The research team, led by sociologist Mark Regnerus of the University of Texas at Austin together with Anne Morse of the Austin Institute, drew on the 2025 Relationships in America Survey, which interviewed 5,460 English-speaking Americans aged 20 to 70. The survey was conducted by Ipsos using its KnowledgePanel, a probability-based pool designed to produce estimates representative of the national adult population. Crucially, the 2025 instrument repeated, word for word, the statement posed to respondents in the 2018 Post-Midterm Election Study: that it should be acceptable for adolescents to transition with hormones or surgery if they identify with another gender. Respondents answered on a five-point scale ranging from strongly agree to strongly disagree, and both datasets were weighted to correct for differential nonresponse, making the two waves directly comparable.

The headline finding is stark. In 2018, 26 percent of American adults agreed that adolescent medical transition should be permissible; by 2025, that figure had fallen to 21 percent, a statistically significant five-point decline. Disagreement surged even more dramatically, rising from 43 percent to 55 percent, an increase of more than twelve percentage points. Meanwhile, the share of respondents who said they neither agreed nor disagree shrank, a pattern the authors attribute to the intense media and political coverage of the issue over the intervening years, which pushed previously ambivalent citizens to form an opinion one way or the other.

The internal composition of the shift is as revealing as its magnitude. The decline in agreement came almost entirely from respondents who had previously said they simply agreed, while the share who strongly agreed showed no statistically significant change. On the other side of the ledger, the proportion who strongly disagree grew substantially and significantly, among both men and women alike. In other words, the population did not become uniformly more hostile; rather, moderate support eroded while firm opposition deepened, a signature of one-sided polarization on an issue that has become a cultural flashpoint.

Perhaps the most striking result concerns age cohorts. Because the two surveys used the same sampling frame and question wording, the researchers could track how specific birth cohorts moved between 2018 and 2025 by comparing, for example, the attitudes of people aged 30 to 34 in 2018 with those of people aged 37 to 41 in 2025. Every cohort born in 1988 or earlier showed a significant increase in disagreement. The cohort aged 35 to 39 in 2018 displayed the largest swing, with disagreement jumping from 40 percent to over 60 percent, a twenty-one-point rise. In 2018, no age cohort had a majority that disagreed with adolescent medical transition; by 2025, every cohort except those aged 32 to 36 did.

These within-cohort shifts challenge a common intuition that the attitudes of older people are fixed while younger generations gradually liberalize the national mood. Demographic churn should, if anything, have pushed support upward: older, less supportive cohorts aged out of the sample while younger, more supportive ones aged in, and the 2025 sample indeed contained more college graduates, more political liberals, and more transgender-identified respondents than the 2018 sample. Yet aggregate support still fell. The authors conclude that a period effect, a cultural shift affecting people of all ages simultaneously, accounts for a substantial portion of the change, echoing sociological research showing that aggregate attitudes often morph through intracohort change rather than generational turnover. Unusually, however, the direction here is conservative rather than liberalizing.

One cohort stands apart. Adults who were aged 25 to 29 in 2018, born between 1989 and 1993, remained the most supportive and least opposed group at both time points, even as their elders shifted beneath them. The sex dynamics within younger cohorts are also notable. Among Americans aged 20 to 24 in 2018, only 21 percent of men agreed with adolescent medical transition, compared with 42 percent of women, a wide gap. Seven years later, the men in that cohort had become somewhat more likely to agree while the women had become less so, narrowing the difference to 25 percent versus 29 percent. Across the full sample, men and women both showed significant increases in disagreement, and the most common pattern was no statistically significant difference between the sexes at all.

To probe what drives these attitudes, the team replicated the nested regression models from the original 2018 study, building from baseline demographics through sexuality and relationship variables, then religion, and finally political identity. The predictors themselves proved remarkably stable: no significant variable changed direction between the two surveys. But some effects shifted in revealing ways. In 2018, being female was significantly associated with greater support in every model; in 2025, that effect vanished once political self-identification was added, suggesting that the apparent sex difference operates indirectly through the growing alignment of women with liberal politics. Religious affiliation effects from evangelical Protestant and LDS identities, significant in 2018 even after political controls, were drowned out by political variables in 2025. Meanwhile, the coefficient for having voted for Donald Trump grew larger than any other significant coefficient, and identifying as transgender, now measured in a sample more than twice the size of 2018, emerged as a significant positive predictor of support, even though roughly a quarter of transgender-identified respondents themselves disagreed with adolescent medical transition.

