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Hydrogenases are metalloenzymes that catalyze the reversible oxidation of molecular hydrogen into protons and electrons. For this purpose, [FeFe]-hydrogenases utilize a hexanuclear iron cofactor, the H-cluster. This biologically unique cofactor provides the enzyme with outstanding catalytic activities, but it is also highly oxygen sensitive. Under in vitro conditions, oxygen stable forms of the H-cluster denoted Htrans and Hinact can be generated via treatment with sulfide under oxidizing conditions. Herein, we show that an Htrans-like species forms spontaneously under intracellular conditions on a time scale of hours, concurrent with the cells ceasing H2 production. Addition of cysteine or sulfide during the maturation promotes the formation of this H-cluster state. Moreover, it is found that formation of the observed Htrans-like species is influenced by both steric factors and proton transfer, underscoring the importance of outer coordination sphere effects on H-cluster reactivity. [ABSTRACT FROM AUTHOR]

Abstract Disease progression modeling (DPM) of Alzheimer's disease (AD) aims at revealing long term pathological trajectories from short term clinical data. Along with the ability of providing a data-driven description of the natural evolution of the pathology, DPM has the potential of representing a valuable clinical instrument for automatic diagnosis, by explicitly describing the biomarker transition from normal to pathological stages along the disease time axis. In this work we reformulated DPM within a probabilistic setting to quantify the diagnostic uncertainty of individual disease severity in an hypothetical clinical scenario, with respect to missing measurements, biomarkers, and follow-up information. We show that the staging provided by the model on 582 amyloid positive testing individuals has high face validity with respect to the clinical diagnosis. Using follow-up measurements largely reduces the prediction uncertainties, while the transition from normal to pathological stages is mostly associated with the increase of brain hypo-metabolism, temporal atrophy, and worsening of clinical scores. The proposed formulation of DPM provides a statistical reference for the accurate probabilistic assessment of the pathological stage of de-novo individuals, and represents a valuable instrument for quantifying the variability and the diagnostic value of biomarkers across disease stages. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients. Therefore, AI applications in MS have the great potential of helping us better support the diagnosis, find markers for prognosis to eventually design more powerful randomised clinical trials and improve patient management in clinical practice and eventually understand the mechanisms of the disease. This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions. [ABSTRACT FROM AUTHOR]

The joint modeling of brain imaging information and genetic data is a promising research avenue to highlight the functional role of genes in determining the pathophysiological mechanisms of Alzheimer's disease (AD). However, since genome-wide association (GWA) studies are essentially limited to the exploration of statistical correlations between genetic variants and phenotype, the validation and interpretation of the findings are usually nontrivial and prone to false positives. To address this issue, in this work, we investigate the functional genetic mechanisms underlying brain atrophy in AD by studying the involvement of candidate variants in known genetic regulatory functions. This approach, here termed functional prioritization, aims at testing the sets of gene variants identified by high-dimensional multivariate statistical modeling with respect to known biological processes to introduce a biology-driven validation scheme. When applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, the functional prioritization allowed for identifying a link between tribbles pseudokinase 3 (TRIB3) and the stereotypical pattern of gray matter loss in AD, which was confirmed in an independent validation sample, and that provides evidence about the relation between this gene and known mechanisms of neurodegeneration. [ABSTRACT FROM AUTHOR]

We propose a framework for developing a comprehensive biophysical model that could predict and simulate realistic longitudinal MRIs of patients with Alzheimer's disease (AD). The framework includes three major building blocks: i) atrophy generation, ii) brain deformation, and iii) realistic MRI generation. Within this framework, this paper focuses on a detailed implementation of the brain deformation block with a carefully designed biomechanics-based tissue loss model. For a given baseline brain MRI, the model yields a deformation field imposing the desired atrophy at each voxel of the brain parenchyma while allowing the CSF to expand as required to globally compensate for the locally prescribed volume loss. Our approach is inspired by biomechanical principles and involves a system of equations similar to Stokes equations in fluid mechanics but with the presence of a non-zero mass source term. We use this model to simulate longitudinal MRIs by prescribing complex patterns of atrophy. We present experiments that provide an insight into the role of different biomechanical parameters in the model. The model allows simulating images with exactly the same tissue atrophy but with different underlying deformation fields in the image. We explore the influence of different spatial distributions of atrophy on the image appearance and on the measurements of atrophy reported by various global and local atrophy estimation algorithms. We also present a pipeline that allows evaluating atrophy estimation algorithms by simulating longitudinal MRIs from large number of real subject MRIs with complex subject-specific atrophy patterns. The proposed framework could help understand the implications of different model assumptions, regularization choices, and spatial priors for the detection and measurement of brain atrophy from longitudinal brain MRIs. [ABSTRACT FROM AUTHOR]

