It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Wednesday, December 13, 2023
Cells move in groups differently than they do when alone
NYU LANGONE HEALTH / NYU GROSSMAN SCHOOL OF MEDICINE
VIDEO:
PICTURED HERE IS A GROUP OF CELLS MOVING TOWARD ITS CORRECT FINAL POSITION IN THE TAIL OF A FORMING ZEBRAFISH EMBRYO. CELL MEMBRANES ARE GREEN AND THE CELL NUCLEI RED.
A protein that helps generate the force needed for single cells to move works differently in cells moving in groups, a new study shows.
Cells push and pull on each other and surrounding tissue to move as they form organs in an embryo, heal wounds, track down invading bacteria, and become cancerous and spread. Led by researchers at NYU Grossman School of Medicine, the new study examined how forces are generated by a group of 140 cells called the primordium that adhere to each other as they move in zebrafish embryos. Zebrafish are a major model in the study of development because they are transparent and share cellular mechanisms with humans.
Published online December 13 in Current Biology, the new work reveals how the cells in the primordium use a protein called RhoA to trigger forces that move the group into place in the developing embryo. To move, cells push out part of themselves called protrusions, use the protrusions to hold on to nearby tissues, and then haul them back in to pull forward, like casting out and hauling in an anchor.
“This finding surprised us because we had no reason to suspect that the RhoA machinery required to move groups of cells would be different from that used by single cells,” said senior study author Holger Knaut, PhD, associate professor in the Department of Cell Biology at NYU Langone Health.
The current study found that the cells in the primordium instead activate RhoA in pulses in the front of the cells where it does two jobs. At the front tip of the cell, RhoA grows the cell skeleton, called the actin meshwork, outward, forming protrusions that grip the surface. At the base of protrusions, RhoA triggers non-muscle myosin II to pull on the actin meshwork and haul in the protrusions. The pulling by myosin II makes the actin flow toward the center and back of the cells, pushing the cell group forward the way a banana slug moves along the ground, but at a different size scale.
“Our findings suggest that RhoA-induced actin flow on the basal sides of cells constitutes the motor that pulls the primordium forward, a scenario that likely underlies the movement of many cell groups,” added Dr. Knaut. “The machinery suggests that the movement of single cells and groups of cells is similar, but that RhoA contributes to that machinery differently in each case. Within moving cell groups, RhoA generates actin flow directed toward the rear to propel the group forward.”
Dr. Knaut notes that a better understanding of the mechanisms by which cell groups move has the potential to be useful in stopping the spread of cancer, perhaps by guiding the design of treatments that block the action of proteins noted in the study.
Along with Dr. Knaut, study authors were Weiyi Qian (co-corresponding author), Naoya Yamaguchi, and Patrycja Lis in the Department of Cell Biology, and Michael Cammer from the Microscopy Laboratory, at NYU Langone Health. The study was funded by Perlmutter Cancer Center Support Grant P30CA016087, National Institutions of Health grant R01NS119449, NYSTEM training grants C322560GG and C322560GG, two American Heart Association fellowships, 903886 and 20PRE3518016, and by the NYU Dean’s Undergraduate Research Fund.
THIS IMAGE METAPHORICALLY CAPTURES THE CELL DIFFERENTIATION PROCESS DURING RETINAL ORGANOID DEVELOPMENT. LOOSE YARN REPRESENTING UNCOMPACTED DNA IS WOUND BY A CROCHET HOOK AROUND BUTTONS REPRESENTING NUCLEOSOMES AND CULMINATING IN A TIGHTLY CONDENSED BALL OF CHROMATIN THAT FORMS AN EYE-LIKE SHAPE.
Researchers at the National Institutes of Health have mapped the 3D organization of genetic material of key developmental stages of human retinal formation, using intricate models of a retina grown in the lab. The findings lay a foundation for understanding clinical traits in many eye diseases, and reveal a highly dynamic process by which the architecture of chromatin, the DNA and proteins that form chromosomes, regulates gene expression. The findings were published in Cell Reports.
“These results provide insights into the heritable genetic landscape of the developing human retina, especially for the most abundant cell types that are commonly associated with vision impairment in retinal diseases,” said the study’s lead investigator, Anand Swaroop, Ph.D., chief of the Neurobiology, Neurodegeneration, and Repair Laboratory at the National Eye Institute (NEI), part of NIH.
