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AbstractIn this study phenolic resin (Phen)-loaded PCS (Phen-PCS) films were prepared and quartz fiber/Phen-PCS composites based on the resin films were also prepared. The quartz fiber/Phen-PCS composites were prepared by impregnating 2.5 Dimension (2.5D was an arrangement of fibers) quartz fibers using the resin film infusion (RFI) process. The properties of the composites were investigated. The study demonstrated that the density of the composites, after curing gradually increased with the increase in PCS. The flexural strength of the composites exhibited a trend of increasing and then decreasing after curing and pyrolysis at 1100 °C under an N2 atmosphere. The flexural strength of the composites reached 13.73 MPa when the addition of PCS was 30% of the phenolic resin, representing an increase of 74.23% compared to that of the composites without PCS. The porosity was found to be as low as 2.3% after curing and 2.1% after pyrolysis. X-ray diffraction (XRD) analysis and infrared spectroscopy tests demonstrated that ceramicization products, such as SiO2 and SiC, were produced after pyrolysis of the composites containing PCS. This transformation significantly enhanced the ablation resistance of the composites. Butane flame tests were performed, and the lowest line and mass ablation rates were 0.026 mm/s and 0.196 mg/s after curing. The results of the study revealed the potential of PCS as a precursor to improve the mechanical properties and heat resistance of composites, which offers promising material options for high-temperature applications. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation of stern slamming and global responses using a segmented model test. A model of large cruise ship with a long and flat stern is selected for the test, with a scale ratio of 1:60. Experimental random uncertainty is analyzed by considering the model responses in a series of regular waves. It is found that different types of response have different levels of dispersion. A calculation model that can reproduce ship–wave relative motions during stern slamming is established based on the experimental data. The influences of the instantaneous impact velocity and longitudinal deadrise angle on the distribution of local slamming pressure are analyzed. Finally, the effect of stern slamming on global responses is discussed. This investigation shows that the longitudinal deadrise angles are usually small in following waves, and this results in a large slamming force loaded on the stern in a short time. In addition, a whipping response of the hull girder will be excited. • The characteristics of stern slamming and whipping responses to a large cruise ship in different wave heights are revealed. [ABSTRACT FROM AUTHOR]

CONSTANS-like (COL) genes play crucial roles in the regulation of photoperiodic flowering and responses to biotic and abiotic stresses; however, COL genes in sunflower (HaCOL) have not been extensively studied. In the present study, we identified 22 putative HaCOL genes in the whole genome sequence of sunflower, which were unevenly distributed on 10 chromosomes. The phylogenetic analysis showed that the HaCOLs could be divided into three well-defined groups (groups I, II and III). Group III had the highest number of COL genes among the three tested monocots, while the highest number of COL genes were observed in group I among the five tested dicots. Among all eight species, group II had the fewest COL family members. Segmental duplications contributed greatly to the expansion of this gene family. Furthermore, 11 tissue expression profiles of 22 HaCOL genes were analyzed through RNA-seq, which displayed their tissue-specific expression profiles. In addition, the expression levels of these HaCOL genes were analyzed under hormone, cadmium (Cd), heat, and drought stresses, and the results showed that HaCOL3 , HaCOL6 , and HaCOL19 were more sensitive than other HaCOLs to certain abiotic stresses. Moreover, the duplicated genes conferred important neofunctionalization in response to abiotic stresses. On the basis of our findings, we speculate that this conserved, duplicated and neofunctionalized COL gene family may play pivotal roles in the adaptation of sunflower to adverse environments. • A total of 22 putative CONSTANS-like genes identified in sunflower genome. • Sunflower CONSTANS-like genes could be divided into three well defined groups. • Segmental duplications contribute greatly to the expansion of this gene family. • Duplicated genes confer their novel functions in response to abiotic stresses. • The neofunctionalized sunflower COLs involved in environmental adaptation. [ABSTRACT FROM AUTHOR]

The original version of this Article contained an error in Reference 34, which was incorrectly given as: [ABSTRACT FROM AUTHOR]

