Uncertainty quantification in energy management procedures

Complex energy systems are made up of a number of components interacting together via different energy vectors. The assessment of their performance under dynamic working conditions, where user demand and energy prices vary over time, requires a simulation tool. Regardless of the accuracy of this procedure, the uncertainty in data, obtained both by measurements or by forecasting, is usually non-negligible and requires the study of the sensitivity of results versus input data. In this work, polynomial chaos expansion technique is used to evaluate the variation of cogeneration plant performance with respect to the uncertainty of energy prices and user requests. The procedure allows to obtain this information with a much lower computational cost than that of usual Monte-Carlo approaches. Furthermore, all the tools used in this paper, which were developed in Python, are published as free and open source software

The Role of MicroRNAs in Influencing Body Growth and Development

Body growth and development are regulated among others by genetic and epigenetic factors. MicroRNAs (miRNAs) are epigenetic regulators of gene expression that act at the post-transcriptional level, thereby exerting a strong influence on regulatory gene networks. Increasing studies suggest the importance of miRNAs in the regulation of the growth plate and growth hormone (GH)-insulin-like growth factor (IGF) axis during the life course in a broad spectrum of animal species, contributing to longitudinal growth. This review summarizes the role of miRNAs in regulating growth in different in vitro and in vivo models acting on GH, GH receptor (GHR), IGFs, and IGF1R genes besides current knowledge in humans, and highlights that this regulatory system is of importance for growth

Energy efficiency measures for buildings in Hebron city and their expected impacts in the distribution grid

Theenergye ffi ciencyinbuildingscouldrepresentoneofthemainopportunity,withinawidestrategicscenario,toachieveenergy independenceofPalestine.Infact,thereductionofbuildingenergydemandduetotheimplementationofenergye ffi ciency measuresleadstoaconsequentdecreaseoftheenergyprovisionneeds.Forthisreasonananalysisofthepotentialreduction oftheenergyconsumptioninbuildingneedtobeperformedandapossibleestimationofcostsshouldbeidentifiedfordefining aenergystrategicplanofPalestine.Thispaperintendstohighlightthepotentialoftheenergye ffi ciencymeasuresindi ff erent buildingtypologiesofHebroncityinPalestineandtheirimpactintheelectricaldistributiongrid.Thee ff ectivenessofthedi ff erent measuresareestimatedbymeansofsoftwaresimulationsandtheiroptimalcombinationisalsoidentifiedinordertomaximize thereductionofenergydemands.Finally,thevariationofpowerlossesinthedistributiongridduetotheretrofitactionanda preliminaryestimationofpossibleeconomice ff ortfortheimplementationoftheproposedactionsarealsoexpose

Assessment of resistance mechanisms and clinical implications in patients with kras mutated-metastatic breast cancer and resistance to cdk4/6 inhibitors

Simple SummaryPalbociclib in combination with fulvestrant is used globally to treat metastatic breast cancer, but it was recognized that not all patients benefit from this combination of drugs. However, the predictive factors remain unknown. Here, we show KRAS ctDNA levels as predictive mechanisms of resistance to palbociclib and fulvestrant, and their association with the time to treatment discontinuation of the above treatment. These observations shed light on the potential clinical applications of ctDNA analysis in this setting of patients, in order to provide critical information about tumour dynamics, and to predict who will take advantage from CDK4/6 inhibitors.Despite therapeutic improvements, resistance to palbociclib is a growing clinical challenge which is poorly understood. This study was conducted in order to understand the molecular mechanisms of resistance to palbociclib, and to identify biomarkers to predict who will take advantage from cyclin-dependent kinase 4/6 inhibitors (CDK4/6i). A total of about a thousand blood samples were collected from 106 patients with hormone receptor positive (HR+) human epidermal growth factor receptor 2 (HER2) negative metastatic breast cancer who received palbociclib in combination with fulvestrant as the first-line metastatic therapy enrolled in this study. The genotyping of their plasma cell-free DNA was studied, including serial plasma samples. Collectively, our findings identify the appearance of KRAS mutations leading to palbociclib resistance acquisition within 6 months, and provide critical information for the prediction of therapeutic responses in metastatic breast cancer. By monitoring KRAS status through liquid biopsy, we could predict who will take advantage from the combination of palbociclib and fulvestrant, offering highly-individualized treatment plans, thus ensuring the best patient quality of life

The Iterative Signature Algorithm for the analysis of large scale gene expression data

We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure