Technology

Via TechHive

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The iPhone 5 is the latest smartphone to hop on-board the LTE (Long Term Evolution) bandwagon, and for good reason: The mobile broadband standard is fast, flexible, and designed for the future. Yet LTE is still a young technology, full of growing pains. Here’s an overview of where it came from, where it is now, and where it might go from here.

The evolution of ‘Long Term Evolution’

LTE is a mobile broadband standard developed by the 3GPP (3rd Generation Partnership Project), a group that has developed all GSM standards since 1999. (Though GSM and CDMA—the network Verizon and Sprint use in the United States—were at one time close competitors, GSM has emerged as the dominant worldwide mobile standard.)

Cell networks began as analog, circuit-switched systems nearly identical in function to the public switched telephone network (PSTN), which placed a finite limit on calls regardless of how many people were speaking on a line at one time.

The second-generation, GPRS, added data (at dial-up modem speed). GPRS led to EDGE, and then 3G, which treated both voice and data as bits passing simultaneously over the same network (allowing you to surf the web and talk on the phone at the same time).

GSM-evolved 3G (which brought faster speeds) started with UMTS, and then accelerated into faster and faster variants of 3G, 3G+, and “4G” networks (HSPA, HSDPA, HSUPA, HSPA+, and DC-HSPA).

Until now, the term “evolution” meant that no new standard broke or failed to work with the older ones. GSM, GPRS, UMTS, and so on all work simultaneously over the same frequency bands: They’re intercompatible, which made it easier for carriers to roll them out without losing customers on older equipment. But these networks were being held back by compatibility.

That’s where LTE comes in. The “long term” part means: “Hey, it’s time to make a big, big change that will break things for the better.”

LTE needs its own space, man

LTE has “evolved” beyond 3G networks by incorporating new radio technology and adopting new spectrum. It allows much higher speeds than GSM-compatible standards through better encoding and wider channels. (It’s more “spectrally efficient,” in the jargon.)

LTE is more flexible than earlier GSM-evolved flavors, too: Where GSM’s 3G variants use 5 megahertz (MHz) channels, LTE can use a channel size from 1.4 MHz to 20 MHz; this lets it work in markets where spectrum is scarce and sliced into tiny pieces, or broadly when there are wide swaths of unused or reassigned frequencies. In short, the wider the channel—everything else being equal—the higher the throughput.

Speeds are also boosted through MIMO (multiple input, multiple output), just as in 802.11n Wi-Fi. Multiple antennas allow two separate benefits: better reception, and multiple data streams on the same spectrum.

LTE complications

Unfortunately, in practice, LTE implementation gets sticky: There are 33 potential bands for LTE, based on a carrier’s local regulatory domain. In contrast, GSM has just 14 bands, and only five of those are widely used. (In broad usage, a band is two sets of paired frequencies, one devoted to upstream traffic and the other committed to downstream. They can be a few MHz apart or hundreds of MHz apart.)

And while LTE allows voice, no standard has yet been agreed upon; different carriers could ultimately choose different approaches, leaving it to handset makers to build multiple methods into a single phone, though they’re trying to avoid that. In the meantime, in the U.S., Verizon and AT&T use the older CDMA and GSM networks for voice calls, and LTE for data.

LTE in the United States

Of the four major U.S. carriers, AT&T, Verizon, and Sprint have LTE networks, with T-Mobile set to start supporting LTE in the next year. But that doesn’t mean they’re set to play nice. We said earlier that current LTE frequencies are divided up into 33 spectrum bands: With the exception of AT&T and T-Mobile, which share frequencies in band 4, each of the major U.S. carriers has its own band. Verizon uses band 13; Sprint has spectrum in band 26; and AT&T holds band 17 in addition to some crossover in band 4.

In addition, smaller U.S. carriers, like C Spire, U.S. Cellular, and Clearwire, all have their own separate piece of the spectrum pie: C Spire and U.S. Cellular use band 12, while Clearwire uses band 41.

