Variational bayes learning of time-varying graphical models

Pipe material and labor costs constitute about 70% of pipeline construction costs. Pipe and labor costs are subject to considerable fluctuations over time. These fluctuations are problematic for cost estimation and bid preparation in pipeline projects, which are mostly large and long-term projects. The accurate prediction of pipe and labor costs is invaluable for cost estimators to prepare accurate bids and manage the cost contingencies. However, the existing literature does not take advantage of the leading indicators of pipeline construction cost time series to accurately forecast cost fluctuations in pipeline projects. The objective of this research is to identify the leading indicators of pipeline construction costs and develop multivariate time series models for forecasting cost fluctuations in pipeline projects. Nineteen potential leading indicators of pipe and labor costs were initially selected based on a comprehensive review of construction cost forecasting literature. The leading indicators were identified from this pool of potential leading indicators based on unit root tests and Granger causality tests. Multivariate time series models were developed based on the results of cointegration tests. Vector error correction (VEC) models were developed for the cointegrated variables, while vector autoregressive (VAR) models were developed for the non-cointegrated variables. Since multivariate time series models include information from the identified leading indicators, multivariate time series models are often expected to deliver more accurate forecasts than univariate time series models. The forecasting accuracies of multivariate time series models were compared with those of univariate time series models based on three common error measures: mean absolute prediction error (MAPE), root-mean-squared error (RMSE), and mean average error (MAE). The results show that multivariate time series models outperform univariate models for forecasting cost fluctuations in pipeline projects. The findings of this research contribute to the state of knowledge by identifying leading indicators of pipe and labor costs and developing multivariate time series models to forecast them. The multivariate time series models with leading indicators are more accurate than univariate models for forecasting cost fluctuations in pipeline projects. It is expected that the proposed multivariate time series forecasting models contribute to the enhancement of the theory and practice of pipeline construction cost forecasting and help cost engineers and investment planners to prepare more accurate bids, cost estimates, and budgets for pipeline projects.

Self-Rated and Caregiver-Rated Quality of Life in Alzheimer Disease with a Focus on Evolving Patient Ability to Respond to Questionnaires: 5-Year Prospective ALSOVA Cohort Study

Alteration in sleep architecture and electroencephalogram as an early sign of Alzheimer's disease preceding the disease pathology and cognitive decline

Disappointing outcomes from clinical trials involving amyloid-modifying therapies for Alzheimer's disease (AD) have prompted more focus on the concept of early-stage (E) amnestic mild cognitive impairment (E-aMCI). However, limited evidence suggests that E-aMCI may represent aMCI at a very early stage of AD. Furthermore, the nature of the progression of E-aMCI to late-stage aMCI (L-aMCI) remains unclear. Therefore, the aim of the present study was to characterize patterns of cortical thinning in both E-aMCI and L-aMCI patients. Cortical thicknesses were measured in 190 patients with aMCI and 147 subjects with normal cognition. In accordance with memory test scores involving delayed recall items, aMCI patients were divided into two subgroups, containing 73 E-aMCI subjects with milder memory impairment [scores between -1.5 standard deviation (SD) and -1.0 SD compared with age- and education-matched norms] and 117 L-aMCI subjects with more severe memory impairment (scores lower than -1.5 SD). Compared with controls, the E-aMCI group exhibited cortical thinning in the left medial temporal and insular regions, whereas the L-aMCI group showed cortical thinning in widespread regions, including the bilateral dorsolateral prefrontal, anterior and medial temporal, and temporo-parietal association cortices, and the precuneus. When the two aMCI groups were directly compared, the L-aMCI group showed greater cortical thinning in the right superior prefrontal, medial temporal, posterior cingulate and lateral parietal cortices. Our findings suggest that E-aMCI might represent an early symptomatic stage of AD. Furthermore, L-aMCI might resemble AD more closely than E-aMCI, in terms of the topography of cortical thinning.

