Identifying multiple detachment horizons and an evolving thrust history through cross-section restoration and appraisal in the Moine Thrust Belt, NW Scotland

Peer reviewedPublisher PD

Influence of structural position on fracture networks in the Torridon Group, Achnashellach fold and thrust belt, NW Scotland

Acknowledgements This research is funded by a NERC CASE studentship (NERC code NE/I018166/1) in partnership with Midland Valley. The authors thank Midland Valley for use of FieldMove Clino software for fracture data collection, and Move software for cross section construction, and strain modelling. 3D Field software is acknowledged for contour map creation. We also thank Toru Takeshita for overseeing the editorial process, and Catherine Hanks and Ole Petter Wennberg for constructive reviews.Peer reviewedPublisher PD

Using laterally compatible cross sections to infer fault growth and linkage models in foreland thrust belts

This research is partially funded by a CGG-financed pathfinder project, and formed part of a study by the Fold-Thrust Research Group at the University of Aberdeen, co-funded by InterOil, Oil Search and Santos. We also thank Nicolas Bellahsen and Stefano Tavani for constructive reviews.Peer reviewedPublisher PD

Interpreting structural geometry in fold-thrust belts : Why style matters

The Fold-Thrust Research Group is funded by InterOil, Oil Search and Santos. We thank David Ferrill and Chris Morley for robust reviews of an early draft of this paper. We also thank Bill Dunne for his patience and his usual editorial rigor – although of course authors alone are responsible for the views expressed here.Peer reviewedPostprin

Fold-thrust structures : where have all the buckles gone?

Special publication title: Folding and Fracturing of Rocks: 50 Years of Research since the Seminal Text Book of J. G. Ramsay We dedicate the paper to the memory of Martin Casey (1948-2008), who did much through good-humored argument to ensure that buckling ideas were not lost to what he called “the Ramping Club” (the thrust belt community). The Fold – Thrust Research Group has been funded by InterOil, OilSearch and Santos. We thank Paul Griffiths and anonymous referee for comments together with Hermann Lebit for scientific editing. The views expressed here of course remain those of the authors.Peer reviewedPostprin

Validation of a Predictive Model for Survival in Patients With Advanced Cancer: Secondary Analysis of RTOG 9714.

BackgroundThe objective of this study was to validate a simple predictive model for survival of patients with advanced cancer.MethodsPrevious studies with training and validation datasets developed a model predicting survival of patients referred for palliative radiotherapy using three readily available factors: primary cancer site, site of metastases and Karnofsky performance score (KPS). This predictive model was used in the current study, where each factor was assigned a value proportional to its prognostic weight and the sum of the weighted scores for each patient was survival prediction score (SPS). Patients were also classified according to their number of risk factors (NRF). Three risk groups were established. The Radiation Therapy and Oncology Group (RTOG) 9714 data was used to provide an additional external validation set comprised of patients treated among multiple institutions with appropriate statistical tests.ResultsThe RTOG external validation set comprised of 908 patients treated at 66 different radiation facilities from 1998 to 2002. The SPS method classified all patients into the low-risk group. Based on the NRF, two distinct risk groups with significantly different survival estimates were identified. The ability to predict survival was similar to that of the training and previous validation datasets for both the SPS and NRF methods.ConclusionsThe three variable NRF model is preferred because of its relative simplicity

Transcription of the Streptococcus Pyogenes Hyaluronic Acid Capsule Biosynthesis Operon is Regulated by Previously Unknown Upstream Elements

