ALK immunohistochemistry is highly sensitive and specific for the detection of ALK translocated lung adenocarcinomas: lessons from an audit of lung cancer molecular testing in South-East of Scotland

Background The approval of novel targeted treatments for epidermal growth factor receptor (EGFR)-positive and anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer has led to the increased requirement for mutation testing. Results We report our experience of ALK testing with immunohistochemistry (IHC) and fluorescence in-situ hybridisation (FISH) and present the prevalence of EGFR, Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) and ALK mutations. From January 2011 to May 2014, we found mutation rates of EGFR, KRAS and ALK to be 10.4% (67/643), 35.8% (86/240) and 2.3% (7/304), respectively. ALK-rearrangements were found to be associated with never smokers (p < 0.001) and younger patients (≤ 50 years old) (p < 0.001). ALK IHC protein expression in tumour cells is 100% sensitive (7 IHC+/7 FISH+) and 96.6% specific (113 IHC-/117 FISH-) for ALK-rearrangements by FISH. ALK-rearranged tumours were wild-type for EGFR and KRAS. Conclusion Our findings support the use of ALK protein expression and KRAS mutation testing as part of the molecular diagnostic algorithm for lung adenocarcinomas

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Experimental Quantum Hamiltonian Learning

Efficiently characterising quantum systems, verifying operations of quantum devices and validating underpinning physical models, are central challenges for the development of quantum technologies and for our continued understanding of foundational physics. Machine-learning enhanced by quantum simulators has been proposed as a route to improve the computational cost of performing these studies. Here we interface two different quantum systems through a classical channel - a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen-vacancy centre - and use the former to learn the latter's Hamiltonian via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately $10^{-5}$. Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model itself. We go on to implement an interactive version of the protocol and experimentally show its ability to characterise the operation of the quantum photonic device. This work demonstrates powerful new quantum-enhanced techniques for investigating foundational physical models and characterising quantum technologies

A proposed potential role for increasing atmospheric CO2 as a promoter of weight gain and obesity

Human obesity has evolved into a global epidemic. Interestingly, a similar trend has been observed in many animal species, although diet composition, food availability and physical activity have essentially remained unchanged. This suggests a common factor—potentially an environmental factor affecting all species. Coinciding with the increase in obesity, atmospheric CO2 concentration has increased more than 40%. Furthermore, in modern societies, we spend more time indoors, where CO2 often reaches even higher concentrations. Increased CO2 concentration in inhaled air decreases the pH of blood, which in turn spills over to cerebrospinal fluids. Nerve cells in the hypothalamus that regulate appetite and wakefulness have been shown to be extremely sensitive to pH, doubling their activity if pH decreases by 0.1 units. We hypothesize that an increased acidic load from atmospheric CO2 may potentially lead to increased appetite and energy intake, and decreased energy expenditure, and thereby contribute to the current obesity epidemic

Caveolin-1 protects B6129 mice against Helicobacter pylori gastritis.

Caveolin-1 (Cav1) is a scaffold protein and pathogen receptor in the mucosa of the gastrointestinal tract. Chronic infection of gastric epithelial cells by Helicobacter pylori (H. pylori) is a major risk factor for human gastric cancer (GC) where Cav1 is frequently down-regulated. However, the function of Cav1 in H. pylori infection and pathogenesis of GC remained unknown. We show here that Cav1-deficient mice, infected for 11 months with the CagA-delivery deficient H. pylori strain SS1, developed more severe gastritis and tissue damage, including loss of parietal cells and foveolar hyperplasia, and displayed lower colonisation of the gastric mucosa than wild-type B6129 littermates. Cav1-null mice showed enhanced infiltration of macrophages and B-cells and secretion of chemokines (RANTES) but had reduced levels of CD25+ regulatory T-cells. Cav1-deficient human GC cells (AGS), infected with the CagA-delivery proficient H. pylori strain G27, were more sensitive to CagA-related cytoskeletal stress morphologies ("humming bird") compared to AGS cells stably transfected with Cav1 (AGS/Cav1). Infection of AGS/Cav1 cells triggered the recruitment of p120 RhoGTPase-activating protein/deleted in liver cancer-1 (p120RhoGAP/DLC1) to Cav1 and counteracted CagA-induced cytoskeletal rearrangements. In human GC cell lines (MKN45, N87) and mouse stomach tissue, H. pylori down-regulated endogenous expression of Cav1 independently of CagA. Mechanistically, H. pylori activated sterol-responsive element-binding protein-1 (SREBP1) to repress transcription of the human Cav1 gene from sterol-responsive elements (SREs) in the proximal Cav1 promoter. These data suggested a protective role of Cav1 against H. pylori-induced inflammation and tissue damage. We propose that H. pylori exploits down-regulation of Cav1 to subvert the host's immune response and to promote signalling of its virulence factors in host cells

The MRN complex is transcriptionally regulated by MYCN during neural cell proliferation to control replication stress

The MRE11/RAD50/NBS1 (MRN) complex is a major sensor of DNA double strand breaks, whose role in controlling faithful DNA replication and preventing replication stress is also emerging. Inactivation of the MRN complex invariably leads to developmental and/or degenerative neuronal defects, the pathogenesis of which still remains poorly understood. In particular, NBS1 gene mutations are associated with microcephaly and strongly impaired cerebellar development, both in humans and in the mouse model. These phenotypes strikingly overlap those induced by inactivation of MYCN, an essential promoter of the expansion of neuronal stem and progenitor cells, suggesting that MYCN and the MRN complex might be connected on a unique pathway essential for the safe expansion of neuronal cells. Here, we show that MYCN transcriptionally controls the expression of each component of the MRN complex. By genetic and pharmacological inhibition of the MRN complex in a MYCN overexpression model and in the more physiological context of the Hedgehog-dependent expansion of primary cerebellar granule progenitor cells, we also show that the MRN complex is required for MYCN-dependent proliferation. Indeed, its inhibition resulted in DNA damage, activation of a DNA damage response, and cell death in a MYCN- and replication-dependent manner. Our data indicate the MRN complex is essential to restrain MYCN-induced replication stress during neural cell proliferation and support the hypothesis that replication-born DNA damage is responsible for the neuronal defects associated with MRN dysfunctions.Cell Death and Differentiation advance online publication, 12 June 2015; doi:10.1038/cdd.2015.81

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables

A cohort evaluation on arterial stiffness and hypertensive disorders in pregnancy

10.1186/1471-2393-12-160BMC Pregnancy and Childbirth12-BPCM

On the status and mechanisms of coastal erosion in Marawila Beach, Sri Lanka

Coastal erosion remains a problem in many developing countries because of a limited understating of erosion mechanisms and management. Sri Lanka is one of the countries that recognized coastal erosion management as a governmental responsibility, in 1984. Nevertheless, erosion mechanisms have not yet been fully understood. We investigate the status and mechanisms of coastal erosion using empirically collected data and various techniques, such as Geographic Information System analysis of satellite images, drone mapping, bathymetric surveys, hindcasting of wind-induced wave climate, questionnaires, and semi-structured interview surveys. We identified wave climate change, reduction in river sand supply, interruptions from previous erosion management measures, and offshore sand mining as potential causes of erosion considering sediment flux and rates of erosion. Erosion of Marawila Beach began during 2005–2010 and has been continuing ever since, due to a lack of integration in the beach and the entire sediment system. It is necessary to identify the long-term, large-scale changes in the sediment system through data collection. This study highlights the importance of an integrated coastal erosion management plan and could facilitate better coastal erosion management in Sri Lanka, as well as in other developing countries