Using deep Hi-C sequencing, a tool used for studying 3D genome organization, the researchers created a high-resolution map of chromatin in a human retinal organoid at five key points in development. Organoids are tissue models grown in a lab and engineered to replicate the function and biology of a specific type of tissue in a living body.
Genes, the sequences that code for RNA and proteins, are interspersed throughout long strands of DNA. Those DNA strands get packaged into chromatin fibers, which are spooled around histone proteins and then repeatedly looped to form highly compact structures that fit into the cell nucleus.
All those loops create millions of contact points where genes encounter non-coding DNA sequences, such as super enhancers, promoters, and silencers that regulate gene expression. Long considered “junk DNA”, these non-coding sequences are now recognized to play a crucial role in controlling which genes get expressed in a cell and when. Studies of chromatin’s 3D architecture shed light on how these non-coding regulatory elements exert control even when their location on a DNA strand is remote from the genes they regulate.
At each of the five key retinal organoid developmental stages, billions of chromatin contact point pairs were sequenced and analyzed.
The findings reveal a dynamic picture: Spatial organization of the genome within the nucleus is transformed during retinal development, facilitating expression of specific genes at key time periods. For example, at a stage when immature cells start developing retinal cell characteristics, chromatin contact points shift from a mostly proximal-enriched state to add more distal interactions.
There also appears to be a hierarchy of compartments that organize the contact point interactions. Some of these compartments, called “A” and “B”, are stable, but others swap during development, which further serves to enhance or inhibit gene expression.
“The datasets resulting from this research serve as a foundation for future investigations into how non-coding sections of the genome are relevant for understanding divergent phenotypes in single gene mutation (Mendelian) disorders, as well as complex retinal diseases,” Swaroop said.
The study was funded by the NEI Intramural Research Program (ZIAEY000450 and ZIAEY000546). NEI is part of the National Institutes of Health.
Reference:
Qu Z, Batz Z, Singh N, Marchal C, Swaroop A, “Stage-specific dynamic reorganization of genome topology shapes transcriptional neighborhoods in developing human retinal organoids”. Published December 2, 2023 in Cell https://doi.org/10.1016/j.celrep.2023.113543
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This press release describes a basic research finding. Basic research increases our understanding of human behavior and biology, which is foundational to advancing new and better ways to prevent, diagnose, and treat disease. Science is an unpredictable and incremental process— each research advance builds on past discoveries, often in unexpected ways. Most clinical advances would not be possible without the knowledge of fundamental basic research. To learn more about basic research, visit https://www.nih.gov/news-events/basic-research-digital-media-kit.
NEI leads the federal government’s efforts to eliminate vision loss and improve quality of life through vision research…driving innovation, fostering collaboration, expanding the vision workforce, and educating the public and key stakeholders. NEI supports basic and clinical science programs to develop sight-saving treatments and to broaden opportunities for people with vision impairment. For more information, visit https://www.nei.nih.gov.
About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit https://www.nih.gov/.
THE RETINA OF VERTEBRATE SPECIES, SUCH AS MICE AND HUMANS, ARE REMARKABLY CONSERVED SINCE THE ORIGIN OF JAWED VERTEBRATES MORE THAN 400 MILLION YEARS AGO. THIS DIAGRAM SHOWS THE SIMILARITIES BETWEEN THE RETINAL CELLS OF HUMANS AND MICE, INCLUDING THE ON AND OFF “MIDGET” RETINAL GANGLION CELLS (MGCS).
CREDIT: ILLUSTRATION BY HUGO SALAIS, METAZOA STUDIO, SPAIN
Karthik Shekhar and his colleagues raised a few eyebrows as they collected cow and pig eyes from Boston butchers, but those eyes — eventually from 17 separate species, including humans — are providing insights into the evolution of the vertebrate retina and could lead to better animal models for human eye diseases.
The retina is a miniature computer containing diverse types of cells that collectively process visual information before transmitting it to the rest of the brain. In a comparative analysis across animals of the many cell types in the retina — mice alone have 130 types of cells in the retina, as Shekhar’s previous studies have shown — the researchers concluded that most cell types have an ancient evolutionary history. These cell types, distinguished by their differences at the molecular level, give clues to their functions and how they participate in building our visual world.
