Explore the vast range of titles available.

Fuel combustion for electricity generation emits a mix of health- and climate-relevant air emissions, with the potential for technology or fuel switching to impact multiple emissions together. While there has been extensive research on the co-benefits of climate policies on air quality improvements, few studies have quantified the effect of air pollution controls on carbon emissions. Here we evaluate three multi-pollutant emission reduction strategies, focused on sulfur dioxide (SO 2 ) controls in the electricity sector. Traditional ‘add-on’ pollution controls like flue gas desulfurization (FGD) reduce SO 2 emissions from coal combustion but increase emissions of nitrogen oxides (NO X ), volatile organic compounds (VOCs), fine particulate matter (PM 2.5 ), and carbon dioxide (CO 2 ) due to heat efficiency loss. Fuel switching from coal to natural gas and renewables potentially reduces all pollutants. We identified 135 electricity generation units (EGUs) without SO 2 controls in the contiguous US in 2017 and quantified the unit-level emission changes using pollution control efficiencies, emission rates, fuel heat input, and electricity load. A cost-benefit analysis is conducted, considering pollution control costs, fuel costs, capital and operation and maintenance (O&M) costs, the monetized health benefits from avoided multi-pollutant, and the social cost of carbon as the benefit for carbon reduction. We find that add-on SO 2 controls result in an average annual net benefit of$179.3 million (95% CI: $ 137.5- $221.0 million) per EGU, fuel switching from coal to natural gas, $ 432.7 million (95% CI:$366.4-$ 498.9 million) per EGU; and fuel switching from coal to renewable energy sources,$537.9 million (95% CI: $ 457.1-$618.9 million) per EGU. Our results highlight multi-pollutant emission reduction strategy as a cost-effective way to synergistically control air pollution and mitigate climate change.

Circular RNA (circRNA), a novel class of noncoding RNAs, has been used extensively to complement transcriptome remodeling in the central nervous system, although the genomic coverage provided has rarely been studied in intracerebral hemorrhage (ICH) and is limited and fails to provide a detailed picture of the cerebral transcriptome landscape. Here, we described sequencing-based transcriptome profiling, providing comprehensive analysis of cerebral circRNA, messenger RNA (mRNA) and microRNA (miRNA) expression in ICH rats. In the study, male Sprague–Dawley rats were subjected to ICH, and next-generation sequencing of RNAs isolated from non-hemorrhagic (Sham) and hemorrhagic (ICH) rat brain samples collected 7 (early phase) and 28 (chronic phase) days after insults, was conducted. Bioinformatics analysis was performed to determine miRNA binding sites and gene ontology of circRNAs, target genes of miRNAs, as well as biological functions of mRNAs, altered after ICH. These analyses revealed different expression profiles of circRNAs, mRNAs and miRNAs in day-7 and day-28 ICH groups, respectively, compared with the Sham. In addition, the expression signature of circRNAs was more sensitive to disease progression than that of mRNAs or miRNAs. Further analysis suggested two temporally specific circRNA-miRNA-mRNA networks based on the competitive endogenous RNA theory, which had profound impacts on brain activities after ICH. In summary, these results suggested an important role for circRNAs in the pathogenesis of ICH and in reverse remodeling based on self-protection support, providing deep insights into diverse possibilities for ICH therapy through targeting circRNAs.

