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This paper develops a theory for group Lasso using a concept called strong group sparsity. Our result shows that group Lasso is superior to standard Lasso for strongly group-sparse signals. This provides a convincing theoretical justification for using group sparse regularization when the underlying group structure is consistent with the data. Moreover, the theory predicts some limitations of the group Lasso formulation that are confirmed by simulation studies.

Human steroid 5α-reductase 2 (SRD5A2) is an integral membrane enzyme in steroid metabolism and catalyzes the reduction of testosterone to dihydrotestosterone. Mutations in the SRD5A2 gene have been linked to 5α-reductase deficiency and prostate cancer. Finasteride and dutasteride, as SRD5A2 inhibitors, are widely used antiandrogen drugs for benign prostate hyperplasia. The molecular mechanisms underlying enzyme catalysis and inhibition for SRD5A2 and other eukaryotic integral membrane steroid reductases remain elusive due to a lack of structural information. Here, we report a crystal structure of human SRD5A2 at 2.8 Å, revealing a unique 7-TM structural topology and an intermediate adduct of finasteride and NADPH as NADP-dihydrofinasteride in a largely enclosed binding cavity inside the transmembrane domain. Structural analysis together with computational and mutagenesis studies reveal the molecular mechanisms of the catalyzed reaction and of finasteride inhibition involving residues E57 and Y91. Molecular dynamics simulation results indicate high conformational dynamics of the cytosolic region that regulate NADPH/NADP + exchange. Mapping disease-causing mutations of SRD5A2 to our structure suggests molecular mechanisms for their pathological effects. Our results offer critical structural insights into the function of integral membrane steroid reductases and may facilitate drug development. Human steroid 5α-reductase 2 (SRD5A2) is an integral membrane enzyme and catalyzes 5α-reduction of testosterone to dihydrotestosterone. Structural analysis accompanied by computational and mutagenesis studies reveal the mechanisms of catalysis and inhibition by clinically relevant drugs targeting SRD5A2.

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources. The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern and early assessment would be vital. Here, the authors show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission.

To satisfy the stringent requirements for computational resources in the field of real-time semantic segmentation, most approaches focus on the hand-crafted design of light-weight segmentation networks. To enjoy the ability of model auto-design, Neural Architecture Search (NAS) has been introduced to search for the optimal building blocks of networks automatically. However, the network depth, downsampling strategy, and feature aggregation method are still set in advance and nonadjustable during searching. Moreover, these key properties are highly correlated and essential for a remarkable real-time segmentation model. In this paper, we propose a joint search framework, called AutoRTNet, to automate all the aforementioned key properties in semantic segmentation. Specifically, we propose hyper-cells to jointly decide the network depth and the downsampling strategy via a novel cell-level pruning process. Furthermore, we propose an aggregation cell to achieve automatic multi-scale feature aggregation. Extensive experimental results on Cityscapes and CamVid datasets demonstrate that the proposed AutoRTNet achieves the new state-of-the-art trade-off between accuracy and speed. Notably, our AutoRTNet achieves 73.9% mIoU on Cityscapes and 110.0 FPS on an NVIDIA TitanXP GPU card with input images at a resolution of 768×1536.

This study presents an innovative solution to improve the mechanical performance of traditional cemented tailings backfill (CTB) by incorporating 3D-printed polymer lattice (3DPPL) reinforcements. We systematically investigated three distinct 3DPPL configurations (four-column FC, six-column SC, and cross-shaped CO) through comprehensive experimental methods including Brazilian splitting tests, digital image correlation (DIC), and scanning electron microscopy (SEM). The results show that the 3DPPL reinforcement significantly enhances the CTB’s tensile properties, with the CO structure demonstrating the most substantial improvement—increasing the tensile strength by 85.6% (to 0.386 MPa) at a cement-to-tailings ratio of 1:8. The 3DPPL-modified CTB exhibited superior ductility and progressive failure characteristics, as evidenced by multi-stage load-deflection behavior and a significantly higher strain capacity (41.698–51.765%) compared to unreinforced specimens (2.504–4.841%). The reinforcement mechanism involved synergistic effects of macroscopic truss behavior and microscopic interfacial bonding, which effectively redistributed the stress and dissipated energy. This multi-scale approach successfully transforms CTB’s failure mode from brittle to progressive while optimizing both strength and toughness, providing a promising advancement for mine backfill material design.

