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PurposeAbnormalities of the running pattern of choroidal vessel have been reported in eyes with pachychoroid diseases. However, it is difficult for clinicians to judge the running pattern with high reproducibility. Thus, the purpose of this study was to compare the degree of concordance of the running pattern of the choroidal vessels between that determined by artificial intelligence (AI) to that determined by experienced clinicians.MethodsThe running pattern of the choroidal vessels in en face images of Haller’s layer of 413 normal and pachychoroid diseased eyes was classified as symmetrical or asymmetrical by human raters and by three supervised machine learning models; the support vector machine (SVM), Xception, and random forest models. The data from the human raters were used as the supervised data. The accuracy rates of the human raters and the certainty of AI’s answers were compared using confidence scores (CSs).ResultsThe choroidal vascular running pattern could be determined by each AI model with an area under the curve better than 0.94. The random forest method was able to discriminate with the highest accuracy among the three AIs. In the CS analyses, the percentage of certainty was highest (66.4%) and that of uncertainty was lowest (6.1%) in the agreement group. On the other hand, the rate of uncertainty was highest (27.3%) in the disagreement group.ConclusionAI algorithm can automatically classify with ambiguous criteria the presence or absence of a symmetrical blood vessel running pattern of the choroid. The classification was as good as that of supervised humans in accuracy and reproducibility.

Hydrolysis of the phospholipid phosphatidylinositol 4,5-bisphosphate (PIP2) at the cell membrane induces the release of inositol 1,4,5-trisphosphate (IP3) into the cytoplasm and diffusion of diacylglycerol (DAG) through the membrane, respectively. Release of IP3 subsequently increases Ca2+ levels in the cytoplasm, which results in activation of protein kinase C α (PKCα) by Ca2+ and DAG, and finally the translocation of PKCα from the cytoplasm to the membrane. In this study, we developed a metabolic reaction–diffusion framework to simulate PKCα translocation via PIP2 hydrolysis in an endothelial cell. A three-dimensional cell model, divided into membrane and cytoplasm domains, was reconstructed from confocal microscopy images. The associated metabolic reactions were divided into their corresponding domain; PIP2 hydrolysis at the membrane domain resulted in DAG diffusion at the membrane domain and IP3 release into the cytoplasm domain. In the cytoplasm domain, Ca2+ was released from the endoplasmic reticulum, and IP3, Ca2+, and PKCα diffused through the cytoplasm. PKCα bound Ca2+ at, and diffused through, the cytoplasm, and was finally activated by binding with DAG at the membrane. Using our model, we analyzed IP3 and DAG dynamics, Ca2+ waves, and PKCα translocation in response to a microscopic stimulus. We found a qualitative agreement between our simulation results and our experimental results obtained by live-cell imaging. Interestingly, our results suggest that PKCα translocation is dominated by DAG dynamics. This three-dimensional reaction–diffusion mathematical framework could be used to investigate the link between PKCα activation in a cell and cell function.

This study aims to develop a novel cross-sectional imaging of fluorescence in over-1000 nm near-infrared (OTN-NIR), which allows in vivo deep imaging, using computed tomography (CT) system. Cylindrical specimens of composite of OTN-NIR fluorophore, NaGdF4 co-doped with Yb3+ and Ho3+ (ex: 980 nm, em: 1150 nm), were embedded in cubic agar (10.5-12 mm) or in the peritoneal cavity of mice and placed on a rotatable stage. When the fluorescence from inside of the samples was serially captured from multiple angles, the images were disrupted by the reflection and refraction of emitted light on the sample-air interface. Immersing the sample into water filled in a rectangular bath suppressed the disruption at the interface and successfully reconstructed the position and concentration of OTN-NIR fluorophores on the cross-sectional images using a CT technique. This is promising as a novel three-dimensional imaging technique for OTN-NIR fluorescent image projections of small animals captured from multiple angles.

A pulsed laser cause vaporization of tissue by plasma if a laser can provide high-density energy within a very short pulse duration. Such phenomena are called laser-induced plasma ablation. The influence of the laser-induced plasma ablation for tissue is unclear because the ablation mechanism is differing regardless of two lasers provide almost the same power density. The two kinds of lasers' vaporization mechanism (Nanosecond laser output could cause an optical breakdown in the air depending on power density and pulse duration of the laser and Femtosecond laser output could cause a breakdown only on solids surface since pulse irradiation time is shorter than energy transfer time) are evaluated by using thermal damage and destruction of tissue. The experimental results show that nanosecond laser caused vaporization without thermal damage and destruction at the tissue approximant 300 µm away from the ablation area. The pulsed laser which has high power density and longer pulse duration than energy transfer time is suitable for plasma ablation not depending on thermal process.

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. The speculated position is determined as the exact point if the RI ratio ranges between 0.97 and 1.03 by immersing the body in an RI matching liquid. The results can help in experimental settings of fluorescence CT for acquiring three-dimensional positional information.

