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Despite consistent evidence highlighting the benefits of exclusive breastfeeding (EBF) for the first six months, EBF duration sometimes falls short of six months or exceeds it. This research seeks to explore factors influencing the practice of optimal duration of EBF and identify factors associated with suboptimal EBF durations. A cross-sectional survey was conducted in 16 districts across four Northern regions of Ghana with 2000 caregivers of children under five years old. The analysis specifically focused on a subset of 1761 biological mothers. Logistic regression was conducted to discern socio-demographic, care-seeking, and behavioral determinants influencing the practice of EBF for a six-month duration. Additionally, multinomial logistic regression was used to identify factors associated with suboptimal EBF durations, categorized as less than and beyond six months. All biological mothers breastfed their youngest child, varying in durations: 75% exclusively breastfed for six months, 19.2% for less than six months, and 5.9% for beyond six months. Several behavioral determinants influenced EBF for six months, with biological mothers ' confidence in their ability to EBF (aOR: 6.8 95% CI, 4.13-11.33), willingness to recommend EBF practice to friends and family (aOR: 10.5 95% CI, 4.13-26.62) and perception of practicing EBF as normative in the community showing statistically significant associations (aOR: 6.3, 95% CI, 4.41-9.10). Education and religion of biological mothers were also significantly associated with EBF for six months. While there was overlap in behavioral factors associated with EBF for less than and beyond six months, the socio-demographic characteristics of biological mothers associated with these suboptimal durations of EBF differed. Among the factors included in this study, social norms, caregiver confidence, and approval of EBF were key factors influencing the recommended duration of EBF practices. These findings highlight the importance of community and cultural contexts in shaping biological mothers' behavior and emphasize the need to address socio-cultural barriers and facilitators that influence EBF practices, as well as empower biological mothers to adopt and sustain these practices.

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure-property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co3O4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the size-dependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an onset radius, which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

Our study examined how students' perceived teacher beliefs and classroom goal structures, gender (of teachers and students) and own perceived talent, controlling for prior achievements, together explained motivational outcomes of students' achievement goals, intrinsic value and enrollment choices in mathematics and English. Participants were 1086 grades 9-11 students (respective Ns = 380, 369, 337) from 3 coeducational middle-class schools in metropolitan Sydney, Australia. Hierarchical linear modeling revealed student-perceived teacher beliefs as the most consistent predictor of motivational outcomes in mathematics and English, over and above the effects of other measured influences. Perceived teacher beliefs moderated the effects of classroom goal structures, as well as relationships of gender with motivational outcomes in English. Grade-level effects were more positive among older students which coincided with the grade 11 transition.

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is "out-of-distribution" from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

After the use of facemasks, other isolation measures enacted during the SARS-CoV-2 pandemic were lifted, respiratory pathogens, such as RSV, reappeared, but until the November 2023 WHO alert for China, M. pneumoniae had virtually disappeared. After observing a similar reappearance in our hospital, a retrospective analysis of the number of positive M. pneumoniae tests. Between 2018 and December 2023, 1619 PCR tests were ordered and 43 (2.6%) of them were positive. Two outbreaks, one in 2018 and one in 2023, accounted for the majority of cases. Tests were usually ordered in an outpatient setting (53.54%, n = 23) and most of them were paediatric patients with a mean age (sd) of 10.2 (6.2) years. As for the severity of the cases, in the 2018 outbreak, of 15 children who tested positive, 53.3% (n = 8) were admitted to the ward and 6.7% (n = 1) at the intensive care unit. Whereas in 2023, 2 patients were tested in the ward (10.5%) and one in the intensive care unit (5.2%) from a total of 19 patients. The positive rate in 2023 was significantly higher in comparison with years 2020, 2021 and 2022 and significantly lower in comparison with 2018 (P-value=0.003). The outbreak in late 2023 can be explained by the seasonality of Mycoplasma pneumonia alone, which has shown outbreaks every 3–5 years, and it does not appear to be more severe than the previous one.

