Large-scale additive manufacturing processes for construction utilise computer-controlled placement of extruded cement-based mortar to create physical objects layer-by-layer. Demonstrated ...applications include component manufacture and placement of in-situ walls for buildings. These applications vary the constraints on design parameters and present different technical issues for the production process. In this paper, published and new work are utilised to explore the relationship between fresh and hardened paste, mortar, and concrete material properties and how they influence the geometry of the created object. Findings are classified by construction application to create a matrix of issues that identifies the spectrum of future research exploration in this emerging field.
Neuroinflammation is a key part of the etio-pathogenesis of Alzheimer's disease (AD). We tested the relationship between neuroinflammation and the disruption of functional connectivity in large-scale ...networks, and their joint influence on cognitive impairment. We combined
CPK11195 positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) in 28 patients (12 females/16 males) with clinical diagnosis of probable AD or mild cognitive impairment with positive PET biomarker for amyloid, and 14 age-, sex-, and education-matched healthy controls (8 females/6 males). Source-based "inflammetry" was used to extract principal components of
CPK11195 PET signal variance across all participants. rs-fMRI data were preprocessed via independent component analyses to classify neuronal and non-neuronal signals. Multiple linear regression models identified sources of signal covariance between neuroinflammation and brain connectivity profiles, in relation to the diagnostic group (patients, controls) and cognitive status.Patients showed significantly higher
CPK11195 binding relative to controls, in a distributed spatial pattern including the hippocampus, frontal, and inferior temporal cortex. Patients with enhanced loading on this
CPK11195 binding distribution displayed diffuse abnormal functional connectivity. The expression of a stronger association between such abnormal connectivity and higher levels of neuroinflammation correlated with worse cognitive deficits.Our study suggests that neuroinflammation relates to the pathophysiological changes in network function that underlie cognitive deficits in Alzheimer's disease. Neuroinflammation, and its association with functionally-relevant reorganization of brain networks, is proposed as a target for emerging immunotherapeutic strategies aimed at preventing or slowing the emergence of dementia.
Neuroinflammation is an important aspect of Alzheimer's disease (AD), but it was not known whether the influence of neuroinflammation on brain network function in humans was important for cognitive deficit. Our study provides clear evidence that
neuroinflammation in AD impairs large-scale network connectivity; and that the link between neuro inflammation and functional network connectivity is relevant to cognitive impairment. We suggest that future studies should address how neuroinflammation relates to network function as AD progresses, and whether the neuroinflammation in AD is reversible, as the basis of immunotherapeutic strategies to slow the progression of AD.
The unprecedented availability of 6-hourly data from a multi-model GCM ensemble in the CMIP5 data archive presents the new opportunity to dynamically downscale multiple GCMs to develop ...high-resolution climate projections relevant to detailed assessment of climate vulnerability and climate change impacts. This enables the development of high resolution projections derived from the same set of models that are used to characterise the range of future climate changes at the global and large-scale, and as assessed in the IPCC AR5. However, the technical and human resource required to dynamically-downscale the full CMIP5 ensemble are significant and not necessary if the aim is to develop scenarios covering a representative range of future climate conditions relevant to a climate change risk assessment. This paper illustrates a methodology for selecting from the available CMIP5 models in order to identify a set of 8–10 GCMs for use in regional climate change assessments. The selection focuses on their suitability across multiple regions—Southeast Asia, Europe and Africa. The selection (a) avoids the inclusion of the least realistic models for each region and (b) simultaneously captures the maximum possible range of changes in surface temperature and precipitation for three continental-scale regions. We find that, of the CMIP5 GCMs with 6-hourly fields available, three simulate the key regional aspects of climate sufficiently poorly that we consider the projections from those models ‘implausible’ (
MIROC
-
ESM, MIROC
-
ESM
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CHEM,
and
IPSL
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CM5B
-
LR
). From the remaining models, we demonstrate a selection methodology which avoids the poorest models by including them in the set only if their exclusion would significantly reduce the range of projections sampled. The result of this process is a set of models suitable for using to generate downscaled climate change information for a consistent multi-regional assessment of climate change impacts and adaptation.
We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and ...precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.
Mechanical hypersensitivity of the colon underlies in part the chronic abdominal pain experienced by patients with irritable bowel syndrome, yet the molecules that confer mechanosensitivity to colon ...sensory neurons and their contribution to visceral pain are unknown. We tested the hypothesis that transient receptor potential vanilloid 1 (TRPV1) and acid-sensing ion channel 3 (ASIC3) are peripheral mechanosensors in colon afferent neuronal fibers that mediate visceral nociceptive behavior in mice. Visceral nociception, modeled by the visceromotor response to colorectal distension, and colon afferent fiber mechanosensitivity were assessed in control (C57BL/6) mice and two congenic knock-out mouse strains with deletions of either TRPV1 or ASIC3. Phasic colon distension (15-60 mmHg) produced graded behavioral responses in all three mouse strains. However, both TRPV1 and ASIC3 knock-out mice were significantly less sensitive to distension, with an average response magnitude only 58 and 50% of controls, respectively. The behavioral deficits observed in both strains of knock-out mice were associated with a significant and selective reduction in afferent fiber sensitivity to circumferential stretch of the colon, an effect that was mimicked in control preparations by pretreatment with capsazepine, a TRPV1 antagonist, but not amiloride, a nonselective ASIC antagonist (both 500 microM). In addition, whereas stretch-evoked afferent fiber responses were enhanced by chemical inflammatory mediators in control mice, this effect was differentially impaired in both knock-out mouse strains. These results demonstrate a peripheral mechanosensory role for TRPV1 and ASIC3 in the mouse colon that contributes to nociceptive behavior and possibly peripheral sensitization during tissue insult.
