Search

The Silk Road was an important channel for economic exchanges between China and foreign countries in ancient times, and glass products were an important physical evidence of trade exchanges. Early glass was introduced to China in the form of bead-shaped products, and on this basis, craftsmen improved it to form the unique ancient Chinese glass. Glass products are important physical evidence of the ancient Silken Road, but they are vulnerable to weathering due to the influence of the burial environment. In this paper, we use a batch of ancient glass artifacts as the research object, and build a machine learning classification model and a clustering model based on the different chemical composition content to analyze the classification basis and the relationship between the chemical composition content of the glass artifacts and optimize the research.

To explore the function and mechanism of lncRNA HOXA-AS2 in cancer-associated fibroblasts (CAFs)-derived exosomes in gallbladder cancer metastasis, and provide new research targets for the treatment of gallbladder cancer. At the same time, in order to clarify the early predictive value of lncRNA HOXA-AS2 for gallbladder cancer metastasis, and to provide a theoretical basis for clinical individualized treatment of gallbladder cancer. Methods. In our previous work, we used TCGA database analysis to find that lncRNA HOXA-AS2 was highly expressed in gallbladder cancer tissues compared with normal tissues. In this study, the expression levels of HOXA-AS2 in gallbladder cancer cell lines and control cells were first verified by QPCR and Western blot methods. Then, lentiviral tools were used to construct knockdown vectors (RNAi#1, RNAi#2) and negative control vectors targeting two different sites of HOXA-AS2, and the vectors were transfected into NOZ and OCUG-1 cells, respectively. Real-time PCR was used to detect knockdown efficiency. Then, the effects of silencing HOXA-AS2 on the proliferation, cell viability, cell migration, and invasion ability of gallbladder cancer cells were detected by MTT, plate cloning assay, Transwell migration chamber assay, and Transwell invasion chamber assay. Finally, the interaction between HOXA-AS2 and miR-6867 and the 3 ′ UTR of YAP1 protein was detected by luciferase reporter gene. The results showed that the expression level of HOXA-AS2 in gallbladder cancer cell lines was higher than that in control cells. The expression of HOXA-AS2 in gallbladder carcinoma tissues was significantly higher than that in adjacent tissues ( p < 0.05 ). After successful knockout of HOXA-AS2 by lentiviral transfection, the expression of HOXA-AS2 in gallbladder cancer cell lines was significantly decreased. Through cell proliferation and plate clone detection, it was found that silencing HOXA-AS2 inhibited cell proliferation and invasion. Through software prediction and fluorescein reporter gene detection, it was found that HOXA-AS2 has a binding site with miR-6867, and the two are negatively correlated, that is, the expression of miR-6867 is enhanced after the expression of HOXA-AS2 is downregulated. And the 3 ′ UTR of YAP1 protein in the Hippo signaling pathway binds to miR-6867. Therefore, HOXA-AS2 may affect the expression of YAP1 protein by regulating miR-6867, thereby inhibiting the Hippo signaling pathway and promoting the proliferation and metastasis of gallbladder cancer cells. HOXA-AS2 is abnormally expressed in gallbladder cancer cells. HOXA-AS2 may promote the migration and invasion of gallbladder cancer cells by regulating the Hippo signaling pathway through miR-6867. HOXA-AS2 may serve as a potential diagnostic and therapeutic target for gallbladder cancer in clinic.

The immunogenicity of SARS-CoV-2 vaccines is poor in kidney transplant recipients (KTRs). The factors related to poor immunogenicity to vaccination in KTRs are not well defined. Here, observational study demonstrated no severe adverse effects were observed in KTRs and healthy participants (HPs) after first or second dose of SARS-CoV-2 inactivated vaccine. Different from HPs with excellent immunity against SARS-CoV-2, IgG antibodies against S1 subunit of spike protein, receptor-binding domain, and nucleocapsid protein were not effectively induced in a majority of KTRs after the second dose of inactivated vaccine. Specific T cell immunity response was detectable in 40% KTRs after the second dose of inactivated vaccine. KTRs who developed specific T cell immunity were more likely to be female, and have lower levels of total bilirubin, unconjugated bilirubin, and blood tacrolimus concentrations. Multivariate logistic regression analysis found that blood unconjugated bilirubin and tacrolimus concentration were significantly negatively associated with SARS-CoV-2 specific T cell immunity response in KTRs. Altogether, these data suggest compared to humoral immunity, SARS-CoV-2 specific T cell immunity response are more likely to be induced in KTRs after administration of inactivated vaccine. Reduction of unconjugated bilirubin and tacrolimus concentration might benefit specific cellular immunity response in KTRs following vaccination.

