Navigator channel adaptation to reconstruct three dimensional heart volumes from two dimensional radiotherapy planning data

BACKGROUND: Biologically-based models that utilize 3D radiation dosimetry data to estimate the risk of late cardiac effects could have significant utility for planning radiotherapy in young patients. A major challenge arises from having only 2D treatment planning data for patients with long-term follow-up. In this study, we evaluate the accuracy of an advanced deformable image registration (DIR) and navigator channels (NC) adaptation technique to reconstruct 3D heart volumes from 2D radiotherapy planning images for Hodgkin's Lymphoma (HL) patients. METHODS: Planning CT images were obtained for 50 HL patients who underwent mediastinal radiotherapy. Twelve image sets (6 male, 6 female) were used to construct a male and a female population heart model, which was registered to 23 HL "Reference" patients' CT images using a DIR algorithm, MORFEUS. This generated a series of population-to-Reference patient specific 3D deformation maps. The technique was independently tested on 15 additional "Test" patients by reconstructing their 3D heart volumes using 2D digitally reconstructed radiographs (DRR). The technique involved: 1) identifying a matching Reference patient for each Test patient using thorax measurements, 2) placement of six NCs on matching Reference and Test patients' DRRs to capture differences in significant heart curvatures, 3) adapting the population-to-Reference patient-specific deformation maps to generate population-to-Test patient-specific deformation maps using linear and bilinear interpolation methods, 4) applying population-to-Test patient specific deformation to the population model to reconstruct Test-patient specific 3D heart models. The percentage volume overlap between the NC-adapted reconstruction and actual Test patient's true heart volume was calculated using the Dice coefficient. RESULTS: The average Dice coefficient expressed as a percentage between the NC-adapted and actual Test model was 89.4 ± 2.8%. The modified NC adaptation technique made significant improvements to the population deformation heart models (p = 0.01). As standard evaluation, the residual Dice error after adaptation was comparable to the volumetric differences observed in free-breathing heart volumes (p = 0.62). CONCLUSIONS: The reconstruction technique described generates accurate 3D heart models from limited 2D planning data. This development could potentially be used to retrospectively calculate delivered dose to the heart for historically treated patients and thereby provide a better understanding of late radiation-related cardiac effects

Optimal leverage from non-ergodicity

In modern portfolio theory, the balancing of expected returns on investments against uncertainties in those returns is aided by the use of utility functions. The Kelly criterion offers another approach, rooted in information theory, that always implies logarithmic utility. The two approaches seem incompatible, too loosely or too tightly constraining investors' risk preferences, from their respective perspectives. The conflict can be understood on the basis that the multiplicative models used in both approaches are non-ergodic which leads to ensemble-average returns differing from time-average returns in single realizations. The classic treatments, from the very beginning of probability theory, use ensemble-averages, whereas the Kelly-result is obtained by considering time-averages. Maximizing the time-average growth rates for an investment defines an optimal leverage, whereas growth rates derived from ensemble-average returns depend linearly on leverage. The latter measure can thus incentivize investors to maximize leverage, which is detrimental to time-average growth and overall market stability. The Sharpe ratio is insensitive to leverage. Its relation to optimal leverage is discussed. A better understanding of the significance of time-irreversibility and non-ergodicity and the resulting bounds on leverage may help policy makers in reshaping financial risk controls.Comment: 17 pages, 3 figures. Updated figures and extended discussion of ergodicit

Engineering Archeal Surrogate Systems for the Development of Protein-Protein Interaction Inhibitors against Human RAD51

Protein-protein interactions (PPIs) are increasingly important targets for drug discovery. Efficient fragment-based drug discovery approaches to tackle PPIs are often stymied by difficulties in the production of stable, unliganded target proteins. Here, we report an approach that exploits protein engineering to "humanise" thermophilic archeal surrogate proteins as targets for small-molecule inhibitor discovery and to exemplify this approach in the development of inhibitors against the PPI between the recombinase RAD51 and tumour suppressor BRCA2. As human RAD51 has proved impossible to produce in a form that is compatible with the requirements of fragment-based drug discovery, we have developed a surrogate protein system using RadA from $\textit{Pyrococcus furiosus}$. Using a monomerised RadA as our starting point, we have adopted two parallel and mutually instructive approaches to mimic the human enzyme: firstly by mutating RadA to increase sequence identity with RAD51 in the BRC repeat binding sites, and secondly by generating a chimeric archaeal human protein. Both approaches generate proteins that interact with a fourth BRC repeat with affinity and stoichiometry comparable to human RAD51. Stepwise humanisation has also allowed us to elucidate the determinants of RAD51 binding to BRC repeats and the contributions of key interacting residues to this interaction. These surrogate proteins have enabled the development of biochemical and biophysical assays in our ongoing fragment-based small-molecule inhibitor programme and they have allowed us to determine hundreds of liganded structures in support of our structure-guided design process, demonstrating the feasibility and advantages of using archeal surrogates to overcome difficulties in handling human proteins.Wellcome Trust Translational (Grant ID: 080083/Z/06/Z) and Seeding Drug Discovery Initiative (Grant ID: 91050/Z/10/Z) award

African Trypanosomes undermine humoral responses and vaccine development : link with inflammatory responses?

