Computing Networks Enabled Semantic Communications

Semantic communication has shown great potential in boosting the effectiveness and reliability of communications. However, its systems to date are mostly enabled by deep learning, which requires demanding computing resources. This article proposes a framework for the computing networks enabled semantic communication system, aiming to offer sufficient computing resources for semantic processing and transmission. Key techniques including semantic sampling and reconstruction, semantic-channel coding, semantic-aware resource allocation and optimization are introduced based on the cloud-edge-end computing coordination. Two use cases are demonstrated to show advantages of the proposed framework. The article concludes with several future research directions

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h

Numerical study on the evolution law and correction method of turbine characteristics of the gas turbine under alternative fuel conditions

Reducing carbon emissions is an urgent need in the field of marine power. Gas turbines are of great importance in the marine industry. The use of clean or industrial-associated fuels can increase the fuel adaptability of designed, manufactured, or in-service gas turbines to realize the goal of expanding fuel sources, reducing fuel waste, lowering energy demand, and remitting environmental pressure. By changing from fossil fuel to alternative energy, the change in the physical properties of the combustion products will lead to changes in the working medium of the turbines, which result in a profound effect on the performance. In this study, based on the actual law of working medium property change, the evolution mechanism of turbine characteristics is lucubrated in depth, focusing on the key parameters of the influence of working medium properties on turbine characteristics under alternative fuel conditions, and a correction method is proposed to predict the evolution law of the turbine characteristics as working medium varies

The association and dose–response relationship between dietary intake of α-linolenic acid and risk of CHD: a systematic review and meta-analysis of cohort studies

Abstract Previous studies show inconsistent associations between α -linolenic acid (ALA) and risk of CHD. We aimed to examine an aggregate association between ALA intake and risk of CHD, and assess for any dose–response relationship. We searched the PubMed, EMBASE and Web of Science databases for prospective cohort studies examining associations between ALA intake and CHD, including composite CHD and fatal CHD. Data were pooled using random-effects meta-analysis models, comparing the highest category of ALA intake with the lowest across studies. Subgroup analysis was conducted based on study design, geographic region, age and sex. For dose–response analyses, we used two-stage random-effects dose–response models. In all, fourteen studies of thirteen cohorts were identified and included in the meta-analysis. The pooled results showed that higher ALA intake was associated with modest reduced risk of composite CHD (risk ratios (RR)=0·91; 95 % CI 0·85, 0·97) and fatal CHD (RR=0·85; 95 % CI 0·75, 0·96). The analysis showed a J-shaped relationship between ALA intake and relative risk of composite CHD ( χ 2 =21·95, P <0·001). Compared with people without ALA intake, only people with ALA intake <1·4 g/d showed reduced risk of composite CHD. ALA intake was linearly associated with fatal CHD – every 1 g/d increase in ALA intake was associated with a 12 % decrease in fatal CHD risk (95 % CI −0·21, −0·04). Though a higher dietary ALA intake was associated with reduced risk of composite and fatal CHD, the excess composite CHD risk at higher ALA intakes warrants further investigation, especially through randomised controlled trials

The Cell Cycle Checkpoint Gene, RAD17 rs1045051, Is Associated with Prostate Cancer Risk

Human RAD17, as an agonist of checkpoint signaling, plays an essential role in mediating DNA damage. This hospital-based case-control study aimed to explore the association between RAD17 rs1045051, a missense sin-gle nucleotide polymorphism (SNP), and prostate cancer risk. Subjects were 358 prostate cancer patients and 314 cancer-free urology patients undergoing treatment at the Zhujiang Hospital of Southern Medical University in China. RAD17 gene polymorphism rs1045051 was evaluated by the SNaPshot method. Compared with the RAD17 gene polymorphism rs1045051 AA genotype, there was a higher risk of prostate cancer for the CC gen-otype (adjusted odds ratio [AOR] = 1.731, 95% confidence interval [95%CI] = 1.031−2.908, p = 0.038). Compared with the A allele, the C allele was significantly associated with the disease status (AOR = 1.302, 95%CI = 1.037−1.634, p = 0.023). All these findings indicate that in the SNP rs1045051, both the CC genotype and C allele may have a substantial influence on the prostate cancer risk