Tyler O'Neal, Staff Editor ASTRONOMY

Spanish researchers predict sea level changes along many coasts around the globe

Researchers at the Image Processing Laboratory (IPL) of the University of Valencia have developed a machine learning approach to model and predict short-term sea-level changes in the coastal regions of the Pacific, Indian and Atlantic oceans. The study, especially useful for coastal protection, has been published in an academic.

All ocean basins have experienced significant warming and sea-level rise in recent decades, driven by climate change. However, there are important regional differences, resulting from different processes on different time scales, such as those associated with temperature changes due to natural causes.

To better interpret observations of sea-level variations on coastal regions at a local level, the team of Verónica Nieves, Distinguished Researcher of the GenT Program at the Image Processing Laboratory (IPL) of the University of Valencia, has developed a machine learning approach that exploits sea temperature estimates to model coastal sea-level variability and associated uncertainty across a range of timescales ranging from months to several years. Nieves Radin 300x225 d37f5

The study now published in the journal also shows that the physical relationships between temperature variables in the upper layers of open sea regions and estimates of sea level anomalies on the coastal sites of these regions can be used in combination with machine learning methods to make reasonably accurate short-term predictions of the sea-level tendency (for one to several years).

They conclude that, to date, short-term regional coastal sea level variations are still largely influenced by natural processes in large open ocean regions, such as the open ocean, temperature changes down the water column to 700 meters, which are closely tied to internal natural climate variability. These processes are superimposed on the influence of other effects, like high tides or storms, among others.

“Climate is a highly complex and dynamical system that can change naturally in unexpected ways; and, in this sense, machine learning methods can provide useful insight to better interpret data that exhibits complex nonlinear patterns and identify near-future regional sea-level changes”, said Verónica Nieves, the article’s first author and head of the AI4OCEANS group, in the IPL, where this line of research is being pursued. “Our models perform particularly well in the coastal areas most influenced by internal climate variability, but they are widely applicable to evaluate the rising and falling sea level patterns across many places around the globe”, added Cristina Radín, a member of the team with which professor Gustau Camps-Valls has also collaborated.

This is the first study to use Artificial Intelligence techniques in the oceans to make this type of prediction. Modeling and anticipating sea-level changes in the coming years is crucial for near-term decision-making and strategic planning about coastal protection measures.

The team has also developed an interactive map, as a support tool that will allow inspecting individual regions where the machine learning model prediction was made.

TU Delft Forze Hydrogen Racing team moves infrastructure to Greenhouse Datacenters facility

Tyler O'Neal, Staff Editor ASTRONOMY

Forze Hydrogen Racing, the hydrogen-electric racing team comprised of students from Delft University of Technology (TU Delft), has moved its whole IT infrastructure to a co-located environment in Greenhouse Datacenters flagship data center in Naaldwijk, the Netherlands. Previously, the team had its IT infrastructure located at the university campus. Greenhouse provides Forze with a colocation offering that features an extremely energy efficient PUE figure of 1.15, matching Forze’s drive to make the world a better place through sustainability and better energy sources.
 
Forze Hydrogen Racing is the high-tech hydrogen racing team of students of the Delft University of Technology. Forze is one of only two hydrogen racing teams worldwide, participating in the ‘traditional’ gasoline-powered Dutch Supercar Challenge.
 
The Forze team consists of over sixty TU Delft students in changing composition, of which 25 each year take a gap year. The team has been working on developing hydrogen-powered racecars since 2007. Their goal is to promote a shift towards clean transportation in the future, with pure water as the only emission. Forze Hydrogen Racing
 
Greenhouse offers the team rack space in their DC2 facility in Naaldwijk, the Netherlands. Apart from the energy-efficient Power Usage Effectiveness figure of 1.15, Greenhouse DC2 offers ample international and regional connectivity options including carriers and IXs, cloud onramps, zero-latency links with the Internet hub in Amsterdam, 24/7 engineering support onsite, and more.
 
Server Cluster and NAS
 
The migration of Forze’s cluster of servers to the Greenhouse flagship colocation data center in Naaldwijk, the Netherlands, is now completed. Forze’s IT infrastructure includes the server cluster with immense computational power and also a comprehensive Network Attached Storage (NAS) platform with a large number of hard disk drives (HDDs).
 
“The new co-located data center environment in Greenhouse’s flagship colocation facility is a strong improvement with our previous situation. It’s not far away from the university campus in Delft actually, only a half-hour drive,” says Martijn Loonen, Chief Aerodynamics at Forze Hydrogen Racing. “Greenhouse has built an impressive greenfield data center facility in Naaldwijk, with extremely energy-efficient operations which seamlessly fits in with our own sustainability drives and goals. The previous cabinet layout at the TU Delft university campus was a bit different from our new situation, but the Greenhouse engineers were very helpful in adapting to our new situation.”
 
Aerodynamics - Racing Against Porsches and Lamborghini’s
 
The IT infrastructure being deployed and now migrated by Forze Hydrogen Racing to Greenhouse is mainly intended for the use of Computational Fluid Dynamics (CFD). For solving fluid flow issues and optimizing the race car’s aerodynamics - to continuously improve the speed and handling of their hydrogen-powered race car. The cluster of servers acts as a sort of supercomputer, allowing the team to repeatedly solve differential equations through different runs in order to make well-founded modifications to the race car’s bodywork.
 