BackgroundDevelopment of new therapies for Alzheimer's disease (AD) is increasingly focused on more mildly affected populations, and requires new assessment and outcome strategies. Patients in early stages of AD have...

Although changes in extracellular matrix (ECM) scaffold have been reported previously in Alzheimer's disease (AD) compared to normal ageing, it is not known how alterations in the numerous components of the perivascular ECM might occur at different stages of AD. This study therefore investigates potential changes in basement membrane-associated ECM molecules in relation to increasing Braak stages. Thirty patients were divided into three groups (control subject, subclinical AD and AD patients). ECM levels of collagen IV, perlecan and fibronectin as well as human platelet endothelial cell adhesion molecule (hPECAM) were quantified by immunohistochemistry. Von Willebrand factor staining was measured to assess vessel density. Expression levels were correlated with the presence of amyloid plaques. Collagen IV, perlecan and fibronectin expression was increased in subclinical AD and AD patients when compared to controls, in frontal and temporal cortex, whilst no further increase was detected between subclinical AD and AD. These changes were not associated with an increase in vessel density, which was instead decreased in the temporal cortex of AD patients. In contrast, hPECAM levels remained unchanged. Finally, we found similar pattern in levels of amyloid deposition between the different Braak stages and showed that changes in ECM components correlated with amyloid deposition. Present data support the hypothesis that significant ECM changes occur during the early stages of AD. ECM changes affecting brain microvascular functions could therefore drive disease progression and provide potential new early investigational biomarkers in AD.

Sleep architecture, including spindles and slow oscillations, is disrupted in Alzheimer disease (AD). How changes in these sleep elements relate to cognitive decline is less clear. Our objectives were to examine changes in sleep macroarchitecture and microarchitecture in early clinical stages of AD compared with older adult controls (CTLs) and to investigate their associations with longitudinal cognitive change. This was both a cross-sectional and longitudinal study performed at Mass General Brigham Hospitals, where participants with early AD or CTLs underwent overnight ambulatory scalp EEG and longitudinal cognitive testing. We compared sleep microarchitectural features extracted from the EEG, including spindle activity, across the brain topography and between groups. We then performed longitudinal analyses using mixed-effects models to test the association of these sleep features with changes in cognition on the Montreal Cognitive Assessment (MoCA), collected annually for up to 7 years. AD (n = 47, mean age 74.1 years, 66% female) and CTL (n = 43, mean age 72.6 years, 56% female) groups spent a similar proportion of sleep time in each stage of sleep. Sleep efficiency, however, was lower in the AD group (mean: CTL 75.1% vs AD 70.9%; p = 0.034). We found a significant reduction in spindle range power (11-16 Hz) in patients with AD compared with CTLs, particularly in the temporal regions (mean normalized power at EEG channels T3/T4: CTL 3.13 ± 1.13 vs AD 2.48 ± 1.01; p = 0.005). In participants with longitudinal MoCA scores (AD = 26, CTL = 25), reduced temporal lobe spindle density (β = 0.61, 95% CI 0.35-0.87; false discovery rate [FDR]-adjusted p < 0.001) and temporal lobe spindle power (β = 0.56, 95% CI 0.22-0.88; FDR-adjusted p = 0.005) were each associated with a faster rate of cognitive decline. Temporal lobe sleep spindle activity is reduced in early clinical stages of AD and is associated with a faster rate of cognitive decline. Our results underscore the importance of including temporal lobe measurements when assessing sleep neurophysiology in AD, which is not standard in polysomnography. Future work examining the relationship between AD biomarkers and reduced spindle activity is needed to elucidate the potential mechanisms underlying these findings.

