Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State

Standardized methods for effectively and rapidly monitoring changes in the biodiversity of marine ecosystems are critical to assess status and trends in ways that are comparable between locations and over time. In intertidal and subtidal habitats, estimates of fractional cover and abundance of organisms are typically obtained with traditional quadrat-based methods, and collection of photoquadrat imagery is a standard practice. However, visual analysis of quadrats, either in the field or from photographs, can be very time-consuming. Cutting-edge machine learning tools are now being used to annotate species records from photoquadrat imagery automatically, significantly reducing processing time of image collections. However, it is not always clear whether information is lost, and if so to what degree, using automated approaches. In this study, we compared results from visual quadrats versus automated photoquadrat assessments of macroalgae and sessile organisms on rocky shores across the American continent, from Patagonia (Argentina), Galapagos Islands (Ecuador), Gorgona Island (Colombian Pacific), and the northeast coast of the United States (Gulf of Maine) using the automated software CoralNet. Photoquadrat imagery was collected at the same time as visual surveys following a protocol implemented across the Americas by the Marine Biodiversity Observation Network (MBON) Pole to Pole of the Americas program. Our results show that photoquadrat machine learning annotations can estimate percent cover levels of intertidal benthic cover categories and functional groups (algae, bare substrate, and invertebrate cover) nearly identical to those from visual quadrat analysis. We found no statistical differences of cover estimations of dominant groups in photoquadrat images annotated by humans and those processed in CoralNet (binomial generalized linear mixed model or GLMM). Differences between these analyses were not significant, resulting in a Bray-Curtis average distance of 0.13 (sd 0.11) for the full label set, and 0.12 (sd 0.14) for functional groups. This is the first time that CoralNet automated annotation software has been used to monitor “Invertebrate Abundance and Distribution” and “Macroalgal Canopy Cover and Composition” Essential Ocean Variables (EOVs) in intertidal habitats. We recommend its use for rapid, continuous surveys over expanded geographical scales and monitoring of intertidal areas globally.

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Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

Marine Ecology Progress Series ◽

10.3354/meps13302 ◽

2020 ◽

Vol 641 ◽

pp. 159-175

Author(s):

J Runnebaum ◽

KR Tanaka ◽

L Guan ◽

J Cao ◽

L O’Brien ◽

...

Keyword(s):

Habitat Suitability ◽

Mixed Model ◽

Linear Mixed Model ◽

Gulf Of Maine ◽

Homarus Americanus ◽

Management Tool ◽

American Lobster ◽

Suitable Habitat ◽

Climate Conditions ◽

Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

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Influencing cardiovascular health habits in the rural, deep south: results of a cluster randomized trial

Health Education Research ◽

10.1093/her/cyy052 ◽

2019 ◽

Vol 34 (2) ◽

pp. 200-208

Author(s):

Laurie S Abbott ◽

Elizabeth H Slate ◽

Jennifer L Lemacks

Keyword(s):

Health Program ◽

Mixed Model ◽

Cardiovascular Health ◽

Linear Mixed Model ◽

Intervention Group ◽

The United States ◽

Control Group ◽

Cvd Risk ◽

Health Habits ◽

Cluster Randomized

Abstract Cardiovascular disease (CVD) is a major cause of death among people living in the United States. Populations, especially minorities, living in the rural South are disproportionately affected by CVD and have greater CVD risk, morbidity and mortality. Culturally relevant cardiovascular health programs implemented in rural community settings can potentially reduce CVD risk and facilitate health behavior modification. The purpose of this study was to examine the effects of a cardiovascular health promotion intervention on the health habits of a group of rural African American adults. The study had a cluster randomized controlled trial design involving 12 rural churches that served as statistical clusters. From the churches (n = 6) randomized to the intervention group, 115 participants were enrolled, received the 6-week health program and completed pretest–posttest measures. The 114 participants from the control group churches (n = 6) did not receive the health program and completed the same pretest–posttest measures. The linear mixed model was used to compare group differences from pretest to posttest. The educational health intervention positively influenced select dietary and confidence factors that may contribute toward CVD risk reduction.

