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Many species of fishes around the world are soniferous. The types of sounds fishes produce vary among species and regions but consist typically of low-frequency (< 1.5 kHz) pulses and grunts. These sounds can potentially be used to monitor fishes non-intrusively and could complement traditional monitoring techniques. However, the significant time required for human analysts to manually label fish sounds in acoustic recordings does not yet allow passive acoustics to be used as a viable tool for monitoring fishes. In this paper, we compare two different approaches to automatically detect fish sounds. One is a more traditional machine learning technique based on the detection of acoustic transients in the spectrogram and the classification using Random Forest (RF). The other is using a deep learning approach and is based on the classification of overlapping segments (0.2 s) of spectrogram using a ResNet18 Convolutional Neural Network (CNN). Both algorithms were trained using 21,950 manually annotated fish and non-fish sounds collected from 2014 to 2019 at five different locations in the Strait of Georgia, British Columbia, Canada. The performance of the detectors was tested on part of the data from the Strait of Georgia that was withheld from the training phase, data from Barkley Sound, British Columbia, and data collected in the Port of Miami, Florida, United States. The CNN performed up to 1.9 times better than the RF (F1 score: 0.82 vs. 0.43). In some cases, the CNN was able to find more faint fish sounds than the analyst and performed well in environments different from the one it was trained in (Miami F1 score: 0.88). Noise analysis in the 20--1,000 Hz frequency band shows that the CNN is still reliable in noise levels greater than 130 dB re 1 μPa in the Port of Miami but becomes less reliable in Barkley Sound past 100 dB re 1 μPa due to mooring noise. The proposed approach can efficiently monitor (unidentified) fish sounds in a variety of environments and can also facilitate the development of species-specific detectors. We provide the software FishSound Finder, an easy-to-use open-source implementation of the CNN detector with detailed documentation. [ABSTRACT FROM AUTHOR]

Physical and climatic variables such as Tree Canopy coverage, Normalized Difference Vegetation Index (NDVI), Distance to Roads, Distance to the Coast, Impervious Surface, and Precipitation can affect land surface temperature (LST). This paper examines the relationships using linear regression models and explores LST trends in the Miami Statistical Area (MSA) between 2002 and 2021. This study evaluates the effect of dry and wet seasons as well as day and night data on LST. A multiscale investigation is used to examine LST trends at the MSA scale, the individual county level, and at the pixel level to provide a detailed local perspective. The multiscale results are needed to understand spatiotemporal LST distributions to plan mitigation measures such as planting trees or greenery to regulate temperature and reduce the impacts of surface urban heat islands. The results indicate that LST values are rising in the MSA with a positive trend throughout the 20-year study period. The rate of change (RoC) for the wet season is smaller than for the dry season. The pixel-level analysis suggests that the RoC is primarily in rural areas and less apparent in urban areas. New development in rural areas may trigger increased RoC. This RoC relates to LST in the MSA and is different from global or regional RoC using air temperature. Results also suggest that climatic explanatory variables have different impacts during the night than they do in the daytime. For instance, the Tree Canopy variable has a positive coefficient, while during the day, the Tree Canopy variable has a negative relationship with LST. The Distance to the Coast variable changes from day to night as well. The increased granularity achieved with the multiscale analysis provides critical information needed to improve the effectiveness of potential mitigation efforts. [ABSTRACT FROM AUTHOR]

Extant research has highlighted the pros and cons of the use of (a) competition and (b) positive (peer) pressure as two mechanisms for invoking peer effects to bring about long-term behavior change. However, these mechanisms have been studied separately and in different contexts. In this paper, we compare the differential impacts of competition versus positive pressure-induced pro-environmental behavior change in the same target population. This study spans a seven-year horizon between 2013 and 2019 (pre-COVID) during which the workforce of about 2500 employees in the Stephan P. Clark Government Center in Miami, Florida, was split into four groups - one control and three treatment groups. Each treatment group was subjected to (a) a healthy inter-group competition through tracking, benchmarking, and comparing monthly group level electricity saving, and (b) positive pressure through the appointment of champions who encouraged pro-environmental behavior among their group members. Studying these mechanisms concurrently in the same population allows for a nuanced comparison of their differential impacts on pro-environmental behavior change. • In this paper, we compare the differential impacts of competition-induced spillover and supervision-induced peer pressure in affecting pro-environmental behavior change in the same target over a seven-year analysis horizon. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • These results have implications for the design of pro-environment campaigns that could leverage peer effects through fostering an environment of intragroup pride and healthy inter-group rivalry sprinkled with intermittent supervisory interventions for making long-term impacts. [ABSTRACT FROM AUTHOR]

