Neuronal avalanches differ from wakefulness to deep sleep - evidence from intracranial depth recordings in humans

Neuronal activity differs between wakefulness and sleep states. In contrast, an attractor state, called self-organized critical (SOC), was proposed to govern brain dynamics because it allows for optimal information coding. But is the human brain SOC for each vigilance state despite the variations in neuronal dynamics? We characterized neuronal avalanches – spatiotemporal waves of enhanced activity - from dense intracranial depth recordings in humans. We showed that avalanche distributions closely follow a power law – the hallmark feature of SOC - for each vigilance state. However, avalanches clearly differ with vigilance states: slow wave sleep (SWS) shows large avalanches, wakefulness intermediate, and rapid eye movement (REM) sleep small ones. Our SOC model, together with the data, suggested first that the differences are mediated by global but tiny changes in synaptic strength, and second, that the changes with vigilance states reflect small deviations from criticality to the subcritical regime, implying that the human brain does not operate at criticality proper but close to SOC. Independent of criticality, the analysis confirms that SWS shows increased correlations between cortical areas, and reveals that REM sleep shows more fragmented cortical dynamics

Neuronal avalanches change from wakefulness to deep sleep - a study of intracranial depth recordings in humans

Neuronal dynamics differs between wakefulness and sleep stages, so does the cognitive state. In contrast, a single attractor state, called self-organized critical (SOC), has been proposed to govern human brain dynamics for its optimal information coding and processing capabilities. Here we address two open questions: First, does the human brain always operate in this computationally optimal state, even during deep sleep? Second, previous evidence for SOC was based on activity within single brain areas, however, the interaction between brain areas may be organized differently. Here we asked whether the interaction between brain areas is SOC. ..

Practical Experiences of a Smart Livestock Location Monitoring System leveraging GNSS, LoRaWAN and Cloud Services.

Livestock farming is, in most cases in Europe, unsupervised, thus making it difficult to ensure adequate control of the position of the animals for the improvement of animal welfare. In addition, the geographical areas involved in livestock grazing usually have difficult access with harsh orography and lack of communications infrastructure, thus the need to provide a low-power livestock localization and monitoring system is of paramount importance, which is crucial not for a sustainable agriculture, but also for the protection of native breeds and meats thanks to their controlled supervision. In this context, this work presents an Internet of things (IoT)-based system integrating low-power wide area (LPWA) technology, cloud and virtualization services to provide real-time livestock location monitoring. Taking into account the constraints coming from the environment in terms of energy supply and network connectivity, our proposed system is based on a wearable device equipped with inertial sensors, Global Positioning System (GPS) receiver and LoRaWAN transceiver, which can provide a satisfactory compromise between performance, cost and energy consumption. At first, this article provides the state-of-the-art localization techniques and technologies applied to smart livestock. Then, we proceed to provide the hardware and firmware co-design to achieve very low energy consumption, thus providing a significant positive impact to the battery life. The proposed platform has been evaluated in a pilot test in the Northern part of Italy, evaluating different configurations in terms of sampling period, experimental duration and number of devices. The results are analyzed and discussed for packe delivery ratio, energy consumption, localization accuracy, battery discharge measurement and delay

Computing and Experiments

The question about the scientific nature of computing has been widely debated with no universal consensus reached about its disciplinary status. Positions vary from acknowledging computing as the science of computers to defining it as a synthetic engineering discipline. In this paper, we aim at discussing the nature of computing from a methodological perspective. We consider, in particular, the nature and role of experiments in this field, whether they can be considered close to the traditional experimental scientific method or, instead, they possess peculiar and unique features. We argue that this experimental perspective should be taken into account when discussing the status of computing. We critically survey how the experimental method has been conceived and applied in computing, and some open issues that could be tackled with the aid of the history of science, the philosophy of science, and the philosophy of technology

Growth factors and mechano-regulated reciprocal crosstalk with extracellular matrix tune the keratocyte–fibroblast/myofibroblast transition

