Plasma myoglobin indicates muscle damage associated with acceleration/deceleration during football.

BACKGROUND: Monitoring muscle damage in athletes assists not only coaches to adjust the training workload but also medical staff to prevent injury. Measuring blood myoglobin concentration can help evaluate muscle damage. The novel portable device utilized in this study allows for easy on-site measurement of myoglobin, providing real-time data on the player's muscle damage. This study investigated the relationship between external load (global positioning system parameters) and internal loads (myoglobin concentration and creatine kinase activity) in 15 male professional football players before and after a match. METHODS: Whole blood samples from participants' fingertips were collected before the match (baseline) and at 2, 16, and 40 h after the match. Myoglobin concentrations were measured using the IA-100 compact immunoassay system. Creatine kinase concentrations were measured in a clinical laboratory, and match loads were monitored using a global positioning system device. RESULTS: The mean myoglobin concentration was significantly higher at 2 h than at the other time points (P<0.05), and decreased to baseline levels within 16 h post-match. The mean creatine kinase concentration increased after the match but did not reach a significant level. Muscle damage monitored by myoglobin after football match-play was strongly associated with acceleration/deceleration metrics rather than the sprint/high-speed running distance. CONCLUSIONS: Our findings indicate that myoglobin is a more sensitive marker of muscle damage than creatine kinase after football match-play. Monitoring myoglobin in athletes can aid in determining their recovery status from the previous training load and help practitioners manage the training load.

Multidimensional cerebellar computations for flexible kinematic control of movements.

Both the environment and our body keep changing dynamically. Hence, ensuring movement precision requires adaptation to multiple demands occurring simultaneously. Here we show that the cerebellum performs the necessary multi-dimensional computations for the flexible control of different movement parameters depending on the prevailing context. This conclusion is based on the identification of a manifold-like activity in both mossy fibers (MFs, network input) and Purkinje cells (PCs, output), recorded from monkeys performing a saccade task. Unlike MFs, the PC manifolds developed selective representations of individual movement parameters. Error feedback-driven climbing fiber input modulated the PC manifolds to predict specific, error type-dependent changes in subsequent actions. Furthermore, a feed-forward network model that simulated MF-to-PC transformations revealed that amplification and restructuring of the lesser variability in the MF activity is a pivotal circuit mechanism. Therefore, the flexible control of movements by the cerebellum crucially depends on its capacity for multi-dimensional computations.

Exploration of Chemical Space Guided by PixelCNN for Fragment-Based De Novo Drug Discovery.

We report a novel framework for achieving fragment-based molecular design using pixel convolutional neural network (PixelCNN) combined with the simplified molecular input line entry system (SMILES) as molecular representation. While a widely used recurrent neural network (RNN) assumes monotonically decaying correlations in strings, PixelCNN captures a periodicity among characters of SMILES. Thus, PixelCNN provides us with a novel solution for the analysis of chemical space by extracting the periodicity of molecular structures that will be buried in SMILES. Moreover, this characteristic enables us to generate molecules by combining several simple building blocks, such as a benzene ring and side-chain structures, which contributes to the effective exploration of chemical space by step-by-step searching for molecules from a target fragment. In conclusion, PixelCNN could be a powerful approach focusing on the periodicity of molecules to explore chemical space for the fragment-based molecular design.

Identification of microstructures critically affecting material properties using machine learning framework based on metallurgists' thinking process.

In materials science, machine learning has been intensively researched and used in various applications. However, it is still far from achieving intelligence comparable to that of human experts in terms of creativity and explainability. In this paper, we investigate whether machine learning can acquire explainable knowledge without directly introducing problem-specific information such as explicit physical mechanisms. In particular, a potential of machine learning to obtain the capability to identify a part of material structures that critically affects a physical property without human prior knowledge is mainly discussed. The guide for constructing the machine learning framework adopted in this paper is to imitate human researchers' process of thinking in the interpretation and development of materials. Our framework was applied to the optimization of structures of artificial dual-phase steels in terms of a fracture property. A comparison of results of the framework with those of numerical simulation based on governing physical laws demonstrated the potential of our framework for the identification of a part of microstructures critically affecting the target property. Consequently, this implies that our framework can implicitly acquire an intuition in a similar way that human researchers empirically attain the general strategy for material design consistent with the physical background.