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Abstract P358: Discovery Loci and Generalization of Platelet Count Associations in the Hispanic Community Health Study: Study of Latinos (HCHS/SoL)

Circulation ◽

10.1161/circ.131.suppl_1.p358 ◽

2015 ◽

Vol 131 (suppl_1) ◽

Author(s):

Chani J Hodonsky ◽

Ursula Schick ◽

Jean Morrison ◽

Cathy Laurie ◽

John H Eckfeldt ◽

...

Keyword(s):

Platelet Count ◽

Mixed Model ◽

Linear Mixed Model ◽

Structural Integrity ◽

Genome Wide Association Study ◽

Genetic Model ◽

The United States ◽

Actin Binding ◽

Health Study ◽

A Genome

Background: The biology of platelets_anuclear megakaryocyte fragments required for clotting_is well defined, with abnormalities resulting in clotting disorders ranging from asymptomatic to severe. Although platelet count (PLT) is highly heritable (h 2 =0.57 in NHANES), genetic regulation of this trait remains incompletely characterized. In particular, Hispanic Latinos are a diverse population in which the genetic variability of PLT has not been analyzed. We aimed to 1) identify novel loci associated with PLT in participants of HCHS/SoL; and 2) determine if previously identified PLT loci generalize to the Hispanic Latino population. Methods: We performed a genome-wide association study of PLT in 12,491 participants of HCHS/SoL, which includes individuals with Dominican, Puerto Rican, Mexican, Cuban, South American, and Central American ancestral origins residing in the United States. Participants were genotyped on the Illumina SoL Omni2.5M array and imputed to the 1000 Genomes Phase I Reference panel. We implemented linear mixed-model regression assuming an additive genetic model, adjusting for sex, age, study center, ancestry and sample weights, and including random effects for individual relatedness, household, and block group. Results: Ten independent loci were significantly (α = 5x10 -8 ) associated with PLT in HCHS/SoL and 13 of 57 previously identified platelet-count GWAS loci generalized to HCHS/SoL (+/-500kb, α = 8.78x10 -4 ). Discovery loci included a significant association near GABBR1 (rs3131857, MAF = 40%, β = -0.16) and a suggestive association near ETV7 (rs9470264, MAF = 20%, β = -0.19). Furthermore, a noncoding variant in ACTN1 was associated with increased PLT (rs117672662, MAF = 6%, β = 0.61); ACTN1 codes for alpha-actinin, a multi-isoform actin-binding protein involved in cytoskeleton organization and platelet/megakaryocyte structural integrity. Missense mutations in ACTN1 were recently implicated in congenital thrombocytopenia; exome sequencing also associated this locus with PLT, which supports a potential functional role in PLT for this gene. Conditional regression analyses to assess secondary association signals are in progress. Conclusions: Genetic associations in two discovery loci ( ETV7 and GABBR1 ) underscore the benefit of using diverse populations in GWAS. Expanding the number of loci associated with platelet count will help elucidate disease mechanisms and develop approaches to treat platelet disorders in populations of all ancestries.

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Code-switching attitudes and their effects on acceptability judgment tasks

Linguistic Approaches to Bilingualism ◽

10.1075/lab.16006.bad ◽

2017 ◽

Vol 8 (1) ◽

pp. 5-24 ◽

Cited By ~ 18

Author(s):

Lucia Badiola ◽

Rodrigo Delgado ◽

Ariane Sande ◽

Sara Stefanich

Keyword(s):

