Adopting improved Adam optimizer to train dendritic neuron model for water quality prediction

As one of continuous concern all over the world, the problem of water quality may cause diseases and poisoning and even endanger people's lives. Therefore, the prediction of water quality is of great significance to the efficient management of water resources. However, existing prediction algorithms not only require more operation time but also have low accuracy. In recent years, neural networks are widely used to predict water quality, and the computational power of individual neurons has attracted more and more attention. The main content of this research is to use a novel dendritic neuron model (DNM) to predict water quality. In DNM, dendrites combine synapses of different states instead of simple linear weighting, which has a better fitting ability compared with traditional neural networks. In addition, a recent optimization algorithm called AMSGrad (Adaptive Gradient Method) has been introduced to improve the performance of the Adam dendritic neuron model (ADNM). The performance of ADNM is compared with that of traditional neural networks, and the simulation results show that ADNM is better than traditional neural networks in mean square error, root mean square error and other indicators. Furthermore, the stability and accuracy of ADNM are better than those of other conventional models. Based on trained neural networks, policymakers and managers can use the model to predict the water quality. Real-time water quality level at the monitoring site can be presented so that measures can be taken to avoid diseases caused by water quality problems

統計モデルとニューラルネットワークを用いた時序列の予測研究

富山大学・富理工博甲第119号・虞瑩・2017/03/23富山大学201

新たな進化的及びニューロン計算による分類問題に関する研究

富山大学・富理工博甲第172号・銭孝孝・2020/3/24富山大学202

Understanding cellular differentiation by modelling of single-cell gene expression data

Over the course of the last decade single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity, as one experiment routinely covers the expression of thousands of genes in tens or hundreds of thousands of cells. By quantifying differences between the single cell transcriptomes it is possible to reconstruct the process that gives rise to different cell fates from a progenitor population and gain access to trajectories of gene expression over developmental time. Tree reconstruction algorithms must deal with the high levels of noise, the high dimensionality of gene expression space, and strong non-linear dependencies between genes. In this thesis we address three aspects of working with scRNA-seq data: (1) lineage tree reconstruction, where we propose MERLoT, a novel trajectory inference method, (2) method comparison, where we propose PROSSTT, a novel algorithm that simulates scRNA-seq count data of complex differentiation trajectories, and (3) noise modelling, where we propose a novel probabilistic description of count data, a statistically motivated local averaging strategy, and an adaptation of the cross validation approach for the evaluation of gene expression imputation strategies. While statistical modelling of the data was our primary motivation, due to time constraints we did not manage to fully realize our plans for it. Increasingly complex processes like whole-organism development are being studied by single-cell transcriptomics, producing large amounts of data. Methods for trajectory inference must therefore efficiently reconstruct \textit{a priori} unknown lineage trees with many cell fates. We propose MERLoT, a method that can reconstruct trees in sub-quadratic time by utilizing a local averaging strategy, scaling very well on large datasets. MERLoT compares favorably to the state of the art, both on real data and a large synthetic benchmark. The absence of data with known complex underlying topologies makes it challenging to quantitatively compare tree reconstruction methods to each other. PROSSTT is a novel algorithm that simulates count data from complex differentiation processes, facilitating comparisons between algorithms. We created the largest synthetic dataset to-date, and the first to contain simulations with up to 12 cell fates. Additionally, PROSSTT can learn simulation parameters from reconstructed lineage trees and produce cells with expression profiles similar to the real data. Quantifying similarity between single-cell transcriptomes is crucial for clustering scRNA-seq profiles to cell types or inferring developmental trajectories, and appropriate statistical modelling of the data should improve such similarity calculations. We propose a Gaussian mixture of negative binomial distributions where gene expression variance depends on the square of the average expression. The model hyperparameters can be learned via the hybrid Monte Carlo algorithm, and a good initialization of average expression and variance parameters can be obtained by trajectory inference. A way to limit noise in the data is to apply local averaging, using the nearest neighbours of each cell to recover expression of non-captured mRNA. Our proposal, nearest neighbour smoothing with optimal bias-variance trade-off, optimizes the k-nearest neighbours approach by reducing the contribution of inappropriate neighbours. We also propose a way to assess the quality of gene expression imputation. After reconstructing a trajectory with imputed data, each cell can be projected to the trajectory using non-overlapping subsets of genes. The robustness of these assignments over multiple partitions of the genes is a novel estimator of imputation performance. Finally, I was involved in the planning and initial stages of a mouse ovary cell atlas as a collaboration