• A multi-channel model based on recurrent variational autoencoders was proposed to capture spatial and temporal evolution of AD using multimodal data. • Proposed model was evaluated on synthetic and real datasets. • Model outperforms a set of baselines for missing data reconstruction across modalities. [Display omitted] The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N = 897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5 % (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores. [ABSTRACT FROM AUTHOR]

Computational anatomy aims at developing models to understand the anatomical variability of organs and tissues. A widely used and validated instrument for comparing the anatomy in medical images is non-linear diffeomorphic registration which is based on a rich mathematical background. For instance, the 'large deformation diffeomorphic metric mapping' (LDDMM) framework defines a Riemannian setting by providing a right invariant metric on the tangent spaces, and solves the registration problem by computing geodesics parametrized by time-varying velocity fields. A simpler alternative based on stationary velocity fields (SVF) has been proposed, using the one-parameter subgroups from Lie groups theory. In spite of its better computational efficiency, the geometrical setting of the SVF is more vague, especially regarding the relationship between one-parameter subgroups and geodesics. In this work, we detail the properties of finite dimensional Lie groups that highlight the geometric foundations of one-parameter subgroups. We show that one can define a proper underlying geometric structure (an affine manifold) based on the canonical Cartan connections, for which one-parameter subgroups and their translations are geodesics. This geometric structure is perfectly compatible with all the group operations (left, right composition and inversion), contrarily to left- (or right-) invariant Riemannian metrics. Moreover, we derive closed-form expressions for the parallel transport. Then, we investigate the generalization of such properties to infinite dimensional Lie groups. We suggest that some of the theoretical objections might actually be ruled out by the practical implementation of both the LDDMM and the SVF frameworks for image registration. This leads us to a more practical study comparing the parameterization (initial velocity field) of metric and Cartan geodesics in the specific optimization context of longitudinal and inter-subject image registration.Our experimental results suggests that stationarity is a good approximation for longitudinal deformations, while metric geodesics notably differ from stationary ones for inter-subject registration, which involves much larger and non-physical deformations. Then, we turn to the practical comparison of five parallel transport techniques along one-parameter subgroups. Our results point out the fundamental role played by the numerical implementation, which may hide the theoretical differences between the different schemes. Interestingly, even if the parallel transport generally depends on the path used, an experiment comparing the Cartan parallel transport along the one-parameter subgroup and the LDDMM (metric) geodesics from inter-subject registration suggests that our parallel transport methods are not so sensitive to the path. [ABSTRACT FROM AUTHOR]

Background: Memantine is an approved symptomatic treatment for moderate to severe Alzheimer's disease that reduces the excitotoxic effects of hyperactive glutamatergic transmission. However, the exact mechanism of the effect of memantine in Alzheimer's disease patients is poorly understood. Importantly, the default mode network (DMN), which plays a key role in attention, is hypoactive in Alzheimer's disease and is under glutamatergic control. Objective: To assess the effect of memantine on the activity of the DMN in moderate to severe Alzheimer's disease. Methods: Functional magnetic resonance imaging (MRI) data from 15 patients with moderate to severe Alzheimer's disease, seven treated with memantine (mean ± SD age 77 ± 8 years, mean ± SD Mini-Mental State Examination [MMSE] score 16 ± 5) and eight with placebo (mean ± SD age 76 ± 6 years, mean±SD MMSE score 13±1), were acquired at baseline (T0) and after 6 months of treatment (T6). Resting state components were extracted after spatial normalization in individual patients with independent component analysis. The consistency of the components was assessed using ICASSO and the DMN was recognized through spatial correlation with a pre-defined template. Voxel-based statistical analyses were performed to study the change in DMN activity from T0 to T6 in the two groups. Results: At T0, the two groups showed similar DMN activity except in the precuneus and cuneus, where the patients who started treatment with memantine had slightly greater activity (p<0.05 corrected for familywise error [FWE]). The prospective comparison between T0 and T6 in the treated patients showed increased DMN activation mapping in the precuneus (p<0.05, FWE corrected), while the prospective comparison in the untreated patients did not show significant changes. The treatment×time interaction term was significant at p<0.05, FWE corrected. Conclusions: The results suggest a positive effect of memantine treatment in patients with moderate to severe Alzheimer's disease, resulting in an increased resting DMN activity in the precuneus region over 6 months. Future studies confirming the present findings are required to further demonstrate the beneficial effects of memantine on the DMN in Alzheimer's disease. [ABSTRACT FROM AUTHOR]

