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Abstract Bordetella bronchiseptica (Bb) is recognized as a leading cause of respiratory diseases in dogs and cats. However, epidemiological data on Bb in dogs and cats in China are still limited, and there is no commercially available vaccine. Live vaccines containing Bb that are widely used abroad are generally effective but can establish latency and potentially reactivate to cause illness in some immunodeficient vaccinated recipients, raising safety concerns. In this study, 34 canine-derived and two feline-derived Bb strains were isolated from 1809 canine and 113 feline nasopharyngeal swab samples collected from eight provinces in China from 2021 to 2023. The PCR results showed that the percentage of positive Bb was 22.94% (441/1922), and more than 90% of the Bb isolates had four virulence factor-encoding genes (VFGs), namely, fhaB, prn, betA and dnt. All the isolated strains displayed a multidrug-resistant phenotype. The virulence of 10 Bb strains isolated from dogs with respiratory symptoms was tested in mice, and we found that eight isolates were highly virulent. Furthermore, the eight Bb isolates with high virulence were inactivated and intramuscularly injected into mice, and three Bb strains (WH1218, WH1203 and WH1224) with the best protective efficacy were selected. Dogs immunized with these three strains exhibited strong protection against challenge with the Bb field strain WH1218. Ultimately, the WH1218 strain with the greatest protection in dogs was selected as the vaccine candidate. Dogs and cats that received a vaccine containing 109 CFU of the inactivated WH1218 strain showed complete protection against challenge with the Bb field strain WH1218. This study revealed that Bb is an important pathogen that causes respiratory diseases in domestic dogs and cats in China, and all the isolates exhibited multidrug resistance. The present work contributes to the current understanding of the prevalence, antimicrobial resistance, and virulence genes of Bb in domestic dogs and cats. Additionally, our results suggest that the WH1218 strain is a promising candidate safe and efficacious inactivated Bb vaccine.

Abstract Dysfunction of the hypothalamus is associated with endocrine imbalances, growth abnormalities, and reproductive disorders. However, there is a lack of targeted treatment strategies focused on the hypothalamus. In this study, we constructed a multifunctional nanocarrier system (S@ANP) to directly target the hypothalamic neurokinin receptor 3 (NK3R) via an intranasal delivery strategy. This system could overcome the primary obstacles in drug delivery for hypothalamus‐related diseases. Under the guidance of a modified (Trp7, β‐Ala8)‐neurokinin A (4‐10) peptide with cysteine, nanoparticles encapsulated with SB222200, an NK3R inhibitor, were found to readily penetrate hypothalamic cells with substantial loading capacity, encapsulation efficiency, and sustained release in vitro. Moreover, intranasal delivery represents an optimal delivery strategy that allows for a significant reduction in oral dosage and enables nanoparticles to bypass the blood‒brain barrier and target relevant parts of the brain. The mucolytic agent N‐acetyl‐L‐cysteine (NAC) was loaded into the nanoparticles (S@ANP + NAC) to increase mucosal solubility and intranasal delivery efficiency. In vivo evaluations showed that S@ANP + NAC could effectively target the hypothalamus and modulate NK3R‐regulated hypothalamic functions in mice. Due to its high hypothalamic targeting efficiency and low toxicity, this intranasal nanoparticle drug delivery system may serve as a potential strategy for precision therapy of hypothalamic disorders.

Abstract Background The bidirectional causal association between daytime napping frequency and schizophrenia is unclear. Methods A bidirectional two-sample Mendelian randomization (MR) analysis was conducted with summary statistics of top genetic variants associated with daytime napping frequency and schizophrenia from genome-wide association studies (GWAS). The single nucleotide polymorphisms (SNPs) data of daytime napping frequency GWAS came from the UK Biobank (n = 452,633) and 23andMe study cohort (n = 541,333), while the schizophrenia GWAS came from the Psychiatric Genomics Consortium (PGC, 36,989 cases and 113,075 controls). The inverse variance weighted (IVW) analysis was the primary method, with the weighted median, MR-Robust Adjusted Profile Score (RAPS), Radial MR and MR-Pleiotropy Residual Sum Outlier (PRESSO) as sensitivity analysis. Results The MR analysis showed a bidirectional causal relationship between more frequent daytime napping and the occurrence of schizophrenia, with the odds ratio (OR) for one-unit increase in napping category (never, sometimes, usually) on schizophrenia was 3.38 (95% confidence interval [CI]: 2.02–5.65, P = 3.58 × 10–6), and the beta for the occurrence of schizophrenia on daytime napping frequency was 0.0112 (95%CI: 0.0060–0.0163, P = 2.04 × 10–5). The sensitivity analysis obtained the same conclusions. Conclusion Our findings support the bidirectional causal association between more daytime napping frequency and schizophrenia, implying that daytime napping frequency is a potential intervention for the progression and treatment of schizophrenia.

