A deep learning algorithm to identify carotid plaques and assess their stability.

Background: Carotid plaques are major risk factors for stroke. Carotid ultrasound can help to assess the risk and incidence rate of stroke. However, large-scale carotid artery screening is time-consuming and laborious, the diagnostic results inevitably involve the subjectivity of the diagnostician to a certain extent. Deep learning demonstrates the ability to solve the aforementioned challenges. Thus, we attempted to develop an automated algorithm to provide a more consistent and objective diagnostic method and to identify the presence and stability of carotid plaques using deep learning. Methods: A total of 3,860 ultrasound images from 1,339 participants who underwent carotid plaque assessment between January 2021 and March 2023 at the Shanghai Eighth People's Hospital were divided into a 4:1 ratio for training and internal testing. The external test included 1,564 ultrasound images from 674 participants who underwent carotid plaque assessment between January 2022 and May 2023 at Xinhua Hospital affiliated with Dalian University. Deep learning algorithms, based on the fusion of a bilinear convolutional neural network with a residual neural network (BCNN-ResNet), were used for modeling to detect carotid plaques and assess plaque stability. We chose AUC as the main evaluation index, along with accuracy, sensitivity, and specificity as auxiliary evaluation indices. Results: Modeling for detecting carotid plaques involved training and internal testing on 1,291 ultrasound images, with 617 images showing plaques and 674 without plaques. The external test comprised 470 ultrasound images, including 321 images with plaques and 149 without. Modeling for assessing plaque stability involved training and internal testing on 764 ultrasound images, consisting of 494 images with unstable plaques and 270 with stable plaques. The external test was composed of 279 ultrasound images, including 197 images with unstable plaques and 82 with stable plaques. For the task of identifying the presence of carotid plaques, our model achieved an AUC of 0.989 (95% CI: 0.840, 0.998) with a sensitivity of 93.2% and a specificity of 99.21% on the internal test. On the external test, the AUC was 0.951 (95% CI: 0.962, 0.939) with a sensitivity of 95.3% and a specificity of 82.24%. For the task of identifying the stability of carotid plaques, our model achieved an AUC of 0.896 (95% CI: 0.865, 0.922) on the internal test with a sensitivity of 81.63% and a specificity of 87.27%. On the external test, the AUC was 0.854 (95% CI: 0.889, 0.830) with a sensitivity of 68.52% and a specificity of 89.49%. Conclusion: Deep learning using BCNN-ResNet algorithms based on routine ultrasound images could be useful for detecting carotid plaques and assessing plaque instability.

Mechanochemical Synthesis of α-halo Alkylboronic Esters.

α-halo alkylboronic esters, acting as ambiphilic synthons, play a pivotal role as versatile intermediates in fields like pharmaceutical science and organic chemistry. The sequential transformation of carbon-boron and carbon-halogen bonds into a broad range of carbon-X bonds allows for programmable bond formation, facilitating the incorporation of multiple substituents at a single position and streamlining the synthesis of complex molecules. Nevertheless, the synthetic potential of these compounds is constrained by limited reaction patterns. Additionally, the conventional methods often necessitate the use of bulk toxic solvents, exhibit sensitivity to air/moisture, rely on expensive metal catalysts, and involve extended reaction times. In this report, a ball milling technique is introduced that overcomes these limitations, enabling the external catalyst-free multicomponent coupling of aryl diazonium salts, alkenes, and simple metal halides. This approach offers a general and straightforward method for obtaining a diverse array of α-halo alkylboronic esters, thereby paving the way for the extensive utilization of these synthons in the synthesis of fine chemicals.

Screen-Printable Iontronic Pressure Sensor with Thermal Expansion Microspheres for Pulse Monitoring.

