The diagnosis of BCR/ABL-negative chronic myeloproliferative diseases (CMPD): a comprehensive approach based on morphology, cytogenetics, and molecular markers

Recent years showed significant progress in the molecular characterization of the chronic myeloproliferative disorders (CMPD) which are classified according to the WHO classification of 2001 as polycythemia vera (PV), chronic idiopathic myelofibrosis (CIMF), essential thrombocythemia (ET), CMPD/unclassifiable (CMPD-U), chronic neutrophilic leukemia, and chronic eosinophilic leukemia (CEL)/hypereosinophilic syndrome, all to be delineated from BCR/ABL-positive chronic myeloid leukemia (CML). After 2001, the detection of the high frequency of the JAK2V617F mutation in PV, CIMF, and ET, and of the FIP1L1–PDGFRA fusion gene in CEL further added important information in the diagnosis of CMPD. These findings also enhanced the importance of tyrosine kinase mutations in CMPD and paved the way to a more detailed classification and to an improved definition of prognosis using also novel minimal residual disease (MRD) markers. Simultaneously, the broadening of therapeutic strategies in the CMPD, e.g., due to reduced intensity conditioning in allogeneic hematopoietic stem cell transplantation and the introduction of tyrosine kinase inhibitors in CML, in CEL, and in other ABL and PDGRFB rearrangements, increased the demands to diagnostics. Therefore, today, a multimodal diagnostic approach combining cytomorphology, cytogenetics, and individual molecular methods is needed in BCR/ABL-negative CMPD. A stringent diagnostic algorithm for characterization, choice of treatment, and monitoring of MRD will be proposed in this review

Relevant Safety Falsification by Automata Constrained Reinforcement Learning

Complex safety-critical cyber-physical systems, such as autonomous cars or collaborative robots, are becoming increasingly common. Simulation-based falsification is a testing method for uncovering safety hazards of such systems already in the design phase. Conventionally, the falsification method takes the form of a static optimization. Recently, dynamic optimization methods such as reinforcement learning have gained interest for their ability to uncover harder-to-find safety hazards. However, these methods may converge to risk-maximising, but irrelevant behaviors. This paper proposes a principled formulation and solution of the falsification problem by automata constrained reinforcement learning, in which rewards for relevant behavior are tuned via Lagrangian relaxation. The challenges and proposed methods are demonstrated in a use-case example from the domain of industrial human-robot collaboration, where falsification is used to identify hazardous human worker behaviors that result in human-robot collisions. Compared to random sampling and conventional approximate Q-learning, we show that the proposed method generates equally hazardous, but at the same time more relevant testing conditions that expose safety flaws

Hazard Analysis of Collaborative Automation Systems: A Two-layer Approach based on Supervisory Control and Simulation

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory Control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system

Downregulation of the TGF-beta pseudoreceptor BAMBI in non-small cell lung cancer enhances TGF-beta signaling and invasion.

Non-small cell lung cancer (NSCLC) is characterized by early metastasis and has the highest mortality rate among all solid tumors, with the majority of patients diagnosed at an advanced stage where curative therapeutic options are lacking. In this study, we identify a targetable mechanism involving TGF-beta elevation that orchestrates tumor progression in this disease. Substantial activation of this pathway was detected in human lung cancer tissues with concomitant downregulation of BAMBI, a negative regulator of the TGF-beta signaling pathway. Alterations of epithelial-to-mesenchymal transition (EMT) marker expression were observed in lung cancer samples compared to tumor-free tissues. Distinct alterations in the DNA methylation of the gene regions encoding TGF-beta pathway components were detected in NSCLC samples compared to tumor-free lung tissues. In particular, epigenetic silencing of BAMBI was identified as a hallmark of NSCLC. Reconstitution of BAMBI expression in NSCLC cells resulted in a marked reduction of TGF-beta-induced EMT, migration and invasion in vitro, along with reduced tumor burden and tumor growth in vivo. In conclusion, our results demonstrate how BAMBI downregulation drives the invasiveness of NSCLC, highlighting TGF-beta signaling as a candidate therapeutic target in this setting