Psychometric Evaluation of the Altered States of Consciousness Rating Scale (OAV)

BACKGROUND: The OAV questionnaire has been developed to integrate research on altered states of consciousness (ASC). It measures three primary and one secondary dimensions of ASC that are hypothesized to be invariant across ASC induction methods. The OAV rating scale has been in use for more than 20 years and applied internationally in a broad range of research fields, yet its factorial structure has never been tested by structural equation modeling techniques and its psychometric properties have never been examined in large samples of experimentally induced ASC. METHODOLOGY/PRINCIPAL FINDINGS: The present study conducted a psychometric evaluation of the OAV in a sample of psilocybin (n = 327), ketamine (n = 162), and MDMA (n = 102) induced ASC that was obtained by pooling data from 43 experimental studies. The factorial structure was examined by confirmatory factor analysis, exploratory structural equation modeling, hierarchical item clustering (ICLUST), and multiple indicators multiple causes (MIMIC) modeling. The originally proposed model did not fit the data well even if zero-constraints on non-target factor loadings and residual correlations were relaxed. Furthermore, ICLUST suggested that the "oceanic boundlessness" and "visionary restructuralization" factors could be combined on a high level of the construct hierarchy. However, because these factors were multidimensional, we extracted and examined 11 new lower order factors. MIMIC modeling indicated that these factors were highly measurement invariant across drugs, settings, questionnaire versions, and sexes. The new factors were also demonstrated to have improved homogeneities, satisfactory reliabilities, discriminant and convergent validities, and to differentiate well among the three drug groups. CONCLUSIONS/SIGNIFICANCE: The original scales of the OAV were shown to be multidimensional constructs. Eleven new lower order scales were constructed and demonstrated to have desirable psychometric properties. The new lower order scales are most likely better suited to assess drug induced ASC

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

A glimpse of the paleome in endolithic microbial communities

Abstract Background The terrestrial subsurface is home to a significant proportion of the Earth’s microbial biomass. Our understanding about terrestrial subsurface microbiomes is almost exclusively derived from groundwater and porous sediments mainly by using 16S rRNA gene surveys. To obtain more insights about biomass of consolidated rocks and the metabolic status of endolithic microbiomes, we investigated interbedded limestone and mudstone from the vadose zone, fractured aquifers, and deep aquitards. Results By adapting methods from microbial archaeology and paleogenomics, we could recover sufficient DNA for downstream metagenomic analysis from seven rock specimens independent of porosity, lithology, and depth. Based on the extracted DNA, we estimated between 2.81 and 4.25 × 105 cells × g−1 rock. Analyzing DNA damage patterns revealed paleome signatures (genetic records of past microbial communities) for three rock specimens, all obtained from the vadose zone. DNA obtained from deep aquitards isolated from surface input was not affected by DNA decay indicating that water saturation and not flow is controlling subsurface microbial survival. Decoding the taxonomy and functional potential of paleome communities revealed increased abundances for sequences affiliated with chemolithoautotrophs and taxa such as Cand. Rokubacteria. We also found a broader metabolic potential in terms of aromatic hydrocarbon breakdown, suggesting a preferred utilization of sedimentary organic matter in the past. Conclusions Our study suggests that limestones function as archives for genetic records of past microbial communities including those sensitive to environmental stress at modern times, due to their specific conditions facilitating long-term DNA preservation. Video Abstrac

nf-core/taxprofiler: v1.0.0 - Dodgy Dachshund [2023-03-13]

v1.0.0 - Dodgy Dachshund [2023-03-13] Added <ul> <li>Add read quality control (sequencing QC, adapter removal and merging)</li> <li>Add read complexity filtering</li> <li>Add host-reads removal step</li> <li>Add run merging</li> <li>Add taxonomic classification</li> <li>Add taxon table standardisation</li> <li>Add post-classification visualisation</li> </ul> <p>Contributed by: @jfy133 @sofstam @Midnighter @ljmesi @MillironX @jianhong @mjamy @rafalstepien @maxibor @talnor</p&gt

nf-core/taxprofiler: v1.0.1 - Dodgy Dachshund Patch [2023-05-15]

<code>Fixed</code> <ul> <li><a href="https://github.com/nf-core/taxprofiler/pull/291">#291</a> - Fix Taxpasta not receiving taxonomy directory (❤️ to SannaAb for reporting, fix by @jfy133)</li> </ul&gt

Additional file 2 of A glimpse of the paleome in endolithic microbial communities

Additional file 2: Supplementary tables: Table S1. Statistics sequence data processing. pwd = powdered sample, pc = rock pieces sample, QC = quality control. Table S2. Identified contaminants for different taxonomic ranks. Freq = frequency, prev = prevalence, p.freq = tail probability at value R, p.prev = tail probability of the chi-square distribution for the respective taxon based on presence/absence in true samples and negative controls, p = p-value from Fisher's exact test, NA = not available. Please see [112] for details regarding the mentioned metrics. Table S3. Phylum-level taxonomic profiles. lib_blk = library blank, ex_blank = extraction blank, pc = rock pieces sample, pwd = powdered sample. Table S4. Basic assembly statistics and results from read recruitment. Table S5. Phylum-level taxonomic profiles of assembled contigs (> 1 kbp). Table S6. Taxonomy and quality information regarding recovered genome bins based on checkm [64] output. Table S7. Functional profile based on KEGG Lvl3 Orthologies. Abundances are given as CoPM. CoPM = copies per million. Table S8. Functional profile based on a subset of KEGG pathways. CoPM = copies per million, logCoPM = log copies per million