Constructing microstructures to improve the sensitivity of flexible pressure sensors is an effective approach. However, the preparation of microstructures usually involves inverted molds or subtractive manufacturing methods, which are difficult in large-scale (e.g., in screen printing) preparation. To solve this problem, we introduced thermally expandable microspheres for screen printing to fabricate flexible sensors. Thermally expandable microspheres can be constructed into microstructures by simple heating after printing, which simplifies the microstructure fabrication step. In addition, the added microspheres can also be used as ionic liquid reservoir materials to further increase the capacitance change and improve the sensitivity. The prepared sensors exhibited superior performance, including ultrahigh sensitivity (Smax = 49999.5 kPa-1) and wide detection range (0-350 kPa). Even after 30,000 cycles at a high pressure of 300 kPa and a low pressure of 30 kPa, the sensor showed minimal signal degradation, demonstrating long-term cycling stability. In order to verify the practical potential of the sensors, we performed human radial artery beat detection experiments using these sensors. The variations in the intensity of the 3D radial artery pulse wave can be observed very clearly, which is important for human health monitoring. The above demonstrates that our strategy can provide an effective approach for the large-scale preparation of high-performance flexible pressure sensors.

Wearable multichannel-active pressurized pulse sensing platform.

With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human-computer interaction. Due to the high sensitivity (460.1 kPa-1), high linearity (R 2 > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual's blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.

Carbon storage through China's planted forest expansion.

China's extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of China's planted forest area and its carbon storage remain uncaptured. Here we reveal such changes in China's planted forests from 1990 to 2020 using satellite and field data. Results show a doubling of planted forest area, a trend that intensified post-2000. These changes lead to China's planted forest carbon storage increasing from 675.6 ± 12.5 Tg C in 1990 to 1,873.1 ± 16.2 Tg C in 2020, with an average rate of ~ 40 Tg C yr-1. The area expansion of planted forests contributed ~ 53% (637.2 ± 5.4 Tg C) of the total above increased carbon storage in planted forests compared with planted forest growth. This proactive policy-driven expansion of planted forests has catalyzed a swift increase in carbon storage, aligning with China's Carbon Neutrality Target for 2060.

A Review of Epidermal Flexible Pressure Sensing Arrays.

In recent years, flexible pressure sensing arrays applied in medical monitoring, human-machine interaction, and the Internet of Things have received a lot of attention for their excellent performance. Epidermal sensing arrays can enable the sensing of physiological information, pressure, and other information such as haptics, providing new avenues for the development of wearable devices. This paper reviews the recent research progress on epidermal flexible pressure sensing arrays. Firstly, the fantastic performance materials currently used to prepare flexible pressure sensing arrays are outlined in terms of substrate layer, electrode layer, and sensitive layer. In addition, the general fabrication processes of the materials are summarized, including three-dimensional (3D) printing, screen printing, and laser engraving. Subsequently, the electrode layer structures and sensitive layer microstructures used to further improve the performance design of sensing arrays are discussed based on the limitations of the materials. Furthermore, we present recent advances in the application of fantastic-performance epidermal flexible pressure sensing arrays and their integration with back-end circuits. Finally, the potential challenges and development prospects of flexible pressure sensing arrays are discussed in a comprehensive manner.

Construction and Evaluation of the Immunogenicity and Protective Efficacy of Recombinant Replication-Deficient Human Adenovirus-5 Expressing Genotype VII Newcastle Disease Virus F Protein and Infectious Bursal Disease Virus VP2 Protein.

