Deep Clustering of Compressed Variational Embeddings

Motivated by the ever-increasing demands for limited communication bandwidth and low-power consumption, we propose a new methodology, named joint Variational Autoencoders with Bernoulli mixture models (VAB), for performing clustering in the compressed data domain. The idea is to reduce the data dimension by Variational Autoencoders (VAEs) and group data representations by Bernoulli mixture models (BMMs). Once jointly trained for compression and clustering, the model can be decomposed into two parts: a data vendor that encodes the raw data into compressed data, and a data consumer that classifies the received (compressed) data. In this way, the data vendor benefits from data security and communication bandwidth, while the data consumer benefits from low computational complexity. To enable training using the gradient descent algorithm, we propose to use the Gumbel-Softmax distribution to resolve the infeasibility of the back-propagation algorithm when assessing categorical samples.Comment: Submitted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 202

Robust Quickest Change Detection for Unnormalized Models

Detecting an abrupt and persistent change in the underlying distribution of online data streams is an important problem in many applications. This paper proposes a new robust score-based algorithm called RSCUSUM, which can be applied to unnormalized models and addresses the issue of unknown post-change distributions. RSCUSUM replaces the Kullback-Leibler divergence with the Fisher divergence between pre- and post-change distributions for computational efficiency in unnormalized statistical models and introduces a notion of the ``least favorable'' distribution for robust change detection. The algorithm and its theoretical analysis are demonstrated through simulation studies.Comment: Accepted for the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023). arXiv admin note: text overlap with arXiv:2302.0025

Minimax Concave Penalty Regularized Adaptive System Identification

We develop a recursive least square (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communication channel. The proposed algorithm recursively yields its estimate of the tap-vector, from noisy streaming observations of a received signal, using expectation-maximization (EM) update. We prove the convergence of our algorithm to a local optimum and provide bounds for the steady state error. Using simulation studies of Rayleigh fading channel, Volterra system and multivariate time series model, we demonstrate that our algorithm outperforms, in the mean-squared error (MSE) sense, the standard RLS and the $\ell_1$-regularized RLS

Identification of long non-protein coding RNAs in chicken skeletal muscle using next generation sequencing

AbstractVertebrate genomes encode thousands of non-coding RNAs including short non-coding RNAs (such as microRNAs) and long non-coding RNAs (lncRNAs). Chicken (Gallus gallus) is an important model organism for developmental biology, and the recently assembled genome sequences for chicken will facilitate the understanding of the functional roles of non-coding RNA genes during development. The present study concerns the first systematic identification of lncRNAs using RNA-Seq to sample the transcriptome during chicken muscle development. A computational approach was used to identify 281 new intergenic lncRNAs in the chicken genome. Novel lncRNAs in general are less conserved than protein-coding genes and slightly more conserved than random non-coding sequences. The present study has provided an initial chicken lncRNA catalog and greatly increased the number of chicken ncRNAs in the non-protein coding RNA database. Furthermore, the computational pipeline presented in the current work will be useful for characterizing lncRNAs obtained from deep sequencing data

A novel DSPP mutation causes dentinogenesis imperfecta type II in a large Mongolian family

<p>Abstract</p> <p>Background</p> <p>Several studies have shown that the clinical phenotypes of dentinogenesis imperfecta type II (DGI-II) may be caused by mutations in <it>dentin sialophosphoprotein </it>(<it>DSPP</it>). However, no previous studies have documented the clinical phenotype and genetic basis of DGI-II in a Mongolian family from China.</p> <p>Methods</p> <p>We identified a large five-generation Mongolian family from China with DGI-II, comprising 64 living family members of whom 22 were affected. Linkage analysis of five polymorphic markers flanking <it>DSPP </it>gene was used to genotype the families and to construct the haplotypes of these families. All five DSPP exons including the intron-exon boundaries were PCR-amplified and sequenced in 48 members of this large family.</p> <p>Results</p> <p>All affected individuals showed discoloration and severe attrition of their teeth, with obliterated pulp chambers and without progressive high frequency hearing loss or skeletal abnormalities. No recombination was found at five polymorphic markers flanking DSPP in the family. Direct DNA sequencing identified a novel A→G transition mutation adjacent to the donor splicing site within intron 3 in all affected individuals but not in the unaffected family members and 50 unrelated Mongolian individuals.</p> <p>Conclusion</p> <p>This study identified a novel mutation (IVS3+3A→G) in <it>DSPP</it>, which caused DGI-II in a large Mongolian family. This expands the spectrum of mutations leading to DGI-II.</p

Preparation and Product Characterization of Microwaveable Food Using <i>Lentinus edodes</i> Protein through 3D Printing

The Lentinus edodes protein (LP) is a high-quality protein known for its well-balanced amino acid composition. In this study, we developed three-dimensional (3D)-printed microwaveable food using a combination of LP and potato flour, and optimized the formulation to achieve a ratio of LP: potato flour: xanthan gum: water = 2:8:1:23. The 3D-printed samples exhibited better shape, weight, and size compared to the molded samples after microwave treatment, with the most favorable microwave effect observed at a 90% filling ratio. The LP content affected the viscosity and retrogradation value of the LP–potato starch mixture. Microwave duration affected the surface hardness, interior softness, and moisture content of the product. The highest overall score of 8.295 points was obtained with a microwave processing duration of 2 min. This study lays a foundation for the development of LP-based 3D-printed food

Synthesis of 2H-pyrroles via iron catalyzed dehydrogenative coupling and C–C bond cleavage

Iron catalyzed coupling of β-amino alcohols with allylic alcohols for the synthesis of 3,4-dihydro-2H-pyrrole derivatives has been developed. Mechanistic studies suggest that the reaction involves iron catalyzed dehydrogenative coupling and C–C bond cleavage processes

Targeted sequencing of the BDNF gene in young Chinese Han people with major depressive disorder

Abstract Background Adolescence and young adulthood are considered the peak age for the emergence of many psychiatric disorders, in particular major depressive disorder (MDD). Previous research has shown substantial heritability for MDD. In addition, the brain‐derived neurotrophic factor (BDNF) gene is known to be associated with MDD. However, there has been no study conducting targeted sequencing of the BDNF gene in young MDD patients so far. Method To examine whether the BDNF gene is associated with the occurrence of MDD in young patients, we used targeted sequencing to detect the BDNF gene variants in 259 young Chinese Han people (105 MDD patients and 154 healthy subjects). Results The BDNF variant rs4030470 was associated with MDD in young Chinese Han people (uncorrected p = 0.046), but this was no longer significant after applying FDR correction (p = 0.552, after FDR correction). We did not find any significant differences in genotype or haplotype frequencies between the case and control groups, and furthermore discovered no rare mutation variants any of the 259 subjects. Conclusion Our results do not support an association of the BDNF gene variants with MDD in young people in the Chinese Han population