A Generalized Encoding System for Alpha Oscillations Through Visual Saliency Analysis.

By learning how the brain reacts to external visual stimuli and examining possible triggered brain statuses, we conduct a systematic study on an encoding problem that estimates ongoing EEG dynamics from visual information. A novel generalized system is proposed to encode the alpha oscillations modulated during video viewing by employing the visual saliency involved in the presented natural video stimuli. Focusing on the parietal and occipital lobes, the encoding effects at different alpha frequency bins and brain locations are examined by a real-valued genetic algorithm (GA), and possible links between alpha features and saliency patterns are constructed.

UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images.

Recently, there has been rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from stacks of two-dimensional (2D) electron microscopy (EM) images. The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for CNN-based segmentation.

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.

An unsupervised EEG decoding system for human emotion recognition.

Emotion plays a vital role in human health and many aspects of life, including relationships, behaviors and decision-making. An intelligent emotion recognition system may provide a flexible method to monitor emotion changes in daily life and send warning information when unusual/unhealthy emotional states occur. Here, we proposed a novel unsupervised learning-based emotion recognition system in an attempt to decode emotional states from electroencephalography (EEG) signals.

Characterization of electroencephalography signals for estimating saliency features in videos.

Understanding the functions of the visual system has been one of the major targets in neuroscience for many years. However, the relation between spontaneous brain activities and visual saliency in natural stimuli has yet to be elucidated. In this study, we developed an optimized machine learning-based decoding model to explore the possible relationships between the electroencephalography (EEG) characteristics and visual saliency.

Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone.

Biological cells express intracellular biomolecular information to the extracellular environment as various physical responses. We show a novel computational approach to estimate intracellular biomolecular pathways from growth cone electrophysiological responses. Previously, it was shown that cGMP signaling regulates membrane potential (MP) shifts that control the growth cone turning direction during neuronal development.

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

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.

Constructing a meta-tracker using Dropout to imitate the behavior of an arbitrary black-box tracker.

Imitating the behaviors of an arbitrary visual tracking algorithm enables many higher level tasks such as tracker identification and efficient tracker-fusion. It is also useful for discovering the features essential in a black-box tracker or learning from several trackers to form a super-tracker. In this study, we propose a non-linear feature fusion framework, "MIMIC" that imitates many popular trackers by mixing a pool of heterogeneous features.

Numerical indices based on circulating tumor DNA for the evaluation of therapeutic response and disease progression in lung cancer patients.

Monitoring of disease/therapeutic conditions is an important application of circulating tumor DNA (ctDNA). We devised numerical indices, based on ctDNA dynamics, for therapeutic response and disease progression. 52 lung cancer patients subjected to the EGFR-TKI treatment were prospectively collected, and ctDNA levels represented by the activating and T790M mutations were measured using deep sequencing.

Empirical Bayesian significance measure of neuronal spike response.

Background: Functional connectivity analyses of multiple neurons provide a powerful bottom-up approach to reveal functions of local neuronal circuits by using simultaneous recording of neuronal activity. A statistical methodology, generalized linear modeling (GLM) of the spike response function, is one of the most promising methodologies to reduce false link discoveries arising from pseudo-correlation based on common inputs. Although recent advancement of fluorescent imaging techniques has increased the number of simultaneously recoded neurons up to the hundreds or thousands, the amount of information per pair of neurons has not correspondingly increased, partly because of the instruments' limitations, and partly because the number of neuron pairs increase in a quadratic manner.

Monitoring of treatment responses and clonal evolution of tumor cells by circulating tumor DNA of heterogeneous mutant EGFR genes in lung cancer.

Objectives: Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have dramatic effects on EGFR-mutant non-small-cell lung cancer (NSCLC). However, most patients experience disease recurrences, approximately half of which are T790M-mediated. Monitoring EGFR status with re-biopsy has spatiotemporal limitations.

Dynamics of circulating tumor DNA represented by the activating and resistant mutations in epidermal growth factor receptor tyrosine kinase inhibitor treatment.

Circulating tumor DNA (ctDNA) is an emerging field of cancer research. For lung cancer, non-invasive genotyping of EGFR is the foremost application. The activating mutations represent the ctDNA from all cancer cells, and the T790M-resistant mutation represents that from resistant cells.

