Spiking Semantic Communication for Feature Transmission with HARQ

In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is divided between the edge and the cloud, with intermediate features being sent from the edge to the cloud for inference. Several deep learning-based Semantic Communication (SC) models have been proposed to reduce feature transmission overhead and mitigate channel noise interference. Previous research has demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater robustness on digital channels compared to Deep Neural Network (DNN)-based SC models. However, the existing SNN-based SC models require fixed time steps, resulting in fixed transmission bandwidths that cannot be adaptively adjusted based on channel conditions. To address this issue, this paper introduces a novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with the Hybrid Automatic Repeat Request (HARQ) mechanism. SNN-SC-HARQ comprises an SNN-based SC model that supports the transmission of features at varying bandwidths, along with a policy model that determines the appropriate bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust the bandwidth according to the channel conditions without performance loss

Spatial distribution of tuberculosis and its association with meteorological factors in mainland China

BACKGROUND: The incidence of tuberculosis (TB) remains high worldwide. Current strategies will not eradicate TB by 2035; instead, by 2182 is more likely. Therefore, it is urgent that new risk factors be identified. METHODS: An ecological study was conducted in 340 prefectures in China from 2005 to 2015. The spatial distribution of TB incidence was shown by clustering and hotspot analysis. The relationship between the distribution patterns and six meteorological factors was evaluated by the geographically weighted regression (GWR) model. RESULTS: During the 11 years of the study period, TB incidence was persistently low in the east and high in the west. Local coefficients from the GWR model showed a positive correlation between TB incidence and yearly average rainfall (AR) but a negative correlation with other meteorological factors. Average relative humidity (ARH) was negatively correlated with the incidence of TB in all prefectures (p \u3c 0.05). CONCLUSION: Meteorological factors may play an important role in the prevention and control of TB

Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes

Radiomics has shown potential in disease diagnosis, but its feasibility for non-small cell lung carcinoma (NSCLC) subtype classification is unclear. This study aims to explore the diagnosis value of texture and colour features from positron emission tomography computed tomography (PET-CT) images in differentiation of NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Two patient cohorts were retrospectively collected into a dataset of 341 18F-labeled 2-deoxy-2fluoro-d-glucose ([18F] FDG) PET-CT images of NSCLC tumours (125 ADC, 174 SqCC, and 42 cases with unknown subtype). Quantification of texture and colour features was performed using freehand regions of interest. The relation between extracted features and commonly used parameters such as age, gender, tumour size, and standard uptake value (SUVmax) was explored. To classify NSCLC subtypes, support vector machine algorithm was applied on these features and the classification performance was evaluated by receiver operating characteristic curve analysis. There was a significant difference between ADC and SqCC subtypes in texture and colour features (P  <  0.05); this showed that imaging features were significantly correlated to both SUVmax and tumour diameter (P  <  0.05). When evaluating classification performance, features combining texture and colour showed an AUC of 0.89 (95% CI, 0.78–1.00), colour features showed an AUC of 0.85 (95% CI, 0.71–0.99), and texture features showed an AUC of 0.68 (95% CI, 0.48–0.88). DeLong's test showed that AUC was higher for features combining texture and colour than that for texture features only (P  =  0.010), but not significantly different from that for colour features only (P  =  0.328). HSV colour features showed a similar performance to RGB colour features (P  =  0.473). The colour features are promising in the refinement of NSCLC subtype differentiation, and features combining texture and colour of PET-CT images could result in better classification performance

Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection.

MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here, we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 patients with COVID-19 using the high-throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the patients with COVID-19, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective.MethodsWe developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors.ResultFrom April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination.ConclusionAs found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice

Catalytic Function of PLA2G6 Is Impaired by Mutations Associated with Infantile Neuroaxonal Dystrophy but Not Dystonia-Parkinsonism

