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Background: The gut microbiome is an important determinant of health and disease in preterm infants. Objectives: The objective of this article was to share our current protocol for other neonatal intensive care units to potentially expand their existing protocols, aiming to characterize the relationship between the intestinal microbiome and health outcomes in preterm infants. Methods : This prospective, longitudinal study planned to recruit 160 preterm infants born <32 weeks gestational age or weighing <1,500 g and admitted to one of two Level III/IV neonatal intensive care units. During the neonatal intensive care unit period, the primary measures included events of early life pain/stress, gut microbiome, host genetic variations, and neurobehavioral assessment. During follow-up visits, gut microbiome; pain sensitivity; and medical, growth, and developmental outcomes at 4, 8-12, and 18-24 months corrected age were measured. Discussion : As of February 14, 2020, 214 preterm infants have been recruited. We hypothesize that infants who experience greater levels of pain/stress will have altered gut microbiome, including potential adverse outcomes such as necrotizing enterocolitis and host genetic variations, feeding intolerance, and/or neurodevelopmental impairments. These will differ from the intestinal microbiome of preterm infants who do not develop these adverse outcomes. To test this hypothesis, we will determine how alterations in the intestinal microbiome affect the risk of developing necrotizing enterocolitis, feeding intolerance, and neurodevelopmental impairments in preterm infants. In addition, we will examine the interaction between the intestinal microbiome and host genetics in the regulation of intestinal health and neurodevelopmental outcomes. [ABSTRACT FROM AUTHOR]

Working waterfront space is limited, leading to competition for coastal space among different waterfront‐dependent industries as well as non‐waterfront dependent users. Coastal margins will be shrinking as sea levels rise due to climate change, squeezing already scarce port resources, exacerbating competition for these spaces, and generating conflicts. This paper focuses on the competition for waterfront space on the Thames River of Connecticut bordered by the municipalities of New London, Groton, Norwich, Ledyard, and Montville. Emerging wind power in the new Blue Economy looks to reshape port usage in the Thames River. The recent focus on wind generation by the State of Connecticut, coupled with several competitive bidding events for wind‐generated electricity, has augmented demand for port space that will serve as a staging area for this offshore development. Although the development of wind power will occur offshore, Connecticut ports are anticipating port use by wind companies with winning bids. This paper explores the processes by which port space has been (re)allocated and contested, and examines the nature of the ensuing conflicts and outcomes. [ABSTRACT FROM AUTHOR]

This paper describes a human-centered design approach to investigate unmet adolescent sexual health wants and needs among youth of color in low-income and rural communities in Texas, North Carolina, and Connecticut (n = 149). The approach involved gathering qualitative data through in-depth interviews and other human-centered design activities between April 2016 and August 2016. Data were analyzed using thematic content analysis after each round of data collection. Themes were triangulated across multiple data sources to identify key insights. Results highlighted several important unmet wants and needs. Participants shared that schools were not the preferred place to learn about sexual health and healthy relationships, due to uncomfortable and sometimes even hostile environments. When discussing the potential to use internet- and technology-based sexual health resources, participants expressed concerns over privacy, safety, and credibility of information available. Similarly, participants preferred mobile apps over websites due to the privacy of the experience. Most importantly, key emotions impacting adolescents' access to and use of sexual health resources were identified. Results indicated a preference for consuming story-based information in a text message format that described diverse experiences related to sexual health topics. Together, these findings led to the development of an innovative, mobile health intervention for adolescents, the Real Talk mobile app. This human-centered approach can support researchers and practitioners in strengthening intervention development efforts to improve the reach, adoption, and implementation of sexual health interventions. [ABSTRACT FROM AUTHOR]

Palliative care and medical-legal partnership are complementary disciplines dedicated to integrating care to treat the whole patient and intervening before a legal or medical issue is at a crisis point. In this paper, we discuss the founding and operations of the Yale Palliative Medical Legal Partnership, give examples of typical cases, explain special considerations in this area of law, and propose areas for further research. [ABSTRACT FROM AUTHOR]

