Design of high-performance entangling logic in silicon quantum dot systems with Bayesian optimization.

Device engineering based on computer-aided simulations is essential to make silicon (Si) quantum bits (qubits) be competitive to commercial platforms based on superconductors and trapped ions. Combining device simulations with the Bayesian optimization (BO), here we propose a systematic design approach that is quite useful to procure fast and precise entangling operations of qubits encoded to electron spins in electrode-driven Si quantum dot (QD) systems. For a target problem of the controlled-X (CNOT) logic operation, we employ BO with the Gaussian process regression to evolve design factors of a Si double QD system to the ones that are optimal in terms of speed and fidelity of a CNOT logic driven by a single microwave pulse. The design framework not only clearly contributes to cost-efficient securing of solutions that enhance performance of the target quantum operation, but can be extended to implement more complicated logics with Si QD structures in experimentally unprecedented ways.

scRNA-seq analysis discovered suppression of immunomodulatory dependent inflammatory response in PMBCs exposed to silver nanoparticles.

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 IL1ß 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.

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

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.

Correction: Peters et al. Benchmarking the ACEnano Toolbox for Characterisation of Nanoparticle Size and Concentration by Interlaboratory Comparison. Molecules 2021, 26, 5315.

Due to miscommunication a number of potential co-authors were not listed in the original publication [...].

Mass Cytometry Exploration of Immunomodulatory Responses of Human Immune Cells Exposed to Silver Nanoparticles.

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, MIP1ß, 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.

Methods, models, mechanisms and metadata: Introducing the Nanotoxicology collection at F1000Research.

Nanotoxicology is a relatively new field of research concerning the study and application of nanomaterials to evaluate the potential for harmful effects in parallel with the development of applications. Nanotoxicology as a field spans materials synthesis and characterisation, assessment of fate and behaviour, exposure science, toxicology / ecotoxicology, molecular biology and toxicogenomics, epidemiology, safe and sustainable by design approaches, and chemoinformatics and nanoinformatics, thus requiring scientists to work collaboratively, often outside their core expertise area. This interdisciplinarity can lead to challenges in terms of interpretation and reporting, and calls for a platform for sharing of best-practice in nanotoxicology research. The F1000Research Nanotoxicology collection, introduced via this editorial, will provide a place to share accumulated best practice, via original research reports including no-effects studies, protocols and methods papers, software reports and living systematic reviews, which can be updated as new knowledge emerges or as the domain of applicability of the method, model or software is expanded. This editorial introduces the Nanotoxicology Collection in F1000Research. The aim of the collection is to provide an open access platform for nanotoxicology researchers, to support an improved culture of data sharing and documentation of evolving protocols, biological and computational models, software tools and datasets, that can be applied and built upon to develop predictive models and move towards in silico nanotoxicology and nanoinformatics. Submissions will be assessed for fit to the collection and subjected to the F1000Research open peer review process.

Benchmarking the ACEnano Toolbox for Characterisation of Nanoparticle Size and Concentration by Interlaboratory Comparisons.

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.

Gross motor function and health fitness in adults with autistic spectrum disorder and intellectual disability: single-blind retrospective trial.

This study aimed at providing an exercise program for each type of disability after analyzing the exercise program performed by adults with intellectual disability (ID) or autistic spectrum disorder (ASD). Twenty-nine male adults voluntarily took part in this study, whose age ranged from 19 to 28 years and with an average body mass index of 23.98± 4.02 kg/m2. The sample was divided into two groups as follows: ASD group (ASDG; n=15) and ID group (IDG, n=14). The selected tests used to measure gross motor function (GMF, locomotion and object control skills) and health fitness (body composition, flexibility, strength, muscle endurance, and cardiopulmonary endurance) were also used in previous studies. The GMF and health fitness between ASDG and IDG showed no significant differences. This study indicates that exercise programs could provide similar effects, even with other disorder types having similar symptoms.

Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data.

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects.

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.

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.

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.

Implementation of Complementary Model using Optimal Combination of Hematological Parameters for Sepsis Screening in Patients with Fever.

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.

Analysis for reliability and validity of gross motor function and health fitness tests for children with developmental disabilities.