Multi-NbN films manufactured by a hybrid process comprising pre-treatment rotation accelerated shot peening (RASP) and double glow plasma surface metallurgy (DG technique) and designed to protect titanium alloys were assessed to determine their tribological and mechanical properties. The surface microstructures of a modified TA15 alloy were characterized using X-ray diffraction, scanning electron microscopy, 3D-profilometer and micro-Vickers hardness testing. The results reveal that the distribution of N concentration on layers was influenced by the pre-treatment with RASP, in which a dense nitride gradient layer was formed inside the films and a new phase β-Nb 2 N occurred at the film surface. This is because a large number of defects on the surface layer were generated by the pre-treatment with RASP. While multi-NbN films on the original sample without new phase detected can be divided into three zones—Nb Ti interdiffusion layer, Nb-Ti-N interdiffusion layer, and NbN layer. Strong increase in surface hardness was verified in correspondence of elevated peening velocity, and the hardest RASP-60 m/s sample is up to 30.1803 GPa. However, the thickness of films cannot keep increasing trend in agreement with increasing shot peeing velocity. The thickest films occurred on the RASP-40 m/s sample, is 5.85 μm. The results show that the RASP-treated samples exhibited more serious damage than the untreated sample, due to the formation of β-Nb 2 N phase with brittle structure which bonding on the course surface are incapable of overcoming the damage of shearing stress from counterparts. And once starting to crack, the films that are supposed to protect the substrate are turned into hard abrasive particles that adversely affect the wear resistance of the substrate. • Multi-NbN films were manufactured by rotation accelerated shot peening and double-glow plasma technique on titanium alloys. • The effects of peening velocity for multi-NbN films deposition process and films-failure mechanism were discussed. • The Jonsson-Höymark model was used to evaluate the proper hardness of modified layers. [ABSTRACT FROM AUTHOR]

Background: Valproic acid (VPA) is a commonly used broad-spectrum antiepileptic drug. For elderly epileptic patients, VPA plasma concentrations have a considerable variation. We aim to establish a prediction model via a combination of machine learning and population pharmacokinetics (PPK) for VPA plasma concentration., Methods: A retrospective study was performed incorporating 43 variables, including PPK parameters. Recursive Feature Elimination with Cross-Validation was used for feature selection. Multiple algorithms were employed for ensemble model, and the model was interpreted by Shapley Additive exPlanations., Results: The inclusion of PPK parameters significantly enhances the performance of individual algorithm model. The composition of categorical boosting, light gradient boosting machine, and random forest (7:2:1) with the highest R 2 (0.74) was determined as the ensemble model. The model included 11 variables after feature selection, of which the predictive performance was comparable to the model that incorporated all variables., Conclusions: Our model was specifically tailored for elderly epileptic patients, providing an efficient and cost-effective approach to predict VPA plasma concentration. The model combined classical PPK with machine learning, and underwent optimization through feature selection and algorithm integration. Our model can serve as a fundamental tool for clinicians in determining VPA plasma concentration and individualized dosing regimens accordingly., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Background: Teicoplanin has been widely used in patients with infections caused by Staphylococcus aureus, especially for critically ill patients. The pharmacokinetics (PK) of teicoplanin vary between individuals and within the same individual. We aim to establish a prediction model via a combination of machine learning and population PK (PPK) to support personalized medication decisions for critically ill patients., Methods: A retrospective study was performed incorporating 33 variables, including PPK parameters (clearance and volume of distribution). Multiple algorithms and Shapley additive explanations were employed for feature selection of variables to determine the strongest driving factors., Results: The performance of each algorithm with PPK parameters was superior to that without PPK parameters. The composition of support vector regression, categorical boosting and a backpropagation neural network (7:2:1) with the highest R2 (0.809) was determined as the final ensemble model. The model included 15 variables after feature selection, of which the predictive performance was superior to that of models considering all variables or using only PPK. The R2, mean absolute error, mean squared error, absolute accuracy (±5 mg/L) and relative accuracy (±30%) of external validation were 0.649, 3.913, 28.347, 76.12% and 76.12%, respectively., Conclusions: Our study offers a non-invasive, fast and cost-effective prediction model of teicoplanin plasma concentration in critically ill patients. The model serves as a fundamental tool for clinicians to determine the effective plasma concentration range of teicoplanin and formulate individualized dosing regimens accordingly., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

