New Insights into Mucosa-Associated Microbiota in Paired Tumor and Non-Tumor Adjacent Mucosal Tissues in Colorectal Cancer Patients.

Background/objective: Colorectal cancer (CRC) is one of the most common cancers worldwide. Increasing scientific evidence supports the idea that gut microbiota dysbiosis accompanies colorectal tumorigenesis, and these changes could be causative. Implementing gut microbiota analysis in clinical practice is limited by sample type, sequencing platform and taxonomic classification.

Conjugated linoleic acid metabolite impact in colorectal cancer: a potential microbiome-based precision nutrition approach.

Colorectal cancer (CRC) is the second most deadly and the third most diagnosed cancer in both sexes worldwide. CRC pathogenesis is associated with risk factors such as genetics, alcohol, smoking, sedentariness, obesity, unbalanced diets, and gut microbiota dysbiosis. The gut microbiota is the microbial community living in symbiosis in the intestine, in a dynamic balance vital for health.

Sensor-based gait analysis in the premotor stage of LRRK2 G2019S-associated Parkinson's disease.

Introduction: There is a need for biomarkers to monitor the earliest phases of Parkinson's disease (PD), especially in premotor stages. Here, we studied whether there are early gait alterations in carriers of the G2019S mutation of LRRK2 that can be detected by means of an inertial sensor system.

Methods: Twenty-one idiopathic PD patients, 20 LRRK2-G2019S PD, 27 asymptomatic carriers of LRRK2-G2019S mutation (AsG2019S) and 36 controls walked equipped with 16 lightweight inertial sensors in three different experiments: i/normal gait, ii/fast gait and iii/dual-task gait.

User Interaction Modeling and Profile Extraction in Interactive Systems: A Groupware Application Case Study.

A relevant goal in human-computer interaction is to produce applications that are easy to use and well-adjusted to their users' needs. To address this problem it is important to know how users interact with the system. This work constitutes a methodological contribution capable of identifying the context of use in which users perform interactions with a groupware application (synchronous or asynchronous) and provides, using machine learning techniques, generative models of how users behave.

Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study.

Imagine an agent that performs tasks according to different strategies. The goal of Behavioral Recognition (BR) is to identify which of the available strategies is the one being used by the agent, by simply observing the agent's actions and the environmental conditions during a certain period of time. The goal of Behavioral Cloning (BC) is more ambitious.