Their remarkable conservation across species suggests that the retina of the last common ancestor of all mammals, which roamed the earth some 200 million year ago, must have had a complexity rivaling the retina of modern mammals. In fact, there are clear hints that some of these cell types can be traced back more than 400 million years ago to the common ancestors of all vertebrates — that is, mammals, reptiles, birds and jawed fish.
The results will be published Dec. 13 in the journal Nature as part of a 10-paper package reporting the latest results of the BRAIN Initiative Cell Census Network's efforts to create a cell-type atlas of the adult mouse brain. The first author is Joshua Hahn, a chemical and biomolecular engineering graduate student in Shekhar’s group at the University of California, Berkeley. The work was an equal collaboration with the group of Joshua Sanes at Harvard University.
The findings were a surprise, since vertebrate vision varies so widely from species to species. Fish need to see underwater, mice and cats require good night vision, monkeys and humans evolved very sharp daytime eyesight for hunting and foraging. Some animals see vivid colors, while others are content with seeing the world in black and white.
Yet, numerous cell types are shared across a range of vertebrate species, suggesting that the gene expression programs that define these types likely trace back to the common ancestor of jawed vertebrates, the researchers concluded.
The team found, for example, that one cell type — the “midget” retinal ganglion cell — that is responsible for our ability to see fine detail, is not unique to primates, as it was thought to be. By analyzing large-scale gene expression data using statistical inference approaches, the researchers discovered evolutionary counterparts of midget cells in all other mammals, though these counterparts occurred in much smaller proportions.
“What we are seeing is that something thought to be unique to primates is clearly not unique. It’s a remodeled version of a cell type that is probably very ancient," said Shekhar, a UC Berkeley assistant professor of chemical and biomolecular engineering. "The early vertebrate retina was probably extremely sophisticated, but the parts list has been used, expanded, repurposed or refurbished in all the species that have descended since then."
Coincidentally, one of Shekhar's UC Berkeley colleagues, Teresa Puthussery of the School of Optometry, reported last month in Nature that another cell type thought to have been lost in the human eye — a type of retinal ganglion cell responsible for gaze stabilization — is still there. Puthussery and her colleagues used information from a previous paper co-authored by Shekhar to select molecular markers that helped identify this cell type in primate retinal tissue samples.
The discoveries are, in a sense, not a total surprise, since the eyes of vertebrates have a similar plan: Light is detected by photoreceptors, which relay the signal to bipolar, horizontal and amacrine cells, which in turn connect with retinal ganglion cells, which then relay the results to the brain's visual cortex. Shekhar uses new technologies, in particular single-cell genomics, to assay the molecular composition of thousands to tens of thousands of neurons at once within the visual system, from the retina to the visual cortex.
Because the number of identified retinal cell types varies widely in vertebrates — about 70 in humans, but 130 in mice, based on previous studies by Shekhar and his colleagues — the origins of these diverse cell types were a mystery.
One possibility that emerged from the new research, Shekhar said, is that as the primate brain became more complex, primates began to rely less on signal processing within the eye — which is key to reflexive actions, such as reacting to an approaching predator — and more on analysis within the visual cortex. Hence the apparent decrease in molecularly distinct cell types in the human eye.
"Our study is saying that the human retina may have evolved to trade cell types that perform sophisticated visual computations for cell types that basically just transmit a relatively unprocessed image of the visual world with the brain so that we can do a lot more sophisticated things with that," Shekhar said. "We are giving up speed for finesse."
The team's new detailed map of cell types in a variety of vertebrate retinas could aid research on human eye disease. Shekhar’s group is also studying molecular hallmarks of glaucoma, the leading cause of irreversible blindness in the world and, in the U.S., the second most common cause of blindness after macular degeneration.
Yet, while mice are a favorite model animal for studying glaucoma, they have very few of the midget retinal ganglion cell counterparts. These cell types make up only 2% to 4% of all ganglion cells in mice, whereas 90% of retinal ganglion cells are midget cells in humans.