Increased GADD34 in oligodendrocytes in Alzheimer’s disease

ABSTRACTThere is a debate over the extent to which personal identity or the self is preserved in patients with Alzheimer’s disease (AD). Autobiographical memory deficits at early stages of AD could contribute to altering patients’ self. However, the nature of the relationship between autobiographical memory deficits and the self in AD has not been much investigated experimentally. In the present study, we aimed to investigate the integrative meaning of self-defining memories (SDMs) in early stages of AD and to analyse its relationship with the self-concept. The results showed that, when compared to the control group, AD patients less frequently extracted meaning from their SDMs and the meaning was less frequently tied to the self. Patients exhibited some altered aspects of the self-concept (i.e., complexity and strength), though some other components still persisted (i.e., valence and certainty). Correlation analyses showed that the impaired integrative meaning in the AD group was correlated with some changes in self-concept. We suggest that integrative meaning may act as a bridge between autobiographical memories and the self-concept, with reduced integration abilities appearing as a potential mechanism for the deterioration of the self-concept in AD.

Alzheimer's disease (AD) is a complex, incurable, and fatal neurodegenerative disorder characterized by progressive memory loss with a very high mortality rate. AD neuropathological alterations begin years before the onset of clinical symptoms and about 99.6% of candidate drugs targeting Aβ plaque the causal factor of AD have failed at various stages of clinical trials. Thus, early diagnosis is an essential requirement for effective intervention. Phosphorylation of collapsin response mediator protein 2 (CRMP2), a member of the CRMPs family, at the S522 site has been previously reported to be aberrantly high in end-stage AD. In this study, the key phosphosignaling events at the early stage of AD were investigated using hippocampal phosphoproteomic analysis of the AppNL-F mice model of early-stage AD. This was followed by protein class enrichment and pathway analyses of the overrepresented protein classes and pathways in the upregulated and downregulated phosphosites. Validation of phosphosites of the AD-related proteins identified by the enrichment analyses of the early-stage AD mice hippocampus was carried out using the medial-temporal lobes of human subjects diagnosed with early-stage AD. CRMP2 dephosphorylation was observed in human subjects with early-stage AD compared to normal subjects. In this study, the implications of CRMP2 dephosphorylation with respect to AD and potential use as a biomarker and therapeutic intervention in early-stage AD are presented.

In real-world scenarios, partial information losses of multivariate time series degrade the time series analysis. Hence, the time series imputation technique has been adopted to compensate for the missing values. Existing methods focus on investigating temporal correlations, cross-variable correlations, and bidirectional dynamics of time series, and most of these methods rely on recurrent neural networks (RNNs) to capture temporal dependency. However, the RNN-based models suffer from the common problems of slow speed and high complexity when dealing with long-term dependency. While some self-attention-based models without any recurrent structures can tackle long-term dependency with parallel computing, they do not fully learn and utilize correlations across the temporal and cross-variable dimensions. To address the limitations of existing methods, we propose a novel so-called dual-branch cross-dimensional self-attention-based imputation (DCSAI) model for multivariate time series, which is capable of performing global and auxiliary cross-dimensional analyses when imputing the missing values. In particular, this model contains masked multi-head self-attention-based encoders aligned with auxiliary generators to obtain global and auxiliary correlations in two dimensions, and these correlations are then combined into one final representation through three weighted combinations. Extensive experiments are presented to show that our model performs better than other state-of-the-art benchmarkers on three real-world public datasets under various missing rates. Furthermore, ablation study results demonstrate the efficacy of each component of the model.

Accumulation of beta amyloid peptide, including As1-42, is a hallmark of Alzheimer's disease (AD). In the early stages of AD, neuronal death is observed in the septum and the hippocampus of the brain. This neuronal death causes memory and cognitive dysfunction which are clinical manifestations of AD. A newly-discovered neurotransmitter receptor subtype, the a7s2 nicotinic acetylcholine receptor (a7s2-nAChR) is expressed in the septum and the hippocampus of the rodent and human brain. This pentameric receptor has similar functional characteristics to a7-nAChR, a more prevalent subtype. It has been shown that a7-nAChRs mediate internalization of As1-42. Others have shown that As1-42 internalization may cause neuronal dysfunction and death. The aims of the present study are to determine if a7s2-nAChR mediates internalization of As1-42, if this is toxic to the cells, and if it affects intracellular calcium activity. We have used multiphoton microscopy to show internalization of the peptide in SH-EP1 cells expressing a7s2-nAChR and a7-nAChR, and live/dead assays for measuring cell death. Epi-fluorescence microscopy and calcium dyes are being used to compare calcium activity of cells expressing these receptors. These are preliminary steps toward determining the pathogenic molecular mechanisms of neuronal dysfunction and cell death in early stages of AD.

