In one of the most comprehensive investigations of its kind, researchers have taken a major step toward untangling the biology underlying suicide risk, moving the field beyond long-standing debates about whether suicidal behavior is simply a consequence of psychiatric illness. By integrating large-scale genetic data with cutting-edge approaches that map how genes act in specific brain cells and circuits, an international team has identified shared genetic liability that cuts across psychiatric diagnoses while also pinpointing druggable biological pathways that could one day inform prevention strategies. The study, published in Translational Psychiatry, offers a sweeping synthesis of how inherited risk factors may translate into molecular vulnerabilities, and it does so with a level of resolution that previous candidate-gene approaches could never achieve.

Suicide remains one of the leading causes of death worldwide, claiming more than 700,000 lives each year according to global health estimates. Despite decades of research, clinical prediction remains stubbornly imprecise, and biological models of suicidal behavior have historically been hampered by small sample sizes, inconsistent findings, and a tendency to conflate suicide risk with the psychiatric disorders with which it so often co-occurs. Major depression, bipolar disorder, schizophrenia, and substance use disorders all carry elevated suicide risk, yet most people with these conditions never attempt suicide, while some individuals who die by suicide have no diagnosed psychiatric illness at all. This paradox has long suggested to researchers that suicidal behavior may possess partially distinct biological underpinnings, and the new study set out to test that hypothesis with unprecedented statistical power.

At the heart of the work lies genome-wide association study methodology, or GWAS, which scans hundreds of thousands to millions of genetic variants spread across the entire genome in very large groups of people, comparing individuals who have experienced suicidal thoughts or behaviors with those who have not. Each variant is tested for statistical association with the outcome, and the results can be combined into polygenic risk scores, numerical summaries of a person’s inherited liability based on the cumulative small effects of thousands of variants. Because no single common variant exerts a large effect, the field depends on aggregating data across massive biobanks and cohorts, a strategy that has only become feasible in the past decade as datasets containing hundreds of thousands of individuals became available.

The research team took particular care to address a central confound: psychiatric disorders themselves are highly heritable, and any genetic signal linked to suicide could simply reflect inherited liability to depression or schizophrenia rather than to suicide specifically. To disentangle this, the investigators applied sophisticated statistical techniques, including genetic correlation analysis and conditional modeling, which allow researchers to ask how much genetic overlap remains between suicidal behavior and psychiatric conditions after accounting for their shared heritability. Their findings revealed a complex architecture. Substantial genetic correlations confirmed that suicide risk shares a meaningful fraction of its inherited basis with major psychiatric disorders, supporting the idea of a common pool of genetic liability. Yet the analyses also uncovered signals that persisted beyond what could be explained by those disorders, pointing to biological mechanisms that may be more specific to the emergence of suicidal thoughts and actions.

A key conceptual innovation of the study is its framing of “shared genetic liability” as a bridge between diagnoses. Rather than treating suicide as an epiphenomenon of any single disorder, the researchers modeled it as a transdiagnostic phenomenon, one whose risk emerges partly from inherited vulnerabilities that cut across diagnostic categories. This perspective aligns with a growing movement in biological psychiatry that emphasizes symptom dimensions and mechanisms over traditional diagnostic boundaries. From this vantage point, the genetic variants that raise risk for depression, PTSD, and schizophrenia may do so partly because they converge on shared molecular pathways, and those same pathways may govern an individual’s threshold for suicidal crises regardless of which diagnosis they carry.

Identifying genetic variants, however, is only the first step. A GWAS signal typically points to a genomic region rather than a specific causal gene or mechanism, since many associated variants fall in non-coding stretches of DNA that regulate gene activity from a distance. To translate statistical associations into biological insight, the team deployed a battery of functional genomics approaches. Transcriptome-wide association studies, known as TWAS, integrate gene expression data to identify genes whose genetically predicted expression levels are linked to suicide risk. Tissue and cell-type enrichment analyses then ask whether the implicated genes are unusually active in particular organs or cell populations. The results converged strikingly on brain tissue, and more specifically on neuronal populations, providing support for the idea that inherited suicide risk is mediated largely through gene regulation within the central nervous system.

