Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways.

This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.

Metabolomic Signatures of Exposure to Nitrate and Trihalomethanes in Drinking Water and Colorectal Cancer Risk in a Spanish Multicentric Study (MCC-Spain).

We investigated the metabolomic profile associated with exposure to trihalomethanes (THMs) and nitrate in drinking water and with colorectal cancer risk in 296 cases and 295 controls from the Multi Case-Control Spain project. Untargeted metabolomic analysis was conducted in blood samples using ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. A variety of univariate and multivariate association analyses were conducted after data quality control, normalization, and imputation. Linear regression and partial least-squares analyses were conducted for chloroform, brominated THMs, total THMs, and nitrate among controls and for case-control status, together with a N-integration model discriminating colorectal cancer cases from controls through interrogation of correlations between the exposure variables and the metabolomic features. Results revealed a total of 568 metabolomic features associated with at least one water contaminant or colorectal cancer. Annotated metabolites and pathway analysis suggest a number of pathways as potentially involved in the link between exposure to these water contaminants and colorectal cancer, including nicotinamide, cytochrome P-450, and tyrosine metabolism. These findings provide insights into the underlying biological mechanisms and potential biomarkers associated with water contaminant exposure and colorectal cancer risk. Further research in this area is needed to better understand the causal relationship and the public health implications.

Analyzing drop coalescence in microfluidic devices with a deep learning generative model.

Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering.

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys.

The performance of advanced materials for extreme environments is underpinned by their microstructure, such as the size and distribution of nano- to micro-sized reinforcing phase(s). Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, e.g., Concentrated Solar Power. Their development requires the determination of precipitate volume fraction and size distribution using Electron Microscopy (EM), as these properties are crucial for the thermal stability and mechanical properties of chromium superalloys. Traditional approaches to EM image processing utilise filtering with a fixed contrast threshold, leads to weak robustness to background noise and poor generalisability to different materials. It also requires an enormous amount of time for manual object measurements on large datasets. Efficient and accurate object detection and segmentation are therefore highly desired to accelerate the development of novel materials like chromium-based superalloys. To address these bottlenecks, based on YOLOv5 and SegFormer structures, this study proposes an end-to-end, two-stage deep learning scheme, DT-SegNet, to perform object detection and segmentation for EM images. The proposed approach can thus benefit from the training efficiency of CNNs at the detection stage (i.e., a small number of training images required) and the accuracy of the ViT at the segmentation stage. Extensive numerical experiments demonstrate that the proposed DT-SegNet significantly outperforms the state-of-the-art segmentation tools offered by Weka and ilastik regarding a large number of metrics, including accuracy, precision, recall and F1-score. This model forms a useful tool to aid alloy development microstructure examinations, and offers significant advantages to address the large datasets associated with high-throughput alloy development approaches.

Self-Healing, Adhesive, and Highly Stretchable Ionogel as a Strain Sensor for Extremely Large Deformation.

Fabricating a strain sensor that can detect large deformation over a curved object with a high sensitivity is crucial in wearable electronics, human/machine interfaces, and soft robotics. Herein, an ionogel nanocomposite is presented for this purpose. Tuning the composition of the ionogel nanocomposites allows the attainment of the best features, such as excellent self-healing (>95% healing efficiency), strong adhesion (347.3 N m-1 ), high stretchability (2000%), and more than ten times change in resistance under stretching. Furthermore, the ionogel nanocomposite-based sensor exhibits good reliability and excellent durability after 500 cycles, as well as a large gauge factor of 20 when it is stretched under a strain of 800-1400%. Moreover, the nanocomposite can self-heal under arduous conditions, such as a temperature as low as -20 °C and a temperature as high as 60 °C. All these merits are achieved mainly due to the integration of dynamic metal coordination bonds inside a loosely cross-linked network of ionogel nanocomposite doped with Fe3 O4 nanoparticles.

Liquid-Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks.

We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder-decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics. Details of the methodology are presented, which include data preprocessing, RNN model exploration, and methods for model performance visualization; an ensemble-based procedure is also introduced to provide a measure of the model uncertainty. The workflow is designed to be generic and can be deployed to make predictions in other industrial applications with similar time-series data.

Spectral Cross-Domain Neural Network With Soft-Adaptive Threshold Spectral Enhancement.

