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Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself’s ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches. Moreover, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret them in the context of the fluorescence images, and analyze the performance contributions of each component of our approach.

Elucidating the developmental process of an organism will require the complete cartography of cellular lineages in the spatial, temporal, and molecular domains. We present Zebrahub, a comprehensive dynamic atlas of zebrafish embryonic development that combines single-cell sequencing time course data with light-sheet microscopy-based lineage reconstructions. Zebrahub is a foundational resource to study developmental processes at both transcriptional and spatiotemporal levels. It is publicly accessible as a web-based resource, providing an open-access collection of datasets and tools. Using this resource we shed new light on the pluripotency of Neuro-Mesodermal Progenitors (NMPs). We find that NMPs are pluripotent only during early axis elongation before becoming exclusively mesodermal progenitors. We attribute this restriction in NMP cell fate to emerging morphodynamic features that compartmentalize tissue motion.

The promise of single-objective light-sheet microscopy is to combine the convenience of standard single-objective microscopes with the speed, coverage, resolution and gentleness of light-sheet microscopes. We present DaXi, a single-objective light-sheet microscope design based on oblique plane illumination that achieves: (1) a wider field of view and high-resolution imaging via a custom remote focusing objective; (2) fast volumetric imaging over larger volumes without compromising image quality or necessitating tiled acquisition; (3) fuller image coverage for large samples via multi-view imaging and (4) higher throughput multi-well imaging via remote coverslip placement. Our instrument achieves a resolution of 450 nm laterally and 2 μm axially over an imaging volume of 3,000 × 800 × 300 μm. We demonstrate the speed, field of view, resolution and versatility of our instrument by imaging various systems, including Drosophila egg chamber development, zebrafish whole-brain activity and zebrafish embryonic development – up to nine embryos at a time.

A 76-year-old woman with a past history of diabetes mellitus and Hashimoto's disease, in regard to her personal history, she did not smoke or drink alcohol. In March, year X-1, she became aware of cervical lymphadenopathy. Based on the findings of lymph node biopsy, she was diagnosed as having diffuse large B-cell lymphoma(DLBCL). An upper gastrointestinal endoscopy(U-GIE)revealed white granular prominences in the gastric fornix, and biopsy of these lesions revealed the diagnosis of Russell body gastritis(RBG). Neither lymphoma infiltration nor other malignant findings were found. Diagnostic tests for Helicobacter pylori were negative. The clinical stage of the DLBCL was determined as stage ⅢA, and the International Prognostic Index was"high intermediate". She received 6 cycles of R-CHOP therapy, with concomitant use of a proton pump inhibitor. Complete remission was confirmed in November, year X-1. An U-GIE performed again no longer showed the white granular prominences in the gastric fornix. The present report is the first of DLBCL complicated by RBG; our findings suggested that the two diseases were associated with each other.

Underwater walking is one of the most common hydrotherapeutic exercises. Therefore, understanding muscular control during underwater walking is important for optimizing training regimens. The effects of the water environment on walking are mainly related to the hydrostatic and hydrodynamic theories of buoyancy and drag force. To date, muscular control during underwater walking has been investigated at the individual muscle level. However, it is recognized that the human nervous system modularly controls multiple muscles through muscle synergies, which are sets of muscles that work together. We found that the same set of muscle synergies was shared between the two walking tasks. However, some task-dependent modulation was found in the activation combination across muscles and temporal activation patterns of the muscle synergies. The results suggest that the human nervous system modulates activation of lower-limb muscles during water walking by finely tuning basic locomotor muscle synergies that are used during land walking to meet the biomechanical requirements for walking in the water environment.

Background We recently discovered that individuals with complete spinal cord injury (SCI) have a higher grip force control ability in their intact upper limbs than able-bodied subjects. However, the neural basis for this phenomenon is unknown. Objective This study aimed to investigate the neural basis of the higher grip force control in the brains of individuals with SCI using multimodal magnetic resonance imaging (MRI). Methods Eight SCI subjects and 10 able-bodied subjects performed hand grip force control tasks at 10%, 20%, and 30% of their maximal voluntary contraction during functional MRI (fMRI). Resting-state fMRI and T1-weighted structural images were obtained to investigate changes in brain networks and structures after SCI. Results SCI subjects showed higher grip force steadiness than able-bodied subjects ( P < .05, corrected), smaller activation in the primary motor cortex ( P < .05, corrected), and deactivation of the visual cortex ( P < .001, uncorrected). Furthermore, SCI subjects had stronger functional connectivity between the superior parietal lobule and the left primary motor cortex ( P < .001, uncorrected), as well as larger gray matter volume in the bilateral superior parietal lobule ( P < .001, uncorrected). Conclusions The structural and functional reorganization observed in the superior parietal lobule of SCI subjects may represent the neural basis underlying the observed higher grip force control, and is likely responsible for the smaller activation in the primary motor cortex observed in these individuals. These findings could have applications in the fields of neurorehabilitation for improvement of intact limb functions after SCI.

