The Tumor Microenvironment in Liver Metastases from Colorectal Carcinoma in the Context of the Histologic Growth Patterns

Colorectal carcinoma (CRC) is the third most common cancer. Likewise, it is a disease that has a long survival if it is prematurely detected. However, more than 50% of patients will develop metastases, mainly in the liver (LM-CRC), throughout the evolution of their disease, which accounts for most CRC-related deaths. Treatment it is certainly a controversial issue, since it has not been shown to increase overall survival in the adjuvant setting, although it does improve disease free survival (DFS). Moreover, current chemotherapy combinations are administered based on data extrapolated from primary tumors (PT), not considering that LM-CRC present a very particular tumor microenvironment that can radically condition the effectiveness of treatments designed for a PT. The liver has a particular histology and microenvironment that can determine tumor growth and response to treatments: double blood supply, vascularization through fenestrated sinusoids and the presence of different mesenchymal cell types, among other particularities. Likewise, the liver presents a peculiar immune response against tumor cells, a fact that correlates with the poor response to immunotherapy. All these aspects will be addressed in this review, putting them in the context of the histological growth patterns of LM-CRC, a particular pathologic feature with both prognostic and predictive repercussions.     

Synthesis and characterization of cross‐linked poly(sodium acrylate)/sodium silicate hydrogels

Transparent and intumescent polymer‐silicate hydrogels were synthesized by free‐radical polymerization, in the presence of the redox initiators system (potassium persulphate/sodium thiosulphate) and cross‐linking monomer (N,N′‐methylenebisacrylamide). Hydrogels obtained in such a manner were rheologically tested and it was found that the same sample with a different sodium acrylate concentration polymerized faster when its content was lower. The spectroscopic and thermal analysis proved that the polymer combines with sodium silicate by hydrogen bonds and during thermal degradation only small polymer fragments and water molecules were released. NMR studies have shown that the content of water glass causes shorter relaxation times. Conducted fire tests showed that glass panes systems filled with the tested hydrogels meet the relevant construction standards and what is their big advantage, a thin 1 mm layer of polymer‐silicate gel is sufficient for this purpose. We believe that these results will contribute to the development of intumescent hydrogels with enhanced fire‐retardant properties. POLYM. ENG. SCI., 59:1279–1287 2019. © 2019 Society of Plastics Engineers     

Connecting the real world with the digital overlay with smart ambient media—applying Peirce’s categories in the context of ambient media

This article is based on the keynote held at the workshop on Events in Multimedia (EiMM09) that took place in conjunction with the ACM Multimedia conference in Beijing in October 2009. The idea of the keynote was to develop and explain the idea of ambient media in general, it is principles, and additionally relate ambient media to information theoretical approaches such as Peirce’s categories, to the theories of decision making, and to theories discussing how smart objects can be made ‘smart’ by simulating the human mind. This article rounds up with practical examples underlining the presented ideas and theories.     

Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries

Given a large collection of transactions containing items, a basic common data mining problem is to extract the so-called frequent itemsets (i.e., sets of items appearing in at least a given number of transactions). In this paper, we propose a structure called free-sets, from which we can approximate any itemset support (i.e., the number of transactions containing the itemset) and we formalize this notion in the framework of -adequate representations (H. Mannila and H. Toivonen, 1996. In Proc. of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96), pp. 189–194). We show that frequent free-sets can be efficiently extracted using pruning strategies developed for frequent itemset discovery, and that they can be used to approximate the support of any frequent itemset. Experiments on real dense data sets show a significant reduction of the size of the output when compared with standard frequent itemset extraction. Furthermore, the experiments show that the extraction of frequent free-sets is still possible when the extraction of frequent itemsets becomes intractable, and that the supports of the frequent free-sets can be used to approximate very closely the supports of the frequent itemsets. Finally, we consider the effect of this approximation on association rules (a popular kind of patterns that can be derived from frequent itemsets) and show that the corresponding errors remain very low in practice.     