As the COVID-19 pandemic unfolds, every aspect of daily life is being altered in response to the virus. The pandemic has altered secondary education. Classes online, teachers struggling to learn Zoom and make lessons meaningful and relevant to students. Students struggling to make sense of this moment, struggling with mental health issues due to the loss of routine and in many cases contact with adult role models. Unfortunately, in times of crisis such as these some of the most disenfranchized people in society are completely overlooked and forgotten, such as refugees. However, if more teachers were to leverage the social studies potential of current events such as the Coronavirus, greater empathy would be felt for marginalized people more starkly impacted by the pandemic, leading ultimately to a heightened sense of civic engagement among the next generation. The purpose of this paper is to assist teachers in guiding their students through analyzing current events, such as COVID-19's impact on refugees, toward developing civic mindedness. In addition to this, the paper will discuss some of the broader societal impacts the virus is having within the United States, as well as ways in which this event may be viewed as a historical subject in the future. The paper will begin by building the content knowledge of high school social studies teachers through addressing the following question: "What is the difference between a refugee entering the United States now versus one year ago before the COVID-19 pandemic?" Following this, the authors will present an inquiry-based learning segment designed to teach the History correlated to the COVID-19 pandemic to a classroom of secondary education students. The inquiry template follows the standard C3 format utilized by the State of Connecticut. [ABSTRACT FROM AUTHOR]

The search for reliable tools to assess potential deleterious reactions in sulfide-bearing aggregates, is driven by recent field structure damage involving such types of aggregates. The paper presents a new test scheme with detailed procedures and limit criteria. Five aggregates, including pyrrhotite-bearing reactive ones (Connecticut and MSK 0.9), an alkali-silica reactive low-sulfur limestone (Spratt), a non-reactive control (PKA), and a low-sulfide aggregate (MSK 0.2), were used to manufacture concrete specimens that were subjected to the proposed test. The limit criterion, based on modulus of elasticity loss (RMoE), effectively distinguishes non-reactive, reactive, and potentially reactive aggregates. The scheme's reliability is validated against an existing protocol involving sulfur determination and mortar bar expansion testing. Four of five aggregates exhibit similar potential sulfide reactivity. Despite excessive mortar bar expansion, MSK 0.2, with traces of pyrrhotite, shows non-reactive behavior in concrete specimens after electrochemical treatment. The proposed test scheme offers a practical and timely tool for assessing sulfide-bearing aggregate reactivity before concrete production. [ABSTRACT FROM AUTHOR]

Background: Although Good Samaritan laws (GSLs) have been widely adopted throughout the United States, their efficacy in individual states is often unknown. This paper offers an approach for assessing the impact of GSLs and insight for policy-makers and public health officials who wish to know whether they should expect to see outcomes from similar policy interventions.Methods: Utilizing a system dynamics (SD) modeling approach, the research team conducted a policy evaluation to determine the impact of GSLs on opioid use disorder (OUD) in Connecticut and evaluated the GSL based upon the following health outcomes: (1) emergency department (ED) visits for overdose, (2) behavioral changes of bystanders, and (3) overdose deaths.Results: The simulation model suggests that Connecticut's GSL has not yet affected overdose deaths but has resulted in bystander behavioral changes, such as increased 911 calls for overdose. ED visits have increased as the number of opioid users has increased.Conclusions: The simulation results indicate that the number of opioid-related deaths will continue to increase and that the GSL alone cannot effectively control the crisis. However, the SD approach that was used will allow policymakers to evaluate the effectiveness of the GSL over time using a simulation framework. This SD model demonstrates great potential by producing simulations that allow policymakers to assess multiple strategies for combating the opioid crisis and select optimal public health interventions. [ABSTRACT FROM AUTHOR]

