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Flood disasters caused by typhoon rainfall seriously threaten regional social and economic development. Accurately assessing the risk of typhoons and their secondary disasters is a great challenge in disaster prevention and reduction. To address this, the city of Fuzhou, Fujian Province, which was severely affected by Typhoon “Lupit” (2109), was selected as a case study. A typhoon rainfall flood disaster system including four components (the disaster-causing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention and reduction capacity) was constructed. A typhoon-rainfall process comprehensive intensity index (TPCI) based on different time scales within the typhoon process was developed to accurately evaluate the flood risk. The TPCI represented the disaster-causing factors of rainfall intensity, duration, and concentration features. Geographical similarity and random forest (RF) were applied to screen 23 typical indices for an urban flood disaster risk assessment model. The results indicated that the TPCI based on a 6 h precipitation simulation at a 24 h time scale was highly effective in highlighting the role of short-term precipitation in the typhoon process. A total of 66.5% of the floodplain area had a medium-grade or higher TPCI value, while 32.5% of the area had a low-grade TPCI. Only 1% of the flooded areas were not identified, which indicated that the TPCI could accurately capture the risk of typhoon rainfall. The urban flood disaster risk assessment model comprehensively considered socioeconomic and natural environment conditions. High-risk areas were identified as regions with extreme precipitation and dense populations. The dynamic evaluation results accurately described the spatiotemporal differences in the flood disaster risk. A period of extreme precipitation lagged the landfall time of Typhoon “Lupit”, causing the proportion of areas above the medium–high-risk threshold of flood disasters to rapidly increase from 8.29% before the landfall of the typhoon to 23.57% before its demise. The high-risk areas of flood disasters were mainly distributed in the towns of Shangjie, Nanyu, and Gaishan, which was consistent with the observed disasters. These study findings could contribute to the development of effective measures for disaster prevention and reduction, and improve the resilience of urban areas to typhoon disasters.

The autonomous underwater vehicle (AUV) has a problem with feature loss when recognizing small targets underwater. At present, algorithms usually use multi-scale feature extraction to solve the problem, but this method increases the computational effort of the algorithm. In addition, low underwater light and turbid water result in incomplete information on target features. This paper proposes an enhanced dilated convolution framework (EHDC) for underwater blurred target recognition. Firstly, this paper extracts small target features through hybrid dilated convolution networks, increasing the perceptive field of the algorithm without increasing the computational power of the algorithm. Secondly, the proposed algorithm learns spatial semantic features through an adaptive correlation matrix and compensates for the missing features of the target. Finally, this paper fuses spatial semantic features and visual features for the recognition of small underwater blurred targets. Experiments show that the proposed method improves the recognition accuracy by 1.04% compared to existing methods when recognizing small underwater blurred targets.

Microfluidic devices have been widely used in mechanical, biomedical, chemical, and materials research. As a result, it is becoming increasingly important to have a cheap, fast, and reliable method for rapid microfabrication. However, prototyping of microfluidic devices typically suffers from the high initial cost, low resolution, rough surface finish, and long turn-around time. Here we present a strategic approach to closed-loop control of deterministic fabrication process based on in-situ image analysis called image-guided in-situ maskless lithography (IGIs-ML). Using the closed-loop control along with flush-flow functionality and leveraging the swelling behavior of the photocurable polymer, we demonstrate the fabrication of sub-micron high aspect ratio channels (800 nm width and >10 µm height) close to the light diffraction limit. This outperforms any reported rapid prototyping platforms which can typically reach tens of µm in channel width. Such dimensional capability is even comparable to some of the most advanced fabrication methods currently available. Dynamic image analysis simultaneously provides superior repeatability of densely packed patterns across a large area and multiple devices. A general and robust approach is established to fabricate a wide variety of microfluidic devices. This resolves the critical over-curing issues and size limitations in rapid prototyping of microfluidic devices, enabling affordable and reliable exploration of design space beyond the resolution of traditional photolithography.

Cancer metastasis counts for 90% of cancer fatalities, and its development process is still a mystery. The dynamic process of tumor metastatic transport in the blood vessel is not well understood, in which some biomechanical factors, such as shear stress and various flow patterns, may have significant impacts. Here, we report a microfluidic vessel-on-a-chip platform for recapitulating several key metastatic steps of tumor cells in blood vessels on the same chip, including intravasation, circulating tumor cell (CTC) vascular adhesion, and extravasation. Due to its excellent adaptability, our system can reproduce various microenvironments to investigate the specific interactions between CTCs and blood vessels. On the basis of this platform, effects of important biomechanical factors on CTC adhesion such as vascular surface properties and vessel geometry-dependent hemodynamics were specifically inspected. We demonstrated that CTC adhesion is more likely to occur under certain mechano-physiological situations, such as vessels with vascular glycocalyx (VGCX) shedding and hemodynamic disturbances. Finally, computational models of both the fluidic dynamics in vessels and CTC adhesion were established based on the confocal scanned 3D images. The modeling results are believed to provide insights into exploring tumor metastasis progression and inspire new ideas for anticancer therapy development.

