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Abstract Sinonasal squamous cell carcinoma (SNSCC) is an aggressive cancer affecting the nasal and sinus regions, with its progression factors, particularly genetic ones, not yet fully understood. Here, we first conducted a retrospective study with 219 SNSCC patients to identify clinical factors affecting SNSCC prognosis. Additionally, we mined a vast literature dataset to uncover genetic factors associated with SNSCC progression. Based on this data, we constructed SNSCC prognosis pathways and performed a gene set enrichment analysis (GSEA). Clear operative margins were linked to a 73.5–86.3% improvement in overall survival and a 73.5–88.9% lower risk of recurrence. Nasal cavity-originated cases exhibited a 67.6–97.4% decrease in mortality and an 80.7–96.7% lower recurrence rate. Patients at T1-2 staging had a 65.0–80.6% reduced risk of death and recurrence compared to those at T3 stage. Additionally, we identified 53 genes associated with SNSCC, with 14 also implicated in primary tumor site, T stage, and operative margin. These genes, including EGFR, PIK3CA, ERBB2, PTEN, BCL2, BRAF, KRAS, and PRL, form a complex SNSCC-prognosis pathway and were significantly enriched in 42 KEGG pathways and Gene Ontology (GO) terms (FDR-corrected p-value

Abstract Background Recent data have demonstrated that in locally advanced rectal cancer (LARC), a total neoadjuvant therapy (TNT) approach improves compliance with chemotherapy and increases rates of tumor response compared to neoadjuvant chemoradiation (CRT) alone. They further indicate that the optimal sequencing of TNT involves consolidation (rather than induction) chemotherapy to optimize complete response rates. Data, largely from retrospective studies, have also shown that patients with clinical complete response (cCR) after TNT may be managed safely with the watch and wait approach (WW) instead of preemptive total mesorectal resection (TME). However, the optimal consolidation chemotherapy regimen to achieve cCR has not been established, and a randomized clinical trial has not robustly evaluated cCR as a primary endpoint. Collaborating with a multidisciplinary oncology team and patient groups, we designed this NCI-sponsored study of chemotherapy intensification to address these issues and to drive up cCR rates, to provide opportunity for organ preservation, improve quality of life for patients and improve survival outcomes. Methods In this NCI-sponsored multi-group randomized, seamless phase II/III trial (1:1), up to 760 patients with LARC, T4N0, any T with node positive disease (any T, N +) or T3N0 requiring abdominoperineal resection or coloanal anastomosis and distal margin within 12 cm of anal verge will be enrolled. Stratification factors include tumor stage (T4 vs T1-3), nodal stage (N + vs N0) and distance from anal verge (0–4; 4–8; 8–12 cm). Patients will be randomized to receive neoadjuvant long-course chemoradiation (LCRT) followed by consolidation doublet (mFOLFOX6 or CAPOX) or triplet chemotherapy (mFOLFIRINOX) for 3–4 months. LCRT in both arms involves 4500 cGy in 25 fractions over 5 weeks + 900 cGy boost in 5 fractions with a fluoropyrimidine (capecitabine preferred). Patients will undergo assessment 8–12 (± 4) weeks post-TNT completion. The primary endpoint for the phase II portion will compare cCR between treatment arms. A total number of 312 evaluable patients (156 per arm) will provide statistical power of 90.5% to detect a 17% increase in cCR rate, at a one-sided alpha = 0.048. The primary endpoint for the phase III portion will compare disease-free survival (DFS) between treatment arms. A total of 285 DFS events will provide 85% power to detect an effect size of hazard ratio 0.70 at a one-sided alpha of 0.025, requiring enrollment of 760 patients (380 per arm). Secondary objectives include time-to event outcomes (overall survival, organ preservation time and time to distant metastasis) and adverse event rates. Biospecimens including archival tumor tissue, plasma and buffy coat, and serial rectal MRIs will be collected for exploratory correlative research. This study, activated in late 2022, is open across the NCTN and had accrued 330 patients as of May 2024. Study support: U10CA180821, U10CA180882, U24 CA196171; https://acknowledgments.alliancefound.org . Discussion Building on data from modern day rectal cancer trials and patient input from national advocacy groups, we have designed The Janus Rectal Cancer Trial studying chemotherapy intensification via a consolidation chemotherapy approach with the intent to enhance cCR and DFS rates, increase organ preservation rates, and improve quality of life for patients with rectal cancer. Trial registration Clinicaltrials.gov ID: NCT05610163; Support includes U10CA180868 (NRG) and U10CA180888 (SWOG).

Objective: To characterize the clinical and imaging features of circumscribed choroidal hemangioma (CCH), and to evaluate individualized treatment efficiency of photodynamic therapy (PDT), transpupillary thermotherapy (TTT), or their combination, followed by retrobulbar injection of betamethasone on CCH resolvement. Methods: Forty-nine patients with CCHs who underwent PDT, TTT or PDT+TTT treatments were retrospectively analyzed. Their treatment efficacy was compared by analyzing the change of best corrected visual acuity (BCVA), subretinal fluid (SRF) and CCH lesion characteristics. Results: PDT, TTT and PDT+TTT were respectively administrated in 17, 11 and 21 patients. No significant difference in age, gender, affected eyes and tumor location across the three groups. Baseline BCVA were 0.41 ± 0.28, 0.62 ± 0.30 and 0.24 ± 0.24 for PDT, TTT and PDT+TTT groups, respectively (F = 6.572, P = 0.003). CCH treated by three strategies showed significant difference in maximum tumor basal diameter, SRF areas and macula involvement prior to the treatment (P < 0.05). Patients receiving PDT+TTT exhibited larger tumor basal diameter, more SRF, higher ratio of macular involvement than other groups. A total of 38 (77.6 %) cases had good visual acidity with final BCVA ≥0.5 after treatments. PDT and PDT+TTT treatment groups acquired more vision improvement (0.27 ± 0.23 and 0.31 ± 0.26) in BCVA than TTT group (0.09 ± 0.13). All SRF were resolved within two weeks of treatment and no recurrent SRF were found. Conclusion: The three treatments showed good performance in improving visual function and controlling SRF, and individualized treatment should be selected primarily by the tumor location, and then the tumor size and presence of SRF.

ABSTRACTSpatial-temporal dynamics monitoring of Arctic vegetation structure (i.e. distribution range of tundra and forest) is of great significance for evaluating global warming effect. Currently, time-series monitoring of Arctic vegetation structure relies primarily on the Normalized Difference Vegetation Index (NDVI), which is derived from optical remote sensing images. However, because of factors such as the long revisit period of satellites and the impact of climate, optical observations are severely lacking in the Arctic region. This results in NDVI time-series data highly discontinuous and difficult to reflect actual variations in Arctic vegetation structure, and the traditional time-series reconstruction method would usually fail for severe missing conditions. Therefore, this study developed a Time Series Reconstruction method considering Periodic Trend (TSR-PT), which is specifically for alleviating the severe missing observation condition in the Arctic region. It can separate the phenological change and trend change of the incomplete time series NDVI, and borrow the information from the neighboring unchanged years for compensate of the missing observations in current years, based on the learned inter-annual and intra-annual correlation. We explore its usability in monitoring vegetation structure variation in Vorkuta region (transition zone of tundra and taiga in the Arctic Circle) based on MODIS data. It is found that the proposed TSR-PT is able to reconstruct NDVI with reasonable phenological feature even the missing rate reaches over 70%, which is usually falsely constructed by traditional filtering or fitting method, and suppress them by 0.038 in terms of RMSE; besides, we find that since 21-century, the Arctic trees have continued to increase and encroach the original tundra ecosystem, which caused a largely Arctic vegetation structural change, and we believe the proposed method would largely promote the Arctic vegetation research.

