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  • Neural correlates and reinstatement of recent and remote memory in children and young adults

    Neural correlates and reinstatement of recent and remote memory in children and young adults

    To investigate how neural activation for correctly recalled memories varied across different time delays, we examined the contrast of remote >recent correct trials during object presentation at retrieval (Figure 4, ‘Retrieval fMRI’).

    Mean signal differences between correct remote and recent memories.

    The figure presents mean signal difference for remote > recent contrast across sessions and groups during the object presentation time window in (A) anterior and posterior hippocampus; (B) anterior and posterior parahippocampal gyrus; (C) cerebellum; (D) medial prefrontal cortex; (E) ventrolateral prefrontal cortex; (F) precuneus; (G) retrosplenial cortex; (H) lateral occipital cortex. Note: Bars indicate the group mean for each session (solid lines for day 1, dashed lines for day 14), plotted separately for children and young adults. Error bars represent ± 1 standard error of the mean. The color indicated the age groups: purple for children and khaki yellow for young adults. Across all panels, the mean of individual subject data is shown with transparent points. The connecting faint lines reflect within-subject differences across sessions. Orange asterisks denote significant difference of remote > recent contrast from zero. An upward orange arrow indicates that this difference is greater than zero, while a downward arrow indicates that this is less than zero. *p<0.05; **p<0.01; ***p<0.001 (significant difference); nonsignificant differences were not specifically highlighted. Significant main and interaction effects are highlighted by the corresponding asterisks. All main and interaction p-values were false discovery rate (FDR)-adjusted for multiple comparisons.

    We first tested whether the remote > recent contrast significantly differed from zero in each age group and session (day 1 and day 14), as an indicator of differential engagement during memory retrieval. FDR-adjusted results showed no significant results in the anterior and posterior HC (Figure 4A), anterior PHG (Figure 4B), and RSC (Figure 4G) across sessions and age groups (all p>0.054; see Supplementary file 3 for details). To rule out the possibility that these nonsignificant differences reflect an overall absence of retrieval-related activation, we tested whether mean activation for recent and remote items – each relative to the implicit baseline – was significantly above zero. FDR-adjusted results revealed that activation in these ROIs was significantly greater than zero (all p<0.031), except in the recent day 1 condition in children for the posterior HC (p>0.141) and the precuneus (p>0.056, see Supplementary file 4 and Figure 4—figure supplement 1 for details). These findings indicate that the anterior and posterior HC, anterior PHG, and RSC are similarly engaged during successful retrieval of both recent and remote memories, regardless of delay or age group. (As a control analysis, we tested whether the anterior and posterior HC, anterior PHG, and RSC were similarly engaged during retrieval of recent and remote items over time using the LME models. These models included mean activation relative to the implicit baseline, a Session × Delay × Group interaction, and Subject as a random intercept. The results were consistent with the earlier findings, showing no significant main effect of Delay [all p>0.106], Group [all p>0.060], or Session × Delay interaction [all p>0.340], indicating comparable engagement of these ROIs across delays and age groups [see Supplementary file 6 for full statistical details].) Other ROIs showed more differentiated patterns, which are discussed below. (In contrast, the vlPFC, CE, posterior PHG and LOC, precuneus, and mPFC showed a significant main effect of Delay [all p<0.009, see Supplementary file 5 for details], indicating time-related changes in the remote > recent contrast. These effects are examined in more detail below. Notably, these findings are consistent with results from the whole-brain analyses; Supplementary file 7.)

    To further explore the more differentiated patterns observed in other ROIs, we examined changes in the remote >recent contrast across age groups and sessions (day 1 and day 14) using LME models, controlling for sex, handedness, general intelligence, and mean reaction time. All main and interaction effects were FDR-adjusted, and all post hoc tests were Sidak-corrected (see Supplementary file 5 for details).