Abstract BackgroundAs the vaccination campaign against COVID-19 progresses, it becomes crucial to comprehend the lasting effects of vaccination on safeguarding against new infections or reinfections. ObjectiveThis study aimed to assess the risk of new SARS-CoV-2 infections based on the number of vaccine doses, prior infections, and other clinical characteristics. MethodsWe defined a cohort of 800 health care workers in a 24-month study (March 2020 to December 2022) in northern Barcelona to determine new infections by SARS-CoV-2. We used extended Cox models, specifically Andersen-Gill (AG) and Prentice-Williams-Peterson, and we examined the risk of new infections. The AG model incorporated variables such as sex, age, job title, number of chronic conditions, vaccine doses, and prior infections. Additionally, 2 Prentice-Williams-Peterson models were adjusted, one for those individuals with no or 1 infection and another for those with 2 or 3 infections, both with the same covariates as the AG model. ResultsThe 800 participants (n=605, 75.6% women) received 1, 2, 3, and 4 doses of the vaccine. Compared to those who were unvaccinated, the number of vaccine doses significantly reduced (PPPPPP ConclusionsThe study suggests that both prior infections and vaccination status significantly contribute to SARS-CoV-2 immunity, supporting vaccine effectiveness in reducing risk of reinfection for up to 24 months after follow-up from the onset of the pandemic. These insights contribute to our understanding of long-term immunity dynamics and inform strategies for mitigating the impact of COVID-19.

Background: There is limited evidence comparing hepatitis A seroprevalence among HIV-exposed uninfected (HEU), HIV-infected (HIV), and unexposed uninfected (HUU) children. This compromises rational vaccine decision-making. Methods: This study comprised a retrospective health facility-based population of children aged 1 month–12 years. Archival sera were tested for markers of acute (anti-HAV IgM) or past (total anti-HAV) HAV infection. Subgroup analysis was conducted based on perinatal HIV exposure or infection status. Results: Among 513 children, the median age was 10 (IQR: 4–25) months. The median maternal age was 29 (IQR: 25–34) years. An anti-HAV seropositivity of 95.1% (117/122 [95% CI 90.2–98.4]) was found among those ≤6 months of age, indicative of the rate of transplacental antibody transfer. Among 1–12-year-olds, hepatitis A seroprevalence was 19.3% (37/192 [95% CI 14.1–25.7]), while 1.1% (2/188 [95% CI 0.12–2.76]) had evidence of acute infection. Compared to HIV-exposed subgroups (HIV = 60%, 6/10 [95% CI 27.4–86.3] and HEU = 45%, 9/20 [95% CI 23.8–68]), hepatitis A seroprevalence among HUU children was low (29.2%, 47/161 [95% CI 22.4–37.0]). Conclusions: Natural immunity among HIV-exposed and unexposed children in South Africa is insufficient to protect against severe liver complications associated with HAV infection later in adulthood.

The paper explores demographic characteristics, migration history, and impact of flooding on households and communities. The main objective is to explore the different ways in which floods impact households and communities in Accra. Specifically, the paper analyzes how floods alter the set of resources available to households and communities. The results indicate that urbanization and governmental policies have rendered more people, especially the poor and recent migrants, homeless. These homeless people have become more vulnerable to flooding than the average Accra resident. The results also show that the homeless community contrast with the fixed community in terms of socio-economic characteristics, degree of social cohesion, and physical location. The paper concludes that the unchanging pattern of vulnerability shows the inability of a society to cope and adjust to familiar hazards.

The misuse of the Internet that takes advantage of adolescents and young adults' lack of understanding along with their growing online presence has left them at risk. A study comprising 1,092 South Korean high school students investigated ways in which adolescents and young adults may be exploited while engaged in popular and everyday online activities. Specifically, exposure to sexually explicit material (SEM) and violent content, cyberbullying, malware, phishing, and identity theft were examined in the contexts of social media, streaming movies and/or television, streaming and/or posting videos, downloading and/or listening to music, downloading and/or sharing files, involvement in free and/or subscription-based online gaming, reading news, emailing, surfing, and completing school assignments. The findings reveal prevalence rates consistent with the literature on young people's technology use and online activities; moreover, exposure to SEM and cyberbullying were found to have statistically significant relationships with these activities. These are findings consistent with studies concluding that exposure to SEM and online bullying are two of the most serious issues related to young people today and, therefore, warrant continued attention to identifying strategies that can be used to curb risky online behavior. With more and more students using the Internet to access educational content and complete schoolwork in online and blended courses, it is imperative that young people are taught how to safeguard themselves when online.