Cell Attention Networks

Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks. However, these networks consider only pairwise relationships among nodes and then they are not able to fully exploit higher-order interactions present in many real world data-sets. In this paper, we introduce Cell Attention Networks (CANs), a neural architecture operating on data defined over the vertices of a graph, representing the graph as the 1-skeleton of a cell complex introduced to capture higher order interactions. In particular, we exploit the lower and upper neighborhoods, as encoded in the cell complex, to design two independent masked self-attention mechanisms, thus generalizing the conventional graph attention strategy. The approach used in CANs is hierarchical and it incorporates the following steps: i) a lifting algorithm that learns {\it edge features} from {\it node features}; ii) a cell attention mechanism to find the optimal combination of edge features over both lower and upper neighbors; iii) a hierarchical {\it edge pooling} mechanism to extract a compact meaningful set of features. The experimental results show that CAN is a low complexity strategy that compares favorably with state of the art results on graph-based learning tasks.Comment: Preprint, under revie

Generalized Simplicial Attention Neural Networks

The aim of this work is to introduce Generalized Simplicial Attention Neural Networks (GSANs), i.e., novel neural architectures designed to process data defined on simplicial complexes using masked self-attentional layers. Hinging on topological signal processing principles, we devise a series of self-attention schemes capable of processing data components defined at different simplicial orders, such as nodes, edges, triangles, and beyond. These schemes learn how to weight the neighborhoods of the given topological domain in a task-oriented fashion, leveraging the interplay among simplices of different orders through the Dirac operator and its Dirac decomposition. We also theoretically establish that GSANs are permutation equivariant and simplicial-aware. Finally, we illustrate how our approach compares favorably with other methods when applied to several (inductive and transductive) tasks such as trajectory prediction, missing data imputation, graph classification, and simplex prediction.Comment: arXiv admin note: text overlap with arXiv:2203.0748

CIN++: Enhancing Topological Message Passing

Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, they face significant limitations in expressive power, struggling with long-range interactions and lacking a principled approach to modeling higher-order structures and group interactions. Cellular Isomorphism Networks (CINs) recently addressed most of these challenges with a message passing scheme based on cell complexes. Despite their advantages, CINs make use only of boundary and upper messages which do not consider a direct interaction between the rings present in the underlying complex. Accounting for these interactions might be crucial for learning representations of many real-world complex phenomena such as the dynamics of supramolecular assemblies, neural activity within the brain, and gene regulation processes. In this work, we propose CIN++, an enhancement of the topological message passing scheme introduced in CINs. Our message passing scheme accounts for the aforementioned limitations by letting the cells to receive also lower messages within each layer. By providing a more comprehensive representation of higher-order and long-range interactions, our enhanced topological message passing scheme achieves state-of-the-art results on large-scale and long-range chemistry benchmarks.Comment: 21 pages, 9 figure

A Conventional Multiplex PCR Assay for the Detection of Toxic Gemfish Species (Ruvettus pretiosus and Lepidocybium flavobrunneum): A Simple Method to Combat Health Frauds

The meat of Ruvettus pretiosus and Lepidocybium flavobrunneum (gemfishes) contains high amounts of indigestible wax esters that provoke gastrointestinal disorders. Although some countries have banned the sale of these species, mislabeling cases have been reported in sushi catering. This work developed a simple conventional multiplex PCR, which discriminates the two toxic gemfishes from other potentially replaced species, such as tunas, cod, and sablefish. A common degenerate forward primer and three species-specific reverse primers were designed to amplify cytochrome oxidase subunit I (COI) gene regions of different lengths (479, 403, and 291 bp) of gemfishes, tunas, and sablefish, respectively. A primer pair was designed to amplify a fragment (193 bp) of the cytb gene of cod species. Furthermore, a primer pair targeting the 16S rRNA gene was intended as common positive control (115 bp). The method developed in this study, by producing the expected amplicon for all of the DNA samples tested (reference and commercial), provides a rapid and reliable response in identifying the two toxic species to combat health frauds

Enhanced copy number variants detection from whole-exome sequencing data using EXCAVATOR2

