42 research outputs found
Automated Quantification of Traffic Particulate Emissions via an Image Analysis Pipeline
Traffic emissions are known to contribute significantly to air pollution
around the world, especially in heavily urbanized cities such as Singapore. It
has been previously shown that the particulate pollution along major roadways
exhibit strong correlation with increased traffic during peak hours, and that
reductions in traffic emissions can lead to better health outcomes. However, in
many instances, obtaining proper counts of vehicular traffic remains manual and
extremely laborious. This then restricts one's ability to carry out
longitudinal monitoring for extended periods, for example, when trying to
understand the efficacy of intervention measures such as new traffic
regulations (e.g. car-pooling) or for computational modelling. Hence, in this
study, we propose and implement an integrated machine learning pipeline that
utilizes traffic images to obtain vehicular counts that can be easily
integrated with other measurements to facilitate various studies. We verify the
utility and accuracy of this pipeline on an open-source dataset of traffic
images obtained for a location in Singapore and compare the obtained vehicular
counts with collocated particulate measurement data obtained over a 2-week
period in 2022. The roadside particulate emission is observed to correlate well
with obtained vehicular counts with a correlation coefficient of 0.93,
indicating that this method can indeed serve as a quick and effective correlate
of particulate emissions
Robustness of Physics-Informed Neural Networks to Noise in Sensor Data
Physics-Informed Neural Networks (PINNs) have been shown to be an effective
way of incorporating physics-based domain knowledge into neural network models
for many important real-world systems. They have been particularly effective as
a means of inferring system information based on data, even in cases where data
is scarce. Most of the current work however assumes the availability of
high-quality data. In this work, we further conduct a preliminary investigation
of the robustness of physics-informed neural networks to the magnitude of noise
in the data. Interestingly, our experiments reveal that the inclusion of
physics in the neural network is sufficient to negate the impact of noise in
data originating from hypothetical low quality sensors with high
signal-to-noise ratios of up to 1. The resultant predictions for this test case
are seen to still match the predictive value obtained for equivalent data
obtained from high-quality sensors with potentially 10x less noise. This
further implies the utility of physics-informed neural network modeling for
making sense of data from sensor networks in the future, especially with the
advent of Industry 4.0 and the increasing trend towards ubiquitous deployment
of low-cost sensors which are typically noisier
Design of Turing Systems with Physics-Informed Neural Networks
Reaction-diffusion (Turing) systems are fundamental to the formation of
spatial patterns in nature and engineering. These systems are governed by a set
of non-linear partial differential equations containing parameters that
determine the rate of constituent diffusion and reaction. Critically, these
parameters, such as diffusion coefficient, heavily influence the mode and type
of the final pattern, and quantitative characterization and knowledge of these
parameters can aid in bio-mimetic design or understanding of real-world
systems. However, the use of numerical methods to infer these parameters can be
difficult and computationally expensive. Typically, adjoint solvers may be
used, but they are frequently unstable for very non-linear systems.
Alternatively, massive amounts of iterative forward simulations are used to
find the best match, but this is extremely effortful. Recently,
physics-informed neural networks have been proposed as a means for data-driven
discovery of partial differential equations, and have seen success in various
applications. Thus, we investigate the use of physics-informed neural networks
as a tool to infer key parameters in reaction-diffusion systems in the
steady-state for scientific discovery or design. Our proof-of-concept results
show that the method is able to infer parameters for different pattern modes
and types with errors of less than 10\%. In addition, the stochastic nature of
this method can be exploited to provide multiple parameter alternatives to the
desired pattern, highlighting the versatility of this method for bio-mimetic
design. This work thus demonstrates the utility of physics-informed neural
networks for inverse parameter inference of reaction-diffusion systems to
enhance scientific discovery and design
FastFlow: AI for Fast Urban Wind Velocity Prediction
Data-driven approaches, including deep learning, have shown great promise as
surrogate models across many domains. These extend to various areas in
sustainability. An interesting direction for which data-driven methods have not
been applied much yet is in the quick quantitative evaluation of urban layouts
for planning and design. In particular, urban designs typically involve complex
trade-offs between multiple objectives, including limits on urban build-up
and/or consideration of urban heat island effect. Hence, it can be beneficial
to urban planners to have a fast surrogate model to predict urban
characteristics of a hypothetical layout, e.g. pedestrian-level wind velocity,
without having to run computationally expensive and time-consuming
high-fidelity numerical simulations. This fast surrogate can then be
potentially integrated into other design optimization frameworks, including
generative models or other gradient-based methods. Here we present the use of
