New frontiers in liver ultrasound: From mono to multi parametricity

Modern liver ultrasonography (US) has become a "one-stop shop " able to provide not only anatomic and morphologic but also functional information about vascularity, stiffness and other various liver tissue properties. Modern US techniques allow a quantitative assessment of various liver diseases. US scanning is no more limited to the visualized plane, but three-dimensional, volumetric acquisition and consequent post-processing are also possible. Further, US scan can be consistently merged and visualized in real time with Computed Tomography and Magnetic Resonance Imaging examinations. Effective and safe microbubble-based contrast agents allow a real time, dynamic study of contrast kinetic for the detection and characterization of focal liver lesions. Ultrasound can be used to guide loco-regional treatment of liver malignancies and to assess tumoral response either to interventional procedures or medical therapies. Microbubbles may also carry and deliver drugs under ultrasound exposure. US plays a crucial role in diagnosing, treating and monitoring focal and diffuse liver disease. On the basis of personal experience and literature data, this paper is aimed to review the main topics involving recent advances in the field of liver ultrasound

The stability and activity of human neuroserpin are modulated by a salt bridge that stabilises the reactive centre loop

Neuroserpin (NS) is an inhibitory protein belonging to the serpin family and involved in several pathologies, including the dementia Familial Encephalopathy with Neuroserpin Inclusion Bodies (FENIB), a genetic neurodegenerative disease caused by accumulation of NS polymers. Our Molecular Dynamics simulations revealed the formation of a persistent salt bridge between Glu289 on strand s2C and Arg362 on the Reactive Centre Loop (RCL), a region important for the inhibitory activity of NS. Here, we validated this structural feature by simulating the Glu289Ala mutant, where the salt bridge is not present. Further, MD predictions were tested in vitro by purifying recombinant Glu289Ala NS from E. coli. The thermal and chemical stability along with the polymerisation propensity of both Wild Type and Glu289Ala NS were characterised by circular dichroism, emission spectroscopy and non-denaturant gel electrophoresis, respectively. The activity of both variants against the main target protease, tissue-type plasminogen activator (tPA), was assessed by SDS-PAGE and chromogenic kinetic assay. Our results showed that deletion of the salt bridge leads to a moderate but clear reduction of the overall protein stability and activity

Topological Gradient-based Competitive Learning

Topological learning is a wide research area aiming at uncovering the mutual spatial relationships between the elements of a set. Some of the most common and oldest approaches involve the use of unsupervised competitive neural networks. However, these methods are not based on gradient optimization which has been proven to provide striking results in feature extraction also in unsupervised learning. Unfortunately, by focusing mostly on algorithmic efficiency and accuracy, deep clustering techniques are composed of overly complex feature extractors, while using trivial algorithms in their top layer. The aim of this work is to present a novel comprehensive theory aspiring at bridging competitive learning with gradient-based learning, thus allowing the use of extremely powerful deep neural networks for feature extraction and projection combined with the remarkable flexibility and expressiveness of competitive learning. In this paper we fully demonstrate the theoretical equivalence of two novel gradient-based competitive layers. Preliminary experiments show how the dual approach, trained on the transpose of the input matrix i.e. X T , lead to faster convergence rate and higher training accuracy both in low and high-dimensional scenarios

Gradient-Based Competitive Learning: Theory

Deep learning has been recently used to extract the relevant features for representing input data also in the unsupervised setting. However, state-of-the-art techniques focus mostly on algorithmic efficiency and accuracy rather than mimicking the input manifold. On the contrary, competitive learning is a powerful tool for replicating the input distribution topology. It is cognitive/biologically inspired as it is founded on Hebbian learning, a neuropsychological theory claiming that neurons can increase their specialization by competing for the right to respond to/represent a subset of the input data. This paper introduces a novel perspective by combining these two techniques: unsupervised gradient-based and competitive learning. The theory is based on the intuition that neural networks can learn topological structures by working directly on the transpose of the input matrix. At this purpose, the vanilla competitive layer and its dual are presented. The former is representative of a standard competitive layer for deep clustering, while the latter is trained on the transposed matrix. The equivalence of the layers is extensively proven both theoretically and experimentally. The dual competitive layer has better properties. Unlike the vanilla layer, it directly outputs the prototypes of the data inputs, while still allowing learning by backpropagation. More importantly, this paper proves theoretically that the dual layer is better suited for handling high-dimensional data (e.g., for biological applications), because the estimation of the weights is driven by a constraining subspace which does not depend on the input dimensionality, but only on the dataset cardinality. This paper has introduced a novel approach for unsupervised gradient-based competitive learning. This approach is very promising both in the case of small datasets of high-dimensional data and for better exploiting the advantages of a deep architecture: the dual layer perfectly integrates with the deep layers. A theoretical justification is also given by using the analysis of the gradient flow for both vanilla and dual layers

Comparison of Genetic and Reinforcement Learning Algorithms for Energy Cogeneration Optimization