Copy Number Variants (CNVs) are structural rear- rangements contributing to phenotypic variation that have been proved to be associated with many dis- ease states. Over the last years, the identification of CNVs from whole-exome sequencing (WES) data has become a common practice for research and clinical purpose and, consequently, the demand for more and more efficient and accurate methods has increased. In this paper, we demonstrate that more than 30% of WES data map outside the targeted re- gions and that these reads, usually discarded, can be exploited to enhance the identification of CNVs from WES experiments. Here, we present EXCAVATOR2, the first read count based tool that exploits all the reads produced by WES experiments to detect CNVs with a genome-wide resolution. To evaluate the per- formance of our novel tool we use it for analysing two WES data sets, a population data set sequenced by the 1000 Genomes Project and a tumor data set made of bladder cancer samples. The results obtained from these analyses demonstrate that EXCAVATOR2 out- performs other four state-of-the-art methods and that our combined approach enlarge the spectrum of detectable CNVs from WES data with an unprece- dented resolution

Steel sieves filter and stripping for the quality of extra virgin olive oil

Filtration is a widely spread procedure adopted after the olive oil extraction process to remove the suspended solids and to eliminate humidity, making the oil more brilliant and more stable. In Tuscany, the most common filtration equipment are filter-presses. Those devices are able to reach the aims of filtration but they show some disadvantages. First of all, filter-presses consume not re-generable filter sheets. These represents a direct purchasing cost as well as an indirect cost due to the trapping of a relevant oil amount. Furthermore, the use of filter sheets implies complications for their disposal. To partially overcome these issues a new filtration equipment able to reduce the filter sheets consumption has been designed. The main idea is the addition of steel sieves before the filter-press capable to retain the suspended solids. In this way, the filter sheets only have to hold the humidity of oil. The addition of the sieves increases the amount of processed olive oil up to about five times before the filter sheets has to be substituted. In addition, the opportunity of performing the stripping techniques to remove the dissolved oxygen from the olive oil is provided. The dissolved oxygen is shortly consumed by the oil in a few days and seems to act as a starter for the subsequent autoxidation reactions. This was confirmed by the faster quality decay kinetics during shelf-life of the oils with higher dissolved oxygen concentration, according to previous researches. In the presented device, the adoption of the stripping technique was able to halve the dissolved oxygen concentration in the treated extra virgin olive oils. Thus, the innovative filter should be able to considerably reduce the filter sheets consumption, and to improve the olive oil shelf-life through the reduction of the dissolved oxygen amounts. However, before the adoption of this kind of devices at the industrial scale, further investigations are necessar

The human health impact of waste management practices: a review of the literature and an evaluation of evidence

A literature review was carried out of the health impacts of incineration, landfill, composting, landspreading sewage sludge and sewage discharges. A protocol for making judgements about the strength and reliability of the evidence was applied using an algorithm with defined criteria. Possible judgements were “convincing”, “probable”, “possible” or “insufficient”. The review found that the evidence linking any adverse health outcomes with incineration, landfill or landspreading sewage sludge was “insufficient” to claim a causal association. The evidence is “insufficient” to link residence near a centralised composting facility with adverse health outcomes but it is “possible” that working at a centralised composting facility causes health problems. Working in sewage treatment plants “probably” causes gastrointestinal tract problems, headache, fatigue and airways symptoms. The only “convincing” evidence is that gastrointestinal symptoms result from bathing in sewage contaminated recreational waters

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology

Message Passing Neural Networks (MPNNs) are instances of Graph Neural Networks that leverage the graph to send messages over the edges. This inductive bias leads to a phenomenon known as over-squashing, where a node feature is insensitive to information contained at distant nodes. Despite recent methods introduced to mitigate this issue, an understanding of the causes for over-squashing and of possible solutions are lacking. In this theoretical work, we prove that: (i) Neural network width can mitigate over-squashing, but at the cost of making the whole network more sensitive; (ii) Conversely, depth cannot help mitigate over-squashing: increasing the number of layers leads to over-squashing being dominated by vanishing gradients; (iii) The graph topology plays the greatest role, since over-squashing occurs between nodes at high commute (access) time. Our analysis provides a unified framework to study different recent methods introduced to cope with over-squashing and serves as a justification for a class of methods that fall under `graph rewiring'.Comment: Accepted to ICML23; 21 page