Intracellular and intercellular Ca2+ waves play key roles in cellular functions, and focal stimulation triggers Ca2+ wave propagation from stimulation points to neighboring cells, involving localized metabolism reactions and specific diffusion processes. Among these, inositol 1,4,5-trisphosphate (IP3) is produced at membranes and diffuses into the cytoplasm to release Ca2+ from endoplasmic reticulum (ER). In this study, we developed a three-dimensional (3D) simulation model for intercellular and intracellular Ca2+ waves in endothelial cells (ECs). 3D model of 2 cells was reconstructed from confocal microscopic images and was connected via gap junctions. Cells have membrane and cytoplasm domains, and metabolic reactions were divided into each domain. Finally, the intracellular and intercellular Ca2+ wave propagations were induced using microscopic stimulation and were compared between numerical simulations and experiments. The experiments showed that initial sharp increases in intracellular Ca2+ occurred approximately 0.3 s after application of stimuli. In addition, Ca2+ wave speeds remained constant in cells, with intracellular and intercellular speeds of approximately 35 and 15 μm/s, respectively. Simulations indicated initial increases in Ca2+ concentrations at points of stimulation, and these were then propagated across stimulated and neighboring cells. In particular, initial rapid increases in intracellular Ca2+ were delayed and subsequent intracellular and intercellular Ca2+ wave speeds were approximately 25 and 12 μm/s, respectively. Simulation results were in agreement with those from cell culture experiments, indicating the utility of our 3D model for investigations of intracellular and intercellular messaging in ECs.

Time-lapse confocal fluorescence microscopy images from mouse embryonic stem cells (ESCs) carrying reporter genes, histone H2B-mCherry and Mvh-Venus, have been used to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. Accurate cell nucleus segmentation is required to analyse the ESC dynamics and differentiation at a single cell resolution. Several methods used concavities on nucleus contours to segment overlapping cell nuclei. Our proposed method evaluates not only the concavities but also the size and shape of every 2D nucleus region to determine if any of the strait, extrusion, convexity and large diameter criteria is satisfied to segment overlapping nuclei inside the region. We then use a 3D segmentation method to reconstruct simple, convex, and reasonably sized 3D nuclei along the image stacking direction using the radius and centre of every segmented region in respective microscopy images. To avoid false concavities on nucleus boundaries, fluorescence images of the H2B-mCherry reporter are used for localisation of cell nuclei and Venus fluorescence images are used for determining the cell colony ranges. We use a series of image preprocessing procedures to remove noise outside and inside cell colonies, and in respective nuclei, and to smooth nucleus boundaries based on the colony ranges. We propose dynamic data structures to record every segmented nucleus region and solid in sets (volumes) of 3D confocal images. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei on microscopy images and that the segmentation method effectively segment out every nucleus with a reasonable size and shape. All 3D nuclei in a set (volume) of confocal microscopy images can be accessed by the dynamic data structures for 3D reconstruction. The 3D nuclei in time-lapse confocal microscopy images can be tracked to calculate cell movement and proliferation in consecutive volumes for understanding the dynamics of the differentiation characteristics about ESCs. LAY DESCRIPTION: Embryonic stem cells (ESCs) are considered as an ideal source for basic cell biology study and producing medically useful cells in vitro. This study uses time-lapse confocal fluorescence microscopy images from mouse ESCs carrying reporter gene to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. To automate analyses of ESC differentiation behaviours, accurate cell nucleus segmentation to distinguish respective cells are required. A series of image preprocessing procedures are implemented to remove noise in live-cell fluorescence images but yield overlapping cell nuclei. A segmentation method that evaluates boundary concavities and the size and shape of every nucleus is then used to determine if any of the strait, extrusion, convexity, large and local minimum diameter criteria satisfied to segment overlapping nuclei. We propose a dynamic data structure to record every newly segmented nucleus. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei and that the segmentation method effectively detects overlapping nuclei. All segmented nuclei in confocal images can be accessed using the dynamic data structures to be visualised and manipulated for quantitative analyses of the ESC differentiation behaviours. The manipulation can be tracking of segmented 3D cell nuclei in time-lapse images to calculate their dynamics of differentiation characteristics.

