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Background: Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes., Objective: This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto., Methods: We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models., Results: In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm 3 , 33.7 nm, and 1225 ng/m 3 , respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm 3 , 29.7 nm, and 1060 ng/m 3 , respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R 2 ) was slightly greater (1-2 percentage points) for the combined models than the generalized additive models and a greater (approximately 10 percentage points) than the deep CNN models. The Toronto combined model R 2 values in the test set were 0.73, 0.55, and 0.61 for UFP concentrations, UFP size, and BC concentration, respectively. The Montreal combined model R 2 values were 0.60, 0.49, and 0.60 for UFP concentration, UFP size, and BC concentration models respectively. For each pollutant, predictions from the combined, deep CNN, and generalized additive models were highly correlated with each other and differences between models were explored in sensitivity analyses., Conclusion: Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Scott Weichenthal reports financial support was provided by Health Effects Institute., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Microbiome has emerged as a promising indicator or predictor of human diseases. However, previous studies have typically labeled each specimen as either healthy or with a specific disease, ignoring prevalence of complications or comorbidities in actual cohorts, which may confound the microbial–disease associations. For instance, a patient may suffer from multiple diseases, making it challenging to detect their health status accurately. Furthermore, host phenotypes like physiological characteristics and lifestyles can alter microbiome structure, but such information has not yet been fully utilized in data models. To address these issues, a highly explainable deep learning (DL) method called Meta‐Spec is proposed. Using a deep neural network (DNN)‐based approach, it encodes and embeds refined host variables with microbiome features, enabling the detection of multiple diseases simultaneously. Experiments show that Meta‐Spec outperforms regular machine learning (ML) strategies for multilabel disease screening in several cohorts. More importantly, Meta‐Spec successfully detects comorbidities that are often missed by other approaches. In addition, for its high interpretability, Meta‐Spec captures key factors that shape disease patterns from host variables and microbes. Hence, these efforts improve the feasibility and sensitivity of microbiome‐based disease screening in practical scenarios, representing a significant step toward personalized medicine and better health outcomes. [ABSTRACT FROM AUTHOR]

This paper presents the rationale for incorporating engineering design into project-based laboratory learning. To ensure an effective and efficient pedagogy for the new laboratory format, we placed the emphasis of the pedagogical framework on constructivist learning for deep laboratory learning, and integrated experiential learning cycle with cyclic engineering design to formulate a sequential instruction and formative assessment methodology. The implementation of the pedagogy was exemplified using a case study of a concrete distillation design consisting of conceptualizing the design, reasoning the adequacy and experiment-based validation of the design correlations, and verifying the final design as per experimental observations. The impact of the novel lab format on student learning experience was surveyed and compared to that of a traditional laboratory. The survey results revealed that the project-based laboratory with design resulted in an improved learning experience in addressing high-level learning outcomes and engineering skills. Evidence of the survey also suggested that the sequential instruction and formative assessment methodology was effective with every stage of the experiential learning and formative assessment essential for the successful and efficient implementation of the project-based laboratory learning. • Project-based laboratory (PBL) with design promotes deep laboratory learning. • Integration of experiential learning with design for an effective instruction pedagogy. • A practical distillation design case exemplifies implementation of the novel pedagogy. • PBL with design improves students' learning experience in laboratory learning. [ABSTRACT FROM AUTHOR]

Although deep learning-based RGB-D salient object detection methods have achieved impressive results in the recent years, there are still some issues need to be addressed including multi-modal fusion and multi-level aggregation. In this paper, we propose a bifurcated multi-modal fusion network (BMFNet) to address these two issues cooperatively. First, we design a multi-modal feature interaction (MFI) module to fully capture the complementary information between the RGB and depth features by leveraging the channel attention and spatial attention. Second, unlike the widely used layer-by-layer progressive fusion, we adopt a bifurcated fusion strategy for all the multi-level unimodal and cross-modal features to effectively reduce the gaps between features at different levels. For the intra-group feature aggregation, a multi-modal feature fusion (MFF) module is designed to integrate the intra-group multi-modal features to produce a low-level/high-level saliency feature. For the inter-group aggregation, a multi-scale feature learning (MFL) module is introduced to exploit the contextual interactions between different scales to boost fusion performance. Experimental results on five public RGB-D datasets demonstrate the effectiveness and superiority of our proposed network. The code and prediction maps will be available at https://github.com/ZhangQing0329/BMFNet • We propose a bifurcated multi-modal fusion network for RGB-D saliency detection. • We design an MFI module to capture the complementary information of RGB and depth. • We use a bifurcated fusion strategy to reduce the gaps between multi-level features. • The proposed method performs competitively against the recent methods. [ABSTRACT FROM AUTHOR]