8 Best Travel Adapters (2026): 20+ Models Tested
If your iPhone is one of the 15 models on Apple’s Apple Intelligence device list, the cheapest way in costs nothing: iOS 27 arrives free on Monday, September 14. If it is not, the least expensive route Apple sells is the $699 iPhone 17e, and the same model was listed at $599 before September 9. […]
The post The Cheapest Way Into Apple Intelligence in 2026: Free on 15 iPhones, $699 If Yours Is Not One appeared first on The Mac Observer.
Apple’s headphone range is down to three names and four prices, and the choice comes apart cleanly by what you do rather than what you spend. AirPods 5 at $129 or $149 are the open-ear pick for anyone who wants to hear the room, AirPods Pro 3 at $249 are the only model with a […]
The post AirPods 5, AirPods Pro 3 or AirPods Max 2: Apple’s Three Sets, Decided by What You Do appeared first on The Mac Observer.
Apple now sells three Apple Watches, and for most sports the answer is the middle one. The Apple Watch Series 12 at $399 covers running, swimming and everyday health tracking on Apple’s own numbers, the Ultra 4 at $799 earns its price only if your day runs past 10 hours of workout tracking or your […]
The post Apple Watch by Activity: What the Specs Say for a Runner, a Swimmer, a Hiker and a Desk Worker appeared first on The Mac Observer.
Apple sells seven iPhones this week, from the $699 iPhone 17e to the $1,999 iPhone Duo, and only two of them carry a dedicated telephoto lens. That makes the decision simpler than the size of the lineup suggests: the iPhone 18 Pro at $1,199 is the cheapest iPhone Apple sells with a real zoom lens, […]
The post Which iPhone Should You Buy in 2026? One Decision Table for All Seven Apple Sells appeared first on The Mac Observer.
Apple has published no walk-in policy for 18 September, and it has published no stock figures for any store, anywhere. If you want a straight answer to whether you can turn up on launch day without a pre-order and leave with a phone, Apple has not given one, and nobody else is in a position […]
The post Can You Buy an iPhone 18 Pro in Store on September 18 Without Pre-Ordering? appeared first on The Mac Observer.
On May 11, 2026, the School of Science hosted the inaugural 1932 Award Dinner to honor Marilyn Simons and the late Jim Simons as philanthropists and as trusted partners of fundamental scientific researchers at MIT. The 1932 Award recognizes philanthropy that ignites new pathways of curiosity throughout the School of Science and celebrates its impact on basic science. Faculty, students, and friends of MIT gathered to commend Jim and Marilyn Simons and the Simons Foundation as visionary partners of the School of Science, who have advanced discovery across mathematics, neuroscience, and the physical sciences for three decades.
The Simons’s generosity reflects a deep and enduring partnership grounded in curiosity, collaboration, and a shared commitment to advancing fundamental science for the benefit of humanity. By recognizing their achievements with the 1932 Award, Dean Nergis Mavalvala and the School of Science community expressed gratitude to exceptional generosity that drives forward the scientific enterprise.
The post Marilyn Simons receives School of Science’s 1932 Award appeared first on MIT School of Science.
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.
Article Title: From shared genetic liability to druggable biology in suicide risk
Article References: Yao, K., Zhuo, C., Chen, X., Li, C., Song, H., Zou, J., & Tian, H. (2026). From shared genetic liability to druggable biology in suicide risk. Translational Psychiatry. https://doi.org/10.1038/s41398-026-04428-1
Image Credits: AI Generated
DOI: 10.1038/s41398-026-04428-1
Keywords: suicide risk, genetic liability, genome-wide association study, polygenic risk score, druggable genome, transdiagnostic psychiatry, TWAS, brain gene expression, glutamatergic signaling, psychiatric genetics, Translational Psychiatry
| Mac mini (M6) | Mac mini (M5 Pro) | Mac Studio (M5 Max) | Mac Studio (M5 Ultra) |
|---|---|---|---|
| M6 chip | M5 Pro chip | M5 Max chip | M5 Ultra chip (two dual-die M5 Max chips joined by UltraFusion) |
| Made with TSMC's 2nm process | Made with TSMC's 3nm process | Made with TSMC's 3nm process | Made with TSMC's 3nm process |