Abstract Background For patients with liver-confined metastatic colorectal cancer (mCRC), local therapy of isolated metastases has been associated with long-term progression-free and overall survival (OS). However, for patients with more advanced mCRC, including those with extrahepatic disease, the efficacy of local therapy is less clear although increasingly being used in clinical practice. Prospective studies to clarify the role of metastatic-directed therapies in patients with mCRC are needed. Methods The Evaluating Radiation, Ablation, and Surgery (ERASur) A022101/NRG-GI009 trial is a randomized, National Cancer Institute-sponsored phase III study evaluating if the addition of metastatic-directed therapy to standard of care systemic therapy improves OS in patients with newly diagnosed limited mCRC. Eligible patients require a pathologic diagnosis of CRC, have BRAF wild-type and microsatellite stable disease, and have 4 or fewer sites of metastatic disease identified on baseline imaging. Liver-only metastatic disease is not permitted. All metastatic lesions must be amenable to total ablative therapy (TAT), which includes surgical resection, microwave ablation, and/or stereotactic ablative body radiotherapy (SABR) with SABR required for at least one lesion. Patients without overt disease progression after 16–26 weeks of first-line systemic therapy will be randomized 1:1 to continuation of systemic therapy with or without TAT. The trial activated through the Cancer Trials Support Unit on January 10, 2023. The primary endpoint is OS. Secondary endpoints include event-free survival, adverse events profile, and time to local recurrence with exploratory biomarker analyses. This study requires a total of 346 evaluable patients to provide 80% power with a one-sided alpha of 0.05 to detect an improvement in OS from a median of 26 months in the control arm to 37 months in the experimental arm with a hazard ratio of 0.7. The trial uses a group sequential design with two interim analyses for futility. Discussion The ERASur trial employs a pragmatic interventional design to test the efficacy and safety of adding multimodality TAT to standard of care systemic therapy in patients with limited mCRC. Trial registration ClinicalTrials.gov: NCT05673148, registered December 21, 2022.

ObjectiveAlthough lipoprotein(a) [Lp(a)] and high-sensitivity C-reactive protein (Hs-CRP) are closely associated with the mortality of acute myocardial infarction (AMI), their synergistic effect on the risk of death remains unknown. Therefore, this study aimed to explore the combined effect of Lp(a) and Hs-CRP on the incidence of all-cause and cardiovascular death in AMI patients.MethodsA comprehensive cohort study enrolled 912 AMI patients, categorizing them into four groups based on Lp(a) and Hs-CRP levels: Group 1 [Lp(a) < 30 mg/dL & Hs-CRP < 2 mg/L], Group 2 [Lp(a) < 30 mg/dL & Hs-CRP ≥ 2 mg/L], Group 3 [Lp(a) ≥ 30 mg/dL & Hs-CRP < 2 mg/L], and Group 4 [Lp(a) ≥ 30 mg/dL & Hs-CRP ≥ 2 mg/L]. Cox regression analysis, Kaplan-Meier survival analysis and sensitivity analysis were employed to determine the combined effects of Lp(a) and Hs-CRP on the risk of all-cause and cardiovascular death.ResultsOver a median observation period of 38.98 months, 217 patients passed away, with 137 deaths attributed to cardiovascular causes. The multivariate Cox regression analysis revealed that in the comprehensively adjusted Model 3, only Lp(a) and the combination of Lp(a) and Hs-CRP exhibited a strong association with cardiovascular death risk. Specifically, for Lp(a) levels ≥ 30 mg/dL compared to < 30 mg/dL, the hazard ratio (HR) was 2.434 with a 95% confidence interval (CI) of 1.653–3.583 (P < 0.001); for log10(Lp(a)), the HR was 2.630 with a 95% CI of 1.530–4.523 (P < 0.001); for Group 4 versus Group 1, the HR was 2.346 with a 95% CI of 1.054–5.220 (P = 0.037); and for Group 4 versus Groups 1 + 2 + 3, the HR was 1.878 with a 95% CI of 1.284–2.748 (P = 0.001). Sensitivity analysis indicated that the synergy between Lp(a) and Hs-CRP continued to be independently associated with the risk of cardiovascular death. For Group 3 versus Group 1, the HR was 3.353 with a 95% CI of 1.133–9.917 (P = 0.029); for Group 4 versus Group 1, the HR was 3.710 with a 95% CI of 1.466–9.392 (P = 0.006); and for Group 4 versus Groups 1 + 2 + 3, the HR was 2.433 with a 95% CI of 1.620–3.656 (P < 0.001).ConclusionsCompared to elevated levels of either Lp(a) or Hs-CRP alone, the concurrent high levels of both significantly increased the risk of cardiovascular death in patients with AMI, underscoring the importance of considering their combined effects in the prognostic management of AMI patients.

Spatiotemporal fusion (STF) can provide dense satellite image series with high spatial resolution. However, most spatiotemporal fusion approaches are time-consuming, which seriously limits their applicability in large-scale areas. To address this problem, some efforts have been paid for accelerating STF approaches with help of graphics processing units (GPUs), whose effect is dramatic. However, this strategy is hardware dependent, which may not be always satisfied. In this paper, we develop a hardware independent accelerating strategy, named AcSTF. The proposed AcSTF consists of two steps, which are medium resolution STF (MSTF) and local normalization-based fast fusion (LNFM). The MSTF utilizes STF methods to improve the coarse spatial resolution images to a medium spatial resolution, while the LNFM further refines the medium spatial resolution images to provide fine spatial resolution images. To test the AcSTF, the experiments are conducted using five commonly used STF approaches on two public Landsat-MODIS datasets. The experimental results indicate that AcSTF can not only reduce 87%–95% running time of current STF approaches, but also preserve their qualitative and quantitative performance well. After that, we apply the AcSTF to produce an intact 30 m image of the whole Ukraine mainland. Without any hardware which can speed up computing,the time for reconstructing the 30 m image is 5.42 h just using an unremarkable central processing unit (CPU). Compared to the real Landsat image, the reconstructed image achieves remarkable qualitative and quantitative performance, which demonstrates the practicability of the AcSTF.

Urban renewal has led to the proliferation of informal urban habitats, such as slums, shanty towns, and urban villages (UVs). As an important component of urban renewal, UVs influence urban spatial structure and land use patterns. Therefore, the fine extraction of UV is of great theoretical and practical significance. Existing UV classification techniques mostly employ machine learning and convolutional neural network based models, which struggle to perceive long-range global semantic information. In this article, based on high-resolution remote sensing images, we propose a multiscale mask transformer model for UV (MaskUV). It can extract both local texture features and global features. The multiscale mask transformer module with mask attention can aggregate different levels of pixel and object features, enhancing the model's recognition and generalization abilities. We extracted UV in seven cities in the Pearl River Delta (PRD) using MaskUV and analyzed the spatial pattern and accessibility of UV. Due to the scarcity of fine-grained UV detection datasets, we also provide a novel dataset (UVSet) containing 3415 pairs of 512 × 512 high-resolution UV images and labels, with a spatial resolution of 1 m. Comparative experiments with several UV extraction models demonstrate the effectiveness of MaskUV, achieving an F1 score of 84.39% and an IoU of 73.00% on UVSet. Besides, MaskUV achieves highly accurate detection results in seven cities in the PRD, with average F1 and IoU values of 84.41% and 72.44%, respectively.

Building height dataset is crucial in urban studies, holding significant importance in understanding the interactions of human activities and the built-up environment. However, high-resolution three-dimensional building datasets covering large areas are limited. A rapid and accurate method for revealing fine-scale urban morphology is required. In this study, we developed a method for estimating building heights at the building scale. First, we integrated multisource datasets (i.e., synthetic aperture radar, optical, terrain, social-economic, and vector-based datasets) and built the machine learning model for building height estimation. Second, we applied the model to 11 cities in the U.S. and assessed its performance. Our results were consistent with the reference data, indicating that the effectiveness of our method is applicable [i.e., the R2 was 0.82, and the root mean square error (RMSE) was 3.39 m]. The evaluated results in various cities, across different height intervals, and within distinct regions also show the good agreement with reference heights according to the correlation (R2: 0.51–0.86, RMSE: 2.57–5.97 m in cities) and similar height distribution. Moreover, our results also showed the superiority by comparing with other height datasets at different scales. Finally, we mapped the building-scale height to characterize the urban morphology. These results demonstrate our proposed method's usable accuracy and the vast application potential in estimating building heights. Our proposed method's refined building height information can significantly help socioeconomic and climatological urban studies.

