Occurrence of aflatoxins in selected processed foods from pakistan

A total of 125 (ready to eat) processed food samples (70 intended for infant and 55 for adult intake) belonging to 20 different food categories were analyzed for aflatoxins contamination using Reverse Phase High Performance Liquid Chromatography (RP-HPLC) with fluorescent detection. A solvent mixture of acetonitrile-water was used for the extraction followed by immunoaffinity clean-up to enhance sensitivity of the method. The limit of detection (LOD) (0.01-0.02 ng·g(-1)) and limit of quantification (LOQ) (0.02 ng·g(-1)) was established for aflatoxins based on signal to noise ratio of 3:1 and 10:1, respectively. Of the processed food samples tested, 38% were contaminated with four types of aflatoxins, i.e., AFB1 (0.02-1.24 μg·kg(-1)), AFB2 (0.02-0.37 μg·kg(-1)), AFG1 (0.25-2.7 μg·kg(-1)) and AFG2 (0.21-1.3 μg·kg(-1)). In addition, the results showed that 21% of the processed foods intended for infants contained AFB1 levels higher than the European Union permissible limits (0.1 μg·kg(-1)), while all of those intended for adult consumption had aflatoxin contamination levels within the permitted limits.     

Investigation on the Antioxidant Activity of Leaves, Peels, Stems Bark, and Kernel of Mango (Mangifera indica L.)

Bioactive polyphenols, cartenoids, and anthocyanins present in fruits and vegetables are receiving much attention because of their potential antioxidant activity. This study was conducted to determine antioxidant activity of leaves, peels, stem bark, and kernel of mango varieties langra and chonsa. Total phenolic (TPC) and total flavonoid contents (TFCs) in segments of langra ranged from 63.89 to 116.80 mg GAE/g DW and 45.56 to 90.89 mg CE/g DW, respectively, and that of chonsa were 69.24 to 122.60 mg GAE/g DW and 48.43 to 92.55 mg CE/g DW, respectively. The 2, 2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity and linoleic inhibition capacity in segments of langra ranged from 53.30% to 61.10% and 40.0% to 47.20%, respectively, whereas for chonsa; 56.40% to 66.0% and 48.1% to 49.0%, respectively. The reducing potentials of different segments of langra and chonsa at concentration of 10 mg/mL were 0.512 to 0.850 and 0.595 to 0.665 mV, respectively. Comparison between both varieties showed chonsa exhibited better antioxidant activity. Data were analyzed by analysis of variance (ANOVA) using completely randomised design (CRD) under factorial.     

Investigation of structural and electrochemical properties of Na2Mn2(SO4)3 cathode for sodium-ion batteries

Journal of Materials Science: Materials in Electronics - Electrode materials with the benefits of high working voltage, low cost, and environmental benign are needed for the realization of...     

Mesogenic structures and charge-transfer reactions in poly(ethylene oxide) complexes

After discussion of a schematic model for the crystalline phases of some poly(ethylene oxide) (PEO)-Na⁺ complexes with organic anions, three types of material are discussed. (i) Complexes of sodium salts of organic acids substituted by hydrophobic groups are deposited from methanol as macrodomains of uniaxially oriented material; these rearrange to microdomain morphologies on heating above ca. 60°C. Similar anions substituted by polar groups form spherulitic complexes or they rearrange thermotropically to microdomain structures. (ii) The preparation of polythiocyanogen by a ‘canal’ polymerisation within a PEO-NaSCN lattice is described and its extraction to give a material of conductivity ca. 10^?1S cm^?1 after doping is discussed, (iii) Charge-transfer reactions with PEO-NaI and tetracyano-quinodimethan (TCNQ) to give films having conductivities of 10^?3-10Ω^?1 cm^?1 are also described.     

Development of a lumbar EMG-based coactivation index for the assessment of complex dynamic tasks

The objective of this study was to develop and test an EMG-based coactivation index and compare it to a coactivation index defined by a biologically assisted lumbar spine model to differentiate between tasks. The purpose was to provide a universal approach to assess coactivation of a multi-muscle system when a computational model is not accessible. The EMG-based index developed utilised anthropometric-defined muscle characteristics driven by torso kinematics and EMG. Muscles were classified as agonists/antagonists based upon ‘simulated’ moments of the muscles relative to the total ‘simulated’ moment. Different tasks were used to test the range of the index including lifting, pushing and Valsalva. Results showed that the EMG-based index was comparable to the index defined by a biologically assisted model (r² = 0.78). Overall, the EMG-based index provides a universal, usable method to assess the neuromuscular effort associated with coactivation for complex dynamic tasks when the benefit of a biomechanical model is not available.

