“These objects are way more massive? than anyone expected,” said Joel Leja, assistant professor of astronomy and astrophysics at Penn State, who modeled light from these galaxies. “We expected only to find tiny, young, baby galaxies at this point in time, but we’ve discovered galaxies as mature as our own in what was previously understood to be the dawn of the universe.”

Using the first dataset released from NASA’s James Webb Space Telescope, the international team of scientists discovered objects as mature as the Milky Way when the universe was only 3% of its current age, about 500-700 million years after the Big Bang. The telescope is equipped with infrared-sensing instruments capable of detecting light that was emitted by the most ancient stars and galaxies. Essentially, the telescope allows scientists to see back in time roughly 13.5 billion years, near the beginning of the universe as we know it, Leja explained.

“This is our first glimpse back this far, so it’s important that we keep an open mind about what we are seeing,” Leja said. “While the data indicates they are likely galaxies, I think there is a real possibility that a few of these objects turn out to be obscured supermassive black holes. Regardless, the amount of mass we discovered means that the known mass in stars at this period of our universe is up to 100 times greater than we had previously thought. Even if we cut the sample in half, this is still an astounding change.”

In a paper published today (Feb. 22) in Nature, the researchers show evidence that the six galaxies are far more massive than anyone expected and call into question what scientists previously understood about galaxy formation at the very beginning of the universe.

“The revelation that massive galaxy formation began extremely early in the history of the universe upends what many of us had thought was settled science,” said Leja. “We’ve been informally calling these objects ‘universe breakers’ — and they have been living up to their name so far.”

Leja explained that the galaxies the team discovered are so massive that they are in tension with 99% percent of models for cosmology. Accounting for such a high amount of mass would require either altering the models for cosmology or revising the scientific understanding of galaxy formation in the early universe — that galaxies started as small clouds of stars and dust that gradually grew larger over time. Either scenario requires a fundamental shift in our understanding of how the universe came to be, he added.

“We looked into the very early universe for the first time and had no idea what we were going to find,” Leja said. “It turns out we found something so unexpected it actually creates problems for science. It calls the whole picture of early galaxy formation into question.”

On July 12, NASA released the first full-color images and spectroscopic data from the James Webb Space Telescope. The largest infrared telescope in space, Webb was designed to see the genesis of the cosmos, its high resolution allowing it to view objects too old, distant or faint for the Hubble Space Telescope.

“When we got the data, everyone just started diving in and these massive things popped out really fast,” Leja said. “We started doing the modeling and tried to figure out what they were, because they were so big and bright. My first thought was we had made a mistake and we would just find it and move on with our lives. But we have yet to find that mistake, despite a lot of trying.”

Leja explained that one way to confirm the team’s finding and alleviate any remaining concerns would be to take a spectrum image of the massive galaxies. That would provide the team data on the true distances, and also the gasses and other elements that made up the galaxies. The team could then use the data to model a clearer of picture of what the galaxies looked like, and how massive they truly were.

“A spectrum will immediately tell us whether or not these things are real,” Leja said. “It will show us how big they are, how far away they are. What’s funny is we have all these things we hope to learn from James Webb and this was nowhere near the top of the list. We’ve found something we never thought to ask the universe — and it happened way faster than I thought, but here we are.”

The other co-authors on the paper are Elijah Mathews and Bingjie Wang of Penn State, Ivo Labbe of the Swinburne University of Technology, Pieter van Dokkum of Yale University, Erica Nelson of the University of Colorado, Rachel Bezanson of the University of Pittsburgh, Katherine A. Suess of the University of California and Stanford University, Gabriel Brammer of the University of Copenhagen, Katherine Whitaker of the University of Massachusetts and the University of Copenhagen, and Mauro Stefanon of the Universitat de Valencia.

This magnetic sense, or ‘magnetoreception’, was first noticed in birds, and particularly in migratory songbirds. It has now been observed in many other species including mammals, fish and insects. However, the exact relationship between the magnetic field and the behaviour is difficult to pin down because it can be masked by other environmental factors. Experiments must be very carefully designed if their results are to be statistically sound.