A 74-year-old woman was diagnosed with Waldenstrom macroglobulinemia/lymphoplasmacytic lymphoma (WM/LPL) in X-18. Fludarabine plus rituximab (FR) was started, and she showed remission. In July X-7, the serum creatinine (Cr) level increased to 1.67 mg/dL, and bendamustine plus rituximab (BR) was started. By November X-7, the Cr level had increased to 8.41 mg/dL, so she was started on hemodialysis (HD). In September X-1, she developed nephrotic syndrome. She was started on tirabrutinib at 480 mg. In July X, her nephrotic syndrome had improved, and a complete response (CR) was achieved. This is the first case of the administration of tirabrutinib in a patient undergoing HD.

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

The efficiency of upfront consolidation with high‐dose chemotherapy/autologous stem‐cell transplantation (HDCT/ASCT) for newly diagnosed high‐risk diffuse large B‐cell lymphoma (DLBCL) may be influenced by induction chemotherapy. To select better induction chemotherapy regimens for HDCT/ASCT, a randomized phase II study was conducted in high‐risk DLBCL patients having an age‐adjusted International Prognostic Index (aaIPI) score of 2 or 3. As induction chemotherapy, 6 cycles of R‐CHOP‐14 (arm A) or 3 cycles of R‐CHOP‐14 followed by 3 cycles of CHASER (arm B) were planned, and patients who responded proceeded to HDCT with LEED and ASCT. The primary endpoint was 2‐y progression‐free survival (PFS), and the main secondary endpoints included overall survival, overall response rate, and adverse events (AEs). In total, 71 patients were enrolled. With a median follow‐up of 40.3 mo, 2‐y PFS in arms A and B were 68.6% (95% confidence interval [CI], 50.5%‐81.2%) and 66.7% (95% CI: 48.8%‐79.5%), respectively. Overall survival at 2 y in arms A and B was 74.3% (95% CI: 56.4%‐85.7%) and 83.3% (95% CI: 66.6%‐92.1%). Overall response rates were 82.9% in arm A and 69.4% in arm B. During induction chemotherapy, 45.7% and 75.0% of patients in arms A and B, respectively, had grade ≥ 3 non‐hematologic toxicities. One patient in arm A and 6 in arm B discontinued induction chemotherapy due to AEs. In conclusion, R‐CHOP‐14 showed higher 2‐y PFS and less toxicity compared with R‐CHOP‐14/CHASER in patients with high‐risk DLBCL, suggesting the former to be a more promising induction regimen for further investigations (UMIN‐CTR, UMIN000003823)., The efficiency of upfront consolidation with high‐dose chemotherapy/autologous stem‐cell transplantation (HDCT/ASCT) for newly diagnosed high‐risk diffuse large B‐cell lymphoma (DLBCL) may be influenced by induction chemotherapy. To select better induction chemotherapy regimens for HDCT/ASCT, a randomized phase II study was conducted in patients with high‐risk DLBCL who had an age‐adjusted International Prognostic Index (aaIPI) score of 2 or 3. R‐CHOP‐14 showed higher 2‐y PFS and less toxicity compared with R‐CHOP‐14/CHASER in patients with high‐risk DLBCL, suggesting the former to be a more promising induction regimen for further investigations.

