Proposing 3D Thermal Technology for Heritage Building Energy Monitoring

[EN] The energy monitoring of heritage buildings has, to date, been governed by methodologies and standards that have been defined in terms of sensors that record scalar magnitudes and that are placed in specific positions in the scene, thus recording only some of the values sampled in that space. In this paper, however, we present an alternative to the aforementioned technologies in the form of new sensors based on 3D computer vision that are able to record dense thermal information in a three-dimensional space. These thermal computer vision-based technologies (3D-TCV) entail a revision and updating of the current building energy monitoring methodologies. This paper provides a detailed definition of the most significant aspects of this new extended methodology and presents a case study showing the potential of 3D-TCV techniques and how they may complement current techniques. The results obtained lead us to believe that 3D computer vision can provide the field of building monitoring with a decisive boost, particularly in the case of heritage buildingsThis research was funded by the European Regional Development Fund (SBPLY/19/180501/000094 project) and the Ministry of Science and Innovation (PID2019-108271RB-C31 and PID2019108271RB-C33).Adan, A.; Pérez, V.; Vivancos, J.; Aparicio Fernandez, CS.; Prieto, SA. (2021). Proposing 3D Thermal Technology for Heritage Building Energy Monitoring. Remote Sensing. 13(8):1-25. https://doi.org/10.3390/rs13081537S12513

Expression microarray reproducibility is improved by optimising purification steps in RNA amplification and labelling.

BACKGROUND: Expression microarrays have evolved into a powerful tool with great potential for clinical application and therefore reliability of data is essential. RNA amplification is used when the amount of starting material is scarce, as is frequently the case with clinical samples. Purification steps are critical in RNA amplification and labelling protocols, and there is a lack of sufficient data to validate and optimise the process. RESULTS: Here the purification steps involved in the protocol for indirect labelling of amplified RNA are evaluated and the experimentally determined best method for each step with respect to yield, purity, size distribution of the transcripts, and dye coupling is used to generate targets tested in replicate hybridisations. DNase treatment of diluted total RNA samples followed by phenol extraction is the optimal way to remove genomic DNA contamination. Purification of double-stranded cDNA is best achieved by phenol extraction followed by isopropanol precipitation at room temperature. Extraction with guanidinium-phenol and Lithium Chloride precipitation are the optimal methods for purification of amplified RNA and labelled aRNA respectively. CONCLUSION: This protocol provides targets that generate highly reproducible microarray data with good representation of transcripts across the size spectrum and a coefficient of repeatability significantly better than that reported previously.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Mid-Infrared laser spectroscopy detection and quantification of explosives in soils using multivariate analysis and artificial intelligence

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils

Multimodal sensory reliance in the nocturnal homing of the amblypygid \u3ci\u3ePhrynus pseudoparvulus\u3c/i\u3e (Class Arachnida, Order Amblypygi)?

Like many other nocturnal arthropods, the amblypygid Phrynus pseudoparvulus is capable of homing. The environment through which these predators navigate is a dense and heterogeneous tropical forest understory and the mechanism(s) underlying their putatively complex navigational abilities are presently unknown. This study explores the sensory inputs that might facilitate nocturnal navigation in the amblypygid P. pseudoparvulus. Specifically, we use sensory system manipulations in conjunction with field displacements to examine the potential involvement of multimodal—olfactory and visual—stimuli in P. pseudoparvulus’ homing behavior. In a first experiment, we deprived individuals of their olfactory capacity and displaced them to the opposite side of their home trees (\u3c5 m). We found that olfaction-intact individuals were more likely to be re-sighted in their home refuges than olfaction-deprived individuals. In a second experiment, we independently manipulated both olfactory and visual sensory capacities in conjunction with longer-distance displacements (8 m) from home trees. We found that sensory-intact individuals tended to be re-sighted on their home tree more often than sensory-deprived individuals, with a stronger effect of olfactory deprivation than visual deprivation. Comparing across sensory modality manipulations, olfaction-manipulated individuals took longer to return to their home trees than vision-manipulated individuals. Together, our results indicate that olfaction is important in the nocturnal navigation of P. pseudoparvulus and suggest that vision may also play a more minor role

The Orphan Adhesion-GPCR GPR126 Is Required for Embryonic Development in the Mouse

Adhesion-GPCRs provide essential cell-cell and cell-matrix interactions in development, and have been implicated in inherited human diseases like Usher Syndrome and bilateral frontoparietal polymicrogyria. They are the second largest subfamily of seven-transmembrane spanning proteins in vertebrates, but the function of most of these receptors is still not understood. The orphan Adhesion-GPCR GPR126 has recently been shown to play an essential role in the myelination of peripheral nerves in zebrafish. In parallel, whole-genome association studies have implicated variation at the GPR126 locus as a determinant of body height in the human population. The physiological function of GPR126 in mammals is still unknown. We describe a targeted mutation of GPR126 in the mouse, and show that GPR126 is required for embryonic viability and cardiovascular development

Potentiation of amyloid beta phagocytosis and amelioration of synaptic dysfunction upon FAAH deletion in a mouse model of Alzheimer’s disease.