Mutations in the PLA2G6 gene have been identified in autosomal recessive neurodegenerative diseases classified as infantile neuroaxonal dystrophy (INAD), neurodegeneration with brain iron accumulation (NBIA), and dystonia-parkinsonism. These clinical syndromes display two significantly different disease phenotypes. NBIA and INAD are very similar, involving widespread neurodegeneration that begins within the first 1-2 years of life. In contrast, patients with dystonia-parkinsonism present with a parkinsonian movement disorder beginning at 15 to 30 years of age. The PLA2G6 gene encodes the PLA2G6 enzyme, also known as group VIA calcium-independent phospholipase A(2), which has previously been shown to hydrolyze the sn-2 acyl chain of phospholipids, generating free fatty acids and lysophospholipids.We produced purified recombinant wildtype (WT) and mutant human PLA2G6 proteins and examined their catalytic function using in vitro assays with radiolabeled lipid substrates. We find that human PLA2G6 enzyme hydrolyzes both phospholipids and lysophospholipids, releasing free fatty acids. Mutations associated with different disease phenotypes have different effects on catalytic activity. Mutations associated with INAD/NBIA cause loss of enzyme activity, with mutant proteins exhibiting less than 20% of the specific activity of WT protein in both lysophospholipase and phospholipase assays. In contrast, mutations associated with dystonia-parkinsonism do not impair catalytic activity, and two mutations produce a significant increase in specific activity for phospholipid but not lysophospholipid substrates.These results indicate that different alterations in PLA2G6 function produce the different disease phenotypes of NBIA/INAD and dystonia-parkinsonism. INAD/NBIA is caused by loss of the ability of PLA2G6 to catalyze fatty acid release from phospholipids, which predicts accumulation of PLA2G6 phospholipid substrates and provides a mechanistic explanation for the accumulation of membranes in neuroaxonal spheroids previously observed in histopathological studies of INAD/NBIA. In contrast, dystonia-parkinsonism mutations do not appear to directly impair catalytic function, but may modify substrate preferences or regulatory mechanisms for PLA2G6

The enormous repetitive Antarctic krill genome reveals environmental adaptations and population insights

Antarctic krill (Euphausia superba) is Earth’smost abundant wild animal, and its enormous biomass is vital to the Southern Ocean ecosystem. Here, we report a 48.01-Gb chromosome-level Antarctic krill genome, whose large genome size appears to have resulted from inter-genic transposable element expansions. Our assembly reveals the molecular architecture of the Antarctic krill circadian clock and uncovers expanded gene families associated with molting and energy metabolism, providing insights into adaptations to the cold and highly seasonal Antarctic environment. Population-level genome re-sequencing from four geographical sites around the Antarctic continent reveals no clear population structure but highlights natural selection associated with environmental variables. An apparent drastic reduction in krill population size 10 mya and a subsequent rebound 100 thousand years ago coincides with climate change events. Our findings uncover the genomic basis of Antarctic krill adaptations to the Southern Ocean and provide valuable resources for future Antarctic research

Vliv sdružené aktivace systému feroniklové strusky a karbidu vápníku na vlastnosti betonu