The study’s most clinically provocative findings concern druggable biology. Using databases that catalog the protein targets of approved and investigational medications, the researchers examined whether genes implicated in suicide risk were over-represented among known drug targets. The analysis identified several classes of candidate targets, including components of neurotransmitter signaling systems, ion channels, and intracellular signaling cascades that are already the focus of pharmacological development for other conditions. This “druggable genome” approach, which has gained traction in genetics research across many diseases, offers a pragmatic shortcut for translational science: rather than waiting decades for entirely new therapeutic classes to be invented, researchers can prioritize existing or in-development compounds whose molecular targets intersect with genetically implicated pathways.

Among the pathways receiving attention in the analysis were those involving glutamatergic and GABAergic neurotransmission, the primary excitatory and inhibitory systems of the brain, which have repeatedly surfaced in studies of mood disorders and stress response. The endocannabinoid system and inflammatory signaling pathways also appeared in the convergent analyses, consistent with a broader literature linking immune dysregulation and allostatic load to suicidal ideation. Importantly, the authors emphasize that these findings identify hypotheses rather than ready-made treatments. Genetic association with a drug target does not guarantee that modulating that target will reduce suicide risk, and any therapeutic application would require extensive preclinical validation and carefully designed clinical trials. Nevertheless, the work provides a principled, data-driven map for where to look, replacing decades of largely failed candidate-gene speculation with a genome-wide evidence base.

The methodological rigor underpinning the study deserves emphasis, because the genetics of suicidal behavior has a troubled history. Early candidate-gene studies, which typically examined a handful of variants in genes related to serotonin signaling, produced findings that largely failed to replicate and in some cases were later shown to reflect statistical artifacts, small samples, and publication bias. The field has since undergone a reckoning, and modern efforts adhere to far stricter standards: preregistered analyses, very large and ancestrally diverse cohorts, replication in independent samples, and conservative multiple-testing corrections that demand genome-wide significance thresholds on the order of five times ten to the negative eighth power. The new study reflects these lessons, applying sensitivity analyses to guard against confounding from psychiatric comorbidity and examining whether findings generalize across ancestral groups, an issue of considerable importance given that most genetic discovery to date has been conducted in populations of European ancestry.

The implications for clinical practice, while still distant, are worth considering. Polygenic risk scores for suicide-related outcomes currently explain only a small fraction of individual differences in risk, far too little to be used as stand-alone screening tools, and their predictive performance is degraded in populations not represented in the discovery data. Researchers in the field are careful to stress that genetic risk is not destiny: suicidal behavior arises from an intricate interplay of inherited vulnerability, mental illness, trauma, substance use, social isolation, access to lethal means, and the availability of support. What genetics offers is not a crystal ball but a window into mechanism, and mechanistic knowledge is the raw material from which better biomarkers, risk stratification tools, and ultimately targeted interventions are built. In the near term, the druggable pathway findings are most valuable as a guide for laboratory science, directing neurobiologists toward molecular systems whose manipulation might alter suicide-relevant phenotypes in animal models and cellular assays.

Looking forward, the study signals where the field must go next. Sample sizes for suicide-specific genetic analyses remain smaller than those for major psychiatric disorders, and expanding representation across ancestries is essential if the benefits of this research are to be shared equitably. Integrating genetic data with longitudinal measures of life stress, neural imaging, and digital phenotyping may eventually allow researchers to model how inherited liability interacts with environmental triggers across the lifespan. Functional experiments in induced pluripotent stem cell-derived neurons and brain organoids could test whether the implicated genes genuinely alter stress-response circuitry or resilience. And randomized trials in patients carrying high genetic liability, though ethically and logistically complex, represent the logical endpoint of the druggable-target approach.

