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Summary: Cytokine storms caused by viruses are associated with elevated cytokine levels and uncontrolled inflammatory responses that can lead to acute respiratory distress syndrome. Current antiviral therapies are not sufficient to prevent or treat these complications. Cyclophilin A (CypA) is a key factor that regulates the production of multiple cytokines and could be a potential therapeutic target for cytokine storms. Here, three proteolysis targeting chimeras (PROTACs) targeting CypA were designed. These PROTACs bind to CypA, enhance its ubiquitination, and promote its degradation in both cell lines and mouse organs. During influenza B virus (IBV) infection, PROTAC-mediated CypA depletion reduces P65 phosphorylation and NF-κB-mediated proinflammatory cytokine production in A549 cells. Moreover, Comp-K targeting CypA suppresses excessive secretion of proinflammatory cytokines in bronchoalveolar lavage fluid, reduces lung injury, and enhances survival rates of IBV-infected mice. Collectively, we provide PROTACs targeting CypA, which are potential candidates for the control of cytokine storms.

Introduction: The advancement and advocacy of the 3R principle and safety evaluation and risk assessment technology has given rise to an innovative method on a global scale, which known as the Next Generation Risk Assessment (NGRA). This progressive approach adopts a tiered assessment strategy based on prepositive hypothesis and exposure analysis. Unlike traditional risk assessment methods that rely on toxicological endpoint animal tests, NGRA lays emphasis on evaluating the exposure process by outlining the mechanisms and significant key events leading to adverse outcomes. The NGRA process entails prioritizing the robust high-throughput screening and prediction tools to enhance the accuracy, efficiency, and relevance of the assessment. Result: This review provides an overview of the principles, framework, and tools of NGRA as recommended by the International Cosmetics Regulatory Cooperation Organization (ICCR). Furthermore, the review encompasses published case studies of cosmetics ingredients and elucidates the potential applications and technical challenges associated with NGRA while considering the extant safety evaluation requirements for cosmetics ingredients in China. Moreover, the review synthesizes a selection of case studies of cosmetics ingredients and hope to proffer implications for the assessment of risk in traditional Chinese medicine. Conclusion: It was hoped to help researchers to investigate novel techniques of safety risk assessment and propose research concepts for both cosmetics and traditional Chinese medicine.

Spins defined in single-walled carbon nanotubes promise ultra-long spin relaxation times, but qubit implementations require confinement of isolated spins. Here the authors report highly confined long-lived electron spins in chemically functionalized nanotubes and demonstrate their coherent control.

Abstract With the rapid development of electric vehicles (EV), the problem of charging safety is a multifaceted and complex issue. Taking the integration of electric vehicle charging as the research object, including power batteries, charging piles, and power distribution grids, charging data is collected based on data mining technology. At the same time, the factors affecting charging safety are analysed, and an integrated index set is established and optimised. The comprehensive fuzzy evaluation method is used to comprehensively analyse the monitoring data of the electric vehicle charging process, the weight is determined based on the grey correlation method and the expert scoring mechanism, and an integrated evaluation model of the electric vehicle charging process is established. Analyse five sets of charging data in Nanjing through calculation examples, and output the integrated health degree of the electric vehicle charging process so that the equipment can be maintained in a targeted manner, which effectively proves the practicability and reliability of the assessment model.

Abstract A large number of epidemics, including COVID-19 and SARS, quickly swept the world and claimed the precious lives of large numbers of people. Due to the concealment and rapid spread of the virus, it is difficult to track down individuals with mild or asymptomatic symptoms with limited human resources. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications.

Hyaline cartilage plays a critical role in maintaining joint function and pain. However, the lack of blood supply, nerves, and lymphatic vessels greatly limited the self-repair and regeneration of damaged cartilage, giving rise to various tricky issues in medicine. In the past 30 years, numerous treatment techniques and commercial products have been developed and practiced in the clinic for promoting defected cartilage repair and regeneration. Here, the current therapies and their relevant advantages and disadvantages will be summarized, particularly the tissue engineering strategies. Furthermore, the fabrication of tissue-engineered cartilage under research or in the clinic was discussed based on the traid of tissue engineering, that is the materials, seed cells, and bioactive factors. Finally, the commercialized cartilage repair products were listed and the regulatory issues and challenges of tissue-engineered cartilage repair products and clinical application would be reviewed.

From the perspective of improving the physicochemical properties of nerve tissue regeneration materials, biocompatible photocrosslinked gelatin (GelMA) and hydrophilic polymer polyacrylamide (PAM) were selected to construct a composite hydrogel scaffold for supporting Schwann cell growth. With pure GelMA as a control, the morphology, physicochemical properties and mechanical properties of photocrosslinked GelMA-PAM composite hydrogels with different concentrations were tested; The biocompatibility, growth, and differentiation of Schwann cells on scaffolds were observed. The experimental results show that PAM can improve the mechanical properties of GelMA scaffolds. When the mass fractions of GelMA and PAM are 10% and 2.5%, respectively, the scaffolds have good biocompatibility and can promote the differentiation of Schwann neurons. Therefore, the GelMA-PAM composite hydrogel scaffold developed in this experiment can be used as an ideal neural tissue engineering construct.

Tumor mutation burden (TMB) has emerged as an essential biomarker for assessing the efficacy of cancer immunotherapy. However, due to the inherent complexity of tumors, TMB is not always correlated with the responsiveness of immune checkpoint inhibitors (ICIs). Thus, refining the interpretation and contextualization of TMB is a requisite for enhancing clinical outcomes. In this study, we conducted a comprehensive investigation of the relationship between TMB and multi-omics data across 33 human cancer types. Our analysis revealed distinct biological changes associated with varying TMB statuses in STAD, COAD, and UCEC. While multi-omics data offer an opportunity to dissect the intricacies of tumors, extracting meaningful biological insights from such massive information remains a formidable challenge. To address this, we developed and implemented the PGLCN, a biologically informed graph neural network based on pathway interaction information. This model facilitates the stratification of patients into subgroups with distinct TMB statuses and enables the evaluation of driver biological processes through enhanced interpretability. By integrating multi-omics data for TMB prediction, our PGLCN model outperformed previous traditional machine learning methodologies, demonstrating superior TMB status prediction accuracy (STAD AUC: 0.976 ± 0.007; COAD AUC: 0.994 ± 0.007; UCEC AUC: 0.947 ± 0.023) and enhanced interpretability (BA-House: 1.0; BA-Community: 0.999; BA-Grid: 0.994; Tree-Cycles: 0.917; Tree-Grids: 0.867). Furthermore, the biological interpretability inherent to PGLCN identified the Toll-like receptor family and DNA repair pathways as potential combined biomarkers in conjunction with TMB status in gastric cancer. This finding suggests a potential synergistic targeting strategy with immunotherapy for gastric cancer, thus advancing the field of precision oncology.

Motion segmentation is a formidable computer vision task, aiming to segment moving targets from a dynamic scene. In this paper, we choose to introduce an additional modality to bolster the robustness. The event camera is a bio-inspired sensor that accurately detects and captures intensity changes with exceptional temporal resolution and dynamic range, which is an optimal choice for motion segmentation. Therefore, we present a novel framework for event-based motion segmentation and propose Multi-Scale Recurrent Neural Network (MSRNN) to fuse temporal information efficiently. To our best knowledge, it is the first time that a multi-scale recurrent architecture is implemented in event-based motion segmentation. The proposed framework is evaluated through experiments conducted on the EV-IMO dataset. Our method achieves a mean Intersection-over-Union (mIoU) of 82.0%, which sets a new state-of-the-art in motion segmentation. To further validate our approach in arduous real-world scenarios, we introduce the Event Challenging Motion dataset, consisting of 350 images and corresponding events, in which our method outperforms the other methods by 1.5% in Intersection-over-Union (IoU).

