Parallel Samples Research Articles

A dual-process of targeted and unbiased Nanopore sequencing enables accurate and rapid diagnosis of lower respiratory infections

Nanopore metagenomics has been used for infectious disease diagnosis for bacterial pathogens. However, this technology currently lacks comprehensive performance studies in clinical settings for simultaneous detection of bacteria, fungi, and viruses. We developed a dual-process of Nanopore sequencing for one sample, with unbiased metagenomics in Meta process and target enrichment in Panel process (Nanopore Meta-Panel process, NanoMP) and prospectively enrolled 450 respiratory specimens from multiple centers. The filter system of pathogen detection was established with machine learning and receiver operator characteristic (ROC) curve to optimize the detection accuracy based on orthogonal test of 21 species. Antimicrobial resistance (AMR) genes were identified based on the Comprehensive Antibiotic Resistance Database (CARD) and single-nucleotide polymorphism matrix. Our approach showed high sensitivity in Meta process, with 82.9%, 88.7%, and 75.0% for bacteria, fungi (except Aspergillus), and Mycobacterium tuberculosis groups, respectively. Moreover, target amplification improved the sensitivity of virus (>80.0% vs. 39.4%) and Aspergillus (81.8% vs. 42.3%) groups in Panel process compared with Meta process. Overall, NanoMP achieved 80.2% sensitivity and 98.8% specificity compared with the composite reference standard, and we were able to accurately detect AMR genes including blaKPC-2, blaOXA-23 and mecA and distinguish their parent organisms in patients with mixed infections. We combined metagenomic and enriched Nanopore sequencing for one sample in parallel. Our NanoMP approach simultaneously covered bacteria, viruses and fungi in respiratory specimens and demonstrated good diagnostic performance in real clinical settings. National Key Research and Development Program of China and National Natural Science Foundation of China.

Implementation of Asynchronous Distributed Gauss-Newton Optimization Algorithms for Uncertainty Quantification by Conditioning to Production Data

Summary Previous implementation of the distributed Gauss-Newton (DGN) optimization algorithm ran multiple optimization threads in parallel, employing a synchronous running mode (S-DGN). As a result, it waits for all simulations submitted in each iteration to complete, which may significantly degrade performance because a few simulations may run much longer than others, especially for time-consuming real-field cases. To overcome this limitation and thus improve the DGN optimizer’s execution, we propose two asynchronous DGN (A-DGN) optimization algorithms in this paper. The two A-DGN optimization algorithms are (1) the local-search algorithm (A-DGN-LS) to locate multiple maximum a-posteriori (MAP) estimates and (2) the integrated global-search algorithm with the randomized maximum likelihood (RML) method (A-DGN + RML) to generate hundreds of RML samples in parallel for uncertainty quantification. We propose using batch together with a checking time interval to control the optimization process. The A-DGN optimizers check the status of all running simulations after every checking time frame. The iteration index of each optimization thread is updated dynamically according to its simulation status. Thus, different optimization threads may have different iteration indices in the same batch. A new simulation case is proposed immediately once the simulation of an optimization thread is completed, without waiting for the completion of other simulations. We modified the training data set updating algorithm using each thread’s dynamically updated iteration index to implement the asynchronous running mode. We apply the modified QR decomposition method to estimate the sensitivity matrix at the best solution of each optimization thread by linear interpolation of all or a subset of the training data to avoid the issue of solving a linear system with a singular matrix because of insufficient training data points in early batches. A new simulation case (or search point) is generated by solving the Gauss-Newton (GN) trust-region subproblem (GNTRS) using the estimated sensitivity matrix. We developed a more efficient and robust GNTRS solver using eigenvalue decomposition (EVD). The proposed A-DGN optimization methods are tested and validated on a 2D analytical toy problem and a synthetic history-matching problem and then applied to a real-field deepwater reservoir model. Numerical tests confirm that the proposed A-DGN optimization methods can converge to solutions with matching quality comparable to those obtained by the S-DGN optimizers, saving on the time required for the optimizer to converge by a factor ranging from 1.3 to 2 when compared to the S-DGN optimizer depending on the problem. The new A-DGN optimization algorithms improve efficiency and robustness in solving history-matching or inversion problems, especially for uncertainty quantification of subsurface model parameters and production forecasts of real-field reservoirs by conditioning production data.

