AlphaFold2-guided engineering of split-GFP technology enables labeling of endogenous tubulins across species while preserving function.

Dynamic properties are essential for microtubule (MT) physiology. Current techniques for in vivo imaging of MTs present intrinsic limitations in elucidating the isotype-specific nuances of tubulins, which contribute to their versatile functions. Harnessing the power of the AlphaFold2 pipeline, we engineered a strategy for the minimally invasive fluorescence labeling of endogenous tubulin isotypes or those harboring missense mutations. We demonstrated that a specifically designed 16-amino acid linker, coupled with sfGFP11 from the split-sfGFP system and integration into the H1-S2 loop of tubulin, facilitated tubulin labeling without compromising MT dynamics, embryonic development, or ciliogenesis in Caenorhabditis elegans. Extending this technique to human cells and murine oocytes, we visualized MTs with the minimal background fluorescence and a pathogenic tubulin isoform with fidelity. The utility of our approach across biological contexts and species set an additional paradigm for studying tubulin dynamics and functional specificity, with implications for understanding tubulin-related diseases known as tubulinopathies.

Specialized metabolites of the endophyte Annulohypoxylon areolatum hosted by Aconitum carmichaelii.

Seven previously undescribed compounds, including one amino acid hybrid sesquiterpene areolatol A (1), two unusual natural sesquiterpenoid skeleton areolatones A-B (2-3) and four benzo[j]fluoranthene areolaranes A-D (4-7) were characterized from Annulohypoxylon areolatum. The structures of the compounds were determined by extensive spectroscopic analysis, X-ray diffraction analysis, and ECD and NMR computational. Notably, areolatol A (1) was the first reported sesquiterpene featuring a 5/7/3-ring system and hybridized with two molecular amino acids. In addition, areolaranes A-D (4-7) were identified as possible chemophenetic markers.

Composted maize straw under fungi inoculation reduces soil N2O emissions and mitigates the microbial N limitation in a wheat upland.

Enhancement of microbial assimilation of inorganic nitrogen (N) by straw addition is believed to be an effective pathway to improve farmland N cycling. However, the effectiveness of differently pretreated straws on soil N2O emissions and soil N-acquiring enzyme activities remains unclear. In this study, a pot experiment with four treatments (I, no addition, CK; II, respective addition of maize straw, S; III, composted maize straw under no fungi inoculation, SC; and IV, composted maize straw under fungi inoculation, SCPA) at the same quantity of carbon (C) input was conducted under the same amount of inorganic N fertilization. Results showed that the seasonal cumulative N2O emissions following the SCPA treatment were the lowest at 4.03 kg N ha-1, representing a significant reduction of 19 % compared with the CK treatment. The S and SC treatments had no significant effects on N2O emissions. The decrease of soil N2O emissions following the SCPA treatment was mainly attributed to the increase of microbial N assimilation and the increased abundance of functional genes related to N2O reductase. The SCPA treatment significantly decreased soil alkaline phosphatase (ALP) activity and increased leucine aminopeptidase (LAP) activity at the basal fertilization, while increased soil ALP and LAP activity, decreased soil N-Acetyl-ß-D-Glucosidase (NAG) activity at harvest. Compared with the CK treatment, the S, SC, and SCPA treatment significantly increased soil ß-glucosidase (ß-GC) activity at harvest. The decrease in the (NAG+LAP)/ALP ratio following the SCPA treatment indicated that the composted maize straw under fungi inoculation alleviated microbial N limitation at harvest. Moreover, PICRUSt analysis also suggested that the SCPA treatment increased the abundance of bacterial genes associated with N assimilation and N2O reduction, whereas the S and SC treatment did not significantly affect the abundance of N2O reduction genes compared with the CK treatment. Our results suggest that the composted maize straw under fungi inoculation would reduce the risk of N2O emissions and effectively mitigate the microbial N limitation in dryland wheat system.

Nutritional Indices Predict All Cause Mortality in Patients with Multi-/Rifampicin-Drug Resistant Tuberculosis.