Non-viral vectors are considered to be an attractive approach for gene delivery, since an artificial material is less immunogenic and oncogenic compared to a viral vector. We previously reported on the hepatic delivery of plasmid DNA (pDNA) by using lipid-like material (an SS-cleavable and pH-activated lipid-like material: ssPalm) which mounts two hydrophobic scaffolds, proton-accepting motifs (tertiary amines), and a cleavable unit (disulfide bonding). In the present study, we report on an advanced hepatic gene delivery system that uses a new type of ssPalm derivative: ssPalmE-Paz4-C2. The hepatic transgene expression of the intravenously administrated lipid nanoparticle (LNP) that was formed with the ssPalmE-Paz4-C2 (LNPssPalmE-Paz4-C2) was significantly higher than that of conventional LNPs formed with a myristic acid-scaffold ssPalm (LNPssPalmM). However, the LNPssPalmE-Paz4-C2 particle induced a severe innate immune response that involved the production of the pro-inflammatory cytokines (IL-6 and TNFα), intracellular DNA sensor-related cytokine (IL-1β) and interferon (IFNβ), even when a pDNA free from CpG-motifs was encapsulated. The production of the pro-inflammatory cytokines and the DNA sensor-related cytokines is attributed to the combination of vitamin E scaffolds and encapsulated pDNA. The depletion of macrophages by chlodronate-encapsulating liposomes dramatically reduced inflammatory gene expression. Based on the above findings, we conclude that the use of a certain type of non-viral carrier that shows a robust gene expression activity is attended by a risk of eliciting an innate immune response. When a highly hydrophobic derivative of dexamethasone, an anti-inflammatory glucocorticoid compound, was co-loaded to the particle, this inflammatory response was relieved, and gene expression efficiency was enhanced. It is thus concluded that the co-delivery of dexamethasone and pDNA is a promising approach for reducing these risks.

An absorbing boundary condition (ABC) is presented for the absorption of electromagnetic waves in dispersive media in problems where the frequency bandwidth of interest is limited. The condition relies on an operator designed to absorb the waves in the considered bandwidth. Numerical experiments demonstrate the effectiveness of the operator in human body media. Due to its simple implementation, the operator ABC may be an alternative to the perfectly matched layer in applications where the bandwidth does not exceed one or two decades of frequency.

Summary 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 Ca2+ imaging to measure complex spikes (CSs) evoked by climbing fiber inputs over the entire dorsal surface of the cerebellum simultaneously. The surface is divided into approximately 200 segments, each composed of ∼100 Purkinje cells that fire CSs synchronously. Our in vivo imaging reveals that, although stimulation of four limb muscles individually elicits similar global CS responses across nearly all segments, the timing and location of a stimulus are derived by Bayesian inference from coordinated activation and inactivation of multiple segments on a single trial basis. We propose that the cerebellum performs segment-based, distributed-population coding that represents the conditional probability of sensory events.

This study aims to verify whether the three-dimensional internal information acquisition system we have developed can be applied successfully to the microstructures of consecutively precision-machined biological samples, and to those of metallic samples. Therefore, this study mainly deals with biological hard tissue samples like bones. In this paper, we first studied the precision-machining characteristics of bones. From this, we determined that, to obtain machined surfaces sufficient for internal observations, we need to determine the maximum uncut chip thickness and the cutting speeds, taking the bone’s anisotropy into consideration. Next, we acquired three-dimensional internal information on consecutively precision-machined bone samples using the three-dimensional internal acquisition system we developed. Subsequently, we visualized the internal structures of these machined samples. Our tiling observations acquired an 18×9×3 mm segment as a 6.2×6.2×10μm resolution image. We obtained a three-dimensionally reconstructed image of complex blood vessel networks inside the bone by making the acquired images binary.

Fox odour 2,4,5-trimethyl thiazoline (TMT) is known to activate multiple glomeruli in the mouse olfactory bulb (OB) and elicits strong fear responses. In this study, we screened TMT-reactive odourant receptors and identified Olfr1019 with high ligand reactivity and selectivity, whose glomeruli are located in the posterodorsal OB. In the channelrhodopsin knock-in mice for Olfr1019, TMT-responsive olfactory-cortical regions were activated by photostimulation, leading to the induction of immobility, but not aversive behaviour. Distribution of photoactivation signals was overlapped with that of TMT-induced signals, but restricted to the narrower regions. In the knockout mice, immobility responses were reduced, but not entirely abolished likely due to the compensatory function of other TMT-responsive glomeruli. Our results demonstrate that the activation of a single glomerular species in the posterodorsal OB is sufficient to elicit immobility responses and that TMT-induced fear may be separated into at least two different components of immobility and aversion., The olfactory bulb is arranged in glomeruli defined by their olfactory receptor expression. The authors identify an olfactory receptor for fox odour, TMT, and show that activation of the glomerulus expressing that receptor in mice leads to immobility, but does not lead to other fear behaviours.

Time Reversal (TR) imaging methods have recently generated considerable interest due to their robustness and ability to locate multiple targets. However, problems arise when the targets are behind an obstructing material. We develop a preprocessing algorithm for TR imaging methods to obtain images of targets hidden behind a wall, applying temporal and spatial windows on the full-Multistatic Data Matrix (MDM). By using temporal-windows, we divide the full-MDM into sub-MDMs. We further segment the sub-MDMs spatially by selecting its elements corresponding to a predetermined number of antennas in space.We take the singular value decomposition of the sub-MDMs to obtain singular value distributions as well as singular vectors which we analyse individually and use for the MUltiple SIgnal Classification (MUSIC) TR imaging method. The obtained images from the these localised sub-MDMs collectively yield to clearerimages of scenarios than we can obtain using the full-MDM.

