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Creativity is pivotal to solving complex problems of many kinds, yet how cognitive flexibility dynamically supports creative processes is largely unexplored. Despite being a crucial multi-faceted contributor in creative thinking, cognitive flexibility, as typically assessed, does not fully capture how people adaptively shift between varying or persisting in their current problem-solving efforts. To fill this theoretical and methodological gap, we introduce a new operationalization of cognitive flexibility: the process-based Self-Guided Transition (SGT) measures, which assess when participants autonomously choose to continue working on one of two concurrently presented items (dwell length) and how often they choose to switch between the two items (shift count). We examine how these measures correlate with three diverse creativity tasks, and with creative performance on a more complex \"garden design\" task. Analyses of the relations between these new cognitive flexibility measures in 66 young adults revealed that SGT dwell length positively correlated with creative performance across several tasks. The SGT shift count positively correlated with within-task performance for a two-item choice task tapping divergent thinking (Alternative Uses Task) but not for a two-item choice task calling on convergent thinking (Anagram task). Multiple regression analyses revealed that, taken together, both the shift count and dwell length measures from the Alternative Uses Task explained a significant proportion of variance in measures of fluency, and originality, on a composite measure of the three independently-assessed creative tasks. Relations of SGTs to the Garden Design task were weaker, though shift count on the Alternative Uses Task was predictive of a composite measure of overall Garden Design quality. Taken together, these results highlight the promise of our new process-based measures to better chart the dynamically flexible processes supporting creative thinking and action.

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence. The authors measured high-resolution fMRI activity from eight individuals who saw and memorized thousands of annotated natural images over 1 year. This massive dataset enables new paths of inquiry in cognitive neuroscience and artificial intelligence.

Deep neural networks (DNNs) optimized for visual tasks learn representations that align layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this finding is that hierarchical representations are necessary to accurately predict brain activity in the primate visual system. To test this interpretation, we optimized DNNs to directly predict brain activity measured with fMRI in human visual areas V1-V4. We trained a single-branch DNN to predict activity in all four visual areas jointly, and a multi-branch DNN to predict each visual area independently. Although it was possible for the multi-branch DNN to learn hierarchical representations, only the single-branch DNN did so. This result shows that hierarchical representations are not necessary to accurately predict human brain activity in V1-V4, and that DNNs that encode brain-like visual representations may differ widely in their architecture, ranging from strict serial hierarchies to multiple independent branches. Whether or not deep neural networks require hierarchical representations to predict brain activity is not known. Here, the authors show that a multi-branch deep neural network can predict neural activity independently in visual areas in the absence of hierarchical representations.

Using synthetic biology techniques, bacteria have been engineered to serve as microrobots for diagnosing diseases and delivering treatments. These engineered bacteria can be used individually or in combination as microbial consortia. The components within these consortia complement each other, enhancing diagnostic accuracy and providing synergistic effects that improve treatment efficacy. The application of microbial therapies in cancer, intestinal diseases, and metabolic disorders underscores their significant potential. The impact of these therapies on the host's native microbiota is crucial, as engineered microbes can modulate and interact with the host's microbial environment, influencing treatment outcomes and overall health. Despite numerous advancements, challenges remain. These include ensuring the long‐term survival and safety of bacteria, developing new chassis microbes and gene editing techniques for non‐model strains, minimising potential toxicity, and understanding bacterial interactions with the host microbiota. This mini‐review examines the current state of engineered bacteria and microbial consortia in disease diagnosis and treatment, highlighting advancements, challenges, and future directions in this promising field. Bacteria including probiotics, genetically engineered bacteria, and microbial consortia are employed in disease diagnosis and treatment via diverse mechanisms.

It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb. Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable. The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties.