The advent of image-activated cell sorting and imaging-based cell picking has advanced our knowledge and exploitation of biological systems in the last decade. Unfortunately, they generally rely on fluorescent labeling for cellular phenotyping, an indirect measure of the molecular landscape in the cell, which has critical limitations. Here we demonstrate Raman image-activated cell sorting by directly probing chemically specific intracellular molecular vibrations via ultrafast multicolor stimulated Raman scattering (SRS) microscopy for cellular phenotyping. Specifically, the technology enables real-time SRS-image-based sorting of single live cells with a throughput of up to ~100 events per second without the need for fluorescent labeling. To show the broad utility of the technology, we show its applicability to diverse cell types and sizes. The technology is highly versatile and holds promise for numerous applications that are previously difficult or undesirable with fluorescence-based technologies., Most current cell sorting methods are based on fluorescence detection with no imaging capability. Here the authors generate and use Raman image-activated cell sorting with a throughput of around 100 events per second, providing molecular images with no need for labeling.

Imaging flow cytometry is a powerful tool by virtue of its capability for high-throughput cell analysis. The advent of high-speed optical imaging methods on a microfluidic platform has significantly improved cell throughput and brought many degrees of freedom to instrumentation and applications over the last decade, but it also poses a predicament on microfluidic chips. Specifically, as the throughput increases, the flow speed also increases (currently reaching 10 m/s): consequently, the increased hydrodynamic pressure on the microfluidic chip deforms the wall of the microchannel and produces detrimental effects lead to defocused and blur image. Here, we present a comprehensive study of the effects of flow-induced microfluidic chip wall deformation on imaging flow cytometry. We fabricated three types of microfluidic chips with the same geometry and different degrees of stiffness made of polydimethylsiloxane (PDMS) and glass to investigate material influence on image quality. First, we found the maximum deformation of a PDMS microchannel was >60 μm at a pressure of 0.6 MPa, while no appreciable deformation was identified in a glass microchannel at the same pressure. Second, we found the deviation of lag time that indicating velocity difference of migrating microbeads due to the deformation of the microchannel was 29.3 ms in a PDMS microchannel and 14.9 ms in a glass microchannel. Third, the glass microchannel focused cells into a slightly narrower stream in the X-Y plane and a significantly narrower stream in the Z-axis direction (focusing percentages were increased 30%, 32%, and 5.7% in the glass channel at flow velocities of 0.5, 1.5, and 3 m/s, respectively), and the glass microchannel showed stabler equilibrium positions of focused cells regardless of flow velocity. Finally, we achieved the world's fastest imaging flow cytometry by combining a glass microfluidic device with an optofluidic time-stretch microscopy imaging technique at a flow velocity of 25 m/s. © 2019 International Society for Advancement of Cytometry.

In the past few decades, aquatic animals have become popular model organisms in biology, spurring a growing need for establishing aquatic facilities. Zebrafish are widely studied and relatively easy to culture using commercial systems. However, a challenging aspect of maintaining aquatic facilities is animal feeding, which is both time- and resource-consuming. We have developed an open-source fully automatic daily feeding system, Zebrafish Automatic Feeder (ZAF). ZAF is reliable, provides a standardized amount of food to every tank, is cost-efficient and easy to build. The advanced version, ZAF+, allows for the precise control of food distribution as a function of fish density per tank, and has a user-friendly interface. Both ZAF and ZAF+ are adaptable to any laboratory environment and facilitate the implementation of aquatic colonies. Here, we provide all blueprints and instructions for building the mechanics, electronics, fluidics, as well as to setup the control software and its user-friendly graphical interface. Importantly, the design is modular and can be scaled to meet different user needs. Furthermore, our results show that ZAF and ZAF+ do not adversely affect zebrafish culture, enabling fully automatic feeding for any aquatic facility.

Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself's ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches. Moreover, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret them in the context of the fluorescence images, and analyze the performance contributions of each component of our approach.

The promise of single-objective light-sheet microscopy is to combine the convenience of standard single-objective microscopes with the speed, coverage, resolution and gentleness of light-sheet microscopes. We present DaXi, a single-objective light-sheet microscope design based on oblique plane illumination that achieves: (1) a wider field of view and high-resolution imaging via a custom remote focusing objective; (2) fast volumetric imaging over larger volumes without compromising image quality or necessitating tiled acquisition; (3) fuller image coverage for large samples via multi-view imaging and (4) higher throughput multi-well imaging via remote coverslip placement. Our instrument achieves a resolution of 450 nm laterally and 2 μm axially over an imaging volume of 3,000 × 800 × 300 μm. We demonstrate the speed, field of view, resolution and versatility of our instrument by imaging various systems, including Drosophila egg chamber development, zebrafish whole-brain activity and zebrafish embryonic development - up to nine embryos at a time.

