TGF-beta mediated G1 arrest in a human melanoma cell line lacking p15INK4B: evidence for cooperation between p21Cip1/WAF1 and p27Kip1

We have studied TGF-beta mediated G1 arrest in WM35, an early stage human melanoma cell line. These cells have lost p15INK4B expression through loss of one chromosome 9 and rearrangement of the other. In asynchronously growing WM35, TGF-beta caused reductions in cyclin D1, cyclin A and cdk4 proteins and their associated kinase activities and an increase in both p21Cip1/WAF1 and p27Kip1. These findings were confirmed in cells released from quiescence in the presence of TGF-beta, in which TGF-beta inhibited or delayed the reduction in the cdk inhibitors that normally occurs in late G1. In contrast to observations in other cell types, there was an increased association of both p21Cip1/WAF1 and p27Kip1 with cyclin D1/cdk4 and with cyclin E/cdk2 during TGF-beta mediated arrest of asynchronously growing cells. Upregulation of p21Cip1/WAF1 preceded that of p27Kip1. Furthermore, p21Cip1/WAF1 and p27Kip1 were not present in the same cdk complexes but bound distinct populations of target cdk molecules. Both p21Cip1/WAF1 and p27Kip1 immunoprecipitates from asynchronously growing cells contained active kinase complexes. These KIP-associated kinase activities were reduced in TGF-beta arrested cells. It has been proposed that in TGF-beta arrested epithelial cells, up-regulation of p15INK4B and of p15INK4B binding to cdk4 serves to destabilize the association of p27Kip1 with cyclin D1/cdk4, promoting p27Kip1 binding and inhibition of cyclin E/cdk2. Our findings demonstrate that this is not a universal mechanism of G1 arrest by TGF-beta. In TGF-beta arrested WM35, which lack p15INK4B, the increased p21Cip1/WAF1 may serve a similar function to that of p15INK4B: initiating kinase inhibition and providing an additional mechanism to supplement the effect of p27Kip1 on G1 cyclin/cdks.     

Effect of magnetic field on Cu–water nanofluid heat transfer using GMDH-type neural network

Heat transfer of Cu–water nanofluid over a stretching cylinder in the presence of magnetic field has been investigated. The group method of data handling (GMDH) type neural networks (NNs) is used to calculate Nusselt number formulation. Results indicate that GMDH-type NN in comparison with fourth-order Runge–Kutta integration scheme provides an effective means of efficiently recognizing the patterns in data and accurately predicting a performance. The effects of nanoparticle volume fraction, magnetic parameter and Reynolds number on Nusselt number are studied by sensitivity analyses. The results show that Nusselt number is an increasing function of Reynolds number and volume fraction of nanoparticles while it is a decreasing function of magnetic parameter. As volume fraction of nanoparticles increases, the effect of this parameter on Nusselt number also increases, but opposite behavior is obtained for magnetic parameter and Reynolds number.     

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction. For temporal sequence, this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short Term Memory (BiLSTM) to capture long-term dependencies. Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes. In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters. Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data. In contrast, the other uses optical flow images. Finally, the proposed ensemble approach using max hard voting outperforms state-of-the-art methods with 96.30% and 90.07% accuracies on the UCF101 and HMDB51 datasets.     

Short-term and long-term memory analysis of learning using 2D and 3D educational contents

ABSTRACT

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM.     

Modeling moisture sorption isotherms in roasted green wheat using least square regression and neural-fuzzy techniques

Roasted green wheat at moisture content from 0.052 to 0.25 (decimal d.b.) and temperatures from 25 to 43 °C was used to model moisture sorption isotherms using conventional non-linear least square regression (NLR) and neural-fuzzy (NF) techniques. The results showed that neural-fuzzy techniques provided a better fit than conventional least square regression with: R² = 0.99 and 0.97, RMSE = 0.01 and 0.0038, E% = 1.01 and 5.9 and SSE = 0.0008 and 0.009, for NF and NLR techniques, respectively. Differential enthalpy decreased from 477.9 kJ/kg at 0.052 (decimal d.b. mc) to 44.7 kJ/kg at 0.25 (decimal d.b. mc) and entropy decreased from 1.16 kJ/kg K at 0.052 (decimal d.b. mc) to 0.014 kJ/kg K at 0.25 (decimal d.b. mc). A linear plot between enthalpy and entropy showed that compensation exists. The isokinetic temperature T_β was 376.13 K which was larger than the harmonic mean temperature T_hm = 307.31 K, showing that the water sorption was entropy-driven. The free energy change ΔG was positive (+38.42 kJ/kg) indicating a non-spontaneous water sorption process.     

The production of a cellular graphene array by scanning probe lithography and its ability to store electrical charge

A graphene cellular array on an insulating SiO₂ layer was fabricated by scanning probe lithography. The graphene layer was oxidized by an electric field which was applied between the cantilever tip and Si substrate without any electrode directly connected to the graphene layer. When the bias voltage was applied on a cell of patterned graphene through the cantilever tip, charge was accumulated on the cell and preserved for a long time without decay. The accumulated charge and the surface potential were measured by an electrostatic force microscope. The charge retention was measured as a function of time, and the decay time constant was estimated to be ～70 min.     

Improvement of Grain Size and Dome Height of Microalloyed Steels Using Taguchi Method Based on Grey Relational Grade in Controlled Rolling

An efficient approach was introduced for improving the condition of major controlled rolling process parameters of roughing, finishing and coiling temperatures and optimizing these parameters to obtain minimum grain size and maximum dome height simultaneously. Taguchi method combined with grey relational analysis was applied to achieve optimum grain size and dome height during controlled rolling process. For this purpose, four levels for the above temperatures were chosen and sixteen experiments were conducted based on orthogonal array of Taguchi method. Based on Taguchi approach, signal-to-noise (S/N) ratios were calculated and used in order to obtain the optimum levels for every input parameter. Analysis of variance revealed that finishing and coiling temperatures have the maximum effect on the grain size and dome height of microalloyed steels. The confirmation tests with the optimal levels of parameters indicated that the grain size and dome height of controlled rolled microalloyed steels can be improved effectively through this approach.     

Boron nitride based polyaniline nanocomposite: Preparation,property, and application

In this study, we report first time the electrical properties and photocatalytic activity of HCl doped polyaniline (Pani) and Pani/boron nitride (Pani/BN) nanocomposite prepared by in situ polymerization of aniline using potassium persulfate (K₂S₂O₈) in the presence of hexagonal boron nitride (h‐BN). The prepared Pani and Pani/BN nanocomposite were characterized by Fourier transform infrared, X‐ray diffraction, Thermogravimetric analysis, Scanning electron microscope, and Transmission electron microscope. The stability of the Pani/BN nanocomposite in comparison of Pani in terms of the DC electrical conductivity retention was investigated under isothermal and cyclic aging conditions. The Pani/BN nanocomposite in terms of DC electrical conductivity was observed to be comparatively more thermally stable than Pani. The degradation of Methylene blue (MB) and Rhodamine B (RhB) under UV‐light irradiation were 50 and 56.4%, respectively, over Pani and 65.7 and 71.6%, respectively, over Pani/BN. The results indicated that the extent of degradation of MB and RhB was greater over nanocomposite material than Pani, which may result due to high electron–hole pairs charge separation under UV light. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43989.