Improper healing of the cornea after injury, infections or surgery can lead to corneal scar formation, which is associated with the transition of resident corneal keratocytes into activated fibroblasts and myofibroblasts (K–F/M). Myofibroblasts can create an extracellular matrix (ECM) niche in which fibrosis is promoted and perpetuated, resulting in progressive tissue opacification and vision loss. As a reversion back to quiescent keratocytes is essential to restore corneal transparency after injury, we characterized how growth factors with demonstrated profibrotic effects (PDGF, FGF, FBS, TGFβ1) induce the K–F/M transition, and whether their withdrawal can revert it. Indeed, the upregulated expression of αSMA and the associated changes in cytoskeletal architecture correlated with increases in cell contractility, fibronectin (Fn) and collagen matrix density and Fn fiber strain, as revealed by 2D cell culture, nanopillar cellular force mapping and a FRET-labeled Fn tension probe. Substrate mechanosensing drove a more complete K–F/M transition reversal following growth factor withdrawal on nanopillar arrays than on planar glass substrates. Using decellularized ECM scaffolds, we demonstrated that the K–F/M transition was inhibited in keratocytes reseeded onto myofibroblast-assembled, and/or collagen-1-rich ECM. This supports the presence of a myofibroblast-derived ECM niche that contains cues favoring tissue homeostasis rather than fibrosis

Hybrid visibility compositing and masking for illustrative rendering

In this paper, we introduce a novel framework for the compositing of interactively rendered 3D layers tailored to the needs of scientific illustration. Currently, traditional scientific illustrations are produced in a series of composition stages, combining different pictorial elements using 2D digital layering. Our approach extends the layer metaphor into 3D without giving up the advantages of 2D methods. The new compositing approach allows for effects such as selective transparency, occlusion overrides, and soft depth buffering. Furthermore, we show how common manipulation techniques such as masking can be integrated into this concept. These tools behave just like in 2D, but their influence extends beyond a single viewpoint. Since the presented approach makes no assumptions about the underlying rendering algorithms, layers can be generated based on polygonal geometry, volumetric data, point-based representations, or others. Our implementation exploits current graphics hardware and permits real-time interaction and rendering.publishedVersio

A New Approach to LCA Evaluation of Lamb Meat Production in Two Different Breeding Systems inNorthern Italy

Lamb meat production provides vital landscape-management and ecosystem services; however, ruminant farming produces a considerable share of the world’s greenhouse gas emissions. To measure and compare the advantages and disadvantages of the intensification of livestock farming, an integrative analysis was conducted in this study by combining environmental impact analysis and animal welfare assessment. This approach is the first of its kind and is the innovative aspect of this paper. The methodology of Life Cycle Assessment (LCA) entails the holistic analysis of various impact categories and the associated emission quantities of products, services, and resources over their life cycle, including resource extraction and processing, production processes, transport, usage, and the end of life. The outlines of LCA are standardized in DIN EN ISO 14040/14044. To assess the environmental impacts of the production of lamb meat in northern Italy, two case studies were undertaken using the LCA software GaBi. The analysis is based on primary data from two sheep-breeding systems (semi-extensive and semi-intensive in alpine and continental bioregions, respectively) combined with inventory data from the GaBi database and data from the literature. The assessment was conducted for the functional unit of 1 kg of lamb meat and focuses on the impact categories global warming potential, acidification potential, and eutrophication potential. For an overall evaluation of the supply chain, we have also considered a parameter indicating animal welfare, in keeping with consumer concerns, employing an analysis of chronic stress as shown by cortisol accumulation. The goal is to derive models and recommendations for an efficient, more sustainable use of resources without compromising animal welfare, meat quality, and competitiveness. The aim of this study is to provide a standard for individualized sustainability analyses for European lamb production systems in the future. From the LCA perspective, the more intensive case-study farm showed a lower impact in global impact factors and a higher impact in local impact categories in comparison with the more extensively run farm that was studied. From the animal welfare perspective, lower amounts of the stress hormone cortisol were found on the extensively managed case-study far