“We aim to provide a balanced overview for researchers who wish to enter this exciting area of sensory biology,” explains Schneider. He and his co-authors outlined a range of methods that are used to deduce whether an animal’s behaviour is affected by a magnetic field. These include using GPS to mark animals’ alignment with the Earth’s field during normal activities, such as cows grazing; observing behaviour after tissues thought to be responsible for magnetoreception have been removed, or genes knocked out; and attaching small magnets on or near the animals’ bodies to disrupt the mechanism. Further work by animal physiologists, neuroscientists, geneticists and others will also be necessary to truly understand this phenomenon.

And this research is not only of academic interest. “Understanding animal magnetoreception will help us to protect animals released into unknown environments in the wild,” adds Lindecke.

The study, published in the American Journal of Obstetrics and Gynecology, comprises a total of more than 11,000 women who had given birth vaginally in Sweden, twice, in the years 1987-2000.

The researchers have previously described how the risk of accidental bowel leakage increases after obstetric anal sphincter injuries (OASIs) during childbirth. The present study focuses on the severity of these problems and their potential impact on women’s lives two decades later.

Three groups were studied: women who had no anal sphincter injuries, those who incurred the OASI during one of the births, and those with an OASI both times they gave birth. The data analysed came from the Swedish National Birth Register and replies to a questionnaire concerning the women’s symptoms of bowel leakage and the psychological impact and effect on social life of the incontinence after 20 years.

Problems increase with number of injuries

Of the women with two OASIs, 10.5 percent report leakage of liquid feces two times or more monthly; this is categorized in the study as high-frequency anal incontinence. When the women with low-frequency incontinence are included as well, the proportion is 34.9 percent. Of all the women with two injuries, 29.6 percent state that leakage affects their everyday life.

The results thereafter follow a descending scale. Of the women with one anal sphincter injury, 6.4 percent report high-frequency leakage and 21.7 percent leakage of both high and low frequency. For 19.7 percent of the latter, the injury affects their everyday life. Of the women with no OASI, 2.7 percent report high-frequency leakage and 10.8 percent either high- or low-frequency leakage, while 8.6 percent report that the incontinence impacts everyday life.

In terms of the severity of these problems, going from no injury to one injury is roughly equivalent to the step from one to two injuries. Thus, the problems are cumulative, and this is also reflected in the women’s subjective perception of how their everyday lives are affected by fecal incontinence.

Incontinence pads for fecal leakage are used by 2.3 percent of the women with no OASI, 7.1 percent of those with one OASI and 8.4 percent of those with two OASIs. The study found no influence of OASIs on other pelvic floor disorders or symptoms in the lower urinary tract.

Major impact on quality of life

Attitudes vary, too. In women with no OASI, fecal incontinence is found “bothersome” by 28.2 percent. The corresponding share in the group with one injury is 43.9 percent, against 46.0 percent among those with two injuries.

The first and corresponding author of the study is Ida Nilsson, a researcher affiliated with Sahlgrenska Academy, University of Gothenburg, in obstetrics and gynecology. She is also a resident obstetrician at Södra Älvsborg Hospital Women’s Clinic in Borås.

“An anal sphincter injury considerably raises the risk of accidental bowel leakage later in life. With repetition of the injury, the risk of persistent fecal incontinence is doubled. The degree of severity also rises, with a higher frequency of leakage occasions, more severe incontinence, and a greater impact on quality of life,” Nilsson states.

The study is reported online today in the journal PLOS ONE.

“If we can identify which people are naturally able to avoid infection by SARS-CoV-2, we may be able to learn — in addition to societal and behavioral factors — which genetic and environmental differences influence their defense against the virus,” says lead study author Karen (Kai-Wen) Yang, a biomedical engineering graduate student in the Translational Informatics Research and Innovation Lab at The Johns Hopkins University. “That insight could lead to new preventive measures and more highly targeted treatments.”

For its study, the research team set out to determine if a machine-learning statistical model could use health characteristics stored in electronic health records — providing patient data such as comorbidities (other medical conditions) and prescribed medications — as a means to pinpoint people with a natural ability to avoid SARS-CoV-2 infection. Those persons, says Yang, could then be studied to better understand the factors enabling their resistance.

A machine-learning model is a computer program or system that uses mathematical algorithms to find statistical patterns, and then apply the patterns moving forward. This gives such systems the ability to imitate human thinking and reasoning, and similar to the brain, learn over time.