•The brain entropy (BEN) in the sensorimotor-auditory and association cortices positively correlated with postnatal age (PNA), and negatively correlated with gestational age (GA), respectively.•The BEN in the right rolandic operculum correlated significantly with both GA and PNA.•Preterm-born infants exhibited increased BEN values in the visual-motor cortex compared with term-born infants.•We identified five BEN-related genes (DNAJC12, FIG4, STX12, CETN2, and IRF2BP2) associated with synaptic vesicle transportation and cell division.•The fMRI-based BEN can serve as an indicator of age-dependent brain functional development in human neonates, which may be influenced by specific genes. The relationship between brain entropy (BEN) and early brain development has been established through animal studies. However, it remains unclear whether the BEN can be used to identify age-dependent functional changes in human neonatal brains and the genetic underpinning of the new neuroimaging marker remains to be elucidated. In this study, we analyzed resting-state fMRI data from the Developing Human Connectome Project, including 280 infants who were scanned at 37.5–43.5 weeks postmenstrual age. The BEN maps were calculated for each subject, and a voxel-wise analysis was conducted using a general linear model to examine the effects of age, sex, and preterm birth on BEN. Additionally, we evaluated the correlation between regional BEN and gene expression levels. Our results demonstrated that the BEN in the sensorimotor-auditory and association cortices, along the ‘S-A’ axis, was significantly positively correlated with postnatal age (PNA), and negatively correlated with gestational age (GA), respectively. Meanwhile, the BEN in the right rolandic operculum correlated significantly with both GA and PNA. Preterm-born infants exhibited increased BEN values in widespread cortical areas, particularly in the visual-motor cortex, when compared to term-born infants. Moreover, we identified five BEN-related genes (DNAJC12, FIG4, STX12, CETN2, and IRF2BP2), which were involved in protein folding, synaptic vesicle transportation and cell division. These findings suggest that the fMRI-based BEN can serve as an indicator of age-dependent brain functional development in human neonates, which may be influenced by specific genes.

Bacteria contain an immense untapped trove of novel secondary metabolites in the form of ‘silent’ biosynthetic gene clusters (BGCs). These can be identified bioinformatically but are not expressed under normal laboratory growth conditions. Methods to access their products would dramatically expand the pool of bioactive compounds. We report a universal high-throughput method for activating silent BGCs in diverse microorganisms. Our approach relies on elicitor screening to induce the secondary metabolome of a given strain and imaging mass spectrometry to visualize the resulting metabolomes in response to ~500 conditions. Because it does not require challenging genetic, cloning, or culturing procedures, this method can be used with both sequenced and unsequenced bacteria. We demonstrate the power of the approach by applying it to diverse bacteria and report the discovery of nine cryptic metabolites with potentially therapeutic bioactivities, including a new glycopeptide chemotype with potent inhibitory activity against a pathogenic virus. A combination of elicitor screening to induce expression of silent biosynthetic gene clusters with imaging mass spectrometry to visualize the resulting metabolome enables the discovery of nine cryptic natural products.

•The VSSL method successfully measured CSF flow in the ventricles and around major arteries, including the MCA, ACA, and PCA in SAS.•The study underscores the potential of VSSL MRI as a non-invasive tool for mapping rapid CSF flow in the SAS system and investigating CSF dynamics.•The diffusion component in the VSSL method was well suppressed by flow-compensated gradients, allowing comprehensive mapping of the CSF flow.•Calibration of the VSSL-derived velocity maps to the velocity determined by PC-MRI, showing a clear linear correlation. This study aims to evaluate cerebrospinal fluid (CSF) flow dynamics within ventricles, and the subarachnoid space (SAS) using the velocity selective spin labeling (VSSL) MRI method with Fourier-transform-based velocity selective inversion preparation. The study included healthy volunteers who underwent MRI scanning with specific VSSL parameters optimized for CSF flow quantification. The VSSL sequence was calibrated against phase-contrast MRI (PC-MRI) to ensure accurate flow velocity measurements. The CSF flow patterns observed in the ventricles were consistent with those obtained using 3D amplified MRI and other advanced MRI techniques, verifying the reliability of the VSSL method. The VSSL method successfully measured CSF flow in the SAS along major arteries, including the middle cerebral artery (MCA), anterior cerebral artery (ACA), and posterior cerebral artery (PCA), with an average flow velocity of 0.339±0.117cm/s. The diffusion component was well suppressed by flow-compensated gradients, enabling comprehensive mapping of the rapid CSF flow pattern in the SAS system and ventricles. The flow pattern in the SAS system closely resembles the recently discovered perivascular subarachnoid space (PVSAS) system. CSF flow around the MCA, PCA, and ACA arteries in the SAS exhibited a weak orientation dependency. CSF flow in the ventricles was also measured, with an average flow velocity of0.309±0.116cm/s, and the highest velocity observed along the superior-inferior direction. This study underscores the potential of VSSL MRI as a non-invasive tool for investigating CSF dynamics in both SAS and ventricles.

