The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
Yin-yang haplotypes arise when a stretch of DNA evolves to present two divergent forms. A group of engineers at Washington University in St. Louis showed a massive yin-yang haplotype pair encompassing the gene gephyrin on human chromosome 14. This image shows the states for markers in the region for 934 individuals in eight global populations. Dark blue and red horizontal lines in the yin-yang region represent carriers of two yin and two yang haplotypes, respectively, and light blue represents carriers of both haplotypes.
Computer scientists at Washington University in St. Louis’ School of Engineering & Applied Science analyzed a huge amount of data regarding an important protein and discovered its connection in human history as well as clues about its role in complex neurological diseases.
Through a novel method of analyzing these big data, Sharlee Climer, PhD, research assistant professor in computer science, and Weixiong Zhang, PhD, professor of computer science and of genetics at the School of Medicine, discovered a region encompassing the gephyrin gene on chromosome 14 that underwent rapid evolution after splitting in two completely opposite directions thousands of years ago. Those opposite directions, known as yin and yang, are still strongly evident across different populations of people around the world today.
The results of their research, done with Alan Templeton, PhD, the Charles Rebstock professor emeritus in the Department of Biology in the College of Arts & Sciences, appear in the March 27 issue of Nature Communications.
The gephyrin protein is a master regulator of receptors in the brain that transmit messages. Malfunction of the protein has been associated with epilepsy, Alzheimer’s disease, schizophrenia and other neurological diseases. Additionally, without gephyrin, our bodies are unable to synthesize an essential trace nutrient.
The research team used big data from the International HapMap Project, a public resource of genetic data from populations worldwide designed help researchers find genes associated with human disease, as well as from the 1000 Genomes project, another public data source of sequenced human genomes. In total, they looked at the genetic data from 3,438 individuals.
When they analyzed the data, they made an interesting discovery in a sequence of markers, called a haplotype, enveloping the gephyrin gene: up to 80 percent of the haplotypes were perfect yin and yang types, or complete opposites of the other. They were able to trace the split back to what is known as the Ancestral haplotype, or that of the most recent common human ancestor.
“We observed that the Ancestral haplotype split into two distinct haplotypes and subsequently underwent rapid evolution, as each haplotype possesses about 140 markers that are different from the Ancestral haplotype,” Climer said. “These numerous mutations should have produced a large number of intermediate haplotypes, but the intermediates have almost entirely disappeared, and the divergent yin and yang haplotypes are prevalent in populations representing every major human ancestry.”
Using the data from the HapMap Project, they looked at the gephyrin region in several populations of people, including European, East and South Asian, and African heritage, and found variations in the haplotype frequencies of each of these populations. Those from African origin generally have more yang haplotypes, while those of European origin have more yin haplotypes. Those of Asian descent have nearly equal numbers of yin and yang haplotypes.
Humans carry pairs of chromosomes, and 30 percent of Japanese individuals carry two yin haplotypes or two yang haplotypes. Another 30 percent of these individuals possess both a yin and a yang haplotype, reflecting the roughly equal probability of inheriting either one.
To find this pattern within the huge datasets, the research team used a novel method to assess correlations between genetic markers called single nucleotide polymorphisms, or SNPs, which are variations in a DNA sequence that make humans different from each other.
The team’s method, called BlocBuster, computes correlations between each pair of SNPs, then builds a network of those correlations. By observing the network, researchers can see clusters of correlated markers.
“For example, you could build a Facebook network using all of your Facebook friends,” Climer said. “If two of your friends are friends with each other, you would connect them in the network. If you see that a cluster of people is interconnected with each other, they probably share something in common, such as a family relationship, a school, or some type of social interaction. Similarly, with an efficient algorithm and an adequate number of processors and time, we can look at every pair of SNPs, build these networks and observe clusters of interconnected SNPs.
“The BlocBuster approach is a paradigm shift from the conventional methods for genome-wide association studies, or popularly known as GWAS, where one or a few markers were examined at a time,” Zhang said. “It is truly a data mining technique for big data like those from HapMap and 1000 Genomes projects.”
The researchers also can design this approach to look at complex traits and diseases.
“BlocBuster is able to detect combinations of networked genetic markers that are characteristic of complex traits,” Zhang said. “It is suitable for analyzing traits, such as body weights, which are determined by multiple genetic factors, and genetic patterns in populations, such as the yin-yang haplotypes we discovered.”
Ultimately, they expect this method will shed light on the genetic roots of disease.
“Most complex diseases arise due to a group of genetic variations interacting together,” Climer said. “Different groups of people who get a disease may be affected by different groups of variations. There’s not enough power to see most of these intricate associations when looking at single markers one at a time. We’re taking a combinatorial approach — looking at combinations of markers together — and we’re able to see the patterns.”
Source: Washington University in St. Louis.
By Beth Miller
© Copyright 2019 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.