Individual tree segmentation (ITS) is a pivotal technique in orchard research, estimating tree counts and delineating crown contours. This method provides foundational data for assessing orchard health, nutritional composition, and predicting yield. Unmanned aerial vehicles (UAVs) have become an essential data source for (ITS) due to their capability to capture ultra-fine details. However, current deep-learning-based ITS methods struggle to accurately handle densely overlapping fruit tree distributions with similar characteristics in UAVs images, primarily due to the intricate nature of spatial arrangements in such scenarios. In this article, we propose CEDAnet, a context enhancement, and density adjustment network, to address the challenge of dense fruit trees segmentation. Specifically, a transformer-based contextual aggregation module is designed to distinguish different instances and refine the boundary of the instances. We have proposed a density-guided nonmaximum suppression method to adaptively generate sufficient candidate bounding boxes, aiming to retain more potential instances in dense trees. To evaluate the effectiveness and robustness of our proposal, we curated two ITS datasets constructed with imagery captured by UAVs, namely instance segmentation in Conghua images dataset (iSCHID) and instance segmentation in Maoming images dataset (iSMMID) based on their respective spatial characteristics. Experimental results on both two datasets demonstrated that CEDAnet yields competitive results in ITS tasks, with the bounding box AP of 0.498, segmentation AP of 0.493 in iSCHID, and the bounding box AP of 0.706, segmentation AP of 0.703 in iSMMID.

Abstract Computational methods have been proposed to leverage spatially resolved transcriptomic data, pinpointing genes with spatial expression patterns and delineating tissue domains. However, existing approaches fall short in uniformly quantifying spatially variable genes (SVGs). Moreover, from a methodological viewpoint, while SVGs are naturally associated with depicting spatial domains, they are technically dissociated in most methods. Here, we present a framework (PROST) for the quantitative recognition of spatial transcriptomic patterns, consisting of (i) quantitatively characterizing spatial variations in gene expression patterns through the PROST Index; and (ii) unsupervised clustering of spatial domains via a self-attention mechanism. We demonstrate that PROST performs superior SVG identification and domain segmentation with various spatial resolutions, from multicellular to cellular levels. Importantly, PROST Index can be applied to prioritize spatial expression variations, facilitating the exploration of biological insights. Together, our study provides a flexible and robust framework for analyzing diverse spatial transcriptomic data.

Abstract Recently, ground coverings change detection (CD) driven by bitemporal hyperspectral images (HSIs) has become a hot topic in the remote sensing community. There are two challenges in the HSI‐CD task: (1) attribute feature representation of pixel pairs and (2) feature extraction of attribute patterns of pixel pairs. To solve the above problems, a novel spectral‐spatial sequence characteristics‐based convolutional transformer (S3C‐CT) method is proposed for the HSI‐CD task. In the designed method, firstly, an eigenvalue extrema‐based band selection strategy is introduced to pick up spectral information with salient attribute patterns. Then, a 3D tensor with spectral‐spatial sequence characteristics is proposed to represent the attribute features of pixel pairs in the bitemporal HSIs. Next, a fusion framework of the convolutional neural network (CNN) and Transformer encoder (TE) is designed to extract high‐order sequence semantic features, taking into account both local context information and global sequence dependencies. Specifically, a spatial‐spectral attention mechanism is employed to prevent information reduction and enhance dimensional interactivity between the CNN and TE. Finally, the binary change map is determined according to the fully‐connected layer. Experimental results on real HSI datasets indicated that the proposed S3C‐CT method outperforms other well‐known and state‐of‐the‐art detection approaches in terms of detection performance.

Paddy field leveling is an essential step before rice transplanting. During the operation of a paddy field grader, a common issue is the wrapping of rice straw around the blades, resulting in a low rice straw burial rate. This study focused on analyzing the operating parameters of a disc spring–tooth-combined paddy field grader. A soil–straw mechanism simulation model was created using EDEM 2021 software to simulate the field operation status. Firstly, the single-factor test was carried out, with the working speed, the working depth of the disc cutter roller, and the rotation speed of the cutter roller as the factors and the straw-buried rate (SBR) and the machine forward resistance (MFR) as the test indexes, and the parameter range was optimized. The parameters were optimized by the response surface method (RSM) and machine learning algorithms. The results indicated that the genetic algorithm–back propagation (GA-BP) neural network outperformed other optimization models in terms of prediction accuracy and stability. By utilizing the GA-BP regression model and RSM model for regression fitting, two sets of optimal parameter combinations were obtained. Verification experiments were carried out using two sets of parameter combinations. Taking the average of the experimental results, the simulation results showed that the straw burial rate was 93.47% and the forward resistance was 6487 N for the parameter combinations of RSM, and the straw burial rate was 94.86% and the forward resistance was 6352 N for the parameter combinations of GA-BP; the field experiments showed that the straw burial rate was 92.86% and the forward resistance was 6518 N for the parameter combinations of RSM, and the straw burial rate was 95.17% and the forward resistance was 6249 N for the parameter combinations of GA-BP. The results demonstrated that the GA-BP prediction model exhibited better predictive capabilities compared to the traditional RSM, providing more accurate predictions of the paddy field grader’s field operation performance.

This study addresses the challenge of simulating fluid flow around flexible net panels, which are critical components in aquaculture systems. Traditional grid-based Eulerian methods struggle with the complex deformations and interactions of such panels. To fill this gap, a numerical approach leveraging the Smoothed Particle Hydrodynamics (SPH) method is introduced. The proposed approach employs a knot-spring model to represent the flexible net panels. The nets are idealized as a structure of physical net-knots linked by virtual elastic springs. The SPH method is used to simulate the fluid flow, capturing the movement of the knots and the resulting deformation of the nets. The model's parameters and numerical schemes are detailed, along with the implementation process. The accuracy of the model is validated through comparisons with existing experimental and simulation data. The results indicate that the SPH-based approach accurately simulates the flow past flexible net panels, providing a detailed velocity distribution and deformation analysis. The study's findings demonstrate the potential of the SPH method for aquaculture engineering applications. The proposed approach offers a promising alternative to traditional methods, particularly in scenarios involving significant net deformation or interaction with other objects. The implications for the design and operation of aquaculture facilities are discussed, along with suggestions for future research directions.

The global population is on the rise, leading to an increased demand for food resources. Accurate mapping and monitoring of paddy rice fields have become crucial for effective food management and yield estimation. Several large-scale paddy rice mapping products with medium-to-high resolution have been made by using threshold-based phenological methods. The main shortcoming is that paddy rice varies in different regions due to differences in climate and planting habits, which results in the limitations of fixed phenological indicator thresholds for large-scale paddy rice mapping. Therefore, paddy rice samples from different regions are needed to account for regional differences between paddy rice and improve paddy rice mapping accuracy. To address two key shortcomings in existing sample generation methods (the non-robustness of DTW distance and the limitation of fixed phenological indicator thresholds for generating samples) and the three difficulties of cross-regional sample generation (different planting times, growth cycles, and planting intensities), this study proposed a novel approach for cross-regional paddy rice sample generation based on spatial domain transfer, achieving cross-regional paddy rice mapping. The accuracy results (OA of 95.16–97.54%) in three study regions with different climatic and topographic conditions in China and improved accuracy (OA improvement of 5.89–19.06%) compared with the method of NESEA-Rice10, proved the effectiveness of the proposed method and the potential of the proposed method in large-scale paddy rice mapping. The proposed method can obtain high-accuracy paddy rice mapping results in regions without paddy rice samples and be applied to the mapping of other crops and land cover types.