The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the prognosis of breast cancer patients. Several investigations have shown high intraobserver and interobserver variability in the evaluation of HER2 staining by visual examination. In this study, we aim to propose an artificial intelligence (AI)–assisted microscope to improve the HER2 assessment accuracy and reliability. Our AI-assisted microscope was equipped with a conventional microscope with a cell-level classification-based HER2 scoring algorithm and an augmented reality module to enable pathologists to obtain AI results in real time. We organized a three-round ring study of 50 infiltrating duct carcinoma not otherwise specified (NOS) cases without neoadjuvant treatment, and recruited 33 pathologists from 6 hospitals. In the first ring study (RS1), the pathologists read 50 HER2 whole-slide images (WSIs) through an online system. After a 2-week washout period, they read the HER2 slides using a conventional microscope in RS2. After another 2-week washout period, the pathologists used our AI microscope for assisted interpretation in RS3. The consistency and accuracy of HER2 assessment by the AI-assisted microscope were significantly improved (p < 0.001) over those obtained using a conventional microscope and online WSI. Specifically, our AI-assisted microscope improved the precision of immunohistochemistry (IHC) 3 + and 2 + scoring while ensuring the recall of fluorescent in situ hybridization (FISH)–positive results in IHC 2 + . Also, the average acceptance rate of AI for all pathologists was 0.90, demonstrating that the pathologists agreed with most AI scoring results.

The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which prevents them from discovering novel reactions. To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates. As far as we know, this is the first method that uses machine learning to compose reaction templates for retrosynthesis prediction. Besides, we propose an effective reactant candidate scoring model that can capture atom-level transformations, which helps our method outperform previous methods on the USPTO-50K dataset. Experimental results show that our method can produce novel templates for 15 USPTO-50K test reactions that are not covered by training templates. We have released our source implementation.

Computational pathology is a rapidly expanding area for research due to the current global transformation of histopathology through the adoption of digital workflows. Survival prediction of breast cancer patients is an important task that currently depends on histopathology assessment of cancer morphological features, immunohistochemical biomarker expression and patient clinical findings. To facilitate the manual process of survival risk prediction, we developed a computational pathology framework for survival prediction using digitally scanned haematoxylin and eosin-stained tissue microarray images of clinically aggressive triple negative breast cancer. Our results show that the model can produce an average concordance index of 0.616. Our model predictions are analysed for independent prognostic significance in univariate analysis (hazard ratio = 3.12, 95% confidence interval [1.69,5.75], p < 0.005) and multivariate analysis using clinicopathological data (hazard ratio = 2.68, 95% confidence interval [1.44,4.99], p < 0.005). Through qualitative analysis of heatmaps generated from our model, an expert pathologist is able to associate tissue features highlighted in the attention heatmaps of high-risk predictions with morphological features associated with more aggressive behaviour such as low levels of tumour infiltrating lymphocytes, stroma rich tissues and high-grade invasive carcinoma, providing explainability of our method for triple negative breast cancer.

Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-consuming, and as a result, is not available for most patients, especially those in low-resource settings. We aimed to develop an annotation-free Deep learning-enabled artificial intelligence method to predict GEne Mutations (DeepGEM) from routinely acquired histological slides. In this multicentre retrospective study, we collected data for patients with lung cancer who had a biopsy and multigene next-generation sequencing done at 16 hospitals in China (with no restrictions on age, sex, or histology type), to form a large multicentre dataset comprising paired pathological image and multiple gene mutation information. We also included patients from The Cancer Genome Atlas (TCGA) publicly available dataset. Our developed model is an instance-level and bag-level co-supervised multiple instance learning method with label disambiguation design. We trained and initially tested the DeepGEM model on the internal dataset (patients from the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China), and further evaluated it on the external dataset (patients from the remaining 15 centres) and the public TCGA dataset. Additionally, a dataset of patients from the same medical centre as the internal dataset, but without overlap, was used to evaluate the model's generalisation ability to biopsy samples from lymph node metastases. The primary objective was the performance of the DeepGEM model in predicting gene mutations (area under the curve [AUC] and accuracy) in the four prespecified groups (ie, the hold-out internal test set, multicentre external test set, TCGA set, and lymph node metastases set). Assessable pathological images and multigene testing information were available for 3697 patients who had biopsy and multigene next-generation sequencing done between Jan 1, 2018, and March 31, 2022, at the 16 centres. We excluded 60 patients with low-quality images. We included 3767 images from 3637 consecutive patients (1978 [54·4%] men, 1514 [41·6%] women, 145 [4·0%] unknown; median age 60 years [IQR 52–67]), with 1716 patients in the internal dataset, 1718 patients in the external dataset, and 203 patients in the lymph node metastases dataset. The DeepGEM model showed robust performance in the internal dataset: for excisional biopsy samples, AUC values for gene mutation prediction ranged from 0·90 (95% CI 0·77–1·00) to 0·97 (0·93–1·00) and accuracy values ranged from 0·91 (0·85–0·98) to 0·97 (0·93–1·00); for aspiration biopsy samples, AUC values ranged from 0·85 (0·80–0·91) to 0·95 (0·86–1·00) and accuracy values ranged from 0·79 (0·74–0·85) to 0·99 (0·98–1·00). In the multicentre external dataset, for excisional biopsy samples, AUC values ranged from 0·80 (95% CI 0·75–0·85) to 0·91 (0·88–1·00) and accuracy values ranged from 0·79 (0·76–0·82) to 0·95 (0·93–0·96); for aspiration biopsy samples, AUC values ranged from 0·76 (0·70–0·83) to 0·87 (0·80–0·94) and accuracy values ranged from 0·76 (0·74–0·79) to 0·97 (0·96–0·98). The model also showed strong performance on the TCGA dataset (473 patients; 535 slides; AUC values ranged from 0·82 [95% CI 0·71–0·93] to 0·96 [0·91–1·00], accuracy values ranged from 0·79 [0·70–0·88] to 0·95 [0·90–1·00]). The DeepGEM model, trained on primary region biopsy samples, could be generalised to biopsy samples from lymph node metastases, with AUC values of 0·91 (95% CI 0·88–0·94) for EGFR and 0·88 (0·82–0·93) for KRAS and accuracy values of 0·85 (0·80–0·88) for EGFR and 0·95 (0·92–0·96) for KRAS and showed potential for prognostic prediction of targeted therapy. The model generated spatial gene mutation maps, indicating gene mutation spatial distribution. We developed an AI-based method that can provide an accurate, timely, and economical prediction of gene mutation and mutation spatial distribution. The method showed substantial potential as an assistive tool for guiding the clinical treatment of patients with lung cancer. National Natural Science Foundation of China, the Science and Technology Planning Project of Guangzhou, and the National Key Research and Development Program of China. For the Chinese translation of the abstract see Supplementary Materials section.

Excavation in foundation pits can result in serious issues for nearby tunnel structures like deformation, differential settlement, and seepage damage, which profoundly impact project timelines and potentially endanger life and property safety. Therefore, it is imperative to investigate these impacts before and after construction and to facilitate timely adjustments of construction measures and reinforcement where possible. In this study, a foundation pit construction project near a rail transit line is employed as a case to comprehensive study the impact of on-site deep foundation pit excavation on adjacent rail transit structures by numerical simulation. A three-dimensional finite-element model of the foundation pit based on site geological characteristics and construction procedures is established to study the excavation and maintenance processes. Through analysis of key parameters including soil deformation, displacement, shear force, and bending moment of the tunnel structures, the designed protective structure is found to have effectively mitigated soil deformation, ensuring the stability of the foundation pit. As excavation progresses, lateral soil deformation and vertical uplift gradually increase but remain within specified control values. During various excavation stages, the maximum displacement of the tunnel structure gradually increases, with the increase rates of maximum settlement being 29.09%, 20.51%, and 6.45%, respectively. This indicates a gradual enhancement of the stability of the tunnel structure. Additionally, excavation of the foundation pit has a significant impact on the bending moment distribution of the tunnel structure but does not affect the axial force and shear force of the tunnel structure. The findings of this study offer crucial scientific insights for evaluating the safety and stability of construction near tunnel structures.