Globally, What Affects Primary Caregivers’ Grieving Processes Leading To Subsequent Effective And Ineffective Coping Strategies Following An Infant Mortality.

poster abstractWorldwide in 2013, 4.6 million infant deaths occurred within the first year of life and accounted for 74% of all deaths under the age of 5 years old. As a result of these infant death events, there are varied zcaregiver grief responses and coping strategies. The purpose of this study was to meta-synthesize what factors affect primary caregiver grieving processes and then analyze their effective and ineffective coping strategies. After a rigorous multi-database search, we accessed 9 articles worldwide from years 1995-2013 for inclusion. These 9 papers were assessed for credibility by a primary and secondary reviewer via standardized critical appraisal instruments from the Joanna Briggs Institute. Data extraction and metaaggregation of the findings was carried out to determine intergenerational coping strategies and grieving process after an infant death. Eight peer reviewed articles were included in the aggregation. The data extracted included specific details about intergenerational support, interrelationship support, and lasting emotional impressions following an infant death. We identified that the influence of living children and parents of the primary caregivers are significant sources of intergenerational support. Conversely there is a lack of support between primary caregivers leading to incomplete coping and grieving processes within the relationship. Furthermore, lasting emotional impressions were acknowledged as a recurrent theme among individuals affected by the loss of an infant. Nursing interventions and education should be identified that address caregiver and family member grieving processes and coping strategies such as follow up emotional coping assessments at regular intervals for those at risk for poor coping. Nurses have the ability to play a vital role in improving the family and caregiver outcomes including positive coping strategies and healthy grieving processes following infant mortality

Utilization of Small Commercial Grade Nickel Cadmium (NiCd) Cells in Low Earth Orbit (LEO) Applications

The Defense Advanced Research Projects Agency (DARPA) has sponsored the Advanced Space Technology Program (ASTP) to enhance the cost-effectiveness and responsiveness of military space systems. One of the major themes of this program is the development of highly capable small satellites, generally referred to as \u27\u27LightSats, which can perform selected defense missions at relatively low cost. A key element of the programmatic approach is the utilization of commercial grade parts and practices where practical, as opposed to the much more conservative aerospace grade parts. ASTP has incorporated commercial grade batteries into its first generation LightSats; however, an attempt has been made to study the trade-offs and design considerations to optimally employ these batteries on small satellites. For certain applications, particularly for small relatively inexpensive satellites, commercial grade cells may be a viable alternative to aerospace cells. Differences between aerospace and commercial grade cells range from physical construction and technology incorporated, to the level of quality control in manufacturing. These differences are reflected in both greater cost and increased lead time for the aerospace cells. Our research and experience suggest that certain manufacturing technologies are preferable when considering commercial cells for space applications. Once the cell type is chosen, candidate cells must be thoroughly screened to insure survival and acceptable performance in the space environment. To insure optimal performance, cells should be rigorously matched in electrical characteristics when forming batteries. Test procedures should be tailored to fit the application in order to yield the best performance in a specific physical, electrical, and operational environment. An acceptance test plan for screening and matching cells is discussed. The present paper is the first in a series of reports which will document the approach, results, and lessons learned from ASTP\u27s commercial battery studies

Child and Infant Mortality; Risk Factors Related to SUID in Marion County

poster abstractBetween 2003- 2012, Indiana had 434 child deaths, including 53 Sudden Unexpected Infant Death (SUID) cases. Marion County has a high rate of SUID at 14%. The purpose of our research is to identify the risk factors for suffocation and to determine if SUID can be better prevented. In a pilot exploratory study, we analyzed five de-identified Marion County SUID cases to identify the asphyxia variables. The Fetal Infant Mortality Review (FIMR) cases allowed for thematic analysis. We used a meta-aggregation program NOTARI (Narrative, opinion, text assessment, and review instrument) to focus on categorical variables. Results identified asphyxia variables such as swaddling, blanket suffocation, wedging, parents bedding, soft bedding with pillows. Common maternal variables were obesity, hypertension, and STDs. Infant variables included breathing problems and cardio-respiratory pathologies. We found four cases with documented safe sleep education. The education that parents receive on safe sleep is not a guarantee that they will practice safe sleep with their infants. The education might not be effective enough to help them comprehend its importance; therefore nurses and other healthcare professionals need to consider changing the way they educate and advocate for parents. We suggest the introduction of more primary educational programs that will help the community understand safe sleep and SUID. This intervention would help decrease the incidence of sudden unexpected infant death