In coal mining areas, surface subsidence poses significant risks to human life and property. Fortunately, surface subsidence caused by coal mining can be monitored and predicted by using various methods, e.g., probability integral method and deep learning (DL) methods. Although DL methods show promise in predicting subsidence, they often lack accuracy due to insufficient consideration of spatial correlation and temporal nonlinearity. Considering this issue, we propose a novel DL-based approach for predicting mining surface subsidence. Our method employs K-means clustering to partition spatial data, allowing the application of a gate recurrent unit (GRU) model to capture nonlinear relationships in subsidence time series within each partition. Optimization using snake optimization (SO) further enhances model accuracy globally. Validation shows our method outperforms traditional Long Short-Term Memory (LSTM) and GRU models, achieving 99.1% of sample pixels with less than 8 mm absolute error., (© 2024. The Author(s).)

High-energy exciton emission could allow single-component multi-colour display or white light-emitting diodes. However, the thermal relaxation of high-energy excitons is much faster than the photon emission of them, making them non-emissive. Here, we report quantum dots with light hole-heavy hole splitting exhibiting strong high-energy exciton electroluminescence from high-lying light holes, opening a gate for high-performance multi-colour light sources. The high-energy electroluminescence can reach 44.5% of the band-edge heavy-hole exciton emission at an electron flux density Φ e of 0.71 × 10 19  s -1 cm -2 - 600 times lower than the photon flux density Φ p (4.3 × 10 21  s -1 cm -2 ) required for the similar ratio. Our simulation and experimental results suggest that the oscillator strength of heavy holes reduces more than that of light holes under electric fields. We attribute this as the main reason for strong light-hole electroluminescence. We observe this phenomenon in both Cd x Zn 1-x Se-ZnS and CdSe-CdS core-shell quantum dots exhibiting large light hole-heavy hole splittings., (© 2024. The Author(s).)

Introduction: Sleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients' suffering., Methods: To this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany., Results: The experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%., Discussion: All of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor's diagnostic process and provide a better medical experience for patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xiong, Wang, He, Wang, Liu, Sun, Zhang and Zhang.)

Electron and hole spin polarization is crucial for quantum dots to be used in spin lasers and quantum information processing. However, the degree of spin polarization in II-VI and III-V semiconductor quantum dots is low because of the degenerated valence band. Here, we increase the light and heavy hole degeneracy by introducing biaxial strain into CdSe-based quantum dots, enabling the degree of spin polarization to be increased from 20% to 50% under photoexcitation. The optical gain threshold measurement further reveals that the increase in polarization helps to reduce the gain threshold.

Background: Emotion recognition using EEG signals enables clinicians to assess patients' emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy., Methods: We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state., Results: Experiments' results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data., Discussion: Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Chao, Ma, Sha, Sun, Li and Chang.)

Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods struggle to meet both high accuracy and interpretability. We propose a deep learning model called the auditory decoding transformer (ADT) network for speech envelope reconstruction from EEG signals to address these issues. The ADT network uses spatio-temporal convolution for feature extraction, followed by a transformer decoder to decode the speech envelopes. Through anticausal masking, the ADT considers only the current and future EEG features to match the natural relationship of speech and EEG. Performance evaluation shows that the ADT network achieves average reconstruction scores of 0.168 and 0.167 on the SparrKULee and DTU datasets, respectively, rivaling those of other nonlinear models. Furthermore, by visualizing the weights of the spatio-temporal convolution layer as time-domain filters and brain topographies, combined with an ablation study of the temporal convolution kernels, we analyze the behavioral patterns of the ADT network in decoding speech envelopes. The results indicate that low- (0.5-8 Hz) and high-frequency (14-32 Hz) EEG signals are more critical for envelope reconstruction and that the active brain regions are primarily distributed bilaterally in the auditory cortex, consistent with previous research. Visualization of attention scores further validated previous research. In summary, the ADT network balances high performance and interpretability, making it a promising tool for studying neural speech envelope tracking., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The monitoring of potassium ion (K + ) levels in human sweat can provide valuable insights into electrolyte balance and muscle fatigue non-invasively. However, existing laboratory techniques for sweat testing are complex, while wearable sensors face limitations like drift, fouling and interference from ions such as Na + . This work develops printed electrodes using β-cyclodextrin functionalized reduced graphene oxide (β-CD-RGO) for selective K + quantification in sweat. The β-CD prevents the aggregation of RGO sheets while also providing selective binding sites for K + capture. Electrodes were fabricated by screen printing the β-CD-RGO ink onto conductive carbon substrates. Material characterization confirmed the successful functionalization of RGO with β-CD. Cyclic voltammetry (CV) showed enhanced electrochemical behavior for β-CD-RGO-printed electrodes compared with bare carbon and RGO. Sensor optimization resulted in a formulation with 30% β-CD-RGO loading. The printed electrodes were drop-casted with an ion-selective polyvinyl chloride (PVC) membrane. A linear range from 10 μM to 100 mM was obtained along with a sensitivity of 54.7 mV/decade. The sensor showed good reproducibility over 10 cycles in 10 mM KCl. Minimal interference from 100 mM Na + and other common sweat constituents validated the sensor's selectivity. On-body trials were performed by mounting the printed electrodes on human subjects during exercise. The K + levels measured in sweat were found to correlate well with serum analysis, demonstrating the sensor's ability for non-invasive electrolyte monitoring. Overall, the facile synthesis of stable β-CD-RGO inks enables the scalable fabrication of wearable sensors for sweat potassium detection.

Given the rise of the tourism industry, there is an increasing urgency among tourists to access information about various tourist attractions. To address this challenge, innovative solutions have emerged, utilizing recommendation algorithms to offer customers personalized product recommendations. Nonetheless, existing recommendation algorithms predominantly rely on textual data, which is insufficient to harness the full potential of online tourism data. The most valuable tourism information is often found in the multi-modal data on social media, characterized by its voluminous and content-rich nature. Against this backdrop, our article posits a groundbreaking travel recommendation algorithm that leverages multi-modal data mining techniques. The proposed algorithm uses a travel recommendation platform, designed using multi-vector word sense segmentation and multi-modal data fusion, to improve the recommendation performance by introducing topic words. In our final experimental comparison, we verify the recommendation performance of the proposed algorithm on the real data set of TripAdvisor. Our proposed algorithm has the best degree of confusion with various topics. With a LOP of 20, the Precision and MAP values reach 0.0026 and 0.0089, respectively. It has the potential to better serve the tourism industry in terms of tourist destination recommendations. It can effectively mine the multi-modal data of the tourism industry to generate more excellent economic and social value., Competing Interests: The author declare that they have no competing interests., (©2023 Liu.)

Background: T-DM1 and T-DXd are two promising antibody-drug conjugates for treating advanced HER2-positive breast cancer and HER2-mutated lung cancer. Understanding the differences in the adverse events (AEs) profile of both drugs may help clinicians make an appropriate treatment decision., Research Design and Methods: All data obtained from the FDA Adverse Event Reporting System (FAERS) database from Q1 2004 to Q3 2022 underwent disproportionality analysis and Bayesian analysis to detect and assess the AE signals of T-DM1 and T-DXd for comparison., Results: A total of 2,113 and 1,269 AE reports associated with T-DM1 and T-Dxd, respectively, were retrieved from FAERS database, in which, respondents were mostly elderly women. Their statistical differences ( p  < 0.001), poses high incidence of thrombocytopenia, including cardiotoxicity ( p  < 0.05) for T-DM1, while myelosuppression, interstitial lung disease (ILD), and pneumonitis for T-DXd. Splenomegaly, nodular regenerative hyperplasia, hepatic cirrhosis, portal hypertension, neuropathy peripheral, and spider nevus, are particular to T-DM1. Similarly, febrile neutropenia, pneumocystis jirovecii pneumonia, neutrophil count decreased, and KL-6 increased, are unique to T-DXd., Conclusions: T-DXd is more likely to induce ILD/pneumonia and myelosuppression than T-DM1, whereas T-DM1 has higher risk of hepatotoxicity, cardiotoxicity, and thrombocytopenia than T-DXd. T-DM1-related hepatotoxicity may need redefinition. Clinicians may need to balance the benefits and risks of antibody-drug conjugates treatment for certain patients.