Summary Background Standard treatment for untreated advanced-stage follicular lymphoma is rituximab plus chemotherapy. The incidence of histological transformation of follicular lymphoma has been reported only in heterogeneously treated populations and rarely with long-term follow-up. Additionally, the incidence of secondary malignancies after treatment, without high-dose therapy for follicular lymphoma, is largely unknown. The aim of our study was to assess progression-free survival, overall survival, incidence of secondary malignancies, and incidence of histological transformation in a 10-year follow-up analysis of the JCOG0203 trial. Methods In the phase 2–3 randomised JCOG0203 trial, previously untreated patients with stage III or IV indolent B-cell lymphoma, including grades 1–3 follicular lymphoma, from 44 hospital centres in Japan, were randomly assigned (1:1) by use of a minimisation method to receive six cycles of R-CHOP (rituximab [375 mg/m2], given on day 1, plus cyclophosphamide [750 mg/m2], doxorubicin [50 mg/m2], vincristine [1·4 mg/m2, capped at 2·0 mg] given intravenously on day 3, and oral prednisone [100 mg once daily on days 3–7]) every 3 weeks (R-CHOP-21) or every 2 weeks (enabled by mandatory granulocyte-colony stimulating factor administration once daily for 6 days, starting on day 8; R-CHOP-14) without rituximab maintenance. Age, bulky disease (nodal or extranodal mass ≥10 cm in diameter on CT), and institution were used as adjustment factors. Investigators enrolled participants, and assignment to trial groups was done with a computer-assisted randomisation allocation sequence that took place centrally at the Japan Clinical Oncology Group Data Center, without the intervention of investigators. Interventions were not masked for patients or investigators. Data were collected 10 years after enrolment of the last patient. The primary endpoint of the phase 3 part of the study was progression-free survival, and the primary endpoint of the phase 2 part of the study was the proportion of patients who achieved a complete response. Accrual was 4·5 years, and follow-up was 3 years after registration was closed. Data were updated on the cutoff date of Feb 28, 2017. Intention-to-treat analyses (ie, progression-free survival, overall survival, and incidence of secondary malignancies) were predefined, to be done at 10 years after the last patient was enrolled. An additional analysis of the incidence of histological transformation was defined 15 years after the protocol, on May 8, 2017, in a supplementary analysis plan, and assessed at 10 years after the last patient was enrolled. Follow-up is ongoing. This trial is registered with ClinicalTrials.gov, number NCT00147121. Findings Between Sept 1, 2002, and Feb 28, 2007, 300 patients were enrolled, and 149 (50%) were assigned to the R-CHOP-21 group and 151 (50%) were assigned to the R-CHOP-14 group. After eligibility was assessed, one patient was excluded from the R-CHOP-21 group. 10-year progression-free survival was not different between groups (R-CHOP-21 33%, 95% CI 25–41; R-CHOP-14 39%, 31–47; hazard ratio 0·89, 95% CI 0·67–1·17). In 248 patients with grade 1–3a follicular lymphoma, progression-free survival was 39% (33–45) at 8 years and 36% (30–42) at 10 years. The cumulative incidence of histological transformation was 3·2% (95% CI 1·5–6·0) at 5 years, 8·5% (5·4–12·4) at 8 years, and 9·3% (6·1–13·4) at 10 years after enrolment. At 10 years, the cumulative incidence of secondary malignancies was 8·1% (5·1–12·0) and the cumulative incidence of haematological secondary malignancies was 2·9% (1·3–5·5). Interpretation R-CHOP is a viable option for first-line treatment in patients with newly diagnosed advanced follicular lymphoma. Clinicians choosing a first-line treatment for patients with follicular lymphoma should be cautious of secondary malignancies caused by immunochemotherapy and severe complications of infectious diseases in the long-term follow-up—both of which could lead to death. Funding National Cancer Center and Ministry of Health, Labour and Welfare of Japan.

Elucidating the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here, we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. cytoself leverages a self-supervised training scheme that does not require pre-existing knowledge, categories, or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes such as nuclear, cytoplasmic and vesicular, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself’s ability to cluster proteins into organelles and protein complex clusters using a clustering score, and show that cytoself attains higher scores than previous unsupervised or self-supervised approaches. Finally, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret these features in the context of the fluorescence images, and analyze the performance contributions of the different components of our approach.

