Estimation of Anthropogenic VOCs Emission Based on Volatile Chemical Products: A Canadian Perspective.

Volatile organic compounds (VOCs) in urban areas are of great interest due to their significant role in forming ground-level ozone and adverse public health effects. Emission inventories usually compile the outdoor VOCs emission sources (e.g., traffic and industrial emissions). However, considering emissions from volatile chemical products (e.g., solvents, printing ink, personal care products) is challenging because of scattered data and the lack of an effective method to estimate the VOCs emission rate from these chemical products. This paper aims to systematically analyse potential sources of VOCs emission in Canada's built environment, including volatile chemical products. Also, spatial variation of VOCs level in the ambient atmosphere is examined to understand the VOC relationship with ozone and secondary organic aerosol formation. The study shows that VOCs level may vary among everyday microenvironments (e.g., residential areas, offices, and retail stores) depending on the frequency of product consumption, building age, ventilation condition, and background ambient concentration in the atmosphere. However, it is very difficult to establish VOC speciation and apportionment to different volatile chemical products that contribute most significantly to exposure and target subpopulations with elevated levels. Thus, tracer compounds can be used to identify inventory sources at the consumer end. A critical overview highlights the limitations of existing VOC estimation methods and possible approaches to control VOC emissions. The findings provide crucial information to establish an emission inventory framework for volatile chemical products at a national scale and enable policymakers to limit VOCs emission from various volatile chemical products.

CNV-PCC: An efficient method for detecting copy number variations from next-generation sequencing data.

Copy number variations (CNVs) significantly influence the diversity of the human genome and the occurrence of many complex diseases. The next-generation sequencing (NGS) technology provides rich data for detecting CNVs, and the read depth (RD)-based approach is widely used. However, low CN (copy number of 3-4) duplication events are challenging to identify with existing methods, especially when the size of CNVs is small. In addition, the RD-based approach can only obtain rough breakpoints. We propose a new method, CNV-PCC (detection of CNVs based on Principal Component Classifier), to identify CNVs in whole genome sequencing data. CNV-PPC first uses the split read signal to search for potential breakpoints. A two-stage segmentation strategy is then implemented to enhance the identification capabilities of low CN duplications and small CNVs. Next, the outlier scores are calculated for each segment by PCC (Principal Component Classifier). Finally, the OTSU algorithm calculates the threshold to determine the CNVs regions. The analysis of simulated data results indicates that CNV-PCC outperforms the other methods for sensitivity and F1-score and improves breakpoint accuracy. Furthermore, CNV-PCC shows high consistency on real sequencing samples with other methods. This study demonstrates that CNV-PCC is an effective method for detecting CNVs, even for low CN duplications and small CNVs.

Deciphering landscape dynamics of cell fate decision via a Lyapunov method.

The embryonic stem cell (ESC) has the capacity to self-renew and maintain pluripotent, while continuously offering a source of various differentiated cell types. The fate decision process of remaining in the ground state or transiting to a differentiated state can be read out by the regulatory network of key transcription factors (TFs). However, its underlying mechanism remains to be fully elucidated. In this paper, we tackle this problem by proposing a novel cellular differentiation model for mouse embryonic stem cell (MESC) dynamics regulation: MESC-DRM. We employ nonlinear least-squares algorithm to infer model parameters by using benchmark datasets, construct a potential function by exploiting multivariate Gaussian distributions, and project the potential landscape into a 3D space to validate and replicate the stable cell states observed in experiments. The traditional cell landscape modeling techniques rely on the potential function visualization to decide the stable states of cells. But the visualization will be almost impossible when the dimensionality of the potential function is greater than 3. We handle the challenge by innovatively employing a Lyapunov method to resolve it through a more straightforward analytical approach. It also provides a more rigorous and robust way for accurate cell fate decision. The study not only validates the previous experimental results but also provides an insightful guide for cell fate decision besides inspiring future study on this topic.

A regional numerical environmental multimedia modeling approach to assess spatial Eco-Environmental exposure risk of perfluorooctane sulfonate (PFOS) in the Pearl river basin.