Today, large-scale Penaeus monodon farms no longer incubate eggs but instead purchase larvae from large-scale hatcheries for rearing. The accurate counting of tens of thousands of larvae in these transactions is a challenging task due to the small size of the larvae and the highly congested scenes. To address this issue, we present the Penaeus Larvae Counting Strategy (PLCS), a simple and efficient method for counting Penaeus monodon larvae that only requires a smartphone to capture images without the need for any additional equipment. Our approach treats two different types of keypoints as equip keypoints based on keypoint regression to determine the number of shrimp larvae in the image. We constructed a high-resolution image dataset named Penaeus&#95;1k using images captured by five smartphones. This dataset contains 1420 images of Penaeus monodon larvae and includes general annotations for three keypoints, making it suitable for density map counting, keypoint regression, and other methods. The effectiveness of the proposed method was evaluated on a real Penaeus monodon larvae dataset. The average accuracy of 720 images with seven different density groups in the test dataset was 93.79%, outperforming the classical density map algorithm and demonstrating the efficacy of the PLCS.

Background: Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease., Methods: To study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts., Results: After using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods., Conclusion: Thus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Xiong, Feng, Zhang, Wu, Yi, Wang, Liu and He.)

Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

Thanks to the narrow line width and high brightness, colloidal quantum dot (CQD) lasers show promising applications in next-generation displays. However, CQD laser-based displays have yet to be demonstrated because of two challenges in integrating red, green, and blue (RGB) lasers: absorption from red CQDs deteriorates the optical gain of blue and green CQDs, and imbalanced white spectra lack blue lasing due to the high lasing threshold of blue CQDs. Herein, we introduce a facile surfactant-free self-assembly method to assemble RGB CQDs into high-quality whispering-gallery-mode (WGM) RGB lasers with close lasing thresholds among them. Moreover, these RGB lasers can lase nearly independently even when they are closely integrated, and they can construct an ultrawide color space whose color gamut is 105% of that of the BT.2020 standard. These combined strategies allow us to demonstrate the first full-color liquid crystal displays using CQD lasers as the backlight source.

Background: The purpose of this study is to identify and characterize ocular adverse events (AEs) that are significantly associated with anti-VEGF drugs for treatment of neovascular age-related macular degeneration and compare the differences between each drug, and provide clinical reference. Methods: Ocular AEs submitted to the US Food and Drug Administration were analyzed to map the safety profile of anti-VEGF drugs. The Pharmacovigilance tools used for the quantitative detection of signals were reporting odds ratio and bayesian confidence propagation neural network. Results: A total of 10,608,503 AE reports were retrieved from FAERS, with 20,836 for ranibizumab, 19,107 for aflibercept, and 2,442 for brolucizumab between the reporting period of Q1, 2004 and Q3, 2021. We found and analyzed the different AEs with the strongest signal in each drug-ranibizumab-macular ischaemia (ROR = 205.27, IC-2SD = 3.70), retinal pigment epithelial tear (ROR = 836.54, IC-2SD = 7.19); aflibercept-intraocular pressure increased (ROR = 31.09, IC-2SD = 4.61), endophthalmitis (ROR = 178.27, IC-2SD = 6.70); brolucizumab-retinal vasculitis (ROR = 2930.41, IC-2SD = 7.47) and/or retinal artery occlusion (ROR = 391.11, IC-2SD = 6.10), dry eye (ROR = 12.48, IC-2SD = 2.88). Conclusion: The presence of AEs should bring clinical attention. The use of anti-VEGF drugs should be based on the patient's underlying or present medical condition to reduce any adverse event associated with the treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ma, Pan, Liu, Qu, Xie, Xie, Cao and Chen.)

