Age-related changes in the primary motor cortex of newborn to adult domestic pig sus scrofa domesticus

The pig has been increasingly used as a suitable animal model in translational neuroscience. However, several features of the fast-growing, immediately motor-competent cerebral cortex of this species have been adequately described. This study analyzes the cytoarchitecture of the primary motor cortex (M1) of newborn, young and adult pigs (Sus scrofa domesticus). Moreover, we investigated the distribution of the neural cells expressing the calcium-binding proteins (CaBPs) (calretinin, CR; parvalbumin, PV) throughout M1. The primary motor cortex of newborn piglets was characterized by a dense neuronal arrangement that made the discrimination of the cell layers difficult, except for layer one. The absence of a clearly recognizable layer four, typical of the agranular cortex, was noted in young and adult pigs. The morphometric and immunohistochemical analy-ses revealed age-associated changes characterized by (1) thickness increase and neuronal density (number of cells/mm2 of M1) reduction during the first year of life; (2) morphological changes of CR-immunoreactive neurons in the first months of life; (3) higher density of CR-and PV-immunopositive neurons in newborns when compared to young and adult pigs. Since most of the present findings match with those of the human M1, this study strengthens the growing evidence that the brain of the pig can be used as a potentially valuable translational animal model during growth and development

Morfologia del modello drammaturgico tra Seicento e Settecento. Il tiranno e la follia

Il saggio ripercorre alcuni momenti nella storia letteraria italiana della diffusione della maschera del tiranno sulle scene del teatro seicentesco

GitLab: work where you want, when you want

GitLab is a software company that works “all remote” at the scale of more than 1000 employees located in more than 60 countries. GitLab has no physical office and its employees can work from anywhere they choose. Any step of the organizational life of a GitLab employee (e.g., hiring, onboarding and firing) is performed remotely, except for a yearly companywide gathering. GitLab strongly relies on asynchronous coordination, allowing employees to work anytime they want. After highlighting some of the main practices implemented by GitLab to effectively work all remotely and asynchronously, I asked renowned organizational scientists their thoughts on this interesting case and to question the generalizability of the all remote asynchronous model. Understanding whether and under what conditions this model can succeed can be of guidance for organizational designers that are now considering different remote models in response of the COVID-19 shock and its aftermath

Coopetitive business models in future mobile broadband with licensed shared access (LSA)

6siopenSpectrum scarcity forces mobile network operators (MNOs) providing mobile broadband services to develop new business models that address spectrum sharing. It engages MNOs into coopetitive relationship with incumbents. Licensed Shared Access (LSA) concept complements traditional licensing and helps MNOs to access new spectrum bands on a shared basis. This paper discusses spectrum sharing with LSA from business perspective. It describes how coopetition and business model are linked conceptually, and identifies the influence of coopetition on future business models in LSA. We develop business models for dominant and challenger MNOs in traditional licensing and future with LSA. The results indicate that coopetition and business model concepts are linked via value co-creation and value co-capture. LSA offers different business opportunities to dominant and challenger MNOs. Offering, value proposition, customer segments and differentiation in business models become critical in mobile broadband.openP. Ahokangas; M. Matinmikko; I. Atkova; L.F. Minervini; S. Yrjölä; M. MustonenP., Ahokangas; M., Matinmikko; I., Atkova; Minervini, LEO FULVIO; S., Yrjölä; M., Mustone

Polyphenols as potential agents in the management of temporomandibular disorders

Temporomandibular disorders (TMD) consist of multifactorial musculoskeletal disorders associated with the muscles of mastication, temporomandibular joint (TMJ), and annexed structures. This clinical condition is characterized by temporomandibular pain, restricted mandibular movement, and TMJ synovial inflammation, resulting in reduced quality of life of affected people. Commonly, TMD management aims to reduce pain and inflammation by using pharmacologic therapies that show efficacy in pain relief but their long-term use is frequently associated with adverse effects. For this reason, the use of natural compounds as an effective alternative to conventional drugs appears extremely interesting. Indeed, polyphenols could represent a potential therapeutic strategy, related to their ability to modulate the inflammatory responses involved in TMD. The present work reviews the mechanisms underlying inflammation-related TMD, highlighting the potential role of polyphenols as a promising approach to develop innovative management of temporomandibular diseases

An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks

Access to external knowledge is essential for many natural language processing tasks, such as question answering and dialogue. Existing methods often rely on a parametric model that stores knowledge in its parameters, or use a retrieval-augmented model that has access to an external knowledge source. Parametric and retrieval-augmented models have complementary strengths in terms of computational efficiency and predictive accuracy. To combine the strength of both approaches, we propose the Efficient Memory-Augmented Transformer (EMAT) – it encodes external knowledge into a key-value memory and exploits the fast maximum inner product search for memory querying. We also introduce pre-training tasks that allow EMAT to encode informative key-value representations, and to learn an implicit strategy to integrate multiple memory slots into the transformer. Experiments on various knowledge-intensive tasks such as question answering and dialogue datasets show that, simply augmenting parametric models (T5-base) using our method produces more accurate results (e.g., 25.8 → 44.3 EM on NQ) while retaining a high throughput (e.g., 1000 queries/s on NQ). Compared to retrievalaugmented models, EMAT runs substantially faster across the board and produces more accurate results on WoW and ELI5.

Minimally invasive treatment of benign complete stenosis of colorectal anastomosis

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