Elucidating the wiring diagram of the human cell is one of the central goals of the post-genomic era. Here, we integrate genome engineering, confocal imaging, mass spectrometry and data science to systematically map protein localization in live cells and protein interactions under endogenous expression conditions. For this, we generated a library of 1,311 CRISPR-edited cell lines harboring fluorescent tags that also serve as handles for affinity capture, and applied a new machine learning framework to encode the interaction and localization profiles of each protein. Our approach provides a data-driven description of the molecular and spatial networks that organize the human proteome. We show that unsupervised clustering of these networks delineates functional groups and facilitates biological discovery, while hierarchical analyses uncover the core features that template cellular architecture. Furthermore, we discover that localization signatures are remarkably predictive of protein function, and often contain enough information to identify molecular interactions. Paired with a fully interactive website (opencell.czbiohub.org), OpenCell is a resource for the quantitative cartography of human cellular organization.

The present study was a cross-sectional comparison of probabilistic structure in the distribution of pitching location among baseball pitchers of various age groups (25 elementary school (ES), 20 junior high school (JH), 15 high school (HS), and 18 college students (CL)). In the results, despite the general age-dependent variations in pitching precision, the difference was reflected not only in error 'size' but also in the 'shape' of error as it was shown by fitting 95% confidence ellipse to the two dimensional distribution of pitch location. While the precision measure as a reflection of trial-by-trial variability of release timing (major axis length of the ellipse) was constant, minor axis length of the ellipse as a reflection of variability in the pitching form of each participant demonstrated significant differences among the groups. In the ES group particularly, the trial-by-trial variability in the trajectory angle of the throwing arm was significantly correlated with the minor axis length; this correlation was far greater than those in older groups. The present study is the first to demonstrate the detailed structure of the variability of pitching location of baseball dependent on age.

How do skilled players change their motion patterns depending on motion effort? Pitchers commonly accelerate wrist and elbow joint rotations via proximal joint motions. Contrastingly, they show individually different pitching motions, such as in wind-up or follow-through. Despite the generality of the uniform and diverse features, effort-dependent effects on these features are unclear. Here, we reveal the effort dependence based on muscle activity data in natural three-dimensional pitching performed by skilled players. We extract motor modules and their effort dependence from the muscle activity data via tensor decomposition. Then, we reveal the unknown relations among motor modules, common features, unique features, and effort dependence. The current study clarifies that common features are obvious in distinguishing between low and high effort and that unique features are evident in differentiating high and highest efforts.

Recent developments in Oblique Plane Microscopy (OPM) have shown that it can achieve high spatio-temporal resolution. Here we describe a single objective light-sheet microscope based on oblique plane illumination that achieves: (i) large field of view and high-resolution imaging via a custom remote focusing objective; (ii) fast volumetric imaging by means of light-sheet stabilised stage scanning – a novel scanning modality that extends the imaging volume without compromising imaging speed nor quality; (iii) multi-view imaging by alternating the orientation of light-sheet illumination and detection to improve the image quality on large samples; (iv) simpler design and ergonomics by remote placement of coverslips to allow inverted imaging, enabling imaging across scales in a high-throughput format. Overall, we achieved a resolution of 450 nm laterally and 2 μm axially and a field of view of 3000 μm × 800 μm × 300 μm. We demonstrate the speed, field of view, resolution and versatility of our novel instrument by imaging various systems, including zebrafish whole brain activity, Drosophila egg chamber development, and zebrafish development – up to nine embryos simultaneously.