The findings arrive against a backdrop of rapid legal and political change. The study period encompassed the surge of state legislation restricting gender-affirming care for minors, the Tennessee law banning puberty blockers and hormone therapy that the Supreme Court upheld in June 2025, and a 2025 executive order withholding federal funding from medical transitions for minors. Pew Research Center data cited in the paper show that support for banning such care rose ten percentage points between 2022 and 2025, with declines among Democrats as well as Republicans. The new analysis adds nuance to that picture: the liberal-to-conservative gap widened, but the widening was driven by movement among conservatives and moderates, while liberals held steady.

The authors are careful about limitations. The single survey item compresses a multifaceted issue into one agree-disagree continuum, combines hormonal and surgical interventions without distinguishing them, and says nothing about social transitions or adult care. The comparisons are cross-sectional rather than a true panel, and cannot adjust for cohort attrition through mortality or migration, though a sensitivity check on the 401 respondents who participated in both surveys found no evidence of differential attrition distorting the results. Robustness checks using ordered logistic regression and multiple imputation confirmed the substantive findings. Whether the documented decline proves durable or temporary remains an open question, but the study makes clear that public opinion on adolescent medical transition is not static, that it has moved within cohorts of both sexes, and that political identity now overshadows nearly every other predictor of where Americans stand.

Subject of Research: Cohort changes in American public attitudes toward adolescent medical gender transition between 2018 and 2025

Article Title: Cohort Change in Attitudes About Adolescent Gender Transitions Between 2018 and 2025

Article References: Regnerus, M., & Morse, A. (2026). Cohort Change in Attitudes About Adolescent Gender Transitions Between 2018 and 2025. Archives of Sexual Behavior. https://doi.org/10.1007/s10508-026-03659-z

Image Credits: AI Generated

DOI: 10.1007/s10508-026-03659-z

Keywords: public opinion, gender dysphoria, adolescent gender transition, cohort analysis, transgender health, hormone therapy, puberty blockers, gender-affirming surgery, survey research, political polarization, Archives of Sexual Behavior, period effects

The platform, called lncRNA Seeker Hub, was developed by Pok-Son Kim of Kookmin University, Klaus Heese of Hanyang University’s Graduate School of Biomedical Science and Engineering, and Arne Kutzner of Hanyang University’s Department of Information Systems. Writing in the open-access journal BMC Bioinformatics, the team describes a system that aggregates RNA sequencing evidence across entire cohorts of samples and then lets researchers manually reconstruct transcript structures that automated assemblers cannot reliably recover. The work was supported by a grant from the National Research Foundation of Korea, funded by the Ministry of Science and ICT, and the software’s case studies focus on two of the most famous non-coding RNA loci in human biology: MALAT1 and PCA3.

The core problem the team attacks is statistical and computational at once. When a gene is expressed weakly, any single RNA-seq sample may show only sparse local coverage along the genome, and the reads that do map across splice junctions, the points where RNA segments are stitched together after introns are removed, may be too few to convince an assembler that a genuine transcript exists there. Individual samples also differ from one another in unpredictable ways, so the splicing pattern supported by one patient’s tissue may barely register in another’s. Yet when many samples are pooled, these faint, distributed signals can become reproducible and interpretable. Automated assembly tools, which typically operate sample by sample or merge results only crudely, are poorly suited to exploit that cohort-level pattern. Genome browsers that researchers routinely rely on, including IGV, JBrowse, and the UCSC Genome Browser, offer superb visualization but, according to the authors, lack integrated support for cohort-level aggregation and the kind of hands-on transcript reconstruction their workflow demands.