Epilepsy as a common disease of the nervous system, with high incidence, sudden and recurrent characteristics. Therefore, timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. Epilepsy seizures is the result of temporal and spatial evolution, Existing deep learning methods often ignore its spatial features, in order to make better use of the temporal and spatial characteristics of epileptic EEG signals. We propose a CBAM-3D CNN-LSTM model to predict epilepsy seizures. First, we apply short-time Fourier transform(STFT) to preprocess EEG signals. Secondly, the 3D CNN model was used to extract the features of preictal stage and interictal stage from the preprocessed signals. Thirdly, Bi-LSTM is connected to 3D CNN for classification. Finally CBAM is introduced into the model. Different attention is given to the data channel and space to extract key information, so that the model can accurately extract interictal and pre-ictal features. Our proposed approach achieved an accuracy of 97.95%, a sensitivity of 98.40%, and a false alarm rate of 0.017 h−1 on 11 patients from the public CHB-MIT scalp EEG dataset. Clinical and Translational Impact Statement—Timely prediction of epileptic seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients.

Due to the complexity of terrain in natural environments, the soft exoskeleton cannot adaptively adjust parameters to achieve the optimal performance. To this end, a design for a soft exoskeleton assistive force parameter optimization method on multi-terrains is presented in this paper. Firstly, the core control parameters are determined by analyzing the system’s motion dynamics. Then, the collected data from inertial measurement unit (IMU) is transferred to the convolutional neural network (CNN) to recognize the certain terrain. In the meanwhile, the control parameters corresponding to the different terrains are optimized by the Bayesian algorithm. Finally, the optimal assistive force parameters are transferred to the system for improving the performance of the soft exoskeleton. The experiment is conducted on three participants, wherein the net metabolic rates of the subjects are compared with and without the assistive force. The final results show that the metabolic rates of the subjects reduce the average value of 19.6% on flat ground, 11.6% on walking uphill, and 12.7% on walking upstairs. The experimental results confirm the effectiveness of the proposed method.

Abstract Background Whole genome bisulfite sequencing (WGBS), possesses the aptitude to dissect methylation status at the nucleotide-level resolution of 5-methylcytosine (5-mC) on a genome-wide scale. It is a powerful technique for epigenome in various cell types, and tissues. As a recently established next-generation sequencing (NGS) platform, GenoLab M is a promising alternative platform. However, its comprehensive evaluation for WGBS has not been reported. We sequenced two bisulfite-converted mammal DNA in this research using our GenoLab M and NovaSeq 6000, respectively. Then, we systematically compared those data via four widely used WGBS tools (BSMAP, Bismark, BatMeth2, BS-Seeker2) and a new bisulfite-seq tool (BSBolt). We interrogated their computational time, genome depth and coverage, and evaluated their percentage of methylated Cs. Result Here, benchmarking a combination of pre- and post-processing methods, we found that trimming improved the performance of mapping efficiency in eight datasets. The data from two platforms uncovered ~ 80% of CpG sites genome-wide in the human cell line. Those data sequenced by GenoLab M achieved a far lower proportion of duplicates (~ 5.5%). Among pipelines, BSMAP provided an intriguing representation of 5-mC distribution at CpG sites with 5-mC levels > ~ 78% in datasets from human cell lines, especially in the GenoLab M. BSMAP performed more advantages in running time, uniquely mapped reads percentages, genomic coverage, and quantitative accuracy. Finally, compared with the previous methylation pattern of human cell line and mouse tissue, we confirmed that the data from GenoLab M performed similar consistency and accuracy in methylation levels of CpG sites with that from NovaSeq 6000. Conclusion Together we confirmed that GenoLab M was a qualified NGS platform for WGBS with high performance. Our results showed that BSMAP was the suitable pipeline that allowed for WGBS studies on the GenoLab M platform.

Objective: The prognosis of preterm premature rupture of membranes (PPROM) combined with chorioamnionitis is often unsatisfactory for both mother and newborn. Although tragic outcomes can be avoided if treated early, no effective prediction method for decision-making is available currently. This study aimed to establish an effective method with maternal inflammation indexes to predict preterm premature rupture of membranes with concomitant chorioamnionitis. Materials and methods: This retrospective study examined the data of 206 singleton PPROM cases and 60 normal full-term cases. The PPROM cases included 93 cases of PPROM with chorioamnionitis and 113 cases of PPROM without chorioamnionitis based on clinical manifestations, laboratory examinations, and histopathological diagnosis. Normal full-term cases were included as the control group. Peripheral blood levels of selected inflammatory indicators were observed 12 h after fetal membrane rupture. Associations between selected inflammatory indicators and chorioamnionitis diagnosis were analyzed. Results: Selected factors except for procalcitonin predicted chorioamnionitis in PPROM patients. Combined results of C-reactive protein and white blood cell (WBC) count showed best predictive ability with area under curve, sensitivity, and specificity of 0.702, 60.22%, and 76.11%, respectively. Furthermore including Interleukine-6 and neutrophil count provided similar predictive results. Conclusions: The best predictive factor combinations for PPROM-CAM were C-reactive protein and white blood cell count. Results of this study provide a useful clinical reference for PPROM-CAM and may improve maternal and infant prognostic outcomes.

Point set registration (PSR) from correspondences is a basic problem in the area of computer vision, robotics and remote sensing. Nevertheless, because of the limited accuracy of current correspondence building and feature matching technologies, PSR is frequently afflicted by the problem of outliers. In this work, we put forward VODRAC (VOting-based Double-point RAndom sampling with Compatibility weighting), a fast, highly robust and practically effective solution for the PSR problem as well as its real-world applications. To realize this, our first contribution is to integrate the scale-invariant constraint with a double-point random sampling framework to achieve the rapid seeking of inlier candidates and the rough rejection of outliers in the meantime. The second contribution is that we introduce the concept of weight matrix for PSR which is to use the robust loss function for weight computation between pairwise correspondences and also propose a time-efficient way to embed the construction of this matrix into the operation of the algorithm without requiring additional computational time. Moreover, we employ the correspondence voting technique to accelerate the consensus maximization (convergence) of the algorithm, as our third contribution. Through comprehensive experiments over multiple actual datasets, we demonstrate that VODRAC is fast, accurate, and highly robust against up to 99% of outliers (e.g. solving PSR within 2 s when there exist 99% outliers among 1000 correspondences), outperforming existing state-of-the-art (SOTA) robust algorithms.

ABSTRACT: The discovery of graphene has led to the devotion of intensive efforts, theoretical and experimental, to produce two-dimensional (2D) materials that can be used for developing functional materials and devices. This work provides a brief review of the recent developments in the lattice models of 2D Dirac materials and their relevant real material counterparts that are crucial for understanding the origins of 2D Dirac cones in electronic band structures as well as their material design and device applications. We focus on the roles of lattice symmetry, atomic orbital hybridization, and spin–orbit coupling in the presence of a Dirac cone. A number of lattice models, such as honeycomb, kagome, ruby, star, Cairo, and line-centered honeycomb, with different symmetries are reviewed based on the tight-binding approach. Inorganic and organic 2D materials, theoretically proposed or experimentally synthesized to satisfy these 2D Dirac lattice models, are summarized.

