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

Toward an integrated modeling of the dairy product transformations, a review of the existing mathematical models

The large diversity of dairy products is the consequence of complex processes involving series of unit operations with a wide range of controls that makes the completemodeling awkward. Due to the variety of milk components and process conditions, a generic model describing the milk processing can only be achieved by the integration of various models into a generic modeling framework. Moreover, the building of such an approach involves the coupling of transformation models describing each unit operation. In this scope, the present work aims to review existing mathematical models of the dairy products processing with a special focus on some main process units: thermal treatment, homogenization and coagulation. For each unit operation, several transformation models are investigated according to their complexities, relevance to depict actual phenomena and ability to be integrated with others models to represent complex transformations. As a first step for the integration of models, a focus on their input parameters and predicted variables is achieved

Multi-resolution selective ensemble extreme learning machine for electricity consumption prediction

We propose a multi-resolution selective ensemble extreme learning machine (MRSE-ELM) method for time-series prediction with the application to the next-step and next-day electricity consumption prediction. Specifically, at the current time stamp, the preceding timeseries data is sampled at different time intervals (i.e. resolutions) to constitute the time windows used for the prediction. The value at each sampled point can be certain statistics calculated from its associated time interval. At each resolution, multiple extreme learning machines (ELMs) with different numbers of hidden neurons are first trained. Then, sequential forward selection and least square regression are used to select an optimal set of trained ELMs to constitute the final ensemble model. The experimental results demonstrate that the proposed MRSE-ELM outperforms the best single ELM model across all resolutions. Compared to three state-of-the-art prediction models, MRSE-ELM shows its superiority on the next-step and next-day electricity consumption prediction tasks

Enhanced diagnosis of advanced fibrosis and cirrhosis in individuals with NAFLD using FibroScan-based Agile scores

Background & Aims: Currently available non-invasive tests, including fibrosis-4 index (FIB-4) and liver stiffness measurement (LSM by VCTE), are highly effective at excluding advanced fibrosis (AF) (F ≥3) or cirrhosis in people with non-alcoholic fatty liver disease (NAFLD), but only have moderate ability to rule-in these conditions. Our objective was to develop and validate two new scores (Agile 4 and Agile 3+) to identify cirrhosis or AF, respectively, with optimized positive predictive value and fewer indeterminate results, in individuals with NAFLD attending liver clinics. Methods: This international study included seven adult cohorts with suspected NAFLD who underwent liver biopsy, LSM and blood sampling during routine clinical practice or screening for trials. The population was randomly divided into a training set and an internal validation set, on which the best-fitting logistic regression model was built, and performance and goodness of fit were assessed, respectively. Furthermore, both scores were externally validated on two large cohorts. Cut-offs for high sensitivity and specificity were derived in the training set to rule-out and rule-in cirrhosis or AF and then tested in the validation set and compared to FIB-4 and LSM. Results: Each score combined LSM, AST/ALT ratio, platelets, sex and diabetes status, as well as age for Agile 3+. Calibration plots for Agile 4 and Agile 3+ indicated satisfactory to excellent goodness of fit. Agile 4 and Agile 3+ outperformed FIB-4 and LSM in terms of AUROC, percentage of patients with indeterminate results and positive predictive value to rule-in cirrhosis or AF. Conclusions: The two novel non-invasive scores improve identification of cirrhosis or AF among individuals with NAFLD attending liver clinics and reduce the need for liver biopsy in this population. Impact and implications: Non-invasive tests currently used to identify patients with advanced fibrosis or cirrhosis, such as fibrosis-4 index and liver stiffness measurement by vibration-controlled transient elastography, have high negative predictive values but high false positive rates, while results are indeterminate for a large number of cases. This study provides scores that will help the clinician diagnose advanced fibrosis or cirrhosis. These new easy-to-implement scores will help liver specialists to better identify (1) patients who need more intensive follow-up, (2) patients who should be referred for inclusion in therapeutic trials, and (3) which patients should be treated with pharmacological agents when effective therapies are approve