EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics.

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.

The nature of Pu-bearing particles from the Maralinga nuclear testing site, Australia.

The high-energy release of plutonium (Pu) and uranium (U) during the Maralinga nuclear trials (1955-1963) in Australia, designed to simulate high temperature, non-critical nuclear accidents, resulted in wide dispersion µm-sized, radioactive, Pu-U-bearing 'hot' particles that persist in soils. By combining non-destructive, multi-technique synchrotron-based micro-characterization with the first nano-scale imagining of the composition and textures of six Maralinga particles, we find that all particles display intricate physical and chemical make-ups consistent with formation via condensation and cooling of polymetallic melts (immiscible Fe-Al-Pu-U; and Pb ± Pu-U) within the detonation plumes. Plutonium and U are present predominantly in micro- to nano-particulate forms, and most hot particles contain low valence Pu-U-C compounds; these chemically reactive phases are protected by their inclusion in metallic alloys. Plutonium reworking was observed within an oxidised rim in a Pb-rich particle; however overall Pu remained immobile in the studied particles, while small-scale oxidation and mobility of U is widespread. It is notoriously difficult to predict the long-term environmental behaviour of hot particles. Nano-scale characterization of the hot particles suggests that long-term, slow release of Pu from the hot particles may take place via a range of chemical and physical processes, likely contributing to on-going Pu uptake by wildlife at Maralinga.

Future migration: Key environmental indicators of Pu accumulation in terrestrial sediments of Queensland, Australia.

Plutonium (Pu) interactions in the environment are highly complex. Site-specific variables play an integral role in determining the chemical and physical form of Pu, and its migration, bioavailability, and immobility. This paper aims to identify the key variables that can be used to highlight regions of radioecological sensitivity and guide remediation strategies in Australia. Plutonium is present in the Australian environment as a result of global fallout and the British nuclear testing program of 1952-1958 in central and west Australia (Maralinga and Montebello islands). We report the first systematic measurements of 239+240Pu and 238Pu activity concentrations in distal (≥1000 km from test sites) catchment outlet sediments from Queensland, Australia. The average 239+240Pu activity concentration was 0.29 mBq.g -1 (n = 73 samples) with a maximum of 4.88 mBq.g -1.238Pu/239+240Pu isotope ratios identified a large range (0.02-0.29 (RSD: 74%)) which is congruent with the heterogeneous nuclear material used for the British nuclear testing programme at Maralinga and Montebello Islands. The use of a modified PCA relying on non-linear distance correlation (dCorr) provided broader insight into the impact of environmental variables on the transport and migration of Pu in this soil system. Primary key environmental indicators of Pu presence were determined to be actinide/lanthanide/heavier transition metals, elevation, electrical conductivity (EC), CaO, SiO2, SO3, landform, geomorphology, land use, and climate explaining 81.7% of the variance of the system. Overall this highlighted that trace level Pu accumulations are associated with the coarse, refractive components of Australian soils, and are more likely regulated by the climate of the region and overall soil type.

Transmission of and susceptibility to seasonal influenza in Switzerland from 2003 to 2015.

Understanding the seasonal patterns of influenza transmission is critical to help plan public health measures for the management and control of epidemics. Mathematical models of infectious disease transmission have been widely used to quantify the transmissibility of and susceptibility to past influenza seasons in many countries. The objective of this study was to obtain a detailed picture of the transmission dynamics of seasonal influenza in Switzerland from 2003 to 2015. To this end, we developed a compartmental influenza transmission model taking into account social mixing between different age groups and seasonal forcing. We applied a Bayesian approach using Markov chain Monte Carlo (MCMC) methods to fit the model to the reported incidence of influenza-like-illness (ILI) and virological data from Sentinella, the Swiss Sentinel Surveillance Network. The maximal basic reproduction number, R0, ranged from 1.46 to 1.81 (median). Median estimates of susceptibility to influenza ranged from 29% to 98% for different age groups, and typically decreased with age. We also found a decline in ascertainability of influenza cases with age. Our study illustrates how influenza surveillance data from Switzerland can be integrated into a Bayesian modeling framework in order to assess age-specific transmission of and susceptibility to influenza.