United States ◽

Mixed Model ◽

Linear Mixed Model ◽

The United States ◽

Code Switching ◽

Negative Attitudes ◽

Bilingual Speakers ◽

Positive Attitudes ◽

Acceptability Judgment

Abstract The present study examines the effects of code-switching (CS) attitudes in Acceptability Judgment Tasks (AJTs) among early Spanish/English bilinguals in the United States. In doing so, we explore whether negative attitudes towards CS result in lower/degraded ratings, and, likewise, whether positive attitudes result in higher acceptability ratings. Fifty Spanish/English bilinguals completed a survey that comprised a linguistic background questionnaire and a set of monolingual and code-switched sentences featuring two sets of stimuli, pro-drop (Sande, 2015) and pronouns (Koronkiewicz, 2014), that they rated on a 1–7 Likert scale; additionally, the survey included a final component that gathered information about the speakers’ attitudes towards CS. The pro-drop and pronouns code-switched stimuli gave rise to a total of four conditions. Results from a Linear Mixed Model revealed that all participants, regardless of attitude, distinguished between all Conditions. Furthermore, an effect for attitude was found for two of the conditions, such that the more positive the attitude, the higher the rating given on the AJT. In fact, these two conditions were composed of the CS structures that were rated higher by participants in Sande (2015) and Koronkiewicz (2014). No effect for attitude was found for CS structures that were rated low in the original studies. Thus, this investigation suggests that the attitudes that bilingual speakers have towards CS play a role in the ratings that they provide in AJTs, but in a manner that highlights, rather than obscures, the rule-governed nature of CS.

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Influence of Intensity and Duration of Invasion by Amur Honeysuckle (Lonicera maackii) on Mixed Hardwood Forests of Indiana

Invasive Plant Science and Management ◽

10.1614/ipsm-d-14-00044.1 ◽

2015 ◽

Vol 8 (1) ◽

pp. 44-56 ◽

Cited By ~ 4

Author(s):

Joshua M. Shields ◽

Michael A. Jenkins ◽

Michael R. Saunders ◽

Kevin D. Gibson ◽

Patrick A. Zollner ◽

...

Keyword(s):

Invasive Species ◽

Mixed Model ◽

Linear Mixed Model ◽

Taxonomic Diversity ◽

Hardwood Forests ◽

Percent Cover ◽

Native Plant ◽

Light Competition ◽

Negative Effects ◽

Amur Honeysuckle

AbstractThe expansion of populations of invasive species continues to compromise the ecological and economic integrity of our natural resources. The negative effects of invasive species on native biota are widely reported. However, less is known about how the duration (i.e., age of oldest invaders) and intensity (i.e., density and percent cover) of an invasion influences native plant diversity and abundance at the microsite scale. We examined the influence of density, percent cover, and age of Amur honeysuckle (a nonnative invasive shrub), and several environmental factors on native plant taxa at 12 mixed hardwood forests in Indiana, USA. Overall, study sites with the greatest taxonomic diversity (Shannon's Diversity; H′), richness (S), percent cover, and density of native vegetation also had the lowest percent cover of Amur honeysuckle in the upper vertical stratum (1.01 to 5 m). Based on linear mixed model analyses, percent cover of Amur honeysuckle in the upper vertical stratum was consistently and negatively correlated with H′, S, total percent cover, and woody seedling density of native taxa at the microsite scale (P < 0.05). Duration of Amur honeysuckle at the microsite scale was not significant when percent cover of Amur honeysuckle in the upper vertical stratum was included in models. However, duration of Amur honeysuckle invasion was significantly correlated with dependent variables and with upper-stratum honeysuckle cover, suggesting that older Amur honeysuckle in a microsite resulted in greater light competition from above for native understory plant species. Beyond increased cover and shading, our results do not provide evidence of duration-related effects from long-term dominance of honeysuckle in our sampled mixed hardwood forest sites.

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Distance Traveled and Cross-State Commuting to Opioid Treatment Programs in the United States

Journal of Environmental and Public Health ◽

10.1155/2011/948789 ◽

2011 ◽

Vol 2011 ◽

pp. 1-10 ◽

Cited By ~ 48

Author(s):

Andrew Rosenblum ◽

Charles M. Cleland ◽

Chunki Fong ◽

Deborah J. Kayman ◽

Barbara Tempalski ◽

...

