Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system

Still under debate is the question of whether machine learning is capable of going beyond black-box modeling for complex physical systems. We investigate the generalizing and interpretability properties of learning algorithms. To this end, we use supervised and unsupervised learning to infer the phase boundaries of the active Ising model, starting from an ensemble of configurations of the system. We illustrate that unsupervised learning techniques are powerful at identifying the phase boundaries in the control parameter space, even in situations of phase coexistence. It is demonstrated that supervised learning with neural networks is capable of learning the characteristics of the phase diagram, such that the knowledge obtained at a limited set of control variables can be used to determine the phase boundaries across the phase diagram. In this way, we show that properly designed supervised learning provides predictive power to regions in the phase diagram that are not included in the training phase of the algorithm. We stress the importance of introducing interpretability methods in order to perform a physically relevant classification of the phases with deep learning

Avelumab as neoadjuvant therapy in patients with urothelial non-metastatic muscle invasive bladder cancer: a multicenter, randomized, non-comparative, phase II study (Oncodistinct 004 - AURA trial)

Introduction Cisplatin-based neoadjuvant chemotherapy (NAC) followed by surgery is the standard treatment for patients with non-metastatic muscle invasive bladder cancer (MIBC). Unfortunately, many patients are not candidates to receive cisplatin due to renal impairment. Additionally, no predictive biomarkers for pathological complete response (pCR) are currently validated in clinical practice. Studies evaluating immune checkpoint inhibitors in the peri-operative setting are emerging with promising results. Clinical trials are clearly required in the neoadjuvant setting in order to improve therapeutic strategies. Methods and analysis Oncodistinct 004 - AURA is an ongoing multicenter phase II randomized trial assessing the efficacy and safety of avelumab single-agent or combined to different NAC regimens in patients with non-metastatic MIBC. Patients are enrolled in two distinct cohorts according to their eligibility to receive cisplatin-based NAC. In the cisplatin eligible cohort, patients are randomized in a 1:1 fashion to receive avelumab combined with cisplatin-gemcitabine or with dose-dense methotrexate-vinblastine-doxorubicin-cisplatin. In the cisplatin ineligible cohort, patients are randomized at a 1:1 ratio to paclitaxel-gemcitabine associated to avelumab or avelumab alone. Primary endpoint is pCR. Secondary endpoints are pathological response and safety. Ethics and dissemination The study is approved by ethics committee from all participating centers. All participants provide informed consent prior inclusion to the study. Once completed, results will be published in peer-reviewed journals

EV-TRACK: Transparent reporting and centralizing knowledge in extracellular vesicle research

We argue that the field of extracellular vesicle (EV) biology needs more transparent reporting to facilitate interpretation and replication of experiments. To achieve this, we describe EV-TRACK, a crowdsourcing knowledgebase (http://evtrack.org) that centralizes EV biology and methodology with the goal of stimulating authors, reviewers, editors and funders to put experimental guidelines into practice

EV-TRACK : transparent reporting and centralizing knowledge in extracellular vesicle research

We argue that the field of extracellular vesicle (EV) biology needs more transparent reporting to facilitate interpretation and replication of experiments. To achieve this, we describe EV-TRACK, a crowdsourcing knowledgebase (http://evtrack.org) that centralizes EV biology and methodology with the goal of stimulating authors, reviewers, editors and funders to put experimental guidelines into practice