Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. ...The embedding can be used to estimate the community structure of the network, with strong consistency results in the stochastic blockmodel framework. One of the main practical limitations of standard algorithms for community detection from spectral embeddings is that the number of communities and the latent dimension of the embedding must be specified in advance. In this article, a novel Bayesian model for simultaneous and automatic selection of the appropriate dimension of the latent space and the number of blocks is proposed. Extensions to directed and bipartite graphs are discussed. The model is tested on simulated and real world network data, showing promising performance for recovering latent community structure.
Spectral embedding of network adjacency matrices often produces node representations living approximately around low-dimensional submanifold structures. In particular, hidden substructure is expected ...to arise when the graph is generated from a latent position model. Furthermore, the presence of communities within the network might generate community-specific submanifold structures in the embedding, but this is not explicitly accounted for in most statistical models for networks. In this article, a class of models called latent structure block models (LSBM) is proposed to address such scenarios, allowing for graph clustering when community-specific one-dimensional manifold structure is present. LSBMs focus on a specific class of latent space model, the random dot product graph (RDPG), and assign a latent submanifold to the latent positions of each community. A Bayesian model for the embeddings arising from LSBMs is discussed, and shown to have a good performance on simulated and real-world network data. The model is able to correctly recover the underlying communities living in a one-dimensional manifold, even when the parametric form of the underlying curves is unknown, achieving remarkable results on a variety of real data.
A new class of models for dynamic networks is proposed, called mutually exciting point process graphs (MEG). MEG is a scalable network-wide statistical model for point processes with dyadic marks, ...which can be used for anomaly detection when assessing the significance of future events, including previously unobserved connections between nodes. The model combines mutually exciting point processes to estimate dependencies between events and latent space models to infer relationships between the nodes. The intensity functions for each network edge are characterized exclusively by node-specific parameters, which allows information to be shared across the network. This construction enables estimation of intensities even for unobserved edges, which is particularly important in real world applications, such as computer networks arising in cyber-security. A recursive form of the log-likelihood function for MEG is obtained, which is used to derive fast inferential procedures via modern gradient ascent algorithms. An alternative EM algorithm is also derived. The model and algorithms are tested on simulated graphs and real world datasets, demonstrating excellent performance.
Supplementary materials
for this article are available online.
Periodic patterns can often be observed in real-world event time data, possibly mixed with non-periodic arrival times. For modelling purposes, it is necessary to correctly distinguish the two types ...of events. This task has particularly important implications in computer network security; there, separating automated polling traffic and human-generated activity in a computer network is important for building realistic statistical models for normal activity, which in turn can be used for anomaly detection. Since automated events commonly occur at a fixed periodicity, statistical tests using Fourier analysis can efficiently detect whether the arrival times present an automated component. In this article, sequences of arrival times which contain automated events are further examined, to separate polling and non-periodic activity. This is first achieved using a simple mixture model on the unit circle based on the angular positions of each event time on the
p
-clock, where
p
represents the main periodicity associated with the automated activity; this model is then extended by combining a second source of information, the time of day of each event. Efficient implementations exploiting conjugate Bayesian models are discussed, and performance is assessed on real network flow data collected at Imperial College London.
Sardinia, located in Italy, is a significant producer of Protected Designation of Origin (PDO) sheep cheeses. In response to the growing demand for high-quality, safe, and traceable food products, ...the elemental fingerprints of Pecorino Romano PDO and Pecorino Sardo PDO were determined on 200 samples of cheese using validated, inductively coupled plasma methods. The aim of this study was to collect data for food authentication studies, evaluate nutritional and safety aspects, and verify the influence of cheesemaking technology and seasonality on elemental fingerprints. According to European regulations, one 100 g serving of both cheeses provides over 30% of the recommended dietary allowance for calcium, sodium, zinc, selenium, and phosphorus, and over 15% of the recommended dietary intake for copper and magnesium. Toxic elements, such as Cd, As, Hg, and Pb, were frequently not quantified or measured at concentrations of toxicological interest. Linear discriminant analysis was used to discriminate between the two types of pecorino cheese with an accuracy of over 95%. The cheese-making process affects the elemental fingerprint, which can be used for authentication purposes. Seasonal variations in several elements have been observed and discussed.
In this article, we describe the effects of tail pinch (TP), a mild acute stressor, on the levels of brain-derived neurotrophic factor (BDNF) and its tyrosine kinase receptor B (trkB) proteins in the ...hippocampus (HC) of the outbred Roman High- (RHA) and Low-Avoidance (RLA) rats, one of the most validated genetic models for the study of fear/anxiety- and stress-related behaviors. Using Western blot (WB) and immunohistochemistry assays, we show for the first time that TP induces distinct changes in the levels of BDNF and trkB proteins in the dorsal (dHC) and ventral (vHC) HC of RHA and RLA rats. The WB assays showed that TP increases BDNF and trkB levels in the dHC of both lines but induces opposite changes in the vHC, decreasing BDNF levels in RHA rats and trkB levels in RLA rats. These results suggest that TP may enhance plastic events in the dHC and hinder them in the vHC. Immunohistochemical assays, carried out in parallel to assess the location of changes revealed by the WB, showed that, in the dHC, TP increases BDNF-like immunoreactivity (LI) in the CA2 sector of the Ammon's horn of both Roman lines and in the CA3 sector of the Ammon's horn of RLA rats while, in the dentate gyrus (DG), TP increases trkB-LI in RHA rats. In contrast, in the vHC, TP elicits only a few changes, represented by decreases of BDNF- and trkB-LI in the CA1 sector of the Ammon's horn of RHA rats. These results support the view that the genotypic/phenotypic features of the experimental subjects influence the effects of an acute stressor, even as mild as TP, on the basal BDNF/trkB signaling, leading to different changes in the dorsal and ventral subdivisions of the HC.
