Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer.

Background: Multiple first-line treatment options have been developed for advanced non-small cell lung cancer (NSCLC) in each subgroup determined by predictive biomarkers, specifically driver oncogene and programmed cell death ligand-1 (PD-L1) status. However, the methodology for optimal treatment selection in individual patients is not established. This study aimed to develop artificial intelligence (AI)-based personalized survival prediction model according to treatment selection.

Deep learning for quantifying spatial patterning and formation process of early differentiated human-induced pluripotent stem cells with micropattern images.

Micropatterning is reliable method for quantifying pluripotency of human-induced pluripotent stem cells (hiPSCs) that differentiate to form a spatial pattern of sorted, ordered and nonoverlapped three germ layers on the micropattern. In this study, we propose a deep learning method to quantify spatial patterning of the germ layers in the early differentiation stage of hiPSCs using micropattern images. We propose decoding and encoding U-net structures learning labelled Hoechst (DNA-stained) hiPSC regions with corresponding Hoechst and bright-field micropattern images to segment hiPSCs on Hoechst or bright-field images.

Development of a visible to 1600 nm hyperspectral imaging rigid-scope system using supercontinuum light and an acousto-optic tunable filter.

In this study, we developed a rigid-scope system that can perform hyperspectral imaging (HSI) between visible and 1600 nm wavelengths using a supercontinuum light source and an acousto-optic tunable filter to emit specific wavelengths. The system optical performance was verified, and the classification ability was investigated. Consequently, it was demonstrated that HSI (490-1600 nm) could be performed.

Assessing Molecular Localization of Symbiont Microalgae in Coral Branches Through Mass Spectrometry Imaging.

Reef-building corals are a fundamental pillar of coral reef ecosystems in tropical and subtropical shallow environments. Corals harbor symbiotic dinoflagellates belonging to the family Symbiodiniaceae, commonly known as zooxanthellae. Extensive research has been conducted on this symbiotic relationship, yet the fundamental information about the distribution and localization of Symbiodiniaceae cells in corals is still limited.

In situ FRET measurement of cellular tension using conventional confocal laser microscopy in newly established reporter mice expressing actinin tension sensor.

FRET-based sensors are utilized for real-time measurements of cellular tension. However, transfection of the sensor gene shows low efficacy and is only effective for a short period. Reporter mice expressing such sensors have been developed, but sensor fluorescence has not been measured successfully using conventional confocal microscopy.

Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells by Deep Learning for Cell Center Detection using Three-Dimensional Confocal Fluorescence Microscopy Images.

Accurate single cell segmentation provides means to monitor the behavior of single cell within a population of cells. Time-lapse fluorescence images are used to reveal heterogeneous nature of single mouse embryonic stem cell (ESC) colony and monitor fluctuations of the cell states. Automatic quantification of speed and status shifts of the ESCs depends on accurate single cell segmentation that is used to calculate the 3D center of every cell and track this cell for the quantification.

Quantitative Analyses for Early Tempo-spatial Patterning of Differentiated Human Induced Pluripotent Stem Cells on Micropatterns using Time-lapse Bright-field Microscopy Images.

Three germ layer formation on micropatterns are extremely useful for quantitative analysis of hiPSC (human induced pluripotent stem cells) pluripotency. Spatial patterns of stem cells differentiated on the micropatterns will be formed from about 24 hours after differentiation induction and usually quantitated near 48 hours. To delineate the germ layer formation process, temporal changes in spatial patterning of germ layers should be analyzed by noninvasive microscopy.

Evaluating the identification of the extent of gastric cancer by over-1000 nm near-infrared hyperspectral imaging using surgical specimens.

Significance: Determining the extent of gastric cancer (GC) is necessary for evaluating the gastrectomy margin for GC. Additionally, determining the extent of the GC that is not exposed to the mucosal surface remains difficult. However, near-infrared (NIR) can penetrate mucosal tissues highly efficiently.

Computer-aided demarcation of early gastric cancer: a pilot comparative study with endoscopists.

Background: Precise area diagnosis of early gastric cancer (EGC) is critical for reliable endoscopic resection. Computer-aided diagnosis (CAD) shows strong potential for detecting EGC and reducing cancer-care disparities caused by differences in endoscopists' skills. To be used in clinical practice, CAD should enable both the detection and the demarcation of lesions.

A covalently linked probe to monitor local membrane properties surrounding plasma membrane proteins.

