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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.

Abstract Background Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. Methods In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. Results The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02–0.06, P

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.

Abstract Background Existing treatments for cholangiocarcinoma have poor efficacy. However, chimeric antigen receptor‐T (CAR‐T) cells are emerging as a potential therapeutic strategy. Solid tumors possess multiple adverse factors in an immunosuppressive microenvironment that impair CAR‐T cell infiltration and function. This study aimed to improve the function of CAR‐T cells through knock down immune checkpoints and immunosuppressive molecular receptors. Methods We evaluated the expression of epidermal growth factor receptor (EGFR) and B7 homolog 3 protein (B7H3) antigens in cholangiocarcinoma tissues using immunohistochemistry and screened specific immune checkpoints in the cholangiocarcinoma microenvironment via flow cytometry. Subsequently, we engineered CAR‐T cells targeting EGFR and B7H3 antigens. We simultaneously knocked down immune checkpoints and immunosuppressive molecular receptors in CAR‐T cells by constructing two clusters of small hairpin RNAs and evaluated the engineered CAR‐T cells for antitumor activity both in vitro, using tumor cell lines and cholangiocarcinoma organoid models, and in vivo, using humanized mouse models. Results We observed high expression of EGFR and B7H3 antigens in cholangiocarcinoma tissues. EGFR‐CAR‐T and B7H3‐CAR‐T cells demonstrated specific anti‐tumor activity. We found an abundance of programmed cell death protein 1 (PD‐1), T cell immunoglobulin and mucin domain‐containing protein 3 (Tim‐3), and T cell immunoglobulin and ITIM domain (Tigit) on infiltrated CD8+ T cells in the cholangiocarcinoma microenvironment. We then decreased the expression of these 3 proteins on the surface of CAR‐T cells, named PTG‐scFV‐CAR‐T cells. Furthermore, we knocked‐down the expression of transforming growth factor beta receptor (TGFβR), interleukin‐10 receptor (IL‐10R), and interleukin‐6 receptor (IL‐6R) of PTG‐scFV‐CAR‐T cells. Those cells, named PTG‐T16R‐scFV‐CAR‐T cells, potently killed tumor cells in vitro and promoted apoptosis of tumor cells in a cholangiocarcinoma organoid model. Finally, the PTG‐T16R‐scFv‐CAR‐T cells showed greater inhibitory effect on tumor growth in vivo, and were superior in prolonging the survival of mice. Conclusions Our results revealed that PTG‐T16R‐scFV‐CAR‐T cells with knockdown of sextuplet inhibitory molecules exhibited strong immunity against cholangiocarcinoma and long‐term efficacy both in vitro and in vivo. This strategy provides an effective and personalized immune cell therapy against cholangiocarcinoma.

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.

Abstract Background Impaired respiratory function caused by respiratory muscle dysfunction is one of the common consequences of chronic obstructive pulmonary disease (COPD). In this study, two-dimensional shear wave elastography (2D-SWE) was used to measure diaphragm stiffness (DS) and intercostal muscle stiffness (IMS) in patients with COPD; in addition, the value of 2D-SWE in evaluating respiratory function was determined. Methods In total, 219 consecutive patients with COPD and 20 healthy adults were included. 2D-SWE was used to measure the DS and IMS, and lung function was also measured. The correlation between respiratory muscle stiffness and lung function and the differences in respiratory muscle stiffness in COPD patients with different severities were analysed. Results 2D-SWE measurements of the DS and IMS presented with high repeatability and consistency, with ICCs of 0.756 and 0.876, respectively, and average differences between physicians of 0.10 ± 1.61 and 0.07 ± 1.65, respectively. In patients with COPD, the DS and IMS increased with disease severity (F1 = 224.50, F2 = 84.63, P

By analyzing the differences of mineral element contents in dried tangerine peel from different producing areas, combined with multivariate statistical analysis, the effective origin traceability indexes based on mineral element fingerprint analysis technology were selected to construct its discrimination model of different origin identification. In this study, 206 dried tangerine peel samples were collected from three different producing areas as Guangdong, Fujian and Chongqing. The contents of 32 mineral elements were determined by inductively coupled plasma mass spectrometer (ICP-MS) and inductively coupled plasma atomic emission spectrometer (ICP-AES). Combined with analysis of variance, principal component analysis, linear discriminant analysis and orthogonal partial least squares discriminant analysis, a discriminant model of dried tangerine peel producing area was established. The results showed that 26 of the 32 mineral elements in the dried tangerine peel samples had significant differences between Guangdong and other two producing areas, and 11 of them had significant differences between the three different regions. After principal component analysis, four principal components could be extracted from the 32 mineral elements, representing 70.0% of the total index. Based on the principal component analysis, the dried tangerine peel samples could be preliminarily clustered according to their different origin. The main variables of the first two principal components were Dy, Sm, Gd, Pr, Nd, Y, La, Fe, Be, V, Ce, Sc, Co, P, Mo, As, Pb and B. Through linear discriminant analysis, 21 mineral elements K, P, Ca, Co, Cu, Mn, Mo, V, Ni, B, Li, Pb, As, Sr, Ti, Th, Gd, Sc, Nd, Pr and Y were determined as effective traceability indexes of the dried tangerine peel. Additionally, based on the discriminant model established by orthogonal partial least squares discriminant analysis, the importance of 13 elements Sc, B, Y, Co, Nd, La, Pr, Be, Gd, Dy, Sm, Mo and Fe were determined. For the discrimination models established by the two above-mentioned discrimination analysis methods, their overall correct discrimination rates of cross validation and external sample validation were both 100%, which basically achieved the origin discrimination of dried tangerine peel. The research suggested that the mineral element fingerprint analysis technology can be used for the origin traceability discrimination of dried tangerine peel.

