A Brief Online Implicit Bias Intervention for School Mental Health Clinicians

Clinician bias has been identified as a potential contributor to persistent healthcare disparities across many medical specialties and service settings. Few studies have examined strategies to reduce clinician bias, especially in mental healthcare, despite decades of research evidencing service and outcome disparities in adult and pediatric populations. This manuscript describes an intervention development study and a pilot feasibility trial of the Virtual Implicit Bias Reduction and Neutralization Training (VIBRANT) for mental health clinicians in schools—where most youth in the U.S. access mental healthcare. Clinicians (N = 12) in the feasibility study—a non-randomized open trial—rated VIBRANT as highly usable, appropriate, acceptable, and feasible for their school-based practice. Preliminarily, clinicians appeared to demonstrate improvements in implicit bias knowledge, use of bias-management strategies, and implicit biases (as measured by the Implicit Association Test [IAT]) post-training. Moreover, putative mediators (e.g., clinicians’ VIBRANT strategies use, IAT D scores) and outcome variables (e.g., clinician-rated quality of rapport) generally demonstrated correlations in the expected directions. These pilot results suggest that brief and highly scalable online interventions such as VIBRANT are feasible and promising for addressing implicit bias among healthcare providers (e.g., mental health clinicians) and can have potential downstream impacts on minoritized youth’s care experience.

Neuro-Linguistic Programming, Positive Psychology & VR in Special Education

In the era of rapid change, special education is in the quest to ‘drive up standards’ with alternative intervention strategies ensuring optimal outcomes for parents, teachers and learners. Automatic thoughts, negative beliefs and implicit bias demotivate, disrupt students’ behavior, and lower the quality of learning outcomes. Neuro-linguistic programming (NLP) is a psychological approach that employs appropriate techniques to help individuals deal with their dysfunctional schemas. The present paper aims at reviewing the research studies regarding the effectiveness of neurolinguistic programming (NLP) in challenging situations as those that people with disabilities face. In addition, we will co-examine the possible applications of NLP on virtual reality (VR) environments. The findings of this review support the idea that neuro-linguistic programming provides influencing strategies for students with special educational needs to be rapidly engaged in those states of mind that eliminate implicit bias and promote positive behaviors and academic achievement. It was found that VR is in line with NLP methodology contributing to unintended bias reduction, cultivating users’ ability to change perspective with flexibility, expecting a positive future and perceiving themselves more realistically with less symptoms of depression. This study takes the view of a new pedagogy in Special Education that integrates the overlapping areas of neurolinguistic programming, positive and social psychology and recognizes their role in developing brain rewiring and sub-conscious training techniques -even in virtual environments-.

How well can inverse analyses of high-resolution satellite data resolve heterogeneous methane fluxes? Observing system simulation experiments with the GEOS-Chem adjoint model (v35)

We perform observing system simulation experiments (OSSEs) with the GEOS-Chem adjoint model to test how well methane emissions over North America can be resolved using measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and similar high-resolution satellite sensors. We focus analysis on the impacts of (i) spatial errors in the prior emissions and (ii) model transport errors. Along with a standard scale factor (SF) optimization we conduct a set of inversions using alternative formalisms that aim to overcome limitations in the SF-based approach that arise for missing sources. We show that 4D-Var analysis of the TROPOMI data can improve monthly emission estimates at 25 km even with a spatially biased prior or model transport errors (42 %–93 % domain-wide bias reduction; R increases from 0.51 up to 0.73). However, when both errors are present, no single inversion framework can successfully improve both the overall bias and spatial distribution of fluxes relative to the prior on the 25 km model grid. In that case, the ensemble-mean optimized fluxes have a domain-wide bias of 77 Gg d−1 (comparable to that in the prior), with spurious source adjustments compensating for the transport errors. Increasing observational coverage through longer-timeframe inversions does not significantly change this picture. An inversion formalism that optimizes emission enhancements rather than scale factors exhibits the best performance for identifying missing sources, while an approach combining a uniform background emission with the prior inventory yields the best performance in terms of overall spatial fidelity – even in the presence of model transport errors. However, the standard SF optimization outperforms both of these for the magnitude of the domain-wide flux. For the common scenario in which prior errors are non-random, approximate posterior error reduction calculations (derived via gradient-based randomization) for the inversions reflect the sensitivity to observations but have no spatial correlation with the actual emission improvements. This demonstrates that such information content analysis can be used for general observing system characterization but does not describe the spatial accuracy of the posterior emissions or of the actual emission improvements. Findings here highlight the need for careful evaluation of potential missing sources in prior emission datasets and for robust accounting of model transport errors in inverse analyses of the methane budget.

Solutions to problems of nonexistence of parameter estimates and sparse data bias in Poisson regression

Poisson regression can be challenging with sparse data, in particular with certain data constellations where maximum likelihood estimates of regression coefficients do not exist. This paper provides a comprehensive evaluation of methods that give finite regression coefficients when maximum likelihood estimates do not exist, including Firth’s general approach to bias reduction, exact conditional Poisson regression, and a Bayesian estimator using weakly informative priors that can be obtained via data augmentation. Furthermore, we include in our evaluation a new proposal for a modification of Firth’s approach, improving its performance for predictions without compromising its attractive bias-correcting properties for regression coefficients. We illustrate the issue of the nonexistence of maximum likelihood estimates with a dataset arising from the recent outbreak of COVID-19 and an example from implant dentistry. All methods are evaluated in a comprehensive simulation study under a variety of realistic scenarios, evaluating their performance for prediction and estimation. To conclude, while exact conditional Poisson regression may be confined to small data sets only, both the modification of Firth’s approach and the Bayesian estimator are universally applicable solutions with attractive properties for prediction and estimation. While the Bayesian method needs specification of prior variances for the regression coefficients, the modified Firth approach does not require any user input.

SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

Soluble N-ethylmaleimide sensitive factor activating protein receptor (SNARE) proteins are a large family of transmembrane proteins located in organelles and vesicles. The important roles of SNARE proteins include initiating the vesicle fusion process and activating and fusing proteins as they undergo exocytosis activity, and SNARE proteins are also vital for the transport regulation of membrane proteins and non-regulatory vesicles. Therefore, there is great significance in establishing a method to efficiently identify SNARE proteins. However, the identification accuracy of the existing methods such as SNARE CNN is not satisfied. In our study, we developed a method based on a support vector machine (SVM) that can effectively recognize SNARE proteins. We used the position-specific scoring matrix (PSSM) method to extract features of SNARE protein sequences, used the support vector machine recursive elimination correlation bias reduction (SVM-RFE-CBR) algorithm to rank the importance of features, and then screened out the optimal subset of feature data based on the sorted results. We input the feature data into the model when building the model, used 10-fold crossing validation for training, and tested model performance by using an independent dataset. In independent tests, the ability of our method to identify SNARE proteins achieved a sensitivity of 68%, specificity of 94%, accuracy of 92%, area under the curve (AUC) of 84%, and Matthew’s correlation coefficient (MCC) of 0.48. The results of the experiment show that the common evaluation indicators of our method are excellent, indicating that our method performs better than other existing classification methods in identifying SNARE proteins.

Comparison of Efficacy among Three Dermal Substitutes in the Management of Critical Lower-Limb Wounds: The Largest Biases-Reduced Single-Center Retrospective Cohort Study in Literature

Background and objectives: The skin recently became the main focus of regenerative medicine and, in this context, skin substitutes are fully entering into the plastic surgeon’s armamentarium. Among the various types of skin substitutes, dermal substitutes (DSs) are the most used. Our study aims to retrospectively compare three renowned and extremely similar DS in the management of critical lower limb wounds in the largest cohort analysis currently present in literature. Materials and Methods: We followed a strict protocol of application and evaluation of the DS for each patient and wound and, after a meticulous bias reduction process, we compared final outcomes in terms of efficacy and speed in achieving the defect coverage. Results: Among patients who did not receive a skin graft after the DS, we registered a wound healed surface of 50% for Pelnac, 52% for Integra, and 19% for Nevelia, after 30 days from the external silicon layer removal; among those who received a skin graft after the DS, we observed a significantly lower mean percentage of graft take after 7 days with Pelnac (53%) compared to Integra and Nevelia (92% and 80%, respectively). The overall percentage of wound healed surface obtained after 30 days from the external silicon sheet removal, either with or without skin graft, was 71% for Pelnac, 63% for Integra and 63% for Nevelia. We also ran a sub-group analysis only including grafted wounds with a negative microbiological test and the mean percentage of graft take was similar this time. Eventually, we assessed the influence of the wound’s “chronicity” on its healing, comparing the mean graft take only in “acute” wounds who received a skin graft and it resulted 63% for Pelnac, 91% for Integra and 75% for Nevelia. Conclusions: Integra demonstrates the highest rate of skin graft viability and the highest rate of skin graft takes after 7 days. Pelnac shows the quickest induction of secondary healing in acute wounds. Nevelia is not different from Integra and shows a superior graft take compared to Pelnac, but features the lowest secondary healing induction rate. No differences exist between the three DSs in terms of wound healing after 30 days from the skin graft or from the removal of the external silicon layer.

Cumulative Concussion and Odds of Stroke in Former National Football League Players

Background and Purpose: Postmortem and experimental studies indicate a potential association between repeated concussions and stroke risk in older contact sport athletes. We examined the relationship between concussion and stroke history in former National Football League players aged ≥50 years. Methods: Former professional football players aged ≥50 years who played ≥1 year in the National Football League were enrolled in the cross-sectional study. Indirect standardization was used to calculate overall and decade-specific standardized prevalence ratios. Logistic regression using Firth’s bias reduction method examined the association between lifetime concussion history 0 (n=119; 12.2%), 1 to 2 (n=152; 15.5%), 3 to 5 (n=242; 24.7%), 6 to 9 (201; 20.5%), and 10+(n=265; 27.1%) and stroke. Adjusted odds ratios for stroke were calculated for concussion history groups, age, and coronary artery disease and/or myocardial infarction. Results: The 979 participants who met inclusion criteria had a mean age of 65.0±9.0 years (range, 50–99). The prevalence of stroke was 3.4% (n=33), significantly lower than expected based on rates of stroke in US men aged 50 and over (standardized prevalence ratio=0.56, Z= −4.56, P <0.001). Greater odds of stroke history were associated with concussion history (10+ versus 0, adjusted odds ratio [95% CI]=5.51 [1.61–28.95]), cardiovascular disease (adjusted odds ratio [95% CI]=2.24 [1.01–4.77]), and age (1-year-increase adjusted odds ratio [95% CI]=1.07 [1.02–1.11]). Conclusions: The prevalence of stroke among former National Football League players aged ≥50 years was lower than the general population, with significantly increased risk among those with 10 or more prior concussions. Findings add to the evidence suggesting that traumatic brain injuries are associated with increased risk of stroke. Clinically, management of cardio- and cerebrovascular health may be pertinent to those with a history of multiple prior concussions.