Wind speed and direction are critical meteorological elements. Multi-rotor unmanned aerial vehicles UAVs are widely used as a premium payload platform in meteorological monitoring. The meteorological UAV is able to improve the spatial and temporal resolution of the elements collected. However, during wind measurement missions, the installed anemometers are susceptible to interference caused by rotor turbulence. This paper puts forward a wind pressure orthogonal decomposition (WPOD) strategy to overcome this limitation in three ways: the location of the sensors, a new wind measurement method, and supporting equipment. A weak turbulence zone (WTZ) is found around the airframe, where the turbulence strength decays rapidly and is more suitable for installing wind measurement sensors. For the sensors to match the spatial structure of this area, a WPOD wind measurement method is proposed. An anemometer based on this principle was mounted on a quadrotor UAV to build a wind measurement system. Compared with a standard anemometer, this system has satisfactory performance. Analysis of the resulting data indicates that the error of the system is ±0.3 m/s and ±2° under hovering conditions and ±0.7 m/s and ±5° under moving conditions. In summary, WPOD points to a new orientation for wind measurement under a small spatial–temporal scale.

BackgroundObservational studies have revealed that dried fruit intake may be associated with cancer incidence; however, confounding factors make the results prone to be disturbed. Therefore, we conducted a two-sample Mendelian randomization (MR) study to explore the causal relationship between dried fruit intake and 11 site-specific cancers.Materials and methodsForty-three single nucleoside polymers (SNPs) with robust genome-wide association study (GWAS) evidence, strongly correlated with dried fruit intake, were used as instrumental variables (IVs) in this study. The summary-level genetic datasets of site-specific cancers were obtained from the Oncoarray oral cavity and oropharyngeal cancer consortium, International Lung Cancer Consortium, Breast Cancer Association Consortium (BCAC), Ovarian Cancer Association Consortium, PanScan1, and GWAS of other scholars. We analyzed the causality between dried fruit intake and 11 site-specific cancers using the inverse-variance-weighted (IVW) and weighted median (WM) methods. For the results of the MR analysis, Cochran’s Q test was used to check for heterogeneity, and multiplicative random effects were used to evaluate the heterogeneity further. Gene pleiotropy was tested using MR-Egger regression and MR-PRESSO methods. In addition, the main results of this study were validated by using the summary statistical data from the FinnGen and UK Biobank databases, and adjusted body mass index (BMI), years of education, fresh fruit intake, and vitamin C using multivariable MR analysis to ensure the stability of the research results.ResultsThe evidence from IVW analyses showed that each increase of dried fruit intake by one standard deviation was statistically significantly associated with 82.68% decrease of oral cavity/pharyngeal cancer incidence risk (P = 0.0131), 67.01% decrease of lung cancer incidence risk (P = 0.0011), 77% decrease of squamous cell lung cancer incidence risk (P = 0.0026), 53.07% decrease of breast cancer incidence risk (P = 4.62 × 10–5), 39.72% decrease of ovarian cancer incidence risk (P = 0.0183), 97.26% decrease of pancreatic cancer incidence risk (P = 0.0280), 0.53% decrease of cervical cancer incidence risk (P = 0.0482); however, there was no significant effect on lung adenocarcinoma (P = 0.4343), endometrial cancer (P = 0.8742), thyroid cancer (P = 0.6352), prostate cancer (P = 0.5354), bladder cancer (P = 0.8996), and brain cancer (P = 0.8164). In the validation part of the study results, the causal relationship between dried fruit intake and lung cancer (P = 0.0043), squamous cell lung cancer (P = 0.0136), and breast cancer (P = 0.0192) was determined. After adjusting for the potential impact of confounders, the causal relationship between dried fruit intake and lung cancer (P = 0.0034), squamous cell lung cancer (P = 0.046), and breast cancer (P = 0.0001) remained. The sensitivity analysis showed that our results were stable and reliable.ConclusionThe intake of dried fruits may have a protective effect against some site-specific cancers. Therefore, health education and a reasonable adjustment of dietary proportions may help in the primary prevention of cancer.