The aim of this study was to determine the reliability and validity of gross motor function and health fitness assessment tests for children with developmental disabilities. All 35 participants who took part in this study on a voluntary basis were male children (age, 10.31±1.25 years). All selected tests for gross motor function and health fitness assessments were used in previous studies to measure basic physical health and motor abilities, which include strength (grip strength test), muscular endurance (modified sit-ups test), flexibility (sit and reach test), and cardiopulmonary endurance (15-m shuttle run test). Reliability was analyzed using intraclass correlation coefficients in the pretest-posttest and Bland-Altman graphs study. Pearson correlation was used to analyze convergent validity and analysis of variance was used to analyze variations among age groups. Lastly, a correlation analysis was conducted between the tests in gross motor function and health fitness assessments. This study indicates that gross motor function and health fitness assessments have obtained adequate reliability parameters and are able to determine differences in children from 9 to 12 years of age. The tests performed were simple to use, safe, and suitable for children with developmental disabilities.

Monitoring Raman emission through state population in cold atoms confined inside a hollow-core fiber.

We study the spontaneous Raman emission in an ensemble of laser-cooled three-level Λ-type atoms confined inside a hollow-core photonic-bandgap fiber using a novel approach to observe the process. Instead of detecting the emitted light, we measure the number of atoms in the ground state as a function of Raman pump time, which eliminates the need to suppress the pump photons with a high-resolution filter. We describe how this measurement can be used to detect superradiant emission from the atomic ensembles and estimate the number of atoms required to observe Raman superradiance in atomic clouds inside a hollow-core fiber.

Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials.

A quasi-QSAR model was developed to predict the cell viability of human lung (BEAS-2B) and skin (HaCaT) cells exposed to 21 types of metal oxide nanomaterials. A wide range of toxicity datasets obtained from the S2NANO (www.s2nano.org) database was used. The data of descriptors representing the physicochemical properties and experimental conditions were coded to quasi-SMILES. In particular, hierarchical cluster analysis (HCA) and min-max normalization method were respectively used in assigning alphanumeric codes for numerical descriptors (e.g., core size, hydrodynamic size, surface charge, and dose) and then quasi-QSAR model performances for both methods were compared. The quasi-QSAR models were developed using CORAL software (www.insilico.eu/coral). Quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. The model showed satisfactory statistical results (Radj2 for the training dataset: 0.71-0.73; Radj2 for the calibration dataset: 0.74-0.82; and Radj2 for the validation dataset: 0.70-0.76).

Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells.

Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R2 for internal validation data sets: 0.60-0.80; R2pred for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 µm; surface area, 11.3-380.0 m2/g; and dose, 0-200 ppm.

Optimal Incorporation Level of Dietary Alternative Phosphate (MgHPO4) and Requirement for Phosphorus in Juvenile Far Eastern Catfish (Silurus asotus).

A growth trial was conducted to determine the optimal incorporation level of dietary magnesium hydrogen phosphate (MHP, MgHPO4), which was manufactured from swine manure and phosphorus (P), required by juvenile far eastern catfish (Silurus asotus). Graded MHP of 0.5%, 1.0%, 1.5%, and 2.0%, and 2.0% monocalcium phosphate (MCP) each was added to the basal diet (control) in lieu of cellulose to become the range of available P (AP) from 0.4% to 0.8% of which diets were designated as control, MHP0.5, MHP1.0, MHP1.5, MHP2.0, and MCP, respectively. Control diet contained fish meal (20%), soybean meal (40%), wheat flour (27%), corn gluten meal (5%), fish oil (2%) and soy oil (2%) as main ingredients. Following a 24 h fasting, 540 fish with a mean body weight of 11.8 g were randomly allotted to 6 groups in triplicate, whereby 18 tanks (0.4×0.6×0.36 cm, water volume of 66 L) were prepared. The feeding experiment lasted for 8 weeks. Fish group fed the control diet showed the lowest weight gain (WG) and feed efficiency (FE) among treatments. The WG was, however, not significantly different (p>0.05) from that of fish group fed MHP0.5. Fish group fed MHP2.0 showed the highest WG and FE of which values were not significantly different from those of fish groups fed diets MHP1.0 and MHP1.5 as well as MCP (p>0.05) except fish groups fed control and MHP0.5. Aspartate aminotransferase was significantly decreased with an increase in available P, while alanine aminotransferase did not show a significant difference among treatment. The highest inorganic P in plasma was observed in fish fed MHP2.0. From the present results, a second-order regression analysis revealed that the optimal dietary MHP level and the AP requirement were found to be 1.62% and 0.7%, respectively.

