A stellar stream remnant of a globular cluster below the metallicity floor.

Stellar ejecta gradually enrich the gas out of which subsequent stars form, making the least chemically enriched stellar systems direct fossils of structures formed in the early Universe1. Although a few hundred stars with metal content below 1,000th of the solar iron content are known in the Galaxy2-4, none of them inhabit globular clusters, some of the oldest known stellar structures. These show metal content of at least approximately 0.2% of the solar metallicity [Formula: see text]. This metallicity floor appears universal5,6, and it has been proposed that protogalaxies that merged into the galaxies we observe today were simply not massive enough to form clusters that survived to the present day7. Here we report observations of a stellar stream, C-19, whose metallicity is less than 0.05% of the solar metallicity [Formula: see text]. The low metallicity dispersion and the chemical abundances of the C-19 stars show that this stream is the tidal remnant of the most metal-poor globular cluster ever discovered, and is significantly below the purported metallicity floor: clusters with significantly lower metallicities than observed today existed in the past and contributed their stars to the Milky Way halo.

Coronaviral ORF6 protein mediates inter-organelle contacts and modulates host cell lipid flux for virus production.

Lipid droplets (LDs) form inter-organelle contacts with the endoplasmic reticulum (ER) that promote their biogenesis, while LD contacts with mitochondria enhance ß-oxidation of contained fatty acids. Viruses have been shown to take advantage of lipid droplets to promote viral production, but it remains unclear whether they also modulate the interactions between LDs and other organelles. Here, we showed that coronavirus ORF6 protein targets LDs and is localized to the mitochondria-LD and ER-LD contact sites, where it regulates LD biogenesis and lipolysis. At the molecular level, we find that ORF6 inserts into the LD lipid monolayer via its two amphipathic helices. ORF6 further interacts with ER membrane proteins BAP31 and USE1 to mediate ER-LDs contact formation. Additionally, ORF6 interacts with the SAM complex in the mitochondrial outer membrane to link mitochondria to LDs. In doing so, ORF6 promotes cellular lipolysis and LD biogenesis to reprogram host cell lipid flux and facilitate viral production.

GR-pKa: a message-passing neural network with retention mechanism for pKa prediction.

During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pKa prediction model named GR-pKa (Graph Retention pKa), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pKa values. The GR-pKa model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-pKa model outperforms several state-of-the-art models in predicting macro-pKa values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R2 of 0.937 on the SAMPL7 dataset.

Observation of gauge invariance in a 71-site Bose-Hubbard quantum simulator.

The modern description of elementary particles, as formulated in the standard model of particle physics, is built on gauge theories1. Gauge theories implement fundamental laws of physics by local symmetry constraints. For example, in quantum electrodynamics Gauss's law introduces an intrinsic local relation between charged matter and electromagnetic fields, which protects many salient physical properties, including massless photons and a long-ranged Coulomb law. Solving gauge theories using classical computers is an extremely arduous task2, which has stimulated an effort to simulate gauge-theory dynamics in microscopically engineered quantum devices3-6. Previous achievements implemented density-dependent Peierls phases without defining a local symmetry7,8, realized mappings onto effective models to integrate out either matter or electric fields9-12, or were limited to very small systems13-16. However, the essential gauge symmetry has not been observed experimentally. Here we report the quantum simulation of an extended U(1) lattice gauge theory, and experimentally quantify the gauge invariance in a many-body system comprising matter and gauge fields. These fields are realized in defect-free arrays of bosonic atoms in an optical superlattice of 71 sites. We demonstrate full tunability of the model parameters and benchmark the matter-gauge interactions by sweeping across a quantum phase transition. Using high-fidelity manipulation techniques, we measure the degree to which Gauss's law is violated by extracting probabilities of locally gauge-invariant states from correlated atom occupations. Our work provides a way to explore gauge symmetry in the interplay of fundamental particles using controllable large-scale quantum simulators.

A GDSL motif-containing lipase modulates Sclerotinia sclerotiorum resistance in Brassica napus.

Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum (Lib.) De Bary is a devastating disease infecting hundreds of plant species. It also restricts the yield, quality, and safe production of rapeseed (Brassica napus) worldwide. However, the lack of resistance sources and genes to S. sclerotiorum has greatly restricted rapeseed SSR-resistance breeding. In this study, a previously identified GDSL motif-containing lipase gene, Brassica napus GDSL LIPASE-LIKE 1 (BnaC07.GLIP1), encoding a protein localized to the intercellular space, was characterized as functioning in plant immunity to S. sclerotiorum. The BnaC07.GLIP1 promoter is S. sclerotiorum-inducible and the expression of BnaC07.GLIP1 is substantially enhanced after S. sclerotiorum infection. Arabidopsis (Arabidopsis thaliana) heterologously expressing and rapeseed lines overexpressing BnaC07.GLIP1 showed enhanced resistance to S. sclerotiorum, whereas RNAi suppression and CRISPR/Cas9 knockout B. napus lines were hyper-susceptible to S. sclerotiorum. Moreover, BnaC07.GLIP1 affected the lipid composition and induced the production of phospholipid molecules, such as phosphatidylethanolamine, phosphatidylcholine and phosphatidic acid, which were correlated with decreased levels of reactive oxygen species (ROS) and enhanced expression of defense-related genes. A B. napus bZIP44 transcription factor specifically binds the CGTCA motif of the BnaC07.GLIP1 promoter to positively regulate its expression. BnbZIP44 responded to S. sclerotiorum infection, and its heterologous expression inhibited ROS accumulation, thereby enhancing S. sclerotiorum resistance in Arabidopsis. Thus, BnaC07.GLIP1 functions downstream of BnbZIP44 and is involved in S. sclerotiorum resistance by modulating the production of phospholipid molecules and ROS homeostasis in B. napus, providing insights into the potential roles and functional mechanisms of BnaC07.GLIP1 in plant immunity and for improving rapeseed SSR disease-resistance breeding.

Single-Cell RNA Sequencing Deconstructs the Distribution of Immune Cells Within Abdominal Aortic Aneurysms in Mice.

BACKGROUND: Inflammation is a key component in the development of abdominal aortic aneurysm (AAA), yet insights into the roles of immune cells and their interactions in this process are limited. METHODS: Using single-cell RNA transcriptomic analysis, we deconstructed the CD45+ cell population in elastase-induced murine AAA at the single-cell level. We isolated each group of immune cells from murine AAA tissue at different time points and divided them into several subtypes, listed the remarkable differentially expressed genes, explored the developmental trajectories of immune cells, and demonstrated the interactions among them. RESULTS: Our findings reveal significant differences in several immune cell subsets, including macrophages, dendritic cells, and T cells, within the AAA microenvironment compared with the normal aorta. Especially, conventional dendritic cell type 1 exclusively existed in the AAA tissue rather than the normal aortas. Via CellChat analysis, we identified several intercellular communication pathways like visfatin, which targets monocyte differentiation and neutrophil extracellular trap-mediated interaction between neutrophils and dendritic cells, which might contribute to AAA development. Some of these pathways were validated in human AAA. CONCLUSIONS: Despite the absence of external pathogenic stimuli, AAA tissues develop a complex inflammatory microenvironment involving numerous immune cells. In-depth studies of the inflammatory network shall provide new strategies for patients with AAA.

Shared Minds, Shared Feedback: tracing the influence of parental feedback on shared neural patterns.

Parental feedback affects children in multiple ways. However, little is known about how children, family, and feedback types affect parental feedback neural mechanisms. The current study used functional near-infrared spectroscopy-based hyperscanning to observe 47 mother-daughter pairs's (mean age of mothers: 35.95 ± 3.99 yr old; mean age of daughters: 6.97 ± 0.75 yr old) brain synchronization in a jigsaw game under various conditions. Between parental negative feedback and praise conditions, mother-daughter brain in supramarginal gyrus, left dorsolateral prefrontal cortex, right inferior frontal gyrus, and right primary somatic (S1) differed. When criticized, conformity family-communication-patterned families had much worse brain synchronization in S1, left dorsolateral prefrontal cortex, and right Wernicke's region than conversational families. Resilient children had better mother-child supramarginal gyrus synchronicity under negative feedback. This study supports the importance of studying children's neurological development in nurturing environments to assess their psychological development.

Sea-ATI unravels novel vocabularies of plant active cistrome.