The innate immune system contributes to the earliest phase of the host defense against foreign organisms and has both soluble and cellular pattern recognition receptors for microbial products. Two ...important members of this receptor group, CD14 and the Toll-like receptor (TLR) pattern recognition receptors, are essential for the innate immune response to components of Gram-negative and Gram-positive bacteria, mycobacteria, spirochetes and yeast. We now find that these receptors function in an antiviral response as well. The innate immune response to the fusion protein of an important respiratory pathogen of humans, respiratory syncytial virus (RSV), was mediated by TLR4 and CD14. RSV persisted longer in the lungs of infected TLR4-deficient mice compared to normal mice. Thus, a common receptor activation pathway can initiate innate immune responses to both bacterial and viral pathogens.
We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13. Submissions were made by three free‐modeling (FM) methods which combine the predictions of ...three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 FM assessors' ranking by summed z‐scores, this system scored highest with 68.3 vs 48.2 for the next closest group (an average GDT_TS of 61.4). The system produced high‐accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 FM domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template‐based methods.
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence
. This problem is of fundamental importance as the structure of a protein ...largely determines its function
; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures
. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force
that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction
(CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores
of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined
.
There is some evidence that statins may have a protective and symptomatic benefit in Alzheimer disease (AD). The LEADe study is a randomized controlled trial (RCT) evaluating the efficacy and safety ...of atorvastatin in patients with mild to moderate AD.
This was an international, multicenter, double-blind, randomized, parallel-group study. Subjects had mild to moderate probable AD (Mini-Mental State Examination score 13-25), were aged 50-90 years, and were taking donepezil 10 mg daily for > or 3 months prior to screening. Entry low-density lipoprotein cholesterol levels (LDL-C) were > 95 and < 195 mg/dL. Patients were randomized to atorvastatin 80 mg/day or placebo for 72 weeks followed by a double-blind, 8-week atorvastatin withdrawal phase. Coprimary endpoints were changes in cognition (Alzheimer's Disease Assessment Scale-Cognitive Subscale ADAS-Cog) and global function (Alzheimer's Disease Cooperative Study Clinical Global Impression of Change ADCS-CGIC) at 72 weeks.
A total of 640 patients were randomized in the study. There were no significant differences in the coprimary endpoints of ADAS-cog or ADCS-CGIC or the secondary endpoints. Atorvastatin was generally well-tolerated.
In this large-scale randomized controlled trial evaluating statin therapy as a treatment for mild to moderate Alzheimer disease, atorvastatin was not associated with significant clinical benefit over 72 weeks. This treatment was generally well-tolerated without unexpected adverse events.
This study provides Class II evidence that intensive lipid lowering with atorvastatin 80 mg/day in patients with mild to moderate probable Alzheimer disease (aged 50-90), taking donepezil, with low-density lipoprotein cholesterol levels between 95 and 195 mg/dL over 72 weeks does not benefit cognition (as measured by Alzheimer's Disease Assessment Scale-Cognitive Subscale) (p = 0.26) or global function (as measured by Alzheimer's Disease Cooperative Study Clinical Global Impression of Change) (p = 0.73) compared with placebo.
We investigate the form and evolution of the X-ray luminosity-temperature (L
X-kT) relation of a sample of 114 galaxy clusters observed with Chandra at 0.1 < z < 1.3. The clusters were divided into ...subsamples based on their X-ray morphology or whether they host strong cool cores. We find that when the core regions are excluded, the most relaxed clusters (or those with the strongest cool cores) follow an L
X-kT relation with a slope that agrees well with simple self-similar expectations. This is supported by an analysis of the gas density profiles of the systems, which shows self-similar behaviour of the gas profiles of the relaxed clusters outside the core regions. By comparing our data with clusters in the Representative XMM-Newton Cluster Structure Survey (REXCESS) sample, which extends to lower masses, we find evidence that the self-similar behaviour of even the most relaxed clusters breaks at around 3.5 keV. By contrast, the L
X-kT slopes of the subsamples of unrelaxed systems (or those without strong cool cores) are significantly steeper than the self-similar model, with lower mass systems appearing less luminous and higher mass systems appearing more luminous than the self-similar relation. We argue that these results are consistent with a model of non-gravitational energy input in clusters that combines central heating with entropy enhancements from merger shocks. Such enhancements could extend the impact of central energy input to larger radii in unrelaxed clusters, as suggested by our data. We also examine the evolution of the L
X-kT relation, and find that while the data appear inconsistent with simple self-similar evolution, the differences can be plausibly explained by selection bias, and thus we find no reason to rule out self-similar evolution. We show that the fraction of cool core clusters in our (non-representative) sample decreases at z > 0.5 and discuss the effect of this on measurements of the evolution in the L
X-kT relation.
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