EML4-ALK variants: biological and molecular properties, and the implications for patients

Since the discovery of the fusion between EML4 (echinoderm microtubule associated protein-like 4) and ALK (anaplastic lymphoma kinase), EML4-ALK, in lung adenocarcinomas in 2007, and the subsequent identification of at least 15 different variants in lung cancers, there has been a revolution in molecular-targeted therapy that has transformed the outlook for these patients. Our recent focus has been on understanding how and why the expression of particular variants can affect biological and molecular properties of cancer cells, as well as identifying the key signalling pathways triggered, as a result. In the clinical setting, this understanding led to the discovery that the type of variant influences the response of patients to ALK therapy. Here, we discuss what we know so far about the EML4-ALK variants in molecular signalling pathways and what questions remain to be answered. In the longer term, this analysis may uncover ways to specifically treat patients for a better outcome

Numerical investigation on the performance of coalescence and break-up kernels in subcooled boiling flows in vertical channels

In order to accurately predict the thermal hydraulic of two-phase gas-liquid flows with heat and mass transfer, special numerical considerations are required to capture the underlying physics: characteristics of the heat transfer and bubble dynamics taking place near the heated wall and the evolution of the bubble size distribution caused by the coalescence, break-up, and condensation processes in the bulk subcooled liquid. The evolution of the bubble size distribution is largely driven by the bubble coalescence and break-up mechanisms. In this paper, a numerical assessment on the performance of six different bubble coalescence and break-up kernels is carried out to investigate the bubble size distribution and its impact on local hydrodynamics. The resultant bubble size distributions are compared to achieve a better insight of the prediction mechanisms. Also, the void fraction, bubble Sauter mean diameter, and interfacial area concentration profiles are compared against the experimental data to ensure the validity of the models applied

Correlated enhancement of Hc2 and Jc in carbon nanotube-doped MgB2

The use of MgB2 in superconducting applications still awaits for the development of a MgB2-based material where both current-carrying performance and critical magnetic field are optimized simultaneously. We achieved this by doping MgB2 with double-wall carbon nanotubes (DWCNT) as a source of carbon in polycrystalline samples. The optimum nominal DWCNT content for increasing the critical current density, Jc is in the range 2.5-10%at depending on field and temperature. Record values of the upper critical field, Hc2(4K) = 41.9 T (with extrapolated Hc2(0) ~ 44.4 T) are reached in a bulk sample with 10%at DWCNT content. The measured Hc2 vs T in all samples are successfully described using a theoretical model for a two-gap superconductor in the dirty limit first proposed by Gurevich et al.Comment: 12 pages, 3 figure

Feature selection for high dimensional imbalanced class data using harmony search

Misclassification costs of minority class data in real-world applications can be very high. This is a challenging problem especially when the data is also high in dimensionality because of the increase in overfitting and lower model interpretability. Feature selection is recently a popular way to address this problem by identifying features that best predict a minority class. This paper introduces a novel feature selection method call SYMON which uses symmetrical uncertainty and harmony search. Unlike existing methods, SYMON uses symmetrical uncertainty to weigh features with respect to their dependency to class labels. This helps to identify powerful features in retrieving the least frequent class labels. SYMON also uses harmony search to formulate the feature selection phase as an optimisation problem to select the best possible combination of features. The proposed algorithm is able to deal with situations where a set of features have the same weight, by incorporating two vector tuning operations embedded in the harmony search process. In this paper, SYMON is compared against various benchmark feature selection algorithms that were developed to address the same issue. Our empirical evaluation on different micro-array data sets using G-Mean and AUC measures confirm that SYMON is a comparable or a better solution to current benchmarks

Transdifferentiation of pancreatic progenitor cells to hepatocyte-like cells is not serum-dependent when facilitated by extracellular matrix proteins

The rising prevalence of chronic liver disease, coupled with a permanent shortage of organs for liver transplantation, has sparked enormous interest in alternative treatment strategies. Previous protocols to generate hepatocyte-like cells (HLCs) via pancreas-to-liver transdifferentiation have utilised fetal bovine serum, introducing unknown variables and severely limiting study reproducibility. Therefore, the main goal of this study was to develop a protocol for transdifferentiation of pancreatic progenitor cells to HLCs in a chemically defined, serum-free culture medium. The clonal pancreatic progenitor cell line AR42J-B13 was cultured in basal growth medium on uncoated plastic culture dishes in the absence or presence of Dexamethasone on uncoated, laminin-or fibronectin-coated culture substrata, with or without serum supplementation. The hepatocytic differentiation potential was evaluated: (i) morphologically through bright-field and scanning electron microscopy, (ii) by assessing pancreatic and hepatic marker expression and (iii) by determining the function of HLCs through their ability to synthesise glycogen or take up and release indocyanine green. Here we demonstrate for the first time that transdifferentiation of pancreatic cells to HLCs is not dependent on serum. These results will assist in converting current differentiation protocols into procedures that are compliant with clinical use in future cell-based therapies to treat liver-related metabolic disorders