In this paper we propose a statistical modeling framework that contributes to advancing methods for modeling insurance policy premium in the actuarial literature. Specification of separate frequency and severity models, accounting for territorial risk and performing accurate inference, are some of the challenges an actuary faces while modeling policy premium. Policy premiums are characterized to follow a semi-continuous probability distribution, featuring a non-zero probability at zero along with a positive continuous support. Interpretability is a concern when quantifying unobserved risks premiums face from spatial variation. Commonly used strategies in the literature are known to successfully quantify this risk, but do not necessarily produce interpretable estimates. Resorting to frequency-severity models leaves the actuary indecisive about the specification of covariates and spatial effects. The novelty of our proposed approach lies in the development of a parsimonious and interpretable zero-adjusted modeling framework that allows for joint estimation of the effect of policy and individual characteristics on the mean premium and dispersion, while quantifying spatial variability in the mean model. The developed methods are applied to a database featuring premiums arising from the collision coverage in insurance policies for motor vehicles within the state of Connecticut, USA, for the year 2008. [ABSTRACT FROM AUTHOR]

Demand response (DR) program empowers the dynamic prices to actively optimize the consumption. This optimized consumption plays a vital role in resolving the complex operation and reliability issues in the electricity market. The human behavior aspect of consumers explained by several models that have been reported in the literature. These models depend on the classical utility factor. The effect of price on the consumer's decision in the field of energy efficiency and reduction of consumption based on behavioral characteristics are two important aspects of DR programs. In absence of such characteristics, results become non‐viable. In this paper, the footprint of two time‐based DR programs is explored on the peak reduction namely; Real‐Time Pricing (RTP) and Peak Time Rebate (PTR). Artificial Neural Network (ANN) based topologies for two DR programs are proposed. The proposed topologies employ variation in demand and price, subsequently for simulating an online DR simulator. Demand before and after the RTP and PTR was calculated and compared with four ANN‐based DR topologies namely; Radial Basis Function Neural Network‐Demand Response (RBFN‐DR), Feedforward Backprop‐Demand Response (FFBP‐DR), Layer Recurrent‐Demand Response (LR‐DR), and Generalized Regression‐Demand Response (GR‐DR). The proposed models are tested on three test cases. The first case tested on hourly data of New England ISO of Connecticut on August 18, 2014, the second case tested on hourly data of the same system on August 18, 2020, and the third case tested on hourly residential data of test smart grid. By assessing the results from all three test cases, depicted that RBFN‐DR proved its efficacy by giving better results for both price‐based programs namely; RTP and PTR. [ABSTRACT FROM AUTHOR]

To support public health policymakers in Connecticut, we developed a flexible county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, and estimates of important features of disease transmission and clinical progression. In this paper, we outline the model design, implementation and calibration, and describe how projections and estimates were used to meet the changing requirements of policymakers and officials in Connecticut from March 2020 to February 2021. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We calibrated this model to data on deaths and hospitalizations and developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut. [ABSTRACT FROM AUTHOR]

Objective: To compare the efficacy of gentamicin and steroids in patients with Meniere's disease (MD). Methods: The study protocol was structured in accordance with the Population, Intervention, Comparison and Outcome (PICO) framework. We used "Endnote" (Thomson ResearchSoft; Stanford, Connecticut, the United States) to retrieve database documents. A systematic literature search of Web of Science and PubMed Database through May 2020 was conducted using specific search terms. Inclusion criteria, exclusion criteria, vertigo improvement, and exact hearing changes were defined. Review Manager 5.3 (Cochrane) was used for data analysis. Results: A total of 80 articles were searched from Web of Science and 23 articles were searched from PubMed database. After further reviewing the specific points of the inclusion criteria and exclusion criteria, 9 papers were finally selected. Intratympanic gentamicin (ITG) was superior to intratympanic steroid (ITS) in reducing the number of vertigo attacks (odds ratio (OR) 3.08, 95% confidence intervals (CI) [2.05–3.65]), probability (P) < 0.01). As for hearing improvement, we did the analysis twice. With all the studies included, the odds of hearing improvement (by "x") was 0.31 among patients receiving gentamicin compared to those treated by steroids (OR 0.31, 95% CI [0.16–0.61]). But after removing one study with outlying results from the analysis, this effect disappeared. Hearing loss of ITG and ITS was not statistically significant (P = 0.29). Conclusions: ITG is superior to ITS in reducing the number of vertigo attacks in the treatment of MD (P < 0.01); the impact on hearing is more heterogeneous in the studies. Overall, there was no clear difference between ITG and ITS on hearing improvement and on hearing loss. [ABSTRACT FROM AUTHOR]

