Distributed Data Aggregation for Sparse Recovery in Wireless Sensor Networks

We consider the approximate sparse recovery problem in Wireless Sensor Networks (WSNs) using Compressed Sensing/Compressive Sampling (CS). The goal is to recover the n \mbox{-}dimensional data values by querying only $m \ll n$ sensors based on some linear projection of sensor readings. To solve this problem, a two-tiered sampling model is considered and a novel distributed compressive sparse sampling (DCSS) algorithm is proposed based on sparse binary CS measurement matrix. In the two-tiered sampling model, each sensor first samples the environment independently. Then the fusion center (FC), acting as a pseudo-sensor, samples the sensor network to select a subset of sensors ($m$ out of $n$) that directly respond to the FC for data recovery purpose. The sparse binary matrix is designed using unbalanced expander graph which achieves the state-of-the-art performance for CS schemes. This binary matrix can be interpreted as a sensor selection matrix-whose fairness is analyzed. Extensive experiments on both synthetic and real data set show that by querying only the minimum amount of $m$ sensors using the DCSS algorithm, the CS recovery accuracy can be as good as dense measurement matrices (e.g., Gaussian, Fourier Scrambles). We also show that the sparse binary measurement matrix works well on compressible data which has the closest recovery result to the known best k\mbox{-}term approximation. The recovery is robust against noisy measurements. The sparsity and binary properties of the measurement matrix contribute, to a great extent, the reduction of the in-network communication cost as well as the computational burden

Compressed Sensing in Resource-Constrained Environments: From Sensing Mechanism Design to Recovery Algorithms

Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited environment like visual sensor network (VSNs). There are several challenges to perform sensing due to the characteristic of these platforms, including, for example, needing active user involvement, computational and storage limitations and lower transmission capabilities. This dissertation focuses on the study of CS in resource-constrained environments. First, we try to solve the problem on how to design sensing mechanisms that could better adapt to the resource-limited smartphone platform. We propose the compressed phone sensing (CPS) framework where two challenging issues are studied, the energy drainage issue due to continuous sensing which may impede the normal functionality of the smartphones and the requirement of active user inputs for data collection that may place a high burden on the user. Second, we propose a CS reconstruction algorithm to be used in VSNs for recovery of frames/images. An efficient algorithm, NonLocal Douglas-Rachford (NLDR), is developed. NLDR takes advantage of self-similarity in images using nonlocal means (NL) filtering. We further formulate the nonlocal estimation as the low-rank matrix approximation problem and solve the constrained optimization problem using Douglas-Rachford splitting method. Third, we extend the NLDR algorithm to surveillance video processing in VSNs and propose recursive Low-rank and Sparse estimation through Douglas-Rachford splitting (rLSDR) method for recovery of the video frame into a low-rank background component and sparse component that corresponds to the moving object. The spatial and temporal low-rank features of the video frame, e.g., the nonlocal similar patches within the single video frame and the low-rank background component residing in multiple frames, are successfully exploited

Identification of a dCache-type chemoreceptor in Campylobacter jejuni that specifically mediates chemotaxis towards methyl pyruvate

The foodborne pathogenic bacterium Campylobacter jejuni utilizes chemotaxis to assist in the colonization of host niches. A key to revealing the relationship among chemotaxis and pathogenicity is the discovery of signaling molecules perceived by the chemoreceptors. The C. jejuni chemoreceptor Tlp11 is encoded by the highly infective C. jejuni strains. In the present study, we report that the dCache-type ligand-binding domain (LBD) of C. jejuni ATCC 33560 Tlp11 binds directly to novel ligands methyl pyruvate, toluene, and quinoline using the same pocket. Methyl pyruvate elicits a strong chemoattractant response, while toluene and quinoline function as the antagonists without triggering chemotaxis. The sensory LBD was used to control heterologous proteins by constructing chimeras, indicating that the signal induced by methyl pyruvate is transmitted across the membrane. In addition, bioinformatics and experiments revealed that the dCache domains with methyl pyruvate-binding sites and ability are widely distributed in the order Campylobacterales. This is the first report to identify the class of dCache chemoreceptors that bind to attractant methyl pyruvate and antagonists toluene and quinoline. Our research provides a foundation for understanding the chemotaxis and virulence of C. jejuni and lays a basis for the control of this foodborne pathogen

Gamma-Glutamyl Transpeptidase to Platelet Ratio Is a Novel and Independent Prognostic Marker for Resectable Lung Cancer: A Propensity Score Matching Study.