An efficient calibration method for digital bandwidth interleaving sampling system

Digital bandwidth interleaving (DBI) is a technique that can simultaneously increase system sampling rate and bandwidth. While DBI can reduce the impact of clock jitter on the dynamic performance of the system compared to time-interleaved sampling systems, achieving dynamic performance comparable to time-interleaved analog-to-digital converters (TIADC) remains challenging due to phase misalignment in crossover bands caused by analog filters and nonlinearity in analog mixers. To address these issues, this paper proposes a method to calibrate DBI sampling systems using PRBS signals. This method involves injecting pseudo-random binary sequence (PRBS) signal into the system input as a background signal and calculating the correlation between the output sampling sequence and the PRBS sequence to obtain the response of each sampling channel. Compared with traditional single tone signal or pulse signal, this method offers a more efficient background calibration method. To verify the effectiveness of this method, this paper designed a hybrid parallel sampling system combining DBI and TIADC with a 20 GSPS sampling rate and 8 GHz bandwidth to evaluate the effectiveness of the calibration method. In the performance test of the sampling system before and after calibration, this calibration method improved the system's Signal-to-Noise and Distortion Ratio (SINAD) by up to 13 dB, Spurious Free Dynamic Range (SFDR) by up to 27 dB, and phase response flatness better than 0.3 radians.

Multi-level weighted dynamic adversarial adaptation network for partial set cross-domain fault diagnosis

Domain adaptation (DA)-based methods have been successfully applied in fault diagnosis, but their effectiveness relies on the assumption that the source and target label spaces are identical, which does not always hold in industrial scenarios. A more realistic scenario, named partial transfer diagnosis where the target label space is a subset of the source label space, is explored in this study. To this end, a multi-level weighted dynamic adversarial adaptation network (MWDAAN) is proposed. To prevent the negative effects of outlier data, a parallel sample filtering scheme based on the multi-level weighting strategy is designed, which enhances the effect of correcting noise weights and adequately evaluates the transferability of source samples. On this basis, a dynamic domain adversarial framework embedded with the sample filtering scheme is constructed to maximize the positive transfer of shared classes. Extensive experimental results on four cases demonstrate the effectiveness and superiority of the proposed method.

Top-Down Machine Learning of Coarse-Grained Protein Force Fields.

Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential through differentiable trajectory reweighting. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data derived from extensive simulations or memory-intensive end-to-end differentiable simulations. Once trained, the model can be employed to run parallel molecular dynamics simulations and sample folding events for proteins both within and beyond the training distribution, showcasing its extrapolation capabilities. By applying Markov state models, native-like conformations of the simulated proteins can be predicted from the coarse-grained simulations. Owing to its theoretical transferability and ability to use solely experimental static structures as training data, we anticipate that this approach will prove advantageous for developing new protein force fields and further advancing the study of protein dynamics, folding, and interactions.

Hands-Free Proteomic Profiling of Urinary Extracellular Vesicles with a High-Throughput Automated Workflow.

Extracellular vesicles (EVs) have emerged as a promising source of disease biomarkers for noninvasive early stage diagnoses, but a bottleneck in EV sample processing restricts their immense potential in clinical applications. Existing methods are limited by a low EV yield and integrity, slow processing speeds, low sample capacity, and poor recovery efficiency. We aimed to address these issues with a high-throughput automated workflow for EV isolation, EV lysis, protein extraction, and protein denaturation. The automation can process clinical urine samples in parallel, resulting in protein-covered beads ready for various analytical methods, including immunoassays, protein quantitation assays, and mass spectrometry. Compared to the standard manual lysis method for contamination levels, efficiency, and consistency of EV isolation, the automated protocol shows reproducible and robust proteomic quantitation with less than a 10% median coefficient of variation. When we applied the method to clinical samples, we identified a total 3,793 unique proteins and 40,380 unique peptides, with 992 significantly upregulated proteins in kidney cancer patients versus healthy controls. These upregulated proteins were found to be involved in several important kidney cancer metabolic pathways also identified with a manual control. This hands-free workflow represents a practical EV extraction and profiling approach that can benefit both clinical and research applications, streamlining biomarker discovery, tumor monitoring, and early cancer diagnoses.

Optimal real-time resonant scanner linearization using filtered Hermite interpolation.