Abstract Background Stereotactic radiotherapy (SRT) and hypo-fractionated radiotherapy are feasible treatment options for single glioblastoma multiforme when combined with conventional radiotherapy or delivered alone. HyperArc (HA), a novel linac-based method with 4 noncoplanar arcs, has been introduced into stereotactic radiosurgery (SRS) for single and multiple metastases. In this study, we compared the dosimetric quality of HyperArc with the well-established CyberKnife (CK) and conventional VMAT methods of SRT for a single, large target. Methods Sixteen patients treated in our center with their clinical CK plans were enrolled, and the linac-based plans were designed in silico. From the aspect of normal tissue protection and treatment efficacy, we compared the conformity index (CI), gradient index (GI), homogeneity index (HI), dose distribution in planning target volume, dose in the normal brain tissue, and mean dose of several organs at risk (OARs). All of the data were evaluated with nonparametric Kruskal‒Wallis tests. We further investigated the relationship of the dose distribution with the tumor volume and its location. Results The results showed that with a higher CI (0.94 ± 0.03) and lower GI (2.57 ± 0.53), the HA plans generated a lower dose to the OARs and the normal tissue. Meanwhile, the CK plans achieved a higher HI (0.35 ± 0.10) and generated a higher dose inside the tumor. Although manual VMAT showed slight improvement in dose quality and less monitoring units (2083 ± 225), HA can save half of the delivery time of CK (37 minutes) on average. Conclusion HA plans have higher conformity and spare OARs with lower normal tissue irradiation, while CK plans achieve a higher mean dose in tumors. HA with 4 arcs is sufficient in dosimetric quality for a single tumor with great convenience in planning and treatment processes compared with conventional VMAT. The tumor size and location are factors to be considered when selecting treatment equipment.

Abstract The design, fabrication, and measurement of a 7 × 7 mm‐wave array antenna have been presented for 5G communications. The substrate integrated waveguide (SIW)‐fed antenna element is formed of four parts: a rectangular radiating patch, a coupling slot cut onto the broadwall of the SIW, a transition layer, and a feeding layer at the bottom. The bandwidth and radiation efficiency has been improved by embedding an air cavity below the radiating patch, as the effective dielectric constant of the substrate is reduced. Moreover, a prototype has been fabricated and measured to verify the design principles. The finite array operating over 23–28 GHz has achieved active voltage standing wave ratios of less than 2 and 2.5 in the E‐ and H‐planes while scanning up to 45° respectively. Compared with the theoretical ideal gain, the average gain drop of the measured embedded element gain is 0.73dB.

ObjectiveA string-type mooring system is proposed for the permanent berthing demands of special offshore platforms. So it is necessary to study the hydrodynamic performance of the berthing platform using this mooring system to verify its adaptability to the permanent berthing demands.MethodsBased on the three-dimensional potential flow theory, a numerical model of the platform berthed alongside a terminal wharf is established using the boundary element method, and the frequency domain numerical simulation of the moored platform at different tide levels is carried out. Two mooring systems, i.e., string-type and traditional dock mooring, are used to simulate the hydrodynamic performance of the platform in the time domain under extreme wave conditions, and parameterized analysis is conducted to analyze the influence of different tide levels on the motion response of the platform.ResultsBy providing omni-directional restoring force, the string-type mooring system strengthens the rotational motion limitations of the platform, greatly reduce the most violent rolling motion, transfers the energy of environmental load to translational motion, and further strengthens the restriction of the overall motion of the platform; the motion range of the platform is balanced, stable and controllable; and the effects of the system on the motion limit of the platform are less affected by the change in tide level.ConclusionCompared with the traditional mooring system, the string-type mooring system is more suitable for the permanent berthing of special offshore platforms.

Thyroid hormones (TH), adiponectin and brown adipose tissue (BAT) are regulators of energy homoeostasis. Influence of BAT activity on the relationship between TH and adiponectin remains unexplored. The aim of the study was to identify the relationship between TH and adiponectin and to clarify the impact of active BAT on the metabolic effects of adiponectin before and after the correction of thyrotoxicosis. Twenty-one patients with newly diagnosed hyperthyroidism from Graves’ disease were recruited. A titration dosing regimen of thionamide anti-thyroid drug (ATD) was used to establish euthyroidism over 12–24 weeks. Anthropometric, biochemical and adipocytokine parameters were measured before and after control of hyperthyroidism. BAT activity was quantified by fusion 18 F-fluorodeoxyglucose (18 F-FDG) PET/MR imaging, and patients were grouped based on BAT status. Plasma adiponectin level was significantly increased following correction of hyperthyroidism in the overall sample. Free thyroxine (FT4) was also identified as a predictor of adiponectin level in thyroid dysfunction. However, significant changes in adiponectin level and correlations involving adiponectin were absent in BAT-positive patients but maintained in BAT-negative patients. BAT activity diminishes the correlative relationship with body composition and abolishes TH and adiponectin relationships when transitioning from a hyperthyroid to euthyroid state.

Objectives To evaluate the effectiveness of repetitive thoracic paravertebral block (TPVB) under ultrasound (US) guidance for acute pain associated to herpes zoster (HZ) and its prophylactic effects on post-herpetic neuralgia (PHN). Methods Patients who suffered from acute pain associated to HZ within 1 week of rash onset were randomized in a ratio of 1:1 to receive a seven-day course antiviral therapy, antiviral therapy with additional US-guided repetitive TPVB using transverse short axial (TSA) approach every 48 h for a week after antiviral therapy. All patients were allowed to receive rescue analgesics. The primary endpoint was HZ burden of illness (HZ-BOI) measured by a severity-by-duration composite pain assessment conducted 1-month post inclusion. Adverse events were also recorded. Results A total of 96 patients completed the entire 6-month follow-up. The BOI-30AUC was 112.5 (95%CI: 105.2, 119.9) in control group, and 82.7 (95%CI: 75.4, 90.1) in TPVB group (F = 32.252, p

Pancreatic lipase (PL) is a well-known key target for the prevention and treatment of obesity. Human carboxylesterase 1A (hCES1A) has become an important target for the treatment of hyperlipidaemia. Thus, the discovery of potent dual-target inhibitors based on PL and hCES1A hold great potential for the development of remedies for treating related metabolic diseases. In this study, a series of natural triterpenoids were collected and the inhibitory effects of these triterpenoids on PL and hCES1A were determined using fluorescence-based biochemical assays. It was found that oleanolic acid (OA) and ursolic acid (UA) have the excellent inhibitory effects against PL and hCES1A, and highly selectivity over hCES2A. Subsequently, a number of compounds based on the OA and UA skeletons were synthesised and evaluated. Structure–activity relationship (SAR) analysis of these compounds revealed that the acetyl group at the C-3 site of UA (compound 41) was very essential for both PL and hCES1A inhibition, with IC50 of 0.75 µM and 0.014 µM, respectively. In addition, compound 39 with 2-enol and 3-ketal moiety of OA also has strong inhibitory effects against both PL and hCES1A, with IC50 of 2.13 µM and 0.055 µM, respectively. Furthermore, compound 39 and 41 exhibited good selectivity over other human serine hydrolases including hCES2A, butyrylcholinesterase (BChE) and dipeptidyl peptidase IV (DPP-IV). Inhibitory kinetics and molecular docking studies demonstrated that both compounds 39 and 41 were effective mixed inhibitors of PL, while competitive inhibitors of hCES1A. Further investigations demonstrated that both compounds 39 and 41 could inhibit adipocyte adipogenesis induced by mouse preadipocytes. Collectively, we found two triterpenoid derivatives with strong inhibitory ability on both PL and hCES1A, which can be served as promising lead compounds for the development of more potent dual-target inhibitors targeting on PL and hCES1A.

