Saffron (Crocus sativus L.) extract prevents and improves D-galactose and NaNO2 induced memory impairment in mice

This study was conducted to examine the effects of saffron extract on preventing D-galactose and NaNO2 induced memory impairment and improving learning and memory deficits in amnestic mice. In this study, the learning and memory functions in ovariectomized mice were examined by the one way passive and active avoidance tests. In active avoidance test, training in amnestic treated (AT) and amnestic prophylaxis (AP) groups, was improved so that there was a significant difference between them and the amnestic control (AC) group. In passive avoidance test, animal’s step through latency, as an index for learning, in all test groups was significantly greater than control group. Total time spent in dark room (DS), which opposes the memory retention ability, in AC was significantly greater than AT group at 1 and 2 hours after full training, while there was not any significant difference between this index in AP and AT as compared with normal control (NC) group. Our findings indicate that saffron hydroalcoholic extract prevents and improves amnesia induced by D-galactose and NaNO2 in mice

Histochemistry and anatomy of phylloxera (Daktulosphaira vitifoliae) nodosities on young roots of grapevine (Vitis spp).

Phylloxera (Daktulosphaira vitifoliae FITCH) induce galls (nodosities) on young grapevine roots. Histological and histochemical methods were applied to study the gall's morphology and enzyme activities (peroxidases, leucine aminopeptidases and acidic phosphatases). Susceptible V. vinifera cv. Cabernet Sauvignon was compared to the resistant rootstock 5 BB (V. berlandieri x V. riparia) using aseptic dual culture conditions. The gall induction phase was analyzed before visible signs of potential resistance responses were detected. Elevated metabolic activity has been found in nodosities compared to uninfected roots. Starch granule incorporation was detected in young galls and was highest at the feeding site. As galls mature, the starch density decreased at the feeding site and increased towards the periphery of the gall. Peroxidase, acidic phosphatase and leucine aminopeptidase activities were highest at the incision. No differences in enzyme activities could be detected between the two cultivars tested.

Experimental realization of the Yang-Baxter Equation via NMR interferometry

The Yang-Baxter equation is an important tool in theoretical physics, with many applications in different domains that span from condensed matter to string theory. Recently, the interest on the equation has increased due to its connection to quantum information processing. It has been shown that the Yang-Baxter equation is closely related to quantum entanglement and quantum computation. Therefore, owing to the broad relevance of this equation, besides theoretical studies, it also became significant to pursue its experimental implementation. Here, we show an experimental realization of the Yang-Baxter equation and verify its validity through a Nuclear Magnetic Resonance (NMR) interferometric setup. Our experiment was performed on a liquid state Iodotrifluoroethylene sample which contains molecules with three qubits. We use Controlled-transfer gates that allow us to build a pseudo-pure state from which we are able to apply a quantum information protocol that implements the Yang-Baxter equation.Comment: 10 pages and 6 figure

Nonintrusive reduced order model for parametric solutions of inertia relief problems

The Inertia Relief (IR) technique is widely used by industry and produces equilibrated loads allowing to analyze unconstrained systems without resorting to the more expensive full dynamic analysis. The main goal of this work is to develop a computational framework for the solution of unconstrained parametric structural problems with IR and the Proper Generalized Decomposition (PGD) method. First, the IR method is formulated in a parametric setting for both material and geometric parameters. A reduced order model using the encapsulated PGD suite is then developed to solve the parametric IR problem, circumventing the so-called curse of dimensionality. With just one offline computation, the proposed PGD-IR scheme provides a computational vademecum that contains all the possible solutions for a predefined range of the parameters. The proposed approach is nonintrusive and it is therefore possible to be integrated with commercial finite element (FE) packages. The applicability and potential of the developed technique is shown using a three-dimensional test case and a more complex industrial test case. The first example is used to highlight the numerical properties of the scheme, whereas the second example demonstrates the potential in a more complex setting and it shows the possibility to integrate the proposed framework within a commercial FE package. In addition, the last example shows the possibility to use the generalized solution in a multi-objective optimization setting

Multiple Teachers-Meticulous Student:A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification

Background: Image classification can be considered one of the key pillars of medical image analysis. Deep learning (DL) faces challenges that prevent its practical applications despite the remarkable improvement in medical image classification. The data distribution differences can lead to a drop in the efficiency of DL, known as the domain shift problem. Besides, requiring bulk annotated data for model training, the large size of models, and the privacy-preserving of patients are other challenges of using DL in medical image classification. This study presents a strategy that can address the mentioned issues simultaneously. Method: The proposed domain adaptive model based on knowledge distillation can classify images by receiving limited annotated data of different distributions. The designed multiple teachers-meticulous student model trains a student network that tries to solve the challenges by receiving the parameters of several teacher networks. The proposed model was evaluated using six available datasets of different distributions by defining the respiratory motion artefact detection task. Results: The results of extensive experiments using several datasets show the superiority of the proposed model in addressing the domain shift problem and lack of access to bulk annotated data. Besides, the privacy preservation of patients by receiving only the teacher network parameters instead of the original data and consolidating the knowledge of several DL models into a model with almost similar performance are other advantages of the proposed model. Conclusions: The proposed model can pave the way for practical clinical applications of deep classification methods by achieving the mentioned objectives simultaneously

Primary Central Nervous System Lymphoma

ObjectivePrimary central nervous system lymphoma (PCNSL) is an extremely rare condition in childhood. We report the first case of PCNSL in a child in Iran.Clinical presentationA nine-year-old boy was referred to Mofid Hospital with the history of headache of four months and seizure of 2 months duration. Magnetic resonance imaging of the brain revealed a hyper-intense lesion in left fronto-parietal area with secondary satellite lesions. Biopsy of the brain mass was performed. Pathologic findings showed brain lymphoma and immunohistochemistry confirmed this diagnosis. The treatment started with intrathecal and systemic chemotherapy in combination with radiotherapy

Emerging technologies for the energy systems of the future

The way the world gets its energy is undergoing a rapid transition, driven by both the increased urgency of decarbonizing energy systems and the plummeting costs of renewable energy technologies. The road to the future will not be easy, and indeed, new technologies, markets, architectures, infrastructures, actors, and business models should be developed, and major changes will be required in the regulation of the energy systems to further support new business models and new consumption patterns. Such a transition requires rethinking every single aspect of energy systems, starting from the way energy is produced and harvested to the way that we dispatch and use it. In this area, it is also imperative to understand how to control and manage existing and emerging technologies to enhance the energy economy and efficiency by active participation in different services required by energy networks