Discovering and interpreting transcriptomic drivers of imaging traits using neural networks

Motivation. Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these "radiogenomic" studies often use linear, shallow models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These current studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical traits, such as prognosis. Results. We present an approach based on neural networks that takes high-dimensional gene expressions as input and performs nonlinear mapping to an imaging trait. To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outperform comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that imaging traits had specific transcription patterns, e.g., edema and genes related to cellular invasion, and 15 radiogenomic associations were predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic associations with clinical value

AAO Starbugs: software control and associated algorithms

The Australian Astronomical Observatory's TAIPAN instrument deploys 150 Starbug robots to position optical fibres to accuracies of 0.3 arcsec, on a 32 cm glass field plate on the focal plane of the 1.2 m UK-Schmidt telescope. This paper describes the software system developed to control and monitor the Starbugs, with particular emphasis on the automated path-finding algorithms, and the metrology software which keeps track of the position and motion of individual Starbugs as they independently move in a crowded field. The software employs a tiered approach to find a collision-free path for every Starbug, from its current position to its target location. This consists of three path-finding stages of increasing complexity and computational cost. For each Starbug a path is attempted using a simple method. If unsuccessful, subsequently more complex (and expensive) methods are tried until a valid path is found or the target is flagged as unreachable.Comment: 10 pages, to be published in Proc. SPIE 9913, Software and Cyberinfrastructure for Astronomy IV; 201

Empirical comparison of high gradient achievement for different metals in DC and pulsed mode

For the SwissFEL project, an advanced high gradient low emittance gun is under development. Reliable operation with an electric field, preferably above 125 MV/m at a 4 mm gap, in the presence of an UV laser beam, has to be achieved in a diode configuration in order to minimize the emittance dilution due to space charge effects. In the first phase, a DC breakdown test stand was used to test different metals with different preparation methods at voltages up to 100 kV. In addition high gradient stability tests were also carried out over several days in order to prove reliable spark-free operation with a minimum dark current. In the second phase, electrodes with selected materials were installed in the 250 ns FWHM, 500 kV electron gun and tested for high gradient breakdown and for quantum efficiency using an ultra-violet laser.Comment: 25 pages, 13 figures, 5 tables. Follow up from FEL 2008 conference (Geyongju Korea 2008) New Title in JVST A (2010) : Vacuum breakdown limit and quantum efficiency obtained for various technical metals using DC and pulsed voltage source

Determining intended evidence relations in natural language arguments

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72555/1/j.1467-8640.1991.tb00386.x.pd

The Physicist's Guide to the Orchestra

An experimental study of strings, woodwinds (organ pipe, flute, clarinet, saxophone and recorder), and the voice was undertaken to illustrate the basic principles of sound production in music instruments. The setup used is simple and consists of common laboratory equipment. Although the canonical examples (standing wave on a string, in an open and closed pipe) are easily reproduced, they fail to explain the majority of the measurements. The reasons for these deviations are outlined and discussed.Comment: 11 pages, 10 figures (jpg files). Submitted to European Journal of Physic

Equipping for risk: Lessons learnt from the UK shale-gas experience on assessing environmental risks for the future geoenergy use of the deep subsurface

\ua9 2024 The Authors. Summary findings are presented from an investigation to improve understanding of the environmental risks associated with developing an unconventional-hydrocarbons industry in the UK. The EQUIPT4RISK project, funded by UK Research Councils, focused on investigations around Preston New Road (PNR), Fylde, Lancashire, and Kirby Misperton Site A (KMA), North Yorkshire, where operator licences to explore for shale gas by hydraulic fracturing (HF) were issued in 2016, although exploration only took place at PNR. EQUIPT4RISK considered atmospheric (greenhouse gases, air quality), water (groundwater quality) and solid-earth (seismicity) compartments to characterise and model local conditions and environmental responses to HF activities. Risk assessment was based on the source-pathway-receptor approach. Baseline monitoring of air around the two sites characterised the variability with meteorological conditions, and isotopic signatures were able to discriminate biogenic methane (cattle) from thermogenic (natural-gas) sources. Monitoring of a post-HF nitrogen-lift (well-cleaning) operation at PNR detected the release of atmospheric emissions of methane (4.2 \ub1 1.4 t CH4). Groundwater monitoring around KMA identified high baseline methane concentrations and detected ethane and propane at some locations. Dissolved methane was inferred from stable-isotopic evidence as overwhelmingly of biogenic origin. Groundwater-quality monitoring around PNR found no evidence of HF-induced impacts. Two approaches for modelling induced seismicity and associated seismic risk were developed using observations of seismicity and operational parameters from PNR in 2018 and 2019. Novel methodologies developed for monitoring include use of machine learning to identify fugitive atmospheric methane, Bayesian statistics to assess changes to groundwater quality, a seismicity forecasting model seeded by the HF-fluid injection rate and high-resolution monitoring of soil-gas methane. The project developed a risk-assessment framework, aligned with ISO 31000 risk-management principles, to assess the theoretical combined and cumulative environmental risks from operations over time. This demonstrated the spatial and temporal evolution of risk profiles: seismic and atmospheric impacts from the shale-gas operations are modelled to be localised and short-lived, while risk to groundwater quality is longer-term