Summary: Many motor and neurodegenerative diseases affect the peripheral nervous system (PNS). The myelinated axons in the sciatic nerves offer valuable insights into the pathology of these diseases. Here, we present a protocol for isolating and processing mouse sciatic nerves for confocal immunohistochemistry. We describe steps for mouse perfusion, removing and fixing the sciatic nerve, transferring nerves onto slides, staining, and imaging. This protocol can assist in characterizing pathologies of myelinated fibers resulting from diseases affecting the PNS.For complete details on the use and execution of this protocol, please refer to Chang et al. (2023).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Human Hox genes (Homeobox) play a crucial role in embryonic development and cancer. The HOXC10 gene, a member of the HOX family, has been reported abnormally expressed in several cancers. However, the association between HOXC10 and hepatocellular carcinoma (HCC) remains to be elucidated. In the present study, tissue microarray cohort data showed that high levels of HOXC10 expression predicted a poor survival in HCC patients. Meanwhile, HOXC10 was significantly upregulated in the Huh7 cell line compared with the well differentiated cell line HepG2 and human normal liver cells. Functionally, silencing HOXC10 in Huh7 cells inhibited cell proliferation, increased apoptosis, and inhibited invasion and migration of HCC cells. HOXC10 overexpression in HepG2 cells increased cell proliferation, decreased apoptosis, and increased invasion and migration of HCC cells. In the HepG2 xenograft models, HOXC10 increased the tumor volume and weight compared with control. Mechanistically, the m6A modification of HOXC10 by METTL3 enhanced its expression by enhancing its mRNA stability. Both the in vitro and in vivo results showed that overexpressed HOXC10 activated the PTEN/AKT/mTOR pathway. In summary, the findings highlight the importance of HOXC10 in the regulation of HCC progression. HOXC10 is potentially a future therapeutic target for HCC treatment.

Abstract Background We performed a paired analysis to compare the therapeutic effect between the induction chemotherapy‐based organ‐preservation approach and immediate total laryngectomy in hypopharyngeal squamous cell carcinoma patients requiring total laryngectomy. Methods 351 patients who were treated with organ‐preservation approach were compared with 110 patients who were treated with total laryngectomy. The main measures and outcomes were progression‐free survival (PFS), overall survival (OS), and larynx function preservation survival (LFPS). Results No statistical difference was observed for 3‐, 5‐, and 10‐year PFS and OS in two groups. In the organ‐preservation group, the 3‐, 5‐, and 10‐year LFPS was 30.7%, 23.3%, and 16.6%, respectively. The LFPS of Stage III > Stage IV, N0 > N1 > N2 > N3, T2 > T3 > T4, CR > PR > SD > PD patients (all p values

In order to explore the storage property of Erythropalum scandens Blume, this study compared and analyzed the changes of physiological and metabolic indexes of reducing sugar, soluble sugar, glucose, fructose, vitamin C, protein, crude fiber, water content and enzyme activity of Guichi 1 and Guichi 2 at 4 ℃. The results showed that the changes of water content of Guichi 1 and Guichi 2 were less than 3%, the content of various sugars and vitamin C showed downtrend, the change of protein content was not obvious, and the crude vitamin content showed an increasing trend during storage at 4 ℃. The main differences between the two varieties were reflected in the appearance, carbohydrates, vitamin C and various enzyme activities. At low temperature, the leaves of Guichi 2 shrank faster and faded significantly, carbohydrates and vitamin C decreased faster, and the activities of peroxidase, catalase and superoxide dismutase were lower than those of Guichi 1. Through comprehensive comparison, Guichi 1 was more resistant to storage than Guichi 2, and its nutrients, flavor, taste were also better than Guichi 2. The best shelf life of Guichi 1 was 10 days at 4 ℃ , and the best shelf life of Guichi 2 was 5 days at 4 ℃. This conclusion could provide a reference for the study of postharvest preservation of E. scandens.

Background: The neuropathophysiological mechanisms of brain damage underlying hypothyroidism remain unclear. Fractional amplitude of low-frequency fluctuations (fALFF) has been established as a reliable indicator for investigation of abnormal spontaneous brain activity that occurs at specific frequencies in different types of mental disorder. However, the changes of fALFF in specific frequency bands in hypothyroidism have not yet been investigated. Methods: Fifty-three hypothyroid patients and 39 healthy controls (HCs) underwent thyroid-related hormone levels tests, neuropsychological assessment, and magnetic resonance imaging (MRI) scans. The fALFF in the standard band (0.01–0.1 Hz), slow-4 (0.027–0.073 Hz), and slow-5 bands (0.01–0.027 Hz) were analyzed. An analysis of Pearson correlation was conducted between fALFF, thyroid-related hormone levels, and neuropsychological scores in hypothyroid patients. Results: Compared to HCs, within the routine band, hypothyroidism group showed significantly decreased fALFF in left lingual gyrus, middle temporal gyrus (MTG), precentral gyrus, calcarine cortex, and right inferior occipital gyrus; within the slow-5 band, the hypothyroidism group exhibited decreased fALFF in left lingual gyrus, MTG, superior temporal gyrus, postcentral gyrus, and paracentral lobule, and increased fALFF in supplementary motor area (SMA) and right middle frontal gyrus; additionally, fALFF in the left lingual gyrus within the routine and slow-5 bands were negatively correlated with the level of thyroid stimulating hormone. Conclusions: In this study, the slow-5 frequency band exhibits better sensitivity than the standard band in detecting fALFF values. A decrease of fALFF values in the lingual gyrus and MTG was observed in both the standard and slow-5 bands and might present potential neuroimaging biomarkers for hypothyroidism. Clinical Trial Registration: No: ChiCTR2000028966. Registered 9 January, 2020, https://www.chictr.org.cn.

Abstract Background RP11-296E3.2 is a novel long noncoding RNA (lncRNA) associated with colorectal cancer (CRC) metastasis, that was reported in our previous clinical studies. However, the mechanisms of RP11-296E3.2 in colorectal tumorigenesis remain elusive. Methods RNA sequencing (RNA-seq), Fluorescence in situ hybridization (FISH), Transwell assays and others, were performed to evaluate the function of RP11-296E3.2 for proliferation and metastasis in vitro. In situ and metastatic tumor models were performed to evaluate the function of RP11-296E3.2 for proliferation and metastasis in vivo. RNA-pulldown, RNA-interacting protein immunoprecipitation (RIP), tissue microarray (TMA) assay, a luciferase reporter assay, chromatin immunoprecipitation (ChIP) and others were performed to explore the mechanisms by which RP11-296E3.2 regulates CRC tumorigenesis. Results RP11-296E3.2 was confirmed to be associated with CRC cell proliferation and metastasis in vitro and in vivo. Mechanistically, RP11-296E3.2 directly bound to recombinant Y-Box Binding Protein 1 (YBX1) and enhanced signal transducer and activator of transcription 3 (STAT3) transcription and phosphorylation. YBX1 promoted the CRC cell proliferation and migration, while knockdown of RP11-296E3.2 attenuated the effects of YBX1 on CRC cell proliferation, and metastasis and the expression of several related downstream genes. We are the first to discover and confirm the existence of the YBX1/STAT3 pathway, a pathway dependent on RP11-296E3.2. Conclusion Together, these novel findings show that the RP11-296E3.2/YBX1 pathway promotes colorectal tumorigenesis and progression by activating STAT3 transcription and phosphorylation, and suggest that RP11-296E3.2 is a potential diagnostic biomarker and therapeutic target in CRC.