• Using probe data, we estimated the traffic density information on limited access highways. • We used a mixed effect binomial model to link the odds of speeding to traffic density, roadway characteristics, and COVID-19 phases. • We analyzed and compared speeding in two U.S. states, Maine, and Connecticut. • We found that a better level of service such as A, or B (low density) results in higher odds of speeding. • We found that speeding substantially increased during COVID-19 stay-at-home orders and continued to happen even one year after the orders. Stay-at-home orders - imposed to prevent the spread of COVID-19 - drastically changed the way highways operate. Despite lower traffic volumes during these times, the rate of fatal and serious injury crashes increased significantly across the United States due to increased speeding on roads with less traffic congestion and lower levels of speed enforcement. This paper uses a mixed effect binomial regression model to investigate the impact of stay-at-home orders on odds of speeding on urban limited access highway segments in Maine and Connecticut. This paper also establishes a link between traffic density and the odds of speeding. For this purpose, hourly speed and volume probe data were collected on limited access highway segments for the U.S. states of Maine and Connecticut to estimate the traffic density. The traffic density was then combined with the roadway geometric characteristics, speed limit, as well as dummy variables denoting the time of the week, time of the day, COVID-19 phases (before, during and after stay-at-home order), and the interactions between them. Density, represented in the model as Level of Service, was found to be associated with the odds of speeding, with better levels of service such as A, or B (low density) resulting in the higher odds that drivers would speed. We also found that narrower shoulder width could result in lower odds of speeding. Furthermore, we found that during the stay-at-home order, the odds of speeding by more than 10, 15, and 20 mph increased respectively by 54%, 71% and 85% in Connecticut, and by 15%, 36%, and 65% in Maine during evening peak hours. Additionally, one year after the onset of the pandemic, during evening peak hours, the odds of speeding greater than 10, 15, and 20 mph were still 35%, 29%, and 19% greater in Connecticut and 35% 35% and 20% greater in Maine compared to before pandemic. [ABSTRACT FROM AUTHOR]

• Despite reform efforts, the juvenile justice system is permeated with inequities. • Partnering with stakeholders, we developed an integrated model of juvenile justice. • The model layers public health, socioecological and restorative justice frameworks. • Applied examples illustrate how this approach can benefit youth and communities. • Recommendations for engaging in community-driven reform efforts are provided. Approaches to juvenile justice have undergone significant change in recent years, shifting away from retributive models and toward greater focus on rehabilitation and recognition of the unique developmental needs of youth. However, the juvenile justice system is still permeated with significant challenges, including the overrepresentation of vulnerable youth, particularly youth of color, and limited community-based interventions for youth at risk for justice involvement. Current models of juvenile justice do not fully address the needs of at-risk and justice involved youth, and have struggled to effectively reduce racial and ethnic disparities within the system, suggesting there is a need for a model that is better able to conceptualize and respond to youths' needs. The purpose of this paper is to describe the Integrated Model of Juvenile Justice (IMJJ), a novel conceptual approach to juvenile justice reform, which integrates the public health, social-ecological, and restorative justice models. We worked collaboratively with justice system stakeholders in Connecticut to develop the IMJJ, which proposes that juvenile justice practice must take into account and provide for youths' multi-level needs. Through an applied example, this paper describes how the IMJJ can be utilized to target youth offending and promote equitable treatment of youth in the juvenile justice system. Recommendations for justice system stakeholders are provided and implications for policy and practice are discussed. [ABSTRACT FROM AUTHOR]