Newcastle disease (ND) and infectious bursal disease (IBD) are two key infectious diseases that significantly threaten the health of the poultry industry. Although existing vaccinations can effectively prevent and treat these two diseases through multiple immunizations, frequent immunization stresses significantly impact chicken growth. In this study, three recombinant adenoviruses, rAd5-F expressing the NDV (genotype VII) F protein, rAd5-VP2 expressing the IBDV VP2 protein, and rAd5-VP2-F2A-F co-expressing F and VP2 proteins, were constructed using the AdEasy system. The F and VP2 genes of the recombinant adenoviruses could be transcribed and expressed normally in HEK293A cells as verified by RT-PCR and Western blot. The three recombinant viruses were shown to have similar growth kinetics as rAd5-EGFP. Compared with the PBS and rAd5-EGFP groups, SPF chickens immunized with recombinant adenoviruses produced higher antibody levels, more significant lymphocyte proliferation, and significantly higher CD4+/CD3+ and CD8+/CD3+ cells in peripheral blood. The survival rate of SPF chickens immunized with rAd5-F and rAd5-VP2-F2A-F after the challenge with DHN3 was 100%, and 86% of SPF chickens showed no viral shedding at 7 dpc. The survival rate of SPF chickens immunized with rAd5-VP2 and rAd5-VP2-F2A-F after the challenge with BC6/85 was 86%. rAd5-VP2 and rAd5-VP2-F2A-F significantly inhibited bursal atrophy and pathological changes compared to the rAd5-EGFP and PBS groups. This study provides evidence that these recombinant adenoviruses have the potential to be developed into safe and effective vaccine candidates for the prevention and control of ND and IBD.

Boron nitride-enabled printing of a highly sensitive and flexible iontronic pressure sensing system for spatial mapping.

Recently, flexible iontronic pressure sensors (FIPSs) with higher sensitivities and wider sensing ranges than conventional capacitive sensors have been widely investigated. Due to the difficulty of fabricating the nanostructures that are commonly used on electrodes and ionic layers by screen printing techniques, strategies for fabricating such devices using these techniques to drive their mass production have rarely been reported. Herein, for the first time, we employed a 2-dimensional (2D) hexagonal boron nitride (h-BN) as both an additive and an ionic liquid reservoir in an ionic film, making the sensor printable and significantly improving its sensitivity and sensing range through screen printing. The engineered sensor exhibited high sensitivity (Smin> 261.4 kPa-1) and a broad sensing range (0.05-450 kPa), and it was capable of stable operation at a high pressure (400 kPa) for more than 5000 cycles. In addition, the integrated sensor array system allowed accurate monitoring of wrist pressure and showed great potential for health care systems. We believe that using h-BN as an additive in an ionic material for screen-printed FIPS could greatly inspire research on 2D materials for similar systems and other types of sensors. Hexagonal boron nitride (h-BN) was employed for the first time to make iontronic pressure sensor arrays with high sensitivity and a broad sensing range by screen printing.

Construction and Transcriptomic Study of Chicken IFNAR1-Knockout Cell Line Reveals the Essential Roles of Cell Growth- and Apoptosis-Related Pathways in Duck Tembusu Virus Infection.

For industrial vaccine production, overwhelming the existing antiviral innate immune response dominated by type I interferons (IFN-I) in cells would be a key factor improving the effectiveness and production cost of vaccines. In this study, we report the construction of an IFN-I receptor 1 (IFNAR1)-knockout DF-1 cell line (KO-IFNAR1), which supports much more efficient replication of the duck Tembusu virus (DTMUV), Newcastle disease virus (NDV) and gammacoronavirus infectious bronchitis virus (IBV). Transcriptomic analysis of DTMUV-infected KO-IFNAR1 cells demonstrated that DTMUV mainly activated genes and signaling pathways related to cell growth and apoptosis. Among them, JUN, MYC and NFKBIA were significantly up-regulated. Furthermore, knockdown of zinc-fingered helicase 2 (HELZ2) and interferon-α-inducible protein 6 (IFI6), the two genes up-regulated in both wild type and KO-IFNAR1 cells, significantly increased the replication of DTMUV RNA. This study paves the way for further studying the mechanism underlying the DTMUV-mediated IFN-I-independent regulation of virus replication, and meanwhile provides a potential cell resource for efficient production of cell-based avian virus vaccines.

Avian IRF1 and IRF7 Play Overlapping and Distinct Roles in Regulating IFN-Dependent and -Independent Antiviral Responses to Duck Tembusu Virus Infection.