This paper presents a novel numerical environmental multimedia modeling system (RNEMM) for assessing the environmental fate of emerging organic contaminants and their relative health risk at a regional scale. The RNEMM is developed based on an integrated numerical algorithm that comprises four sub-models: a river network simulation module, a gaseous phase simulation module, a mass balance based simulation module for soil compartment, and a food web analysis module. This RNEMM has been applied to simulate the spatial distribution of PFOS and assess the consequent health risks for a central water basin region of the Pearl River in China. The study region includes the urban areas of Guangzhou, Foshan, and Dongguan Cities with emission sources of PFOS, which was detected in local water, sediments, and air environment. The spatial concentration distributions of PFOS in water, sediment, air, soil, and various fish species are examined based on RNEMM and compared with the measured data. With a focus on water environment, it shows that the simulated results essentially agree well with measured concentrations. Comparing the simulated results and the measured data collected in 2013, the relative errors are mostly less than 40 % in the surface water and sediment zones for this regional scale field study. Whereas the relative error in the atmosphere zone is less than 5%. In addition, the health risk assessment for children and adults is conducted based on the RNEMM approach. The hazard quotient (HQ) values for the 95th percentile in most subareas of the study region are higher than 0.1, showing a low-risk level for the study period. The results indicate that the RNEMM is a useful modeling tool to manage the environmental and health risks associated with emerging contaminants on regional air, water, soil, and ecosystem at an adequate spatial-temporal resolution.

DINTD: Detection and Inference of Tandem Duplications From Short Sequencing Reads.

Tandem duplication (TD) is an important type of structural variation (SV) in the human genome and has biological significance for human cancer evolution and tumor genesis. Accurate and reliable detection of TDs plays an important role in advancing early detection, diagnosis, and treatment of disease. The advent of next-generation sequencing technologies has made it possible for the study of TDs. However, detection is still challenging due to the uneven distribution of reads and the uncertain amplitude of TD regions. In this paper, we present a new method, DINTD (Detection and INference of Tandem Duplications), to detect and infer TDs using short sequencing reads. The major principle of the proposed method is that it first extracts read depth and mapping quality signals, then uses the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to find the possible TD regions. The total variation penalized least squares model is fitted with read depth and mapping quality signals to denoise signals. A 2D binary search tree is used to search the neighbor points effectively. To further identify the exact breakpoints of the TD regions, split-read signals are integrated into DINTD. The experimental results of DINTD on simulated data sets showed that DINTD can outperform other methods for sensitivity, precision, F1-score, and boundary bias. DINTD is further validated on real samples, and the experiment results indicate that it is consistent with other methods. This study indicates that DINTD can be used as an effective tool for detecting TDs.

In Vivo Demonstration of Photoacoustic Image Guidance and Robotic Visual Servoing for Cardiac Catheter-Based Interventions.

Cardiac interventional procedures are often performed under fluoroscopic guidance, exposing both the patient and operators to ionizing radiation. To reduce this risk of radiation exposure, we are exploring the use of photoacoustic imaging paired with robotic visual servoing for cardiac catheter visualization and surgical guidance. A cardiac catheterization procedure was performed on two in vivo swine after inserting an optical fiber into the cardiac catheter to produce photoacoustic signals from the tip of the fiber-catheter pair. A combination of photoacoustic imaging and robotic visual servoing was employed to visualize and maintain constant sight of the catheter tip in order to guide the catheter through the femoral or jugular vein, toward the heart. Fluoroscopy provided initial ground truth estimates for 1D validation of the catheter tip positions, and these estimates were refined using a 3D electromagnetic-based cardiac mapping system as the ground truth. The 1D and 3D root mean square errors ranged 0.25-2.28 mm and 1.24-1.54 mm, respectively. The catheter tip was additionally visualized at three locations within the heart: (1) inside the right atrium, (2) in contact with the right ventricular outflow tract, and (3) inside the right ventricle. Lasered regions of cardiac tissue were resected for histopathological analysis, which revealed no laser-related tissue damage, despite the use of 2.98 mJ per pulse at the fiber tip (379.2 mJ/cm2 fluence). In addition, there was a 19 dB difference in photoacoustic signal contrast when visualizing the catheter tip pre- and post-endocardial tissue contact, which is promising for contact confirmation during cardiac interventional procedures (e.g., cardiac radiofrequency ablation). These results are additionally promising for the use of photoacoustic imaging to guide cardiac interventions by providing depth information and enhanced visualization of catheter tip locations within blood vessels and within the beating heart.