Stochastic characterization and reconstruction of material microstructures for establishment of process-structure-property linkage using the deep generative model.

In material design, microstructure characterization and reconstruction are indispensable for understanding the role of a structure in a process-structure-property relation. The significant contribution of this paper is to introduce a methodology for the characterization and generation of material microstructures using deep generative networks as the first step in the establishment of a process-structure-property linkage for forward or inverse material design. Our approach can be divided into two parts: (i) characterization of material microstructures by a vector quantized variational auto-encoder, and (ii) determination of the correlation between the extracted microstructure characterizations and the given conditions, such as processing parameters and/or material properties, by a pixel convolutional neural network. As an example, we tested our framework in the generation of low-carbon-steel microstructures from the given material processing. The results were in satisfactory agreement with the experimental observation qualitatively and quantitatively, demonstrating the potential of applying the proposed method to forward or inverse material design. One of the advantages of the proposed methodology lies in the capability to capture the stochastic nature behind the microstructure generation. As a result, this methodology enables us to build a process-structure-property linkage while quantifying uncertainties, which not only makes a prediction more robust but also shows a way toward enhancing our understanding of the stochastic competitive phenomena behind the generation of material microstructures.

Cerebellar complex spikes multiplex complementary behavioral information.

Purkinje cell (PC) discharge, the only output of cerebellar cortex, involves 2 types of action potentials, high-frequency simple spikes (SSs) and low-frequency complex spikes (CSs). While there is consensus that SSs convey information needed to optimize movement kinematics, the function of CSs, determined by the PC's climbing fiber input, remains controversial. While initially thought to be specialized in reporting information on motor error for the subsequent amendment of behavior, CSs seem to contribute to other aspects of motor behavior as well. When faced with the bewildering diversity of findings and views unraveled by highly specific tasks, one may wonder if there is just one true function with all the other attributions wrong? Or is the diversity of findings a reflection of distinct pools of PCs, each processing specific streams of information conveyed by climbing fibers? With these questions in mind, we recorded CSs from the monkey oculomotor vermis deploying a repetitive saccade task that entailed sizable motor errors as well as small amplitude saccades, correcting them. We demonstrate that, in addition to carrying error-related information, CSs carry information on the metrics of both primary and small corrective saccades in a time-specific manner, with changes in CS firing probability coupled with changes in CS duration. Furthermore, we also found CS activity that seemed to predict the upcoming events. Hence PCs receive a multiplexed climbing fiber input that merges complementary streams of information on the behavior, separable by the recipient PC because they are staggered in time.

Unsupervised microstructure segmentation by mimicking metallurgists' approach to pattern recognition.

An efficient deep learning method is presented for distinguishing microstructures of a low carbon steel. There have been numerous endeavors to reproduce the human capability of perceptually classifying different textures using machine learning methods, but this is still very challenging owing to the need for a vast labeled image dataset. In this study, we introduce an unsupervised machine learning technique based on convolutional neural networks and a superpixel algorithm for the segmentation of a low-carbon steel microstructure without the need for labeled images. The effectiveness of the method is demonstrated with optical microscopy images of steel microstructures having different patterns taken at different resolutions. In addition, several evaluation criteria for unsupervised segmentation results are investigated along with the hyperparameter optimization.

Using deep neural networks to detect complex spikes of cerebellar Purkinje cells.

One of the most powerful excitatory synapses in the brain is formed by cerebellar climbing fibers, originating from neurons in the inferior olive, that wrap around the proximal dendrites of cerebellar Purkinje cells. The activation of a single olivary neuron is capable of generating a large electrical event, called "complex spike," at the level of the postsynaptic Purkinje cell, comprising of an initial large-amplitude spike followed by a long polyphasic tail of small-amplitude spikelets. Several ideas discussing the role of the cerebellum in motor control are centered on these complex spike events. However, these events, only occurring one to two times per second, are extremely rare relative to Purkinje cell "simple spikes" (standard sodium-potassium action potentials). As a result, drawing conclusions about their functional role has been very challenging. In fact, because standard spike sorting approaches cannot fully handle the polyphasic shape of complex spike waveforms, the only safe way to avoid omissions and false detections has been to rely on visual inspection by experts, which is both tedious and, because of attentional fluctuations, error prone. Here we present a deep learning algorithm for rapidly and reliably detecting complex spikes. Our algorithm, utilizing both action potential and local field potential signals, not only detects complex spikes much faster than human experts, but it also reliably provides complex spike duration measures similar to those of the experts. A quantitative comparison of our algorithm's performance to both classic and novel published approaches addressing the same problem reveals that it clearly outperforms these approaches.NEW & NOTEWORTHY Purkinje cell "complex spikes", fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.

