Subclonal TP53 copy number is associated with prognosis in multiple myeloma.

Multiple myeloma (MM) is a genetically heterogeneous cancer of bone marrow plasma cells with variable outcome. To assess the prognostic relevance of clonal heterogeneity of TP53 copy number, we profiled tumors from 1777 newly diagnosed Myeloma XI trial patients with multiplex ligation-dependent probe amplification (MLPA). Subclonal TP53 deletions were independently associated with shorter overall survival, with a hazard ratio of 1.8 (95% confidence interval, 1.2-2.8; P = .01). Clonal, but not subclonal, TP53 deletions were associated with clinical markers of advanced disease, specifically lower platelet counts (P < .001) and increased lactate dehydrogenase (P < .001), as well as a higher frequency of features indicative of genomic instability, del(13q) (P = .002) or del(1p) (P = .006). Biallelic TP53 loss-of-function by mutation and deletion was rare (2.4%) and associated with advanced disease. We present a framework for identifying subclonal TP53 deletions by MLPA, to improve patient stratification in MM and tailor therapy, enabling management strategies.

Motion grading of high-resolution quantitative computed tomography supported by deep convolutional neural networks.

Image quality degradation due to subject motion confounds the precision and reproducibility of measurements of bone density, morphology and mechanical properties from high-resolution peripheral quantitative computed tomography (HR-pQCT). Time-consuming operator-based scoring of motion artefacts remains the gold standard to determine the degree of acceptable motion. However, due to the subjectiveness of manual grading, HR-pQCT scans of poor quality, which cannot be used for analysis, may be accepted upon initial review, leaving patients with incomplete or inaccurate imaging results. Convolutional Neural Networks (CNNs) enable fast image analysis with relatively few pre-processing requirements in an operator-independent and fully automated way for image classification tasks. This study aimed to develop a CNN that can predict motion scores from HR-pQCT images, while also being aware of uncertain predictions that require manual confirmation. The CNN calculated motion scores within seconds and achieved a high F1-score (86.8 ± 2.8 %), with good precision (87.5 ± 2.7 %), recall (86.7 ± 2.9 %) and a substantial agreement with the ground truth measured by Cohen's kappa (κ = 68.6 ± 6.2 %); motion scores of the test dataset were predicted by the algorithm with comparable accuracy, precision, sensitivity and agreement as by the operators (p > 0.05). This post-processing approach may be used to assess the effect of motion scores on microstructural analysis and can be immediately implemented into clinical protocols, significantly reducing the time for quality assessment and control of HR-pQCT scans.

An in silico micro-multiphysics agent-based approach for simulating bone regeneration in a mouse femur defect model.

Bone defects represent a challenging clinical problem as they can lead to non-union. In silico models are well suited to study bone regeneration under varying conditions by linking both cellular and systems scales. This paper presents an in silico micro-multiphysics agent-based (micro-MPA) model for bone regeneration following an osteotomy. The model includes vasculature, bone, and immune cells, as well as their interaction with the local environment. The model was calibrated by time-lapsed micro-computed tomography data of femoral osteotomies in C57Bl/6J mice, and the differences between predicted bone volume fractions and the longitudinal in vivo measurements were quantitatively evaluated using root mean square error (RMSE). The model performed well in simulating bone regeneration across the osteotomy gap, with no difference (5.5% RMSE, p = 0.68) between the in silico and in vivo groups for the 5-week healing period - from the inflammatory phase to the remodelling phase - in the volume spanning the osteotomy gap. Overall, the proposed micro-MPA model was able to simulate the influence of the local mechanical environment on bone regeneration, and both this environment and cytokine concentrations were found to be key factors in promoting bone regeneration. Further, the validated model matched clinical observations that larger gap sizes correlate with worse healing outcomes and ultimately simulated non-union. This model could help design and guide future experimental studies in bone repair, by identifying which are the most critical in vivo experiments to perform.

Trabecular bone remodeling in the aging mouse: A micro-multiphysics agent-based in silico model using single-cell mechanomics.

