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Paper-based materials made from cellulose have been sought after as a sustainable and inexpensive packaging option. However, the porous structure and high hydrophilicity of paper-based materials result in inadequate water and oil repellency, as well as a limited water vapor barrier. In this work, lignin nanoparticles (LNPs) were prepared using a high-speed homogenizer, and subsequently coated on base paper along with cationic starch to enhance its multi-barrier performance to facilitate the packaging application. The LNPs obtained through such a facile process formed stable colloidal dispersion in water, which exhibited excellent interfacial compatibility with cationic starch. During the coating process, a highly adhesive emulsion consisting of cationic starch and LNPs were coated on the surface of base paper, imparting good hydrophobic properties to the paper. The resulting paper material exhibited good water resistance (Cobb value of 37.5 g m−2), high oil resistance (Kit rating 9) and tensile strength (48.93 MPa). The reduction in water vapor transmission rate (WVTR) exceeds sixfold. This study provides a new avenue for the application of lignin in high-barrier, fluorine-free, water-and oil-resistant packaging materials.

Background: Bone marrow mesenchymal stem cells (BMSCs) have immense potential in applications for the enhancement of tendon-bone (T-B) healing. Recently, it has been well-reported that skeletal stem cells (SSCs) could induce bone and cartilage regeneration. Therefore, SSCs represent a promising choice for cell-based therapies to improve T-B healing. In this study, we aimed to compare the therapeutic potential of SSCs and BMSCs for tendon-bone healing. Methods: SSCs and BMSCs were isolated by flow cytometry, and their proliferation ability was measured by CCK-8 assay. The osteogenic, chondrogenic, and adipogenic gene expression in cells was detected by quantitative real-time polymerase chain reaction (qRT-PCR). C57BL/6 mice underwent unilateral supraspinatus tendon detachment and repair, and the mice were then randomly allocated to 4 groups: control group (tendon-bone interface without any treatment), hydrogel group (administration of blank hydrogel into the tendon-bone interface), hydrogel + BMSCs group (administration of hydrogel with BMSCs into the tendon-bone interface), and hydrogel + SSCs group (administration of hydrogel with SSCs into the tendon-bone interface). Histological staining, Micro-computed tomography (Micro-CT) scanning, biomechanical testing, and qRT-PCR were performed to assay T-B healing at 4 and 8 weeks after surgery. Results: SSCs showed more cell proportion, exhibited stronger multiplication capacity, and expressed higher osteogenic and chondrogenic markers and lower adipogenic markers than BMSCs. In vivo assay, the SSCs group showed a better-maturated interface which was characterized by richer chondrocytes and more proteoglycan deposition, as well as more newly formed bone at the healing site and increased mechanical properties when compared to other there groups. qRT-PCR analysis revealed that the healing interface in the SSCs group expressed more transcription factors essential for osteogenesis and chondrogenesis than the interfaces in the other groups. Conclusions: Overall, the results demonstrated the superior therapeutic potential of SSCs over BMSCs in tendon-bone healing. The translational potential of this article: This current study provides valuable insights that SSCs may be a more effective cell therapy for enhancing T-B healing compared to BMSCs.

As the main by-product of rice processing, rice bran is rich in many nutriments, such as oil, protein, and bioactive substances. The protein content in rice bran accounts for about 15%~17% of rice bran, which is divided into albumin, globulin, gliadin and glutenin with the amino acid composition close to the pattern recommended by FAO/WHO. It is a high-quality plant protein source with low allergenicity and high bio-efficacy. The acquisition of rice bran protein can increase the added value of rice and has broad application prospects in the fields of food and medicine. Therefore, this paper reviewes the extraction methods, functional properties and functional complexes of rice bran protein, and the application status of rice bran protein and its complexes in the fields of bioactive peptides, infant food, food additives, nutrition delivery system are also introduced. Finally, the challenge of future research on rice bran protein is proposed. This review provides certain ideas and references for the industrial production and high-value utilization of rice bran protein.

Abstract Background Air exposure is an inevitable source of stress that leads to significant mortality in Coilia nasus. Our previous research demonstrated that adding 10‰ NaCl to aquatic water could enhance survival rates, albeit the molecular mechanisms involved in air exposure and salinity mitigation remained unclear. Conversely, salinity mitigation resulted in decreased plasma glucose levels and improved antioxidative activity. To shed light on this phenomenon, we characterized the transcriptomic changes in the C. nasus brain upon air exposure and salinity mitigation by integrated miRNA-mRNA analysis. Results The plasma glucose level was elevated during air exposure, whereas it decreased during salinity mitigation. Antioxidant activity was suppressed during air exposure, but was enhanced during salinity mitigation. A total of 629 differentially expressed miRNAs (DEMs) and 791 differentially expressed genes (DEGs) were detected during air exposure, while 429 DEMs and 1016 DEGs were identified during salinity mitigation. GO analysis revealed that the target genes of DEMs and DEGs were enriched in biological process and cellular component during air exposure and salinity mitigation. KEGG analysis revealed that the target genes of DEMs and DEGs were enriched in metabolism. Integrated analysis showed that 24 and 36 predicted miRNA-mRNA regulatory pairs participating in regulating glucose metabolism, Ca2+ transport, inflammation, and oxidative stress. Interestingly, most of these miRNAs were novel miRNAs. Conclusion In this study, substantial miRNA-mRNA regulation pairs were predicted via integrated analysis of small RNA sequencing and RNA-Seq. Based on predicted miRNA-mRNA regulation and potential function of DEGs, miRNA-mRNA regulatory network involved in glucose metabolism and Ca2+ transport, inflammation, and oxidative stress in C. nasus brain during air exposure and salinity mitigation. They regulated the increased/decreased plasma glucose and inhibited/promoted antioxidant activity during air exposure and salinity mitigation. Our findings would propose novel insights to the mechanisms underlying fish responses to air exposure and salinity mitigation.