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients’ transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

Objective: To study the contents of functional components and antioxidant capacity of different varieties of Indocalamus leaves in same area. Methods: Using 11 existing species of Indocalamus leaves resources as raw materials, the contents of basic nutrients, flavonoids, polyphenols, polysaccharides, volatile oil and other functional components were evaluated as well as the antioxidant capacity of alcohol extracts. Then the data were analyzed by weight and scored comprehensively. Results: The results showed that there were significant differences among the content of basic nutrients in leaves of different Indocalamus varieties in the same area. The coefficient of variation of four components was 13.93% only for crude protein, and the coefficient of variation among the other three was no more than 10%. Same phenomenon was found in the four functional components as well. The variation coefficients of flavonoids and polyphenols were calculated for 34.47% and 38.04%, 28.03% and 22.36% were for that of polysaccharide and volatile oil contents, in which the difference was smaller than that of flavonoids and polyphenols. Among them, the contents of flavonoids (0.68%) and polyphenols (0.62%) in the leaves of Indocalamus elongatus were the highest. Polysaccharide (0.69%) in leaves of Indocalamus barbatus was the highest. And the content of volatile oil (0.64%) in broad-leaved Indocalamus broad-leaved was the highest. The antioxidant capacity assay indicated that there was indistinctively difference in antioxidant activity among those Indocalamus leaves, and the coefficient of variation among the scavenging rates of DPPH radical, superoxide anion and hydroxyl radical was less than 10%. From the weight analysis containing the content of four functional components and antioxidant activity, the comprehensive score of Indocalamus Tianmu was the highest (0.8108), which might be used as the preferred material for the development and utilization of Indocalamus leaves. From the above studies, this finding would provide an important information for the utilization of Indocalamus leaves.

Abstract Background The measurement of liver stiffness (LS) and spleen stiffness (SS) based on ultrasound elastography can be used for non-invasive assessment of portal hypertension (PH). However, there are few studies on the corresponding mechanism of increased spleen stiffness. Our aim was to use two-dimensional shear wave elastrography (2D-SWE) to evaluate the relationship between LS and SS and the severity of PH in rats. And explore the mechanism of the increase of LS and SS in PH. Methods Sixty male Sprague–Dawley rats were randomly divided into portal hypertension (PH group, n = 45) and normal control (NC group, n = 15). At 12 weeks, LS and SS was detected by 2D-SWE in vivo. Related hemodynamic parameters and portal vein pressure (PVP) was measured. Spleen and liver 2D-SWE detection was performed again after sacrifice. Pathological changes were observed. Results The SS and LS were increased in PH group (P

Aiming at the problem of cable breakage of an asymmetric single-tower cable-stayed bridge by taking a single-tower cable-stayed bridge that has been in operation for more than 20 years as an engineering example, a spatial finite element model that reflected the actual stress state of the structure was established and a method for solving the time point of cable breakage was proposed. The results show that the strength of the main girder, pylon and cable could meet the stress requirements of the vibration caused by successive cable breaks, and the maximum dynamic displacement of the main girder did not exceed the stiffness limit. Although the breakage of the stay cable caused the stress in the adjacent position and the corresponding position on the opposite side of the tower to increase sharply, the stress fluctuation in other positions was small. The fluctuation of the dynamic stress of the cable tower was relatively small, but the displacement of the tower top changed greatly. The dynamic response of the cables breaking in succession was less than that of simultaneous breaking, and the structural vibration caused by cable breaking was not as severe as that caused by simultaneous breaking.

Abstract Background This study focuses on the processes occurring during the acidogenic step of anaerobic digestion, especially resulting from nutritional interactions between dark fermentation (DF) bacteria and lactic acid bacteria (LAB). Previously, we have confirmed that DF microbial communities (MCs) that fed on molasses are able to convert lactate and acetate to butyrate. The aims of the study were to recognize the biodiversity of DF-MCs able and unable to convert lactate and acetate to butyrate and to define the conditions for the transformation. Results MCs sampled from a DF bioreactor were grown anaerobically in mesophilic conditions on different media containing molasses or sucrose and/or lactate and acetate in five independent static batch experiments. The taxonomic composition (based on 16S_rRNA profiling) of each experimental MC was analysed in reference to its metabolites and pH of the digestive liquids. In the samples where the fermented media contained carbohydrates, the two main tendencies were observed: (i) a low pH (pH ≤ 4), lactate and ethanol as the main fermentation products, MCs dominated with Lactobacillus, Bifidobacterium, Leuconostoc and Fructobacillus was characterized by low biodiversity; (ii) pH in the range 5.0–6.0, butyrate dominated among the fermentation products, the MCs composed mainly of Clostridium (especially Clostridium_sensu_stricto_12), Lactobacillus, Bifidobacterium and Prevotella. The biodiversity increased with the ability to convert acetate and lactate to butyrate. The MC processing exclusively lactate and acetate showed the highest biodiversity and was dominated by Clostridium (especially Clostridium_sensu_stricto_12). LAB were reduced; other genera such as Terrisporobacter, Lachnoclostridium, Paraclostridium or Sutterella were found. Butyrate was the main metabolite and pH was 7. Shotgun metagenomic analysis of the selected butyrate-producing MCs independently on the substrate revealed C.tyrobutyricum as the dominant Clostridium species. Functional analysis confirmed the presence of genes encoding key enzymes of the fermentation routes. Conclusions Batch tests revealed the dynamics of metabolic activity and composition of DF-MCs dependent on fermentation conditions. The balance between LAB and the butyrate producers and the pH values were shown to be the most relevant for the process of lactate and acetate conversion to butyrate. To close the knowledge gaps is to find signalling factors responsible for the metabolic shift of the DF-MCs towards lactate fermentation. Video Abstract

BackgroundDiabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes and the strongest initiating risk factor for diabetic foot ulceration. Early diagnosis of DPN through screening measures is, therefore, of great importance for diabetic patients. Recently, shear wave elastography (SWE) has been used as a method that is complementary to neuroelectrophysiological examination in the diagnosis of DPN. We aimed to conduct a meta-analysis based on currently available data to evaluate the performance of tibial nerve stiffness on SWE for diagnosing DPN.MethodsBoth PubMed, EMBASE, the Cochrane Library, and Web of Science were searched for studies that investigated the diagnostic performance of SWE for DPN up to March 1th, 2022. Three measures of diagnostic test performance, including the summary area under receiver operating characteristics curve (AUROC), the summary sensitivity and specificity, and the summary diagnostic odds ratios were used to assess the diagnostic accuracy of SWE. All included studies were published between 2017 and 2021.ResultsSix eligible studies (with 170 DPN patients, 28 clinically defined DPN patients, 168 non-DPN patients, and 154 control participants) that evaluated tibial nerve stiffness were included for meta-analysis. The summary sensitivity and specificity of SWE for tibial nerve stiffness were 75% (95% confidence interval [CI]: 68–80%) and 86% (95% CI: 80–90%), respectively, and the summary AUROC was 0.84 (95% CI: 0.81–0.87), for diagnosing DPN. A subgroup analysis of five two-dimensional SWE studies revealed similar diagnostic performance, showing the summary sensitivity and specificity of 77% (95% CI: 69–83%) and 86% (95% CI: 79–91%), respectively, and a summary AUROC value of 0.86 (95% CI: 0.83–0.89).ConclusionsSWE is found to have good diagnostic accuracy for detecting DPN and has considerable potential as an important and noninvasive adjunctive tool in the management of patients with DPN.

BackgroundMeasurement of hepatic venous pressure gradients is the gold standard for assessing portal hypertension (PH) but is invasive with potential complications. We aimed to assess the performance in liver and spleen stiffness measurement (LSM and SSM, respectively) by two-dimensional shear wave elastography (2D-SWE) and composite scores including liver stiffness-spleen diameter to platelet ratio score (LSPS), platelet (PLT) count/spleen diameter ratio (PSR), aspartate aminotransferase (AST)/alanine aminotransferase ratio (AAR), and AST-to-PLT ratio index (APRI) for diagnosing PH in nonalcoholic fatty liver disease (NAFLD) rat models.MethodsAnimal models with PH in NAFLD were established in 65 rats, which then underwent 2D-SWE measurements. Morphological and biological parameters were collected for calculation of four composite scores. Correlations of noninvasive methods with portal venous pressure were evaluated by Spearman correlation analysis. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of noninvasive methods in predicting PH.ResultsLSM and SSM were significantly associated with portal venous pressure (r = 0.636 and 0.602, respectively; all P < 0.001). The AUCs of LSM and SSM in the diagnosis of PH were 0.906 (95% confidence interval [CI]:0.841–0.97) and 0.87 (95% CI:0.776–0.964), respectively, and were significantly higher than those in composite scores. The AUCs for LSPS, PSR, AAR, and APRI were 0.793, 0.52, 0.668, and 0.533, respectively, for diagnosing PH. The AUCs of the combined models of LSM and SSM, LSM and PLT, SSM and PLT, and LSM, SSM and PLT were 0.923, 0.913, 0.872, and 0.923, respectively. The four combined models showed no statistical differences compared to LSM and SSM in evaluating PH (all P > 0.05).ConclusionsLSM and SSM by 2D-SWE can be used as promising noninvasive parameters for diagnosing PH in NAFLD and have higher accuracy than composite scores. The combined models, compared to LSM and SSM, did not significantly improve the performance in diagnosing PH.