Simulation and Design of Biological and Biologically-Motivated Computing Systems

In life science, there is a great need in understandings of biological systems for therapeutics, synthetic biology, and biomedical applications. However, complex behaviors and dynamics of biological systems are hard to understand and design. In the mean time, the design of traditional computer architectures faces challenges from power consumption, device reliability, and process variations. In recent years, the convergence of computer science, computer engineering and life science has enabled new applications targeting the challenges from both engineering and biological fields. On one hand, computer modeling and simulation provides quantitative analysis and predictions of functions and behaviors of biological systems, and further facilitates the design of synthetic biological systems. On the other hand, bio-inspired devices and systems are designed for real world applications by mimicking biological functions and behaviors. This dissertation develops techniques for modeling and analyzing dynamic behaviors of biologically realistic genetic circuits and brain models and design of brain-inspired computing systems. The stability of genetic memory circuits is studied to understand its functions for its potential applications in synthetic biology. Based on the electrical-equivalent models of biochemical reactions, simulation techniques widely used for electronic systems are applied to provide quantitative analysis capabilities. In particular, system-theoretical techniques are used to study the dynamic behaviors of genetic memory circuits, where the notion of stability boundary is employed to characterize the bistability of such circuits. To facilitate the simulation-based studies of physiological and pathological behaviors in brain disorders, we construct large-scale brain models with detailed cellular mechanisms. By developing dedicated numerical techniques for brain simulation, the simulation speed is greatly improved such that dynamic simulation of large thalamocortical models with more than one million multi-compartment neurons and hundreds of synapses on commodity computer servers becomes feasible. Simulation of such large model produces biologically meaningful results demonstrating the emergence of sigma and delta waves in the early and deep stages of sleep, and suggesting the underlying cellular mechanisms that may be responsible for generation of absence seizure. Brain-inspired computing paradigms may offer promising solutions to many challenges facing the main stream Von Neumann computer architecture. To this end, we develop a biologically inspired learning system amenable to VLSI implementation. The proposed solution consists of a digitized liquid state machine (LSM) and a spike-based learning rule, providing a fully biologically inspired learning paradigm. The key design parameters of this liquid state machine are optimized to maximize the learning performance while considering hardware implementation cost. When applied to speech recognition of isolated word using TI46 speech corpus, the performance of the proposed LSM rivals several existing state-of-art techniques including the Hidden Markov Model based recognizer Sphinx-4

Dendrimers: A Themed Issue in Honor of Professor Donald A. Tomalia on the Occasion of His 80th Birthday

Dendrimers have firmly established their space in the macromolecular field since their first discovery in 1978. These monodispersed and hyperbranched macromolecules present unique properties with demonstrated potential in varied scientific disciplines. Dr. Donald A Tomalia is one of the pioneers in this area whose name is synonym for polyamidoamine (PAMAM) dendrimers, one of the most extensively investigated macromolecular architectures. In this monograph, his colleagues and friends celebrate Don’s achievements and contributions to the field, on the occasion of his 80th birthday in 2018, which also coincides with the 40th anniversary of the first report on dendrimers. It provides the reader with excellent reviews on different aspects of dendritic architectures, followed by research articles that explore the state-of-the-art in synthesis, properties and varied applications, including in biology. Collectively, it provides scientists just beginning their careers, as well as firmly established ones, with the pulse of the field and inspiration to continue to explore these intriguing macromolecules