Brassinosteroid (BR) has been indicated to induce the production of hydrogen peroxide (H 2 O 2 ) in plants in response to various environmental stimuli. However, it remains largely unknown how BR induces H 2 O 2 production. In this study, we found that BR treatment significantly raised the kinase activity of maize (Zea mays L.) brassinosteroid-signaling kinase 1 (ZmBSK1) using the immunoprecipitation kinase assay. ZmBSK1 could modulate the gene expressions and activities of nicotinamide adenine dinucleotide phosphate (NADPH) oxidases (EC 1.6.3.1) to modulate BR-induced H 2 O 2 production. BR could enhance the interaction between ZmBSK1 and maize calcium/calmodulin-dependent protein kinase (ZmCCaMK), a previously identified substrate of ZmBSK1. The BR-induced phosphorylation and kinase activity of ZmCCaMK are dependent on ZmBSK1. Moreover, we showed that ZmBSK1 regulated the NADPH oxidase gene expression and activity via directly phosphorylating ZmCCaMK. Genetic analysis suggested that ZmBSK1-ZmCCaMK module strengthened plant tolerance to oxidative stress induced by exogenous application of H 2 O 2 through improving the activities of antioxidant defense enzyme and alleviating the malondialdehyde (MDA) accumulation and electrolyte leakage rate. In conclusion, these findings provide the new insights of ZmBSK1 functioning in BR-induced H 2 O 2 production and the theoretical supports for breeding stress-tolerant crops., (Copyright © 2022 Elsevier Masson SAS. All rights reserved.)

Plant growth-promoting rhizobacteria are a type of beneficial bacteria which inhabit in the rhizosphere and possess the abilities to promote plant growth. Pseudomonas putida LWPZF is a plant growth-promoting bacterium isolated from the rhizosphere soil of Cercidiphyllum japonicum. Inoculation treatment with LWPZF could significantly promote the growth of C. japonicum seedlings. P. putida LWPZF has a variety of plant growth-promoting properties, including the ability to solubilize phosphate, synthesize ACC deaminase and IAA. The P. putida LWPZF genome contained a circular chromosome (6,259,530 bp) and a circular plasmid (160,969 bp) with G+C contents of 61.75% and 58.25%, respectively. There were 5632 and 169 predicted protein-coding sequences (CDSs) on the chromosome and the plasmid respectively. Genome sequence analysis revealed lots of genes associated with biosynthesis of IAA, pyoverdine, ACC deaminase, trehalose, volatiles acetoin and 2,3-butanediol, 4-hydroxybenzoate, as well as gluconic acid contributing phosphate solubilization. Additionally, we identified many heavy metal resistance genes, including arsenate, copper, chromate, cobalt-zinc-cadmium, and mercury. These results suggest that P. putida LWPZF shows strong potential in the fields of biofertilizer, biocontrol and heavy metal contamination soil remediation. The data presented in this study will allow us to better understand the mechanisms of plant growth promotion, biocontrol, and anti-heavy metal of P. putida LWPZF., (© 2022. The Author(s).)

Objective: To establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method., Methods: A retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with higher model performance (top 3) were selected to establish the ensemble prediction model and SHAP was employed to interpret the model., Results: Three algorithms (SVR, GBRT, and RF) with high R 2 scores (0.676, 0.670, and 0.656, respectively) were selected to construct the ensemble model at the ratio of 6:3:1. The model with R 2 = 0.720, MAE = 3.628, MSE = 22.571, absolute accuracy of 83.93%, and relative accuracy of 60.71% was obtained, which performed better in model fitting and had better prediction accuracy than any single algorithm. The feature importance and direction of each variable were visually demonstrated by SHAP values, in which teicoplanin administration and renal function were the most important factors., Conclusion: We firstly adopted a machine learning approach to predict the teicoplanin trough concentration, and interpreted the prediction model by the SHAP method, which is of great significance and value for the clinical medication guidance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ma, Liu, Gu, Dai, Gan, Cen, Shang, Liu and Chen.)