High-speed laser scanning microscopy frequently relies on resonant scanners due to their order of magnitude increase in imaging rate compared to conventional galvanometer scanners. However, the use of a nonlinear scan trajectory introduces distortion that must be corrected. This manuscript derives a new algorithm based on filtered Hermite polynomial interpolation that provides the optimal shot-noise-limited SNR for a fixed number of photons and provides higher spatial accuracy than previous methods. An open-source library is presented using the Intel advanced vector instruction set (AVX) to process up to 32 samples in parallel. Using this approach, I simultaneously demonstrate lower shot noise variance, moderately higher spatial accuracy and greater than 1 gigapixel per second interpolation rate on a desktop CPU.

An RNA-Ligation-Based RACE-PAT Assay to Monitor Poly(A) Tail Length of mRNAs of Interest.

In eukaryotes, a non-templated poly-adenosine (poly(A)) tail is added co-transcriptionally to almost every messenger RNA (mRNA). The length of this poly(A) tail changes during the lifetime of mRNAs and has been shown in many circumstances to be an important factor controlling transcript fates. Yet, the measure of the length of this homogenous nucleotide sequence is technically challenging, making it difficult to assess its dynamic variation. In this chapter, we describe an RNA-ligation-based RACE-PAT (Rapid Amplification of cDNA End-Poly(A) Tail) assay to monitor the poly(A) tail length of mRNAs. In the first step, an RNA oligonucleotide is ligated to mRNA 3' ends providing an anchoring site to prime cDNA synthesis, avoiding the bias introduced by oligo(dT)-derived primers. Afterward, reverse transcription is performed with an anchor primer with a unique 5' extension. The choice of the oligonucleotide 3' end at this step allows further flexibility to amplify modified tails, for example, by uridylation. Next, short DNA fragments encompassing the poly(A) tails are amplified by Polymerase Chain Reaction (PCR) using as forward primer, a transcript-specific primer hybridizing close to the transcript polyadenylation signal, and as reverse primer, an oligonucleotide corresponding to the 5' extension of the primer used for cDNA synthesis, ensuring that only cDNAs are amplified. The resulting DNA fragments are then visualized after size fractionation by electrophoresis. This method does not provide exact nucleotide count and composition but has the advantage of allowing the processing of many samples in parallel at a low cost.

Convolutional Tsetlin Machine-based Training and Inference Accelerator for 2-D Pattern Classification

The Tsetlin Machine (TM) is a machine learning algorithm based on an ensemble of Tsetlin Automata (TAs) that learns propositional logic expressions from Boolean input features. In this paper, the design and implementation of a Field Programmable Gate Array (FPGA) accelerator based on the Convolutional Tsetlin Machine (CTM) is presented. The accelerator performs classification of two pattern classes in 4 × 4 Boolean images with a 2 × 2 convolution window. Specifically, there are two separate TMs, one per class. Each TM comprises 40 propositional logic formulas, denoted as clauses, which are conjunctions of literals. Include/exclude actions from the TAs determine which literals are included in each clause. The accelerator supports full training, including random patch selection during convolution based on parallel reservoir sampling across all clauses. The design is implemented on a Xilinx Zynq XC7Z020 FPGA platform. With an operating clock speed of 40 MHz, the accelerator achieves a classification rate of 4.4 million images per second with an energy per classification of 0.6 μJ. The mean test accuracy is 99.9% when trained on the 2-dimensional Noisy XOR dataset with 40% noise in the training labels. To achieve this performance, which is on par with the original software implementation, Linear Feedback Shift Register (LFSR) random number generators of minimum 16 bits are required. The solution demonstrates the core principles of a CTM and can be scaled to operate on multi-class systems for larger images.

Analysis on the mechanism of the enforced imbibition effect of tight oil reservoir by the activated water

