Research Notebooks Workshop

The workshop was a meetup to discuss practical steps for institutions wishing to providing central support for research notebook tools

Research Notebooks Workshop

The workshop was a meetup to discuss practical steps for institutions wishing to providing central support for research notebook tools

Carbon Sequestration in Biogenic Magnesite and Other Magnesium Carbonate Minerals

The stability and longevity of carbonate minerals make them an ideal sink for surplus atmospheric carbon dioxide. Biogenic magnesium carbonate mineral precipitation from the magnesium-rich tailings generated by many mining operations could offset net mining greenhouse gas emissions, while simultaneously giving value to mine waste products. In this investigation, cyanobacteria in a wetland bioreactor enabled the precipitation of magnesite (MgCO3), hydromagnesite [Mg5(CO3)4(OH)2·4H2O], and dypingite [Mg5(CO3)4(OH)2·5H2O] from a synthetic wastewater comparable in chemistry to what is produced by acid leaching of ultramafic mine tailings. These precipitates occurred as micrometer-scale mineral grains and microcrystalline carbonate coatings that entombed filamentous cyanobacteria. This provides the first laboratory demonstration of low temperature, biogenic magnesite precipitation for carbon sequestration purposes. These findings demonstrate the importance of extracellular polymeric substances in microbially enabled carbonate mineral nucleation. Fluid composition was monitored to determine carbon sequestration rates. The results demonstrate that up to 238 t of CO2 could be stored per hectare of wetland/year if this method of carbon dioxide sequestration was implemented at an ultramafic mine tailing storage facility. The abundance of tailings available for carbonation and the anticipated global implementation of carbon pricing make this method of mineral carbonation worth further investigation

Pathways to post-traumatic stress disorder and alcohol dependence: Trauma, executive functioning, and family history of alcoholism in adolescents and young adults

INTRODUCTION: Family history (FH) of alcohol dependence is likely to increase the risk of trauma exposure, post-traumatic stress disorder (PTSD), and alcohol dependence. FH of alcohol dependence and trauma has been separately shown to adversely affect planning/problem-solving aspects of executive function. However, few studies have examined these risk factors in an integrated model. METHODS: Using data from trauma-exposed individuals from the Collaborative Study on the Genetics of Alcoholism prospective cohort (N = 1,860), comprising offspring from alcohol-dependent high-risk and comparison families (mean age [SE] = 21.9 [4.2]), we investigated associations of trauma (nonsexual assaultive, nonassaultive, sexual assaultive) with DSM-IV PTSD and alcohol dependence symptom counts, and planning/problem-solving abilities assessed using the Tower of London Test (TOLT). Moderating effects of family history density of alcohol use disorder (FHD) on these associations and sex differences were explored. RESULTS: Family history density was positively associated with PTSD in female participants who endorsed a sexual assaultive trauma. Exposure to nonsexual assaultive trauma was associated with more excess moves made on the TOLT. CONCLUSION: Findings from this study demonstrate associations with PTSD and alcohol dependence symptom counts, as well as poor problem-solving ability in trauma-exposed individuals from families densely affected with alcohol dependence, depending on trauma type, FHD, and sex. This suggests that having a FH of alcohol dependence and exposure to trauma during adolescence may be associated with more PTSD and alcohol dependence symptoms, and poor problem-solving abilities in adulthood

Predicting alcohol-related memory problems in older adults: A machine learning study with multi-domain features

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive uplift life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life

Diagnostic criteria for identifying individuals at high risk of progression from mild or moderate to severe alcohol use disorder

IMPORTANCE: Current Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) diagnoses of substance use disorders rely on criterion count-based approaches, disregarding severity grading indexed by individual criteria. OBJECTIVE: To examine correlates of alcohol use disorder (AUD) across count-based severity groups (ie, mild, moderate, mild-to-moderate, severe), identify specific diagnostic criteria indicative of greater severity, and evaluate whether specific criteria within mild-to-moderate AUD differentiate across relevant correlates and manifest in greater hazards of severe AUD development. DESIGN, SETTING, AND PARTICIPANTS: This cohort study involved 2 cohorts from the family-based Collaborative Study on the Genetics of Alcoholism (COGA) with 7 sites across the United States: cross-sectional (assessed 1991-2005) and longitudinal (assessed 2004-2019). Statistical analyses were conducted from December 2022 to June 2023. MAIN OUTCOMES AND MEASURES: Sociodemographic, alcohol-related, psychiatric comorbidity, brain electroencephalography (EEG), and AUD polygenic score measures as correlates of DSM-5 AUD levels (ie, mild, moderate, severe) and criterion severity-defined mild-to-moderate AUD diagnostic groups (ie, low-risk vs high-risk mild-to-moderate). RESULTS: A total of 13 110 individuals from the cross-sectional COGA cohort (mean [SD] age, 37.8 [14.2] years) and 2818 individuals from the longitudinal COGA cohort (mean baseline [SD] age, 16.1 [3.2] years) were included. Associations with alcohol-related, psychiatric, EEG, and AUD polygenic score measures reinforced the role of increasing criterion counts as indexing severity. Yet within mild-to-moderate AUD (2-5 criteria), the presence of specific high-risk criteria (eg, withdrawal) identified a group reporting heavier drinking and greater psychiatric comorbidity even after accounting for criterion count differences. In longitudinal analyses, prior mild-to-moderate AUD characterized by endorsement of at least 1 high-risk criterion was associated with more accelerated progression to severe AUD (adjusted hazard ratio [aHR], 11.62; 95% CI, 7.54-17.92) compared with prior mild-to-moderate AUD without endorsement of high-risk criteria (aHR, 5.64; 95% CI, 3.28-9.70), independent of criterion count. CONCLUSIONS AND RELEVANCE: In this cohort study of a combined 15 928 individuals, findings suggested that simple count-based AUD diagnostic approaches to estimating severe AUD vulnerability, which ignore heterogeneity among criteria, may be improved by emphasizing specific high-risk criteria. Such emphasis may allow better focus on individuals at the greatest risk and improve understanding of the development of AUD

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

BACKGROUND Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001). CONCLUSION The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research

Age-dependent effects of protein restriction on dopamine release

FUNDING AND DISCLOSURE This work was supported by the Biotechnology and Biological Sciences Research Council [grant # BB/M007391/1 to J.E.M.], the European Commission [grant # GA 631404 to J.E.M.], The Leverhulme Trust [grant # RPG-2017-417 to J.E.M.] and the Tromsø Research Foundation [grant # 19-SG-JMcC to J. E. M.). The authors declare no conflict of interest. ACKNOWLEDGEMENTS The authors would like to acknowledge the help and support from the staff of the Division of Biomedical Services, Preclinical Research Facility, University of Leicester, for technical support and the care of experimental animals.Peer reviewedPublisher PD