The transfer characteristics of an underwater Wireless Power Transfer (WPT) system with a resonant frequency of 460kHz are studied experimentally. The coils have an outer diameter of 22.5 cm. Through this study, it was found that when the transfer distance is small, the transmission effect of WPT system in seawater is similar to that in air; when the transfer distance is larger than 20 cm, the load voltage of the WPT system in seawater is 4 times higher than that in air, and the transmission efficiency in seawater can reach 17 times of that in air on average. The experimental results in this paper are compared with studies published, and a circuit model is proposed to explain and predict the experimental phenomena, but the specific model still needs further work. Finally, the underwater WPT system has a low transfer efficiency of only 20% at midrange, so the findings of this research may be used for distance first scenarios.

BACKGROUND: This paper aimed to summarize our experience in the nursing of acute graft-versus-host disease (aGVHD) after simultaneous pancreas-kidney transplantation (SPK). METHODS: We retrospectively collected and analyzed the demographic characteristics, preoperative evaluation, donor evaluation, screening, and surgical methods of patients with aGVHD after SPK in our center from September 2016 to September 2019. RESULTS: One patient developed intractable diarrhea with decline in platelet (PLT), white blood cell (WBC), and red blood cell (RBC) counts. Meanwhile, the other two patients experienced facial and trunk rashes, hepatic impairment, as well as decreased PLT, WBC, and RBC counts. We took the following nursing interventions: establishing an intensive care team and close monitoring of changes in the condition; protective isolation to minimize exogenous infections; nursing of pulmonary infections; and nutritional support. However, despite careful treatment and nursing, the conditions of the three patients subsequently worsened rapidly and became uncontrollable, and all died. CONCLUSIONS: aGVHD is extremely rare after SPK, and no literature exists concerning nursing care or management related to this condition. Clinical manifestations and histopathology are helpful for diagnosis; however, treatment outcomes might be unsatisfactory and the prognosis is poor. Early detection, diagnosis, and intervention have a positive impact on the prognosis of aGVHD, and proper nursing can benefit patients.

A novel paradigm that combines federated learning with blockchain to empower edge intelligence over vehicular networks (FBVN) can enable latency-sensitive deep neural network-based applications to be executed in a distributed pattern. However, the complex environments in FBVN make the system latency much harder to minimize by traditional methods. In this paper, we model the training and transmission latency of each autonomous vehicle (AV) and consensus latency of the blockchain in-edge side in FBVN. Considering the dynamic and time-varying wireless channel conditions, unpredictable packet error rate, and unstable datasets quality, we adopt duel deep Q-learning (DDQL) as the solving approach. We propose a federated DDQL algorithm, in which the learning agent is deployed on each AV side, and the sensing states on each AV do not need to be shared so that increases scalability and flexibility for practical implementation. Simulation results show that the proposed algorithm has better performance in reducing system latency compared with the other schemes.

Trigger-action programming (TAP) is an intelligent tool, which makes it easy for users to make intelligent rules for IoT devices and applications. Unfortunately, with the popularization of TAP and more and more rules, the rule chain from multiple rules appears gradually and brings more and more threats. Previous work pays more attention to the construction of the security model, but few people focus on how to accurately identify the rule chain from multiple rules. Inaccurate identification of rule chains will lead to the omission of rule chains with threats. This paper proposes a rule chain recognition model based on multiple features, TapChain, which can more accurately identify the rule chain without source code. We design a correction algorithm for TapChain to help us get the correct NLP analysis results. We extract 12 features from 5 aspects of the rules to make the recognition of the rule chain more accurate. According to the evaluation, compared with the previous work, the accuracy rate of TapChain is increased by 3.1%, the recall rate is increased by 1.4%, and the precision rate can reach 88.2%. More accurate identification of the rule chain can help to better implement the security policies and better balance security and availability. What’s more, according to the rule chain that TapChain can recognize, there is a new kind of rule chain with threats. We give the relevant case studies in the evaluation.

