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Early stages of deadly respiratory diseases including COVID-19 are challenging to elucidate in humans. Here, we define cellular tropism and transcriptomic effects of SARS-CoV-2 virus by productively infecting healthy human lung tissue and using scRNA-seq to reconstruct the transcriptional program in "infection pseudotime" for individual lung cell types. SARS-CoV-2 predominantly infected activated interstitial macrophages (IMs), which can accumulate thousands of viral RNA molecules, taking over 60% of the cell transcriptome and forming dense viral RNA bodies while inducing host profibrotic (TGFB1, SPP1) and inflammatory (early interferon response, CCL2/7/8/13, CXCL10, and IL6/10) programs and destroying host cell architecture. Infected alveolar macrophages (AMs) showed none of these extreme responses. Spike-dependent viral entry into AMs used ACE2 and Sialoadhesin/CD169, whereas IM entry used DC-SIGN/CD209. These results identify activated IMs as a prominent site of viral takeover, the focus of inflammation and fibrosis, and suggest targeting CD209 to prevent early pathology in COVID-19 pneumonia. This approach can be generalized to any human lung infection and to evaluate therapeutics.

Knowing the amplitudes in a spectrum calculated using the Discrete Fourier Transform (DFT) algorithm is essential for precisely estimating the frequencies of a real-valued signal. Since the amplitudes are affected by the signal's initial phase, it is crucial to consider this aspect when estimating the signal frequencies. This paper presents the initial signal phase's effect on the values calculated for the real and imaginary parts and the magnitudes in the DFT of a real-valued signal. Analyzing the evolution of the amplitudes with the initial phase for a complete cycle for the initial phase, we found that the values in the magnitude DFT are subject to minor alteration, while the values of the real and imaginary parts of the DFT are strongly affected by the initial phase.

In this work, we have considered a cantilever beam with a rectangular plastic cross-section, reinforced by four wire reinforcements made of ductile Iron. It was studied using numerical resolution based on theoretical formulae and then simulated using Solidworks software. A comparison of the results on the frequency ratios between the theoretical and numerical by Solidworks models gave a low error of 1.389% with a high correlation coefficient of around 0.959. Then, two top reinforcements inside the beam were cut in order to study the behavior of the intact beam and the damaged beam. Using the simulation results, we found an accurate damage severity using a mathematical method based on error deviation indicators or Model Performance Analysis (MPA).

In energy system transformation studies, the assumption of linearly rising CO2 prices is common and based on the decreasing issued European allowances (EUA). The actual auction price for allowances is influenced by market dynamics, resulting in non-linear variations in CO2 prices due to the possibility of banking allowances. This study explores the impact of non-linear CO2 price trajectories on the European electricity market. Various scenarios, such as increasing prices with the market interest rate, early price peaks, collapse, or fluctuations, are examined until 2030. The study quantifies the effects on power plant investments, profitability, consumer costs, and government revenues. The findings reveal that early and high price signals lead to an earlier transition with 7 % more wind power plants but at 4 % higher costs. Conversely, lower initial prices delay power plant investments in wind power plants by 6 % and result in 21 % higher emissions. Compensation for climate impact costs can yield a positive cost-benefit analysis in scenarios with early and high price signals when emissions are avoided through mechanisms like the Market Stability Reserve (MSR).

Traditional vibration-based damage detection methods often involve human intervention in decision-making, therefore being time-consuming and error-prone. In this study, we propose using Artificial Neural Networks (ANNs) to detect patterns in the structural response and create accurate predictions. The features extracted from the response signal are the Relative Frequency Shifts (RFSs) of the first eight weak-axis bending vibration modes, and the predictions refer to the damage location. To increase the accuracy of the predictions, we propose a novel stacked neural network approach, capable of detecting damage locations with high accuracy. The dataset used for training involves, as input data, the RFSs calculated with an original method for numerous damage locations and severities. The following models were used as building blocks for our stacked approach: Multilayer Perceptron, Recurrent Neural Network, Long Short-term Memory, and Gated Recurrent Units. The entire beam was thus split into segments and each network was trained in this stacked model on one beam segment. All results obtained with the models are also compared to a standard neural network trained on the entire beam. The results obtained show that the model that performs the best contains 14 stacked two-layer feedforward networks.

Cell-free RNA from liquid biopsies can be analyzed to determine disease tissue of origin. We extend this concept to identify cell types of origin using the Tabula Sapiens transcriptomic cell atlas as well as individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset. We define cell type signature scores, which allow the inference of cell types that contribute to cell-free RNA for a variety of diseases.

Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type-specific RNA splicing was discovered and analyzed across tissues within an individual.

Abstract The important roles of bacterial outer membrane vesicles (OMVs) in various diseases and their emergence as a promising platform for vaccine development and targeted drug delivery necessitates the development of imaging techniques suitable for quantifying their biodistribution with high precision. To address this requirement, we aimed to develop an OMV specific radiolabeling technique for positron emission tomography (PET). A novel bacterial strain (E. coli BL21(DE3) ΔnlpI, ΔlpxM) was created for efficient OMV production, and OMVs were characterized using various methods. SpyCatcher was anchored to the OMV outer membrane using autotransporter-based surface display systems. Synthetic SpyTag-NODAGA conjugates were tested for OMV surface binding and 64Cu labeling efficiency. The final labeling protocol shows a radiochemical purity of 100% with a ~ 29% radiolabeling efficiency and excellent serum stability. The in vivo biodistribution of OMVs labeled with 64Cu was determined in mice using PET/MRI imaging which revealed that the biodistribution of radiolabeled OMVs in mice is characteristic of previously reported data with the highest organ uptakes corresponding to the liver and spleen 3, 6, and 12 h following intravenous administration. This novel method can serve as a basis for a general OMV radiolabeling scheme and could be used in vaccine- and drug-carrier development based on bioengineered OMVs.

There is an increasingly rich literature on the decarbonisation of heat and the evolution of heat networks. This paper investigates whether a novel fifth Generation District Heating and Cooling Network (5GDHC) could be retrofitted to an existing National Health Service (NHS) hospital campus for the purpose of heating and cooling. The building load was simulated and input into a custom-written script to carry out a series of parametric studies and optimise design options. The model was calibrated against site data available from hospital facilities management. The research found that it is feasible to use a 5GDHC consisting of a large single mass of water to utilise inter-seasonal thermal storage. A natural water resource such as an aquifer was not required. The model tested sizing options and found that larger thermal storage, heat pumps and chillers reduce operating costs and improve flexibility. The paper closes with a discussion of the practical factors in retrofitting 5GDHC networks to a densely occupied and highly constrained campus environment. The findings are novel in further describing the circumstances for which 5GDHC networks are suitable.

This study investigated the impact of the depulping of cocoa beans after pod opening, as well as the influences of pod storage (PS) and fermentation time on the fermentation dynamics and the overall quality of beans and liquors made thereof. Twelve variations were conducted in three experimental runs (with/without depulping; 1-/3-day PS; and fermentation times of 3, 4, 5, 6 or 7 days). Fermentation dynamics (e.g., temperature and pH) and the quality of dried beans (e.g., cut-test and fermentation index) and liquors (sensory assessment, quantification of cocoa key-odorants and tastants) were investigated. It was demonstrated that 17–20% of cocoa pulp, relative to the total bean-pulp-mass weight, could be mechanically removed without negatively affecting the bean quality. No significant differences were found in the percentages of well-fermented beans after 5–6 days fermentation with 1-day PS, resulting in 49 ± 9% with, and 48 ± 12% without depulping. There were no significant differences in key tastants present in the liquors; however, significantly less volatile acids and esters were found when liquors were produced from 5–6 day-fermented depulped beans, with 1-day PS, without negatively affecting the sensory profiles. This strategy allows producers to maximize the cacao fruit’s value by integrating part of the pulp into the cocoa value chain.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.

Traditional vibration-based damage detection methods often involve human intervention in decision-making, therefore being time-consuming and error-prone. In this study, we propose using Artificial Neural Networks (ANNs) to detect patterns in the structural response and create accurate predictions. The features extracted from the response signal are the Relative Frequency Shifts (RFSs) of the first eight weak-axis bending vibration modes, and the predictions refer to the damage location. To increase the accuracy of the predictions, we propose a novel stacked neural network approach, capable of detecting damage locations with high accuracy. The dataset used for training involves, as input data, the RFSs calculated with an original method for numerous damage locations and severities. The following models were used as building blocks for our stacked approach: Multilayer Perceptron, Recurrent Neural Network, Long Short-term Memory, and Gated Recurrent Units. The entire beam was thus split into segments and each network was trained in this stacked model on one beam segment. All results obtained with the models are also compared to a standard neural network trained on the entire beam. The results obtained show that the model that performs the best contains 14 stacked two-layer feedforward networks. [ABSTRACT FROM AUTHOR]

