Bigwigs in a more than 600-year-old South American population were easy to spot. Their artificially elongated, teardrop-shaped heads screamed prestige, a new study finds.

During the 300 years before the Incas’ arrival in 1450, intentional head shaping among prominent members of the Collagua ethnic community in Peru increasingly centered on a stretched-out look, says bioarchaeologist Matthew Velasco of Cornell University. Having long, narrow noggins cemented bonds among members of a power elite — a unity that may have helped pave a relatively peaceful incorporation into the Incan Empire, Velasco proposes in the February Current Anthropology.
“Increasingly uniform head shapes may have encouraged a collective identity and political unity among Collagua elites,” Velasco says. These Collagua leaders may have negotiated ways to coexist with the encroaching Inca rather than fight them, he speculates. But the fate of the Collaguas and a neighboring population, the Cavanas, remains hazy. Those populations lived during a conflict-ridden time — after the collapse of two major Andean societies around 1100 (SN: 8/1/09, p. 16) and before the expansion of the Inca Empire starting in the 15th century.

For at least the past several thousand years, human groups in various parts of the world have intentionally modified skull shapes by wrapping infants’ heads with cloth or binding the head between two pieces of wood (SN: 4/29/17, p. 18). Researchers generally assume that this practice signified membership in ethnic or kin groups, or perhaps social rank.
The Callagua people lived in Colca Valley in southeastern Peru and raised alpaca for wool. By tracking Collagua skull shapes over 300 years, Velasco found that elongated skulls became increasingly linked to high social status. By the 1300s, for instance, Collagua women with deliberately distended heads suffered much less skull damage from physical attacks than other females did, he reports. Chemical analyses of bones indicates that long-headed women ate a particularly wide variety of foods.
Until now, knowledge of head-shaping practices in ancient Peru primarily came from Spanish accounts written in the 1500s. Those documents referred to tall, thin heads among Collaguas and wide, long heads among Cavanas, implying that a single shape had always characterized each group.

“Velasco has discovered that the practice of cranial modification was much more dynamic over time and across social [groups],” says bioarchaeologist Deborah Blom of the University of Vermont in Burlington.

Velasco examined 211 skulls of mummified humans interred in either of two Collagua cemeteries. Burial structures built against a cliff face were probably reserved for high-ranking individuals, whereas common burial grounds in several caves and under nearby rocky overhangs belonged to regular folk.
Radiocarbon analyses of 13 bone and sediment samples allowed Velasco to sort Collagua skulls into early and late pre-Inca groups. A total of 97 skulls, including all 76 found in common burial grounds, belonged to the early group, which dated to between 1150 and 1300. Among these skulls, 38 — or about 39 percent — had been intentionally modified. Head shapes included sharply and slightly elongated forms as well as skulls compressed into wide, squat configurations.

Of the 14 skulls with extreme elongation, 13 came from low-ranking individuals, a pattern that might suggest regular folk first adopted elongated head shapes. But with only 21 skulls from elites, the finding may underestimate the early frequency of elongated heads among the high-status crowd. Various local groups may have adopted their own styles of head modification at that time, Velasco suggests.

In contrast, among 114 skulls from elite burial sites in the late pre-Inca period, dating to between 1300 and 1450, 84 — or about 74 percent — displayed altered shapes. A large majority of those modified skulls — about 64 percent — were sharply elongated. Shortly before the Incas’ arrival, prominent Collaguas embraced an elongated style as their preferred head shape, Velasco says. No skeletal evidence has been found to determine whether low-ranking individuals also adopted elongated skulls as a signature look in the late pre-Inca period.

In courtrooms around the United States, computer programs give testimony that helps decide who gets locked up and who walks free.

These algorithms are criminal recidivism predictors, which use personal information about defendants — like family and employment history — to assess that person’s likelihood of committing future crimes. Judges factor those risk ratings into verdicts on everything from bail to sentencing to parole.

Computers get a say in these life-changing decisions because their crime forecasts are supposedly less biased and more accurate than human guesswork.
But investigations into algorithms’ treatment of different demographics have revealed how machines perpetuate human prejudices. Now there’s reason to doubt whether crime-prediction algorithms can even boast superhuman accuracy.

