Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

Background Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection?Methods A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near-total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were preprocessed, and a total of 15720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm.Results A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the testing cohort, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the validation set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761.Conclusion In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

3E-LDA

Linear discriminant analysis (LDA) is one of the important techniques for dimensionality reduction, machine learning, and pattern recognition. However, in many applications, applying the classical LDA often faces the following problems: (1) sensitivity to outliers, (2) absence of local geometric information, and (3) small sample size or matrix singularity that can result in weak robustness and efficiency. Although several researchers have attempted to address one or more of the problems, little work has been done to address all of them together to produce a more effective and efficient LDA algorithm. This article proposes 3E-LDA, an enhanced LDA algorithm, that deals with all three problems as an attempt to further improve LDA. It proposes to learn a weighted median rather than the mean of the samples to deal with (1), to embed both between-class and within-class local geometric information to deal with (2), and to calculate the projection vectors in the null space of the matrix to deal with (3). Experiments on six benchmark datasets show that these three enhancements enable 3E-LDA to markedly outperform state-of-the-art LDA baselines in both accuracy and efficiency.

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

PRODUTIVIDADE DO TRATOR FLORESTAL CHOCKER SKIDDER NA EXTRAÇÃO DE MADEIRA EM TERRENOS DECLIVOSOS

Este trabalho objetivou analisar os tempos do ciclo operacional e a produtividade do trator florestal chocker skidder na extração de madeira em diferentes classes de declividade e distância, visando gerar informações para o planejamento eficiente das operações e o melhor aproveitamento dos recursos disponíveis. O trabalho foi realizado em uma empresa florestal localizada no município de Itararé, SP. Foi realizado um estudo de tempos e movimentos do ciclo operacional, determinando disponibilidade mecânica, disponibilidade técnica, eficiência operacional e produtividade da máquina em diferentes condições de distância de extração e declividade do terreno. Os resultados indicaram que as atividades que consumiram a maior parte do tempo do ciclo de trabalho foram o “arraste do cabo e engate” e “desengate das correntes”, com 28% e 11% do tempo total, respectivamente. Houve redução da produtividade da máquina com o aumento da declividade do terreno, sendo mais evidente nas menores distâncias de extração e mostrando a sensibilidade dessa variável nessa condição operacional.AbstractProductivity of forestry tractor chocker skidder in extraction of wood on steep terrain. The objective of this study was to evaluate the time consumed by the elements of the work cycle and the productivity of a chocker skidder in extraction of wood at different slope terrain and distances classes, generating information for efficient planning of operations and better utilization of available resources. The study was conducted in a forestry company located in the municipality of Itararé, São Paulo State. A motion and time study of the operational cycle was performed, determining the mechanical availability, technical availability, operational efficiency and productivity of the machine in different conditions of extraction distance and terrain slope. The results showed that elements of the work cycle which most time consuming were the "drag cable and engagement" and "disengagement of the currents" that consumed the greatest operational cycle of the machine with 28% and 11% of the total time, respectively. A reduction machine productivity with increasing of the terrain slope, being stronger in the smaller extraction distances and, showing the sensitivity of this variable in this operating condition.Keywords: Timber harvesting; operational planning; optimization.

An Operational Semantics of Real-Time Process Algebra (RTPA)

The need for new forms of mathematics to express software engineering concepts and entities has been widely recognized. Real-time process algebra (RTPA) is a denotational mathematical structure and a system modeling methodology for describing the architectures and behaviors of real-time and nonrealtime software systems. This article presents an operational semantics of RTPA, which explains how syntactic constructs in RTPA can be reduced to values on an abstract reduction machine. The operational semantics of RTPA provides a comprehensive paradigm of formal semantics that establishes an entire set of operational semantic rules of software. RTPA has been successfully applied in real-world system modeling and code generation for software systems, human cognitive processes, and intelligent systems.

An Operational Semantics of Real-Time Process Algebra (RTPA)

The need for new forms of mathematics to express software engineering concepts and entities has been widely recognized. Real-time process algebra (RTPA) is a denotational mathematical structure and a system modeling methodology for describing the architectures and behaviors of real-time and nonreal-time software systems. This article presents an operational semantics of RTPA, which explains how syntactic constructs in RTPA can be reduced to values on an abstract reduction machine. The operational semantics of RTPA provides a comprehensive paradigm of formal semantics that establishes an entire set of operational semantic rules of software. RTPA has been successfully applied in real-world system modeling and code generation for software systems, human cognitive processes, and intelligent systems.