Isolation of exosomes from whole blood by integrating acoustics and microfluidics

Exosomes are nanoscale extracellular vesicles that play an important role in many biological processes, including intercellular communications, antigen presentation, and the transport of proteins, RNA, and other molecules. Recently there has been significant interest in exosome-related fundamental research, seeking new exosome-based biomarkers for health monitoring and disease diagnoses. Here, we report a separation method based on acoustofluidics (i.e., the integration of acoustics and microfluidics) to isolate exosomes directly from whole blood in a label-free and contact-free manner. This acoustofluidic platform consists of two modules: a microscale cell-removal module that first removes larger blood components, followed by extracellular vesicle subgroup separation in the exosome-isolation module. In the cell-removal module, we demonstrate the isolation of 110-nm particles from a mixture of micro- and nanosized particles with a yield greater than 99%. In the exosome-isolation module, we isolate exosomes from an extracellular vesicle mixture with a purity of 98.4%. Integrating the two acoustofluidic modules onto a single chip, we isolated exosomes from whole blood with a blood cell removal rate of over 99.999%. With its ability to perform rapid, biocompatible, label-free, contact-free, and continuous-flow exosome isolation, the integrated acoustofluidic device offers a unique approach to investigate the role of exosomes in the onset and progression of human diseases with potential applications in health monitoring, medical diagnosis, targeted drug delivery, and personalized medicine. Keywords: extracellular vesicles; exosomes; blood-borne vesicles; surface acoustic waves; acoustic tweezersNational Science Foundation (U.S.) (Grant R01 HD086325)National Science Foundation (U.S.) (Grant IIP-1534645

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Effect of Sintering Process on Ionic Conductivity of Li7-xLa3Zr2-xNbxO12 (x = 0, 0.2, 0.4, 0.6) Solid Electrolytes

Garnet-type Li7La3Zr2O12 (LLZO) is considered as a promising solid electrolyte. Nb-doped LLZO ceramics exhibit significantly improved ion conductivity. However, how to prepare the Nb-doped LLZO ceramics in a simple and economical way, meanwhile to investigate the relationship between process conditions and properties in Li7-xLa3Zr2-xNbxO12 ceramics, is particularly important. In this study, Li7-xLa3Zr2-xNbxO12 (LLZNxO, x = 0, 0.2, 0.4, 0.6) ceramics were prepared by conventional solid-state reaction. The effect of sintering process on the structure, microstructure, and ionic conductivity of LLZNxO (x = 0, 0.2, 0.4, 0.6) ceramics was investigated. Due to the more contractive Nb-O bonds in LLZNxO ceramics, the cubic structures are much easier to form and stabilize, which could induce the decreased preparation time. High-performance garnet LLZNxO ceramics can be obtained by optimizing the sintering process with lower calcining temperature and shorter holding time. The garnet samples with x = 0.4 calcined at 850 °C for 10 h and sintered at 1250 °C for 4 h exhibit the highest ionic conductivity of 3.86 × 10−4 S·cm−1 at room temperature and an activation energy of 0.32 eV, which can be correlated to the highest relative density of 96.1%, and good crystallinity of the grains

Landslide Extraction Using Mask R-CNN with Background-Enhancement Method

The application of deep learning methods has brought improvements to the accuracy and automation of landslide extractions based on remote sensing images because deep learning techniques have independent feature learning and powerful computing ability. However, in application, the quality of training samples often fails the requirement for training deep networks, causing insufficient feature learning. Furthermore, some background objects (e.g., river, bare land, building) share similar shapes, colors, and textures with landslides. They can be confusing to automatic tasks, contributing false and missed extractions. To solve the above problems, a background-enhancement method was proposed to enrich the complexity of samples. Models can learn the differences between landslides and background objects more efficiently through background-enhanced samples, then reduce false extractions on background objects. Considering that the environments of disaster areas play dominant roles in the formation of landslides, landslide-inducing attributes (DEM, slope, distance from river) were used as supplements, providing additional information for landslide extraction models to further improve the accuracy of extraction results. The proposed methods were applied to extract landslides that occurred in Ludian county, Yunnan Province, in August 2014. Comparative experiments were conducted using a mask R-CNN model. The experiment using both background-enhanced samples and landslide-inducing information showed a satisfying result with an F1 score of 89.08%. Compared with the F1 score from the experiment using only satellite images as input data, it was significantly improved by 22.38%, underscoring the applicability and effectiveness of our background-enhancement method

An Extended Building Damage (EBD) dataset constructed from disaster-related bi-temporal remote sensing images.

The Extended Building Damage (EBD) dataset is a large-size remote sensing sample library for building damage assessment tasks. It contains over 18,000 bi-temporal images and over 175,000 connected components for buildings. The raw images were collected from 12 disaster events and multiple natural disaster types, including hurricanes, earthquakes, flooding, volcanic eruptions, and tornadoes. The raw images of EBD were collected from the Maxar Open-data program (https://www.maxar.com/open-data).It is worth noting that our EBD is constructed by machine labeling and manual post-supervision. The annotation model was first pre-trained on the xBD dataset (https://xview2.org/dataset) and then fine-tuned on disaster-specific images in a semi-supervised setting. Accordingly, the EBD dataset can be referred to as:An extended set of the original xBD dataset.An extendable dataset for new disasters, following a machine-driven and knowledge transfer paradigm of sample labeling.Details for EBD dataset:Each sample is constructed as pre-disaster and post-disaster images, with 512×512 size and RGB 3-channels.Annotation for each sample includes a binary building localization mask (255: building; 0: background), and a building damage mask (1: no-damage; 2: minor damage; 3: major damage; 4: destroyed; 0: background).Each sample is named as "{disaster event}_{image ID}_{pre/post}_disaster".</p

Support Vector Machine Regression Algorithm Based on Chunking Incremental Learning

Abstract. On the basis of least squares support vector machine regression (LSSVR), an adaptive and iterative support vector machine regression algorithm based on chunking incremental learning (CISVR) is presented in this paper. CISVR is an iterative algorithm and the samples are added to the working set in batches. The inverse of the matrix of coefficients from previous iteration is used to calculate the regression parameters. Therefore, the proposed approach permits to avoid the calculation of the inverse of a large-scale matrix and improves the learning speed of the algorithm. Support vectors are selected adaptively in the iteration to maintain the sparseness. Experimental results show that the learning speed of CISVR is improved greatly compared with LSSVR for the similar training accuracy. At the same time the number of the support vectors obtained by the presented algorithm is less than that obtained by LSSVR greatly

Fixability–Flexibility Relations in Sustainable Territorial Spatial Planning in China: A Review from the Food–Energy–Water Nexus Perspective

Territorial spatial planning involves fixability and flexibility in different driving factors related to control and development orientation, and they play an important role in regional sustainable development, especially in developing countries such as China. With rapid urbanisation and industrialisation, China has been impacted by conflicts between development and protection in territorial space. To integrate the contradictions among different territorial spatial planning measures, planners and scholars have started to focus on studies regarding fixability–flexibility relationships and integration. However, the relationship between and integration of fixability and flexibility in territorial spatial planning have yet to be clearly summarised. This paper explores an innovative research direction for the fixability–flexibility relations in territorial spatial planning from a new perspective, the Food–Energy–Water Nexus, which is a dynamic and comprehensive framework for Sustainable Development Goals (SDGs) studies. This paper covers the existing research on fixability and flexibility in territorial spatial planning. Moreover, after summarising the conflicts of fixability and flexibility, the dialectical relationship between and the integration of fixability and flexibility are researched