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Efficacy of glutamate-GABA modulator riluzole for the treatment of cognitive and psychotic symptoms in 22q11.2 deletion syndrome: A placebo-controlled crossover trial
No full textIndividuals with 22q11.2 deletion syndrome are susceptible to psychosis and cognitive impairments. These symptoms have been linked to a disruption in the balance of glutamate (excitatory) to GABA (inhibitory) transmission. This clinical trial aimed to determine whether the glutamate and GABA-modulating drug riluzole reduces psychotic or cognitive symptoms within 22q11.2 deletion syndrome. 32 participants with a 22q11.2 deletion and psychotic and/or cognitive symptoms were enrolled in this placebo-controlled, fixed-order crossover trial. Participants received placebo for 8 weeks, followed by 8 weeks of 100mg/day riluzole. The Positive and Negative Syndrome Scale (PANSS) and Pennsylvania Computerized Neurocognitive Battery (CNB) were used to assess psychotic and cognitive symptoms, with PANSS total and subscale scores and CNB accuracy and reaction time as primary outcome measures. Correcting for family-wise error rate, riluzole improved performance on executive function (p = 0.036), social cognition (p = 0.041), and non-verbal reasoning (p = 0.005) tasks in comparison to baseline. Following correction, no significant effects of riluzole were found on the PANSS total score and subscales, or on accuracy and reaction time on the CNB compared to placebo. Exploratory analyses of individual PANSS items indicated that riluzole reduced anxiety (p = 0.001) and impairments in abstract thinking (p = 0.039) compared to baseline. Our results suggest riluzole may have beneficial effects on mental health and cognition. Further research is needed to confirm these findings and establish a responsive phenotype.Received 8 October 2025, Revised 5 December 2025, Accepted 16 December 2025, Available online 16 December 2025, Version of Record 19 December 2025
Ocean-Land-Atmosphere Research
No full textSurface air temperature (SAT) in polar regions is rising faster than the global average. This study analyzes the rapid increase in anthropogenic influences throughout the industrial period on Arctic and Antarctic warming, utilizing climate models. Our results show that while the SAT trend in the Arctic due to greenhouse gas (GHG) forcing is approximately 0.6 °C/decade, twice that of land use (LU) forcing at 0.3 °C/decade, the amplification of Arctic warming from LU forcings (with an amplification factor of 2.37) is stronger than the GHG forcings (amplification factor of 2.25). Anthropogenic aerosols cool the Arctic 1.5 times more than Antarctica, driven by higher aerosol concentrations from long-range pollutant transport from lower latitudes. Since 1950, rapid industrialization in the Northern Hemisphere has caused Arctic warming to accelerate, with SAT rising by 0.34 °C/decade due to anthropogenic forcings?over twice the global average of 0.17 °C/decade. In contrast, Antarctic warming has remained closer to global trends, buffered by its remoteness from the anthropogenic influence. Under the high-emission scenario (RCP8.5), both polar regions are projected to experience substantial temperature increases by the end of the 21st century, underscoring the significant role of human activities in polar warming and the need for targeted interventions addressing regional and global changes in LU, GHG emissions, and anthropogenic aerosols
The social learning and development of intra- and inter-ethnic sharing norms in the Congo Basin
No full textCompared to other species, the extent of human cooperation is unparalleled. Such cooperation is coordinated between community members via social norms. Developmental research has demonstrated that very young children are sensitive to social norms, and that social norms are internalized by middle childhood. Most research on social norm acquisition has focused on norms that modulate intra-group cooperation. Yet around the world, multi-ethnic communities also cooperate, and this cooperation is often shaped by distinct inter-group social norms. In the present study, we investigated whether intra-ethnic and inter-ethnic social norm acquisition follows the same, or distinct, developmental trajectories. Specifically, we worked with BaYaka foragers and Bandongo fisher-farmers who inhabit multi-ethnic villages in the Republic of the Congo. In these villages, inter-ethnic cooperation is regulated by sharing norms. Based on our ethnographic knowledge of the participating communities, we predicted that children’s intra-ethnic sharing choices would match those of adults at an earlier age than their inter-ethnic sharing choices. To test this prediction, children (5–17 years) and adults (17+years) participated in a modified Dictator Game to investigate the developmental trajectories of children’s intra- and inter-ethnic sharing choices. Contrary to our prediction, both intra- and inter-ethnic sharing norms were acquired in middle childhood. Interviews with adult participants suggested that intra- and inter-ethnic sharing norms are acquired from multiple sources, including parents and peers. Further, Bandongo adults primarily reported learning sharing norms via Instruction, whereas BaYaka adults primarily reported learning via Observation/ Imitation. These cross-cultural differences may reflect variation in norm complexity. Together, these findings suggest that when social contexts regularly expose children to out-group collaboration, inter-ethnic norms are acquired at similar timelines to intra-ethnic ones, as part of children’s broader cooperative repertoire. © 2026 Lew-Levy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Reinforcement Learning for Quantum Technology
No full textMany challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges -- such as scalability, interpretability, and integration with experimental platforms -- and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies
Characterizing diurnal variations and driving factors of major gaseous pollutants across China
No full textIndustrial chemical glycan synthesis (ICGS): process intensification of glycosylations
No full textThe industrial production of biologically active glycans is pivotal to the development and manufacture of glycan-based medicinal products. Chemical synthesis is critical to acquiring well-defined glycans for glycobiology investigations and the advancement of glycan-enabled applications. Efforts toward industrial chemical glycan synthesis (ICGS) are hampered by unique challenges associated with their structural complexity and diversity. The major bottleneck in ICGS are regio- and stereoselective glycosylations. In this Perspective, we review the process intensification of glycosylations based on innovative methodologies and devices, and highlight its contributions to ICGS. Five topics including optimization of glycan assembly sequence, modulation of glycosylation reactivity, adjustment of environmental parameters, development of an automated chemical glycan synthesizer, and application of artificial intelligence (AI) to glycan synthesis will be discussed. We also present prospects for accelerating ICGS through process intensification of glycosylation, including the integration of AI techniques, the development of glycosylation-oriented special large-scale reactors and flow chemistry platforms