As such, for a manufacturer to support LTE networks in the United States alone, it would need to build a receiver that could tune into seven different LTE bands—let alone the various flavors of GSM-evolved or CDMA networks.

With the iPhone, Apple tried to cut through the current Gordian Knot by releasing two separate models, the A1428 and A1429, which cover a limited number of different frequencies depending on where they’re activated. (Apple has kindly released a list of countries that support its three iPhone 5 models.) Other companies have chosen to restrict devices to certain frequencies, or to make numerous models of the same phone.

Banded together

Other solutions are coming. Qualcomm made a regulatory filing in June regarding a seven-band LTE chip, which could be in shipping devices before the end of 2012 and could allow a future iPhone to be activated in different fashions. Within a year or so, we should see most-of-the-world phones, tablets, and other LTE mobile devices that work on the majority of large-scale LTE networks.

That will be just in time for the next big thing: LTE-Advanced, the true fulfillment of what was once called 4G networking, with rates that could hit 1 Gbps in the best possible cases of wide channels and short distances. By then, perhaps the chip, handset, and carrier worlds will have converged to make it all work neatly together.

Via PLOS genetics

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Abstract

Inter-individual variation in facial shape is one of the most noticeable phenotypes in humans, and it is clearly under genetic regulation; however, almost nothing is known about the genetic basis of normal human facial morphology. We therefore conducted a genome-wide association study for facial shape phenotypes in multiple discovery and replication cohorts, considering almost ten thousand individuals of European descent from several countries. Phenotyping of facial shape features was based on landmark data obtained from three-dimensional head magnetic resonance images (MRIs) and two-dimensional portrait images. We identified five independent genetic loci associated with different facial phenotypes, suggesting the involvement of five candidate genes—PRDM16, PAX3, TP63, C5orf50, and COL17A1—in the determination of the human face. Three of them have been implicated previously in vertebrate craniofacial development and disease, and the remaining two genes potentially represent novel players in the molecular networks governing facial development. Our finding at PAX3 influencing the position of the nasion replicates a recent GWAS of facial features. In addition to the reported GWA findings, we established links between common DNA variants previously associated with NSCL/P at 2p21, 8q24, 13q31, and 17q22 and normal facial-shape variations based on a candidate gene approach. Overall our study implies that DNA variants in genes essential for craniofacial development contribute with relatively small effect size to the spectrum of normal variation in human facial morphology. This observation has important consequences for future studies aiming to identify more genes involved in the human facial morphology, as well as for potential applications of DNA prediction of facial shape such as in future forensic applications.

Introduction

The morphogenesis and patterning of the face is one of the most complex events in mammalian embryogenesis. Signaling cascades initiated from both facial and neighboring tissues mediate transcriptional networks that act to direct fundamental cellular processes such as migration, proliferation, differentiation and controlled cell death. The complexity of human facial development is reflected in the high incidence of congenital craniofacial anomalies, and almost certainly underlies the vast spectrum of subtle variation that characterizes facial appearance in the human population.

Facial appearance has a strong genetic component; monozygotic (MZ) twins look more similar than dizygotic (DZ) twins or unrelated individuals. The heritability of craniofacial morphology is as high as 0.8 in twins and families [1], [2], [3]. Some craniofacial traits, such as facial height and position of the lower jaw, appear to be more heritable than others [1], [2], [3]. The general morphology of craniofacial bones is largely genetically determined and partly attributable to environmental factors [4]–[11]. Although genes have been mapped for various rare craniofacial syndromes largely inherited in Mendelian form [12], the genetic basis of normal variation in human facial shape is still poorly understood. An appreciation of the genetic basis of facial shape variation has far reaching implications for understanding the etiology of facial pathologies, the origin of major sensory organ systems, and even the evolution of vertebrates [13], [14]. In addition, it is feasible to speculate that once the majority of genetic determinants of facial morphology are understood, predicting facial appearance from DNA found at a crime scene will become useful as investigative tool in forensic case work [15]. Some externally visible human characteristics, such as eye color [16]–[18] and hair color [19], can already be inferred from a DNA sample with practically useful accuracies.