Functional membrane proteins in the plasma membrane are suggested to have specific membrane environments that play important roles to maintain and regulate their function. However, the local membrane environments of membrane proteins remain largely unexplored due to the lack of available techniques. We have developed a method to probe the local membrane environment surrounding membrane proteins in the plasma membrane by covalently tethering a solvatochromic, environment-sensitive dye, Nile Red, to a GPI-anchored protein and the insulin receptor through a flexible linker.

Human induced pluripotent stem cell formation and morphology prediction during reprogramming with time-lapse bright-field microscopy images using deep learning methods.

Background And Objective: Human induced pluripotent stem cells (hiPSCs) represent an ideal source for patient specific cell-based regenerative medicine; however, efficiency of hiPSC formation from reprogramming cells is low. We use several deep-learning results from time-lapse brightfield microscopy images during culture, to early detect the cells potentially reprogramming into hiPSCs and predict the colony morphology of these cells for improving efficiency of culturing a new hiPSC line.

Methods: Sets of time-lapse bright-field images are taken to track reprogramming process of CD34+ cells biologically identified as just beginning reprogramming.

U-net structures for segmentation of single mouse embryonic stem cells using three-dimensional confocal microscopy images.

Cell segmentation at a single cell resolution is required to provide insights for basic biology and application study. However, there are issues of low signal-to-noise ratio, weak fluorescence response, and insufficient resolution along the image stacking direction in 3D confocal images (volume). It has been difficult to segment out single cells from close or contacted cells in a cell volume using image processing methods or together with geometric processing methods.

Prediction of Human Induced Pluripotent Stem Cell Formation Based on Deep Learning Analyses Using Time-lapse Brightfield Microscopy Images.

We use deep learning methods to predict human induced pluripotent stem cell (hiPSC) formation using time-lapse brightfield microscopy images taken from a cell identified as the beginning of entered into the reprogramming process. A U-net is used to segment cells and a CNN is used to classify the segmented cells into eight types of cells during the reprogramming and hiPSC formation based on cellular morphology on the microscopy images. The numbers of respective types of cells in cell clusters before the hiPSC formation stage are used to predict if hiPSC regions can be well formed lately.

Modeling the musculoskeletal system of an insect thorax for flapping flight.

Indirect actuation of the wings via thoracic deformation is a unique mechanism widely observed in flying insect species. The physical properties of the thorax have been intensively studied in terms of their ability to efficiently generate wingbeats. The basic mechanism of indirect wing actuation is generally explained as a lever model on a cross-sectional plane, where the dorsoventral movement of the mesonotum (dorsal exoskeleton of the mesothorax) generated by contractions of indirect muscles actuates the wing.

Contributing role of mitochondrial energy metabolism on platelet adhesion, activation and thrombus formation under blood flow conditions.

Platelets have an active energy metabolism mediated by mitochondria. However, the role of mitochondria in platelet adhesion, activation, and thrombus formation under blood flow conditions remains to be elucidated. Blood specimens were obtained from healthy adult volunteers.

Influence of the difference in refractive index on the interface of an object and the surroundings in near-infrared fluorescence tomography.

The refraction of fluorescence from the inside of a sample at the surface results in fluctuations in fluorescence computed tomography (CT). We evaluated the influence of the difference in refractive index (RI) between the sample body and the surroundings on fluorescence CT results. The brightest fluorescent point is away from the correct point on the tomograms owing to the refraction.

Visualization and quantitative analyses for mouse embryonic stem cell tracking by manipulating hierarchical data structures using time-lapse confocal microscopy images.

We present a cell tracking method for time-lapse confocal microscopy (3D) images that uses dynamic hierarchical data structures to assist cell and colony segmentation and tracking. During the segmentation, the cell and colony numbers and their geometric data are recorded for each 3D image set. In tracking, the colony correspondences between neighboring frames of time-lapse 3D images are first computed using the recorded colony centers.

Distributed sensory coding by cerebellar complex spikes in units of cortical segments.

Sensory processing is essential for motor control. Climbing fibers from the inferior olive transmit sensory signals to Purkinje cells, but how the signals are represented in the cerebellar cortex remains elusive. To examine the olivocerebellar organization of the mouse brain, we perform quantitative Ca imaging to measure complex spikes (CSs) evoked by climbing fiber inputs over the entire dorsal surface of the cerebellum simultaneously.

Detecting colon polyps in endoscopic images using artificial intelligence constructed with automated collection of annotated images from an endoscopy reporting system.

Background: Artificial intelligence (AI) has made considerable progress in image recognition, especially in the analysis of endoscopic images. The availability of large-scale annotated datasets has contributed to the recent progress in this field. Datasets of high-quality annotated endoscopic images are widely available, particularly in Japan.