Helcococcus kunzii isolated from a sow with purulent urocystitis

Helcococcus kunzii has never been reported in veterinary medicine. The isolation of H. kunzii from a sow with purulent urocystitis is described, suggesting this organism's potential pathogenic role in swine

Comparative genomics of Salmonella enterica serovar Enteritidis ST-11 isolated in Uruguay reveals lineages associated with particular epidemiological traits

Salmonella enterica serovar Enteritidis is a major cause of foodborne disease in Uruguay since 1995. We used a genomic approach to study a set of isolates from different sources and years. Whole genome phylogeny showed that most of the strains are distributed in two major lineages (E1 and E2), both belonging to MLST sequence type 11 the major ST among serovar Enteritidis. Strikingly, E2 isolates are over-represented in periods of outbreak abundance in Uruguay, while E1 span all epidemic periods. Both lineages circulate in neighbor countries at the same timescale as in Uruguay, and are present in minor numbers in distant countries. We identified allelic variants associated with each lineage. Three genes, ycdX, pduD and hsdM, have distinctive variants in E1 that may result in defective products. Another four genes (ybiO, yiaN, aas, aceA) present variants specific for the E2 lineage. Overall this work shows that S. enterica serovar Enteritidis strains circulating in Uruguay have the same phylogenetic profile than strains circulating in the region, as well as in more distant countries. Based on these results we hypothesize that the E2 lineage, which is more prevalent during epidemics, exhibits a combination of allelic variants that could be associated with its epidemic ability

Morganella morganii septicemia and concurrent renal crassicaudiasis in a Cuvier’s beaked whale (Ziphius cavirostris) stranded in Italy

Information regarding bacterial diseases in Cuvier's beaked whale (CBW, Ziphius cavirostris) is scattered and mostly incomplete. This report describes a case of septicemia by Morganella morganii in a juvenile male CBW with concurrent renal crassicaudiasis. The animal stranded along the Ligurian coastline (Italy) and underwent a systematic post-mortem examination to determine the cause of death. Histopathology showed lesions consistent with a septicemic infection, severe meningoencephalitis, and renal crassicaudiasis. An M. morganii alpha-hemolytic strain was isolated in pure culture from liver, lung, prescapular lymph node, spleen, hepatic and renal abscesses, and central nervous system (CNS). The antimicrobial susceptibility profile of the strain was evaluated with the minimum inhibitory concentrations (MICs) method and reduced susceptibility to Trimethoprim-Sulfamethoxazole is reported. Crassicauda sp. nematodes were retrieved from both kidneys. No other pathogens were detected by immunohistochemistry, serology, or biomolecular analyses. Toxicological investigations detected high concentrations of immunosuppressant pollutants in the blubber. The chronic parasitic infestation and the toxic effects of xenobiotics likely compromised the animal's health, predisposing it to an opportunistic bacterial infection. To our knowledge, this is the first description of M. morganii septicemia with CNS involvement in a wild cetacean

Spectral Clustering with Graph Neural Networks for Graph Pooling

Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement pooling operations that aggregate nodes belonging to the same cluster. However, the eigendecomposition of the Laplacian is expensive and, since clustering results are graph-specific, pooling methods based on SC must perform a new optimization for each new sample. In this paper, we propose a graph clustering approach that addresses these limitations of SC. We formulate a continuous relaxation of the normalized minCUT problem and train a GNN to compute cluster assignments that minimize this objective. Our GNN-based implementation is differentiable, does not require to compute the spectral decomposition, and learns a clustering function that can be quickly evaluated on out-of-sample graphs. From the proposed clustering method, we design a graph pooling operator that overcomes some important limitations of state-of-the-art graph pooling techniques and achieves the best performance in several supervised and unsupervised tasks

Scanning force microscopy on live cultured cells: Imaging and force\u2010versus\u2010distance investigations

Extensive measurements with the scanning force microscope on living cells in their native liquid environment are described with the purpose of critically assessing the extent of the interaction between the SFM tip and the (soft) cell materials and the effect of such interaction on topographic information. Images are obtained under various force conditions and systematically correlated with force\u2010versus\u2010distance curves. As a result, detailed indications about tip indentation are given, thickness estimates deduced and identification of submembranous cytoplasmic structures suggested. 1994 Blackwell Science Lt

Graph Neural Networks in TensorFlow and Keras with Spektral

Graph neural networks have enabled the application of deep learning to problems that can be described by graphs, which are found throughout the different fields of science, from physics to biology, natural language processing, telecommunications or medicine. In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-fr iendliness and quick prototyping on which Keras is based. Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral’s features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression

Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling

In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that learn hierarchical representations. In this work, we propose the Node Decimation Pooling (NDP), a pooling operator for GNNs that generates coarser graphs while preserving the overall graph topology. During training, the GNN learns new node representations and fits them to a pyramid of coarsened graphs, which is computed offline in a preprocessing stage. NDP consists of three steps. First, a node decimation procedure selects the nodes belonging to one side of the partition identified by a spectral algorithm that approximates the MAXCUT solution. Afterward, the selected nodes are connected with Kron reduction to form the coarsened graph. Finally, since the resulting graph is very dense, we apply a sparsification procedure that prunes the adjacency matrix of the coarsened graph to reduce the computational cost in the GNN. Notably, we show that it is possible to remove many edges without significantly altering the graph structure. Experimental results show that NDP is more efficient compared to state-of-the-art graph pooling operators while reaching, at the same time, competitive performance on a significant variety of graph classification tasks

Mechanical and morphological properties of living 3T6 cells probed via scanning force microscopy

Scanning Force Microscopy (SFM) is utilized to study living confluent 3T6 cells. Images based on mechanical contrast are obtained and related morphological details, mostly regarding the cell cytoskeleton, are analyzed. Moreover, numerical estimates of the local mechanical properties of the living cells are given, by extensive use of the 'force-vs.-distance' operation mode. On the basis of the results obtained, the potentialities of SFM as an optimal new technique available for probing the cell cytoskeleton of unstained living cells, and assessing related models, are shortly discussed

Autoregressive Models for Sequences of Graphs

This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very general family of graphs, characterised by a variable topology, and attributes associated with nodes and edges. A graph neural network (GNN) is also proposed to learn the AR function associated with the graph-generating process (GGP), and subsequently predict the next graph in a sequence. The proposed method is compared with four baselines on synthetic GGPs, denoting a significantly better performance on all considered problems