BACKGROUND We report this propensity score matching (PSM) analysis to assess prognostic roles of preoperative gamma-glutamyl transpeptidase to platelet ratio (GPR) in video-assisted thoracoscopic (VATS) lobectomy for stage I-II non-small-cell lung cancer (NSCLC). METHODS The PSM-based study conducted on our single-center prospectively collected database from January 2014 to August 2015 provided Kaplan-Meier survival analyses using the log-rank test to discriminate differences in overall survival (OS) and disease-free survival (DFS) between patients stratified by preoperative GPR. RESULTS Our study includes 379 patients diagnosed with operable primary stage I-II NSCLC. A GPR value at 0.16 was recognized as the optimal cutoff point for prognostic prediction. Both OS and DFS of patients with GPR ≥0.16 were significantly shortened when compared to those of patients with GPR <0.16. Patients with GPR ≥0.16 had significantly lower 5-year rates of OS and DFS than those of patients with GPR <0.16 (P <0.001). Significant associations between GPR and unfavorable survival still are validated in the PSM analysis. Multivariable Cox regression models on both the entire cohort and the PSM cohort consistently demonstrated that an elevated preoperative GPR could be an independent prognostic marker for both OS and DFS of resectable NSCLC. CONCLUSIONS GPR may be an effective and noninvasive prognostic biomarker in VATS lobectomy for surgically resectable NSCLC

Burden of liver cancer due to hepatitis C from 1990 to 2019 at the global, regional, and national levels

BackgroundLiver cancer due to hepatitis C (LCDHC) is one of the leading causes of cancer-related deaths worldwide, and the burden of LCDHC is increasing. We aimed to report the burden of LCDHC at the global, regional, and national levels in 204 countries from 1990 to 2019, stratified by etiology, sex, age, and Sociodemographic Index.MethodsData on LCDHC were available from the Global Burden of Disease, Injuries, and Risk Factors (GBD) study 2019. Numbers and age-standardized mortality, incidence, and disability-adjusted life year (DALY) rates per 100,000 population were estimated through a systematic analysis of modeled data from the GBD 2019 study. The trends in the LCDHC burden were assessed using the annual percentage change.ResultsGlobally, in 2019, there were 152,225 new cases, 141,810 deaths, and 2,878,024 DALYs due to LCDHC. From 1990 to 2019, the number of incidences, mortality, and DALY cases increased by 80.68%, 67.50%, and 37.20%, respectively. However, the age-standardized incidence, mortality, and DALY rate had a decreasing trend during this period. In 2019, the highest age-standardized incidence rates (ASIRs) of LCDHC were found in high-income Asia Pacific, North Africa and the Middle East, and Central Asia. At the regional level, Mongolia, Egypt, and Japan had the three highest ASIRs in 2019. The incidence rates of LCDHC were higher in men and increased with age, with a peak incidence in the 95+ age group for women and the 85–89 age group for men in 2019. A nonlinear association was found between the age-standardized rates of LCDHC and sociodemographic index values at the regional and national levels.ConclusionsAlthough the age-standardized rates of LCDHC have decreased, the absolute numbers of incident cases, deaths, and DALYs have increased, indicating that LCDHC remains a significant global burden. In addition, the burden of LCDHC varies geographically. Male and older adult/s individuals have a higher burden of LCDHC. Our findings provide insight into the global burden trend of LCDHC. Policymakers should establish appropriate methods to achieve the HCV elimination target by 2030 and reducing the burden of LCDHC

Identification and validation of potential diagnostic signature and immune cell infiltration for HIRI based on cuproptosis-related genes through bioinformatics analysis and machine learning

Background and aimsCuproptosis has emerged as a significant contributor in the progression of various diseases. This study aimed to assess the potential impact of cuproptosis-related genes (CRGs) on the development of hepatic ischemia and reperfusion injury (HIRI).MethodsThe datasets related to HIRI were sourced from the Gene Expression Omnibus database. The comparative analysis of differential gene expression involving CRGs was performed between HIRI and normal liver samples. Correlation analysis, function enrichment analyses, and protein-protein interactions were employed to understand the interactions and roles of these genes. Machine learning techniques were used to identify hub genes. Additionally, differences in immune cell infiltration between HIRI patients and controls were analyzed. Quantitative real-time PCR and western blotting were used to verify the expression of the hub genes.ResultsSeventy-five HIRI and 80 control samples from three databases were included in the bioinformatics analysis. Three hub CRGs (NLRP3, ATP7B and NFE2L2) were identified using three machine learning models. Diagnostic accuracy was assessed using a receiver operating characteristic (ROC) curve for the hub genes, which yielded an area under the ROC curve (AUC) of 0.832. Remarkably, in the validation datasets GSE15480 and GSE228782, the three hub genes had AUC reached 0.904. Additional analyses, including nomograms, decision curves, and calibration curves, supported their predictive power for diagnosis. Enrichment analyses indicated the involvement of these genes in multiple pathways associated with HIRI progression. Comparative assessments using CIBERSORT and gene set enrichment analysis suggested elevated expression of these hub genes in activated dendritic cells, neutrophils, activated CD4 memory T cells, and activated mast cells in HIRI samples versus controls. A ceRNA network underscored a complex regulatory interplay among genes. The genes mRNA and protein levels were also verified in HIRI-affected mouse liver tissues.ConclusionOur findings have provided a comprehensive understanding of the association between cuproptosis and HIRI, establishing a promising diagnostic pattern and identifying latent therapeutic targets for HIRI treatment. Additionally, our study offers novel insights to delve deeper into the underlying mechanisms of HIRI