Connecting to smart cities : analyzing energy times series to visualize monthly electricity peak load in residential buildings

Rapidly growing energy consumption rate is considered an alarming threat to economic stability and environmental sustainability. There is an urgent need of proposing novel solutions to mitigate the drastic impact of increased energy demand in urban cities to improve energy efficiency in smart buildings. It is commonly agreed that exploring, analyzing and visualizing energy consumption patterns in residential buildings can help to estimate their energy demands. Moreover, visualizing energy consumption patterns of residential buildings can also help to diagnose if there is any unpredictable increase in energy demand at a certain time period. However, visualizing and inferring energy consumption patterns from typical line graphs, bar charts, scatter plots is obsolete, less informative and do not provide deep and significant insight of the daily domestic demand of energy utilization. Moreover, these methods become less significant when high temporal resolution is required. In this research work, advanced data exploratory and data analytics techniques are applied on energy time series. Data exploration results are presented in the form of heatmap. Heatmap provides a significant insight of energy utilization behavior during different times of the day. Heatmap results are articulated from three analytical perspectives; descriptive analysis, diagnostic analysis and contextual analysis

Design and validation of the 1-week memory battery for assessing episodic memory and accelerated long-term forgetting in cognitively unimpaired subjects

Subtle decline in memory is thought to arise in the preclinical phase of Alzheimer's disease (AD). However, detecting these initial cognitive difficulties cross-sectionally has been challenging, and the exact nature of the decline is still debated. Accelerated long-term forgetting (ALF) has been recently suggested as one of the earliest and most sensitive indicators of memory dysfunction in subjects at risk of developing AD. The objective of this study was to design and validate the 1-week memory battery (1WMB) for assessing episodic memory and ALF in cognitively unimpaired individuals.The 1WMB is unique in that it assesses multimodal memory and measures recall at both short delay (20 min) and at long term (1 week). Forty-five cognitively unimpaired subjects were assessed with 1WMB and standardized neuropsychological tests. Subjective cognitive decline (SCD), levels of anxiety and depression, and cognitive reserve were also measured.The tests of 1WMB showed a high internal consistency, and concurrent validity was observed with standard tests of episodic memory and executive functions. The analysis revealed a greater loss of information at 1 week compared to short-term forgetting (20 min). Performance in the 1WMB was affected by age and educational level, but was not associated with levels of anxiety and depression. Unlike standard tests, performance in the 1WMB correlated with measures of SCD.Our findings indicate that the 1WMB has good psychometric properties, and future studies are needed to explore its potential usefulness to assess cognitively unimpaired subjects at increased risk of developing AD. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Energía gastada en el primer ciclo de histéresis como parámetro de selección de un biomaterial

Veinticuatro muestras de pericardio de ternera estabilizado, material similar al utilizado en la fabricación de velos de bioprótesis cardiacas, fueron sometidas a una prueba de esfuerzo a fatiga. Tras seis bloques de 100 ciclos a 1 MPa de presión máxima se rompieron 12 muestras, el 50%. La energía media gastada en este primer ciclo, en las muestras que sobrevivieron, fue de 0.16J, un valor inferior a la energía gastada en las muestra que terminaron rompiendo, 0.28J (p=0.005). Utilizando el cociente entre la energía disipada en el primer ciclo y el espesor medio de la muestra, con un valor de corte de 0.48J/mm para la selección de las mejores muestras, se obtiene un índice de validez del 87.5%, y un área bajo la curva ROC de 0.917. Este método no destructivo debe ayudar a los métodos ópticos en el reconocimiento y selección de las muestras más resistentes y en la obtención del material biológico más homogéne

Growth-promoting effects of sustained swimming in fingerlings of glithead sea bream (Sparus aurata L.)

Fish growth is strongly influenced by environmental and nutritional factors and changing culture conditions can help optimize it. The importance of early-life experience on the muscle phenotype later in life is well known. Here, we study the effects of 5 weeks of moderate and sustained swimming activity (5 BL s−1) in gilthead sea bream during early development. We analysed growth and body indexes, plasma IGF-I and GH levels, feed conversion, composition [proximate and isotopic (15N/13C)] and metabolic key enzymes (COX, CS, LDH, HOAD, HK, ALAT, ASAT) of white muscle. Moderate and continuous exercise in fingerlings of gilthead sea bream increased plasma IGF-I, whereas it reduced plasma GH. Under these conditions, growth rate improved without any modification to feed intake through an increase in muscle mass and a reduction in mesenteric fat deposits. There were no changes in the content and turnover of muscle proteins and lipid reserves. Glycogen stores were maintained, but glycogen turnover was higher in white muscle of exercised fish. A lower LDH/CS ratio demonstrated an improvement in the aerobic capacity of white muscle, while a reduction in the COX/CS ratio possibly indicated a functional adaptation of mitochondria to adjust to the tissue-specific energy demand and metabolic fuel availability in exercised fish. We discuss the synergistic effects of dietary nutrients and sustained exercise on the different mitochondrial responses

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia

Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC.We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity.We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability.We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.© 2023. The Author(s)

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC

Magnesium Exchanged Zirconium Metal−Organic Frameworks with Improved Detoxification Properties of Nerve Agents

