Korelacija između nekih biohemijskih parametara šaranskih riba (Cyprinidae) do starosti od jedne godine

Cilj ispitivanja je da se prouče neki od biohemijskih parametara krvnog seruma i tela, kao i eventualnih korelacije između njih. Ispitivane vrste su bile šaran, tolstolobik i amur do jedne godine starosti, pre i posle zimovanja u zimovnicima. Korelacija koja je bila ustanovljena između ispitivanih parova parametara ukupnih serum protein i baktericidne aktivnosti za ispitivane tipova i starosti riba, varira od umerene do znatne, značajne i različitog pravca. Kod tolstolobika je konstatovana umerena, obrnuta i znatna korelacija između masti i glukoze krvi, kao i proteina i glukoze krvi. Korelacija koja je bila ustanovljena između protein u telu i ukupnih protein kod jednogodišnjeg šarana, tolstolobika i amura varira od umerene do znatne, obrnuta je i značajna (r = 0.54; r =0.73; Р <0.047; Р <0.0009)

Effects of the CDK-inhibitor CYC202 on p38 MAPK, ERK1/2 and c-Myc activities in papillomavirus type 16 E6- and E7-transformed human keratinocytes

In the present study, we have investigated the effect of the chemical CDK-inhibitor CYC202 on E6 and E7-transformed keratinocytes, in which the function of the cellular cell cycle inhibitor p21Cip1 is abrogated by the viral genes. The cyto-toxicity and the inhibition of the cell growth were analysed by MTT assay and analysis of DNA synthesis respectively. The effect on some signalling molecules was tested by Western blot analysis. CYC202 effectively inhibited the proliferation of E6 and E7 keratinocytes in a dose-dependent manner. Treatment with CYC202 strongly increased the activity of p38 MAP kinase. Furthermore, it inhibited ERK1/2 at the highest concentration used and had no effect on the activity of JNK1/2. CYC202 also increased the phosphorylation of HSP27 and decreased the phosphorylation and DNA-binding activity of the transcriptional regulator c-Myc, in correlation with the corresponding upstream kinases p38 MAPK and ERK1/2. Our results provide additional data for the anti-proliferative actions and potency of the chemical CDK-inhibitor CYC202

EFFECT OF FLUNARIZINE ON CONTRACTILE ACTIVITY OF ANTRUM AND FUNDUS OF GUINEA PIG STOMACH

No abstrac

Time series analysis and modelling of the freezing of gait phenomenon

Freezing of Gait (FOG) is one of the most debilitating symptoms of Parkinson's Disease and is associated with falls and loss of independence. The patho-physiological mechanisms underpinning FOG are currently poorly understood. In this paper we combine time series analysis and mathematical modelling to study the FOG phenomenon's dynamics. We focus on the transition from stepping in place into freezing and treat this phenomenon in the context of an escape from an oscillatory attractor into an equilibrium attractor state. We extract a discrete-time discrete-space Markov chain from experimental data and divide its state space into communicating classes to identify the transition into freezing. This allows us to develop a methodology for computationally estimating the time to freezing as well as the phase along the oscillatory (stepping) cycle of a patient experiencing Freezing Episodes (FE). The developed methodology is general and could be applied to any time series featuring transitions between different dynamic regimes including time series data from forward walking in people with FOG.Comment: this version: correction of author spelling, submitted, 25 pages, 20 figure

A Ca2+-Based Computational Model for NMDA Receptor-Dependent Synaptic Plasticity at Individual Post-Synaptic Spines in the Hippocampus

Associative synaptic plasticity is synapse specific and requires coincident activity in pre-synaptic and post-synaptic neurons to activate NMDA receptors (NMDARs). The resultant Ca2+ influx is the critical trigger for the induction of synaptic plasticity. Given its centrality for the induction of synaptic plasticity, a model for NMDAR activation incorporating the timing of pre-synaptic glutamate release and post-synaptic depolarization by back-propagating action potentials could potentially predict the pre- and post-synaptic spike patterns required to induce synaptic plasticity. We have developed such a model by incorporating currently available data on the timecourse and amplitude of the post-synaptic membrane potential within individual spines. We couple this with data on the kinetics of synaptic NMDARs and then use the model to predict the continuous spine [Ca2+] in response to regular or irregular pre- and post-synaptic spike patterns. We then incorporate experimental data from synaptic plasticity induction protocols by regular activity patterns to couple the predicted local peak [Ca2+] to changes in synaptic strength. We find that our model accurately describes [Ca2+] in dendritic spines resulting from NMDAR activation during pre-synaptic and post-synaptic activity when compared to previous experimental observations. The model also replicates the experimentally determined plasticity outcome of regular and irregular spike patterns when applied to a single synapse. This model could therefore be used to predict the induction of synaptic plasticity under a variety of experimental conditions and spike patterns

