Sandy’s Effects on NYC Predicted by Two Recent Studies

E. B. White wrote, “It is a miracle that New York works at all. By rights New York should have destroyed itself long ago, from panic or fire or rioting or failure of some vital supply line in its circulatory system.” On Monday, Hurricane Sandy managed to cut off many of New York’s supply lines in ways they’ve never been tested before. The city lost power, water, and lives. But it was not only White’s fears, but also the predictions of scientists that were realized. Two separate papers, published earlier this year and last, predicted what would happen to New York City if it were struck by a severe storm.

In 2011, a state agency assembled a massive report on climate change in New York. In it, Klaus H. Jacob, a climate scientist at Columbia University’s Lamont-Doherty Earth Observatory, conducted a case study (PDF) on the impact of a 100-year flood on New York City’s transportation system. A 100-year flood is a flood whose severity, on average, is seen only once every hundred years (or has a 1 percent chance of occurring in any given year), which the study equates with a category 1 to 2 hurricane. Jacob looked at three scenarios: a 100-year flood alone, one combined with a 2-foot sea level rise, and another with a 4-foot sea level rise as a result of climate change.

Virtually all the subway lines in lower Manhattan and the tunnels under the East River would be flooded under the worst-case scenario, which indeed closely mimicked reality. (Jacob, 2011)

Jacob identified the areas that would be flooded under each scenario. Using a base flood elevation map of the city as well as known elevations of transportation structures, he found that low-lying streets, subways, and tunnels in the Battery, Jamaica Bay, the Rockaways and other neighborhoods near the city’s shoreline would be particularly vulnerable to flooding. Indeed, those areas were among those that suffered most from Hurricane Sandy. In fact, a record 14 feet, or 4.25 meters, of water swept over the Battery on Monday, matching the case study’s worst-case scenario. Jacob also predicted that the total economic and physical damages for NYC would be $58 billion, $70 billion, and $84 billion in order of worsening scenario. One current estimate stands at $20 billion in losses for the entire Northeast and Mid-Atlantic due to Sandy, so Jacob’s estimates seem to have overshot it. One thing is for sure though. Investing in infrastructure that protect the city from future storms can save money in the long run. As Jacob told New York Magazine, “For every dollar that you spend today, you probably save $4 of not incurred costs later.”

Just months after Jacob’s case study, Ning Lin, a climate scientist at MIT, and colleagues used computer models to predict the impact of a hurricane on New York City. Published in Nature Climate Change in February this year, the study used four climate models to simulate 10,000 synthetic storms, half under the current climate and half under projected warming conditions. The researchers programmed the storm to be within a 200-km radius from the Battery and to gust at wind speeds greater than 20 m/s, or 45 mph (Hurricane Sandy exceeded the models, with 60 mph recorded at Central Park). They found that in the worst-case scenarios, a hurricane would cause a storm surge as high as 4.57 m to 4.75 m at the Battery, which came fairly close to the 4.25 m caused by Sandy.

Two worst-case scenarios estimate surge height at the Battery to range from 4.75 m (left) to 4.57 m (right), coming fairly close to the 4.25 m record surge caused by Hurricane Sandy. (Lin, et al. 2012)

The researchers also found that climate change will only increase the risk of storm surges for the city. Based on historical data on NY-region storms, they predicted that a 1 m rise in sea level in the future will increase the likelihood of a 100-year surge flood occurring as frequently as every 3-20 years and a 500-year flood every 25-240 years by the end of the century. Of course, predicting something as unpredictable as a hurricane is extremely difficult. But the fact that the climate models closely mirrored Hurricane Sandy makes the need to prepare for future severe weather all the more urgent.


The Worm’s Altruistic Suicide

Caenorhabditis elegans, a millimeter-long nematode or roundworm, has been poked and prodded, dissected and inspected. Every cell in its body has been mapped, the circuitry of its neurons traced, and its entire genome sequenced. For the past 50 years, it has been the experimental animal of choice, the subject of over 15,000 articles on everything from genetics to drug development. Biologically speaking, we know more about this animal than any other in the world—including ourselves.