The phased array antenna is one of the critical components of the space-borne SAR system and plays a key role in the quality of images. This paper focuses on the design of active phased array antenna for Gaofen-3 satellite, including its architecture, system design methods, key hardware, and the performance for the design implementation. The proposed phased array antenna have capabilities of beam broadening and scanning, left and right side look, polarization switching, in-orbit calibration, uploading beam-steering correction data, collecting self-checking information, and self-protection caused by over pulse width or duty cycle. The phased array antenna is mainly consisted of dual-polarized slotted waveguide antennas, TR modules, time-delay and amplifier modules and power distribution networks. Moreover, the solutions of the direct problem and inverse problem for accurately predicting the pattern and determining the states of TR modules for the required beam have been proposed. The obtained results can confirm that the cross-polarization level of both polarizations for the phased antenna array is below −35 dB. By employing the proposed antenna model, the beam steering error and dispersion error between the predicted and the measured beams in the range plane are less than 4% (100% probability) and less than 2% (95% probability). For beam steering in the azimuth plane, the beam steering error is less than 2% (90% probability), and less than 4% (100% probability). The ground and on-orbit test results have indicated that the functions and performance of the antenna meet the requirements of the SAR system.

The IMC (interfacial intermetallic compound) produced by the metallurgical reaction between the solder and the Cu plate enables the packaging of IGBT (insulated gate bipolar transistor) modules. In this paper, the growth behavior of IMCs of Sn58Bi/Cu and Sn58Bi-0.05AlN/Cu at different times (10, 30 and 60 min) and temperatures (200, 250 and 300 °C) was investigated, which can provide a reference for the connection of IGBT. It was shown that the interfacial IMC thickness of both solders increased with time and temperature, but the growth of interfacial IMC was suppressed for Sn58Bi solder containing 0.05 wt% AlN. The effect of AlN on the inhibition of grain growth was also observed by EBSD testing. Because of the adsorption effect of AlN nanoparticles, they can adhere to the surface of IMC layer and decrease the effective the diffusion path of unreacted Cu atoms, thereby inhibiting the growth of interfacial IMC. And the effect of AlN particles on interfacial IMC was analyzed via phase field simulation. In addition, under the heating conditions with high temperature and long time, there were fewer cracks in the solder joints containing AlN particles, compared to the Sn58Bi solder. Therefore, AlN nanoparticles can enhance the reliability of Sn58Bi/Cu solder joints.

Traditional voice quality optimization relies on field test, case accumulation and expert experience.It is costly and inefficient to analyze problems by manual test.Through the application of data mining, decision tree machine learning algorithm, geographic visualization and other technologies, a voice experience optimization visualization platform based on big data analysis was developed, which could effectively identify the laws in voice big data, and realize the functions of correlation analysis between user voice experience index and wireless network performance index, intelligent recognition of degradation threshold and image analysis of poor quality areas.It is conducive to reduce the skill threshold of network engineers, improve the work efficiency of network optimization, save network operation and maintenance costs, and provide accurate and effective voice experience improvement solutions for the industry.

Abstract The highly mutated and transmissible Omicron (BA.1) and its more contagious lineage BA.2 have provoked serious concerns over their decreased sensitivity to the current COVID-19 vaccines and evasion from most anti-SARS-CoV-2 neutralizing antibodies (NAbs). In this study, we explored the possibility of combating the Omicron and BA.2 by constructing bispecific antibodies based on non-Omicron NAbs. We engineered 10 IgG-like bispecific antibodies with non-Omicron NAbs named GW01, 16L9, 4L12, and REGN10987 by fusing the single-chain variable fragments (scFvs) of two antibodies through a linker and then connecting them to the Fc region of IgG1. Surprisingly, 8 out of 10 bispecific antibodies showed high binding affinities to the Omicron receptor-binding domain (RBD) and exhibited extreme breadth and potency against pseudotyped SARS-CoV-2 variants of concern (VOCs) including Omicron and BA.2, with geometric mean of 50% inhibitory concentration (GM IC50) values ranging from 4.5 ng/mL to 103.94 ng/mL, as well as the authentic BA.1.1. Six bispecific antibodies containing the cross-NAb GW01 not only neutralized Omicron and BA.2, but also neutralized the sarbecoviruses including SARS-CoV and SARS-related coronaviruses (SARSr-CoVs) RS3367 and WIV1, with GM IC50 ranging from 11.6 ng/mL to 103.9 ng/mL. Mapping analyses of 42 spike (S) variant single mutants of Omicron and BA.2 elucidated that these bispecific antibodies accommodated the S371L/F mutations, which were resistant to most of the non-Omicron NAbs. A cryo-electron microscopy (cryo-EM) structure study of the representative bispecific antibody GW01-16L9 (FD01) in its native full-length IgG form in complex with the Omicron S trimer revealed 5 distinct trimers and one novel trimer dimer conformation. 16L9 scFv binds the receptor-binding motif (RBM), while GW01 scFv binds a epitope outside the RBM. Two scFvs of the bispecific antibody synergistically induced the RBD-down conformation into 3 RBD-up conformation, improved the affinity between IgG and the Omicron RBD, induced the formation of trimer dimer, and inhibited RBD binding to ACE2. The trimer dimer conformation might induce the aggregation of virions and contribute to the neutralization ability of FD01. These novel bispecific antibodies are strong candidates for the treatment and prevention of infection with the Omicron, BA.2, VOCs, and other sarbecoviruses. Engineering bispecific antibodies based on non-Omicron NAbs could turn the majority of NAbs into a powerful arsenal to aid the battle against the pandemic.

Abstract Background Tumor characteristics can be prognostically relevant in patients with high‐grade serous ovarian carcinoma (HGSOC). This study aimed to determine whether different subtypes of HGSOC, especially the mesenchymal subtype, are associated with overall survival (OS) or progression‐free survival (PFS) in patients with HGSOC. Methods PubMed, Embase, and the Cochrane Library were searched for studies published up to September 2020. The eligibility criteria were (1) population: patients with HGSOG with molecular subtyping of their tumor, (2) exposure: mesenchymal subtype, (3) non‐exposure: differentiated, immunoreactive, proliferative, and other non‐mesenchymal subtypes, (4) outcome: survival, with hazard ratios (HRs), and (5) English language. Results The mesenchymal subtype showed no statistically significant difference in OS compared with the immunoreactive subtype (HR = 1.47, 95% CI: 0.78–2.78, p = 0.238; I2 = 81.2%, pheterogeneity = 0.005) or all non‐mesenchymal subtypes (HR = 1.65, 95% CI: 0.97–2.80, p = 0.063; I2 = 79.4%, pheterogeneity = 0.008). The mesenchymal subtype showed no statistically significant difference in PFS compared with the immunoreactive subtype (HR = 1.19, 95% CI: 0.71–2.00, p = 0.514; I2 = 71.6%, pheterogeneity = 0.030) but a significant differences was observed when using all non‐mesenchymal subtypes as reference (HR = 1.51, 95% CI: 1.00–2.28, p = 0.049). The results were robust according to the sensitivity analyses. Conclusions There are no statistically significant differences in OS between the mesenchymal subtype of HGSOC and other subtypes of HGSOC. Because of statistical power, this meta‐analysis cannot conclude about non‐inferiority, and the relationship between the molecular subtypes and HGSOC prognosis remains controversial. Based on one study, the mesenchymal subtype could have a poorer PFS than the non‐mesenchymal subtypes of HGSOC, but this conclusion requires further evidence.