Computer scientist Julia Dressel recently analyzed the prognostic powers of a widely used recidivism predictor called COMPAS. This software determines whether a defendant will commit a crime within the next two years based on six defendant features — although what features COMPAS uses and how it weighs various data points is a trade secret.

Dressel, who conducted the study while at Dartmouth College, recruited 400 online volunteers, who were presumed to have little or no criminal justice expertise. The researchers split their volunteers into groups of 20, and had each group read descriptions of 50 defendants. Using such information as sex, age and criminal history, the volunteers predicted which defendants would reoffend.
A comparison of the volunteers’ answers with COMPAS’ predictions for the same 1,000 defendants found that both were about 65 percent accurate. “We were like, ‘Holy crap, that’s amazing,’” says study coauthor Hany Farid, a computer scientist at Dartmouth. “You have this commercial software that’s been used for years in courts around the country — how is it that we just asked a bunch of people online and [the results] are the same?”

There’s nothing inherently wrong with an algorithm that only performs as well as its human counterparts. But this finding, reported online January 17 in Science Advances, should be a wake-up call to law enforcement personnel who might have “a disproportionate confidence in these algorithms,” Farid says.

“Imagine you’re a judge, and I tell you I have this highly secretive, highly proprietary, expensive software built on big data, and it says the person standing in front of you is high risk” for reoffending, he says. “The judge would be like, ‘Yeah, that sounds quite serious.’ But now imagine if I tell you, ‘Twenty people online said this person is high risk.’ I imagine you’d weigh that information a little bit differently.” Maybe these predictions deserve the same amount of consideration.

Judges could get some better perspective on recidivism predictors’ performance if the Department of Justice or National Institute for Standards and Technology established a vetting process for new software, Farid says. Researchers could test computer programs against a large, diverse dataset of defendants and OK algorithms for courtroom use only if they get a passing grade for prediction.

Farid has his doubts that computers can show much improvement. He and Dressel built several simple and complex algorithms that used two to seven defendant features to predict recidivism. Like COMPAS, all their algorithms maxed out at about D-level accuracy. That makes Farid wonder whether trying to predict crime with anything approaching A+ accuracy is an exercise in futility.

“Maybe there will be huge breakthroughs in data analytics and machine learning over the next decade that [help us] do this with a high accuracy,” he says. But until then, humans may make better crime predictors than machines. After all, if a bunch of average Joe online recruits gave COMPAS a run for its money, criminal justice experts — like social workers, parole officers, judges or detectives — might just outperform the algorithm.

Even if computer programs aren’t used to predict recidivism, that doesn’t mean they can’t aid law enforcement, says Chelsea Barabas, a media researcher at MIT. Instead of creating algorithms that use historic crime data to predict who will reoffend, programmers could build algorithms that examine crime data to find trends that inform criminal justice research, Barabas and colleagues argue in a paper to be presented at the Conference on Fairness, Accountability and Transparency in New York City on February 23.

For instance, if a computer program studies crime statistics and discovers that certain features — like a person’s age or socioeconomic status — are highly related to repeated criminal activity, that could inspire new studies to see whether certain interventions, like therapy, help those at-risk groups. In this way, computer programs would do one better than just predict future crime. They could help prevent it.

Engineers are taking a counterintuitive approach to protecting future spacecraft: shooting at their experiments. The image above and high-speed video below capture a 2.8-millimeter aluminum bullet plowing through a test material for a space shield at 7 kilometers per second. The work is an effort to find structures that could stand up to the impact of space debris.

Earth is surrounded by a cloud of debris, both natural — such as micrometeorites and comet dust, which create meteor showers — and unnatural, including dead satellites and the cast-off detritus of space launches. Those pieces of flotsam can damage other spacecraft if they collide at high speeds, and bits smaller than about a centimeter are hard to track and avoid, says ESA materials engineer Benoit Bonvoisin in a statement.
To defend future spacecraft from taking a hit, Bonvoisin and colleagues are developing armor made from fiber metal laminates, or several thin metal layers bonded together. The laminates are arranged in multiple layers separated by 10 to 30 centimeters, a configuration called a Whipple shield.