Paddy rice mapping is crucial for cultivation management, yield estimation, and food security. Guangdong, straddling tropics and subtropics, is a major rice-producing region in China. Mapping paddy rice in Guangdong is essential. However, there are 2 main difficulties in tropical and subtropical paddy rice mapping, including the lack of high-quality optical images and differences in paddy rice planting times. This study proposed a paddy rice mapping framework using phenology matching, integrating Sentinel-1 and Sentinel-2 data to incorporate prior knowledge into the classifiers. The transplanting periods of paddy rice were identified with Sentinel-1 data, and the subsequent 3 months were defined as the growth periods. Features during growth periods obtained by Sentinel-1 and Sentinel-2 were inputted into machine learning classifiers. The classifiers using matched features substantially improved mapping accuracy compared with those using unmatched features, both for early and late rice mapping. The proposed method also improved the accuracy by 6.44% to 16.10% compared with 3 other comparison methods. The model, utilizing matched features, was applied to early and late rice mapping in Guangdong in 2020. Regression results between mapping area and statistical data validate paddy rice mapping credibility. Our analysis revealed that thermal conditions, especially cold severity during growing stages, are the primary determinant of paddy rice phenology. Spatial patterns of paddy rice in Guangdong result from a blend of human and physical factors, with slope and minimum temperature emerging as the most important limitations. These findings enhance our understanding of rice ecosystems’ dynamics, offering insights for formulating relevant agricultural policies.

Building, as an integral aspect of human life, is vital in the domains of urban management and urban analysis. To facilitate large-scale urban planning applications, the acquisition of complete and reliable building data becomes imperative. There are a few publicly available products that provide a lot of building data, such as Microsoft and Open Street Map. However, in East Asia, due to the more complex distribution of buildings and the scarcity of auxiliary data, there is a lack of building data in these regions, hindering the large-scale application in East Asia. Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression. However, the reliance on inaccurate buildings data introduces cumulative errors, rendering this simulation data highly unreliable, leading to limitations in achieving precise research in East Asian region. Therefore, we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia, and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings. The evaluation shows the validity of our building product, with an average overall accuracy of 89.63% and an F1 score of 82.55%. In addition, a comparison with existing products further shows the high quality and completeness of our building data. Finally, we conduct spatial analysis of our building data, revealing its value in supporting urban-related research. The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931.

Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales. However, monitoring water bodies with highly seasonal hydrological variability, particularly using medium-resolution satellite imagery such as Landsat 4-9, presents substantial challenges. This study introduces the Normalized Difference Water Fraction Index (NDWFI) based on spectral mixture analysis (SMA) to improve the detection of subtle and dynamically changing water bodies. First, the effectiveness of NDWFI is rigorously assessed across four challenging sites. The findings reveal that NDWFI achieves an average overall accuracy (OA) of 98.2% in water extraction across a range of water-covered scenarios, surpassing conventional water indices. Subsequently, using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine (GEE) platform, this study generates a high-resolution surface water (SW) map for Jiangsu Province, China, exhibiting an impressive OA of 95.91% ± 0.23%. We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps. This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring, contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes.

Objective To investigate the correlation between long-term glycemic variability and cognitive function in middle-aged and elderly patients with type 2 diabetes mellitus (T2DM). Methods This retrospective analysis includes 222 patients hospitalized at Second Affiliated Hospital of Anhui Medical University from June 2021 to June 2023. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). All patients were categorized into the MCI group and the non-MCI group based on their MoCA score. Long-term blood glucose fluctuations were measured using glycated hemoglobin A1c standard deviation (HbA1c-SD) and fasting plasma glucose standard deviation (FPG-SD). The study compared general clinical data, blood biochemical indicators, and glycemic variability indicators between the two groups. The differences between the groups were compared using t-test, Chi-Square Test, or Mann–Whitney U test. Kendall’s correlation analysis, multivariate logistic regression analysis and ROC curve correlation analysis were further used to analyze the correlation and diagnostic power. Results The differences in age, MoCA scores, MMSE scores, HOMA-β, HbA1c-M, HbA1c-SD, FPG-M, FPG-SD, eGFR, Smoking, GLP-1RA and SGLT-2i usage were statistically significant between the two groups (P

BackgroundThe association between waist-to-height ratio (WHtR) with hypertension has not been adequately explained, so in this study we sought to clarify the predictive role of WHtR on the incidence of hypertension as well as the potential nonlinear associations in the general population.MethodsIn this large prospective cohort study, a total of 4,458 individuals from the China Health and Nutrition Survey (CHNS) were included in the analysis. Multivariate Cox regression analyses, subgroup analyses, receiver operator characteristic (ROC) and restricted cubic spline (RCS) analyses were used to examine the association of WHtR with the risk of new-onset hypertension.ResultsHypertension occurred in 32.8% of participants during the maximum six-year follow-up period. Compared with the group with lower WHtR, the group with higher WHtR had a higher incidence of hypertension (p

Safe and efficient recycling of industrially generated machined chips is a high-priority technological issue. In this study, the effect of SiC particles (SiCp) on the microstructure and mechanical properties of SiCp/AZ91D composites is systematically analyzed, and the reinforcement mechanism of SiCp on composites is investigated. Different contents of SiCp/AZ91D composites are fabricated by solid-state synthesis. The results show that the incorporation of SiCp refined the grains of SiCp/AZ91D composites, which is related to the uniform distribution of SiCp at the grain boundaries. The strong bonding of SiCp with the AZ91D matrix inhibited the generation and extension of cracks, which led to the simultaneous increase in the yield strength (YS) and elongation (EL) of the SiCp/AZ91D composites. The mechanical properties of the 3 wt.% SiCp/AZ91D composites are the most superior, with an average grain size, Vickers hardness, ultimate tensile strength (UTS), YS, and EL of 6.69 ± 4.48 μm, 89.5 ± 2.5 HV, 341 ± 11 MPa, 172 ± 8 MPa, and 4.43 ± 0.18%, respectively. The reinforcement mechanisms of SiCp/AZ91D composites are mainly grain refinement and dislocation strengthening. Solid-state synthesis is an effective method for recycling magnesium alloy chips.

Elymus dahucirus is an essential plant for ecological restoration in fragile ecological areas and mining area restoration. As lawn grass, it can quickly cover soil and prevent soil erosion, so it is commonly used as a pioneer grass for lawn greening and slope protection. In recent years, with the development of mineral resources, Qinghai–Tibet Plateau soil is facing the threat of heavy metal cadmium (Cd) pollution. E. dahuricus can host the filamentous fungus Epichloë bromicola. To make better use of the advantages that Epichloë bring to host plants to alleviate heavy metal pollution in soil, plant growth and antioxidant capacity effects on E. bromicola infected (E+) and uninfected (E−) E. dahuricus were determined under Cd stress. During Cd treatment, plant growth was decreased by Cd stress, while E+ plants exhibited equal or better growth compared to E− plants. Cd treatment induces a proline and antioxidant enzyme burst in infected plants, while malondialdehyde (MDA) increases. E. bromicola improved plant growth and antioxidant capacity. E. dahuricus breeding strategies could use the information here in efforts to improve the performance of E. dahuricus in both environmental protection and agronomic contexts.

Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images’ spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In this study, we developed a method based on random forest (RF) models to obtain Arctic SIM in winter by incorporating wind field and coastal geographic location information. These random forest models were trained using Synthetic Aperture Radar (SAR) SIM data. Our results show good consistency with SIM data retrieved from satellite imagery and buoy observations. With respect to the SAR data, compared with SIM estimated with RF model training using reanalysis surface wind, the results by additional coastal information input had a lower root mean square error (RMSE) and a higher correlation coefficient by 31% and 14% relative improvement, respectively. The latter SIM result also showed a better performance for magnitude, especially within 100 km of the coastline in the north of the Canadian Arctic Archipelago. In addition, the influence of coastline on SIM is quantified through variable importance calculation, at 22% and 28% importance of all RF variables for east and north SIM components, respectively. These results indicate the great potential of RF models for estimating SIM over the whole Arctic Ocean in winter.

Summary: The Contactin-associated protein 1 (Cntnap1) mouse mutants fail to establish proper axonal domains in myelinated axons. Human CNTNAP1 mutations are linked to hypomyelinating neuropathy-3, which causes severe neurological deficits. To understand the human neuropathology and to model human CNTNAP1C323R and CNTNAP1R764C mutations, we generated Cntnap1C324R and Cntnap1R765C mouse mutants, respectively. Both Cntnap1 mutants show weight loss, reduced nerve conduction, and progressive motor dysfunction. The paranodal ultrastructure shows everted myelin loops and the absence of axo-glial junctions. Biochemical analysis reveals that these Cntnap1 mutant proteins are nearly undetectable in the paranodes, have reduced surface expression and stability, and are retained in the neuronal soma. Postnatal transgenic expression of Cntnap1 in the mutant backgrounds rescues the phenotypes and restores the organization of axonal domains with improved motor function. This study uncovers the mechanistic impact of two human CNTNAP1 mutations in a mouse model and provides proof of concept for gene therapy for CNTNAP1 patients.