Mindfulness and accordant interventions are often used as complementary treatments to psychological or psychosomatic problems. This has also been gradually integrated into daily lives for the promotion of psychological well-being in non-clinical populations. The experience of mindful acceptance in a non-judgmental way brought about the state, which was less interfered by a negative effect. Mindfulness practice often begins with focused attention (FA) meditation restricted to an inner experience. We postulate that the brain areas related to an interoceptive function would demonstrate an intrinsic functional change after mindfulness training for the mindful novices along with paying more attention to internal processes. To further explore the influence of mindfulness on the organization of the brain regions, both functional connectivity (FC) in the voxel and the region of interest (ROI) level were calculated. In the current study, 32 healthy volunteers, without any meditation experiences, were enrolled and randomly assigned to a mindfulness-based stress reduction group (MBSR) or control group (CON). Participants in the MBSR group completed 8 weeks of mindfulness-based stress reduction (MBSR) and rated their mindfulness skills before and after MBSR. All subjects were evaluated via resting-state functional MRI (rs-fMRI) in both baselines and after 8 weeks. They also completed a self-report measure of their state and trait anxiety as well as a positive and negative affect. Pre- and post-MBSR assessments revealed a decreased amplitude of low-frequency fluctuations (ALFF) in the right anterior cingulate gyrus (ACC.R), left anterior and posterior insula (aIC.L, pIC.L), as well as left superior medial frontal gyrus (SFGmed.L) in MBSR practitioners. Strengthened FC between right anterior cingulate cortex (ACC.R) and aIC.R was observed. The mean ALFF values of those regions were inversely and positively linked to newly acquired mindful abilities. Along with a decreased negative affect score, our results suggest that the brain regions related to attention and interoceptive function were involved at the beginning of mindfulness. This study provides new clues in elucidating the time of evaluating the brain mechanisms of mindfulness novices., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gan, Ding, Bi, Liu, Zhao, Zhong, Wu, Zeng, Cui, Wu, Fu and Chen.)

Current state-of-the-art quantum dot light-emitting diodes have reached close to unity internal quantum efficiency. Further improvement in external quantum efficiency requires more efficient photon out-coupling. Improving the directivity of the photon emission is considered to be the most feasible approach. Here, we report improved emission directivity from colloidal quantum dot films. By growing an asymmetric compressive shell, we are able to lift their band-edge state degeneracy, which leads to an overwhelming population of exciton with in-plane dipole moment, as desired for high-efficiency photon out-coupling. The in-plane dipole proportion determined by back-focal plane imaging method is 88%, remarkably higher than 70% obtained from conventional hydrostatically strained colloidal quantum dots. Enhanced emission directivity obtained here opens a path to increasing the external quantum efficiencies notably.

Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA&#95;OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA&#95;withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA&#95;OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex., (© 2021. The Author(s).)