Patients with the target gene mutation frequently derive significant clinical benefits from target therapy. However, differences in the abundance level of mutations among patients resulted in varying survival benefits, even among patients with the same target gene mutations. Currently, there is a lack of rational and interpretable models to assess the risk of treatment failure. In this study, we investigated the underlying coupled factors contributing to variations in medication sensitivity and established a statistically interpretable framework, named SAFE-MIL, for risk estimation. We first constructed an effectiveness label for each patient from the perspective of exploring the optimal grouping of patients’ positive judgment values and sampled patients into 600 and 1,000 groups, respectively, based on multi-instance learning (MIL). A novel and interpretable loss function was further designed based on the Hosmer-Lemeshow test for this framework. By integrating multi-instance learning with the Hosmer-Lemeshow test, SAFE-MIL is capable of accurately estimating the risk of drug treatment failure across diverse patient cohorts and providing the optimal threshold for assessing the risk stratification simultaneously. We conducted a comprehensive case study involving 457 non-small cell lung cancer patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors. Results demonstrate that SAFE-MIL outperforms traditional regression methods with higher accuracy and can accurately assess patients’ risk stratification. This underscores its ability to accurately capture inter-patient variability in risk while providing statistical interpretability. SAFE-MIL is able to effectively guide clinical decision-making regarding the use of drugs in targeted therapy and provides an interpretable computational framework for other patient stratification problems. The SAFE-MIL framework has proven its effectiveness in capturing inter-patient variability in risk and providing statistical interpretability. It outperforms traditional regression methods and can effectively guide clinical decision-making in the use of drugs for targeted therapy. SAFE-MIL offers a valuable interpretable computational framework that can be applied to other patient stratification problems, enhancing the precision of risk assessment in personalized medicine. The source code for SAFE-MIL is available for further exploration and application at https://github.com/Nevermore233/SAFE-MIL.

ABSTRACT In order to provide a highly feasible research pathway for the control of larch shoot blight, healthy larch branches and leaves were collected from 13 sampling sites in 8 provinces in China. The antagonistic endophytic bacteria obtained from the screening were used to carry out disease control experiments in potted seedlings. The safety evaluation test was conducted on the antagonistic bacteria. Subsequently, the strains with better preventive effect and high safety were identified by morphological and molecular methods. A total of 391 strains of endophytic bacteria were isolated from healthy larch branches and leaves. Seventy-eight strains of larch endophytic bacteria with antagonistic effect were obtained by primary sieving. Ten strains of endophytic bacteria with obvious antagonism were further obtained by secondary sieving, and all of them had an inhibition rate of more than 57%. Among them, strains YN 2, JL 6, NMG 23, and JL 54 showed the highest inhibition rate of 63.16%–65.08%, which was significantly different from the other treatments. The results of the pot test showed that 14 days after inoculation with the pathogen, strains YN 2 and JL 54 were more effective in the control of larch shoot blight, with the preventive effects reaching 57.7% and 50.0%, respectively. Strains JL 6 and JL 54 were biologically safe in the safety evaluation test. Therefore, strain JL 54 was selected for identification. It was identified as Bacillus amyloliquefaciens through morphological observation, 16S rDNA sequence, gyrB gene sequence and 16S rDNA-gyrB tandem feature sequence analysis.IMPORTANCELarch shoot blight is a widely distributed, damaging, and rapidly spreading fungal disease of forest trees that poses a serious threat to larch plantations. Endophytic bacteria have biological effects on host plants against pests and diseases, and they have a growth-promoting effect on plants. In this paper, we investigated for the first time the biocontrol effect of endophytic bacteria on larch shoot blight by screening endophytic bacteria with the function of antagonizing dieback fungi. Bacillus amyloliquefaciens JL 54 has a better prospect of biocontrol against larch shoot blight, which lays the foundation for the application of this bacterium in the future.

ObjectiveParkinson’s disease (PD) is the second most common neurodegenerative disease in the world, and type 2 diabetes (T2DM) and PD are influenced by common genetic and environmental factors. Mitochondrial dysfunction and inflammation are common pathogenic mechanisms of both diseases. However, the close association between PD and T2DM and the specific relationship between them are not yet clear. This study aimed to reveal the specific connection between the two diseases by establishing a mouse model of comorbid PD and T2DM, as well as a Bv2 cell model.MethodsC57BL/6 mouse were used to construct a model of PD with T2DM using streptozotocin and rotenone, while Bv2 cells were used to simulate the microenvironment of PD and T2DM using rotenone and palmitate. Behavioral tests were conducted to assess any differences in motor and cognitive functions in mouse. Immunohistochemistry was used to analyze the number of dopaminergic neurons in the substantia nigra region of mouse. Western blotting was used to detect the expression levels of TH, P-NFκB, NFκB, Cyclic GMP-AMP synthase (cGAS), and Stimulator of interferon genes (STING) proteins in the substantia nigra region of mouse and Bv2 cells. qRT-PCR was used to analyze the expression levels of IL1β, IL6, and TNF-α. Seahorse technology was used to assess mitochondrial function in Bv2 cells.Results:T2DM exacerbated the motor and cognitive symptoms in mouse with PD. This effect may be mediated by disrupting mitochondrial function in microglial cells, leading to damaged mtDNA leakage into the cytoplasm, subsequently activating the cGAS-STING pathway and downstream P-NFκB/NFκB proteins, triggering an inflammatory response in microglial cells. Microglial cells release inflammatory factors such as IL1β, IL6, and TNF-α, exacerbating neuronal damage caused by PD.ConclusionOur study results suggest that T2DM may exacerbate the progression of PD by damaging mitochondrial function, and activating microglial cell inflammation. The detrimental effects on Parkinson’s disease may be achieved through the activating of the cGAS-STING protein pathway.

