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An Introduction To Genetic Analysis

Preface IIGeneral Remarks on Japanese Art Culture IIIZen and the Study of Confucianism IVZen and the Samurai VZen and Swordsmanship I VIZen and Swordsmanship II VIIZen and Haiku VIIIZen and the Art of Tea I IXZen and the Art of Tea II XRikyu and Other Teamen XILove of Nature Appendices ITwo Mondo from the \"Hekigan-shu\" IIThe Vimalakirti Sutra III\"Yama-uba,\" a No Play IVThe Swordsman and the Cat VChuang-tzu Bibliography Index

Zen and Japanese Culture

Developing reliable and user-friendly electroencephalography (EEG) electrodes remains a challenge for emerging real-world EEG applications. Classic wet electrodes are the gold standard for recording EEG; however, they are difficult to implement and make users uncomfortable, thus severely restricting their widespread application in real-life scenarios. An alternative is dry electrodes, which do not require conductive gels or skin preparation. Despite their quick setup and improved user-friendliness, dry electrodes still have some inherent problems (invasive, relatively poor signal quality, or sensitivity to motion artifacts), which limit their practical utilization. In recent years, semi-dry electrodes, which require only a small amount of electrolyte fluid, have been successfully developed, combining the advantages of both wet and dry electrodes while addressing their respective drawbacks. Semi-dry electrodes can collect reliable EEG signals comparable to wet electrodes. Moreover, their setup is as fast and convenient similar to that of dry electrodes. Hence, semi-dry electrodes have shown tremendous application prospects for real-world EEG acquisition. Herein, we systematically summarize the development, evaluation methods, and practical design considerations of semi-dry electrodes. Some feasible suggestions and new ideas for the development of semi-dry electrodes have been presented. This review provides valuable technical support for the development of semi-dry electrodes toward emerging practical applications.

Review of semi-dry electrodes for EEG recording

There is no consensus on the role of insulin secreted from pancreatic β‐cells in regulating its own secretion, either in rodent islets or in human islets. We have now investigated whether there is an autocrine signalling role for insulin in human islets by determining insulin receptor expression and assessing the effects of insulin receptor activation using a non‐peptidyl insulin mimetic termed L‐783,281. Human insulin receptor mRNA was detected by PCR amplification of human islet cDNA, and translation of the message in human islets was confirmed by Western blotting. Perifusion experiments revealed that both glucose‐stimulated and basal insulin secretion were significantly inhibited following human islet insulin receptor activation with L‐783,281, and that signalling through phosphatidylinositol 3‐kinase (PI 3‐kinase) was responsible, at least in part, for this inhibitory effect. These studies indicate that human islets express insulin receptors and that they are functionally coupled to a PI 3‐kinase‐dependent inhibition of insulin secretion.

Insulin receptor activation inhibits insulin secretion from human islets of Langerhans

Function perturbation impact on stability in distribution of probabilistic Boolean networks

Electroencephalography (EEG) is a highly promising medium for brain-computer interfaces (BCI) with potentially extraordinary applications such as the direct control of prosthetics and exoskeletons. However, effective analysis and modeling of EEG data has been limited by its poor spatial resolution. EEG's low amplitude, brief and sporadic nature, compounded by extra-cranial noise, contribute to the difficulty of this problem. This systematic analysis provides strong evidence to guide future research in machine learning applied to real-time analysis of brain states using EEGs. The main goal of this research is to understand how the construction of data sets used in training models affects the accuracy of prominent machine learning algorithms, specifically: Random Forest, Boosting, Naive Bayesian Classifier, k-Nearest Neighbors (KNN) and Support Vector Machine (SVM). Herein, we present a systematic method (N = 153) to test the accuracy of prominent machine learning algorithms when varying three main components of the training data set: the permutations of subjects, the number of unique data sets used to generate the training data set, and the number of samples in each training data set. Our results strongly indicate that Random Forest consistently yields superior results when analyzing EEG data compared to other prominent machine learning algorithms. Furthermore, a pilot investigation was conducted on a mean-normalized feature for EEG data analysis. The pilot analysis (N = 28) confirmed Random Forest's analytical superiority in EEG data, shows signs of improved accuracy, and identifies a distinctive correlation between beta and delta waves and their respective active or idle brain states.

