The protozoan parasite Leishmania (Viannia) braziliensis (L. braziliensis) is the main cause of human tegumentary leishmaniasis in the New World, a disease affecting the skin and/or mucosal tissues. Despite its importance, the study of the unique biology of L. braziliensis through reverse genetics analyses has so far lagged behind in comparison with Old World Leishmania spp. In this study, we successfully applied a cloning-free, PCR-based CRISPR–Cas9 technology in L. braziliensis that was previously developed for Old World Leishmania major and New World L. mexicana species. As proof of principle, we demonstrate the targeted replacement of a transgene (eGFP) and two L. braziliensis single-copy genes (HSP23 and HSP100). We obtained homozygous Cas9-free HSP23-and HSP100-null mutants in L. braziliensis that matched the phenotypes reported previously for the respective L. donovani null mutants. The function of HSP23 is indeed conserved throughout the Trypanosomatida as L. major HSP23 null mutants could be complemented phenotypically with transgenes from a range of trypanosomatids. In summary, the feasibility of genetic manipulation of L. braziliensis by CRISPR–Cas9-mediated gene editing sets the stage for testing the role of specific genes in that parasite’s biology, including functional studies of virulence factors in relevant animal models to reveal novel therapeutic targets to combat American tegumentary leishmaniasis. / Alexander von Humboldt-Stiftung / Revisión por pares

<div>Drought is the leading cause of crop loss globally. Breeding for drought tolerance is difficult due to the polygenetic nature of the trait and low heritability of yield under drought. Plant relative water content is a secondary trait that may advance drought breeding programs.</div><div><br></div><div>The LeafSpec, a newly developed hyperspectral leaf scanner, was used to test the hypothesis that distribution of hyperspectral information across the leaf can be used to improve prediction of leaf relative water content. Data was collected across two experiments from five different maize genotypes representing temperate and tropical hybrids with varying levels of drought tolerance and inbreds with varying stomatal densities. The hyperspectral intensity averaged across the entire leaf was used to predict relative water content with an R2Prediction of 0.7989. Model performance was tested using additional predictors that quantify:</div><div><br></div><div>• Spectral information from multiple regions in the leaf (e.g. base, middle, tip).</div><div>• Spectral information from regions segmented by tissue type.</div><div>• The distribution of hyperspectral intensity in a cross section parallel to the midrib or in a cross-section perpendicular to the midrib.</div><div>• A contour pattern of hyperspectral intensity from the outside edge of the leaf to the midrib.</div><div>• Texture features extracted from each wavelength.</div><div><br></div><div>The mean spectrum model outperformed previously reported results, potentially due to the elimination of sources of noise and higher quality data produced by the LeafSpec. None of the models with expanded feature sets outperformed the mean spectrum model at a statistically significant level. The hyperspectral signal from the green tissue a third of the way from the base of the leaf and half way between the midrib and edge was the most correlated with relative water content. Models without midrib and vein tissue signals had increased performance. Distribution of the Water Index visually showed improved ability to discriminate leaf RWC as compared to individual wavelengths but this did not translate to improved model performance.</div><div><br></div><div>For future work, more data should be collected to improve model robustness and hyperspectral imaging should include SWIR wavelengths that have previously been found useful for predicting relative water content. Exploring indices composed from current spectral bands may lead to improved prediction performance.</div>

<p>Thermal and multispectral imagery can provide users with insights into the water stress status and evapotranspiration demand of crops. However, traditional platforms, such as satellites, for these thermal and multispectral sensors are limited in their usefulness due to low spatial and temporal resolution. Small unmanned aircraft system (UAS) have the potential to have similar sensors installed and provide canopy temperature and reflectance information at spatial and temporal resolutions more useful for crop management; however, most of the existing research on the calibration or the estimation of water status were established based on the satellite platforms either for the sensors calibration or water status quantification. There is, therefore, a need to develop methods specifically for UAS-mounted sensors. In this research, a pixel-based calibration and an atmospheric correction method based on in-field approximate blackbody sources were developed for an uncooled thermal camera, and the higher accurate vegetative temperature acquired after calibration was used as inputs to an algorithm developed for high-resolution thermal imagery for calculating crop latent heat flux. At last, a thermal index based on the Bowen ratio is proposed to quantify the water deficit stress in a crop field, along with this, a method for plot-level analysis of various vegetation and thermal indices have been demonstrated to illustrate its broad application to genetic selection. The objective was to develop a workflow to use high-resolution thermal and multispectral imagery to derive indices that can quantify crops water status on a plot level which will facilitate the research related to breeding selection.</p> <p>The camera calibration method can effectively reduce the root mean square error (RMSE) and variability of measurements. The pixel-based thermal calibration method presented here was able to reduce the measurement uncertainty across all the pixels in the images, thus improving the accuracy and reducing the between-pixel variability of the measurements. During field calibration, the RMSE values relative to ground reference targets for two flights in 2017 were reduced from 6.36°C to 1.24°C and from 4.56°C to 1.32°C, respectively. The latent heat flux estimation algorithm yields an RMSE of 65.23 W/m² compared with the ground reference data acquired from porometer. The Bowen ratio has a high correlation with drought conditions quantified using the soil moisture index, stomatal conductance, and crop water stress index (CWSI), which indicates the potential of this index to be used as a water deficit stress indicator. The thermal and multispectral indices on a plot level displayed will facilitate the breeding selection.</p>