Background: Multidrug- and rifampicin-resistant tuberculosis (MDR/RR-TB) with high mortality remains a public health crisis and health security threat. This study aimed to explore the predictive value of nutritional indices for all-cause mortality (ACM) in MDR/RR-TB patients. Methods: We retrospectively recruited MDR/RR-TB patients between January 2015 and December 2021, randomly assigning them to training and validation cohorts. Patients were divided into high nutritional risk groups (HNRGs) and low nutritional risk groups (LNRGs) based on the optimal cut-off value obtained from receiver operating characteristic (ROC) analyses of the hemoglobin-albumin-lymphocyte-platelet (HALP) score, prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score. In the training cohort, Kaplan-Meier survival curves and Log rank tests were used to compare overall survival (OS) between the groups. Cox risk proportion regression analyses were used to explore the risk factors of ACM in patients with MDR/RR-TB. The predictive performance of ACM was assessed using area under the curve (AUC), sensitivity and specificity of ROC analyses. Results: A total of 524 MDR/RR-TB patients, with 255 in the training cohort and 269 in the validation cohort, were included. Survival analyses in the training cohort revealed significantly lower OS in the HNRGs compared to the LNRGs. After adjusting for covariates, multivariate analysis identified low HALP score, low PNI and high CONUT score were independent risk factors for ACM in MDR/RR-TB patients. ROC analyses demonstrated good predictive performance for ACM with AUCs of 0.765, 0.783, 0.807, and 0.811 for HALP score, PNI, CONUT score, and their combination, respectively. Similar results were observed in the validation set. Conclusion: HALP score, PNI, and CONUT scores could effectively predict ACM in patients with MDR/RR-TB. Hence, routine screening for malnutrition should be given more attention in clinical practice to identify MDR/RR-TB patients at higher risk of mortality and provide them with nutritional support to reduce mortality.

Design and Numerical Modeling of Terahertz Metasurface with Dual Functions of Sensing and Filtering.

This study proposes a dual-functional terahertz device based on the Dirac semimetal, serving as both a sensing element and a band-pass filter. The device's operating mode can switch between these two functions by utilizing the phase transition property of vanadium dioxide (VO2). When VO2 is in the insulating state, the device functions as a sensing element. The simulation results demonstrate an impressive refractive index sensitivity of 374.40 GHz/RIU (Refractive Index Unit). When VO2 is in the metallic state, the device functions as a band-pass filter, exhibiting a center frequency of 2.01 THz and a 3 dB fractional bandwidth of 0.91 THz. The integration of these dual functionalities within a single terahertz device enhances its utility in both sensing and filtering applications.

A Machine Learning Method for Differentiation Crohn's Disease and Intestinal Tuberculosis.

Background: Whether machine learning (ML) can assist in the diagnosis of Crohn's disease (CD) and intestinal tuberculosis (ITB) remains to be explored. Methods: We collected clinical data from 241 patients, and 51 parameters were included. Six ML methods were tested, including logistic regression, decision tree, k-nearest neighbor, multinomial NB, multilayer perceptron, and XGBoost. SHAP and LIME were subsequently introduced as interpretability methods. The ML model was tested in a real-world clinical practice and compared with a multidisciplinary team (MDT) meeting. Results: XGBoost displays the best performance among the six ML models. The diagnostic AUROC and the accuracy of XGBoost were 0.946 and 0.884, respectively. The top three clinical features affecting our ML model's result prediction were T-spot, pulmonary tuberculosis, and onset age. The ML model's accuracy, sensitivity, and specificity in clinical practice were 0.860, 0.833, and 0.871, respectively. The agreement rate and kappa coefficient of the ML and MDT methods were 90.7% and 0.780, respectively (P<0.001). Conclusion: We developed an ML model based on XGBoost. The ML model could provide effective and efficient differential diagnoses of ITB and CD with diagnostic bases. The ML model performs well in real-world clinical practice, and the agreement between the ML model and MDT is strong.

Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.

To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enrolled and categorized into low (< 2 lymph nodes involved (LNs+)) and high (≥ 2 LNs+) burden groups based on surgical pathology. PE was evaluated on T2WI, and intra- and peri-tumoral radiomic features were extracted from MRI-visible tumors in DCE-MRI. Deep learning models were developed for LN burden prediction in the training cohort and validated in an independent cohort. The incremental value of PE was evaluated through receiver operating characteristic (ROC) analysis, confirming the improvement in the area under the curve (AUC) using the Delong test. This was complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. The deep learning combined model, incorporating PE with selected radiomic features, demonstrated significantly higher AUC values compared to the MRI model and the DLR model in the training cohort (n = 177) (AUC: 0.953 vs. 0.849 and 0.867, p < 0.05) and the validation cohort (n = 111) (AUC: 0.963 vs. 0.883 and 0.882, p < 0.05). The complementary analysis demonstrated that PE significantly enhances the prediction performance of the DLR model (Categorical NRI: 0.551, p < 0.001; IDI = 0.343, p < 0.001). These findings were confirmed in the validation cohort (Categorical NRI: 0.539, p < 0.001; IDI = 0.387, p < 0.001). PE improved preoperative ALNM burden prediction of DLR model, facilitating personalized axillary management in breast cancer patients.