Abstract Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that involves T follicular helper (TFH) cell‐mediated humoral immune responses. Absent in melanoma 2 (human AIM2 and murine Aim2) is a well‐known component of the inflammasome in the innate immune system. Surprisingly, we observed that in SLE patients, upregulated levels of AIM2 expression were found in peripheral blood and skin lesions, with the highest levels detected in TFH‐like cells. In the CD4creAim2fl/fl conditional knockout mice, a markedly reduced TFH cell response was observed, with significantly lower levels of serum autoantibodies and proteinuria, as well as profoundly reduced renal IgG deposition in pristane‐induced lupus mice. Mechanistically, IL‐21 was found to recruit hydroxymethyltransferase ten‐eleven translocation 2 (TET2) to the AIM2 promoter, resulting in DNA demethylation and increased transcription of AIM2. In addition, AIM2 could regulate c‐MAF expression to enhance IL‐21 production, which consequently promoted TFH cell differentiation. Our results have identified a role of AIM2 in promoting the TFH cell response and further revealed that the IL‐21‐TET2‐AIM2‐c‐MAF signalling pathway is dysregulated in lupus pathogenesis, which provides a potential therapeutic target for SLE.

Systemic lupus erythematosus (SLE) is an autoimmune disease that is characterized by abnormal T and B cells. B-cell activating factor (BAFF) has been suggested to play a crucial role in lupus by promoting the proliferation, differentiation, and survival of B cells. Increased serum levels of BAFF have been found in patients with lupus. However, the expression of BAFF and its receptors on immune cells and in skin has not been systematically reported before. Here, we report that SLE patients showed increased levels of BAFF on circulating CD3+ T cells and B-cell maturation antigen (BCMA) on CD14+ monocytes and dramatically increased expression of BAFF in lupus skin lesions compared with those of healthy controls. TACI was undetectable on circulating immune cells. An increased serum level of BAFF was also confirmed in lupus patients in this study. Our findings may provide a better understanding of the pathogenesis and predictors of BAFF antibody treatment response, as well as potential targets for skin therapies.

The control over specific biological functions of a single cell or between different cells is an exciting goal in the fields of biophysics and biochemistry. Besides the fast‐developed chemical biology methods, such as chemical labeling and gene editing, researchers are still seeking for efficient ways to manipulate cell behaviors. Over the past decades, self‐assembled DNA nanostructures have emerged as novel and versatile tools for the study of biological science. Featured with structural programmability, customized functionality, and marked biocompatibility, DNA nanostructures have been immensely exploited for a variety of fascinating biological applications including bioimaging and drug delivery. In addition, DNA nanostructures also hold great potential in the regulation of physiological functions at various biological interfaces. In this minireview, the recent progress of the regulation of biological processes and functions at cell interface using DNA nanostructures is summarized. Current challenges and opportunities in applying DNA nanoassemblies as tools for cell behavior manipulation are also briefly discussed.

Ethnopharmacological relevance: Curcumin is a bright yellow chemical produced by plants of the Curcuma longa species. Chemically, curcumin is a diarylheptanoid, belonging to the group of curcuminoids. The therapeutic potential of curcumin has been widely investigated, including its utilization in various of cardiovascular diseases. However, its effect in cardiac remodeling post myocardial infarction and underlying mechanism remains to be uncover.Aim: To evaluate the therapeutic effect and underlying mechanism of curcumin on cardiac fibrosis after myocardial infarction via macrophage-fibroblast crosstalk.Methods: Male C57BL/6 (C57) mice were subjected to left anterior descending coronary artery ligation to establish myocardial infarction and intragastrically fed vehicle or curcumin (50 mg/kg or 100 mg/kg) for 4 weeks. In parallel, neonatal rat cardiac fibroblasts were isolated and co-cultured with liposaccharide (LPS− or LPS+) curcumin-treated macrophages, followed by TGF-β stimulation for 24 h. Cardiac function was determined by 2-dimensional echocardiography, and cardiac fibrosis was measured by picrosirius red staining. Apoptosis of macrophages was investigated by flow cytometry; all pro-fibrotic protein expression (EDA-Fibronectin, Periostin, Vimentin, and α-SMA) as well as TGF-βR1 downstream signaling activation reflected by phosphorylated SMAD2/3 (p-SMAD2 and p-SMAD3) were demonstrated by western blotting.Results: Curcumin significantly ameliorated the inflammation process subsequent to myocardial infarction, reflected by decreased expression of CD68+ and CD3+ cells, accompanied by dramatically improved cardiac function compared with the placebo group. In addition, cardiac fibrosis is inhibited by curcumin administration. Interestingly, no significant reduction in fibrotic gene expression was observed when isolated cardiac fibroblasts were directly treated with curcumin in vitro; however, pro-fibrotic protein expression was significantly attenuated in CF, which was co-cultured with LPS-stimulated macrophages under curcumin treatment compared with the placebo group. Mechanistically, we discovered that curcumin significantly downregulated pro-inflammatory cytokines in macrophages, which in turn inhibited IL18 expression in co-cultured cardiac fibroblasts using bulk RNA sequencing, and the TGF-β1-p-SMAD2/3 signaling network was also discovered as the eventual target downstream of IL18 in curcumin-mediated anti-fibrosis signaling.Conclusion: Curcumin improves cardiac function and reduces cardiac fibrosis after myocardial infarction. This effect is mediated by the inhibition of macrophage-fibroblast crosstalk in the acute phase post-MI and retrained activation of IL18-TGFβ1-p-SMAD2/3 signaling in cardiac fibroblasts.

