The association between ultrafiltration rate and mortality in a co- hort of chronic hemodialysis patients with and without diabetes mellitus: a 7-year retrospective observational study.

Background: The ultrafiltration rate (UFR) is one of the important factors involved in long-term mortality in hemodialysis (HD) patients. Presence of diabetes mellitus often affects UFR due to abrupt hypotension during dialysis. In this study, we aimed to find the optimal UFR to improve the mortality in this population with and without diabetes mellitus (DM).Methods: The effect of the UFR on mortality was retrospectively evaluated in 707 patients un- dergoing regular HD from 1 June 2010 to 30 June 2017. The relationship between the UFR and mortality in patients in the non-DM group and those in the DM group was evaluated. Logistic regression analyses were used to select the determinants of mortality. Receiver operating char- acteristic (ROC) curve analyses and survival analysis were used to determine the optimal cutoff points of UFR for mortality.Results: The cutoff UFR values of the non-DM and DM groups were 12.07 ml/hr/kg and 9.66 ml/ hr/kg, respectively. A survival curve showed that in the non-DM group, the 7-year survival rate of patients with a UFR <12.07 ml/hr/kg was 72.6% and that in those with a UFR ≥12.07 ml/hr/kg was 19.6% (p<0.0001). In the DM group, the 7-year survival rate of those with a UFR <9.66 ml/ hr/kg was 66.7%, and it was 33.4% in those with a UFR ≥9.66 ml/hr/kg (p<0.0001).Conclusion: Lower UFR is essential for the long-term mortality of HD patients, and optimal UFR would be different between patients with and without DM

Spike Code Flow in Cultured Neuronal Networks

We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of “1101” and “1011,” which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the “maximum cross-correlations” among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network

Simulation of Spike Wave Propagation and Two-to-one Communication with Dynamic Time Warping

Although intercommunication among the different areas of the brain is well known, the rules of communication in the brain are not clear. Many previous studies have examined the firing patterns of neural networks in general, while we have examined the involvement of the firing patterns of neural networks in communication. In order to understand information processing in the brain, we simulated the interactions of the firing activities of a large number of neural networks in a 25 × 25 two-dimensional array for analyzing spike behavior. We stimulated the transmitting neurons at 0.1 msec. Then we observed the generated spike propagation for 120 msec. In addition, the positions of the firing neurons were determined with spike waves for different variances in the temporal fluctuations of the neuronal characteristics. These results suggested that for the changes (diversity) in the propagation routes of neuronal transmission resulted from variance in synaptic propagation delays and refractory periods. The simulation was used to examine differences in the percentages of neurons with significantly larger test statistics and the variances in the synaptic delay and refractory period. These results suggested that multiplex communication was more stable if the synaptic delay and refractory period varied

Learning Times Required to Identify the Stimulated Position and Shortening of Propagation Path by Hebb’s Rule in Neural Network

To deepen the understanding of the human brain, many researchers have created a new way of analyzing neural data. In many previous studies, researchers have examined neural networks from a macroscopic point of view, based on neuronal firing patterns. On the contrary, we have studied neural networks locally, in order to understand their communication strategies. To understand information processing in the brain, we simulated the firing activities of neural networks in a 9 × 9 two-dimensional neural network to analyze spike behavior. In this research study, we used two kinds of learning processes. As the main learning process, we implemented the learning process to identify the stimulated position. As the subsidiary one, we implemented Hebb’s learning rule which changes weight between neurons. Three channels with transmission and reception were preset, each of which has a different distance and direction. When all three channels succeeded in identifying the source stimulation in the receiving neuron group, it was regarded as an overall success and the learning was termed as successful. Furthermore, in order to see the effect of the second learning procedure, we elucidated the average of necessary learning times in each channel type and compared the firing propagation time of the first trial and an overall successful trial, in each channel. We found that the firing path after learning is shorter than the firing path before learning. Therefore, we deduced that Hebb’s rule contributes to shortening the firing path. Thus, Hebb’s rule contributes to speeding up communication in a neuronal network

Neuroscience instrumentation and distributed analysis of brain activity data: a case for eScience on global Grids

