On the construction of Dialectical Databases

Argumentation systems have substantially evolved in the past few years, resulting in adequate tools to model some forms of common sense reasoning. This has sprung a new set of argument-based applications in diverse areas. In previous work, we defined how to use precompiled knowledge to obtain significant speed-ups in the inference process of an argument-based system. This development is based on a logic programming system with an argumentationdriven inference engine, called Observation Based Defeasible Logic Programming (ODeLP). In this setting was first presented the concept of dialectical databases, that is, data structures for storing precompiled knowledge. These structures provide precompiled information about inferences and can be used to speed up the inference process, as TMS do in general problem solvers. In this work, we present detailed algorithms for the creation of dialectical databases in ODeLP and analyze these algorithms in terms of their computational complexity

Charcot foot reconstruction with combined internal and external fixation: case report

Charcot neuroarthropathy is a destructive and often-limb threatening process that can affect patients with peripheral neuropathy of any etiology. Early recognition and appropriate management is crucial to prevention of catastrophic outcomes. Delayed diagnosis and subsequent pedal collapse often preclude successful conservative management of these deformities and necessitate surgical intervention for limb salvage. We review the current literature on surgical reconstruction of Charcot neuroarthropathy and present a case report of foot reconstruction with combined internal and external fixation methods

TGF-β2 dictates disseminated tumour cell fate in target organs through TGF-β-RIII and p38α/β signalling

In patients, non-proliferative disseminated tumour cells (DTCs) can persist in the bone marrow (BM) while other organs (such as lung) present growing metastasis. This suggested that the BM might be a metastasis ‘restrictive soil’ by encoding dormancy-inducing cues in DTCs. Here we show in a head and neck squamous cell carcinoma (HNSCC) model that strong and specific transforming growth factor-β2 (TGF-β2) signalling in the BM activates the MAPK p38α/β, inducing an (ERK/p38)low signalling ratio. This results in induction of DEC2/SHARP1 and p27, downregulation of cyclin-dependent kinase 4 (CDK4) and dormancy of malignant DTCs. TGF-β2-induced dormancy required TGF-β receptor-I (TGF-β-RI), TGF-β-RIII and SMAD1/5 activation to induce p27. In lungs, a metastasis ‘permissive soil’ with low TGF-β2 levels, DTC dormancy was short-lived and followed by metastatic growth. Importantly, systemic inhibition of TGF-β-RI or p38α/β activities awakened dormant DTCs, fuelling multi-organ metastasis. Our work reveals a ‘seed and soil’ mechanism where TGF-β2 and TGF-β-RIII signalling through p38α/β regulates DTC dormancy and defines restrictive (BM) and permissive (lung) microenvironments for HNSCC metastasis.Fil: Bragado, Paloma. Mount Sinai School of Medicine. Tisch Cancer Institute; Estados UnidosFil: Estrada, Yeriel. Mount Sinai School of Medicine. Tisch Cancer Institute; Estados UnidosFil: Parikh, Falguni. Mount Sinai School of Medicine. Tisch Cancer Institute; Estados UnidosFil: Krause, Sarah. University Hospital of Schleswig-Holstein; AlemaniaFil: Capobianco, Carla Sabrina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Oncología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Farina, Hernán Gabriel. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Oncología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schewe, Denis M.. Mount Sinai School of Medicine. Tisch Cancer Institute; Estados UnidosFil: Aguirre Ghiso, Julio A.. Mount Sinai School of Medicine. Tisch Cancer Institute; Estados Unido

Autologous Hematopoietic Stem Cell Transplantation (AHSCT): Standard of Care for Relapsing–Remitting Multiple Sclerosis Patients

Abstract Autologous hematopoietic stem cell transplantation (AHSCT) has been used in the treatment of highly active multiple sclerosis (MS) for over two decades. It has been demonstrated to be highly efficacious in relapsing–remitting (RR) MS patients failing to respond to disease-modifying drugs (DMDs). AHSCT guarantees higher rates of no evidence of disease activity (NEDA) than those achieved with any other DMDs, but it is also associated with greater short-term risks which have limited its use. In the 2019 updated EBMT and ASBMT guidelines, which review the clinical evidence of AHSCT in MS, AHSCT indication for highly active RRMS has changed from “clinical option” to “standard of care”. On this basis, AHSCT must be proposed on equal footing with second-line DMDs to patients with highly active RRMS, instead of being considered as a last resort after failure of all available treatments. The decision-making process requires a close collaboration between transplant hematologists and neurologists and a full discussion of risk–benefit of AHSCT and alternative treatments. In this context, we propose a standardized protocol for decision-making and informed consent process

Graphene-assisted control of coupling between optical waveguides

The unique properties of optical waveguides electrically controlled by means of graphene layers are investigated. We demonstrate that, thanks to tunable losses induced by graphene layers, a careful design of silicon on silica ridge waveguides can be used to explore passive PT-symmetry breaking in directional couplers. We prove that the exceptional point of the system can be probed by varying the applied voltage and we thus propose very compact photonic structures which can be exploited to control coupling between waveguides and to tailor discrete diffraction in arrays

Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks

Data-driven methods open up unprecedented possibilities for maritime surveillance using Automatic Identification System (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation of results shows: i) the superiority of attention pooling over static pooling for the specific application, and ii) the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS.Comment: Accepted for publications in IEEE Transactions on Aerospace and Electronic Systems, 17 pages, 9 figure