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Coral reef limestone at different depositional depths and facies differ remarkably on the textural and mineralogical characteristics, owing to the complex sedimentary diagenesis. To explore the effects of pore structure and mineral composition associated with diagenetic variation on the mechanical behavior of reef limestone, a series of quasi-static and dynamic compression tests along with microscopic examinations were performed on the reef limestone at shallow and deep burial depths. It is revealed that the shallow reef limestone (SRL) is classified as a porous aragonite-type carbonate rock with high porosity (55.3±3.2)% and pore connectivity. In comparison, the deep reef limestone (DRL) is mainly composed of dense calcite-type calcium carbonate with low porosity (4.9±1.6)% and pore connectivity. The DRL strengthened and stiffened by the tight grain framework consistently displays much higher values of the dynamic compressive strength, elastic modulus, brittleness index, and specific energy absorption than those of the SRL. The gap between two types of limestone further increases with an increase in strain rate. It appears that the failure pattern of SRL is dominated by the inherent defects like weak bonding interfaces and growth lines, revealed by the intricate fracturing network and mixed failure. Likewise, although the preexisting megapores in DRL may affect the crack propagation on pore tips to a certain distance, it hardly alters the axial splitting failure of DRL under impacts. The stress wave propagation and attenuation in SRL is primarily controlled by the reflection and diffusion caused by plenty mesopores, as well as an energy dissipation in layer-wise pore collapse and adjacent grain crushing, while the stress wave in DRL is highly hinged on the insulation and diffraction induced by the isolated megapores. This process is accompanied by the energy dissipation behavior of inelastic deformation resulted from the pore-emanated microcracking.

Abstract Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of clinical stage N0 NSCLC. An internal cohort (n = 1911) is included to construct the deep learning nodal metastasis signature (DLNMS). Subsequently, an external cohort (n = 355) and a prospective cohort (n = 999) are utilized to fully validate the predictive performances of the DLNMS. Here, we show areas under the receiver operating characteristic curve of the DLNMS for occult N1 prediction are 0.958, 0.879 and 0.914 in the validation set, external cohort and prospective cohort, respectively, and for occult N2 prediction are 0.942, 0.875 and 0.919, respectively, which are significantly better than the single-modal deep learning models, clinical model and physicians. This study demonstrates that the DLNMS harbors the potential to predict ONM of clinical stage N0 NSCLC.

Objective Osteoarthritis (OA) is a degenerative joint disease associated with excessive mechanical loading. The aim here was to elucidate whether different subpopulations of chondrocytes exhibit distinct phenotypes in response to variations in loading conditions. Furthermore, we seek to investigate the transcriptional switches and cell crosstalk among these chondrocytes subsets.Methods Proteomic analysis was performed on cartilage tissues isolated from weight-bearing and non-weight-bearing regions. Additionally, single-cell RNA sequencing was employed to identify different subsets of chondrocytes. For disease-specific cells, in vitro differentiation induction was performed, and their presence was confirmed in human cartilage tissue sections using immunofluorescence. The molecular mechanisms underlying transcriptional changes in these cells were analysed through whole-transcriptome sequencing.Results In the weight-bearing regions of OA cartilage tissue, a subpopulation of chondrocytes called OA hypertrophic chondrocytes (OAHCs) expressing the marker genes SLC39A14 and COL10A1 are present. These cells exhibit unique characteristics of active cellular interactions mediated by the TGFβ signalling pathway and express OA phenotypes, distinct from hypertrophic chondrocytes in healthy cartilage. OAHCs are mainly distributed in the superficial region of damaged cartilage in human OA tissue, and on TGFβ stimulation, exhibit activation of transcriptional expression of iron metabolism-related genes, along with enrichment of associated pathways.Conclusion This study identified and validated the existence of a subset of OAHCs in the weight-bearing area of OA cartilage tissue. Our findings provide a theoretical basis for targeting OAHCs to slow down the progression of OA and facilitate the repair of cartilage injuries.

Introduction: To validate the residual tumor (R) classification proposed by the International Association for the Study of Lung Cancer (IASLC) in NSCLC after sleeve lobectomy. Methods: A total of 682 patients were analyzed. The R status, on the basis of the Union for International Cancer Control (UICC) criteria, was recategorized according to the IASLC descriptors. Recurrence-free survival (RFS) and overall survival (OS) among different R classifications were assessed for the entire cohort and pathologic node (pN) subgroups. Results: All in all, 631 (92.5%), 48 (7.1%), and three patients (0.4%) were classified as R0, R1, and R2, respectively, by the UICC criteria, whereas 489 (71.7%), 110 (16.1%), and 83 patients (12.2%), received R0, uncertain resection (R[un]), and R1/2 resection, respectively, according to the IASLC criteria. There were 96 patients (15.2%) with UICC R0 who were reclassified as R(un), mainly because of the positive highest mediastinal node station (82 of 96, 85.4%). A total of 46 patients (7.3%) were reassigned from UICC R0 to IASLC R1/2 owing to extracapsular extension. For the entire cohort, patients with R(un) and R1/2 exhibited worse RFS (R[un], adjusted p = 0.023; R1/2, adjusted p = 0.001) and OS (R[un], adjusted p = 0.040; R1/2, adjusted p = 0.051) compared with R0. No significant differences were observed between R(un) and R1/2 (RFS, adjusted p = 0.586; OS, adjusted p = 0.781). Furthermore, subgroup analysis revealed a distinct prognostic impact of the IASLC R status—with prognostic significances in the pN1 and pN2 subgroups, but not in the pN0 subgroup. Conclusions: The IASLC R descriptors helped to stratify the prognosis of NSCLC after sleeve lobectomy, with its prognostic impact varied among pN stages.

