Most biochemistry labs that study DNA isolate it within a water-based solution that allows scientists to manipulate DNA without interacting with other molecules. They also tend to use heat to separate strands, heating the DNA to over 150 degrees Fahrenheit, a temperature a cell would never naturally reach. By contrast, in a living cell DNA lives in a very crowded environment, and special proteins attach to DNA to mechanically unwind the double helix and then pry it apart.

“The interior of the cell is super crowded with molecules, and most biochemistry experiments are super uncrowded,” said Northwestern professor John Marko. “You can think of extra molecules as billiard balls. They’re pounding against the DNA double helix and keeping it from opening.”

Marko, a professor of molecular biosciences as well as physics in Northwestern’s Weinberg College of Arts and Sciences, led the research along with Northwestern post-doctoral researcher Parth Desai. In Marko’s lab, for their experiments, he and Desai use microscopic magnetic tweezers to separate DNA and then carefully attach strands of it to surfaces on one end, and tiny magnetic particles on the other, then conduct high-tech imaging. The technology has been around for 25 years, and Marko was one of the first researchers theorizing about and then using it.

Marko and Desai wrote the paper that not only identifies but quantifies the amount of stress imposed by crowding, that will be published on June 17 in the Biophysical Journal.

Desai introduced three types of molecules to the solution holding DNA to mimic proteins and investigated interactions among glycerol, ethylene glycol and polyethylene glycol (each approximately the size of one DNA double helix, two or three nanometers).

“We wanted to have a wide variety of molecules where some cause dehydration, destabilizing DNA mechanically, and then others that stabilize DNA,” Desai said. “It’s not exactly analogous to things found in cells, but you could imagine that other competing proteins in cells will have a similar effect. If they’re competing for water, for instance, they would dehydrate DNA, and if they’re not competing for water, they would crowd the DNA and have this entropic effect.”

While fundamental, research like this has “been the basis for many, many, many medical advances,” Marko said, such as deep sequencing of DNA, where scientists can now sequence an entire human genome in under a day. He also thinks their findings may be broadly applicable to other elements of fundamental biochemical processes.

“If this affects DNA strand separation, all protein interactions with DNA are also going to be affected,” Marko said. “For example, the tendency for proteins to stick to specific sites on DNA and to control specific processes — this is also going to be altered by crowding.”

In addition to running more experiments that incorporate multiple crowding agents, the team hopes to move closer to a true representation of a cell, and from there, study how interactions between enzymes and DNA are impacted by crowding.

This work was supported by the National Institutes of Health (grant R01-GM105847) and by subcontract to the University of Massachusetts Center for 3D Structure and Physics of the Genome (under NIH grant UM1-HG011536).

Data centers house and use large computers to run massive amounts of data. Oftentimes, the processors can’t keep up with this workload because it’s taxing to predict and prepare instructions to carry out. This slows the flow of data. Thus, when you type a question into a search engine, the answer generates more slowly or doesn’t provide the information you need.

To remedy this issue, researchers at Texas A&M University developed a new technique called Skia in collaboration with Intel, AheadComputing, and Princeton to help computer processors better predict future instructions and improve computing performance.

The team includes Dr. Paul V. Gratz, a professor in the Department of Electrical and Computer Engineering, Dr. Daniel A. Jiménez, a professor in the Department of Computer Science and Engineering, and Chrysanthos Pepi, a graduate student in the Department of Electrical and Computer Engineering.

“Processing instructions has become a major bottleneck in modern processor design,” Gratz said. “We developed a new technique, Skia, to better predict what’s coming next and alleviate that bottleneck.”

A common problem for modern data center workloads is that the instruction stream — the steps a computer must take for processing — can be too large or difficult to process. Skia, a Greek word for shadow, can not only help better predict future instructions, but based upon that information, it can improve the throughput of instructions on the system. Throughput refers to units of completed processing per units of time.

“Think of throughput in terms of being a server in a restaurant,” Gratz said. “You have lots and lots of jobs to do. How many tasks can you complete, or how many instructions can you execute per unit time? You want high throughput, especially for computing.”

Improving throughput can lead to quicker performance and less power consumption for the data center.

“There are new bottlenecks in data center workloads associated with the instruction footprint and by fixing these, we can make the hardware better mapped and suited to that workload,” Gratz added. “If we make it up to 10% more efficient, a company previously needing to make 100 data centers around the country, now only needs to make 90, which is 10 less data centers. That’s pretty significant. These data centers cost millions of dollars, and they consume roughly the equivalent of the entire output of a power plant.”