In this paper, we aim to make the best joint decision of device selection and computing and spectrum resource allocation for optimizing federated learning (FL) performance in distributed industrial Internet of Things (IIoT) networks. To implement efficient FL over geographically dispersed data, we introduce a three-layer collaborative FL architecture to support deep neural network (DNN) training. Specifically, using the data dispersed in IIoT devices, the industrial gateways locally train the DNN model and the local models can be aggregated by their associated edge servers every FL epoch or by a cloud server every a few FL epochs for obtaining the global model. To optimally select participating devices and allocate computing and spectrum resources for training and transmitting the model parameters, we formulate a stochastic optimization problem with the objective of minimizing FL evaluating loss while satisfying delay and long-term energy consumption requirements. Since the objective function of the FL evaluating loss is implicit and the energy consumption is temporally correlated, it is difficult to solve the problem via traditional optimization methods. Thus, we propose a “ Reinforcement on Federated ” (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem. Specifically, the RoF scheme is executed decentralizedly at edge servers, which can cooperatively make the optimal device selection and resource allocation decisions. Moreover, a device refinement subroutine is embedded into the RoF scheme to accelerate convergence while effectively saving the on-device energy. Simulation results demonstrate that the RoF scheme can facilitate efficient FL and achieve better performance compared with state-of-the-art benchmarks.

Background Genitourinary syndrome of menopause (GSM) comprises genital symptoms (dryness, burning, itching, irritation, bleeding), sexual symptoms (dyspareunia and other sexual dysfunctions) and urinary symptoms (dysuria, frequency, urgency, recurrent urinary infections) associated with menopause. To avoid invasive testing and painful physical examinations, validated questionaries, which can assess the prevalence and risk factors associated with symptoms of GSM. We aimed to investigate the prevalence and risk factors associated with GSM in middle-aged and older women in the communities of Beijing, China. Methods A cross-sectional, questionnaire study was performed among 35–70 years old Chinese woman. Vaginal health index score and urinary distress inventory (UDI-6) was used to evaluate vulvovaginal atrophy (VVA) and urinary incontinence (UI). Stages of pelvic organ prolapse (POP) was measured during gynecological examination with POP-Q system. Mean ± standard deviation (SD) and proportion/percentages were used to summarize continuous and categorial variables respectively. The Bonferroni method was used to adjust for multiple comparisons. Results A total of 2702/3000 participants completed the questionnaire survey. The mean ± SD age of participants was 53.7 ± 7.0 years and prevalence of VVA among participants was 34.8% (941/2702). In UDI-6 questionnaires total 47.5% (1284/2702) participants reported experiencing urinary incontinence (UI). Further, POP was highly prevalent in anterior vaginal wall 38.9% (1050/2702) followed by posterior vaginal wall 25.3% (683/2702) and uterine 22.2% (599/2702). Besides, multiple logistic regression analysis inferred older age (45–54 years [OR (95% CI): 3.38 (2.03, 5.64)]; 55–64 years [OR (95% CI): 8.63 (5.09, 14.64)]), menopause [OR (95% CI): 2.20 (1.71, 2.85)] and Faecal Inconsistence (FI) [OR (95% CI): 1.31(1.00, 1.72)] as independent risk factors for VVA. Conclusions Our study evidenced that GSM is prevalent in old age Chinese women. GSM is related with UI, POP and VVA. Further older age, menopause and FI were risk factors associated with VVA. Our findings could help health care personnel to get a comprehensive overview of factors associated with VVA and urinal distress, which may facilitate early detection and prevention of GSM.