"This work is clinically important because, ultimately, the midget cells are probably what we should care about the most in human glaucoma," Shekhar said. "Knowing their counterparts in the mouse will hopefully help us design and interpret these glaucoma mouse models a little better."
Single-cell transcriptomics
Shekhar and Sanes have, for the past eight years, been applying single-cell genomic approaches to profile the mRNA molecules in cells to categorize them according to their gene expression fingerprints. That technique has gradually helped identify more and more distinct cell types within the retina, many of them through studies that Shekhar initiated while a postdoctoral fellow with Aviv Regev, one of the pioneers of single-cell genomics, at the Broad Institute. It was in her lab that Shekhar began working with Sanes, a renowned retinal neurobiologist who became Shekhar’s co-advisor and collaborator.
In the current study, they wanted to expand their single-cell transcriptomic approach to other species to understand how retinal cell types have changed through evolution. They gathered, in all, eyes from 17 species: human, two monkeys (macaque and marmoset), four rodents (three species of mice and one ground squirrel), three ungulates (cow, sheep and pig), tree shrew, opossum, ferret, chicken, lizard, zebrafish and lamprey.
With Sanes' team at Harvard conducting the transcriptomic experiments and Shekhar's team at UC Berkeley conducting the computational analysis, many new cell types were identified in each of the species. They then mapped this variety to a smaller set of "orthotypes" — cell types that have likely descended from the same ancestral cell type in early vertebrates.
For bipolar cells, which are a class of neurons that lie between the photoreceptors and retinal ganglion cells, they found 14 distinct orthotypes. Most extant species contain 13 to 16 bipolar types, suggesting that these types have evolved little. In contrast, they found 21 orthotypes of retinal ganglion cells, which exhibit greater variation among species. Studies thus far have identified more than 40 distinct types in mice and about 20 different types in humans.
Interestingly, the pronounced evolutionary divergence among types of retinal ganglion cells, as compared to other retinal classes, suggests that natural selection acts more strongly on diversifying neuron types that transmit information from the retina to the rest of the brain.
They also found that numerous transcription factors, which have been implicated in retinal cell type specification in mice, are highly conserved, suggesting that the molecular steps leading to the development of the retina might be evolutionarily conserved, as well.
Based on the new work, Shekhar is refocusing his glaucoma research on the analogs of midget cells, called alpha cells, in mice.
The work was supported primarily by the National Institutes of Health (K99EY033457, R00EY028625, R21EY028633, U01MH105960, T32GM007103), the Chan-Zuckerberg Initiative (CZF-2019-002459) and the Glaucoma Research Foundation (CFC4). Shekhar also acknowledges support from the Hellman Fellows Program. Sanes was funded in part by NIH’s Brain Research Through Advancing Innovative Neurotechnologies Initiative, or the BRAIN Initiative.
JOURNAL
Nature
METHOD OF RESEARCH
Data/statistical analysis
SUBJECT OF RESEARCH
Animals
ARTICLE TITLE
Evolution of neuronal cell classes and types in the vertebrate retina
ARTICLE PUBLICATION DATE
13-Dec-2023
Machine learning sees into the future to prevent sight loss in humans
MYOPIC MACULOPATHY, ALSO KNOWN AS MYOPIC MACULAR DEGENERATION, IS A KEY FEATURE OF PATHOLOGIC MYOPIA. IN THE META-PM CLASSIFICATION SYSTEM, MYOPIC MACULOPATHY LESIONS ARE CATEGORIZED INTO FIVE CATEGORIES FROM NO MYOPIC RETINAL LESIONS (CATEGORY 0), TESSELLATED FUNDUS ONLY (CATEGORY 1, FIGURE 1A), DIFFUSE CHORIORETINAL ATROPHY (CATEGORY 2, FIGURE 1B&C), PATCHY CHORIORETINAL ATROPHY (CATEGORY 3, FIGURE 1D ARROWS), TO MACULAR ATROPHY (CATEGORY 4, FIGURE 1E&F).
