Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning.

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable.

Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales.

The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues about the underlying biophysical mechanisms.

Data-Driven EEG Band Discovery with Decision Trees.

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta. While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear.

Changes in Gene Expression in Leaves of Cacao Genotypes Resistant and Susceptible to Infection.

Black pod rot, caused by , is a devastating disease of L. (cacao) leading to huge losses for farmers and limiting chocolate industry supplies. To understand resistance responses of cacao leaves to , Stage 2 leaves of genotypes Imperial College Selection 1 (ICS1), Colección Castro Naranjal 51 (CCN51), and Pound7 were inoculated with zoospores and monitored for symptoms up to 48 h.

Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry.

Electrochemistry of surface-bound molecules is of high importance for numerous electronic and sensor applications. Extracting the electron transfer rate is beneficial for understanding surface-bound processes, but it requires experimental or computational rigor. We evaluate methods to determine electron transfer rates from large voltammetry sets from experiments via machine learning using decision tree ensembles, neural networks, and gaussian process regression models.

Machine Learning for Light Sensor Calibration.

Sunlight incident on the Earth's atmosphere is essential for life, and it is the driving force of a host of photo-chemical and environmental processes, such as the radiative heating of the atmosphere. We report the description and application of a physical methodology relative to how an ensemble of very low-cost sensors (with a total cost of <$20, less than 0.5% of the cost of the reference sensor) can be used to provide wavelength resolved irradiance spectra with a resolution of 1 nm between 360-780 nm by calibrating against a reference sensor using machine learning.

UV/Vis photochemistry database: Structure, content and applications.

The "science-softCon UV/Vis Photochemistry Database" (www.photochemistry.org) is a large and comprehensive collection of EUV-VUV-UV-Vis-NIR spectral data and other photochemical information assembled from published peer-reviewed papers.

Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning.

This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and it is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points.

Using Machine Learning for the Calibration of Airborne Particulate Sensors.

Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment agencies, the number of sensors that can be deployed is limited.

Combining domain filling with a self-organizing map to analyze multi-species hydrocarbon signatures on a regional scale.

For the period of the Barnett Coordinated Campaign, October 16-31, 2013, hourly concentrations for 46 volatile organic compounds (VOCs) were recorded at 14 air monitoring stations within the Barnett Shale of North Texas. These measurements are used to identify and analyze multi-species hydrocarbon signatures on a regional scale through the novel combination of two techniques: domain filling with Lagrangian trajectories and the machine learning unsupervised classification algorithm called a self-organizing map (SOM). This combination of techniques is shown to accurately identify concentration enhancements in the lightest measured alkane species at and downwind of the locations of active-permit oil and gas facilities, despite the model having no a priori knowledge of these source locations.

Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.

Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in North America.

Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS.

In order to examine associations between asthma morbidity and local ambient air pollution in an area with relatively low levels of pollution, we conducted a time-series analysis of asthma hospital admissions and fine particulate matter pollution (PM) in and around Jackson, MS, for the period 2003 to 2011. Daily patient-level records were obtained from the Mississippi State Department of Health (MSDH) Asthma Surveillance System. Patient geolocations were aggregated into a grid with 0.

Using machine learning to examine the relationship between asthma and absenteeism.

In this study, we found that machine learning was able to effectively estimate student learning outcomes geo-spatially across all the campuses in a large, urban, independent school district. The machine learning showed that key factors in estimating the student learning outcomes included the number of days students were absent from school. In turn, one of the most important factors in estimating the number of days a student was absent was whether or not the student had asthma.

Using machine learning to understand the temporal morphology of the PM annual cycle in East Asia.

PM air pollution is a significant issue for human health all over the world, especially in East Asia. A large number of ground-based measurement sites have been established over the last decade to monitor real-time PM concentration. However, even this enhanced observational network leaves many gaps in characterizing the PM spatial distribution.

Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.

Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen depend on the ambient weather conditions.

Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Pollen.

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne pollen using a suite of machine learning approaches including deep learning and ensemble learners.

Advanced Metrics for Assessing Holistic Care: The "Epidaurus 2" Project.