In data centers, modern processors improve efficiency by predicting instructions and retrieving them before they’re needed, relying on a system known as Fetch Directed Instruction Prefetching (FDIP). FDIP uses Branch Prediction Unit to anticipate and fetch instructions.

However, as data center applications grow more complex, issues can occur when the Branch Target Buffer (BTB), which helps to monitor and track instructions, faults. This hinders FDIPS’s effectiveness, causing incorrect predictions and cache pollution. Many of these missed branches, termed “Shadow Branches,” exist in previously fetched cache lines but aren’t being used by the current instruction sequence and remain undecoded.

Skia identifies and decodes these shadow branches in unused bytes, storing them in a memory area called the Shadow Branch Buffer, which can be accessed alongside the BTB.

“What makes this technique interesting is that most of the future instructions were already available, and we demonstrate that Skia, with a minimal hardware budget, can make data centers more efficient, nearly twice the performance improvement versus adding the same amount of storage to the existing hardware as we observe,” Pepi said.

Their findings, “Skia: Exposing Shadow Branches,” were published in one of the leading computer architecture conferences, the ACM International Conference on Architectural Support for Programming Languages and Operating Systems. The team also traveled to the Netherlands to present their work to colleagues from around the globe.

Other collaborators on the project include David I. August, a professor in the Department of Computer Science from Princeton University, Krishnam Tibrewala, a graduate student in the Computer Science and Engineering Department at Texas A&M, Gilles Pokam, a senior principal engineer at Intel Corporation, and Bhargav Reddy Godala and Gino Chacon, senior central processing unit architects at AheadComputing.

Funding for this research is administered by the Texas A&M Engineering Experiment Station (TEES), the official research agency for Texas A&M Engineering.

A University of Washington-led team has developed a system, called ProxiCycle, that logs when a passing car comes too close to a cyclist (within four feet). A small, inexpensive sensor plugs into bicycle handlebars and tracks the passes, sending them to the rider’s phone. The team tested the system for two months with 15 cyclists in Seattle and found a significant correlation between the locations of close passes and other indicators of poor safety, such as collisions. Deployed at scale, the system could support mapping or navigating cyclists on safer bike routes through cities.

“Experienced cyclists have this mental map of which streets are safe and which are unsafe, and I wanted to find a simple way to pass that knowledge down to novice cyclists,” said lead author Joseph Breda, a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. “Cycling is really good for your health and for the environment. Getting more people biking more often is how we reap those rewards and increase safety in numbers for cyclists on the roads.”

The team presented its research Apr. 29 at the ACM CHI Conference on Human Factors in Computing Systems in Yokohama, Japan.

To start, researchers surveyed 389 people in Seattle. Respondents of all cycling experience levels ranked the threat of cars as the factor which most discouraged them from cycling, and said they’d be very likely to use a map that helps navigate for safety. But a key factor preventing this is limited data on road safety.

The team then built a small sensor system that plugs into a bike’s left handlebar. The system, which costs less than $25 to build, consists of a 3D printed plastic casing that houses a pair of sensors and a Bluetooth antenna. The antenna transmits data to the rider’s phone, where the team’s algorithm susses out what’s a passing car rather than a person, or another cyclist, or a tree.

The team validated the system both by testing it in a parking lot, with a car passing at different distances, and with seven cyclists riding through Seattle with GoPro cameras on their handlebars. Researchers watched the footage from these rides and compared this to the sensor output.

The team then recruited 15 cyclists through the newsletter of Seattle Neighborhood Greenways, a local advocacy group. Each got a ProxiCycle sensor, a custom Android application and instructions. The cyclists took 240 bike rides over two months and recorded 2,050 close passes. Researchers then compared the locations of close passes with riders’ perceived safety at different locations in the city — which they measured by showing cyclists images of locations and having them rate how safe they felt at those locations (referred to as “perceived safety”) — and with the locations of known automobile-to-bike collisions in the last five years.

The team found a significant correlation between close passes and both other indicators of cycling risk. They also found that this measure of close passes was a better indicator of actual safety than the surveyed perceived safety, which is the current standard used by policymakers to study safety when collisions aren’t enough.

In the future, researchers hope to scale the study up and potentially account for other risk factors, such as cyclists being hit by opening car doors, and deploy ProxiCylce in other cities. With enough data, existing bike mapping apps, such as Google Maps or Strava, might include safer route suggestions for cyclists.

Some of those routes involve only minor adjustments.

“One study participant, who was living down by Seattle Center, was biking down Mercer all the time,” Breda said. “It’s this busy, multi-lane road. But just before the study, they found out that there’s a great bike lane on a quieter street, just one block north.”

