Definición empírica de la relación agua / cemento efectiva en morteros con árido reciclado en función de la absorción

The use of recycled aggregates from construction and demolition wastes for the manufacture of mortars and concretes is a subject of great interest from the point of view of sustainable construction since it can reduce the exploitation of quarries replacing natural aggregate by recycled aggregate and it can reduce the volume of wastes in landfills. In order to study the influence of recycled aggregate on concrete and mortar strength, the effective water/cement ratio must be the same in concretes or mortars compared. The effective water/cement ratio is defined as the amount of water available to react with the cement of the mixture. Discrepancies among authors arise in the definition of how much is the amount of available water, which depends on the absorption and moisture of the aggregates at the time of the batch. Therefore, in this research, an experimental study is developed empirically to find the amount of water which reacts with the cement mortar in various mixtures with different ratios of recycled aggregate depending on the absorption of the aggregates. Subsequently, the relations between the amount of water which doesn’t react with the cement and aggregate absorption of each of the mixtures were analyzed. Finally, a definition of the effective water/cement ratio depending on absorption is proposed, based on the empirical study developed.La utilización de áridos reciclados procedentes de construcción y demolición, para la fabricación de morteros y hormigones es un tema de gran interés desde el punto de vista de la construcción sostenible puesto que, además de disminuir la explotación de las canteras al sustituir el árido natural por árido reciclado, también se reduce el volumen de residuos depositados en vertederos. Para poder estudiar la influencia que tiene el uso de árido reciclado en la resistencia de los hormigones y morteros, es necesario que la relación agua/cemento efectiva sea a misma en todas las mezclas comparadas, La relación agua/cemento efectiva se define como la cantidad de agua disponible que reacciona con el cemento de la mezcla. Las discrepancias entre autores surgen en la definición de cuál es esa “cantidad de agua disponible”, La cantidad de agua disponible que reacciona con el cemento depende de la absorción de los áridos Por ello, en esta investigación, se desarrolla un estudio experimental para hallar de forma empírica la cantidad de agua que reacciona con el cemento en varias mezclas de mortero con distintos porcentajes de sustitución de árido reciclado, en función de la absorción de los áridos. Posteriormente se analiza qué relaciones existen entre la cantidad de agua que no reacciona con el cemento y el agua total de absorción de los áridos de cada una de las mezclas. Finalmente se propone una definición de la relación agua/cemento efectiva en función de la absorción basada en este estudio empírico

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance

Estradiol regulates brown adipose tissue thermogenesis via hypothalamic AMPK

Copyright © 2014 Elsevier Inc. All rights reserved.Peer reviewedPublisher PD

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients

The impact of silent vascular brain burden in cognitive impairment in Parkinson's disease

White matter hyperintensities (WMHs) detected by magnetic resonance imaging (MRI) of the brain are associated with dementia and cognitive impairment in the general population and in Alzheimer's disease. Their effect in cognitive decline and dementia associated with Parkinson's disease (PD) is still unclear. METHODS: We studied the relationship between WMHs and cognitive state in 111 patients with PD classified as cognitively normal (n = 39), with a mild cognitive impairment (MCI) (n = 46) or dementia (n = 26), in a cross-sectional and follow-up study. Cognitive state was evaluated with a comprehensive neuropsychological battery, and WMHs were identified in FLAIR and T2-weighted MRI. The burden of WMHs was rated using the Scheltens scale. RESULTS: No differences in WMHs were found between the three groups in the cross-sectional study. A negative correlation was observed between semantic fluency and the subscore for WMHs in the frontal lobe. Of the 36 non-demented patients re-evaluated after a mean follow-up of 30 months, three patients converted into MCI and 5 into dementia. Progression of periventricular WMHs was associated with an increased conversion to dementia. A marginal association between the increase in total WMHs burden and worsening in the Mini Mental State Examination was encountered. CONCLUSIONS: White matter hyperintensities do not influence the cognitive status of patients with PD. Frontal WMHs have a negative impact on semantic fluency. Brain vascular burden may have an effect on cognitive impairment in patients with PD as WMHs increase overtime might increase the risk of conversion to dementia. This finding needs further confirmation in larger prospective studies

ZMC 211-3 - KAEDAH MATEMATIK II MAC-APRIL 1989.pdf

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients

In vivo CRISPR/Cas9 targeting of fusion oncogenes for selective elimination of cancer cells