On account of current algorithm and parameter design difficulties and low detection accuracy in feature extractions of small target detections in sea clutter environment, this paper proposes a correspondingly improved four feature extraction method by FAST. After the short-time Fourier transform is applied, a time–frequency distribution spectrogram of original data is generated. Candidate feature points (CFP) are first extracted by FAST algorithm, and then a four-feature extraction is implemented with FAST and DBSCAN combined. The feature distinction is enhanced through a feature optimization. Upon the construction of the four-dimensional feature vectors, XGBoost classifier algorithm classifies and detects these feature vectors. The genetic algorithm optimizes the hyperparameters in XGBoost and updates the decision threshold in real time to control the detection method’s false alarm rate. The IPIX dataset is employed for experimental verification. Verification results confirm that this proposed detection method has better performance than several other currently used detection methods. The detection performance is improved by 7% and 13.8% when observation time is set at 0.512 s and 1.024 s, respectively.

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

Due to its non-intrusive manner, blade tip timing (BTT) is considered a potential tool for the condition monitoring of turbomachinery. The challenge of BTT relates to significant under-sampled signal processing, which is induced by a lower number of probes. Signal processing assumes that the ability of the hardware system can meet the requirements of the software algorithm. The abilities of the hardware, including the time resolution of the data acquisition system (DAS) and the dynamic characteristics of rigs, are compromised, particularly when the rotating speed increases. This increase in speed causes two problems for BTT: (1) the rig is less stable, due to the reduction of dynamic stiffness; (2) the time resolution of the DAS can be inadequate for identification. To promote the performance of the hardware system, here a BTT rig was designed with high dynamic performance, including a new DAS with a time resolution of 10 ns. A variety of commonly used BTT signal processing methods are used to analyze the experimental data and verify the good reliability and validity of the experimental platform.

Human behavior refers to the way humans act and interact. Understanding human behavior is a cornerstone of observational practice, especially in psychotherapy. An important cue of behavior analysis is the dynamical changes of emotions during the conversation. Domain experts integrate emotional information in a highly nonlinear manner; thus, it is challenging to explicitly quantify the relationship between emotions and behaviors. In this work, we employ deep transfer learning to analyze their inferential capacity and contextual importance. We first train a network to quantify emotions from acoustic signals and then use information from the emotion recognition network as features for behavior recognition. We treat this emotion-related information as behavioral primitives and further train higher level layers towards behavior quantification. Through our analysis, we find that emotion-related information is an important cue for behavior recognition. Further, we investigate the importance of emotional-context in the expression of behavior by constraining (or not) the neural networks’ contextual view of the data. This demonstrates that the sequence of emotions is critical in behavior expression. To achieve these frameworks we employ hybrid architectures of convolutional networks and recurrent networks to extract emotion-related behavior primitives and facilitate automatic behavior recognition from speech.

Plasmon-induced hot-electron transfer from metal nanostructures is being intensely pursed in current photocatalytic research, however it remains elusive whether molecular-like metal clusters with excitonic behavior can be used as light-harvesting materials in solar energy utilization such as photocatalytic methanol steam reforming. In this work, we report an atomically precise Cu13 cluster protected by dual ligands of thiolate and phosphine that can be viewed as the assembly of one top Cu atom and three Cu4 tetrahedra. The Cu13H10(SR)3(PR’3)7 (SR = 2,4-dichlorobenzenethiol, PR’3 = P(4-FC6H4)3) cluster can give rise to highly efficient light-driven activity for methanol steam reforming toward H2 production.

Abstract Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R2 = 0.78216, root mean squared error (RMSE) = 285.8964 gCm−2y−1, and mean absolute error (MAE) = 180.4186 gCm−2y−1, which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs.

Soil respiration (Rs), the second-largest flux in the global carbon cycle, is a crucial but uncertain component. To improve the understanding of global Rs, we constructed single global models, and specific models classified by climate type, land cover type, year of the data record, and elevation range using the random forest algorithm to predict global Rs values and explore the associated uncertainty in the models. The results showed a similar overall predictive performance for the models, with an R-squared value greater than 0.63; however, significant differences were observed compared to the global Rs estimate (23 Pg C). All the models estimated larger values of Rs than the single global model, mainly owing to imbalances in the sample data on which the prediction models were based. One exception to this result is the land cover model, which estimates a smaller global Rs for 2020 (95.1 Pg C). Overall, the single global model estimates were closer to those obtained for temperate zones owing to differences in the training data distribution, which resulted in smaller global estimates than those of other classification-specific models. Prediction models using observations before 2000 tend to underestimate the global Rs. However, the use of classification-specific Rs models proved helpful in addressing the persistent temporal and spatial imbalances in Rs sampling. Expanding the coverage of Rs records both temporally and spatially and updating the global Rs database promptly would improve the estimation accuracy of global Rs prediction models while enhancing the understanding of the overall global carbon budget and the feedback of soil carbon with regard to climate warming.