• Multi-contrast framework for 3D histology reconstruction that is robust against outliers and computationally efficient. • Graph theoretical approach that explicitly models accuracy and smoothness of the reconstructions. • Probabilistic model of spatial deformations optimized using linear programming. • 3D histology reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas and the corresponding mapping to MNI space. [Display omitted] Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ 2 norm, which can be minimised in closed form) and Laplacian (ℓ 1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest. [ABSTRACT FROM AUTHOR]

Digital impression provides several advantages in implant prosthodontics; however, its use in full-arch rehabilitations, especially immediately after surgery, has yet to be validated. The aim of this study was to retrospectively analyse the fit of immediate full-arch prostheses, fabricated using conventional or digital impressions. Patients requiring a full-arch immediate loading rehabilitation were divided into three groups: T1 (digital impression taken immediately after surgery), T2 (Preoperative digital impression, guided surgery—prefabricated temporary bridge) and C (conventional impression taken immediately after surgery). Immediate temporary prostheses were delivered within 24 h after surgery. X-rays were obtained at the time of prosthesis delivery and at the 2-year follow-up. Primary outcomes were cumulative survival rate (CSR) and prosthesis fit. Secondary outcomes were marginal bone level (MBL) and patient satisfaction. One hundred and fifty patients were treated from 2018 to 2020, with 50 in each group. Seven implants failed during the observation period. The CSR was 99% for T1, 98% for T2 and 99.5% for C. A statistically significant difference in prosthesis fit was found among T1 and T2 vs. C. A statistically significant difference was found in the MBL between T1 and C. The outcomes of the present study suggest that digital impression is a viable alternative to conventional protocols for the realisation of full-arch immediate loading prostheses. [ABSTRACT FROM AUTHOR]

Reports the relevance of integrating readily available diagnostic means in making correct diagnosis of adult colonic intussusception. Relevance of associating of abdominal radiography, hydrosoluble contrast enema and ultrasonography in emergency settings of colonic intussusception; Treatment of choice; Choice and timing of operation.

• Statistical modelling of brain-heart interaction by including prior cardiac physiological knowledge. • Biophysical modelling constrained by a model of joint heart-brain variability. • The model identifies physiologically plausible mechanisms and significant differences associated to clinical conditions. • We present the first study on patient-specific modelling relating cardiac function and brain damage. • This study is based on the largest presented cohort of personalised subjects to date (3445 individuals). [Display omitted] Initiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of the cardiovascular function, such as cardiac contractility, cannot be measured in-vivo. While these non-observable parameters can be estimated by means of biophysical models, their personalisation is generally an ill-posed problem, often lacking critical data and only applied to small datasets. Therefore, to jointly study brain and heart, we propose an approach in which the parameter personalisation of a lumped cardiovascular model is constrained by the statistical relationships observed between model parameters and brain-volumetric indices extracted from imaging, i.e. ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We explored the plausibility of the learnt relationships by inferring the model parameters conditioned on the absence of part of the target clinical features, applying this framework in a cohort of more than 3 000 subjects and in a pathological subgroup of 59 subjects diagnosed with atrial fibrillation. Our results demonstrate the impact of such external features in the cardiovascular model personalisation by learning more informative parameter-space constraints. Moreover, physiologically plausible mechanisms are captured through these personalised models as well as significant differences associated to specific clinical conditions. [ABSTRACT FROM AUTHOR]

