In-person and cyber dating abuse: A longitudinal investigation

While studies have identified associations between cyber and in-person dating abuse, most research has relied on cross-sectional data, limiting the ability to determine temporality. This study tested the longitudinal associations between cyber and physical and psychological forms of in-person dating abuse. Data were from an ongoing longitudinal study following a group of high school students originally recruited in Southeast Texas, US, into their young adulthood. Three waves of data (Waves 4–6) were used, with each wave collected one year apart. At Wave 4, participants’ age ranged from 16 years to 20 years (mean = 18.1, median = 18.0, SD = .78). The analytical sample consisted of 879 adolescents/young adults (59% female, 41% male; 32% Hispanics, 28% Black, 29% White, and 11% other) who completed the dating abuse questions. Cross-lagged panel analysis showed that dating abuse victimization and perpetration were predictive of subsequent dating abuse of the same type. Cyber dating abuse perpetration was found to predict subsequent physical dating abuse perpetration as well as physical dating abuse victimization, but not vice versa. Further, cyber dating abuse perpetration predicted psychological dating abuse victimization, but not vice versa. Cyber dating abuse victimization was not significantly associated with either physical or psychological dating abuse temporally. Overall, findings suggest that cyber dating abuse perpetration may be a risk marker for both physical and psychological forms of in-person dating abuse. Interventions may benefit from targeting cyber dating abuse perpetration as means to prevent in-person dating abuse.

Regional county-level housing inventory predictions and the effects on hurricane risk

Regional hurricane risk is often assessed assuming a static housing inventory, yet a region’s housing inventory changes continually. Failing to include changes in the built environment in hurricane risk modeling can substantially underestimate expected losses. This study uses publicly available data and a long short-term memory (LSTM) neural network model to forecast the annual number of housing units for each of 1,000 individual counties in the southeastern United States over the next 20 years. When evaluated using testing data, the estimated number of housing units was almost always (97.3 % of the time), no more than one percentage point different than the observed number, predictive errors that are acceptable for most practical purposes. Comparisons suggest the LSTM outperforms ARIMA and simpler linear trend models. The housing unit projections can help facilitate a quantification of changes in future expected losses and other impacts caused by hurricanes. For example, this study finds that if a hurricane with similar characteristics as Hurricane Harvey were to impact southeast Texas in 20 years, the residential property and flood losses would be nearly US$4 billion (38 %) greater due to the expected increase of 1.3 million new housing units (41 %) in the region.

Investigation of Contributing Factors to Traffic Crash Severity in Southeast Texas Using Multiple Correspondence Analysis

Driving is the essential means of travel in Southeast Texas, a highly urbanized and populous area that serves as an economic powerhouse of the whole state. However, driving in Southeast Texas is subject to many risks as this region features a typical humid subtropical climate with long hot summers and short mild winters. Local drivers would encounter intense precipitation, heavy fog, strong sunlight, standing water, slick road surface, and even frequent extreme weather such as tropical storms, hurricanes and flood during their year-around travels. Meanwhile, research has revealed that the fatality rate per 100 million vehicle miles driven in urban Texas became considerably higher than national average since 2010, and no conclusive study has elucidated the association between Southeast Texas crash severity and potential contributing factors. This study used multiple correspondence analysis (MCA) to examine a group of contributing factors on how their combinatorial influences determine crash severity by creating combination clouds on a factor map. Results revealed numerous significant combinatorial effects. For example, driving in rain and extreme weather on a wet road surface has a higher chance in causing crashes that incur severe or deadly injuries. Besides, other contributing factors involving risky behavioral factors, road designs, and vehicle factors were well discussed. The research outcomes could inspire local traffic administration to take more effective countermeasures to systematically mitigate road crash severity.

Examining the Validity and Reliability of Measures for Individual-Level Constructs related to Implementation of School-Based Physical Activity Approaches

Valid and reliable measures are important to understanding the implementation of physical activity approaches in schools. The study purpose is to examine the psychometric properties of measures of individual-level constructs (knowledge, attitudes, outcome expectations, self-efficacy, innovativeness, and support) in the context of implementing school-based physical activity approaches. We collected data from a sample of elementary school employees (administrators, classroom teachers, physical educators, and support staff) from an urban school district in southeast Texas. Confirmatory factor analysis (CFA) models were used to examine structural validity. We also examined correlations between constructs to assess discriminant and convergent validity. Last, we used a CFA-based approach to examine point estimates for reliability. The analytic sample consisted of 205 employees. CFA results for each individual measure revealed good-fitting models for most measures (χ2(df)>0.05, RMSEA<0.08, CFI>0.90, TLI>0.90, SRMR≤0.07). A combined model that included all the measures also indicated good fit across indices: χ2(306)=485, p<0.001; RMSEA=0.05, CFI=0.93, TLI=0.92, SRMR=0.07. All correlations between constructs were <0.70, and all but one construct (innovativeness) demonstrated moderate correlations with support for classroom-based physical activity approaches (>0.30). In addition, reliability point estimates were all >0.70. The measures tested in this study were found to have good reliability, as well as good structural, discriminant, and convergent validity. These measures are useful in efforts to better understand how individual-level constructs relate to implementation behaviors for physical activity approaches in schools.

Characterization of a Dengue Virus Serotype 1 Isolated from a Patient in Ciudad Juarez, Mexico

Dengue (DEN) is the most important human arboviral disease worldwide. Sporadic outbreaks of DEN have been reported since 1980 in urban communities located along the border in southeast Texas and northern Mexico. Other than the Rio Grande Valley region of TX, autochthonous transmission of DENV has not been reported from any other US border communities. As part of a surveillance program for arthropod-borne viruses in Ciudad Juarez, Mexico, during November 2015, a blood sample was obtained from a female patient who experienced an undifferentiated fever and arthralgia. The plasma of the sample was tested for virus in Vero-76 and C6/36 cells. DENV serotype 1 (DENV-1) was isolated in the C6/36 cells, and nucleotide sequencing of the envelope gene and full genome grouped the DENV-1 isolate in the Central America clade. The patient had not traveled outside of Ciudad Juarez, Mexico, thus suggesting DENV-1 infection was acquired in this community.

We Are Together Now: Notes on the Film Hoktiwe: Two Poems in Ishakkoy

Artist and historian of the Atakapa-Ishak Nation, Jeffery Darensbourg’s 2020 film with Fernando López features poetry in Ishakkoy, an indigenous language from what is now southwest Louisiana and southeast Texas, composed during an artist residency at A Studio in the Woods. The companion essay shares some of the process of composing creative works in this language, and especially of writing centos, also known as patchwork or collage poems, during COVID-19 sequestration.