Ferroptosis has emerged to be a promising approach in cancer therapies; however, colorectal cancer (CRC) is relatively insensitive to ferroptosis. Exactly how the gut microenvironment impacts the ferroptotic sensitivity of CRC remains unknown. Herein, by performing metabolomics, we discovered that butyrate concentrations were significantly decreased in CRC patients. Butyrate supplementation sensitized CRC mice to ferroptosis induction, showing great in vivo translatability. Particularly, butyrate treatment reduced ferroptotic resistance of cancer stem cells. Mechanistically, butyrate inhibited xCT expression and xCT-dependent glutathione synthesis. Moreover, we identified c-Fos as a novel xCT suppressor, and further elucidated that butyrate induced c-Fos expression via disrupting class I HDAC activity. In CRC patients, butyrate negatively correlated with tumor xCT expression and positively correlated with c-Fos expression. Finally, butyrate was found to boost the pro-ferroptotic function of oxaliplatin (OXA). Immunohistochemistry data showed that OXA non-responders exhibited higher xCT expression compared to OXA responders. Hence, butyrate supplementation is a promising approach to break the ferroptosis resistance in CRC.

Leaf area index (LAI) is an important indicator for crop growth monitoring. Due to the small number of ground-measured samples, the hybrid method, using the radiative transfer model (RTM) to generate simulated samples and combining with the regression model, is popular for LAI estimation. However, there is still difference between simulated spectrum and measured spectrum, which may affect the inversion results. In this study, an iterative hybrid method combines BP neural network and PROSAIL model, and an optimal sample selection method for crop LAI estimation was proposed. A small number of ground-measured samples and unmanned aerial vehicle (UAV) hyperspectral data were used to estimate LAI firstly. Then, the initial LAI result was used as the parameter of PROSAIL model to generate the simulated spectrum. The simulated spectrum with high similarity with the UAV spectrum and corresponding LAI value would be treated as new samples for BP neural network. After several iterations, a reasonable sample set was obtained to estimate winter wheat LAI. The method proposed in this study is evaluated using ground-measured test samples and compared with the common hybrid methods. Results indicate that with the increase of the number of training samples, the accuracy of estimation model is improved (RMSE/MAE decreased from 0.4685/0.0301 to 0.4377/0.0272, respectively, while R$^{2}$ increased from 0.5857 to 0.6384). Also, the accuracy of proposed iterative hybrid model is higher than that of commonly used hybrid model. The experiments demonstrate the relatively high accuracy of the proposed iterative hybrid method, which could be used for vegetation parameter estimation with only a small number of ground samples.

Occlusion handling is crucial for improving the performance of convolutional neural networks (CNNs) in real-world remote sensing images, which are often captured in complex and unconstrained environments. In particular, occlusion scene classification has received significant attention due to its usefulness in various remote sensing tasks. However, existing methods are limited by their dependence on close-world learning, which assumes that all test cases are included in the training set. This is problematic because occlusion is too complex to be thoroughly annotated. To address this issue, we propose a novel contrastive learning-based CNN that can classify out-of-distribution occluded scenes without the need for occlusion annotation. Our approach uses a two-branch subnetwork to learn representations of unoccluded anchor images and occlusion-augmented images. We then employ cascade supervised contrastive learning to make the network's representations invariant to occlusion. Unlike standard contrastive learning, our method leverages category information to avoid incompact intraclass distribution and uses a cascade strategy to hierarchically learn occlusion-invariant representations. Finally, we use a multilayer perceptron to classify the learned representations and assess representation quality. We evaluate our method on the UAVDT dataset and two simulated datasets, and the results demonstrate that our approach accurately characterizes occluded objects and achieves more precise classification in occlusion scenarios.

Farmland is a significant resource for human survival and development. Rapid acquisition of farmland information is the basis for dynamic crop detection and sustainable land development. The continuous development of high-resolution remote sensing imagery makes it possible to make a wide range of refined earth observation. With better image interpretation ability, image segmentation method based on deep learning can bring specific results from high-resolution imagery and is widely used in remote sensing. However, existing image segmentation methods based on semantic segmentation have difficulties to extracting refined farmland parcels. Deep neural network is used to detect farmland edge. We use high-resolution network to achieve feature extraction that retains high-resolution features, strengthens the feature representation of network context information based on object-contextual representations module, and carries out more complete interpretation of farmland and its boundary. Finally, we design a farmland edge postprocessing method to connect the disconnected boundary based on the direction information generated by the connectivity attention module, and finally obtained the farmland boundary which is complete enough to be closed for generating farmland parcels. To verify our method, we used Google Earth image to label farmland boundaries and conduct experimental verification. The results show that our proposed model has a higher precision for farmland edge detection, and the postprocessing method of boundary connection can effectively close the boundary lines and achieve more detailed and complete farmland parcels.

As the basic unit of farmland, parcel is crucial for remote sensing tasks, such as urban management. Previous studies of farmland parcels extraction are based on boundary detection and instance segmentation methods. However, these methods perform poorly in the parcels with complex shape and fuzzy boundary due to the insufficient feature extraction capability. Moreover, for the lack of multiscale features extraction and fusion, they are difficult to extract different scale farmland parcels accurately. Based on these issues, we propose a fuzzy-boundary enhanced trident network, named FBETNet, to enhance the feature of fuzzy boundary and generate multiscale parcels. First, a semantic-guided multitask strategy is introduced in order to enhance the feature of fuzzy boundary. Second, we design a multiscale trident module to further improve the performance of multiscale feature extraction. Finally, an adversarial data augmentation strategy is employed in the training phase to strengthen the robustness and stability of our proposed method. Experiments show that our proposed method improves significantly in both accuracy and visualization, especially for the parcels with fuzzy boundary and complex shape.

Abstract Background Striatin interacting protein 2 (STRIP2) is a core component of the striatin-interacting phosphatase and kinase (STRIPAK) complexes, which is involved in tumor initiation and progression via the regulation of cell contractile and metastasis. However, the underlying molecular mechanisms of STRIP2 in non-small cell lung cancer (NSCLC) progression remain largely unknown. Methods The expressions of STRIP2 and IGF2BP3 in human NSCLC specimens and NSCLC cell lines were detected using quantitative RT-PCR, western blotting, and immunohistochemistry (IHC) analyses. The roles and molecular mechanisms of STRIP2 in promoting NSCLC progression were investigated in vitro and in vivo. Results Here, we found that STRIP2 expression was significantly elevated in NSCLC tissues and high STRIP2 expression was associated with a poor prognosis. Knockdown of STRIP2 suppressed tumor growth and metastasis in vitro and in vivo, while STRIP2 overexpression obtained the opposite effect. Mechanistically, P300/CBP-mediated H3K27 acetylation activation in the promoter of STRIP2 induced STRIP2 transcription, which interacted with insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) and upregulated IGF2BP3 transcription. In addition, STRIP2-IGF2BP3 axis stimulated m6A modification of TMBIM6 mRNA and enhanced TMBIM6 stability. Consequently, TMBIM6 involved NSCLC cell proliferation, migration and invasion dependent on STRIP2 and IGF2BP3. In NSCLC patients, high co-expression of STRIP2, IGF2BP3 and TMBIM6 was associated with poor outcomes. Conclusions Our findings indicate that STRIP2 interacts with IGF2BP3 to regulate TMBIM6 mRNA stability in an m6A-dependent manner and may represent a potential prognostic biomarker and therapeutic target for NSCLC.