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We present a novel platform for testing the effects of interventions on the life- and healthspan of a short-lived freshwater organism with complex behavior and physiology-the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioral features of both routine motion and response to stimuli are continuously quantified over large synchronized cohorts via an automated phenotyping pipeline. We build predictive machine-learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using a long-term dose-response profile of metformin and a short-term assay of well-studied substances such as caffeine and alcohol.

Chronic low back pain (CLBP) is a nonspecific and persistent ailment that entails many physiological, psychological, social, and economic consequences for individuals and societies. Although there is a plethora of treatments available to treat CLBP, each treatment has varying efficacy for different patients, and it is currently unknown how to best link patients to their ideal treatment. However, it is known that biopsychosocial influences associated with CLBP affect the way that we move. It has been hypothesized that identifying phenotypes of spinal motion could facilitate an objective and repeatable method of determining the optimal treatment for each patient. The objective of this research was to develop an array of high deflection strain gauges to monitor spinal motion, and use that information to identify spinal-motion phenotypes. The high deflection strain gauges used in this endeavor exhibit highly nonlinear electrical signal due to their viscoelastic material properties. Two sub-models were developed to account for these nonlinearities: the first characterizes the relationship between quasistatic strain and resistance, and the second accounts for transient electrical phenomena due to the viscoelastic response to dynamic loads. These sub-models are superimposed to predict and interpret the electrical signal under a wide range of applications. The combined model accurately predicts sensor strain with a mean absolute error (MAE) of 1.4% strain and strain rate with an MAE of 0.036 mm/s. Additionally, a multilayered architecture was developed for the strain gauges to provide mechanical support during high strain, cyclic loads. The architecture significantly mitigates sensor creep and viscoplastic deformation, thereby reducing electrical signal drift by 74%. This research also evaluates the effects of CLBP on patient-reported outcomes. An exploratory factor analysis revealed that there are five primary components of well-being: Pain and Physical Limitations, Psychological Distress, Physical Activity, Sleep Deprivation, and Pain Catastrophizing. The presence of CLBP has adverse effects on all these components. It was also observed that different patient reported outcomes are highly correlated with each other, and the presence of CLBP is a significant moderating factor in many of these relationships. Arrays of high-deflection strain gauges were used to collect spinal kinematic data from 274 subjects. Seven phenotypes of spinal motion were identified among study participants. Statistical analyses revealed significant differences in the patient-reported outcomes of subjects who exhibited different phenotypes. This is a promising indication that the phenotypes may also provide important information to clinicians who treat patients suffering from CLBP. Future research will be conducted to develop and identify the optimal treatments for patients according to their phenotypes, which has the potential to reduce medical costs, expedite recovery, and improve the lives of millions of patients worldwide.

<p>High-throughput phenotyping is a recent multidisciplinary research field that investigates the accurate acquisition and analysis of multidimensional phenotypes on large and diverse populations of plants. High-throughput phenotyping is at the crossroad between plant biology and computer vision, and profits from advances in plant modeling, plant reconstruction, and plant structure understanding. So far, most of the data analysis is done on 2D images, yet plants are inherently 3D shapes, and measurements made in 2D can be biased. For example, leaf angles change when they are reprojected in 2D images. Although some research works investigate the 3D reconstruction of plants, high-throughput phenotyping is still limited in its ability to automatically measure a large population of plants in 3D. In fact, plants are difficult to 3D reconstruct because they look self-similar, feature highly irregular geometries, and self-occlusion. </p> <p><br></p> <p>In this dissertation, we investigate the research question \textit{whether we can design and validate high-throughput phenotyping algorithms that take advantage of the 3D nature of the plants to outperform existing algorithms based on 2D images?} We present four contributions that address this question. First, we show a voxel 3D reconstruction pipeline and measure phenotypic traits related to canopy architecture over a population of 351 sorghum plants. Second, we show a machine learning-based skeletonization and segmentation algorithm for sorghum plants, which automatically learns from a set of 100 manually annotated plants. Third, we estimate individual leaf angles over a population of 1,098 sorghum plants. Finally, we present a sparse 3D reconstruction algorithm that can triangulate thousands of points of interest from up to 15 views without correspondences, even in the presence of noise and occlusion. We show that our approach outperforms single-view methods by using multiple views for sorghum leaf counting.</p> <p><br></p> <p>Progress made towards improving high-throughput phenotyping has the potential to benefit society with a better adaptation of crops to climate change, which will limit food insecurity in the world.</p>