Ferronickel slag (FNS) is a by-product of ferronickel alloy which causes pollution. Calcium carbide slag (CS) is an industrial waste residue from the hydrolysis of CaC2 with the main components of Ca(OH)2 and CaCO3. The hydraulicity of FNS can be improved by alkali activation to prepare cement-free clinker. Sodium carbonate (SC), sodium sulfate (SS), hemihydrate phosphogypsum (HG) and dihydrate phosphogypsum (DG) are used to study the combined effect with CS on the activation of FNS. The characteristics of FNS are studied firstly, then FNS with the particle size of D50=8.4μm is selected to be the precursor. The main conclusions are as follows: The main chemical composition of FNS is Al2O3, SiO2, CaO, MgO and Fe2O3. Cr is the primary heavy metal in ferronickel slag. The existing MgO phases in ferronickel slag are mainly spinel and forsterite, which is essential for the low grindability and negligible decrease in binding energy. CS and SC can decrease the fluidity and setting time of specimens by increasing the alkali degree of paste. However, SS increases the fluidity and setting time. Although the dissolution of FNS mainly depends on the alkalinity of the pore solution, the heat release and strength development are retarded when high Na2O-E is dosed in the systems. The gel product is believed to be C-(N)-A-S-H gel. SC-activated system showed looser structure and micro-crack in interfacial transition zone (ITZ). Although the larger critical pore diameter and fewer gel products are exhibited in SS-activated system, the compressive strength is higher than SC-activated system at 28d due to the better interfacial transition zone caused by lower autogenous shrinkage and the reasonable hydration products. The formation of CaSO4·2H2O in HG-activated system can build a structure rapidly to decrease the fluidity and setting time sharply. DG shows inert to prolong the setting time, and the decreased degree of fluidity is not apparent. The impurities in phosphogypsum can retard the occurred time of exothermal peak. HG and DG show a positive effect on compressive strength at later ages. But CS shows negative effect on strength development in both systems. High CS content results in a high Ca/Si ratio in ITZ and large crack can be observed. All the HG and DG-activated systems specimens present a stable deformation after a quick expansion increment. SS-activated and DG-activated concrete were studied. The workability and mechanical property of the SS-activated system is better than DG-activated system. The two systems present a decreased strength at 60d but recover after 90d. The carbonization degree of the SS-activated system is much lower than the DG-activated system. The specimens after sulfate curing present higher compressive strength than standard curing. The integrity of DG-activated system is still well after being calcined at 800℃, but SS-activated system shows better high-temperature resistance from 200℃ to 600℃.Prostředí zatěžující feroniklová struska (FNS) je vedlejším produktem slitiny feroniklu. Struska karbidu vápenatého (CS) je průmyslový odpad hydrolýzy CaC2 s využitím hlavních složek Ca(OH)2 a CaCO3. Hydraulicitu FNS lze zlepšit aktivací alkálií pro přípravu slínku bez cementu. Ke studiu kombinovaného účinku CS na aktivaci FNS se používá uhličitan sodný (SC), síran sodný (SS), hemihydrát fosfosádrovce (HG) a dihydrát fosfosádrovce (DG). V práci jsou nejprve studovány vlastnosti FNS, poté byl jako prekurzor vybrán FNS s velikostí částic D50=8,4μm. Hlavní závěry tohoto studia jsou následující: FNS obsahuje především Al2O3, SiO2, CaO, MgO a Fe2O3. Cr je primární těžký kov ve strusce feroniklu. Stávající MgO fáze ve feroniklové strusce jsou převážně spinel a forsterit, to je zásadní pro nízkou brousitelnost a zanedbatelný pokles vazebné energie. CS a SC mohou snížit tekutost a dobu tuhnutí pasty zvýšením jejího alkalického stupně. SS však zvyšuje tekutost a také dobu tuhnutí. Přestože rozpouštění FNS závisí hlavně na alkalitě roztoku pórů, uvolňování tepla a vývoj pevnosti jsou zpomaleny, když je do systémů dávkováno vysoké množství Na2O-E. Gelem je v tomto případě C-(N)-A-S-H. SC aktivovaný systém vykazoval volnější strukturu a mikrotrhlinu v interfaciální přechodové zóně (ITZ). Ačkoli je u tohoto systému větší kritický průměr pórů a menší obsah gelových produktů, pevnost v tlaku u SS-aktivovaného systému je vyšší než u SC-aktivovaného systému. Je to způsobeno zejména lepší přechodovou zónou rozhraní způsobenou nižším autogenním smrštěním a generací správných hydratačních produktů. Tvorba CaSO4·2H2O v systému aktivovaném HG může rychle vybudovat strukturu, která prudce sníží tekutost a dobu tuhnutí. DG je inertní vůči vodě a tím prodlužuje dobu tuhnutí, redukce stupně tekutosti není patrný. Nečistoty ve fosfosádrovci mohou zpomalit dobu exotermického peaku. HG a DG vykazují pozitivní vliv na pevnost v tlaku v pozdějších stádiích. CS však vykazuje negativní vliv na rozvoj pevnosti v obou systémech. Vysoký obsah CS má za následek vysoký poměr Ca/Si v ITZ a byla pozorována i trhlina. Všechny vzorky HG a DG aktivovaných systémů vykazují stabilní deformaci po rychlém nárůstu expanze. Byly studovány betony aktivované jak SS, tak i DG. Zpracovatelnost a mechanické vlastnosti systému aktivovaného SS jsou lepší než systému aktivovaného DG. Tyto dva systémy vykazují sníženou pevnost po 60 dnech, ale obnoví se hned po 90 dnech. Stupeň karbonizace systému aktivovaného SS je mnohem nižší než systému aktivovaného DG. Vzorky po sulfátovém vytvrzování vykazují vyšší pevnost v tlaku než standardní vytvrzování. Integrita systému aktivovaného DG je stále dobrá i po kalcinaci při 800 ℃, ale systém aktivovaný SS vykazuje lepší odolnost vůči vysokým teplotám v rozmezí od 200 ℃ do 600 ℃.636 - Katedra materiálového inženýrstvívyhově

Ecological effect life cycle assessment of house buildings based on emergy footprint model

Abstract Construction is an important sector for climate action. The construction, operation and maintenance, demolition and disposal stages of house buildings consume many resources and have a significant impact on society, the economy and the environment. To assess such efforts, we propose the emergy footprint model of house buildings, which can quantitatively analyse the ecological effect in the house buildings life cycle. The research shows the following. China’s ecological efficiency of the housing sector is characterized by improvement. In the house building fifty-year life cycle, the emergy footprint of the operation and maintenance stage is the largest (75.92%), followed by the construction stage (21.95%), but the emergy footprint intensity of the latter is 4.82 times that of the former. Reducing energy consumption and carbon dioxide emissions in the operation and maintenance stage is the key to reducing the life cycle emergy footprint of house buildings. The ecological impact coefficient of house buildings is negatively exponentially correlated with their service life. It reaches ecological break-even when the service period of the house building is equal to 36.73 years. If the house building is demolished after less than nine years of service, the impact is extremely unfavourable