Practitioner Summary: A universal coactivation index for the lumbar spine was developed to assess complex dynamic tasks. This method was validated relative to a model-based index for use when a high-end computational model is not available. Its simplicity allows for fewer inputs and usability for assessment of task ergonomics and rehabilitation.     

Gonokendra model: a response to “information poverty” in rural areas of Bangladesh

We argue that there is a need to understand impacts of information and communication technologies (ICT) projects in their local context, considering the participants' perspectives at the micro (community) level. Hence, this paper reports on the development and refinement of an extended framework to investigate ICT impact on development in three village areas in the developing country, Bangladesh. Through an interpretive study, we argue that previous studies failed to encapsulate many aspects such as mobility restriction, negative attitude towards female group and religious influences of ICT impact, especially at the micro level. Our extended framework demonstrates that ICT projects can lead to development, but only when social constraints are addressed.     

Reconfigurable Compact Wideband Circularly Polarised Dielectric Resonator Antenna for Wireless Applications

In this work, a novel compact wideband reconfigurable circularly polarised (CP) dielectric resonator antenna (DRA) is presented. The L-shaped Dielectric resonator antenna is excited by an inverted question mark shaped feed. This arrangement of feed-line helps to generate two orthogonal modes inside the DR, which makes the design circularly polarised. A thin micro-strip line placed on the defected ground plane not only helps to generate a wideband response but also assist in the positioning of the two diode switches. These switches located at the left and right of the micro-strip line helps in performing two switching operations. The novel compact design offers the reconfigurability between 2.9–3.8 GHz which can be used for different important wireless applications. For the switching operation I, the achieved impedance bandwidth is 24% while axial ratio bandwidth (ARBW) is 42%. For this switching state, the design has 100% CP performance. Similarly, the switching operation II achieves 60% impedance bandwidth and 58.88% ARBW with 76.36% CP performance. The proposed design has a maximum measured gain of 3.4 dBi and 93% radiation efficiency. The proposed design is novel in terms of compactness and performance parameters. The prototype is fabricated for the performance analysis which shows that the simulated and measured results are in close agreement.     

CNR: A Cluster-Based Solution for Connectivity Restoration for Mobile WSNs

Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving the challenges associated with connectivity restoration in sensor networks. Since energy is a scarce resource in sensor networks, it becomes the focus of research, and researchers strive to propose new solutions that are energy efficient. The common issue that is well studied and considered is how to increase the network’s life span by solving the node failure problem and achieving efficient energy utilization. This paper introduces a Cluster-based Node Recovery (CNR) connectivity restoration mechanism based on the concept of clustering. Clustering is a well-known mechanism in sensor networks, and it is known for its energy-efficient operation and scalability. The proposed technique utilizes a distributed cluster-based approach to identify the failed nodes, while Cluster Heads (CHs) play a significant role in the restoration of connectivity. Extensive simulations were conducted to evaluate the performance of the proposed technique and compare it with the existing techniques. The simulation results show that the proposed technique efficiently addresses node failure and restores connectivity by moving fewer nodes than other existing connectivity restoration mechanisms. The proposed mechanism also yields an improved field coverage as well as a lesser number of packets exchanged as compared to existing state-of-the-art mechanisms.     

Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept. Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells. Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data. The proposed technique is validated on acquired 203 fluorescent microscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate (CFO@BTO) magnetoelectric nanoparticles in vitro. The developed approach achieved high prediction with accuracy of 97.5% and sensitivity of 100% and outperformed other approaches. The high performance reveals the effectiveness of the approach. It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung, brain tumor and breast cancer.     

Deep transfer learning based hepatitis B virus diagnosis using spectroscopic images

Accurate identification of Hepatitis B virus (HBV) disease by analyzing the Raman spectroscopic images is a challenge for pathologists. To save precious human lives, an efficient technique is required with higher diagnostic accuracy at early‐stage of HBV. We proposed a novel method of HBV diagnosis using deep neural networks with the concept of transfer learning and Raman spectroscopic images. The proposed approach developed by utilizing pretrained convolutional neural networks ResNet101 by employing transfer learning on a real dataset of HBV‐infected blood plasma samples. Dataset consists of 1000 Raman images in which 600 are HBV‐infected blood plasma samples, and 400 are healthy ones. The developed model is capable to detect minute variation between infected and healthy samples and achieved enhanced performance. The proposed approach has been assessed and attained high classification accuracy, sensitivity, specificity, and AUC of 99.7%, 100%, 99.25%, and 98.7%, respectively. The proposed TL‐ResNet101 model outperformed the conventional approaches such as PCA‐SVM and PCA‐LDA and demonstrated improved accuracy more than 7%. High performance indicates that the developed TL‐ResNet101 model has potential to use for HBV diagnosis. Moreover, the developed automated approach can be extended for other disease.