Fluoride contamination in drinking water poses a health threat. Activated carbon suffers from low fluoride adsorption capacity, which can be enhanced by loading with nitrogen-rich compounds. In this study, graphitic carbon nitride (g-C3N4) nanosheets were loaded onto activated carbon using ultrasonic irradiation to synthesize a g-C3N4-activated carbon composite. The ultrasonically synthesized composite exhibited significantly higher fluoride removal capacity of 92.7 mg/g compared to 2.1 mg/g for activated carbon alone. The adsorption followed the Langmuir isotherm model. Thermodynamic analysis revealed the adsorption was spontaneous and exothermic. Characterization confirmed the role of surface complexation between fluoride ions and C-N/CN groups in g-C3N4 for the enhanced adsorption. The composite achieved 82–90% defluoridation of real wastewater samples containing initially 8.7–12 mg/L fluoride. The composite could be regenerated using NaOH and reused for multiple cycles. The study demonstrates the potential of a simple ultrasonic irradiation method for synthesizing a g-C3N4-activated carbon composite with remarkably enhanced defluoridation capacity. The composite is promising for practical decentralized water defluoridation applications, especially in regions affected by high fluoride levels.

The rise of large-scale Transformers has led to challenges regarding computational costs and energy consumption. In this context, spiking neural networks (SNNs) offer potential solutions due to their energy efficiency and processing speed. However, the inaccuracy of surrogate gradients and feature space quantization pose challenges for directly training deep SNN Transformers. To tackle these challenges, we propose a method (called LDD) to align ANN and SNN features across different abstraction levels in a Transformer network. LDD incorporates structured feature knowledge from ANNs to guide SNN training, ensuring the preservation of crucial information and addressing inaccuracies in surrogate gradients through designing layer-wise distillation losses. The proposed approach outperforms existing methods on the CIFAR10 (96.1%), CIFAR100 (82.3%), and ImageNet (80.9%) datasets, and enables training of the deepest SNN Transformer network using ImageNet.

In the context of intensive urban development, urban complexes have emerged as crucial public spaces that address the needs of urban populations. However, current research on urban complexes is predominantly qualitative and lacks a rigorous scientific and quantitative analysis. Therefore, this study employs the analytic hierarchy process (AHP) to construct a standardized system encompassing five dimensions: spatial function, spatial perception, architectural style, surrounding environment, and energy-saving technology. The objective is to determine the weights of the indices that influence people’s use of urban complexes under the goal of “humanization”. Additionally, the study quantitatively analyzes key indices using spatial syntax and other analytical methods. Subsequently, we employ multi-criteria decision making (MCDM) analysis to examine three real-world cases in China, aiming to validate further the importance of the AHP + MCDM approach, which incorporates the TOPSIS method based on grey correlation. This methodology considers both the subjective factors of crowd evaluations of urban complex usage and the interrelationships among indicators, ensuring that the statistical calculations of the indicators remain objective and scientifically robust. The results indicate that (1) the degree of facility improvement has the greatest impact on the crowd’s use of urban complexes; (2) there is a discrepancy between the results of the TOPSIS method and the MCDM evaluation model, with the MCDM evaluation method aligning more closely with real-world scenarios; and (3) the Shanghai MOSCHINO received the highest evaluation score, while the Nanjing Central Emporium received the lowest. Finally, we discuss the experimental results and propose targeted strategies for optimizing the design of urban complexes to achieve the goal of “humanization”.

Aims: It has been established that mechanical stimulation benefits tendon-bone (T-B) healing, and macrophage phenotype can be regulated by mechanical cues; moreover, the interaction between macrophages and mesenchymal stem cells (MSCs) plays a fundamental role in tissue repair. This study aimed to investigate the role of macrophage-mediated MSC chondrogenesis in load-induced T-B healing in depth. Methods: C57BL/6 mice rotator cuff (RC) repair model was established to explore the effects of mechanical stimulation on macrophage polarization, transforming growth factor (TGF)-β1 generation, and MSC chondrogenesis within T-B enthesis by immunofluorescence and enzyme-linked immunosorbent assay (ELISA). Macrophage depletion was performed by clodronate liposomes, and T-B healing quality was evaluated by histology and biomechanics. In vitro, bone marrow-derived macrophages (BMDMs) were stretched with CELLOAD-300 load system and macrophage polarization was identified by flow cytometry and quantitative real-time polymerase chain reaction (qRT-PCR). MSC chondrogenic differentiation was measured by histochemical analysis and qRT-PCR. ELISA and qRT-PCR were performed to screen the candidate molecules that mediated the pro-chondrogenic function of mechanical stimulated BMDMs. Results: Mechanical stimulation promoted macrophage M2 polarization in vivo and in vitro. The conditioned media from mechanically stimulated BMDMs (MS-CM) enhanced MSC chondrogenic differentiation, and mechanically stimulated BMDMs generated more TGF-β1. Further, neutralizing TGF-β1 in MS-CM can attenuate its pro-chondrogenic effect. In vivo, mechanical stimulation promoted TGF-β1 generation, MSC chondrogenesis, and T-B healing, which were abolished following macrophage depletion. Conclusion: Macrophages subjected to appropriate mechanical stimulation could polarize toward the M2 phenotype and secrete TGF-β1 to promote MSC chondrogenesis, which subsequently augments T-B healing. Cite this article: Bone Joint Res 2023;12(3):219–230.