In a recent candidate gene study carried out in two independent European population samples, we investigated a potential association between risk alleles for non-syndromic cleft lip with or without cleft palate (NSCL/P) and nose width and facial width in the normal population [20]. Two NSCL/P associated single nucleotide polymorphisms (SNPs) showed association with different facial phenotypes in different populations. However, facial landmarks derived from 3-Dimensional (3D) magnetic resonance images (MRI) in one population and 2-Dimensional (2D) portrait images in the other population were not completely comparable, posing a challenge for combining phenotype data. In the present study, we focus on the MRI-based approach for capturing facial morphology since previous facial imaging studies by some of us have demonstrated that MRI-derived soft tissue landmarks represent a reliable data source [21], [22].

In geometric morphometrics, there are different ways to deal with the confounders of position and orientation of the landmark configurations, such as (1) superimposition [23], [24] that places the landmarks into a consensus reference frame; (2) deformation [25]–[27], where shape differences are described in terms of deformation fields of one object onto another; and (3) linear distances [28], [29], where Euclidean distances between landmarks instead of their coordinates are measured. Rationality and efficacy of these approaches have been reviewed and compared elsewhere [30]–[32]. We briefly compared these methods in the context of our genome-wide association study (GWAS) (see Methods section) and applied them when appropriate.

We extracted facial landmarks from 3D head MRI in 5,388 individuals of European origin from Netherlands, Australia, and Germany, and used partial Procrustes superimposition (PS) [24], [30], [33] to superimpose different sets of facial landmarks onto a consensus 3D Euclidean space. We derived 48 facial shape features from the superimposed landmarks and estimated their heritability in 79 MZ and 90 DZ Australian twin pairs. Subsequently, we conducted a series of GWAS separately for these facial shape dimensions, and attempted to replicate the identified associations in 568 Canadians of European (French) ancestry with similar 3D head MRI phenotypes and additionally sought supporting evidence in further 1,530 individuals from the UK and 2,337 from Australia for whom facial phenotypes were derived from 2D portrait images.

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The full article@PLOS genetics

Via Motherboard

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Wearable computing is all the rage this year as Google pulls back the curtain on their Glass technology, but some scientists want to take the idea a stage further. The emerging field of stretchable electronics is taking advantage of new polymers that allow you to not just wear your computer but actually become a part of the circuitry. By embedding the wiring into a stretchable polymer, these cutting edge devices resemble human skin more than they do circuit boards. And with a whole host of possible medical uses, that’s kind of the point.

A Cambridge, Massachusetts startup called MC10 is leading the way in stretchable electronics. So far, their products are fairly simple. There’s a patch that’s meant to be installed right on the skin like a temporary tattoo that can sense whether or not the user is hydrated as well as an inflatable balloon catheter that can measure the electronic signals of the user’s heartbeat to search for irregularities like arrythmias. Later this year, they’re launching a mysterious product with Reebok that’s expected to take advantage of the technology’s ability to detect not only heartbeat but also respiration, body temperature, blood oxygenation and so forth.

The joy of stretchable electronics is that the manufacturing process is not unlike that of regular electronics. Just like with a normal microchip, gold electrodes and wires are deposited on to thin silicone wafers, but they’re also embedded in the stretchable polymer substrate. When everything’s in place, the polymer substrate with embedded circuitry can be peeled off and later installed on a new surface. The components that can be added to stretchable surface include sensors, LEDs, transistors, wireless antennas and solar cells for power.

For now, the technology is still the nascent stages, but scientists have high hopes. In the future, you could wear a temporary tattoo that would monitor your vital signs, or doctors might install stretchable electronics on your organs to keep track of their behavior. Stretchable electronics could also be integrated into clothing or paired with a smartphone. Of course, if all else fails, it’ll probably make for some great children’s toys.

See@MC10