A Novel Preparation Method of Composite Bolted T-Joint with High Bending Performance Based on the Prepreg-RTM Co-Curing Process.

A co-curing resin system consisting of 9368 epoxy resin for prepreg and 6808 epoxy resin for resin transfer molding (RTM) was developed. A corresponding preparation method for a novel polymer composite bolted T-joint with internal skeleton and external skin was proposed based on the prepreg-RTM co-curing process, and novel T-joints were fabricated. A series of conventional configuration T-joints based on the RTM process and T-joints made of 2A12 aluminum alloy were prepared simultaneously. Bending performances were studied on these T-joints experimentally. The results indicate that 9368 epoxy resin and 6808 epoxy resin exhibit good compatibility in rheological and thermophysical properties. The novel T-joints prepared with the prepreg-RTM co-curing process show no obvious fiber local winding or resin-rich regions inside, and the interface quality between the internal skeleton and the external skin is excellent. The main failure modes of the novel T-joint under bending load include the separation of the skin and skeleton and the fracture along the thickness on the base panel; the skeleton carries the main bending load, but there is still load transfer between external skin and internal skeleton through their interface. The internal damages of the novel T-joint are highly consistent with surface damages observed visually, facilitating the detection and timely discovery of damages. The initial stiffness, damage initiation load, and ultimate load of the novel T-joint are 1.65 times, 5.89 times, and 3.45 times that of the conventional T-joint, respectively. When considering the influence of the density, the relative initial stiffness and relative ultimate load of the novel T-joint are 1.44 times and 2.07 times that of the aluminum alloy T-joint, respectively.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer.

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

A joint brain extraction and image quality assessment framework for fetal brain MRI slices.

Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.

Reaction-Initiated Single-Molecule Tracking of Mass Transfer in Core-Shell Mesoporous Silica Particles.

Understanding the host-guest interactions in porous materials is of great importance in the field of separation science. Probing it at the single-molecule level uncovers the inter- and intraparticle inhomogeneity and establishes structure-property relationships for guiding the design of porous materials for better separation performance. In this work, we investigated the dynamics of host-guest interactions in core-shell mesoporous silica particles under in situ conditions by using a fluorogenic reaction-initiated single-molecule tracking (riSMT) approach. Taking advantage of the low fluorescence background, three-dimensional (3D) tracking of the dynamics of the molecules inside the mesoporous silica pore was achieved with high spatial precision. Compared to the commonly used two-dimensional (2D) tracking method, the 3D tracking results show that the diffusion coefficients of the molecules are three times larger on average. Using riSMT, we quantitatively analyzed the mass transfer of probe molecules in the mesoporous silica pore, including the fraction of adsorption versus diffusion, diffusion coefficients, and residence time. Large interparticle inhomogeneity was revealed and is expected to contribute to the peak broadening for separation application at the ensemble level. We further investigated the impact of electrostatic interaction on the mass transfer of molecules in the mesoporous silica pore and discovered that the primary effect is on the fraction rather than their diffusion rates of resorufin molecules undergoing diffusion.

PETS-Nets: Joint Pose Estimation and Tissue Segmentation of Fetal Brains Using Anatomy-Guided Networks.

Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.

Comprehensive genomic profiling of breast cancers characterizes germline-somatic mutation interactions mediating therapeutic vulnerabilities.

Germline-somatic mutation interactions are universal and associated with tumorigenesis, but their role in breast cancer, especially in non-Caucasians, remains poorly characterized. We performed large-scale prospective targeted sequencing of matched tumor-blood samples from 4079 Chinese females, coupled with detailed clinical annotation, to map interactions between germline and somatic alterations. We discovered 368 pathogenic germline variants and identified 5 breast cancer DNA repair-associated genes (BCDGs; BRCA1/BRCA2/CHEK2/PALB2/TP53). BCDG mutation carriers, especially those with two-hit inactivation, demonstrated younger onset, higher tumor mutation burden, and greater clinical benefits from platinum drugs, PARP inhibitors, and immune checkpoint inhibitors. Furthermore, we leveraged a multiomics cohort to reveal that clinical benefits derived from two-hit events are associated with increased genome instability and an immune-activated tumor microenvironment. We also established an ethnicity-specific tool to predict BCDG mutation and two-hit status for genetic evaluation and therapeutic decisions. Overall, this study leveraged the large sequencing cohort of Chinese breast cancers, optimizing genomics-guided selection of DNA damaging-targeted therapy and immunotherapy within a broader population.