Phosphofructokinase-1 in Cancer: A Promising Target for Diagnosis and Therapy.

Tumor cells have distorted enzymatic houses, which change the metabolic state from oxidative phosphorylation to glycolysis with high lactate levels in a hypoxic environment. Redrafting the metabolic profile is an emerging hallmark of cancer. Glycolytic enzyme amplification occurs in about 70% of all malignancies. Current studies have found that PFK-1 overexpression is linked to cell migration, proliferation, and Overall Survival (OS) rate in various human cancer cell lines. This review intended to uncover the bona fide therapeutic target for cancer therapy and elucidate the role of PFK-1 in cancer. Furthermore, this review has outlined the listed pharmacological and genetic inhibitors of PFK-1. Following this review, future studies on PFK-1 should emphasize the molecular pathways implicated in PFK-1 overexpression in cancer development. The terms "PFK-1", "PFKP-1", "PFKL-1", "PFKM-1", "PFKM-1 and cancer", "PFKP-1 and cancer", "PFKL-1 and cancer", and "inhibitors of PFK-1" were used to retrieve the information from a variety of databases, including PubMed, Scopus, Google Scholar, and ScienceDirect. In a variety of malignancies, inhibiting the expression of PFK-1 isoforms has been reported to be the most effective therapeutic method. Overexpression of PFK-1 isoforms induces the Warburg effect, cell proliferation, and carcinogenesis by downregulating apoptotic proteins, such as active caspase-3, caspase-9, and caspase-8. YY1, synoviolin, Sh-RNA-507, SNAI, miR-520a/b/e, miR-128, and ß-miR-6517 are some of the putative genetic inhibitors against PFK-1 that have been used to manage the development of malignancies. Pharmacological inhibitors, such as penfluridol, synoviolin/HRD1, quercetin, ginsenoside 20(S)-Rg3, triptolide, worenine, acetylsalicylic acid, and salicylic acid, can regulate the advancement of malignancies by inhibiting PFK-1. Thus, PFK-1 is a promising molecular biomarker for cancer treatment. A prospective investigation can validate the unbiased approaches for discovering brandnew PFK-1 inhibitors for cancer treatment.

Experimental quantum Byzantine agreement on a three-user quantum network with integrated photonics.

Quantum communication networks are crucial for both secure communication and cryptographic networked tasks. Building quantum communication networks in a scalable and cost-effective way is essential for their widespread adoption. Here, we establish a complete polarization entanglement-based fully connected network, which features an ultrabright integrated Bragg reflection waveguide quantum source, managed by an untrusted service provider, and a streamlined polarization analysis module, which requires only one single-photon detector for each user. We perform a continuously working quantum entanglement distribution and create correlated bit strings between users. Within the framework of one-time universal hashing, we provide the experimental implementation of source-independent quantum digital signatures using imperfect keys circumventing the necessity for private amplification. We further beat the 1/3 fault tolerance bound in the Byzantine agreement, achieving unconditional security without relying on sophisticated techniques. Our results offer an affordable and practical route for addressing consensus challenges within the emerging quantum network landscape.

Natural Derivatives of Selective HDAC8 Inhibitors with Potent in Vivo Antitumor Efficacy against Breast Cancer.

HDAC8 is a therapeutic target with great promise for breast cancer. Here, we reported a novel compound corallorazine D from Nocardiopsis sp. XZB108, selectively inhibited HDAC8 (IC50 = 0.90 ± 0.014 µM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. Upon additional modifications of corallorazine D, a candidate compound 5k, demonstrated remarkable inhibitory potency against HDAC8 (IC50 = 0.12 ± 0.01 nM), 89-fold superior to PCI-34051. The selectivity of 5k was at least 439-fold, superior to corallorazine D, confirming the efficacy of our modifications. In an orthotopic mouse model of breast cancer, 5k displayed nearly 4-fold superior antitumor activity than SAHA. Furthermore, 5k triggered antitumor immunity by activating T cells. Treatment with 5k significantly increased the proportion of M1 macrophages and decreased the proportion of M2 macrophages (M1/M2 ratio = 2.67 ± 0.25). 5k represents a promising compound for further investigation as a potential treatment for breast cancer.