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics., eLife digest Platelets are small cells in the blood that primarily help stop bleeding after an injury by sticking together with other blood cells to form a clot that seals the broken blood vessel. Blood clots, however, can sometimes cause harm. For example, if a clot blocks the blood flow to the heart or the brain, it can result in a heart attack or stroke, respectively. Blood clots have also been linked to harmful inflammation and the spread of cancer, and there are now preliminary reports of remarkably high rates of clotting in COVID-19 patients in intensive care units. A variety of chemicals can cause platelets to stick together. It has long been assumed that it would be impossible to tell apart the clots formed by different chemicals (which are also known as agonists). This is largely because these aggregates all look very similar under a microscope, making it incredibly time consuming for someone to look at enough microscopy images to reliably identify the subtle differences between them. However, finding a way to distinguish the different types of platelet aggregates could lead to better ways to diagnose or treat blood vessel-clogging diseases. To make this possible, Zhou, Yasumoto et al. have developed a method called the “intelligent platelet aggregate classifier” or iPAC for short. First, numerous clot-causing chemicals were added to separate samples of platelets taken from healthy human blood. The method then involved using high-throughput techniques to take thousands of images of these samples. Then, a sophisticated computer algorithm called a deep learning model analyzed the resulting image dataset and “learned” to distinguish the chemical causes of the platelet aggregates based on subtle differences in their shapes. Finally, Zhou, Yasumoto et al. verified iPAC method’s accuracy using a new set of human platelet samples. The iPAC method may help scientists studying the steps that lead to clot formation. It may also help clinicians distinguish which clot-causing chemical led to a patient’s heart attack or stroke. This could help them choose whether aspirin or another anti-platelet drug would be the best treatment. But first more studies are needed to confirm whether this method is a useful tool for drug selection or diagnosis.

This study explored the mechanical factors that determine accuracy of a baseball pitching. In particular, we focused on the mechanical parameters at ball release, referred to as release parameters. The aim was to understand which parameter has the most deterministic influence on pitch location by measuring the release parameters during actual pitching and developing a simulation that predicts the pitch location from given release parameters. By comparing the fluctuation of the simulated pitch location when varying each release parameter, it was found that the elevation pitching angle and speed significantly influenced the vertical pitch location, and the azimuth pitching angle significantly influenced the horizontal pitch location. Moreover, a regression model was obtained to predict the pitch location, and it became clear that the significant predictors for the vertical pitch location were the elevation pitching angle, the speed, and spin axis, and those for the horizontal pitch location were the azimuth pitching angle, the spin axis, and horizontal release point. Therefore, it was suggested that the parameter most affecting pitch location weas pitching angle. On the other hand, multiple regression analyses revealed that the relation between release parameters varied between pitchers. The result is expected to contribute to an understanding of the mechanisms underlying accurate ball control skill in baseball pitching.

The traditional diagnosis of leukemia relies on pathologists to observe and classify cells on bone marrow smears, which is low-throughput, time-consuming, and subject to human bias. To overcome these limitations, we demonstrate intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging (OTS-QPI) that acquires bright-field and quantitative phase images of white blood cells (WBCs) containing leukemia cells with high throughput (15,000 cells/s) for deep-learning-based classification. After trained with 64,000 images, a convolutional neural network (CNN) distinguishes three different types of leukemia cells from WBCs with an accuracy of over 96%. Our method provides new possibilities for high-throughput, label-free, and intelligent leukemia diagnosis.

Platelets participate in both physiological hemostasis and pathological thrombosis by forming aggregates activated by various agonists. However, it has been considered impossible to identify the stimuli and classify the aggregates. Here we present an intelligent method for classifying platelet aggregates by agonist type based on the combination of high-throughput imaging flow cytometry and a convolutional neural network. It morphologically identifies the contributions of different agonists to platelet aggregation with high accuracy. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to develop a new class of clinical diagnostics and therapeutics.

In cross-sectional studies, we examined the long-term practice effects of QiGong exercise on perceived health and quality of life (QoL) in middle-aged (over 50 years) Japanese individuals. In Study 1, Japanese adults (n = 320) who practised QiGong responded to a questionnaire concerning the perceived benefits of QiGong practice and QoL. In Study 2, we collected data from QiGong participants who attended a QiGong conference (n = 799). Participants in Study 1 perceived that QiGong affords physical, psychological, and social benefits and QiGong duration in years correlated strongly with QoL. In Study 2, those who practised QiGong for 0–3 years vs. 13+ years reported a greater likelihood of perceived palpitation, insomnia, a lack of vigour, and attention deficit (odd ratios 1.56–2.60, all p .05). QiGong is a multi-component form of physical activity, which – if practised for prolonged periods – affords motor, cognitive, social, and QoL benefits.