This work was supported by CaixaImpulse (CI18-00017;FuGe) to S.R-P. RT-R. is supported by a postdoctoral fellowship from the Asociación Española Contra el Cáncer (AECC). J.C.S. is supported by the Spanish Cell Therapy cooperative research network (TERCEL)(RD16/0011/0011). P.M. also acknowledges the financial support from the Obra Social La Caixa-Fundaciò Josep Carreras. P.M. is an investigator of the Spanish Cell Therapy cooperative research network (TERCEL). A.M.C. acknowledges funding fromXarxa de Bancs de Tumors de Catalunya (XBTC; sponsored by Pla Director d'Oncologia de Catalunya).Fusion oncogenes (FOs) are common in many cancer types and are powerful drivers of tumor development. Because their expression is exclusive to cancer cells and their elimination induces cell apoptosis in FO-driven cancers, FOs are attractive therapeutic targets. However, specifically targeting the resulting chimeric products is challenging. Based on CRISPR/Cas9 technology, here we devise a simple, efficient and non-patient-specific gene-editing strategy through targeting of two introns of the genes involved in the rearrangement, allowing for robust disruption of the FO specifically in cancer cells. As a proof-of-concept of its potential, we demonstrate the efficacy of intron-based targeting of transcription factors or tyrosine kinase FOs in reducing tumor burden/mortality in in vivo models. The FO targeting approach presented here might open new horizons for the selective elimination of cancer cells

Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networks

Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods: We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. Results: Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. Conclusion: Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size. Keywords: Alzheimer’s disease; amyloid β; artificial neural networks; machine learning; neurodegeneration; plasma proteomics; ta

Gray matter network properties show distinct associations with CSF p-tau 181 levels and amyloid status in individuals without dementia

Gray matter networks are altered with amyloid accumulation in the earliest stage of AD, and are associated with decline throughout the AD spectrum. It remains unclear to what extent gray matter network abnormalities are associated with hyperphosphorylated-tau (p-tau). We studied the relationship of cerebrospinal fluid (CSF) p-tau181 with gray matter networks in non-demented participants from the European Prevention of Alzheimer’s Dementia (EPAD) cohort, and studied dependencies on amyloid and cognitive status. Gray matter networks were extracted from baseline structural 3D T1w MRI. P-tau181 and abeta were measured with the Roche cobas Elecsys System. We studied the associations of CSF biomarkers levels with several network’s graph properties. We further studied whether the relationships of p-tau 181 and network measures were dependent on amyloid status and cognitive stage (CDR). We repeated these analyses for network properties at a regional level, where we averaged local network values across cubes within each of 116 areas as defined by the automated anatomical labeling (AAL) atlas. Amyloid positivity was associated with higher network size and betweenness centrality, and lower gamma, clustering and small-world coefficients. Higher CSF p-tau 181 levels were related to lower betweenness centrality, path length and lambda coefficients (all p < 0.01). Three-way interactions between p-tau181, amyloid status and CDR were found for path length, lambda and clustering (all p < 0.05): Cognitively unimpaired amyloid-negative participants showed lower path length and lambda values with higher CSF p-tau181 levels. Amyloid-positive participants with impaired cognition demonstrated lower clustering coefficients in association to higher CSF p-tau181 levels. Our results suggest that alterations in gray matter network clustering coefficient is an early and specific event in AD

Eigenvector centrality dynamics are related to Alzheimer’s disease pathological changes in non-demented individuals

Amyloid-β accumulation starts in highly connected brain regions and is associated with functional connectivity alterations in the early stages of Alzheimer's disease. This regional vulnerability is related to the high neuronal activity and strong fluctuations typical of these regions. Recently, dynamic functional connectivity was introduced to investigate changes in functional network organization over time. High dynamic functional connectivity variations indicate increased regional flexibility to participate in multiple subnetworks, promoting functional integration. Currently, only a limited number of studies have explored the temporal dynamics of functional connectivity in the pre-dementia stages of Alzheimer's disease. We study the associations between abnormal cerebrospinal fluid amyloid and both static and dynamic properties of functional hubs, using eigenvector centrality, and their relationship with cognitive performance, in 701 non-demented participants from the European Prevention of Alzheimer's Dementia cohort. Voxel-wise eigenvector centrality was computed for the whole functional magnetic resonance imaging time series (static), and within a sliding window (dynamic). Differences in static eigenvector centrality between amyloid positive (A+) and negative (A-) participants and amyloid-tau groups were found in a general linear model. Dynamic eigenvector centrality standard deviation and range were compared between groups within clusters of significant static eigenvector centrality differences, and within 10 canonical resting-state networks. The effect of the interaction between amyloid status and cognitive performance on dynamic eigenvector centrality variability was also evaluated with linear models. Models were corrected for age, sex, and education level. Lower static centrality was found in A+ participants in posterior brain areas including a parietal and an occipital cluster; higher static centrality was found in a medio-frontal cluster. Lower eigenvector centrality variability (standard deviation) occurred in A+ participants in the frontal cluster. The default mode network and the dorsal visual networks of A+ participants had lower dynamic eigenvector centrality variability. Centrality variability in the default mode network and dorsal visual networks were associated with cognitive performance in the A- and A+ groups, with lower variability being observed in A+ participants with good cognitive scores. Our results support the role and timing of eigenvector centrality alterations in very early stages of Alzheimer's disease and show that centrality variability over time adds relevant information on the dynamic patterns that cause static eigenvector centrality alterations. We propose that dynamic eigenvector centrality is an early biomarker of the interplay between early Alzheimer's disease pathology and cognitive decline