Background: It is well known that appropriate mechanical stimulation benefits tendon-bone (T-B) healing, however, the mechanisms behind this are still uncovered completely. Here, we aimed to explore whether the IL-4/JAK/STAT signaling pathway mediated macrophage polarization was involved in mechanical stimulation induced T-B healing. Method: C57BL/6 mice rotator cuff (RC) repair model was established, and the mice were randomly allocated to the following group. 1. Mice were allowed for free cage activities after surgery (FC group); 2. Mice received treadmill running initiated on postoperative day 7 (TR group); 3. Mice only received a local injection of hydrogel containing IL-4 neutralizing antibody without postoperative intervention (FC ​+ ​AF-404-SP group); 4. Mice received a local injection of hydrogel containing IL-4 neutralizing antibody and postoperative treadmill running (TR ​+ ​AF-404-SP group). The expression of IL-4 within supraspinatus tendon (SST) enthesis was measured by Enzyme-linked immunosorbent assay (ELISA). In addition, the activation of JAK/STAT signaling pathway in macrophages and identification of macrophage phenotype at the RC insertion site was detected by Flow cytometry and qRT-PCR. T-B healing quality in this RC repair model was evaluated by histological staining, Micro-computed tomography (Micro-CT) scanning, and biomechanical testing. Result: In this study, using the RC repair model, we confirmed that generation of IL-4, activation of the JAK/STAT signaling pathway in macrophages, the ability of macrophages to polarize towards M2 subtype, and T-B healing quality were significantly enhanced in TR group compared to FC group. When comparing FC ​+ ​AF-404-SP group with TR ​+ ​AF-404-SP group, it was found that the mechanical stimulation induced this effect was depleted following the blockade of the IL-4/JAK/STAT signaling pathway. Conclusion: Our finding suggested that mechanical stimulation could accelerate T-B healing via activating the IL-4/JAK/STAT signaling pathway that modulates macrophages to polarize towards M2 subtype. The translational potential of this article: This is the first study to reveal a significant role of mechanical stimulation in the IL-4/JAK/STAT signaling pathway activation and macrophage polarization during RC T-B healing, which highlights the IL-4/JAK/STAT signaling pathway as a potential target to mediate macrophage M2 polarization and improves T-B healing for RC repair.

Background: The repair of rotator cuff injury is affected by lifestyle and metabolic factors. Intermittent fasting (IF) can promote repair of damaged tissue by regulating intestinal flora, which provides an idea of therapy for rotator cuff injury. The aim of this study was to investigate the effects of fasting on rotator cuff repair after injury, and the role of intestinal flora or a single strain in this process. Methods: Mice underwent rotator cuff injury were treated with intermittent fasting or fed ad libitum. Fasting began one month before surgery and continued until euthanasia. Fresh feces were collected at 2 weeks before surgery, on the day of surgery, and 2, 4, 8 weeks postoperatively for 16S rRNA microbiome sequencing. Supraspinatus tendon-humerus ​(SSTH) complex was collected at 2, 4 and 8 weeks after surgery. Live parabacteroides distasonis (Parabacteroides distasonis) was used for repair of rotator cuff injury, with equal amount of pasteurized P. distasonis (KPD) or sterile anaerobic phosphate buffer saline (PBS) as control. Biomechanical, radiological, histological analysis were used to assess the effect of rotator cuff repair. Results: Biomechanical, radiological and histological analysis indicated that intermittent fasting significantly promoted the repair of rotator cuff injury in the early postoperative period (P ＜ 0.05), but significantly inhibited the repair of rotator cuff injury at 4 weeks postoperatively (P ＜ 0.05). 16S rRNA Microbiome sequencing result showed that P. distasonis was the species with the most obvious changes in intestinal flora of mice after fasting. The results of tensile test, X-ray analysis and histological analysis indicated that the live P. distasonis (LPD) significantly impaired the biomechanical properties, bone regeneration and fibrocartilage regeneration of enthesis postoperatively (P ＜ 0.05). Conclusion: Intermittent fasting promoted repair of rotator cuff injury in the early postoperative period by regulating the gut microbiota, in which P. distasonis played an important role. The translational potential of this article: Intermittent fasting (IF) may be a beneficial lifestyle for the repair of rotator cuff injury in the early postoperative period in clinical, and the influence of a certain strain on the repair of rotator cuff injury may also provide an idea for the treatment of rotator cuff injury in the future.

Open chromatin regions are the genomic regions associated with basic cellular physiological activities, while chromatin accessibility is reported to affect gene expressions and functions. A basic computational problem is to efficiently estimate open chromatin regions, which could facilitate both genomic and epigenetic studies. Currently, ATAC-seq and cfDNA-seq (plasma cell-free DNA sequencing) are two popular strategies to detect OCRs. As cfDNA-seq can obtain more biomarkers in one round of sequencing, it is considered more effective and convenient. However, in processing cfDNA-seq data, due to the dynamically variable chromatin accessibility, it is quite difficult to obtain the training data with pure OCRs or non-OCRs, and leads to a noise problem for either feature-based approaches or learning-based approaches. In this paper, we propose a learning-based OCR estimation approach with a noise-tolerance design. The proposed approach, named OCRFinder, incorporates the ideas of ensemble learning framework and semi-supervised strategy to avoid potential overfitting of noisy labels, which are the false positives on OCRs and non-OCRs. Compared to different noise control strategies and state-of-the-art approaches, OCRFinder achieved higher accuracies and sensitivities in the experiments. In addition, OCRFinder also has an excellent performance in ATAC-seq or DNase-seq comparison experiments.

Tumor mutation burden (TMB) is a widely recognized biomarker for predicting the efficacy of immunotherapy. However, its use still remains highly controversial. In this study, we examine the underlying causes of this controversy based on clinical needs. By tracing the source of the TMB errors and analyzing the design philosophy behind variant callers, we identify the conflict between the incompleteness of biostatistics rules and the variety of clinical samples as the critical issue that renders TMB an ambivalent biomarker. A series of experiments were conducted to illustrate the challenges of mutation detection in clinical practice. Additionally, we also discuss potential strategies for overcoming these conflict issues to enable the application of TMB in guiding decision-making in real clinical settings.