CREDIT: DEPARTMENT OF OPHTHALMOLOGY AND VISUAL SCIENCE, TMDU
Researchers from Tokyo Medical and Dental University (TMDU) develop models based on machine learning that predict long-term visual acuity in patients with high myopia, one of the top three causes of irreversible blindness in many regions of the world
Tokyo, Japan – Machine learning has been found to predict well the outcomes of many health conditions. Now, researchers from Japan have found a way to predict whether people with severe shortsightedness will have good or bad vision in the future.
In a study recently published in JAMA Ophthalmology, researchers from the Tokyo Medical and Dental University (TMDU) developed a machine-learning model that works well for predicting—and visualizing—the risk of visual impairment over the long term.
People with extreme shortsightedness (called high myopia) can clearly see objects that are near to them but cannot focus on objects at a distance. Contacts, glasses, or surgery can be used to correct their vision, but having high myopia is not just inconvenient; half of the time it leads to a condition called pathologic myopia, and complications from pathologic myopia are the leading causes of blindness.
“We know that machine-learning algorithms work well on tasks such as identifying changes and complications in myopia,” says Yining Wang, lead author of the study, “but in this study, we wanted to investigate something different, namely how good these algorithms are at long-term predictions.”
To do this, the team performed a cohort study and looked at the visual acuity of 967 Japanese patients at TDMU’s Advanced Clinical Center for Myopia after 3 and 5 years had passed. They formed a dataset from 34 variables that are commonly collected during ophthalmic examinations, such as age, current visual acuity, and the diameter of the cornea. They then tested several popular machine-learning models such as random forests and support vector machines. Of these models, the logistic regression-based model performed the best at predicting visual impairment at 5 years.
However, predicting outcomes is only part of the story. “It’s also important to present the model’s output in a way that is easy for patients to understand and convenient for making clinical decisions,” says Kyoko Ohno-Matsui, senior author. To do this, the researchers used a nomogram to visualize the classification model. Each variable is assigned a line with a length that indicates how important it is for predicting visual acuity. These lengths can be converted into points that can be added up to obtain a final score explaining the risk of visual impairment in future.
People who permanently lose their vision often suffer both financially and physically as a result of their loss of independence. The decrease in global productivity caused by severe visual impairment was estimated to be USD94.5 billion in 2019. Although the model still has to be evaluated on a wider population, this study has shown that machine-learning models have good potential to help address this increasingly important public health concern, which will benefit both individuals and society as a whole.
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The article, “Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes,” was published in JAMA Ophthalmology at DOI: 10.1001/jamaophthalmol.2023.4786
Maternity-related employment gaps may cause job candidates to be unfairly screened out of positions for which they are otherwise qualified, according to new research from NYU Tandon School of Engineering.
A research team led by Siddharth Garg, Institute Associate Professor of Electrical and Computer Engineering, examined bias in Large Language Models (LLMs) – advanced AI systems trained to understand and generate human language – when used in hiring processes.
The team will present its findings in a paper presented at NeurIPS 2023 R0-FoMo Workshop on December 15. Akshaj Kumar Veldanda, PhD candidate in Department of Electrical and Computer Engineering, is the paper's lead researcher.
AI algorithms have come under scrutiny recently when used in employment. President Biden's October 2023 AI executive order underscored the pressing need to address potential bias when employers rely on AI to help with hiring. New York City has enacted a first-of-its-kind law requiring regular audits to assess the transparency and fairness of algorithmic hiring decisions.
“Our research is helping develop a robust auditing methodology that can uncover hiring biases in LLMs, aiding researchers and practitioners in efforts to intervene before discrimination occurs,” said Garg. “Our study unearths some of the very biases that the New York City law intends to prohibit.”
In the study, researchers assessed the ability of three popular LLMs, namely ChatGPT (GPT-3.5), Bard, and Claude, to disregard irrelevant personal attributes such as race or political affiliations — factors that are both legally and ethically inappropriate to consider — while evaluating job candidates' resumes.
To do this, researchers added “sensitive attributes” to experimental resumes, including race and gender signaled through first and last names associated with either Black or white men or women; language indicating periods of absence from employment for parental duties, affiliation with either the Democratic or Republican party, and disclosure of pregnancy status.
After being fed the resumes, the LLMs were presented with two queries that human resource professionals could reasonably use in hiring: identifying whether the information presented on a resume aligns it with a specific job category – such as “teaching” or “construction” – and summarizing resumes to include only information relevant for employment.