What the study ultimately delivers is a reframe. Suicide risk, in this analysis, is not an amorphous clinical outcome but a measurable, partly heritable trait whose genetic architecture is now being resolved with the same tools that have transformed our understanding of heart disease, diabetes, and schizophrenia. By demonstrating that shared genetic liability links suicide risk across diagnostic boundaries and by mapping that liability onto specific, pharmacologically tractable biology, the researchers have converted a long-standing clinical puzzle into a concrete research agenda. The path from genetic correlation to effective prevention is long, and no genetic finding will soon replace the human work of crisis intervention, connection, and care. But for a problem that has defied biological explanation for generations, the identification of shared, druggable mechanisms represents a genuine and hard-won advance, one that may shape suicide research for years to come.

AI visibility tools track brand mentions and citations, but marketers need outcome data to know what those movements mean and what to do next.

The post Brands Are Tracking AI Visibility, But Are They Measuring The Right Things? appeared first on Search Engine Journal.

Trump has touted a recent Census report. WIRED found grave flaws in its analysis and the process behind it, and confirmed the identity of several of its authors—among them a one-time DOGE affiliate.

Trump has touted a recent Census report. WIRED found grave flaws in its analysis and the process behind it, and confirmed the identity of several of its authors—among them a one-time DOGE affiliate.

Modeled data now fills the gaps left by signal loss, and mistaking estimates for measurements can lead to costly budget misallocation.

The post When Attribution Looks More Precise Than The Data Behind It appeared first on Search Engine Journal.

An analysis of dozens of strange objects captured in official U.S. government videos has revealed new insights—and challenges—involving the study ofunidentified anomalous phenomena (UAP).

Since May 8, 2026, the U.S. Department of War has released more than 100 videos that reportedly capture evidence of unknown objects in U.S. airspace and near American military operations around the world.

Now, a preprint paper by researchers Jacob Haqq-Misra of Blue Marble Space and Ravi Kopparapu of NASA’s Goddard Space Flight Center examined 112 videos released this year under the Presidential Unsealing and Reporting System for UAP Encounters (PURSUE).

The researchers, both of whom serve on the UAP Science Advisory Council to the U.S. government, say their study highlights significant limitations on analysis of the purported UAP videos due to missing or redacted sensor data, which prevents civilian researchers from having enough of the tools necessary to determine whether seemingly fast-moving objects in the videos actually display anomalous velocities.

Kopparapu and Haqq-Misra’s new study, which has not yet undergone peer review, highlights a fundamental problem that has impeded analytical efforts involving UAP videos collected by U.S. military personnel: that the apparent speed of an object suggested by its movement across a camera’s field of view is not necessarily an accurate indication of its real physical velocity.

In an email to The Debrief, Haqq-Misra explained that while the new study is among the first to explore the limitations on what can be discerned about these objects based on analysis of the current batch of PURSUE releases, the underlying problem is not a new one for researchers tackling the UAP problem.

“I think our conclusion will come as no surprise to people in the aviation, aerospace, and intelligence communities who have already been closely following UAP disclosure,” Haqq-Misra told The Debrief, though expressing that the conclusions he and Kopparapu present “may not be fully appreciated in the broader public discussion.”

“I get lots of questions from friends, family, and other scientists about my opinion of the government ‘UAP disclosure’ data,” Haqq-Misra said. “A lot of people haven’t even looked at the data and may assume that there is more available than we really are getting.”

In terms of what data has been made available to the public since May, Haqq-Misra says the limitations are obvious.

“Most of the useful quantities are redacted,” he said. “It is also not immediately obvious that what appears to be a fast-moving object in an infrared sensor video might be a mundane object (like a bird) much closer to the camera.”

Analyzing the DOW’s ‘PURSUE’ UAP Videos

The videos Haqq-Misra and Kopparapu analyzed, all of which are currently available at the U.S. Department of War website, were acquired by targeting pods and other turret-mounted camera systems aboard military aircraft or similar government platforms. The collection includes infrared and electro-optical imagery, much of it showing unidentified objects either crossing the camera’s field of view or being actively tracked by the sensor.

Haqq-Misra and Kopparapu reviewed all 112 videos to determine what kinematic information could be extracted from them. At its simplest, analysts can measure how quickly an object moves across a video in pixels per frame. However, the problem arises when it comes to converting that apparent motion into an actual velocity—a process that requires several additional pieces of information.

