Charting the single-cell landscape of colorectal cancer stem cell polarisation

Colonic epithelia is regulated by cell-intrinsic and cell-extrinsic cues, both in homeostatic tissues and colorectal cancer (CRC), where the tumour microenvironment closely interacts with mutated epithelia. Our understanding on how these cues polarise colonic stem cell (CSC) states remains incomplete. Indeed, charting the interaction between intrinsic and stromal cues requires a systematic study yet to be found in the literature. In this work I present my efforts towards computationally studying colonic stem cell polarisation at single-cell resolution. Leveraging the scalability of organoid models, my colleagues and I dissected the heterocellular CRC organoid system presented in Qin & Cardoso Rodriguez et al. using single-cell omic analyses, resolving complex interaction and polarisation processes. First, I identified bottlenecks in common mass cytometry (MC) analysis workflows benefiting from either increased accessibility or automation; designing the CyGNAL pipeline and developing a cell-state classifier to tackle these points respectively. I then used single-cell RNA sequencing (scRNA-seq) data to reveal a shared landscape of CSC polarisation; wherein stromal cues polarise the epithelia towards slow-cycling revival CSC (revCSC) and oncogenic mutations trap cells in a hyper-proliferative CSC (proCSC) state. I then developed a method to visualise single-cell differentation using a novel valley-ridge (VR) score, which can generate data-driven Waddington-like landscapes that recapitulate differentiation dynamics of the colonic epithelia. Finally, I explored an approach for holistic inter- and intra-cellular communication analysis by incorporating literature information as a directed knowledge graph (KG), showing that low-dimensional representations of the graph retain biological information and that projected cellular profiles recapitulate their transcriptomes. These results reveal a polarisation landscape where CRC epithelia is trapped in a proCSC state refractory to stromal cues, and broadly show the importance of joint collaborative wet- and dry-lab work; central towards targeting gaps in the method space and generating a comprehensive analysis of heterocellular signalling in cancer

USEFULNESS AND METABOLIC IMPLICATIONS OF A 60-SECOND REPEATED JUMPS TEST AS A PREDICTOR OF ACROBATIC JUMPING PERFORMANCE IN GYMNASTS

Gymnastics floor exercises are composed of a set of four to five successive acrobatic jumps usually called a �series�. The aims of the study were: 1) to relate the acrobatic gymnastics performance of these series with a repeated jumps test of similar duration (R60), 2) to study the relation between R60 and physiological parameters (heart rate and blood lactate), and the performance obtained in different kinds of jumps, 3) to confirm whether R60, executed without a damped jumping technique, can be considered an anaerobic lactic power test. Twenty male and twenty-four female gymnasts performed three repeated jumps tests for 5 s (R5), 10 s (R10) and 60 s (R60) and vertical jumps, such as drop jumps (DJ), squat jumps (SJ) and countermovement jumps (CMJ). We assessed heart rate (HR) and blood lactate during R10 and R60. The average values of the maximal blood lactate concentration (Lmax) after R10 (males = 2.5±0.6 mmol.l-1; females = 2.1±0.8 mmol.l-1) confirm that anaerobic glycolysis is not activated to a high level. In R60, the Lmax (males = 7.5±1.7 mmol.l-1; females = 5.9±2.1 mmol.l-1) that was recorded does not validate R60 as an anaerobic lactic power test. We confirmed the relation between the average power obtained in R60 (R60Wm) and the acrobatic performance on the floor. The inclusion in the multiple regression equation of the best power in DJ and the best flight-contact ratio (FC) in R5 confirms the influence of other non-metabolic components on the variability in R60 performance, at least in gymnasts

Graph Element Networks: adaptive, structured computation and memory

We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure. Similar to finite element analysis, we assign nodes of a GNN to spatial locations and use a computational process defined on the graph to model the relationship between an initial function defined over a space and a resulting function in the same space. We use GNNs as a computational substrate, and show that the locations of the nodes in space as well as their connectivity can be optimized to focus on the most complex parts of the space. Moreover, this representational strategy allows the learned input-output relationship to generalize over the size of the underlying space and run the same model at different levels of precision, trading computation for accuracy. We demonstrate this method on a traditional PDE problem, a physical prediction problem from robotics, and learning to predict scene images from novel viewpoints.Comment: Accepted to ICML 201

Are Quality of Randomized Clinical Trials and ESMO-Magnitude of Clinical Benefit Scale Two Sides of the Same Coin, to Grade Recommendations for Drug Approval?