High resolution of global land cover dynamic is indicative for understanding the influence of anthropogenic activity on environmental change. However, most of the land cover products are based on Landsat image that only has 30 m resolution, which is insufficient to distinguish the heterogenous urban structure; while very high spatial resolution image usually has low temporal resolution, which is difficult to monitor the urban dynamic. Deep-learning-driven super-resolution mapping is a prevailing way of achieving very-high-resolution land cover dynamic products in aspect of alleviating the mixed pixel problem of Landsat image. However, two limitations are obvious: 1) the fixed grid of kernel during the upsampling process favors spatial homogeneity and suppresses the learning of spatial heterogeneity of urban composition and 2) geometric or radiation variation over large spatial and long temporal extent in remote sensing images makes the super-resolution mapping approach difficult to transfer for application. Here, we attempt to solve these two limitations: 1) a progressive edge-guided super-resolution architecture is designed to allow nonuniformed kernel specific at the low-confidence edge region and intensify the learning of heterogenous compositions’ patterns and 2) an alternating optimization strategy is designed to minimize the resultant entropy and modulate the classification hyperplane to accommodate to the manifold of the discrepant region. Validation experiments are investigated based on a fine-grained and large-extent super-resolution (FLAS) dataset constructed in this study, and it is found that our approach remarkably enhances rich detailed patterns of heterogenous region and outperforms other state-of-the-art algorithms. Besides, we applied DETNet to the large spatial extent of 28 metropolises in China (>40,000 km2) and the large temporal extent of continuous 21-year (2000–2020) in Wuhan city to examine transferability. From the land cover areas variation, we find that the expansion rate of cropland is faster than the urban expansion over the past 10 years, which are gradually becoming the principal source for the encroachment of forest and lakes. From detailed urban dynamic reflected by the 21-year products, we find that urban-villages between the old city zone and the outer high-tech development zone are gradually disappeared. The captured dynamic is consistence with the urban-village renovation policy during this period, which is meant to redistribute the spatial configuration of the city for a more sustainable urban structure. We believe that the proposed method can facilitate a seamless and fine-grained observation system that can fill the weakness of the existing land cover activities and provide a brand-new insight into the urban dynamic and its underlying mechanism.

[Introduction] Fossil fuels and new energy power are facing problems and challenges in utilization and development. In order to solve the problem of carbon emissions in hydrogen production from fossil fuels for traditional refining and chemical enterprises and provide suggestions on the volatility in the development of new energy power, it is necessary to review the application and development of hydrogen preparation technology and the hydrogen network status of traditional refining and chemical enterprises. [Method] The utilization and development of hydrogen power were summarized, and special attention was given to the development of fuel cell electrolysis for hydrogen production technology. The hydrogen network in refining and chemical enterprise was in-depth analyzed from hydrogen production, hydrogen utilization, and hydrogen purification points of view. [Result] Based on these, the research points out the idea of combining the waste renewable energy power with the hydrogen network in refining and chemical enterprises by the water-electrolytic hydrogen production technology. With the clean energy device settled near the traditional refinery and chemical enterprise, the daily electricity requirement is satisfied and the processes hydrogen need is met. [Conclusion] To reduce the CO2 emissions from fossil fuel usage and the volatility of renewable energy power usage and to realize the idea of applying water-electrolytic hydrogen production technology in traditional refinery and chemical enterprises, there are still many challenges to overcome.

Objective With the aim of standardizing and improving the use of ultrasound-guided PLA on PTMC, a panel of experts from China and Italy, jointly issued this expert consensus on the clinical use of PLA for low-risk PTMC.Methods This expert consensus was developed by Chinese and Italian experts who have specific competence and expertise in this area. An evidence-based approach combining the knowledge and practical experience of the panelists was utilized.Results Twenty-six expert consensus recommendations were developed, spanning topics including the indications and contraindications of PLA for PTMC, physician training, preoperative preparation of patients, intraoperative technical procedures, possible complications, efficacy assessment, follow-up strategy, the approach to new PTMC and metastatic lymph nodes after treatment, thyroid-stimulating hormone inhibition therapy, and quality control of the entire procedure.Conclusion We summarized practical recommendations about standardized and improved PLA treatment for PTMC.

In strongly correlated materials multiple electronic phases can form simultaneously in real space, offering the potential of devices with multiple electronic properties, if such phase separation can be controlled. Here, Li et al study this electronic phase separation in LaMnO3/CaMnO3/PrMnO3 superlattice, and find a nano-network electronic phase separation resulting from local strain relaxation that persists through thermal cycling.

Long non-coding RNA (lncRNA) is involved in the progression of head and neck squamous cell carcinoma (HNSCC). The molecular mechanism of lncRNA SOX2-OT in HNSCC remains unclear. Therefore, we aimed to elucidate the oncogenic role of SOX2-OT in HNSCC. QRT-PCR analysis was performed in 61 pairs of HNSCC cancer tissues, adjacent normal tissues, and 68 plasma samples confirmed that lncRNA SOX2-OT was overexpressed in cancer tissues and plasma samples, which served as a poor prognostic factor for HNSCC. The FISH assay demonstrated that SOX2-OT was localized in the nucleus and cytoplasm of HNSCC cell lines. Further, the cell function assay confirmed that SOX2-OT promoted cell proliferation and metastasis in vitro and in vivo. RNA pulldown and RIP assay results revealed that SOX2-OT bonds with ILF3 in HNSCC, and the rescue assay confirmed that SOX2-OT played an oncogenic role depending on ILF3 protein expression. Ingenuity pathway analysis and Western blotting indicated that SOX2-OT regulated HNSCC progression by promoting STAT3 phosphorylation and modulating the crosstalk between STAT3 and TGF-β signaling. These results reveal evidence for the role of SOX2-OT in HNSCC progression and metastasis by binding to ILF3, which may serve as a therapeutic target and prognostic biomarker in HNSCC.

The urban heat island effect poses a growing threat to human society, especially in densely populated and developed megacities. With the introduction of the Local Climate Zones (LCZ) framework, new perspectives and findings have been brought to urban heat island studies. This study investigated the cooling effect of vegetation and albedo on the surface urban heat island (SUHI) in the classification system of LCZ during different seasons, using three Chinese megacities as case study areas. Single-factor linear regression and Pearson’s correlation coefficient were applied to analyze the seasonal cooling effect of both albedo and the NDVI on the SUHI within different LCZs. The results show that (1) the variability of the SUHI is reflected in its dominance and intensity within certain LCZs in different cities and in the efficiency of cooling factors; (2) the cooling effect of vegetation is dominant in each season, and the cooling effect produced by albedo within specific seasons can be differentiated by LCZs. This study provides valuable information for the mitigation of the SUHI magnitude in specific regions and at specific times of the year.

Abstract Background Interleukin‐15 (IL‐15) is a promising immunotherapeutic agent owing to its powerful immune‐activating effects. However, the clinical benefits of these treatments are limited. Crosstalk between tumor cells and immune cells plays an important role in immune escape and immunotherapy drug resistance. Herein, this study aimed to obtain in‐depth understanding of crosstalk in the tumor microenvironment for providing potential therapeutic strategies to prevent tumor progression. Methods T‐cell killing assays and co‐culture models were developed to determine the role of crosstalk between macrophages and tumor cells in breast cancer resistant to IL‐15. Western blotting, histological analysis, CRISPR‐Cas9 knockout, multi‐parameter flow cytometry, and tumor cell‐macrophage co‐injection mouse models were developed to examine the mechanism by which IL‐15Rα+ tumor‐associated macrophages (TAMs) regulate breast cancer cell resistance to IL‐15. Results We found that macrophages contributed to the resistance of tumor cells to IL‐15, and tumor cells induced macrophages to express high levels of the α subunit of the IL‐15 receptor (IL‐15Rα). Further investigation showed that IL‐15Rα+ TAMs reduced the protein levels of chemokine CX3C chemokine ligand 1 (CX3CL1) in tumor cells to inhibit the recruitment of CD8+ T cells by releasing the IL‐15/IL‐15Rα complex (IL‐15Rc). Administration of an IL‐15Rc blocking peptide markedly suppressed breast tumor growth and overcame the resistance of cancer cells to anti‐ programmed cell death protein 1 (PD‐1) antibody immunotherapy. Interestingly, Granulocyte‐macrophage colony‐stimulating factor (GMCSF) induced γ chain (γc) expression to promote tumor cell‐macrophage crosstalk, which facilitated tumor resistance to IL‐15. Additionally, we observed that the non‐transcriptional regulatory function of hypoxia inducible factor‐1alpha (HIF‐1α) was essential for IL‐15Rc to regulate CX3CL1 expression in tumor cells. Conclusions The IL‐15Rc‐HIF‐1α‐CX3CL1 signaling pathway serves as a crosstalk between macrophages and tumor cells in the tumor microenvironment of breast cancer. Targeting this pathway may provide a potential therapeutic strategy for enhancing the efficacy of cancer immunotherapy.

