Mitigating human impacts including climate change on proliferative kidney disease in salmonids of running waters.

Over the last two decades, an increasing number of reports have identified a decline in salmonid populations, possibly linked to infection with the parasite Tetracapsuloides bryosalmonae and the corresponding disease, that is, proliferative kidney disease (PKD). The life cycle of this myxozoan parasite includes sessile bryozoan species as invertebrate host, which facilitates the distribution of the parasite in running waters. As the disease outcome is temperature dependent, the impact of the disease on salmonid populations is increasing with global warming due to climate change. The goal of this review is to provide a detailed overview of measures to mitigate the effects of PKD on salmonid populations. It first summarizes the parasite life cycle, temperature-driven disease dynamics and new immunological and molecular research into disease resistance and, based on this, discusses management possibilities. Sophisticated management actions focusing on local adaptation of salmonid populations, restoration of the riverine ecosystem and keeping water temperatures cool are necessary to reduce the negative effects of PKD. Such actions include temporary stocking with PKD-resistant salmonids, as this may assist in conserving current populations that fail to reproduce

Characterisation of supra- and infratentorial ICP profiles

In pathophysiology and clinical practice, the intracranial pressure (ICP) profiles in the supratentorial and infratentorial compartments are unclear. We know that the pressure within the skull is unevenly distributed, with demonstrated ICP gradients. We recorded and characterised the supra- and infratentorial ICP patterns to understand what drives the transtentorial ICP gradient.A 70-year-old man was operated on for acute cerebellar infarction. One supratentorial probe and one cerebellar probe were implanted. Both signals were recorded concurrently and analysed off-line. We calculated mean ICP, ICP pulse amplitude, respiratory waves, slow waves and the RAP index of supra- and infratentorial ICP signals. Then, we measured transtentorial difference and performed correlation analysis for every index.Supratentorial ICP mean was 8.5 mmHg lower than infratentorial ICP, but the difference lessens for higher values. Both signals across the tentorium showed close correlation. Supra- and infratentorial pulse amplitude, respiratory waves and slow waves also showed a high degree of correlation. The compensatory reserve (RAP) showed good correlation. In this case report, we demonstrate that the mean value of ICP is higher in the posterior fossa, with a strong correlation across the tentorium. All other ICP-derived parameters display a symmetrical profile

A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic

The continuous evolution of Internet traffic and its applications makes the classification of network traffic a topic far from being completely solved. An essential problem in this field is that most of proposed techniques in the literature are based on a static view of the network traffic (i.e., they build a model or a set of patterns from a static, invariable dataset). However, very little work has addressed the practical limitations that arise when facing a more realistic scenario with an infinite, continuously evolving stream of network traffic flows. In this paper, we propose a streaming flow-based classification solution based on Hoeffding Adaptive Tree, a machine learning technique specifically designed for evolving data streams. The main novelty of our proposal is that it is able to automatically adapt to the continuous evolution of the network traffic without storing any traffic data. We apply our solution to a 12 + 1 year-long dataset from a transit link in Japan, and show that it can sustain a very high accuracy over the years, with significantly less cost and complexity than existing alternatives based on static learning algorithms, such as C4.5.Peer ReviewedPostprint (author's final draft

Building a Graph-based Deep Learning network model from captured traffic traces

Currently the state of the art network models are based or depend on Discrete Event Simulation (DES). While DES is highly accurate, it is also computationally costly and cumbersome to parallelize, making it unpractical to simulate high performance networks. Additionally, simulated scenarios fail to capture all of the complexities present in real network scenarios. While there exists network models based on Machine Learning (ML) techniques to minimize these issues, these models are also trained with simulated data and hence vulnerable to the same pitfalls. Consequently, the Graph Neural Networking Challenge 2023 introduces a dataset of captured traffic traces that can be used to build a ML-based network model without these limitations. In this paper we propose a Graph Neural Network (GNN)-based solution specifically designed to better capture the complexities of real network scenarios. This is done through a novel encoding method to capture information from the sequence of captured packets, and an improved message passing algorithm to better represent the dependencies present in physical networks. We show that the proposed solution it is able to learn and generalize to unseen captured network scenarios.Comment: 8 pages, 4 figure