Fluorescence microscopy imaging of live cells has drawn the attention of many researchers in the past decade. Because of the large amount of image data produced by a fluorescence microscope, automated cell detection and tracking algorithms have become an emerging need to help cell biologists visualize and analyze cell kinematics such as changes in cell population caused by proliferation, attachment, and differentiation. This study uses the adaptive background subtraction technique to detect cell clusters in time-lapse fluorescence microscopy images and the discrete Kalman filter to track the position, motion, merging and splitting of these clusters. A set of two-dimensional (2D) time-series images from mouse embryonic stem cells obtained using wide-field microscopy and a set of three-dimensional (3D) time-series from HeLa cells obtained using confocal microscopy are used to demonstrate that biologists can visualize various quantitative measures such as motion, merging and splitting, centroids, areas, and growth rates of cell clusters to acquire useful information through 2D and 3D interfaces.

Gaussian convolution and its discrete analogue, Gauss transform, have many science and engineering applications, such as mathematical statistics, thermodynamics and machine learning, and are widely applied to computer vision and image processing tasks. Due to its computational expense (quadratic and exponential complexities with respect to the number of points and dimensionality, respectively) and rapid spreading of high quality data (bit depth/dynamic range), accurate approximation has become important in practice compared with conventional fast methods, such as recursive or box kernel methods. In this paper, we propose a novel approximation method for fast Gaussian convolution of two-dimensional uniform point sets, such as 2D images. Our method employs L1 distance metric for Gaussian function and domain splitting approach to achieve fast computation (linear computational complexity) while preserving high accuracy. Our numerical experiments show the advantages over conventional methods in terms of speed and precision. We also introduce a novel and effective joint image filtering approach based on the proposed method, and demonstrate its capability on edge-aware smoothing and detail enhancement. The experiments show that filters based on the proposed L1 Gauss transform give higher quality of the result and are faster than the original filters that use box kernel for Gaussian convolution approximation.

This paper describes large-scale three-dimensional (3D) observation of microstructures in ferrous materials, using a three-dimensional internal structure microscope that enables automated serial sectioning of ferrous materials, assisted by efficient exploring of etching conditions. Our system uses precision cutting for surface fabrication, which can create a mirror-like cross-section within a minute at depth intervals of 1 μm. Our approach consists of two steps: exploring the etching conditions and 3D observation. For the first stage, etching times were automatically changed for each cross-section from five to 40 s in 2.5 s steps, and an optical microscope with a digital camera captured the cross-sections. In this process, the specimen did not need to be detached from the device, and it took less than an hour to obtain the images for 15 conditions. A suitable image could be quickly selected from these. The following 3D observation step demonstrated automated large-scale serial sectioning of 0.15C-1.5Mn steel using the etching condition. The 3D model offered a range of 867 × 645 × 1500 μm3, which had 1000 cross-sections at 1.5-μm intervals and a resolution of 0.066 × 0.066 × 1.5 μm3. The total voxel size became 13196 × 9824 × 1000 voxels. The observation time was 3.5 min per section and took about 60 h in total. The quality of the 3D image was sufficient for recognizing clear microstructures even in the reconstructed side surfaces.

Background: Platelet play crucial role for the onset of acute coronary syndrome. The initial binding following vessel injury is exclusively mediated by the binding of von Willebrand factor (VWF) and platelet glycoprotein (GP) Ib α . Previous biological experiments revealed loss of function of platelet expressing GPIb α with G233D mutation. Objective: To elucidate the mechanism underlining the loss of function in G233D mutant. Methods: Dynamic fluctuating three-dimensional structures and the Potential of Mean Force (PMF) were calculated for the binding of VWF and platelet GPIb α in wild-type or G233D mutant. PMF were calculated at each 0.5Å for 25 Å to 65 Å of mass center distance between GPIb α and VWF. The energy required to dissociate the bond between GPIb α and VWF was calculated by subtracting the lowest PMF from the PMF at 65 Å mass center distance. Chemistry at HARvard Molecular Mechanics (CHARMM) force field with NAnoscale Molecular Dynamics (NAMD) was used for calculation. The initial structure of each mutant was obtained by inducing single amino-acid substitution with Visual Molecular Dynamics (VMD) to the stable water-soluble binding structure of wild-type VWF and GPIb α . Results: The energetically most stable binding structure of VWF and GPIb α in wild-type and G233D did not differ substantially (Figure panel A). However, The energy required to dissociate the bond between GPIb α and VWF was 4.32 kcal/mol (19.5 %) lower for G233D mutant compared to wild-type (Figure panel B). Conclusions: Our results suggest that the reduction of dissociation energy of single molecule of G233D GPIb α mutant as a possible explanation for the reduced platelet adhesion under blood flow condition.

