Category: Statistics

J Youth Adolesc. 2024 Jun 29. doi: 10.1007/s10964-024-02039-x. Online ahead of print.

ABSTRACT

Schools in the United States are increasingly offering ethnic studies classes, which focus on exploring students’ ethnic-racial identities (ERI) and critical analysis of systemic racism, to their diverse student bodies, yet scant research exists on their effectiveness for students of different ethnic-racial backgrounds in multiracial classrooms. A policy change to require all high school students in one school district to take an ethnic studies class facilitated a natural experiment for comparing the effects of quasi-random assignment to an ethnic studies class (treatment) relative to a traditional social studies class (control; e.g., U.S. Government, Human Geography). Student surveys and school administrative data were used to compare students’ ERI development, well-being, and academic outcomes across ethnic studies and control classes. Participants (N = 535 9th graders; 66.1% ethnic studies) had diverse ethnic-racial (33.5% non-Latine White, 29.5% Black, 21.1% Latine, 10.7% biracial, 2.8% Asian, 2.2% Native American) and gender identities (44.7% female, 7.1% non-binary). Ethnic studies students reported marginally higher ERI exploration and resolution than controls, and sensitivity analyses showed a statistically significant effect on ERI among participants with complete midpoint surveys. Higher resolution was associated with better psychological well-being for all students and higher attendance for White students. Students with low middle school grades (GPA < 2.0) had better high school grades in core subjects when enrolled in ethnic studies than the control class. Overall, the results of this natural experiment provide preliminary support for ethnic studies classes as a method for promoting ERI development, well-being, attendance, and academic achievement for students from diverse ethnic-racial backgrounds.

PMID:38949674 | DOI:10.1007/s10964-024-02039-x

Glob Health Action. 2024 Dec 31;17(1):2371184. doi: 10.1080/16549716.2024.2371184. Epub 2024 Jul 1.

ABSTRACT

BACKGROUND: The COVID-19 pandemic prompted varied policy responses globally, with Latin America facing unique challenges. A detailed examination of these policies’ impacts on health systems is crucial, particularly in Bolivia, where information about policy implementation and outcomes is limited.

OBJECTIVE: To describe the COVID-19 testing trends and evaluate the effects of quarantine measures on these trends in Cochabamba, Bolivia.

METHODS: Utilizing COVID-19 testing data from the Cochabamba Department Health Service for the 2020-2022 period. Stratified testing rates in the health system sectors were first estimated followed by an interrupted time series analysis using a quasi-Poisson regression model for assessing the quarantine effects on the mitigation of cases during surge periods.

RESULTS: The public sector reported the larger percentage of tests (65%), followed by the private sector (23%) with almost double as many tests as the public-social security sector (11%). In the time series analysis, a correlation between the implementation of quarantine policies and a decrease in the slope of positive rates of COVID-19 cases was observed compared to periods without or with reduced quarantine policies.

CONCLUSION: This research underscores the local health system disparities and the effectiveness of stringent quarantine measures in curbing COVID-19 transmission in the Cochabamba region. The findings stress the importance of the measures’ intensity and duration, providing valuable lessons for Bolivia and beyond. As the global community learns from the pandemic, these insights are critical for shaping resilient and effective health policy responses.

PMID:38949664 | DOI:10.1080/16549716.2024.2371184

J Nerv Ment Dis. 2024 Jul 1;212(7):398-402. doi: 10.1097/NMD.0000000000001778.

ABSTRACT

The DSM-III symptomatic criteria for major depression (MD) were derived from those proposed by Feighner and colleagues in 1972, which closely resembled those published by Cassidy in 1957. I here present a counter-factual history in which Feighner carefully read a key reference in Cassidy, a large 1953 follow-up study by Campbell of depressed patients with detailed tables of depressive signs and symptoms. In this alternative timeline, the Feighner criteria for MD were modified by Campbell’s results, which then changed DSM-III and subsequent MD criteria sets. The historical pathway to the current DSM MD criteria was contingent on a range of historical events and could easily have been different. This story is not meant to criticize DSM MD criteria that perform well. Rather, it suggests that these criteria represent a useful but fallible set of symptoms/signs that index but do not constitute MD and therefore are not to be reified.

PMID:38949660 | DOI:10.1097/NMD.0000000000001778

J Nerv Ment Dis. 2024 Jul 1;212(7):392-397. doi: 10.1097/NMD.0000000000001777.

ABSTRACT

The study was set out to establish the potential for psychotherapy to effect improvements in patients with narcissistic personality disorder (NPD). Eight patients with NPD who improved in treatment were identified. Consensus clinician/investigator diagnostic scores from before and after the psychotherapies were retroactively established on the Diagnostic Interview for Narcissism (DIN) and the Diagnostic Statistic Manual for Psychiatric Disorders, 5th Edition (DSM-5) Personality Disorder Section II criteria. Psychosocial functioning (work or school, romantic relationships) before and after the psychotherapies was retroactively evaluated as well. At the completion of the therapies after 2.5 to 5 years, all patients had improved, no longer met DIN or DSM-5 criteria for NPD, and showed better psychosocial functioning. Symptomatic improvements were associated with large effect sizes. In conclusion, changes in NPD can occur in treatment after 2.5 to 5 years. Future research should identify patient characteristics, interventions, and common processes in such improved cases that could help with development of treatments.

PMID:38949659 | DOI:10.1097/NMD.0000000000001777

bioRxiv [Preprint]. 2024 Jun 18:2024.06.17.599426. doi: 10.1101/2024.06.17.599426.