The distribution of knowledge (by scientists) and data sources (advanced scientific instruments), and the need for large-scale computational resources for analyzing massive scientific data are two major problems commonly observed in scientific disciplines. Two popular scientific disciplines of this nature are brain science and high-energy physics. The analysis of brain-activity data gathered from the MEG (magnetoencephalography) instrument is an important research topic in medical science since it helps doctors in identifying symptoms of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and requires access to large-scale computational resources. The potential platform for solving such resource intensive applications is the Grid. This paper presents the design and development of MEG data analysis system by leveraging Grid technologies, primarily Nimrod-G, Gridbus, and Globus. It describes the composition of the neuroscience (brain-activity analysis) application as parameter-sweep application and its on-demand deployment on global Grids for distributed execution. The results of economic-based scheduling of analysis jobs for three different optimizations scenarios on the world-wide Grid testhed resources are presented along with their graphical visualization

Classification of Spike Wave Propagations in a Cultured Neuronal Network: Investigating a Brain Communication Mechanism

In brain information science, it is still unclear how multiple data can be stored and transmitted in ambiguously behaving neuronal networks. In the present study, we analyze the spatiotemporal propagation of spike trains in neuronal networks. Recently, spike propagation was observed functioning as a cluster of excitation waves (spike wave propagation) in cultured neuronal networks. We now assume that spike wave propagations are just events of communications in the brain. However, in reality, various spike wave propagations are generated in neuronal networks. Thus, there should be some mechanism to classify these spike wave propagations so that multiple communications in brain can be distinguished. To prove this assumption, we attempt to classify various spike wave propagations generated from different stimulated neurons using our original spatiotemporal pattern matching method for spike temporal patterns at each neuron in spike wave propagation in the cultured neuronal network. Based on the experimental results, it became clear that spike wave propagations have various temporal patterns from stimulated neurons. Therefore these stimulated neurons could be classified at several neurons away from the stimulated neurons. These are the classifiable neurons. Moreover, distribution of classifiable neurons in a network is also different when stimulated neurons generating spike wave propagations are different. These results suggest that distinct communications occur via multiple communication links and that classifiable neurons serve this function

Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network

Neuronal networks have fluctuating characteristics, unlike the stable characteristics seen in computers. The underlying mechanisms that drive reliable communication among neuronal networks and their ability to perform intelligible tasks remain unknown. Recently, in an attempt to resolve this issue, we showed that stimulated neurons communicate via spikes that propagate temporally, in the form of spike trains. We named this phenomenon “spike wave propagation”. In these previous studies, using neural networks cultured from rat hippocampal neurons, we found that multiple neurons, e.g., 3 neurons, correlate to identify various spike wave propagations in a cultured neuronal network. Specifically, the number of classifiable neurons in the neuronal network increased through correlation of spike trains between current and adjacent neurons. Although we previously obtained similar findings through stimulation, here we report these observations on a physiological level. Considering that individual spike wave propagation corresponds to individual communication, a correlation between some adjacent neurons to improve the quality of communication classification in a neuronal network, similar to a diversity antenna, which is used to improve the quality of communication in artificial data communication systems, is suggested

Expression of Flavone Synthase II and Flavonoid 3′-Hydroxylase is Associated with Color Variation in Tan-colored Injured Leaves of Sorghum

Sorghum (Sorghum bicolor L. Moench) exhibits various color changes in injured leaves in response to cutting stress. Here, we aimed to identify key genes for the light brown and dark brown color variations in tan-colored injured leaves of sorghum. For this purpose, sorghum M36001 (light brown injured leaves), Nakei-MS3B (purple), and a progeny, #7 (dark brown), from Nakei-MS3B × M36001, were used. Accumulated pigments were detected by using high-performance liquid chromatography: M36001 accumulated only apigenin in its light brown leaves; #7 accumulated both luteolin and a small amount of apigenin in its dark brown leaves, and Nakei-MS3B accumulated 3-deoxyanthocyanidins (apigeninidin and luteolinidin) in its purple leaves. Apigenin or luteolin glucoside derivatives were also accumulated, in different proportions. Differentially expressed genes before and after cutting stress were identified by using RNA-seq. Integration of our metabolic and RNA-seq analyses suggested that expression of only flavone synthase II (FNSII) led to the synthesis of apigenin in M36001, expression of both FNSII and flavonoid 3′-hydroxylase (F3′H) led to the synthesis of apigenin and luteolin in #7, and expression of both flavanone 4-reductase and F3’H led to the synthesis of 3-deoxyanthocyanidins in Nakei-MS3B. These results suggest that expression of FNSII is related to the synthesis of flavones (apigenin and luteolin) and the expression level of F3′H is related to the balance of apigenin and luteolin. Expression of FNSII and F3′H is thus associated with dark or light brown coloration in tan-colored injured leaves of sorghum