But for all that we know about C. elegans, one aspect remains a mystery. In an abnormal birth process called matricide, the offspring eat and kill their mother. Researchers have shown that this unusual phenomenon may in fact be an evolutionary adaptation. By committing suicide for the sake of her young, the mother provides them the opportunity to become dauers, larvae that are incredibly stress-resistant.

The C. elegans‘ transparent body contains exactly 959 cells.
From J.E. Sulston and H.R. Horvitz, Dev. Biol. 56:110-156, 1977.

Sydney Brenner, a biologist at Oxford, first saw the nematode’s potential in the 1960’s. C. elegans, he realized, is the ideal multicellular organism to study in the lab—simple yet possessing the basic tissues common to all animals. Almost all C. elegans are hermaphrodites, essentially female bodies capable of producing and self-fertilizing with their own sperm. About four days after birth, the worm reaches maturity and self-breeds, laying up to three hundred eggs, which hatch outside its body. In a defective worm that is unable to form a vulva or opening necessary to expel the eggs from its body, the eggs hatch inside. As biologists Diana McCulloch and David Gems from the University College London described, “eggs eventually hatch within the uterus, and the emerging larvae devour the mother.”

Such a worm is often called a “bag of worms,” because under the microscope it looks like a bloated worm. (Watch the “bag of worms” in action here.) The eggs hatch inside and, with nowhere else to go, the offspring writhe frantically about and eat their mother’s insides until they pierce their way out of her body. Once they escape, many of the larvae who have inherited the genes involved in matricide will encounter the same fate as their mother.

In the underbelly of the worm, a special cell called the anchor cell signals three precursor cells to form the vulva in preparation for breeding. In a worm carrying the genetic mutation, however, the anchor cell breaks down in relaying its messages to the precursor cells. The worm is unable to form a vulva and is then fated to become a bag of worms.

While matricide has often been cast as a defect, emerging research has shed light on its evolutionary value. In 2003, Jianjun Chen and Edward P. Caswell-Chen, scientists in the department of nematology at the University of California, Davis, found that far from being a rare phenomenon of the laboratory, matricide occurs in nematodes living in the wild as well.

In the lab or the wild, severe stress can cause matricide, even in worms that do not carry the mutation. Starve a pregnant C. elegans, expose it to toxic substances, or transfer it from a solid to a liquid environment, and it is likely to develop into a bag of worms. In their experiment, the researchers starved batches of C. elegans and watched their response under the microscope for several hours. They found that when starved, the mother “sacrifices its body” to provide nutrition to its offspring. Interestingly, they also discovered that matricide is reversible: feeding a starving mother allows it to lay its eggs normally, assuming offspring that already hatched inside did not cause too much damage already.

Most importantly, the researchers found that in matricide, the mother provides her offspring with a mechanism for coping with stress through the dauer stage, a larval stage that is a kind of emergency survival mode. When the mother’s body has been consumed and food is still not available, the larva can enter developmental arrest, reducing its metabolism and increasing its capacity to withstand stress. Compared to the typical two- to three-week lifespan of C. elegans, dauer larvae can survive months without food. In particular, the researchers found that the longer they starved the mother, the fewer the offspring that survived (due to competition for resources), but a higher proportion of those survivors were able to reach the dauer stage.

Leaving behind even a single dauer is an evolutionary fitness advantage for C. elegans. By sacrificing herself, the mother is able to ensure that her young live on. In the survival of the fittest, dog eat dog does not always win the game. Sometimes, altruism goes a long way—even if it means being the one that gets eaten.

Images of Architecture, Architecture of Images: Computer Reveals Patterns in Cities

In his famous 1903 essay “The Metropolis and Mental Life,” sociologist Georg Simmel wrote that “with each crossing of the street” the city bombards its inhabitants with “rapid crowding of changing images, the sharp discontinuity in the grasp of a single glance, and the unexpectedness of onrushing impressions.” Simmel’s notion of the city as a chaotic jumble of images resounds to this day. But a new study suggests that if one looks closely enough, architectural features emerge that contribute to a city’s distinctive “look and feel.”

