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Abstract The pharmacokinetics of vancomycin exhibit significant inter-individual variability, particularly among elderly patients. This study aims to develop a predictive model that integrates machine learning with population pharmacokinetics (popPK) to facilitate personalized medication management for this demographic. A retrospective analysis incorporating 33 features, including popPK parameters such as clearance and volume of distribution. A combination of multiple algorithms and Shapley Additive Explanations was utilized for feature selection to identify the most influential factors affecting drug concentrations. The performance of each algorithm with popPK parameters was superior to that without popPK parameters. Our final ensemble model, composed of support vector regression, light gradient boosting machine, and categorical boosting in a 6:3:1 ratio, included 16 optimized features. This model demonstrated superior predictive accuracy compared to models utilizing all features, with testing group metrics including an R2 of 0.656, mean absolute error of 3.458, mean square error of 28.103, absolute accuracy within ± 5 mg/L of 81.82%, and relative accuracy within ± 30% of 76.62%. This study presents a rapid and cost-effective predictive model for estimating vancomycin plasma concentrations in elderly patients. The model offers a valuable tool for clinicians to accurately determine effective plasma concentration ranges and tailor individualized dosing regimens, thereby enhancing therapeutic outcomes and safety.

The development of Si–HfO2/Yb2Si2O7/Yb2SiO5 environmental barrier coatings (EBCs) aims to improve the operational temperature and longevity of ceramic matrix composites (CMCs) in turbine environments. Nevertheless, several critical questions remain unanswered, including the oxidation mechanism of Si–HfO2 bond coating, the compatibility of its mixed thermally grown oxide (m-TGO) with adjacent layers during thermal cycling, and the evolution pattern of vertical mud-cracks that impact the overall performance in service. Using plasma spraying physical vapor deposition (PS-PVD), we fabricated these EBCs on a CMC substrate, and thermal cycling tests at 1400, 1450, and 1500 °C revealed that their durability reached 200 h. m-TGO growth followed a parabolic model, with the oxygen diffusion activation energy being 133.69 kJ/mol between 1400 and 1450 °C and 101.47 kJ/mol from 1450 to 1500 °C, emphasizing that the transport of molecular oxygen is key to controlling the oxidation of m-TGO in this EBC system. Although residual stresses and stored elastic strain energy build up between m-TGO and adjacent layers, especially around the cristobalite phase transition temperature, causing interlaminar crack formation in later thermal cycles, the stored elastic strain energy remains lower than that of the silicon oxide–thermally grown oxide (SiO2–TGO) formed in Si bond coating system. In addition to [110] dislocations, (001) twinning and interaction zones between twinning and dislocations were discovered for the first time, driving the bifurcation of mud cracks. Notably, controlling the mud-crack density is vital for protection of Yb2SiO5 layer, as bifurcated mud-crack tips may converge with adjacent mud-cracks.

Abstract 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.

Abstract 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 × 1019 s−1 cm−2 − 600 times lower than the photon flux density Φ p (4.3 × 1021 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 CdxZn1-xSe-ZnS and CdSe-CdS core-shell quantum dots exhibiting large light hole-heavy hole splittings.

The emergence of the Digital Twin of Earth (DTE) signifies a pivotal advancement in the field of the Digital Earth, which includes observations, simulations, and predictions regarding the state of the Earth system and its temporal evolution. While the Geospatial Data Cubes (GDCs) hold promise in probing the DTE across time, existing GDC approaches are not well-suited for real-time data ingestion and processing. This paper proposes the design and implementation of a real-time ingestion and processing approach in Geospatial Data Cube (RTGDC), achieved by ingesting real-time observation streams into data cubes. The methodology employs a publish/subscribe model for efficient observation ingestion. To optimize observation processing within the data cube, a distributed streaming computing framework is utilized in the implementation of the RTGDC. This approach significantly enhances the real-time capabilities of GDCs, providing an efficient solution that brings the realization of the DTE concept within closer reach. In RTGDC, two cases involving local and global scales were implemented, and their real-time performance was evaluated based on latency, throughput, and parallel efficiency.