The effects of turbidity and sedimentation stress on early life stages of corals are poorly understood, particularly in Atlantic species. Dredging operations, beach nourishment, and other coastal construction activities can increase sedimentation and turbidity in nearby coral reef habitats and have the potential to negatively affect coral larval development and metamorphosis, reducing sexual reproduction success. In this study, we investigated the performance of larvae of the threatened Caribbean coral species Orbicella faveolata exposed to suspended sediments collected from a reef site in southeast Florida recently impacted by dredging (Port of Miami), and compared it to the performance of larvae exposed to sediments collected from the offshore, natal reef of the parent colonies. In a laboratory experiment, we tested whether low and high doses of each of these sediment types affected the survival, settlement, and respiration of coral larvae compared to a no-sediment control treatment. In addition, we analyzed the sediments used in the experiments with 16S rRNA gene amplicon sequencing to assess differences in the microbial communities present in the Port versus Reef sediments, and their potential impact on coral performance. Overall, only O. faveolata larvae exposed to the high-dose Port sediment treatment had significantly lower survival rates compared to the control treatment, suggesting an initial tolerance to elevated suspended sediments. However, significantly lower settlement rates were observed in both Port treatments (low- and high-dose) compared to the control treatment one week after exposure, suggesting strong latent effects. Sediments collected near the Port also contained different microbial communities than Reef sediments, and higher relative abundances of the bacteria Desulfobacterales, which has been associated with coral disease. We hypothesize that differences in microbial communities between the two sediments may be a contributing factor in explaining the observed differences in larval performance. Together, these results suggest that the settlement success and survival of O. faveolata larvae are more readily compromised by encountering port inlet sediments compared to reef sediments, with potentially important consequences for the recruitment success of this species in affected areas. [ABSTRACT FROM AUTHOR]

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline. [ABSTRACT FROM AUTHOR]

The insurance industry has used parametric solutions to transfer catastrophe risks since the 1990s. Instead of relying on a lengthy process to assess a claim, these products pay the insured a pre-agreed amount if the physical characteristics of the event fulfill pre-defined conditions. Cat-in-a-box or cat-in-a-circle triggers, commonly used tools for tropical cyclone risk transfer, provide a payout to the insured if the track of a hurricane crosses the perimeter of a geographic area defined by a polygon or a circle with a certain intensity. Cat-in-a-grid solutions are novel and more sophisticated. They rely on a set of multiple cat-in-a-box triggers arranged on an orthogonal grid. The consideration of multiple geographic domains instead of a single box or circle is helpful to reduce basis risk, i.e., the difference between the parametric loss estimate and the target loss. In the case study for Miami presented here, for instance, a cat-in-a-grid solution showed 18.5% less basis risk than a typical cat-in-a-box alternative. To organize the different types of triggers within a common framework, we classify the existing alternatives based on whether they use a single geographic domain (like a box or a circle) or multiple domains (like a grid). We discuss their advantages and disadvantages and describe the process required to calibrate any one solution with the help of a catastrophe-risk model. We focus, in particular, on the analysis and construction of cat-in-a-grid triggers, the alternative that we believe offers the greatest potential for global standardization and adoption. [ABSTRACT FROM AUTHOR]

