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The KM3NeT-Italia underwater neutrino detection unit, the tower, consists of 14 floors. Each floor supports 6 Optical Modules containing front-end electronics needed to digitize the PMT signal, format and transmit the data and 2 hydrophones that reconstruct in real-time the position of Optical Modules, for a maximum tower throughput of more than 600 MB/s. All floor data are collected by the Floor Control Module (FCM) board and transmitted by optical bidirectional virtual point-to-point connections to the on-shore laboratory, each FCM needing an on-shore counterpart as communication endpoint. In this contribution we present NaNet3, an on-shore readout board based on Altera Stratix V GX FPGA able to manage multiple FCM data channels with a capability of 800 Mbps each. The design is a NaNet customization for the KM3NeT-Italia experiment, adding support in its I/O interface for a synchronous link protocol with deterministic latency at physical level and for a Time Division Multiplexing protocol at data level.

Neuroscience is moving toward a more integrative discipline where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow-wave activity (<1 Hz), which occurs during unconscious brain states like sleep and general anesthesia and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow-wave characteristics across multiple, openly available electrocorticography (ECoG) and calcium imaging datasets., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Glioblastoma (GBM) is the most malignant form of primary brain tumor. It is characterized by the presence of highly invasive cancer cells infiltrating the brain by hijacking neuronal mechanisms and interacting with non-neuronal cell types, such as astrocytes and endothelial cells. To enter the interstitial space of the brain parenchyma, GBM cells significantly shrink their volume and extend the invadopodia and lamellipodia by modulating their membrane conductance repertoire. However, the changes in the compartment-specific ionic dynamics involved in this process are still not fully understood. Here, using noninvasive perforated patch-clamp and live imaging approaches on various GBM cell lines during a wound-healing assay, we demonstrate that the sodium-calcium exchanger (NCX) is highly expressed in the lamellipodia compartment, is functionally active during GBM cell migration, and correlates with the overexpression of large conductance K+ channel (BK) potassium channels. Furthermore, a NCX blockade impairs lamellipodia formation and maintenance, as well as GBM cell migration. In conclusion, the functional expression of the NCX in the lamellipodia of GBM cells at the migrating front is a conditio sine qua non for the invasion strategy of these malignant cells and thus represents a potential target for brain tumor treatment.

A growing number of studies is focusing on the pharmacology and feasibility of bioactive compounds as a novel valuable approach to target a variety of human diseases related to neurological degeneration. Among the group of the so-called medicinal mushrooms (MMs), Hericium erinaceus has become one of the most promising candidates. In fact, some of the bioactive compounds extracted from H . erinaceus have been shown to recover, or at least ameliorate, a wide range of pathological brain conditions such as Alzheimer's disease, depression, Parkinson's disease, and spinal cord injury. In a large body of in vitro and in vivo preclinical studies on the central nervous system (CNS), the effects of erinacines have been correlated with a significant increase in the production of neurotrophic factors. Despite the promising outcome of preclinical investigations, only a limited number of clinical trials have been carried out so far in different neurological conditions. In this survey, we summarized the current state of knowledge on H . erinaceus dietary supplementation and its therapeutic potential in clinical settings. The bulk collected evidence underlies the urgent need to carry out further/wider clinical trials to prove the safety and efficacy of H . erinaceus supplementation, offering significant neuroprotective applications in brain pathologies.

The development of novel techniques to record wide-field brain activity enables estimation of data-driven models from thousands of recording channels and hence across large regions of cortex. These in turn improve our understanding of the modulation of brain states and the richness of traveling waves dynamics. Here, we infer data-driven models from high-resolution in-vivo recordings of mouse brain obtained from wide-field calcium imaging. We then assimilate experimental and simulated data through the characterization of the spatio-temporal features of cortical waves in experimental recordings. Inference is built in two steps: an inner loop that optimizes a mean-field model by likelihood maximization, and an outer loop that optimizes a periodic neuro-modulation via direct comparison of observables that characterize cortical slow waves. The model reproduces most of the features of the non-stationary and non-linear dynamics present in the high-resolution in-vivo recordings of the mouse brain. The proposed approach offers new methods of characterizing and understanding cortical waves for experimental and computational neuroscientists., (© 2023. The Author(s).)