lncRNA Seeker Hub fills that gap with an architecture that reflects modern software engineering as much as genomics. At its heart sits a high-performance backend written in Rust, a systems programming language prized for speed and memory safety. The backend performs targeted aggregation of BAM files, the standard binary format in which aligned sequencing reads are stored, pulling only the genomic regions of interest rather than loading entire chromosomes. To keep interactive exploration responsive, the system employs binary caching, so that previously computed aggregations can be served instantly on repeat queries. On top of this engine runs an interactive visualization interface built with Bokeh, a Python-based plotting library designed for browser-delivered, data-dense graphics. The combination means a researcher can zoom into a specific gene locus across dozens or hundreds of samples and see aggregated evidence render quickly enough to support genuine, exploratory analysis rather than batch-and-wait processing.

What makes the platform distinctive is not merely speed but the way it synchronizes different layers of evidence. In a single view, a researcher can examine read coverage along the genome, the splice junctions supported by split reads, and the individual reads themselves, all aligned and all drawn from the aggregated cohort rather than from one sample at a time. This synchronized exploration is precisely what weak-lncRNA discovery requires. A candidate exon may show barely detectable coverage in any single individual, yet appear consistently, sample after sample, once the cohort is pooled. A splice junction may lack the read support an assembler demands in isolation, but reveal itself as a recurring pattern when evidence is aggregated. The software then supports manual transcript reconstruction: researchers can build, inspect, and refine candidate transcript structures directly from that aggregated evidence, effectively acting as expert curators of their own data.

The published case studies illustrate the approach at the MALAT1 and PCA3 loci. MALAT1, or metastasis-associated lung adenocarcinoma transcript 1, is one of the most abundant and intensively studied lncRNAs in mammals, involved in regulating gene expression in the cell nucleus and strongly implicated in cancer metastasis. PCA3 is a prostate-specific non-coding RNA used as a biomarker in prostate cancer diagnostics, notable because it is expressed at very low levels compared with most coding genes. At both loci, the authors report that lncRNA Seeker Hub enabled the detection of candidate cohort-supported transcriptional structures and splice-graph heterogeneity, meaning variability in how the splice junctions connect exons across samples, that they characterize as not readily identifiable using automated assembly or single-sample visualization. In other words, at even these well-trodden genomic addresses, the cohort-level view surfaced structural detail that mainstream tools had been leaving on the table.

Performance matters as much as discovery in a tool meant for daily research use, and the team benchmarked the platform across representative genes spanning a wide range of read-depth regimes, including extreme local coverage of the kind encountered at hyper-expressed loci such as MALAT1. The benchmarks show predictable latency and memory scaling, meaning that response times and memory consumption grow in a controlled, anticipatable way as the amount of sequencing data at a locus increases. That predictability is more than an engineering nicety. Researchers exploring weak transcriptional signals need to know that panning across a gene with enormous depth of coverage will not crash the session or freeze the interface, and the benchmarking suggests the Rust-and-caching design delivers exactly that stability even under the punishing local coverage that some of the most biologically interesting regions produce.

The broader significance of the work lies in a quiet tension running through modern genomics. Automated pipelines are indispensable, and no one proposes abandoning them; they process thousands of samples with consistency and scale no human can match. But they encode assumptions, and those assumptions are calibrated to the signals that dominate most datasets: well-expressed, cleanly spliced, protein-coding transcripts. Non-coding RNAs, with their low abundance, tissue specificity, and splicing variability, sit precisely at the margins where those assumptions break. By combining cohort-level aggregation with expert-driven, manual reconstruction, lncRNA Seeker Hub represents a hybrid model of discovery in which the machine does the heavy lifting of data handling and the human contributes pattern recognition and biological judgment. The authors position the platform, accordingly, as a practical framework for cohort-level RNA-seq exploration and manual transcript validation rather than as a replacement for assembly pipelines.

