Missing data in multi-omics integration: Recent advances through artificial intelligence.

Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.

Benchmarking animal handling outcomes on cow-calf operations and identifying associated factors.

The assessment of animal handling is commonly included in cattle care programs. The guidelines set in the National Cattlemen's Beef Association Beef Checkoff funded Beef Quality Assurance (BQA) program are often used for assessing handling on feedlot, stocker, and cow-calf operations. There is limited information about animal handling on cow-calf operations. Thus, the objectives of this study were to: (1) quantify handling outcomes on cow-calf operations and compare them to national BQA program thresholds, and (2) investigate factors associated with handling outcomes. Researchers visited 76 operations across the United States to observe the following outcomes, adapted from the BQA program, during the processing of cows or yearling heifers: Prod Use, Miscatch, Vocalization, Jump, Slip/Stumble, Fall and Run. One hundred cows or less (depending on herd size) were observed moving through a restraint system at each operation. Other information specific to the animal type, facilities, and management were also gathered to be explored as potential predictors of handling outcomes. Data were summarized using descriptive statistics on an operation basis and analyzed with multi-predictor ANOVA or Kruskal-Wallis tests to assess the relationship between outcomes and possible explanatory factors. Predictors included in the final analyses were: BQA certification (BQA), animal temperament (TEMP), region (REGION), chute style (CHUTE), and visual contact with humans (VISUAL). The 76 operations were sampled in 24 states (Central, n = 17; East, 30; West, 29), with herd sizes ranging from 10 animals to more than 5,000 animals. A total of 4,804 animals were observed. There were a substantial number of operations exceeding BQA thresholds for Prod Use (34.0%, 26), Miscatch (46.0%, 35), and Fall (31.6%, 24); the averages of these outcomes also exceeded the BQA thresholds (< 10%, 0%, and 2%, respectively). There was an association between Prod Use and several explanatory factors, including SIZE (P = 0.072), TEMP (P = 0.001), VISUAL (P = 0.027), and BQA (P = 0.104). Miscatch, Vocalization, and Fall all had single associated factors (REGION, P = 0.019; REGION, P = 0.002; VISUAL, P = 0.002, respectively). The VISUAL and TEMP factors had an association with the majority of outcomes. The findings suggest an opportunity for improving handling outcomes, which could be achieved through education and training support regarding the importance of animal handling on-farm. Future work should consider additional aspects of facilities and management that could impact cattle handling outcomes.

I Can Step Clearly Now, the TENS Is On: Transcutaneous Electric Nerve Stimulation Decreases Sensorimotor Uncertainty during Stepping Movements.

Transcutaneous electric nerve stimulation (TENS) is a method of electrical stimulation that elicits activity in sensory nerves and leads to improvements in the clinical metrics of mobility. However, the underlying perceptual mechanisms leading to this improvement are unknown. The aim of this study was to apply a Bayesian inference model to understand how TENS impacts sensorimotor uncertainty during full body stepping movements. Thirty healthy adults visited the lab on two occasions and completed a motor learning protocol in virtual reality (VR) on both visits. Participants were randomly assigned to one of three groups: TENS on first visit only (TN), TENS on second visit only (NT), or a control group where TENS was not applied on either visit (NN). Using methods of Bayesian inference, we calculated the amount of uncertainty in the participants' center of mass (CoM) position estimates on each visit. We found that groups TN and NT decreased the amount of uncertainty in the CoM position estimates in their second visit while group NN showed no difference. The least amount of uncertainty was seen in the TN group. These results suggest that TENS reduces the amount of uncertainty in sensory information, which may be a cause for the observed benefits with TENS.

Novel applications of Bayesian inference clarify sensorimotor uncertainty during stepping movements.

The human nervous system relies on sensory information from the feet and legs to control the way we balance and walk. However, even in healthy individuals this sensory information is inherently variable and clouded with uncertainty. Researchers have found that the central nervous system (CNS) estimates body position amid the uncertainty of sensory signals in a way consistent with Bayesian inference. Bayesian inference posits that the brain accounts for variability in sensory data by combining it with learned expectations built from previous movement attempts. While initial findings on this topic are promising, they have neglected to study full-body movements such as gait and balance. The purpose of this research was to determine if the CNS controls balance-related stepping tasks in a way that fits a Bayesian framework. To address this purpose, we created a virtual reality protocol where participants moved their center of mass (CoM) to various targets while relying on uncertain visual cues and compensating for an alternating shift to the cursor position. We showed that as incoming sensory information became less certain, participants relied more on their learned expectation of body position and demonstrated more uncertainty in their responses. Accordingly, as participants learned to control and estimate their CoM position during our mobility task, they relied both on the sensory information they were receiving as well as learned expectations for its location. These results provide further evidence that the CNS is aware of the variability in sensory information and is proficient at compensating for the resultant uncertainty. We aim to apply these findings as a method for measuring the efficacy of interventions targeting sensory function.