Summary: Background: This study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predicting major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) and further explore the biological basis under its prediction. Methods: 274 patients undergoing curative surgery after neoadjuvant chemoimmunotherapy for NSCLC at 4 centres from January 2019 to December 2021 were included and divided into a training cohort, an internal validation cohort, and an external validation cohort. ShuffleNetV2x05-based features of the primary tumour on the CT scans within the 2 weeks preceding neoadjuvant administration were employed to develop a deep learning score for distinguishing MPR and non-MPR. To reveal the underlying biological basis of the deep learning score, a genetic analysis was conducted based on 25 patients with RNA-sequencing data. Findings: MPR was achieved in 54.0% (n = 148) patients. The area under the curve (AUC) of the deep learning score to predict MPR was 0.73 (95% confidence interval [CI]: 0.58–0.86) and 0.72 (95% CI: 0.58–0.85) in the internal validation and external validation cohorts, respectively. After integrating the clinical characteristic into the deep learning score, the combined model achieved satisfactory performance in the internal validation (AUC: 0.77, 95% CI: 0.64–0.89) and external validation cohorts (AUC: 0.75, 95% CI: 0.62–0.87). In the biological basis exploration for the deep learning score, a high deep learning score was associated with the downregulation of pathways mediating tumour proliferation and the promotion of antitumour immune cell infiltration in the microenvironment. Interpretation: The proposed deep learning model could effectively predict MPR in NSCLC patients treated with neoadjuvant chemoimmunotherapy. Funding: This study was supported by National Key Research and Development Program of China, China (2017YFA0205200); National Natural Science Foundation of China, China (91959126, 82022036, 91959130, 81971776, 81771924, 6202790004, 81930053, 9195910169, 62176013, 8210071009); Beijing Natural Science Foundation, China (L182061); Strategic Priority Research Program of Chinese Academy of Sciences, China (XDB38040200); Chinese Academy of Sciences, China (GJJSTD20170004, QYZDJ-SSW-JSC005); Shanghai Hospital Development Center, China (SHDC2020CR3047B); and Science and Technology Commission of Shanghai Municipality, China (21YF1438200).

Summary: Immunotherapy shows durable response but only in a subset of patients, and test for predictive biomarkers requires procedures in addition to routine workflow. We proposed a confounder-aware representation learning-based system, genopathomic biomarker for immunotherapy response (PITER), that uses only diagnosis-acquired hematoxylin-eosin (H&E)-stained pathological slides by leveraging histopathological and genetic characteristics to identify candidates for immunotherapy. PITER was generated and tested with three datasets containing 1944 slides of 1239 patients. PITER was found to be a useful biomarker to identify patients of lung adenocarcinoma with both favorable progression-free and overall survival in the immunotherapy cohort (p

Background: Non-small-cell lung cancer (NSCLC) with additional nodule(s) located in the same lobe or ipsilateral different lobe were designated as T3 and T4, respectively, which was merely defined by anatomical location of additional nodule(s), regardless of other prognostic factors. Methods: A total of 4711 patients with T1-4, N0-2, M0 NSCLC undergoing complete resection were identified between 2009 and 2014, including 145 patients with additional nodule(s) in the same lobe (T3-Add) and 174 patients with additional tumor nodule(s) in ipsilateral different lobe (T4-Add). Overall survival (OS) was compared using multivariable Cox regression models and propensity score matching analysis (PSM). Results: T3-Add patients [T3-Add versus T3, hazard ratio (HR), 0.695; 95% confidence interval (CI), 0.528–0.915; p = 0.009] and comparable OS with T2b patients through multivariable Cox analysis, and further validated by PSM. T4-Add patients carried a wide spectrum of prognosis, and the largest diameter of single tumor was screened out as the most effective indicator for distinguishing prognosis. T4-Add (⩽3 cm) patients had better OS than T4 patients [T4-Add (⩽3 cm) versus T4, HR, 0.629; 95% CI, 0.455–0.869; p = 0.005] and comparable OS with T3 patients. And T4-Add (>3 cm) patients had comparable OS with T4 patients. Conclusion: NSCLC patients with additional nodule(s) in the same lobe and ipsilateral different lobe (maximum tumor diameter ⩽ 3 cm) should be further validated and considered restaging as T2b and T3 in the forthcoming 9th tumor, node, and metastasis staging system.