In this experiment at the Fraunhofer Institute for High-Speed Dynamics in Germany, the first layer shatters the aluminum bullet into a cloud of smaller pieces, which the second layer is able to deflect. This configuration has been used for decades, but the materials are new. The next step is to test the shield in orbit with a small CubeSat, Bonvoisin says.

To craft a new color-switching material, scientists have again taken inspiration from one of nature’s masters of disguise: the chameleon.

Thin films made of heart cells and hydrogel change hues when the films shrink or stretch, much like chameleon skin. This material, described online March 28 in Science Robotics, could be used to test new medications or possibly to build camouflaging robots.

The material is made of a paper-thin hydrogel sheet engraved with nanocrystal patterns, topped with a layer of living heart muscle cells from rats. These cells contract and expand — just as they would inside an actual rat heart to make it beat — causing the underlying hydrogel to shrink and stretch too. That movement changes the way light bounces off the etched crystal, making the material reflect more blue light when it contracts and more red light when it’s relaxed.
This design is modeled after nanocrystals embedded in chameleon skin, which also reflect different colors of light when stretched (SN Online: 3/13/15).

When researchers treated the material with a drug normally used to boost heart rate, the films changed color more quickly — indicating the heart cells were pulsating more rapidly. That finding suggests the material could help drug developers monitor how heart cells react to new medications, says study coauthor Luoran Shang, a physicist at Southeast University in Nanjing, China. Or these kinds of films could also be used to make color-changing skins for soft robots, Shang says.

The center of the Milky Way may be abuzz with black holes. For the first time, a dozen small black holes have been spotted within the inner region of the galaxy in an area spanning just a few light-years — and there could be thousands more.

Astrophysicist Charles Hailey of Columbia University and his colleagues spotted the black holes thanks to the holes’ interactions with stars slowly spiraling inward, the team reports in Nature on April 4. Isolated black holes emit no light, but black holes stealing material from orbiting stars will heat that material until it emits X-rays.
In 12 years of telescope data from NASA’s orbiting Chandra X-ray Observatory, Hailey and colleagues found 12 objects emitting the right X-ray energy to be black holes with stellar companions. Based on theoretical predictions of how many black holes are paired with stars, there should be up to 20,000 invisible solo black holes just in that small part of the galaxy.
The discovery follows decades of astronomers searching for small black holes in the galactic center, where a supermassive black hole lives (SN: 3/4/17, p. 8). Theory predicted that the galaxy should contain millions or even 100 million black holes overall, with a glut of black holes piled up near the center (SN: 9/16/17, p. 7). But none had been found.
“It was always kind of a mystery,” Hailey says. “If there’s so many that are supposed to be jammed into the central parsec [about 3.26 light-years], why haven’t we seen any evidence?” Finding the 12 was “really hard,” he admits.

It’s unclear how the black holes got to the galaxy’s center. Gravity could have tugged them toward the supermassive black hole. Or a new theory from Columbia astronomer Aleksey Generozov suggests black holes could be born in a disk around the supermassive black hole.

The researchers ruled out other objects emitting X-rays, such as neutron stars and white dwarfs, but acknowledged that up to half of the sources they found could be fast-spinning stellar corpses called millisecond pulsars rather than black holes. That could add to the debate over whether a mysterious excess in gamma rays at the galactic center is from pulsars or dark matter (SN: 12/23/17, p. 12).

“The theorists are going to have to slug it out and figure out what’s going on,” Hailey says.

Every few years, a buzz fills the air in the southeastern United States as adolescent cicadas crawl out from the soil to molt and make babies. After a childhood spent sipping tree sap underground, some species emerge every 13 years, others every 17 years, rarely overlapping. Yet somehow in this giant cicada orgy, hybridization happens between species that should be out of sync.

Researchers have sought to explain how the two life cycle lengths developed. A new study published online April 19 in Communications Biology fails to pin the difference on genetics, but finds some interesting things along the way.
Cicadas fall into three species groups that diverged from one another about 3.9 million to 2.5 million years ago. Within each of those groups, species on a 13-year schedule diverged from 17-year-cycle cicadas about 200,000 to 100,000 years ago, the researchers from the United States and Japan report.