Carbon emissions from forest ecosystems are greatly impacted by the acceleration of fragmentation and edge effects. Understanding these effects requires accurate monitoring of changes in fragmented forest landscapes. However, these changes are often low-intensity and small-scale, making it difficult to detect them using medium spatial resolution satellite images (e.g., Landsat). To address this challenge, this study developed the Pure Forest Index (PFI), which uses a combination of the existing vegetation index (VI) and spectral mixture analysis (SMA) to more effectively detect and characterize the contribution of forests to the observed spectral response of a pixel. The PFI was applied to detect forest changes in the Amazon rainforest from 1986 to 2020 using the Continuous Change Detection and Classification (CCDC) algorithm (hereafter referred to as the CCDC-PFI algorithm). The results showed reliable performance in mapping forest changes, with an overall accuracy of 0.94 (±0.03) at the spatial scale and a temporal accuracy of 91.1 % (within a two-year window). Comparison with other indices revealed that the PFI improves the ability to monitor forest dynamics with an increased overall accuracy of 0.02–0.35. The PFI also demonstrated advantages in enhancing sub-pixel forest information and suppressing non-forest backgrounds in various scenes compared to conventional VIs. The proposed approach is expected to benefit further research on forests and ecosystems.

Abstract State estimation is the core function of distribution management system, and its estimation accuracy directly affects the operation of other advanced application functions of distribution management system. However, the access of a large number of distributed generation brings non‐Gaussian noise and bad data problems, which affect the accuracy of state estimation. Therefore, based on the conventional cubature particle filter (CPF), two new state estimation operators, robust cubature filter 1 (RCPF1) and robust cubature filter 2 (RCPF2), are proposed by introducing the Huber's M‐estimation theory into the CPF algorithm and integrating a time‐varying, multi‐dimensional scale factor with the CPF, respectively. The simulation results based on IEEE33 bus system showed that: (1) RCPF has better estimation accuracy and robustness than conventional cubature particle filter and cubature Kalman filter; (2) RCPF2 has better estimation performance than RCPF1 in the case of non‐Gaussian noise and bad data.

Establishing appropriate inter-organizational partnership in disaster response is of great help to the improvement of disaster relief performance. However, the selection of proper disaster response organizations for government agencies to build partnerships among the numerous relief organizations remains to be addressed. To fill the gap, this paper adopted a complex network modeling method to explain the formation mechanism of inter-organizational partnership in disaster response. Then, based on a numerical simulation method, the paper compared different inter-organizational partnering strategies, and explored the optimal inter-organizational partnering strategy in the disaster response process. Results demonstrate that the optimal partnering strategy is contingent on disaster response conditions, with accurate disaster information and abundant relief materials to choose the material difference principle, and vague disaster information to choose the organizational influence principle. At the same time, frequent information communication and material cooperation allocation can be transformed into low-frequency information communication and government-led material allocation. This study provides practical guidance for disaster relief organizations to choose inter-organizational partnering strategies, and enriches the disaster organization management theory.

Metastatic colorectal cancer (mCRC) is a heterogeneous disease that can evoke discordant responses to therapy among different lesions in individual patients. The Response Evaluation Criteria in Solid Tumors (RECIST) criteria do not take into consideration response heterogeneity. We explored and developed lesion-based measurement response criteria to evaluate their prognostic effect on overall survival (OS). Patients and Methods: Patients enrolled in 17 first-line clinical trials, who had mCRC with ≥ 2 lesions at baseline, and a restaging scan by 12 weeks were included. For each patient, lesions were categorized as a progressing lesion (PL: > 20% increase in the longest diameter (LD)), responding lesion (RL: > 30% decrease in LD), or stable lesion (SL: neither PL nor RL) based on the 12-week scan. Lesion-based response criteria were defined for each patient as follows: PL only, SL only, RL only, and varied responses (mixture of RL, SL, and PL). Lesion-based response criteria and OS were correlated using stratified multivariable Cox models. The concordance between OS and classifications was measured using the C statistic. Results: Among 10,551 patients with mCRC from 17 first-line studies, varied responses were noted in 51.6% of patients, among whom, 3.3% had RL/PL at 12 weeks. Among patients with RL/SL, 52% had stable disease (SD) by RECIST 1.1, and they had a longer OS (median OS (mOS) = 19.9 months) than those with SL only (mOS = 16.8 months, HR (95% CI) = 0.81 (0.76, 0.85), p < 0.001), although a shorter OS than those with RL only (mOS = 25.8 months, HR (95% CI) = 1.42 (1.32, 1.53), p < 0.001). Among patients with SL/PL, 74% had SD by RECIST 1.1, and they had a longer OS (mOS = 9.0 months) than those with PL only (mOS = 8.0 months, HR (95% CI) = 0.75 (0.57, 0.98), p = 0.040), yet a shorter OS than those with SL only (mOS = 16.8 months, HR (95% CI) = 1.98 (1.80, 2.18), p < 0.001). These associations were consistent across treatment regimen subgroups. The lesion-based response criteria showed slightly higher concordance than RECIST 1.1, although it was not statistically significant. Conclusion: Varied responses at first restaging are common among patients receiving first-line therapy for mCRC. Our lesion-based measurement criteria allowed for better mortality discrimination, which could potentially be informative for treatment decision-making and influence patient outcomes.

Floods are threatening agricultural production and causing enormous crop losses globally. In June–July 2020, the Yangtze and Huai River basins were hit again by exceptionally heavy rainfall and flooding, followed by massive inundation of croplands. However, quantitative studies on the impact of flooding on crops are still lacking. Therefore, quick and accurate mapping of flood and quantitative assessment of inundation impact on crops is vital. The main objectives of this article are to map the flooding areas and analyze the inundation impact on crops. The result of flood mapping with SAR images achieves an overall accuracy of 0.9023, which facilitates the analyses of phenological temporal change and Normalized Difference Vegetation Index (NDVI) losses of crops. It is found that the inundation resulted in a phenological lag in crops grown in the 2020 summer. The phenological lag resulted in a significant NDVI loss, especially in the earlier stage. With the extension of phenology latency, crops suffered even greater losses. Conversely, in the winter of 2020, the crops show obvious phenological advances, and compared with 2019, there was no obvious NDVI loss.

Local climate zone (LCZ) has become a new standard classification scheme in urban landscapes and showed great potential in urban climate research. Traditional classifiers and ordinary neural networks only consider the spectral or local spatial features of the pixel, ignoring the effect of nonlocal information on the LCZ classification. The graph convolutional network (GCN) has been used to exploit the relationship between adjacent and global land covers owing to the ability to conduct flexible convolution over graphs. In this work, we integrated a convolutional neural network and two GCNs into an end-to-end hybrid framework and generated LCZs directly from the original images. Local-, regional-, and global-level features were extracted and grouped complementarily to foster better performance. Experiments were conducted in six cities around the world to verify the effectiveness of our method. Results showed that the average classification accuracy of the six cities reached 0.956 and performed better than any other comparable model. Ablation experiments also demonstrated the mutual promotion of the different modules. Finally, the small sample experiment provided a practical reference for the LCZ classification in the absence of samples in future.