A high tumor mutation burden (TMB) is known to drive the response to immune checkpoint inhibitors (ICI) and is associated with favorable prognoses. However, because it is a one-dimensional numerical representation of non-synonymous genetic alterations, TMB suffers from clinical challenges due to its equal quantification. Since not all mutations elicit the same antitumor rejection, the effect on immunity of neoantigens encoded by different types or locations of somatic mutations may vary. In addition, other typical genomic features, including complex structural variants, are not captured by the conventional TMB metric. Given the diversity of cancer subtypes and the complexity of treatment regimens, this paper proposes that tumor mutations capable of causing various degrees of immunogenicity should be calculated separately. TMB should therefore, be segmented into more exact, higher dimensional feature vectors to exhaustively measure the foreignness of tumors. We systematically reviewed patients’ multifaceted efficacy based on a refined TMB metric, investigated the association between multidimensional mutations and integrative immunotherapy outcomes, and developed a convergent categorical decision-making framework, TMBserval (Statistical Explainable machine learning with Regression-based VALidation). TMBserval integrates a multiple-instance learning concept with statistics to create a statistically interpretable model that addresses the broad interdependencies between multidimensional mutation burdens and decision endpoints. TMBserval is a pan-cancer-oriented many-to-many nonlinear regression model with discrimination and calibration power. Simulations and experimental analyses using data from 137 actual patients both demonstrated that our method could discriminate between patient groups in a high-dimensional feature space, thereby rationally expanding the beneficiary population of immunotherapy.

Tuning the optical properties of carbon dots (CDs) and figuring out the mechanisms underneath the emissive phenomena have been one of the most cutting-edge topics in the development of carbon-based nanomaterials. Dual-emissive CDs possess the intrinsic dual-emission character upon single-wavelength excitation, which significantly benefits their multi-purpose applications. Explosive exploitations of dual-emissive CDs have been reported during the past five years. Nevertheless, there is a lack of a systematic summary of the rising star nanomaterial. In this review, we summarize the synthesis strategies and optical mechanisms of the dual-emissive CDs. The applications in the areas of biosensing, bioimaging, as well as photoelectronic devices are also outlined. The last section presents the main challenges and perspectives in further promoting the development of dual-emissive CDs. By covering the most vital publications, we anticipate that the review is of referential significance for researchers in the synthesis, characterization, and application of dual-emissive CDs.

PurposeThis study examined the role of Evergrande FC's club debt on the Chinese Super League (CSL)'s profitability from 2014 to 2019.MethodsWe extracted the financial statements of Evergrande FC and evaluated its correlation with the profitability of CSL and Evergrande Group, which serves as a direct indicator of commercial growth.ResultsThe association between Evergrande FC's net loss and gross debt and CSL's profitability is positive, strong (all correlation coefficients > .89), and statistically significant (all p

In 2014, the Chinese government unveiled the New Urbanization Plan and Document No. 46, which profoundly influenced the development trajectory of the regional economy and sports industry. Using the coupling coordination model, this study aimed to assess the development progress of the sports industry and urban clusters economy. This study sampled Greater Bay Area urban clusters (GBAUC) and Yangtze River Delta urban clusters (YRDUC). The statistics covered one year after the release of the policies to date. We developed a total of 15 macro indicators to evaluate the sports industry and urban cluster economy as two distinct, yet interdependent, economic systems. Using the entropy weight method, we determined the standardized values and weights for the two systems before calculating the coupling coordination degree (D). Between 2015 and 2021, the sampled sports industry and urban clusters economy exhibited coordinated high growth across all economic metrics, with multiple sports industry metrics exhibiting double-digit growth. In 2015, both showed extreme imbalance: D of GBAUC = 0.092, D of YRDUC = 0.091. In 2017, both improved to bare coordination: D of GBAUC = 0.600, D of YRDUC = 0.566. In 2019, both reached well coordination: D of GBAUC = 0.851, D of YRDUC = 0.814. By 2021, both achieved quality coordination: D of GBAUC = 0.990, D of YRDUC = 1. This study provides the first evidence from the sports industry that China's new urbanization model and Document No. 46 are highly effective for synergistic regional economic growth.

A lot of bioinformatics tools were released to detect structural variants from the sequencing data during the past decade. For a data analyst, a natural question is about the selection of a tool fits for the data. Thus, this study presents an automatic tool recommendation method to facilitate data analysis. The optimal variant calling tool was recommended from a set of state-of-the-art bioinformatics tools by given a sequencing data. This recommendation method was implemented under a meta-learning framework, identifying the relationships between data features and the performance of tools. First, the meta-features were extracted to characterize the sequencing data and meta-targets were identified to pinpoint the optimal caller for the sequencing data. Second, a meta-model was constructed to bridge the meta-features and meta-targets. Finally, the recommendation was made according to the evaluation from the meta-model. A series of experiments were conducted to validate this recommendation method on both the simulated and real sequencing data. The results revealed that different SV callers often fit different sequencing data. The recommendation accuracy averaged more than 80% across all experimental configurations, outperforming the random- and fixed-pick strategy. To further facilitate the research community, we incorporated the recommendation method into an online cloud services for genomic data analysis, which is available at https://c.solargenomics.com/via a simple registration. In addition, the source code and a pre-trained model is available at https://github.com/hello-json/CallerRecommendation for academic usages only.

There has been little research on the impact resistance of mortar–rock slope protection structures. To ensure that the mortar–rock interface has good adhesion properties under the action of impact loading, in this paper, based on fracture mechanics theory, a theoretical impact model was established for mortar–rock binary material. Dynamic fracture tests were carried out on mortar–rock interfaces using the split-Hopkinson pressure bar (SHPB) system. The Brazilian disc (CSTBD) specimen was prepared with one half in granite and the other half in mortar. The specimen used for the dynamic impact test was 48 mm in diameter and 25 mm thick. The effects caused by the change in interface inclination and interface shape on the dynamic fracture mode were discussed. The dynamic model parameters were obtained for different inclination angles and interfaces. The results show that both the interface inclination and interface shape have significant effects on the dynamic mechanical properties of the mortar–rock binary material. The fracture modes of the mortar–rock specimens can be classified into three types. When the interface inclination is 0°, the specimen shows shear damage with an interface fracture; when the interface inclination is in the range of 0–90°, the dynamic splitting strength of the mortar–rock material increases with increasing interface inclination, and the interface undergoes composite fracture; and when the interface inclination is 90°, the dynamic splitting strength of the specimen reaches its peak, and the interface undergoes tensile fracture. The mortar–rock interface damage follows the M-C criterion. The roughness of the interface shape has a large influence on the dynamic splitting strength of the specimens. The rougher the interface shape, the higher the interface cleavage strength and the higher the peak load that causes the material to damage. The results of this study can provide a reference for the design of mortar–rubble structures to meet the demand for impact resistance and have strong engineering application value.