But the researchers also found that the 17-year and 13-year broods within each group share genetic code — evidence of hybridization. It’s possible that neighboring broods swapped DNA when their emergence overlapped — something that happens every 221 years — or if stragglers emerged early or late.

Everybody agrees that medical treatments should be based on sound evidence. Hardly anybody agrees on what sort of evidence counts as sound.

Sure, some people say the “gold standard” of medical evidence is the randomized controlled clinical trial. But such trials have their flaws, and translating their findings into sound real-world advice isn’t so straightforward. Besides, the best evidence rarely resides within any single study. Sound decisions come from considering the evidentiary database as a whole.
That’s why meta-analyses are also a popular candidate for best evidence. And in principle, meta-analyses make sense. By aggregating many studies and subjecting them to sophisticated statistical analysis, a meta-analysis can identify beneficial effects (or potential dangers) that escape detection in small studies. But those statistical techniques are justified only if all the studies done on the subject can be obtained and if they all use essential similar methods on sufficiently similar populations. Those criteria are seldom met. So it is usually not wise to accept a meta-analysis as the final word.

Still, meta-analysis is often a part of what some people consider to be the best way of evaluating medical evidence: the systematic review.

A systematic review entails using “a predetermined structured method to search, screen, select, appraise and summarize study findings to answer a narrowly focused research question,” physician and health care researcher Trisha Greenhalgh of the University of Oxford and colleagues write in a new paper. “Using an exhaustive search methodology, the reviewer extracts all possibly relevant primary studies, and then limits the dataset using explicit inclusion and exclusion criteria.”

Systematic reviews are highly focused; while hundreds or thousands of studies are initially identified, most are culled out so only a few are reviewed thoroughly with respect to the evidence they provide on a specific medical issue. The resulting published paper reaches a supposedly objective conclusion often from a quantitative analysis of the data.
Sounds good, right? And in fact, systematic reviews have gained a reputation as a superior form of medical evidence. In many quarters of medical practice and publishing, systematic reviews are considered the soundest evidence you can get.

But “systematic” is not synonymous with “high quality,” as Greenhalgh, Sally Thorne (University of British Columbia, Vancouver) and Kirsti Malterud (Uni Research Health, Bergen, Norway) point out in their paper, accepted for publication in the European Journal of Clinical Investigation. Sometimes systematic reviews are valuable, they acknowledge. “But sometimes, the term ‘systematic review’ allows a data aggregation to claim a more privileged position within the knowledge hierarchy than it actually deserves.”

Greenhalgh and colleagues question, for instance, why systematic reviews should be regarded as superior to “narrative” reviews. In a narrative review, an expert in the field surveys relevant publications and then interprets and critiques them. Such a review’s goal is to produce “an authoritative argument, based on informed wisdom,” Greenhalgh and colleagues write. Rather than just producing a paper that announces a specific conclusion, a narrative review reflects the choices and judgments by an expert about what research is worth considering and how to best interpret the body of evidence and apply it to a variety of medical issues and questions. Systematic reviews are like products recommended to you by Amazon’s computers; narrative reviews are birthday presents from friends who’ve known you long and well.

For some reason, though, an expert reviewer’s “informed wisdom” is considered an inferior source of reliable advice for medical practitioners, Greenhalgh and colleagues write. “Reviews crafted through the experience and judgment of experts are often viewed as untrustworthy (‘eminence-based’ is a pejorative term).”

Yet if you really want the best evidence, it might be a good idea to seek the counsel of people who know good evidence when they see it.

A systematic review might be fine for answering “a very specific question about how to treat a particular disease in a particular target group,” Greenhalgh and colleagues write. “But the doctor in the clinic, the nurse on the ward or the social worker in the community will encounter patients with a wide diversity of health states, cultural backgrounds, illnesses, sufferings and resources.” Real-life patients often have little in common with participants in research studies. A meaningful synthesis of evidence relevant to real life requires a reviewer to use “creativity and judgment” in assessing “a broad range of knowledge sources and strategies.”

