Evaluation of the physical interpretability of calibrated building model parameters

Identifying building envelope thermal properties from the calibration of a lumped model raises identifiability issues. Not only needs the simplified model to be structurally identifiable, i.e. deliver unique estimates after calibration, but also the data used might not be informative enough to result in either or both accurate estimates and physically interpretable values. This could particularly be the case when data is extracted from non intrusive in situ measurements, in the sense not disturbing potential occupancy. In this frame, this paper develops a method to investigate the physical interpretation of the parameters of lumped models through a numerical tests procedure. Each test runs a simulation of a comprehensive thermal model of a building, with variations in thermal resistance properties of the envelope. Each simulation delivers data used to calibrate a lumped model. The parameters of the lumped model are then physically interpretable if their value vary according to the variations of the comprehensive model. The overall test procedure is applied to the study of a 2R2C model. Results show that the calibration of this model delivers robust calibration results for all parameters but one and also shows satisfactory robustness of the estimation of the overall thermal resistance. This paper concludes that the numerical test procedure does allow to evaluate practical identifiability of lumped models, and will in future work be used to examine more complex lumped model

Developing a hybrid model of prediction and classification algorithms for building energy consumption

Artificial intelligence algorithms have been applied separately or integrally for prediction, classification or optimization of buildings energy consumption. However, there is a salient gap in the literature on the investigation of hybrid objective function development for energy optimization problems including qualitative and quantitative datasets in their constructs. To tackle this challenge, this paper presents a hybrid objective function of machine learning algorithms in optimizing energy consumption of residential buildings through considering both continuous and discrete parameters of energy simultaneously. To do this, a comprehensive dataset including significant parameters of building envelop, building design layout and HVAC was established, Artificial Neural Network as a prediction and Decision Tree as a classification algorithm were employed via cross-training ensemble equation to create the hybrid function and the model was finally validated via the weighted average of the error decomposed for the performance. The developed model could effectively enhance the accuracy of the objective functions used in the building energy prediction and optimization problems. Furthermore, the results of this novel approach resolved the inclusion issue of both continuous and discrete parameters of energy in a unified objective function without threatening the integrity and consistency of the building energy datasets

Assessing Pharmacodynamic Interactions in Mice Using the Multistate Tuberculosis Pharmacometric and General Pharmacodynamic Interaction Models

The aim of this study was to investigate pharmacodynamic (PD) interactions in mice infected with Mycobacterium tuberculosis using population pharmacokinetics (PKs), the Multistate Tuberculosis Pharmacometric (MTP) model, and the General Pharmacodynamic Interaction (GPDI) model. Rifampicin, isoniazid, ethambutol, or pyrazinamide were administered in monotherapy for 4 weeks. Rifampicin and isoniazid showed effects in monotherapy, whereas the animals became moribund after 7 days with ethambutol or pyrazinamide alone. No PD interactions were observed against fast-multiplying bacteria. Interactions between rifampicin and isoniazid on killing slow and non-multiplying bacteria were identified, which led to an increase of 0.86 log10 colony-forming unit (CFU)/lungs at 28 days after treatment compared to expected additivity (i.e., antagonism). An interaction between rifampicin and ethambutol on killing non-multiplying bacteria was quantified, which led to a decrease of 2.84 log10 CFU/lungs at 28 days after treatment (i.e., synergism). These results show the value of pharmacometrics to quantitatively assess PD interactions in preclinical tuberculosis drug development

Kinome rewiring reveals AURKA limits PI3K-pathway inhibitor efficacy in breast cancer.

Dysregulation of the PI3K-AKT-mTOR signaling network is a prominent feature of breast cancers. However, clinical responses to drugs targeting this pathway have been modest, possibly because of dynamic changes in cellular signaling that drive resistance and limit drug efficacy. Using a quantitative chemoproteomics approach, we mapped kinome dynamics in response to inhibitors of this pathway and identified signaling changes that correlate with drug sensitivity. Maintenance of AURKA after drug treatment was associated with resistance in breast cancer models. Incomplete inhibition of AURKA was a common source of therapy failure, and combinations of PI3K, AKT or mTOR inhibitors with the AURKA inhibitor MLN8237 were highly synergistic and durably suppressed mTOR signaling, resulting in apoptosis and tumor regression in vivo. This signaling map identifies survival factors whose presence limits the efficacy of targeted therapies and reveals new drug combinations that may unlock the full potential of PI3K-AKT-mTOR pathway inhibitors in breast cancer

The candidate antimalarial drug MMV665909 causes oxygen-dependent mRNA mistranslation and synergises with quinoline-derived antimalarials

To cope with growing resistance to current antimalarials, new drugs with novel modes of action are urgently needed. Molecules targeting protein synthesis appear to be promising candidates. We identified a compound (MMV665909) from the MMV Malaria Box of candidate antimalarials that could produce synergistic growth inhibition with the aminoglycoside antibiotic paromomycin, suggesting a possible action of the compound in mRNA mistranslation. This mechanism of action was substantiated with the yeast cell model using available reporters of mistranslation and other genetic tools. Mistranslation induced by MMV665909 was oxygen-dependent, suggesting a role for reactive oxygen species (ROS). Overexpression of Rli1 (a ROS-sensitive, conserved FeS protein essential in mRNA translation) rescued inhibition by MMV665909, consistent with the drug’s action on translation fidelity being mediated through Rli1. The MMV drug also synergised with major quinoline-derived antimalarials which can perturb amino acid availability or promote ROS stress: chloroquine, amodiaquine and primaquine. The data collectively suggest translation-fidelity as a novel target of antimalarial action and support MMV665909 as a promising drug candidate

Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: a review

The strict CO2 emission targets set to tackle the global climate change associated with greenhouse gas emission exerts so much pressure on our cities which contribute up to 75% of the global carbon dioxide emission level, with buildings being the largest contributor (UNEP, 2015). Premised on this fact, urban planners are required to implement proactive energy planning strategies not only to meet these targets but also ensure that future cities development is performed in a way that promotes energy-efficiency. This article gives an overview of the state-of-art of energy planning and forecasting approaches for aiding physical improvement strategies in the building sector. Unlike previous reviews, which have only addressed the strengths as well as weaknesses of some of the approaches while referring to some relevant examples from the literature, this article focuses on critically analysing more approaches namely; 2D GIS and 3DGIS (CityGML) based energy prediction approaches, based on their frequent intervention scale, applicability in the building life cycle, and conventional prediction process. This will be followed by unravelling the gaps and issues pertaining to the reviewed approaches. Finally, based on the identified problems, future research prospects are recommended

Monoamine involvement in the antidepressant-like effect induced by P2 blockade

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