While lumbar kinematics can be measured in vivo, most measurements are invasive (such as percutaneous bone pins), provide high doses of radiation (such as biplane fluoroscopy), or are taken with the patient in a static position (such as MRIs). Recent work suggests that lumbar kinematics can be determined by dynamic measurements of epidermal strain in the lower back. This work aims to develop and examine a method of examining lumbar kinematics via optoelectronic motion capture utilizing skin-mounted markers in the lumbar region. Two studies were performed. One study examined lumbar epidermal strain in 28 asymptomatic subjects during diagnostic movements, while the second study used fresh/frozen cadavers to compare segmental lumbar kinematics as measured by both an optoelectronic motion capture system utilizing skin-mounted markers and an electromagnetic motion capture system utilizing sensors mounted to percutaneous bone pins inserted into the spinous processes. In the first study, participants had a grid-like marker set adhered to their lower back. They were instructed to perform 17 diagnostic movements, with data from three trials of each exercise being captured. Data was analyzed in MATLAB to examine segmental lumbar kinematics. Analysis shows trends consistent with expected movement patterns for asymptomatic individuals with measurement values consistent with those found in previous studies. Trends of symmetry in motion for left versus right motions were observed, as well as a trend for the return motion in a movement to be faster than the outgoing motion. In the second study, three fresh/frozen cadavers were outfitted with electromagnetic motion tracking sensors mounted to bone pins which were placed in the spinous processes of L1-S2. Each cadaver also had a similar grid-like marker set of optical motion tracking markers adhered in the lumbar region. The cadavers were moved through 10 of the same diagnostic exercises with data from seven trials from each exercise being captured. Data was analyzed in MATLAB to compare the end range of motion as measured by the optical system to the same measurements by the electromagnetic system. End ROM values showed statistically different measurements for five of the twelve segment measurements compared. Data collected in this work contributes to the establishment of normative dynamic kinematics of the lumbar spine in the asymptomatic population. It also outlines the strengths and weaknesses of the methodology utilized.

<p dir="ltr">The increasing global need for food security and sustainable agriculture underscores the urgency of advancing field phenotyping for enhanced plant breeding and crop management. Soybean, a major global protein source, is at the forefront of these advancements. Proximal sensing in soybean phenotyping offers a higher signal-to-noise ratio and resolution but has been underutilized in large-scale field applications due to low throughput and high labor costs. Moreover, there is an absence of automated solutions for in vivo proximal phenotyping of dicot plants. This thesis addresses these gaps by introducing a comprehensive, technologically sophisticated approach to modern field phenotyping.</p><p dir="ltr">Fully Automated Proximal Hyperspectral Imaging System: The first chapter presents the development of a cutting-edge hyperspectral imaging system integrated with a robotic arm. This system surpasses traditional imaging limitations, providing enhanced close-range data for accurate plant health assessment.</p><p dir="ltr">Robust Leaf Pose Estimation: The second chapter discusses the application of deep learning for accurate leaf pose estimation. This advancement is crucial for in-depth plant analysis, fostering better insights into plant health and growth, thereby contributing to increased crop yield and disease resistance.</p><p dir="ltr">PhenoBee – A Drone Mobility Platform: The third chapter introduces 'PhenoBee,' a dronebased platform designed for extensive field phenotyping. This innovative technology significantly broadens the capabilities of field data collection, showcasing its viability for widespread aerial phenotyping.</p><p dir="ltr">Adaptive Sampling for Dynamic Waypoint Planning: The final chapter details an adaptive sampling algorithm for efficient, real-time waypoint planning. This strategic approach enhances field scouting efficiency and precision, ensuring optimal data acquisition.</p><p dir="ltr">By integrating deep learning, robotic automation, aerial mobility, and intelligent sampling algorithms, the proposed solution revolutionizes the adaptation of in vivo proximal phenotyping on a large scale. The findings of this study highlight the potential to automate agriculture activities with high scalability and identify nutrient deficiencies, diseases, and chemical damage in crops earlier, thereby preventing yield loss, improving food quality, and expediting the development of agricultural products. Collectively, these advancements pave the way for more effective and efficient plant breeding and crop management, directly contributing to the enhancement of global food production systems. This study not only addresses current limitations in field phenotyping but also sets a new standard for technological innovation in agriculture.</p>