Statistical Depth for Text Data: An Application to the Classification of Healthcare Data

This manuscript introduces a new concept of statistical depth function: the compositional D-depth. It is the first data depth developed exclusively for text data, in particular, for those data vectorized according to a frequency-based criterion, such as the tf-idf (term frequency?inverse document frequency) statistic, which results in most vector entries taking a value of zero. The proposed data depth consists of considering the inverse discrete Fourier transform of the vectorized text fragments and then applying a statistical depth for functional data, D. This depth is intended to address the problem of sparsity of numerical features resulting from the transformation of qualitative text data into quantitative data, which is a common procedure in most natural language processing frameworks. Indeed, this sparsity hinders the use of traditional statistical depths and machine learning techniques for classification purposes. In order to demonstrate the potential value of this new proposal, it is applied to a real-world case study which involves mapping Consolidated Framework for Implementation and Research (CFIR) constructs to qualitative healthcare data. It is shown that the DDG -classifier yields competitive results and outperforms all studied traditional machine learning techniques (logistic regression with LASSO regularization, artificial neural networks, decision trees, and support vector machines) when used in combination with the newly defined compositional D-depth.Funding: A.N.-R. is supported by Grant 21.VP67.64662 funded by “Proyectos Puente 2022” from the Spanish Government of Cantabria. For H.L.R., the qualitative data used in study were funded by Instituto de Salud Carlos III through the project “PI17/02070” (co-funded by the European Regional Development Fund/European Social Fund “A way to make Europe”/“Investing in your future”) and the Basque Government Department of Health project “2017111086”. The funding bodies had no role in the design of the study, collection, analysis, interpretation of data nor the writing of the manuscript

Supervised Classification of Healthcare Text Data Based on Context-Defined Categories

Achieving a good success rate in supervised classification analysis of a text dataset, where the relationship between the text and its label can be extracted from the context, but not from isolated words in the text, is still an important challenge facing the fields of statistics and machine learning. For this purpose, we present a novel mathematical framework. We then conduct a comparative study between established classification methods for the case where the relationship between the text and the corresponding label is clearly depicted by specific words in the text. In particular, we use logistic LASSO, artificial neural networks, support vector machines, and decision-tree-like procedures. This methodology is applied to a real case study involving mapping Consolidated Framework for Implementation and Research (CFIR) constructs to health-related text data and achieves a prediction success rate of over 80% when just the first 55% of the text, or more, is used for training and the remaining for testing. The results indicate that the methodology can be useful to accelerate the CFIR coding process.A.N.-R. is supported by Grant MTM2017-86061-C2-2-P funded by “ERDF A way of making Europe” and MCIN/AEI/10.13039/501100011033. For H.L.R., this study was funded by Instituto de Salud Carlos III through the project “PI17/02070” (co-funded by the European Regional Development Fund/European Social Fund “A way to make Europe”/“Investing in your future”) and the Basque Government Department of Health project “2017111086”. The funding bodies had no role in the design of the study, collection, analysis, nor interpretation of data, nor the writing of the manuscript. The APC was paid by PI17/0207

Household-level factors associated with relapse following discharge from treatment for moderate acute malnutrition

AbstractFactors associated with relapse among children who are discharged after reaching a threshold denoted ‘recovered’ from moderate acute malnutrition (MAM) are not well understood. The aim of this study was to identify factors associated with sustained recovery, defined as maintaining a mid-upper-arm circumference≥12·5 cm for 1 year after release from treatment. On the basis of an observational study design, we analysed data from an in-depth household (HH) survey on a sub-sample of participants within a larger cluster randomised controlled trial (cRCT) that followed up children for 1 year after recovery from MAM. Out of 1497 children participating in the cRCT, a subset of 315 children participated in this sub-study. Accounting for other factors, HH with fitted lids on water storage containers (P=0·004) was a significant predictor of sustained recovery. In addition, sustained recovery was better among children whose caregivers were observed to have clean hands (P=0·053) and in HH using an improved sanitation facility (P=0·083). By contrast, socio-economic status and infant and young child feeding practices at the time of discharge and HH food security throughout the follow-up period were not significant. Given these results, we hypothesise that improved water, sanitation and hygiene conditions in tandem with management of MAM through supplemental feeding programmes have the possibility to decrease relapse following recovery from MAM. Furthermore, the absence of associations between relapse and nearly all HH-level factors indicates that the causal factors of relapse may be related mostly to the child’s individual, underlying health and nutrition status.</jats:p