The Roman High-Avoidance (RHA) and the Roman Low-Avoidance (RLA) rats, represent two psychogenetically-selected lines that are, respectively, resistant and prone to displaying depression-like ...behavior, induced by stressors. In the view of the key role played by the neurotrophic factors and neuronal plasticity, in the pathophysiology of depression, we aimed at assessing the effects of acute stress, i.e., forced swimming (FS), on the expression of brain-derived neurotrophic factor (BDNF), its trkB receptor, and the Polysialilated-Neural Cell Adhesion Molecule (PSA-NCAM), in the dorsal (dHC) and ventral (vHC) hippocampus of the RHA and the RLA rats, by means of western blot and immunohistochemical assays. A 15 min session of FS elicited different changes in the expression of BDNF in the dHC and the vHC. In RLA rats, an increment in the CA2 and CA3 subfields of the dHC, and a decrease in the CA1 and CA3 subfields and the dentate gyrus (DG) of the vHC, was observed. On the other hand, in the RHA rats, no significant changes in the BDNF levels was seen in the dHC and there was a decrease in the CA1, CA3, and DG of the vHC. Line-related changes were also observed in the expression of trkB and PSA-NCAM. The results are consistent with the hypothesis that the differences in the BDNF/trkB signaling and neuroplastic mechanisms are involved in the susceptibility of RLA rats and resistance of RHA rats to stress-induced depression.
The present work was undertaken to investigate the effects of acute forced swimming (FS) on the levels of brain-derived neurotrophic factor (BDNF) and tyrosine kinase receptor B (trkB) proteins in: ...the ventral tegmental area (VTA); the nucleus accumbens (Acb) shell and core compartments; and the anterior cingulate (ACg), prelimbic (PL) and infralimbic (IL) territories of the prefrontal cortex of genetic models of vulnerability (RLA, Roman low-avoidance rats) and resistance (RHA, Roman high-avoidance rats) to stress-induced depression. We report for the first time that FS induced very rapid and distinct changes in the levels of BDNF and trkB proteins in different areas of the mesocorticolimbic system of RHA and RLA rats. Thus, (1) in the VTA and Acb core, FS elicited a significant increase of both BDNF- and trkB-LI in RHA but not RLA rats, whereas in the Acb shell no significant changes in BDNF- and trkB-LI across the line and treatment were observed; (2) in RLA rats, the basal levels of BDNF-LI in the IL/PL cortex and of trkB-LI in the ACg cortex were markedly lower than those of RHA rats; moreover, BDNF- and trkB-LI in the IL/PL and ACg cortex were increased by FS in RLA rats but decreased in their RHA counterparts. These results provide compelling evidence that the genetic background influences the effects of stress on BDNF/trkB signaling and support the view that the same stressor may impact differently on the expression of BDNF in discrete brain areas.
Graph link prediction is an important task in cybersecurity: relationships between entities within a computer network, such as users interacting with computers or system libraries and the ...corresponding processes that use them, can provide key insights into adversary behaviour. Poisson matrix factorisation (PMF) is a popular model for link prediction in large networks, particularly useful for its scalability. In this article PMF is extended to include scenarios that are commonly encountered in cybersecurity applications. Specifically, an extension is proposed to explicitly handle binary adjacency matrices and include known categorical covariates associated with the graph nodes. A seasonal PMF model is also presented to handle seasonal networks. To allow the methods to scale to large graphs, variational methods are discussed for performing fast inference. The results show an improved performance over the standard PMF model and other statistical network models.
Fish populations play an active role in the maintenance of aquatic ecosystems biodiversity. Their intestinal microbiota and fillet chemistry depend on abiotic and biotic factors of the water ...environments that they inhabit. The present study investigated the grey mullets’ gut microbiota from a transitional aquatic ecosystem (Santa Giusta Lagoon, Sardinia, Italy) by a multidisciplinary approach which refers the results of (1) gut cultivable microbiota analyses (MA), (2) the trace metal assessment of fish muscle (TM), (3) the physico-chemical water monitoring (PC). MA detected the greatest number of total aerobic heterotrophic bacteria, Enterobacteriaceae and coliforms in Autumn (mean values 1.3 × 105, 2.4 × 104, 1.1 × 104 cfu g−1, respectively) when the accumulated rain and mean values of nutrients (reactive phosphorous and silica) were the highest. Marine bacteria were more numerous in Summer (mean value 7.4 × 105 cfu g−1) when the highest mean values of water temperature and salinity were registered. The gut bacteria were identified as Pseudomonas spp. (64%), Aeromonas spp. (17%), Ochrobactrum pseudogrignonense (10%), Providencia spp. (5%), Enterobacter ludwigii (2%) and Kocuria tytonicola (2%). TM showed that Ca, Na, B and Ni increased their concentrations in Winter while maxima of P, Zn, Cu and Fe were found in muscles of fish sampled in Summer. This study highlighted that the fish intestinal microbiota and metal composition of the fillet reflected the seasonal aquatic environmental variability.
PDF