Narrative reviews come in many versions. Some are systematic in their own way. But a key difference is that the standard systematic review focuses on process (search strategies, exclusion criteria, mathematical method) while narrative reviews emphasize thinking and interpretation. Ranking systematic reviews superior to narrative reviews “elevates the mechanistic processes of exhaustive search, wide exclusion and mathematical averaging over the thoughtful, in-depth, critically reflective processes of engagement with ideas,” Greenhalgh and collaborators assert.

Tabulating data and calculating confidence intervals are important skills, they agree. But the rigidity of the systematic review approach has its downsides. It omits the outliers, the diversity and variations in people and their diseases, diminishing the depth and nuance of medical knowledge. In some cases, a systematic review may be the right approach to a specific question. But “the absence of thoughtful, interpretive critical reflection can render such products hollow, misleading and potentially harmful,” Greenhalgh and colleagues contend.

And even when systematic reviews are useful for answering a particular question, they don’t serve many other important purposes — such as identifying new questions also in need of answers. A narrative review can provide not only guidance for current treatment but also advice on what research is needed to improve treatment in the future. Without the perspective provided by more wide-ranging narrative reviews, research funding may flow “into questions that are of limited importance, and which have often already been answered.”

Their point extends beyond the realm of medical evidence. There is value in knowledge, wisdom and especially judgment that is lost when process trumps substance. In many realms of science (and life in general), wisdom is often subordinated to following rules. Some rules, or course, are worthwhile guides to life (see Gibbs’ list, for example). But as the writing expert Robert Gunning once articulated nicely, rules are substitutes for thought.

In situations where thought is unnecessary, or needlessly time-consuming, obeying the rules is a useful strategy. But many other circumstances call for actual informed thinking and sound judgment. All too often in such cases the non-thinkers of the world rely instead on algorithms, usually designed to implement business models, with no respect for the judgments of informed and wise human experts.

In other words, bots are dolts. They are like a disease. Finding the right treatment will require gathering sound evidence. You probably won’t get it from a systematic review.

Genetic modifications to a plant that makes artemisinin, a key compound used in malaria drugs, more than tripled the amount of the ingredient naturally produced in leaves.

Previous attempts to genetically engineer Artemisia annua to increase the yield of artemisinin had failed. So Kexuan Tang, a plant scientist at Shanghai Jiao Tong University, and colleagues determined A. annua’s entire genetic instruction book and identified three genes crucial to artemisinin production. Genetic modifications to increase the activity of these genes boosted the artemisinin level in leaves from 0.1–1 percent of their dry weight to 3.2 percent, the researchers report April 24 in Molecular Plant.
Malaria kills about 440,000 people worldwide every year. The scientists hope to save lives by increasing and stabilizing the global supply of artemisinin, which has been in shortage due to unstable supply, Tang says. Seeds of these modified plants have been shipped to Madagascar, which grows the most A. annua in Africa, as part of a field trial.

“This is a milestone paper for artemisinin,” says Akhil Vaidya, an immunologist at Drexel University in Philadelphia who was not involved in the research. Artemisinin was discovered by Chinese chemist Youyou Tu in 1972, as she was investigating thousands of traditional Chinese remedies. The discovery, which has saved millions of lives, earned her the 2015 Nobel Prize in medicine (SN Online: 10/5/15).

Drug companies have used genetically modified yeast to produce semisynthetic artemisinin (SN: 5/4/13, p. 20), which is also effective against malaria. But artemisinin from plants is cheaper, Vaidya says. “Let the sun shine. Let the plants do their job,” he says.

Toastier nest temperatures, rather than sex chromosomes, turn baby turtles female. Now, a genetic explanation for how temperature determines turtles’ sex is emerging: Scientists have identified a temperature-responsive gene that sets turtle embryos on a path to being either male or female. When researchers dialed down that gene early in development, turtle embryos incubating at the cooler climes that would normally yield males turned out female instead, researchers report in the May 11 Science.

