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This meta-analytic study examined the associations between child-father attachment in early childhood and children's externalizing and internalizing behavior problems. Based on 15 samples (N = 1,304 dyads), the association between child-father attachment insecurity and externalizing behaviors was significant and moderate in magnitude (r = 0.18, 95% CI: 0.10, 0.27 or d = 0.37, 95% CI: 0.20, 0.55). No moderators of this association were identified. Based on 12 samples (N = 1,073), the association between child-father attachment insecurity and internalizing behaviors was also significant, albeit smaller in magnitude (r = 0.09, 95% CI: 0.02, 0.15; or d = 0.17, 95% CI: 0.03, 0.31). Between-study heterogeneity was insufficient to consider moderators. When compared to the effect sizes of prior meta-analyses on child-mother attachment and behavior problems, the quality of the attachment relationship with fathers yields a similar magnitude of associations to children's externalizing and internalizing behaviors. Results support the need to consider the role of the attachment network, which notably includes attachment relationships to both fathers and mothers, to understand how attachment relationships contribute to child development.

In this study, we report on the development and psychometric evaluation of the Risk-Taking (RT) and Self-Harm (SH) Inventory for Adolescents (RTSHIA), a self-report measure designed to assess adolescent RT and SH in community and clinical settings. 651 young people from secondary schools in England ranging in age from 11.6 years to 18.7 years and 71 young people referred to mental health services for SH behavior in London between the ages of 11.9 years and 17.5 years completed the RTSHIA along with standardized measures of adolescent psychopathology. Two factors emerged from the principal axis factoring, and RT and SH were further validated by a confirmatory factor analysis as related, but different, constructs, rather than elements of a single continuum. Inter-item and test-retest reliabilities were high for both components (Cronbach's [alpha = 0.85, r[subscript tt] = 0.90; Cronbach's [alpha] 0.93, r[subscript tt] = 0.87), and considerable evidence emerged in support of the measure's convergent, concurrent, and divergent validity. The findings are discussed with regard to potential usefulness of the RTSHIA for research and clinical purposes with adolescents. (Contains 1 figure and 7 tables.)

The development of low stress input/output (I/O) pin attachment structures for glass-ceramic modules in mainframe computers is discussed. The use of these I/O pin structures in conjunction with stress reducing soft metal cushion layer can reduce the stress of the pin joint and brazing alloy significantly and protect the glass-ceramic substrate from fractures. Low modulus and low yield strength solder could also contribute to the production of low stress pin joint.

Presents a study which examined the combined effects of composition, average grain size, and the thickness of the passivating overlayer in order to assess their relative importance in determining the rate of electromigration in aluminum based conductors. Significance of the variations in time to failure data for electromigration process; Motive for the application of temperature-ramp resistance analysis; Effects of physical properties.

School reopenings in 2021 and 2022 coincided with the rapid emergence of new SARS-CoV-2 variants in the United States. In-school mitigation efforts varied, depending on local COVID-19 mandates and resources. Using a stochastic age-stratified agent-based model of SARS-CoV-2 transmission, we estimate the impacts of multiple in-school strategies on both infection rates and absenteeism, relative to a baseline scenario in which only symptomatic cases are tested and positive tests trigger a 10-day isolation of the case and 10-day quarantine of their household and classroom. We find that monthly asymptomatic screening coupled with the 10-day isolation and quarantine period is expected to avert 55.4% of infections while increasing absenteeism by 104.3%. Replacing quarantine with test-to-stay would reduce absenteeism by 66.3% (while hardly impacting infection rates), but would require roughly 10-fold more testing resources. Alternatively, vaccination or mask wearing by 50% of the student body is expected to avert 54.1% or 43.1% of infections while decreasing absenteeism by 34.1% or 27.4%, respectively. Separating students into classrooms based on mask usage is expected to reduce infection risks among those who wear masks (by 23.1%), exacerbate risks among those who do not (by 27.8%), but have little impact on overall risk. A combined strategy of monthly screening, household and classroom quarantine, a 50% vaccination rate, and a 50% masking rate (in mixed classrooms) is expected to avert 81.7% of infections while increasing absenteeism by 90.6%. During future public health emergencies, such analyses can inform the rapid design of resource-constrained strategies that mitigate both public health and educational risks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Colleges and universities in the US struggled to provide safe in-person education throughout the COVID-19 pandemic. Testing coupled with isolation is a nimble intervention strategy that can be tailored to mitigate the changing health and economic risks associated with SARS-CoV-2. We developed a decision-support tool to aid in the design of university-based screening strategies using a mathematical model of SARS-CoV-2 transmission. Applying this framework to a large public university reopening in the fall of 2021 with a 60% student vaccination rate, we find that the optimal strategy, in terms of health and economic costs, is twice weekly antigen testing of all students. This strategy provides a 95% guarantee that, throughout the fall semester, case counts would not exceed twice the CDC's original high transmission threshold of 100 cases per 100k persons over 7 days. As the virus and our medical armament continue to evolve, testing will remain a flexible tool for managing risks and keeping campuses open. We have implemented this model as an online tool to facilitate the design of testing strategies that adjust for COVID-19 conditions as well as campus-specific populations, resources, and priorities., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