The sub-mountain plains area at the northern foothills of the Yinshan Mountains in Inner Mongolia is an important grain producing area in the cold and arid regions of China, but due to climatic and environmental factors, it has resulted in a fragile ecological environment, degradation of arable land, and serious soil erosion. Quinoa (Chenopodium quinoa Willd.) is a hardy, cold and saline tolerant crop with much higher protein content than other crops. Therefore, it has begun to be grown on a large scale in the region in recent years. In order to investigate the distribution characteristics of soil aggregates and organic carbon (SOC) in quinoa fields in ecologically fragile areas, four quinoa fields with different soil textures, namely, sandy soil, sandy loam, loamy soil and clay loam, were selected for this experiment, and the mass percentages of different soil aggregate particle sizes and the organic carbon content of each particle size were determined before and after quinoa planting, and the organic carbon mineralisation in soil aggregates of quinoa fields with different textures was analysed through indoor organic carbon mineralisation cultures. The dynamic characteristics were analysed. The results showed that the stability of soil aggregates was enhanced after planting quinoa, and the highest mass percentage of aggregates with >2 mm particle size was 27.95% in clay loam soil. Planting quinoa in loamy soil had the most obvious effect on promoting the formation of soil macroaggregates, while the organic carbon content increased the most. The organic carbon (SOC) content of soil aggregates of all grain sizes increased by 0.45–3.09 g kg-1 compared to the pre-sowing period, with the fastest rate and greatest intensity of organic carbon mineralisation in aggregates of 2–0.25 mm grain size. The study showed that growing quinoa in loamy soils improves soil structure, slows down the rate of soil organic carbon mineralization, and increases soil carbon sequestration capacity.

The “seesaw relationship” between thermal conductivity and thermal expansion coefficient (CTE) in most high temperature ceramics has become an obstacle to the design of long-life multilayer thermal/environmental barrier coatings (T/EBC). Due to low thermal conductivity and CTE, defect fluorite type high-entropy rare earth (RE) hafnates have drawn a lot of interest for potential application in T/EBC systems. This work designs and synthesizes the (Dy0.2Ho0.2Er0.2Tm0.2Lu0.2)2Hf2O7 with comprehensive thermal performance and investigates the thermophysical mechanism from the phonon scale. In addition to the lattice distortion effect caused by the point defects of multicomponent substitutional atoms in (Dy0.2Ho0.2Er0.2Tm0.2Lu0.2)2Hf2O7, the oxygen vacancies in the defect fluorite lattice also play a critical role in reducing the thermal conductivity. From microscopic thermal expansion behavior, the low-frequency optic phonons originated from the vibration of RE atoms are the key factors in altering CTE for hafnates. And doping smaller RE ions is beneficial for enhancing the RE–O bond strength and further reducing CTE. The results contribute to the understanding of high-entropy strategic design and suggest that (Dy0.2Ho0.2Er0.2Tm0.2Lu0.2)2Hf2O7 is a promising top layer material in the implementation of T/EBC.

IntroductionSleep 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.MethodsTo 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.ResultsThe 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%.DiscussionAll 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.

BackgroundEmotion 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.MethodsWe 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.ResultsExperiments’ 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.DiscussionGiven its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.

The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web of Data. They are also limited in efficient association capabilities for large-scale datasets. This paper extends the data integration capability based on the spatial metrics in the open geospatial engine OGE. These metrics include topological relationships and spatial matching between geospatial entities within multiple geospatial data sources. Thus, the tool can be employed by data publishers to set geospatial-aware links to facilitate geospatial data and knowledge discovery in the Web of Data. Several geospatial data sources are used to demonstrate the usability and effectiveness of the approach and tool implementation.

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.

Abstract 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.

BackgroundSubstance 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.MethodsTo 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.ResultsAfter 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.ConclusionThus, 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.

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.