Inferring directed spectral information flow between mixed-frequency time series.

Identifying directed spectral information flow between multivariate time series is important for many applications in finance, climate, geophysics and neuroscience. Spectral Granger causality (SGC) is a prediction-based measure characterizing directed information flow at specific oscillatory frequencies. However, traditional vector autoregressive (VAR) approaches are insufficient to assess SGC when time series have mixed frequencies (MF) or are coupled by nonlinearity. Here we propose a time-frequency canonical correlation analysis approach ("MF-TFCCA") to assess the strength and driving frequency of spectral information flow. We validate the approach with intensive computer simulations on MF time series under various interaction conditions and assess statistical significance of the estimate with surrogate data. We further apply MF-TFCCA to real-life finance, climate and neuroscience data. Our analysis framework provides an exploratory and computationally efficient approach to quantify directed information flow between MF time series in the presence of complex and nonlinear interactions.

An ECG denoising technique based on AHIN block and gradient difference max loss.

The electrocardiogram (ECG) signal is susceptible to interference from unknown noises during the acquisition process due to their low frequency and amplitude, resulting in the loss of significant information in the signals. Recent advancements in deep learning models have shown promising results in signal processing. However, these models lack robustness against various types of noise and often overlook the gradient difference between denoised and original signals. In this study, we propose a novel deep learning denoising method based on an attention half instance normalization block (AHIN block) and a gradient difference max loss function (GDM Loss). Our approach consists of two stages: firstly, we input the noisy ECG signal to obtain a denoised version; secondly, we reconstruct the denoised signal by fusing preliminary results from the first stage while correcting waveform distortions caused by initial denoising to minimize information loss. Additionally, we introduce a new loss function that considers differences between slopes of the denoised ECG signal and clean ECG signal. To validate our proposed method's effectiveness, extensive experiments were conducted on both our model architecture and loss function compared with other state-of-the-art methods. Results demonstrate that our approach achieves excellent performance in terms of both signal-to-noise ratio (SNR) and root-mean-square error (RMSE). The proposed noise reduction method improves 8.86%, 12.05% and 10.50% respectively under BW, MA and EM noise.

The metabolic clock of ketamine abuse in rats by a machine learning model.

Ketamine has recently become an anesthetic drug used in human and veterinary clinical medicine for illicit abuse worldwide, but the detection of illicit abuse and inference of time intervals following ketamine abuse are challenging issues in forensic toxicological investigations. Here, we developed methods to estimate time intervals since ketamine use is based on significant metabolite changes in rat serum over time after a single intraperitoneal injection of ketamine, and global metabolomics was quantified by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Thirty-five rats were treated with saline (control) or ketamine at 3 doses (30, 60, and 90 mg/kg), and the serum was collected at 21 time points (0 h to 29 d). Time-dependent rather than dose-dependent features were observed. Thirty-nine potential biomarkers were identified, including ketamine and its metabolites, lipids, serotonin and other molecules, which were used for building a random forest model to estimate time intervals up to 29 days after ketamine treatment. The accuracy of the model was 85.37% in the cross-validation set and 58.33% in the validation set. This study provides further understanding of the time-dependent changes in metabolites induced by ketamine abuse.

RN486, a Bruton's Tyrosine Kinase inhibitor, antagonizes multidrug resistance in ABCG2-overexpressing cancer cells.

Background and Objectives: Overcoming ATP-binding cassette subfamily G member 2 (ABCG2)-mediated multidrug resistance (MDR) has attracted the attention of scientists because one of the critical factors resulting in MDR in cancer is the overexpression of ABCG2. RN486, a Bruton's Tyrosine Kinase (BTK) inhibitor, was discovered to potentially reverse ABCB1-mediated MDR. However, there is still uncertainty about whether RN486 has a reversal off-target impact on ABCG2-mediated MDR. Methods: MTT assay was used to detect the reversal effect of RN486 on ABCG2-overexpressing cancer cells. The ABCG2 expression level and subcellular localization were examined by Western blotting and immunofluorescence. Drug accumulation and eflux assay and ATPase assay were performed to analyze the ABCG2 transporter function and ATPase activity. Molecular modeling predicted the binding between RN486 and ABCG2 protein. Results: Non-toxic concentrations of RN486 remarkably increased the sensitivity of ABCG2-overexpressing cancer cells to conventional anticancer drugs mitoxantrone and topotecan. The reversal mechanistic studies showed that RN486 elevated the drug accumulation because of reducing the eflux of ABCG2 substrate drug in ABCG2-overexpressing cancer cells. In addition, the inhibitory efect of RN486 on ABCG2-associated ATPase activity was also verified. Molecular docking study implied a strong binding afinity between RN486 and ABCG2 transporter. Meanwhile, the ABCG2 subcellular localization was not altered by the treatment of RN486, but the expression level of ABCG2 was down-regulated. Conclusions: Our studies propose that RN486 can antagonize ABCG2-mediated MDR in cancer cells via down-regulating the expression level of ABCG2 protein, reducing ATPase activity of ABCG2 transporter, and inhibiting the transporting function. RN486 could be potentially used in conjunction with chemotherapy to alleviate MDR mediated by ABCG2 in cancer.

Preoperative prediction of extensive intraductal component in invasive breast cancer based on intra- and peri-tumoral heterogeneity in high-resolution ultrafast DCE-MRI.

Preoperatively predicting extensive intraductal component in invasive breast cancer through imaging is crucial for informed decision-making, guiding surgical planning to mitigate risks of incomplete resection or re-operation for positive margins in breast-conserving surgery. This study aimed to characterize intra- and peri-tumor heterogeneity using high-spatial resolution ultrafast DCE-MRI to predict the extensive intraductal component in invasive breast cancer (IBC-EIC) preoperatively. A retrospective analysis included invasive breast cancer patients who underwent preoperative high-spatial resolution ultrafast DCE-MRI, categorized based on intraductal component status (IBC-EIC vs. IBC without EIC). Propensity score matching (PSM) was employed to balance clinicopathological covariates between the groups. Personalized kinetic intra-tumor heterogeneity (ITHkinetic) and peri-tumor heterogeneity (PTHkinetic) scores were quantified using clustered voxels with similar enhancement patterns. An image combined model, incorporating MRI features, ITHkinetic, and PTHkinetic scores, was developed and assessed. Of 368 patients, 26.4% (97/368) had IBC-EIC. PSM yielded well-matched pairs of 97 patients each. After PSM, ITHkinetic and PTHkinetic scores were significantly higher in the IBC-EIC group (ITHkinetic: 0.68 ± 0.23; PTHkinetic: 0.58 ± 0.19) compared to IBC without EIC (ITHkinetic: 0.32 ± 0.25; PTHkinetic: 0.42 ± 0.18; p < 0.001). Before PSM, ITHkinetic (0.71 ± 0.20 vs. 0.49 ± 0.28, p < 0.001) and PTHkinetic (0.61 ± 0.18 vs. 0.50 ± 0.20, p < 0.001) scores remained higher in the IBC-EIC group. The Image Combined Model demonstrated good predictive performance for IBC-EIC, with an AUC of 0.91 (95% CI 0.86-0.95) after PSM and 0.85 (95% CI 0.81-0.90) before PSM. Inclusion of ITHkinetic and PTHkinetic scores significantly improved prediction capability. ITHkinetic and PTHkinetic characterization from high-spatial resolution ultrafast DCE-MRI kinetic curves enhances preoperative prediction of IBC-EIC, offering valuable insights for personalized breast cancer management.

Bridging cultures: Chinese elements in scientific illustrations.

In the context of globalization, the integration of cultural elements into scientific research, particularly through the incorporation of traditional Chinese cultural motifs in scientific illustrations, represents a novel frontier in enhancing the universality and appeal of scientific discoveries. This paper explores the innovative practice of embedding traditional Chinese cultural elements into scientific paper illustrations, highlighting its significant role in augmenting the global appeal of research findings, promoting diversity and innovation in scientific inquiry, and facilitating cross-cultural understanding. Through a series of case studies, including symbolic representations of ancient myths and the use of traditional themes to elucidate complex scientific phenomena, we demonstrate how this cultural integration not only makes scientific content more accessible and engaging but also fosters a deeper appreciation of Chinese heritage among international audiences. This approach not only bridges the gap between science and culture but also contributes to a more inclusive and interconnected global scientific community, underscoring the importance of cultural diversity in enriching scientific exploration and communication.

Research Note: Effects of different light-emitting diode lights during egg incubation on hatching performance and embryo development in White King pigeons.