Accurate streamflow prediction in human‐regulated catchments remains a formidable challenge due to the complex disturbance of hydrological processes. To consider human disturbance in hydrological modeling, this study introduces a novel static attribute collection that combines river‐reach attributes with catchment attributes, referred to as multiscale attributes. The attribute collection is assembled into two deep learning (DL) methods, that is, the Long Short‐Term Memory (named as Multiscale LSTM) and the Differentiable Parameter Learning (DPL) model, and the performance is evaluated across 95 human‐regulated catchments in the United States (USA) and 24 catchments in the Yellow River Basin in China. In the USA, the Multiscale LSTM and the DPL models achieve similar performance with median Kling‐Gupta Efficiency (KGE) of 0.78 and 0.71, respectively. However, in the Yellow River Basin, the KGE values are 0.58 for Multiscale LSTM and 0.24 for DPL. These results highlight the DL models' ability to leverage multiscale attributes for improved performance compared to traditional catchment attributes. The performance of Multiscale LSTM and DPL models is predominantly influenced by river‐scale attributes, encompassing factors such as connectivity status index (CSI), degree of regulation (DOR), sediment trapping (SED), and number of dams. Additionally, satellite‐derived attributes such as mean and maximum river width (Width), slope and mean water surface elevation (WSE) from the Surface Water and Ocean Topography River Database (SWORD) contribute valuable insights into anthropogenic influences. Moreover, our study highlights the significance of selecting the appropriate training data period, which emerges as the most dominant factor affecting model performance across human‐regulated catchments. The diversity of data during the training period enables the model to capture a broad spectrum of hydrological signatures within these catchments. Consequently, this study emphasizes the advantages of Multiscale LSTM and underscores the significance of considering both natural and anthropogenic signatures to enhance hydrological predictions within human‐regulated environments. Plain Language Summary Understanding how water flows in rivers is crucial for managing reservoirs, preventing floods, and making smart decisions about water use in human‐regulated catchments. Previous research using data‐driven models has predominantly focused on long short‐term memory (LSTM) and differentiable parameter learning (DPL) models in natural catchments with minimal human influences. Identifying the effectiveness of these models in human‐regulated catchments has posed a significant challenge. To address this gap, we intentionally compared the performance of LSTM and DPL models, incorporating multiscale attributes as inputs. The results underscore the significant superiority of the LSTM model over the DPL model in human‐regulated catchments, emphasizing the crucial role of river attributes in enhancing model performance. Additionally, we observed that the DPL model exhibited higher sensitivity to the dynamics of human activities, and struggles to provide accurate simulations during periods of elevated human impacts. These findings elucidate the divergent capabilities of these models in representing hydrological processes in human‐regulated catchments. Key Points Long Short‐Term Memory is able to capture irregular streamflow patterns by utilizing a novel collection of multiscale attributes as input The performance of the model is extremely sensitive to the training data period across human‐regulated catchments Differentiable Parameter Learning may be susceptible to the dynamics of human activities, and struggles to provide accurate simulations

Hemispheric lateralization constitutes a core architectural principle of human brain organization underlying cognition, often argued to represent a stable, trait-like feature. However, emerging evidence underlines the inherently dynamic nature of brain networks, in which time-resolved alterations in functional lateralization remain uncharted. Integrating dynamic network approaches with the concept of hemispheric laterality, we map the spatiotemporal architecture of whole-brain lateralization in a large sample of high-quality resting-state fMRI data ( N = 991, Human Connectome Project). We reveal distinct laterality dynamics across lower-order sensorimotor systems and higher-order associative networks. Specifically, we expose 2 aspects of the laterality dynamics: laterality fluctuations (LF), defined as the standard deviation of laterality time series, and laterality reversal (LR), referring to the number of zero crossings in laterality time series. These 2 measures are associated with moderate and extreme changes in laterality over time, respectively. While LF depict positive association with language function and cognitive flexibility, LR shows a negative association with the same cognitive abilities. These opposing interactions indicate a dynamic balance between intra and interhemispheric communication, i.e., segregation and integration of information across hemispheres. Furthermore, in their time-resolved laterality index, the default mode and language networks correlate negatively with visual/sensorimotor and attention networks, which are linked to better cognitive abilities. Finally, the laterality dynamics are associated with functional connectivity changes of higher-order brain networks and correlate with regional metabolism and structural connectivity. Our results provide insights into the adaptive nature of the lateralized brain and new perspectives for future studies of human cognition, genetics, and brain disorders.

