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Since the actual operation of the bearing inevitably exists in both noise and variable working conditions, most of the traditional networks can only deal with them alone, and the fault identification result will be significantly reduced under such complex conditions. Therefore, Parallel Multichannel Deep Convolution Neural Network (PMDCNN) and Long Short-Term Memory (LSTM) are proposed as PMDCNN-LSTM model to enable better performance. And a local sparse structure is used to greatly reduce the number of model parameters, the Exponential Linear Unit (ELU) activation function is used to further improve accuracy and stability. The results show that the proposed method is strongly resistant to noise and variable working conditions, as verified by the Case Western Reserve University (CWRU) bearing dataset and bearing fault simulation experimental platform dataset. The comparison with other model shows that the proposed model has good applicability, strong stability and high accuracy of bearing fault identification.

The presence of polyhalogenated carbazoles (PHCZs), which are emerging as dioxin-like contaminants, in remote polar regions has not been reported. This study investigated 11 target PHCZs (Σ11PHCZs) and 25 unknown PHCZs (Σ25UNPs) in soil and sediment samples collected from Ny-Ålesund in the Arctic. The findings revealed that Σ11PHCZs concentrations reached 165.6 ng/g dry weight (dw), with 3,6-dichlorocarbazole and 3-chlorocarbazole being the predominant congeners. The Σ25UNPs concentrations ranged from 1.4 to 92.4 ng/g dw (median: 17.0 ng/g dw), primarily comprising a 1,3,6,8-tetrabromocarbazole isomer. The sediment contained ∼6 times more Σ25UNPs than Σ11PHCZs. Long-range atmospheric transport, oceanic transport, and human activity can potentially affect soils and sediments concurrently. For the first time, halogen substitution patterns in PHCZs, including ClI, ClI2, and ClI3, were detected in soil and sediment. Using the toxic equivalent (TEQ) approach, the potential toxic effects linked to the target PHCZ levels were assessed. The TEQPHCZsin Arctic soils and sediments indicated low risk, ranging from 8.0 × 10–3to 17 pg TEQ/g dw (median: 2.8 pg TEQ/g dw) and 0.1 to 0.4 pg TEQ/g dw (median: 0.3 pg TEQ/g dw), respectively. This study marks the first report on the occurrence, composition, sources, and potential risks posed by PHCZs in the Arctic region.

Novel sigma-2 (σ2) receptor ligands with benzimidazolone and 5,6-dimethoxyisoindoline as pharmacophores were designed and synthesized. Compound 4exhibited low nanomolar affinity for the σ2receptors (Ki(σ2) = 2.30 nM) and high subtype selectivity (Ki(σ1)/Ki(σ2) > 1500). Radioligand [18F]4was prepared in radiochemical yields of 18 ± 7%, with >99% radiochemical purity and molar activity of 244 ± 136 GBq/μmol. Biodistribution and blocking studies in mice and small animal PET/CT imaging in rats indicated highly specific binding of [18F]4in organs known to express the σ2receptors. Small animal PET/CT imaging with [18F]4showed clear visualization of the tumors in subcutaneous A549 lung cancer and U87MG glioma xenografts, and intracranial orthotopic U87MG glioma models. Co-administration of CM398 with [18F]4significantly reduced activity uptake in the tumors, indicating that [18F]4specifically binds to the σ2receptors expressed in A549 and U87MG xenografts.

The industrial Internet of Things (IIoT) generates a large amount of private data, which can be used to improve production efficiency and optimize production management decisions. However, the openness of the network and the limited resources of sensor devices pose security threats to industrial privacy data, such as illegal access and leakage. Attribute-based encryption (ABE) is a promising solution for solving the problem of private data sharing. Nevertheless, many time-consuming operations, such as bilinear pairing, security of access policies, and attribute revocation issues, pose challenges for ABE in practical applications. To address the above issues, we propose an efficient policy hiding ciphertext-policy ABE (CP-ABE) scheme based on a multivalued attribute access structure with wildcards. It introduces fog computing to achieve fully outsourced computation, reducing the computational overhead of resource constrained terminals. Meanwhile, attribute revocation and user revocation mechanisms were designed to achieve flexible and fine-grained access control. Based on the idea of reduction, we have demonstrated that our scheme is secure under the assumption of the decision q-bilinear Diffie-Hellman exponent. In addition, our scheme has both backward and forward securities. Finally, we compared and analyzed the proposed scheme with the existing schemes in terms of functionality and performance. Theoretical analysis and experimental simulation results show that our scheme has relatively complete functions and has certain advantages in communication costs and computational overhead.