Bone remodeling is regulated by the interaction between different cells and tissues across many spatial and temporal scales. Notably, in silico models are regarded as powerful tools to further understand the signaling pathways that regulate this intricate spatial cellular interplay. To this end, we have established a 3D multiscale micro-multiphysics agent-based (micro-MPA) in silico model of trabecular bone remodeling using longitudinal in vivo data from the sixth caudal vertebra (CV6) of PolgA(D257A/D257A) mice, a mouse model of premature aging. Our in silico model includes a variety of cells as single agents and receptor-ligand kinetics, mechanomics, diffusion and decay of cytokines which regulate the cells' behavior. We highlighted its capabilities by simulating trabecular bone remodeling in the CV6 of five mice over 4 weeks and we evaluated the static and dynamic morphometry of the trabecular bone microarchitecture. Based on the progression of the average trabecular bone volume fraction (BV/TV), we identified a configuration of the model parameters to simulate homeostatic trabecular bone remodeling, here named basal. Crucially, we also produced anabolic, anti-anabolic, catabolic and anti-catabolic responses with an increase or decrease by one standard deviation in the levels of osteoprotegerin (OPG), receptor activator of nuclear factor kB ligand (RANKL), and sclerostin (Scl) produced by the osteocytes. Our results showed that changes in the levels of OPG and RANKL were positively and negatively correlated with the BV/TV values after 4 weeks in comparison to basal levels, respectively. Conversely, changes in Scl levels produced small fluctuations in BV/TV in comparison to the basal state. From these results, Scl was deemed to be the main driver of equilibrium while RANKL and OPG were shown to be involved in changes in bone volume fraction with potential relevance for age-related bone features. Ultimately, this micro-MPA model provides valuable insights into how cells respond to their local mechanical environment and can help to identify critical pathways affected by degenerative conditions and ageing.

Evaluation of the computational capabilities of a memristive random network (MN3) under the context of reservoir computing.

This work presents the simulation results of a novel recurrent, memristive neuromorphic architecture, the MN3 and explores its computational capabilities in the performance of a temporal pattern recognition task by considering the principles of the reservoir computing approach. A simple methodology based on the definitions of ordered and chaotic dynamical systems was used to determine the separation and fading memory properties of the architecture. The results show the potential use of this architecture as a reservoir for the on-line processing of time-varying inputs.

Memristive nanowires exhibit small-world connectivity.

Small-world networks provide an excellent balance of efficiency and robustness that is not available with other network topologies. These characteristics are exhibited in the Memristive Nanowire Neural Network (MN3), a novel neuromorphic hardware architecture. This architecture is composed of an electrode array connected by stochastically deposited core-shell nanowires. We simulate the stochastic behavior of the nanowires by making various assumptions on their paths. First, we assume that the nanowires follow straight paths. Next, we assume that they follow arc paths with varying radii. Last, we assume that they follow paths generated by pink noise. For each of the three methods, we present a method to find whether a nanowire passes over an electrode, allowing us to represent the architecture as a bipartite graph. We find that the small-worldness coefficient increases logarithmically and is consistently greater than one, which is indicative of a small-world network.

Molecular Cytogenetics in Trough Shells (Mactridae, Bivalvia): Divergent GC-Rich Heterochromatin Content.

The family Mactridae is composed of a diverse group of marine organisms, commonly known as trough shells or surf clams, which illustrate a global distribution. Although this family includes some of the most fished and cultured bivalve species, their chromosomes are poorly studied. In this work, we analyzed the chromosomes of Spisula solida, Spisula subtruncata and Mactra stultorum by means of fluorochrome staining, C-banding and fluorescent in situ hybridization using 28S ribosomal DNA (rDNA), 5S rDNA, H3 histone gene and telomeric probes. All three trough shells presented 2n = 38 chromosomes but different karyotype compositions. As happens in most bivalves, GC-rich regions were limited to the nucleolus organizing regions in Spisula solida. In contrast, many GC-rich heterochromatic bands were detected in both Spisula subtruncata and Mactra stultorum. Although the three trough shells presented single 5S rDNA and H3 histone gene clusters, their chromosomal locations differed. Regarding major rDNA clusters, while Spisula subtruncata presented a single cluster, both Spisula solida and Mactra stultorum showed two. No evidence of intercalary telomeric signals was detected in these species. The molecular cytogenetic characterization of these taxa will contribute to understanding the role played by chromosome changes in the evolution of trough shells.