Identification of Dendritic Processing in Spiking Neural Circuits

A large body of experimental evidence points to sophisticated signal processing taking place at the level of dendritic trees and dendritic branches of neurons. This evidence suggests that, in addition to inferring the connectivity between neurons, identifying analog dendritic processing in individual cells is fundamentally important to understanding the underlying principles of neural computation. In this thesis, we develop a novel theoretical framework for the identification of dendritic processing directly from spike times produced by spiking neurons. The problem setting of spiking neurons is necessary since such neurons make up the majority of electrically excitable cells in most nervous systems and it is often hard or even impossible to directly monitor the activity within dendrites. Thus, action potentials produced by neurons often constitute the only causal and observable correlate of dendritic processing. In order to remain true to the underlying biophysics of electrically excitable cells, we employ well-established mechanistic models of action potential generation to describe the nonlinear mapping of the aggregate current produced by the tree into an asynchronous sequence of spikes. Specific models of spike generation considered include conductance-based models such as Hodgkin-Huxley, Morris-Lecar, Fitzhugh-Nagumo, as well as simpler models of the integrate-and-fire and threshold-and-fire type. The aggregate time-varying current driving the spike generator is taken to be produced by a dendritic stimulus processor, which is a nonlinear dynamical system capable of describing arbitrary linear and nonlinear transformations performed on one or more input stimuli. In the case of multiple stimuli, it can also describe the cross-coupling, or interaction, between various stimulus features. The behavior of the dendritic stimulus processor is fully captured by one or more kernels, which provide a characterization of the signal processing that is consistent with the broader cable theory description of dendritic trees. We prove that the neural identification problem, stated in terms of identifying the kernels of the dendritic stimulus processor, is mathematically dual to the neural population encoding problem. Specifically, we show that the collection of spikes produced by a single neuron in multiple experimental trials can be treated as a single multidimensional spike train of a population of neurons encoding the parameters of the dendritic stimulus processor. Using the theory of sampling in reproducing kernel Hilbert spaces, we then derive precise results demonstrating that, during any experiment, the entire neural circuit is projected onto the space of input stimuli and parameters of this projection are faithfully encoded in the spike train. Spike times are shown to correspond to generalized samples, or measurements, of this projection in a system of coordinates that is not fixed but is both neuron- and stimulus-dependent. We examine the theoretical conditions under which it may be possible to reconstruct the dendritic stimulus processor from these samples and derive corresponding experimental conditions for the minimum number of spikes and stimuli that need to be used. We also provide explicit algorithms for reconstructing the kernel projection and demonstrate that, under natural conditions, this projection converges to the true kernel. The developed methodology is quite general and can be applied to a number of neural circuits. In particular, the methods discussed span all sensory modalities, including vision, audition and olfaction, in which external stimuli are typically continuous functions of time and space. The results can also be applied to circuits in higher brain centers that receive multi-dimensional spike trains as input stimuli instead of continuous signals. In addition, the modularity of the approach allows one to extend it to mixed-signal circuits processing both continuous and spiking stimuli, to circuits with extensive lateral connections and feedback, as well as to multisensory circuits concurrently processing multiple stimuli of different dimensions, such as audio and video. Another important extension of the approach can be used to estimate the phase response curves of a neuron. All of the theoretical results are accompanied by detailed examples demonstrating the performance of the proposed identification algorithms. We employ both synthetic and naturalistic stimuli such as natural video and audio to highlight the power of the approach. Finally, we consider the implication of our work on problems pertaining to neural encoding and decoding and discuss promising directions for future research

Proceedings of the 26th Project Integration Meeting

Progress made by the Flat-plate Solar Array (FSA) Project is described for the period July 1985 to April 1986. Included are reports on silicon sheet growth and characterization, silicon material, process development, high-efficienty cells, environmental isolation, engineering sciences, and reliability physics. Also included are technical and plenary presentations made at the 26th Project Integration Meeting (PIM) held on April 29 to 30 and May 1, 1986

The molecular genetic basis of the association of TNFSF4 with SLE

The tumour necrosis factor ligand superfamily member 4 gene (TNFSF4), also known as OX40L, is an established susceptibility locus in the autoimmune disease systemic lupus erythematosus (SLE). Genetic association studies map polymorphisms that associate with disease, but linkage disequilibrium often hinders the identification of the actual casual allele(s) at a disease susceptibility locus. At TNFSF4 genetic association studies had shown that an extended 100kb haplotype upstream of the coding region of the gene was associated with SLE risk. The principle aim of the project was to conduct genetic association analyses in cohorts with different ancestry in an attempt to fine map the TNFSF4 association signal and thereby identify the causal genetic variants that underlie the genetic risk. Utilizing >17,900 subjects of European, African-American, Hispanic-American and Southeast Asian ancestry a transancestral fine mapping analysis was performed. The results demonstrate the strong association of TNFSF4 risk alleles in all populations tested. The most consistent and strongest evidence of association came from the single nucleotide polymorphism (SNP), rs2205960-T (P = 7.1 x 10-32, odds ratio = 1.63). This variant was also associated with autoantibody production in three independent cohorts. In silico analysis of the DNA sequence encompassing rs2205960-T predicts it to form part of a decameric motif, which binds the RelA (p65) component of the NF-κB transcription factor complex. A second associated SNP, rs16845607-A in TNFSF4 intron 1 was identified in Hispanic-Americans (P = 9.17 x 10-9, odds ratio = 2.06). In an attempt to further refine the association, resequencing was performed in 80 individuals who were selected on the basis of their genotype to carry risk or non-risk haplotypes upstream of TNFSF4. This sequencing study identified >200 novel variants, mostly small insertion-deletion polymorphisms indels. The data presented in this thesis largely resolves the genetic basis of the immediate upstream association signal observed at TNFSF4 with SLE and will facilitate the unraveling of the molecular basis of this genetic risk in systemic autoimmunity.Open Acces

A Textbook of Advanced Oral and Maxillofacial Surgery

The scope of OMF surgery has expanded; encompassing treatment of diseases, disorders, defects and injuries of the head, face, jaws and oral cavity. This internationally-recognized specialty is evolving with advancements in technology and instrumentation. Specialists of this discipline treat patients with impacted teeth, facial pain, misaligned jaws, facial trauma, oral cancer, cysts and tumors; they also perform facial cosmetic surgery and place dental implants. The contents of this volume essentially complements the volume 1; with chapters that cover both basic and advanced concepts on complex topics in oral and maxillofacial surgery