Genetic mapping of the rice ionome in leaves and grain:Identification of QTLs for 17 elements including arsenic, cadmium, iron and selenium

Research into the composition of cereal grains is motivated by increased interest in food quality. Here multi-element analysis is conducted on leaves and grain of the Bala x Azucena rice mapping population grown in the field. Quantitative trait loci (QTLs) for the concentration of 17 elements were detected, revealing 36 QTLs for leaves and 41 for grains. Epistasis was detected for most elements. There was very little correlation between leaf and grain element concentrations. For selenium, lead, phosphorus and magnesium QTLs were detected in the same location for both tissues. In general, there were no major QTL clusters, suggesting separate regulation of each element. QTLs for grain iron, zinc, molybdenum and selenium are potential targets for marker assisted selection to improve seed nutritional quality. An epistatic interaction for grain arsenic also looks promising to decrease the concentration of this carcinogenic element. © Springer Science+Business Media B.V. 2009.

Ultra-Compact and Robust FPGA-Based PUF Identification Generator

Physically Unclonable Functions (PUFs), exploit inherent manufacturing variations and present a promising solution for hardware security. They can be used for key storage, authentication and ID generations. Low power cryptographic design is also very important for security applications. However, research to date on digital PUF designs, such as Arbiter PUFs and RO PUFs, is not very efficient. These PUF designs are difficult to implement on Field Programmable Gate Arrays (FPGAs) or consume many FPGA hardware resources. In previous work, a new and efficient PUF identification generator was presented for FPGA. The PUF identification generator is designed to fit in a single slice per response bit by using a 1-bit PUF identification generator cell formed as a hard-macro. In this work, we propose an ultra-compact PUF identification generator design. It is implemented on ten low-cost Xilinx Spartan-6 FPGA LX9 microboards. The resource utilization is only 2.23%, which, to the best of the authors' knowledge, is the most compact and robust FPGA-based PUF identification generator design reported to date. This PUF identification generator delivers a stable range of uniqueness of around 50% and good reliability between 85% and 100%.

Resonantly loaded apertures for high-resolution near field surface imaging

A novel type of microwave probes based on the loaded aperture geometry has been proposed and experimentally evaluated for dielectrics characterisation and high-resolution near-field imaging. Experimental results demonstrate the possibility of very accurate microwave spectroscopic characterisation of thin lossy dielectric samples and biological materials containing water. High-resolution images of the subwavelength lossy dielectric strips and wet and dry leaves have been obtained with amplitude contrast around 10-20 dB and spatial resolution better than one-tenth of a wavelength in the near-field zone. A microwave imaging scenario for the early-stage skin cancer identification based on the artificial dielectric model has also been explored. This model study shows that the typical resolution of an artificial malignant tumour with a characteristic size of one-tenth of a wavelength can be discriminated with at least 6 dB amplitude and 50° phase contrast from the artificial healthy skin and with more than 3 dB contrast from a benign lesion of the same size. It has also been demonstrated that the proposed device can efficiently deliver microwave energy to very small, subwavelength, focal areas which is highly sought in the microwave hyperthermia applications.