Powered by these CFD-based modifications, Forze Hydrogen Racing expects to launch the latest version of its hydrogen-electric race car, Forze IX, this summer. After two years of thorough engineering work, as a successor to the Forze VIII race car, the team until now has managed to achieve the following specs: acceleration 0 – 100: < 3 seconds; top speed: 300km/h; fuel cell power: 240 kW (327 hp); maximum boost power: 600 kW (805 hp) and all-wheel drive. The new car weighs 1500kg, and has a size of 519x 190 cm.
 
“With its innovative designs, our team is able to show the potential of this clean energy solution for mobility,” says Maxime van Kekem, Marketing Manager of Forze Hydrogen Racing. “Our hydrogen-powered racing results until now are certainly impressive. We hope to achieve even better results with our new car, Forze IX. With this new car, we’re aiming at participating in the GT Class of the Dutch Supercar Challenge. In our newest endeavor, we’ll be up against the fastest cars including Porsches and Lamborghini’s.”
 
“To have our IT infrastructure now located in such a professional colocation environment really adds to our capabilities,” added Van Kekem. “The more efficient the data center environment set up, the faster we can execute the process of CFD-based modeling, simulation, and modification.”

Center for Precision Animal Modeling at UAB wins $9.3 million grant from NIH

Tyler O'Neal, Staff Editor ASTRONOMY

Precision disease modeling involves the creation of patient-specific disease models that mimic the molecular character of a condition present in a patient, enabling more precise diagnoses and treatments.

A new Center for Precision Animal Modeling, or C-PAM, has been created at the University of Alabama at Birmingham, supported by a five-year, $9.3 million grant from the National Institutes of Health's Office of Research Infrastructure Programs.

The UAB C-PAM is one of only three centers in the United States funded through a highly competitive NIH program to create national centers for "precision disease modeling." UAB submitted a 15-member team proposal led by Brad Yoder, Ph.D., chair of the UAB Department of Cell, Developmental and Integrative Biology, and Matt Might, Ph.D., a professor in the UAB Department of Medicine and director of the Hugh Kaul Precision Medicine Institute.  A new Center for Precision Animal Modeling has been created, supported by a five-year, $9.3 million grant from the National Institutes of Health's Office of Research Infrastructure Programs. Precision disease modeling involves creation of patient-specific disease models -- often using yeast, worms, fruit flies, zebrafish, frogs, mice or rats -- that mimic the molecular character of a condition present in a patient.{module INSIDE STORY}

Yoder and Might say the new center is a recognition of UAB's national reputation for leadership in both precision medicine and model organism research.

Precision disease modeling involves the creation of patient-specific disease models -- often using yeast, worms, fruit flies, zebrafish, frogs, mice, or rats -- that mimic the molecular character of a condition present in a patient. For example, if a patient has a disease caused by a sequence variant leading to the dysfunction of a gene, then C-PAM will create an animal model with this same variant. Studying the effect that the variant has in the model makes it possible to do science that is not possible in the human patient.

Computational capabilities at UAB -- including advanced data science and artificial intelligence -- will help predict possible treatments that can be tested in the models. Therapies that help treat the model would then become candidates for the treatment of the patient.

Biology and medicine have used animal models to help understand the disease for centuries, often by painstakingly breeding lines of animals that have, or are predisposed to have a specific disease. By taking advantage of recent advances in genetic engineering at UAB and elsewhere, C-PAM's transformative leap will make animal model research directly available to individual patients and their physicians. The use of such models can confirm tentative or novel diagnoses and aid the search for possible treatments, some of which could be unique to the patient.

Underlying the need for C-PAM is the recognition that every undiagnosed disease program at research universities, including UAB's Undiagnosed Diseases Program, faces a huge problem. While they have identified significant numbers of genomic variants, they still lack sufficient functional evidence for clinical reporting and clinical treatment. The C-PAM approach can reveal whether a genomic variant in a patient is causal of the disease, determine its significance for gene function and disease pathophysiology, and identify and evaluate therapeutic targets.

A distinguishing feature of the UAB C-PAM is the establishment of direct interfaces and services for physicians and patients, in addition to services for other scientists. C-PAM will allow a treating physician to request the creation of a customized model for a patient, and the physician can work with C-PAM to further understand the disease and potential treatment.

Operationally, C-PAM will have five interconnected components to bring cutting-edge, precision disease modeling to patients. They are the:

  • Coordinating Component, led by Yoder and Might;
  • Pre/Co-Clinical Component, led by Bruce Korf, M.D., Ph.D., Might and Andy Crouse, Ph.D.;
  • Bioinformatics Component, led by Brittany Lasseigne, Ph.D., and Elizabeth Worthey, Ph.D.;
  • Disease Modeling Unit, led by Yoder, Robert Kesterson, Ph.D., and Craig Powell, M.D., Ph.D.; and
  • Resource and Services Component, led by Deeann Wallis, Ph.D.

The Disease Modeling Unit will host cores that have expertise in each model: 1) frogs, led by Chinbei Chang, Ph.D.; 2) nematode worms, led by Yoder and Courtney Haycraft, Ph.D.; 3) zebrafish, led by John Parant, Ph.D., and Matthew Alexander, Ph.D.; 4) mice, led by Kesterson; and 5) rats, led by Laura Lambert, Ph.D. Powell will oversee efforts in behavioral phenotyping, while Jeremy Foote, Ph.D., will oversee anatomical phenotyping.

C-PAM, Yoder and Might say, will leverage UAB's existing expertise to create a national resource for efficient and cost-effective analysis of pathogenicity of gene variants identified in patients with rare disorders. Its informative models will help pursue disease mechanisms and targeted therapeutics.