Abstract Alien species invasion and habitat destruction are among the primary threats to native animal communities, particularly for native predator–prey systems. However, when predator invasion and habitat destruction co‐occur, it remains unclear whether their respective threats to native systems compensate each other or accumulate, as well as how these effects respond to the different characteristics of predator invasion and habitat destruction. In this study, we developed a spatially explicit simulation model with one prey species and one predator species and exposed it to invasive predators and habitat destruction with different properties. The results revealed the following insights: (1) Habitat destruction can compensate threats to native predator–prey systems from global predator invasion only when native predators possess predation capability similar to those of the invaders. In other scenarios, cumulative effects arise from predator invasion and habitat destruction. (2) Low levels of habitat destruction occurring at a faster rate, in conjunction with a substantial number of global invasive predators being present, can better compensate their respective threats to native predator–prey systems than the other scenarios. These findings provide valuable insights into situations where habitat destruction and alien species invasion coincide. They raise the question of whether we can leverage the interaction between them to reduce threats to biodiversity.

BackgroundThe efficacy of Chinese herbal medicine (CHM) in managing irritable bowel syndrome with diarrhea (IBS-D) accompanied by anxiety and depression remains uncertain. Thus, a systematic review was carried out employing meta-analysis and network pharmacology to ascertain the efficacy and underlying mechanisms of CHM therapy.MethodsBy conducting a systematic review, including literature search, screening, and data extraction, we identified 25 randomized controlled trials to assess CHM’s effectiveness in treating irritable bowel syndrome alongside anxiety and depression. Network pharmacology was utilized to scrutinize the metabolite utility of CHM in addressing this condition. Potential primary mechanisms were synthesized using information sourced from the PubMed database.ResultsTwenty-five studies, including 2055 patients, were analyzed, revealing significant treatment efficacy for IBS-D in the trial group compared to controls [OR = 4.01, 95% CI (2.99, 5.36), I2 = 0%] Additionally, treatment for depression [SMD = −1.08, 95% CI (-1.30, −0.86), p < 0.00001, I2 = 68%; SDS: SMD = -1.69, 95% CI (-2.48, −0.90), p < 0.0001, I2 = 96%] and anxiety [HAMA: SMD = -1.29, 95% CI (-1.68, −0.91), p < 0.00001, I2 = 89%; SAS: SMD = -1.75, 95% CI (-2.55, −0.95), p < 0.00001, I2 = 96%] significantly improved in the trial group. Furthermore, the trial group exhibited a significantly lower disease relapse rate [OR = 0.30, 95% CI (0.20, 0.44), p < 0.00001, I2 = 0%]. CHM treatment consistently improved IBS severity (IBS-SSS) and symptom scores. Network pharmacology analysis identified key chemical metabolites in traditional Chinese medicine formulations, including Beta-sitosterol, Stigmasterol, Quercetin, Naringenin, Luteolin, Kaempferol, Nobiletin, Wogonin, Formononetin, and Isorhamnetin. Utilizing the STRING database and Cytoscape v3.9.0 software, a protein-protein interaction (PPI) network revealed the top eight key targets: IL-6, TNF, PPARG, PTGS2, ESR1, NOS3, MAPK8, and AKT1, implicated in anti-inflammatory responses, antioxidant stress modulation, and neurotransmitter homeostasis maintenance.ConclusionChinese Herbal Medicine (CHM) offers a promising and safe treatment approach for patients dealing with Diarrheal Irritable Bowel Syndrome (IBS-D) accompanied by anxiety and depression; thus, indicating its potential for practical implementation. The most active metabolites of CHM could simultaneously act on the pathological targets of IBS-D, anxiety, and depression.The diverse scope of CHM’s therapeutic role includes various aspects and objectives, underscoring its potential for broad utilization.

Intracerebral hemorrhage (ICH), a common subtype of hemorrhagic stroke, often causes severe disability or death. ICH induces adverse events that might lead to secondary brain injury (SBI), and there is currently a lack of specific effective treatment strategies. To provide a new direction for SBI treatment post-ICH, the systematic review discussed how thrombin impacts secondary injury after ICH through several potentially deleterious or protective mechanisms. We included 39 studies and evaluated them using SYRCLE’s ROB tool. Subsequently, we explored the potential molecular mechanisms of thrombin-mediated effects on SBI post-ICH in terms of inflammation, iron deposition, autophagy, and angiogenesis. Furthermore, we described the effects of thrombin in endothelial cells, astrocytes, pericytes, microglia, and neurons, as well as the harmful and beneficial effects of high and low thrombin concentrations on ICH. Finally, we concluded the current research status of thrombin therapy for ICH, which will provide a basis for the future clinical application of thrombin in the treatment of ICH.