ABSTRACT The ubiquity and long-range transport of the microorganisms inhabiting dust can pose a serious risk to human, animal, and plant health. The well-recognized importance of dust-associated microorganisms contrasts starkly with our limited understanding of the factors determining the variation in the composition of these communities at the global scale. Here, we provide the first insight into the global determinants of dust-associated microorganisms by quantifying the environmental factors shaping bacterial and fungal community composition in 467 outdoor settled dust samples collected from 33 countries and 6 continents. Our results show that the global variation in dust-associated bacterial and fungal community composition was, to some degree, predictable from mean annual precipitation and temperature. Notably, our results show that the fungal genera Alternaria and Aspergillus, which contain many species that can serve as triggers of allergenic disease in humans and as plant pathogens, were more abundant in drier regions. Collectively, these results highlight the key influence of climate on the global distribution of dust-associated microorganisms and provide the baseline information needed to build a more comprehensive understanding of how microbial exposures vary across the globe and in response to climate change. IMPORTANCE A broad diversity of microorganisms can be found in dust, with some of these microorganisms capable of causing allergenic disease in human via inhalation or affecting plant health by acting as plant pathogens. However, the spatial variation in dust microbiomes and the environmental factors associated with this variation have not been comprehensively assessed at the global scale. Here, we investigated the bacteria and fungi found in outdoor settled dust samples spanning 33 countries and 6 continents. Our results show that dust-associated bacteria and fungi exhibit climate-driven variability in community composition at the global scale. Our results call for the development of strategies to predict the geographic distribution of dust-associated microorganisms and to identify the potential associations between microbial exposures and the health of humans, animals, and plants.

The purpose of the present study aims to develop a satisfactory model for predicting pro-social and pro-cognitive effects on azinesulfonamides of cyclic amine derivatives as potential antipsychotics. The three dimensional-quantitative structure affinity relationship (3D-QSAR) study was performed on a series of azinesulfonamides of cyclic amine derivative using comparative molecular similarity indices analysis (CoMSIA). The best statistical model of CoMSIA q2, r2, SEE and F values are 0.664, 0.973, 0.087, and 82.344, respectively. Based on the model contour maps and the highest activity structure of the 43rd compound, serial new structures were designed and the 43k1 compound was selected as the best structure. The dock results showed a good binding of 43k1 with the protein (PDB ID: 6A93). The QSAR model analysis of the contour maps can help us to provide guidelines for finding novel potential antipsychotics.

Background: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer.Methods: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558.Findings: the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites.Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics.

Abstract Background In this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated. Methods The ultrasound images of 276 patients were retrospectively selected. The diagnoses of the radiologists were determined according to the Thyroid Imaging Reporting and Data System; the images were automatically recognized and diagnosed by the established artificial intelligence system. Pathological diagnosis was the gold standard for the final diagnosis. The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared. Results The artificial intelligence diagnosis system correctly identified the lesion area, with an area under the receiver operating characteristic (ROC) curve of 0.902, which is higher than that of the radiologists (0.859). This finding indicates a higher diagnostic accuracy (p = 0.0434). The sensitivity, positive predictive value, negative predictive value, and accuracy of the artificial intelligence diagnosis system for the diagnosis of malignant thyroid nodules were 90.5%, 95.22%, 80.99%, and 90.31%, respectively, and the performance did not significantly differ from that of the radiologists (p > 0.05). The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98%, p = 0.026). Conclusions Compared with the performance of experienced radiologists, the artificial intelligence system has comparable sensitivity and accuracy for the diagnosis of malignant thyroid nodules and better diagnostic ability for benign thyroid nodules. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules.

This study describes the dynamics and complexity of microbial communities producing hydrogen-rich fermentation gas from sugar-beet molasses in five packed-bed reactors (PBRs). The bioreactors constitute a part of a system producing hydrogen from the by-products of the sugar-beet industry that has been operating continuously in one of the Polish sugar factories. PBRs with different working volumes, packing materials, construction and inocula were tested. This study focused on analysis (based on 16S rRNA profiling and shotgun metagenomics sequencing) of the microbial communities selected in the PBRs under the conditions of high (>100 cm3/g COD of molasses) and low (

In order to improve the seismic performance of long-span double deck steel truss continuous girder bridge, taking Dao Qing Chau Bridge in Fuzhou as an engineering background, the isolation scheme of friction pendulum bearing (FPB) and friction pendulum bearing combined with viscous dampers is applied to study seismic performance. A three-dimensional dynamic model of the bridge is established using SAP2000. Taking three artificial seismic waves as seismic excitation, the seismic response of the seismic structure is calculated by nonlinear time history integration, and is then compared with the seismic response of the seismic reduction and isolation structure. The results show that the friction pendulum bearing (FPB) scheme and combined seismic dissipation and isolation (CSDI) scheme show a good seismic dissipation and isolation effect and ensure the safety of the bridge structure. However, for whole-bridge isolation, friction pendulum bearing (FPB) will produce certain residual deformations and additional stress of the bearing under the conditions of temperature and external load. For the purpose of protecting the bearing, it is recommended to use the combined seismic dissipation and isolation (CSDI) scheme.