What Are the Factors that Influence Caregiver/Parent Co-sleeping Education?

poster abstractBackground: In the United States, 13% of infants routinely co-sleep with a caregiver, and 50% of infants share a bed with a caregiver for part of the night. Co-sleeping has been identified as a risk factor for infant death by Sudden Unexplained Infant Death Syndrome (SUIDS). The purpose of this research was to carry out a systematic review for determining best practices related to education to caregivers on the risks of co-sleeping. Method: After a rigorous multi-database search, we accessed 100 research articles related to SUIDS from years 2002-2015 for inclusion for this review. A total of 20 papers related to co-sleeping and SUIDS met the inclusion criteria and were assessed for validity by a primary and secondary reviewer via standardized critical appraisal instruments from the Joanna Briggs Institute. Due to the articles’ descriptive methods, NOTARI (Narrative, Opinion, and Text Assessment and Review Instrument) was used to appraise, extract data, and thematically organize the findings resulting in meta-aggregation. Results: The data extracted included specific details for co-sleeping. We identified that a) educational, b) family dynamics, c) racial/cultural, and d) socioeconomic factors were the significant concepts that influenced the caregivers’ attitude toward co-sleeping and their likelihood to co-sleep. Heterogeneity for the study’s methods was represented in the results. Conclusions: Many caregivers and families that practice co-sleeping display resistance to education about the discontinuation of co-sleeping based on the belief that healthcare providers do not take into account the family’s personal situation. The caregivers are more likely to be receptive to advice regarding safer co-sleeping practices as opposed to omitting the practice of co-sleeping. Family-centered interventions and tailored education delivered by nurses should be identified. Caregiver safe practices for sleep, taking into account situational factors such as socioeconomic level, race, culture, and core beliefs, should be encouraged

Haste or Speed? Alterations in the Impact of Incentive Cues on Task Performance in Remitted and Depressed Patients With Bipolar Disorder

A variety of evidence suggests that bipolar disorder is associated with disruptions of reward related processes, although the properties, and scope of these changes are not well understood. In the present study, we aimed to address this question by examining performance of patients with bipolar disorder (30 depressed bipolar; 35 euthymic bipolar) on a motivated choice reaction time task. We compared performance with a group of healthy control individuals (n = 44) and a group of patients with unipolar depression (n = 41), who were matched on several demographic variables. The task consists of an “odd-one-out” discrimination, in the presence of a cue signaling the probability of reward on a given trial (10, 50, or 90%) given a sufficiently fast response. All groups showed similar reaction time (RT) performance, and similar shortening of RT following the presentation of a reward predictive cue. However, compared to healthy individuals, the euthymic bipolar group showed a relative increase in commission errors during the high reward compared to low condition. Further correlational analysis revealed that in the healthy control and unipolar depression groups, participants tended either to shorten RTs for the high rather than low reward cue a relatively large amount with an increase in error rate, or to shorten RTs to a lesser extent but without increasing errors to the same degree. By contrast, reward-related speeding and reward-related increase in errors were less well coupled in the bipolar groups, significantly so in the BPD group. These findings suggest that although RT performance on the present task is relatively well matched, there may be a specific failure of individuals with bipolar disorder to calibrate RT speed and accuracy in a strategic way in the presence of reward-related stimuli

Disposition of Federally Owned Surpluses

PDZ domains are scaffolding modules in protein-protein interactions that mediate numerous physiological functions by interacting canonically with the C-terminus or non-canonically with an internal motif of protein ligands. A conserved carboxylate-binding site in the PDZ domain facilitates binding via backbone hydrogen bonds; however, little is known about the role of these hydrogen bonds due to experimental challenges with backbone mutations. Here we address this interaction by generating semisynthetic PDZ domains containing backbone amide-to-ester mutations and evaluating the importance of individual hydrogen bonds for ligand binding. We observe substantial and differential effects upon amide-to-ester mutation in PDZ2 of postsynaptic density protein 95 and other PDZ domains, suggesting that hydrogen bonding at the carboxylate-binding site contributes to both affinity and selectivity. In particular, the hydrogen-bonding pattern is surprisingly different between the non-canonical and canonical interaction. Our data provide a detailed understanding of the role of hydrogen bonds in protein-protein interactions