    For the posterior PHG (Figure 4B), a significant Session × Group interaction, F(1,83) = 9.54, p=0.020, ω2=0.09, indicated a more pronounced increase in remote >recent mean signal difference over time in young adults compared to children, b=0.11, t(83) = 3.09, p=0.003.

    Similarly, also for the cerebellum (Figure 4C), a significant Session × Group interaction, F(1,161) = 7.68, p=0.020, ω2=0.04, indicated a stronger increase in remote > recent mean signal difference over time in young adults compared to children, b=0.09, t(160) = 2.77, p=0.006.

    For the mPFC (Figure 4D), a significant main effect of Group, F(1,86) = 7.61, p=0.023, ω2=0.07, denoted that the overall remote > recent mean signal difference in children was higher than in young adults, b=–0.10, t(86) = –2.76, p=0.007.

    For the vlPFC (Figure 4E), a significant main effect of Group, F(1,82) = 31.35, p=<0.001, ω2=0.13, indicated an overall lower remote > recent mean signal difference in children compared to young adults, b=–0.125, t(108) = –3.91, p<0.001. In addition, a significant main effect of Session, F(1,99)=10.68, p=0.005, ω2=0.09, pointed out overall higher remote > recent mean signal difference on day 14 compared to day 1, b=0.08, t(99) = 3.27, p=0.001.

    For the precuneus (Figure 4F), a significant main effect of Group, F(1,161) = 5.09, p=0.027, ω2=0.02, indicated an overall lower remote > recent mean signal difference in adults compared to children, b=–0.05, t(160) = –2.26, p=0.037. In addition, a significant main effect of Session, F(1,161) = 6.50, p=0.036, ω2=0.03, denoted an overall lower remote > recent contrast for day 14 compared to day 1, b=–0.05, t(160) = –2.55, p=0.012. Although the remote > recent contrasts were mostly negative, the mean activation for recent and remote items – each relative to the implicit baseline – was significantly greater than zero for all delays and group (all p<0.023), except for children’s recent items on day 1 (p=0.056).

    For the LOC (Figure 4H), a significant main effect of Group, F(1,82) = 9.12, p=0.015, ω2=0.09, indicated a higher remote > recent mean signal difference in young adults compared to children, b=0.07, t(82) = 3.02, p=0.003. Additionally, a significant main effect of Session, F(1,97) = 16.76, p=<0.001, ω2=0.14, showed an overall increase in remote > recent mean signal difference on day 14 compared to day 1, b=0.07, t(97) = 4.10, p=<0.001. Furthermore, a significant Session × Group interaction, F(1,81) = 6.42, p=0.032, ω2=0.06, demonstrated higher increase in remote > recent mean signal difference over time in adults compared to children, b=0.09, t(81) = 2.53, p=0.013.

    Of note, we conducted an additional univariate analysis using a subsample that included only participants who needed two learning cycles to reach the learning criteria (see Supplementary file 8 for details). The subsampled results fully replicated the findings from the full sample and demonstrated that the amount of re-exposure to stimuli during encoding did not affect consolidation-related changes in memory retrieval at the neural level.

    In summary, our findings revealed distinct consolidation-related neural upregulation for remote memory between children and adults. From day 1 to day 14, adults showed a higher increase in remote > recent signal difference for remembered items in the posterior PHG, LOC, and cerebellum than children. Adults showed overall higher remote > recent difference in the vlPFC than children, while children showed overall higher remote > recent difference in the mPFC than adults. Furthermore, we observed a constant activation of anterior and posterior HC, anterior PHG, and RSC in memory retrieval across age groups irrespective of memory type or delay.

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  • Tired of Running Out of Ink? Snag the HP Smart Tank 5000 With 2 Years of Ink Included for $150 Right Now

    Tired of Running Out of Ink? Snag the HP Smart Tank 5000 With 2 Years of Ink Included for $150 Right Now

    There are few things as frustrating as running out of ink when you really need to print something. This printer deal will help you avoid that all-too-common occurrence with two years of free ink included with the purchase of the HP Smart Tank…

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  • Down 40% in the Past Month, Morgan Stanley Says This 1 Stock Is Key to the Future of AI

    Down 40% in the Past Month, Morgan Stanley Says This 1 Stock Is Key to the Future of AI

    Amid the intense focus on artificial intelligence, machine learning, and large language models, a burgeoning problem is emerging: how will AI developers have enough computing power to train and run AI programs when thousands of companies are seeking to build or use AI products?