African trypanosomosis is a debilitating disease of great medical and socioeconomical importance. It is caused by strictly extracellular protozoan parasites capable of infecting all vertebrate classes including human, livestock, and game animals. To survive within their mammalian host, trypanosomes have evolved efficient immune escape mechanisms and manipulate the entire host immune response, including the humoral response. This report provides an overview of how trypanosomes initially trigger and subsequently undermine the development of an effective host antibody response. Indeed, results available to date obtained in both natural and experimental infection models show that trypanosomes impair homeostatic B-cell lymphopoiesis, B-cell maturation and survival and B-cell memory development. Data on B-cell dysfunctioning in correlation with parasite virulence and trypanosome-mediated inflammation will be discussed, as well as the impact of trypanosomosis on heterologous vaccine efficacy and diagnosis. Therefore, new strategies aiming at enhancing vaccination efficacy could benefit from a combination of (i) early parasite diagnosis, (ii) anti-trypanosome (drugs) treatment, and (iii) anti-inflammatory treatment that collectively might allow B-cell recovery and improve vaccination

Evaluation of the IgG antibody response to SARS CoV-2 infection and performance of a lateral flow immunoassay: cross-sectional and longitudinal analysis over 11 months

OBJECTIVE: To evaluate the dynamics and longevity of the humoral immune response to SARS-CoV-2 infection and assess the performance of professional use of the UK-RTC AbC-19 Rapid Test lateral flow immunoassay (LFIA) for the target condition of SARS-CoV-2 spike protein IgG antibodies. DESIGN: Nationwide serological study. SETTING: Northern Ireland, UK, May 2020–February 2021. PARTICIPANTS: Plasma samples were collected from a diverse cohort of individuals from the general public (n=279), Northern Ireland healthcare workers (n=195), pre-pandemic blood donations and research studies (n=223) and through a convalescent plasma programme (n=183). Plasma donors (n=101) were followed with sequential samples over 11 months post-symptom onset. MAIN OUTCOME MEASURES: SARS-CoV-2 antibody levels in plasma samples using Roche Elecsys Anti-SARS-CoV-2 IgG/IgA/IgM, Abbott SARS-CoV-2 IgG and EuroImmun IgG SARS-CoV-2 ELISA immunoassays over time. UK-RTC AbC-19 LFIA sensitivity and specificity, estimated using a three-reference standard system to establish a characterised panel of 330 positive and 488 negative SARS-CoV-2 IgG samples. RESULTS: We detected persistence of SARS-CoV-2 IgG antibodies for up to 10 months post-infection, across a minimum of two laboratory immunoassays. On the known positive cohort, the UK-RTC AbC-19 LFIA showed a sensitivity of 97.58% (95.28% to 98.95%) and on known negatives, showed specificity of 99.59% (98.53 % to 99.95%). CONCLUSIONS: Through comprehensive analysis of a cohort of pre-pandemic and pandemic individuals, we show detectable levels of IgG antibodies, lasting over 46 weeks when assessed by EuroImmun ELISA, providing insight to antibody levels at later time points post-infection. We show good laboratory validation performance metrics for the AbC-19 rapid test for SARS-CoV-2 spike protein IgG antibody detection in a laboratory-based setting

Identification of Mechanosensitive Genes during Embryonic Bone Formation

Although it is known that mechanical forces are needed for normal bone development, the current understanding of how biophysical stimuli are interpreted by and integrated with genetic regulatory mechanisms is limited. Mechanical forces are thought to be mediated in cells by “mechanosensitive” genes, but it is a challenge to demonstrate that the genetic regulation of the biological system is dependant on particular mechanical forces in vivo. We propose a new means of selecting candidate mechanosensitive genes by comparing in vivo gene expression patterns with patterns of biophysical stimuli, computed using finite element analysis. In this study, finite element analyses of the avian embryonic limb were performed using anatomically realistic rudiment and muscle morphologies, and patterns of biophysical stimuli were compared with the expression patterns of four candidate mechanosensitive genes integral to bone development. The expression patterns of two genes, Collagen X (ColX) and Indian hedgehog (Ihh), were shown to colocalise with biophysical stimuli induced by embryonic muscle contractions, identifying them as potentially being involved in the mechanoregulation of bone formation. An altered mechanical environment was induced in the embryonic chick, where a neuromuscular blocking agent was administered in ovo to modify skeletal muscle contractions. Finite element analyses predicted dramatic changes in levels and patterns of biophysical stimuli, and a number of immobilised specimens exhibited differences in ColX and Ihh expression. The results obtained indicate that computationally derived patterns of biophysical stimuli can be used to inform a directed search for genes that may play a mechanoregulatory role in particular in vivo events or processes. Furthermore, the experimental data demonstrate that ColX and Ihh are involved in mechanoregulatory pathways and may be key mediators in translating information from the mechanical environment to the molecular regulation of bone formation in the embryo

Nanoencapsulation of luminescent 3-hydroxypicolinate lanthanide complexes

We have synthesized luminescent nanoparticles comprising a core of lanthanide complexes and shells of amorphous silica using reverse micelles as nanoreactors. 3-Hydroxypicolinate complexes of Eu(III), Tb(III), and the corresponding heteronuclear complexes have been investigated as the photoactive cores. The size of the silica particles is within the nanometer scale, which, together with the ability for surface biofunctionalization, opens up perspectives for their use in bioapplications. Optical studies of the as-prepared nanoparticles reveal that the luminescence properties of the 3-hydroxypicolinate complexes in the matrices are markedly different from their original features

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

In this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT