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Exercise has been found to be important in maintaining neurocognitive health. However, the effect of exercise intensity level remains relatively underexplored. Thus, to test the hypothesis that self-paced high-intensity exercise and cardiorespiratory fitness (peak aerobic capacity; VO2peak) increase grey matter (GM) volume, we examined the effect of a 6-month exercise intervention on frontal lobe GM regions that support the executive functions in older adults.
Methods:
Ninety-eight cognitively normal participants (age = 69.06 ± 5.2 years; n = 54 female) were randomised into either a self-paced high- or moderate-intensity cycle-based exercise intervention group, or a no-intervention control group. Participants underwent magnetic resonance imaging and fitness assessment pre-intervention, immediately post-intervention, and 12-months post-intervention.
Results:
The intervention was found to increase fitness in the exercise groups, as compared with the control group (F = 9.88, p = <0.001). Changes in pre-to-post-intervention fitness were associated with increased volume in the right frontal lobe (β = 0.29, p = 0.036, r = 0.27), right supplementary motor area (β = 0.30, p = 0.031, r = 0.29), and both right (β = 0.32, p = 0.034, r = 0.30) and left gyrus rectus (β = 0.30, p = 0.037, r = 0.29) for intervention, but not control participants. No differences in volume were observed across groups.
Conclusions:
At an aggregate level, six months of self-paced high- or moderate-intensity exercise did not increase frontal GM volume. However, experimentally-induced changes in individual cardiorespiratory fitness was positively associated with frontal GM volume in our sample of older adults. These results provide evidence of individual variability in exercise-induced fitness on brain structure.
Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
Aims
To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
Method
Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
Results
Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
Conclusions
AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
The oceans have a huge capability to store, release, and transport heat, water, and various chemical species on timescales from seasons to centuries. Their transports affect global energy, water, and biogeochemical cycles and are crucial elements of Earth’s climate system. Ocean variability, as represented, for example, by sea surface temperature (SST) variations, can result in anomalous diabatic heating or cooling of the overlying atmosphere, which can in turn alter atmospheric circulation in such a way as to feedback on ocean thermal and current structures to modify the original SST variations. Ocean–atmosphere interactions in one ocean basin can also influence remote regions via interbasin teleconnections that can trigger responses having both local and far-field impacts. This chapter highlights the defining aspects of the climate in individual ocean basins, including mean states, seasonal cycles, interannual-to-interdecadal variability, and interactions with other basins. Key components of the global and tropical ocean observing system are also described.
In the Introduction to his Treatise of Human Nature, David Hume credits “my Lord Shaftesbury” as one of the “philosophers in England, who have begun to put the science of man on a new footing.” I describe aspects of Shaftesbury’s philosophy that justify the credit Hume gives him. I focus on Shaftesbury’s refutation of psychological egoism, his examination of partiality, and his views on how to promote impartial virtue. I also discuss Shaftesbury’s political commitments, and raise questions about recent interpretations that have taken his Characteristicks to be a polemic, partisan text.
Estimation of RMR using prediction equations is the basis for calculating energy requirements. In the present study, RMR was predicted by Harris–Benedict, Schofield, Henry, Mifflin–St Jeor and Owen equations and measured by indirect calorimetry in 125 healthy adult women of varying BMI (17–44 kg/m2). Agreement between methods was assessed by Bland–Altman analyses and each equation was assessed for accuracy by calculating the percentage of individuals predicted within ± 10 % of measured RMR. Slopes and intercepts of bias as a function of average RMR (mean of predicted and measured RMR) were calculated by regression analyses. Predictors of equation bias were investigated using univariate and multivariate linear regression. At group level, bias (the difference between predicted and measured RMR) was not different from zero only for Mifflin–St Jeor (0 (sd 153) kcal/d (0 (sd 640) kJ/d)) and Henry (8 (sd 163) kcal/d (33 (sd 682) kJ/d)) equations. Mifflin–St Jeor and Henry equations were most accurate at the individual level and predicted RMR within 10 % of measured RMR in 71 and 66 % of participants, respectively. For all equations, limits of agreement were wide, slopes of bias were negative, and intercepts of bias were positive and significantly (P < 0⋅05) different from zero. Increasing age, height and BMI were associated with underestimation of RMR, but collectively these variables explained only 15 % of the variance in estimation bias. Overall accuracy of equations for prediction of RMR is low at the individual level, particularly in women with low and high RMR. The Mifflin–St Jeor equation was the most accurate for this dataset, but prediction errors were still observed in about one-third of participants.