    Morgan Stanley analysts are projecting that there will be a cumulative shortfall of 47 gigawatts of computing power through 2028—and the next phase of AI investing will not be who’s building the best GPUs, but who can best provide data center infrastructure and the power to use them.

    A solution may be the business model provided by Iren Limited (IREN), an Australian miner of Bitcoin (BTCUSD) that has expanded its offerings to deliver next-generation data centers and large-scale GPU clusters for AI training and inference. Iren recently signed Microsoft (MSFT) to a five-year lease for computing power—a short-term arrangement that Morgan Stanley says may be a powerful model for investors to consider in the future.

    Is Iren really a key part of what Morgan Stanley analysts identify as the next generation of AI investing?

    Based in Sydney, Australia, Iren makes most of its money by mining Bitcoin, but its data centers are also available for rent for developers and companies that want to train and run AI models. That’s the model that Iren used last month to sign a $9.7 billion deal with Microsoft for cloud computing services, using Nvidia (NVDA) GPUs. As part of the deal, Iren announced that it entered into an agreement with Dell Technologies (DELL) to purchase $5.8 billion of GPUs and ancillary equipment.

    The company has three data centers in Canada and one in Texas, which will supply the computing power for the Microsoft deal. It’s also in the process of building a second data center in Texas.

    The growing interest in data center capacity has been a major tailwind for IREN stock, which, despite its recent weakness (down 40% in the past month), is up nearly 355% so far this year, helping to push its market capitalization over $13 billion.

    www.barchart.com

    But despite the stock price growth, IREN stock is still surprisingly affordable, with a trailing price-to-earnings ratio of only 25.2 and a forward P/E of 37.6. Iren has a lower P/E than Nvidia, which is the biggest company in the world by market capitalization. And competitors Nebius Group (NBIS) and CoreWeave (CRWV), which also offer data center services, aren’t profitable yet.

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  • 10 scientific breakthroughs from Microsoft researchers  

    10 scientific breakthroughs from Microsoft researchers  

    As AI becomes a bigger part of everyday life, scientists are finding exciting new ways of harnessing its transformative power to tackle some of society’s biggest challenges.

    From designing new materials to mapping flood risks through…

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  • New York Times sues AI startup for ‘illegal’ copying of millions of articles | Artificial intelligence (AI)

    New York Times sues AI startup for ‘illegal’ copying of millions of articles | Artificial intelligence (AI)

    The New York Times sued an embattled artificial intelligence startup on Friday, accusing the firm of illegal copying of millions of articles. The newspaper alleged Perplexity AI had distributed and displayed journalists’ work without permission en masse.

    The Times said that Perplexity AI is also violating its trademarks under the Lanham Act, claiming the startup’s generative AI products create fabricated content, or “hallucinations”, and falsely attribute them to the newspaper by displaying them alongside its registered trademarks.

    The newspaper said that Perplexity’s business model relies on scraping and copying content, including paywalled material, to power its generative AI products. Other publishers have made similar allegations.

    The lawsuit is the latest salvo in a bitter, ongoing battle between publishers and tech companies over the use of copyrighted content without authorization to build and operate their AI systems.

    Perplexity in particular has become a target of multiple legal disputes and faces similar accusations from a number of publishers as it tries to aggressively build market share in a hyper-competitive market for generative AI tools. Cloudflare, one of the world’s most prominent digital infrastructure companies, accused Perplexity earlier this year of hiding its web-crawling activities and scraping websites without permission – a serious accusation with potential copyright implications. Perplexity denied the allegations.

