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Detecting and tracking multiple moving objects in a video is a challenging task. For living cells, the task becomes even more arduous as cells change their morphology over time, can partially overlap, and mitosis leads to new cells. Differently from fluorescence microscopy, label-free techniques can be easily applied to almost all cell lines, reducing sample preparation complexity and phototoxicity. In this study, we present ALFI, a dataset of images and annotations for label-free microscopy, made publicly available to the scientific community, that notably extends the current panorama of expertly labeled data for detection and tracking of cultured living nontransformed and cancer human cells. It consists of 29 time-lapse image sequences from HeLa, U2OS, and hTERT RPE-1 cells under different experimental conditions, acquired by differential interference contrast microscopy, for a total of 237.9 hours. It contains various annotations (pixel-wise segmentation masks, object-wise bounding boxes, tracking information). The dataset is useful for testing and comparing methods for identifying interphase and mitotic events and reconstructing their lineage, and for discriminating different cellular phenotypes. [ABSTRACT FROM AUTHOR]

As the number of graph-level embedding techniques increases at an unprecedented speed, questions arise about their behavior and performance when training data undergo perturbations. This is the case when an external entity maliciously alters training data to invalidate the embedding. This paper explores the effects of such attacks on some graph datasets by applying different graph-level embedding techniques. The main attack strategy involves manipulating training data to produce an altered model. In this context, our goal is to go in-depth about methods, resources, experimental settings, and performance results to observe and study all the aspects that derive from the attack stage. [ABSTRACT FROM AUTHOR]

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER metagenomics diagnosis for inflammatory bowel disease challenge investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed Taxonomy- and Function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants' predictions performed better than random predictions in classifying IBD versus nonIBD, Ulcerative Colitis (UC) versus nonIBD, and Crohn's Disease (CD) versus nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification. [ABSTRACT FROM AUTHOR]

Studies about the metabolic alterations during tumorigenesis have increased our knowledge of the underlying mechanisms and consequences, which are important for diagnostic and therapeutic investigations. In this scenario and in the era of systems biology, metabolic networks have become a powerful tool to unravel the complexity of the cancer metabolic machinery and the heterogeneity of this disease. Here, we present TumorMet, a repository of tumor metabolic networks extracted from context-specific Genome-Scale Metabolic Models, as a benchmark for graph machine learning algorithms and network analyses. This repository has an extended scope for use in graph classification, clustering, community detection, and graph embedding studies. Along with the data, we developed and provided Met2Graph, an R package for creating three different types of metabolic graphs, depending on the desired nodes and edges: Metabolites-, Enzymes-, and Reactions-based graphs. This package allows the easy generation of datasets for downstream analysis. Measurement(s) gene expression, metabolic relationships Technology Type(s) Genome Scale Metabolic Models; Computational network biology Sample Characteristic - Organism Homo sapiens [ABSTRACT FROM AUTHOR]

Groundwater is a common resource that has been wasted for years. Today, we pay the consequences of such inappropriate exploitation and we are aware that it is necessary to realize policies in order to guarantee the use of this resource for future generations. In fact, the irrational exploitation of water by agents, nevertheless it is a renewable resource, may cause its exhaustion. In our paper, we develop a differential game to determine the efficient extraction of groundwater resource among overlapping generations. We consider intragenerational as well as intergenerational competition between extractors that exploit the resource in different time intervals, and so the horizons of the players in the game are asynchronous. Feedback equilibria have been computed in order to determine the optimal extraction rate of "young" and "old" agents that coexist in the economy. The effects of the withdrawal by several generations are numerically and graphically analyzed in order to obtain results on the efficiency of the groundwater resource. [ABSTRACT FROM AUTHOR]

Many real applications require the representation of complex entities and their relations. Frequently, networks are the chosen data structures, due to their ability to highlight topological and qualitative characteristics. In this work, we are interested in supervised classification models for data in the form of networks. Given two or more classes whose members are networks, we build mathematical models to classify them, based on various graph distances. Due to the complexity of the models, made of tens of thousands of nodes and edges, we focus on model simplification solutions to reduce execution times, still maintaining high accuracy. Experimental results on three datasets of biological interest show the achieved performance improvements. [ABSTRACT FROM AUTHOR]

Background subtraction from color and depth data is a fundamental task for video surveillance applications that use data acquired by RGBD sensors. We present a method that adopts a self-organizing neural background model previously adopted for RGB videos to model the color and depth background separately. The resulting color and depth detection masks are combined to guide the selective model update procedure and to achieve the final result. Extensive experimental results and comparisons with several state-of-the-art methods on a publicly available dataset show that the exploitation of depth information allows achieving much higher performance than just using color, accurately handling color and depth background maintenance challenges. [ABSTRACT FROM AUTHOR]

We study the exploitation of a common groundwater resource, first as a static and then as a differential game, in order to take into account the strategic and dynamic interactions among the users of the resource. We suppose that firms can form coalitions or can decide not to cooperate. The non-cooperation regime is characterized by pumping that lead to depletion of the aquifer; the cooperation preserves the resource. Open-loop and feedback equilibria have been computed and compared in order to characterize the existence of cooperators and defectors in water resources management. [ABSTRACT FROM AUTHOR]

Correction to: I Scientific Data i https://doi.org/10.1038/s41597-022-01702-x, published online 07 October 2022 In this article the funding from 'European Union PON "Ricerca e Innovazione 2014-2020" FSC - Project PON03PE 00060 5 MEDIA ' was omitted. In the Acknowledgements section, the second sentence was also incorrect and should have read 'It was carried out also within the activities of the authors as members of the INdAM Research group GNCS and the ICAR-CNR INdAM Research Unit'. [Extracted from the article]

The detection of moving objects from stationary cameras is usually approached by background subtraction, i.e. by constructing and maintaining an up-to-date model of the background and detecting moving objects as those that deviate from such a model. We adopt a previously proposed approach to background subtraction based on self-organization through artificial neural networks, that has been shown to well cope with several of the well known issues for background maintenance. Here, we propose a spatial coherence variant to such approach to enhance robustness against false detections and formulate a fuzzy model to deal with decision problems typically arising when crisp settings are involved. We show through experimental results and comparisons that higher accuracy values can be reached for color video sequences that represent typical situations critical for moving object detection. [ABSTRACT FROM AUTHOR]