Innovations in optical microscopy have opened new windows onto scientific research, industrial quality control, and medical practice over the last few decades. One of such innovations is optofluidic time-stretch quantitative phase microscopy - an emerging method for high-throughput quantitative phase imaging that builds on the interference between temporally stretched signal and reference pulses by using dispersive properties of light in both spatial and temporal domains in an interferometric configuration on a microfluidic platform. It achieves the continuous acquisition of both intensity and phase images with a high throughput of more than 10,000 particles or cells per second by overcoming speed limitations that exist in conventional quantitative phase imaging methods. Applications enabled by such capabilities are versatile and include characterization of cancer cells and microalgal cultures. In this paper, we review the principles and applications of optofluidic time-stretch quantitative phase microscopy and discuss its future perspective.

The development of reliable, sustainable, and economical sources of alternative fuels to petroleum is required to tackle the global energy crisis. One such alternative is microalgal biofuel, which is expected to play a key role in reducing the detrimental effects of global warming as microalgae absorb atmospheric CO2 via photosynthesis. Unfortunately, conventional analytical methods only provide population-averaged lipid amounts and fail to characterize a diverse population of microalgal cells with single-cell resolution in a non-invasive and interference-free manner. Here high-throughput label-free single-cell screening of lipid-producing microalgal cells with optofluidic time-stretch quantitative phase microscopy was demonstrated. In particular, Euglena gracilis, an attractive microalgal species that produces wax esters (suitable for biodiesel and aviation fuel after refinement), within lipid droplets was investigated. The optofluidic time-stretch quantitative phase microscope is based on an integration of a hydrodynamic-focusing microfluidic chip, an optical time-stretch quantitative phase microscope, and a digital image processor equipped with machine learning. As a result, it provides both the opacity and phase maps of every single cell at a high throughput of 10,000 cells/s, enabling accurate cell classification without the need for fluorescent staining. Specifically, the dataset was used to characterize heterogeneous populations of E. gracilis cells under two different culture conditions (nitrogen-sufficient and nitrogen-deficient) and achieve the cell classification with an error rate of only 2.15%. The method holds promise as an effective analytical tool for microalgae-based biofuel production. © 2017 International Society for Advancement of Cytometry.

Optical time-stretch microscopy has recently attracted intensive attention for its capability of acquiring images at an ultrahigh frame rate. Unfortunately, its achievable frame rate is limited by the requirement of having no overlap between consecutive frames, which leads to a tradeoff between the frame rate (pulse repetition rate) and the amount of the temporal dispersion used for optical image serialization. In this paper, we demonstrate compressive sensing on the platform of optical time-stretch microscopy to overcome the tradeoff between frame rate and temporal dispersion (time stretch) and achieve 50 times higher frame rate than conventional optical time-stretch microscopy. Specifically, we computationally perform compressed optical time-stretch microscopy with an experimental dataset acquired by conventional optical time-stretch microscopy and demonstrate its effects in terms of spatial resolution and cell classification accuracy. Our results indicate that the spatial resolution and cell classification accuracy reach 780 nm and 95% at a line scan rate of 675 MHz and 6.75 GHz, respectively, which correspond to five times and 50 times higher frame rates than what conventional optical time-stretch microscopy can achieve with the same dispersion amount and digitizer sampling rate.

This study aimed to assess the long-term outcomes of radiotherapy in patients with localized gastric mucosa–associated lymphoid tissue (MALT) lymphoma. Twenty-seven patients with Stage I gastric MALT lymphoma were treated with radiotherapy from 1999 to 2010. The median age was 65 years (range: 31–84). Fifteen patients were Helicobacter pylori–negative. Thirteen patients were treated with definitive radiotherapy alone. The other 14 patients who had refractory or residual disease following a prior treatment received salvage radiotherapy. The median dose of the radiotherapy was 30 Gy in 20 fractions (range: 30–39.5 Gy). The median follow-up period was 121 months (range: 8–176 months). The 5- and 10-year overall survival rates for all patients were 92% and 87%, respectively. No patients died from MALT lymphoma. Three patients died of other diseases at 8, 33 and 74 months after radiotherapy (myocardial infarction, pneumonia and hepatocellular carcinoma, respectively). No cases of local recurrence were observed during the follow-up period. There were no serious late gastric, liver or kidney complications during a median follow-up period of over 10 years. Two patients remain alive with distant metastases: a lung metastasis and an abdominal lymph node metastasis at 104 months and 21 months after radiotherapy, respectively. Excellent long-term local control was observed in patients with localized gastric MALT lymphoma after radiotherapy. However, lifelong follow-up should be conducted to detect cases of late recurrence, especially distant metastases.