Tumor mutation burden (TMB) is a widely recognized stratification biomarker for predicting the efficacy of immunotherapy; however, the number and universal definition of the categorizing thresholds remain debatable due to the multifaceted nature of efficacy and the imprecision of TMB measurements. We proposed a minimal joint p-value criterion from the perspective of differentiating the comprehensive therapeutic advantages, termed TMBcat, optimized TMB categorization across distinct cancer cohorts and surpassed known benchmarks. The statistical framework applies to multidimensional endpoints and is fault-tolerant to TMB measurement errors. To explore the association between TMB and various immunotherapy outcomes, we performed a retrospective analysis on 78 patients with non-small cell lung cancer and 64 patients with nasopharyngeal carcinomas who underwent anti-PD-(L)1 therapy. The stratification results of TMBcat confirmed that the relationship between TMB and immunotherapy is non-linear, i.e., treatment gains do not inherently increase with higher TMB, and the pattern varies across carcinomas. Thus, multiple TMB classification thresholds could distinguish patient prognosis flexibly. These findings were further validated in an assembled cohort of 943 patients obtained from 11 published studies. In conclusion, our work presents a general criterion and an accessible software package; together, they enable optimal TMB subgrouping. Our study has the potential to yield innovative insights into therapeutic selection and treatment strategies for patients.

Tumor mutation burden (TMB) is a recognized stratification biomarker for immunotherapy. Nevertheless, the general TMB-high threshold is unstandardized due to severe clinical controversies, with the underlying cause being inconsistency between multiple assessment criteria and imprecision of the TMB value. The existing methods for determining TMB thresholds all consider only a single dimension of clinical benefit and ignore the interference of the TMB error. Our research aims to determine the TMB threshold optimally based on multifaceted clinical efficacies accounting for measurement errors. We report a multi-endpoint joint model as a generalized method for inferring the TMB thresholds, facilitating consistent statistical inference using an iterative numerical estimation procedure considering mis-specified covariates. The model optimizes the division by combining objective response rate and time-to-event outcomes, which may be interrelated due to some shared traits. We augment previous works by enabling subject-specific random effects to govern the communication among distinct endpoints. Our simulations show that the proposed model has advantages over the standard model in terms of precision and stability in parameter estimation and threshold determination. To validate the feasibility of the proposed thresholds, we pool a cohort of 73 patients with non-small-cell lung cancer and 64 patients with nasopharyngeal carcinoma who underwent anti-PD-(L)1 treatment, as well as validation cohorts of 943 patients. Analyses revealed that our approach could grant clinicians a holistic efficacy assessment, culminating in a robust determination of the TMB screening threshold for superior patients. Our methodology has the potential to yield innovative insights into therapeutic selection and support precision immuno-oncology.

Plant cytochrome P450s play key roles in the diversification and functional modification of plant natural products. Although over 200,000 plant P450 gene sequences have been recorded, only seven crystalized P450 genes severely hampered the functional characterization, gene mining and engineering of important P450s. Here, we combined Rosetta homologous modeling and MD-based refinement to construct a high-resolution P450 structure prediction process (PCPCM), which was applied to 181 plant P450s with identified functions. Furthermore, we constructed a ligand docking process (PCPLD) that can be applied for plant P450s virtual screening. 10 examples of virtual screening indicated the process can reduce about 80% screening space for next experimental verification. Finally, we constructed a plant P450 database (PCPD: http://p450.biodesign.ac.cn/), which includes the sequences, structures and functions of the 181 plant P450s, and a web service based on PCPCM and PCPLD. Our study not only developed methods for the P450-specific structure analysis, but also introduced a universal approach that can assist the mining and functional analysis of P450 enzymes.

Abstract Cellulose nanocrystals (CNCs) are featured with low toxicity, non‐trivial biocompatibility, and cell membrane penetration capability, which allow the constructions of nanoplatforms for biosensing and in vivo imaging. Interfacing CNCs and fluorescent materials into sensors/probes is thus highly topical and has received tremendous interest. This review covers the development of CNC extraction methods and, in particular, their impacts on the surficial properties of CNCs. Whereafter, recently reported strategies for fluorescent functionalizations of CNCs are summarized based on chemical modification, physical adsorption, or in situ growth. Choosing the most suitable strategy, according to the properties of both CNCs and fluorophores, for constructing fluorescent CNCs is also emphasized. With regard to applications of the fluorescent CNCs, this work focuses on the studies which have involved but not been limited to metal ion sensing, physiological pH detection, cell imaging, and tumor antiproliferation. Being aware of the highly flexible construction, appealing structural/optical properties, and outstanding performances in analysis/imaging, it is believed that CNCs are bound to be increasingly investigated in the future and widely applied in the biomedical area.

Recent studies highlight the potential of T cell receptor (TCR) repertoires in accurately detecting cancers via noninvasive sampling. Unfortunately, due to the complicated associations among cancer antigens and the possible induced T cell responses, currently, the practical strategy for identifying cancer-associated TCRs is the computational prediction based on TCR repertoire data. Several state-of-the-art methods were proposed in recent year or two; however, the prediction algorithms were still weakened by two major issues. To facilitate the computational processes, the algorithms prefer to decompose the original TCR sequences into length-fixed amino acid fragments, while the first dilemma comes as the lengths of cancer-associated motifs are suggested to be various. Moreover, the correlations among TCRs in the same repertoire should be further considered, which are often ignored by the existing methods. We here developed a deep multi-instance learning method, named DeepLION, to improve the prediction of cancer-associated TCRs by considering these issues. First, DeepLION introduced a deep learning framework with alternative convolution filters and 1-max pooling operations to handle the amino acid fragments with different lengths. Then, the multi-instance learning framework modeled the TCR correlations and assigned adjusted weights for each TCR sequence during the predicting process. To validate the performance of DeepLION, we conducted a series of experiments on several cohorts of patients from nine cancer types. Compared to the existing methods, DeepLION achieved, on most of the cohorts, higher prediction accuracies, sensitivities, specificities, and areas under the curve (AUCs), where the AUC reached notably 0.97 and 0.90 for thyroid and lung cancer cohorts, respectively. Thus, DeepLION may further support the detection of cancers from TCR repertoire data. DeepLION is publicly available on GitHub, at https://github.com/Bioinformatics7181/DeepLION, for academic usage only.