High stereographic resolution texture and residual stress evaluation using time-of-flight neutron diffraction.

Neutron diffraction texture measurements provide bulk averaged textures with excellent grain orientation statistics, even for large-grained materials, owing to the probed volume being of the order of 1 cm3. Furthermore, crystallographic parameters and other valuable microstructure information such as phase fraction, coherent crystallite size, root-mean-square microstrain, macroscopic or intergranular strain and stress, etc. can be derived from neutron diffractograms. A procedure for combined high stereographic resolution texture and residual stress evaluation was established on the pulsed-neutron-source-based engineering materials diffractometer TAKUMI at the Materials and Life Science Experimental Facility of the Japan Proton Accelerator Research Center, through division of the neutron detector panel regions. Pole figure evaluation of a limestone standard sample with a well known texture suggested that the precision obtained for texture measurement is comparable to that of the established neutron beamlines utilized for texture measurement, such as the HIPPO diffractometer at the Los Alamos Neutron Science Center (New Mexico, USA) and the D20 angle-dispersive neutron diffractometer at the Institut Laue-Langevin (Grenoble, France). A high-strength martensite-austenite multilayered steel was employed for further verification of the reliability of simultaneous Rietveld analysis of multiphase textures and macro stress tensors. By using a texture-weighted geometric mean micromechanical (BulkPathGEO) model, a macro stress tensor analysis with a plane stress assumption showed a rolling direction-transverse direction (RD-TD) in-plane compressive stress (about -330 MPa) in the martensite layers and an RD-TD in-plane tensile stress (about 320 MPa) in the austenite layers. The phase stress partitioning was ascribed mainly to the additive effect of the volume expansion during martensite transformation and the linear contraction misfit between austenite layers and newly transformed martensite layers during the water quenching process.

Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model.

We propose a method to predict grain growth based on data assimilation by using a four-dimensional variational method (4DVar). When implemented on a multi-phase-field model, the proposed method allows us to calculate the predicted grain structures and uncertainties in them that depend on the quality and quantity of the observational data. We confirm through numerical tests involving synthetic data that the proposed method correctly reproduces the true phase-field assumed in advance. Furthermore, it successfully quantifies uncertainties in the predicted grain structures, where such uncertainty quantifications provide valuable information to optimize the experimental design.

Data assimilation for massive autonomous systems based on a second-order adjoint method.

Data assimilation (DA) is a fundamental computational technique that integrates numerical simulation models and observation data on the basis of Bayesian statistics. Originally developed for meteorology, especially weather forecasting, DA is now an accepted technique in various scientific fields. One key issue that remains controversial is the implementation of DA in massive simulation models under the constraints of limited computation time and resources. In this paper, we propose an adjoint-based DA method for massive autonomous models that produces optimum estimates and their uncertainties within reasonable computation time and resource constraints. The uncertainties are given as several diagonal elements of an inverse Hessian matrix, which is the covariance matrix of a normal distribution that approximates the target posterior probability density function in the neighborhood of the optimum. Conventional algorithms for deriving the inverse Hessian matrix require O(CN^{2}+N^{3}) computations and O(N^{2}) memory, where N is the number of degrees of freedom of a given autonomous system and C is the number of computations needed to simulate time series of suitable length. The proposed method using a second-order adjoint method allows us to directly evaluate the diagonal elements of the inverse Hessian matrix without computing all of its elements. This drastically reduces the number of computations to O(C) and the amount of memory to O(N) for each diagonal element. The proposed method is validated through numerical tests using a massive two-dimensional Kobayashi phase-field model. We confirm that the proposed method correctly reproduces the parameter and initial state assumed in advance, and successfully evaluates the uncertainty of the parameter. Such information regarding uncertainty is valuable, as it can be used to optimize the design of experiments.

A novel highly sensitive and specific flow cytometry system for cervical cancer screening.