We present a LSTM (Long Short-Term Memory) based RNN (recurrent neural network) method for predicting human induced Pluripotent Stem (hiPS) cells in the reprogramming process. The method uses a trained LSTM network by time-lapse microscopy images to predict growth and transition of reprogramming processes of CD34+ human cord blood cells into hiPS cells. The prediction can be visualized by output time-series probability images. The growth and transition are thus analyzed quantitatively by region areas of distinct cells emerged during the iPS formation processes. The experimental results show that our LSTM network is a potentially powerful tool to predict the cells at the distinct phases of the reprogramming to hiPS cells. This method should be extremely useful not only for basic biology of iPS cells but also detection of the reprogramming cells that will become genuine hiPS cells even at early stages of hiPS formation. Such predictive power should greatly reduce cost, labor and time required for establishment of the genuine hiPS cells, thereby accelerating the practical use of hiPS cells in regenerative medicine.

This paper presents a retinal thickness analysis method from 3D images acquired by optical coherence tomography (OCT). Given OCT images with segmented boundaries of retinal layers, medial axes of the layers are computed by medial axis transforms (MAT), and thickness is evaluated based on Euclidean distance fields. Since the MAT computes the closest points on the boundary of the layer, it can compute more correct thickness than conventional methods that evaluate Y-axis-aligned thickness. Experimental results show that our method can detect thin-parts around distorted regions, or a clue of high myopia. This is useful for early diagnosis of high myopia and other eye diseases.

The objective of this study was to build deep learning models with optical coherence tomography (OCT) images to classify normal and age related macular degeneration (AMD), AMD with fluid, and AMD without any fluid. In this study, 185 normal OCT images from 49 normal subjects, 535 OCT images of AMD with fluid, and 514 OCT mages of AMD without fluid from 120 AMD eyes as training data, while 49 normal images from 25 normal eyes, 188 AMD OCT images with fluid and 154 AMD images without any fluid from 77 AMD eyes as test data, were enrolled. Data augmentation was applied to increase the number of images to build deep learning models. Totally, two classification models were built in two steps. In the first step, a VGG16 model pre-trained on ImageNet dataset was transfer learned to classify normal and AMD, including AMD with fluid and/or without any fluid. Then, in the second step, the fine-tuned model in the first step was transfer learned again to distinguish the images of AMD with fluid from the ones without any fluid. With the first model, normal and AMD OCT images were classified with 0.999 area under receiver operating characteristic curve (AUC), and 99.2% accuracy. With the second model, AMD with the presence of any fluid, and AMD without fluid were classified with 0.992 AUC, and 95.1% accuracy. Compared with a transfer learned VGG16 model pre-trained on ImageNet dataset, to classify the three categories directly, higher classification performance was achieved with our notable approach. Conclusively, two classification models for AMD clinical practice were built with high classification performance, and these models should help improve the early diagnosis and treatment for AMD.

Mitotic event detection is a fundamental step in investigating of cell behaviors. The event can be used to analyze various diseases, but most mitotic event detections performed previously focused only on two-dimensional (2D) images with time information. Owing to the complex background (normal cells) and mitotic event orientations, the 2D detection methods yield many false positive and false negative results. To solve this problem, we proposed a 2.5 dimensional (2.5D) cascaded end-to-end network combined with 3D anchors for accurate detection of mitotic events in 4D microscopic images. Our proposed network uses a convolutional long short-term memory to handle issues relating to time sequence; this helps to improve the detection accuracy (reduction of false positives). Furthermore, it uses 3D anchors to capture volume information used to address the orientation problem (reduction of false negatives). The experimental results show that the proposed method can achieve higher precision and recall compared with state-of-the-art methods.

This paper describes the simulation of airflow in human nasal airways using voxel-based modeling characterized by robust, automatic, and objective grid generation. Computed tomography scans of a healthy adult nose are used to reconstruct 3D virtual models of the nasal airways. Voxel-based simulations of restful inspiratory flow are then performed using various mesh sizes to determine the level of granularity required to adequately resolve the airflow. For meshes with close voxel spacings, the model successfully reconstructs the nasal structure and predicts the overall pressure drop through the nasal cavity.