Extraction of DNA from Plant and Fungus Tissues in situ

Background: When samples are collected in the field and transported to the lab, degradation of the nucleic acids contained in the samples is frequently observed. Immediate extraction and precipitation of the nucleic acids reduces degradation to a minimum, thus preserving accurate sequence information. An extraction method to obtain high quality DNA in field studies is described. Findings. DNA extracted immediately after sampling was compared to DNA extracted after allowing the sampled tissues to air dry at 21°C for 48 or 72 hours. While DNA extracted from fresh tissues exhibited little degradation, DNA extracted from all tissues exposed to 21°C air for 48 or 72 hours exhibited varying degrees of degradation. Yield was higher for extractions from fresh tissues in most cases. Four microcentrifuges were compared for DNA yield: one standard electric laboratory microcentrifuge (max rcf=16,000×g), two battery-operated microcentrifuges (max rcf=5,000 and 3,000 ×g), and one manually-operated microcentrifuge (max rcf=120×g). Yields for all centrifuges were similar. DNA extracted under simulated field conditions was similar in yield and quality to DNA extracted in the laboratory using the same equipment. Conclusions: This CTAB (cetyltrimethylammonium bromide) DNA extraction method employs battery-operated and manually-operated equipment to isolate high quality DNA in the field. The method was tested on plant and fungus tissues, and may be adapted for other types of organisms. The method produced high quality DNA in laboratory tests and under simulated field conditions. The field extraction method should prove useful for working in remote sites, where ice, dry ice, and liquid nitrogen are unavailable; where degradation is likely to occur due to the long distances between the sample site and the laboratory; and in instances where other DNA preservation and transportation methods have been unsuccessful. It may be possible to adapt this method for genomic, metagenomic, transcriptomic and metabolomic projects using samples collected in situ. © 2012 Abu Almakarem et al.; licensee BioMed Central Ltd

Mentoring, Training, and Scholarly Productivity Experiences of Cancer-Related Health Disparities Research Trainees: Do Outcomes Differ for Underrepresented Scientists?

The study aims to explore variation in scholarly productivity outcomes by underrepresented status among a diverse sample of researchers in a community-engaged training program. We identified 141 trainees from a web-based survey of researchers in the National Cancer Institute-funded, Community Networks Program Centers (CNPCs) (2011-2016). We conducted a series of multiple logistic regression models to estimate the effect of National Institutes of Health (NIH)-defined underrepresented status on four, self-reported, scholarly productivity outcomes in the previous 5 years: number of publications (first-authored and total) and funded grants (NIH and any agency). Sixty-five percent (n = 92) indicated NIH underrepresented status. In final adjusted models, non-NIH underrepresented (vs. underrepresented) trainees reported an increased odds of having more than the median number of total publications (&gt; 9) (OR = 3.14, 95% CI 1.21-8.65) and any grant funding (OR = 5.10, 95% CI 1.77-14.65). Reporting ≥ 1 mentors (vs. none) was also positively associated (p &lt; 0.05) with these outcomes. The CNPC underrepresented trainees had similar success in first-authored publications and NIH funding as non-underrepresented trainees, but not total publications and grants. Examining trainees' mentoring experiences over time in relation to scholarly productivity outcomes is needed

Traumatic brain injury causes selective, CD74-dependent peripheral lymphocyte activation that exacerbates neurodegeneration

INTRODUCTION: Traumatic brain injury (TBI), a significant cause of death and disability, causes, as in any injury, an acute, innate immune response. A key component in the transition between innate and adaptive immunity is the processing and presentation of antigen by professional antigen presenting cells (APCs). Whether an adaptive immune response to brain injury is beneficial or detrimental is not known. Current efforts to understand the contribution of the immune system after TBI have focused on neuroinflammation and brain-infiltrating immune cells. Here, we characterize and target TBI-induced expansion of peripheral immune cells that may act as potential APCs. Because MHC Class II-associated invariant peptide (CLIP) is important for antigen processing and presentation, we engineered a competitive antagonist (CAP) for CLIP, and tested the hypothesis that peptide competition could reverse or prevent neurodegeneration after TBI. RESULTS: We show that after fluid percussion injury (FPI), peripheral splenic lymphocytes, including CD4+ and CD8+ T cells, regulatory T cells (Tregs), and γδ T cells, are increased in number within 24 hours after FPI. These increases were reversed by CAP treatment and this antagonism of CLIP also reduced neuroinflammation and neurodegeneration after TBI. Using a mouse deficient for the precursor of CLIP, CD74, we observed decreased peripheral lymphocyte activation, decreased neurodegeneration, and a significantly smaller lesion size following TBI. CONCLUSION: Taken together, the data support the hypothesis that neurodegeneration following TBI is dependent upon antigen processing and presentation that requires CD74. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40478-014-0143-5) contains supplementary material, which is available to authorized users

Psychological factors and cardiac repolarization instability during anger in implantable cardioverter defibrillator patients

