281 research outputs found
Symmetric polarization insensitive directional couplers fabricated by femtosecond laser waveguide writing
We study analytically the polarization behaviour of directional couplers
composed of birefringent waveguides, showing that they can induce polarization
transformations that depend on the specific input-output path considered. On
the basis of this study, we propose and demonstrate experimentally, by
femtosecond laser writing, directional couplers that are free from this problem
and also yield a polarization independent power-splitting ratio. More in
detail, we devise two different approaches to realize such devices: the first
one is based on local birefringence engineering, while the second one exploits
ultra-low birefringence waveguides obtained by thermal annealing
Herpes Simplex Virus type-1 infection induces synaptic dysfunction in cultured cortical neurons via GSK-3 activation and intraneuronal amyloid-β protein accumulation
Increasing evidence suggests that recurrent Herpes Simplex Virus type 1 (HSV-1) infection spreading to the CNS is a risk factor for Alzheimer's Disease (AD) but the underlying mechanisms have not been fully elucidated yet. Here we demonstrate that in cultured mouse cortical neurons HSV-1 induced Ca 2+ -dependent activation of glycogen synthase kinase (GSK)-3. This event was critical for the HSV-1-dependent phosphorylation of amyloid precursor protein (APP) at Thr668 and the following intraneuronal accumulation of amyloid-β protein (Aβ). HSV-1-infected neurons also exhibited: i) significantly reduced expression of the presynaptic proteins synapsin-1 and synaptophysin; ii) depressed synaptic transmission. These effects depended on GSK-3 activation and intraneuronal accumulation of Aβ. In fact, either the selective GSK-3 inhibitor, SB216763, or a specific antibody recognizing Aβ (4G8) significantly counteracted the effects induced by HSV-1 at the synaptic level. Moreover, in neurons derived from APP KO mice and infected with HSV-1 Aβ accumulation was not found and synaptic protein expression was only slightly reduced when compared to wild-type infected neurons. These data further support our contention that HSV-1 infections spreading to the CNS may contribute to AD phenotype
Phylogeny and classification of the Bucconidae (Aves, Galbuliformes) based on osteological characters
The puffbirds (Bucconidae) are relatively poorly studied birds whose intrafamilial relationships have not yet been explored within a phylogenetic framework in a published study. Here, we performed a parsimony analysis of osteological data obtained following the examination of all the genera and 32 out of the 36 species recognized in Bucconidae currently. The analysis yielded eight equally parsimonious trees (426 minimum steps). Ambiguous relationships were observed only in Notharcus ordii, Malacoptila fusca, and Nonnula rubecula. Notably, Bucco was polyphyletic, leading to the resurrection of Cyphos and Tamatia. In addition, the osteological data provided a well-resolved phylogeny (topological dichotomies) and the support indices indicated that most of the nodes were robust at all hierarchical levels. We thus propose the first revised classification of the Bucconidae
Anti-Inflammatory activity of a polyphenolic extract from Arabidopsis thaliana in in vitro and in vivo models of Alzheimer's Disease
Alzheimer's disease (AD) is the most common neurodegenerative disorder and the primary form of dementia in the elderly. One of the main features of AD is the increase in amyloid-beta (Aβ) peptide production and aggregation, leading to oxidative stress, neuroinflammation and neurodegeneration. Polyphenols are well known for their antioxidant, anti-inflammatory and neuroprotective effects and have been proposed as possible therapeutic agents against AD. Here, we investigated the effects of a polyphenolic extract of Arabidopsis thaliana (a plant belonging to the Brassicaceae family) on inflammatory response induced by Aβ. BV2 murine microglia cells treated with both Aβ25⁻35 peptide and extract showed a lower pro-inflammatory (IL-6, IL-1β, TNF-α) and a higher anti-inflammatory (IL-4, IL-10, IL-13) cytokine production compared to cells treated with Aβ only. The activation of the Nrf2-antioxidant response element signaling pathway in treated cells resulted in the upregulation of heme oxygenase-1 mRNA and in an increase of NAD(P)H:quinone oxidoreductase 1 activity. To establish whether the extract is also effective against Aβ-induced neurotoxicity in vivo, we evaluated its effect on the impaired climbing ability of AD Drosophila flies expressing human Aβ1⁻42. Arabidopsis extract significantly restored the locomotor activity of these flies, thus confirming its neuroprotective effects also in vivo. These results point to a protective effect of the Arabidopsis extract in AD, and prompt its use as a model in studying the impact of complex mixtures derived from plant-based food on neurodegenerative diseases
Do the Fastest Open-Water Swimmers have A Higher Speed in Middle- and Long-Distance Pool Swimming Events?