Deep learning (DL) has inaugurated new approaches to implement fault diagnosis of roller bearings, which is essential to deal with the current industrial big data era. Unfortunately, many existing deep learning models, particularly convolutional neural network (CNN) models, have the following drawbacks. Single-channel convolution neural network in pooling layer has the problem of data loss. The feature information extracted by CNN is not integrated due to lack of feature learning ability, which will induce unsatisfactory diagnostic results and poor generalization ability. To deal with the above issues, a dual-flow convolutional neural network (DFCNN) is proposed for intelligent diagnosis the faults of roller bearings. The multi-channel convolutional neural network is used for feature extraction to solve the problem of data loss in the pooling layer. More comprehensive fault features can be extracted by multiple feature fusion after the input of two dimensions. Network parameters are adjusted using model optimization. The proposed method is demonstrated by the bearing experiment and collected vibration data including different types of faults. The results show that the accuracy rate is more superior than other six different neural network, and it is suitable for roller bearing fault diagnosis.

Sleep disorders have increasingly impacted healthy lifestyles. Accurate scoring of sleep stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging differs notably between healthy individuals and those with sleep apnea (SA). SA disrupts the regularity of sleep stages, affecting the performance of sleep stage detection and influencing the accuracy of sleep staging, thereby impacting sleep quality assessment results. The study compares the accuracy of sleep staging between healthy individuals and SA patients using the same algorithm, revealing variations in performance based on different severities of sleep apnea. This suggests limitations in the generalization ability of current sleep staging methods. Accordingly, researchers are working to develop sleep staging methods that can diminish the impact of sleep apnea and exhibit better generalization capabilities. Furthermore, the study emphasizes the advantages of automated methods over manual scoring due to being less subjective and resource-intensive, making them more suitable for practical applications. The emphasis is on recent research findings on automatic sleep stage classification based on electroencephalography (EEG). The study outlines potential applications and distinctions of various algorithm models rooted in machine learning and deep learning within the context of sleep staging. These methods are applied to the well-known public EEG dataset Sleep-EDF. The study applies four widely studied algorithms to the single-channel EEG of 20 subjects, comparing the results of the models’ automatic sleep staging with the manual sleep staging annotations by clinical experts.

AbstractAims/BackgroundBlended learning has been a commonly adopted teaching mode in the medical education community in recent years. Many studies have shown that the blended learning mode is superior to the traditional teaching mode. Nonetheless, pinpointing the specific advantages provided by blended teaching methods is challenging, since multiple elements influence their effectiveness. This study aimed to investigate the reliability of the conclusions of published randomised controlled trials (RCTs) on blended learning in medical education by assessing their quality, and to provide suggestions for future related studies.MethodsTwo investigators searched PUBMED and EMBASE, and assessed RCTs related to medical blended learning published from January 1, 2010 to December 31, 2021. The analysis of the overall quality of each report was based on the 2010 consolidated standard of reporting trials (CONSORT) Statement applying a 28-point overall quality score. We also conducted a multivariate assessment including year of publication, region of the trial, journal, impact factor, sample size, and the primary outcome.ResultsA total of 22 RCTs closely relevant to medical blended learning were eventually selected for study. The results demonstrated that half of the studies failed to explicitly describe at least 34% of the items in the 2010 CONSORT Statement. Medical blended learning is an emerging new teaching mode, with 95.45% of RCTs published since 2010. However, many issues that we consider crucial were not satisfactorily addressed in the selected RCTs.ConclusionAlthough the 2010 CONSORT Statement was published more than a decade ago, the quality of RCTs remains unsatisfactory. Some important items were inadequately reported in many RCTs such as sample size, blinding, and concealment. We encourage researchers who focus on the effects of blended learning in medical education to incorporate the guidelines in the 2010 CONSORT Statement when designing and conducting relevant research. Researchers, reviewers, and editors also need to work together to improve the quality of relevant RCTs in accordance with the requirements of the 2010 CONSORT Statement.