CNNs for urban layout characterization that is typically done via high-fidelity
numerical simulation. We further apply this model towards a first demonstration
of its utility for data-driven pedestrian-level wind velocity prediction. The
data set in this work comprises results from high-fidelity numerical
simulations of wind velocities for a diverse set of realistic urban layouts,
based on randomized samples from a real-world, highly built-up urban city. We
then provide prediction results obtained from the trained CNN, demonstrating
test errors of under 0.1 m/s for previously unseen urban layouts. We further
illustrate how this can be useful for purposes such as rapid evaluation of
pedestrian wind velocity for a potential new layout. It is hoped that this data
set will further accelerate research in data-driven urban AI, even as our
baseline model facilitates quantitative comparison to future methods
Blood-Based Biomarkers of Aggressive Prostate Cancer
Purpose: Prostate cancer is a bimodal disease with aggressive and indolent forms. Current prostate-specific-antigen testing and digital rectal examination screening provide ambiguous results leading to both under-and over-treatment. Accurate, consistent diagnosis is crucial to risk-stratify patients and facilitate clinical decision making as to treatment versus active surveillance. Diagnosis is currently achieved by needle biopsy, a painful procedure. Thus, there is a clinical need for a minimally-invasive test to determine prostate cancer aggressiveness. A blood sample to predict Gleason score, which is known to reflect aggressiveness of the cancer, could serve as such a test. Materials and Methods: Blood mRNA was isolated from North American and Malaysian prostate cancer patients/controls. Microarray analysis was conducted utilizing the Affymetrix U133 plus 2·0 platform. Expression profiles from 255 patients/controls generated 85 candidate biomarkers. Following quantitative real-time PCR (qRT-PCR) analysis, ten disease-associated biomarkers remained for paired statistical analysis and normalization. Results: Microarray analysis was conducted to identify 85 genes differentially expressed between aggressive prostate cancer (Gleason score ≥8) and controls. Expression of these genes was qRT-PCR verified. Statistical analysis yielded a final seven-gene panel evaluated as six gene-ratio duplexes. This molecular signature predicted as aggressive (ie, Gleason score ≥8) 55% of G6 samples, 49% of G7(3+4), 79% of G7(4+3) and 83% of G8-10, while rejecting 98% of controls. Conclusion: In this study, we have developed a novel, blood-based biomarker panel which can be used as the basis of a simple blood test to identify men with aggressive prostate cancer and thereby reduce the overdiagnosis and overtreatment that currently results from diagnosis using PSA alone. We discuss possible clinical uses of the panel to identify men more likely to benefit from biopsy and immediate therapy versus those more suited to an “active surveillance” strategy
Whole blood transcriptome correlates with treatment response in nasopharyngeal carcinoma
<p>Abstract</p> <p>Background</p> <p>Treatment protocols for nasopharyngeal carcinoma (NPC) developed in the past decade have significantly improved patient survival. In most NPC patients, however, the disease is diagnosed at late stages, and for some patients treatment response is less than optimal. This investigation has two aims: to identify a blood-based gene-expression signature that differentiates NPC from other medical conditions and from controls and to identify a biomarker signature that correlates with NPC treatment response.</p> <p>Methods</p> <p>RNA was isolated from peripheral whole blood samples (2 x 10 ml) collected from NPC patients/controls (EDTA vacutainer). Gene expression patterns from 99 samples (66 NPC; 33 controls) were assessed using the Affymetrix array. We also collected expression data from 447 patients with other cancers (201 patients) and non-cancer conditions (246 patients). Multivariate logistic regression analysis was used to obtain biomarker signatures differentiating NPC samples from controls and other diseases. Differences were also analysed within a subset (n = 28) of a pre-intervention case cohort of patients whom we followed post-treatment.</p> <p>Results</p> <p>A blood-based gene expression signature composed of three genes — LDLRAP1, PHF20, and LUC7L3 — is able to differentiate NPC from various other diseases and from unaffected controls with significant accuracy (area under the receiver operating characteristic curve of over 0·90). By subdividing our NPC cohort according to the degree of patient response to treatment we have been able to identify a blood gene signature that may be able to guide the selection of treatment.</p> <p>Conclusion</p> <p>We have identified a blood-based gene signature that accurately distinguished NPC patients from controls and from patients with other diseases. The genes in the signature, LDLRAP1, PHF20, and LUC7L3, are known to be involved in carcinoma of the head and neck, tumour-associated antigens, and/or cellular signalling. We have also identified blood-based biomarkers that are (potentially) able to predict those patients who are more likely to respond to treatment for NPC. These findings have significant clinical implications for optimizing NPC therapy.</p
Multigene profiling of single circulating tumor cells
Numerous techniques for isolating circulating tumor cells (CTCs) have been developed. Concurrently, single-cell techniques that can reveal molecular components of CTCs have become widely available. We discuss how the combination of isolation and multigene profiling of single CTCs in our platform can facilitate eventual translation to the clinic