Incremental execution of rule-based model transformation

When model transformations are used to implement consistency relations between very large models, incrementality plays a cornerstone role in detecting and resolving inconsistencies efficiently when models are updated. Given a directed consistency relation between two models, the problem studied in this work consists in propagating model changes from a source model to a target model in order to ensure consistency while minimizing computational costs. The mechanism that enforces such consistency is called consistency maintainer and, in this context, its scalability is a required non-functional requirement. State-of-the-art model transformation engines with support for incrementality normally rely on an observer pattern for linking model changes, also known as deltas, to the application of model transformation rules, in so-called dependencies, at run time. These model changes can then be propagated along an already executed model transformation. Only a few approaches to model transformation provide domain-specific languages for representing and storing model changes in order to enable their use in asynchronous, event-based execution environments. The principal contribution of this work is the design of a forward change propagation mechanism for incremental execution of model transformations, which decouples dependency tracking from change propagation using two innovations. First, the observer pattern-based model is replaced with dependency injection, decoupling domain models from consistency maintainers. Second, a standardized representation of model changes is reused, enabling interoperability with EMF-compliant tools, both for defining model changes and for processing them asynchronously. This procedure has been implemented in a model transformation engine, whose performance has been evaluated experimentally using the VIATRA CPS benchmark. In the experiments performed, the new transformation engine shows gains in the form of several orders of magnitude in the initial phase of the incremental execution of the benchmark model transformation and change propagation is performed in real time for those model sizes that are processable by other tools and, in addition, is able to process much larger models.

     

Domain-specific discrete event modelling and simulation using graph transformation

Graph transformation is being increasingly used to express the semantics of domain-specific visual languages since its graphical nature makes rules intuitive. However, many application domains require an explicit handling of time to accurately represent the behaviour of a real system and to obtain useful simulation metrics to measure throughputs, utilization times and average delays. Inspired by the vast knowledge and experience accumulated by the discrete event simulation community, we propose a novel way of adding explicit time to graph transformation rules. In particular, we take the event scheduling discrete simulation world view and provide rules with the ability to schedule the occurrence of other rules in the future. Hence, our work combines standard, efficient techniques for discrete event simulation (based on the handling of a future event set) and the intuitive, visual nature of graph transformation. Moreover, we show how our formalism can be used to give semantics to other timed approaches and provide an implementation on top of the rewriting logic system Maude.     

Learning locomotion reflexes: A self-supervised neural system for a mobile robot

This article is concerned with an artificial neural system for a mobile robot reactive navigation in an unknown, cluttered environment. Reactive navigation is a process of immediately choosing locomotion actions in response to measured spatial situations, while no planning occurs. A task of a presented system is to provide a steering angle signal letting a robot reach a goal while avoiding collisions with obstacles. Basic reactive navigation methods are briefly characterized, special attention is paid to a neural approach to the considered problem. The authors describe the system's architecture and important details of the algorithm. The main parts of the system are: the Fuzzy ART neural self-organizing classifier, performing a perceptual space partitioning, and a neural associative memory, memorizing the system's experience and superposing influences of different behaviors. Tests show that the learning process, starting from zero, is efficient, despite some initial fluctuations of its effectiveness.     

The Connectionist Inductive Learning and Logic Programming System

This paper presents the Connectionist Inductive Learning and Logic Programming System (C-IL²P). C-IL²P is a new massively parallel computational model based on a feedforward Artificial Neural Network that integrates inductive learning from examples and background knowledge, with deductive learning from Logic Programming. Starting with the background knowledge represented by a propositional logic program, a translation algorithm is applied generating a neural network that can be trained with examples. The results obtained with this refined network can be explained by extracting a revised logic program from it. Moreover, the neural network computes the stable model of the logic program inserted in it as background knowledge, or learned with the examples, thus functioning as a parallel system for Logic Programming. We have successfully applied C-IL²P to two real-world problems of computational biology, specifically DNA sequence analyses. Comparisons with the results obtained by some of the main neural, symbolic, and hybrid inductive learning systems, using the same domain knowledge, show the effectiveness of C-IL²P.     

A Closed-Loop Automatic Data-Mining Approach for Preventive Network Monitoring

As telecommunication networks grow in size and complexity, monitoring systems need to scale up accordingly. Alarm data generated in a large network are often highly correlated. These correlations can be explored to simplify the process of network fault management, by reducing the number of alarms presented to the network-monitoring operator. This makes it easier to react to network failures. But in some scenarios, it is highly desired to prevent the occurrence of these failures by predicting the occurrence of alarms before hand. This work investigates the usage of data mining methods to generate knowledge from historical alarm data, and using such knowledge to train a machine learning system, in order to predict the occurrence of the most relevant alarms in the network. The learning system was designed to be retrained periodically in order to keep an updated knowledge base.