Background and aims: The atherogenic index of plasma (AIP) is supposed to be associated with cardiovascular disease (CVD), but there is limited evidence on its longitudinal effect. Our study aimed to explore the associations between baseline and long-term AIP with the risk of stroke and its subtypes. Methods and results: A total of 97,959 participants free of stroke at baseline were included in the Kailuan study. The AIP was calculated as the logarithm of the ratio of triglyceride to high-density lipoprotein cholesterol. The updated mean AIP was calculated as the average of the AIP from baseline to the first occurrence of outcome or the end of follow-up. The outcome was the first occurrence of stroke, including ischemic stroke (IS) and intracerebral hemorrhage (ICH). We used univariable and multivariable Cox proportional hazard models to explore the association between AIP and the risk of stroke. During a median follow-up of 12.79 years, a total of 6307 participants developed stroke, including 5482 IS and 1024 ICH. Compared with the 1st quartile of baseline AIP, the multivariate-adjusted HR in the 4th quartile was 1.12 (95% CI, 1.03–1.22, pfor trend <0.001) for stroke risk. Same results were found in IS, but no significant association was found for ICH. The associations between updated mean AIP and stroke and its subtypes showed similar results. Conclusions: Elevated levels of both baseline and long-term updated mean AIP were associated with the risk of stroke and IS but not ICH in the general population.

NHC-catalyzed [4+2] annulation of 2H-azirine-2-carbaldehydes with ketimines and isocyanates has been developed, providing straightforward synthetic protocols for constructing structurally intriguing pyrimido[1,2-a]indolediones and pyrimidinediones under mild conditions with excellent yields. This protocol can be used to synthesize the core skeleton of pharmaceutically important drugs and pyrimido[1,2-a]indoledione-containing natural products, making it potentially valuable for creating biologically active derivatives.

In the trend of network middleboxes as a service, enterprise customers adopt in-the-cloud deep packet inspection (DPI) services to protect networks. As network misconfigurations and hardware failures notoriously exist, recent efforts envision to ensure the execution integrity of DPI services in untrusted clouds. However, they either require enterprise customers to know proprietary DPI rulesets of cloud providers or introduce forbidden overhead in the network context. In the paper, we propose a privacy-preserving and lightweight verification scheme that efficiently checks whether in-the-cloud DPI services run correctly without leaking private DPI rulesets. Particularly, our design introduces one trusted third party to perform privacy-preserving and trustworthy ruleset evaluation and DPI execution verification. Meanwhile, it devises a novel DPI ruleset authentication method that enables tamper-proof DPI operations and facilitates fast proof generation. The proofs can be verified without requiring the verifier to always maintain all rulesets. To further reduce the verification costs while resisting cloud cheating behaviors like bias treatments of packets, it employs a commitment-based delayed sampling mechanism which requires the DPI services to first demonstrate that all packets have been processed before receiving sampling decisions. Moreover, extensive experiments are conducted based on Click modules. The results show that the proposed scheme is practical and only incurs the real-time overhead of 10–20 microseconds.

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations across different slices. This presents a significant hurdle to medical image segmentation, as conventional segmentation methods may either overlook the minority class or overly emphasize the majority class, ultimately leading to a decrease in the overall generalization ability of the segmentation results. To overcome this, we propose a novel approach based on multi-step reinforcement learning, which integrates prior knowledge of medical images and pixel-wise segmentation difficulty into the reward function. Our method treats each pixel as an individual agent, utilizing diverse actions to evaluate its relevance for segmentation. To validate the effectiveness of our approach, we conduct experiments on four imbalanced medical datasets, and the results show that our approach surpasses other state-of-the-art methods in highly imbalanced scenarios. These findings hold substantial implications for clinical diagnosis and research.

Internet of things (IoT) networks allow cross-device interactions to achieve various intelligent applications, for example, smart homes and smart commercial spaces. However, cross-device interactions are often protected by inadequate authorization mechanisms, making them susceptible to various attacks, including connection-based attacks, application impersonation attacks, and so on. In this article, we propose a zero-trust IoT network architecture, OUTSIDE, designed to provide fine-grained authorization for IoT applications. It achieves the application-level authorization at the network layer by encoding the capability information of applications into verifiable tokens. Meanwhile, it enables a zero-trust service for per-packet verification, ensuring that every packet is sent by an authorized application with proper access privileges. Particularly, our architecture is versatile and compatible with various IoT protocols. We prototype and deploy OUTSIDE in Raspberry Pis and ESP32 microcontrollers running over the constrained application protocol (CoAP). The experimental results show that our architecture incurs negligible performance degradation.