Unraveling the Pivotal Network of Ultrasound and Somatic Mutations in Triple-Negative and Non-Triple-Negative Breast Cancer.

Purpose: The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures. Patients and Methods: Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features. Results: Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC. Conclusion: The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

An In Vivo CRISPR Screen Identifies That SNRPC Promotes Triple-Negative Breast Cancer Progression.

Dysregulation of RNA-binding proteins (RBP) is one of the characteristics of cancer. Investigating the biological functions and molecular mechanisms of abnormal RBPs can help uncover new cancer biomarkers and treatment strategies. To identify oncogenic RBPs in triple-negative breast cancer (TNBC), we employed an in vivo CRISPR screen and a TNBC progression model, which revealed small nuclear ribonucleoprotein polypeptide C (SNRPC), a subunit of the U1 small nuclear ribonucleoprotein particle (U1 snRNP), as a key modulator of TNBC progression. SNRPC was frequently upregulated, which corresponded to poor prognosis in patients with TNBC. SNRPC ablation significantly impaired the proliferation, migration, and invasion of TNBC cells in vitro and in vivo. In addition, SNRPC was essential for the stability of U1 snRNP and contributed to the RNA Pol II-controlled transcriptional program. Knockdown of SNRPC decreased RNA Pol II enrichment on a subset of oncogenes (TNFAIP2, E2F2, and CDK4) and reduced their expression levels. Furthermore, SNRPC deletion was confirmed to inhibit TNBC progression partially through regulation of the TNFAIP2-Rac1-ß-catenin signaling pathway. Taken together, this data suggests that SNRPC plays an oncogenic role in TNBC, is a marker of poor prognosis, and may be a valuable therapeutic target for patients with intractable TNBC. SIGNIFICANCE: A functional CRISPR screen identifies SNRPC as an RNA-binding protein that promotes the aggressiveness of breast cancer by facilitating Pol II-controlled transcription of oncogenes.

The clinical application of 68Ga-PSMA PET/CT and regulating mechanism of PSMA expression in patients with brain metastases of lung cancer.

Brain metastases (BMs) of lung cancer are common malignant intracranial tumours associated with severe neurological symptoms and an abysmal prognosis. Prostate-specific membrane antigen (PSMA) has been reported to express significantly in a variety of solid tumours. However, the clinical applications of 68Ga-PSMA PET/CT and the mechanism of PSMA expression in patients with BMs of lung cancer have rarely been reported. Experiments with 68Ga-PSMA PET/CT and immunohistochemical staining were conducted to evaluate the expression of PSMA from seven patients with BMs of lung cancer who accepted surgical treatment in Fudan University Shanghai Cancer Center between October 2020 and October 2021. The mechanism of PSMA expression in BMs of lung cancer was explored by using single-cell RNA sequencing. The median maximum standardized uptake value (SUVmax) in BMs was higher than that in primary lung cancer (8.6 ± 2.8 vs. 3.6 ± 1.3, P < 0.01). The mean SUVmax in BMs was 1.76-fold higher than that in the liver, which indicated the potential of PSMA radioligand therapy (PSMA-RLT) for BMs. BMs showed intense PSMA staining, while normal lung tissue had no PSMA staining and there was only faint primary lung cancer staining by immunohistochemistry (IHC). Single-cell RNA sequencing (scRNA-seq) analysis found that PSMA was mainly expressed in oligodendrocytes of BMs, whereas it was expressed at lower levels in solid cells of lung cancer. PSMA expression in oligodendrocytes might be regulated by the factors ATF3 and NR4A1, which were associated with ER stress.