Avian interferon regulatory factors 1 and 7 (IRF1 and IRF7) play important roles in the host's innate immunity against viral infection. Our previous study revealed that duck tembusu virus (DTMUV) infection of chicken fibroblasts (DF1) and duck embryo fibroblasts (DEFs) induced the expression of a variety of IFN-stimulated genes (ISGs), including VIPERIN, IFIT5, CMPK2, IRF1, and IRF7. IRF1 was further shown to play a significant role in regulating the up-expression of VIPERIN, IFIT5, and CMPK2 and inhibiting DTMUV replication. In this study, we confirm, through overexpression and knockout approaches, that both IRF1 and IRF7 inhibit DTMUV replication, mainly via regulation of type I IFN expression, as well as the induction of IRF1, VIPERIN, IFIT5, CMPK2, and MX1. In addition, IRF1 directly promoted the expression of VIPERIN and CMPK2 in an IFN-independent manner when IRF7 and type I IFN signaling were undermined. We also found that non-structural protein 2B (NS2B) of DTMUV was able to inhibit the induction of IFN-ß mRNA triggered by Newcastle disease virus (NDV) infection or poly(I:C) treatment, revealing a strategy employed by DTMUV to evade host's immunosurveillance. This study demonstrates that avian IRF7 and IRF1 play distinct roles in the regulation of type I IFN response during DTMUV infection.

Mechanical Modeling of Tube Bending Considering Elastoplastic Evolution of Tube Cross-Section.

Aluminum alloy tubes are widely used in various industries because of their excellent performance. Up to now, when the tube is bent, the elastoplastic deformation evolution mechanism of the cross-section has not been clear, and no direct analytical proof has been found. In this paper, based on the bilinear material model assumption, a new mechanical model of tube plane bending deformation is constructed. The analytical model can describe in detail the evolution mechanism of elastic-plastic deformation on the cross-section of the tube after bending deformation, the position of the elastic-plastic boundary, the position of the radius of the strain neutral layer, and the relationship between the bending moment over the section and the bending radius. According to this model, the deformation law of the tube cross-section during bending is elucidated. The results are as follows: (1) the deformation evolution of the cross-section of the bending deformed tube calculated by the analytical model is in good agreement with the finite element model (FEM) of pure bending. (2) By comparing the results of the analytical model with FEM results, and the processing test of the self-designed four-axis free bending forming tube bender, the bending moments are in good agreement. (3) Compared with the bending moments calculated by several other analytical models of tube bending, this model has a relatively small deviation value.

Optimized Multiscale Entropy Model Based on Resting-State fMRI for Appraising Cognitive Performance in Healthy Elderly.

Many studies have indicated that an entropy model can capture the dynamic characteristics of resting-state functional magnetic resonance imaging (rfMRI) signals. However, there are problems of subjectivity and lack of uniform standards in the selection of model parameters relying on experience when using the entropy model to analyze rfMRI. To address this issue, an optimized multiscale entropy (MSE) model was proposed to confirm the parameters objectively. All healthy elderly volunteers were divided into two groups, namely, excellent and poor, by the scores estimated through traditional scale tests before the rfMRI scan. The parameters of the MSE model were optimized with the help of sensitivity parameters such as receiver operating characteristic (ROC) and area under the ROC curve (AUC) in a comparison study between the two groups. The brain regions with significant differences in entropy values were considered biomarkers. Their entropy values were regarded as feature vectors to use as input for the probabilistic neural network in the classification of cognitive scores. Classification accuracy of 80.05% was obtained using machine learning. These results show that the optimized MSE model can accurately select the brain regions sensitive to cognitive performance and objectively select fixed parameters for MSE. This work was expected to provide the basis for entropy to test the cognitive scores of the healthy elderly.

Research on Chest Disease Recognition Based on Deep Hierarchical Learning Algorithm.

