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Presynaptic Trafficking of Glutamate Decarboxylase Isoforms Is Dispensable for Basal GABAergic Neurotransmission

Two major glutamate decarboxylase isoforms (i.e., GAD65 and GAD67) together synthesize the majority of γ-aminobutyric acid (GABA) in our nervous system. However, the subcellular distribution of these enzymes and their relative impacts on synaptic GABA release remain unclear. To address this important question, here we monitored their synaptic trafficking in male and female mouse brains and dissociated neuronal cultures. We noticed that, unlike some major glutamate-biosynthesizing enzymes, e.g., glutaminase and glutamate dehydrogenase, which were primarily associated with perisomatic mitochondria, both GADs together were highly enriched at GABAergic presynapses. Nevertheless, when expressed separately in GAD-deficient human neurons derived from a male stem cell line, GAD65 exhibited preferential distribution at presynapses over GAD67. Despite these differences in subcellular localization, both GADs produced equivalent levels of intracellular GABA, which adequately diffused to axon terminals, and triggered robust GABAergic activities. These findings raised the question of whether the presynaptic recruitment of GADs is, after all, necessary for reliable GABAergic transmission. To examine this hypothesis, we further swapped or removed the trafficking signals from both GAD isoforms and even artificially restricted them at nonsynaptic compartments, including the cell nucleus. Despite our attempts, the chimeric and mutant GAD variants continued to produce sufficient amount of intracellular GABA for vesicular loading and presynaptic release. These results indicate that GAD65 and GAD67 are functionally redundant in GABA production, if expressed equitably in neurons, and irrespective of GADs’ subcellular trafficking profile, diffusion of GABA molecules from distant sources can effectively supply and replenish the presynaptic terminals for functional activities.

Automated Rapid Synthesis of High-Purity Head-to-Tail Cyclic Peptides via a Diaminonicotinic Acid Scaffold

The Camellia sinensis var. sinensis cv. Fuding Dabaicha genome unveils structural variation-driven metabolic innovation

A multimodal spatiotemporal convolutional network with attention mechanism for athlete anxiety behavior recognition

Abstract Athletic performance is significantly impacted by anxiety, yet traditional assessment methods rely on subjective questionnaires that lack real-time capability. This study presents an automated anxiety recognition system for athletes using multimodal data fusion of physiological signals, facial expressions, and body movements. The proposed approach employs spatiotemporal convolutional networks with adaptive attention mechanisms to capture behavioral patterns across multiple modalities simultaneously. The system achieves 94.6% accuracy in anxiety detection while maintaining real-time processing capability for practical sports applications. This objective assessment tool enables coaches and sports psychologists to implement timely interventions, potentially improving both athletic performance and athlete mental well-being. The multimodal approach demonstrates significant advantages over single-modal methods, providing a comprehensive solution for anxiety monitoring in competitive sports environments.

Dynamic Respiration–Neural Coupling in Substantia Nigra across Sleep and Anesthesia

Respiration is increasingly recognized as a coordinator of neural activity across widespread brain regions and behavioral states. Even during sleep, respiration rhythms modulate sleep-related oscillations. While the basal ganglia are known to play roles in both sleep and respiratory regulation, their interaction with respiration rhythms remains poorly understood. Here, we examined respiration–neural couplings in the substantia nigra pars reticulata (SNr), a major output nucleus of the basal ganglia, and the primary motor cortex (M1) across multiple states in male and female mice, including non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, quiet wakefulness, and anesthesia. Simultaneous recordings of local field potentials (LFPs) from M1 and SNr along with diaphragm muscle activities revealed state-dependent, region-specific patterns of respiration–neural coupling. Coupling strength in both SNr and M1 was attenuated during NREM sleep compared with REM sleep and quiet wakefulness. However, under ketamine/xylazine anesthesia, coupling was markedly enhanced in the SNr, but not in M1, indicating region-specific sensitivity to arousal and anesthesia state. Notably, respiration–neural coupling was systematically related to delta sub-band power; coupling strength was reduced with increased slow delta (0.5–2 Hz) and decreased fast delta (2.5–4 Hz) powers. In addition, slow delta was associated with SNr-M1 synchronization, suggesting that interregional communication during deep sleep may suppress respiration locking. Together, these findings highlight dynamic, state-dependent modulation of respiration–neural couplings in corticobasal ganglia circuits, underscoring its potential role in coordinating body–brain interactions during sleep and anesthesia.