Anatomical attention-based prediction of postoperative pulmonary venous obstruction via CTA images.

Total anomalous pulmonary venous connection (TAPVC) is a rare congenital heart disease, with which some patients suffer from postoperative pulmonary venous obstruction (PPVO), requiring particular follow-up strategies and treatments. PPVO prediction has important clinical significance, while building a PPVO prediction model is challenging due to limited data and class imbalance distribution. Inspired by the anatomical evidence of PPVO, which is related to the structure of the left atrium (LA) and pulmonary vein (PV), we design an effective multi-task network for PPVO classification. The proposed method incorporates clinical priors and merits of the segmentation-based network into the classification task. The features learned from segmenting LA and PV are concatenated into the PPVO classification branch to constrain the learning of discriminative features. Anatomical-guided attention is applied in the aggregation of these features to restrict them focusing on TAPVC-related regions. To deal with the imbalance classification problem of PPVO, a novel classification loss derived by masked class activation map (MCAM) is designed to improve the classification performance. Computed tomography angiography (CTA) images of 146 patients diagnosed with supracardiac TAPVC in Shanghai Children's Medical Center and Guangdong Provincial People's Hospital were enrolled in this work. The comprehensive experiments demonstrate the effectiveness and generalization of our proposed method. The automatic PPVO prediction model shows the potential application in helping clinicians develop follow-up strategies, thereby improving the survival rate of TAPVC patients.

Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets.

BACKGROUND: We established a radiogenomic model to predict pathological complete response (pCR) in triple-negative breast cancer (TNBC) and explored the association between high-frequency mutations and drug resistance. METHODS: From April 2018 to September 2019, 112 patients who had received neoadjuvant chemotherapy were included. We randomly split the study population into training and validation sets (2:1 ratio). Contrast-enhanced magnetic resonance imaging scans were obtained at baseline and after two cycles of treatment and were used to extract quantitative radiomic features and to construct two radiomics-only models using a light gradient boosting machine. By incorporating the variant allele frequency features obtained from baseline core tissues, a radiogenomic model was constructed to predict pCR. Additionally, we explored the association between recurrent mutations and drug resistance. RESULTS: The two radiomics-only models showed similar performance with AUCs of 0.71 and 0.73 (p = 0.55). The radiogenomic model had a higher predictive ability than the radiomics-only model in the validation set (p = 0.04), with a corresponding AUC of 0.87 (0.73-0.91). Two highly frequent mutations were selected after comparing the mutation sites of pCR and non-pCR populations. The MED23 mutation p.P394H caused epirubicin resistance in vitro (p < 0.01). The expression levels of γ-H2A.X, p-ATM and p-CHK2 in MED23 p.P394H cells were significantly lower than those in wild type cells (p < 0.01). In the HR repair system, the GFP positivity rate of MED23 p.P394H cells was higher than that in wild-type cells (p < 0.01). CONCLUSIONS: The proposed radiogenomic model has the potential to accurately predict pCR in TNBC patients. Epirubicin resistance after MED23 p.P394H mutation might be affected by HR repair through regulation of the p-ATM-γ-H2A.X-p-CHK2 pathway.

In vivo multidimensional CRISPR screens identify Lgals2 as an immunotherapy target in triple-negative breast cancer.

Immune checkpoint inhibitors exhibit limited response rates in patients with triple-negative breast cancer (TNBC), suggesting that additional immune escape mechanisms may exist. Here, we performed two-step customized in vivo CRISPR screens targeting disease-related immune genes using different mouse models with multidimensional immune-deficiency characteristics. In vivo screens characterized gene functions in the different tumor microenvironments and recovered canonical immunotherapy targets such as Ido1. In addition, functional screening and transcriptomic analysis identified Lgals2 as a candidate regulator in TNBC involving immune escape. Mechanistic studies demonstrated that tumor cell-intrinsic Lgals2 induced the increased number of tumor-associated macrophages, as well as the M2-like polarization and proliferation of macrophages through the CSF1/CSF1R axis, which resulted in the immunosuppressive nature of the TNBC microenvironment. Blockade of LGALS2 using an inhibitory antibody successfully arrested tumor growth and reversed the immune suppression. Collectively, our results provide a theoretical basis for LGALS2 as a potential immunotherapy target in TNBC.

