Data-driven and machine-learning based prediction of wave propagation behavior in dam-break flood

The computational prediction of wave propagation in dam-break floods is a long-standing problem in hydrodynamics and hydrology. Until now, conventional numerical models based on Saint-Venant equations are the dominant approaches. Here we show that a machine learning model that is well-trained on a minimal amount of data, can help predict the long-term dynamic behavior of a one-dimensional dam-break flood with satisfactory accuracy. For this purpose, we solve the Saint-Venant equations for a one-dimensional dam-break flood scenario using the Lax-Wendroff numerical scheme and train the reservoir computing echo state network (RC-ESN) with the dataset by the simulation results consisting of time-sequence flow depths. We demonstrate a good prediction ability of the RC-ESN model, which ahead predicts wave propagation behavior 286 time-steps in the dam-break flood with a root mean square error (RMSE) smaller than 0.01, outperforming the conventional long short-term memory (LSTM) model which reaches a comparable RMSE of only 81 time-steps ahead. To show the performance of the RC-ESN model, we also provide a sensitivity analysis of the prediction accuracy concerning the key parameters including training set size, reservoir size, and spectral radius. Results indicate that the RC-ESN are less dependent on the training set size, a medium reservoir size K=1200~2600 is sufficient. We confirm that the spectral radius \r{ho} shows a complex influence on the prediction accuracy and suggest a smaller spectral radius \r{ho} currently. By changing the initial flow depth of the dam break, we also obtained the conclusion that the prediction horizon of RC-ESN is larger than that of LSTM

Association of obstructive sleep apnea with hypertension: A systematic review and meta-analysis

Results: Twenty-six studies with 51 623 participants (28 314 men, 23 309 women; mean age 51.8 years) met inclusion criteria and were included in this study. Among them, six studies showed a significant association between OSA and resistant hypertension (pooled OR = 2.842, 95% CI = 1.703-3.980, P \u3c 0.05). Meanwhile, the combination of 20 original studies on the association of OSA with essential hypertension also presented significant results with the pooled ORs of 1.184 (95% CI = 1.093-1.274, P \u3c 0.05) for mild OSA, 1.316 (95% CI = 1.197-1.433, P \u3c 0.05) for moderate OSA and 1.561 (95% CI = 1.287-1.835, P \u3c 0.05) for severe OSA. Conclusions: Our findings indicated that OSA is related to an increased risk of resistant hypertension. Mild, moderate and severe OSA are associated essential hypertension, as well a dose-response manner relationship is manifested. The associations are relatively stronger among Caucasians and male OSA patients. Background: Obstructive sleep apnea (OSA) is a sleep disorder characterized as complete or partial upper airflow cessation during sleep. Although it has been widely accepted that OSA is a risk factor for the development of hypertension, the studies focusing on this topic revealed inconsistent results. We aimed to clarify the association between OSA and hypertension, including essential and medication-resistant hypertension. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was followed. PubMed and Embase databases were used for searching the relevant studies published up to December 31, 2016. A quantitative approach of meta-analysis was performed to estimate the pooled odds ratio (OR) and 95% confidence interval (CI)

Sculpting Molecules in 3D: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization

The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research. However, the challenge of designing molecular drugs or materials that incorporate multi-modality prior knowledge remains a critical and complex undertaking. Specifically, achieving a practical molecular design necessitates not only meeting the diversity requirements but also addressing structural and textural constraints with various symmetries outlined by domain experts. In this article, we present an innovative approach to tackle this inverse design problem by formulating it as a multi-modality guidance generation/optimization task. Our proposed solution involves a textural-structure alignment symmetric diffusion framework for the implementation of molecular generation/optimization tasks, namely 3DToMolo. 3DToMolo aims to harmonize diverse modalities, aligning them seamlessly to produce molecular structures adhere to specified symmetric structural and textural constraints by experts in the field. Experimental trials across three guidance generation settings have shown a superior hit generation performance compared to state-of-the-art methodologies. Moreover, 3DToMolo demonstrates the capability to generate novel molecules, incorporating specified target substructures, without the need for prior knowledge. This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies. 3DToMolo creates opportunities for a more nuanced and effective exploration of the vast chemical space, opening new frontiers in the development of molecular entities with tailored properties and functionalities

A classifier graph based recurring concept detection and prediction approach

It is common in real-world data streams that previously seen concepts will reappear, which suggests a unique kind of concept drift, known as recurring concepts. Unfortunately, most of existing algorithms do not take full account of this case. Motivated by this challenge, a novel paradigm was proposed for capturing and exploiting recurring concepts in data streams. It not only incorporates a distribution-based change detector for handling concept drift but also captures recurring concept by storing recurring concepts in a classifier graph. The possibility of detecting recurring drifts allows reusing previously learnt models and enhancing the overall learning performance. Extensive experiments on both synthetic and real-world data streams reveal that the approach performs significantly better than the state-of-the-art algorithms, especially when concepts reappear