While race and gender did not trigger biased results in the resume-matching experiment, the other sensitive attributes did, meaning at least one of the LLMs erroneously factored them into whether it included or excluded a resume from a job category.
Maternity- and paternity employment gaps triggered pronounced biased results. Claude performed the worst on that attribute, most frequently using it to wrongly assign a resume either inside or outside its correct job category. ChatGPT also showed consistently biased results on that attribute, although less frequently than Claude.
“Employment gaps for parental responsibility, frequently exercised by mothers of young children, are an understudied area of potential hiring bias,” said Garg. “This research suggests those gaps can wrongly weed out otherwise qualified candidates when employers rely on LLMs to filter applicants.”
Both political affiliation and pregnancy triggered incorrect resume classification as well, with Claude once again performing the worst and ChatGPT coming in behind it.
Bard performed strongest across the board, exhibiting a remarkably consistent lack of bias across all sensitive attributes.
“Claude is the most prone to bias in our study, followed by GPT-3.5. But Bard’s performance shows that bias is not a fait accompli,” said Garg “LLMs can be trained to withstand bias on attributes that are infrequently tested against, although in the case of Bard it could be biased along sensitive attributes that were not in this study.”
When it comes to producing resume summaries, researchers found stark differences between models. GPT-3.5 largely excludes political affiliation and pregnancy status from the generated summaries, whereas Claude is more likely to include all sensitive attributes. Bard frequently refuses to summarize, but is more likely to include sensitive information in cases where it generates the summaries. In general, job category classification on summaries – rather than full resumes – improves fairness of all LLMs including Claude, potentially because summaries make it easier for a model to attend to relevant information.
“The summary experiment also points to the relative weakness of Claude compared to the other LLMs tested,” said Garg. “This study overall tells us that we must continue to interrogate the soundness of using LLMs in employment, ensuring we ask LLMs to prove to us they are unbiased, not the other way around. But we also must embrace the possibility that LLMs can, in fact, play a useful and fair role in hiring.”
Methodology and notes
The study began by using a publicly released dataset of 2484 resumes from livecareer.com, available via Kaggle, spanning 24 job categories, which were anonymized to remove personal information. Due to limitations with state-of-the-art language model APIs, the evaluation initially focused on a subset of three job categories: Information Technology (IT), Teacher, and Construction. This yielded a “raw" resume corpus containing 334 resumes. Researchers subsequently evaluated across all 24 job categories for both Bard and Claude. The researchers manually inspected a sample of the resumes to ensure they matched their ground-truth job categories and had relevant information, such as experience and educational qualifications.
Sensitive attributes like race, gender, maternity/paternity-based employment gaps, pregnancy status, and political affiliation were introduced to the resumes using a specific approach, including that of Sendhil Mullainathan, a behavioral economist and professor at Harvard University who produced seminal research on hiring bias using racially stereotypical names of job candidates. Language added for other sensitive attributes aligned with standard recommendations related to resume creation.
For job category classifications, researchers pose a binary classification problem to the LLM to identify whether a resume belongs to that job category or not. Researchers then evaluated the accuracy, true positive and true negative rates using ground-truth labels from its dataset.
For the summary task, the LLM was asked to briefly summarize a specific resume and keep the most important information for employment. Researchers evaluated bias by identifying whether sensitive attributes were retained and by using summaries for the classification task, mimicking a scenario in which the resume itself is too long for classification analysis. Classification on summaries improves fairness of LLMs, including Claude.
Because ChatGPT, Bard, and Anthropic (Claude) are black box models – meaning they arrive at conclusions or decisions without providing any explanations as to how they were reached – in-depth examination of biases are hindered. To gain a deeper understanding, the researchers conducted an evaluation on Alpaca, a white-box model that provides such explanations. The team observed that Alpaca exhibits biases in classification tasks as well. The team employed an existing method called Contrastive Input Decoding (CID) to explain the biases within the Alpaca model. Through this approach, researchers observed that:
For maternity leave, some responses offered the following reason for rejection: "Including personal information about maternity leave is not relevant to the job and could be seen as a liability."
For pregnancy status, CID rejected candidates because "She is pregnant" or "Because of her pregnancy."