Subtle modulation of ongoing calcium dynamics in astrocytic microdomains by sensory inputs.

Astrocytes communicate with neurons through their processes. In vitro experiments have demonstrated that astrocytic processes exhibit calcium activity both spontaneously and in response to external stimuli; however, it has not been fully determined whether and how astrocytic subcellular domains respond to sensory input in vivo. We visualized the calcium signals in astrocytes in the primary visual cortex of awake, head-fixed mice.

Diagnostic Accuracy of Noninvasive Genotyping of EGFR in Lung Cancer Patients by Deep Sequencing of Plasma Cell-Free DNA.

Background: Genotyping of EGFR (epidermal growth factor receptor) mutations is indispensable for making therapeutic decisions regarding whether to use EGFR tyrosine kinase inhibitors (TKIs) for lung cancer. Because some cases might pose challenges for biopsy, noninvasive genotyping of EGFR in circulating tumor DNA (ctDNA) would be beneficial for lung cancer treatment.

Methods: We developed a detection system for EGFR mutations in ctDNA by use of deep sequencing of plasma DNA.

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.

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.

Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information.

For practical brain-machine interfaces (BMIs), electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are the only current methods that are non-invasive and available in non-laboratory environments. However, the use of EEG and NIRS involves certain inherent problems. EEG signals are generally a mixture of neural activity from broad areas, some of which may not be related to the task targeted by BMI, hence impairing BMI performance.

Quantitative identification of mutant alleles derived from lung cancer in plasma cell-free DNA via anomaly detection using deep sequencing data.

The detection of rare mutants using next generation sequencing has considerable potential for diagnostic applications. Detecting circulating tumor DNA is the foremost application of this approach. The major obstacle to its use is the high read error rate of next-generation sequencers.

High-speed multineuron calcium imaging using Nipkow-type confocal microscopy.

Conventional confocal and two-photon microscopy scan the field of view sequentially with single-point laser illumination. This raster-scanning method constrains video speeds to tens of frames per second, which are too slow to capture the temporal patterns of fast electrical events initiated by neurons. Nipkow-type spinning-disk confocal microscopy resolves this problem by the use of multiple laser beams.

The period of the somite segmentation clock is sensitive to Notch activity.

The number of vertebrae is defined strictly for a given species and depends on the number of somites, which are the earliest metameric structures that form in development. Somites are formed by sequential segmentation. The periodicity of somite segmentation is orchestrated by the synchronous oscillation of gene expression in the presomitic mesoderm (PSM), termed the "somite segmentation clock," in which Notch signaling plays a crucial role.

Oncogenic LMO3 collaborates with HEN2 to enhance neuroblastoma cell growth through transactivation of Mash1.

Expression of Mash1 is dysregulated in human neuroblastoma. We have also reported that LMO3 (LIM-only protein 3) has an oncogenic potential in collaboration with neuronal transcription factor HEN2 in neuroblastoma. However, the precise molecular mechanisms of its transcriptional regulation remain elusive.

Robust model selection for classification of microarrays.

Recently, microarray-based cancer diagnosis systems have been increasingly investigated. However, cost reduction and reliability assurance of such diagnosis systems are still remaining problems in real clinical scenes. To reduce the cost, we need a supervised classifier involving the smallest number of genes, as long as the classifier is sufficiently reliable.

Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles.

Multiclass classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. There have been many studies of aggregating binary classifiers to construct a multiclass classifier based on one-versus-the-rest (1R), one-versus-one (11), or other coding strategies, as well as some comparison studies between them. However, the studies found that the best coding depends on each situation.

High expression of ncRAN, a novel non-coding RNA mapped to chromosome 17q25.1, is associated with poor prognosis in neuroblastoma.

Neuroblastoma shows complex patterns of genetic aberrations including MYCN amplification, deletion of chromosome 1p or 11q, and gain of chromosome 17q. The 17q gain is frequently observed in high-risk neuroblastomas, however, the candidate genes still remain elusive. In the present study, we integrated the data of comparative genomic hybridization of 236 tumors by BAC array and expression profiling of 136 tumors by using the in-house cDNA microarray carrying 5,340 genes derived from primary neuroblastomas.