Background: We proposed a population graph with Transformer-generated and clinical features for the purpose of predicting overall survival (OS) and recurrence-free survival (RFS) for patients with early stage non-small cell lung carcinomas and to compare this model with traditional models. Methods: The study included 1705 patients with lung cancer (stages I and II), and a public data set for external validation (n=127). We proposed a graph with edges representing non-imaging patient characteristics and nodes representing imaging tumour region characteristics generated by a pretrained Vision Transformer. The model was compared with a TNM model and a ResNet-Graph model. To evaluate the models' performance, the area under the receiver operator characteristic curve (ROC-AUC) was calculated for both OS and RFS prediction. The Kaplan–Meier method was used to generate prognostic and survival estimates for low- and high-risk groups, along with net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. An additional subanalysis was conducted to examine the relationship between clinical data and imaging features associated with risk prediction. Results: Our model achieved AUC values of 0.785 (95% confidence interval [CI]: 0.716–0.855) and 0.695 (95% CI: 0.603–0.787) on the testing and external data sets for OS prediction, and 0.726 (95% CI: 0.653–0.800) and 0.700 (95% CI: 0.615–0.785) for RFS prediction. Additional survival analyses indicated that our model outperformed the present TNM and ResNet-Graph models in terms of net benefit for survival prediction. Conclusions: Our Transformer-Graph model was effective at predicting survival in patients with early stage lung cancer, which was constructed using both imaging and non-imaging clinical features. Some high-risk patients were distinguishable by using a similarity score function defined by non-imaging characteristics such as age, gender, histology type, and tumour location, while Transformer-generated features demonstrated additional benefits for patients whose non-imaging characteristics were non-discriminatory for survival outcomes. Funding: The study was supported by the National Natural Science Foundation of China (91959126, 8210071009), and Science and Technology Commission of Shanghai Municipality (20XD1403000, 21YF1438200).

The Hengduan Mountains region is known for its river capture and drainage reversal events during the tectonic upheaval of the Qinghai-Tibetan Plateau. However, little is known of the geomorphological processes at fine scales such as in the upper and middle Yalong River. Here, we investigated whether the phylogeography of Schizopygopsis malacanthus reflected the palaeodrainage changes using the mitochondrial DNA control region and 12 microsatellite markers. A wide-range sampling of fish (439 individuals) was collected across the upper and middle Yalong River (14 populations: 5 in mainstream and 9 in seven major tributaries). Phylogenetic trees, hierarchical AMOVA, STRUCTURE and gene flow confirmed a river capture event in the upper mainstream of Yalong River and Xianshui River during the middle and late Pleistocene, provided evidence for historical drainage connection between the Jiulong and Liqiu rivers before the Gonghe Movement, and revealed a potential secondary contact event in upper reaches of the Litang and Huoqu rivers during the last interglacial period. Our results indicated that the current phylogeographic structure of S. malacanthus was impacted by the geological events and climatic fluctuations during the middle and late Pleistocene. The study provides insights into the historical drainage evolution across the upper and middle Yalong River in the Hengduan Mountains region.

With distinguished advantages over radio frequency communication, visible light communication (VLC) has been considered as one of the potential and promising wireless communication technologies. However, the densely deployed light emitting diode (LED) lamps introduce severe inter channel interference (ICI), which drastically deteriorates the VLC network performance. In addition, the limited wireless access capabilities of heterogeneous LED lamps, as well as the different data transmission requirements of varied user equipments (UEs), will bring new challenges to the VLC network in real-life applications. To mitigate the ICI and improve the VLC network performance, the hypergraph theory is introduced in detail. Effective data links between one UE and all of its available LED access points (APs) are taken as a hyperedge to form an amorphous cell for each UE. Considering the UE satisfaction performance, as well as the spectrum efficiency and service fairness, the non-layered resource allocation (RA) algorithm based on carrier aggregation and the directly overlapped RA algorithm based on carrier aggregation and cooperative transmission are proposed. Numeric results show that the proposed RA algorithms outperform the classic Dsatur RA algorithm in terms of the UE satisfaction, spectrum efficiency and service fairness.

As an important topic in the multimedia and computer vision fields, salient object detection has been researched for years. Recently, state-of-the-art performance has been witnessed with the aid of the fully convolutional networks (FCNs) and the various pyramid-like encoder-decoder frameworks. Starting from a common encoder-decoder architecture, we enhance a residual refinement network with feature purification for better saliency estimation. To this end, we improve the global knowledge streams with intermediate supervisions for global saliency estimation and design a specific feature subtraction module for residual learning, respectively. On the basis of the strengthened network, we also introduce an attribute encoding sub-network (AENet) with a grid aggregation block (GAB) to guide the final saliency predictor to obtain more accurate saliency maps. Furthermore, the network is trained with a novel constraint loss besides the traditional crossentropy loss to yield the finer results. Extensive experiments on five public benchmarks show our method achieves better or comparable performance compared with previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The dynamics of spin orientation in (Ga,Mn)As are studied by time-resolved Kerr rotation. Experimental results reveal that the magnetic ions could be aligned by the polarized holes, and the time scales of spin alignment and relaxation take place in tens and hundreds of picoseconds, respectively.