Purpose: To explore the role of bi-parametric MRI radiomics features in identifying PNI in high-grade PCa and to further develop a combined nomogram with clinical information. Methods: 183 high-grade PCa patients were included in this retrospective study. Tumor regions of interest (ROIs) were manually delineated on T2WI and DWI images. Radiomics features were extracted from lesion area segmented images obtained. Univariate logistic regression analysis and the least absolute shrinkage and selection operator (LASSO) method were used for feature selection. A clinical model, a radiomics model, and a combined model were developed to predict PNI positive. Predictive performance was estimated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: The differential diagnostic efficiency of the clinical model had no statistical difference compared with the radiomics model (area under the curve (AUC) values were 0.766 and 0.823 in the train and test group, respectively). The radiomics model showed better discrimination in both the train cohort and test cohort (train AUC: 0.879 and test AUC: 0.908) than each subcategory image (T2WI train AUC: 0.813 and test AUC: 0.827; DWI train AUC: 0.749 and test AUC: 0.734). The discrimination efficiency improved when combining the radiomics and clinical models (train AUC: 0.906 and test AUC: 0.947). Conclusion: The model including radiomics signatures and clinical factors can accurately predict PNI positive in high-grade PCa patients.

The zebrafishDanio reriohas become a popular model in functional genomics and genetic disease studies. However, when a zebrafish mutant line must be propagated as heterozygotes due to homozygous lethality, using standard genotyping methods to identify a population of homozygous mutant embryos is time-consuming and sometimes impractical due to downstream applications such as large-scale chemical screens. Here, we introduce ‘TICIT’,TargetedIntegration byCRISPR-Cas9 andIntegraseTechnologies, which utilizes the site-specific DNA recombinase – phiC31 integrase – to insert fluorescent markers into CRISPR-Cas9-generated mutant alleles. It allows instantaneous determination of a zebrafish’s genotype simply by examining its color. This technique, which relies on first knocking in a 39-basepair phiC31 landing site via CRISPR-Cas9, enables researchers to insert large DNA fragments at the same genomic location repeatedly and with high precision and efficiency. We demonstrated that TICIT could also be used to create reporter fish driven by an endogenous promoter. Additionally, we created a landing site located in thetyrosinasegene that could support transgene expression in a broad spectrum of tissue and cell types, acting as a putative safe harbor locus. Hence, TICIT can yield predictable and reproducible transgene expression, facilitate diverse applications in zebrafish, and may be applicable to cells in culture and other model organisms.

Pneumocandin B0, the precursor of the antifungal drug caspofungin, is a lipohexapeptide produced by the fungus Glarea lozoyensis. Oxidative stress and the resulting production of reactive oxygen species (ROS) are known to be involved in the regulation of pneumocandin B0 biosynthesis. In this study, the Glyap1 gene of Glarea lozoyensis, a homologue of the yeast redox regulator YAP1, was knocked out. The intracellular ROS levels of the resulting ΔGlyap1 strain were higher than in the wild-type strain, which was caused by the downregulated expression of superoxide dismutase (SOD) and catalase (CAT). Compared with the wild-type strain, ΔGlyap1 exhibited an oxidative phenotype throughout its life cycle, which resulted in significantly higher pneumocandin B0 production per unit biomass. In addition, ΔGlyap1 showed growth inhibition and decreased pneumocandin B0 production in the presence of CCl4, which leads to strong oxidative stress. To overcome the strain’s sensitivity, a three-stage antioxidant addition strategy was developed. This approach significantly improved the growth of ΔGlyap1 while maintaining a high pneumocandin B0 production per unit biomass, which reached 38.78 mg/g DCW. Notably, this result represents a 50% increase over the wild-type strain. These findings provide new insights into the regulatory mechanisms that control pneumocandin B0 production under oxidative stress, which may be applied to improve the production of other secondary metabolites. • Glyap1 is involved in expression of redox and pneumocandin B0 synthesis-related genes. • Addition of a three-stage antioxidant alleviated the sensitivity of ΔGlyap1 strain. • The yield of pneumocandin B0 per unit biomass of ΔGlyap1 strain was 38.78 mg/g DCW.