Single-cell lineage tracing (scLT) has emerged as a powerful tool, providing unparalleled resolution to investigate cellular dynamics, fate determination, and the underlying molecular mechanisms. This review thoroughly examines the latest prospective lineage DNA barcode tracing technologies. It further highlights pivotal studies that leverage single-cell lentiviral integration barcoding technology to unravel the dynamic nature of cell lineages in both developmental biology and cancer research. Additionally, the review navigates through critical considerations for successful experimental design in lineage tracing and addresses challenges inherent in this field, including technical limitations, complexities in data analysis, and the imperative for standardization. It also outlines current gaps in knowledge and suggests future research directions, contributing to the ongoing advancement of scLT studies.

This paper investigates the hybrid impulsive control synchronization problem of multi-term fractional-order neural networks (MFNNs) with switching parameters. Firstly, a novel MFNN with switching parameters model is introduced by incorporating multi-term Caputo fractional-order derivative to extend the existing framework for fractional-order cases. Then, the relationship between multi-term fractional-order derivative and distributed-order derivative is analyzed, and a synchronization criterion for a class of multi-term fractional-order impulsive switched systems is derived by utilizing the properties of the distributed-order derivative weight function. Furthermore, a hybrid impulsive controller is designed to obtain sufficient conditions for synchronization of MFNNs with switching parameters. To validate the effectiveness of the obtained conclusions, a numerical example is presented to demonstrate the validity of the proposed MFNN model and synchronization criterion.

In the initial alignment of the strapdown inertial navigation system, it is difficult to obtain sufficient data information to make the optimized state of convergence in a short time, which cannot realize fast alignment, and the accuracy of the alignment results is poor. The dynamic sliding window is difficult to recover after marginalization, and the linearization points of the Jacobi matrix are inconsistent, leading to the zero-space variation of the information matrix. To address the above problems, we propose an inertial alignment algorithm for sliding window reverse optimization delayed marginalization (RODM) to improve the rapidity and accuracy of the alignment algorithm. The method of storing gyro and accelerometer data during the alignment process and repeatedly solving the stored inertial data using forward and reverse navigation solving and optimization algorithms (OAMs) is used as a way to extend the interval of inertial data collected in a short period of time so that there is enough inertial data to enable the convergence of the state variables of the sliding-window optimization for a short period of time to achieve fast alignment. A delayed marginalization factor is used to update the marginalized prior information, solving the problem that the linearization of the Jacobi matrix must be fixed point to improve the accuracy of the alignment algorithm. We propose an algorithm to improve the accuracy of alignment, which we have verified the rapidity and accuracy of our initial alignment system in the turntable test, where the accuracy of the alignment heading angle reaches 0.136467 mil in 3 min, providing accurate attitude information for navigation and localization.

Power transformer emits continuous vibration signals during working, which contain plenty of impulses and fluctuations caused by mechanical faults, and these signals are the main data source to evaluate the operational condition of a power transformer. This study researches the voiceprint features of vibration sound signals within power transformers under different operational conditions and constructs an automatic and noninvasive condition monitoring and fault diagnosis system based on acoustic characteristics. First, a normal/faulty operational condition classification module is proposed using acoustic short-time energy and zero-crossing rate to evaluate the real-time operational condition of a power transformer. Second, a multifault recognition module with finite signal samples is constructed based on the time–frequency characteristics of acoustic signals, and an automatic fault diagnosis system for power transformers is built. To improve the accuracy and robustness under dynamic noisy environments and complicated operational conditions, a multidimensional performance optimization method is proposed to handle the problems caused by environmental noises, mixed faults, and unknown faults. Experimental results demonstrate that the proposed condition monitoring and fault diagnosis system can fast distinguish the faulty operational conditions and diagnose six types of typical faults of power transformers, as well as mixed faults, under different noisy environments with an accuracy of more than 97.83%.

Camouflaged Object Detection (COD) is a challenging visual task due to its complex contour, diverse scales, and high similarity to the background. Existing COD methods encounter two predicaments: One is that they are prone to falling into local perception, resulting in inaccurate object localization; Another issue is the difficulty in achieving precise object segmentation due to a lack of detailed information. In addition, most COD methods typically require larger parameter amounts and higher computational complexity in pursuit of better performance. To this end, we propose a global localization perception and local guidance refinement network (PRNet), that simultaneously addresses performance and computational costs. Through effective aggregation and use of semantic and details information, the PRNet can achieve accurate localization and refined segmentation of camouflaged objects. Specifically, with the help of a Cascaded Attention Perceptron (CAP) designed, we can effectively integrate and perceive multi-scale information to localize camouflaged objects. We also design a Guided Refinement Decoder (GRD) in a top-down manner to extract context information and aggregate details to further refine camouflaged prediction results. Extensive experimental results demonstrate that our PRNet outperforms 12 state-of-the-art models on 4 challenging datasets. Meanwhile, the PRNet has a smaller number of parameters (12.74M), lower computational complexity (10.24G), and real-time inference speed (105FPS). Source codes are available at https://github.com/hu-xh/PRNet.

