Logical design of oral glucose ingestion pattern minimizing blood glucose in humans.

Excessive increase in blood glucose level after eating increases the risk of macroangiopathy, and a method for not increasing the postprandial blood glucose level is desired. However, a logical design method of the dietary ingestion pattern controlling the postprandial blood glucose level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in three healthy human subjects, and predicted that intermittent ingestion 30 min apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern consistently decreased the peak value of blood glucose level. We also predicted insulin minimization pattern, and found that the intermittent ingestion 30 min apart was optimal, which is similar to that of glucose minimization pattern. Taken together, these results suggest that the glucose minimization is achieved by suppressing the peak value of insulin concentration, rather than by enhancing insulin concentration. This approach could be applied to design optimal dietary ingestion patterns.

Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning.

We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only "virtually" adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets. With a simple enhancement of the algorithm based on the entropy minimization principle, our VAT achieves state-of-the-art performance for semi-supervised learning tasks on SVHN and CIFAR-10.

Non-explosivity of Stochastically Modeled Reaction Networks that are Complex Balanced.

We consider stochastically modeled reaction networks and prove that if a constant solution to the Kolmogorov forward equation decays fast enough relatively to the transition rates, then the model is non-explosive. In particular, complex-balanced reaction networks are non-explosive.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture.

Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in "big data" analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.

Model-based control of the temporal patterns of intracellular signaling in silico.

The functions of intracellular signal transduction systems are determined by the temporal behavior of intracellular molecules and their interactions. Of the many dynamical properties of the system, the relationship between the dynamics of upstream molecules and downstream molecules is particularly important. A useful tool in understanding this relationship is a methodology to control the dynamics of intracellular molecules with an extracellular stimulus. However, this is a difficult task because the relationship between the levels of upstream molecules and those of downstream molecules is often not only stochastic, but also time-inhomogeneous, nonlinear, and not one-to-one. In this paper, we present an easy-to-implement model-based control method that makes the target downstream molecule to trace a desired time course by changing the concentration of a controllable upstream molecule. Our method uses predictions from Monte Carlo simulations of the model to decide the strength of the stimulus, while using a particle-based approach to make inferences regarding unobservable states. We applied our method to in silico control problems of insulin-dependent AKT pathway model and EGF-dependent Akt pathway model with system noise. We show that our method can robustly control the dynamics of the intracellular molecules against unknown system noise of various strengths, even in the absence of complete knowledge of the true model of the target system.

Efficient Monte Carlo image analysis for the location of vascular entity.

Tubular shaped networks appear not only in medical images like X-ray-, time-of-flight MRI- or CT-angiograms but also in microscopic images of neuronal networks. We present EMILOVE (Efficient Monte-carlo Image-analysis for the Location Of Vascular Entity), a novel modeling algorithm for tubular networks in biomedical images. The model is constructed using tablet shaped particles and edges connecting them. The particles encode the intrinsic information of tubular structure, including position, scale and orientation. The edges connecting the particles determine the topology of the networks. For simulated data, EMILOVE was able to accurately extract the tubular network. EMILOVE showed high performance in real data as well; it successfully modeled vascular networks in real cerebral X-ray and time-of-flight MRI angiograms. We also show some promising, preliminary results on microscopic images of neurons.

A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging.

Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron-glia network. We attempted to identify neuron-glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron-glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron-glia systems.

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data.

BACKGROUND: Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. RESULTS: We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. CONCLUSIONS: It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance.