In Vivo Studies of Nanostructure‐Based Photosensitizers for Photodynamic Cancer Therapy

Animal models, particularly rodents, are major translational models for evaluating novel anticancer therapeutics. In this review, different types of nanostructure‐based photosensitizers that have advanced into the in vivo evaluation stage for the photodynamic therapy (PDT) of cancer are described. This article focuses on the in vivo efficacies of the nanostructures as delivery agents and as energy transducers for photosensitizers in animal models. These materials are useful in overcoming solubility issues, lack of tumor specificity, and access to tumors deep in healthy tissue. At the end of this article, the opportunities made possible by these multiplexed nanostructure‐based systems are summarized, as well as the considerable challenges associated with obtaining regulatory approval for such materials. The following questions are also addressed: (1) Is there a pressing demand for more nanoparticle materials? (2) What is the prognosis for regulatory approval of nanoparticles to be used in the clinic?     

Polyphenol-rich seaweed (Eucheuma cottonii) extract suppresses breast tumour via hormone modulation and apoptosis induction

The edible red seaweed Eucheuma cottonii is abundantly cultivated for carrageenan production. This study investigated the effects of dietary E. cottonii polyphenol-rich extract (ECME) on breast cancer. In vitro assays showed that ECME was antiproliferative against oestrogen-dependent MCF-7 and oestrogen-independent MB-MDA-231 human breast-cancer cells (IC₅₀ values of 20 and 42 μg/ml, respectively) but was non-toxic to normal cell lines. The ECME (150 and 300 mg/kg BW) was fed to female rats and, after 4 weeks, rat mammary tumour was induced using LA7 cells (inoculated subcutaneously). The ECME inhibited tumour development and erythrocyte lipid peroxidation in the cancer-induced rats, dose-dependently. It showed anti-oestrogenic effects on the rat estrous cycle and serum hormone levels. Electron microscopy and histopathology observations confirmed apoptosis in the rat mammary tumours. The polyphenol-rich ECME was tumour-suppressive via apoptosis induction, downregulating the endogenous oestrogen biosynthesis, and improving antioxidative status in the rats.     

Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data

Urbanization is commonly accepted as an important contributor to the growth of man-made structures and as a rapid convertor of natural environments to impervious surfaces. Roofs are one class of impervious surface whose materials can highly influence the quality of urban surface water. In this study, two data sources, WorldView-2 (WV-2) imagery and a combination of WV-2 and lidar data, were utilized to map intra-urban targets, with 13 classes. Images were classified using object-based image analysis. Pixel-based classifications using the support vector machine (SVM) and maximum likelihood (ML) methods were also tested for their abilities to use both lidar data and WV-2 imagery. ML and SVM classifications yielded overall accuracies of 72.46% and 75.69%, respectively. The results of these classifiers exhibited mixed pixels and salt-and-pepper effects. Spectral, spatial, and textural attributes as well as various spectral indices were employed in the object-based classification of WV-2 imagery. Feature classification of WV-2 imagery resulted in 85% overall accuracy. Lidar data were added to WV-2 imagery to assist in the spatial and spectral diversities of urban infrastructures. Classified image made from WV-2 imagery and lidar data achieved 92.84% overall accuracy. Rule-sets of these fused datasets effectively reduced the spectral variation and spatial heterogeneities of intra-urban classes, causing finer boundaries among land-cover classes. Therefore, object-based classification of WV-2 imagery and lidar data efficiently improved detailed characterization of roof types and other urban surface materials.     

An overview of spectrum sensing techniques for cognitive LTE and LTE-A radio systems

Advanced communication systems, such as long term evolution (LTE) and LTE-advanced (LTE-A) systems, promise to increase the number of users with high-speed data exchange. However, it leads to spectrum scarcity because of the huge size of data exchange with limited spectrum resources. Cognitive radio (CR) technique is considered the best solution for this spectrum scarcity problem. Spectrum sensing (SS), one of the CR techniques is used to detect the spectrum hole of primary user (PU) without interference with PU. In this paper, several SS approaches for LTE and LTE-A systems are investigated in the CR system. These SS approaches are based on two techniques, namely energy detection and cyclostationary feature detection techniques. The first technique includes four approaches of auto-correlation based advanced energy, time domain detection, Welch periodogram and two-stage model algorithms, while the second technique contains two approaches, namely pilot induced cyclostationary and second order cyclostationary algorithms. According to the analysis, the two-stage model and the second order cyclostationary algorithms are better than the other algorithms because they produce accurate results at the expense of system complexity. Hence, in general a good SS algorithms would require some trade-off between complexity and accuracy.     

Superposition Coding with Unequal Error Protection for the Uplink of Overloaded DS-CDMA System

The overloaded CDMA schemes exploited in direct sequence CDMA (DS-CDMA) systems are mainly to accommodate a greater number of users than the available spreading factor N. In this paper, a superposition coding CDMA (SPC-CDMA) with unequal error protection (UEP) is proposed as one of the overloaded CDMA schemes for the next generation mobile communication systems. It exploits the available power control in most base stations to adapt the transmitted power of active users in the uplink channel. In this scheme, the active users are divided into G groups and each group consists of K users. The K users share the same spreading sequence and are distinguished by different received power levels. At the receiver side, the system first performs despreading for group detection followed by multiuser receiver to estimate the K user signals in each group. It is shown through simulations that better performance are achieved compared to the conventional DS-CDMA and existing overloaded collaborative spreading CDMA (CS-CDMA) schemes, in additive white Gaussian noise (AWGN) and fading channels. Hence, the proposed scheme maximizes the system capacity K-fold compared to conventional DS-CDMA system without requiring extra spreading codes, with average signal to noise ratio (SNR) cost of only 1dB and 2 dB over AWGN and fading channels respectively at BER of 10^?3. On the other hand, for the same N, K and power constraints, SPC-CDMA scheme achieves twofold increase in data rate with 0.7 and 4 dB gains over AWGN and fading channels respectively, compared with overloaded CS-CDMA scheme in the same system capacity. In addition, the proposed scheme can also attain different levels of UEP for different users?? requirements by adjusting their fractions of transmitted powers.     

Green transmission for C-RAN based on SWIPT in 5G: a review

Wireless Networks - C-RAN is a promising new design for the next generation, an important aspect of it in the energy efficiency consideration. Hence, it is considering an innovative candidate to...     

Intelligence Framework Based Analysis of Spatial–Temporal Data with Compressive Sensing Using Wireless Sensor Networks

Wireless sensor networks produce immense sensor readings within a report interval to the sink. So transfer of information in a resource constrained wireless environment is difficult. Compressive sensing overcomes the resource constrains in wireless environment by exploiting sparsity in transfer with fewer measurement and recovery of original signal. In this research Intelligent Neighbor Aided Compressive Sensing (INACS) scheme is proposed for efficient data assembly in spatial and temporal correlated WSNs. Sparse Matrix has been formed with spatial and temporal coordinates for data transfer. In every sensing period, the sensor node just sends the readings within the sensing period to uniquely selected neighbour based on a correlation. The transmission period provides significant improvement with compressed data using INACS with the measurement matrix. Thus INACS provides reduction in number of transmission and higher reconstruction accuracy. INACS has been compared with Compressive wireless sensing for reduction in number of transmissions achieved. The time series analysis with INACS has been done to validate the simultaneous association between number of transmissions and time period.

     

Development of spatial similarity‐based modelling to improve integrated lake water quality management in Malaysia

This study describes similarity‐based modelling techniques used to develop a spatially based water quality prediction model to facilitate sustainable lake water quality management. The lentic nature of lakes allows them to slowly absorb pollutants over a long period of time with readily noticeable signs, causing symptoms to appear only when the water quality has significantly degraded, meaning the risk of improper water quality management can be very high. Thus, failure to establish sustainable planning at the watershed scale was found to be a major threat of water quality degradation, from extemporary approaches often practised in lake management. Accordingly, the developed model is tailored for lakes facing moderate to serious water quality data limitation. The geodatabase integrates the identified driving factors of physical, social and water quality with significant influences on the status of lake water quality. A 1 km buffer radius with percentages of built‐up area, population, lake surface area and rainfall is measured. Calculation of a water quality index was then quantified on the basis of a similarity‐modelling technique. Lake Putrajaya was chosen as a control point for developing the indicator index. A total of 93 recreational lakes within Selangor and Kuala Lumpur were selected as modelling points. The results of this study indicated the similarity technique of spatial modelling is sufficiently reliable to be applied as an early assessment indicator. From the 93 lakes in this study, none feel in the category of either “bad” or “excellent,” with the majority being in class 3 (medium water quality status) and only four considered as having a good water quality condition. The balance of 35 lakes was considered to exhibit poor water quality. The model output is an indicator index, providing classification guidelines for the water quality status of the assessed lake as an early assessment tools.