Background: There is an urgent need in clinical practice to measure the stress of parenting. The Caregiver Strain Questionnaire (CGSQ) was found to be useful to measure parenting stress, but it has not been validated among the Chinese population., Aims: To assess the reliability and construct validity of the Chinese version of CGSQ among Chinese parents., Methods: From 2016 to 2017, 266 parents (patient group) with a child having DSM-5-defined attention deficit hyperactivity disorder (ADHD) (n=107) or autism spectrum disorder (ASD) (n=159) and 268 parents of healthy children (control group) were recruited to the present study in Kunming, Yunnan province. All the parents were asked to fill out the Chinese version of CGSQ. We conducted exploratory factor analysis and confirmatory factor analysis (CFA) to verify construct validity of CGSQ in both patient and control groups. Cronbach's α coefficient as an index of internal consistency was assessed for each subscale. Fourteen days later, 23 subjects filled out the scale again. Intra-class correlation coefficient was calculated to evaluate the test-retest reliability., Results: (1) Cronbach's alpha of the global scale was 0.901 for the control group and 0.952 for the patient group. The test-retest reliability for the whole scale was 0.890; (2) CFA indicated that the three-factor model had better fitting indices compared with the two-factor model in both groups. Besides, the fitting indices in the patient group were more favourable than those of the control group, with χ 2 /df=1.564, Goodness-of-Fit Index=0.841, Comparative Fit Index=0.954, and root mean square error of approximation=0.065 for the patient group at three-factor model; (3) The caregiver strain of ASD parents was statistically higher than that of ADHD parents, and caregiver strain of ADHD parents was higher than that of control group., Conclusion: These findings provide initial evidence to support the construct validity and reliability of CGSQ as a parenting stress measurement tool for Chinese parents, especially for parents of children with ADHD or ASD., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Olfactory receptors (ORs) can bind odor molecules and play a crucial role in odor sensation. Due to the frequent gains and losses of genes during evolution, the number of OR members varies greatly among different species. However, whether the extent of gene gains/losses varies between marine mammals and related terrestrial mammals has not been clarified, and the factors that might underlie these variations are unknown., Results: To address these questions, we identified more than 10,000 members of the OR family in 23 mammals and classified them into 830 orthologous gene groups (OGGs) and 281 singletons. Significant differences occurred in the number of OR repertoires and OGGs among different species. We found that all marine mammals had fewer OR genes than their related terrestrial lineages, with the fewest OR genes found in cetaceans, which may be closely related to olfactory degradation. ORs with more gene duplications or loss events tended to be under weaker purifying selection. The average gain and loss rates of OR genes in terrestrial mammals were higher than those of mammalian gene families, while the average gain and loss rates of OR genes in marine mammals were significantly lower and much higher than those of mammalian gene families, respectively. Additionally, we failed to detect any one-to-one orthologous genes in the focal species, suggesting that OR genes are not well conserved among marine mammals., Conclusions: Marine mammals have experienced large numbers of OR gene losses compared with their related terrestrial lineages, which may result from the frequent birth-and-death evolution under varied functional constrains. Due to their independent degeneration, OR genes present in each lineage are not well conserved among marine mammals. Our study provides a basis for future research on the olfactory receptor function in mammals from the perspective of evolutionary trajectories.

Lishan Mountain is belonged to the Zhongtiao Ranges in Southeast Shanxi, and situated between 35 degrees 16'30" - 35 degrees 27'20" N and 111 degrees 51'-- 112 degrees 5'35" E. The climate is cool and arid in winter, but warm and rainy in summer. This paper studied the species diversity and community structure of forest communities there, based on field investigation data and by using diversity, richness and evenness indices. The results showed that the species diversity and evenness of the communities were in the order of Acer davidii + Acer mono > Acer davidii + Carpinus turczaninouii > Quercus liaolugensis > Quercus varibilis > Pinus armandii + Betula albo-sinensis > Pinus tabulaeformis + Quercus glandulifera > Betula platyphylla > Platycladus orientalis + Quercus variabilis > Betula albo-sinensis > Pinus armandii > Platycladus orientalis > Pinus tabulaeformis > Populus davidiana + Betula albo-sinensis. The diversity and richness of shrub layer were higher than those of herb layer and arbor layer, but the evenness of arbor, shrub and herb layers had no significant difference. There were positive correlations (P < 0.05) between Shannon-Wiener index, Hill index, and species richness from 1 000 m to 1 920 m above sea level, but negative correlations (P < 0.05) from 1920 m to the top of the Mountain. The species diversity showed a "mid-altitude bulge" pattern with the change of altitude, and the reason was that the combination of temperature and moisture conditions from 1000 m to 1920 m above sea level was improved, and human disturbance was decreased gradually.