Notes

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e combined impact of both racial/ethnic minority group status and veteran status.
In low-resource settings, a social autopsy tool has been proposed to measure the effect of delays in access to healthcare on deaths, complementing verbal autopsy questionnaires routinely used to determine cause of death. This study estimates the contribution of various delays in maternal healthcare to subsequent neonatal mortality using a social autopsy case-control design.

This study was conducted at the Child Health and Mortality Prevention Surveillance (CHAMPS) Sierra Leone site (Makeni City and surrounding rural areas). Cases were neonatal deaths in the catchment area, and controls were sex- and area-matched living neonates. Odds ratios for maternal barriers to care and neonatal death were estimated, and stratified models examined this association by neonatal age and medical complications.

Of 53 neonatal deaths, 26.4% of mothers experienced at least one delay during pregnancy or delivery compared to 46.9% of mothers of stillbirths and 18.6% of control mothers. The most commonly reported delay among Centers for Disease Control and Prevention.Persons living with diagnosed HIV (PLWDH) are overrepresented in correctional settings, as are Latinx including those of Puerto Rican (PR) origin. Little is known about this population's HIV care engagement after incarceration. NX-5948 Semi-structured interviews were conducted with 23 PLWDH of PR origin incarcerated in NYC jails using the Behavioral Model for Vulnerable Populations as the theoretical basis. Most participants described a fragile connection to HIV care and inconsistent antiretroviral therapy adherence due to issues including substance use, poverty (e.g. homelessness), and other factors. Most were satisfied with their current communitybased providers and reported that their PR ethnicity and transnational ties to PR did not impact their HIV care, although some preferred Spanish-speaking providers. Greater access to stable housing and HIV care that is convenient to substance use treatment and other services appear to be the greatest needs of PLWDH of PR origin leaving jail.This qualitative study aimed to elucidate and contextualize which and how migration-related stressors may influence Latinx Americans' (i.e., US-born; US-citizens) mental health status, including substance misuse. In 2019, a community sample of 22 Latinx Americans were recruited for an in-depth qualitative interview. Eligibility criteria included being an adult, self-identify as Latinx, report a migration-related stressor, and report at least one symptom of a mental health or substance use problem. Open-ended questions focused on the migrant experience, perceptions and impacts of immigration enforcement, and how these experiences related to their mental health. Using a migration as a social determinant framework, we identified several migration-related stressors at the structural- and social-level of environmental influence that were related to participants' mental health. Findings highlight how migration-related stressors at the structural- and social-level of influence create systematic uncertainty by inducing fear into the daily lives of Latinx Americans.Cross-cultural sex differences in mobility and harm avoidance have been widely reported, often emphasizing fitness benefits of long-distance travel for males and high costs for females. Data emerging from adults in small-scale societies, however, are challenging the assumption that female mobility is restricted during reproduction. Such findings warrant further exploration of the ontogeny of mobility. Here, using a combination of machine-learning, mixed-effects linear regression, and GIS mapping, we analyze range size, daily distance traveled, and harm avoidance among Hadza foragers during middle childhood and adolescence. Distance traveled increased with age and, while male adolescents had the longest daily ranges, average daily distance traveled by each sex was similar. We found few age- or sex-related patterns in harm-avoidant responses and a high degree of individual variation. When queried on the same issues, children and their parents were often in alignment as to expectations pertaining to harm avoidance, and siblings tended to behave in similar ways. To the extent that sex differences in mobility did emerge, they were associated with ecological differences in physical threats associated with sex-specific foraging behaviors. Further, we found no strong association between harm avoidance and mobility. Young Hadza foragers of both sexes are highly mobile, regardless of how harm avoidant they are. Taken together, our findings indicate that the causal arrows between harm avoidance and mobility must be evaluated in ecologically specific frameworks where cultural expectations of juvenile mobility can be contextualized.Electroencephalography (EEG)-based brain computer interface (BCI) enables people to interact directly with computing devices through their brain signals. A BCI typically interprets EEG signals to reflect the user's intent or other mental activity. Motor imagery (MI) is a commonly used technique in BCIs where a user is asked to imagine moving certain part of the body such as a hand or a foot. By correctly interpreting the signal, one can perform a multitude of tasks such as controlling wheel chair, playing computer games, or even typing text. However, the use of motor-imagery-based BCIs outside the laboratory environment is limited due to the lack of their reliability. This work focuses on another kind of mental imagery, namely, the visual imagery (VI). VI is the manipulation of visual information that comes from memory. This work presents a deep convolutional neural network (DCNN)-based system for the recognition of visual/mental imagination of English alphabets so as to enable typing directly via brain signals. The DCNN learns to extract the spatial features hidden in the EEG signal. As opposed to many deep neural networks that use raw EEG signals for classification, this work transforms the raw signals into band powers using Morlet wavelet transformation. The proposed approach is evaluated on two publicly available benchmark MI-EEG datasets and a visual imagery dataset specifically collected for this work. The obtained results demonstrate that the proposed model performs better than the existing state-of-the-art methods for MI-EEG classification and yields an average accuracy of 99.45% on the two public MI-EEG datasets. The model also achieves an average recognition rate of 95.2% for the 26 English-language alphabets. Overall working of the proposed solution for imagined character recognition through EEG signals.
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