Underlaid Sensing Pilot for Integrated Sensing and Communications

This paper investigates a novel underlaid sensing pilot signal design for integrated sensing and communications (ISAC) in an OFDM-based communication system. The proposed two-dimensional (2D) pilot signal is first generated on the delay-Doppler (DD) plane and then converted to the time-frequency (TF) plane for multiplexing with the OFDM data symbols. The sensing signal underlays the OFDM data, allowing for the sharing of time-frequency resources. In this framework, sensing detection is implemented based on a simple 2D correlation, taking advantage of the favorable auto-correlation properties of the sensing pilot. In the communication part, the sensing pilot, served as a known signal, can be utilized for channel estimation and equalization to ensure optimal symbol detection performance. The underlaid sensing pilot demonstrates good scalability and can adapt to different delay and Doppler resolution requirements without violating the OFDM frame structure. Experimental results show the effective sensing performance of the proposed pilot, with only a small fraction of power shared from the OFDM data, while maintaining satisfactory symbol detection performance in communication.Comment: 13 pages, 6 figure

Kinetic modeling of lignin catalytic hydroconversion in a semi-batch reactor

International audienceA kinetic modeling for lignin catalytic hydroconversion over a supported CoMoS catalyst in a semi-batch reactor has been constructed based on analysis results from Gel Permeation Chromatography (GPC) and two-dimensional Gas Chromatography (GC × GC). The model includes 15 pseudo-component lumped products classified by their states and functional groups. Hydrogen consumption, liquid-gas mass transfer resistance and vapor-liquid equilibrium are accounted for. Physical and chemical parameters were estimated from experiments carried out at 350 °C and 80 bar. The resulting model is able to fit the experimental data well. From estimated parameters, it is deduced that the bottlenecks of lignin catalytic hydroconversion are the deeper conversion of the soluble oligomeric fractions and the relatively slow hydrodeoxygenation rate

Associations between the platelet/high-density lipoprotein cholesterol ratio and likelihood of nephrolithiasis: a cross-sectional analysis in United States adults

AimsThe primary objective of this study was to investigate the relationship between the platelet/high-density lipoprotein cholesterol ratio (PHR) and the prevalence of nephrolithiasis within the adult population of the United States.MethodsThe data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2018. The analysis included a non-pregnant population aged 20 years or older, providing proper PHR index and nephrolithiasis data. The research utilized subgroup analyses and weighted univariate and multivariable logistic regression to evaluate the independent association between the PHR and the susceptibility to nephrolithiasis.ResultsThe study comprised 30,899 participants with an average PHR value of 19.30 ± 0.11. The overall prevalence rate of nephrolithiasis was estimated at 9.98% with an increase in the higher PHR tertiles (T1, 8.49%; T2, 10.11%; T3, 11.38%, P < 0.0001). An elevated PHR level was closely linked with a higher susceptibility to nephrolithiasis. Compared with patients in T1, and after adjusting for potential confounders in model 2, the corresponding odds ratio for nephrolithiasis in T3 was 1.48 (95% CI: 1.06 to 2.08), with a P-value = 0.02. The results of the interaction tests revealed a significant impact of chronic kidney disease on the relationship between PHR and nephrolithiasis. Furthermore, the restricted cubic spline analyses exhibited a positive, non-linear correlation between PHR and the risk of nephrolithiasis.ConclusionA convenient biomarker, the PHR, was independently associated with nephrolithiasis and could be a novel biomarker in predicting occurrence in clinical decision

Label-free quantitative imaging of cholesterol in intact tissues by hyperspectral stimulated Raman scattering microscopy

A finger on the pulse: Current molecular analysis of cells and tissues routinely relies on separation, enrichment, and subsequent measurements by various assays. Now, a platform of hyperspectral stimulated Raman scattering microscopy has been developed for the fast, quantitative, and label-free imaging of biomolecules in intact tissues using spectroscopic fingerprints as the contrast mechanism

A Learning Based Approach to Separate Mixed X-Ray Images Associated with Artwork with Concealed Designs

X-ray images are widely used in the study of paintings. When a painting has hidden sub-surface features (e.g., reuse of the canvas or revision of a composition by the artist), the resulting X-ray images can be hard to interpret as they include contributions from both the surface painting and the hidden design. In this paper we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings (‘mixed X-ray images’) to separate them into two hypothetical X-ray images, one containing information related to the visible painting only and the other containing the hidden features. The proposed approach involves two steps: (1) separation of the mixed X-ray image into two images, guided by the combined use of a reconstruction and an exclusion loss; (2) even allocation of the error map into the two individual, separated X-ray images, yielding separation results that have an appearance that is more familiar in relation to Xray images. The proposed method was demonstrated on a real painting with hidden content, Doña Isabel de Porcel by Francisco de Goya, to show its effectiveness

Family-based whole-exome sequencing identifies novel loss-of-function mutations of FBN1 for Marfan syndrome

Background Marfan syndrome (MFS) is an inherited connective tissue disorder affecting the ocular, skeletal and cardiovascular systems. Previous studies of MFS have demonstrated the association between genetic defects and clinical manifestations. Our purpose was to investigate the role of novel genetic variants in determining MFS clinical phenotypes. Methods We sequenced the whole exome of 19 individuals derived from three Han Chinese families. The sequencing data were analyzed by a standard pipeline. Variants were further filtered against the public database and an in-house database. Then, we performed pedigree analysis under different inheritance patterns according to American College of Medical Genetics guidelines. Results were confirmed by Sanger sequencing. Results Two novel loss-of-function indels (c.5027_5028insTGTCCTCC, p.D1677Vfs*8; c.5856delG, p.S1953Lfs*27) and one nonsense variant (c.8034C>A, p.Y2678*) of FBN1 were identified in Family 1, Family 2 and Family 3, respectively. All affected members carried pathogenic mutations, whereas other unaffected family members or control individuals did not. These different kinds of loss of function (LOF) variants of FBN1 were located in the cbEGF region and a conserved domain across species and were not reported previously. Conclusions Our study extended and strengthened the vital role of FBN1 LOF mutations in the pathogenesis of MFS with an autosomal dominant inheritance pattern. We confirm that genetic testing by next-generation sequencing of blood DNA can be fundamental in helping clinicians conduct mutation-based pre- and postnatal screening, genetic diagnosis and clinical management for MFS

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions

Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol–1 Å–1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost

Open-source genomic analysis of Shiga-toxin–producing E. coli O104:H4

An outbreak caused by Shiga-toxin–producing Escherichia coli O104:H4 occurred in Germany in May and June of 2011, with more than 3000 persons infected. Here, we report a cluster of cases associated with a single family and describe an open-source genomic analysis of an isolate from one member of the family. This analysis involved the use of rapid, bench-top DNA sequencing technology, open-source data release, and prompt crowd-sourced analyses. In less than a week, these studies revealed that the outbreak strain belonged to an enteroaggregative E. coli lineage that had acquired genes for Shiga toxin 2 and for antibiotic resistance