Autism spectrum disorders (ASDs) are neurodevelopmental syndromes characterised by repetitive behaviours and restricted interests, impairments in social behaviour and relations, and in language and ...communication. These symptoms are also observed in a number of developmental disorders of known origin, including Fragile X Syndrome, Rett Syndrome, and Foetal Anticonvulsant Syndrome. While these conditions have diverse etiologies, and poorly understood pathologies, emerging evidence suggests that they may all be linked to dysfunction in particular aspects of GABAergic inhibitory signalling in the brain. We review evidence from genetics, molecular neurobiology and systems neuroscience relating to the role of GABA in these conditions. We conclude by discussing how these deficits may relate to the specific symptoms observed.
Data-driven decompositions are becoming essential tools in fluid dynamics, allowing for tracking the evolution of coherent patterns in large datasets, and for constructing low-order models of complex ...phenomena. In this work, we analyse the main limits of two popular decompositions, namely the proper orthogonal decomposition (POD) and the dynamic mode decomposition (DMD), and we propose a novel decomposition which allows for enhanced feature detection capabilities. This novel decomposition is referred to as multi-scale proper orthogonal decomposition (mPOD) and combines multi-resolution analysis (MRA) with a standard POD. Using MRA, the mPOD splits the correlation matrix into the contribution of different scales, retaining non-overlapping portions of the correlation spectra; using the standard POD, the mPOD extracts the optimal basis from each scale. After introducing a matrix factorization framework for data-driven decompositions, the MRA is formulated via one- and two-dimensional filter banks for the dataset and the correlation matrix respectively. The validation of the mPOD, and a comparison with the discrete Fourier transform (DFT), DMD and POD are provided in three test cases. These include a synthetic test case, a numerical simulation of a nonlinear advection–diffusion problem and an experimental dataset obtained by the time-resolved particle image velocimetry (TR-PIV) of an impinging gas jet. For each of these examples, the decompositions are compared in terms of convergence, feature detection capabilities and time–frequency localization.
The oligonucleotide d(TX)9, which consists of an octadecamer sequence with alternating non‐canonical 7‐deazaadenine (X) and canonical thymine (T) as the nucleobases, was synthesized and shown to ...hybridize into double‐stranded DNA through the formation of hydrogen‐bonded Watson–Crick base pairs. dsDNA with metal‐mediated base pairs was then obtained by selectively replacing W‐C hydrogen bonds by coordination bonds to central silver(I) ions. The oligonucleotide I adopts a duplex structure in the absence of Ag+ ions, and its stability is significantly enhanced in the presence of Ag+ ions while its double‐helix structure is retained. Temperature‐dependent UV spectroscopy, circular dichroism spectroscopy, and ESI mass spectrometry were used to confirm the selective formation of the silver(I)‐mediated base pairs. This strategy could become useful for preparing stable metallo‐DNA‐based nanostructures.
Substitution of the original hydrogen bonds by silver(I) coordination bonds in a double‐stranded oligonucleotide comprising Watson–Crick‐paired 7‐deazaadenine and thymine nucleobases leads to a sequential array of metal‐mediated base pairs. The stability of the silver‐containing structure was significantly higher than that of the parent compound while the double‐helix structure was retained.
Vimentin intermediate filament expression is a hallmark of epithelial-to-mesenchymal transitions, and vimentin is involved in the maintenance of cell mechanical properties, cell motility, adhesion, ...and other signaling pathways. A common feature of vimentin-expressing cells is their routine exposure to mechanical stress. Intermediate filaments are unique among cytoskeletal polymers in resisting large deformations in vitro, yet vimentin’s mechanical role in the cell is not clearly understood. We use atomic force microscopy to compare the viscoelastic properties of normal and vimentin-null (vim−/−) mouse embryo fibroblasts (mEFs) on substrates of different stiffnesses, spread to different areas, and subjected to different compression patterns. In minimally perturbed mEF, vimentin contributes little to the elastic modulus at any indentation depth in cells spread to average areas. On a hard substrate however, the elastic moduli of maximally spread mEFs are greater than those of vim−/−mEF. Comparison of the plastic deformation resulting from controlled compression of the cell cortex shows that vimentin’s enhancement of elastic behavior increases with substrate stiffness. The elastic moduli of normal mEFs are more stable over time than those of vim−/−mEFs when cells are subject to ongoing oscillatory compression, particularly on a soft substrate. In contrast, increasing compressive strain over time shows a greater role for vimentin on a hard substrate. Under both conditions, vim−/−mEFs exhibit more variable responses, indicating a loss of regulation. Finally, normal mEFs are more contractile in three-dimensional collagen gels when seeded at low density, when cell-matrix contacts dominate, whereas contractility of vim−/−mEF is greater at higher densities when cell-cell contacts are abundant. Addition of fibronectin to gel constructs equalizes the contractility of the two cell types. These results show that the Young’s moduli of normal and vim−/−mEFs are substrate stiffness dependent even when the spread area is similar, and that vimentin protects against compressive stress and preserves mechanical integrity by enhancing cell elastic behavior.
Germline mutations in DNA damage repair (DDR) genes are identified in a significant proportion of patients with metastatic prostate cancer, but the clinical implications of these genes remain ...unclear. This prospective multicenter cohort study evaluated the prevalence and effect of germline DDR (gDDR) mutations on metastatic castration-resistance prostate cancer (mCRPC) outcomes.