Based on the parallel rock samples of tight oil reservoirs, the imbibition comparison experiments respecting to formation water and active water under different experimental conditions have been initiated. With adoption of the experimental measures i.e. Nuclear Magnetic Resonance (NMR), the micro-production mechanism of the enforcement of the imbibition oil recovery effect by the active agent has been analyzed. Surfactant may promote the imbibition effect of samples with different wettability, different water saturation and different sizes. The imbibition recovery ratios of No. 1 hydrophilic core in formation water and active water are respective 15.79% and 17.68%, and the active water imbibition increased by 1.88%, with an enforcement ratio of 11.97%. The imbibition recovery ratios of No. 3 lipophilic core in formation water and active water are respective 10.64% and 13.26%, and the active water imbibition increased by 2.62%, with an enforcement ratio of​ 24.62%. The enforcement effect of surfactant on the lipophilic core is higher than that on the hydrophilic core. The enforcement ratio of No. 1 core in macropores is 8.78%, and that of No. 3 rock is 36.14%, indicating that the surfactant can effectively reduce the seepage resistance of the lipophilic cores and enhance the imbibition recovery ratio of macropores. The increase magnitude and the enforcement ratio of the imbibition recovery of No. 4 low water-contained core are lower than those of No. 6 high water-contained core, indicating that the surfactant has a greater impact on the imbibition of high water-contained cores. The enforcement ratio in active water imbibition in core No. 4 is 28.59% in micropores and 15.46% in macropores; and respective 1460.74% and 9.56% for No. 6 core. The enforcement ratios of micropores in the two cores are both higher than those in macropores. It shows that the surfactant has a great influence on the permeability of micropores in the core. The increment magnitude in active water imbibition of small No. 2 core is 22%, and that of No. 2 large core is 26.93%, indicating that the surfactant has a greater impact on the imbibition of large cores.

Research on the Application of Translation Parallel Corpus in Interpretation Teaching

Large and organized sets of translated texts between languages are called parallel translation corpora (PTLs). Even though data-driven learning can generate insights from massive datasets and create more tailored learning experiences, it has gained in popularity. There are however a few problems with this strategy, such as poor data quality, privacy concerns, inability to scale, a lack of clear explanations, and high costs. To provide high-quality output, machine translation algorithms are generally trained utilizing parallel corpora generated by human translators. The written word can be deciphered from one language to another through translation. The spoken word can be conveyed from one language to another through translation. Based on the study of real samples, machine translation from corpus linguistics uses its translations to create its translations. Statistical approaches are only one of the many ways a corpus may be used. Translated texts from two or more languages are called parallel corpora. With the emergence of data-driven learning (DDL) in translation training and language instruction, they are becoming increasingly popular in translation and contrastive research. While working as a professional translator, you're likely to encounter a wide range of challenges. These are lexical-semantic, grammar, syntactic, rhetorical, practical, and cultural difficulties. There are limitations to the number of possible translations that dictionaries can provide and difficulty in doing a thorough search. When it comes to translating, contemporary technologies bring up a whole new world of possibilities thanks to the sheer volume of data and the speed at which it is available. Students of translation can benefit from this study's innovative way of employing PTL-DDL, which can help them improve the quality and speed of their translations. In addition, the parallel corpora's sample sentences are readily available, making it easier to choose the best translations from a pool of translation candidates. Because of these characteristics, the approach is well-suited to creating active or encoding dictionaries.

B-246 The Evaluation of Actionable Mutations By Multiplex Analysis Using IntelliPlex® Lung Cancer Panel for Non-Small Cell Cancer Management

Abstract Background Understanding the genetic alterations which occur in genes associated with various cancers can improve therapy outcomes by promoting tailored treatments for each patient. Molecular profiling is therefore a crucial tool for precision medicine and enables for a personalized approach to cancer management. Current guidelines for molecular testing of Non-Small Cell Lung Cancer (NSCLC) patients involves treatment selection of a targeted Tyrosine Kinase Inhibitor based on the minimal testing of EGFR, ALK and ROS1 genes and strongly recommending the additional testing of KRAS, BRAF, MET, RET, NTRK and ERBB2 (Her2) genes to fully evaluate the patient’s mutational status. Method The IntelliPlex Lung Cancer Panel, based on PlexBio’s multiplexing πCode® technology, is an in vitro molecular amplification assay intended for the qualitative identification of 74 DNA mutations in the KRAS, NRAS, PIK3CA, BRAF, EGFR, HER2, MEK1 and AKT1 genes and 28 gene variants of the ALK, ROS1, RET, NTRK1 and MET genes. The assay is suitable for the use with formalin-fixed paraffin-embedded (FFPE) tumor tissues from NSCLC patients. The performance of IntelliPlex Lung Cancer Panel was evaluated on FFPE specimens by comparing the results with two commercially available molecular amplification assays. Discrepant samples were resolved by a corresponding digital PCR method kit. Results The IntelliPlex Lung Cancer Panel can detect DNA mutations at very low allele frequencies (as low as 0.1%) while only requiring DNA quantities of 10ng. Most RNA rearrangements are detected with 10 or more copies present in the sample while only low RNA input quantities (50 ng) are required. The IntelliPlex Lung Cancer Panel can analyze the mutational status of 102 targets in less than 5.5 h from sample to results and allows screening of up to 48 samples in parallel. Among 50 FFPE DNA specimens tested, the overall agreement for EGFR and KRAS mutations between IntelliPlex system and the comparison method were both found to be 98%. And the overall agreement after resolved the discrepancies by digital PCR was 100%. Conclusion The multiplex amplification and detection capabilities of the IntelliPlex Lung Cancer Panel provides a comprehensive and efficient way to evaluate the individual mutational status from lung cancer patients with high sensitivity and specificity. Accurate genetic analysis provides valuable information which can support precision medicine decisions for better cancer management. This can also be used further with non-invasive whole blood sampling (i.e., “liquid biopsy”) for subsequent treatment monitoring.