FastFlow: AI for Fast Urban Wind Velocity Prediction

Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains. These extend to various areas in sustainability. An interesting direction for which data-driven methods have not been applied much yet is in the quick quantitative evaluation of urban layouts for planning and design. In particular, urban designs typically involve complex trade-offs between multiple objectives, including limits on urban build-up and/or consideration of urban heat island effect. Hence, it can be beneficial to urban planners to have a fast surrogate model to predict urban characteristics of a hypothetical layout, e.g. pedestrian-level wind velocity, without having to run computationally expensive and time-consuming high-fidelity numerical simulations. This fast surrogate can then be potentially integrated into other design optimization frameworks, including generative models or other gradient-based methods. Here we present the use of CNNs for urban layout characterization that is typically done via high-fidelity numerical simulation. We further apply this model towards a first demonstration of its utility for data-driven pedestrian-level wind velocity prediction. The data set in this work comprises results from high-fidelity numerical simulations of wind velocities for a diverse set of realistic urban layouts, based on randomized samples from a real-world, highly built-up urban city. We then provide prediction results obtained from the trained CNN, demonstrating test errors of under 0.1 m/s for previously unseen urban layouts. We further illustrate how this can be useful for purposes such as rapid evaluation of pedestrian wind velocity for a potential new layout. It is hoped that this data set will further accelerate research in data-driven urban AI, even as our baseline model facilitates quantitative comparison to future methods

Role of front-line bevacizumab in advanced ovarian cancer: the OSCAR study

Objective Two randomized phase III trials demonstrated the efficacy and safety of combining bevacizumab with front-line carboplatin/paclitaxel for advanced ovarian cancer. The OSCAR (NCT01863693) study assessed the impact of front-line bevacizumab-containing therapy on safety and oncologic outcomes in patients with advanced ovarian cancer in the UK. Methods Between May 2013 and April 2015, patients with high-risk stage IIIB–IV advanced ovarian cancer received bevacizumab (7.5 or 15 mg/kg every 3 weeks, typically for ≤12 months, per UK clinical practice) combined with front-line chemotherapy, with bevacizumab continued as maintenance therapy. Co-primary endpoints were progression-free survival and safety (NCI-CTCAE v4.0). Patients were evaluated per standard practice/physician’s discretion. Results A total of 299 patients received bevacizumab-containing therapy. The median age was 64 years (range 31–83); 80 patients (27%) were aged ≥70 years. Surgical interventions were primary debulking in 21%, interval debulking in 36%, and none in 43%. Most patients (93%) received bevacizumab 7.5 mg/kg with carboplatin/paclitaxel. Median duration of bevacizumab was 10.5 months(range <0.1–41.4); bevacizumab and chemotherapy were given in combination for a median of three cycles (range 1–10). Median progression-free survival was 15.4 (95% CI 14.5 to 16.9) months. Subgroup analyses according to prior surgery showed median progression-free survival of 20.8, 16.1, and 13.6 months in patients with primary debulking, interval debulking, and no surgery, respectively. Median progression-free survival was 16.1 vs 14.8 months in patients aged <70 versus ≥70 years, respectively. The 1-year overall survival rate was 94%. Grade 3/4 adverse events occurred in 54% of patients, the most common being hypertension (16%) and neutropenia (5%). Thirty-five patients (12%) discontinued bevacizumab for toxicity (most often for proteinuria (2%)). Conclusions Median progression-free survival in this study was similar to that in the high-risk subgroup of the ICON7 phase III trial. Median progression-free survival was shortest in patients who did not undergo surgery

Determination of the Loading Mode Dependence of the Proportionality Parameter for the Tearing Energy of Embedded Flaws in Elastomers Under Multiaxial Deformations

In this paper, the relationship between the tearing energy and the far-field cracking energy density (CED) is evaluated for an embedded penny-shaped flaw in a 3D elastomer body under a range of loading modes. A 3D finite element model of the system is used to develop a computational-based fracture mechanics approach which is used to evaluate the tearing energy at the crack in different multiaxial loading states. By analysing the tearing energy’s relationship to the far-field CED, the proportionality parameter in the CED formulation is found to be a function of stretch and biaxiality. Using a definition of biaxiality that gives a unique value for each loading mode, the proportionality parameter becomes a linear function of stretch and biaxiality. Tearing energies predicted through the resulting equation show excellent agreement to those calculated computationally

A Transcriptomic Signature of Mouse Liver Progenitor Cells

Liver progenitor cells (LPCs) can proliferate extensively, are able to differentiate into hepatocytes and cholangiocytes, and contribute to liver regeneration. The presence of LPCs, however, often accompanies liver disease and hepatocellular carcinoma (HCC), indicating that they may be a cancer stem cell. Understanding LPC biology and establishing a sensitive, rapid, and reliable method to detect their presence in the liver will assist diagnosis and facilitate monitoring of treatment outcomes in patients with liver pathologies. A transcriptomic meta-analysis of over 400 microarrays was undertaken to compare LPC lines against datasets of muscle and embryonic stem cell lines, embryonic and developed liver (DL), and HCC. Three gene clusters distinguishing LPCs from other liver cell types were identified. Pathways overrepresented in these clusters denote the proliferative nature of LPCs and their association with HCC. Our analysis also revealed 26 novel markers, LPC markers, including Mcm2 and Ltbp3, and eight known LPC markers, including M2pk and Ncam. These markers specified the presence of LPCs in pathological liver tissue by qPCR and correlated with LPC abundance determined using immunohistochemistry. These results showcase the value of global transcript profiling to identify pathways and markers that may be used to detect LPCs in injured or diseased liver