A motion-decomposition approach to address gimbal lock in the 3-cylinder open chain mechanism description of a joint coordinate system at the glenohumeral joint

In this study, the standard-sequence properties of a joint coordinate system were implemented for the glenohumeral joint by the use of a set of instantaneous geometrical planes. These are: a plane that is bound by the humeral long axis and an orthogonal axis that is the cross product of the scapular anterior axis and this long axis, and a plane that is bounded by the long axis of the humerus and the cross product of the scapular lateral axis and this long axis. The relevant axes are updated after every decomposition of a motion component of a humeral position. Flexion, abduction and rotation are then implemented upon three of these axes and are applied in a step-wise uncoupling of an acquired humeral motion to extract the joint coordinate system angles. This technique was numerically applied to physiological kinematics data from the literature to convert them to the joint coordinate system and to visually reconstruct the motion on a set of glenohumeral bones for validation

A scapular coordinate frame for clinical and kinematic analyses.

Accepted versio

Immersive Video Compression using Implicit Neural Representations

Recent work on implicit neural representations (INRs) has evidenced their potential for efficiently representing and encoding conventional video content. In this paper we, for the first time, extend their application to immersive (multi-view) videos, by proposing MV-HiNeRV, a new INR-based immersive video codec. MV-HiNeRV is an enhanced version of a state-of-the-art INR-based video codec, HiNeRV, which was developed for single-view video compression. We have modified the model to learn a different group of feature grids for each view, and share the learnt network parameters among all views. This enables the model to effectively exploit the spatio-temporal and the inter-view redundancy that exists within multi-view videos. The proposed codec was used to compress multi-view texture and depth video sequences in the MPEG Immersive Video (MIV) Common Test Conditions, and tested against the MIV Test model (TMIV) that uses the VVenC video codec. The results demonstrate the superior performance of MV-HiNeRV, with significant coding gains (up to 72.33\%) over TMIV. The implementation of MV-HiNeRV is published for further development and evaluation

HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation

Learning-based video compression is currently a popular research topic, offering the potential to compete with conventional standard video codecs. In this context, Implicit Neural Representations (INRs) have previously been used to represent and compress image and video content, demonstrating relatively high decoding speed compared to other methods. However, existing INR-based methods have failed to deliver rate quality performance comparable with the state of the art in video compression. This is mainly due to the simplicity of the employed network architectures, which limit their representation capability. In this paper, we propose HiNeRV, an INR that combines light weight layers with novel hierarchical positional encodings. We employs depth-wise convolutional, MLP and interpolation layers to build the deep and wide network architecture with high capacity. HiNeRV is also a unified representation encoding videos in both frames and patches at the same time, which offers higher performance and flexibility than existing methods. We further build a video codec based on HiNeRV and a refined pipeline for training, pruning and quantization that can better preserve HiNeRV's performance during lossy model compression. The proposed method has been evaluated on both UVG and MCL-JCV datasets for video compression, demonstrating significant improvement over all existing INRs baselines and competitive performance when compared to learning-based codecs (72.3% overall bit rate saving over HNeRV and 43.4% over DCVC on the UVG dataset, measured in PSNR)

Estimating relative survival among people registered with cancer in England and Wales

Because routinely collected survival data for cancer patients in England and Wales do not typically specify cause of death, conventional estimates of survival in cancer patients based on such data are a measure of their mortality from all causes rather than their mortality due to cancer. As a result, trends in survival over time are difficult to interpret because changes in overall survival may well reflect changes in the risk of death from other causes, rather than from the cancer of interest. One way of overcoming this problem is to use some form of ‘relative survival’ defined as a measure of survival corrected for the effect of other independent causes of death. Since this concept was first introduced, various methods for calculating relative survival have been proposed and this had led to some confusion as to the most appropriate choice of estimate. This paper aims to provide an introduction to the concept of relative survival and reviews some of the suggested methods of estimation. In addition, a particularly simple, but robust approach, is highlighted based on expected and observed mortality. This method is illustrated using preliminary data from the Office for National Statistics on cancer survival in patients born after 1939 and diagnosed with cancer during 1972–84. The examples presented, although limited to analyses on a small number of selected sites, highlight some encouraging trends in survival in people aged under 35 diagnosed with leukaemia, Hodgkin's disease and testicular cancer during this period. © 1999 Cancer Research Campaig

Exploring cancer register data to find risk factors for recurrence of breast cancer – application of Canonical Correlation Analysis

BACKGROUND: A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA). If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time. One essential outcome after breast cancer treatment is recurrence of the disease. It is important to understand the relationship between different predictors and recurrence, including the time interval until recurrence. This study describes the application of CCA to find important predictors for two different outcomes for breast cancer patients, loco-regional recurrence and occurrence of distant metastasis and to decrease the number of variables in the sets of predictors and outcomes without decreasing the predictive strength of the model. METHODS: Data for 637 malignant breast cancer patients admitted in the south-east region of Sweden were analyzed. By using CCA and looking at the structure coefficients (loadings), relationships between tumor specifications and the two outcomes during different time intervals were analyzed and a correlation model was built. RESULTS: The analysis successfully detected known predictors for breast cancer recurrence during the first two years and distant metastasis 2–4 years after diagnosis. Nottingham Histologic Grading (NHG) was the most important predictor, while age of the patient at the time of diagnosis was not an important predictor. CONCLUSION: In cancer registers with high dimensionality, CCA can be used for identifying the importance of risk factors for breast cancer recurrence. This technique can result in a model ready for further processing by data mining methods through reducing the number of variables to important ones

