KRAS and EGFR Mutations Differentially Alter ABC Drug Transporter Expression in Cisplatin-Resistant Non-Small Cell Lung Cancer

Lung carcinoma is still the most common malignancy worldwide. One of the major subtypes of non-small cell lung cancer (NSCLC) is adenocarcinoma (AC). As driver mutations and hence therapies differ in AC subtypes, we theorized that the expression and function of ABC drug transporters important in multidrug resistance (MDR) would correlate with characteristic driver mutations KRAS or EGFR. Cisplatin resistance (CR) was generated in A549 (KRAS) and PC9 (EGFR) cell lines and gene expression was tested. In three-dimensional (3D) multicellular aggregate cultures, both ABCB1 and ABCG2 transporters, as well as the WNT microenvironment, were investigated. ABCB1 and ABCG2 gene expression levels were different in primary AC samples and correlated with specific driver mutations. The drug transporter expression pattern of parental A549 and PC9, as well as A549-CR and PC9-CR, cell lines differed. Increased mRNA levels of ABCB1 and ABCG2 were detected in A549-CR cells, compared to parental A549, while the trend observed in the case of PC9 cells was different. Dominant alterations were observed in LEF1, RHOU and DACT1 genes of the WNT signalling pathway in a mutation-dependent manner. The study confirmed that, in lung AC-s, KRAS and EGFR driver mutations differentially affect both drug transporter expression and the cisplatin-induced WNT signalling microenvironment.     

Fast and accurate direction estimation of moving pedestrians

This work proposes a simple yet efficient way to estimate pedestrians flow direction based on videos from still cameras. It does that by localizing the extremities of head and feet of silhouettes and fitting them to lines. As the previous in three-dimensional space of these lines are parallel, their intersection point is the vanishing point. Using the computed vanishing point and two internal camera parameters, the horizontal direction of moving pedestrian is determined. Our method competes for the state-of-the-art methods and achieves a high rate accuracy for direction classification.     

Lipid/peptide/nucleotide separation with MALDI-ion mobility-TOF MS

Matrix-assisted laser desorption/ionization when combined with ion mobility-orthogonal time-of-flight mass spectrometry is a viable technique for fast separation and analysis of biomolecules in complex mixtures. Isobaric lipid, peptide, and oligonucleotide ions are preseparated before mass analysis by differences of up to 30% in mobility drift time. Ions of similar chemical type fall along well-defined "trend lines" (with deviations of approximately 3%) when plotted in two-dimensional representations of ion mobility as a function of m/z. Discussion of fundamental and technical limitations of the technique point to its potential for being most useful when applied to systems such as bodily fluids and intact tissue, where an alternative chemical or chromatographic preseparation step prior to mass analysis is either impractical or undesirable.     

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation and classification. In the lesion segmentation task, contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination. Subsequently, the best channel is selected and the lesion map is computed, which is further converted into a binary form using a thresholding function. In the lesion classification task, two pre-trained CNN models were modified and trained using transfer learning. Deep features were extracted from both models and fused using canonical correlation analysis. During the fusion process, a few redundant features were also added, lowering classification accuracy. A new technique called maximum entropy score-based selection (MESbS) is proposed as a solution to this issue. The features selected through this approach are fed into a cubic support vector machine (C-SVM) for the final classification. Results: The experimental process was conducted on two datasets: ISIC 2017 and HAM10000. The ISIC 2017 dataset was used for the lesion segmentation task, whereas the HAM10000 dataset was used for the classification task. The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher than the existing techniques.     

Deep-PHURIE: deep learning based hurricane intensity estimation from infrared satellite imagery

Hurricanes are among the most destructive natural phenomena on Earth. Timely prediction and tracking of hurricane intensity is important as it can help authorities in emergency planning. Several manual, semi and fully automated techniques based on different principles have been developed for hurricane intensity estimation. In this paper, a deep convolutional neural network architecture is proposed for fully automated hurricane intensity estimation from satellite infrared (IR) images. The proposed architecture is robust to errors in annotation of the storm center with a smaller root-mean-squared error (RMSE) (8.82 knots) in comparison to the previous state of the art methods. A web server implementation of Deep-PHURIE and its pre-trained neural network model are available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#Deep-PHURIE.