Inferring PDZ Domain Multi-Mutant Binding Preferences from Single-Mutant Data

Many important cellular protein interactions are mediated by peptide recognition domains. The ability to predict a domain's binding specificity directly from its primary sequence is essential to understanding the complexity of protein-protein interaction networks. One such recognition domain is the PDZ domain, functioning in scaffold proteins that facilitate formation of signaling networks. Predicting the PDZ domain's binding specificity was a part of the DREAM4 Peptide Recognition Domain challenge, the goal of which was to describe, as position weight matrices, the specificity profiles of five multi-mutant ERBB2IP-1 domains. We developed a method that derives multi-mutant binding preferences by generalizing the effects of single point mutations on the wild type domain's binding specificities. Our approach, trained on publicly available ERBB2IP-1 single-mutant phage display data, combined linear regression-based prediction for ligand positions whose specificity is determined by few PDZ positions, and single-mutant position weight matrix averaging for all other ligand columns. The success of our method as the winning entry of the DREAM4 competition, as well as its superior performance over a general PDZ-ligand binding model, demonstrates the advantages of training a model on a well-selected domain-specific data set

Predicting Bipolar Disorder Risk Factors in Distressed Young Adults From Patterns of Brain Activation to Reward: A Machine Learning Approach

BACKGROUND: The aim of this study was to apply multivariate pattern recognition to predict the severity of behavioral traits and symptoms associated with risk for bipolar spectrum disorder from patterns of whole-brain activation during reward expectancy to facilitate the identification of individual-level neural biomarkers of bipolar disorder risk. METHODS: We acquired functional neuroimaging data from two independent samples of transdiagnostically recruited adults (18-25 years of age; n = 56, mean age 21.9 ± 2.2 years, 42 women; n = 36, mean age 21.2 ± 2.2 years, 24 women) during reward expectancy task performance. Pattern recognition model performance in each sample was measured using correlation and mean squared error between actual and whole-brain activation-predicted scores on behavioral traits and symptoms. RESULTS: In the first sample, the model significantly predicted severity of a specific hypo/mania-related symptom, heightened energy, measured by the energy manic subdomain of the Mood Spectrum Structured Interviews (r = .42, p = .001; mean squared error = 9.93, p = .001). The region with the highest contribution to the model was the left ventrolateral prefrontal cortex. Results were confirmed in the second sample (r = .33, p = .01; mean squared error = 8.61, p = .01), in which the severity of this symptom was predicted using a bilateral ventrolateral prefrontal cortical mask (r = .33, p = .009, mean squared error = 9.37, p = .04). CONCLUSIONS: The severity of a specific hypo/mania-related symptom was predicted from patterns of whole-brain activation in two independent samples. Given that emerging manic symptoms predispose to bipolar disorders, these findings could provide neural biomarkers to aid early identification of individual-level bipolar disorder risk in young adults

Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach

BACKGROUND: It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogeneity poses challenges for identifying biological markers associated with dimensions of symptoms and behaviour that could provide targets to guide treatment choice and novel treatment. In response, the research domain criteria (RDoC) (Insel et al., 2010) was developed to advocate a dimensional approach which omits any disease definitions, disorder thresholds, or cut-points for various levels of psychopathology to understanding the pathophysiological processes underlying psychiatry disorders. In the present study we aimed to apply pattern regression analysis to identify brain signatures during dynamic emotional face processing that are predictive of anxiety and depression symptoms in a continuum that ranges from normal to pathological levels, cutting across categorically-defined diagnoses. METHODS: The sample was composed of one-hundred and fifty-four young adults (mean age=21.6 and s.d.=2.0, 103 females) consisting of eighty-two young adults seeking treatment for psychological distress that cut across categorically-defined diagnoses and 72 matched healthy young adults. Participants performed a dynamic face task involving fearful, angry and happy faces (and geometric shapes) while undergoing functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Gaussian Process Regression (GPR) implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r) and normalized mean squared error (MSE) to evaluate the models' performance. Permutation test was applied to estimate significance levels. RESULTS: GPR identified patterns of neural activity to dynamic emotional face processing predictive of self-report anxiety in the whole sample, which covered a continuum that ranged from healthy to different levels of distress, including subthreshold to fully-syndromal psychiatric diagnoses. Results were significant using two different cross validation strategies (two-fold: r=0.28 (p-value=0.001), MSE=4.47 (p-value=0.001) and five fold r=0.28 (p-value=0.002), MSE=4.62 (p-value=0.003). The contributions of individual regions to the predictive model were very small, demonstrating that predictions were based on the overall pattern rather than on a small combination of regions. CONCLUSIONS: These findings represent early evidence that neuroimaging techniques may inform clinical assessment of young adults irrespective of diagnoses by allowing accurate and objective quantitative estimation of psychopathology