• A framework for spatio–temporal modeling of protein dynamics over brain networks from short term imaging data is proposed (GPPM–DS). • GPPM–DS enables the investigation of bio–mechanical hypotheses governing disease progression via Bayesian model comparison. • GPPM–DS provides new insights on the mechanisms of amyloid deposition in Alzheimer's disease, indicating the "Accumulation–Clearance–Propagation" model as the best suited dynamical system for interpretation of amyloid dynamics. • GPPM–DS achieves accurate predictions of individual protein deposition in unseen data and provides plausible simulations of protein propagation. We introduce a theoretical framework for estimating, comparing and interpreting mechanistic hypotheses on long term protein propagation across brain networks in neurodegenerative disorders (ND). The model is expressed within a Bayesian non-parametric regression setting, where mechanisms of protein dynamics are inferred by means of gradient matching on dynamical systems (DS). The Bayesian formalism, combined with stochastic variational inference, naturally allows for model comparison via assessment of model evidence, while providing uncertainty quantification of causal relationship underlying protein progressions. When applied to in–vivo AV45-PET brain imaging data measuring topographic amyloid deposition in Alzheimer's disease (AD), our model identified the mechanisms of accumulation, clearance and propagation as the best suited DS for bio-mechanical description of amyloid dynamics in AD, enabling realistic and accurate personalized simulation of amyloidosis. [ABSTRACT FROM AUTHOR]

• Cycle-consistent perceptual loss improves the quality of TDOCT-to-SDOCT synthesis. • Ensemble of GANs leads to increased segmentation precision of synthesized SDOCT. • Significantly better treatment arm separation in the UK Glaucoma Treatment Study. • The sample size required to distinguish the treatment arms is markedly lower. • RNFL thinning rate responds to treatment similarly to the rate of VF deterioration. Albeit spectral-domain OCT (SDOCT) is now in clinical use for glaucoma management, published clinical trials relied on time-domain OCT (TDOCT) which is characterized by low signal-to-noise ratio, leading to low statistical power. For this reason, such trials require large numbers of patients observed over long intervals and become more costly. We propose a probabilistic ensemble model and a cycle-consistent perceptual loss for improving the statistical power of trials utilizing TDOCT. TDOCT are converted to synthesized SDOCT and segmented via Bayesian fusion of an ensemble of GANs. The final retinal nerve fibre layer segmentation is obtained automatically on an averaged synthesized image using label fusion. We benchmark different networks using i) GAN, ii) Wasserstein GAN (WGAN) (iii) GAN + perceptual loss and iv) WGAN + perceptual loss. For training and validation, an independent dataset is used, while testing is performed on the UK Glaucoma Treatment Study (UKGTS), i.e. a TDOCT-based trial. We quantify the statistical power of the measurements obtained with our method, as compared with those derived from the original TDOCT. The results provide new insights into the UKGTS, showing a significantly better separation between treatment arms, while improving the statistical power of TDOCT on par with visual field measurements. [ABSTRACT FROM AUTHOR]

Small molecules in solution may interfere with mechanistic investigations, as they can affect the stability of catalytic states and produce off-cycle states that can be mistaken for catalytically relevant species. Here we show that the hydride state (Hhyd), a proposed central intermediate in the catalytic cycle of [FeFe]-hydrogenase, can be formed in wild-type [FeFe]-hydrogenases treated with H2 in absence of other, non-biological, reductants. Moreover, we reveal a new state with unclear role in catalysis induced by common low pH buffers. [ABSTRACT FROM AUTHOR]

Epilepsies have numerous specific mechanisms. The understanding of neural dynamics leading to seizures is important for disclosing pathological mechanisms and developing therapeutic approaches. We investigated electrographic activities and neural dynamics leading to convulsive seizures in patients and mouse models of Dravet syndrome (DS), a developmental and epileptic encephalopathy in which hypoexcitability of GABAergic neurons is considered to be the main dysfunction. We analyzed EEGs from DS patients carrying a SCN1A pathogenic variant, as well as epidural electrocorticograms, hippocampal local field potentials, and hippocampal single-unit neuronal activities in Scn1a+/- and Scn1aRH/+ DS mice. Strikingly, most seizures had low-voltage-fast onset in both patients and mice, which is thought to be generated by hyperactivity of GABAergic interneurons, the opposite of the main pathological mechanism of DS. Analyzing single-unit recordings, we observed that temporal disorganization of the firing of putative interneurons in the period immediately before the seizure (preictal) precedes the increase of their activity at seizure onset, together with the entire neuronal network. Moreover, we found early signatures of the preictal period in the spectral features of hippocampal and cortical field potential of Scn1a mice and of patients' EEG, which are consistent with the dysfunctions that we observed in single neurons and that allowed seizure prediction. Therefore, the perturbed preictal activity of interneurons leads to their hyperactivity at the onset of generalized seizures, which have low-voltage-fast features that are similar to those observed in other epilepsies and are triggered by hyperactivity of GABAergic neurons. Preictal spectral features may be used as predictive seizure biomarkers. [ABSTRACT FROM AUTHOR]

