1 The choice of vehicle mostly depends on the demand for microplots per year 66 2.4.2 The cost of imaging devices is similar to that of vehicles that carry sensors 67 2.5 Costs of typical experiments 68 2.5.1 Image analysis: a tradeoff between investment in automated workflows and day-to-day 69 labor costs 70 2.5.2 High costs for data analysis for the identification of traits 71 2.5.3 Costs associated with data storag...

Sorghum (Sorghum bicolor L. Moench) is a C4 tropical grass that plays an essential role in providing nutrition to humans and livestock, particularly in marginal rainfall environments. The timing of head development and the number of heads per unit area are key adaptation traits to consider in agronomy and breeding but are time consuming and labor intensive to measure. We propose a two-step machine-based image processing method to detect and count the number of heads from high-resolution images captured by unmanned aerial vehicles (UAVs) in a breeding trial. To demonstrate the performance of the proposed method, 52 images were manually labeled; the precision and recall of head detection were 0.87 and 0.98, respectively, and the coefficient of determination (R2) between the manual and new methods of counting was 0.84. To verify the utility of the method in breeding programs, a geolocation-based plot segmentation method was applied to pre-processed ortho-mosaic images to extract >1000 plots from original RGB images. Forty of these plots were randomly selected and labeled manually; the precision and recall of detection were 0.82 and 0.98, respectively, and the coefficient of determination between manual and algorithm counting was 0.56, with the major source of error being related to the morphology of plants resulting in heads being displayed both within and outside the plot in which the plants were sown, i.e., being allocated to a neighboring plot. Finally, the potential applications in yield estimation from UAV-based imagery from agronomy experiments and scouting of production fields are also discussed.

Background Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. Methods and findings A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d−1 (95% CI: 1.06 to 1.27 d−1), 0.777 d−1 (0.716 to 0.838 d−1), and 0.450 d−1 (0.378 to 0.522 d−1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. Conclusions In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.