Catalytic Asymmetric Carbosilylation of Methyl Propiolate with Bis-silyl Ketene Acetals

Rainfall sustains multiyear La Niña

A generative AI cybersecurity risks mitigation model for code generation: using ANN-ISM hybrid approach

Temporal and Spatial Scales of Human Resting-State Cortical Activity across the Lifespan

Sensorimotor and cognitive abilities undergo substantial changes throughout the human lifespan, but the corresponding changes in the functional properties of cortical networks remain poorly understood. This can be studied using temporal and spatial scales of functional magnetic resonance imaging (fMRI) signals, which provide a robust description of the topological structure and temporal dynamics of neural activity. For example, timescales of resting-state fMRI signals parsimoniously predict a significant amount of the individual variability in functional connectivity networks identified in adult human brains. In the present study, we quantified and compared temporal and spatial scales in resting-state fMRI data collected from 2,352 subjects of either sex between the ages of 5 and 100 in Developmental, Young Adult, and Aging datasets from the Human Connectome Project. For most cortical regions, we found that both temporal and spatial scales decreased with age throughout the lifespan, with the visual cortex and the limbic network consistently showing the largest and smallest scales, respectively. For some prefrontal regions, however, these two scales displayed non-monotonic trajectories and peaked around the same time during adolescence and decreased throughout the rest of the lifespan. We also found that cortical myelination increased monotonically throughout the lifespan, and its rate of change was significantly correlated with the changes in both temporal and spatial scales across different cortical regions in adulthood. These findings suggest that temporal and spatial scales in fMRI signals, as well as cortical myelination, are closely coordinated during both development and aging.

Total Synthesis of the TIM-1 IgV Domain via N-to-C Serine/Threonine Ligation Enabled by Knorr Pyrazole Synthesis-Mediated Regeneration of Salicylaldehyde Esters

Impact of a single fecal microbiome transplantation in adult women with anorexia nervosa: an open-label feasibility pilot trial

Comprehensive assessment of ground motion amplification in stratified soils with different layer configurations and types

Abstract This study investigates the seismic response of thirty meter deep soil profiles with varying compositions and layering sequences, including homogeneous clay and sand profiles and partially layered profiles composed of 22.5 m of clay over 7.5 m of sand, 7.5 m of clay over 22.5 m of sand, 22.5 m of sand over 7.5 m of clay, 7.5 m of sand over 22.5 m of clay, and evenly layered profiles with 15 m of clay over 15 m of sand or 15 m of sand over 15 m of clay. Nonlinear one-dimensional ground response analyses were performed using RSSeismic software, applying seven strong ground motions scaled to peak ground acceleration levels of 0.10 g, 0.25 g, and 0.50 g. The results demonstrate that seismic amplification is strongly governed by the soil type located at the ground surface, impedance contrasts between adjacent layers, thickness distribution of soft and stiff materials, and nonlinear stiffness degradation under increasing shaking intensity. Profiles with clay at the surface consistently produce higher amplification and longer period response because of greater modulus degradation, whereas sand dominated surfaces generate stronger short period amplification with reduced nonlinear softening. In partially layered profiles the largest amplification, approximately 5.67, occurred when a thin clay layer overlies thick sand in Profile 06, while the lowest amplification, between about 1.36 and 1.88, occurred in profiles with thick sand at the surface such as Profile 04. Deamplification zones were also identified, varying across profiles and shaking levels. These observations highlight the critical importance of accurately characterizing soil stratigraphy for reliable site-specific seismic hazard assessment and earthquake resistant design.

Positional Isomerism Tunes Molecular Reactivities and Mechanisms toward Pathological Targets in Dementia

Growth of non-layered 2D transition metal nitrides enabled by transient chloride templates

Abstract 2D transition metal nitrides (TMNs) have attracted significant attention due to their magnetic, electrical, and chemical properties at atomic thickness. However, the synthesis of 2D TMNs is still challenging, due to their strong isotropic metal-nitrogen bonding networks. Here, we report a universal synthesis of non-layered 2D TMN family by using corresponding metastable metal chlorides as transient templates. This approach takes advantage of the layered structures and low conversion energy barriers of transition metal chlorides (TMCls) to grow 2D TMNs. Fifteen types of 2D TMNs and their alloys were synthesized, demonstrating the versatility of this method. The 2D TMN family exhibits tunable magnetic characteristics ranging from antiferromagnet to hard magnet, which can be modulated by their composition. This work overcomes previous synthesis limitations, thus offering a pathway to explore fundamental properties of 2D TMNs and accelerate their applications.