While a recent upsurge in the application of neuroimaging methods to creative cognition has yielded encouraging progress toward understanding the neural underpinnings of creativity, the neural basis of barriers to creativity are as yet unexplored. Here, we report the first investigation into the neural correlates of one such recently identified barrier to creativity: anxiety specific to creative thinking, or creativity anxiety (Daker et al., 2019). We employed a machine-learning technique for exploring relations between functional connectivity and behavior (connectome-based predictive modeling; CPM) to investigate the functional connections underlying creativity anxiety. Using whole-brain resting-state functional connectivity data, we identified a network of connections or “edges” that predicted individual differences in creativity anxiety, largely comprising connections within and between regions of the executive and default networks and the limbic system. We then found that the edges related to creativity anxiety identified in one sample generalize to predict creativity anxiety in an independent sample. We additionally found evidence that the network of edges related to creativity anxiety were largely distinct from those found in previous work to be related to divergent creative ability (Beaty et al., 2018). In addition to being the first work on the neural correlates of creativity anxiety, this research also included the development of a new Chinese-language version of the Creativity Anxiety Scale, and demonstrated that key behavioral findings from the initial work on creativity anxiety are replicable across cultures and languages.

Accurate simulation of the brain’s intrinsic dynamic activity is essential for understanding human cognition and behavior and developing personalized brain disease therapies. Traditional neurodynamics models depend on structural connectivity to explain the emergence of functional connectivity (FC). However, achieving high-fidelity simulations at the individual level remains challenging, as the models fail to fully capture the brain information. To address these challenges, we introduce the Stable Individualized Brain Computing Model (SI-BCM), a data-driven reverse engineering framework designed to infer spatiotemporal co-activity patterns from fMRI data for simulating whole-brain activity. This model captures the dynamic interactions between brain regions by integrating spatiotemporal dimensional information to extract a stable and shared connectivity pattern, representing the intrinsic functional collaboration pattern of the brain. This connectivity pattern is then used as the core connection weight in the dynamical system. Additionally, the model has a new cost function based on the Phase-Space Association matrix (PSA), enhancing its ability to capture brain activity dynamics. This combination enables the SI-BCM to improve simulation accuracy at the individual level compared to existing models, achieving a correlation coefficient between simulated and empirical FC of 0.87. The SI-BCM also showed enhanced robustness and reliability, and effectively captured brain properties. We found the model sensitively reflected changes in cognitive function, thereby providing valuable insights into the underlying neural mechanisms. Furthermore, the application of SI-BCM in the brain modeling of Alzheimer’s disease (AD) patients substantiated the hypothesis that AD pathogenesis may be due to excessive neuronal excitation. This work establishes a new paradigm for brain network modeling by prioritizing the inference of stable dynamics features from activity data, providing a powerful tool for understanding brain function and pathophysiology.

Emotional regulation is known to be associated with activity in the amygdala. The amygdala is an emotion-generative region that comprises of structurally and functionally distinct nuclei. However, little is known about the contributions of different frontal-amygdala sub-region pathways to emotion regulation. Here, we investigated how functional couplings between frontal regions and amygdala sub-regions are involved in different spontaneous emotion regulation processes by using an individual-difference approach and a generalized psycho-physiological interaction (gPPI) approach. Specifically, 50 healthy participants reported their dispositional use of spontaneous cognitive reappraisal and expressive suppression in daily life and their actual use of these two strategies during the performance of an emotional-picture watching task. Results showed that functional coupling between the orbitofrontal cortex (OFC) and the basolateral amygdala (BLA) was associated with higher scores of both dispositional and actual uses of reappraisal. Similarly, functional coupling between the dorsolateral prefrontal cortex (dlPFC) and the centromedial amygdala (CMA) was associated with higher scores of both dispositional and actual uses of suppression. Mediation analyses indicated that functional coupling of the right OFC-BLA partially mediated the association between reappraisal and emotional response, irrespective of whether reappraisal was measured by dispositional use (indirect effect(SE)=-0.2021 (0.0811), 95%CI(BC)= [-0.3851, -0.0655]) or actual use (indirect effect(SE)=-0.1951 (0.0796), 95%CI(BC)= [-0.3654, -0.0518])). These findings suggest that spontaneous reappraisal and suppression involve distinct frontal- amygdala functional couplings, and the modulation of BLA activity from OFC may be necessary for changing emotional response during spontaneous reappraisal.