Systematic analysis of machine learning algorithms on EEG data for brain state intelligence

This paper presents the study we have done to detect “meditation” brain state by analyzing electroencephalographic (EEG) data. We firstly discuss what is “meditation” state and some prior studies on meditation. We then discuss how meditation state can be reflected in the subject’s brain waves; and what features of the brain waves data can be used in machine learning algorithms to classify meditation state from other states. We studied the suitability of 3 types of entropy: Shannon entropy, approximate entropy, and sample entropy in different circumstances. We found that overall Sample entropy is a good tool to extract information from EEG data. Discretization of EEG data enhances the classification rates by using both the approximate entropy and Shannon entropy.

Using EEG Data Analytics to Measure Meditation

In this article, we discuss how to use a variety of machine learning methods, e.g. tree bagging, random forest, boost, support vector machine, and Gaussian mixture model, for building classifiers for electroencephalogram (EEG) data, which is collected from different brain states on different subjects. Also, we discuss how training data size influences misclassification rate. Moreover, the number of subjects that contributes to the training data affects misclassification rate. Furthermore, we discuss how sample entropy contributes to building a classifier. Our results show that classification based on sample entropy give the smallest misclassification rate. Moreover, two data sets were collected from one channel and seven channels respectively. The classification results of each data set show that the more channels we use, the less misclassification we have. Our results show that it is promising to build a self-adaptive classification system by using EEG data to distinguish idle from active state.

/pdf/statistical-machine-learning-in-brain-state-classification-rkdmqrfk88.pdf

Statistical Machine Learning in Brain State Classification using EEG Data

Chan is a superior mental training methodology derived from Buddhism and absorbed the wisdom of religious practitioners, philosophers, and scholars around Eastern Asia through thousands of years. As the primary way of Chan, meditation has clear effects in bringing practitioners' mind into a tranquil state and promoting both the mental and the physical health. The effect of Chan is measurable. The authors propose to establish a Chan science by applying modern experimental sciences to various models. In particular, machine learning methods have been used to classify brain states using electroencephalogram EEG data. The experimental results show potential in building brain state models for calibrating the routine of meditation. Through these studies, the authors believe they will be able to make Chan a beneficial practice to promote human's life in modern society.

Cultivating Chan with Calibration

Insulin receptors (IR) can localize to the primary cilia of pancreatic {beta} cells. Because primary cilia are known to sensitize or bias the signaling of cell surface receptors, we investigate how ciliary insulin receptors influence glucose-stimulated insulin secretion (GSIS) in {beta} cells by gauging how cytosolic calcium concentration changes in a mouse insulinoma cell line (MIN6). Purified recombinant insulin suppresses calcium elevation in response to glucose in these cells. Interestingly, ciliated cells show attenuated cytosolic calcium elevation compared to cilium-free cells after glucose stimulation even in the absence of exogenous insulin. We observe that ciliary IR is highly phosphorylated, and the phospho-IR density decreases when cells are either treated with an insulin receptor (IR) inhibitor, BMS536924, or ciliary function is disrupted through either IFT88 or BBS1 knockdown. Consistently, the attenuation of calcium elevation in ciliated cells is abrogated when cells are either treated with IR inhibitor or when primary cilia are impaired. We further demonstrate that ciliary IR signaling hyperpolarizes the plasma membrane but has no apparent impact on glucose-induced ATP production. Thus, our results argue that primary cilia sensitize insulin receptor signaling and mediate negative feedback in GSIS in pancreatic {beta} cells.

/pdf/primary-cilia-sensitize-insulin-receptor-mediated-negative-885yay0aso.pdf

Yuezhe Li

Papers

Systematic analysis of machine learning algorithms on EEG data for brain state intelligence

Using EEG Data Analytics to Measure Meditation

Statistical Machine Learning in Brain State Classification using EEG Data

Cultivating Chan with Calibration

Primary cilia sensitize insulin receptor-mediated negative feedback in pancreatic β cells