Ferroptosis is a newly discovered form of non-apoptotic regulated cell death and is characterized by iron-dependent and lipid peroxidation. Due to the enhanced dependence on iron in cancer cells, induction of ferroptosis is becoming a promising therapeutic strategy. However, the precise underlying molecular mechanism and regulation process of ferroptosis remains largely unknown. In the present study, we demonstrate that the protein Frataxin (FXN) is a key regulator of ferroptosis by modulating iron homeostasis and mitochondrial function. Suppression of FXN expression specifically repressed the proliferation, destroyed mitochondrial morphology, impeded Fe–S cluster assembly and activated iron starvation stress. Moreover, suppression of FXN expression significantly enhanced erastin-induced cell death through accelerating free iron accumulation, lipid peroxidation and resulted in dramatic mitochondria morphological damage including enhanced fragmentation and vanished cristae. In addition, this type of cell death was confirmed to be ferroptosis, since it could be pharmacologically restored by ferroptotic inhibitor Fer-1 or GSH, but not by inhibitors of apoptosis, necrosis. Vice versa, enforced expression of FXN blocked iron starvation response and erastin-induced ferroptosis. More importantly, pharmacological or genetic blocking the signal of iron starvation could completely restore the resistance to ferroptosis in FXN knockdown cells and xenograft graft in vivo. This paper suggests that FXN is a novel ferroptosis modulator, as well as a potential provided target to improve the antitumor activity based on ferroptosis.

Urban development is one of the leading causes of biodiversity change. Understanding how soil microorganisms respond to urbanization is particularly important because they are crucial for the provisioning of ecosystem functions and services. Here, we collected monthly soil samples over one year across three locations representing an urbanization gradient (low-moderate-high) in the arid Southwestern USA, and we characterized their microbial communities using marker gene sequencing. Our results showed that microbial richness and community composition exhibited nonsignificant changes over time regardless of the location. Soil fungal richness was lower in moderately and highly urbanized locations, but soil bacterial/archaeal richness was not significantly different among locations. Both bacteria/archaea and fungi exhibited significant differences in community composition across locations. After inferring potential functional groups, soils in the highly urbanized location had lower proportions of arbuscular mycorrhizal fungi and soil saprotrophic fungi but had higher proportions of bacterial taxa involved in aromatic compound degradation, human pathogens, and intracellular parasites. Furthermore, ammonia-oxidizing bacteria were more abundant in the highly urbanized location, but ammonia-oxidizing archaea were more abundant in lowly and moderately urbanized locations. Together, these results highlight the significant changes in belowground microbial communities across an urbanization gradient, and these changes might have important implications for aboveground–belowground interactions, nutrient cycling, and human health.

Autoimmune diseases can be chronic with relapse of inflammatory symptoms, but it can be also acute and life-threatening if immune cells destroy life-supporting organs, such as lupus nephritis. The etiopathogenesis of autoimmune diseases has been revealed as that genetics and environmental factors-mediated dysregulated immune responses contribute to the initiation and development of autoimmune disorders. However, the current understanding of pathogenesis is limited and the underlying mechanism has not been well defined, which lows the development of novel biomarkers and new therapeutic strategies for autoimmune diseases. To improve this, broadening and deepening our understanding of pathogenesis is an unmet need. As genetic susceptibility cannot explain the low accordance rate of incidence in homozygous twins, epigenetic regulations might be an additional explanation. Therefore, this review will summarize current progress of studies on epigenetic dysregulations contributing to autoimmune diseases, including SLE, rheumatoid arthritis (RA), psoriasis, type 1 diabetes (T1D), and systemic sclerosis (SSc), hopefully providing opinions on orientation of future research, as well as discussing the clinical utilization of potential biomarkers and therapeutic strategies for these diseases.

Plant pathogens are increasingly considered as important agents in promoting plant coexistence, while plant symbionts like ectomycorrhizal fungi (EMF) can facilitate plant dominance by helping conspecific individuals to defend against plant pathogens. However, we know little about their relationships with plants at large scales. Here, using soil fungal data collected from 28 forest reserves across China, we explored the latitudinal diversity gradients of overall fungi and different fungal functional guilds, including putative plant pathogens, EMF, and saprotrophic fungi. We further linked the spatial patterns of alpha diversities of putative plant pathogens and EMF to the variation of plant species richness. We found that the relationships between latitude and alpha diversities of putative plant pathogens and EMF were region-dependent with sharp diversity shifts around the mid-latitude (~35°N), which differed from the unimodal diversity distributions of the overall and saprotrophic fungi. The variations in the diversities of putative plant pathogens and EMF were largely explained by the spatial regions (south vs. north/subtropical zone vs. temperate zone). Additionally, the alpha diversities of these two fungal guilds exhibited opposing trends across latitude. EMF could alter the relationship between diversities of putative plant pathogens and plants in the south/subtropical region, but not vice versa. We also found that the ratio of their alpha diversities (EMF to putative plant pathogens) was negatively related to plant species richness across the spatial regions (north to south), and explained ~10% of the variation of plant species richness. Overall, our findings suggest that plant-microbe interactions not only shape the local plant diversity but also may have non-negligible contributions to the large-scale patterns of plant diversity in forest ecosystems.