ObjectiveTo examine the prevalence and influencing factors of thyroid disease among adult residents in Liaoning province. MethodsUsing age and sex stratified cluster sampling, we surveyed 2 931 residents aged 18 – 85 years and living in local regions at least 5 years in 2 villages and 2 communities from coastal Yingkou city and inland Shenyang city. Questionnaire interview, thyroid ultrasound examination, detections of serum iodine (SI), urine iodine (UI), iodine in table salt, thyroid function and antibodies, thyroid-stimulating hormone (TSH), free thyroxine (fT4), free triiodothyronine (fT3), thyroid peroxidase antibody (TPOAb), and thyroglobulin antibody (TgAb) were conducted among the participants from 2017 through 2018. Iodine nutrition, prevalence of thyroid diseases and its influencing factors among the participants were analyzed. ResultsFor all the participants, the median of UI and SI were 167.6 µg/L and 62.1 µg/L, indicat-ing an appropriate overall iodine level; the proportions of the participants with lower (40 < µg /L) and higher (90 > µg /L) SI were 4.3% (n = 125) and 6.4% (n = 186). The overall detection rate of thyroid diseases was 37.15%, and the detection rates were 20.37% for thyroid nodules, 16.17% for autoimmune thyroiditis (AITD), 1.91% for endemic goiter, 1.43% for hypothyroidism, 5.53% for subclinical hypothyroidism, 0.92% for hyperthyroidism, and 0.61% for subclinical hyperthyroidism, respectively. The detection rate of hypothyroidism, hyperthyroidism, AITD, goiter and all thyroid diseases differed significantly among the participants with different SI (P < 0.05 for all). The results of multivariate logistic regression analysis revealed following risk factors for thyroid disease: female gender (odds ratio [OR] = 2.03), family history of thyroid disease (OR = 1.65), advanced age (OR = 1.33), and overweight or obesity (OR = 1.60); the results also indicated that SI, intake of iodine-rich foods and income were significant influencing factors of thyroid disease. ConclusionThe prevalence of thyroid disease is at a moderate level and closely influenced by gender, age, family history of thyroid disease, and body mass index among adult residents in Liaoning province. The results suggest that women and middle-aged/elderly people are key populations in thyroid disease prevention and treatment.

Abstract Noise standard deviation (STD) is an important parameter in many digital image processing applications. This paper presents a Gaussian noise parameter estimation algorithm using multiple singular value decomposition (SVD) and non‐linear fitting. The proposed algorithm adds known noise to the original noise image many times to generate a noise‐corrupted image set and then performs SVD on each image. By analyzing the singular values of the noise‐corrupted images, an overdetermined equation system with respect to the noise STD is established. The Gauss–Newton iteration method and backtracking Armijo line search are used to solve the equations, which improve the convergence speed and reduce computational cost. Compared with other methods, the mean error of the proposed algorithm on the TID2008 dataset is 0.028, which is several times lower than other methods. This shows that the performance of our estimator is significantly improved.

Sonogenetics refers to the use of genetically encoded, ultrasound-responsive mediators for noninvasive and selective control of neural activity. It is a promising tool for studying neural circuits. However, due to its infancy, basic studies and developments are still underway, including gauging key in vivo performance metrics such as spatiotemporal resolution, selectivity, specificity, and safety. In this paper, we summarize recent findings on sonogenetics to highlight technical hurdles that have been cleared, challenges that remain, and future directions for optimization.

Cefquinome is widely used to treat respiratory tract diseases of swine. While extra-label dosages of cefquinome could improve clinical efficacy, they might lead to excessively high residues in animal-derived food. In this study, a physiologically based pharmacokinetic (PBPK) model was calibrated based on the published data and a microdialysis experiment to assess the dosage efficiency and food safety. For the microdialysis experiment, in vitro/in vivo relative recovery and concentration-time curves of cefquinome in the lung interstitium were investigated. This PBPK model is available to predict the drug concentrations in the muscle, kidney, liver, plasma, and lung interstitial fluid. Concentration-time curves of 1000 virtual animals in different tissues were simulated by applying sensitivity and Monte Carlo analyses. By integrating pharmacokinetic/pharmacodynamic target parameters, cefquinome delivered at 3–5 mg/kg twice daily is advised for the effective control of respiratory tract infections of nursery pig, which the bodyweight is around 25 kg. Based on the predicted cefquinome concentrations in edible tissues, the withdrawal interval is 2 and 3 days for label and the extra-label doses, respectively. This study provides a useful tool to optimize the dosage regimen of cefquinome against respiratory tract infections and predicts the concentration of cefquinome residues in edible tissues. This information would be helpful to improve the food safety and guide rational drug usage. Author summary Physiologically-based pharmacokinetic (PBPK) models, a mechanism-based model that incorporate physiological and chemical-specific parameters, can describe the absorption, distribution, metabolism and excretion of chemicals in different organs of animals. It can be applied for chemical risk assessment, dosage optimization and withdrawal intervals determination. The Extra-label use antimicrobial drug is a way to solve the treatment failure resulting from antimicrobial resistance. Integrate with pharmacokinetic/pharmacodynamic (PK/PD) parameters and maximum residual limit, PBPK model can determine a safe and effective dosage. This approach would be helpful to improve the food safety and guide rational drug usage.

Current inspections of seed appearance quality are mainly performed manually, which is time-consuming, tedious, and subjective, and creates difficulties in meeting the needs of practical applications. For rapid and accurate identification of seeds based on appearance quality, this study proposed a seed-quality evaluation method that used an improved Inception-ResNet network with corn seeds of different qualities. First, images of multiple corn seeds were segmented to build a single seed image database. Second, the standard convolution of the Inception-ResNet module was replaced by a depthwise separable convolution to reduce the number of model parameters and computational complexity of the network. In addition, an attention mechanism was applied to improve the feature learning performance of the network model and extract the best image information to express the appearance quality. Finally, the feature fusion strategy was used to fuse the feature information at different levels to prevent the loss of important information. The results showed that the proposed method had decent comprehensive performance in detection of corn seed appearance quality, with an average of 96.03% for detection accuracy, 96.27% for precision, 96.03% for recall rate, 96.15% for F1 value of reconciliation, and the average detection time of an image was about 2.44 seconds. This study realized rapid nondestructive detection of seeds and provided a theoretical basis and technical support for construction of intelligent seed sorting equipment.

IntroductionIn the process of rice production and storage, there are many defects in the traditional detection methods of rice appearance quality, but using modern high-precision instruments to detect the appearance quality of rice has gradually developed into a new research trend at home and abroad with the development of agricultural artificial intelligence.MethodsIn this study, we independently designed a fast automatic rice appearance quality detection system based on machine vision technology by introducing convolutional neural network and image processing technology. In this study, NIR and RGB images were generated into five-channel image data by superposition function, and image are preprocessed by combining the Watershed algorithm with the Otus adaptive threshold function. Different grains in the samples were labeled and put in the convolutional neural network for training. The rice grains were classified and the phenotype data were analyzed by selecting the optimal training model to realize the detection of rice appearance quality.Results and discussionThe experimental results showed that the resolution of the system could reach 92.3%. In the detection process, the system designed with this method not only reduces the subjectivity problems caused by different detection environments, visual fatigue caused large sample size and the inspector’s personal factors, but also significantly improves the detection time and accuracy, which further enhances the detection efficiency of rice appearance quality, and has positive significance for the development of the rice industry.

The 2019 novel coronavirus disease (COVID-19) has lasted for over 3 years and has seriously affected the regular life trajectory of human beings. The severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) has caused non-negligible harm to people’s respiratory systems and multiple organs. Although the pathogenesis of COVID-19 has been fully elucidated, there is still a lack of effective and specific treatment for COVID-19. Mesenchymal stem cells (MSCs) or MSC-derived extracellular vesicles (MSC-EVs) by far have become the most promising candidates in preclinical studies and clinical trials, and MSC-related therapies have shown their potential to treat severe COVID-19. The multidirectional differentiation potential and immunomodulatory function of MSCs have allowed the cells to exert multiple cellular and molecular actions on different immune cells and organs. It is critical to understand the therapeutic roles of MSCs before entering the clinics for COVID-19 and other diseases. This review summarizes the recent progress of particular mechanisms underlying the immunomodulatory and tissue regenerative effects of MSCs toward COVID-19. We focused on discussing the functional roles of MSC-mediated effects on immune cell responses, cell survival, and organ regeneration. Besides, the novel discoveries and recent findings of MSC clinical application in patients with COVID-19 were highlighted. This will provide an overview of the current research on the rapid development of MSC-based therapies not only for COVID-19 but also for other immune-mediated/immune-dysregulating diseases.