Background: In this study, we proposed an averaged airway model design based on four healthy subjects and numerically evaluated its effectiveness for predicting the airflow and particle transport through an airway. Methods: Direct-averaged models of the conducting airways of four subjects were restored by averaging the three-dimensional (3D) skeletons of four healthy airways, which were calculated using an inverse 3D thinning algorithm. We simulated the airflow and particle transport in the individual and the averaged airway models using computational fluid dynamics. Results: The bifurcation geometry differs even among healthy subjects, but the averaged model retains the typical geometrical characteristics of the airways. The Reynolds number of the averaged model varied within the range found in the individual subject models, and the averaged model had similar inspiratory flow characteristics as the individual subject models. The deposition fractions at almost all individual lobes ranged within the variation observed in the subjects, however, the deposition fraction was higher in only one lobe. The deposition distribution at the main bifurcation point differed among the healthy subjects, but the characteristics of the averaged model fell within the variation observed in the individual subject models. On the contrary, the deposition fraction of the averaged model was higher than that of the average of the individual subject models and deviated from the range observed in the subject models. Conclusion: These results indicate that the direct-averaged model may be useful for predicting the individual airflow and particle transport on a macroscopic scale.

Introduction Thrombin inhibitor and anti-Xa are now widely used in clinical practice. However, the difference between thrombin inhibitor and anti-Xa in prevention of thrombosis is still to be elucidated. Materials and methods Computer simulator implementing the function of platelet, coagulation, fibrinolysis and blood flow was developed. The function of thrombin is defined as to activated platelet at the rate of 0.01 s−1 and to produce fibrin at the rate of 0.1 s−1 in control. The effect of thrombin inhibitor was settled to reduce the rate of platelet activation and fibrin generation changed from 10 to 100% as compared to the control. The local thrombin generation rate on activated platelet was settled as 1.0 s−1 as a control. The effect of anti-Xa was settled to reduce to thrombin generation rate on activated platelet from 10% to 100% as compared to the control. The sizes of thrombi formed at site of endothelial injury in the presence and absence of thrombin inhibitor and anti-Xa were compared. Results and conclusions The size of thrombi formed by 30-s perfusion of blood at site of endothelial injury reduced both in the presence of thrombin inhibitor and anti-Xa. There was significant positive relationship between thrombin inhibitor effect and the size of formed thrombi with R value of 0.96. (p

The non-intrusive polynomial chaos (NIPC) expansion method is one of the most frequently used methods for uncertainty quantification (UQ) due to its high computational efficiency and accuracy. However, the number of polynomial bases is known to substantially grow as the number of random parameters increases, leading to excessive computational cost. Various sparse schemes such as the least angle regression method have been utilised to alleviate such a problem. Nevertheless, the computational cost associated with the NIPC method is still nonnegligible in systems which consist of a high number of random parameters. This paper proposes the first versatile UQ method which requires the least computational cost whilst maintaining the UQ accuracy. We combine the hyperbolic scheme with the principal component analysis method and reduce the number of polynomial bases with the simpler procedure than currently available, keeping most information in the system. The ridge regression method is utilised to build a statistical parsimonious model to decrease the number of input samples and the leaveone- out cross-validation method is applied to improve the UQ accuracy .

A multidisciplinary approach reveals key insights into the principles of collective cell migration, which is involved in fundamental biological processes. The conformational plasticity of a single molecule, JRAB/MICAL-L2, provides “law and order” in collective cell migration., In fundamental biological processes, cells often move in groups, a process termed collective cell migration. Collectively migrating cells are much better organized than a random assemblage of individual cells. Many molecules have been identified as factors involved in collective cell migration, and no one molecule is adequate to explain the whole picture. Here we show that JRAB/MICAL-L2, an effector protein of Rab13 GTPase, provides the “law and order” allowing myriad cells to behave as a single unit just by changing its conformation. First, we generated a structural model of JRAB/MICAL-L2 by a combination of bioinformatic and biochemical analyses and showed how JRAB/MICAL-L2 interacts with Rab13 and how its conformational change occurs. We combined cell biology, live imaging, computational biology, and biomechanics to show that impairment of conformational plasticity in JRAB/MICAL-L2 causes excessive rigidity and loss of directionality, leading to imbalance in cell group behavior. This multidisciplinary approach supports the concept that the conformational plasticity of a single molecule provides “law and order” in collective cell migration.

In microwave imaging applications, propagatingsignals undergo additional attenuation in dispersive and lossymedia when compared to non-dispersive and lossless media. Inthis paper, we introduce a threshold approach and Short-TimeFourier transform (STFT) based inverse filters to compensate forsuch additional attenuation in time reversal (TR) based imagingalgorithms. The method introduced here is utilized to reduce theunwanted noise amplification in the received signals during thecompensation stage. Additionally, optimum settings for windowtype and length in the STFT method are obtained through ascanning operation in the propagation medium. Different windowtypes and lengths are studied to achieve best focusing resolutionin TR applications. While utilizing large number of windowswith short spatial lengths provides improved TR focusing performance,it also increases the overall cost and complexity of theimaging system. The threshold method introduced here achievesimproved TR focusing performance without increasing the costby utilizing a lower number of inverse filters.