Autophagy is a complicated cellular mechanism that maintains cellular and tissue homeostasis and integrity via degradation of senescent, defective subcellular organelles, infectious agents, and misfolded proteins. Accumulating evidence has shown that autophagy is involved in numerous immune processes, such as removal of intracellular bacteria, cytokine production, autoantigen presentation, and survival of lymphocytes, indicating an apparent and important role in innate and adaptive immune responses. Indeed, in genome-wide association studies, autophagy-related gene polymorphisms have been suggested to be associated with the pathogenesis of several autoimmune and inflammatory disorders, such as systemic lupus erythematosus, psoriasis, rheumatoid arthritis, inflammatory bowel disease, and multiple sclerosis. In addition, conditional knockdown of autophagy-related genes in mice displayed therapeutic effects on several autoimmune disease models by reducing levels of inflammatory cytokines and autoreactive immune cells. However, the inhibition of autophagy accelerates the progress of some inflammatory and autoimmune diseases via promotion of inflammatory cytokine production. Therefore, this review will summarize the current knowledge of autophagy in immune regulation and discuss the therapeutic and pathogenic role of autophagy in autoimmune diseases to broaden our understanding of the etiopathogenesis of autoimmune diseases and shed light on autophagy-mediated therapies.

Understanding microbial interactions is essential to decipher the mechanisms of community assembly and their effects on ecosystem functioning, however, the conservation of species- and trait-based network interactions along environmental gradient remains largely unknown. Here, by using the network-based analyses with three paralleled data sets derived from 16S rRNA gene pyrosequencing, functional microarray, and predicted metagenome, we test our hypothesis that the network interactions of traits are more conserved than those of taxonomic measures, with significantly lower variation of network characteristics along the environmental gradient in acid mine drainage. The results showed that although the overall network characteristics remained similar, the structural variation was significantly lower at trait levels. The higher conserved individual node topological properties at trait level rather than at species level indicated that the responses of diverse traits remained relatively consistent even though different species played key roles under different environmental conditions. Additionally, the randomization tests revealed that it could not reject the null hypothesis that species-based correlations were random, while the tests suggested that correlation patterns of traits were non-random. Furthermore, relationships between trait-based network characteristics and environmental properties implied that trait-based networks might be more useful in reflecting the variation of ecosystem function. Taken together, our results suggest that deterministic trait-based community assembly results in greater conservation of network interaction, which may ensure ecosystem function across environmental regimes, emphasizing the potential importance of measuring the complexity and conservation of network interaction in evaluating the ecosystem stability and functioning.

A recently proposed product quantization method is efficient for large scale approximate nearest neighbor search, however, its performance on unstructured vectors is limited. This paper introduces residual vector quantization based approaches that are appropriate for unstructured vectors. Database vectors are quantized by residual vector quantizer. The reproductions are represented by short codes composed of their quantization indices. Euclidean distance between query vector and database vector is approximated by asymmetric distance, i.e., the distance between the query vector and the reproduction of the database vector. An efficient exhaustive search approach is proposed by fast computing the asymmetric distance. A straight forward non-exhaustive search approach is proposed for large scale search. Our approaches are compared to two state-of-the-art methods, spectral hashing and product quantization, on both structured and unstructured datasets. Results show that our approaches obtain the best results in terms of the trade-off between search quality and memory usage.

This paper focuses on online scene learning and fast camera relocalisation which are two key problems currently limiting the performance of wide area augmented reality systems. Firstly, we propose to use adaptive random trees to deal with the online scene learning problem. The algorithm can provide more accurate recognition rates than traditional methods, especially with large scale workspaces. Secondly, we use the enhanced PROSAC algorithm to obtain a fast camera relocalisation method. Compared with traditional algorithms, our method can significantly reduce the computation complexity, which facilitates to a large degree the process of online camera relocalisation. Finally, we implement our algorithms in a multithreaded manner by using a parallel-computing scheme. Camera tracking, scene mapping, scene learning and relocalisation are separated into four threads by using multi-CPU hardware architecture. While providing real-time tracking performance, the resulting system also possesses the ability to track multiple maps simultaneously. Some experiments have been conducted to demonstrate the validity of our methods.

Negative emotions of drivers may lead to some dangerous driving behaviors, which in turn lead to serious traffic accidents. However, most of the current studies on driver emotions use a single modality, such as EEG, eye trackers, and driving data. In complex situations, a single modality may not be able to fully consider a driver’s complete emotional characteristics and provides poor robustness. In recent years, some studies have used multimodal thinking to monitor single emotions such as driver fatigue and anger, but in actual driving environments, negative emotions such as sadness, anger, fear, and fatigue all have a significant impact on driving safety. However, there are very few research cases using multimodal data to accurately predict drivers’ comprehensive emotions. Therefore, based on the multi-modal idea, this paper aims to improve drivers’ comprehensive emotion recognition. By combining the three modalities of a driver’s voice, facial image, and video sequence, the six classification tasks of drivers’ emotions are performed as follows: sadness, anger, fear, fatigue, happiness, and emotional neutrality. In order to accurately identify drivers’ negative emotions to improve driving safety, this paper proposes a multi-modal fusion framework based on the CNN + Bi-LSTM + HAM to identify driver emotions. The framework fuses feature vectors of driver audio, facial expressions, and video sequences for comprehensive driver emotion recognition. Experiments have proved the effectiveness of the multi-modal data proposed in this paper for driver emotion recognition, and its recognition accuracy has reached 85.52%. At the same time, the validity of this method is verified by comparing experiments and evaluation indicators such as accuracy and F1 score.