Algal genomics research contributes to a deeper understanding of algal evolution and provides useful genomics inferences correlated with various functions. Published algal genome sequences are very limited owing to genome assembly challenges. Because genome data of freshwater algae are rapidly increasing with the recent boom in next-generation sequencing and bioinformatics, an interface to store, interlink, and display these data is needed. To provide a substantial genomic resource specifically for freshwater algae, we developed the Freshwater Algae Database (FWAlgaeDB), a user-friendly, constantly updated online repository for integrating genomic data and annotation information. This database, which includes information on 204 freshwater algae, allows easy access to gene repertoires and gene clusters of interest and facilitates potential applications. Three functional modules are integrated into FWAlgaeDB: a Basic Local Alignment Search Tool tool for similarity analyses, a Search tool for rapid data retrieval, and a Download function for data downloads. This database tool is freely available at http://www.fwalagedb.com/#/home. To demonstrate the utility of FWAlgaeDB, we also individually mapped metagenomic sequencing reads of 10 water samples to FWAlgaeDB and Nt algae databases we constructed to obtain taxonomic composition information. According to the mapping results, FWAlgaeDB may be a better choice for identifying algal species in freshwater samples, with fewer potential false positives because of its focus on freshwater algal species. FWAlgaeDB can therefore serve as an open-access, sustained platform to provide genomic data and molecular analysis tools specifically for freshwater algae.

This study aims to explore influence mechanism of multi-participants risk perception to the public opinion dissemination. Based on the complex network theory, the dynamic model of network public opinion dissemination is constructed, applied social network method to analyze the influence mechanism of information interaction of network public opinion participants on network public opinion communication, using the method of simulation, this paper discussed the influence of external factors on the communication state and risk of public opinion under the conditions of single interference, information superposition and time series transformation. The results show the advantage of government information release before large-scale dissemination is that can reduce the spread risk of public opinion. The media information release later than the government has a positive effect on the risk prevention and control of public opinion propagation. Under the interaction of media and government information, the information superposition effect is more significant, and superposition effect appearance is related to the order of the participants information release and the reliability of the facts represented by participants.

Background: Metabolic-associated fatty liver disease (MAFLD) has become an important cause of liver cancer. The current understanding of MAFLD-related liver cancer is not sufficient, however. Objective: The objective of this study was to investigate the clinical and metabolic characteristics of inpatients with MAFLD-related liver cancer. Design: This is a cross-sectional investigation. Methods: An investigation was conducted to collect the cases of hepatic malignant tumor hospitalized in Beijing Ditan Hospital, Capital Medical University from 1 January 2010 to December 31 2019. The basic information, medical history, laboratory examination results, and imaging examination results of 273 patients diagnosed with MAFLD-related liver cancer were recorded. The general information and metabolic characteristics of patients with MAFLD-related liver cancer were analyzed. Results: In total, 5958 patients were diagnosed with hepatic malignant tumor. Among them, liver cancer due to other causes accounted for 6.19% (369/5958), MAFLD-related liver cancer was diagnosed in 273 cases of them. From 2010 to 2019, MAFLD-related liver cancer showed an increasing trend. Among 273 patients with MAFLD-related liver cancer, 60.07% were male, 66.30% were ⩾60 years old, and 43.22% had cirrhosis. The 273 patients were comprised by 38 patients with evidence of fatty liver and 235 patients without evidence of fatty liver. There was no significant difference in the proportions of sex, age, overweight/obesity, type 2 diabetes, and the presence of ⩾2 metabolic-related factors between the two groups. In the group without evidence of fatty liver, 47.23% patients had cirrhosis, which was significantly higher than 18.42% in the group with evidence of fatty liver ( p

ABSTRACT The plant essential oil component trans-cinnamaldehyde (t-CIN) exhibits antibacterial activity against a broad range of foodborne pathogenic bacteria, including L. monocytogenes, but its mode of action is not fully understood. In this study, several independent mutants of L. monocytogenes with increased t-CIN tolerance were obtained via experimental evolution. Whole-genome sequencing (WGS) analysis revealed single-nucleotide-variation mutations in the yhfK gene, encoding an oxidoreductase of the short-chain dehydrogenases/reductases superfamily, in each mutant. The deletion of yhfK conferred increased sensitivity to t-CIN and several other α,β-unsaturated aldehydes, including trans-2-hexenal, citral, and 4-hydroxy-2-nonenal. The t-CIN tolerance of the deletion mutant was restored via genetic complementation with yhfK. Based on a gas chromatography-mass spectrometry (GC-MS) analysis of the culture supernatants, it is proposed that YhfK is an ene reductase that converts t-CIN to 3-phenylpropanal by reducing the C=C double bond of the α,β-unsaturated aldehyde moiety. YhfK homologs are widely distributed in Bacteria, and the deletion of the corresponding homolog in Bacillus subtilis also caused increased sensitivity to t-CIN and trans-2-hexenal, suggesting that this protein may have a conserved function to protect bacteria against toxic α,β-unsaturated aldehydes in their environments. IMPORTANCE While bacterial resistance against clinically used antibiotics has been well studied, less is known about resistance against other antimicrobials, such as natural compounds that could replace traditional food preservatives. In this work, we report that the food pathogen Listeria monocytogenes can rapidly develop an elevated tolerance against t-cinnamaldehyde, a natural antimicrobial from cinnamon, by single base pair changes in the yhfK gene. The enzyme encoded by this gene is an oxidoreductase, but its substrates and precise role were hitherto unknown. We demonstrate that the enzyme reduces the double bond in t-cinnamaldehyde and thereby abolishes its antibacterial activity. Furthermore, the mutations linked to t-CIN tolerance increased bacterial sensitivity to a related compound, suggesting that they modify the substrate specificity of the enzyme. Since the family of oxidoreductases to which YhfK belongs is of great interest in the mediation of stereospecific reactions in biocatalysis, our work may also have unanticipated application potential in this field.

Traditional industrial robots often face challenges in achieving a perfectly polished surface on a workpiece because of their high mechanical rigidity. The active compliance force control device installed at the robotic arm’s end enables high-precision contact force control between the grinding tool and the workpiece. However, the complex hysteresis nonlinearity between cylinder air pressure and output force, as well as various random disturbances during the grinding process, can affect the accuracy of the contact force and potentially impact the grinding effect of the workpiece, even causing irreversible damage to the surface of the workpiece. Given the complex random variation of cylinder output force in the actual grinding process, a rate-dependent hysteresis model based on diagonal recurrent neural network and Pradtl–Ishlinskii models named dRNN-PI is designed to compensate for the complex nonlinear hysteresis of the cylinder and calculate the desired air pressure to maintain a steady contact force on the workpiece. The proxy-based sliding mode control (PSMC) is utilized to quickly track the desired air pressure without overshooting. This paper also proves the controller’s stability using the Lyapunov-based methods. Finally, the accuracy of the proposed hysteresis compensation model and the effectiveness and robustness of the PSMC are verified by experiment results.