ABSTRACT

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as default or visual. Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

PMID:38948881 | PMC:PMC11212927 | DOI:10.1101/2024.06.17.599426

bioRxiv [Preprint]. 2024 Jun 22:2024.06.18.599636. doi: 10.1101/2024.06.18.599636.

ABSTRACT

Functional connectivity (FC) is the degree of synchrony of time series between distinct, spatially separated brain regions. While traditional FC analysis assumes the temporal stationarity throughout a brain scan, there is growing recognition that connectivity can change over time and is not stationary, leading to the concept of dynamic FC (dFC). Resting-state functional magnetic resonance imaging (fMRI) can assess dFC using the sliding window method with the correlation analysis of fMRI signals. Accurate statistical inference of sliding window correlation must consider the autocorrelated nature of the time series. Currently, the dynamic consideration is mainly confined to the point estimation of sliding window correlations. Using in vivo resting-state fMRI data, we first demonstrate the non-stationarity in both the cross-correlation function (XCF) and the autocorrelation function (ACF). Then, we propose the variance estimation of the sliding window correlation considering the nonstationary of XCF and ACF. This approach provides a means to dynamically estimate confidence intervals in assessing dynamic connectivity. Using simulations, we compare the performance of the proposed method with other methods, showing the impact of dynamic ACF and XCF on connectivity inference. Accurate variance estimation can help in addressing the critical issue of false positivity and negativity.

PMID:38948863 | PMC:PMC11212997 | DOI:10.1101/2024.06.18.599636

bioRxiv [Preprint]. 2024 Jun 18:2024.06.18.599103. doi: 10.1101/2024.06.18.599103.

ABSTRACT

The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.

PMID:38948820 | PMC:PMC11213021 | DOI:10.1101/2024.06.18.599103

bioRxiv [Preprint]. 2024 Jun 18:2024.06.17.599448. doi: 10.1101/2024.06.17.599448.

ABSTRACT

CRISPR screens are powerful tools to identify key genes that underlie biological processes. One important type of screen uses fluorescence activated cell sorting (FACS) to sort perturbed cells into bins based on the expression level of marker genes, followed by guide RNA (gRNA) sequencing. Analysis of these data presents several statistical challenges due to multiple factors including the discrete nature of the bins and typically small numbers of replicate experiments. To address these challenges, we developed a robust and powerful Bayesian random effects model and software package called Waterbear. Furthermore, we used Waterbear to explore how various experimental design parameters affect statistical power to establish principled guidelines for future screens. Finally, we experimentally validated our experimental design model findings that, when using Waterbear for analysis, high power is maintained even at low cell coverage and a high multiplicity of infection. We anticipate that Waterbear will be of broad utility for analyzing FACS-based CRISPR screens.

PMID:38948774 | PMC:PMC11213010 | DOI:10.1101/2024.06.17.599448

bioRxiv [Preprint]. 2024 Jun 20:2024.06.20.599815. doi: 10.1101/2024.06.20.599815.

ABSTRACT

In this paper, we introduce a new, open-source software developed in Python for analyzing Auditory Brainstem Response (ABR) waveforms. ABRs are a far-field recording of synchronous neural activity generated by the auditory fibers in the ear in response to sound, and used to study acoustic neural information traveling along the ascending auditory pathway. Common ABR data analysis practices are subject to human interpretation and are labor-intensive, requiring manual annotations and visual estimation of hearing thresholds. The proposed new Auditory Brainstem Response Analyzer (ABRA) software is designed to facilitate the analysis of ABRs by supporting batch data import/export, waveform visualization, and statistical analysis. Techniques implemented in this software include algorithmic peak finding, threshold estimation, latency estimation, time warping for curve alignment, and 3D plotting of ABR waveforms over stimulus frequencies and decibels. The excellent performance on a large dataset of ABR collected from three labs in the field of hearing research that use different experimental recording settings illustrates the efficacy, flexibility, and wide utility of ABRA.

PMID:38948763 | PMC:PMC11213013 | DOI:10.1101/2024.06.20.599815

bioRxiv [Preprint]. 2024 Jun 17:2024.06.16.599201. doi: 10.1101/2024.06.16.599201.

ABSTRACT

1Computational methods in biology can infer large molecular interaction networks from multiple data sources and at different resolutions, creating unprecedented opportunities to explore the mechanisms driving complex biological phenomena. Networks can be built to represent distinct conditions and compared to uncover graph-level differences-such as when comparing patterns of gene-gene interactions that change between biological states. Given the importance of the graph comparison problem, there is a clear and growing need for robust and scalable methods that can identify meaningful differences. We introduce node2vec2rank (n2v2r), a method for graph differential analysis that ranks nodes according to the disparities of their representations in joint latent embedding spaces. Improving upon previous bag-of-features approaches, we take advantage of recent advances in machine learning and statistics to compare graphs in higher-order structures and in a data-driven manner. Formulated as a multi-layer spectral embedding algorithm, n2v2r is computationally efficient, incorporates stability as a key feature, and can provably identify the correct ranking of differences between graphs in an overall procedure that adheres to veridical data science principles. By better adapting to the data, node2vec2rank clearly outperformed the commonly used node degree in finding complex differences in simulated data. In the real-world applications of breast cancer subtype characterization, analysis of cell cycle in single-cell data, and searching for sex differences in lung adenocarcinoma, node2vec2rank found meaningful biological differences enabling the hypothesis generation for therapeutic candidates. Software and analysis pipelines implementing n2v2r and used for the analyses presented here are publicly available.

PMID:38948759 | PMC:PMC11212899 | DOI:10.1101/2024.06.16.599201