Osteosarcoma is the most common malignant primary bone cancer, but it is infrequently reported in cats. Feline appendicular osteosarcoma typically exhibits good prognosis when treated with surgery alone. A retrospective multi‐institutional study was conducted to identify possible prognostic factors. Cats diagnosed with appendicular osteosarcoma were included if initial staging and follow‐up information were available. Data including signalment, tumour characteristics, treatment modalities, and survival outcomes were collected and analysed. Fifty‐six cats were included; the femur was the most frequently affected bone. Eight cats had distant metastasis at admission and an additional 9 developed metastatic disease during follow‐up, resulting in an overall metastatic rate of 30%. Forty‐nine (87.5%) cats underwent surgery, and 4 also received adjuvant chemotherapy. Among operated cats, median time to local progression (TTLP), time to distant progression and tumour‐specific survival (TSS) were not reached. One‐ and 2‐year survival rates were 66% and 55%, respectively. Seven (12.5%) cats received no treatment; 1‐ and 2‐year survival rates were 25% and 0%, respectively. Operated cats had significantly longer TTLP (P <.001) and TSS (P =.001) compared with non‐operated cats. Among operated cats, young age negatively impacted local tumour progression, while the presence of distant metastasis at diagnosis was associated with a higher risk of tumour‐related death. This study reaffirms the good prognosis for cats with appendicular osteosarcoma undergoing surgery, but sheds light on some additional factors to consider. Accurate initial staging is recommended, as the metastatic rate may exceed many previous estimations. Surgery substantially extends survival time, whereas the role of chemotherapy remains uncertain. [ABSTRACT FROM AUTHOR]

In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a generative model. A reference morphology is deformed along specific trajectories to match subject specific morphologies. It is used to define two imaging progression markers: 1) a morphological age and 2) a disease score. These markers can be computed regionally in any brain region. The approach is evaluated on brain structural magnetic resonance images (MRI) from the ADNI database. The model is first estimated on a control population using longitudinal data, then, for each testing subject, the markers are computed cross-sectionally for each acquisition. The longitudinal evolution of these markers is then studied in relation with the clinical diagnosis of the subjects and used to generate possible morphological evolutions. In the model, the morphological changes associated with normal aging are mainly found around the ventricles, while the Alzheimer's disease specific changes are located in the temporal lobe and the hippocampal area. The statistical analysis of these markers highlights differences between clinical conditions even though the inter-subject variability is quite high. The model is also generative since it can be used to simulate plausible morphological trajectories associated with the disease. Our method quantifies two interpretable scalar imaging biomarkers assessing respectively the effects of aging and disease on brain morphology, at the individual and population level. These markers confirm the presence of an accelerated apparent aging component in Alzheimer's patients but they also highlight specific morphological changes that can help discriminate clinical conditions even in prodromal stages. More generally, the joint modeling of normal and pathological evolutions shows promising results to describe age-related brain diseases over long time scales. [ABSTRACT FROM AUTHOR]

Abstract Current models of progression in neurodegenerative diseases use neuroimaging measures that are averaged across pre-defined regions of interest (ROIs). Such models are unable to recover fine details of atrophy patterns; they tend to impose an assumption of strong spatial correlation within each ROI and no correlation among ROIs. Such assumptions may be violated by the influence of underlying brain network connectivity on pathology propagation – a strong hypothesis e.g. in Alzheimer's Disease. Here we present DIVE: D ata-driven I nference of V ertexwise E volution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-term patterns of brain pathology from short-term longitudinal data sets. DIVE clusters vertex-wise (i.e. point-wise) biomarker measurements on the cortical surface that have similar temporal dynamics across a patient population, and concurrently estimates an average trajectory of vertex measurements in each cluster. DIVE uniquely outputs a parcellation of the cortex into areas with common progression patterns, leading to a new signature for individual diseases. DIVE further estimates the disease stage and progression speed for every visit of every subject, potentially enhancing stratification for clinical trials or management. On simulated data, DIVE can recover ground truth clusters and their underlying trajectory, provided the average trajectories are sufficiently different between clusters. We demonstrate DIVE on data from two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Dementia Research Centre (DRC), UK. The DRC cohort contains patients with Posterior Cortical Atrophy (PCA) as well as typical Alzheimer's disease (tAD). DIVE finds similar spatial patterns of atrophy for tAD subjects in the two independent datasets (ADNI and DRC), and further reveals distinct patterns of pathology in different diseases (tAD vs PCA) and for distinct types of biomarker data – cortical thickness from Magnetic Resonance Imaging (MRI) vs amyloid load from Positron Emission Tomography (PET). We demonstrate that DIVE stages have potential clinical relevance, despite being based only on imaging data, by showing that the stages correlate with cognitive test scores. Finally, DIVE can be used to estimate a fine-grained spatial distribution of pathology in the brain using any kind of voxelwise or vertexwise measures including Jacobian compression maps, fractional anisotropy (FA) maps from diffusion tensor imaging (DTI) or other PET measures. Graphical abstract Image 1 Highlights • DIVE estimates fine-grained spatial patterns of pathology in neurodegenerative diseases, not available with ROI-based methods. • Further estimates the temporal evolution of brain pathology and subject specific disease stages. • Forecasts the evolution of pathology at population- and subject-level. • Finds plausible patterns of pathology in four distinct datasets comprising different diseases and modalities. • DIVE, available online, can be applied to any registered voxelwise images (e.g.MRI, PET, DTI). [ABSTRACT FROM AUTHOR]