BACKGROUND: Evidence indicates that emotions such as anger are associated with increased incidence of sudden cardiac death, but the biological mechanisms remain unclear. We tested the hypothesis that, in patients with sudden death vulnerability, anger would be associated with arrhythmic vulnerability, indexed by cardiac repolarization instability. METHODS: Patients with coronary artery disease (CAD) and an implantable cardioverter defibrillator (ICD; n = 41) and healthy controls (n = 26) gave an anger‐inducing speech (anger recall), rated their current (state) anger, and completed measures of trait (chronic) levels of Anger and Hostility. Repolarization instability was measured using QT Variability Index (QTVI) at resting baseline and during anger recall using continuous ECG. RESULTS: ICD patients had significantly higher QTVI at baseline and during anger recall compared with controls, indicating greater arrhythmic vulnerability overall. QTVI increased from baseline to anger recall to a similar extent in both groups. In ICD patients but not controls, during anger recall, self‐rated anger was related to QTVI (r = .44, p = .007). Trait (chronic) Anger Expression (r = .26, p = .04), Anger Control (r = −.26, p = .04), and Hostility (r = .25, p = .05) were each associated with the change in QTVI from baseline to anger recall (ΔQTVI). Moderation analyses evaluated whether psychological trait associations with ΔQTVI were specific to the ICD group. Results indicated that Hostility scores predicted ΔQTVI from baseline to anger recall in ICD patients (β = 0.07, p = .01), but not in controls. CONCLUSIONS: Anger increases repolarization lability, but in patients with CAD and arrhythmic vulnerability, chronic and acute anger interact to trigger cardiac repolarization lability associated with susceptibility to malignant arrhythmias

Resilience Testing of Health Systems : How Can It Be Done?

The resilience of health systems has received considerable attention as of late, yet little is known about what a resilience test might look like. We develop a resilience test concept and methodology. We describe key components of a toolkit and a 5-phased approach to implementation of resilience testing that can be adapted to individual health systems. We develop a methodology for a test that is balanced in terms of standardization and system-specific characteristics/needs. We specify how to work with diverse stakeholders from the health ecosystem via participatory processes to assess and identify recommendations for health system strengthening. The proposed resilience test toolkit consists of “what if” adverse scenarios, a menu of health system performance elements and indicators based on an input-output-outcomes framework, a discussion guide for each adverse scenario, and a traffic light scorecard template. The five phases of implementation include Phase 0, a preparatory phase to adapt the toolkit materials; Phase 1: facilitated discussion groups with stakeholders regarding the adverse scenarios; Phase 2: supplemental data collection of relevant quantitative indicators; Phase 3: summarization of results; Phase 4: action planning and health system transformation. The toolkit and 5-phased approach can support countries to test resilience of health systems, and provides a concrete roadmap to its implementation

Alcohol brief interventions practice following training for multidisciplinary health and social care teams: a qualitative interview study

Introduction and Aims: Few studies of the implementation of alcohol brief interventions (ABI) have been conducted in community settings such as mental health, social work and criminal justice teams. This qualitative interview study sought to explore the impact of training on ABI delivery by staff from a variety of such teams. Design and Methods: Fifteen semi-structured telephone interviews were carried out with trained practitioners and with managers to explore the use of, perceived need for and approaches to ABI delivery and recording with clients, and compatibility of ABIs with current practice. Interviews were analysed thematically using an inductive approach. Results: Very few practitioners reported delivery of any ABIs following training primarily because they felt ABIs to be inappropriate for their clients. According to practitioners, this was either because they drank too much or too little to benefit. Practitioners reported a range of current activities relating to alcohol, and some felt that their knowledge and confidence were improved following training. One practitioner reported ABI delivery and was considered a training success, while expectations of ABIs did not fit with current practice including assessment procedures for the remainder. Discussion and Conclusions: Identified barriers to ABI delivery included issues relating to individual practitioners, their teams, current practice and the ABI model. They are likely to be best addressed by strategic team- and setting-specific approaches to implementation, of which training is only one part

Population structure and phylogeography of the Gentoo Penguin (Pygoscelis papua) across the Scotia Arc

Climate change, fisheries' pressure on penguin prey, and direct human disturbance of wildlife have all been implicated in causing large shifts in the abundance and distribution of penguins in the Southern Ocean. Without mark-recapture studies, understanding how colonies form and, by extension, how ranges shift is challenging. Genetic studies, particularly focused on newly established colonies, provide a snapshot of colonization and can reveal the extent to which shifts in abundance and occupancy result from changes in demographic rates (e.g., reproduction and survival) or migration among suitable patches of habitat. Here, we describe the population structure of a colonial seabird breeding across a large latitudinal range in the Southern Ocean. Using multilocus microsatellite genotype data from 510 Gentoo penguin (Pygoscelis papua) individuals from 14 colonies along the Scotia Arc and Antarctic Peninsula, together with mitochondrial DNA data, we find strong genetic differentiation between colonies north and south of the Polar Front, that coincides geographically with the taxonomic boundary separating the subspecies P. p. papua and P. p. ellsworthii. Using a discrete Bayesian phylogeographic approach, we show that southern Gentoos expanded from a possible glacial refuge in the center of their current range, colonizing regions to the north and south through rare, long-distance dispersal. Our findings show that this dispersal is important for new colony foundation and range expansion in a seabird species that ordinarily exhibits high levels of natal philopatry, though persistent oceanographic features serve as barriers to movement