Management-Oriented Modeling: Optimizing Nitrogen Management using Computerized Artificial Intelligence

Increasing nitrate levels in groundwater have caused growing public health concern in recent years. This has prompted research on precision nitrogen management to understand and control nitrogen impact on the environment. Many nitrogen (N) models have been developed to describe the N status and behavior in soil-plant systems, but they are uniformly weak in finding optimal management strategies. To model nitrogen management, Management-Oriented Modeling (MOM), a dynamic simulation model using artificial intelligence (AI) optimization techniques, was developed in this study. MOM was designed as a tool to find optimal solutions for N management to minimize nitrate leaching and maximize production and profits. MOM consists of a generator, a simulator, and an evaluator. In searching for optimal management strategies, the generator produces a group of nodes (management choices). The evaluator uses the built-in knowledge and communication with users to analyze the outputs of the simulator and to guide the generator’s work. A mixed search method that combines hill-climbing method for a global, strategic search with best-first method for a local, tactical search was developed to find the shortest path from start nodes to goals. In this manner, MOM searches for user-weighted goals by simulating the N cycle and management effects on the fate of N in a soil-plant system. In addition to general simulation and evaluation of N fertilization, MOM provides real time decision-aid for within-season management. MOM-guided within-season management uses weather forecasting to estimate rainfall in the near future and simulates the consequences in soil-plant systems. It gives users daily “snapshots” of the N status in soil-plant systems without within-season sampling and testing. Scenarios show that MOM can provide precision nitrogen management that maximizes profits and yields while minimizing nitrate leaching by updating management of irrigation and fertilization within-season. MOM-guided within-season management is a precision tool with high efficiency, low cost and “transparency” for nitrogen management. MOM simulator was evaluated with 11 datasets from Hawaii and Brazil. Calibration and validation results suggest that the model prediction accuracy was acceptable for the field N management

Identification of an Operant Learning Circuit by Whole Brain Functional Imaging in Larval Zebrafish

When confronted with changing environments, animals can generally adjust their behavior to optimize reward and minimize punishment. The process of modifying one's behavior based on its consequences is referred to as operant or instrumental learning. Operant learning makes specific demands on the animal. The animal must exhibit some flexibility in its behavior, switching from unsuccessful motor responses to potentially successful ones. The animal must represent the consequence of its actions. Finally, the animal must select the correct response based on its past history of reinforcement. Studies in mammalian systems have found competing and complementary circuits in the cortex and striatum that mediate different aspects of this learning process. The larval zebrafish is an ideal system to extend the study of operant learning due to its genetic and optical properties. We have developed a behavioral paradigm and imaging system that have allowed us to comprehensively image neural activity throughout the zebrafish brain during the process of operant conditioning. Our analysis of the neural network activity underlying this learning process reveals several classes of neurons whose activity correlates with learning and decision making. The distribution of these learning-related neurons is highly localized to regions of the habenula and forebrain. We describe, in particular, a lateralized habenula circuit that may encode prediction and relief prediction error

Auxin response factor 6A regulates photosynthesis, sugar accumulation, and fruit development in tomato.

Auxin response factors (ARFs) are involved in auxin-mediated transcriptional regulation in plants. In this study, we performed functional characterization of SlARF6A in tomato. SlARF6A is located in the nucleus and exhibits transcriptional activator activity. Overexpression of SlARF6A increased chlorophyll contents in the fruits and leaves of tomato plants, whereas downregulation of SlARF6A decreased chlorophyll contents compared with those of wild-type (WT) plants. Analysis of chloroplasts using transmission electron microscopy indicated increased sizes of chloroplasts in SlARF6A-overexpressing plants and decreased numbers of chloroplasts in SlARF6A-downregulated plants. Overexpression of SlARF6A increased the photosynthesis rate and accumulation of starch and soluble sugars, whereas knockdown of SlARF6A resulted in opposite phenotypes in tomato leaves and fruits. RNA-sequence analysis showed that regulation of SlARF6A expression altered the expression of genes involved in chlorophyll metabolism, photosynthesis and sugar metabolism. SlARF6A directly bound to the promoters of SlGLK1, CAB, and RbcS genes and positively regulated the expression of these genes. Overexpression of SlARF6A also inhibited fruit ripening and ethylene production, whereas downregulation of SlARF6A increased fruit ripening and ethylene production. SlARF6A directly bound to the SAMS1 promoter and negatively regulated SAMS1 expression. Taken together, these results expand our understanding of ARFs with regard to photosynthesis, sugar accumulation and fruit development and provide a potential target for genetic engineering to improve fruit nutrition in horticulture crops

Cardiac Substrate Utilization and Relationship to Invasive Exercise Hemodynamic Parameters in HFpEF

We conducted transcardiac blood sampling in healthy subjects and subjects with heart failure with preserved ejection fraction (HFpEF) to compare cardiac metabolite and lipid substrate use. We demonstrate that fatty acids are less used by HFpEF hearts and that lipid extraction is influenced by hemodynamic factors including pulmonary pressures and cardiac index. The release of many products of protein catabolism is apparent in HFpEF compared to healthy myocardium. In subgroup analyses, differences in energy substrate use between female and male hearts were identified

The Tangential Nucleus Controls a Gravito-inertial Vestibulo-ocular Reflex

Whilst adult vertebrates sense changes in head position using two classes of accelerometer, at larval stages zebrafish lack functional semicircular canals and rely exclusively on their otolithic organs to transduce vestibular information. Despite this limitation, they perform an effective vestibulo-ocular reflex (VOR) that serves to stabilize gaze in response to pitch and roll tilts. Using single-cell electroporations and targeted laser-ablations, we identified a specific class of central vestibular neurons, located in the tangential nucleus, which are essential for the utricle-dependent VOR. Tangential nucleus neurons project contralaterally to extraocular motoneurons, and in addition, to multiple sites within the reticulospinal complex. We propose that tangential neurons function as a broadband inertial accelerometer, processing utricular acceleration signals to control the activity of extraocular and postural neurons, thus completing a fundamental three-neuron circuit responsible for gaze stabilization.Molecular and Cellular Biolog

Integration of Multi-Modal Data to Guide Classification in Studies of Complex Diseases

Having entered the big data era, the unprecedentedly fast-growing volume and variety of biological data have swiftly transformed the landscape of biomedical research. Meanwhile, classification methods as a powerful bioinformatics tool have greatly empowered researchers to uncover new aspects of the complex biological systems. This thesis addresses the statistical and methodological challenges that often exist in different stages of biomedical multi-modal data integration with a focus into the application of classification methods in studies of complex diseases. Data generated from mass spectrometry (MS) platforms are inherently susceptible to systematic biases. Widespread missing values, where certain compounds cannot be identified or quantified, pose a prominent challenge to MS data normalisation. We propose a novel normalisation approach for high-dimensional MS data, called ruvms. This novel method is a one-step procedure that is able to handle missing values in input data and does not require imputation. We also explore a challenging situation in multi-modal data integration where not all types of data of interest are available within the same cohort. In brain studies, brain tissue samples are generally inaccessible from the same brain for which fMRI data can be obtained. We propose a gene-expression-guided fMRI network classification method that distinguishes patients of neurological diseases from the healthy control, called brainClass. brainClass links functional connectivity features to potentially involved biological pathways, to bridge the gap between functional biomarkers of neurological disorders and their underpinning molecular mechanisms. We also introduce a post-hoc interpretation framework to provide gene-expression-guided biological interpretations for predictive functional connectivity features identified by existing generic network classifiers applied to fMRI data