Four-Color SERS Monitoring of Size-dependent Nanoparticle Delivery in the Same Tumor.

The physicochemical properties of nanoparticles (NPs) significantly influence their deposition at the disease site, ultimately impacting the overall therapeutic efficacy; however, precisely assessing the effects of various factors on NP accumulation within a single cell/tumor tissue is challenging due to the lack of appropriate labeling techniques. Surface-enhanced Raman spectroscopy (SERS) tag is a powerful encoding method that has recently been intensively employed for immunodetection of biomarkers. Herein, we introduce a multiplexed SERS tracking approach for systematic investigation of size-dependent accumulation and distribution of NPs within the same tumor. Four-sized (34, 60, 108, and 147 nm) NPs encoded with different SERS "colors" were fabricated, mixed, and incubated with monolayer tumor cells, multicellular tumor spheroids, or injected into mouse models bearing xenograft solid tumors in a single dose. Multicolor SERS detection of the specimens revealed that NP accumulation in tumor cells, tumor spheroids, and solid tumors was in the order of 34 nm > 60 nm > 108 nm > 147 nm, 60 nm > 34 nm > 108 nm > 147 nm, and 34 nm > 147 nm > 108 nm > 60 nm, respectively. Inductively coupled plasma mass spectroscopy determination performed in parallel samples were in alignment with the four-color SERS probing results, demonstrating the effectiveness of this multiplexed evaluation assay. Furthermore, in combination with fluorescence labeling of specific biomolecules, this method can be applied for the colocalization of different NPs in various pathological structures and provide additional information for analysis of the possible mechanisms.

A welding defect detection method based on multiscale feature enhancement and aggregation

ABSTRACT Welding defects can pose significant safety risks in the industrial production. Accurate detection of these defects is crucial for ensuring the industrial production safety. Traditional computer vision methods rely on operator experience and knowledge and are time-consuming. In addition, manual feature extraction and classifier definition become more difficult for complex images or unknown defects. In contrast, deep learning can automatically extract features from raw data and build classifiers with higher accuracy. Additionally, deep learning models have stronger generalization ability and can identify a wider range of defects, without being limited by feature engineering. Consequently, we proposed a welding defect detection model based on multiscale feature enhancement and aggregation based on the advanced deep learning model YOLOv7. The model is composed of a wavelet multiscale attention (WMA), a parallel sampling module (PSS), and a path expansion aggregation network (PEANet). We compared the model with a variety of excellent object detection algorithms. Experiments show that the proposed model can effectively identify welding defects. Compared with the original model, the mAP has increased by approximately 1.2%, the average recall has increased by approximately 5.3%, the parameter volume has increased slightly, and the Flops has decreased by approximately 62.6%..

ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-Oriented Sample Size Allocation and Data Generation

Approximate query processing (AQP) is one of the key techniques to cope with big data querying problem on account that it obtains approximate answers efficiently. To address non-trivial sample selection and heavy sampling cost issues in AQP, we propose ShadowAQP, an efficient and accurate approach based on attribute-oriented sample size allocation and data generation. We select samples according to group-by and join attributes, and determine the sample size for each group of unique value combinations to improve query accuracy. We design a conditional variational autoencoder model with automatic table data encoding and model update strategies. To further improve accuracy and efficiency, we propose a set of extensions, including parallel multi-round sampling aggregation, data outlier-aware sampling, and dimension reduction optimization. Evaluation results on diversified datasets show that, compared with SOTA approaches, ShadowAQP achieves 5.8× query speed performance improvement on average (up to 12.8×), while reducing query error by 74% on average (up to 95%) at the same time.

