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Abstract Radiotherapy (RT), a common cancer treatment, unintentionally harms surrounding tissues, including the skin, and hinders wound healing years after treatment. This study aims to understand the mechanisms behind these late-onset adverse effects. We compare skin biopsies from previously irradiated (RT+) and non-irradiated (RT−) sites in breast cancer survivors who underwent RT years ago. Here we show that the RT+ skin has compromised healing capacity and fibroblast functions. Using ATAC-seq, we discover altered chromatin landscapes in RT+ fibroblasts, with THBS1 identified as a crucial epigenetically primed wound repair-related gene. This is further confirmed by single-cell RNA-sequencing and spatial transcriptomic analysis of human wounds. Notably, fibroblasts in both murine and human post-radiation wound models show heightened and sustained THBS1 expression, impairing fibroblast motility and contractility. Treatment with anti-THBS1 antibodies promotes ex vivo wound closure in RT+ skin from breast cancer survivors. Our findings suggest that fibroblasts retain a long-term radiation memory in the form of epigenetic changes. Targeting this maladaptive epigenetic memory could mitigate RT’s late-onset adverse effects, improving the quality of life for cancer survivors.

Abstract The cell transition from an inflammatory phase to a subsequent proliferative phase is crucial for wound healing, yet the driving mechanism remains unclear. By profiling lncRNA expression changes during human skin wound healing and screening lncRNA functions, we identify SNHG26 as a pivotal regulator in keratinocyte progenitors underpinning this phase transition. Snhg26-deficient mice exhibit impaired wound repair characterized by delayed re-epithelization accompanied by exacerbated inflammation. Single-cell transcriptome analysis combined with gain-of-function and loss-of-function of SNHG26 in vitro and ex vivo reveals its specific role in facilitating inflammatory-to-proliferative state transition of keratinocyte progenitors. A mechanistic study unravels that SNHG26 interacts with and relocates the transcription factor ILF2 from inflammatory genomic loci, such as JUN, IL6, IL8, and CCL20, to the genomic locus of LAMB3. Collectively, our findings suggest that lncRNAs play cardinal roles in expediting tissue repair and regeneration and may constitute an invaluable reservoir of therapeutic targets in reparative medicine.

Acid rain and invasive plants have quintessential adverse impacts on terrestrial ecosystems. As an environmentally safe method for disposal of invasive plants, we tested the effect of biochar produced from these plants in altering soil deterioration under acid rain as compared with lime. Given the impacts of the feedstock type and soil properties on the response of soil to the added biochar, we hypothesized that the microbial community and functions would respond differently to the charred invasive plants under acid rain. A pot experiment was conducted to examine the response of soil microbiomes and functions to the biochar produced from Blackjack (Biden Pilosa), Wedelia (Wedelia trilobata), and Bitter vine (Mikania micrantha Kunth), or quicklime (CaO) at a rate of 1 % (w/w) under acid rain. Like soil pH, the nutrient contents (nitrogen, phosphorus, and potassium), calcium, and cation exchange capacity (CEC) were important as dominant edaphic factors affecting soil microbial community and functionality. In this respect, lime decreased nutrients availability, driven by 11-fold, 44 %, and 2-fold increments in calcium content, pH, and C/N ratio. Meanwhile, biochar improved nutrients availability under acid rain owing to maintaining a neutral pH (∼6.5), increasing calcium (by only 2-fold), and improving CEC, water repellency, and aggregation while decreasing the C/N ratio and aluminum content. Unlike biochar, lime decreased the relative abundance of Nitrosomonadaceae (the dominant ammonia-oxidizing bacteria) while augmenting the relative abundance of some fungal pathogens such as Spizellomycetaceae and Sporormiaceae. Given the highest nitrogen and dissolved organic carbon content than other biochar types, Wedelia-biochar resulted in the greatest relative abundance of Nitrosomonadaceae; thus, the microbial carbon and nitrogen biomasses were maximized. This study outlined the responses of the soil biogeochemical properties and the related microbial community structure and functionality to the biochar produced from invasive plants under acid rain. This study suggests that biochar can replace lime to ameliorate the effects of acid rain on soil physical, chemical and biological properties.

