In a world of plastic water filter pitchers, I tested two of the new generation of stainless-steel filter systems. I will admit that the popularity of those giant, stainless steel, gravity-fed water filters remained a mystery to me for some years--even as multi-gallon water filter systems from brands like British Berkefeld and Berkey seemed to proliferate equally among lovers of doomsday prepping and holistic wellness retreats. I have been testing much different breeds of water filters for more than a year now, including reverse osmosis filters and water pitchers. But often, the big water filter tanks have seemed as much like status symbols as functional items. If you see a big gravity-fed filter, you know the person in question is serious about wellness, survival, or both. What changed my mind about these big stainless steel filters was microplastics . Most water filter pitchers are made of BPA-free plastic. But as new research shows that bottled-water drinkers ingest tens of thousands of excess microplastic particles, wellness lovers have begun to look askance at water filters that are themselves made of plastic.

Three years on from the release of ChatGPT, two broad camps have formed: those people who refuse to use it, and those who use it every day. Three years on from the release of ChatGPT, two broad camps have formed: those people who refuse to use it, and those who use it every day. We asked experts about the most responsible ways to use AI tools - here's what they said Three years on from the release of ChatGPT, two broad camps have formed: those people who refuse to use it, and those who use it every day. A 2025 survey by the Pew Research Center found that one-third of US adults say they have been using ChatGPT. This includes 58% of US adults under 30 - roughly double the share two years ago.

Drug-induced toxicity is one of the leading reasons new drugs fail clinical trials. Machine learning models that predict drug toxicity from molecular structure could help researchers prioritize less toxic drug candidates. However, current toxicity datasets are typically small and limited to a single organ system (e.g., cardio, renal, or liver). Creating these datasets often involved time-intensive expert curation by parsing drug labelling documents that can exceed 100 pages per drug. Here, we introduce UniTox, a unified dataset of 2,418 FDA-approved drugs with drug-induced toxicity summaries and ratings created by using GPT-4o to process FDA drug labels.

The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psychiatric disorders, and cognitive patterns. In functional magnetic resonance imaging (MRI) research, interactions between brain regions are commonly modeled using graph-based representations. The potency of graph machine learning methods has been established across myriad domains, marking a transformative step in data interpretation and predictive modeling. Yet, despite their promise, the transposition of these techniques to the neuroimaging domain has been challenging due to the expansive number of potential preprocessing pipelines and the large parameter search space for graph-based dataset construction. In this paper, we introduce NeuroGraph, a collection of graph-based neuroimaging datasets, and demonstrated its utility for predicting multiple categories of behavioral and cognitive traits.

Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemphasis on the distance effect as outlined in the First Law of Geography. In response, we propose to endow road network representation with the principles of the recent Third Law of Geography. To this end, we propose a novel graph contrastive learning framework that employs geographic configuration-aware graph augmentation and spectral negative sampling, ensuring that road segments with similar geographic configurations yield similar representations, and vice versa, aligning with the principles stated in the Third Law. The framework further fuses the Third Law with the First Law through a dual contrastive learning objective to effectively balance the implications of both laws. We evaluate our framework on two real-world datasets across three downstream tasks. The results show that the integration of the Third Law significantly improves the performance of road segment representations in downstream tasks.

Meghan unveils new As Ever line with Lilibet... amid claims Netflix has been left with huge $10m surplus of her unsold products amid'split' with streamer Outrageous full story of scandalous affair that's the talk of Manhattan's exclusive private schools: Family insiders reveal humiliating sex secrets... shock'confession' letter... and the furious relative who exposed it all Sinister truth about explosive resignation of Trump's top counter-terror chief Joe Kent... and his shock claim Israel is manipulating the president: MARK HALPERIN Canada's ultimate revenge on Trump over tariffs gathers pace Ugly new Nicole Kidman and Keith Urban divorce fight ERUPTS: Her friends share humiliating details of'midlife crisis'... and reveal brutal REAL reason daughter Sunday Rose'snubbed' him Kim Kardashian takes a VERY dramatic tumble in towering $80 'stripper heels' and accidentally grabs an'old lady' as she falls on her way out of Vanity Fair Oscars party USA baseball stars slammed over'disgraceful' national anthem gesture before WBC final vs Venezuela Israel says Iran's intelligence chief has been killed in overnight airstrike in latest attack on regime: Live updates Presidential hopeful JB Pritzker's bold defiant bet against black caucus pays off Supreme Court's top judge issues chilling warning as Trump targets his own appointees Heath Ledger's lookalike daughter Matilda steps out days after 17 year anniversary of late actor's Oscar win Fox News anchor issues blistering takedown of liberal media's delusional take on Iran: 'A stalemate? I ditched my realtor and used ChatGPT to sell my Florida house instead. Here's my exact prompts and steps for you to do it too Hollywood's top insider makes VERY catty observation about Kaitlan Collins Everything JFK Jr told friends about his love affair with'sexual dynamo' Madonna... her unprintable pillow talk... and his perverse incest request that she couldn't go through with Mamdani forces New York beloved preschool to hike annual fee to $36,000... and parents are fuming Alix Earle stuns in white bikini in first glimpse at 2026 Sports Illustrated Swimsuit edition... after turning heads with Tom Brady and Joe Burrow Scientists reveal what a '30% chance of rain' REALLY means Are you always getting caught in the rain without an umbrella? If so, you might be reading your weather forecast app wrong. When many people see a '30% chance of rain' on their app, they think this corresponds to the heaviness of the downpour, or the area of land that will experience it.

Things to Do in L.A. Tap to enable a layout that focuses on the article. AI pilot program in L.A. County courts will help judges craft rulings in some cases This is read by an automated voice. Please report any issues or inconsistencies here . A select panel of L.A. County judges now have access to an artificial intelligence tool that can help them summarize motions and draft rulings in civil court. The tool, Learned Hand, is already in use by judges in 10 states, according to the company's CEO.

In the age of A.I., the term has become as much of a Silicon Valley cliché as "disruption" was in the twenty-tens. With artificial intelligence continuing to dominate corporate strategies and news headlines, Silicon Valley has embraced a new buzzword, one that may feel too close to home for those already feeling embattled by automation. That word is "taste," and in recent months it has become as much of a tech-world cliché as "disruption" was in the twenty-tens. The esteemed technologist Paul Graham posted on X, "In the AI age, taste will become even more important." Koen Bok, a founder of the booming A.I. design tool Framer, said on a podcast that "great taste" is what will create the best new products.

Deep neural networks (DNNs) are widely used models for investigating biological visual representations. However, existing DNNs are mostly designed to analyze neural responses to static images, relying on feedforward structures and lacking physiological neuronal mechanisms. There is limited insight into how the visual cortex represents natural movie stimuli that contain context-rich information. To address these problems, this work proposes the long-range feedback spiking network (LoRaFB-SNet), which mimics top-down connections between cortical regions and incorporates spike information processing mechanisms inherent to biological neurons. Taking into account the temporal dependence of representations under movie stimuli, we present Time-Series Representational Similarity Analysis (TSRSA) to measure the similarity between model representations and visual cortical representations of mice. LoRaFB-SNet exhibits the highest level of representational similarity, outperforming other well-known and leading alternatives across various experimental paradigms, especially when representing long movie stimuli. We further conduct experiments to quantify how temporal structures (dynamic information) and static textures (static information) of the movie stimuli influence representational similarity, suggesting that our model benefits from long-range feedback to encode context-dependent representations just like the brain. Altogether, LoRaFB-SNet is highly competent in capturing both dynamic and static representations of the mouse visual cortex and contributes to the understanding of movie processing mechanisms of the visual system.