With the rapid development of artificial intelligence and industrial Internet of Things (IIoT) technologies, intelligent predictive maintenance (IPdM) has received considerable attention from researchers and practitioners. To efficiently predict impending failures and mitigate unexpected downtime, while satisfying the instant maintenance demands of industrial facilities is very important for improving the production efficiency. In this article, a self-attention based “Perception and Prediction” framework, called DeepHealth, is proposed for the instant IPdM. Specifically, the framework is composed of two submodels (i.e., DH-1 and DH-2), which are respectively utilized to perform the health perception and sequence prediction. By operating the framework, the proposed models can predict the health conditions via predicting the future signal samples, thereby completing the instant IPdM. Considering the potential temporal correlation in time series, we deploy an enhanced attention mechanism to capture global dependencies from the vibration signals, and leverage the long- and short-term sequence prediction of sensor signals to support instant maintenance decision-making. On this basis, we conduct a destructive experiment based on the IIoT-enabled rotating machinery and construct a balanced industrial dataset for model evaluations. Extensive experiment results show that the proposed solution achieves good prediction accuracy for instant IPdM on the automatic washing equipment and Case Western Reserve University datasets.

Performing deep neural network (DNN) inference in real time requires excessive network resources, which poses a big challenge to the resource-limited industrial Internet of things (IIoT) networks. To address the challenge, in this paper, we introduce an end-edge-cloud orchestration architecture, in which the inference task assignment and DNN model placement are flexibly coordinated. Specifically, the DNN models, trained and pre-stored in the cloud, are properly placed at the end and edge to perform DNN inference. To achieve efficient DNN inference, a multi-dimensional resource management problem is formulated to maximize the average inference accuracy while satisfying the strict delay requirements of inference tasks. Due to the mix-integer decision variables, it is difficult to solve the formulated problem directly. Thus, we transform the formulated problem into a Markov decision process which can be solved efficiently. Furthermore, a deep reinforcement learning based resource management scheme is proposed to make real-time optimal resource allocation decisions. Simulation results are provided to demonstrate that the proposed scheme can efficiently allocate the available spectrum, caching, and computing resources, and improve average inference accuracy by 31.4 $\%$ compared with the deep deterministic policy gradient benchmark.

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services, which require low delay and high accuracy. Sampling rate adaption, which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in minimizing the service delay. In this article, we investigate the collaborative DNN inference problem in industrial IoT networks. To capture the channel variation and task arrival randomness, we formulate the problem as a constrained Markov decision process (CMDP). Specifically, sampling rate adaption, inference task offloading, and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services. Since CMDP cannot be directly solved by general reinforcement learning (RL) algorithms due to the intractable long-term constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov optimization technique. Then, a deep RL-based algorithm is proposed to solve the MDP. To expedite the training process, an optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal edge computing resource allocation. Extensive simulation results are provided to demonstrate that the proposed RL-based algorithm can significantly reduce the average service delay while preserving long-term inference accuracy with a high probability.

Background The increasing occurrence of multidrug-resistant organisms (MDROs) infections has posed major challenge to solid organ transplantation (SOT). For SOT recipients, high-dose immunosuppressants and broad-spectrum antibiotics can markedly increase the risk of early postoperative MDRO infections and thus have adverse effects on the outcomes of SOT. Here, we analyzed the incidence and clinical features of early MDRO infections after SOT, in an attempt to provide new evidence for the control and treatment of early MDROs. Methods The clinical data of 133 patients with MDRO infections after SOT in our department from 2017 to 2020 were retrospectively collected, and clinical features including incidence, etiologies, infection sites, and complications, were analyzed. Results The incidence of MDRO infections after SOT was 9.9%. Simultaneous liver and kidney transplantation patients had the highest incidence of MDRO infections, followed by the recipients of liver transplantation and simultaneous pancreas-kidney transplantation; patients undergoing renal transplantation had the lowest incidence of MDRO infections. The most common pathogen was extended spectrum beta-lactamase (ESBL)-producing organisms (n=88, 66.2%), the most common infection site was the urinary system (n=58, 43.6%), and the main postoperative complications were urinary tract infections (n=44, 33.1%) and lung infections (n=41, 30.8%). MDRO infections were cured in most cases. Conclusions A sound knowledge of the clinical features of MDRO infection after SOT is important for the successful prevention and treatment of these infections.