Soil erosion results in dissolved organic matter (DOM) loss and is one of the main paths of soil carbon loss. Bacteria affect the generation and transformation of DOM. However, the effect of bacteria on the composition and slope distribution of DOM has rarely been investigated under field conditions. Based on a long-term experiment of three gradients (3°, 5°, 8°) in a black soil erosion area of Northeast China, the content, composition, and source of DOM were studied. The results showed that the DOM of the 3° and 5° slope was enriched midslope, and the DOM of the 8° slope was enriched downslope. Parallel factor (PARAFAC) analysis indicated that the main substances in DOM were fulvic-like acid, humic-like acid, tryptophan-like protein, and soluble microbial metabolites. The upslope and downslope soils of 3° and 5° slopes showed high DOM bioavailability, while the downslope soil of the 8° slope showed high DOM bioavailability. The content of new DOM in downslope soil increased with the gradient. Bacteria played an important role in the synthesis and transformation of DOM and affected its composition and slope distribution. Verrucomicrobiota, Firmicutes, Planctomycetota, and Gemmatimonadota were the main factors affecting soil DOM. The results could be helpful in understanding the loss mechanism of DOM in eroded black soil and provide support for soil carbon sequestration.

Hydroxamic acid and fatty acid collectors are commonly used in cassiterite flotation but face issues like poor selectivity, high dosage, and strict requirements on ore composition and grinding fineness. This study investigates the collecting performance of a novel flotation reagent, 5-dodecylsalicylaldoxime (DSA), in cassiterite flotation. DSA exhibits remarkable selectivity, achieving an impressive 82.5% recovery of Sn at a concentration of only 9 × 10−5 mol/L in single mineral flotation tests. Moreover, DSA significantly outperforms benzohydroxamic acid (BHA), enhancing Sn recovery by 33.55% in artificially mixed ore flotation experiments. In the flotation test of a copper–tin polymetallic ore, compared with the BHA flotation effect, the recovery rate of DSA increased by 12.29% when the Sn grade remained basically unchanged. Analyses such as zeta potential, FT-IR, and XPS indicate that DSA’s superior collecting performance stems from its stable adsorption onto cassiterite surfaces through a chelating ring formation, resembling the adsorption mechanism of hydroxamic acid collectors. Furthermore, DSA’s larger cluster size in the solution compared to BHA contributes to its enhanced selectivity and collectability. Overall, DSA emerges as a promising alternative to traditional cassiterite flotation collectors, offering a combination of enhanced selectivity, lower dosage requirements, and robustness in complex ore systems.

Visual-inertial odometry (VIO) algorithms, fusing various features such as points and lines, are able to improve their performance in challenging scenes while the running time severely increases. In this paper, we propose a novel lightweight point–line visual–inertial odometry algorithm to solve this problem, called LRPL-VIO. Firstly, a fast line matching method is proposed based on the assumption that the photometric values of endpoints and midpoints are invariant between consecutive frames, which greatly reduces the time consumption of the front end. Then, an efficient filter-based state estimation framework is designed to finish information fusion (point, line, and inertial). Fresh measurements of line features with good tracking quality are selected for state estimation using a unique feature selection scheme, which improves the efficiency of the proposed algorithm. Finally, validation experiments are conducted on public datasets and in real-world tests to evaluate the performance of LRPL-VIO and the results show that we outperform other state-of-the-art algorithms especially in terms of speed and robustness.

Simultaneous measurements of atmospheric volatile organic compounds (VOCs), conventional gases and meteorological parameters were performed at an urban site in Jinan, East China, in June 2021 to explore the formation and evolution mechanisms of summertime ozone (O3) pollution events. O3 Episode Ⅰ, O3 Episode II, and non-O3 episodes were identified based on the China Ambient Air Quality Standards and the differences in precursor concentrations. The O3 concentrations in Episode I and Episode II were 145.4 μg/m3 and 166.4 μg/m3, respectively, which were significantly higher than that in non-O3 episode (90 μg/m3). For O3 precursors, VOCs and NOx concentrations increased by 48% and 34% in Episode I, and decreased by 21% and 27% in Episode II compared to non-O3 episode days. The analysis of the m,p-xylene to ethylbenzene ratio (X/E) and OH exposure demonstrated that the aging of the air masses in Episode II was significantly higher than the other two episodes, and the differences could not be explained by localized photochemical consumption. Therefore, we speculate that the high O3 concentrations in Episode II were driven by the regional transport of O3 and its precursors. Backward trajectory simulations indicated that the air masses during Episode II were concentrated from the south. In contrast, the combination of high precursor concentrations and favorable meteorological conditions (high temperatures and low humidity) led to an excess of O3 in Episode I. Positive matrix factorization (PMF) model results indicated that increased emissions from combustion and gasoline vehicle exhausts contributed to the elevated concentrations of VOCs in Episode I, and solvent usage may be an important contributor to O3 formation. The results of this study emphasize the importance of strengthening regional joint control of O3 and its precursors with neighboring cities, especially in the south, which is crucial for Jinan to mitigate O3 pollution.

The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides deep Hubble Space Telescope (HST) F275W and F435W imaging over four CANDELS fields (GOODS-N, GOODS-S, COSMOS, and Extended Groth Strip). We combine this newly acquired UV imaging with existing HST imaging from CANDELS as well as existing ancillary data to obtain robust photometric redshifts and reliable estimates for galaxy physical properties for over 150,000 galaxies in the ∼430 arcmin ^2 UVCANDELS area. Here, we leverage the power of the new UV photometry to not only improve the photometric redshift measurements in these fields, but also constrain the full redshift probability distribution combining multiple redshift-fitting tools. Furthermore, using the full UV-to-IR photometric data set, we measure the galaxy physical properties by fitting templates from population synthesis models with two different parameterizations (flexible and fixed form) of the star formation histories (SFHs). Compared to the flexible SFH parameterization, we find that the fixed-form SFHs systematically underestimate the galaxy stellar masses, both at the low-mass (≲10 ^9 M _⊙ ) and high-mass (≳10 ^10 M _⊙ ) end, by as much as ∼0.5 dex. This underestimation is primarily due the limited ability of fixed-form SFH parameterization to simultaneously capture the chaotic nature of star formation in these galaxies.

UVCANDELS is a Hubble Space Telescope Cycle-26 Treasury Program awarded 164 orbits of primary ultraviolet (UV) F275W imaging and coordinated parallel optical F435W imaging in four CANDELS fields—GOODS-N, GOODS-S, EGS, and COSMOS—covering a total area of ∼426 arcmin ^2 . This is ∼2.7 times larger than the area covered by previous deep-field space UV data combined, reaching a depth of about 27 and 28 ABmag (5 σ in 0.”2 apertures) for F275W and F435W, respectively. Along with new photometric catalogs, we present an analysis of the rest-frame UV luminosity function (LF), relying on our UV-optimized aperture photometry method, yielding a factor of 1.5 increase over H-isophot aperture photometry in the signal-to-noise ratios of galaxies in our F275W imaging. Using well-tested photometric redshift measurements, we identify 5810 galaxies at redshifts 0.6 < z < 1, down to an absolute magnitude of M _UV = −14.2. In order to minimize the effect of uncertainties in estimating the completeness function, especially at the faint end, we restrict our analysis to sources above 30% completeness, which provides a final sample of 4726 galaxies at −21.5 < M _UV < −15.5. We performed a maximum likelihood estimate to derive the best-fit parameters of the UV LF. We report a best-fit faint-end slope of $\alpha =-{1.359}_{-0.041}^{+0.041}$ at z ∼ 0.8. Creating subsamples at z ∼ 0.7 and z ∼ 0.9, we observe a possible evolution of α with redshift. The unobscured UV luminosity density at M _UV < −10 is derived as ${\rho }_{{\rm{U}}{\rm{V}}}={1.339}_{-0.030}^{+0.027}\,(\times {10}^{26}\,{\rm{e}}{\rm{r}}{\rm{g}}\,{{\rm{s}}}^{-1}\,{{\rm{H}}{\rm{z}}}^{-1}\,{{\rm{M}}{\rm{p}}{\rm{c}}}^{-3})$ using our best-fit LF parameters. The new F275W and F435 photometric catalogs from UVCANDELS have been made publicly available on the Barbara A. Mikulski Archive for Space Telescopes.

