Explore the vast range of titles available.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

The assessment of AgNPs toxicity in vitro and in vivo models are frequently conflicting and inaccurate. Nevertheless, single cell immunological responses in a heterogenous environment have received little attention. Therefore, in this study, we have performed in-depth analysis which clearly revealed cellular-metal ion association as well as specific immunological response. Our study didn’t show significant population differences in PMBC between control and AgNPs group implying no toxicological response. To confirm it further, deep profiling identified differences in subsets and differentially expressed genes (DEGs) of monocytes, B cells and T cells. Notably, monocyte subsets showed significant upregulation of metallothionein (MT) gene expression such as MT1G , MT1X , MT1E , MT1A , and MT1F. On the other hand, downregulation of pro-inflammatory genes such as  IL1β and CCL3 in both CD16 + and CD16- monocyte subsets were observed. This result indicated that AgNPs association with monocyte subsets de-promoted inflammatory responsive genes suggesting no significant toxicity observed in AgNPs treated group. Other cell types such as B cells and T cells also showed negligible differences in their subsets suggesting no toxicity response. Further, AgNPs treated group showed upregulation of cell proliferation, ribosomal synthesis, downregulation of cytokine release, and T cell differentiation inhibition. Overall, our results conclude that treatment of AgNPs to PMBC cells didn’t display immunological related cytotoxicity response and thus motivate researchers to use them actively for biomedical applications.

The prospective use of food additive titanium dioxide (E171 TiO 2 ) in a variety of fields (food, pharmaceutics, and cosmetics) prompts proper cellular cytotoxicity and transcriptomic assessment. Interestingly, smaller-sized E171 TiO 2 can translocate in bloodstream and induce a diverse immunological response by activating the immune system, which can be either pro-inflammatory or immune-suppressive. Nevertheless, their cellular or immunologic responses in a heterogeneous population of the immune system following exposure of food additive E171 TiO 2 is yet to be elucidated. For this purpose, we have used male Sprague-Dawley rats to deliver E171 TiO 2 (5 mg/kg bw per day) via non-invasive intratracheal instillation for 13 weeks. After the 4 weeks recovery period, 3 mL of blood samples from both treated and untreated groups were collected for scRNAseq analysis. Firstly, granulocyte G1 activated innate immune response through the upregulation of genes involved in pro-inflammatory cytokine mediated cytotoxicity. Whereas NK cells resulted in heterogeneity role depending on the subsets where NK1 significantly inhibited cytotoxicity, whereas NK2 and NK3 subsets activated pro-B cell population & inhibited T cell mediated cytotoxicity respectively. While NKT_1 activated innate inflammatory responses which was confirmed by cytotoxic CD8+ T killer cell suppression. Similarly, NKT_2 cells promote inflammatory response by releasing lytic granules and MHC-I complex inhibition to arrest cytotoxic T killer cell responses. Conversely, NKT_3 suppressed inflammatory response by release of anti-inflammatory cytokines suggesting the functional heterogeneity of NKT subset. The formation of MHC-I or MHC-II complexes with T-cell subsets resulted in neither B and T cell dysfunction nor cytotoxic T killer cell inhibition suppressing adaptive immune response. Overall, our research offers an innovative high-dimensional approach to reveal immunological and transcriptomic responses of each cell types at the single cell level in a complex heterogeneous cellular environment by reassuring a precise assessment of immunological response of E171 TiO 2 . Graphical Abstract

The precise identification of immune cell type responses to alcoholic steatohepatitis (ASH) at the single‐cell level remains unresolved. Therefore, in this study, we analyzed heterogeneous immune leukocytes associated with ASH at the single‐cell level using high‐dimensional single‐cell RNA sequencing in alcoholic liver disease (ALD)‐induced and healthy control mice. A t‐distributed stochastic neighbor embedding plot for dimensionality reduction and 2D visualization was used to visualize heterogeneous immune cell types. Moreover, singleR was used for automated cell annotation to identify the cell types and differentially expressed genes from each cell type and their subsets. We observed a decline in the population of B cells and their subsets, with up and downregulated genes signifying an innate proinflammatory response as an important indication of alcohol‐induced liver fibrosis. Additionally, neutrophil deficiency in the alcohol‐induced mouse group was associated with ASH. An increase in eosinophils diverts further complications in liver fibrosis, suggesting the functional heterogeneity of granulocyte subsets. Overall, our findings may assist in discovering potential ALD biomarker cell types that are significantly reduced by frequent alcohol exposure and enhance our understanding of the circulating immune leukocytes that lead to alcohol‐induced liver fibrosis. In this study, we explored comprehensive profiling of heterogeneous immune leukocytes at a single‐cell level using scRNAseq analysis to discover the regulation of inflammatory‐mediated liver fibrosis in ALD‐induced mice compared with healthy controls.