Dryland vegetation underpins ecosystem services and livelihoods. Understanding the influencing factors of its dynamics is critical for effective restoration and degradation risk reduction. Most assessments still rely on unvalidated vegetation indices, assume monotonic trends over a single period, and use coarse attribution approaches that blur the respective roles of climate, soil–water conditions, and land use. This paper verifies NDVI (Normalized Vegetation Index) and kNDVI (Kernel Normalized Vegetation Index) using unmanned aerial vehicle (UAV) observation data. The temporal and spatial changes of vegetation in Inner Mongolia from 2000 to 2024 and the driving mechanisms of climate-soil-groundwater and land use were analyzed by using the sequence Mann-Kendall mutation test, the trend analysis of Theil-Sen (Theil-Sen) + MK (Mann-Kendall), and the Hurst index, pixel-wise correlations and a Geodetector model. Main findings: (1) compared with NDVI, kNDVI better identifies low-cover/poor-growth areas; (2) vegetation shows a fluctuating upward trend (slope ≈ 0.0034 yr⁻ 1 ) with a mean kNDVI of 0.255, and a northeast-to-southwest decline in greenness with peaks in Hulunbuir; (3) vegetation conditions improved over 77.29% of the region (mainly in the northeast) and degraded over 22.71% (chiefly central–eastern); Theil–Sen slope estimator combined with the Hurst exponent indicates kNDVI is likely to increase over most areas, with ~ 10.65% showing a declining tendency; (4) groundwater depth and precipitation are the principal natural drivers of interannual fluctuations, with groundwater showing the strongest association (up to r  = 0.95, p  < 0.01). In contrast, spatial heterogeneity is mainly shaped by soil nutrients, land use, and topography, among which total nitrogen provides the highest explanatory power (q = 0.41). Overall, the results underscore the central role of groundwater and soil conditions, calling for restoration strategies that integrate water management and land-use planning.

To explore the structure and assembly of the rare bacterial community within sediment samples, as well as their responses their responses to environmental influencing factors, we collected surface sediment and overlying water samples from Sancha Lake across four seasons. MiSeq high-throughput sequencing was applied to the V3-V4 hypervariable regions of the 16 S rRNA genes, and the β - Nearest Taxon index (βNTI) was utilized to analyze the bacterial community assembly in the sediment samples. Our findings uncovered abundant bacterial diversity within the sediment samples of Sancha Lake, with 9314 operational taxonomic units (OTUs) identified, encompassing 59 phyla, 198 classes, 279 orders, 447 families, and 758 genera of bacteria. Proteobacteria and Chloroflexi were the dominant rare bacteria at the phylum level, whereas Coxiella and hgcl_clade were the principal rare bacteria at the genus level. The variety index of rare communities across diverse seasons was notably higher than that of abundant ones ( P  < 0.01). Bacterial community structure differed between spring and other seasons, and the rare bacterial community exhibited substantial seasonal alterations during non-spring periods. pH, dissolved oxygen (DO), total phosphorus (TP), and soluble reactive phosphorus (SRP) were the predominant environmental factors, exerting an even greater influence on rare bacteria. Within the co-occurrence network, rare bacteria constituted the majority of nodes and connections and were the dominant key species throughout all seasons. The assembly of their community was chiefly deterministic in autumn and random in other seasons. This study indicated that rare bacteria in Sancha Lake were diverse. They were keystone taxa for maintaining community interactions and stable operation, and their assembly process was influenced by both stochastic and deterministic factors.