Copy number variants (CNVs) play a significant role in disease pathogenesis in a small subset of individuals with schizophrenia (~2.5%). Chromosomal microarray testing is a first-tier genetic test for many neurodevelopmental disorders. Similar testing could be useful in schizophrenia.
Aims
To determine whether clinically identifiable phenotypic features could be used to successfully model schizophrenia-associated (SCZ-associated) CNV carrier status in a large schizophrenia cohort.
Method
Logistic regression and receiver operating characteristic (ROC) curves tested the accuracy of readily identifiable phenotypic features in modelling SCZ-associated CNV status in a discovery data-set of 1215 individuals with psychosis. A replication analysis was undertaken in a second psychosis data-set (n = 479).
Results
In the discovery cohort, specific learning disorder (OR = 8.12; 95% CI 1.16–34.88, P = 0.012), developmental delay (OR = 5.19; 95% CI 1.58–14.76, P = 0.003) and comorbid neurodevelopmental disorder (OR = 5.87; 95% CI 1.28–19.69, P = 0.009) were significant independent variables in modelling positive carrier status for a SCZ-associated CNV, with an area under the ROC (AUROC) of 74.2% (95% CI 61.9–86.4%). A model constructed from the discovery cohort including developmental delay and comorbid neurodevelopmental disorder variables resulted in an AUROC of 83% (95% CI 52.0–100.0%) for the replication cohort.
Conclusions
These findings suggest that careful clinical history taking to document specific neurodevelopmental features may be informative in screening for individuals with schizophrenia who are at higher risk of carrying known SCZ-associated CNVs. Identification of genomic disorders in these individuals is likely to have clinical benefits similar to those demonstrated for other neurodevelopmental disorders.
Indicators are necessary to monitor national progress toward commitments made to the Convention on Biological Diversity (CBD), but countries often struggle to mobilize quantitative indicators for many biodiversity targets. Assessing the extent to which countries are using measurable indicators from global and national sources by surveying 5th National Reports to the CBD, we found that nationally generated indicators were used 11 times more frequently than global indicators and only one-fifth of indicators matched those recommended by the CBD, suggesting that countries and indicator experts should work more closely to agree upon measurable, scalable, fit-for-purpose indicators for the next generation of CBD targets.
Previous studies using resting-state functional neuroimaging have revealed alterations in whole-brain images, connectome-wide functional connectivity and graph-based metrics in groups of patients with schizophrenia relative to groups of healthy controls. However, it is unclear which of these measures best captures the neural correlates of this disorder at the level of the individual patient.
Methods
Here we investigated the relative diagnostic value of these measures. A total of 295 patients with schizophrenia and 452 healthy controls were investigated using resting-state functional Magnetic Resonance Imaging at five research centres. Connectome-wide functional networks were constructed by thresholding correlation matrices of 90 brain regions, and their topological properties were analyzed using graph theory-based methods. Single-subject classification was performed using three machine learning (ML) approaches associated with varying degrees of complexity and abstraction, namely logistic regression, support vector machine and deep learning technology.
Results
Connectome-wide functional connectivity allowed single-subject classification of patients and controls with higher accuracy (average: 81%) than both whole-brain images (average: 53%) and graph-based metrics (average: 69%). Classification based on connectome-wide functional connectivity was driven by a distributed bilateral network including the thalamus and temporal regions.
Conclusion
These results were replicated across the three employed ML approaches. Connectome-wide functional connectivity permits differentiation of patients with schizophrenia from healthy controls at single-subject level with greater accuracy; this pattern of results is consistent with the ‘dysconnectivity hypothesis’ of schizophrenia, which states that the neural basis of the disorder is best understood in terms of system-level functional connectivity alterations.
We report the results of a computer enumeration that found that there are 3155 perfect 1-factorisations (P1Fs) of the complete graph $K_{16}$. Of these, 89 have a nontrivial automorphism group (correcting an earlier claim of 88 by Meszka and Rosa [‘Perfect 1-factorisations of $K_{16}$ with nontrivial automorphism group’, J. Combin. Math. Combin. Comput.47 (2003), 97–111]). We also (i) describe a new invariant which distinguishes between the P1Fs of $K_{16}$, (ii) observe that the new P1Fs produce no atomic Latin squares of order 15 and (iii) record P1Fs for a number of large orders that exceed prime powers by one.