Stenosis severity may escalate over the course of coronary artery disease (CAD), increasing the risk of death for the patient. Conventionally, the assessment of stenosis degree relies on invasive coronary angiography (ICA), an invasive examination unsuitable for patients in poor physical condition or those with contrast allergies and one that imposes a psychological burden on patients. Although abnormal serum N-glycan profiles have exhibited robust associations with various cardiovascular diseases, including CAD, their potential in diagnosing CAD stenosis remains to be determined. In this study, we performed a comprehensive analysis of serum N-glycome from 132 patients who underwent ICA and 27 healthy controls using MALDI-TOF-mass spectrometry. The patients who underwent ICA examination were categorized into four groups based on stenosis severity: no/mild/moderate/severe stenosis. Twenty-seven N-glycans were directly quantified, and 47 derived glycan traits were obtained. Notably, among these 74 glycan features, 18 exhibited variations across the study groups. Using a combination of least absolute shrinkage and selection operator and logistic regression analyses, we developed five diagnostic models for recognizing stenosis degree. Our results suggested that alterations in serum N-glycosylation modifications might be valuable for identifying stenosis degree and monitoring disease progression in individuals with CAD. It is expected to offer a noninvasive alternative for those who could not undergo ICA because of various reasons. However, the diagnostic potential of serum N-glycan panels as biomarkers requires multicenter, large cohort validation in the future.

Abstract Gray mold is a destructive disease caused by Botrytis cinerea, a pervasive plant pathogen, which poses a threat to both tomato growth and postharvest storage. The utilization of induced resistance presents a potential strategy for combating plant pathogenic attacks. ZNC (zhinengcong), an extract derived from the endophytic fungus Paecilomyces variotii, has been discovered to play a vital role in preventing diverse forms of bacterial infections. Nevertheless, the precise mechanism behind its ability to enhance tomato resistance to fungi remains unclear. In this study, we found that the exogenous spraying of ZNC could significantly improve the resistance of tomato plants to B. cinerea. The results of both the metabolomic analysis and high-performance liquid chromatography (HPLC) demonstrated that tomato plants responded to ZNC treatment by accumulating high levels of rutin. Additional transcriptome analysis uncovered that rutin enhances tomato resistance possible by initiating the generation of reactive oxygen species (ROS) and phosphorylation of mitogen-activated protein kinases (MPKs) related genes expression during the initial phase of invasion by B. cinerea. In addition, we also found that rutin might activate plant immunity by eliciting ethylene (ET) and jasmonic acid (JA)-mediated pathways. Therefore, plant immune inducer ZNC and rutin has bright application prospects and high utilization value to control gray mold. Graphical Abstract

Objectives: Patient medication safety can affect their clinical outcomes and plays an important role in patient safety management. However, few tools have been developed to assess patient medication safety. This study aimed to develop and validate the self-reported patient medication safety scale (SR-PMSS). Methods: We developed SR-PMSS guided by the Donabedian Structure-Process-Outcome framework and used psychometric methods to test its validity and reliability. Results: A total of 501 patients with an average age of 56.81 ± 14.47 were enrolled in this study. The SR-PMSS consisted of 21 items and 5 factors. The content validity was good with item-level content validity index (CVI) > 0.78, average scale-level CVI (S-CVI) > 0.9, and universal agreement S-CVI > 0.8. Exploratory factor analysis extracted a five-factor solution with eigenvalues > 0.1, explaining 67.766% of the variance. Confirmatory factor analysis showed good model fit, acceptable convergent validity, and discriminant validity. The Cronbach’s α coefficient for SR-PMSS was 0.929, the split-half reliability coefficient was 0.855, and the test–retest reliability coefficient was 0.978. Conclusions: The SR-PMSS was a valid and reliable instrument with good reliability and validity to evaluate the level of patient medication safety. The target users of the SR-PMSS are all people who are taking or have used prescription medications. The SR-PMSS can be used by healthcare providers in clinical practice and research to identify patients at risk for medication use and intervene with them to reduce adverse medication events and provide support for patient safety management. Plain Language Summary SR-PMSS – a self-reported tool to assess patient medication safety Medication therapy was the most common and frequent treatment method to prevent and cure diseases. Medication safety issues may occur in the process of medication use. Patient medication safety can affect their clinical outcomes and plays an important role in patient safety management. However, there are few tools to assess patient medication safety currently, and most of them focused on medication safety related to hospitals or healthcare workers. We developed the self-reported patient medication safety scale (SR-PMSS) guided by the Donabedian Structure-Process-Outcome framework. Then, we conducted a two-round expert consultation, clarity verification, and item simplification to determine the final version of the scale. The SR-PMSS consisted of 21 items and 5 factors and it had good validity and reliability. The target users of the SR-PMSS are all people who are taking or have used prescription medications. Healthcare providers can use the SR-PMSS in clinical practice and research to identify patients at risk for medication use and intervene with them to reduce adverse medication events and provide support for patient safety management.

