SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis

Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection

3D Printing‐Enabled Design and Manufacturing Strategies for Batteries: A Review

Lithium-ion batteries (LIBs) have significantly impacted the daily lives, finding broad applications in various industries such as consumer electronics, electric vehicles, medical devices, aerospace, and power tools. However, they still face issues (i.e., safety due to dendrite propagation, manufacturing cost, random porosities, and basic & planar geometries) that hinder their widespread applications as the demand for LIBs rapidly increases in all sectors due to their high energy and power density values compared to other batteries. Additive manufacturing (AM) is a promising technique for creating precise and programmable structures in energy storage devices. This review first summarizes light, filament, powder, and jetting-based 3D printing methods with the status on current trends and limitations for each AM technology. The paper also delves into 3D printing-enabled electrodes (both anodes and cathodes) and solid-state electrolytes for LIBs, emphasizing the current state-of-the-art materials, manufacturing methods, and properties/performance. Additionally, the current challenges in the AM for electrochemical energy storage (EES) applications, including limited materials, low processing precision, codesign/comanufacturing concepts for complete battery printing, machine learning (ML)/artificial intelligence (AI) for processing optimization and data analysis, environmental risks, and the potential of 4D printing in advanced battery applications, are also presented

Comparative genomics reveals adaptive evolution of Asian tapeworm in switching to a new intermediate host

Taenia saginata, Taenia solium and Taenia asiatica (beef, pork and Asian tapeworms, respectively) are parasitic flatworms of major public health and food safety importance. Among them, T. asiatica is a newly recognized species that split from T. saginata via an intermediate host switch ∼1.14 Myr ago. Here we report the 169- and 168-Mb draft genomes of T. saginata and T. asiatica. Comparative analysis reveals that high rates of gene duplications and functional diversifications might have partially driven the divergence between T. asiatica and T. saginata. We observe accelerated evolutionary rates, adaptive evolutions in homeostasis regulation, tegument maintenance and lipid uptakes, and differential/specialized gene family expansions in T. asiatica that may favour its hepatotropism in the new intermediate host. We also identify potential targets for developing diagnostic or intervention tools against human tapeworms. These data provide new insights into the evolution of Taenia parasites, particularly the recent speciation of T. asiatica

Bi-objective optimization models for network interdiction

This paper designs models for the network interdiction problem. The interdiction problem under study has two contradicting goals: disrupting the network to minimize the profit of one set of agents, while as much as possible preserve the profit of another set of agents. Three bi-objective optimization methods are employed to form the optimal objectives. Also, we develop two formulations (MILP and multi-stage LP) used to deal with congestion cost which is a piecewise cost function. A numerical instance is also presented to better illustrate those models

High-Resolution Swin Transformer for Automatic Medical Image Segmentation

The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation adopt a U-Net-like architecture, which contains an encoder that converts the high-resolution input image into low-resolution feature maps using a sequence of Transformer blocks and a decoder that gradually generates high-resolution representations from low-resolution feature maps. However, the procedure of recovering high-resolution representations from low-resolution representations may harm the spatial precision of the generated segmentation masks. Unlike previous studies, in this study, we utilized the high-resolution network (HRNet) design style by replacing the convolutional layers with Transformer blocks, continuously exchanging feature map information with different resolutions generated by the Transformer blocks. The proposed Transformer-based network is named the high-resolution Swin Transformer network (HRSTNet). Extensive experiments demonstrated that the HRSTNet can achieve performance comparable with that of the state-of-the-art Transformer-based U-Net-like architecture on the 2021 Brain Tumor Segmentation dataset, the Medical Segmentation Decathlon’s liver dataset, and the BTCV multi-organ segmentation dataset

HiCAT: a tool for automatic annotation of centromere structure

Abstract Significant improvements in long-read sequencing technologies have unlocked complex genomic areas, such as centromeres, in the genome and introduced the centromere annotation problem. Currently, centromeres are annotated in a semi-manual way. Here, we propose HiCAT, a generalizable automatic centromere annotation tool, based on hierarchical tandem repeat mining to facilitate decoding of centromere architecture. We apply HiCAT to simulated datasets, human CHM13-T2T and gapless Arabidopsis thaliana genomes. Our results are generally consistent with previous inferences but also greatly improve annotation continuity and reveal additional fine structures, demonstrating HiCAT’s performance and general applicability

Ferroelectric and Spectroscopic Properties of Ho³⁺/Yb³⁺ Co-Doped Pb(Mg_1/3Nb_2/3)O₃-32PbTiO₃ Crystal

In order to design a new multifunctional crystal with excellent ferroelectric and spectroscopic properties, experiments were carried out for Ho3+/Yb3+ co-doped Pb(Mg1/3Nb2/3)O3 -32PbTiO3 ferroelectric crystal using the flux method, the coercive field Ec of which is 11.86 kV/cm. Up-conversion luminescence emission bands, including an intense green emission band at 553 nm, a red band at 663 nm, and a weak infra-red band at 755 nm, are generated at 980 nm excitation. The main spectroscopic parameters, including radiative transition probability A(J′ − J), radiative lifetimes τrad, and branching ratios β, were predicted by applying Judd–Ofelt treatment. The obtained J-O intensity parameters are Ω2 = 0.531 × 10−20 cm2, Ω4 = 1.738 × 10−20 cm2, Ω6 = 0.530 × 10−20 cm2. The radiative lifetime of 5I7 level is 5.45 ms. The fluorescence lifetime of is 5F5 is 92.568 μs. The investigations show that Ho3+/Yb3+ co-doped Pb(Mg1/3Nb2/3)O3-0.32PbTiO3 crystal is a new type of multifunctional crystal integrating ferroelectric and spectroscopic properties, which has a potential application in the developing innovative multifunctional devices and lasers

Effects of winter irrigation on soil salinity and jujube growth in arid regions.

The considerably high evapotranspiration and the low leaching fraction of the soil in arid regions are likely the primary causes of the enhanced soil salinity in such regions. Winter irrigation has proven to be very effective for promoting the leaching of salts from the rooting-zone. In this study, we investigated the effects of different irrigation methods (flood irrigation and drip irrigation) and winter irrigation quotas (450, 1350, 2250, 3150, 4050, and 4950 m3/hm2) on soil salinity and plant growth in an arid region. The sum of ECe in the 0-100 cm soil layer was 56.26-29.32 ms/cm under flood irrigation, 61.37-17.90 ms/cm under drip irrigation, and 64.13 ms/cm under no irrigation. The survival rates of jujube trees reached 65% and 77%, respectively, for drip irrigation and flood irrigation with a quota of 2250 m3/hm2. Furthermore, at irrigation quotas in excess of 3150 m3/hm2 the ground diameter and height of jujube trees were significantly greater than those observed under nonwinter irrigation and several other winter irrigation treatments. These findings indicated that winter irrigation significantly reduced soil salinity, changed the soil salt distribution, created a good environment for the growth of jujube trees and improved the survival rate of young jujube trees, especially under winter drip irrigation with a quota of 3150 m3/hm2. In addition, 1-year-old jujube trees emerging in spring may benefit from an ECe lower than 5 ms/cm