Resonant scattering (RS) of X-ray line emission in supernova remnants (SNRs) may modify the observed line profiles and fluxes and has a potential impact on estimating the physical properties of the hot gas and hence on understanding the SNR physics, but has not been theoretically modeled ever. Here we present our Monte Carlo simulation of the RS effect on X-ray resonant-line emission, typified by the O vii He α r line, from SNRs. We employ the physical conditions characterized by the Sedov–Taylor solution and some basic parameters similar to those in Cygnus Loop. We show that the impact of the RS effect is most significant near the edge of the remnant. The line profiles are predicted to be asymmetric because of different temperatures and photon production efficiencies of the expanding gas at different radii. We also predict the surface brightness of the line emission would decrease in the outer projected region but is slightly enhanced in the inner. The G-ratio of the O vii He α triplet can be effectively elevated by RS in the outer region. We show that the RS effect of the O vii He α r line in the southwestern boundary region of Cygnus Loop is nonnegligible. The observed O vii G-ratio of ∼1.8 of the region could be achieved with RS taken into account for properly elevated O abundance from the previous estimates. Additional simulation performed for the SNRs in ejecta-dominated phases like Cas A shows that RS in the shocked ejecta may have some apparent effects on the observational properties of oxygen-resonant lines.

Bacterial resistance is a growing problem worldwide, and the number of deaths due to drug resistance is increasing every year. We must pay great attention to bacterial resistance. Otherwise, we may go back to the pre-antibiotic era and have no drugs on which to rely. Bacterial resistance is the result of several causes, with efflux mechanisms widely recognised as a significant factor in the development of resistance to a variety of chemotherapeutic and antimicrobial medications. Efflux pump inhibitors, small molecules capable of restoring the effectiveness of existing antibiotics, are considered potential solutions to antibiotic resistance and have been an active area of research in recent years. This article provides a review of the efflux mechanisms of common clinical pathogenic bacteria and their efflux pump inhibitors and describes the effects of efflux pump inhibitors on biofilm formation, bacterial virulence, the formation of bacterial persister cells, the transfer of drug resistance among bacteria, and mismatch repair. Numerous efforts have been made in the past 20 years to find novel efflux pump inhibitors which are known to increase the effectiveness of medicines against multidrug-resistant strains. Therefore, the application of efflux pump inhibitors has excellent potential to address and reduce bacterial resistance.

An intelligent lower-limb prosthesis can provide walking support and convenience for lower-limb amputees. Trajectory planning of prosthesis joints plays an important role in the intelligent prosthetic control system, which directly determines the performance and helps improve comfort when wearing the prosthesis. Due to the differences in physiology and walking habits, humans have their own walking mode that requires the prosthesis to consider the individual’s demands when planning the prosthesis joint trajectories. The human is an integral part of the control loop, whose subjective feeling is important feedback information, as humans can evaluate many indicators that are difficult to quantify and model. In this study, trajectories were built using the phase variable method by normalizing the gait curve to a unified range. The deviations between the optimal trajectory and current were represented using Fourier series expansion. A gait dataset that contains multi-subject kinematics data is used in the experiments to prove the feasibility and effectiveness of this method. In the experiments, we optimized the subjects’ gait trajectories from an average to an individual gait trajectory. By using the individual trajectory planning algorithm, the average gait trajectory can be effectively optimized into a personalized trajectory, which is beneficial for improving walking comfort and safety and bringing the prosthesis closer to intelligence.

Despite advancements in analytical technologies, the complex nature of cosmetic matrices, coupled with the presence of diverse and trace unauthorized additives, hinders the application of these technologies in cosmetics analysis. This not only impedes effective regulation of cosmetics but also leads to the continual infiltration of illegal products into the market, posing serious health risks to consumers. The establishment of cosmetic regulations is often based on extensive scientific experiments, resulting in a certain degree of latency. Therefore, timely advancement in laboratory research is crucial to ensure the timely update and adaptability of regulations. A comprehensive understanding of the composition of cosmetic matrices and their pretreatment technologies is vital for enhancing the efficiency and accuracy of cosmetic detection. Drawing upon the China National Medical Products Administration’s 2021 Cosmetic Classification Rules and Classification Catalogue, we streamline the wide array of cosmetics into four principal categories based on the following compositions: emulsified, liquid, powdered, and wax-based cosmetics. In this review, the characteristics, compositional elements, and physicochemical properties inherent to each category, as well as an extensive overview of the evolution of pretreatment methods for different categories, will be explored. Our objective is to provide a clear and comprehensive guide, equipping researchers with profound insights into the core compositions and pretreatment methods of cosmetics, which will in turn advance cosmetic analysis and improve detection and regulatory approaches in the industry.

Existing maize production is grappling with the hurdles of not applying nitrogen fertilizer accurately due to subpar detection accuracy and responsiveness. This situation presents a significant challenge, as it has the potential to impact the optimal yield of maize and ultimately, the profit margins associated with its cultivation. In this study, an automatic modeling prediction method for nitrogen content in maize leaves was proposed based on machine vision and convolutional neural network. We developed a program designed to streamline the image preprocessing workflow. This program can process multiple images in batches, automatically carrying out the necessary preprocessing steps. Additionally, it integrates an automated training and modeling system that correlates the images with nitrogen content values. The primary objective of this program is to enhance the accuracy of the models by leveraging a larger dataset of image samples. Secondly, the fully connected layer of the convolutional neural network was reconstructed to transform the optimization goal from classification based on 0–1 tags into regression prediction, so that the model can output numerical values of nitrogen content. Furthermore, the prediction model of nitrogen content in maize leaves was gained by training many samples, and samples were collected in three key additional fertilizing stages throughout the growth period of maize (i.e., jointing stage, bell mouth stage, and tasseling stage). In addition, the proposed method was compared with the spectral detection method under full-wave band and characteristic wavelengths. It was verified that our machine vision and CNN (Convolutional Neural Network)-based method offers a high prediction accuracy rate that is not only consistently better—by approximately 5% to 45%—than spectral detection approaches but also features the benefits of easy operation and low cost. This technology can significantly contribute to the implementation of more precise fertilization practices in maize production, leading to potential yield optimization and increased profitability.

As an MFI-type zeolite, ZSM-5 zeolite has wide applications in industry, such as in the fine chemical, petrochemical, and coal chemical industries. However, shape control of ZSM-5 nanocrystals constitutes one of the major challenges of current nanotechnology. Here, the MFI framework structure was used as the theoretical model of pure silicon ZSM-5 to investigate the surface energy and Wulff shape. The models with different crystal surfaces were simulated by molecular dynamics (MD) with the assistance of machine learning potentials (MLPs). The factors influencing the crystal surface energy, such as temperature, pH, and ionic concentration, have been studied in detail. Depending on the calculated surface energies, the crystal surface morphology and its ratio were obtained by means of the Wulff theorem. The results show that the area in the equilibrium shape of the (110) surface is usually the largest, and its proportion varies with external conditions. A high temperature and high concentration of the aluminum source promoted the growth of the (110) crystal surface, and the theoretical value of the crystal surface ratio was as high as 90%. This study provides theoretical insight into the synthesis of zeolites with different morphologies of all-silicon or low-aluminum.