Environmental Injustices of Leaks from Urban Natural Gas Distribution Systems: Patterns among and within 13 U.S. Metro Areas.

Natural gas leaks in local distribution systems can develop as underground pipeline infrastructure degrades over time. These leaks lead to safety, economic, and climate change burdens on society. We develop an environmental justice analysis of natural gas leaks discovered using advanced leak detection in 13 U.S. metropolitan areas. We use Bayesian spatial regression models to study the relationship between the density of leak indications and sociodemographic indicators in census tracts. Across all metro areas combined, we found that leak densities increase with increasing percent people of color and with decreasing median household income. These patterns of infrastructure injustice also existed within most metro areas, even after accounting for housing age and the spatial structure of the data. Considering the injustices described here, we identify actions available to utilities, regulators, and advocacy groups that can be taken to improve the equity of local natural gas distribution systems.

Impact of the wood-burning Justa cookstove on fine particulate matter exposure: A stepped-wedge randomized trial in rural Honduras.

TRIAL DESIGN: We evaluated the impact of a biomass stove intervention on fine particulate matter (PM2.5) concentrations using an individual-level, stepped-wedge randomized trial. METHODS: We enrolled 230 women in rural Honduran households using traditional biomass stoves and randomly allocated them to one of two study arms. The Justa stove, the study intervention, was locally-sourced, wood-burning, and included an engineered combustion chamber and chimney. At each of 6 visits over 3 years, we measured 24-hour gravimetric personal and kitchen PM2.5 concentrations. Half of the households received the intervention after Visit 2 and half after Visit 4. We conducted intent-to-treat analyses to evaluate the intervention effect using linear mixed models with log-transformed kitchen or personal PM2.5 (separately) as the dependent variable, adjusting for time. We also compared PM2.5 concentrations to World Health Organization (WHO) guidelines. RESULTS: Arms 1 and 2 each had 115 participants with 664 and 632 completed visits, respectively. Median 24-hour average personal PM2.5 exposures were 81 µg/m3 (25th-75th percentile: 50-141 µg/m3) for the traditional stove condition (n=622) and 43 µg/m3 (25th-75th percentile: 27-73 µg/m3) for the Justa stove condition (n=585). Median 24-hour average kitchen concentrations were 178 µg/m3 (25th-75th percentile: 69-440 µg/m3; n=629) and 53 µg/m3 (25th-75th percentile: 29-103 µg/m3; n=578) for the traditional and Justa stove conditions, respectively. The Justa intervention resulted in a 32% reduction in geometric mean personal PM2.5 (95% confidence interval [CI]: 20-43%) and a 56% reduction (95% CI: 46-65%) in geometric mean kitchen PM2.5. During rainy and dry seasons, 53% and 41% of participants with the Justa intervention had 24-hour average personal PM2.5 exposures below the WHO interim target-3 guideline (37.5 µg/m3), respectively. CONCLUSION: The Justa stove intervention substantially lowered personal and kitchen PM2.5 and may be a provisional solution that is feasible for Latin American communities where cleaner fuels may not be available, affordable, or acceptable for some time. Clinicaltrials.gov: NCT02658383.

An approach for calibrating laser-based N2 O isotopic analyzers for soil biogeochemistry research.

RATIONALE: Technological advances have motivated researchers to transition from traditional gas chromatography/isotope ratio mass spectrometry to rapid, high-throughput, laser-based instrumentation for N2 O isotopic research. However, calibrating laser-based instruments to yield accurate and precise isotope ratios has been an ongoing challenge. To streamline the N2 O isotope research pipeline, we developed the calibration protocol for laser-based analyzers described here. While our approach is targeted at laboratory soil incubations, we anticipate that it will be broadly applicable for diverse types of stable isotope research. METHODS: We prepared standards diluted from USGS52 and from a commercial cylinder to develop a calibration curve spanning from 0.3 to 300 ppm N2 O. To calibrate over this broad range, we binned each isotopocule (N2 O, N15 NO, 15 NNO, and NN18 O) into low, medium, and high concentration ranges and then used mathematically similar polynomial functions to calibrate the isotopocules within each concentration range. We also assessed the temporal stability of the instrument and the capacity for our calibration approach to work with isotopically enriched gas samples. RESULTS: Our calibration approach yielded generally accurate and precise data when isotopocules were calibrated in concentration ranges, and the measurements appeared to be temporally stable. For all isotopocules at natural abundance, the residual percentage error was smallest in the medium N2 O range. There was more noise in the corrected isotopomers and isotopologue at natural abundance in samples with the lowest and highest N2 O concentrations. Corrected isotopomer results from isotopically enriched samples were very precise. CONCLUSIONS: Developing our calibration strategy involved learning several key lessons: (1) calibrate isotopocules in distinct concentration ranges, (2) use mathematically similar models to calibrate the isotopocules in each range, (3) calibrated N2 O concentrations and δ values tend to be most accurate and precise in the medium N2 O range, and (4) we encourage users to take advantage of isotopic enrichment to capitalize on laser-based instrument strengths.