The approval of a new drug for cancer treatment by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) is based on positive, well-designed randomized phase III clinical trials (RCTs). However, not all of them are analyzed to support the recommendations. For this reason, there are different scales to quantify and evaluate the quality of RCTs and the magnitude of the clinical benefits of new drugs for treating solid tumors. In this review, we discuss the value of the progression-free survival (PFS) as an endpoint in RCTs and the concordance between it and the overall survival (OS) as a measure of the quality of clinical trial designs. We summarize and analyze the different scales to evaluate the clinical benefits of new drugs such as the The American Society of Clinical Oncology value framework (ASCO-VF-NHB16) and European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS) and the concordance between them, focusing on metastatic colorectal cancer (mCRC). We propose several definitions that would help to evaluate the quality of RCT, the magnitude of clinical benefit and the appropriate approval of new drugs in oncology

Comparison of ultrasound-guided versus blind interventions for supraspinatus tendinopathy : A cadaveric study

Background. The treatment of supraspinatus tendinopathy remains a challenge for the health professional. This study aims to analyze the precision of needle interventions in lesions of the supraspinatus tendon when conducting them in an ultrasound-guided or non-ultrasound guided (blind) manner. Methods. Study on cadaver with infiltrations performed under ultrasound control or blind after randomization of the parts and participants. Twenty fresh cadaveric shoulders and 30 practitioners with experience using musculoskeletal ultrasound and doing needle interventions. Each practitioner performed 4 ultrasound-guided and 4 unguided punctures. This provided 240 punctures that were analyzed in 3 different anatomical cuts, thus providing a database of 720 measurements for statistical analysis. Results. Statistically significant differences were observed (p<0.0001) in the distance to the bullet point between the ultrasound-guided and the non-guided infiltrations. It was estimated that the unguided punctures were performed on average 10mm farther from the bullet point than the 'ultrasound-guided' punctures. The ultrasound-guided punctures demonstrated 95% precision while the unguided punctures had a precision rate of 12.5% (p <0.0001). Conclusion. Interventions of the supraspinatus tendon should be performed in an ultrasound-guided manner to facilitate administration of the treatment in the proper area

Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis

Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.Comment: 5 pages, 3 figure

Surface electromyography applications

The electrophysiological techniques (neurography and needle electromyography) allow us an approach to the knowledge of the neuromuscular function. Electromyography obtains the electrical activity from the muscle in rest or in contraction (maximum and static voluntary contraction) In its clinical application, electromyography helps to the diagnosis and follow-up of a process of neuromuscular type. On the other hand, kinesiological or surface electromyography (SEMG) allows the study of the muscular activity in dynamics, which we can apply to the biomechanical movement analysis, gait analysis, studies of muscular fatigue, sport performance| and applications in work medicine and ergonomics. SEMG brings advantages like the fact that is a bloodless test, of being able to analyze varying muscles at the same time, in motion and in actions of non limited duration. The processed one brings us parameters of amplitude and frequencies, which we will use for descriptive and comparative studies. As a balancing entry, it does not allow us to study deep musculature, and some aspects of definition are lost in the obtained outlines

Estudi de l'activitat muscular durant el relevé en primera i sisena posició

Fonaments: Conèixer les diferències existents en l'activació muscular durant el relevé. Participants i mètode: Divuit ballarines de ballet clàssic (14-32 anys). Es practica anàlisi cinètica i cinemàtica. S'analitza el grau de flexió plantar aconseguit i la participació de diversos músculs. Es comparen (test de Wilcoxon) els resultats obtinguts en executar el gest en sisena posició (en parallel) respecte de la primera (en déhors) i en primera posició correcta respecte de la primera posició amb pronació del peu. Resultats: El rang de flexió plantar aconseguit és millor en sisena posició que en primera, i millor en primera correcta que en primera pronada. El múscul bessó intern (BI) presenta més activitat en primera posició; l'abductor del dit gros (ADG), en paral·lel o sisena posició, i els peroneals i bessó extern (BE), en pronació del peu en primera posició. El peroneal lateral (PL) presenta diferències durant el relevé (en posició estàtica inicial i en fase ascendent) i l'ADG durant la pujada al relevé. El BI presenta una activació més precoç i d'inici més lent que el BE en la flexió plantar del relevé. Discussió: L'activitat de l'ADG en primera posició tradueix dèficit d'estabilitat de l'arc intern i del primer radi i, per tant, un major risc de lesió. El relevé en sisena posició permet millor grau de flexió plantar que en primera. La pronació del peu ­secundària en mala tècnica­ produeix menor flexió plantar. L'ADG treballa més en parallel que en déhors, tot evitant la rotació externa del maluc. Les diferències del PL es relacionen amb la presència de pronació del peu: treballa més en pronació