| 12-core CPU (2 super cores + 4 performance cores + 6 efficiency cores) | 15- or 18-core CPU (up to 6 super cores + 12 performance cores) | 18-core CPU (6 super cores + 12 performance cores) | 30- or 36-core CPU (up to 12 super cores + 24 performance cores) |
| 12-core GPU | 16- or 20-core GPU | 32- or 40-core GPU | 64- or 80-core GPU |
| Dual 16-core Neural Engine | 16-core Neural Engine | 16-core Neural Engine | 32-core Neural Engine |
| Up to 170GB/s memory bandwidth | 307GB/s memory bandwidth | Up to 614GB/s memory bandwidth | 1.2TB/s memory bandwidth |
| 16GB, 24GB, or 32GB unified memory | 24GB, 48GB, or 64GB unified memory | 32-core GPU: 36GB unified memory
40-core GPU: 48GB, 64GB, or 128GB unified memory |
64-core GPU: 96GB or 256GB unified memory
80-core GPU: 96GB, 256GB, or 512GB unified memory |
| 256GB, 512GB, 1TB, or 2TB storage | 512GB, 1TB, 2TB, 4TB, or 8TB storage | 512GB, 1TB, 2TB, 4TB, or 8TB storage | 1TB, 2TB, 4TB, 8TB, or 16TB storage |
| Video decode engine | Video decode engine | Video decode engine | Two video decode engines |
| Video encode engine | Video encode engine | Two video encode engines | Four video encode engines |
| ProRes encode and decode engine | ProRes encode and decode engine | Two ProRes encode and decode engines | Four ProRes encode and decode engines |
| Support for up to three displays: two at 6K 60Hz or 4K 165Hz, plus a third at 5K 60Hz over Thunderbolt or 4K 60Hz over HDMI | Support for up to three displays, all at 6K 60Hz or 4K 165Hz | Support for up to five 4K displays, four 6K displays, or two 8K displays | Support for up to eight 4K displays, eight 6K displays, or four 8K displays |
| Two displays per Thunderbolt port | All three displays through a single Thunderbolt port | ||
| Three rear Thunderbolt 4 ports | Three rear Thunderbolt 5 ports | Four rear Thunderbolt 5 ports | Four rear Thunderbolt 5 ports |
| Two front USB-C ports | Two front USB-C ports | Two front USB-C ports | Two front Thunderbolt 5 ports |
| Two rear USB-A ports | Two rear USB-A ports | ||
| 2.5Gb Ethernet or 10Gb Ethernet port | 2.5Gb Ethernet or 10Gb Ethernet port | 10Gb Ethernet port | 10Gb Ethernet port |
| SDXC card slot (UHS-II) | SDXC card slot (UHS-II) | ||
| Bottom power button | Bottom power button | Rear power button | Rear power button |
| Starts at $899 | Starts at $1,699 | Starts at $2,499 | Starts at $5,499 |
| Mac mini | Mac Studio | |
|---|---|---|
| Height | 2.0 inches (5.0 cm) | 3.7 inches (9.5 cm) |
| Width | 5.0 inches (12.7 cm) | 7.7 inches (19.7 cm) |
| Depth | 5.0 inches (12.7 cm) | 7.7 inches (19.7 cm) |
| Weight | M6: 1.5 pounds (0.67 kg)
M5 Pro: 1.6 pounds (0.73 kg) |
M5 Max: 6.0 pounds (2.7 kg)
M5 Ultra: 8.0 pounds (3.6 kg) |
LibreDB Studio traz IDE SQL self-hosted com 16 bancos, editor Monaco, ER diagrams e IA opcional para consultas e otimização.
O post LibreDB Studio chega como IDE SQL self-hosted para 16 bancos apareceu primeiro em Blog do Edivaldo - Informações e Notícias sobre Linux.
A provocative new paper suggests consciousness may not be unique to Earth and could arise in alien life forms far stranger than we can imagine. A new philosophical paper argues that consciousness may not be exclusive to life as we know it. According to Eric Schwitzgebel, a distinguished professor of philosophy at the University of [...]
A biodegradable wash may help remove surface pesticide residues and keep fruit fresh longer, though more testing is needed before real-world use. Many shoppers follow the same pattern after buying produce: they bring fruits and vegetables home, wash them, dry them, and hope they stay good long enough to eat. Fresh produce, however, can deteriorate [...]
Neil Fraser (via Hacker News):
Nearly twenty-five years ago I registered neil.fraser.name to provide a stable presence on the Internet. It has been the home of this website, my email address, and a server for APIs.
[…]
On 15 April 2026 Verisign proposed the destruction of the entire 3rd level of the ‘.name’ hierarchy in order to simplify their administration. Astonishingly, on 28 July 2026 ICANN approved this action.
[…]
One of my original reasons for choosing ‘.name’ was that it was run by Global Name Registry -- more specifically, it was not run by Verisign. I had history with Verisign and did not trust them. Unfortunately, Verisign acquired Global Name Registry a few years later.
He’s going to lose his site and e-mail address, and someone else could potentially register the domain and hijack his accounts.
Previously:

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