For political affiliation, CID analysis indicated that certain candidates were unsuitable because, "The candidate is a member of the Republican party, which may be a conflict of interest for some employers."
It is important to note that CID does only sometimes offer these reasons, potentially because CID picks one of the potentially many reasons for rejection. Nonetheless, these results suggest that CID could be an effective tool to analyze bias even on larger models, given white-box access.
About the New York University Tandon School of Engineering
The NYU Tandon School of Engineering dates to 1854, the founding date for both the New York University School of Civil Engineering and Architecture and the Brooklyn Collegiate and Polytechnic Institute. A January 2014 merger created a comprehensive school of education and research in engineering and applied sciences as part of a global university, with close connections to engineering programs at NYU Abu Dhabi and NYU Shanghai. NYU Tandon is rooted in a vibrant tradition of entrepreneurship, intellectual curiosity, and innovative solutions to humanity’s most pressing global challenges. Research at Tandon focuses on vital intersections between communications/IT, cybersecurity, and data science/AI/robotics systems and tools and critical areas of society that they influence, including emerging media, health, sustainability, and urban living. We believe diversity is integral to excellence, and are creating a vibrant, inclusive, and equitable environment for all of our students, faculty and staff. For more information, visit engineering.nyu.edu.
Poor diet quality during adolescence is linked to serious health risks
Research published in the Journal of Nutrition Education and Behavior closely measured cardiometabolic risk factors over two years
KATHY HU AND ALYSSA M. BUTTON, PHD, DISCUSS FINDINGS THAT SHOWED ADOLESCENTS WITH POOR ADHERENCE TO THE 2015-2020 DIETARY GUIDELINES FOR AMERICANS AND ASSOCIATED CARDIOMETABOLIC RISK FACTORS CONTINUED THESE SAME PATTERNS OVER THE TWO YEARS OF THE STUDY, SUGGESTING THAT THE ADVERSE EFFECTS OF A POOR-QUALITY DIET HAD ALREADY ESTABLISHED THE HEALTH RISKS THESE TEENAGERS WILL FACE THROUGHOUT LIFE.
CREDIT: JOURNAL OF NUTRITION EDUCATION AND BEHAVIOR
Philadelphia, December 13, 2023 – Diet quality among adolescents in the United States is among the worst across all age groups, putting young people at risk for heart attack, stroke, and diabetes, among other cardiometabolic diseases later in life. The research brief shared in the Journal of Nutrition Education and Behavior, published by Elsevier, used the Healthy Eating Index-2015 and medical testing to assess a group of youth aged 10-16 years.
This study examined data from the Translational Investigation of Growth and Everyday Routine in Kids cohort. This study measured physical activity, sleep, and overall dietary guidelines for youth living in metropolitan areas of Louisiana, which are typically medically underserved and characterized by high poverty levels, food insecurity, obesity, and related diseases. Study participants provided a baseline data set with follow-up measures two years later.
Corresponding author Amanda E. Staiano, PhD, Pennington Biomedical Research Center, Louisiana State University, explained, “Examining the data related to diet quality may help identify targets for future interventions in families, homes, and communities. Effective and timely interventions focusing on adherence to dietary guidelines are necessary for improving diet quality and reducing health risks.”
Of the 342 eligible and enrolled adolescents, the final study sample included 192 participants with complete baseline and follow-up data. At baseline and follow-up, the adolescents were asked to wear an accelerometer for at least seven days and complete two 24-hour dietary recalls for their food and beverage intake during research visits that included body composition, blood pressure, and clinical chemistry measurements and anthropometrics.
Considering overall eating patterns, the findings showed that adolescents with poor adherence to the 2015-2020 Dietary Guidelines for Americans and associated cardiometabolic risk factors continued these same patterns over the two years of the study, suggesting that the adverse effects of a poor-quality diet had already established the health risks these teenagers will face throughout life.
Dr. Staiano concluded, “This study found specific dietary quality patterns associated with adolescent cardiometabolic risk factors. Promotion of nutrition knowledge is necessary, but knowledge is not consistently linked with food consumption behavior. Identifying barriers to consuming a healthful diet and investigating effective strategies to overcome these barriers may curtail future health risks.”