The propagation of thermal runaway in a battery system is safety-critical in almost every application, such as electric vehicles or home storage. Abuse models can help to undestand propagation mechanisms and assist in designing safe battery systems, but need to be well-parametrized. Most of the heat during thermal runaway is released by venting that is why the characteristic of the vent flow plays an important part in the safety assessment. During venting, the cell generates a recoil force like a rocket, which depends on the flow speed and flow rate of the gas. This principle is used in this work to measure the velocity and mass flow rate of the vent gas. High-power and high-energy 18650 format lithium-ion batteries were overheated and the recoil and weight forces were measured to determine the venting parameter during thermal runaway. Our results show, that the linearized gas flow rate for the high-power and high-energy cell is 22.15gs−1 and 27.92gs−1, respectively. The progress of the gas velocity differs between the two cell types and in case of the high-energy cell, it follows a single peak asymmetrical pattern with a peak of 398.5ms−1, while the high-power cell shows a bumpy pattern with a maximum gas velocity of 260.9ms−1. The developed test bench and gained results can contribute insights in the venting behavior, characterize venting, support safety assessments, simulations and pack design studies.

The current paper explores a novel approach for determining temperature variations by integrating the modal parameters and AI techniques. The research focuses on the development of a comprehensive dataset for training an AI model encompassing an analytical method that considers thermal conditions and natural frequencies. Traditional methods of temperature measurement, like infrared and platinum resistance thermometers, often face limitations in terms of accuracy, especially in complex or dynamic environments having an uncertainty of ±3.6°C [1], respectively ±0.2°C [2]. In this study, we propose a methodology that harnesses the inherent relationship between axial loads caused by temperature variations and the change in natural frequencies of a double clamped steel beam. The measured natural frequency data is collected and fed into the AI model, specifically, for a robust temperature estimation, obtaining a maximum predicted temperature deviation of 0.386°C.

This paper introduces a device dedicated to acoustic excitation for modal analysis of plates. A particular setup is requested since the system should produce acoustic excitation with high power in the 10…120 Hz frequency range. The system comprises a signal generator, an amplifier powered by a car starter or a battery, and a subwoofer. This system permits setting the generated frequency around the resonance frequency with reasonable frequency and amplitude stability. We present the system's design and test its performance on a rectangular plate. In our laboratory experiments, we achieved the desired structural behavior.

In the second part of the paper, the dynamic behavior of a doubly clamped beam is presented, where the right clamped end of the beam is weakened by introducing a weakening coefficient. The analytical calculation is based on the determination of the bending moment from the weakened clamped end expressed as a function of slope, after which the modal function, strain energy, and the characteristic equation are determined to obtain the eigenvalues of the first six vibration modes depending on the weakened coefficient of the clamped end. The obtained mode shapes and strain energies are determined for seven values of the weakened coefficient.

The paper presents the strain energy of the circular plate clamped all around through a relation obtained analytically and the graphic representation of the modal shapes and the maximum normalized strain energy along the x axis. Depending on the number of nodal circles s and the nodal diameters n, the maximum strain energy can be both in the center of the circular plate clamped all around, as well as the first ventral points from the center of the circular plate towards the outside of the plate.

Using analytical equations, the paper aims to solve the dynamic behavior of beams where a clamped end of the beam does not respect the ideal boundary conditions by introducing a weakening coefficient. In the paper, the characteristic equation for determining the eigenvalues and the relationship of the modal function and strain energy are derived. The results show the first six vibration modes for different values of the weakening coefficient which is considered in the clamped end and the evolution of the strain energy.

The first part of the paper presents a study regarding the dynamics behavior of thin circular plate clamped all around. An analytical investigation is performed and the results in terms of mode shapes are used to highlight the plate’s dynamics. The modal shapes are obtained using Bessel functions and their graphic representation is presented in 3D by using MS Excel software.

Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death1. Despite rapid advances over recent years, many of the molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood2. To gain a better insight into these processes, here we generate a single-cell transcriptomic atlas across the lifespan of Mus musculus that includes data from 23 tissues and organs. We found cell-specific changes occurring across multiple cell types and organs, as well as age-related changes in the cellular composition of different organs. Using single-cell transcriptomic data, we assessed cell-type-specific manifestations of different hallmarks of ageing-such as senescence3, genomic instability4 and changes in the immune system2. This transcriptomic atlas-which we denote Tabula Muris Senis, or 'Mouse Ageing Cell Atlas'-provides molecular information about how the most important hallmarks of ageing are reflected in a broad range of tissues and cell types.