Identifying genetic risk factors underpinning different aspects of Alzheimer's disease has the potential to provide important insights into pathogenesis. Moving away from simple case-control definitions, there is considerable interest in using quantitative endophenotypes, such as those derived from imaging as outcome measures. Previous genome-wide association studies of imaging-derived biomarkers in sporadic late-onset Alzheimer's disease focused only on phenotypes derived from single imaging modalities. In contrast, we computed a novel multi-modal neuroimaging phenotype comprising cortical amyloid burden and bilateral hippocampal volume. Both imaging biomarkers were used as input to a disease progression modelling algorithm, which estimates the biomarkers' long-term evolution curves from population-based longitudinal data. Among other parameters, the algorithm computes the shift in time required to optimally align a subjects' biomarker trajectories with these population curves. This time shift serves as a disease progression score and it was used as a quantitative trait in a discovery genome-wide association study with n = 944 subjects from the Alzheimer's Disease Neuroimaging Initiative database diagnosed as Alzheimer's disease, mild cognitive impairment or healthy at the time of imaging. We identified a genome-wide significant locus implicating LCORL (rs6850306, chromosome 4; P = 1.03 × 10-8). The top variant rs6850306 was found to act as an expression quantitative trait locus for LCORL in brain tissue. The clinical role of rs6850306 in conversion from healthy ageing to mild cognitive impairment or Alzheimer's disease was further validated in an independent cohort comprising healthy, older subjects from the National Alzheimer's Coordinating Center database. Specifically, possession of a minor allele at rs6850306 was protective against conversion from mild cognitive impairment to Alzheimer's disease in the National Alzheimer's Coordinating Center cohort (hazard ratio = 0.593, 95% confidence interval = 0.387-0.907, n = 911, PBonf = 0.032), in keeping with the negative direction of effect reported in the genome-wide association study (βdisease progression score = -0.07 ± 0.01). The implicated locus is linked to genes with known connections to Alzheimer's disease pathophysiology and other neurodegenerative diseases. Using multimodal imaging phenotypes in association studies may assist in unveiling the genetic drivers of the onset and progression of complex diseases. [ABSTRACT FROM AUTHOR]

Nickel-containing enzymes are diverse in terms of function and active site structure. In many cases, the biosynthesis of the active site depends on accessory proteins which transport and insert the Ni ion. We review and discuss the literature related to the maturation of carbon monoxide dehydrogenases (CODH) which bear a nickel-containing active site consisting of a [Ni-4Fe-4S] center called the C-cluster. The maturation of this center has been much less studied than that of other nickel-containing enzymes such as urease and NiFe hydrogenase. Several proteins present in certain CODH operons, including the nickel-binding proteins CooT and CooJ, still have unclear functions. We question the conception that the maturation of all CODH depends on the accessory protein CooC described as essential for nickel insertion into the active site. The available literature reveals biological variations in CODH active site biosynthesis. [ABSTRACT FROM AUTHOR]

Describes a graft reconstruction technique using an intraluminal balloon catheter in ruptured abdominal aortic aneurysms. Complications of replacing ruptured abdominal aortic aneurysms; Dissection of the aneurysmal neck after opening and emptying aneurysms.