For the lncRNA field specifically, the implications could be considerable. Thousands of long non-coding RNAs have been catalogued, but annotations remain incomplete and uneven, and many candidate transcripts in public databases rest on thin or inconsistent evidence. Tools that can marshal the collective weight of large sequencing cohorts, from population genomics projects to cancer specimen collections, offer a route to confirming which of these faint transcriptional whispers are real, reproducible biology and which are noise. If weak lncRNAs regulating cancer pathways, immune responses, or developmental programs have been hiding below the detection threshold of automated assembly, platforms like this one give researchers a credible way to bring them into view, and potentially into the clinic, since molecules like PCA3 already demonstrate that non-coding transcripts can serve as disease biomarkers.

The software arrives as open-access research, citable under a permanent digital object identifier in BMC Bioinformatics, and the published version represents peer-reviewed, accepted work. Whether lncRNA Seeker Hub becomes a standard fixture in transcriptomics laboratories will depend on adoption and on how the community balances automated throughput with manual curation. But the message of the paper is clear and likely to resonate well beyond bioinformatics: some of the genome’s most important signals are individually too faint for machines to trust, yet collectively unmistakable when the right tools make them visible. Sometimes the next discovery is not hidden in the data so much as hidden in the aggregation of the data, waiting for a browser built to show it.

Subject of Research: Cohort-level RNA-seq visualization software for discovering weakly expressed long non-coding RNAs

Article Title: lncRNA Seeker Hub: cohort-level RNA-seq visualization and manual transcript reconstruction for weak lncRNA discovery

Article References: Kim, P.-S., Heese, K., & Kutzner, A. (2026). lncRNA Seeker Hub: cohort-level RNA-seq visualization and manual transcript reconstruction for weak lncRNA discovery. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06671-1

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06671-1

Keywords: lncRNA, RNA-seq, transcript discovery, bioinformatics software, genome visualization, splice junctions, MALAT1, PCA3, cohort analysis, Rust programming, BAM aggregation, non-coding RNA

The research team, led by Yunxiang Wan of the Max Planck Institute for Demographic Research in Rostock, together with Marwân-al-Qays Bousmah and Marília R. Nepomuceno, drew on the Chinese Longitudinal Healthy Longevity Survey, one of the largest ongoing studies of its kind in the world. Covering the period from 2002 to 2018, the survey follows thousands of older Chinese adults across repeated interviews, recording not only whether they survive from one wave to the next but how they assess their own lives. The researchers used this rich dataset to estimate what demographers call happy life expectancy: the expected number of years a person at a given age will live, and of those, how many will be spent in a state of happiness.

Technically, the study employs a cohort-based multistate life table method, an approach that deserves unpacking because it represents a methodological advance over most previous work. Traditional healthy life expectancy calculations typically rely on period measures, blending mortality rates and happiness prevalence from a single point in time as if today’s older population would experience tomorrow’s conditions. The multistate framework, by contrast, treats life as a set of transitions: individuals can move between a happy state and an unhappy state, and eventually to death, with probabilities estimated from the observed data. By structuring the analysis around birth cohorts, the researchers could compare people born roughly ten years apart as they moved through the same age ranges, isolating genuine generational change from the noise of period fluctuations.

Because the survey data do not follow each cohort from birth to death, the authors estimated what they term partial-cohort happy life expectancies, covering specific age ranges rather than full lifespans. This is a careful and honest solution to a common data limitation in longitudinal ageing studies. The core comparison pits paired cohorts against each other: for each age range examined, the experience of the earlier-born cohort is set against that of the later-born cohort a decade younger. If the later cohort spends a larger fraction of its expected years happy, that is evidence of real generational improvement rather than a statistical artifact of comparing different ages at different calendar times.

The headline finding is striking. Across all the age ranges examined, later-born cohorts of older Chinese adults showed statistically significant increases in the proportion of life spent happy. Crucially, the mechanism behind this improvement matters. The gains came primarily from an expansion in the absolute number of happy years, while total life expectancy itself remained largely stable across cohorts. In other words, these generations were not living dramatically longer, but they were living happier within roughly the same span of years. This combination, more happy years without more total years, is precisely what researchers call compression of unhappiness: the unhappy portion of life is being squeezed into a smaller and smaller window, typically pushed toward the very end of life.