Abstract Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

Abstract Background Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC. Methods Data on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. Results A total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well. Conclusions The XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.

Abstract 9,9‐bis (diphenylphosphorylphenyl) fluorene (FDPO) and dibenzotetrathienoacene (DBTTA), are synthesized as the neutral and anionic ligands, respectively, to prepare the ErIII coordination polymer [Er(DBTTA)3(FDPO)]n. Based on the intramolecular energy transfer, optical gains at 1.5 µm are demonstrated in [Er(DBTTA)3(FDPO)]n‐doped polymer waveguides under excitations of low‐power light‐emitting diodes (LEDs) instead of laser pumping. A ligand‐sensitization scheme between organic ligands and Er3+ ions under an excitation of an ultraviolet (UV) LED is established. Relative gains of 10.5 and 8.5 dB cm−1 are achieved at 1.53 and 1.55 µm, respectively, on a 1‐cm‐long SU‐8 channel waveguide with a cross‐section of 2 × 3 µm2 and a 1.5‐µm‐thick [Er(DBTTA)3(FDPO)]n‐doped polymethylmethacrylate (PMMA) as upper cladding. The ErIII coordination polymer [Er(DBTTA)3(FDPO)]n can be conveniently integrated with various low‐loss inorganic waveguides to compensate for optical losses in the C‐band window. Moreover, by relying on the intramolecular energy transfer and UV LED top‐pumping technology, it is easy to achieve coupling packaging of erbium‐doped waveguide amplifiers (EDWAs) with pump sources in planar photonic integrated chips, effectively reducing the commercial costs.

ObjectiveTo compare the diagnostic effectiveness of the Gynecologic Imaging Reporting and Data System (GI-RADS) and Neoplasias in the Adnexa (ADNEX) model for the diagnosis of benign and malignant ovarian tumors by junior physicians.MethodsThe sonographic data of 634 patients with ovarian tumors confirmed by pathology in our hospital over 4 years were analyzed retrospectively by junior doctors. The diagnostic efficacy of the GI-RADS and ADNEX models was compared based on pathology.Results(1) Regarding the diagnostic efficacy of the GI-RADS and ADNEX models, the sensitivity was 90.15% and 84.85%, the specificity was 87.65% and 85.86%, the accuracy rates were 88.17% and 85.65%, and the Youden Indices were 0.778 and 0.707, respectively. The areas under the receiver operating characteristic (ROC) curves were 0.924 (95% CI: 0.900-0.943) and 0.933 (95% CI: 0.911-0.951), respectively. The GI-RADS classification was equivalent to that of the ADNEX model in the diagnosis of adnexal tumors (P>0.05). These findings were highly consistent with the pathological results (Kappa values were 0.684 and 0.691, respectively). (2) When differentiating between different pathological types of adnexal tumors, the ADNEX model had the best diagnostic value for distinguishing between benign tumors and stage II-IV ovarian cancer (AUC=0.990, 95% CI: 0.978-0.997).Conclusions(1) The diagnostic efficacy of the GI-RADS and ADNEX models in the diagnosis of benign and malignant ovarian tumors by junior physicians is excellent and comparable. (2) The ADNEX model shows good value for differentiating ovarian tumors of different pathological types by junior physicians.