Abstract The low‐cost and high‐safety zinc‐ion batteries (ZIBs) are expected to be promising alternatives of lithium‐ion batteries (LIBs). Nevertheless, sluggish kinetics and unstable structure of cathode lead to the low‐rate capability and poor cycling stability of ZIBs, which severely hinder their commercial application. Herein, a (NH4)xMnO2 cathode with ammonium ions (NH4+) pre‐intercalation is reported. The NH4+ ions with small molar mass expand the layer spacing, accelerating ion diffusion and act as “structural pillars”, preventing structural collapse. Moreover, hydrogen bond between NH4+ and MnO2 promotes the charge transfer and further stabilize the layered structure. Therefore, the (NH4)xMnO2 cathode delivers a high specific capacity (228.7 mAh g−1 at 0.3 A g−1), a superior high‐rate performance (122.5 mAh g−1 at 3.0 A g−1), and an outstanding long‐term cycling stability without obvious capacity fading after 1500 cycles at 2.0 A g−1. This work provides a new perspective for future development of high‐performance ZIBs.

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.

The goal of this work is to analyze how double inversion layers affect fog–haze events and boundary layers over Eastern China based on unmanned aerial vehicle observations. During 10–12 December 2020, two successive fog–haze events occurred in Eastern China. Based on the unmanned aerial vehicle (UAV), wind, temperature, relative humidity (RH), and aerosol mass concentration were monitored simultaneously in Lianyungang, China. Several observations were found after analyzing the physical fields of these data. The results show that the concentrations of air pollutants during fog events were negatively correlated with horizontal visibilities. The mass concentrations of particulate matter increased rapidly (After 08:00 BJT 11) in the fog dissipation stages (PM2.5 and PM10 increased from 97 and 150 μg/m3 to 213 and 300 μg/m3, respectively). Double temperature inversion significantly affected fog events, where the enhancement of the lower-level temperature inversion (where the inversion layer top height was between 150 and 300 m) corresponded to the explosive growth of fog and the dissipation of the upper-level inversion layer. (The bottom height of the inversion layer was between 350 m and 600 m) This indicates the end of fog events. Fog layer thicknesses are negatively correlated with the concentrations of air pollutants in the near-surface layer. The thickening of the inversion layer near the surface corresponds to an increase (after 05:00 BJT 11 and 03:00 BJT 12) in fog layer height. The fog event is maintained when water vapor conditions are favorable enough or when there is a deep near-surface temperature inversion but the maintenance of the near-surface temperature inversion at an intensity of above 2 °C (100 m)−1 mainly contributes to the durations of these two fog events. Calculations through the trajectory model reveal that the air pollutant source varied during these two events, which was the main reason for the difference in air pollutant concentrations between the two events.

Abstract 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_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_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_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.

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_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.

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.

ObjectiveTo 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.MethodsA 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.ResultsThree algorithms (SVR, GBRT, and RF) with high R2 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 R2 = 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.ConclusionWe 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.

Abstract 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.

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 χ2/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.

The original version of this Article contained an error in Reference 34, which was incorrectly given as:

Mindfulness is described as the non-judgmental awareness of experiences in the present moment. The sustained practice of mindfulness may also have beneficial effects on an individual’s well-being. For instance, mindfulness meditation is an effective approach for improving emotion regulation. Specifically, the early stage of mindfulness meditation training enhances emotional monitoring systems related to attention regulation and executive function. Reduced activity in the default mode network (DMN) would probably be observed corresponding to the attenuated mind wandering. In the present study, we hypothesized that alterations in functional activity in the frontal-parietal cortex and DMN may be induced by short-term mindfulness meditation. In this study, before and after 8 weeks of weekly Mindfulness-Based Stress Reduction (MBSR) training, healthy participants were evaluated using a mindfulness questionnaire and an affect schedule, as well as via resting-state functional magnetic resonance imaging. Sixteen right-handed non-meditators were enrolled. Another 16 demographically matched healthy adults without any meditation experience were recruited as controls. Pre- and post-MBSR assessments were compared. Increased regional homogeneity in the right superior parietal lobule and left postcentral gyrus (PoCG), as well as altered functional connectivity in PoCG-related networks, were observed post-MBSR. The mindfulness questionnaire scores also improved and negative affect was significantly decreased after MBSR. Together with reduced involvement of the posterior brain, our results suggest a tendency toward stronger involvement of the parietal cortex in mindfulness beginners. This study provides novel evidence regarding the optimization of emotional processing with short-term mindfulness meditation.