The early detection and timely treatment are the most important factors for improving the outcome of patients with sepsis. Sepsis-related clinical score, such as SIRS, SOFA and LODS, were defined to identify patients with suspected infection and to predict severity and mortality. A few hematological parameters associated with organ dysfunction and infection were included in the score although various clinical pathology parameters (hematology, serum chemistry and plasma coagulation) in blood sample have been found to be associated with outcome in patients with sepsis. The investigation of the parameters facilitates the implementation of a complementary model for screening sepsis to existing sepsis clinical criteria and other laboratory signs. In this study, statistical analysis on the multiple clinical pathology parameters obtained from two groups, patients with sepsis and patients with fever, was performed and the complementary model was elaborated by stepwise parameter selection and machine learning. The complementary model showed statistically better performance (AUC 0.86 vs. 0.74–0.51) than models built up with specific hematology parameters involved in each existing sepsis-related clinical score. Our study presents the complementary model based on the optimal combination of hematological parameters for sepsis screening in patients with fever.

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

Increasing production and application of silver nanoparticles (Ag NPs) have raised concerns on their possible adverse effects on human health. However, a comprehensive understanding of their effects on biological systems, especially immunomodulatory responses involving various immune cell types and biomolecules (e.g., cytokines and chemokines), is still incomplete. In this study, a single-cell-based, high-dimensional mass cytometry approach is used to investigate the immunomodulatory responses of Ag NPs using human peripheral blood mononuclear cells (hPBMCs) exposed to poly-vinyl-pyrrolidone (PVP)-coated Ag NPs of different core sizes (i.e., 10-, 20-, and 40-nm). Although there were no severe cytotoxic effects observed, PVPAg10 and PVPAg20 were excessively found in monocytes and dendritic cells, while PVPAg40 displayed more affinity with B cells and natural killer cells, thereby triggering the release of proinflammatory cytokines such as IL-2, IL-17A, IL-17F, MIP1β, TNFα, and IFNγ. Our findings indicate that under the exposure conditions tested in this study, Ag NPs only triggered the inflammatory responses in a size-dependent manner rather than induce cytotoxicity in hPBMCs. Our study provides an appropriate ex vivo model to better understand the human immune responses against Ag NP at a single-cell level, which can contribute to the development of targeted drug delivery, vaccine developments, and cancer radiotherapy treatments.

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms’ responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

ACEnano is an EU-funded project which aims at developing, optimising and validating methods for the detection and characterisation of nanomaterials (NMs) in increasingly complex matrices to improve confidence in the results and support their use in regulation. Within this project, several interlaboratory comparisons (ILCs) for the determination of particle size and concentration have been organised to benchmark existing analytical methods. In this paper the results of a number of these ILCs for the characterisation of NMs are presented and discussed. The results of the analyses of pristine well-defined particles such as 60 nm Au NMs in a simple aqueous suspension showed that laboratories are well capable of determining the sizes of these particles. The analysis of particles in complex matrices or formulations such as consumer products resulted in larger variations in particle sizes within technologies and clear differences in capability between techniques. Sunscreen lotion sample analysis by laboratories using spICP-MS and TEM/SEM identified and confirmed the TiO2 particles as being nanoscale and compliant with the EU definition of an NM for regulatory purposes. In a toothpaste sample orthogonal results by PTA, spICP-MS and TEM/SEM agreed and stated the TiO2 particles as not fitting the EU definition of an NM. In general, from the results of these ILCs we conclude that laboratories are well capable of determining particle sizes of NM, even in fairly complex formulations.

Microbialites are sedimentary deposits associated with microbial mat communities and are thought to be evidence of some of the oldest life on Earth. Despite extensive studies of such deposits, little is known about the role of microorganisms in their formation. In addition, unambiguous criteria proving their biogenicity have yet to be established. In this study, we characterize modern calcareous microbialites from the alkaline Lake Van, Turkey, at the nanometer scale by combining x-ray and electron microscopies. We describe a simple way to locate microorganisms entombed in calcium carbonate precipitates by probing aromatic carbon functional groups and peptide bonds. Near-edge x-ray absorption fine structure spectra at the C and N K-edges provide unique signatures for microbes. Aragonite crystals, which range in size from 30 to 100 nm, comprise the largest part of the microbialites. These crystals are surrounded by a 10-nm-thick amorphous calcium carbonate layer containing organic molecules and are embedded in an organic matrix, likely consisting of polysaccharides, which helps explain the unusual sizes and shapes of these crystals. These results provide biosignatures for these deposits and suggest that microbial organisms significantly impacted the mineralogy of Lake Van carbonates.