• A framework for characterizing inundation depth (ID) during compound events is presented. • Relative importance of surge and inland flows in controlling flood ID is quantified. • A new topographic index is introduced to capture the variability of flood ID from river to coast. • With the index areas are identified where compound events are more prominent. Storm surge and river runoff can result in compound flooding in coastal areas. The impact of these events can be more significant than that generated by each component individually. This paper describes a framework for characterizing flooding during compound events and quantifying the relative importance of surge and river flows in controlling inundation depth (ID). For the analysis, we considered 1051 simulations of historical flood events covering about 40 years in Connecticut (CT), USA, and several simulated storms associated with synthetic climate scenarios. We simulated river discharge time series for each event using a physically based distributed hydrological model and retrieved the storm surge from tidal stations. These time series are used as upstream and downstream boundary conditions in 2D hydrodynamic simulation. We focused the analysis on seven locations along the coast of Connecticut covered by LiDAR-derived 1 m DEM. To capture the variability of inundation characteristics over the full-scale gradient from river to coast, we introduced a new topographic index, D-Index, that distinguishes topologic variabilities. Results from this study highlight that there is a correlation of ID to different drivers in distinct categories of the D-Index, which can correctly label the main source of hazard, either coast, river, or compound. We identified thresholds of standard deviation (σ) of the D-Index to identify areas where ID strongly correlates with either flow peak and volume, surge peak, or a combination of both. The study also shows that it is possible to use the results obtained from the 1 m analysis to generalize the findings for the entire CT coast to derive zones dominated by surge, river flow, or the compound effect of the two. This study can help coastal communities better understand the risk of the compound impacts of coastal storms, which is essential for increasing climate resilience. [ABSTRACT FROM AUTHOR]

This paper aims to understand whether outbreaks of Covid-19 cases may increase after major storms due to the displacement and movement of a large number of people affected by fallen trees, power lines, and resultant power outages. After Tropical Storm Isais caused widespread power outages in Connecticut (CT), we compiled hourly power outage data, and daily COVID-19 cases for 21 days before and after the storm. First, we identified towns with statistically significant increasing trends of daily COVID-19 cases before and after the storm using time series functions. Using a retrospective space-time scan statistic, we further identified COVID-19 space-time clusters that were active (i.e., more observed than expected cases) before and after the storm. Second, we measured the movement of people from cell phone data aggregated by Safegraph. Third, we measured statistical associations between power outages and movement of people with the increase in COVID-19 cases. Results showed positive associations between power outages and COVTD-19 cases increase. It may be possible to predict the location of future outbreaks after major storms using power outage information. We can estimate times to power restoration by locations using outage predictions and then direct appropriate public health interventions such as increased testing and contact tracing when people return to their homes and businesses to prevent future disease outbreaks. This paper has the potential for advancing the understanding of coupled human-environment systems through an investigation of the relationships between extreme weather events, environmental management, and human health. [ABSTRACT FROM AUTHOR]

Due to the variability of working conditions and the scarcity of fault samples, the existing diagnosis models still have a big gap under the condition of covering more practical application scenarios. Therefore, it is of great significance to study an intelligent diagnosis scheme that takes few samples in the training source domain and zero samples in the test target domain (FST-ZST) into account. A Brownian correlation metric prototypical network (BCMPN) algorithm based on a multi-scale mask preprocessing mechanism is proposed for the above problem. First, this paper constructs a multi-scale mask preprocessing mechanism (MMP) to improve the optimization starting point. Second, the multi-scale feature embedding is realized through the dilation convolution module and the effective light channel attention (ELCA) module. Third, based on the Brownian distance similarity measurement, we learn the feature representation by measuring the difference between the joint feature function and the edge product in the field of diagnosis. Finally, based on the gear data set of the Connecticut university (UConn) and the data collected in the laboratory, it is proved that the BCMPN has better performance in the problem of FST-ZST. [ABSTRACT FROM AUTHOR]

