Gene discovery for improvement of kernel quality-related traits in maize

Developing maize plants with improved kernel quality traits involves the ability to use existing genetic variation and to identify and manipulate commercially important genes. This will open avenues for designing novel variation in grain composition and will provide the basis for the development of the next generation of specialty maize. This paper provides an overview of current knowledge on the identification and exploitation of genes affecting the composition, development, and structure of the maize kernel with particular emphasis on pathways relevant to endosperm growth and development, differentiation of starch-filled cells, and biosynthesis of starches, storage proteins, lipids, and carotenoids. The potential that the new technologies of cell and molecular biology will provide for the creation of new variation in the future are also indicated and discussed

Assessing the feasibility of augmenting fall detection systems by relying on UWB-based position tracking and a home robot

Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand

Grain quality-related traits in maize : gene identification and exploitation

Maize grain is a relevant source of food, feed, and industrial row materials. Developing plants with improved grain structure and quality traits involves the ability to use existing genetic variation and to identify and manipulate economically important genes. This will open new avenues for designing novel variation in kernel size, structures, and composition and will provide the basis for the development of the next generation of specialty maize. This paper provides an overview of current knowledge on the identification and exploitation of genes affecting the development, structure, and composition of the maize kernel with particular emphasis on pathways relevant to endosperm growth and development, and biosynthesis of storage proteins, starch, lipids, and carotenoids. The potential that the new technologies of cell and molecular biology will provide for the creation of new variation or novel compounds in the future are indicated and discussed

Evaluation of maize germplasm based on zein polymorphism from the archipelago of Madeira

Zein polypeptides are a group of proteins that accumulate in maize endosperm during seed development, representing more than 60% of the total endosperm proteins in the mature seeds. To evaluate genotype variability of Madeiran maize germplasm, a biochemical study was conducted based on the prolamins of maize, zeins, extracted from endosperm meal of 43 populations of Zea mays L. maintained in the Germplasm bank of Madeira University along with the inbreed W64A which was used as a polymorphic zein polypeptide standard profile. The zein polymorphism of these 44 maize populations were compared using two different electrophoresis techniques, SDS-PAGE in 15% discontinuous polyacrylamide gel and Acid-PAGE in 10% continuous polyacrylamide gel. SDS-PAGE allowed up to 16 polypeptides to be identified with apparent molecular mass ranging from 28-kDa to 10-kDa. Acid-PAGE allowed up to 20 zein fractions to be identified. The data was submitted to principal component analysis (PCA) and canonical discriminate and similarity analysis. The SDS-PAGE zein polymorphism allowed us to detect 6 groups, assembling all maize populations and explaining 55.32% of all variability. The similarity analysis of zein patterns obtained by Acid-PAGE showed that among regional maize germplasm, 22.5 % of all population seems to be related and have a common ancestor. The ISOP71 seems to be the population more closed to the common ancestor and appears related with the remaining maize populations, excluding the ISOP125.The obtained results and the importance of zein polymorphism in the evaluation of maize germplasm from Madeiran Archipelago are discussedinfo:eu-repo/semantics/publishedVersio

Live Wire - A Low-Complexity Body Channel Communication System for Landmark Identification

This paper presents a robust simplex Body Channel Communication (BCC) system aimed at providing an interactive infrastructure solution for visually impaired people. Compared to existing BCC solutions, it provides high versatility, weara- bility and installability in an environment in a low complexity hardware-software solution. It operates with a ground referred transmitter (TX) and it is based on an asynchronous thresh- old receiver (RX) architecture. Synchronization, demodulation and packetizing and threshold control are completely software defined and implemented using MicroPython. The RX includes Bluetooth® (BT) radio connectivity and a cell-phone application provides push text-to-speech notifications to a smartphone. The hardware achieves a Packet Error Rate (PER) of ∼0.1 at 550 kHz pulse center frequency, Synchronized-On Off Keying (S- OOK) modulation and 1 kbps data rate, for an average current consumption of 44mA