This study aimed to determine the influence of time and precipitation as covariates on the flood frequency distribution in the Songhua River tributaries under the nonstationarity assumption and to investigate the possibility of nonstationary models’ application in river management scope demarcation work. Nonstationary flood frequency analysis (NS-FFA) was conducted in three typical basins of the Songhua River (in Northeastern China) based on the generalized additive models for location, scale, and shape (GAMLSS), and stationary flood frequency analysis was used as a comparison. Under the stationarity assumption, the Pearson type Ⅲ (P-Ⅲ) distribution is the main theoretical distribution for the flood extremum at hydrological stations, followed by a lognormal (LN) distribution. Under the nonstationarity assumption, when time is considered a covariate, the optimal theoretical distribution of the flood extremum is mainly LN (with 63.75%), followed by the Weibull distribution (with 18.75%). When precipitation is considered as a covariate, the optimal theoretical distribution of the flood extremum is mainly LN (with 57.5%). We attempted to apply several FFA methods to calculate the design frequency in this study, referring to the work requirements for river management scope demarcation in three typical basins, and came to the following conclusions. From the simulation results of the p = 10% flood at the export stations of typical basins, it can be seen that time-covariate NS-FFA obtained the best simulation results. Two cases of the simulation under the stationarity assumption are positive, which will lead to a high design scale. The time-covariate GAMLSS in NS-FFA has the advantages of higher calculation accuracy and simpler processes. To better balance construction costs and disaster protection requirements, NS-FFA can be used to determine the design scale of water conservation projects; additionally, it can be used to demarcate the scope of river management. The accuracy of GAMLSS for FFA is also influenced by the complexity of the terrain, with basins with relatively simple terrain having higher calculation accuracy.

BackgroundMedication literacy is one of the key indicators that can affect the self-management of medications and medication safety. This study aimed to revise the Chinese Medication Literacy Scale for hypertensive patients (C-MLSHP) and test the reliability and validity of the revised scale.MethodsWe revised the C-MLSHP by several methods, i.e., focus group discussion, expert consultation, patient interview, and pilot study, based on the established evaluation index system of medication literacy for hypertensive patients. Then, a formal survey using the revised Chinese Medication Literacy Scale for hypertensive patients (C-MLSHP-R) was carried out on hypertensive patients from hospitals and community healthcare centers in Changsha city of China to test its reliability and validity. The reliability was evaluated with Cronbach's α coefficient, split-half reliability, and test–retest reliability. The validity was evaluated with content validity, construct validity, convergent validity, discriminant validity, and criterion-related validity.ResultsThe C-MLSHP-R contained 18 items within four domains, i.e., the knowledge domain included four items, the attitude domain had three items, the skill domain involved seven items, and the practice domain included four items. A total of 339 hypertensive patients participated in the formal survey. The results showed that the Cronbach's α coefficient of C-MLSHP-R was 0.802, and for each domain ranged from 0.639 to 0.815. The split-half reliability coefficient of C-MLSHP-R was 0.709, and for each domain ranged from 0.648 to 0.792. The test–retest reliability coefficient of C-MLSHP-R was 0.851, and for each domain ranged from 0.655 to 0.857. The I-CVI of each item ranged from 0.833 to 1.000, the S-CVI/Ave of C-MLSHP-R was 0.981, the S-CVI/UA was 0.889, and for each domain ranged from 0.958 to 1.000. Confirmatory factor analysis results showed that the model fitted well. The convergent validity of C-MLSHP-R was acceptable, and the discriminant validity was good. The criterion coefficient between C-MLSHP-R and C-MLSHP was 0.797, and for each domain ranged from 0.609 to 0.755.ConclusionCompared with C-MLSHP, the C-MLSHP-R with 18 items was much shorter for measuring, and had decreased reliability within the acceptable range and better validity, which was more appropriate and time-saving to assess the medication literacy level for hypertensive patients scientifically and conveniently.