Reinforcement learning-driven model predictive control for optimizing counter-rotating permanent magnet synchronous motor in submarine propulsion system

Lattice Oxygen-Mediated Electrochemical Carbon Dioxide Reduction

Efficient and stable catalytic hydrolysis of perfluorocarbon enabled by SO2-mediated proton supply

Abstract Catalytic hydrolysis is an effective strategy for decomposing tetrafluoromethane (CF 4 ), one of the most chemically inert per- and polyfluoroalkyl substances (PFAS). A key challenge in this process lies in enhancing proton availability to facilitate efficient and stable C–F bond activation while ensuring long-term catalyst stability. Here we present an SO 2 -driven approach to significantly enhance H 2 O dissociation and proton-supplying through the in situ formation of Al–HSO 4 and Ga–HS species. Combined experimental and theoretical investigations reveal that these species not only lower the energy barrier for C–F bond activation but also promote active site regeneration by facilitating defluorination, thus effectively overcoming catalyst deactivation. As a result, the optimized catalyst enables complete CF 4 decomposition at a low temperature of 550°C, with stable operation for over 2500 hours. This work establishes a new paradigm for regulating proton transfer and offers a viable route for the efficient, durable degradation of gaseous PFAS.

Modeling and inference of mixed dynamics and detection of causal emergent features

Abstract Many real-world problems feature nonlinear dynamic processes. Classical mathematical models may be adequate to describe a single dynamic process in isolation, but can be easily undermined by two natural and simple kinds of phenomenological variations: the emergence (or activation) of an additional dynamic process, and events that affect the parameters of an active process. COVID-19 data offers an important case study expressing these phenomenological variations that deeply challenge the classical SIR epidemiological model, and call for novel mathematical methods to detect and adapt to these critical variations. We address the modeling issues with a novel mathematical framework that reenvisions data as a mixture of multiple causal generating processes, each subject to possible parameter change-points. The new viewpoint extends nonlinear classical models in a manner that overcomes many of these types of phenomenological variations and enables a highly adaptive modeling closely linked to causal events. The new model space unifies a wider class of dynamics and is particularly effective at fitting multi-surge data and explaining key causal events related to surge origination. To demonstrate, we construct a mixture of logistic models termed the Adaptive Logistic Model (ALM), and then formulate appropriate nonlinear least squares optimization and regularization goals, and then apply ALM to data. To validate the approach, we return to COVID-19 forecasting (for case count), and compare ALM directly to other forecasting methods. ALM forecast accuracy is competitive with all leading forecast methods, but its greatest utility may be in how it detects changing dynamics (change-points) and retains far fewer but more interpretable parameters relating naturally to cause and intervening change. The method can be applied more generally as it adapts well to the multi-generative nature of many time series data problems. We demonstrate ALM robustness through data experiments in hydrology, economics, cybersecurity, and social media.

Genetic Code Expansion in Probiotics Enables the Secretion of Covalent Protein Drugs in Mice

6-Phosphogluconate dehydrogenase promotes mitochondrial fusion and immune suppression in tumor-associated monocytic suppressor cells

Abstract The mechanisms underlying the metabolic adaptation of myeloid cells within the tumor microenvironment remain incompletely understood. Here, we identify 6-phosphogluconate dehydrogenase (6PGD), a rate-limiting enzyme in the pentose phosphate pathway (PPP), as an important regulator of monocytic-myeloid derived suppressor cell (M-MDSC) function. Our findings reveal that tumor M-MDSCs upregulate 6PGD expression via IL-6/STAT3 signaling. Blocking 6PGD, using either genetic or pharmacological approaches, impairs the immunosuppressive function of M-MDSCs and suppresses tumor growth. Mechanistically, 6PGD inhibition leads to the accumulation of its substrate, 6-phosphogluconate (6PG), within M-MDSCs, activates the JNK1-IRS1 and PI3K-AKT-pDRP1 signaling pathways, leading to mitochondrial fragmentation and elevated mitochondrial reactive oxygen species (ROS). This metabolic shift drives M-MDSCs toward an M1-like proinflammatory phenotype. Furthermore, 6PGD blockade synergizes with anti-PD-1 immunotherapy in a preclinical tumor model, substantially improving therapeutic outcomes. Our data reveals 6PGD as a possible therapeutic target to disrupt M-MDSC function and improve cancer immunotherapy outcomes.