Glioblastoma stem cells

Glioblastomas are highly malignant primary brain tumors with one of the worst survival rates among all human cancers. With a more profound understanding of the cellular and molecular mechanisms of tumor initiation and acquired resistance to conventional radio- and chemotherapy, novel therapeutic targets might be discovered to optimize therapeutic approaches. In this regard, the identification of a small cellular subpopulation, called glioblastoma stem cell or stem-like cells or glioma-initiating cells or brain tumor propagating cells, has gained attention. In this article, we briefly summarize the current state of knowledge about this tumor cell population and discuss future directions for basic and clinical researc

Feature importance for machine learning redshifts applied to SDSS galaxies

We present an analysis of importance feature selection applied to photometric redshift estimation using the machine learning architecture Decision Trees with the ensemble learning routine Adaboost (hereafter RDF). We select a list of 85 easily measured (or derived) photometric quantities (or `features') and spectroscopic redshifts for almost two million galaxies from the Sloan Digital Sky Survey Data Release 10. After identifying which features have the most predictive power, we use standard artificial Neural Networks (aNN) to show that the addition of these features, in combination with the standard magnitudes and colours, improves the machine learning redshift estimate by 18% and decreases the catastrophic outlier rate by 32%. We further compare the redshift estimate using RDF with those from two different aNNs, and with photometric redshifts available from the SDSS. We find that the RDF requires orders of magnitude less computation time than the aNNs to obtain a machine learning redshift while reducing both the catastrophic outlier rate by up to 43%, and the redshift error by up to 25%. When compared to the SDSS photometric redshifts, the RDF machine learning redshifts both decreases the standard deviation of residuals scaled by 1/(1+z) by 36% from 0.066 to 0.041, and decreases the fraction of catastrophic outliers by 57% from 2.32% to 0.99%.Comment: 10 pages, 4 figures, updated to match version accepted in MNRA

Stacking for machine learning redshifts applied to SDSS galaxies

We present an analysis of a general machine learning technique called 'stacking' for the estimation of photometric redshifts. Stacking techniques can feed the photometric redshift estimate, as output by a base algorithm, back into the same algorithm as an additional input feature in a subsequent learning round. We shown how all tested base algorithms benefit from at least one additional stacking round (or layer). To demonstrate the benefit of stacking, we apply the method to both unsupervised machine learning techniques based on self-organising maps (SOMs), and supervised machine learning methods based on decision trees. We explore a range of stacking architectures, such as the number of layers and the number of base learners per layer. Finally we explore the effectiveness of stacking even when using a successful algorithm such as AdaBoost. We observe a significant improvement of between 1.9% and 21% on all computed metrics when stacking is applied to weak learners (such as SOMs and decision trees). When applied to strong learning algorithms (such as AdaBoost) the ratio of improvement shrinks, but still remains positive and is between 0.4% and 2.5% for the explored metrics and comes at almost no additional computational cost.Comment: 13 pages, 3 tables, 7 figures version accepted by MNRAS, minor text updates. Results and conclusions unchange

Anomaly detection for machine learning redshifts applied to SDSS galaxies

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million 'clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 'anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed 'anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80% when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.Comment: 13 pages, 8 figures, 1 table, minor text updates to macth MNRAS accepted versio

Electron Shock Waves

In this paper we describe numerical investigations of breakdown waves concentrating on antiforce waves. We employed one-dimensional electron fluid dynamical equations for a luminous pulse wave propagating into a neutral gas region and subjected to an applied electric field. We assumed that the electrons were the main element in the propagation of the wave and that the electron gas partial pressure provided the driving force. These waves are considered to be shock fronted and are composed of two regions: the thin sheath region behind the shock front and the thicker quasi-neutral region following the sheath region. Our set of equations, known as the electron fluid dynamical (EFD) equations, is composed of the equations of conservation of mass, momentum, and energy coupled with Poisson\u27s equation. For antiforce waves, we were able to successfully integrate the set of EFD equations through the sheath region using a set of initial boundary conditions at the wave front. By using values of electron gas temperature, electron number density, ionization rate, and also the existing conditions at the end of the sheath region as initial boundary values for the thermal region of the gas, we were able to integrate the electron fluid dynamical equations, modified for the thermal region of the gas, through that region. Our results satisfy the required conditions at the end of the sheath and quasi-neutral regions. The wave profiles for electric field, electron velocity, electron number density, electron gas temperature, and ionization rate within the sheath and quasi-neutral regions were determined

Tuning target selection algorithms to improve galaxy redshift estimates

We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient target selection can improve the ML redshift uncertainties as calculated on an independent sample, while requiring less targets to be observed. We compare the ML targeting algorithms with the Sloan Digital Sky Survey (SDSS) target order, and with a random targeting algorithm. The ML inspired algorithms are constructed iteratively by estimating which of the remaining target galaxies will be most difficult for the machine learning methods to accurately estimate redshifts using the previously observed data. This is performed by predicting the expected redshift error and redshift offset (or bias) of all of the remaining target galaxies. We find that the predicted values of bias and error are accurate to better than 10-30% of the true values, even with only limited training sample sizes. We construct a hypothetical follow-up survey and find that some of the ML targeting algorithms are able to obtain the same redshift predictive power with 2-3 times less observing time, as compared to that of the SDSS, or random, target selection algorithms. The reduction in the required follow up resources could allow for a change to the follow-up strategy, for example by obtaining deeper spectroscopy, which could improve ML redshift estimates for deeper test data.Comment: 16 pages, 9 figures, updated to match MNRAS accepted version. Minor text changes, results unchange

Clinical Implications of Molecular Neuropathology and Biomarkers for Malignant Glioma

Malignant gliomas are currently diagnosed based on morphological criteria and graded according to the World Health Organization classification of primary brain tumors. This algorithm of diagnosis and classification provides clinicians with an estimated prognosis of the natural course of the disease. It does not reflect the expected response to specific treatments beyond surgery (eg, radiotherapy or alkylating chemotherapy). Clinical experience has revealed that gliomas sharing similar histomorphological criteria might indeed have different clinical courses and exhibit highly heterogenous responses to treatments. This was very impressively demonstrated first for oligodendrogliomas. The presence or lack of combined deletions of the chromosomal segments 1p/19q was associated with different benefit from radiotherapy and chemotherapy. We review current molecular markers for malignant gliomas and discuss their current and future impact on clinical neuro-oncolog