Transposable elements (TEs) have been shown to have important gene regulatory functions and their alteration could lead to disease phenotypes. Acute myeloid leukemia (AML) develops as a consequence of a series of genetic changes in hematopoietic precursor cells, including mutations in epigenetic factors. Here, we set out to study the gene regulatory role of TEs in AML. We first explored the epigenetic landscape of TEs in AML patients using ATAC-seq data. We show that a large number of TEs in general, and more specifically mammalian-wide interspersed repeats (MIRs), are more enriched in AML cells than in normal blood cells. We obtained a similar finding when analyzing histone modification data in AML patients. Gene Ontology enrichment analysis showed that genes near MIRs in open chromatin regions are involved in leukemogenesis. To functionally validate their regulatory role, we selected 19 MIR regions in AML cells, and tested them for enhancer activity in an AML cell line (Kasumi-1) and a chronic myeloid leukemia (CML) cell line (K562); the results revealed several MIRs to be functional enhancers. Taken together, our results suggest that TEs are potentially involved in myeloid leukemogenesis and highlight these sequences as potential candidates harboring AML-associated variation.

Cerebral palsy (CP) is the most common motor disability in children. To ascertain the role of major genetic variants in the etiology of CP, we conducted exome sequencing on a large-scale cohort with clinical manifestations of CP. The study cohort comprised 505 girls and 1,073 boys. Utilizing the current gold standard in genetic diagnostics, 387 of these 1,578 children (24.5%) received genetic diagnoses. We identified 412 pathogenic and likely pathogenic (P/LP) variants across 219 genes associated with neurodevelopmental disorders, and 59 P/LP copy number variants. The genetic diagnostic rate of children with CP labeled at birth with perinatal asphyxia was higher than the rate in children without asphyxia (P= 0.0033). Also, 33 children with CP manifestations (8.5%, 33 of 387) had findings that were clinically actionable. These results highlight the need for early genetic testing in children with CP, especially those with risk factors like perinatal asphyxia, to enable evidence-based medical decision-making.

Cauliflower (Brassica oleraceaL. var. botrytis) is a distinctive vegetable that supplies a nutrient-rich edible inflorescence meristem for the human diet. However, the genomic bases of its selective breeding have not been studied extensively. Herein, we present a high-quality reference genome assembly C-8 (V2) and a comprehensive genomic variation map consisting of 971 diverse accessions of cauliflower and its relatives. Genomic selection analysis and deep-mined divergences were used to explore a stepwise domestication process for cauliflower that initially evolved from broccoli (Curd-emergence and Curd-improvement), revealing that three MADS-box genes, CAULIFLOWER1(CAL1), CAL2and FRUITFULL(FUL2), could have essential roles during curd formation. Genome-wide association studies identified nine loci significantly associated with morphological and biological characters and demonstrated that a zinc-finger protein (BOB06G135460) positively regulates stem height in cauliflower. This study offers valuable genomic resources for better understanding the genetic bases of curd biogenesis and florescent development in crops.