UiO-66, MOF-808 and NU-1000 metal-organic frameworks exhibit a differentiated reactivity toward [Mg(OMe)2(MeOH)2]4 related to their pore accessibility. Microporous UiO-66 remains unchanged while mesoporous MOF-808 and hierarchical micro/mesoporous NU-1000 materials yield doped systems containing exposed MgZr5O2(OH)6 clusters in the mesoporous cavities. This modification is responsible for a remarkable enhancement of the catalytic activity toward the hydrolytic degradation of P-F and P-S bonds of toxic nerve agents, at room temperature, in unbuffered aqueous solutions

Contribution of CSF biomarkers to early-onset Alzheimer's disease and frontotemporal dementia neuroimaging signatures

Prior studies have described distinct patterns of brain gray matter and white matter alterations in Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), as well as differences in their cerebrospinal fluid (CSF) biomarkers profiles. We aim to investigate the relationship between early‐onset AD (EOAD) and FTLD structural alterations and CSF biomarker levels. We included 138 subjects (64 EOAD, 26 FTLD, and 48 controls), all of them with a 3T MRI brain scan and CSF biomarkers available (the 42 amino acid‐long form of the amyloid‐beta protein [Aβ42], total‐tau protein [T‐tau], neurofilament light chain [NfL], neurogranin [Ng], and 14‐3‐3 levels). We used FreeSurfer and FSL to obtain cortical thickness (CTh) and fraction anisotropy (FA) maps. We studied group differences in CTh and FA and described the “AD signature” and “FTLD signature.” We tested multiple regression models to find which CSF‐biomarkers better explained each disease neuroimaging signature. CTh and FA maps corresponding to the AD and FTLD signatures were in accordance with previous literature. Multiple regression analyses showed that the biomarkers that better explained CTh values within the AD signature were Aβ and 14‐3‐3; whereas NfL and 14‐3‐3 levels explained CTh values within the FTLD signature. Similarly, NfL levels explained FA values in the FTLD signature. Ng levels were not predictive in any of the models. Biochemical markers contribute differently to structural (CTh and FA) changes typical of AD and FTLD

Sprouty2 and Spred1-2 Proteins Inhibit the Activation of the ERK Pathway Elicited by Cyclopentenone Prostanoids

Sprouty and Spred proteins have been widely implicated in the negative regulation of the fibroblast growth factor receptor-extracellular regulated kinase (ERK) pathway. In considering the functional role of these proteins, we explored their effects on ERK activation induced by cyclopentenone prostanoids, which bind to and activate Ras proteins. We therefore found that ectopic overexpression in HeLa cells of human Sprouty2, or human Spred1 or 2, inhibits ERK1/2 and Elk-1 activation triggered by the cyclopentenone prostanoids PGA1 and 15d-PGJ2. Furthermore, we found that in HT cells that do not express Sprouty2 due to hypermethylation of its gene-promoter, PGA1-provoked ERK activation was more intense and sustained compared to other hematopoietic cell lines with unaltered Sprouty2 expression. Cyclopentenone prostanoids did not induce Sprouty2 tyrosine phosphorylation, in agreement with its incapability to activate tyrosine-kinase receptors. However, Sprouty2 Y55F, which acts as a defective mutant upon tyrosine-kinase receptor stimulation, did not inhibit cyclopentenone prostanoids-elicited ERK pathway activation. In addition, Sprouty2 did not affect the Ras-GTP levels promoted by cyclopentenone prostanoids. These results unveil both common and differential features in the activation of Ras-dependent pathways by cyclopentenone prostanoids and growth factors. Moreover, they provide the first evidence that Sprouty and Spred proteins are negative regulators of the ERK/Elk-1 pathway activation induced not only by growth-factors, but also by reactive lipidic mediators

Full-Exon Pyrosequencing Screening of BRCA Germline Mutations in Mexican Women with Inherited Breast and Ovarian Cancer

Hereditary breast cancer comprises 10% of all breast cancers. The most prevalent genes causing this pathology are BRCA1 and BRCA2 (breast cancer early onset 1 and 2), which also predispose to other cancers. Despite the outstanding relevance of genetic screening of BRCA deleterious variants in patients with a history of familial cancer, this practice is not common in Latin American public institutions. In this work we assessed mutations in the entire exonic and splice-site regions of BRCA in 39 patients with breast and ovarian cancer and with familial history of breast cancer or with clinical features suggestive for BRCA mutations by massive parallel pyrosequencing. First we evaluated the method with controls and found 41–485 reads per sequence in BRCA pathogenic mutations. Negative controls did not show deleterious variants, confirming the suitability of the approach. In patients diagnosed with cancer we found 4 novel deleterious mutations (c.2805_2808delAGAT and c.3124_3133delAGCAATATTA in BRCA1; c.2639_2640delTG and c.5114_5117delTAAA in BRCA2). The prevalence of BRCA mutations in these patients was 10.2%. Moreover, we discovered 16 variants with unknown clinical significance (11 in exons and 5 in introns); 4 were predicted as possibly pathogenic by in silico analyses, and 3 have not been described previously. This study illustrates how massive pyrosequencing technology can be applied to screen for BRCA mutations in the whole exonic and splice regions in patients with suspected BRCA-related cancers. This is the first effort to analyse the mutational status of BRCA genes on a Mexican-mestizo population by means of pyrosequencing