International audience

We describe two new species of Lophocoronidae: Acanthocorona hedida Zhang, Shih and Engel sp. n. and Acanthocorona venulosa Zhang, Shih and Engel sp. n., and an undetermined specimen from mid-Cretaceous Kachin amber. Phylogenetic analysis of basal lepidopteran lineages, including three extinct families, was undertaken. The analysis supported monophyly of Glossata although internal relationships remain controversial. Acanthocorona and Lophocorona form a monophyletic group. It is likely that short and simply structured proboscides of Acanthocorona were used to sip water droplets, pollination drops from gymnosperms, nectar from early flowers, or sap from injured leaves. Both retracted and extended ovipositors are preserved in the material reported here, revealing their morphology and indicating that these Cretaceous lophocoronids inserted eggs into the tissues of their host plants.

The default mode network (DMN) of the brain is involved in cognition, emotion regulation, impulsivity, and balancing between internally and externally focused states. DMN dysregulation has been implicated in several neurological and neuropsychiatric disorders. In this study, we used functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and spectral fiber-photometry to investigate the selective neuromodulatory effect of norepinephrine (NE)-releasing noradrenergic neurons in the locus coeruleus (LC) on the DMN in mice. Chemogenetic-induced tonic LC-NE activity decreased cerebral blood volume (CBV) and glucose uptake, and increased synchronous low frequency fMRI activity within the frontal cortices of the DMN. Fiber-photometry results corroborated these findings, showing that LC-NE activation induced NE release, enhanced calcium-weighted neuronal spiking, and reduced CBV in the anterior cingulate cortex. These data suggest that LC-NE alters conventional stimulus-evoked coupling between neuronal activity and CBV in the frontal DMN. We also demonstrated that chemogenetic activation of LC-NE neurons strengthened functional connectivity within the frontal DMN, and this effect was causally mediated by reduced modulatory inputs from retrosplenial and hippocampal regions to the association cortices of the DMN.

Background To establish a dietary self-management evaluation indicators scale for kidney transplant recipients and to test the reliability and validity of the scale. Methods Based on the knowledge attitude practice model (KAP model) of health-related behavior changes, an evaluation indicators scale of dietary self-management ability of kidney transplant recipients was constructed through a literature review, expert consultation and group discussion. A questionnaire survey was carried out with 102 kidney transplant recipients from a large tertiary hospital to test the reliability and validity of the constructed scale. Results The dietary self-management ability scale for kidney transplant recipients had 29 items in 3 dimensions. The content validity at the scale level was 0.969, and the content validity at the item level was 0.778-1.000. Exploratory factor analysis extracted 3 common factors, and the cumulative variance contribution rate was 68.610%; the correlation coefficient between each dimension was 0.467-0.629, and the correlation coefficient between each dimension and the total score of the scale was 0.648-0.845. Cronbach's α for the scale was 0.831, and the test-retest reliability of the scale was 0.910. Conclusions The constructed dietary self-management ability scale for kidney transplant recipients has good reliability and validity and can be used as a tool to evaluate the dietary self-management ability of kidney transplant recipients.

This paper proposes a patent citation classification model based on deep learning, and collects the patent datasets in text analysis and communication area from Google patent database to evaluate the classification effect of the model. At the same time, considering the technical relevance between the examiners’ citations and the pending patent, this paper proposes a hypothesis to take the output value of the model as the technology similarity of two patents. The rationality of the hypothesis is verified from the perspective of machine statistics and manual spot check. The experimental results show that the model effect based on deep learning proposed in this paper is significantly better than the traditional text representation and classification method, while having higher robustness than the method combining Doc2vec and traditional classification technology. In addition, we compare between the proposed method based on deep learning and the traditional similarity method by a triple verification. It shows that the proposed method is more accurate in calculating technology similarity of patents. And the results of manual sampling show that it is reasonable to use the output value of the proposed model to represent the technology similarity of patents.