Insulin resistance is a common feature of obesity and type 2 diabetes and partly results from an imbalance between food intake and energy expenditure. Therefore, efficient and safe insulin resistance treatment therapies are warranted. This work is aim to access the impact of protocatechuic acid (PCA), a catechol-type O-diphenol phenolic acid, in high fat diet (HFD)-induced glucose, and lipid dysregulation.Five-week-old male C57BL/6 mice are fed with HFD for 4 weeks and then are randomly divided into two cohorts: one cohort feed with HFD is free access to sterile water for 4 weeks, another cohort is free access to PCA-containing water (2.7 mM) for 4 weeks with HFD. In this study, using a hyperinsulinemic-euglycemic mouse clamp, it is showed that PCA-treated mice display improved systemic insulin resistance via enhanced fatty acid mobilization and utilization, thereby reducing ectopic lipid accumulation and promoting hepatic and peripheral insulin action.This study provides insights on the potent pharmacological effects of PCA from food sources on improving high fat diet (HFD)-induced whole-body insulin resistance and type 2 diabetes.

COVID-19 has challenged the world’s public health and led to over 4.5 million deaths. A rapid, sensitive, and cost-effective point-of-care virus detection device is crucial to the control and surveillance of the contagious severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic. Here we demonstrate a solid phase isothermal recombinase polymerase amplification coupled CRISPR-based (spRPA-CRISPR) assay for on-chip multiplexed, sensitive and visual COVID-19 DNA detection. By targeting the SARS-CoV-2 structure protein encoded genomes, two specific genes were simultaneously detected with the control sample without cross-interaction with other sequences. The endpoint signal can be directly visualized for rapid detection of COVID-19. The amplified target sequences were immobilized on the one-pot device surface and detected using the mixed Cas12a-crRNA collateral cleavage of reporter released fluorescent signal when specific genes were recognized. The system was tested with samples of a broad range of concentrations (20 to 2×105 copies) and showed analytical sensitivity down to 20 copies per reaction. Furthermore, a low-cost LED UV flashlight (∼$12) was used to provide a visible SARS-CoV-2 detection signal of the spRPA-CRISPR assay which could be purchased online easily. Thus, our platform provides a sensitive and easy-to-read multiplexed gene detection method with the capacity to specifically identify low concentration genes. Similar CRISPR biosensor chips can support a broad range of applications such as HPV DNA detection, influenza SARS-CoV-2 multiplex detection, and other infectious disease testing assays.HighlightsA commercially available UV flashlight excited CRISPR-Cas12a based visual sensor combined with solid-phase RPAThe developed assay could detect SARS-CoV-2 down to 20 copies with no signal crosswalkThis multiplexed on-chip virus detection system would provide great insight for detecting and differentiating SARS-CoV-2 from other viral infections with a single sample simultaneously.

Chemotherapy is one of the most effective cancer treatments. Starting from the discovery of new molecular entities, it usually takes about 10 years and 2 billion U.S. dollars to bring an effective anti-cancer drug from the benchtop to patients. Due to the physiological differences between animal models and humans, more than 90% of drug candidates failed in phase I clinical trials. Thus, a more efficient drug screening system to identify feasible compounds and pre-exclude less promising drug candidates is strongly desired. For their capability to accurately construct in vitro tumor models derived from human cells to reproduce pathological and physiological processes, microfluidic tumor chips are reliable platforms for preclinical drug screening, personalized medicine, and fundamental oncology research. This review summarizes the recent progress of the microfluidic tumor chip and highlights tumor vascularization strategies. In addition, promising imaging modalities for enhancing data acquisition and machine learning-based image analysis methods to accurately quantify the dynamics of tumor spheroids are introduced. It is believed that the microfluidic tumor chip will serve as a high-throughput, biomimetic, and multi-sensor integrated system for efficient preclinical drug evaluation in the future.

Detection and characterization of rare circulating tumor cells (CTCs) in patients' blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of CTCs from patient blood samples, by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network, a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and CTCs. It requires minimal data pre-processing and has an easy experimental setup. Through our experiments, we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying CTCs. With more data becoming available in the future, the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for CTC analysis.

To identify factors that regulate gut microbiota density and the impact of varied microbiota density on health, we assayed this fundamental ecosystem property in fecal samples across mammals, human disease, and therapeutic interventions. Physiologic features of the host (carrying capacity) and the fitness of the gut microbiota shape microbiota density. Therapeutic manipulation of microbiota density in mice altered host metabolic and immune homeostasis. In humans, gut microbiota density was reduced in Crohn’s disease, ulcerative colitis, and ileal pouch-anal anastomosis. The gut microbiota in recurrent Clostridium difficile infection had lower density and reduced fitness that were restored by fecal microbiota transplantation. Understanding the interplay between microbiota and disease in terms of microbiota density, host carrying capacity, and microbiota fitness provide new insights into microbiome structure and microbiome targeted therapeutics.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Mass transfer kinetics plays a dominant role in membrane morphology. In this study, the effects of polymer concentration, supercritical CO2 (ScCO2) pressure and temperature on the morphology of polyetherimide (PEI) membranes prepared using ScCO2 phase inversion were investigated from the view of the instantaneous velocity of ScCO2 in-diffusion and the difference between average solvent out-diffusion velocity ( V o ¯ ) and average ScCO2 in-diffusion velocity ( V i ¯ ) through the interface between the casting solution and ScCO2, V o ¯ − V i ¯ . The high instantaneous velocity of ScCO2 in-diffusion at the moment of onset of phase separation led to a cellular pore structure in the membrane. In addition, the dramatic decrease in the V o ¯ − V i ¯ resulted in the increased trends of pore size and porosity of the membrane with the increases in polymer concentration, ScCO2 pressure and temperature.