Fragrant rice has a high market value, and it is a popular rice type among consumers owing to its pleasant flavor. Plantation methods, nitrogen (N) fertilizers, and silicon (Si) fertilizers can affect the grain yield and fragrance of fragrant rice. However, the core commercial rice production attributes, namely the head rice yield (HRY) and 2-acetyl-1-pyrroline (2-AP) content of fragrant rice, under various nitrogen and silicon (N-Si) fertilization levels and different plantation methods remain unknown. The field experiment in this study was performed in the early seasons of 2018 and 2019 with two popular indica fragrant rice cultivars (Yuxiangyouzhan and Xiangyaxiangzhan). They were grown under six N-Si fertilization treatments (combinations of two levels of Si fertilizer, 0 kg Si ha−1 (Si0) and 150 kg Si ha−1 (Si1), and three levels of N fertilizer, 0 kg N ha−1 (N0), 150 kg N ha−1 (N1), and 220 kg N ha−1 (N2)) and three plantation methods (artificial transplanting (AT), mechanical transplanting (MT), and mechanical direct-seeding (MD)). The results showed that the N-Si fertilization treatments and all the plantation methods significantly affected the HRY and 2-AP content and related parameters of the two different fragrant rice cultivars. Compared with the Si0N0 treatment, the N-Si fertilization treatments resulted in higher HRY and 2-AP contents. The rates of brown rice, milled rice, head rice, and chalky rice of the fragrant rice also improved with the N-Si fertilization treatments. The N-Si fertilization treatments increased the activities of N metabolism enzymes and the accumulation of N and Si in various parts of the fragrant rice, and affected their antioxidant response parameters. The key parameters for the HRY and 2-AP content were assessed by redundancy analysis. Furthermore, the structural equation model revealed that the Si and N accumulation levels indirectly affected the HRY by affecting the N metabolism enzyme activity, N use efficiency, and grain quality of fragrant rice. Moreover, high N and Si accumulation directly promoted the 2-AP content or affected the antioxidant response parameters and indirectly regulated 2-AP synthesis. The interactions of the MT method with the N-Si fertilization treatments varied in the fragrant rice cultivars in terms of the HRY and 2-AP content, whereas the MD method was beneficial to the 2-AP content in both fragrant rice cultivars under the N-Si fertilization treatments.

A Zn2TiO4 crystalline photovoltaic glass ink was prepared by fast firing at 700 °C for 5 min by the glass crystallisation method, which effectively improved the reflectivity and acid resistance of the photovoltaic glass ink coating. The phase, morphology and properties of the samples were tested by XRD, SEM and UV-vis diffuse reflection, etc. The enhanced reflectivity mechanism was proposed. The results showed that the increase in ZnO/SiO2 ratio reduced the transition temperature (Tg) and crystallisation temperature (Tp) of the glass melt, which could promote the crystallinity of Zn2TiO4 in photovoltaic glass ink coatings and thus improve the acid resistance of photovoltaic glass inks. Significant improvement in reflectance and whiteness is due to the Zn2TiO4 crystallinity growth, which fills in the pores of the ink surface, and TiO2 fillers keep almost the same surface roughness (0.2 µm) and wetting angle (5.2°). Typical samples achieved 89.2% of the whiteness and 88.0% of the reflectance, and the weight loss in acid was 3.9 mg/cm2, which could improve the efficiency of solar power generation.

This paper investigates an adaptive fault-tolerant control scheme for an unmanned aerial vehicle (UAV) attitude control system in the presence of multiple faults, input saturation, and external disturbances. The attitude control system of a UAV is divided into an outer loop consisting of attitude angles and an inner loop consisting of attitude angle velocities. The nonlinear dynamic inversion (NDI) method based on sliding mode surface is used to construct the outer loop controller to ensure fast response and accurate attitude tracking. The treatment of the inner loop with multiple faults, input saturation, and external disturbances is more complex. First, input saturation and sensor faults are converted successively for ease of analysis. After the conversion and reorganization of such uncertainties including actuator faults, sensor faults, input saturation, and external disturbances, a nonlinear uncertain system model is developed. Second, a L1 neural network adaptive fault-tolerant controller is designed to deal with uncertainties, where radial basis function neural networks (RBFNNs) are applied to approximate the nonlinear function in the system model. The L1 adaptive fault-tolerant controller includes three parts: state predictor, adaptive law, and control law. Adaptive laws based on the projection operator estimate and update the unknown parameters in the system and transfer them to the control law and the state predictor to compensate for the effects of uncertainties. The control law with a low-pass filter also eliminates the high-frequency oscillations caused by a high adaptive gain in conventional adaptive algorithms, which guarantee the robustness and fast self-adaptation. Finally, simulation results are given to verify the effectiveness and feasibility of the proposed controller.

Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained by at least three aspects: cost-effectiveness, the dynamic nature of market prices, and the issue of missing bidding values. To address these challenges, we propose Imagine and Imitate Bidding (IIBidder), which includes Strategy Imitation and Imagination modules, to generate cost-effective bidding strategies under partially observable price landscapes. Experimental results on the iPinYou and YOYI datasets demonstrate that IIBidder reduces investment costs, optimizes bidding strategies, and improves future market price predictions.