The cistrome consists of all cis-acting regulatory elements recognized by transcription factors (TFs). However, only a portion of the cistrome is active for TF binding in a specific tissue. Resolving the active cistrome in plants remains challenging. In this study, we report the assay sequential extraction assisted-active TF identification (sea-ATI), a low-input method that profiles the DNA sequences recognized by TFs in a target tissue. We applied sea-ATI to seven plant tissues to survey their active cistrome and generated 41 motif models, including 15 new models that represent previously unidentified cis-regulatory vocabularies. ATAC-seq and RNA-seq analyses confirmed the functionality of the cis-elements from the new models, in that they are actively bound in vivo, located near the transcription start site, and influence chromatin accessibility and transcription. Furthermore, comparing dimeric WRKY CREs between sea-ATI and DAP-seq libraries revealed that thermodynamics and genetic drifts cooperatively shaped their evolution. Notably, sea-ATI can identify not only positive but also negative regulatory cis-elements, thereby providing unique insights into the functional non-coding genome of plants.

Artificial intelligence-aided optical imaging for cancer theranostics.

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

Bridging Stories and Science: An fNIRS-based hyperscanning investigation into child learning in STEM.

Early STEM education is crucial for later learning. This novel study utilised fNIRS to examine how STEM teaching methods (i.e., traditional, storytelling, storyboarding) affect neural activity synchronisation between teachers and students. Our results showed that left and right inferior frontal gyrus (IFG) for storytelling teaching versus traditional teaching, superior temporal gyrus for storyboard teaching versus traditional teaching, and left angular gyrus for storyboard and storytelling teaching were significant different in brain synchronisation. In the storytelling teaching condition, left supramarginal gyrus brain synchrony was found to improve STEM learning outcomes. In the storyboard teaching condition, IFG brain synchrony correlated positively with STEM learning improvement. The findings confirmed that story-based teaching and storyboarding can improve STEM learning efficacy at the neural level and unscored the significant role of neural synchronization as a predictor of learning outcomes.

Concurrent behavioral modeling and multimodal neuroimaging reveals how feedback affects the performance of decision making in internet gaming disorder.

Internet gaming disorder (IGD) prompts inquiry into how feedback from prior gaming rounds influences subsequent risk-taking behavior and potential neural mechanisms. Forty-two participants, including 15 with IGD and 27 health controls (HCs), underwent a sequential risk-taking task. Hierarchy Bayesian modeling was adopted to measure risky propensity, behavioral consistence, and affection by emotion ratings from last trial. Concurrent electroencephalogram and functional near-infrared spectroscopy (EEG-fNIRS) recordings were performed to demonstrate when, where and how the previous-round feedback affects the decision making to the next round. We discovered that the IGD illustrated heightened risk-taking propensity as compared to the HCs, indicating by the computational modeling (p = 0.028). EEG results also showed significant time window differences in univariate and multivariate pattern analysis between the IGD and HCs after the loss of the game. Further, reduced brain activation in the prefrontal cortex during the task was detected in IGD as compared to that of the control group. The findings underscore the importance of understanding the aberrant decision-making processes in IGD and suggest potential implications for future interventions and treatments aimed at addressing this behavioral addiction.

Common and unique brain aging patterns between females and males quantified by large-scale deep learning.

There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently between males and females. Based on resting-state brain functional connectivity (FC) of 25,582 healthy participants (13,373 females) aged 49-76 years from the UK Biobank project, we employ deep learning with explainable AI to discover primary FCs related to progressive aging and reveal similarity and difference between females and males in brain aging. Using a nested cross-validation scheme, we conduct 4200 deep learning models to classify all paired age groups on the main data for females and males separately and then extract gender-common and gender-specific aging-related FCs. Next, we validate those FCs using additional 21,000 classifiers on the independent data. Our results support that aging results in reduced brain functional interactions for both females and males, primarily relating to the positive connectivity within the same functional domain and the negative connectivity between different functional domains. Regions linked to cognitive control show the most significant age-related changes in both genders. Unique aging effects in males and females mainly involve the interaction between cognitive control and the default mode, vision, auditory, and frontoparietal domains. Results also indicate females exhibit faster brain functional changes than males. Overall, our study provides new evidence about common and unique patterns of brain aging in females and males.

The short-term efficacy of neoadjuvant SOX versus SOX plus immune checkpoint inhibitor following laparoscopic gastrectomy for locally advanced gastric cancer: a multicenter retrospective cohort study in China.