The important human pathogen Streptococcus pyogenes (group A Streptococcus [GAS]) produces a hyaluronic acid (HA) capsule that plays critical roles in immune evasion. Previous studies showed that the hasABC operon encoding the capsule biosynthesis enzymes is under the control of a single promoter, P1, which is negatively regulated by the two-component regulatory system CovR/S. In this work, we characterize the sequence upstream of P1 and identify a novel regulatory region controlling transcription of the capsule biosynthesis operon in the M1 serotype strain MGAS2221. This region consists of a promoter, P2, which initiates transcription of a novel small RNA, HasS, an intrinsic transcriptional terminator that inefficiently terminates HasS, permitting read-through transcription of hasABC, and a putative promoter which lies upstream of P2. Electrophoretic mobility shift assays, quantitative reverse transcription-PCR, and transcriptional reporter data identified CovR as a negative regulator of P2. We found that the P1 and P2 promoters are completely repressed by CovR, and capsule expression is regulated by the putative promoter upstream of P2. Deletion of hasS or of the terminator eliminates CovR-binding sequences, relieving repression and increasing read-through, hasA transcription, and capsule production. Sequence analysis of 44 GAS genomes revealed a high level of polymorphism in the HasS sequence region. Most of the HasS variations were located in the terminator sequences, suggesting that this region is under strong selective pressure. We discovered that the terminator deletion mutant is highly resistant to neutrophil-mediated killing and is significantly more virulent in a mouse model of GAS invasive disease than the wild-type strain. Together, these results are consistent with the naturally occurring mutations in this region modulating GAS virulence

Response to “Limitations in the Hilbert Transform Approach to Locating Solar Cycle Terminators” by R. Booth

Booth (Solar Phys.296, 108, 2021; hereafter B21) is essentially a critique of the Hilbert transform techniques used in our paper (Leamon et al., Solar Phys.295, 36, 2020; hereafter L20) to predict the termination of solar cycles. Here we respond to his arguments; our methodology and parameter choices do extract a mathematically robust signature of terminators from the historical sunspot record. We agree that the attempt in L20 to extrapolate beyond the sunspot record gives a failed prediction for the next terminator of May 2020, and we identify both a possible cause and remedy here. However, we disagree with the B21 assessment that the likely termination of Solar Cycle 24 is two years after the date predicted in L20, and we show why

Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models

Long-Range Dependence (LRD) and heavy-tailed distributions are ubiquitous in natural and socio-economic data. Such data can be self-similar whereby both LRD and heavy-tailed distributions contribute to the self-similarity as measured by the Hurst exponent. Some methods widely used in the physical sciences separately estimate these two parameters, which can lead to estimation bias. Those which do simultaneous estimation are based on frequentist methods such as Whittle’s approximate maximum likelihood estimator. Here we present a new and systematic Bayesian framework for the simultaneous inference of the LRD and heavy-tailed distribution parameters of a parametric ARFIMA model with non-Gaussian innovations. As innovations we use the α-stable and t-distributions which have power law tails. Our algorithm also provides parameter uncertainty estimates. We test our algorithm using synthetic data, and also data from the Geostationary Operational Environmental Satellite system (GOES) solar X-ray time series. These tests show that our algorithm is able to accurately and robustly estimate the LRD and heavy-tailed distribution parameters

Overlapping Magnetic Activity Cycles and the Sunspot Number: Forecasting Sunspot Cycle 25 Amplitude

The Sun exhibits a well-observed modulation in the number of spots on its disk over a period of about 11 years. From the dawn of modern observational astronomy, sunspots have presented a challenge to understanding—their quasi-periodic variation in number, first noted 175 years ago, has stimulated community-wide interest to this day. A large number of techniques are able to explain the temporal landmarks, (geometric) shape, and amplitude of sunspot “cycles,” however, forecasting these features accurately in advance remains elusive. Recent observationally-motivated studies have illustrated a relationship between the Sun’s 22-year (Hale) magnetic cycle and the production of the sunspot cycle landmarks and patterns, but not the amplitude of the sunspot cycle. Using (discrete) Hilbert transforms on more than 270 years of (monthly) sunspot numbers we robustly identify the so-called “termination” events that mark the end of the previous 11-yr sunspot cycle, the enhancement/acceleration of the present cycle, and the end of 22-yr magnetic activity cycles. Using these we extract a relationship between the temporal spacing of terminators and the magnitude of sunspot cycles. Given this relationship and our prediction of a terminator event in 2020, we deduce that sunspot Solar Cycle 25 could have a magnitude that rivals the top few since records began. This outcome would be in stark contrast to the community consensus estimate of sunspot Solar Cycle 25 magnitude