Keyword(s):

United States ◽

Mixed Model ◽

Linear Mixed Model ◽

The United States ◽

Treatment Programs ◽

Prescription Opioid ◽

Heroin Use ◽

Opioid Treatment ◽

Mixed Model Analysis ◽

Commuting Patterns

This study examined commuting patterns among 23,141 methadone patients enrolling in 84 opioid treatment programs (OTPs) in the United States. Patients completed an anonymous one-page survey. A linear mixed model analysis was used to predict distance traveled to the OTP. More than half (60%) the patients traveled <10 miles and 6% travelled between 50 and 200 miles to attend an OTP; 8% travelled across a state border to attend an OTP. In the multivariate model (n=17,792), factors significantly (P<.05) associated with distance were, residing in the Southeast or Midwest, low urbanicity, area of the patient's ZIP code, younger age, non-Hispanic white race/ethnicity, prescription opioid abuse, and no heroin use. A significant number of OTP patients travel considerable distances to access treatment. To reduce obstacles to OTP access, policy makers and treatment providers should be alert to patients' commuting patterns and to factors associated with them.

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1432. Estimating the Incubation Period of Salmonella Urinary Tract Infection (UTI) Using Foodborne Outbreak Data

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.1296 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S522-S523

Author(s):

Kara M Jacobs Slifka ◽

Anna Blackstock ◽

Von Nguyen ◽

Colin Schwensohn ◽

Laura Gieraltowski ◽

...

Keyword(s):

Urinary Tract ◽

Incubation Period ◽

Bacterial Infections ◽

Mixed Model ◽

Linear Mixed Model ◽

Random Effect ◽

The United States ◽

Food History ◽

Foodborne Outbreaks ◽

Tract Infections

Abstract Background Urinary tract infections (UTI) are common bacterial infections that may occur as a part of foodborne outbreaks. Salmonella, a less common cause of UTI, has been identified during foodborne outbreaks, but the epidemiology and pathogenesis are poorly understood. Methods PulseNet, the United States national molecular subtyping network for foodborne disease surveillance, was used to identify Salmonella isolates associated with outbreaks from 2004 to 2013 containing at least one urine and one stool isolate in which the duration was ≤1 year and a food vehicle was suspected or confirmed. We standardized isolation dates across outbreaks by calculating the mean date for stool isolation within an outbreak and subtracting this from the date of each stool/urine isolate in that outbreak. A linear-mixed model with random effect for stool/urine was used to estimate the difference in incubation periods between stool/urine isolates. We also surveyed patients from a 2012 Salmonella Cubana outbreak with many urinary isolates and associated with sprouts, to ask about diarrhea, UTI symptoms, diagnosis, and treatment. Descriptive statistics were calculated. Results Urine isolates had later isolation dates than stool isolates for 102 of the 110 outbreaks identified. The average difference between stool and urine isolates was 10.6 days (95% CI: 6.0, 15.2). Seven women from the Salmonella Cubana outbreak were reached. All women were diagnosed with either a UTI (6/7 = 86%) and/or kidney infection (2/7 = 29%) and were treated with antibiotics (7/7 = 100%). All six women completing the survey reported multiple signs and/or symptoms including frequency, urgency, dysuria, and hematuria with only two women reporting diarrhea prior to UTI. Conclusion Salmonella UTI seen during foodborne outbreaks are symptomatic foodborne infections not associated with diarrhea and appear to have a longer incubation period than Salmonella gastrointestinal (GI) illness. A 13- to 16-day incubation period for Salmonella UTI may be more appropriate, calculated by adding a 3- to 4-day GI illness incubation period plus delay in obtaining a stool isolate. Foodborne UTI investigation may need to change as the current method of obtaining a food history for the 6–72 hours prior to illness does not accurately reflect the incubation period for Salmonella UTI. Disclosures All authors: No reported disclosures.

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Work-Hour Trajectories and Associated Socioeconomic Characteristics among Single-Mother Families

Social Work Research ◽

10.1093/swr/svz029 ◽

2020 ◽

Vol 44 (1) ◽

pp. 47-57

Author(s):

Chi-Fang Wu ◽

Yu-Ling Chang ◽

Emily Rhodes ◽

Salma Musaad ◽

Woojin Jung

Keyword(s):