The concept of compression is central to modern longevity science. For decades, researchers have debated whether added years of life come bundled with added years of illness and dependency, a scenario known as expansion of morbidity, or whether the burden of poor health is compressed into a shorter terminal period. Evidence has been mixed across countries and across measures of health. By shifting the outcome from physical health to subjective wellbeing, this study adds a new dimension to that debate. A society can, in principle, compress disability while leaving misery intact, or extend physical health while emotional suffering lingers. The Chinese evidence suggests that, at the population level, the emotional dimension has been improving in the favorable direction, with unhappiness increasingly concentrated at the end of life rather than spread across the older years.

But the study’s second major finding tempers any celebratory reading. The cohort improvements were unevenly distributed across the population in ways that mirror, and in some cases amplify, existing social inequalities. Women experienced larger gains than men in both absolute and relative terms. This is notable because women in many societies report lower levels of subjective wellbeing than men despite living longer, a paradox sometimes attributed to widowhood, disability, and economic insecurity in old age. In China, the analysis found that women’s gains were large enough to reverse an initial disadvantage in happy life expectancy, meaning that earlier cohorts of women could expect fewer happy years than men, while later cohorts overtook them. For a country where older women have historically faced structural disadvantages, this reversal is a significant demographic shift.

The socioeconomic gradient is equally consequential. Urban residents and those with formal education saw greater improvements in happy longevity than their rural and less-educated counterparts. This pattern suggests that the benefits of China’s rapid economic development over the study period, which encompassed extraordinary growth in incomes, pensions, healthcare coverage, and infrastructure, flowed disproportionately to groups already better positioned to take advantage of them. Rural older adults, who often lack adequate pension coverage and depend on family support or continued labor, and those who never entered the formal education system, appear to have been left behind in the happiness transition. The result is a widening gap in happy life expectancy between social groups, a trend that echoes findings on healthy life expectancy in many countries but is especially sharp given the pace of change in China.

These findings arrive at a critical policy moment. China’s population is ageing faster than almost any in history, driven by decades of low fertility and rising longevity, and the country’s old-age support systems are under mounting strain. If the additional years older Chinese people are living were years of misery, the fiscal and social costs of ageing would be compounded by an epidemic of late-life suffering. The compression of unhappiness documented here offers a more optimistic baseline: the ageing of the population is not, on current trends, synonymous with a growing mass of unhappy elders. Yet the widening disparities complicate that picture. A national average that conceals a growing divide between urban educated women thriving in their later years and rural uneducated men struggling through them is a fragile foundation for policy planning. Pension adequacy, rural healthcare access, and educational expansion all emerge, implicitly, as levers of happy longevity, not merely of economic policy.

The study also carries a broader methodological message for the international research community. Measuring happy life expectancy at all remains rare, and doing so with a cohort perspective is rarer still. Most national statistics systems track life expectancy with great precision but treat subjective wellbeing as a soft afterthought, if they track it at all. This analysis demonstrates that wellbeing can be integrated rigorously into the demographic toolkit, yielding estimates that are comparable across groups and over time. As other societies with rapidly ageing populations, from Japan to Italy to the United States, confront the same question of whether longer lives are better lives, the Chinese experience documented by Wan and colleagues offers both a template for measurement and a cautionary tale: happiness in old age can improve generation after generation, but without deliberate attention to equity, the happy years may belong mostly to those who need them least.

Subject of Research: Trends in happy life expectancy and socioeconomic inequality among older adults in China, 2002–2018

Article Title: Are older adults in China living longer happy years? A cohort-based multistate analysis, 2002–2018

Article References: Wan, Y., Bousmah, M.-A.-Q., & Nepomuceno, M. R. (2026). Are older adults in China living longer happy years? A cohort-based multistate analysis, 2002–2018. Genus. https://doi.org/10.1186/s41118-026-00298-z

Image Credits: AI Generated

DOI: 10.1186/s41118-026-00298-z

Keywords: happy life expectancy, population ageing, China, cohort analysis, multistate life table, compression of morbidity, subjective wellbeing, socioeconomic inequality, older adults, gender differences, longitudinal survey, demography