The light provide during incubation can influence hatching characteristics (hatching time, hatchability, etc.) and embryo development in chickens, geese, and turkeys. However, relevant studies on this factor in pigeons are lacking. This study investigated the effects of in ovo photostimulation during embryogenesis on hatching performance, squab quality, and embryo development in pigeons. 400 eggs from paired- bred pigeons were randomly distributed into 4 incubation lighting treatments, with 2 replicates per treatment. The treatments included dark as a control (NL), 12-h light, and 12-h dark photoperiods of white light (WL), red light (RL), and green light (GL) (100 lx at egg level) during the first 15 d of incubation. A total of 1,600 eggs in 4 batches from White King pigeons were used. The results showed that hatching time of the WL group was significantly shorter than that of the dark light group (P < 0.05). The hatchability of fertile eggs in the WL group was significantly higher (P < 0.05), whereas the hatchability of fertile eggs in the RL group was significantly lower (P < 0.05) than that of in the control group. Light stimulation had no effect on time to 90% hatching or average hatching time (P > 0.05). In addition, the hatch window was not extended by light stimulation (P > 0.05). The group incubated under GL showed an increase in embryo weight and relative leg muscle on embryonic d 14 and the hatching day compared to the dark incubation (P < 0.05). Green light stimulated the heart and liver development during the early and middle stages of embryogenesis. It was concluded that white light stimulation during embryogenesis accelerated the hatching process, whereas monochromatic green light had a positive effect on embryo development. Our findings provide important guidance for developing light protocols for pigeon egg incubation.

Shared genetic etiology of vessel diseases: A genome-wide multi-traits association analysis.

BACKGROUND: The comorbidity among vascular diseases has been widely reported, however, the contribution of shared genetic components remains ambiguous. METHODS: Based on genome-wide association study summary statistics, we employed statistical genetics methodologies to explore the shared genetic basis of eight vascular diseases: coronary artery disease, abdominal aortic aneurysm, ischemic stroke, peripheral artery disease, thoracic aortic aneurysm, phlebitis, varicose veins, and venous thromboembolism. We assessed global and local genetic correlations among these disorders by linkage disequilibrium score regression, high-definition likelihood, and local analysis of variant association. Cross-trait analyses conducted with CPASSOC identified pleiotropic variants and loci. Further, biological pathways at the multi-omics level were explored using multimarker analysis of genomic annotation, transcriptome-wide and proteome-wide association studies. Causal associations among the vascular diseases were evaluated by mendelian randomization and latent causal variable to assess vertical pleiotropic effects. RESULTS: We found significant global genetic associations in 18 pairs of vascular diseases. Additionally, we discovered 317 unique genomic regions where at least one pair of traits demonstrated significant correlation. Multi-trait association analysis identified 19,361 significant potential pleiotropic variants in 274 independent pleiotropic loci. Multi-trait colocalization analysis revealed 56 colocalized loci in specific disease sets. Gene-based analysis identified 700 potential pleiotropic genes, which were subsequently validated at both transcriptome and protein levels. Gene-set enrichment analysis supports the role of biological pathways such as vessel wall structure, coagulation and lipid transport in vascular disease. Additionally, 7 pairs of vascular diseases have a causal relationship. CONCLUSIONS: Our study indicates a shared genetic basis and the presence of common risk genes among vascular diseases. These findings offer novel insights into potential mechanisms underlying the association between vascular diseases, as well as provide guidance for interventions and treatments of multi-vascular conditions.

ARBUR, a machine learning-based analysis system for relating behaviors and ultrasonic vocalizations of rats.

Deciphering how different behaviors and ultrasonic vocalizations (USVs) of rats interact can yield insights into the neural basis of social interaction. However, the behavior-vocalization interplay of rats remains elusive because of the challenges of relating the two communication media in complex social contexts. Here, we propose a machine learning-based analysis system (ARBUR) that can cluster without bias both non-step (continuous) and step USVs, hierarchically detect eight types of behavior of two freely behaving rats with high accuracy, and locate the vocal rat in 3-D space. ARBUR reveals that rats communicate via distinct USVs during different behaviors. Moreover, we show that ARBUR can indicate findings that are long neglected by former manual analysis, especially regarding the non-continuous USVs during easy-to-confuse social behaviors. This work could help mechanistically understand the behavior-vocalization interplay of rats and highlights the potential of machine learning algorithms in automatic animal behavioral and acoustic analysis.