“Using a machine-learning system to recognize complex patterns in large numbers of people with COVID-19 enabled another team of Johns Hopkins Medicine researchers in 2021 to predict the course of an individual patient’s case and determine the likelihood that it would become severe,” says co-senior study author Stuart Ray, M.D., vice chair of medicine for data integrity and analytics, and professor of medicine at the Johns Hopkins University School of Medicine. “Based on their success, our team wondered if the same approach also might be applied to predicting who could be exposed to SARS-CoV-2 in close quarters and still not get infected.”

To demonstrate the model’s ability to predict COVID-19 resistance, the researchers first acquired data from a clinical registry called the Johns Hopkins COVID-19 Precision Medicine Analytics Platform Registry (JH-CROWN). The registry contains information for patients seen within the Johns Hopkins Health System who have been suspected of, or confirmed as, having a SARS-CoV-2 infection.

For their resistance study, the researchers only included individuals who received a COVID-19 test between June 10, 2020, and Dec. 15, 2020, and who reported “potential exposure to the virus” as the reason for testing.

The ending date was the point at which large-scale COVID-19 vaccination efforts started in the United States. Choosing this date, the researchers say, enabled them to avoid the effects on their findings of vaccines preventing infection rather than natural resistance.

The 8,536 study participants who reported exposure as their reason for getting COVID tested were divided into two groups: those who did not share a residence (called a “household” in this study) with any COVID-19 patients or their residence had 10 or more patients; and those who shared a residence with 10 or fewer people, with at least one being a COVID-19 patient. The first group, with 8,476 of the participants, was designated as the Training and Testing Set, while the second group, called the Household Index (HHI) Set, had 60 members, and was used as a separate testing set.

Keeping the household number to 10 or fewer, the researchers say, excluded people living in apartment complexes, dormitories and other higher-density, multi-unit living areas where exposure to a particular person positive for SARS-CoV-2 would be less intense.

To identify patterns and cluster participants so that those naturally resistant to SARS-CoV-2 stand out, both study sets were analyzed using the Maximal-frequent All-confident pattern Selection Pattern-based Clustering (MASPC) algorithm. MASPC is specifically designed for electronic health record data analysis that combines patient demographic information (age, sex and race), the International Statistical Classification of Diseases and Related Health Problems (ICD) medical diagnostic codes relevant to each case, outpatient medication orders and the number of comorbidities (other diseases) present.

“We hypothesized that MASPC would enable us to cluster patients with similar patterns in their data to define them as resistant and non-resistant to SARS-CoV-2, and with the hope that the algorithm would learn with each analysis how to improve the accuracy and reliability of future assignments,” says Ray. “This initial study using JH-CROWN data was conducted to give life to that hypothesis, a proof-of-concept trial of our statistical model to show that resistance to COVID-19 might be predictable based a patient’s clinical and demographic profile.”

“In the Training and Testing Set, we identified 56 patterns of ICD codes split into two groups: associated with resistance or not associated,” Yang says. “Statistical analyses of how well these patterns differentiated between resistance and non-resistance yielded five patterns that did the best job for our small and localized [Baltimore-Washington, D.C., metroplex] study population to define who was most likely exposed to SARS-CoV-2.”

“Looking for these patterns in HHI Set — the individuals most likely to have been exposed to SARS-CoV-2 in close quarters — and then statistically analyzing the results, our model’s best performance was 0.61,” says Ray. “Since a score of 0.5 shows only chance association between the prediction and reality, and 1 is 100% association, this shows the model has promise as a tool for identifying people with COVID-19 resistance who can be further studied,” says Ray.

Limitations to the study, says Ray, include potential bias from self-reporting of COVID-19 exposure by participants, the small number of participants in the HHI group, the possibility that participants tested for SARS-CoV-2 using home kits or at facilities outside the Johns Hopkins system (and therefore, the tests were not recorded in the JH-CROWN database), and the short timeframe of the study itself. He adds that future trails using national patient data are needed to validate the model’s ability.

Along with Yang and Ray, the members of the study team from Johns Hopkins Medicine and Johns Hopkins University are graduate and undergraduate students Yijia Chen, Jacob Desman, Kevin Gorman, Chloé Paris, Ilia Rattsev, Tony Wei and Rebecca Yoo; and faculty co-senior authors Joseph Greenstein and Casey Overby Taylor.

The study authors report no conflicts of interest.