Large process plants generally require energy in different forms: mechanical, electrical, or thermal (in the form of steam or hot water). A commonly used source of energy is cogeneration, also defined as Combined Heat and Power (CHP). Cogeneration can offer substantial economic as well as energy savings; however, its real-time operation scheduling is still a challenge today. Multiple algorithms have been proposed for the CHP control problem in the literature, such as genetic algorithms (GAs), particle swarm optimization algorithms, artificial neural networks, fuzzy decision making systems and, most recently, reinforcement learning (RL) algorithms.This paper presents the comparison of a RL approach and a GA for the control of a cogenerator, using as a case study a thermal power plant serving a factory during the year 2021. The two methods were compared based on an earnings before interest, taxes, depreciation, and amortization (EBITDA) metric. The EBITDA that could be obtained using the RL algorithm, exceeds both the EBITDA that could be generated using a per-week genetic algorithm and the one from the manual scheduling of the CHP. Thus, the RL algorithm proves to be the most cost-effective strategy for the control of a CHP

Focal Pancreatic Lesions: Role of Contrast-Enhanced Ultrasonography

The introduction of contrast-enhanced ultrasonography (CEUS) has led to a significant improvement in the diagnostic accuracy of ultrasound in the characterization of a pancreatic mass. CEUS, by using a blood pool contrast agent, can provide dynamic information concerning macro- and micro-circulation of focal lesions and of normal parenchyma, without the use of ionizing radiation. On the basis of personal experience and literature data, the purpose of this article is to describe and discuss CEUS imaging findings of the main solid and cystic pancreatic lesions with varying prevalence

Influence of olive cake dietary supplementation on fecal microbiota of dairy cows

Olive by-products represent a valuable low-price feed supplement for animal nutrition. In the present study, the effect of the dietary destoned olive cake supplementation, on both composition and dynamics of the fecal bacterial biota of cow, was assessed by Illumina MiSeq analysis of the 16S rRNA gene. In addition, metabolic pathways were predicted by using the PICRUSt2 bioinformatic tool. Eighteen lactating cows, according to the body condition score, the days from calving, and the daily milk production were homogeneously allocated into two groups, control or experimental, and subjected to different dietary treatments. In detail, the experimental diet contained, along with the components of the control one, 8% of destoned olive cake. Metagenomics data revealed significant differences in abundance rather than in richness between the two groups. Results showed that Bacteroidota and Firmicutes were identified as the dominant phyla, accounting for over 90% of the total bacterial population. The Desulfobacterota phylum, able to reduce sulfur compounds, was detected only in fecal samples of cows allocated to the experimental diet whereas the Elusimicrobia phylum, a common endosymbiont or ectosymbiont of various flagellated protists, was detected only in cows subjected to the control diet. In addition, both Oscillospiraceae and Ruminococcaceae families were mainly found in the experimental group whereas fecal samples of control cows showed the presence of Rikenellaceae and Bacteroidaceae families, usually associated with the high roughage or low concentrate diet. Based on the PICRUSt2 bioinformatic tool, pathways related to carbohydrate, fatty acid, lipid, and amino acids biosynthesis were mainly up regulated in the experimental group. On the contrary, in the control group, the metabolic pathways detected with the highest occurrence were associated with amino acids biosynthesis and degradation, aromatic compounds degradation, nucleosides and nucleotides biosynthesis. Hence, the present study confirms that the destoned olive cake is a valuable feed supplement able to modulate the fecal microbiota of cows. Further studies will be conducted in order to deepen the inter-relationships between the GIT microbiota and the host

Polydatin beneficial effects in zebrafish larvae undergoing multiple stress types

none8noPolydatin is a polyphenol, whose beneficial properties, including anti-inflammatory and antioxidant activity, have been largely demonstrated. At the same time, copper has an important role in the correct organism homeostasis and alteration of its concentration can induce oxidative stress. In this study, the efficacy of polydatin to counteract the stress induced by CuSO4 exposure or by caudal fin amputation was investigated in zebrafish larvae. The study revealed that polydatin can reduced the stress induced by a 2 h exposure to 10 µM CuSO4 by lowering the levels of il1b and cxcl8b.1 and reducing neutrophils migration in the head and along the lateral line. Similarly, polydatin administration reduced the number of neutrophils in the area of fin cut. In addition, polydatin upregulates the expression of sod1 mRNA and CAT activity, both involved in the antioxidant response. Most of the results obtained in this study support the working hypothesis that polydatin administration can modulate stress response and its action is more effective in mitigating the effects rather than in preventing chemical damages.openPessina A.; Di Vincenzo M.; Maradonna F.; Marchegiani F.; Olivieri F.; Randazzo B.; Gioacchini G.; Carnevali O.Pessina, A.; Di Vincenzo, M.; Maradonna, F.; Marchegiani, F.; Olivieri, F.; Randazzo, B.; Gioacchini, G.; Carnevali, O