Effects of Dietary Supplementation of Magnesium Hydrogen Phosphate (MgHPO4) as an Alternative Phosphorus Source on Growth and Feed Utilization of Juvenile Far Eastern Catfish (Silurus asotus).

The present study was conducted to investigate a supplemental effect of magnesium hydrogen phosphate (MHP, MgHPO4) as an alternative phosphorus (P) source on growth and feed utilization of juvenile far eastern catfish (Silurus asotus) in comparison with three conventional P additives (monocalcium phosphate (MCP), dicalcium phosphate (DCP) and tricalcium phosphate [TCP]) as positive controls. A basal diet as a negative control was prepared without P supplementation and four supplemental P sources were added at the level of 2%. Five groups of 450 fish having mean body weight of 11.3 g following 24 h fasting after three week adaptation period were randomly distributed into each of 15 tanks (30 fish/tank). Fish were hand-fed to apparent satiety twice a day for 8 weeks. Fish fed MHP had weight gain (WG), protein efficiency ratio and specific growth rate comparable to those fed MCP. Fish fed MHP and MCP had feed efficiency (FE) significantly higher (p<0.05) than those fed DCP. Fish groups fed control and TCP showed the lower FE than the other groups which was significantly different (p<0.05) from those of fish fed the other diets. Survival rate was not significantly different (p>0.05) among treatments. Fish fed control had the lowest hematocrit, which was significantly different (p<0.05) from that of fish fed MHP. Fish fed MCP and MHP had plasma P higher (p<0.05) than fish fed the other diets. Relative efficiencies of MCP, DCP and TCP to MHP were found to be 100.5 and 101.3%, 92.0 and 91.6%, and 79.1 and 80.9% for WG and FE, respectively. P availability was determined to be 88.1%, 75.2%, 8.7%, and 90.9% for MCP, DCP, TCP, and MHP, respectively. Consequently, MHP recovered from wastewater stream showed that as an alternative P source its performance was comparative with MCP on growth and feed utilization of juvenile far eastern catfish.

Effects of physiochemical properties of test media on nanoparticle toxicity to Daphnia magna Straus.

The physicochemical property of standard test media significantly influenced the aggregation and dissolution of Ag, CuO and ZnO nanoparticles (NPs) and the toxicity of the NPs to Daphnia magna. For all the NPs, the highest amount of metal ions was released from the ISO medium, whereas acute toxicity to D. magna was highest in the moderately hard water medium (EC50 = 4.94, 980, and 1,950 µg L(-1) for Ag, CuO, and ZnO, respectively). By comparing EC50 values based on the total and dissolved concentrations of NPs with those of metal salt solutions, we found that both particulate and dissolved fractions were likely responsible for the toxicity of Ag NPs, whereas the dissolved fraction mostly contributed to the toxicity of CuO and ZnO NPs.

Non-monotonic concentration-response relationship of TiO(2) nanoparticles in freshwater cladocerans under environmentally relevant UV-A light.

The effects of UV-A on the toxicity of TiO2 nano-particles (NPs) were evaluated using Moina macrocopa and Daphnia magna under environmentally relevant level of UV-A. The waterfleas were exposed to TiO2 NPs with different sizes of ~298nm, ~132nm, or ~72nm for up to 48h, with or without UV-A light. Whole body reactive oxygen species and transcription of antioxidant enzyme genes were measured, as well as the survival of the waterflea. In the presence of UV-A, the survival rates of M. macrocopa significantly decreased in concentration dependent way until ~1mg/L TiO2 NPs, but the survivals were reversed at greater concentrations. This peculiar non-monotonic trend of concentration-response relationship might be explained by changes of particle size under different light conditions. TiO2 NPs within a certain size range could be trapped in the filter apparatus and exert toxicity, and the NPs of greater size were subject to either precipitation or ingestion leading to no or little toxicity. Observed TiO2 toxicity was associated with oxidative stress in the filter apparatus. The results of this study showed that the size change due to UV-A irradiation should be considered in evaluation of ecological risks of TiO2 NP.