Unselected patients were enrolled at diagnosis of mCRPC and were screened for gDDR mutations in 107 genes. The primary aim was to assess the impact of ATM/BRCA1/BRCA2/ PALB2 germline mutations on cause-specific survival (CSS) from diagnosis of mCRPC. Secondary aims included the association of gDDR subgroups with response outcomes for mCRPC treatments. Combined progression-free survival from the first systemic therapy (PFS) until progression on the second systemic therapy (PFS2) was also explored.
We identified 68 carriers (16.2%) of 419 eligible patients, including 14 with BRCA2, eight with ATM, four with BRCA1, and none with PALB2 mutations. The study did not reach its primary end point, because the difference in CSS between ATM/BRCA1/BRCA2/PALB2 carriers and noncarriers was not statistically significant (23.3 v 33.2 months; P = .264). CSS was halved in germline BRCA2 (g BRCA2) carriers (17.4 v 33.2 months; P = .027), and g BRCA2 mutations were identified as an independent prognostic factor for CCS (hazard ratio HR, 2.11; P = .033). Significant interactions between g BRCA2 status and treatment type (androgen signaling inhibitor v taxane therapy) were observed (CSS adjusted P = .014; PFS2 adjusted P = .005). CSS (24.0 v 17.0 months) and PFS2 (18.9 v 8.6 months) were greater in g BRCA2 carriers treated in first line with abiraterone or enzalutamide compared with taxanes. Clinical outcomes did not differ by treatment type in noncarriers.
g BRCA2 mutations have a deleterious impact on mCRPC outcomes that may be affected by the first line of treatment used. Determination of g BRCA2 status may be of assistance for the selection of the initial treatment in mCRPC. Nonetheless, confirmatory studies are required before these results can support a change in clinical practice.
Drylands (hyperarid, arid, semiarid, and dry subhumid ecosystems) cover almost half of Earth's land surface and are highly vulnerable to environmental pressures. Here we provide an inventory of soil ...properties including carbon (C), nitrogen (N), and phosphorus (P) stocks within the current boundaries of drylands, aimed at serving as a benchmark in the face of future challenges including increased population, food security, desertification, and climate change. Aridity limits plant production and results in poorly developed soils, with coarse texture, low C:N and C:P, scarce organic matter, and high vulnerability to erosion. Dryland soils store 646 Pg of organic C to 2 m, the equivalent of 32% of the global soil organic C pool. The magnitude of the historic loss of C from dryland soils due to human land use and cover change and their typically low C:N and C:P suggest high potential to build up soil organic matter, but coarse soil textures may limit protection and stabilization processes. Restoring, preserving, and increasing soil organic matter in drylands may help slow down rising levels of atmospheric carbon dioxide by sequestering C, and is strongly needed to enhance food security and reduce the risk of land degradation and desertification.
Sensory processing abnormalities are common in autism spectrum disorders (ASD), and now form part of the Diagnostic and Statistical Manual 5th Edition (DSM-5) diagnostic criteria, but it is unclear ...whether they characterize the ‘broader phenotype’ of the disorder. We recruited adults (
n
= 772) with and without an ASD and administered the Autism Quotient (AQ) along with the Adult/Adolescent Sensory Profile (AASP), the Cardiff Anomalous Perceptions Scale (CAPS), and the Glasgow Sensory Questionnaire (GSQ), all questionnaire measures of abnormal sensory responsivity. Autism traits were significantly correlated with scores on all three sensory scales (AQ/GSQ
r
= 0.478; AQ/AASP
r
= 0.344; AQ/CAPS
r
= 0.333; all
p
< 0.001). This relationship was linear across the whole range of AQ scores and was true both in those with, and without, an ASD diagnosis. It survived correction for anxiety trait scores, and other potential confounds such as mental illness and migraine.
Increased risks of lung and bladder cancer have been observed in populations exposed to high levels of inorganic arsenic. However, studies at lower exposures (i.e., less than 100 μg/l in water) have ...shown inconsistent results. We therefore conducted an ecological analysis of the association between historical drinking water arsenic concentrations and lung and bladder cancer incidence in U.S. counties. We used drinking water arsenic concentrations measured by the U.S. Geological Survey and state agencies in the 1980s and 1990s as proxies for historical exposures in counties where public groundwater systems and private wells are important sources of drinking water. Relationships between arsenic levels and cancer incidence in 2006-2010 were explored by Poisson regression analyses, adjusted for groundwater dependence and important demographic covariates. The median and 95th percentile county mean arsenic concentrations were 1.5 and 15.4 μg/l, respectively. Water arsenic concentrations were significant and positively associated with female and male bladder cancer, and with female lung cancer. Our findings support an association between low water arsenic concentrations and lung and bladder cancer incidence in the United States. However, the limitations of the ecological study design suggest caution in interpreting these results.
Background
Metabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which ...project data into a latent structure. Biological data are often non-linear, so it is reasonable to hypothesize that metabolomics data may also have a non-linear latent structure, which in turn would be best modelled using non-linear equations. A non-linear ML method with a similar projection equation structure to PLS is artificial neural networks (ANNs). While ANNs were first applied to metabolic profiling data in the 1990s, the lack of community acceptance combined with limitations in computational capacity and the lack of volume of data for robust non-linear model optimisation inhibited their widespread use. Due to recent advances in computational power, modelling improvements, community acceptance, and the more demanding needs for data science, ANNs have made a recent resurgence in interest across research communities, including a small yet growing usage in metabolomics. As metabolomics experiments become more complex and start to be integrated with other omics data, there is potential for ANNs to become a viable alternative to linear projection methods.
Aim of review
We aim to first describe ANNs and their structural equivalence to linear projection-based methods, including PLS regression. We then review the historical, current, and future uses of ANNs in the field of metabolomics.
Key scientific concept of review
Is metabolomics ready for the return of artificial neural networks?
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