Building a Risk Prediction Model for Postoperative Pulmonary Vein Obstruction via Quantitative Analysis of CTA Images.

Total anomalous pulmonary venous connection (TAPVC) is a rare but mortal congenital heart disease in children and can be repaired by surgical operations. However, some patients may suffer from pulmonary venous obstruction (PVO) after surgery with insufficient blood supply, necessitating special follow-up strategy and treatment. Therefore, it is a clinically important yet challenging problem to predict such patients before surgery. In this paper, we address this issue and propose a computational framework to determine the risk factors for postoperative PVO (PPVO) from computed tomography angiography (CTA) images and build the PPVO risk prediction model. From clinical experiences, such risk factors are likely from the left atrium (LA) and pulmonary vein (PV) of the patient. Thus, 3D models of LA and PV are first reconstructed from low-dose CTA images. Then, a feature pool is built by computing different morphological features from 3D models of LA and PV, and the coupling spatial features of LA and PV. Finally, four risk factors are identified from the feature pool using the machine learning techniques, followed by a risk prediction model. As a result, not only PPVO patients can be effectively predicted but also qualitative risk factors reported in the literature can now be quantified. Finally, the risk prediction model is evaluated on two independent clinical datasets from two hospitals. The model can achieve the AUC values of 0.88 and 0.87 respectively, demonstrating its effectiveness in risk prediction.

Longitudinal brain atlases of early developing cynomolgus macaques from birth to 48 months of age.

Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development.

In situ observation of the crystal structure transition of Pt-Sn intermetallic nanoparticles during deactivation and regeneration.

The mechanism of the deactivation and regeneration of PtSn intermetallic compound nanoparticle (iNP) catalysts was studied by in situ TEM investigation. Our study reveals the reversible dynamic structural transition of the iNPs during deactivation and regeneration, which provides a direct correlation between the atomic structure and the catalytic activity of the iNPs.

DIKA-Nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques.

As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnetic resonance imaging (MRI) is a crucial prerequisite in neuroimaging analysis of macaques. Most of the current skull stripping methods can achieve satisfactory results for human brains, but when applied to macaque brains, especially during early brain development, the results are often unsatisfactory. In fact, the early dynamic, regionally-heterogeneous development of macaque brains, accompanied by poor and age-related contrast between different anatomical structures, poses significant challenges for accurate skull stripping. To overcome these challenges, we propose a fully-automated framework to effectively fuse the age-specific intensity information and domain-invariant prior knowledge as important guiding information for robust skull stripping of developing macaques from 0 to 36 months of age. Specifically, we generate Signed Distance Map (SDM) and Center of Gravity Distance Map (CGDM) based on the intermediate segmentation results as guidance. Instead of using local convolution, we fuse all information using the Dual Self-Attention Module (DSAM), which can capture global spatial and channel-dependent information of feature maps. To extensively evaluate the performance, we adopt two relatively-large challenging MRI datasets from rhesus macaques and cynomolgus macaques, respectively, with a total of 361 scans from two different scanners with different imaging protocols. We perform cross-validation by using one dataset for training and the other one for testing. Our method outperforms five popular brain extraction tools and three deep-learning-based methods on cross-source MRI datasets without any transfer learning.

Construction of Longitudinally Consistent 4D Infant Cerebellum Atlases Based on Deep Learning.