In response to COVID-19, schools across the United States closed in early 2020; many did not fully reopen until late 2021. Although regular testing of asymptomatic students, teachers, and staff can reduce transmission risks, few school systems consistently used proactive testing to safeguard return to classrooms. Socioeconomically diverse public school districts might vary testing levels across campuses to ensure fair, effective use of limited resources. We describe a test allocation approach to reduce overall infections and disparities across school districts. Using a model of SARS-CoV-2 transmission in schools fit to data from a large metropolitan school district in Texas, we reduced incidence between the highest and lowest risk schools from a 5.6-fold difference under proportional test allocation to 1.8-fold difference under our optimized test allocation. This approach provides a roadmap to help school districts deploy proactive testing and mitigate risks of future SARS-CoV-2 variants and other pathogen threats.

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities., Competing Interests: The authors declare no competing interest., (Copyright © 2022 the Author(s). Published by PNAS.)

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France's ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As proof-of-concept, we describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in a large US city (Austin, Texas) since May 2020. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.

The novel coronavirus (SARS-CoV-2) emerged in late 2019 and spread globally in early 2020. Initial reports suggested the associated disease, COVID-19, produced rapid epidemic growth and caused high mortality. As the virus sparked local epidemics in new communities, health systems and policy makers were forced to make decisions with limited information about the spread of the disease. We developed a compartmental model to project COVID-19 healthcare demands that combined information regarding SARS-CoV-2 transmission dynamics from international reports with local COVID-19 hospital census data to support response efforts in three Metropolitan Statistical Areas (MSAs) in Texas, USA: Austin-Round Rock, Houston-The Woodlands-Sugar Land, and Beaumont-Port Arthur. Our model projects that strict stay-home orders and other social distancing measures could suppress the spread of the pandemic. Our capacity to provide rapid decision-support in response to emerging threats depends on access to data, validated modeling approaches, careful uncertainty quantification, and adequate computational resources.