PURPOSE: This study assessed the performance of a novel flow cytometry (FCM) cervical cancer screening system compared with human papillomavirus (HPV) Hybrid Capture 2 (HC2). METHODS: Chinese women aged 20years or older were enrolled in this study at Fudan University Shanghai Cancer Center. All participants underwent cytology/pathology testing (gold standard), HPV HC2 testing and FCM testing involving analysis of cell proliferation index (CPIx). RESULTS: Among 437 women enrolled in this study, 185 women (42.3%) were diagnosed as "gold standard positive" by pathology with diseases including cervical intraepithelial neoplasia (CIN) grade 2 (n=11), CIN3 (n=41), squamous cell carcinoma (SCC; n=115), adenocarcinoma in situ (n=2) and adenocarcinoma (n=16). The remaining 252 cases were deemed "gold standard negative". The sensitivity was 87.6% (95% CI, 82.8-92.3) for FCM testing and 89.7% (95% CI, 85.4-94.1; p=0.5121) for HPV HC2 testing. The specificity of FCM testing was 90.5% (95% CI, 86.2-94.7), which was superior to the specificity of HPV HC2 testing (84.5%, 95% CI, 79.3-89.7; p=0.04). In the 20-29years old group, the sensitivity and the specificity of FCM testing were 90.0% (95% CI, 71.4-100.0) and 92.9% (95% CI, 76.9-100.0), respectively. The FCM testing CPIx statistically increased with the transition from normal cervical specimens to SCC specimens. CONCLUSIONS: Our results showed that the FCM screening system had high sensitivity and specificity for women of various ages. The FCM CPIx was able to evaluate the severity of disease quantitatively.

Abscisic acid substantially inhibits senescence of cucumber plants (Cucumis sativus) grown under low nitrogen conditions.

Low nitrogen (N) availability such as that found in both dry land and tropical regions limits plant growth and development. The relationship between the level of abscisic acid (ABA) in a plant and its growth under low-N conditions was investigated. The level of ABA in cucumber (Cucumis sativus) plants under low-N conditions was significantly higher at 10 and 20 d after transplantation compared with that under sufficient-N conditions. Chlorophyll was preserved in the aerial parts of cucumber plants grown under low-N conditions in the presence of ABA, while there was no significant difference between control plants and ABA-applied plants under sufficient-N conditions. ABA suppressed the reduction of chlorophyll biosynthesis under low-N conditions but not under sufficient-N conditions. On the other hand, ABA decreased the expression of the chlorophyll degradation gene in older cucumber plants grown under both conditions. In addition, transcript and protein levels of a gene encoding a chlorophyll a/b binding protein were positively correlated with ABA concentration under low-N conditions. The chloroplasts in control plants were round, and the stack of thylakoid membranes was reduced compared with that of plants treated with ABA 10(-5) M. These results strongly suggest that ABA is accumulated in cucumber plants grown under low-N conditions and that accumulated ABA promotes chlorophyll biosynthesis and inhibits its degradation in those plants.

Feasibility of flow cytometry for the detection of bacteria from body fluid samples.

The detection of microorganisms in body fluids is indispensable for identifying the source of infection and is one of the important examinations that influence subsequent treatment. In order to quickly detect bacteria in body fluid samples, a flow cytometry-based experimental automated bacteria counter (BF-FCM), was tested to determine its clinical value. The results for detectability obtained with the BF-FCM were compared with those obtained by conventional culture and Gram-staining techniques. We evaluated a total of 318 body fluid samples, excluding bile samples from which fungus alone was isolated. The samples consisted of 176 bile, 64 ascites, 42 pleural fluid, and 36 cerebrospinal fluid samples. Among the 318 samples, 154 (48.4%) were culture-positive. Of these 154, the BF-FCM identified 130 as positive (sensitivity, 84.4%). Of the 164 samples that were culture-negative, 141 were negative by the BF-FCM (specificity, 86.0%). Based on the culture results, the BF-FCM detected bacteria with a positive predictive value of 85.0% (130 of 153 samples), a negative predictive value of 85.5% (141 of 165 samples), and percent agreement of 85.2%. Although there were 23/164 (14.0%) false-positive samples, we consider that the BF-FCM, in combination with Gram staining and conventional cultures, would be helpful in the diagnosis and management of patients with diseases such as bacterial meningitis that present emergently.