In an email to The Debrief, Kopparapu, who expressed that his views are based on his own independent analysis and do not reflect those of NASA, said there was one primary form of data that, if it were to accompany future video releases, could make them far more useful to scientists working to understand the performance characteristics of the objects they convey.

“Based on our preliminary mathematical analysis, perhaps the single most important missing quantity is the range; the distance between the object and the observing platform, ideally measured over time,” Kopparapu told The Debrief. “Without range, something moving rapidly across the image could be a small nearby object moving relatively slowly or a much larger, distant object moving extremely fast.”

Additional information Kopparapu says would be very useful would include details about the camera’s field of view, as well as “the motion of the observing platform and viewing geometry.”

In their recent paper, Kopparapu and Haqq-Misra detail the specific measurements that would be required to determine an object’s velocity. However, Kopparapu expressed to The Debrief that “Some apparent anomalies could even arise from sensor or imaging effects,” adding that without the additional data outlined above, “there is simply not much more that can be concluded scientifically.”

Haqq-Misra told The Debrief that one of the goals of the UAP Science Advisory Council is to make recommendations about what kinds of data future PURSUE releases might be able to include that would enable independent scientists to conduct more accurate and meaningful kinematic analyses. 

“We are hopeful that our recommendations based on analyses of sensor videos like these will lead to additional release of data that may even help us resolve some cases,” Haqq-Misra told The Debrief, echoing Kopparapu’s sentiments about the importance of having access to information about the object’s range from the camera system at the time the video was captured.

“Knowing the distance from the camera to the object is essential,” Haqq-Misra said. “This information might be under the redacted black boxes of some of the videos, so unredacted videos would be best.”

UAP Transparency vs National Security

The question of whether information currently redacted from government UAP videos can be safely released to scientists without compromising U.S. national security—as well as how much of that data should be released—remains a sticking point between Pentagon officials and the broader scientific community.

For researchers like Haqq-Misra, one solution might involve having some form of verifiable U.S. government record that certifies certain portions of the redacted information, without necessarily conveying that in the videos themselves in ways that could compromise sensitive information about the systems used to obtain the footage.

“Even just telling us the distance on an official government document would help!” Haqq-Misra told The Debrief.

“The flight trajectory of the aircraft with the observing camera is another important piece of data,” Haqq-Misra added. “In some cases, this data may simply be lost, but if flight logs exist for any of these cases, that would also help.

Although Haqq-Misra adds that technical information about the specific cameras used to capture a given video would “make the data even more useful,” the U.S. Department of War regulates much of this technical data under strict security classification, export control, and Controlled Unclassified Information (CUI) policies. Such restrictions are imposed to protect critical capabilities like sensor resolution, focal lengths, tracking algorithms, and other capabilities or technical properties that might potentially be exploited by foreign adversaries if they were revealed publicly.

While U.S. military concerns over the release of such information is often warranted, without access to it, the nature of how some military systems operate can make it difficult—if not impossible—to make meaningful determinations about the objects detected.

One example includes objects that are being actively tracked while they are filmed. By its design, a targeting system can move its camera to keep an object near the center of the frame, making the subject’s apparent movement approach zero, even if it is traveling rapidly through the environment. The resulting imagery can create the illusion of a stationary object and becomes unhelpful without access to additional information about the object’s movement.

GOFAST: A Case Study in Transparency?

Another example where the appearance of an object’s movement on video can differ significantly from its actual speed appears in the well-known Navy “GOFAST” video. Haqq-Misra and Kopparapu note that this footage was once widely interpreted as evidence of an object moving at extraordinary speed near the ocean surface. However, subsequent analysis using available telemetry showed that much of its apparent motion resulted from parallax generated by the observing aircraft, with its calculated motion ultimately being consistent with wind-borne drift—in other words, the object was most likely a balloon.

The important difference between the GOFAST video and those included in recent PURSUE releases, the researchers note, comes down to the amount of additional information that was available.

“In the GOFAST case, sufficient information was available to constrain the object’s motion relative to the observer and assess it in relation to reported wind conditions,” Haqq-Misra and Kopparapu write in their recent study. Notably, following its initial unauthorized release in 2017 along with two other historical U.S. Navy videos of alleged UAP, the Pentagon eventually determined that GOFAST did not reveal any sensitive information that compromised U.S. national security.