Background: It has been shown that the fastest open-water swimmers (OW-swimmers) increase significantly the speed in the last split of the open-water events. The aim of the present work was to determine if the fastest OW-swimmers have a higher speed in the middle- and long-distance pool swimming events, and to develop a multivariate model that can predict the medalist group in the 10-km competition. Methods: A total of 484 athletes (252-males and 232-females) were included in the analysis. Swimmers were divided into four groups based on their finishing position in the competition. For each swimmer, the absolute best performance (PB) of 200, 400, 800 and 1500-meter in long course, the seasonal best performance (SPB) obtained before the open-water events and critical velocity (CV) were analyzed. Multivariate analysis of variance (MANOVA) was used to detect significant differences between groups and discriminant analysis was used to predict a grouping variable. Results: All the variables analyzed were significantly different between groups (p < 0.001). The first discriminant function correctly classified 50% of the overall female and male swimmers. Conclusion: Fastest OW-swimmers have a higher speed in middle- and long-distance pool swimming events. Further studies should include different anthropometric and physiological variables to increase the accuracy of classification
High-fidelity and polarization insensitive universal photonic processors fabricated by femtosecond laser writing
Universal photonic processors (UPPs) are fully programmable photonic
integrated circuits that are key components in quantum photonics. With this
work, we present a novel platform for the realization of low-loss, low-power
and high-fidelity UPPs based on femtosecond laser writing (FLW) and compatible
with a large wavelength spectrum. In fact, we demonstrate different UPPs,
tailored for operation at 785 nm and 1550 nm, providing similar high-level
performances. Moreover, we show that standard calibration techniques applied to
FLW-UPPs result in Haar random polarization independent photonic
transformations implemented with average amplitude fidelity as high as 0.9979
at 785 nm (0.9970 at 1550 nm), with the possibility of increasing the fidelity
over 0.9990 thanks to novel optimization algorithms. Besides being the first
demonstrations of polarization-transparent UPPs, these devices show the highest
level of control and reconfigurability ever reported for a FLW circuit. These
qualities will be greatly beneficial to applications in quantum information
processing
The impact of a 14-day altitude training camp on olympic-level open-water swimmers' sleep
Despite the common belief that sleep quality at altitude is poor, the scientific evidence to support this notion is still modest. Therefore, the purpose of the present study was to evaluate possible changes of actigraphy-based and subjective sleep parameters in a group of elite open-water swimmers during a 14-day altitude training camp (ATC) at 1500 m. The study subjects were five Olympic-level open-water swimmers (mean age: 25.0 ± 3.2 years; 3 females and 2 males). All subjects wore a wrist activity monitor and filled a sleep diary for 18 consecutive nights, 4 nights before and 14 nights during ATC. The data were then analyzed at four different time points: before ATC (PRE), the first two days of ATC (T1), and after one (T2) and two weeks of ATC (T3). Training load, assessed as the covered distance (km), session rating of perceived exertion (sRPE), and heart rate (HR), was monitored during the week before and the first and second week of ATC. No significant differences in objective and subjective scores of sleep quality were detected, whereas the sleep onset time (p = 0.018; η2p = 0.83, large) and sleep offset time (p < 0.001; η2p = 0.95, large) significantly differed among PRE, T1, T2, and T3: elite athletes started to sleep and woke up ≃ 1 h earlier the first two days of ATC compared to PRE (sleep onset time: p = 0.049; sleep offset time: p = 0.016). Further, an increase in the training volume during the two weeks of the ATC was observed, with the most time spent in a low-intensity regime and an increase in time spent in a high-intensity regime compared to PRE. Sleep quality was not negatively influenced by a 14-day altitude training camp at 1500 m in a group of Olympic-level elite swimmers despite an increase in perceived exertion during training sessions. Nonetheless, early sleep onset and sleep offset times were observed for the first two nights of ATC: elite athletes started to sleep and woke up ≃ 1 h earlier compared to the baseline nights
Deep reinforcement learning for quantum multiparameter estimation
Estimation of physical quantities is at the core of most scientific research, and the use of quantum devices promises to enhance its performances. In real scenarios, it is fundamental to consider that resources are limited, and Bayesian adaptive estimation represents a powerful approach to efficiently allocate, during the estimation process, all the available resources. However, this framework relies on the precise knowledge of the system model, retrieved with a fine calibration, with results that are often computationally and experimentally demanding. We introduce a model-free and deep-learning-based approach to efficiently implement realistic Bayesian quantum metrology tasks accomplishing all the relevant challenges, without relying on any a priori knowledge of the system. To overcome this need, a neural network is trained directly on experimental data to learn the multiparameter Bayesian update. Then the system is set at its optimal working point through feedback provided by a reinforcement learning algorithm trained to reconstruct and enhance experiment heuristics of the investigated quantum sensor. Notably, we prove experimentally the achievement of higher estimation performances than standard methods, demonstrating the strength of the combination of these two black-box algorithms on an integrated photonic circuit. Our work represents an important step toward fully artificial intelligence-based quantum metrology
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