Purpose The immunogenicity of SARS-CoV-2 vaccines is poor in kidney transplant recipients (KTRs). The factors related to poor immunogenicity to vaccination in KTRs are not well defined. Methods An observational study was conducted in KTRs and healthy individuals who had received two doses of SARS-CoV-2 inactivated vaccine. IgG antibodies against the receptor-binding domain found in the S1 subunit of the spike protein, and against nucleocapsid protein were measured using enzyme-linked immunosorbent assay. Receptor-binding domain (RBD)-angiotensin-converting enzyme 2 interaction-blocking antibodies were measured using commercial kits. T cell responses against the spike and nucleocapsid proteins were detected using enzyme-linked immunosorbent spot assay. Results No severe adverse effects were observed in KTRs after first or second dose of SARS-CoV-2 inactivated vaccine. IgG antibodies against the receptor- binding domain, and nucleocapsid protein were not effectively induced in a majority of KTRs after second dose of inactivated vaccine. Specific T cell immunity response was detectable in 32%-40% KTRs after second doses of inactivated vaccine. KTRs who developed specific T cell immunity were more likely to be female, and have lower levels of total bilirubin, unconjugated bilirubin, and blood tacrolimus concentration. Multivariate logistic regression analysis found that blood unconjugated bilirubin was significantly negatively associated with SARS-CoV-2 specific T cell immunity response in k KTRs. Conclusions Specific T cell immunity response could be induced in 32%-40% KTRs after two doses of inactivated vaccine. Blood unconjugated bilirubin was negatively associated with specific cellular immunity response in KTRs following vaccination.

The freezing-induced aggregation of aluminum-based (Alum) adjuvants has been considered as the most important cause of reduced vaccine potency. However, the intrinsic properties that determine the functionality of Alum after freezing have not been elucidated. In this study, we used engineered aluminum oxyhydroxide nanoparticles (AlOOH NPs) and demonstrated that cryogenic freezing led to the mechanical pressure-mediated reduction of surface hydroxyl. The sugar-based surfactant, octyl glucoside (OG), was demonstrated to shield AlOOH NPs from the freezing-induced loss of hydroxyl content and the aggregation through the reduction of recrystallization-induced mechanical stress. As a result, the antigenic adsorption property of frozen AlOOH NPs could be effectively protected. When hepatitis B surface antigen (HBsAg) was adjuvanted with OG-protected frozen AlOOH NPs in mice, the loss of immunogenicity was inhibited. These findings provide insights into the freezing-induced surface decomposition of Alum and can be translated to design of protectants to improve the stability of vaccines.

Somitogenesis, the segmentation of the antero-posterior axis in vertebrates, is thought to result from the interactions between a genetic oscillator and a posterior-moving determination wavefront. The segment (somite) size is set by the product of the oscillator period and the velocity of the determination wavefront. Surprisingly, while the segmentation period can vary by a factor three between 20 °C and 32 °C, the somite size is constant. How this temperature independence is achieved is a mystery that we address in this study. Using RT-qPCR we show that the endogenous fgf8 mRNA concentration decreases during somitogenesis and correlates with the exponent of the shrinking pre-somitic mesoderm (PSM) size. As the temperature decreases, the dynamics of fgf8 and many other gene transcripts, as well as the segmentation frequency and the PSM shortening and tail growth rates slows down as T–Tc (with Tc = 14.4 °C). This behavior characteristic of a system near a critical point may account for the temperature independence of somitogenesis in zebrafish.

IntroductionCroatian superhigh-organic-sulfur Raša coal had been mined for nearly 400 years. The release of hazardous trace elements (HTEs) and toxic organic pollutants (TOPs) into the local environment by coal mining, preparation, and combustion activities has resulted in pollution.MethodsIn this study, the diversity and composition of microbial communities in estuarine sediment and soil samples as well as community function responses to the pollutants were investigated.ResultsThe results showed that PAH degradation does occur following 60 years of natural attenuation, the location is still heavily polluted by polycyclic aromatic hydrocarbons (PAHs) and HTEs. Microbial analyses have shown that high concentrations of PAHs have reduced the diversity and abundance of microbial communities. The pollution exerted an adverse, long-term impact on the microbial community structure and function in the brackish aquatic ecosystem. Microorganisms associated with the degradation of PAHs and sulfur-containing compounds have been enriched although the diversity and abundance of the microbial community have reduced. Fungi which are believed to be the main PAH degrader may play an important role initially, but the activity remains lower thereafter. It is the high concentrations of coal-derived PAHs, rather than HTEs, that have reduced the diversity and abundance of microbial communities and shaped the structure of the local microbiota.DiscussionThis study could provide a basis for the monitoring and restoration of ecosystems impacted by coal mining activities considering the expected decommission of a large number of coal plants on a global scale in the coming years due to growing global climate change concerns.