    Perplexity has raised around $1.5bn in the past three years through multiple funding rounds, most recently closing a $200m round in September that valued the company at $2obn. It has attracted a variety of big-name investors, including Nvidia and Jeff Bezos, as money has flooded the AI industry.

    San Francisco-based Perplexity AI also faced a lawsuit from media baron Rupert Murdoch’s Dow Jones and the New York Post.

    Multiple news outlets, including Forbes and Wired, have accused Perplexity of plagiarizing their content, in one case allegedly copying a Wired article about Perplexity’s own plagiarism issues. The Chicago Tribune, Merriam-Webster Dictionary and Encyclopedia Britannica have all additionally filed lawsuits against Perplexity in recent months, accusing the company of copyright infringement.

    In October, social media company Reddit also sued Perplexity in New York federal court, accusing it and three other companies of unlawfully scraping its data to train Perplexity’s AI-based search engine.

    Perplexity faces legal challenges from its fellow tech companies as well. Amazon last month filed a lawsuit against Perplexity over the search engine’s AI agent shopping feature. The suit alleged that Perplexity was covertly accessing Amazon users’ accounts and masking its AI browsing activities, which Perplexity has denied while accusing Amazon of bullying and attempting to stifle competitors.

    Perplexity did not immediately respond to a Reuters request for comment.

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  • Strava reveals the 3 most-attempted running routes in the world, while London owns the UK’s favourite segment

    Strava reveals the 3 most-attempted running routes in the world, while London owns the UK’s favourite segment

    It’s that magical time of year again – and no, we’re not just talking about the countdown to Christmas. We’re talking about the arrival of our personalised Strava Year in Sport recaps for 2025. Launching for all app users on Monday 8…

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  • Mayer Brown ranks across all categories in Chambers FinTech 2026 | News

    Mayer Brown ranks across all categories in Chambers FinTech 2026 | News

    Mayer Brown has been recognized in five categories in the 2026 edition of Chambers FinTech Legal USA, including earning a Band One ranking in the Payments & Lending category. Additionally, partner David Beam was ranked in Band One for Payments & Lending.

    Chambers FinTech Legal USA offers expert legal insights on critical issues for businesses, highlighting key developments in five practice areas: FinTech Nationwide; Payments & Lending; Blockchain & Cryptocurrencies; Data Protection & Cyber Security; and Corporate, Securities & Financing.

    Review the complete list of Mayer Brown’s rankings.

     

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  • Joint Statement by the Foreign Ministers of Pakistan, Egypt, Jordan, United Arab Emirates, Indonesia, Türkiye, Saudi Arabia and Qatar

    The Foreign Ministers of the Islamic Republic of Pakistan, the Arab Republic of Egypt, the Hashemite Kingdom of Jordan, the United Arab Emirates, the Republic of Indonesia, the Republic of Türkiye, the Kingdom of…

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  • ACC CardiaCast: Innovation in Action: Best Practices of Wearables in Cardiovascular Care

    ACC CardiaCast: Innovation in Action: Best Practices of Wearables in Cardiovascular Care

    Innovation in Action is a podcast series hosted by the ACC Innovation Program aimed at exploring innovations shaping care delivery.

    In this episode, Drs. Rupal O’Quinn and Leon Ptaszek join ACC Chief Innovation Officer Dr. Ami Bhatt to delve into the role of consumer wearables in cardiovascular care. They explore scenarios where consumer wearables can provide meaningful value and offer practical tips to help patients effectively share and use their wearable data to manage their health. They also highlight ways clinicians can integrate this information into care conversations to support better outcomes.





    Clinical Topics:
    Arrhythmias and Clinical EP, Implantable Devices, SCD/Ventricular Arrhythmias, Atrial Fibrillation/Supraventricular Arrhythmias, Cardiovascular Care Team, Prevention


    Keywords:
    CardiaCast, Arrhythmias, Cardiac, Wearable Electronic Devices

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