A primary care Symptoms Clinic for patients with medically unexplained symptoms : pilot randomised trial

Peer reviewedPublisher PD

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

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

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

New insights into acquired temozolomide resistance in glioblastoma?

This scientific commentary refers to ‘c-Myc-miR-29c-REV3L signalling pathway drives the acquisition of temozolomide resistance in glioblastoma' by Luo et al. (doi:10.1093/brain/awv287

Funktionelle und molekulare Charakterisierung von Kaliumkanälen in hippocampalen Astrozyten

Für die Funktion von Astrozyten in der zerebralen Ionen-, Transmitter- und Metabolitenregulation sowie Kontrolle des zerebralen Blutflusses ist ihre hohe Kaliumleitfähigkeit essentiell. Unter pathophysiologischen Bedingungen kommt es oft zu einer lokalen Azidose und die homöosthatische Funktion von Astrozyten ist eingeschränkt. Der Einfluss extrazellulärer Azidose auf die Kaliumleitfähigkeit von hippocampalen Astrozyten der Maus ist jedoch bislang unzureichend verstanden und wird in der vorliegenden Arbeit mit neurophysiologischen und molekularbiologischen Methoden untersucht. Hierzu erfolgen Patch-Clamp-Experimente an akut isolierten Astrozyten des murinem Hippocampus und einem heterologen Expressionssystem unter Applikation verschiedener pH-Werte und Ionenkanalmodulatoren, sowie molekularbiologische Experimente auf Transkriptionsebene mittels Einzelzell-RT-PCR und semiquantitativer RT-PCR. Extrazelluläre Azidifikation führte in Patch-Clamp-Experimenten zu einer Reduktion der astrozytären Kaliumströme. Dies ließ sich in Experimenten an Kir4.1-transfizierten Zellen eines heterologen Expressionssystems nachvollziehen und erfolgt somit vermutlich durch Modulation von Kir-Kanälen. Nach Blockade von Kir-Kanälen führte eine Azidifikation zu einer Vergrößerung der residuellen Kaliumleitfähigkeit hippocampaler Astrozyten. Diese Aktivierung wurde durch Modulatoren von TREK-1-Kanälen aus der Familie der K2P-Kanäle nachgebildet. Molekularbiologische Experimente bestätigten die astrozytäre Expression der K2P-Kanäle TREK-1 und TWIK-1, während TASK-Kanäle nicht nachweisbar waren. Elektrophysiologisch und auf Transkriptionsbene fanden sich keine Hinweise auf eine funktionelle Expression von ASIC und TRPV1-Kanälen in hippocampalen Astrozyten. Zusammenfassend erlaubt die Zusammenstellung astrozytärer Kaliumkanäle die Tolerierung transienter Azidifikation

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