Information-Theoretic Limits of Bistatic Integrated Sensing and Communication

The bistatic integrated sensing and communication (ISAC) system model avoids the strong self-interference in a monostatic ISAC system by employing a pair of physically separated sensing transceiver and maintaining the merit of co-designing radar sensing and communications on shared spectrum and hardware. Inspired by the appealing benefits of bistatic radar, we study bistatic ISAC, where a transmitter sends a message to a communication receiver and a sensing receiver at another location carries out a decoding-and-estimation(DnE) operation to obtain the state of the communication receiver. In this paper, both communication and sensing channels are modelled as state-dependent memoryless channels with independent and identically distributed time-varying state sequences. We consider a rate of reliable communication for the message at the communication receiver as communication metric. The objective of this model is to characterize the capacity-distortion region, i.e., the set of all the achievable rate while simultaneously allowing the sensing receiver to sense the state sequence with a given distortion threshold. In terms of the decoding degree on this message at the sensing receiver, we propose three achievable DnE strategies, the blind estimation, the partial-decoding-based estimation, and the full-decoding-based estimation, respectively. Based on the three strategies, we derive the three achievable rate-distortion regions. In addition, under the constraint of the degraded broadcast channel, i.e., the communication receiver is statistically stronger than the sensing receiver, and the partial-decoding-based estimation, we characterize the capacity region. Examples in both non-degraded and degraded cases are provided to compare the achievable rate-distortion regions under three DnE strategies and demonstrate the advantages of ISAC over independent communication and sensing.Comment: 40 pages, 7 figure

Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation

Many methods of semantic image segmentation have borrowed the success of open compound domain adaptation. They minimize the style gap between the images of source and target domains, more easily predicting the accurate pseudo annotations for target domain's images that train segmentation network. The existing methods globally adapt the scene style of the images, whereas the object styles of different categories or instances are adapted improperly. This paper proposes the Object Style Compensation, where we construct the Object-Level Discrepancy Memory with multiple sets of discrepancy features. The discrepancy features in a set capture the style changes of the same category's object instances adapted from target to source domains. We learn the discrepancy features from the images of source and target domains, storing the discrepancy features in memory. With this memory, we select appropriate discrepancy features for compensating the style information of the object instances of various categories, adapting the object styles to a unified style of source domain. Our method enables a more accurate computation of the pseudo annotations for target domain's images, thus yielding state-of-the-art results on different datasets.Comment: Accepted by NeurlPS202

Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques

Deep learning technique is an effective mean of processing complex data that has emerged in recent years, which has been applied to fault diagnosis of a wide range of equipment. In the present study, three types of deep learning techniques, namely, stacked autoencoder (SAE) network, long short term memory (LSTM) network, and convolutional neural network (CNN) are applied to fault diagnosis of a mixed-flow pump under cavitation conditions. Vibration signals of the mixed-flowed pump are collected from experiment measurements, and then employed as input datasets for deep learning networks. The operation status is clarified into normal, minor cavitation, and severe cavitation conditions according to visualized bubble density. The techniques of FFT and dropout algorithms are also applied to improve diagnosis accuracy. The results show that the diagnosis accuracy based on SAE and LSTM networks is lower than 50%, while is higher than 68% when using CNN. The maximum accuracy can reach 87.2% by mean of a combination of CNN, BN, MLP, and using frequency domain data by FFT as inputs, which validates the feasibility of applying CNN in mixed-flow pumps

Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning

Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma.Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates.Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils.Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis.

Background: Molecular diagnostics are considered the most promising route to achievement of rapid, universal drug susceptibility testing for Mycobacterium tuberculosis complex (MTBC). We aimed to generate a WHO-endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction. Methods: In this systematic analysis, we used a candidate gene approach to identify mutations associated with resistance or consistent with susceptibility for 13 WHO-endorsed antituberculosis drugs. We collected existing worldwide MTBC whole-genome sequencing data and phenotypic data from academic groups and consortia, reference laboratories, public health organisations, and published literature. We categorised phenotypes as follows: methods and critical concentrations currently endorsed by WHO (category 1); critical concentrations previously endorsed by WHO for those methods (category 2); methods or critical concentrations not currently endorsed by WHO (category 3). For each mutation, we used a contingency table of binary phenotypes and presence or absence of the mutation to compute positive predictive value, and we used Fisher's exact tests to generate odds ratios and Benjamini-Hochberg corrected p values. Mutations were graded as associated with resistance if present in at least five isolates, if the odds ratio was more than 1 with a statistically significant corrected p value, and if the lower bound of the 95% CI on the positive predictive value for phenotypic resistance was greater than 25%. A series of expert rules were applied for final confidence grading of each mutation. Findings: We analysed 41 137 MTBC isolates with phenotypic and whole-genome sequencing data from 45 countries. 38 215 MTBC isolates passed quality control steps and were included in the final analysis. 15 667 associations were computed for 13 211 unique mutations linked to one or more drugs. 1149 (7·3%) of 15 667 mutations were classified as associated with phenotypic resistance and 107 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was more than 80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were identified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs. Interpretation: We present the first WHO-endorsed catalogue of molecular targets for MTBC drug susceptibility testing, which is intended to provide a global standard for resistance interpretation. The existence of this catalogue should encourage the implementation of molecular diagnostics by national tuberculosis programmes. Funding: Unitaid, Wellcome Trust, UK Medical Research Council, and Bill and Melinda Gates Foundation

Experimental Study of the Crack Predominance of Rock-Like Material Containing Parallel Double Fissures under Uniaxial Compression

Fractured rock mass is a relatively complex medium in nature. It plays a key role in various projects, such as geotechnical engineering, mining engineering and tunnel engineering. Especially, the interaction between fissures has a practical function in the guidance of safe production. This paper takes its research object as rock-like material which contains prefabricated parallel double fissures. It studies how the fissures&rsquo; length difference and spacing influence the failure of specimens under uniaxial compression, and analyzes them with fracture mechanics theory. The results include two aspects. Firstly, no matter how the length difference and spacing change, the upper fissure always generates new cracks. Secondly, the length difference and spacing produce three effects on the lower fissure. (1) The fissure propagates less obviously as the length difference increases. With the increase to 40mm, the propagation does not occur at all. (2) The decrease of spacing weakens the propagation. As it is reduced to 5 mm, the propagation stops. (3) The crack propagation is more sensitive to length difference than spacing. Regardless of spacing changes, if a length difference is large enough (40 mm or more), the new crack does not expand, while if it is small enough (10 mm or less), propagation always appears

Study on the Interaction of Collinear Cracks and Wing Cracks and Cracking Behavior of Rock under Uniaxial Compression

This paper investigates the crack interaction, initiation, and propagation rules of rock-like materials containing two collinear cracks. Based on the Kachanov method, the formulations for stress intensity factors (SIFs) of two collinear cracks and two winged cracks are derived, respectively. The influences of bridge ligament and crack length on the crack interaction are analyzed theoretically. The results show that the propagation of a long crack is independent of crack interaction when d≥a2 and the same rule applies for a short crack when d≥a1. With the growth of wing cracks, the SIF of wings first remarkably decreases and then it tends toward a steady value. Subsequently, the propagation of collinear cracks and cracking processes under uniaxial compression are analyzed experimentally and numerically. Both the experimental results and simulation results demonstrate that shear cracks tend to initiate and propagate at higher inclination angle. The crack coalescence is affected by the inclination angle of bridge ligament. For increasing the inclination angle, the crack coalescence varies from wing crack failure to shear crack coalescence. As bridge ligament increases, the crack coalescence varies from shear crack coalescence to shear-wing crack coalescence and then to wing crack failure