Specific Receptor Usage in Plasmodium falciparum Cytoadherence Is Associated with Disease Outcome

Our understanding of the basis of severe disease in malaria is incomplete. It is clear that pathology is in part related to the pro-inflammatory nature of the host response but a number of other factors are also thought to be involved, including the interaction between infected erythrocytes and endothelium. This is a complex system involving several host receptors and a major parasite-derived variant antigen (PfEMP1) expressed on the surface of the infected erythrocyte membrane. Previous studies have suggested a role for ICAM-1 in the pathology of cerebral malaria, although these have been inconclusive. In this study we have examined the cytoadherence patterns of 101 patient isolates from varying clinical syndromes to CD36 and ICAM-1, and have used variant ICAM-1 proteins to further characterise this adhesive phenotype. Our results show that increased binding to CD36 is associated with uncomplicated malaria while ICAM-1 adhesion is raised in parasites from cerebral malaria cases

Single cell spatial analysis reveals inflammatory foci of immature neutrophil and CD8 T cells in COVID-19 lungs

Single cell spatial interrogation of the immune-structural interactions in COVID −19 lungs is challenging, mainly because of the marked cellular infiltrate and architecturally distorted microstructure. To address this, we develop a suite of mathematical tools to search for statistically significant co-locations amongst immune and structural cells identified using 37-plex imaging mass cytometry. This unbiased method reveals a cellular map interleaved with an inflammatory network of immature neutrophils, cytotoxic CD8 T cells, megakaryocytes and monocytes co-located with regenerating alveolar progenitors and endothelium. Of note, a highly active cluster of immature neutrophils and CD8 T cells, is found spatially linked with alveolar progenitor cells, and temporally with the diffuse alveolar damage stage. These findings offer further insights into how immune cells interact in the lungs of severe COVID-19 disease. We provide our pipeline [Spatial Omics Oxford Pipeline (SpOOx)] and visual-analytical tool, Multi-Dimensional Viewer (MDV) software, as a resource for spatial analysis

Extant diversity of bryophytes emerged from successive post-Mesozoic diversification bursts

Unraveling the macroevolutionary history of bryophytes, which arose soon after the origin of land plants but exhibit substantially lower species richness than the more recently derived angiosperms, has been challenged by the scarce fossil record. Here we demonstrate that overall estimates of net species diversification are approximately half those reported in ferns and similar to 30% those described for angiosperms. Nevertheless, statistical rate analyses on time-calibrated large-scale phylogenies reveal that mosses and liverworts underwent bursts of diversification since the mid-Mesozoic. The diversification rates further increase in specific lineages towards the Cenozoic to reach, in the most recently derived lineages, values that are comparable to those reported in angiosperms. This suggests that low diversification rates do not fully account for current patterns of bryophyte species richness, and we hypothesize that, as in gymnosperms, the low extant bryophyte species richness also results from massive extinctions.Assembling the Tree of Life programme at NSF; NSF [EF-0531730-002, EF-0531680, EF-0531750]; Scottish Government's Rural and Environment Science and Analytical Services Division; BeiPD-cofund Marie Curie fellowshipinfo:eu-repo/semantics/publishedVersio

CD36 selection of 3D7 Plasmodium falciparum associated with severe childhood malaria results in reduced VAR4 expression

<p>Abstract</p> <p>Background</p> <p>A subset of the <it>Plasmodium falciparum </it>erythrocyte membrane protein 1 (PfEMP1_SM) is involved in the cytoadherence of <it>P. falciparum</it>-infected red blood cells (iRBC) contributing to the pathogenesis of severe disease among young children in malaria endemic areas. The PfEMP1_SMare encoded by group A <it>var </it>genes that are composed of a more constrained range of amino acid sequences than groups B and C <it>var </it>genes encoding PfEMP1_UMassociated with uncomplicated malaria. Also, unlike <it>var </it>genes from groups B and C, those from group A do not have sequences consistent with CD36 binding – a major cytoadhesion phenotype of <it>P. falciparum </it>isolates.</p> <p>Methods</p> <p>A 3D7 PfEMP1_SMsub-line (3D7_SM) expressing VAR4 (PFD1235w/MAL8P1.207) was selected for binding to CD36. The protein expression of this parasite line was monitored by surface staining of iRBC using VAR4-specific antibodies. The serological phenotype of the 3D7_SMparasites was determined by flow cytometry using malaria semi-immune and immune plasma and transcription of the 59 <it>var </it>genes in 3D7 were analysed by real-time quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) using <it>var</it>-specific primers.</p> <p>Results</p> <p>A selection-induced increased adhesion of 3D7_SMiRBC to CD36 resulted in a reduced <it>var4 </it>transcription and VAR4 surface expression.</p> <p>Conclusion</p> <p>VAR4 is not involved in CD36 adhesion. The current findings are consistent with the notion that CD36 adhesion is not associated with particular virulent parasite phenotypes, such as those believed to be exhibited by VAR4 expressing parasites.</p