Single Mothers ◽

Low Income ◽

Mixed Model ◽

Linear Mixed Model ◽

Single Mother ◽

The United States ◽

Policy Implications ◽

Work Hour ◽

Over Time ◽

Single Mother Families

Abstract Although social work research has paid substantial attention to employment patterns among low-income single mothers after welfare reform, little is known about their work-hour trajectories over time. This study uses group-based trajectory modeling to analyze the work-hour trajectories among low-income single mothers in the United States (N = 870). Only approximately two-fifths (41.9 percent) of participants in the sample had stable employment. About 18 percent did not work throughout the study period. Yet several groups experienced changes in working patterns over time: increasing hours (20.7 percent), decreasing then increasing hours (11.3 percent), and decreasing hours (8.4 percent). This study uses a generalized linear mixed model to determine the factors associated with change in work hours over time. Significant factors include marital status, high school completion, race, citizenship, homeownership, child care arrangement, income support program participation, work disability, age of youngest child, age of the mother, state unemployment rate, and state minimum wage. These findings have important policy implications for targeting supports to diverse needs of low-income single-mother families to promote employment stability and economic improvement.

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Fatalism, Social Support and Self-Management Perceptions among Rural African Americans Living with Diabetes and Pre-Diabetes

Nursing Reports ◽

10.3390/nursrep11020024 ◽

2021 ◽

Vol 11 (2) ◽

pp. 242-252

Author(s):

Laurie Abbott ◽

Elizabeth Slate ◽

Lucinda Graven ◽

Jennifer Lemacks ◽

Joan Grant

Keyword(s):

Social Support ◽

Mixed Model ◽

Linear Mixed Model ◽

Diabetes Management ◽

Disease Risk ◽

Public Health Problem ◽

Clinical Trial Data ◽

Population Data ◽

The United States ◽

Self Management

Diabetes is a public health problem and a major risk factor for cardiovascular disease, the leading cause of death in the United States. Diabetes is prevalent among underserved rural populations. The purposes of this study were to perform secondary analyses of existing clinical trial data to determine whether a diabetes health promotion and disease risk reduction intervention had an effect on diabetes fatalism, social support, and perceived diabetes self-management and to provide precise estimates of the mean levels of these variables in an understudied population. Data were collected during a cluster randomized trial implemented among African American participants (n = 146) in a rural, southern area and analyzed using a linear mixed model. The results indicated that the intervention had no significant effect on perceived diabetes management (p = 0.8), diabetes fatalism (p = 0.3), or social support (p = 0.4). However, the estimates showed that, in the population, diabetes fatalism levels were moderate (95% CI = (27.6, 31.3)), and levels of social support (CI = (4.0, 4.4)) and perceived diabetes self-management (CI = (27.7, 29.3)) were high. These findings suggest that diabetes fatalism, social support, and self-management perceptions influence diabetes self-care and rural health outcomes and should be addressed in diabetes interventions.

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Preprocessing Tools for Data Preparation

Multivariate Statistical Machine Learning Methods for Genomic Prediction ◽

10.1007/978-3-030-89010-0_2 ◽

2022 ◽

pp. 35-70

Author(s):

Osval Antonio Montesinos López ◽

Abelardo Montesinos López ◽

Jose Crossa

Keyword(s):

Machine Learning ◽

Mixed Model ◽

Linear Mixed Model ◽

Genomic Relationship Matrix ◽

Relationship Matrix ◽

Data Preparation ◽

Statistical Machine Learning ◽

Major Allele Frequency ◽

Major Allele ◽

Best Linear Unbiased

AbstractThis data preparation chapter is of paramount importance for implementing statistical machine learning methods for genomic selection. We present the basic linear mixed model that gives rise to BLUE and BLUP and explain how to decide when to use fixed or random effects that give rise to best linear unbiased estimates (BLUE or BLUEs) and best linear unbiased predictors (BLUP or BLUPs). The R codes for fitting linear mixed model for the data are given in small examples. We emphasize tools for computing BLUEs and BLUPs for many linear combinations of interest in genomic-enabled prediction and plant breeding. We present tools for cleaning, imputing, and detecting minor and major allele frequency computation, marker recodification, frequency of heterogeneous, frequency of NAs, and three methods for computing the genomic relationship matrix. In addition, scaling and data compression of inputs are important in statistical machine learning. For a more extensive description of linear mixed models, see Chap. 10.1007/978-3-030-89010-0_5.

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