BACKGROUND: This study aims to evaluate the short-term efficacy for locally advanced gastric cancer (LAGC) who accepted laparoscopic gastrectomy (LG) after neoadjuvant SOX versus SOX plus immune checkpoint inhibitors (ICIs). METHODS: LAGC patients who accepted LG after neoadjuvant SOX (SOX-LG, n = 169) and SOX plus ICIs (SOX + ICIs-LG, n = 140) in three medical centers between Jan 2020 and Mar 2024 were analyzed. We compared the tumor regression, treatment-related adverse events (TRAEs), perioperative safety between two groups, and explored the risk factors of postoperative complications (POCs) for LG after neoadjuvant therapy. RESULTS: The baseline characteristics were comparable between two groups (P > 0.05). SOX + ICIs-LG group acquired a higher proportion of objective response (63.6% vs. 46.7%, P = 0.003), major pathological response (43.6% vs. 31.4%, P = 0.001), and pathological complete response (17.9% vs. 9.5%, P = 0.030). There were no significant differences in the TRAEs rates, operation time, R0 resection, retrieved lymph nodes, postoperative first flatus, and hospitalized days, overall and severe POCs between two groups (P > 0.05). Patients in the SOX-ICIs-LG group had lower estimated blood loss (EBL) compared with SOX-LG (P = 0.001). Multivariate analysis showed that more EBL (P = 0.003) and prognostic nutritional index (PNI) < 40 (P = 0.005) were independent risk factors of POCs for LG after neoadjuvant therapy. CONCLUSION: Neoadjuvant SOX plus ICIs brings better tumor regression and similar TRAEs compared with SOX alone for LAGC. SOX + ICIs-LG is safe and feasible to conduct with less EBL. Surgeons should focus on the perioperative management to control POCs for patients with PNI < 40 and more EBL.

Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space.

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

Immune to temperature interference sensor of carbon dioxide gas concentration based on a single modified fiber Bragg grating.

In this study, a novel method that can detect carbon dioxide (CO2) concentration and realize temperature immunity based on only one fiber Bragg grating (FBG) is proposed. The outstanding contribution lies in solving the temperature crosstalk issue of FBG and ensuring the accuracy of detection results under the condition of anti-temperature interference. To achieve immunity to temperature interference without changing the initial structure of FBG, the optical fiber cladding of FBG and adjacent optical fiber cladding at both ends of FBG are modified by a polymer coating. Moreover, a universal immune temperature demodulation algorithm is derived. The experimental results demonstrate that the temperature response sensitivity of the improved FBG is controlled within the range of 0.00407 nm/°C. Compared with the initial FBG (the temperature sensitivity of the initial FBG is 0.04 nm/°C), it decreases by nearly 10 times. Besides, the gas response sensitivity of FBG reaches 1.6 pm/ppm and has overwhelmingly ideal linearity. The detection error results manifest that the gas concentration error in 20 groups of data does not exceed 3.16 ppm. The final reproducibility research shows that the difference in detection sensitivity between the two sensors is 0.08 pm/ppm, and the relative error of linearity is 1.07%. In a word, the proposed method can accurately detect the concentration of CO2 gas and is efficiently immune to temperature interference. The sensor we proposed has the advantages of a simple production process, low cost, and satisfactory reproducibility. It also has the prospect of mass production.

Development of an integrated dual-modality 3D bioluminescence tomography and ultrasound imaging system for small animal tumor imaging.

Ultrasound (US) is a valuable tool for imaging soft tissue and visualizing tumor contours. Taking the benefits of US, we presented an integrated dual-modality imaging system in this paper that achieves three-dimensional (3D) bioluminescence tomography (BLT) with multi-view bioluminescence images and 3D US imaging. The purpose of this system is to perform non-invasive, long-term monitoring of tumor growth in 3D images. US images can enhance the accuracy of the 3D BLT reconstruction and the bioluminescence dose within an object. Furthermore, an integrated co-registered scanning geometry was used to capture the fused BLT and US images. We validated the system with an in vivo experiment involving tumor-bearing mice. The results demonstrated the feasibility of reconstructing 3D BLT images in the tumor region using 3D US images. We used the dice coefficient and locational error to evaluate the similarity between the reconstructed source region and the actual source region. The dice coefficient was 88.5%, and the locational error was 0.4 mm when comparing the BLT and 3D US images. The hybrid BLT/US system could provide significant benefits for reconstructing the source of tumor location and conducting quantitative analysis of tumor size.

Microscopic Study on Superexchange Dynamics of Composite Spin-1 Bosons.

We report on an experimental simulation of the spin-1 Heisenberg model with composite bosons in a one-dimensional chain based on the two-component Bose-Hubbard model. Exploiting our site- and spin-resolved quantum gas microscope, we observed faster superexchange dynamics of the spin-1 system compared to its spin-1/2 counterpart, which is attributed to the enhancement effect of multi-bosons. We further probed the nonequilibrium spin dynamics driven by the superexchange and single-ion anisotropy terms, unveiling the linear expansion of the spin-spin correlations, which is limited by the Lieb-Robinson bound. Based on the superexchange process, we prepared and verified the entangled qutrits pairs with these composite spin-1 bosons, potentially being applied in qutrit-based quantum information processing.