In response to the challenge of military traumatic brain injury and posttraumatic stress disorder, the US military developed a wide range of holistic care modalities at the new Walter Reed National Military Medical Center, Bethesda, MD, from 2001 to 2017, guided by civilian expert consultation via the Epidaurus Project. These projects spanned a range from healing buildings to wellness initiatives and healing through nature, spirituality, and the arts. The next challenge was to develop whole-body metrics to guide the use of these therapies in clinical care.

Remote Sensing of CDOM, CDOM Spectral Slope, and Dissolved Organic Carbon in the Global Ocean.

A Global Ocean Carbon Algorithm Database (GOCAD) has been developed from over 500 oceanographic field campaigns conducted worldwide over the past 30 years including in situ reflectances and coincident satellite imagery, multi- and hyperspectral Chromophoric Dissolved Organic Matter (CDOM) absorption coefficients from 245-715 nm, CDOM spectral slopes in eight visible and ultraviolet wavebands, dissolved and particulate organic carbon (DOC and POC, respectively), and inherent optical, physical, and biogeochemical properties. From field optical and radiometric data and satellite measurements, several semi-analytical, empirical, and machine learning algorithms for retrieving global DOC, CDOM, and CDOM slope were developed, optimized for global retrieval, and validated. Global climatologies of satellite-retrieved CDOM absorption coefficient and spectral slope based on the most robust of these algorithms lag seasonal patterns of phytoplankton biomass belying Case 1 assumptions, and track terrestrial runoff on ocean basin scales.

Insights Into the Morphology of the East Asia PM Annual Cycle Provided by Machine Learning.

The abundance of airborne particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less (PM) is a significant environmental and health issue. Many tools have been used to examine the relationship between PM abundance and meteorological variables, but some of the relationships are nonlinear, non-Gaussian, and even unknown.

Using machine learning to estimate atmospheric pollen concentrations in Tulsa, OK.

This article describes an example of using machine learning to estimate the abundance of airborne pollen for Tulsa, OK. Twenty-seven years of historical pollen observations were used. These pollen observations were combined with machine learning and a very complete meteorological and land surface context of 85 variables to estimate the daily abundance.

and , Causal Agents of Black Pod Rot, Induce Similar Plant Defense Responses Late during Infection of Susceptible Cacao Pods.

(Pmeg) and (Ppal) cause black pod rot of L. (cacao). Of these two clade 4 species, Pmeg is more virulent and is displacing Ppal in many cacao production areas in Africa.

Phytophthora megakarya and P. palmivora, closely related causal agents of cacao black pod rot, underwent increases in genome sizes and gene numbers by different mechanisms.

(Pmeg) and (Ppal) are closely related species causing cacao black pod rot. Although Ppal is a cosmopolitan pathogen, cacao is the only known host of economic importance for Pmeg Pmeg is more virulent on cacao than Ppal We sequenced and compared the Pmeg and Ppal genomes and identified virulence-related putative gene models (PGeneM) that may be responsible for their differences in host specificities and virulence. Pmeg and Ppal have estimated genome sizes of 126.

Near-Field Characterization of Methane Emission Variability from a Compressor Station Using a Model Aircraft.

A model aircraft equipped with a custom laser-based, open-path methane sensor was deployed around a natural gas compressor station to quantify the methane leak rate and its variability at a compressor station in the Barnett Shale. The open-path, laser-based sensor provides fast (10 Hz) and precise (0.1 ppmv) measurements of methane in a compact package while the remote control aircraft provides nimble and safe operation around a local source.

Estimating the global abundance of ground level presence of particulate matter (PM2.5).

With the increasing awareness of the health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground level airborne particulate matter with a diameter of 2.5 microns or less (PM2.5).

The drought response of Theobroma cacao (cacao) and the regulation of genes involved in polyamine biosynthesis by drought and other stresses.

Drought can negatively impact pod production despite the fact that cacao production usually occurs in tropical areas having high rainfall. Polyamines (PAs) have been associated with the response of plants to drought in addition to their roles in responses to many other stresses. The constitutive and drought inducible expression patterns of genes encoding enzymes involved in PA biosynthesis were determined: an ornithine decarboxylase (TcODC), an arginine decarboxylase (TcADC), an S-adenosylmethionine decarboxylase (TcSAMDC), a spermidine synthase (TcSPDS), and a spermine synthase (TcSPMS).