This study investigated the local preliminary hardening of ferrite near ferrite–martensite interfaces in dual-phase (DP) steel. Geometrically necessary dislocations (GNDs), generated in the case of interfacial misfit between different phases, may cause preliminary hardening around such interfaces. Firstly, finite-element simulations of nanoindentations were conducted using an ideal cylindrical DP model without preliminary hardening around the ferrite–martensite interface. The simulations indicated gradually decreasing nano-hardness with increasing distance from the ferrite–martensite interface. Second, nano-hardness measurements of ferrite near the ferrite–martensite interface were performed for laboratory-made DP steel containing 10 vol.% martensite. The measurements were consistent with the simulation without preliminary hardening around the interface. That is, interfacial preliminary hardening of ferrite was not indicated by our measurements. The GND density, which has been reported to cause preliminary hardening around the interface, supported this result. The density on ferrite was increased around the interface, but the area was very small compared with the nanoindentation size. We conclude that another hardening mechanism, e.g., GND accumulation under a high strain gradient around the interface, acts as a microstructural local strengthening factor in DP steel deformation.

From cells to organisms, every living system is three-dimensional (3D), but the performance of fluorescence microscopy has been largely limited when attempting to obtain an overview of systems’ dynamic processes in three dimensions. Recently, advanced light-sheet illumination technologies, allowing drastic improvement in spatial discrimination, volumetric imaging times, and phototoxicity/photobleaching, have been making live imaging to collect precise and reliable 3D information increasingly feasible. In particular, lattice light-sheet microscopy (LLSM), using an ultrathin light-sheet, enables whole-cell 3D live imaging of cellular processes, including mitosis, at unprecedented spatiotemporal resolution for extended periods of time. This technology produces immense and complex data, including a significant amount of information, raising new challenges for big image data analysis and new possibilities for data utilization. Once the data are digitally archived in a computer, the data can be reused for various purposes by anyone at any time. Such an information science approach has the potential to revolutionize the use of bioimage data, and provides an alternative method for cell biology research in a data-driven manner. In this article, we introduce examples of analyzing digital mitotic spindles and discuss future perspectives in cell biology.

This paper proposes an accurate 3D segmentation method for visualization and quantitative analysis of differentiation activities of mouse embryonic stem (ES) cells using time-lapse confocal fluorescence microscopy images. One of critical tasks in ES cell segmentation arises due to that ES cell nuclei are often close to each other. Several segmentation methods by convexities and concavities on cell or nucleus contours to detect possible touching cells or nuclei were proposed. Comparing to image processing methods, these methods are more accurate in some conditions, however, still cannot detect touching nuclei without concavities on nucleus contours. Our method uses the nucleus size and convex, concave, strait and extrusion features on nucleus contour to touch a boundary between touching cell nuclei in 2D slices and interslices. Experimental results show our method can well detect touching boundaries of 2D and 3D nucleus for confocal microscopy images of mouse ES cells in an early stage of differentiating into neural progenitor cells. Based on the accurate ES cell segmentation, cell activities (velocities and shape changes) during differentiation can be accurately visualized and quantitatively analyzed.

Intracellular and intercellular Ca

Understanding anatomical structures and biological functions based on gene expression is critical in a systemic approach to address the complexity of the mammalian brain, where >25 000 genes are expressed in a precise manner. Co-expressed genes are thought to regulate cell type- or region-specific brain functions. Thus, well-designed data acquisition and visualization systems for profiling combinatorial gene expression in relation to anatomical structures are crucial. To this purpose, using our techniques of microtomy-based gene expression measurements and WebGL-based visualization programs, we mapped spatial expression densities of genome-wide transcripts to the 3D coordinates of mouse brains at four post-natal stages, and built a database, ViBrism DB (http://vibrism.neuroinf.jp/). With the DB platform, users can access a total of 172 022 expression maps of transcripts, including coding, non-coding and lncRNAs in the whole context of 3D magnetic resonance (MR) images. Co-expression of transcripts is represented in the image space and in topological network graphs. In situ hybridization images and anatomical area maps are browsable in the same space of 3D expression maps using a new browser-based 2D/3D viewer, BAH viewer. Created images are shareable using URLs, including scene-setting parameters. The DB has multiple links and is expandable by community activity.

In joint surgery, evaluation of the relative positions and angles among joint structures (bones, ligaments, muscle, and cartilages, etc.) in range of motion, lifting and weight bearing of the joint is required. However, current volume visualization techniques provide only static 3D images of anatomic structures in volume data. We propose a method to manipulate (reposition, resize and bend) the joint structures in a volume, by which surgeons can visualize and evaluate the critical positions or angles of the joint structures, and thus plan surgery to correct the morphologic pathology of the joint structures. We also propose a system with a real-time cutting simulation function together with the proposed structure manipulation functions by which surgeons can rehearse and verify joint surgery.