Abnormalities in molecular signalling have been implicated in neurodegeneration. It is emerging that glycogen synthase kinase-3 (GSK-3) is a key signalling molecule that induces neurodegeneration and deficits in memory formation related to Alzheimer's disease (AD). Early stages of AD are associated with deficits in memory formation before neuronal cell death is detectable. Recent studies in rodents have suggested that these impairments in memory formation might result from increased GSK-3 signalling, because enhanced GSK-3 activity impairs hippocampal memory formation. GSK-3 activity blocks synaptic long-term potentiation and induces long-term depression. Furthermore, increased GSK-3 signalling is likely to be a key contributor to the formation of the pathological hallmarks in AD, neurofibrillary tangles (NFTs) and amyloid plaques. Recent studies with mouse models have indicated that GSK-3, but not cyclin-dependent kinase 5, is critical for hyperphosphorylation of the cytoskeletal protein tau, which is the prerequisite for NFT formation in AD. Furthermore, increased GSK-3 signalling in AD mice causes abnormal processing of the amyloid precursor protein so that amyloid peptide production augments and neurotoxicity is induced. Taken together, the current evidences suggest that increased GSK-3 signalling may be responsible for the deficits in memory formation in early stages of AD and neurodegeneration in later stages of the disease.

Diminished metabolism and excessive oxidative stress occur in the brains of patients with Alzheimer's Disease (AD). These abnormalities in oxidative processes occur in the brain in early stages of AD, which suggests that the deficits are not just secondary to the neuro-degeneration. Alterations in oxidative processes also occur in early stages of AD in non-neuronal tissues including fluids (e.g., cerebrospinal fluid, plasma and urine), cell like particles (e.g., red blood cells and platelets) and cells (e.g., lymphocytes). AD-related abnormalities also persist in cultured cells such as fibroblasts, which indicates that the AD-related changes are not secondary to pathology, and reflect inherent properties of AD cells. These measures of abnormalities in oxidative processes in peripheral cells from AD patients have the potential to be useful as diagnostic markers, as indicators of the progression of the disease, as a tool to develop therapeutic approaches and as monitors of therapeutic efficacy. The peripheral cells are also useful for discovering mechanisms that underlie the multiple changes in cell signaling pathways that accompany AD. Several experimental approaches suggest that oxidative stress is a convergence factor that leads to many other AD-related changes. This review focuses on the considerable recent progress in the quest for markers of metabolism/oxidative stress in peripheral tissues from AD patients, and on experiments to test their pathophysiological importance.

With the recent advances in the treatment of Alzheimer's disease (AD) over the last decade, the focus has increasingly shifted to accurate detection of the earliest phase of illness. The intermediate state between normal aging and established AD is commonly known as mild cognitive impairment (MCI). Not all patients with MCI progress to AD and hence there is a need to reliably predict which patients with MCI will progress. The current inability of clinical criteria to accurately identify this at-risk group is fuelling the emerging interest in biomarkers to potentially supplement or replace clinical approaches. Biomarkers have the potential to clearly reflect the progressive deterioration that is present even in the earliest stages of AD--a useful feature for demonstrating the effectiveness or otherwise of a particular dementia drug. This paper will review the evidence regarding the use of cerebrospinal fluid, neuroimaging and blood biomarkers, as well as combination biomarkers, from the two standpoints of predicting MCI conversion to AD and monitoring disease progression at the MCI stage, so as to recommend which biomarkers can reasonably be included in early dementia trials, and how they can best be used.