Purpose: To evaluate the association of body mass index (BMI) with risk of first cardiometabolic disease (FCMD), cardiometabolic multimorbidity (CMM) and death. Methods: 87,512 participants free of CMD were included from the Kailuan cohort, which was established during 2006–2007 and followed up until 2020. BMI was classified as underweight ( < 18.5 kg/m2), healthy weight (18.5–23.9 kg/m2), overweight (24.0–27.9 kg/m2), mildly obese (28.0–31.9 kg/m2), and severely obese ( ≥ 32.0 kg/m2). FCMD was defined as the first onset of diabetes, heart disease, or stroke, and CMM as the coexistence of at least two CMD. The hazard ratio (HR) and 95% confidence interval (95%CI) were estimated with multi-state models. Results: 20,577 participants developed FCMD, 2232 developed CMM afterwards, and 10,191 died. Individuals with higher BMI was more likely to develop FCMD and CMM. Compared with healthy weight, the HR (95%CI) of severe obesity for transition from health to FCMD and from FCMD to CMM was 3.12 (2.91, 3.34) and 1.92 (1.60, 2.31), respectively. On the other hand, underweight was consistently associated with higher mortality risk regardless of initial status, whereas severe obesity was only related to increased risk for transition from health to death (HR: 1.36; 95%CI: 1.17, 1.56) but not for transition from FCMD (HR: 0.70; 95%CI: 0.57, 0.87) or CMM (HR: 0.80; 95%CI: 0.54, 1.19) to death. Conclusion: Our findings highlighted the importance of maintaining healthy weight for primary and secondary prevention of CMD and reflected the demand for more accurate measurement and comprehensive management of obesity for CMD patients.

In this study, microbial dynamics in the tomato rhizosphere was explored at seedling, flowering, and mature stages after soil was amended with 50 mg kg−1triiron tetrairon phosphate nanomaterials (Fe7(PO4)6NMs) and conventional ion fertilizer. It was revealed that 50 mg kg−1of Fe7(PO4)6NMs resulted in a significant increase in the relative abundances of Rhodanobacter, Sphingomonas, Gemmatimonas, Flavisolibacter, and Ramlibacterby 35.7–136.0% in tomato rhizospheres at the seedling stage. These beneficial microorganisms involved in nutrient uptake helped the seedling plant to grow better. After growing to the flowering stage, the relative abundance of Pseudomonas, Mesorhizobium, Sphingomonas, and Gemmatimonasincreased by a range 12.6–324.0%, expediting nutrient accumulation and initiating flower formation. The pollen length and ovule size of the flowers were also increased owing to increased photosynthesis and sucrose transportation to the flowers. Furthermore, Fe7(PO4)6NMs enriched the abundance of Nitrospira, Sphingomonas, Massilia, Bryobacter, Chthoniobacter, RB41, and Rubellimicrobiumat the mature stage, contributing to enhanced soil fertility as evidenced by a notable increment in the availability of phosphorus and potassium by 38.0% and 18.7%, respectively. Additionally, the utilization of Fe7(PO4)6NMs led to a remarkable improvement in fruit productivity and quality, which was indicated by a substantial increase in fruit weight by 174.0%, and in the content of soluble sugar, vitamin C, and amino acids by a range 42.2–103.0%. Our study provides an effective nano-enabled strategy for regulating rhizobacteria to meet specific functional demands during different developmental stages, thereby enhancing crop productivity.

Heterosis utilization in a large proportion of crops depends on the use of cytoplasmic male sterility (CMS) tools, requiring the development of homozygous fertile lines and CMS lines1. Although doubled haploid (DH) technology has been developed for several crops to rapidly generate fertile lines2,3, CMS lines are generally created by multiple rounds of backcrossing, which is time consuming and expensive4. Here we describe a method for generating both homozygous fertile and CMS lines through in vivo paternal haploid induction (HI). We generated in-frame deletion and restored frameshift mutants of BoCENH3in Brassica oleraceausing the CRISPR/Cas9 system. The mutants induced paternal haploids by outcrossing. We subsequently generated HI lines with CMS cytoplasm, which enabled the generation of homozygous CMS lines in one step. The BoCENH3-based HI system provides a new DH technology to accelerate breeding in Brassicaand other crops.

The formation of surfaces decorated with biomacromolecules such as proteins, glycans, or nucleic acids with well-controlled orientations and densities is of critical importance for the design of in vitro models, e.g., synthetic cell membranes and interaction assays. To this effect, ligand molecules are often functionalized with an anchor that specifically binds to a surface with a high density of binding sites, providing control over the presentation of the molecules. Here, we present a method to robustly and quantitatively control the surface density of one or several types of anchor-bearing molecules by tuning the relative concentrations of target molecules and free anchors in the incubation solution. We provide a theoretical background that relates incubation concentrations to the final surface density of the molecules of interest and present effective guidelines toward optimizing incubation conditions for the quantitative control of surface densities. Focusing on the biotin anchor, a commonly used anchor for interaction studies, as a salient example, we experimentally demonstrate surface density control over a wide range of densities and target molecule sizes. Conversely, we show how the method can be adapted to quality control the purity of end-grafted biopolymers such as biotinylated glycosaminoglycans by quantifying the amount of residual free biotin reactant in the sample solution.

