DISA: A Dual Inexact Splitting Algorithm for Distributed Convex Composite Optimization

In this paper, we propose a novel Dual Inexact Splitting Algorithm (DISA) for distributed convex composite optimization problems, where the local loss function consists of a smooth term and a possibly nonsmooth term composed with a linear mapping. DISA, for the first time, eliminates the dependence of the convergent step-size range on the Euclidean norm of the linear mapping, while inheriting the advantages of the classic Primal-Dual Proximal Splitting Algorithm (PD-PSA): simple structure and easy implementation. This indicates that DISA can be executed without prior knowledge of the norm, and tiny step-sizes can be avoided when the norm is large. Additionally, we prove sublinear and linear convergence rates of DISA under general convexity and metric subregularity, respectively. Moreover, we provide a variant of DISA with approximate proximal mapping and prove its global convergence and sublinear convergence rate. Numerical experiments corroborate our theoretical analyses and demonstrate a significant acceleration of DISA compared to existing PD-PSAs

Label-Free Fluorescence Spectroscopy for Detecting Key Biomolecules in Brain Tissue from a Mouse Model of Alzheimer’s Disease

In this study, label-free fluorescence spectroscopy was used for the first time to determine spectral profiles of tryptophan, reduced nicotinamide adenine dinucleotide (NADH), and flavin denine dinucleotide (FAD) in fresh brain samples of a mouse model of Alzheimer’s disease (AD). Our results showed that the emission spectral profile levels of tryptophan and NADH were higher in AD samples than normal samples. The intensity ratio of tryptophan to NADH and the change rate of fluorescence intensity with respect to wavelength also increased in AD brain. These results yield an optical method for detecting early stage of AD by comparing spectral profiles of biomolecules

Analysis of Adhesion Characteristics and Carbohydrate Metabolism Pathways of Three Lacticaseibacillus paracasei Strains from Different Sources Based on Genome Sequencing

The surface characteristics and adhesion properties of Lacticaseibacillus paracasei ZY-1, derived from Tibetan kefir grains, and L. paracasei S-NA5 and S-NB, isolated from Xinjiang traditional fermented milk, were evaluated in vitro, and whole genome sequencing was performed to compare the difference among adhesion-related genes in the three strains. The gastrointestinal tolerance and adhesion properties of L. paracasei S-NB to Caco-2 cells were superior to those of S-NA5 and ZY-1. Moreover, two exopolysaccharide (EPS) synthesis gene clusters were found in each strain, and the EPS cluster 1 of strain ZY-1 differed significantly from that of S-NA5 and S-NB. Meanwhile, genes involved in lipoteichoic acid (LTA) synthesis and related domains of protein adhesins were predicted. The secondary structure prediction of the products encoded by the adhesion protein genes in S-NB showed that the secondary structure of 11 adhesion proteins was dominated by α-helix and random coil. Additionally, the complete metabolic pathways of lactose, galactose, fructi-oligosaccharide, inulin and human milk oligosaccharides were found in the three strains, which were all highly conserved

Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing.

International audienceIn abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors

Can tourists become disciples? The formation and mechanism of place conversion in traditional Chinese villages

Place attachment has been extensively studied in the field of tourism. However, it is important to recognize that place attachment alone may not fully capture the emotional expression of tourists’ spiritual beliefs and their sense of belonging to a destination. Therefore, a more comprehensive attachment theory is needed to encompass higher dimensions. This article focuses on the ancient Chinese villages of Yantou, Cangpo, and Furong from a tourist perspective, and introduces genius loci in architectural phenomenology. We set out to expand the study of spiritual dimensions of place attachment on the basis of local attachment, redefining tourists’ place connection and putting forward the new concept of place conversion. A qualitative analysis of online text using Rooting theory was conducted to condense 22 categories into 6 main categories and establish a model. The study shows that the mechanism of place conversion is composed of situational perception (place perception and place identity), physical and mental immersion (spatio-temporal immersion and place conversion), and behavioral willingness (tourists intention and conversion behavior). The study has implications for the conservation and development of traditional villages and future research on the spiritual experiences of tourists

A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation using Deep Learning

In PET/CT imaging, CT is used for PET attenuation correction (AC). Mismatch between CT and PET due to patient body motion results in AC artifacts. In addition, artifact caused by metal, beam-hardening and count-starving in CT itself also introduces inaccurate AC for PET. Maximum likelihood reconstruction of activity and attenuation (MLAA) was proposed to solve those issues by simultaneously reconstructing tracer activity ($\lambda$-MLAA) and attenuation map ($\mu$-MLAA) based on the PET raw data only. However, $\mu$-MLAA suffers from high noise and $\lambda$-MLAA suffers from large bias as compared to the reconstruction using the CT-based attenuation map ($\mu$-CT). Recently, a convolutional neural network (CNN) was applied to predict the CT attenuation map ($\mu$-CNN) from $\lambda$-MLAA and $\mu$-MLAA, in which an image-domain loss (IM-loss) function between the $\mu$-CNN and the ground truth $\mu$-CT was used. However, IM-loss does not directly measure the AC errors according to the PET attenuation physics, where the line-integral projection of the attenuation map ($\mu$) along the path of the two annihilation events, instead of the $\mu$ itself, is used for AC. Therefore, a network trained with the IM-loss may yield suboptimal performance in the $\mu$ generation. Here, we propose a novel line-integral projection loss (LIP-loss) function that incorporates the PET attenuation physics for $\mu$ generation. Eighty training and twenty testing datasets of whole-body 18F-FDG PET and paired ground truth $\mu$-CT were used. Quantitative evaluations showed that the model trained with the additional LIP-loss was able to significantly outperform the model trained solely based on the IM-loss function.Comment: Accepted at MICCAI 201

Toward the development of smart capabilities for understanding seafloor stretching morphology and biogeographic patterns via DenseNet from high-resolution multibeam bathymetric surveys for underwater vehicles

The increasing use of underwater vehicles facilitates deep-sea exploration at a wide range of depths and spatial scales. In this paper, we make an initial attempt to develop online computing strategies to identify seafloor categories and predict biogeographic patterns with a deep learning-based architecture, DenseNet, integrated with joint morphological cues, with the expectation of potentially developing its embedded smart capacities. We utilized high-resolution multibeam bathymetric measurements derived from MBES and denoted a collection of joint morphological cues to help with semantic mapping and localization. We systematically strengthened dominant feature propagation and promoted feature reuse via DenseNet by applying the channel attention module and spatial pyramid pooling. From our experiment results, the seafloor classification accuracy reached up to 89.87%, 82.01%, and 73.52% on average in terms of PA, MPA, and MIoU metrics, achieving comparable performances with the state-of-the-art deep learning frameworks. We made a preliminary study on potential biogeographic distribution statistics, which allowed us to delicately distinguish the functionality of probable submarine benthic habitats. This study demonstrates the premise of using underwater vehicles through unbiased means or pre-programmed path planning to quantify and estimate seafloor categories and the exhibited fine-scale biogeographic patterns