From unloading to trimming: studying bruising in individual slaughter cattle.

Livestock bruising is both an animal welfare concern and a detriment to the economic value of carcasses. Understanding the causes of bruising is challenging due to the numerous factors that have been shown to be related to bruise prevalence. While most cattle bruising studies collect and analyze data on truckload lots of cattle, this study followed a large number (n = 585) of individual animals from unloading through postmortem processing at five different slaughter plants. Both visual bruise presence and location was recorded postmortem prior to carcass trimming. By linking postmortem data to animal sex, breed, trailer compartment, and traumatic events at unloading, a rich analysis of a number of factors related to bruise prevalence was developed. Results showed varying levels of agreement with other published bruising studies, underscoring the complexity of assessing the factors that affect bruising. Bruising prevalence varied across different sex class types (P < 0.001); 36.5% of steers [95% confidence interval (CI): 31.7, 41.6; n = 378], 52.8% of cows (45.6, 60.0; 193), and 64.3% of bulls (no CI calculated due to sample size; 14) were bruised. There was a difference in bruise prevalence by trailer compartment (P = 0.035) in potbelly trailers, indicating that cattle transported in the top deck were less likely to be bruised (95% CI: 26.6, 40.4; n = 63) compared to cattle that were transported in the bottom deck (95% CI: 39.6, 54.2; n = 89). Results indicated that visual assessment of bruising underestimated carcass bruise trimming. While 42.6% of the carcasses were visibly bruised, 57.9% of carcasses were trimmed due to bruising, suggesting that visual assessment is not able to capture all of the carcass loss associated with bruising. Furthermore, bruises that appeared small visually were often indicators of larger, subsurface bruising, creating an "iceberg effect" of trim loss due to bruising.

A National Estimate of Methane Leakage from Pipeline Mains in Natural Gas Local Distribution Systems.

We estimate methane emissions from U.S. local distribution natural gas (NG) pipes using data collected from an advanced mobile leak detection (AMLD) platform. We estimate that there are 630,000 leaks in U.S. distribution mains, resulting in methane emissions of 0.69 Tg/year (95% cr int: 0.25, 1.23). Total emissions are calculated as the product of activity factors and emissions factors. Our analysis leveraged data on >4000 leak indications found using AMLD, combined with utility pipeline GIS information, to allow us to estimate activity factors. We derive emissions factors from AMLD emission rate estimates and correct these emissions factors based on data from in-field studies assessing AMLD emissions estimates. Finally, we quantify uncertainty in both emissions factors and activity factors and propagate the uncertainty to our total emissions estimate. In modeling leak frequency, we find a clear interaction between pipeline material and age with the leakiness of all material types increasing with age. Our national methane emissions estimate is approximately 5× greater (95% cr int: 1.7×, 8.7×) than the U.S. Environmental Protection Agency's current greenhouse gas inventory estimate for pipeline mains in local distribution systems due to both a larger estimated number of leaks and better characterization of the upper tail of the skewed distribution of emission rates.

Study protocol for a stepped-wedge randomized cookstove intervention in rural Honduras: household air pollution and cardiometabolic health.

BACKGROUND: Growing evidence links household air pollution exposure from biomass-burning cookstoves to cardiometabolic disease risk. Few randomized controlled interventions of cookstoves (biomass or otherwise) have quantitatively characterized changes in exposure and indicators of cardiometabolic health, a growing and understudied burden in low- and middle-income countries (LMICs). Ideally, the solution is to transition households to clean cooking, such as with electric or liquefied petroleum gas stoves; however, those unable to afford or to access these options will continue to burn biomass for the foreseeable future. Wood-burning cookstove designs such as the Justa (incorporating an engineered combustion zone and chimney) have the potential to substantially reduce air pollution exposures. Previous cookstove intervention studies have been limited by stove types that did not substantially reduce exposures and/or by low cookstove adoption and sustained use, and few studies have incorporated community-engaged approaches to enhance the intervention. METHODS/DESIGN: We conducted an individual-level, stepped-wedge randomized controlled trial with the Justa cookstove intervention in rural Honduras. We enrolled 230 female primary cooks who were not pregnant, non-smoking, aged 24-59 years old, and used traditional wood-burning cookstoves at baseline. A community advisory board guided survey development and communication with participants, including recruitment and retention strategies. Over a 3-year study period, participants completed 6 study visits approximately 6 months apart. Half of the women received the Justa after visit 2 and half after visit 4. At each visit, we measured 24-h gravimetric personal and kitchen fine particulate matter (PM2.5) concentrations, qualitative and quantitative cookstove use and adoption metrics, and indicators of cardiometabolic health. The primary health endpoints were blood pressure, C-reactive protein, and glycated hemoglobin. Overall study goals are to explore barriers and enablers of new cookstove adoption and sustained use, compare health endpoints by assigned cookstove type, and explore the exposure-response associations between PM2.5 and indicators of cardiometabolic health. DISCUSSION: This trial, utilizing an economically feasible, community-vetted cookstove and evaluating endpoints relevant for the major causes of morbidity and mortality in LMICs, will provide critical information for household air pollution stakeholders globally. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT02658383 , posted January 18, 2016, field work completed May 2018. Official title, "Community-Based Participatory Research: A Tool to Advance Cookstove Interventions." Principal Investigator Maggie L. Clark, Ph.D. Last update posted July 12, 2018.

An open source algorithm to detect natural gas leaks from mobile methane survey data.

The data collected by mobile methane (CH4) sensors can be used to find natural gas (NG) leaks in urban distribution systems. Extracting actionable insights from the large volumes of data collected by these sensors requires several data processing steps. While these survey platforms are commercially available, the associated data processing software largely constitute a black box due to their proprietary nature. In this paper we describe a step-by-step algorithm for developing leak indications using data from mobile CH4 surveys, providing an under-the-hood look at the choices and challenges associated with data analysis. We also describe how our algorithm has evolved over time, and the data-driven insights that have prompted these changes. Applying our algorithm to data collected in 15 cities produced more than 6100 leak indications and estimates of the leaks' size. We use these results to characterize the distribution of leak sizes in local NG distribution systems. Mobile surveys are already an effective and necessary tool for managing NG distribution systems, but improvements in the technology and software will continue to increase its value.

Vehicle-Based Methane Surveys for Finding Natural Gas Leaks and Estimating Their Size: Validation and Uncertainty.

Managing leaks in urban natural gas (NG) distribution systems is important for reducing methane emissions and costly waste. Mobile surveying technologies have emerged as a new tool for monitoring system integrity, but this new technology has not yet been widely adopted. Here, we establish the efficacy of mobile methane surveys for managing local NG distribution systems by evaluating their ability to detect and locate NG leaks and quantify their emissions. In two cities, three-quarters of leak indications from mobile surveys corresponded to NG leaks, but local distribution companies' field crews did not find most of these leaks, indicating that the national CH4 activity factor for leaks in local NG distribution pipelines is underestimated by a factor of 2.4. We found the median distance between mobile-estimated leak locations and actual leak locations was 19 m. A comparison of emission quantification methods (mobile-based, surface enclosure, and tracer ratio) found that the mobile method overestimated leak magnitude for the smallest leaks but accurately estimated size for the largest leaks that are responsible for the majority of total emissions. Across leak sizes, mobile methods adequately rank relative emission rates for repair prioritization, and they are easily deployed and offer efficient spatial coverage.

A meta-analysis of temperature sensitivity as a microbial trait.

Traits-based approaches in microbial ecology provide a valuable way to abstract organismal interaction with the environment and to generate hypotheses about community function. Using macromolecular rate theory (MMRT), we recently identified that temperature sensitivity can be characterized as a distinct microbial trait. As temperature is fundamental in controlling biological reactions, variation in temperature sensitivity across communities, organisms, and processes has the potential to vastly improve understanding of microbial response to climate change. These microbial temperature sensitivity traits include the heat capacity ( ΔCP‡ ), temperature optimum (Topt ), and point of maximum temperature sensitivity (TSmax ), each of which provide unique insights about organismal response to changes in temperature. In this meta-analysis, we analyzed the distribution of these temperature sensitivity traits from bacteria, fungi, and mixed communities across a variety of biological systems (e.g., soils, oceans, foods, wastewater treatment plants) in order to identify commonalities in temperature responses across these diverse organisms and reaction rates. Our analysis of temperature sensitivity traits from over 350 temperature response curves reveals a wide distribution of temperature sensitivity traits, with Topt and TSmax well within biological relevant temperatures. We find that traits vary significantly depending on organism type, microbial diversity, source environment, and biological process, with higher temperature sensitivity found in fungi than bacteria and in less diverse systems. Carbon dioxide production was found to be less temperature sensitive than denitrification, suggesting that changes in temperature will have a potentially larger impact on nitrogen-related processes. As climate changes, these results have important implications for basic understanding of the temperature sensitivity of biological reactions and for ecological understanding of species' trait distributions, as well as for improved treatment of temperature sensitivity in models.