     

The Thinnest Light Disk: Rewritable Data Storage and Encryption on WS2 Monolayers

The thinnest light disk is demonstrated at the atomic level by developing an erasable method to directly write encrypted information onto WS₂ monolayers. The write-in is realized by precise control of photoluminescence emission by means of ozone functionalization and scanning focused laser beam. The visual decryption and reading-out of information are enabled by fluorescence contrast. The high encryption level is ensured by the threshold power upon which the data deletion will be triggered. Owing to the high spatial resolution and power sensitivity, the storage capacity within < 1 nm thickness can be up to ≈ 62.5 MB cm⁻², and the writing speed can reach ≈ 6.25 MB s⁻¹. Density functional theory calculations suggest that the disk formatting is realized by ozone molecule adsorption induced localized unoccupied states, while the read-in relies on the passivation of defects via substitution of the sulfur vacancies with oxygen atoms. The results of this study promote data storage and encryption on the atomic scale.     

Blind image inpainting quality assessment using local features continuity

Multimedia Tools and Applications - This paper deals with Blind Inpainted Image Quality Assessment BIIQA. Herein, we propose a new method that exploits the continuity of features around the...     

SDN/NFV‐based handover management approach for ultradense 5G mobile networks

With the great increase of connected devices and new types of applications, mobile networks are witnessing exponential growth of traffic volume. To meet emerging requirements, it is widely agreed that the fifth‐generation mobile network will be ultradense and heterogeneous. However, the deployment of a high number of small cells in such networks poses challenges for the mobility management, including frequent, undesired, and ping‐pong handovers, not to mention issues related to increased delay and failure of the handover process. The adoption of software‐defined networking (SDN) and network function virtualization (NFV) technologies into 5G networks offers a new way to address the above‐mentioned challenges. These technologies offer tools and mechanisms to make networks flexible, programmable, and more manageable. The SDN has global network control ability so that various functions such as the handover control can be implemented in the SDN architecture to manage the handover efficiently. In this article, we propose a Software‐Defined Handover (SDHO) solution to optimize the handover in future 5G networks. In particular, we design a Software‐Defined Handover Management Engine (SDHME) to handle the handover control mechanism in 5G ultradense networks. The SDHME is defined in the application plane of the SDN architecture, executed by the control plane to orchestrate the data plane. Simulation results demonstrate that, compared with the conventional LTE handover strategy, the proposed approach significantly reduces the handover failure ratio and handover delay.     

Effect of Silicon Wafer Resistivity on Morphology and Wettability of Silicon Nanowires Arrays

Silicon - Herein, we prepare vertical and single crystalline silicon nanowires (SiNWs) via a one-step metal-assisted chemical etching method in aqueous NH4HF2/AgNO3 solution. The effects of silicon...     

Multiscale Observation of the Fatigue‐Induced Damage Mechanisms in Carbon‐Black Filled Styrene‐Butadiene Rubber

Filled rubber materials exhibit a complex macroresponse characterized by stress softening, hysteresis, and dissipative heating when they are cyclically loaded. The relationship of these inelastic features to the microstructure changes is far from being fully established. This paper deals with the damage mechanisms in sulfur‐vulcanized styrene‐butadiene rubber (SBR) specimens in the diablo form reinforced with carbon‐black (CB) and zinc‐oxide (ZnO) fillers, and submitted to tension cyclic loading at room temperature. The microstructure alteration is characterized at different relevant scales and at different zones of the diabolo specimen by means of various technologies in the aim to report valuable insights about the mechanisms responsible for the macroresponse of this rubber‐filler material system. IR absorption spectra reveal that increasing filler content induces more interfacial interaction between CB and SBR chains. The environmental scanning electron microscopy (ESEM) observations show relevant altered morphologies of elastomeric chains with a predominant effect of both CB and ZnO fillers. A mesoscale observation of material density variation is presented using X‐ray computed tomography and the results are compared with those issued from ESEM.