Electrocardiography (ECG) signals can be considered as multivariable time series (TS). The state-of-the-art ECG data classification approaches, based on either feature engineering or deep learning techniques, treat separately spectral and time domains in machine learning systems. No spectral-time domain communication mechanism inside the classifier model can be found in current approaches, leading to difficulties in identifying complex ECG forms. In this article, we proposed a novel deep learning model named spectral cross-domain neural network (SCDNN) with a new block called soft-adaptive threshold spectral enhancement (SATSE), to simultaneously reveal the key information embedded in spectral and time domains inside the neural network. More precisely, the domain-cross information is captured by a general convolutional neural network (CNN) backbone, and different information sources are merged by a self-adaptive mechanism to mine the connection between time and spectral domains. In SATSE, the knowledge from time and spectral domains is extracted via the fast Fourier transformation (FFT) with soft trainable thresholds in modified sigmoid functions. The proposed SCDNN is tested with several classification tasks implemented on the public ECG databases PTB-XL and CPSC2018. SCDNN outperforms the state-of-the-art approaches with a low computational cost regarding a variety of metrics in all classification tasks on both databases, by finding appropriate domains from the infinite spectral mapping. The convergence of the trainable thresholds in the spectral domain is also numerically investigated in this article. The robust performance of SCDNN provides a new perspective to exploit knowledge across deep learning models from time and spectral domains. The code repository can be found: https://github.com/DL-WG/SCDNN-TS.

Real-time updating of dynamic social networks for COVID-19 vaccination strategies.

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.

Adhesive cryogel particles for bridging confined and irregular tissue defects.

BACKGROUND: Reconstruction of damaged tissues requires both surface hemostasis and tissue bridging. Tissues with damage resulting from physical trauma or surgical treatments may have arbitrary surface topographies, making tissue bridging challenging. METHODS: This study proposes a tissue adhesive in the form of adhesive cryogel particles (ACPs) made from chitosan, acrylic acid, 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) and N-hydroxysuccinimide (NHS). The adhesion performance was examined by the 180-degree peel test to a collection of tissues including porcine heart, intestine, liver, muscle, and stomach. Cytotoxicity of ACPs was evaluated by cell proliferation of human normal liver cells (LO2) and human intestinal epithelial cells (Caco-2). The degree of inflammation and biodegradability were examined in dorsal subcutaneous rat models. The ability of ACPs to bridge irregular tissue defects was assessed using porcine heart, liver, and kidney as the ex vivo models. Furthermore, a model of repairing liver rupture in rats and an intestinal anastomosis in rabbits were established to verify the effectiveness, biocompatibility, and applicability in clinical surgery. RESULTS: ACPs are applicable to confined and irregular tissue defects, such as deep herringbone grooves in the parenchyma organs and annular sections in the cavernous organs. ACPs formed tough adhesion between tissues [(670.9 ± 50.1) J/m2 for the heart, (607.6 ± 30.0) J/m2 for the intestine, (473.7 ± 37.0) J/m2 for the liver, (186.1 ± 13.3) J/m2 for muscle, and (579.3 ± 32.3) J/m2 for the stomach]. ACPs showed considerable cytocompatibility in vitro study, with a high level of cell viability for 3 d [(98.8 ± 1.2) % for LO2 and (98.3 ± 1.6) % for Caco-2]. It has comparable inflammation repair in a ruptured rat liver (P = 0.58 compared with suture closure), the same with intestinal anastomosis in rabbits (P = 0.40 compared with suture anastomosis). Additionally, ACPs-based intestinal anastomosis (less than 30 s) was remarkably faster than the conventional suturing process (more than 10 min). When ACPs degrade after surgery, the tissues heal across the adhesion interface. CONCLUSIONS: ACPs are promising as the adhesive for clinical operations and battlefield rescue, with the capability to bridge irregular tissue defects rapidly.

Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device.

A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelling techniques are applied to compress the recorded images into low-dimensional spaces and alleviate the computational cost. Recurrent neural networks are then employed to build a surrogate model of drop interactions by learning the dynamics of compressed variables in the reduced-order space. The surrogate model is integrated with real-time observations using data assimilation. In this paper we developed an ensemble-based latent assimilation algorithm scheme which shows an improvement in terms of accuracy with respect to the previous approaches. This work demonstrates the possibility to create a reliable data-driven model enabling a high fidelity prediction of drop interactions in microfluidics device. The performance of the developed system is evaluated against experimental data (i.e., recorded videos), which are excluded from the training of the surrogate model. The developed scheme is general and can be applied to other dynamical systems.