New Technologies That Accelerate Persistent Organic Pollutant (POP) Sample Preparation

Despite best efforts in 2004 to ban their use, persistent organic pollutants (POPs) remain prevalent across the globe, including in soil. To protect human health, agricultural and environmental soil require careful investigation, but preparing samples for gas chromatography–mass spectrometry (GC–MS) analysis is time-consuming, and mostly done manually. Accelerated solvent extraction (ASE) has been the preferred preparation method for the past few decades, and while we have seen advancements, the method remains manual. Now, new technology offers parallel sample processing, combined extraction and evaporation, and automation—leading to faster analysis, reduced risk of error, and freed-up time for personnel.

The Influence of KE and EW Dipeptides in the Composition of the Thymalin Drug on Gene Expression and Protein Synthesis Involved in the Pathogenesis of COVID-19.

Thymalin is an immunomodulatory drug containing a polypeptide extract of thymus that has demonstrated efficacy in the therapy of acute respiratory distress syndrome and chronic obstructive pulmonary disease, as well as in complex therapy related to severe COVID-19 in middle-aged and elderly patients.. KE and EW dipeptides are active substances of Thymalin. There is evidence that KE stimulates cellular immunity and nonspecific resistance in organisms, exerting an activating effect on macrophages, blood lymphocytes, thymocytes, and neutrophils, while EW reduces angiotensin-induced vasoconstriction and preserves endothelium-dependent vascular relaxation by inhibiting ACE2, the target protein of SARS-CoV-2. However, the mechanism of the immunomodulatory action of Thymalin, KE, and EW during COVID-19 remains unclear. To identify the potential mechanism of action underlying the immunomodulatory activity of Thymalin and its active components, EW and KE dipeptides, we assessed inflammatory response in the context of COVID-19. Interactions between EW and KE dipeptides and double-stranded DNA (dsDNA) were investigated by molecular modeling and docking using ICM-Pro. Analysis of the possible effect of EW and KE dipeptides on gene expression and protein synthesis involved in the pathogenesis of COVID-19 was conducted through the use of bioinformatics methods, including a search for promoter sequences in the Eukaryotic Promoter Database, the determination of genes associated with the development of COVID-19 using the PathCards database of human biological pathways (pathway unification database), identification of the relationship between proteins through cluster analysis in the STRING database ('Search Tool for Retrieval of Interacting Genes/Proteins'), and assessment of the functional enrichment of protein-protein interaction (PPI) using the terms of gene ontology (GO) and the Markov cluster algorithm (MCL). After that, in vitro studying of a lipopolysaccharide (LPS)-induced model of inflammation using human peripheral blood mononuclear cells was performed. ELISA was applied to assess the level of cytokines (IL-1β, IL-6, TNFα) in the supernatant of cells with or without the impact of EW and KE peptides. Blood samples were obtained from four donors; for each cytokine, ELISA was performed 2-4 times, with two parallel experimental or control samples for each experiment (experiments to assess the effects of peptides on LPS-stimulated cells were repeated four times, while additional experiments with unstimulated cells were performed two times). Using molecular docking, GGAG was found to be the best dsDNA sequence in the classical B-form for binding the EW dipeptide, while GCGC is the preferred dsDNA sequence in the curved nucleosomal form for the KE dipeptide. Cluster analysis revealed that potential target genes for the EW and KE peptides encode the AKT1 and AKT2 proteins involved in the development of the cytokine storm. The specific targets for the EW peptide are the ACE2 and CYSLTR1 genes, and specific target for the KE peptide is the CHUK gene. Protein products of the ACE2, CYSLTR1, and CHUK genes are functionally associated with IL-1β, IL-6, TNF-α, IL-4, and IL-10 cytokines. An in vitro model of an inflammatory reaction demonstrated that Thymalin and EW and KE dipeptides reduced the synthesis of IL-1β, IL-6, and TNF-α cytokines in human peripheral blood mononuclear cells by 1.4-6.0 times. The immunomodulatory effect of Thymalin under the inflammatory response conditions in COVID-19 is based on the potential ability of its active components, EW and KE dipeptides, to regulate protein synthesis involved in the development of the cytokine storm.

