An architecture for life-long user modelling

In this paper, we propose a united architecture for the creation of life-long user profiles. Our architecture combines different steps required for a user prole, including feature extraction and representation, reasoning, recommendation and presentation. We discuss various issues that arise in the context of life-long profiling

TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting

none6We introduce TAASRAD19, a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 timesteps of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section at 5 min sampling rate, covering an area of 240 km of diameter at 500 m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validate TAASRAD19 as a benchmark for nowcasting methods by introducing a TrajGRU deep learning model to forecast reflectivity, and a procedure based on the UMAP dimensionality reduction algorithm for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available on GitHub (https://github.com/MPBA/TAASRAD19) for study replication and reproducibility.noneFranch, Gabriele; Maggio, Valerio; Coviello, Luca; Pendesini, Marta; Jurman, Giuseppe; Furlanello, CesareFranch, Gabriele; Maggio, Valerio; Coviello, Luca; Pendesini, Marta; Jurman, Giuseppe; Furlanello, Cesar

Improved Gas Selectivity Based on Carbon Modified SnO2 Nanowires

The analysis of ambient (home, office, outdoor) atmosphere in order to check the presence of dangerous gases is getting more and more important. Therefore, tiny sensors capable to distinguish the presence of specific pollutants is crucial. Herein, a resistive sensor based on a carbon modified tin oxide nanowires, able to classify different gases and estimate their concentration, is presented. The C-SnO2 nanostructures are grown by chemical vapor deposition and then used as a conductometric sensor under a temperature gradient. The device works at lower temperatures than pure SnO2, with a better response. Five outputs are collected and combined to form multidimensional data that are specific of each gas. Machine learning algorithms are applied to these multidimensional data in order to teach the system how to recognize different gases. The six tested gases (acetone, ammonia, CO, ethanol, hydrogen, and toluene) are perfectly classified by three models, demonstrating the goodness of the raw sensor response. The gas concentration can also be estimated, with an average error of 36% on the low concentration range 1-50 ppm, making the sensor suitable for detecting the exceedance of the danger thresholds

GZMKhigh CD8+ T effector memory cells are associated with CD15high neutrophil abundance in non-metastatic colorectal tumors and predict poor clinical outcome.

CD8(+) T cells are a major prognostic determinant in solid tumors, including colorectal cancer (CRC). However, understanding how the interplay between different immune cells impacts on clinical outcome is still in its infancy. Here, we describe that the interaction of tumor infiltrating neutrophils expressing high levels of CD15 with CD8(+) T effector memory cells (T(EM)) correlates with tumor progression. Mechanistically, stromal cell-derived factor-1 (CXCL12/SDF-1) promotes the retention of neutrophils within tumors, increasing the crosstalk with CD8(+) T cells. As a consequence of the contact-mediated interaction with neutrophils, CD8(+) T cells are skewed to produce high levels of GZMK, which in turn decreases E-cadherin on the intestinal epithelium and favors tumor progression. Overall, our results highlight the emergence of GZMK(high) CD8(+) T(EM) in non-metastatic CRC tumors as a hallmark driven by the interaction with neutrophils, which could implement current patient stratification and be targeted by novel therapeutics

reval: A Python package to determine best clustering solutions with stability-based relative clustering validation.

Determining the best partition for a dataset can be a challenging task because of the lack of a priori information within an unsupervised learning framework and the absence of a unique clustering validation approach to evaluate clustering solutions. Here we present reval: a Python package that leverages stability-based relative clustering validation methods to select best clustering solutions as the ones that replicate, via supervised learning, on unseen subsets of data. The implementation of relative validation methods can contribute to the theory of clustering by fostering new approaches for the investigation of clustering results in different situations and for different data distributions. This work aims at contributing to this effort by implementing a package that works with multiple clustering and classification algorithms, hence allowing both the automation of the labeling process and the assessment of the stability of different clustering mechanisms

Charting differentially methylated regions in cancer with Rocker-meth

Matteo Benelli et al. present Rocker-meth, a new Hidden Markov Model (HMM)-based method, to robustly identify differentially methylated regions (DMRs). They use Rocker-meth to analyse more than 6000 methylation profiles across 14 cancer types, providing a catalog of tumor-specific and shared DMRs

Recognition of Users' Activities using Constraint Satisfaction

Ideally designed software allow users to explore and pursue interleaving plans, making it challenging to automatically recognize user interactions. The recognition algorithms presented use constraint satisfaction techniques to compare user interaction histories to a set of ideal solutions. We evaluate these algorithms on data obtained from user interactions with a commercially available pedagogical software, and find that these algorithms identified users’ activities with 93% accuracy.Engineering and Applied Science

Context Relevance Assessment for Recommender systems

Research on context aware recommender systems is taking for granted that context matters. But, often attempts to show the influence of context have failed. In this paper we consider the problem of quantitatively assessing context relevance. For this purpose we are assuming that users can imagine a situation described by a contextual feature, and judge if this feature is relevant for their decision making task. We have designed a UI suited for acquiring such information in a travel planning scenario. In fact, this interface is generic and can also be used for other domains (e.g., music). The experimental results show that it is possible to identify the contextual factors that are relevant for the given task and that the relevancy depends on the type of the place of interest to be included in the plan

Multiscale statistical physics of the pan-viral interactome unravels the systemic nature of SARS-CoV-2 infections

AbstractProtein–protein interaction networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID–19 and providing ground for applications, such as drug repurposing. Characterizing molecular (dis)similarities between SARS-CoV-2 and other viral agents allows one to exploit existing information about the alteration of key biological processes due to known viruses for predicting the potential effects of this new virus. Here, we compare the novel coronavirus network against 92 known viruses, from the perspective of statistical physics and computational biology. We show that regulatory spreading patterns, physical features and enriched biological pathways in targeted proteins lead, overall, to meaningful clusters of viruses which, across scales, provide complementary perspectives to better characterize SARS-CoV-2 and its effects on humans. Our results indicate that the virus responsible for COVID–19 exhibits expected similarities, such as to Influenza A and Human Respiratory Syncytial viruses, and unexpected ones with different infection types and from distant viral families, like HIV1 and Human Herpes virus. Taken together, our findings indicate that COVID–19 is a systemic disease with potential effects on the function of multiple organs and human body sub-systems