The anti-ESD characteristic of the electronic system is paid more and more attention. Moreover, the on-chip electrostatic discharge (ESD) is necessary for integrated circuits to prevent ESD failures. In this paper, the mixed TCAD model of the ESD protection circuit is built and simulated, and the negative transmission line pulse (TLP) injection damage experiment is carried out on the CD4069UBC chip. The circuit model consists of three-dimensional shallow trench isolation (STI) diode TCAD models and a three-dimensional multi-gate Complementary Metal-Oxide-Semiconductor (CMOS) inverter TCAD model. Moreover, the TCAD modeling is based on a 0.25 μm technology node. Through the transient simulation of the electrothermal coupling, the electrical signal of the input and output nodes of the circuit and the distribution of the electrothermal parameters in the device are obtained. Moreover, by analyzing the simulation results, the physical phenomena and the mechanisms of interference and damage mechanism during TLP injection are explained. The location and type of diode damage in the TLP injection simulation results of the circuit model are consistent with the TLP experiment damage results. The proposed ESD protection circuit model and analysis method are beneficial to ESD robustness prediction and ESD soft damage analysis of IC.

To explore the effect of associated bacteria on the low-temperature adaptability of pinewood nematodes (PWNs), transcriptome sequencing (RNA-seq) of PWN AH23 treated with the associated bacterial strain Bacillus cereus GD1 was carried out with reference to the whole PWN genome. Bioinformatic software was utilized to analyze the differentially expressed genes (DEGs). This study was based on the analysis of DEGs to verify the function of daf-11 by RNAi. The results showed that there were 439 DEGs between AH23 treated with GD1 and those treated with ddH2O at 10 °C. There were 207 pathways annotated in the KEGG database and 48 terms annotated in the GO database. It was found that after RNAi of daf-11, the survival rate of PWNs decreased significantly at 10 °C, and fecundity decreased significantly at 15 °C. It can be concluded that the associated bacteria GD1 can enhance the expression of genes related to PWN low-temperature adaptation and improve their adaptability to low temperatures.

The early detection of Parkinson’s disease (PD) is a critical issue in terms of efficiency. Alpha-synuclein (α-Syn) is a biomarker in PD checks. Alpha-synuclein (α-syn) is the major constituent of Lewy bodies and a pathogenic hallmark of all synucleinopathies, including PDs, dementia with Lewy bodies, and multiple system atrophy. In this study, KCC-1-NH-CS2 was conjugated with biotinylated Ab and entrapped in P(β-CD) polymer cavities. Using this approach, a novel electrochemical label-free immunosensor was designed for the quantification of α-syn in real human samples. For this purpose, the glassy carbon electrode electropolymerized with P(β-CD) biopolymer provided an excellent matrix for entrapping of KCC-1-NH-CS2 loaded with the biotinylated antibody of α-syn. Using the chronoamperometric technique, the proposed immunosensor shows a suitable range of 0.02 to 64 ng/mL for the determination of α-syn. Additionally, a low limit of quantification of the engineered biosensor was obtained at 0.02 ng/mL. The developed immunosensor’s adequate stability, sensitivity, and selectivity, together with its ease of manufacture, make it a promising diagnostic technique for further research. This study also will pave the way for further applications of the synergetic effect of β-CD and KCC-1-NH-CS2 for biomedical analysis in the near future.

The gut microbiota is closely associated with the health of the host and is affected by many factors, including exercise. In this study, we compared the gut microbial changes and exercise performance over a 14-week period in mice that performed exercise (NE; n = 15) and mice that did not perform exercise (NC; n = 15). Mice were subjected to stool collection and exercise tests one week prior to adaptive training and after 2, 6, 10, and 14 weeks of exercise. Bacteria associated with the stool samples were assessed via Illumina-based 16S rRNA gene sequencing. While there was no significant difference in body weight between the groups (p > 0.05), the NE group had a significantly higher exercise performance from weeks 2–14 (p < 0.01) and lower fat coefficient (p < 0.01) compared with the NC group. The Shannon index of the gut microbiota in the NC group was higher than that in the NE group at weeks 6 and 10, and the Chao1 index was higher than that in the NE group at week 14. Exercise performance positively correlated with the relative abundance of Phascolarctobacterium. Grouped time series data analysis demonstrated that Bifidobacteria, Coprococcus, and one unnamed genus in the Clostridiales order were significantly increased in the NE group, which correlated with increased glucose, flavonoid, arginine, and proline metabolism. In conclusion, moderate-intensity treadmill exercise significantly increased the exercise performance of mice and changed the core bacteria and bacterial metabolic activity. These results provide a reference for studying the effects of exercise intervention and exercise performance on the gut microbiota of mice.