Gastrectomy for gastric cancer is still performed in Western countries with high morbidity and mortality. Post-operative complications are frequent, and effective diagnosis and treatment of complications is crucial to lower the mortality rates. In 2015, a project was launched by the EGCA with the aim of building an agreement on list and definitions of post-operative complications specific for gastrectomy. In 2018, the platform www.gastrodata.org was launched for collecting cases by utilizing this new complication list. In the present paper, the Italian Research Group for Gastric Cancer endorsed a collection of complicated cases in the period 2015-2019, with the aim of investigating the clinical pictures, diagnostic modalities, and treatment approaches, as well as outcome measures of patients experiencing almost one post-operative complication. Fifteen centers across Italy provided 386 cases with a total of 538 complications (mean 1.4 complication/patient). The most frequent complications were non-surgical infections (gastrointestinal, pulmonary, and urinary) and anastomotic leaks, accounting for 29.2% and 17.3% of complicated patients, with a median Clavien-Dindo score of II and IIIB, respectively. Overall mortality of this series was 12.4%, while mortality of patients with anastomotic leak was 25.4%. The clinical presentation with systemic septic signs, the timing of diagnosis, and the hospital volume were the most relevant factors influencing outcome., (© 2022. The Author(s).)

Spiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and for understanding the relationship between these dynamics and brain function. Furthermore, the continuous development of parallel computing technologies and the growing availability of computational resources are leading to an era of large-scale simulations capable of describing regions of the brain of ever larger dimensions at increasing detail. Recently, the possibility to use MPI-based parallel codes on GPU-equipped clusters to run such complex simulations has emerged, opening up novel paths to further speed-ups. NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. In this work we evaluate its performance on the simulation of a multi-area model of macaque vision-related cortex, made up of about 4 million neurons and 24 billion synapses and representing 32 mm 2 surface area of the macaque cortex. The outcome of the simulations is compared against that obtained using the well-known CPU-based spiking neural network simulator NEST on a high-performance computing cluster. The results show not only an optimal match with the NEST statistical measures of the neural activity in terms of three informative distributions, but also remarkable achievements in terms of simulation time per second of biological activity. Indeed, NEST GPU was able to simulate a second of biological time of the full-scale macaque cortex model in its metastable state 3.1× faster than NEST using 32 compute nodes equipped with an NVIDIA V100 GPU each. Using the same configuration, the ground state of the full-scale macaque cortex model was simulated 2.4× faster than NEST., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tiddia, Golosio, Albers, Senk, Simula, Pronold, Fanti, Pastorelli, Paolucci and van Albada.)

The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories., Competing Interests: The authors have declared that no competing interests exist.

Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, different types of spike generators, tools for recording spikes, state variables and parameters, and it supports user-definable models. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Runge-Kutta method with adaptive step-size control. In this work we evaluate the performance of this library on the simulation of a cortical microcircuit model, based on LIF neurons and current-based synapses, and on balanced networks of excitatory and inhibitory neurons, using AdEx or Izhikevich neuron models and conductance-based or current-based synapses. On these models, we will show that the proposed library achieves state-of-the-art performance in terms of simulation time per second of biological activity. In particular, using a single NVIDIA GeForce RTX 2080 Ti GPU board, the full-scale cortical-microcircuit model, which includes about 77,000 neurons and 3 · 10 8 connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Golosio, Tiddia, De Luca, Pastorelli, Simula and Paolucci.)

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192 × 192 modules, each composed of 1,250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40 GHz clock rate. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1,024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3 × 10 9 and 4.1 × 10 9 synaptic events per second for both cortical states in the explored range of inter-modular interconnections.

The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. During slow oscillations, spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process. It is characterized by both a specific unsupervised enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This hierarchical organization of post-sleep internal representations favours higher performances in retrieval and classification tasks. The mechanism is based on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations. Indeed, when learned patterns are replayed during sleep, cortico-thalamo-cortical connections favour the activation of other neurons coding for similar thalamic inputs, promoting their association. Such mechanism hints at possible applications to artificial learning systems.

A retrospective study has been performed on 155 cases of surgically treated hypopharyngeal carcinomas in order to bring to light what effect the type of surgery has on survival and the reasons for failure. The following operations were performed: 103 hemipharyngectomies with total laryngectomy (HPTL); 28 total circular pharyngectomies with total laryngectomy (CPTL); 16 hemipharyngectomies with partial laryngectomy (HPPL); 4 exereses in lateral pharyngectomy; 2 total laryngectomies and 2 hemipharyngectomies in lateral pharyngotomy. Analysis of the results is only performed on the three most numerous groups of patients. The overall neoplastic mortality rate at 5 years was 79% for the HPTL group while it was 50% and 61% in the CPTL and HPPL groups, respectively. The actuarial survival curves show a higher, statistically significant, cumulative 5-year survival rate for the CPTL group (55.9%) than for the HPTL group (30.5%). The authors conclude that, considering the particular submucosa spreading to which hypopharyngeal carcinoma is subject and the good functional recovery which can be obtained with modern reconstructive surgery of the alimentary tract, CPTL should be the treatment of choice, even in the less advanced cases, in order to significantly reduce the risk of local recurrences.

Acid phosphatase activity estimated cytochemically is markedly increased in myeloma plasma cells compared to that found in normal plasma cells, polyclonal plasma-cytoses and most interestingly in the non-myelomatous monoclonal dysglobulinaemias. In the differential diagnosis of this last group the test has a useful role.