Paclobutrazol (PBZ) is a plant growth inhibitor and fungicide, but it is also carcinogenic and teratogenic, and has potential harm to human health. In this study, two PBZ haptens (PBZ-1, PBZ-2) were synthesized and conjugated with carrier proteins to get artificial antigens. A highly specific monoclonal antibody (mAb) against PBZ was prepared. The antibody subtype was IgG1 and the concentration was 11.03 mg/mL. A sensitive and rapid time-resolved fluorescence microsphere lateral flow immunoassay (TRFMs-LFIA) was established based on the mAb. The activated pH, the mAbs diluents, the mAb reacting concentration and the probe amount were optimized. The visual limit of detection (vLOD) and quantitative limit of detection (qLOD) of the TRFMs-LFIA for PBZ were 50 and 1.72 ng/mL respectively, and the 50% inhibiting concentration (IC50) was 9.38 ng/mL. The pretreatment procedures are simple and rapid, and the detection time of TRFMs-LFIA strip is 6 min. Qualitative and quantitative analysis of PBZ could be achieved under a UV light or with a portable fluorescence immunoassay analyzer. The average recovery rates ranged from 96.2% to 111.9% and the corresponding coefficients of variation (CV) were 4.0%–11.2% in spiked wheat and rice samples. Twenty real wheat and rice samples were measured by the TRFMs-LFIA and compared with Ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS). The measured values showed a good accordance. These results indicated that the proposed assay will provide a novel effective strategy for on-site detection of PBZ.

A novel and efficient immunoaffinity column (IAC) based on bispecific monoclonal antibody (BsMAb) recognizing aflatoxin B1 (AFB1) and ochratoxin A (OTA) was prepared and applied in simultaneous extraction of AFB1 and OTA from food samples and detection of AFB1/OTA combined with ic-ELISA (indirect competitive ELISA). Two deficient cell lines, hypoxanthine guanine phosphoribosyl-transferase (HGPRT) deficient anti-AFB1 hybridoma cell line and thymidine kinase (TK) deficient anti-OTA hybridoma cell line, were fused to generate a hybrid-hybridoma producing BsMAb against AFB1 and OTA. The subtype of the BsMAb was IgG1 via mouse antibody isotyping kit test. The purity and molecular weight of BsMAb were confirmed by SDS-PAGE method. The cross-reaction rate with AFB2 was 37%, with AFG1 15%, with AFM1 48%, with AFM2 10%, and with OTB 36%. Negligible cross-reaction was observed with other tested compounds. The affinity constant (Ka) was determined by ELISA. The Ka (AFB1) and Ka (OTA) was 2.43 × 108 L/mol and 1.57 × 108 L/mol, respectively. Then the anti-AFB1/OTA BsMAb was coupled with CNBr-Sepharose, and an AFB1/OTA IAC was prepared. The coupling time and elution conditions of IAC were optimized. The coupling time was 1 h with 90% coupling rate, the eluent was methanol–water (60:40, v:v, pH 2.3) containing 1 mol/L NaCl, and the eluent volume was 4 mL. The column capacities of AFB1 and OTA were 165.0 ng and 171.3 ng, respectively. After seven times of repeated use, the preservation rates of column capacity for AFB1 and OTA were 69.3% and 68.0%, respectively. The ic-ELISA for AFB1 and OTA were applied combined with IAC. The IC50 (50% inhibiting concentration) of AFB1 was 0.027 ng/mL, the limit of detection (LOD) was 0.004 ng/mL (0.032 µg/kg), and the linear range was 0.006 ng/mL~0.119 ng/mL. The IC50 of OTA was 0.878 ng/mL, the LOD was 0.126 ng/mL (1.008 µg/kg), and the linear range was 0.259 ng/mL~6.178 ng/mL. Under optimum conditions, corn and wheat samples were pretreated with AFB1-OTA IAC. The recovery rates of AFB1 and OTA were 95.4%~105.0% with ic-ELISA, and the correlations between the detection results and LC-MS were above 0.9. The developed IAC combined with ic-ELISA is reliable and could be applied to the detection of AFB1 and OTA in grains.

In this study, forchlorfenuron (CPPU) was coupled with succinic anhydride to yield a CPPU hapten (CPPU-COOH), and a high-affinity monoclonal antibody (mAb) that can specifically recognize CPPU was produced. Using this mAb as a recognition reagent, a sensitive indirect competitive enzyme-linked immunosorbent assay (icELISA) for CPPU was optimized, which exhibits an IC50 of 1.04 ng/mL, a limit of detection of 0.16 ng/mL, and a linear range of 0.31–3.43 ng/mL for CPPU. Cross-reactivity percentages with six analogues were all below 6%. The average recovery rates for cucumber and orange samples were from 85.23% to 119.14%. The analysis results of this icELISA showed good consistency with those from liquid chromatography mass spectrometry. These results suggest that the proposed icELISA provides a sensitive, specific, and reliable strategy for CPPU detection in food samples.