Wood-density has no effect on stomatal control of leaf-level water use efficiency in an Amazonian forest.

Forest disturbances increase the proportion of fast-growing tree species compared to slow-growing ones. To understand their relative capacity for carbon uptake and their vulnerability to climate change, and to represent those differences in Earth system models, it is necessary to characterise the physiological differences in their leaf-level control of water use efficiency and carbon assimilation. We used wood density as a proxy for the fast-slow growth spectrum and tested the assumption that trees with a low wood density (LWD) have a lower water-use efficiency than trees with a high wood density (HWD). We selected 5 LWD tree species and 5 HWD tree species growing in the same location in an Amazonian tropical forest and measured in situ steady-state gas exchange on top-of-canopy leaves with parallel sampling and measurement of leaf mass area and leaf nitrogen content. We found that LWD species invested more nitrogen in photosynthetic capacity than HWD species, had higher photosynthetic rates and higher stomatal conductance. However, contrary to expectations, we showed that the stomatal control of the balance between transpiration and carbon assimilation was similar in LWD and HWD species and that they had the same dark respiration rates.

Significance of lipids, lipoproteins, and apolipoproteins during the first 14-16 months of life.

The aims of this study were to investigate lipid parameters during the first 14-16 months of life, to identify influential factors, and to test whether high concentrations at birth predict high concentrations at 2- and 14-16 months. The Copenhagen Baby Heart Study, including 13,354 umbilical cord blood samples and parallel venous blood samples from children and parents at birth (n = 444), 2 months (n = 364), and 14-16 months (n = 168), was used. Concentrations of lipids, lipoproteins, and apolipoproteins in umbilical cord blood samples correlated highly with venous blood samples from newborns. Concentrations of low-density lipoprotein (LDL) cholesterol, non-high-density lipoprotein (HDL) cholesterol, apolipoprotein B, and lipoprotein(a) increased stepwise from birth to 2 months to 14-16 months. Linear mixed models showed that concentrations of LDL cholesterol, non-HDL cholesterol, and lipoprotein(a) above the 80th percentile at birth were associated with significantly higher concentrations at 2 and 14-16 months. Finally, lipid concentrations differed according to sex, gestational age, birth weight, breastfeeding, and parental lipid concentrations. Lipid parameters changed during the first 14-16 months of life, and sex, gestational age, birth weight, breastfeeding, and high parental concentrations influenced concentrations. Children with high concentrations of atherogenic lipid traits at birth had higher concentrations at 2 and 14-16 months. These findings increase our knowledge of how lipid traits develop over the first 14-16 months of life and may help in deciding the optimal child age for universal familial hypercholesterolaemia screening.

Broadband low-frequency sound-absorbing metastructure based on an impedance-matching coiled-up cavity with continuously variable cross section

Proposed here is a metastructure based on a micro-perforated panel and an impedance-matching coiled-up cavity with continuously variable cross section, which achieves perfect absorption with a resonant frequency of 496 Hz and an absolute bandwidth (α ≥ 0.5) of 468 Hz. The structure thickness is ca. 1/13 of the operating wavelength λ in the deep subwavelength range. A relative bandwidth of 84.04%–111.67% is achieved through parametric studies. Physically, the continuous variation of the cavity cross section through which sound waves enter weakens the acoustic reflections generated by cross-sectional abruptness and enhances the impedance matching with the air. Furthermore, particle swarm optimization is coupled with a theoretical model to tailor the metastructure to realize the maximum absorption coefficient in the defined frequency range. It is shown theoretically that coherent coupling “weak resonance”—in which each unit individually exhibits imperfect absorption peaks—significantly improves the absorption performance in a broad frequency band through the coherent coupling effect. Finally, a hybrid metastructure using a parallel coupling sample is fabricated, and its acoustic properties are measured in an impedance tube. The average absorption coefficient of this metastructure is 0.934 in the quasi-perfect band (α > 0.9) from 400 to 650 Hz, and the thickness is only ca. λ/15. The unique innovation of a cavity with continuously variable cross section provides new ideas for designing broadband low-frequency sound-absorbing metastructures.