Longitudinal infant dedicated cerebellum atlases play a fundamental role in characterizing and understanding the dynamic cerebellum development during infancy. However, due to the limited spatial resolution, low tissue contrast, tiny folding structures, and rapid growth of the cerebellum during this stage, it is challenging to build such atlases while preserving clear folding details. Furthermore, the existing atlas construction methods typically independently build discrete atlases based on samples for each age group without considering the within-subject temporal consistency, which is critical for large-scale longitudinal studies. To fill this gap, we propose an age-conditional multi-stage learning framework to construct longitudinally consistent 4D infant cerebellum atlases. Specifically, 1) A joint affine and deformable atlas construction framework is proposed to accurately build temporally continuous atlases based on the entire cohort, and rapidly warp the new images to the atlas space; 2) A longitudinal constraint is employed to enforce the within-subject temporal consistency during atlas building; 3) A Correntropy based regularization loss is further exploited to enhance the robustness of our framework. Our atlases are constructed based on 405 longitudinal scans from 187 healthy infants with age ranging from 6 to 27 months, and are compared to the atlases built by state-of-the-art algorithms. Results demonstrate that our atlases preserve more structural details and fine-grained cerebellum folding patterns, which ensure higher accuracy in subsequent atlas-based registration and segmentation tasks.

Learning Spatiotemporal Probabilistic Atlas of Fetal Brains with Anatomically Constrained Registration Network.

Brain atlases are of fundamental importance for analyzing the dynamic neurodevelopment in fetal brain studies. Since the brain size, shape, and anatomical structures change rapidly during the prenatal period, it is essential to construct a spatiotemporal (4D) atlas equipped with tissue probability maps, which can preserve sharper early brain folding patterns for accurately characterizing dynamic changes in fetal brains and provide tissue prior informations for related tasks, e.g., segmentation, registration, and parcellation. In this work, we propose a novel unsupervised age-conditional learning framework to build temporally continuous fetal brain atlases by incorporating tissue segmentation maps, which outperforms previous traditional atlas construction methods in three aspects. First, our framework enables learning age-conditional deformable templates by leveraging the entire collection. Second, we leverage reliable brain tissue segmentation maps in addition to the low-contrast noisy intensity images to enhance the alignment of individual images. Third, a novel loss function is designed to enforce the similarity between the learned tissue probability map on the atlas and each subject tissue segmentation map after registration, thereby providing extra anatomical consistency supervision for atlas building. Our 4D temporally-continuous fetal brain atlases are constructed based on 82 healthy fetuses from 22 to 32 gestational weeks. Compared with the atlases built by the state-of-the-art algorithms, our atlases preserve more structural details and sharper folding patterns. Together with the learned tissue probability maps, our 4D fetal atlases provide a valuable reference for spatial normalization and analysis of fetal brain development.

Molecular Features and Functional Implications of Germline Variants in Triple-Negative Breast Cancer.

BACKGROUND: The germline variant spectrum of triple-negative breast cancer (TNBC) is different from that of other subtypes and has demonstrated ethnic differences. However, the germline variants of TNBC among Chinese patients and its clinical significance remain unclear. METHODS: Using our multi-omics TNBC cohort (n = 325), we determined the spectrum of germline variants in TNBC and aimed to illustrate their biological and clinical implications. RESULTS: Overall, 16.0% (52 of 325) of TNBC patients harbored at least 1 pathogenic or likely pathogenic germline variant. These germline variants were associated with early onset of TNBC, the occurrence of contralateral breast cancer, the basal-like immune-suppressed mRNA subtype, and the homologous recombination deficiency (HRD) mutation subtype. Somatic allele-specific imbalance was observed in 54.1% of these germline variants, which was correlated with early onset of breast cancer and elevated HRD. The genes BRCA1 (7.4%), RAD51D (2.8%), and BRCA2 (2.2%) were those most frequently mutated. The RAD51D germline variants, especially K91fs, were enriched in Chinese patients with TNBC compared with Caucasian and African American patients. The Chinese-specific RAD51D germline variants were functionally associated with the instability of the RAD51D protein, HRD, and sensitivity to PARP inhibitors. CONCLUSIONS: Chinese TNBC patients have a distinct spectrum of germline variants, with a remarkable impact on the clinical and molecular characteristics of the tumor. Integrative germline-somatic analysis may help identify TNBC patients who are most likely to be affected by their germline variants and in performing clinical interventions more precisely. The RAD51D variants enriched in our cohort may serve as therapeutic targets and guide precision treatment of TNBC.