Mangroves are considered to be a sink for microplastics (MPs) due to their unique characteristics. Previous studies mainly focused on the spatial distribution of MPs, but few researchers have addressed the influence of tidal variation on this distribution, especially since the MP total number in mangroves was unknown. In this study, surface sediment samples were collected in mangroves from the Beibu Gulf, South China Sea, and the abundance, composition, and number of MPs were investigated. The results showed that MPs were widely present in all mangrove sediment samples, with abundances ranging from 26.67 ± 9.43 to 239.94 ± 37.80 items/kg. The distribution of MPs was heterogeneous among different sampling sites, with the highest levels in the Shankou (SK) area. The MP abundance in the same mangrove forest gradually increased from the low tidal zone to the high tidal zone, with the enrichment factor ranging from 1.50 to 4.00. The MP abundance was significantly correlated with particulate organic carbon (POC) (n = 12, R = 0.664, p < 0.05). Results showed that mangroves had an interception effect on MPs and factors affecting MP distribution in mangrove sediments included not only tides but also human activities, such as aquaculture, agriculture, and residential life. Finally, this paper estimated the MP total number in mangroves at different sampling areas and tidal zones, and the middle tidal zone was considered to be more accurate for MP pollution assessment in mangroves.

Intracerebral hemorrhage (ICH) is a severe, life-threatening subtype of stoke that constitutes a crucial health and socioeconomic problem worldwide. However, the current clinical treatment can only reduce the mortality of patients to a certain extent, but cannot ameliorate neurological dysfunction and has a high recurrence rate. Increasing evidence has demonstrated that mitochondrial dysfunction occurs in the early stages of brain injury and participates in all stages of secondary brain injury (SBI) after ICH. As the energy source of cells, various pathobiological processes that lead to SBI closely interact with the mitochondria, such as oxidative stress, calcium overload, and neuronal injury. In this review, we discussed the structure and function of mitochondria and the abnormal morphological changes after ICH. In addition, we discussed recent research on the involvement of mitochondrial dynamics in the pathological process of SBI after ICH and introduced the pathological variations and related molecular mechanisms of mitochondrial dysfunction in the occurrence of brain injury. Finally, we summarized the latest progress in mitochondrion-targeted agents for ICH, which provides a direction for the development of emerging therapeutic strategies targeting the mitochondria after ICH.

Rice sheath blight (ShB) caused by Rhizoctonia solani is one of the most destructive diseases in rice. Fungicides are widely used to control ShB in agriculture. However, decades of excessive traditional fungicide use have led to environmental pollution and increased pathogen resistance. Generally, plant elicitors are regarded as environmentally friendly biological pesticides that enhance plant disease resistance by triggering plant immunity. Previously, we identified that the plant immune inducer ZhiNengCong (ZNC), a crude extract of the endophyte, has high activity and a strong ability to protect plants against pathogens. Here, we further found that guanine, which had a significant effect on inducing plant resistance to pathogens, might be an active component of ZNC. In our study, guanine activated bursts of reactive oxygen species, callose deposition and mitogen-activated protein kinase phosphorylation. Moreover, guanine-induced plant resistance to pathogens depends on ethylene and jasmonic acid but is independent of the salicylic acid signaling pathway. Most importantly, guanine functions as a new plant elicitor with broad-spectrum resistance to activate plant immunity, providing an efficient and environmentally friendly biological elicitor for bacterial and fungal disease biocontrol.

Compressed sensing theory has been widely used for data aggregation in WSNs due to its capability of containing much information but with light load of transmission. However, there still exist some issues yet to be solved. For instance, the measurement matrix is complex to construct, and it is difficult to implement in hardware and not suitable for WSNs with limited node energy. To solve this problem, a random measurement matrix construction method based on Time Division Multiple Access (TDMA) is proposed based on the sparse random measurement matrix combined with the data transmission method of the TDMA of nodes in the cluster. The reconstruction performance of the number of non-zero elements per column in this matrix construction method for different signals was compared and analyzed through extensive experiments. It is demonstrated that the proposed matrix can not only accurately reconstruct the original signal, but also reduce the construction complexity from O(MN) to O(d2N) (d≪M), on the premise of achieving the same reconstruction effect as that of the sparse random measurement matrix. Moreover, the matrix construction method is further optimized by utilizing the correlation theory of nested matrices. A TDMA-based semi-random and semi-deterministic measurement matrix construction method is also proposed, which significantly reduces the construction complexity of the measurement matrix from O(d2N) to O(dN), and improves the construction efficiency of the measurement matrix. The findings in this work allow more flexible and efficient compressed sensing for data aggregation in WSNs.