Scientists have struggled since the 1960s to explain how a temperature cue can flip the sex switch for turtles and other reptiles (SN Online: 1/8/18). That’s partly because gene-manipulating techniques that are well-established in mice don’t work in reptiles, says study coauthor Blanche Capel, a developmental biologist at Duke University School of Medicine. Previous studies showed certain genes, including one called Kdm6b, behaving differently in developing male and female turtles. But until recently, nobody had been able to tweak those genes to directly test which ones controlled sex.
“This is the first venture down that path,” says Clare Holleley, an evolutionary geneticist at the Australian National Wildlife Collection in Canberra who wasn’t part of the study. “It’s really quite a breakthrough.”

In the new study, Capel’s lab collaborated with a group of Chinese researchers led by Chutian Ge of Zhejiang Wanli University in Ningbo. Ge’s team recently developed a way to lessen the activity of particular reptilian genes by injecting viruses bearing snippets of artificial RNA into developing eggs.

The researchers used the technique to weaken the effects of the Kdm6b gene in the embryos of red-eared slider turtles (Trachemys scripta elegans) before the gonads formed, then tracked the embryos’ development at 26° Celsius.

“To my delight, it resulted in complete sex reversal,” Capel says. That temperature should have yielded all male turtles. Instead, in two separate experiments done with different gene-silencing viruses, 80 and 87 percent of the surviving embryos became female.
Still, something as complex as sex determination can’t be boiled down to a single gene. Kdm6b controls a gene called Dmrt1, which had already been shown to direct male development, Capel’s team also found. And while Kdm6b does behave differently as temperatures rise, it doesn’t show the same response in all tissues. That suggests that the gene doesn’t directly sense temperature, but is instead receiving messages from some higher-up gene that reacts directly to temperature and directs Kdm6b’s behavior in different tissues, the researchers propose.

Whether Kdm6b plays the same role in other reptiles remains to be seen. A 2017 study in Science Advances coauthored by Holleley found that the gene influenced bearded dragons’ sexual fate (SN Online: 6/14/17). But other genes in the same family, Jumonji genes, are also known to influence development in both reptiles and mammals. And those genes might not work exactly the same way in other reptiles.

“There’s this huge diversity of sex determining modes in reptiles,” Holleley says. Even if Kdm6b is an important switch in other reptiles, “the genes that Jumonji genes are activating are probably going to be different in every reptile.”

There are other wrinkles, too. “This is really exciting finding, but we need to remember that everything in a lab is controlled,” says Itzel Sifuentes-Romero of Florida Atlantic University in Boca Raton. Wild turtle eggs are subject to fluctuations in temperature and moisture as they incubate, she says, which means the signals that temperature-sensitive genes are receiving are far more muddled.

A new analysis of satellite images and seismic waves from North Korea’s nuclear test site support theories that the underground facility has at least partially collapsed.

Seismologists across the world have been tracking the clandestine nuclear weapons program for years by analyzing vibrations that emanate from explosions at the test site under Mount Mantap (SN: 8/5/17, p. 18). Now, researchers have paired 3-D satellite images of Mount Mantap with seismic tremor data to simulate how the mountain’s interior might have changed after a hydrogen bomb test on September 3, 2017.
The simulations indicate that the blast — which triggered an earthquake of estimated magnitude 6.3 — caused a cave-in directly above the detonation site, researchers report online May 10 in Science. The simulations also suggest that a second rock collapse, about 700 meters south of the detonation site, caused a smaller quake about eight minutes after the initial explosion.

These rock falls, which caused the face of Mount Mantap to sink about 0.5 meters, could have buried part or all of the underground test facility, rendering it unusable, says study coauthor Teng Wang, a remote sensing and geodesy researcher at the Earth Observatory of Singapore.

The results support another seismic analysis published online April 27 in Geophysical Research Letters suggesting an underground collapse at Mantap. That study led some media to speculate that North Korea didn’t pledge in April to halt nuclear testing because of international pressure, but because the test site could no longer be used.

Ultimately, experts would have to inspect the site to confirm that the Mount Mantap facility is out of order, says study coauthor Douglas Dreger, an earth and planetary scientist at the University of California, Berkeley. “It’s really hard to make that judgment call without having information at that site — getting boots on the ground and investigating it.”