Background: We evaluate the relationship between corneal nerve structure and function in a veteran population. Methods: 83 veterans (mean age: 55 ± 5 years) seen at the Miami Veterans Affairs (VA) eye clinic were included in this study. Each individual filled out questionnaires to evaluate ocular symptoms (5-Item Dry Eye Questionnaire, DEQ5; Ocular Surface Disease Index, OSDI) and ocular pain (Numerical Rating Scale, NRS; Neuropathic Pain Symptom Inventory modified for the Eye, NPSI-Eye). The individuals also underwent an ocular surface examination that captured functional nerve tests including corneal sensation, corneal staining, and the Schirmer test for tear production. Corneal sub-basal nerve analysis was conducted using in vivo confocal microscopy (IVCM) images with corneal nerve density, length, area, width, and fractal dimension captured. IVCM and functional corneal metrics from the right eye were examined using correlational and linear regression analysis. Results: Most corneal structural metrics were not related to functional metrics, except for weak correlations between various IVCM metrics and tear production. In addition, corneal nerve fiber area was positively related to corneal sensation (r = 0.3, p = 0.01). On linear regression analyses, only the corneal fractal dimension remained significantly related to tear production (β = −0.26, p = 0.02) and only the corneal nerve fiber area remained significantly related to corneal sensation (β = 0.3, p = 0.01). Conclusions: Most corneal nerve structural metrics did not relate to functional metrics in our veteran population, apart from a few weak correlations between structural metrics and tear production. This suggests that using corneal nerve anatomy alone may be insufficient for predicting corneal function. [ABSTRACT FROM AUTHOR]

Background: People who inject drugs (PWID) remain a high priority population under the federal Ending the HIV Epidemic initiative with 11% of new HIV infections attributable to injection drug use. There is a critical need for innovative, efficacious, scalable, and community-driven models of healthcare in non-stigmatizing settings for PWID. We seek to test a Comprehensive-TeleHarm Reduction (C-THR) intervention for HIV prevention services delivered via a syringe services program (SSP). Methods: The CHARIOT trial is a hybrid type I effectiveness-implementation study using a parallel two-arm randomized controlled trial design. Participants (i.e., PWID; n = 350) will be recruited from a syringe services program (SSP) in Miami, Florida. Participants will be randomized to receive either C-THR or non-SSP clinic referral and patient navigation. The objectives are: (1) to determine if the C-THR intervention increases engagement in HIV prevention (i.e., HIV pre-exposure prophylaxis; PrEP or medications for opioid use disorder; MOUD) compared to non-SSP clinic referral and patient navigation, (2) to examine the long-term effectiveness and cost-effectiveness of the C-THR intervention, and (3) to assess the barriers and facilitators to implementation and sustainment of the C-THR intervention. The co-primary outcomes are PrEP or MOUD engagement across follow-up at 3, 6, 9 and 12 months. For PrEP, engagement is confirmed by tenofovir on dried blood spot or cabotegravir injection within the previous 8 weeks. For MOUD, engagement is defined as screening positive for norbuprenorphine or methadone on urine drug screen; or naltrexone or buprenorphine injection within the previous 4 weeks. Secondary outcomes include PrEP adherence, engagement in HCV treatment and sustained virologic response, and treatment of sexually transmitted infections. The short and long term cost-effectiveness analyses and mixed-methods implementation evaluation will provide compelling data on the sustainability and possible impact of C-THR on comprehensive HIV prevention delivered via SSPs. Discussion: The CHARIOT trial will be the first to our knowledge to test the efficacy of an innovative, peer-led telehealth intervention with PWID at risk for HIV delivered via an SSP. This innovative healthcare model seeks to transform the way PWID access care by bypassing the traditional healthcare system, reducing multi-level barriers to care, and meeting PWID where they are. Trial Registration: ClinicalTrials.gov NCT05897099. Trial registry name: Comprehensive HIV and Harm Prevention Via Telehealth (CHARIOT). Registration date: 06/12/2023. [ABSTRACT FROM AUTHOR]

