Genomic organization and allelic polymorphism of the human killer cell inhibitory receptor gene KIR103

Killer cell inhibitory receptors (KIR) belong to the immunoglobulin superfamily of molecules and are expressed on natural killer (NK) cells. The KIRs of the p58/p50 family have two immunoglobulin domains and are ligands for HLA-Cw antigens, whereas the p70/p70 delta family has three immunoglobulin domains and comprises ligands for HLA-B antigens and possibly some HLA-A antigens. Members of a third KIR family, KIR103, have two immunoglobulin domains but have highest nucleotide sequence homology to the p70 family. The ligands for KIR103 on target cells are currently unknown. We here report the complete genomic organization of KIR103. It spans about 12 kb of DNA and consists of eight exons of which exon 1 and exon 2 encode the leader sequence. Exon 3 encodes the first immunoglobulin domain (gamma 1), and exon 4 encodes the main part of the second immunoglobulin domain (gamma 3), which also contains sequences contributed by exon 5 and exon 6. Exon 6 encodes the transmembrane domain, whereas exons 7 and 8 encode most of the cytoplasmic domain. KIR103 is polymorphic, and two alleles, 103AS and 103LP, are defined in this study. Additional full-length cDNA clones for KIR103 have been isolated and are shown to be formed by alternative mRNA splicing with exon skipping. Some of these truncated KIR103 cDNA could encode shorter transmembrane molecules, whereas others lack the transmembrane domain and are candidate genes for secreted KIR products. KIR103 is localized to the KIR genetic region on chromosome 19q13.4.     

Biopreservative technologies of food: an alternative to chemical preservation and recent developments

Food Science and Biotechnology - Despite centuries of developing strategies to prevent food-associated illnesses, food safety remains a significant concern, even with multiple technological...     

Test‐Generation‐Based Fault Detection in Analog VLSI Circuits Using Neural Networks

In this paper, we propose a novel test methodology for the detection of catastrophic and parametric faults present in analog very large scale integration circuits. An automatic test pattern generation algorithm is proposed to generate piece‐wise linear (PWL) stimulus using wavelets and a genetic algorithm. The PWL stimulus generated by the test algorithm is used as a test stimulus to the circuit under test. Faults are injected to the circuit under test and the wavelet coefficients obtained from the output response of the circuit. These coefficients are used to train the neural network for fault detection. The proposed method is validated with two IEEE benchmark circuits, namely, an operational amplifier and a state variable filter. This method gives 100% fault coverage for both catastrophic and parametric faults in these circuits.     

Rind of the rambutan, Nephelium lappaceum, a potential source of natural antioxidants

The rind of rambutan, which is normally discarded was found to contain extremely high antioxidant activity when assessed using several methods. Although having a yield of only 18%, the ethanolic rambutan rind extract had a total phenolic content of 762 ± 10 mg GAE/g extract, which is comparable to that of a commercial preparation of grape seed extract. Comparing the extract’s pro-oxidant capabilities with vitamin C, α-tocopherol, grape seed and green tea, the rind had the lowest pro-oxidant capacity. In addition, the extract at 100 μg/ml was seen to limit oxidant-induced cell death (DPPH at 50 μM) by apoptosis to an extent similar to that of grape seed. The extracts were not cytotoxic to normal mouse fibroblast cells or splenocytes while the powderised rind was seen to have heavy metals contents far below the permissible levels for nutraceuticals. Our study for the first time reveals the high phenolic content, low pro-oxidant capacity and strong antioxidant activity of the extract from rind of Nephelium lappaceum. This extract, either alone or in combination with other active principles, can be used in cosmetic, nutraceutical and pharmaceutical applications.     

Common Mode Voltage Reduction Using 3D-SVPWM for 3-level CI-NPC Inverter with Hybrid Energy System

This paper elucidates Common Mode Voltage (CMV) reduction in transformerless 3-phase 3-level Coupled Inductor Neutral Point (CI-NPC) Clamped Inverter with Hybrid Energy System. The three dimensional Space Vector Pulse Width Modulation (3D-SVPWM) with Nearest State Vector (NSV) is implemented to reduce the CMV by proper selection of medium, large and small vectors in 3D cubic space region. This NSV scheme in addition to CMV reduction, reduces the capacitor voltage balancing issues and minimizes switching losses. The proposed control provides full utilization of dc link voltage with reduced harmonics. This 3-level CI-NPC inverter is energized by hybrid energy source which includes photovoltaic system and wind energy system. The results obtained for the proposed scheme through simulation and experimental setup is compared with the conventional 2D-SVPWM and 3D-SVPWM scheme. From the compared results it is evident that the proposed scheme reduces CMV to a larger extent than 2D and 3D-SVPWM control. The simulation and experimental results are verified using matlab-simulink and FPGA-Spartan-6 controller respectively.     

Fractional weighted nuclear norm based two dimensional linear discriminant features for cucumber leaf disease recognition

Multimedia Tools and Applications - The quality and quantity of agricultural products are significantly affected by plant diseases. The plant diseases could be mitigated if identified at an early...     

Design of high-speed 10-Gb/s wired/FSO systems for local area communication networks for maximum reach

Photonic Network Communications - This paper presents the numerical simulation studies of upgraded system for single-mode fiber (SMF wired)/free space optics (FSO wireless) under high-speed...     

Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations

Optimization of cutting parameters is valuable in terms of providing high precision and efficient machining. Optimization of machining parameters for milling is an important step to minimize the machining time and cutting force, increase productivity and tool life and obtain better surface finish. In this work a mathematical model has been developed based on both the material behavior and the machine dynamics to determine cutting force for milling operations. The system used for optimization is based on powerful artificial intelligence called genetic algorithms (GA). The machining time is considered as the objective function and constraints are tool life, limits of feed rate, depth of cut, cutting speed, surface roughness, cutting force and amplitude of vibrations while maintaining a constant material removal rate. The result of the work shows how a complex optimization problem is handled by a genetic algorithm and converges very quickly. Experimental end milling tests have been performed on mild steel to measure surface roughness, cutting force using milling tool dynamometer and vibration using a FFT (fast Fourier transform) analyzer for the optimized cutting parameters in a Universal milling machine using an HSS cutter. From the estimated surface roughness value of 0.71 μm, the optimal cutting parameters that have given a maximum material removal rate of 6.0×10³ mm³/min with less amplitude of vibration at the work piece support 1.66 μm maximum displacement. The good agreement between the GA cutting forces and measured cutting forces clearly demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results indicate that the optimized parameters are capable of machining the work piece more efficiently with better surface finish.