Gastrointestinal stromal tumors (GISTs): Point mutations matter in management, a review

The therapeutic implications of the genomic alterations seen within the drivers of gastrointestinal stromal tumors (GIST) are among the best understood in all of solid tumors. Sequencing of cKIT and PDGFRα should be considered standard practice for the treatment of GIST patients. In this article, we will review the common mutations and how they are utilized in clinical management. In addition, we will review the rare D842V PDGFRα mutation and the diverse molecular group that lacks a mutation in either cKIT or PDGFRα (wild-type GIST) which are best treated on clinical trial. Finally, we will look forward at the future therapies that are ever evolving for management of GIST. Taken together, the scientific advances in understanding the molecular basis of GIST validates the importance of knowing and understanding the mutations that are present in any one patient

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Nanosecond protein dynamics in a red/green cyanobacteriochrome revealed by transient IR spectroscopy

Over the last decades, photoreceptive proteins were extensively studied with biophysical methods to gain a fundamental understanding of their working mechanisms and further guide the development of optogenetic tools. Time-resolved infrared (IR) spectroscopy is one of the key methods to access their functional non-equilibrium processes with high temporal resolution but has the major drawback that experimental data are usually highly complex. Linking the spectral response to specific molecular events is a major obstacle. Here, we investigate a cyanobacteriochrome photoreceptor with a combined approach of transient absorption spectroscopy in the visible and IR spectral regions. We obtain kinetic information in both spectral regions by analysis with two different fitting methods: global multiexponential fitting and lifetime analysis. We investigate the ground state dynamics that follow photoexcitation in both directions of the bi-stable photocycle (Pr* and Pg*) in the nanosecond and microsecond time regimes. We find two ground state intermediates associated with the decay of Pr* and four with Pg* and report the macroscopic time constants of their interconversions. One of these processes is assigned to a structural change in the protein backbone

Analysis of mineral-rich suspended matter in glacial lakes using simulations and satellite data

The contribution of mineral-rich suspended matter (MSM) to the optics of water bodies is still less treated by bio-optical modeling than that of other water constituents. However, with the increasing number of remote sensing studies on inland waters, optical properties of terrestrial particles gain importance for accurately estimating particle concentrations. We compared two current simulation tools, Hydrolight and WASI, for high MSM concentrations within the realistic context of catchments with glacial erosion. The study area is an extreme form of suspended sediment-dominated Case2 water. We simulated Rrs(0-) spectra with MSM concentrations varying from 5 to 200 g m-3. In a second step, WASI-2D was applied to invert Landsat8. In-situ measured concentrations and reflectance spectra served to assess model performance. Thus, we tested the suitability of the analytical model WASI for high MSM concentrations and point out necessities for future adaptations to (extremely) turbid environments

Nadine Gordimer: De-Linking, Interrupting, Severing

A special issue reassessing the whole of Nadine Gordimer's prolific and versatile work, this collection of articles maps out the numerous breaches, interruptions, and ruptures that traverse the South African writer's novels and short stories, as well as her essays and political commitments. The issue aims at re-circulating and re-directing a number of concepts and critical stances which have emerged over the years about Gordimer's texts. Through their emphasis on processes of de-linking, all eight authors offer new perspectives on her anti-apartheid writing but also on her interventions in a globalisation that has paradoxically entrenched spatial, social and cultural disjunctions