Sport coaches increasingly rely on external load metrics for designing effective training programs. However, their accuracy in estimating internal load is inconsistent, and their ability to predict autonomic nervous system (ANS) deterioration is unknown. This study aimed to evaluate the relationships between internal and external training load metrics and ANS recovery and function in college football players. Football athletes were recruited from a D1 college in the southeastern US and prospectively followed for 27 weeks. Internal load was estimated via exercise cardiac load (ECL; average training heartrate (HR) × session duration) and measured with an armband monitor equipped with electrocardiographic capabilities (Warfighter MonitorTM (WFM), Tiger Tech Solutions, Miami, FL, USA). External load was estimated via the summation and rate of acceleration and decelerations as measured by a triaxial accelerometer using the WFM and an accelerometer-based (ACCEL) device (Catapult Player Load, Catapult Sports, Melbourne, Australia) worn on the mid-upper back. Baseline HR, HR variability (HRV) and HR recovery served as the indicators for ANS recovery and function, respectively. For HRV, two, time-domain metrics were measured: the standard deviation of the NN interval (SDNN) and root mean square of the standard deviation of the NN interval (rMSSD). Linear regression models evaluated the associations between ECL, ACCEL, and the indicators of ANS recovery and function acutely (24 h) and cumulatively (one- and two-week). Athletes (n = 71) were male and, on average, 21.3 ± 1.4 years of age. Acute ECL elicited stronger associations for 24 h baseline HR (R2 0.19 vs. 0.03), HR recovery (R2 0.38 vs. 0.07), SDNN (R2 0.19 vs. 0.02) and rMSSD (R2 0.19 vs. 0.02) compared to ACCEL. Similar results were found for one-week: 24 h baseline HR (R2 0.48 vs. 0.05), HR recovery (R2 0.55 vs. 0.05), SDNN (R2 0.47 vs. 0.05) and rMSSD (R2 0.47 vs. 0.05) and two-week cumulative exposures: 24 h baseline HR (R2 0.52 vs. 0.003), HR recovery (R2 0.57 vs. 0.05), SDNN (R2 0.52 vs. 0.003) and rMSSD (R2 0.52 vs. 0.002). Lastly, the ACCEL devices weakly correlated with ECL (rho = 0.47 and 0.43, p < 0.005). Our findings demonstrate that ACCEL poorly predicted ANS deterioration and underestimated internal training load. ACCEL devices may "miss" the finite window for preventing ANS deterioration by potentially misestimating training loads acutely and cumulatively. [ABSTRACT FROM AUTHOR]

The World Collection of Sugarcane and Related Grasses, maintained at the USDA-ARS in Miami, FL, is one of the largest sugarcane germplasm repositories in the world. However, the genetic integrity of the Saccharum spp. germplasm in this collection has not been fully analyzed. In this study, we employed a single-dose SNP panel to genotype 901 sugarcane accessions, representing six Saccharum species and various hybrids. Our analysis uncovered a high rate of clone mislabeling in the collection. Specifically, we identified 86 groups of duplicates, characterized by identical SNP genotypes, which encompassed 211 accessions (23% of the total clones), while 135 groups, constituting 471 clones (52% of the total), exhibited near-identical genotypes. In addition, twenty-seven homonymous groups were detected, which shared the same clone name but differed in SNP genotypes. Hierarchical analysis of population structure partitioned the Saccharum germplasm into five clusters, corresponding to S. barberi, S. sinense, S. officinarum, S. spontaneum and S. robustum/S. edule. An assignment test, based on the five Saccharum species, enabled correcting 141 instances of mislabeled species memberships and inaccuracies. Moreover, we clarified the species membership and parentage of 298 clones that had ambiguous passport records (e.g., ‘Saccharum spp’, ‘unknown’, and ‘hybrid’). Population structure and genetic diversity in these five species were further supported by Principal Coordinate Analysis and neighbor- joining clustering analysis. Analysis of Molecular Variance revealed that within- species genetic variations accounted for 85% of the total molecular variance, with the remaining 15% attributed to among-species genetic variations. The single-dose SNP markers developed in this study offer a robust tool for characterizing sugarcane germplasm worldwide. These findings have important implications for sugarcane genebank management, germplasm exchange, and crop genetic improvement. [ABSTRACT FROM AUTHOR]