“After a thorough review, the department has determined that the authorized release of these unclassified videos does not reveal any sensitive capabilities or systems, and does not impinge on any subsequent investigations of military air space incursions by unidentified aerial phenomena,” an April 2020 Pentagon statement read. Seemingly, this case would appear to demonstrate that in at least some cases, enough additional information could remain unredacted in future UAP video releases to allow similar technical determinations to be made.

DOW-UAP-PR113: Anomalous Appearance, but Limited Information

Another example Kopparapu and Haqq-Misra look at in their paper involves a video in the recent PURSUE releases designated DOW-UAP-PR113, which features an infrared video reportedly recorded over the western United States in 1996.

The footage depicts what appears to be a compact dark object (or possibly two moving in very close proximity) rapidly crossing the camera’s field of view. Based on Kopparapu and Haqq-Misra’s measurements, the object travels approximately 142 pixels per frame, equivalent to roughly 2.2 frame widths per second, and remains directly visible for only about one-tenth of a second.

One thing that makes the video unique among the PURSUE releases is the presence of markings in the imagery, which the researchers say allows them at least a limited ability to reconstruct an approximate angular scale and horizontal field of view. However, like in other cases, too little information is present to be able to make a complete determination about the object’s motion and capabilities.

For Kopparapu, circumstances like these demonstrate why he advises against drawing conclusions based on what can often appear to be remarkable speed or other capabilities in the PURSUE videos, yet without ruling them out necessarily as being mundane.

“With such limited data, I would urge people to avoid both extraordinary conclusions and outright dismissal,” Kopparapu told The Debrief. “Something can look extremely anomalous when we are seeing only a small part of the information needed to understand it.”

What the PURSUE Videos Can—and Cannot—Tell Scientists

Fundamentally, Haqq-Misra and Kopparapu believe that the videos currently made available under the PURSUE initiative cannot demonstrate—nor can they necessarily be used to exclude—the presence of anomalous kinematics displayed by the alleged UAP they contain.

This issue extends beyond just the PURSUE releases, however, since similar limitations can arise even with imagery captured by conventional cameras, whose technical capabilities are easily discernible. For instance, a single camera primarily measures angles, meaning some aspects of an object’s three-dimensional motion simply cannot be reconstructed based on that imagery alone.

To overcome such problems, Haqq-Misra and Kopparapu point to the use of multi-sensor approaches by independent research efforts.

In terms of what kinds of scientific observations might constitute a “genuine” UAP in future detections, Haqq-Misra says he and his colleagues with the UAP Science Advisory Council are already providing recommendations for the kinds of sensor platforms that could be leveraged to capture better UAP imagery and other data in the future.

“We have already provided recommendations for sensors that could be deployed to collect better quality data for understanding these objects,” Haqq-Misra told The Debrief. “An ideal observatory would be a multi-modal and multi-site observatory, leveraging video observations from infrared to visible and including other modalities such as radio, radar, and audio data collection.”

“The Galileo Project at Harvard is an example of a privately funded effort following this experimental design,” he adds.

Overall, the researchers say that while based on their analysis “the PURSUE sensor videos in their present form cannot fully resolve unidentified cases or conclusively indicate anomalous velocities,” the new research presents a meaningful early step toward identifying future strategies that may help researchers obtain better, more useful data.

“Reality takes time,” Kopparapu told The Debrief, “but fallacy does not.”

“Careful scientific investigation, accompanied by access to the underlying data, is the only reliable way to make progress on these cases,” he adds.

Kopparapu and Haqq-Misra’s recent paper, “Limits on Velocity Recovery from the PURSUE Sensor Videos,” appeared on the arXiv.org preprint server.

Micah Hanks is the Editor-in-Chief and Co-Founder of The Debrief. A longtime reporter on science, defense, and technology with a focus on space and astronomy, he can be reached at micah@thedebrief.org. Follow him on X @MicahHanks, and at micahhanks.com.