Leaf width is an important component of plant architecture that affects light capture during photosynthesis and wind circulation under dense planting conditions. To improve understanding of the genetic mechanisms involved in leaf width at different positions, a comprehensive evaluation using the RIL (Recombinant Inbred Line) and IF2 (Immortalized F2) populations and a subsequent meta-analysis were performed. Forty-seven QTL associated with leaf width at different positions below the tassel were detected. The individual effects of QTL explained 3.5% to 17.0% of the observed phenotypic variation, and ten QTL explained over 10%. The initial QTL were integrated into eight mQTL (meta-QTL) through a meta-analysis. Our results suggested that leaf widths at different positions may be affected by several of the same mQTL and may also be regulated by many different mQTL. These results provide useful information for breeding high density tolerant inbred lines and hybrid cultivars, as well as for using marker-assisted selection for important mQTL.

This paper reports the results of experimental investigation of flow resistance in a coal mine ventilation air methane preheated catalytic oxidation reactor. The experimental system was installed at the Energy Research Institute of Shandong University of Technology. The system has been used to investigate the effects of flow rate (200 Nm3/h to 1000 Nm3/h) and catalytic oxidation bed average temperature (20°C to 560°C) within the preheated catalytic oxidation reactor. The pressure drop and resistance proportion of catalytic oxidation bed, the heat exchanger preheating section, and the heat exchanger flue gas section were measured. In addition, based on a large number of experimental data, the empirical equations of flow resistance are obtained by the least square method. It can also be used in deriving much needed data for preheated catalytic oxidation designs when employed in industry.

With the development of computer technology, foundry CAE technology has made rapid progress. Commercial software packages of casting process simulation, therefore, have become more and more practical. This paper introduces both the recent developments and some applications of InteCAST software, a commercial numerical simulation software package for foundry industry, with more than 120 customers all over the world. The function modules of InteCAST8.0 and some new techniques, such as uneven mesh technology for mold filling simulation and numerical mouse technology for data visulization, were introduced. Several applications on special castings such as investment casting, low pressure die optimize casting process by forecasting casting defect.

The yield of maize grain is a highly complex quantitative trait that is controlled by multiple quantitative trait loci (QTLs) with small effects, and is frequently influenced by multiple genetic and environmental factors. Thus, it is challenging to clone a QTL for grain yield in the maize genome. Previously, we identified a major QTL, qKNPR6, for kernel number per row (KNPR) across multiple environments, and developed two nearly isogenic lines, SL57-6 and Ye478, which differ only in the allelic constitution at the short segment harboring the QTL. Recently, qKNPR6 was re-evaluated in segregating populations derived from SL57-6×Ye478, and was narrowed down to a 2.8 cM interval, which explained 56.3% of the phenotypic variance of KNPR in 201 F(2∶3) families. The QTL simultaneously affected ear length, kernel weight and grain yield. Furthermore, a large F(2) population with more than 12,800 plants, 191 recombinant chromosomes and 10 overlapping recombinant lines placed qKNPR6 into a 0.91 cM interval corresponding to 198Kb of the B73 reference genome. In this region, six genes with expressed sequence tag (EST) evidence were annotated. The expression pattern and DNA diversity of the six genes were assayed in Ye478 and SL57-6. The possible candidate gene and the pathway involved in inflorescence development were discussed.

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The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data. Utilizing this advanced algorithm, the study analyzes the Motor Imagery (MI) dataset from the GigaScience database, GigaDB. The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions, with microstate C showing superior performance in all task states. During imagined movement, microstate A was significantly enhanced. Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method, with an average Calinski-Harabasz Index (CHI) of 35517.29 and a Davis-Bouldin Index (DBI) average of 2.57. Furthermore, an information theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement. By utilizing the extracted microstate sequence parameters as features, the improved algorithm achieved a classification accuracy of 98.41% in EEG signal categorization for motor imagery. A performance of 78.183% accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset. These results demonstrate the potential of the advanced algorithm in microstate analysis, offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals. [ABSTRACT FROM AUTHOR]