Tomato is an economically crucial vegetable/fruit crop globally. Tomato is rich in nutrition and plays an essential role in a healthy human diet. Phenylpropanoid, a critical compound in tomatoes, reduces common degenerative and chronic diseases risk caused by oxidative stress. As an MYB transcription factor, ATMYB12 can increase phenylpropanoid content by activating phenylpropanoid synthesis related genes, such as PAL, C4H, 4CL, CHS. However, the heterologous expression of AtMYB12 in tomatoes can be altered through transgenic technologies, such as unstable expression vectors and promoters with different efficiency. In the current study, the efficiency of other fruit-specific promoters, namely E8S, 2A12, E4, and PG, were compared and screened, and we determined that the expression efficiency of AtMYB12 was driven by the E8S promoter was the highest. As a result, the expression of phenylpropanoid synthesis related genes was regulated by AtMYB12, and the phenylpropanoid accumulation in transgenic tomato fruits increased 16 times. Additionally, the total antioxidant capacity of fruits was measured through Trolox equivalent antioxidant capacity (TEAC) assay, which was increased by 2.4 times in E8S transgenic lines. TEAC was positively correlated with phenylpropanoid content. Since phenylpropanoid plays a crucial role in the human diet, expressing AtMYB12 with stable and effective fruit-specific promoter E8S could improve tomato’s phenylpropanoid and nutrition content and quality. Our results can provide genetic resources for the subsequent improvement of tomato varieties and quality, which is significant for human health.

Background/Aims: High glucose-induced oxidative stress and inflammatory responses play an important role in painful diabetic neuropathy by activating the TLR4/NFκB signal pathway. Schwann cells (SCs) are integral to peripheral nerve biology, contributing to saltatory conduction along axons, nerve and axon development, and axonal regeneration. SCs provide a microenvironment favoring vascular regeneration but their low survival ratio in hyperglycemic conditions suppress the function to promote nerve growth. Nuclear factor erythroid 2-related factor 2 (Nrf2) promotes remyelination after peripheral nerve injury. The aim of this study was to identify the role of Nrf2 in SC-mediated functional recovery after sciatic nerve injury. Methods: We compared plasma inflammatory factors in diabetic patients (DN) with/without diabetic peripheral neuropathy (DPN) and assessed whether Nrf2 expression in SCs could repair peripheral nerve injury in a rat model. Nrf2, TLR4/NFκB signal pathway and apoptosis relative protein expression were detected by western blot. Apoptosis and angiogenesis were determined by immunofluorescence and tubule formation assay, respectively. Regenerated nerves were determined by transmission electron microscope. Results: Higher levels of inflammatory factors and VEGF expression were found in DPN patients. Cellular experiments indicate that Nrf2 expression inhibits hyperglycemia-induced apoptosis and promotes angiogenesis by regulating the TLR4/NFκB signal pathway. Animal experiments show that nerve conduction velocity, myelin sheath thickness, and sciatic vasa nervorum are restored with transplantation of SCs overexpressing Nrf2. Conclusions: Taken together, the high survival ratio of SCs in a DPN rat model indicates that overexpression of Nrf2 restores nerve injury.

In steel-fiber-reinforced composites, when the direction of a steel fiber is parallel to the direction of tensile stress, the capacity of the strengthening and toughening effect of the steel fiber can be fully applied to the composites. In this paper, a method to control the direction of steel fibers from one side surface of the mold full of a fresh mixture of steel-fiber-reinforced concrete using a magnet composed of small magnet squares is proposed, which has the advantage that the approach is suitable for specimens or elements with any size or shape. The influence of the method on the orientation effect of steel fiber is explored, and the flexural properties of the specimens prepared by different orientation methods are compared and analyzed through a three-point bending test. The test results show that the steel fibers in the mixture are successfully aligned by the magnetic treatment and the fiber orientation effective coefficient reaches 0.9 or more. The flexural properties of the aligned steel-fiber-reinforced cementitious composite (ASFRC) prepared by the magnetic device is significantly improved. The test results also show that the properties of the ASFRC prepared by the new method are comparable with that prepared by the previous electromagnetic field in terms of the fiber orientation effective coefficient and flexural strength.