Background: The complex pathophysiology of Alzheimer’s disease (AD) hampers the development of effective treatments. Attempts to prevent neurodegeneration in AD have failed so far, highlighting the need for further clarification of the underlying cellular and molecular mechanisms. Neuroinflammation seems to play a crucial role in disease progression, although its specific contribution to AD pathogenesis remains elusive. We have previously shown that the modulation of the endocannabinoid system (ECS) renders beneficial effects in a context of amyloidosis, which triggers neuroinflammation. In the 5xFAD model, the genetic inactivation of the enzyme that degrades anandamide (AEA), the fatty acid amide hydrolase (FAAH), was associated with a significant amelioration of the memory deficit. Methods: In this work, we use electrophysiology, flow cytometry and molecular analysis to evaluate the cellular and molecular mechanisms underlying the improvement associated to the increased endocannabinoid tone in the 5xFAD mouse− model. Results: We demonstrate that the chronic enhancement of the endocannabinoid tone rescues hippocampal synaptic plasticity in the 5xFAD mouse model. At the CA3–CA1 synapse, both basal synaptic transmission and longterm potentiation (LTP) of synaptic transmission are normalized upon FAAH genetic inactivation, in a CB1 receptor (CB1R)- and TRPV1 receptor-independent manner. Dendritic spine density in CA1 pyramidal neurons, which is notably decreased in 6-month-old 5xFAD animals, is also restored. Importantly, we reveal that the expression of microglial factors linked to phagocytic activity, such as TREM2 and CTSD, and other factors related to amyloid beta clearance and involved in neuron–glia crosstalk, such as complement component C3 and complement receptor C3AR, are specifically upregulated in 5xFAD/FAAH−/− animals. Conclusion: In summary, our findings support the therapeutic potential of modulating, rather than suppressing, neuroinflammation in Alzheimer’s disease. In our model, the long-term enhancement of the endocannabinoid tone triggered augmented microglial activation and amyloid beta phagocytosis, and a consequent reversal in the neuronal phenotype associated to the diseasepost-print4206 K

Loss of CIC promotes mitotic dysregulation and chromosome segregation defects

Background: CIC is a transcriptional repressor inactivated by loss-of-function mutations in several cancer types, including gliomas, lung cancers, and gastric adenocarcinomas. CIC alterations and/or loss of CIC activity have been associated with poorer outcomes and more aggressive phenotypes across cancer types, which is consistent with the notion that CIC functions as a tumour suppressor across a wide range of contexts. Results: Using mammalian cells lacking functional CIC, we found that CIC deficiency was associated with chromosome segregation (CS) defects, resulting in chromosomal instability and aneuploidy. These CS defects were associated with transcriptional dysregulation of spindle assembly checkpoint and cell cycle regulators. We also identified novel CIC interacting proteins, including core members of the SWI/SNF complex, and showed that they cooperatively regulated the expression of genes involved in cell cycle regulation. Finally, we showed that loss of CIC and ARID1A cooperatively increased CS defects and reduced cell viability. Conclusions: Our study ascribes a novel role to CIC as an important regulator of the cell cycle and demonstrates that loss of CIC can lead to chromosomal instability and aneuploidy in human and murine cells through defects in CS, providing insight into the underlying mechanisms of CIC's increasingly apparent role as a "pan-cancer" tumour suppressor

Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models

Feature-based classifiers for somatic mutation detection in tumour–normal paired sequencing data

Motivation: The study of cancer genomes now routinely involves using next-generation sequencing technology (NGS) to profile tumours for single nucleotide variant (SNV) somatic mutations. However, surprisingly few published bioinformatics methods exist for the specific purpose of identifying somatic mutations from NGS data and existing tools are often inaccurate, yielding intolerably high false prediction rates. As such, the computational problem of accurately inferring somatic mutations from paired tumour/normal NGS data remains an unsolved challenge

Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer.

BACKGROUND: High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors. METHODS: We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA). RESULTS: High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to inhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup. CONCLUSIONS: We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity