The influence of serum, glucose and oxygen on intervertebral disc cell growth in vitro: implications for degenerative disc disease

The avascular nature of the human intervertebral disc (IVD) is thought to play a major role in disc pathophysiology by limiting nutrient supply to resident IVD cells. In the human IVD, the central IVD cells at maturity are normally chondrocytic in phenotype. However, abnormal cell phenotypes have been associated with degenerative disc diseases, including cell proliferation and cluster formation, cell death, stellate morphologies, and cell senescence. Therefore, we have examined the relative influence of possible blood-borne factors on the growth characteristics of IVD cells in vitro

Statistically Motivated Second Order Pooling

Second-order pooling, a.k.a.~bilinear pooling, has proven effective for deep learning based visual recognition. However, the resulting second-order networks yield a final representation that is orders of magnitude larger than that of standard, first-order ones, making them memory-intensive and cumbersome to deploy. Here, we introduce a general, parametric compression strategy that can produce more compact representations than existing compression techniques, yet outperform both compressed and uncompressed second-order models. Our approach is motivated by a statistical analysis of the network's activations, relying on operations that lead to a Gaussian-distributed final representation, as inherently used by first-order deep networks. As evidenced by our experiments, this lets us outperform the state-of-the-art first-order and second-order models on several benchmark recognition datasets.Comment: Accepted to ECCV 2018. Camera ready version. 14 page, 5 figures, 3 table

Dynamics of Cortical Degeneration Over a Decade in Huntington's Disease

BACKGROUND: Characterizing changing brain structure in neurodegeneration is fundamental to understanding longterm effects of pathology and ultimately providing therapeutic targets. It is well established that Huntington’s disease (HD) gene carriers undergo progressive brain changes during the course of disease, yet the long-term trajectory of cortical atrophy is not well defined. Given that genetic therapies currently tested in HD are primarily expected to target the cortex, understanding atrophy across this region is essential. METHODS: Capitalizing on a unique longitudinal dataset with a minimum of 3 and maximum of 7 brain scans from 49 HD gene carriers and 49 age-matched control subjects, we implemented a novel dynamical systems approach to infer patterns of regional neurodegeneration over 10 years. We use Bayesian hierarchical modeling to map participant- and group-level trajectories of atrophy spatially and temporally, additionally relating atrophy to the genetic marker of HD (CAG-repeat length) and motor and cognitive symptoms. RESULTS: We show, for the first time, that neurodegenerative changes exhibit complex temporal dynamics with substantial regional variation around the point of clinical diagnosis. Although widespread group differences were seen across the cortex, the occipital and parietal regions undergo the greatest rate of cortical atrophy. We have established links between atrophy and genetic markers of HD while demonstrating that specific cortical changes predict decline in motor and cognitive performance. CONCLUSIONS: HD gene carriers display regional variability in the spatial pattern of cortical atrophy, which relates to genetic factors and motor and cognitive symptoms. Our findings indicate a complex pattern of neuronal loss, which enables greater characterization of HD progression

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

Objective Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD. Method A multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy. Results Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models. Interpretation We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials

Mathematics difficulties in extremely preterm children : evidence of a specific deficit in basic mathematics processing

Background: Extremely preterm (EP, <26 wk gestation) children have been observed to have poor academic achievement in comparison to their term-born peers, especially in mathematics. This study investigated potential underlying causes of this difficulty. Methods: A total of 219 EP participants were compared with 153 term-born control children at 11 y of age. All children were assessed by a psychologist on a battery of standardized cognitive tests and a number estimation test assessing children’s numerical representations. Results: EP children underperformed in all tests in comparison with the term controls (the majority of Ps < 0.001). Different underlying relationships between performance on the number estimation test and mathematical achievement were found in EP as compared with control children. That is, even after controlling for cognitive ability, a relationship between number representations and mathematical performance persisted for EP children only (EP: r = 0.346, n = 186, P < 0.001; control: r = 0.095, n = 146, P = 0.256). Conclusion: Interventions for EP children may target improving children’s numerical representations in order to subsequently remediate their mathematical skills

Screening for Parkinson's disease with response time batteries: A pilot study

BACKGROUND: Although significant response time deficits (both reaction time and movement time) have been identified in numerous studies of patients with Parkinson's disease (PD), few attempts have been made to evaluate the use of these measures in screening for PD. METHODS: Receiver operator characteristic curves were used to identify cutoff scores for a unit-weighted composite of two choice response tasks in a sample of 40 patients and 40 healthy participants. These scores were then cross-validated in an independent sample of 20 patients and 20 healthy participants. RESULTS: The unit-weighted movement time composite demonstrated high sensitivity (90%) and specificity (90%) in the identification of PD. Movement time was also significantly correlated (r = 0.59, p < 0.025) with the motor score of the Unified Parkinson's Disease Rating Scale (UPDRS). CONCLUSIONS: Measures of chronometric speed, assessed without the use of biomechanically complex movements, have a potential role in screening for PD. Furthermore, the significant correlation between movement time and UPDRS motor score suggests that movement time may be useful in the quantification of PD severity

A Multi-Study Model-Based Evaluation of the Sequence of Imaging and Clinical Biomarker Changes in Huntington's Disease

Understanding the order and progression of change in biomarkers of neurodegeneration is essential to detect the effects of pharmacological interventions on these biomarkers. In Huntington’s disease (HD), motor, cognitive and MRI biomarkers are currently used in clinical trials of drug efficacy. Here for the first time we use directly compare data from three large observational studies of HD (total N = 532) using a probabilistic event-based model (EBM) to characterise the order in which motor, cognitive and MRI biomarkers become abnormal. We also investigate the impact of the genetic cause of HD, cytosine-adenine-guanine (CAG) repeat length, on progression through these stages. We find that EBM uncovers a broadly consistent order of events across all three studies; that EBM stage reflects clinical stage; and that EBM stage is related to age and genetic burden. Our findings indicate that measures of subcortical and white matter volume become abnormal prior to clinical and cognitive biomarkers. Importantly, CAG repeat length has a large impact on the timing of onset of each stage and progression through the stages, with a longer repeat length resulting in earlier onset and faster progression. Our results can be used to help design clinical trials of treatments for Huntington’s disease, influencing the choice of biomarkers and the recruitment of participants

Friends of hot Jupiters. II. No correspondence between hot-Jupiter spin-orbit misalignment and the incidence of directly imaged stellar companions

This is the final version of the article. Available from American Astronomical Society / IOP Publishing via the DOI in this record.Multi-star systems are common, yet little is known about a stellar companion's influence on the formation and evolution of planetary systems. For instance, stellar companions may have facilitated the inward migration of hot Jupiters toward to their present day positions. Many observed short-period gas giant planets also have orbits that are misaligned with respect to their star's spin axis, which has also been attributed to the presence of a massive outer companion on a non-coplanar orbit. We present the results of a multi-band direct imaging survey using Keck NIRC2 to measure the fraction of short-period gas giant planets found in multi-star systems. Over three years, we completed a survey of 50 targets ("Friends of Hot Jupiters") with 27 targets showing some signature of multi-body interaction (misaligned or eccentric orbits) and 23 targets in a control sample (well-aligned and circular orbits). We report the masses, projected separations, and confirmed common proper motion for the 19 stellar companions found around 17 stars. Correcting for survey incompleteness, we report companion fractions of 48% ± 9%, 47% ± 12%, and 51% ± 13% in our total, misaligned/eccentric, and control samples, respectively. This total stellar companion fraction is 2.8σ larger than the fraction of field stars with companions approximately 50-2000 AU. We observe no correlation between misaligned/eccentric hot Jupiter systems and the incidence of stellar companions. Combining this result with our previous radial velocity survey, we determine that 72% ± 16% of hot Jupiters are part of multi-planet and/or multi-star systems.This work was supported by NASA grant NNX14AD24G. H.N. is grateful for funding support from the Natural Sciences and Engineering Research Council of Canada. J.A.J. gratefully acknowledges support from generous fellowships from the David & Lucile Packard and Alfred P. Sloan foundations

The effect of crystallization time and temperature on Hydrothermal Synthesis of Zeolite Nax from Bongawan Kaolin

Hydrothermally synthesised zeolite NaX was produced by using kaolin procured from Kg. Gading, Bongawan. The kaolin was treated using sodium hexametaphosphate and calcined at 800oC to form metakaolin. Treated kaolin and prepared metakaolin were characterized using X-ray Fluorescence (XRF) and X-ray Diffraction (XRD). Reaction mixture was obtained by mixing metakaolin, sodium hydroxide and sodium silicate. The reaction mixture underwent aging for 15 hours before they were crystallized at various crystallization times (0 - 48 hours) and temperatures (80 – 130oC). The effect of crystallization time and temperature was studied using SEM and XRD. Optimum time and temperature for the synthesis was found to be 8 hours at 100 oC, respectively