Transcranial direct current stimulation (tDCS) has shown promise in treating neurological disorders, particularly through dorsolateral prefrontal cortex (DLPFC) targeting. However, the effects of DLPFC-tDCS on brain functional networks and the underlying propagation mechanisms remain poorly understood. We present a novel tDCS hybrid brain model (tDCS-HBM) that incorporates tDCS-induced gray matter electric fields into a large-scale brain network model, considering their relationship with membrane potential to effectively predict spatiotemporal dynamics. Using this model, we simulated brain activity in response to tDCS over the left (F3-Fp2) and right DLPFC (F4-Fp1). Our results demonstrate that tDCS enhances brain complexity and flexibility, leading to increased functional connectivity (FC) across the whole brain and an improvement in global network efficiency. Dynamic analysis reveals an initial FC decline, followed by widespread enhancement originating from inferior and orbital frontal regions. Importantly, right DLPFC-tDCS induces strong FC associated with the ventral attention network. These changes in topological metrics and spatiotemporal patterns are consistent with prior modeling and empirical findings, validating the utility of our tDCS-HBM in understanding propagation mechanisms. Our hybrid model holds the potential to predict the stimulation effects of modulation protocols, providing precise guidance for clinical neuromodulation interventions.

Cerebral asymmetry, fundamental to various cognitive functions, is often disrupted in neuropsychiatric disorders. While brain growth has been extensively studied, the maturation of brain asymmetry in children and the factors influencing it in adolescence remain poorly understood. We analyze longitudinal data from 11,270 children aged 10–14 years in the Adolescent Brain Cognitive Development (ABCD) study. Our analysis maps the developmental trajectory of structural brain asymmetry. We identify significant age-related, modality-specific development patterns. These patterns link to crystallized intelligence and mental health problems, but with weak correlations. Genetically, structural asymmetry relates to synaptic processes and neuron projections, likely through asymmetric synaptic pruning. At the microstructural level, corpus callosum integrity emerged as a key factor modulating the developing asymmetry. Environmentally, favorable perinatal conditions were associated with prolonged corpus callosum development, which affected future asymmetry patterns and cognitive outcomes. These findings underscore the dynamic yet predictable interactions between brain asymmetry, its structural determinants, and cognitive and psychiatric outcomes during a pivotal developmental stage. Our results provide empirical support for the adaptive plasticity theory in cerebral asymmetry and offer insights into both cognitive maturation and potential risk for early-onset mental health problems. Cerebral asymmetry, crucial for cognitive functions, often changes in neuropsychiatric disorders. This study maps structural brain asymmetry in 11,000 children aged 10–14 years, identifying age-related patterns linked to genetics, environment, cognition, and psychiatric risks.

Subjective well-being (SWB) reflects the cognitive and emotional evaluations of an individual's life and plays an important role in individual's success in health, work and social relationships. Although previous studies have revealed the spontaneous brain activity underlying SWB, little is known about the relationship between brain network interactions and SWB. The present study investigated the static and dynamic functional connectivity among large-scale brain networks during resting state functional magnetic resonance imaging (fMRI) in relation to SWB in two large independent datasets. The results showed that SWB is negatively correlated with static functional connectivity between the salience network (SN) and the anterior default mode network (DMN). Dynamic functional network connectivity (dFNC) analysis found that SWB is negatively correlated with the fraction of time that participants spent in a brain state characterized by weak cross-network connectivity (between the DMN, SN and frontal-parietal network [FPN]) and strong within-network connectivity (within the DMN and within the FPN). This connectivity profile may account for the good mental adaptability and flexible information communication of people with high levels of SWB. The dFNC results were well replicated with different analysis parameters and further validated in an independent sample. Taken together, these findings reveal that the dynamic interaction between networks involved in self-reflection, emotional regulation and cognitive control underlies SWB.