Identification of Differentially Expressed Hub Genes Associated With Immune Cell Recruitment in Claudin-Low Breast Cancer

Breast cancer (BCa) is the most common malignancy in women and claudin-low breast cancer (CL-BCa) is a newly identified BCa subtype characterized by low expression of claudin 3&4&7. However, the hub genes associated with the recruitment of immune cells into CL-BCa were rarely described. This study aimed at exploring the differentially expressed hub genes associated with tumor-infiltrating immune cells in CL-BCa by a multi-approach bioinformatics analysis. The top 200 genes associated with CL-BCa were screened in the METABRIC dataset; the PPI network was constructed using STRING and Cytoscape; tumor-infiltrating immune cells were analyzed by TIMER 2.0; and the correlation of feature cytokines and claudins on survival was examined in METABRIC and TCGA datasets. Consequently, we found that the fraction of tumor-infiltrating immune cells, especially CD8+T cells and macrophages, increased in the CL-BCa. Differentially expressed cytokines (CCL5, CCL19, CXCL9 and CXCL10) were related to the overall survival, and their expression levels were also examined both in tumor tissues of CL-BCa patients by IHC and in typical CL-BCa cell lines by qPCR. Moreover, the BCa patients with low expression of these differentially expressed claudins (CLDN8, CLDN11 and CLDN19) showed a worse overall survival. This study sheds light on molecular features of CL-BCa on immune microenvironments and contributes to identification of prognosis biomarkers for the CL-BCa patients

Optimization of production of PLA microbubble ultrasound contrast agents for Hydroxycamptothecin delivery

In this paper, ultrasound contrast agents based on a high molecular polymer poly lactic acid (PLA) and loaded with Hydroxycamptothecin (HCPT) were prepared by combining ultrasound method and a Shirasu Porous Glass (SPG) membrane emulsification technique. A special focus was on the optimization of production of RCPT-PLA microbubbles. Different factors, such as the power and the time of ultrasonic action, the ratio of inner aqueous phase against outer oil phase, and the concentration of PLA were evaluated, and the average size of HCPT-PLA microbubbles, the drug carrying efficiency, as well as the acoustically-triggered drug release at 3kHz ultrasound were determined. The study showed that the HCPT-PLA microbubbles prepared using our optimized conditions, were sphere-like in shape with a mean diameter of 1-7 mu m. The drug loading efficiency reached up to 56.48%. In vitro, the drug release of HCPT-PLA microbubbles increased significantly at 3kHz ultrasound for 30s compared with that of ultrasound free condition. In conclusion, the HCPT-PLA microbubbles has the characteristics desirable for an intravenously administered ultrasound contrast agent for further clinical use

Establishment of a Method for the Identification of Plant-Derived Ingredients in Mānuka Honey

To establish a method to identify the authenticity of manuka honey, this study compared different methods of DNA extraction from mānuka honey pollen, and it developed a DNA extraction method for mānuka honey supernatant and a real-time polymerase chain reaction (real-time PCR) method for the detection of plant-derived ingredients in mānuka honey, including plant internal control, mānuka and kānuka. By analyzing the specificity, sensitivity, detection limit and comparing with the method of the New Zealand Ministry for Primary Industries (MPI), the feasibility, accuracy and equivalence of this method were confirmed. The proposed DNA detection method could replace the MPI method, not only shaking off the dependence on kits, but also making up for the deficiency of identifying mānuka honey pollen only. It has an important application value and scientific significance for the identification of mānuka honey

Antifatigue Effects of Antrodia cinnamomea Cultured Mycelium via Modulation of Oxidative Stress Signaling in a Mouse Model

Antrodia cinnamomea, a folk medicinal mushroom, has numerous biological effects. In this study, we aim to assess whether the antifatigue effects of A. cinnamomea mycelia (AC) and its underlying mechanisms are related to oxidative stress signaling using behavioral mouse models and biochemical indices detection. Mice were orally treated with AC at doses of 0.1, 0.3, and 0.9 g/kg for three weeks. AC had no effect on the spontaneous activities of mice indicating its safety on central nervous system. Furthermore, results obtained from weight-loaded forced swimming test, rotary rod test, and exhausted running test confirmed that AC significantly enhanced exercise tolerance of mice. Biochemical indices levels showed that these effects were closely correlated with inhibiting the depletion of glycogen and adenosine triphosphate stores, regulating oxidative stress-related parameters (superoxide dismutase, glutathione peroxidase, reactive oxygen species, and malondialdehyde) in serum, skeletal muscle, and liver of mice. Moreover, the effects of AC may be related with its regulation on the activations of AMP-activated protein kinase, protein kinase B, and mammalian target of rapamycin in liver and skeletal muscle of mice. Altogether, our data suggest that the antifatigue properties of AC may be one such modulation mechanism via oxidative stress-related signaling in mice