In recent years, intravital skin imaging has been increasingly used in mammalian skin research to investigate cell behaviors. A fundamental step of the investigation is mitotic cell (cell division) detection. Because of the complex backgrounds (normal cells), the majority of the existing methods cause several false positives. In this paper, we proposed a 2.5D cascaded end-to-end convolutional neural network (CasDetNet) with temporal information to accurately detect automatic mitotic cell in 4D microscopic images with few training data. The CasDetNet consists of two 2.5D networks. The first one is used for detecting candidate cells with only volume information and the second one, containing temporal information, for reducing false positive and adding mitotic cells that were missed in the first step. The experimental results show that our CasDetNet can achieve higher precision and recall compared to other state-of-the-art methods., Comment: 4 pages, 4 figures, conference paper (submitted to arxiv then update version and submitted to conference. Finally update in arxiv for newest version)

The non-intrusive polynomial chaos (NIPC) expansion method is used to quantify the uncertainty of a stochastic system. It potentially reduces the number of numerical simulations in modelling process, thus improving efficiency, whilstensuring accuracy. However, the number of polynomial bases grows substantially with the increase of random parameters, which may render the technique ineffective due to the excessive computational resources. To address such problems, methods based on the sparse strategy such as the least angle regression (LARS) method with hyperbolic index sets can be used. This paper presents the first work to improve the accuracy of the original LARS method for uncertainty quantification (UQ). We propose an adaptive LARS method in order to quantify the uncertainty of the results from the numerical simulations withhigher accuracy than the original LARS method. The proposed method outperforms the original LARS method in terms of accuracy and stability. The L2 regularisation scheme further reduces the number of input samples while maintaining the accuracy of the LARS method.

Nuclear pore complexes (NPCs) maintain cellular homeostasis by mediating nucleocytoplasmic transport. Although cyclin-dependent kinases (CDKs) regulate NPC assembly in interphase, the location of NPC assembly on the nuclear envelope is not clear. CDKs also regulate the disappearance of pore-free islands, which are nuclear envelope subdomains; this subdomain gradually disappears with increase in homogeneity of the NPC in response to CDK activity. However, a causal relationship between pore-free islands and NPC assembly remains unclear. Here, we elucidated mechanisms underlying NPC assembly from a new perspective by focusing on pore-free islands. We proposed a novel framework for image-based analysis to automatically determine the detailed ‘landscape’ of pore-free islands from a large quantity of images, leading to the identification of NPC intermediates that appear in pore-free islands with increased frequency in response to CDK activity. Comparison of the spatial distribution between simulated and the observed NPC intermediates within pore-free islands showed that their distribution was spatially biased. These results suggested that the disappearance of pore-free islands is highly related to de novo NPC assembly and indicated the existence of specific regulatory mechanisms for the spatial arrangement of NPC assembly on nuclear envelopes.

We present a deep learning architecture Convolutional Neural Networks (CNNs) for automatic classification and recognition of reprogramming and reprogrammed human Induced Pluripotent Stem (iPS) cell regions in microscopy images. The differentiated cells that possibly undergo reprogramming to iPS cells can be detected by this method for screening reagents or culture conditions in iPS induction. The learning results demonstrate that our CNNs can achieve the Top-1 and Top-2 error rates of 9.2% and 0.84%, respectively, to produce probability maps for the automatic analysis. The implementation results show that this automatic method can successfully detect and localize the human iPS cell formation, thereby yield a potential tool for helping iPS cell culture.

This paper explores the feasibility of automatic 3D segmentation, visualization and quantitative analysis for differentiation activities of mouse embryonic stem cells using time-lapse confocal fluorescence microscopy images. Technical approaches include bilateral filtering, mean-shift segmentation, adaptive thresholding, watershed segmentation, connected component labeling, and video tracking. Our method processes simultaneously two image channels, one for cytoplasm and the other for nuclei. The nucleus images are used to segment 2D and then 3D nuclei and to track each nucleus and calculate velocities of the 3D nucleus. The cytoplasm images are used to help nucleus segmentation and calculate the S/V (surface to volume) ratio of cytoplasm surrounding a nucleus. Volume rendering on the time-lapse fluorescence images generates time-series 3D images for visualizing the dynamic changes of cell velocity and S/V ratios. Using our prototype system, cells with different amount of EGFP fluorescent protein possesses different differentiation activity (velocity and S/V ratio) can be visualized and quantitatively analyzed.

We present an automated method for detection and localization of mouse Induced pluripotent stem (iPS) cells formation by using the fluorescence microscopy images. The differentiated cells that possibly undergo reprogramming to iPS cells can be detected by this method for screening reagents or culture conditions in iPS induction. Our method includes image preprocessing to enhance the fluorescence microscopy images, followed by cell detection to detect isolated fluorescent (reprogramming or reprogrammed iPS) cells. Finally, a clustering technique is used to detect and localize the fluorescent cell clusters. A cluster formation indicates that the reprogramming iPS cells may become reprogrammed. The implementation results show that the automatic method can successfully detect and localize the mouse iPS cell formation, thereby yield a potential tool for helping iPS cell culture.