The legal status of cannabis is complex and varies between state and federal governments. While cannabis remains an illegal schedule I substance under federal law, 33 states in the United States allow the use of medical cannabis. Connecticut has legalized use of medical cannabis and partially decriminalized recreational use of cannabis. In this survey-based study, we seek to evaluate legal concerns and perceptions among women with gynecologic malignancies using prescribed medical cannabis through licensed dispensaries in Connecticut. A 43-item paper survey with items exploring legal concerns and perceptions of medical and recreational cannabis was administered to women with gynecologic malignancies who use prescribed medical cannabis through licensed dispensaries in Connecticut. Thirty-six eligible patients were approached for consent, and 31 patients returned completed surveys (86%). Forty-one percent (n=13) of patients had used medical marijuana for greater than 6 months, 16% (n=5) between 3 and 6 months, and 41% (n=13) less than 3 months. Thirty-two percent (n=10) reported using medical marijuana 6-7 days/week, 32% (n=10) 3-5 days/week, and 29% (n=9) 1-2 days/week or less. Thirty-two percent of patients (n=10) had concerns about the legality of medical cannabis at the state or federal level. Twenty-five percent (n=8) reported concern about traveling to a state where medical cannabis was not legal. Of the subset of participants that expressed concern about traveling, half (n=4) reported they would bring medical cannabis regardless of legal ramifications. When asked whether medical cannabis was legal in Connecticut, 77% (n=24) responded 'yes,' 12% (n=4) responded 'no,' and 6% (n=2) were unsure. When the same question was posed about recreational cannabis, 3% (n=1) responded 'yes,' 80% (n=25) responded 'no,' and 12% (n=4) were unsure. Nineteen percent (n=6) were unsure if use of medical cannabis was allowed in their home (i.e., for those living in rental housing). Patients with gynecologic malignancies using prescribed medical cannabis are concerned about the legality of medical cannabis. One fifth of users were unsure of the legal status of medical cannabis or incorrectly thought medical cannabis was not legal in Connecticut. It is possible that such misconceptions are also present among non-users and may hinder patient interest in utilization of medical cannabis. About one fifth of patients were unsure whether the use of medical cannabis was permitted at home, which is an important topic for providers to address as permissibility of medical cannabis is at the discretion of the landlord. Providers should be knowledgeable about the legal status of medical cannabis in their location and educate patients accordingly. [ABSTRACT FROM AUTHOR]

• A machine learning outage prediction model (OPM) capable of predicting power outages at a circuit (the operational units of the power distribution network) resolution is developed to understand the impact of enhanced vegetation management standards on outages in the electrical distribution grid during storm events. • The authors find good matching between the increase in enhanced vegetation management (ETT) and reduction in trouble spots over the years from 2005 to 2019. • Most of the vegetation management on the electric grid was completed over the last five years of the study (2015–2019), and during those five years the OPM estimates annual reductions of trouble spots between 25.7 and 42.5 percent due to the tree trimming performed. • A novel feature of the OPM developed in this study is the ability to dynamically update it with different vegetation management scenarios at the circuit level and predict the effect of each scenario on trouble spots in the electric grid for storm events. • The authors also find increased prediction accuracy (a reduction in mean absolute percentage error of 4.1% and 5.2% for the two models trained) when including vegetation management data as an input variable to predict trouble spots in the electric grid. This paper develops a machine learning outage prediction model (OPM) to serve as a simulation framework capable of quantifying the reduction in damages to the distribution electric grid due to vegetation management for storm events. The model covers the Eversource Energy distribution grid territory in Connecticut and uses a random forest model with input variables for vegetation, vegetation management, land cover, drought, elevation, weather and electrical infrastructure to predict outages for each circuit (the operational units of the power distribution network). The model is trained on 165 storms from the years 2005 to 2019. The results show that over the last five years of the study (2015–2019) the annual reduction in trouble spots in the electric grid due to enhanced tree trimming is between 25.7 and 42.5% and there is good matching between increased trouble spot reduction and increased vegetation management. Further, we demonstrate improved outage predictions when including vegetation management data as an input variable, with a 4.1% reduction in mean absolute percentage error in leave-one-storm-out cross-validation. This framework could be used to examine varying vegetation management scenarios and the results should be useful for decision makers such as utilities, municipalities and regulators in optimizing vegetation management and broader grid resilience enhancement plans. [ABSTRACT FROM AUTHOR]