Following the April 16, 2020 release of the Opening Up America Again guidelines for relaxing coronavirus disease 2019 (COVID-19) social distancing policies, local leaders are concerned about future pandemic waves and lack robust strategies for tracking and suppressing transmission. Here, we present a strategy for triggering short-term shelter-in-place orders when hospital admissions surpass a threshold. We use stochastic optimization to derive triggers that ensure hospital surges will not exceed local capacity and lockdowns are as short as possible. For example, Austin, Texas-the fastest-growing large city in the United States-has adopted a COVID-19 response strategy based on this method. Assuming that the relaxation of social distancing increases the risk of infection sixfold, the optimal strategy will trigger a total of 135 d (90% prediction interval: 126 d to 141 d) of sheltering, allow schools to open in the fall, and result in an expected 2,929 deaths (90% prediction interval: 2,837 to 3,026) by September 2021, which is 29% of the annual mortality rate. In the months ahead, policy makers are likely to face difficult choices, and the extent of public restraint and cocooning of vulnerable populations may save or cost thousands of lives., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Phenology and time of flowering are crucial determinants of rice adaptation to climate variation. A previous study characterized flowering responses of 203 diverse indica rices (the ORYTAGE panel) to ten environments in Senegal (six sowing dates) and Madagascar (two years and two altitudes) under irrigation in the field. This study used the physiological phenology model RIDEV V2 to heuristically estimate component traits of flowering such as cardinal temperatures (base temperature (Tbase) and optimum temperature), basic vegetative phase, photoperiod sensitivity and cold acclimation, and to conduct a genome-wide association study for these traits using 16 232 anonymous single-nucleotide polymorphism (SNP) markers. The RIDEV model after genotypic parameter optimization explained 96% of variation in time to flowering for Senegal alone and 91% for Senegal and Madagascar combined. The latter was improved to 94% by including an acclimation parameter reducing Tbase when the crop experienced low temperatures during early vegetative development. Eighteen significant (P<1.0 × 10-5) quantitative trait loci (QTLs) were identified, namely ten for RIDEV parameters and eight for climatic index variables (difference in time to flowering between key environments). Co-localization of QTLs for different traits were rare. RIDEV parameters gave QTLs that were mostly more significant and distinct from QTLs for index variables. Candidate genes were investigated within the estimated 50% linkage disequilibrium regions of 39 kB. In addition to several known flowering network genes, they included genes related to thermal stress adaptation and epigenetic control mechanisms. The peak SNP for a QTL for the crop parameter Tbase (P=2.0 × 10-7) was located within HD3a, a florigen that was recently identified as implicated in flowering under cool conditions., (© The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Low night and high day temperatures during sensitive reproductive stages cause spikelet sterility in rice. Phenotyping of tolerance traits in the field is difficult because of temporal interactions with phenology and organ temperature differing from ambient. Physiological models can be used to separate these effects. A 203-accession indica rice diversity panel was phenotyped for sterility in ten environments in Senegal and Madagascar and climate data were recorded. Here we report on sterility responses while a companion study reported on phenology. The objectives were to improve the RIDEV model of rice thermal sterility, to estimate response traits by fitting model parameters, and to link the response traits to genomic regions through genome-wide association studies (GWAS). RIDEV captured 64% of variation of sterility when cold acclimation during vegetative stage was simulated, but only 38% when it was not. The RIDEV parameters gave more and stronger quantitative trait loci (QTLs) than index variables derived more directly from observation. The 15 QTLs identified at P<1 × 10-5 (33 at P<1 × 10-4) were related to sterility effects of heat, cold, cold acclimation, or unexplained causes (baseline sterility). Nine annotated genes were found on average within the 50% linkage disequilibrium (LD) region. Among them, one to five plausible candidate genes per QTL were identified based on known expression profiles (organ, stage, stress factors) and function. Meiosis-, development- and flowering-related genes were frequent, as well a stress signaling kinases and transcription factors. Putative epigenetic factors such as DNA methylases or histone-related genes were frequent in cold-acclimation QTLs, and positive-effect alleles were frequent in cold-tolerant highland rice from Madagascar. The results indicate that epigenetic control of acclimation may be important in indica rice genotypes adapted to cool environments., (© The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

This study aimed to develop a superovulation protocol for the spiny mouse (Acomys cahirinus). The spiny mouse is a desert-adapted rodent species, with a long oestrus cycle (11 days) compared with rat and mouse, and gives birth to few (mean litter size is 3) precocial offspring after a relatively long gestation (39 days). We successfully optimised a superovulation protocol that elicited a 5-fold increase in the normal ovulation rate of this species. To induce superovulation in the spiny mouse 2 injections of equine chorionic gonadotrophin (eCG, 10 IU each), 9h apart, were required, followed by 20 IU of human chorionic gonadotrophin (hCG). This protocol was successful in 100% of females trialed and at 33 h post-hCG an average of 14.7 ± 1.5, 1-2 cell embryos were recovered. Histological analysis of ovaries following superovulation revealed large corpus lutea and post-ovulatory follicles occupying a large part of the ovary. Ovulation commenced 6-12 h after the hCG injection and continued until 24-33 h post-hCG as indicated by both histological analysis of ovaries and the presence of oocytes/embryos in the oviduct. This superovulation protocol will facilitate the development of an in vitro culture system for spiny mouse embryos.