Combining computer science and urban architecture, a team of researchers at Carnegie Mellon University and INRIA/Ecole Normale Supérieure looked to Google to unlock patterns in visually complex places like cities. They developed an algorithm that can automatically detect and analyze Google Street View images of twelve cities around the world. They found that these images revealed stylistic architectural elements unique to those cities, such as Paris’ traditional street signs, balustrade windows, and balcony support as distinct from London’s neoclassical columns, Victorian windows, and cast-iron railings.

At left, Paris’ traditional street signs, balustrade windows, and balcony support are distinct from London’s neoclassical columns, Victorian windows, and cast-iron railings, at right. [Courtesy of

This study is one of early attempts to apply data mining to images. Data mining uses computer science and statistics to make sense of large amounts of data (for example, economists track the rise and fall of the stock market and epidemiologists analyze patient charts). Indeed, Simmel discerned early on the need to confront the flood of information we receive every day. The digital age has given rise to more data than we can keep up with. Images like photos and videos, which comprise almost 90 percent of web traffic (thanks to Facebook, Flickr, Youtube and imgur), remain largely untapped. Recognizing the potential opened by Google Street View to study cities, the researchers ventured into a particular brand of visual data mining that they’ve called “computational geo-cultural modeling.” The paper, entitled “What Makes Paris Look Like Paris?” and presented at the SIGGRAPH International Conference on Computer Graphics and Interactive Techniques on August 9, uses Paris as its primary example.

Mining through the virtual landscape of twelve cities for key Parisian elements is like “finding a few needles in a haystack,” the researchers wrote. They collected tens of thousands of Google Street View images and further divided each of those into 25,000 square patches. The researchers wanted to hunt down objects that were both frequent (occur often in Paris) and discriminative (are found only in Paris). For example, trees and cars appear everywhere in Paris, but so do in other cities; the Eiffel Tower is unique to Paris, but since there’s just one, it can tell us little about the city as a whole.

Programmed to recognize objects in images (similar to face detection in cameras and Facebook), the algorithm randomly sampled the patches of images to identify matches, starting with nearby neighbors (images from inside as opposed to outside of Paris). Through repeated sorting, the algorithm was able to build clusters of similar patches by filtering out uninteresting images like sidewalks or the sky found in all cities until elements common but unique to Paris remain.

Visual data mining analyzes tens of thousands of Google Street View images to seek out patterns unique to Paris, above. [Courtesy of

The researchers found that it is not landmarks like the Eiffel Tower and Arc de Triomphe, but ordinary street signs, window railings, balcony supports, lampposts, and doors that best characterize the city. The traditional blue/green/white signs and special style of lampposts that mark Parisian streets are difficult to find anywhere else. “The visual minutiae of daily urban life,” as the researchers put it, that often escape us may actually define our surroundings.

The program can also spot even subtler patterns. For instance, balcony railings are commonly found in main boulevards, while window railings dot the smaller side streets of Paris. And while arch-supporting columns have made Place des Vosges famous, they also appear in the more recent Marché Saint-Germain.

Similarities across cities also emerged, possibly indicating cross-cultural exchange. In the five European cities studied (Paris, Barcelona, Milan, London, and Prague), double arches are found everywhere except for London. Cast-iron balcony railings also appear frequently in Paris, Barcelona, and Milan, while railings in London and Prague are made of stone.

The algorithm had greater trouble characterizing American cities, mostly coming up with cars and road tunnels. The researchers suspect this failure is due to the “melting pot” of architectural styles and the dominance of cars in America.

This new tool may prove particularly useful for computer graphic modelers for films like Pixar’s Ratatouille, which needed to recreate Paris in an animated format. Beyond cities, this technology can potentially seek out patterns in nature, like fields and rivers, and even in home products, like cars and electronics, said the researchers. Can this tool also tap into Google Sky or Google Art Project to uncover patterns in the constellations or in paintings? The possibilities seem enticing.

So is the city, like Simmel once claimed, an onslaught of senseless images? Visual data miners say no. With further advances in this technology, we may perceive and understand our surroundings in a new light.