Keyu Chen, an applied science lead at Gridware, and Thomas Ploetz, a professor at Georgia Institute of Technology, are also co-authors on this paper. Shwetak Patel, a UW professor in the Allen School, is the senior author.

Influenza remains a major respiratory disease, which places a heavy burden on healthcare systems worldwide. Vaccination is the most efficient way to prevent and control influenza. Current seasonal influenza vaccines, however, protect primarily against three selected strains and require annual updates. Their efficacy can significantly decline if the circulating viruses do not match the strains chosen each year for each hemisphere. Moreover, these vaccines fail to guard against human infections caused by animal influenza viruses, such as avian strains, which pose a potential global pandemic threat. The World Health Organization (WHO) has underscored the urgent need for a new generation of universal influenza vaccines.

The research team developed two innovative approaches to create next-generation LAIVs. The first strategy involved inserting a human α-1,3-galactosyltransferase gene into the genome of a human influenza virus. This modification prompts infected host cells to express the α-Gal epitopes on their surface. Since humans naturally produce anti-α-Gal antibodies, this can allow preexisting antibody to recognise cells infected by the vaccine, thereby enhancing vaccine induced immune responses, including antibody-mediated cytotoxicity, opsonisation and phagocytosis. The research data showed that the vaccine is attenuated and is not pathogenic in mouse models. In experiments, vaccinated mice showed strong innate and adaptive immune responses, including antibody and T-cell responses. These immune responses conferred broad protection against various influenza A virus subtypes, including human H1N1 and H3N2, and avian H5N1 strains.

The second approach to developing next-generation LAIVs involved introducing hundreds of silent mutations to a human influenza virus, shifting its codon usage from that of a human influenza virus to that of an avian influenza virus-like pattern. This shift resulted in the attenuation of the virus in mammalian cells, making it safe for use as an LAIV. Additionally, the mutant virus replicated perfectly in chicken eggs, which is crucial for current effective vaccine manufacturing processes. With this approach, the viral protein expression of the LAIV remained identical to the original wildtype virus, providing a robust immune response against the viruses. The research team successfully generated several attenuated viruses with different human influenza virus backbones, including H1N1 and H3N2. The results of in vitro and in vivo experiments confirmed that these viruses were attenuated in mammalian hosts. They can thus be used as LAIVs to protect vaccinated mice from different subtypes of influenza A virus infection, including the human H1N1 and H3N2 viruses, as well as the avian H5N1 and H7N9 influenza viruses.

The development of these two award-winning LAIVs represents a significant advancement in the quest for broadly protective and efficient influenza vaccines. This new generation of LAIVs can both protect humans from seasonal influenza viruses and address the threat posed by emerging viruses, like avian influenza viruses. ‘The advantages of LAIVs lie in their intranasal administration, which has been shown to induce mucosal immune responses along the respiratory tract, providing additional protection against infection,’ highlighted Professor Leo Poon Lit-man, Chair Professor of Public Health Virology and Head of the Division of Public Health Laboratory Sciences, School of Public Health, HKUMed. ‘This needle-free delivery method alleviates the fear of vaccination, particularly in young children, so it will help mitigate vaccine hesitancy.’

These scientific breakthroughs represent a promising step towards a future in which influenza vaccines can offer comprehensive protection against a wide array of viral threats. Moving forward, the research team will leverage the international platform of the Hong Kong Jockey Club Global Health Institute (HKJCGHI) for further development, ensuring continued progress and making a global impact in this vital area.

‘Both HKUMed and the International Vaccine Institute (IVI), one of the collaborators of the HKJCGHI, have initiated discussions and contributed intellectual input towards the vaccine development’, remarked Professor Leo Poon Lit-man, who is also the Co-Director of HKJCGHI. ‘It is anticipated that in the near future, further studies, including research work adhering to Good Laboratory Practice (GLP) standards, will be conducted through the resources of the Institute.’

The research projects were led by Professor Leo Poon Lit-man, Daniel C K Yu Professor in Virology, Chair Professor of Public Health Virology and Head of the Division of Public Health Laboratory Sciences, School of Public Health, HKUMed; the Managing Director and Lead Scientist of the Centre for Immunology & Infection (C2i); the Co-Director of the Hong Kong Jockey Club Global Health Institute (HKJCGHI); and the Co-Director of the HKU-Pasteur Research Pole, HKUMed. Other members included Dr Alex Chin Wing-hong, School of Public Health, HKUMed; and the Centre for Immunology & Infection (C2i).