A large proportion of miscarriages are classified as unexplained miscarriages since no cause is identified. No reliable biomarkers or treatments are available for these pregnancy losses. While our transcriptomic sequencing has revealed substantial upregulation of miR-146b-5p in unexplained miscarriage villous tissues, its role and associated molecular processes have yet to be fully characterized. Our work revealed that relative to samples from normal pregnancy, miR-146b-5p was significantly elevated in villous tissues from unexplained miscarriage patients and displayed promising diagnostic potential. Moreover, miR-146b-5p agomir contributed to higher rates of embryonic resorption in ICR mice. When overexpressed in HTR-8/SVneo cells, miR-146b-5p attenuated the proliferative, invasive, and migratory activity of these cells while suppressing the expression of MMP9 and immune inflammation-associated cytokines, including IL1B, IL11, CXCL1, CXCL8, and CXCL12. Conversely, inhibition of its expression enhanced proliferation, migration, and invasion abilities. Mechanistically, IL-1 receptor-associated kinase-1 and a disintegrin and metalloproteinase 19 were identified as miR-146b-5p targets regulating trophoblast function, and silencing IL-1 receptor-associated kinase-1 had similar effects as miR-146b-5p overexpression, while IL-1 receptor-associated kinase-1 overexpression could partially reverse the inhibitory impact of this microRNA on trophoblasts. miR-146b-5p may inhibit trophoblast proliferation, migration, invasion, and implantation-associated inflammation by downregulating IL-1 receptor-associated kinase-1 and a disintegrin and metalloproteinase 19, participating in the pathogenesis of miscarriage and providing a critical biomarker and a promising therapeutic target for unexplained miscarriage.Overexpressed miR-146b-5p attenuates trophoblast proliferation, migration, invasion, and multiple cytokine expression by downregulating a disintegrin and metalloproteinase 19 and IL-1 receptor-associated kinase-1 in cases of unexplained miscarriage.

tRNA-histidine guanyltransferase 1-like protein (THG1L), located in the mitochondria, plays a crucial role in the tRNA maturation process. Dysfunction of THG1L results in abnormal mitochondrial tRNA modification and neurodevelopmental disorders. To date, few studies have focused on THG1L-related cerebellar ataxia. Whole-exome sequencing revealed compound heterozygous variants NM_017872.5: [c.224A > G]; [c.369-8T > G] in THG1L in a 6-year-old boy with moderate cerebellar ataxia. The variant c.224A > G was demonstrated to downregulate its RNA and protein expression, and c.369-8 T > G resulted in a 7 bp insertion before exon 3. Our case expanded the gene variation and clinical spectrum of THG1L-related cerebellar ataxia.

Cross-sectional studies have shown that remnant cholesterol (RC) was associated with arterial stiffness. The present study evaluated the association of RC and the discordance between RC and low-density lipoprotein cholesterol (LDL-C) with arterial stiffness progression.

Intensive mariculture activities result in eutrophication and enhance coastal deoxygenation. Deoxygenation profoundly influences nitrate reduction processes and further the fate of nitrogen (N) in coastal systems. Herein, 15N isotope labeling, real-time PCR, and high-throughput sequencing techniques were jointly used to investigate the participation and seasonal dynamics of sediment nitrate reduction pathways and the succession of functional microbial communities during the development of seasonal deoxygenation in a coastal aquaculture zone. Denitrification dominated benthic nitrate reduction (46.26–80.91%). Both denitrification and dissimilatory nitrate reduction to ammonium were significantly enhanced by summer deoxygenation (dissolved oxygen levels fell to 2.94 ± 0.28 mg L–1), while anammox remained unchanged. The abundance of the nitrous oxide reductase gene nosZincreased during deoxygenation. The community of the nosZgene was sensitive to deoxygenation, with Azospirillumand Ruegeriaaccounting for the majority. Pelobacterwas overwhelming in the nrfAgene (encoding dissimilatory nitrite reductase) community, which was less affected by deoxygenation. The variations of benthic nitrate reduction processes were driven by bottom water oxygen combined with temperature, chlorophyll a, and microbial gene abundances and community compositions. Our results implicated that seasonal oxygen-deficient zones could be substantial N sinks of coastal ecosystems and important for N balance. Effective management measures need to be developed to avoid further exacerbation of coastal deoxygenation and maintain the sustainable development of mariculture.