Iron oxidation state effect on the Mg-Al- Si-O glassy system

Mg-Al-Si-O glassy systems have a great importance in a wide range of industrial applications, specifically as an electrolyte for molten oxide electrolysis processes in steelmaking. Understanding how the iron oxidation state of the raw material (Fe2+/Fe3+) and its corresponding amount influence this glassy system's properties will be the aim of the current work. Iron oxides (as Fe2O3 or Fe3O4) were used to dope Mg-Al-Si-O system obtaining amorphous materials through an unconventional method: Laser Floating Zone (LFZ). Above 8% mol of Fe formation of magnetic phases or iron clusters, were observed in the glass matrix. Samples with Fe2O3 showed a higher crystal concentration, when compared with Fe3O4. The electron paramagnetic resonance measurements show a strong dependence on the iron source (Fe3O4 or Fe2O3). In addition, the magnetization decreases linearly with iron content, independently of iron oxidation state, except for samples with a higher concentration of Fe2O3(15% mol), due to sample crystallization. Moreover, with Fe3O4 as raw material there is an improvement (~250 times) of the electrical conductivity when compared with Fe2O3. The results show that the presence of Fe2+ on the glass influences the electrical conductivity, which could have impact in the efficiency of molten oxide electrolysis process.publishe

Artificial intelligence, bias and clinical safety

This is the final version. Available on open access from BMJ Publishing group via the DOI in this recordEngineering and Physical Sciences Research Council (EPSRC

Aerial scene classification through fine-tuning with adaptive learning rates and label smoothing

Remote Sensing (RS) image classification has recently attracted great attention for its application in different tasks, including environmental monitoring, battlefield surveillance, and geospatial object detection. The best practices for these tasks often involve transfer learning from pre-trained Convolutional Neural Networks (CNNs). A common approach in the literature is employing CNNs for feature extraction, and subsequently train classifiers exploiting such features. In this paper, we propose the adoption of transfer learning by fine-tuning pre-trained CNNs for end-to-end aerial image classification. Our approach performs feature extraction from the fine-tuned neural networks and remote sensing image classification with a Support Vector Machine (SVM) model with linear and Radial Basis Function (RBF) kernels. To tune the learning rate hyperparameter, we employ a linear decay learning rate scheduler as well as cyclical learning rates. Moreover, in order to mitigate the overfitting problem of pre-trained models, we apply label smoothing regularization. For the fine-tuning and feature extraction process, we adopt the Inception-v3 and Xception inception-based CNNs, as well the residual-based networks ResNet50 and DenseNet121. We present extensive experiments on two real-world remote sensing image datasets: AID and NWPU-RESISC45. The results show that the proposed method exhibits classification accuracy of up to 98%, outperforming other state-of-the-art methods

A phenomenological model of seizure initiation suggests network structure may explain seizure frequency in idiopathic generalised epilepsy

We describe a phenomenological model of seizure initiation, consisting of a bistable switch between stable fixed point and stable limit-cycle attractors. We determine a quasi-analytic formula for the exit time problem for our model in the presence of noise. This formula--which we equate to seizure frequency--is then validated numerically, before we extend our study to explore the combined effects of noise and network structure on escape times. Here, we observe that weakly connected networks of 2, 3 and 4 nodes with equivalent first transitive components all have the same asymptotic escape times. We finally extend this work to larger networks, inferred from electroencephalographic recordings from 35 patients with idiopathic generalised epilepsies and 40 controls. Here, we find that network structure in patients correlates with smaller escape times relative to network structures from controls. These initial findings are suggestive that network structure may play an important role in seizure initiation and seizure frequency