In this work, 2-AG was successfully detected in human plasma samples using a new sandwich-type electrochemical immune device based on poly-β-cyclodextrin P(β-CD) functionalized with AuNPs-DDT and toluidine blue. The P(β-CD) ensured the bioactivity and stability of the immobilized 2-AG antibody by providing a broad surface for the efficient immobilization of the biotinylated antibody. To complete the top section of the immunosensor (reporter), an HRP-conjugated antibody of 2-AG (secondary antibody (Ab2)) was attached to the surface of a glassy carbon electrode (GCE) modified by P(β-CD), as well as a primarily biotinylated antibody (Ab1). The biosensor fabrication process was monitored using field-emission scanning electron microscope (FE-SEM) and EDS methods. Using the differential pulse voltammetry technique, the immunosensor was utilized for detection of 2-AG in real samples. The suggested interface increased the surface area, which allowed for the immobilization of a large quantity of anti-2-AG antibody while also improving biocompatibility, stability, and electrical conductivity. Finally, the suggested immunosensor’s limit of quantitation was determined to be 0.0078 ng/L, with a linear range of 0.0078 to 1.0 ng/L. The results showed that the suggested bioassay can be utilized for diagnosis of 2-AG in clinical samples as a unique and ultrasensitive electrochemical biodevice.

This study examined the development conditions and functional properties of a novel compound tea Lithocarpus polystachyus (wall.) Rehd (L. polystachyus, LPR) black tea (LPRBT). The compound tea was developed by fermentation using fresh leaves (Camellia sinensis cv. Qianmei 601) as the main raw material with LPR powder as an additive. Based on the single factor and orthogonal tests with sensory scores as indicators, a withered leaves–LPR powder mass ratio of 9:1 with a 6 h fermentation time was determined to be the production condition of LPRBT with a sensory score of 89.09. In addition, phlorizin content, anti-oxidation function, hypoglycemic function, and tumor suppressor effect of LPRBT were measured. The results demonstrated that LPRBT phlorizin content was significantly higher than apple. It also showed that the equivalent 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) radical clearance rate with Vitamin C (Vc) and the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical clearance rate was 81% of Vc. Both hydroxyl and superoxide anion radical clearance increased with the increase in LPRBT amount. LPRBT also showed a good inhibitory effect on α-glucosidase and α-amylase, indicating certain hypoglycemic activity. Moreover, it inhibited the growth of HeLa and A549 cancer cells showing tumor suppressor activity. This study provides a reference for the development and application of LPR food products.

Increasing energy consumption has caused power systems to operate close to the limit of their capacity. The distributed power flow controller (DPFC), as a new member of distributed flexible AC transmission systems, is introduced to remove this barrier. This paper proposes an optimal DPFC configuration method to enhance system loadability considering economic performance based on mixed integer linear programming. The conflicting behavior of system loadability and DPFC investment is analyzed and optimal solutions are calculated. Thereafter, the fuzzy decision-making method is implemented for determining the most preferred solution. In the most preferred solution obtained, the investment of DPFCs is minimized to find the optimal number, locations and set points. Simulation results on the IEEE-RTS79 system demonstrate that the proposed method is effective and reasonable.

Abstract Knowledge of changes associated with the progression of growth‐related characteristics in rice plant is essential to make strategies for future breeding and crop management. However, thus far, little attention has been given to the identification of temporal changes of the traits concomitant with the year of release in rice genetic breeding. The objective of this study was to explore genetic gain for yield accrued in association with evolution of the growth phenotypes in a 2‐year field experiment conducted on the Yangtze River Basin of China. Fifteen rice (Oryza sativa L.) cultivars, including 5 super high‐yielding rice, bred or widely cultivated from 1936 to 2005, were grown in irrigated field conditions with appropriate doses of commercial fertilizers during the rice‐growing season from May to October in 2013 and 2014. In both years, SPAD reading was high during vegetative and early grain filling stages, but it declined sharply at maturity. In general, SPAD reading increased with the progression in year of release of the cultivars and it correlated with yield benefit accrued. Similar to SPAD, the evolutionary trend also accounted for significant increases in LAI (leaf area index) and SLW (specific leaf weight), CGR (crop growth rate), LAD (leaf area duration), S/LA (spikelets/leaf area), and NAR (net assimilation rate). All growth parameters, except NAR, correlated positively with grain yield. Conversely, K (extinction coefficient) exhibited significant decline along with year of release, meaning more erect canopies. Gain of SLW in the newer cultivars enabled better light energy interception probably with higher RUE, which associated with increased SPAD amounting to low K values. It was opined that the reduced K improved light distribution across the leaves, which contributed to a grain yield advantage through increase of sink size (S/LA) over the 70‐year period.

This paper presents a novel attitude control design, which combines L 1 adaptive control and backstepping control together, for Autonomous Underwater Vehicles (AUVs) in a highly dynamic and uncertain environment. The Euler angle representation is adopted in this paper to represent the attitude propagation. Kinematics and dynamics of the attitude are in the strict feedback form, which leads the backstepping control strategy serving as the baseline controller. Moreover, by bringing fast and robust adaptation into the backstepping control architecture, our controller is capable of dealing with time-varying uncertainties from modeling and external disturbances in dynamics. This attitude controller is proposed for coupled pitch-yaw channels. For inevitable roll excursions, a Lyapunov function-based optimum linearization method is presented to analyze the stability of the roll angle in the operation region. Theoretical analysis and simulation results are given to demonstrate the feasibility of the developed control strategy.

The inherent variability and randomness of large-scale wind power integration have brought great challenges to power flow control and dispatch. The distributed power flow controller (DPFC) has the higher flexibility and capacity in power flow control in the system with wind generation. This paper proposes a multi-time scale coordinated scheduling model with DPFC to minimize wind power spillage. Configuration of DPFCs is initially determined by stochastic method. Afterward, two sequential procedures containing day-head and real-time scales are applied for determining maximum schedulable wind sources, optimal outputs of generating units and operation setting of DPFCs. The generating plan is obtained initially in day-ahead scheduling stage and modified in real-time scheduling model, while considering the uncertainty of wind power and fast operation of DPFC. Numerical simulation results in IEEE-RTS79 system illustrate that wind power is maximum scheduled with the optimal deployment and operation of DPFC, which confirms the applicability and effectiveness of the proposed method.

The authors would like to make the following correction to their paper[1]. In this paper,we wrongly listed the coordinates of the new silicon allotrope [...]