WASHINGTON, DC, December 13, 2023 –With a severe shortage of affordable housing in the United States, renters living along the East and Gulf coasts are uniquely vulnerable to hurricane disasters. Two new studies based on data from 2009 to 2018 show that renters living along the East and Gulf coasts of the United States face rent increases, higher eviction rates, and a lack of affordable housing in the aftermath of a hurricane. The research will be presented in December at the annual meeting of the 2023 Society for Risk Analysis Annual Conference in Washington, D.C.
Both analytical studies are based on 10 years of data (2009 to 2018) on housing, hurricane disasters, and socioeconomic factors at the county level in 19 coastal states -- from Maine to Texas. The time period includes devastating hurricanes such as Irma (2011), Sandy (2012), and Matthew (2016).
The Impacts of a Hurricane on Rent Affordability
Dr. Kelsea Best of The Ohio State University and her colleagues analyzed how the frequency and intensity of a hurricane correspond to changes in median rent and rental housing affordability over time. They found that median rents rise in the year following more intense hurricanes due to declines in housing availability. Their results also suggest that the occurrence of a hurricane in any given year (or in the previous year) reduces affordable rental housing. This was especially true for counties with a higher percentage of renters and people of color.
More than one-third of the American population (44 million households) live in rental dwellings. Renters have less access to post-disaster government aid programs and to benefits from federal mitigation programs such as home buyouts. In addition, people of renter status are more likely to be underinsured, with only 57% having insurance policies as of 2022 (Insurance Information Institute).
“Most federal post-disaster assistance programs are targeted to homeowners,” says Best. “Our study shows that deliberate attention must be given to renters – especially low-income and minority renters – in recovery efforts immediately following a disaster event and in subsequent years.”
She suggests that future local, state, and federal policies should provide explicit protections and support to renters after disasters. These could include eviction moratoria, limiting late fees on rent payments, increasing access to emergency rental assistance, and freezing rent increases. Additionally, efforts that prioritize affordable and stable housing supply with up-to-date market rent price monitoring could provide a critical reference for policymakers to understand and respond to renters’ struggles, especially during post-disaster periods.
“Without such deliberate consideration of rent and renters, disaster recovery risks exacerbate the affordable housing crisis for some of the most vulnerable populations,” says Best.
Hurricanes and Eviction Risk
Another threat that renters may face following a disaster is eviction due to either loss of income or the lack of effective rental assistance when the housing supply tightens during the recovery phase.
Dr. Qian He of Rowan University and her colleagues investigated how disasters and post-disaster federal aid contribute to renters’ eviction risks. They found that hurricanes corresponded to higher eviction filings and eviction threats by inflating market rent the year of and one year after the hurricane. Counties receiving higher amounts of aggregated federal aid (both post-disaster and hazard mitigation aid) were associated with lower eviction filings and eviction threats two years after the disaster.
According to He, this suggests that post-disaster federal aid programs can help mitigate renters’ housing vulnerability during disaster recovery. “Our findings indicate that coordinated public policies and renter aid programs, specifically after disaster events, can become crucial to ensure that at-risk communities have access to sufficient financial resources and legal support to help renters avoid eviction,” says He.
For example, during the height of the Covid-19 pandemic, the Centers for Disease Control (CDC) issued a national eviction moratorium. This act provided immediate relief for over 6.5 million renter households across the country who were behind on their rent payment and those who were at an increased risk of eviction. “Similar eviction moratoria after a climate-related disaster, potentially as part of federal recovery aid and efforts, could provide valuable protection to renters in affected communities,” says He.
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Presentations are located at The Westin Washington, D.C.
How do Hurricanes and Federal Aid Affect Eviction Risk? Decade-long Evidence from the United States – Monday, December 11, 2:30-2:50 p.m.
Rent Affordability after Hurricanes: Longitudinal Evidence from U.S. Coastal States – Wednesday, December 13, 11:15-11:30 a.m.
About SRA The Society for Risk Analysis is a multidisciplinary, interdisciplinary, scholarly, international society that provides an open forum for all those interested in risk analysis. SRA was established in 1980. Since 1982, it has continuously published Risk Analysis: An International Journal, the leading scholarly journal in the field. For more information, visit www.sra.org.