Similarity and difference in large-scale functional network alternations between behavioral addictions and substance use disorder: a comparative meta-analysis.

Behavioral addiction (BA) and substance use disorder (SUD) share similarities and differences in clinical symptoms, cognitive functions, and behavioral attributes. However, little is known about whether and how functional networks in the human brain manifest commonalities and differences between BA and SUD. Voxel-wise meta-analyses of resting-state functional connectivity (rs-FC) were conducted in BA and SUD separately, followed by quantitative conjunction analyses to identify the common and distinct alterations across both the BA and SUD groups. A total of 92 datasets with 2444 addicted patients and 2712 healthy controls (HCs) were eligible for the meta-analysis. Our findings demonstrated that BA and SUD exhibited common alterations in rs-FC between frontoparietal network (FPN) and other high-level neurocognitive networks (i.e. default mode network (DMN), affective network (AN), and salience network (SN)) as well as hyperconnectivity between SN seeds and the Rolandic operculum in SSN. In addition, compared with BA, SUD exhibited several distinct within- and between-network rs-FC alterations mainly involved in the DMN and FPN. Further, altered within- and between-network rs-FC showed significant association with clinical characteristics such as the severity of addiction in BA and duration of substance usage in SUD. The common rs-FC alterations in BA and SUD exhibited the relationship with consistent aberrant behaviors in both addiction groups, such as impaired inhibition control and salience attribution. By contrast, the distinct rs-FC alterations might suggest specific substance effects on the brain neural transmitter systems in SUD.

Akkermansia muciniphila-derived pentadecanoic acid enhances oxaliplatin sensitivity in gastric cancer by modulating glycolysis.

Accumulating evidence has proved the close association between alterations in gut microbiota and resistance to chemotherapeutic drugs. However, the potential roles of gut microbiota in regulating oxaliplatin sensitivity in gastric cancer (GC) have not been investigated before. We first found that antibiotic treatment diminished the therapeutic efficacy of oxaliplatin in a GC mouse model. Importantly, this effect could be transmitted to germ-free mice via fecal microbiota transplantation, indicating a potential role of gut microbiota modulation in oxaliplatin efficacy. Further, metagenomics data showed that Akkermansia muciniphila (A. muciniphila) ranked first among the bacterial species with decreased relative abundances after antibiotic treatment. Metabolically active A. muciniphila promotes oxaliplatin efficacy. As shown by metabolomics analysis, the metabolic pattern of gut microbiota was disrupted with significantly downregulated levels of pentadecanoic acid (PEA), and the use of PEA significantly promoted oxaliplatin efficacy. Mechanistically, FUBP1 positively regulated aerobic glycolysis of GC cells to hinder the therapeutic efficacy of oxaliplatin. A. muciniphila-derived PEA functioned as an inhibitory factor of glycolysis by directly antagonizing the activity of FUBP1, which potentiated GC responses to oxaliplatin. Our research suggested a key role for intestinal A. muciniphila and its metabolite PEA in promoting oxaliplatin efficacy, thus providing a new perspective for probiotic and prebiotic intervention in GC patients during chemotherapy.

Mapping cross-species connectome atlas of human and macaque striatum.

Cross-species connectome atlas (CCA) that can provide connectionally homogeneous and homologous brain nodes is essential and customized for cross-species neuroscience. However, existing CCAs were flawed in design and coarse-grained in results. In this study, a normative mapping framework of CCA was proposed and applied on human and macaque striatum. Specifically, all striatal voxels in the 2 species were mixed together and classified based on their represented and characterized feature of within-striatum resting-state functional connectivity, which was shared between the species. Six pairs of striatal parcels in these species were delineated in both hemispheres. Furthermore, this striatal parcellation was demonstrated by the best-matched whole-brain functional and structural connectivity between interspecies corresponding subregions. Besides, detailed interspecies differences in whole-brain multimodal connectivities and involved brain functions of these subregions were described to flesh out this CCA of striatum. In particular, this flexible and scalable mapping framework enables reliable construction of CCA of the whole brain, which would enable reliable findings in future cross-species research and advance our understandings into how the human brain works.