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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.