A Graph Clustering Approach to Localization for Adaptive Covariance Tuning in Data Assimilation Based on State-Observation Mapping.

An original graph clustering approach for the efficient localization of error covariances is proposed within an ensemble-variational data assimilation framework. Here, the localization term is very generic and refers to the idea of breaking up a global assimilation into subproblems. This unsupervised localization technique based on a linearized state-observation measure is general and does not rely on any prior information such as relevant spatial scales, empirical cutoff radii or homogeneity assumptions. Localization is performed via graph theory, a branch of mathematics emerging as a powerful approach to capturing complex and highly interconnected Earth and environmental systems in computational geosciences. The novel approach automatically segregates the state and observation variables in an optimal number of clusters, and it is more amenable to scalable data assimilation. The application of this method does not require underlying block-diagonal structures of prior covariance matrices. To address intercluster connectivity, two alternative data adaptations are proposed. Once the localization is completed, a covariance diagnosis and tuning are performed within each cluster, whose contribution is sequentially integrated into the entire covariance matrix. Numerical twin experiment tests show the reduced cost and added flexibility of this approach compared to global covariance tuning, and more accurate results yielded for both observation- and background-error parameter tuning.

Ionotronic Luminescent Fibers, Fabrics, and Other Configurations.

A family of recently developed devices, hydrogel ionotronics, uses hydrogels as ionic conductors, and uses hydrophobic elastomers as dielectrics. This development has posed a challenge: integrate hydrogels and hydrophobic elastomers-in various manufacturing processes-with strong, stretchable, and transparent adhesion. Here, a multistep dip-coating process is described to enable hydrogel ionotronics of diverse configurations. In doing so, a hydrophobic surface is primed to let a hydrophilic precursor wet it, and then polymers of different layers are interlinked with covalent bonds. As a representative example, an ionotronic luminescent fiber that can be lengthened to ≈2.5 times its original length and keeps functioning after 10 000 cycles of stretching is fabricated. A luminescent fabric that displays movable pixels and other configurations is also demonstrated. The proposed method of fabrication expands the design space for hydrogel ionotronics.

Comprehensive techno-economic analysis of wastewater-based algal biofuel production: A case study.

Combining algae cultivation and wastewater treatment for biofuel production is considered the feasible way for resource utilization. An updated comprehensive techno-economic analysis method that integrates resources availability into techno-economic analysis was employed to evaluate the wastewater-based algal biofuel production with the consideration of wastewater treatment improvement, greenhouse gases emissions, biofuel production costs, and coproduct utilization. An innovative approach consisting of microalgae cultivation on centrate wastewater, microalgae harvest through flocculation, solar drying of biomass, pyrolysis of biomass to bio-oil, and utilization of co-products, was analyzed and shown to yield profound positive results in comparison with others. The estimated break even selling price of biofuel ($2.23/gallon) is very close to the acceptable level. The approach would have better overall benefits and the internal rate of return would increase up to 18.7% if three critical components, namely cultivation, harvest, and downstream conversion could achieve breakthroughs.

Growing Chlorella sp. on meat processing wastewater for nutrient removal and biomass production.

In this work, Chlorella sp. (UM6151) was selected to treat meat processing wastewater for nutrient removal and biomass production. To balance the nutrient profile and improve biomass yield at low cost, an innovative algae cultivation model based on wastewater mixing was developed. The result showed that biomass yield (0.675-1.538 g/L) of algae grown on mixed wastewater was much higher than that on individual wastewater and artificial medium. Wastewater mixing eased the bottleneck for algae growth and contributed to the improved biomass yield. Furthermore, in mixed wastewater with sufficient nitrogen, ammonia nitrogen removal efficiencies (68.75-90.38%) and total nitrogen removal efficiencies (30.06-50.94%) were improved. Wastewater mixing also promoted the synthesis of protein in algal cells. Protein content of algae growing on mixed wastewater reached 60.87-68.65%, which is much higher than that of traditional protein source. Algae cultivation model based on wastewater mixing is an efficient and economical way to improve biomass yield.