Chest X-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases. However, it is still a challenge for expert radiologists to assess thousands of cases in a short period so that deep learning methods are introduced to tackle this problem. Since the diseases have correlations with each other and have hierarchical features, the traditional classification scheme could not achieve a good performance. In order to extract the correlation features among the diseases, some GCN-based models are introduced to combine the features extracted from the images to make prediction. This scheme can work well with the high quality of image features, so backbone with high computation cost plays a vital role in this scheme. However, a fast prediction in diagnostic radiology is also needed especially in case of emergency or region with low computation facilities, so we proposed an efficient convolutional neural network with GCN, which is named SGGCN, to meet the need of efficient computation and considerable accuracy. SGGCN used SGNet-101 as backbone, which is built by ShuffleGhost Block (Huang et al., 2021) to extract features with a low computation cost. In order to make sufficient usage of the information in GCN, a new GCN architecture is designed to combine information from different layers together in GCNM module so that we can utilize various hierarchical features and meanwhile make the GCN scheme faster. The experiment on CheXPert datasets illustrated that SGGCN achieves a considerable performance. Compared with GCN and ResNet-101 (He et al., 2015) backbone (test AUC 0.8080, parameters 4.7M and FLOPs 16.0B), the SGGCN achieves 0.7831 (-3.08%) test AUC with parameters 1.2M (-73.73%) and FLOPs 3.1B (-80.82%), where GCN with MobileNet (Sandler and Howard, 2018) backbone achieves 0.7531 (-6.79%) test AUC with parameters 0.5M (-88.46%) and FLOPs 0.66B (-95.88%).

Preparation of molecular imprinted polymer based on attapulgite and evaluation of its performance for adsorption of benzoic acid in water.

In order to reduce the environmental impact of benzoic acid (BA), molecular imprinted polymers based on attapulgite were facilely prepared by molecular imprinted technique. The samples were characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and thermal gravimetric analysis. The adsorption performance, regeneration stability, and competitive selectivity of BA by benzoic acid-surface molecular imprinted polymers (BA-MIP) were systematically investigated by experiments. For this material, it has a high adsorption capacity of 41 mg/g and an equilibrium adsorption time of about 150 min. Compared with non-imprinted polymers, BA-MIP has a higher adsorption capacity for BA, and the dynamic adsorption behavior of BA by both of them conforms to the quasi-second-order kinetic model. The Langmuir adsorption isotherm equation was fitted the isothermal adsorption experiment. The thermodynamic analysis shows that the adsorption process is an exothermic reaction. The adsorption capacity of BA first increases and then decreases with an increase in pH, and the maximum adsorption capacity is reached at pH = 5. BA-MIP also has excellent selective adsorption capacity and regeneration stability for BA.

[Construction and analysis of muscle functional network for exoskeleton robot].

Exoskeleton nursing robot is a typical human-machine co-drive system. To full play the subjective control and action orientation of human, it is necessary to comprehensively analyze exoskeleton wearer's surface electromyography (EMG) in the process of moving patients, especially identifying the spatial distribution and internal relationship of the EMG information. Aiming at the location of electrodes and internal relation between EMG channels, the complex muscle system at the upper limb was abstracted as a muscle functional network. Firstly, the correlation characteristics were analyzed among EMG channels of the upper limb using the mutual information method, so that the muscle function network was established. Secondly, by calculating the characteristic index of network node, the features of muscle function network were analyzed for different movements. Finally, the node contraction method was applied to determine the key muscle group that reflected the intention of wearer's movement, and the characteristics of muscle function network were analyzed in each stage of moving patients. Experimental results showed that the location of the myoelectric collection could be determined quickly and efficiently, and also various stages of the moving process could effectively be distinguished using the muscle functional network with the key muscle groups. This study provides new ideas and methods to decode the relationship between neural controls of upper limb and physical motion.

Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data.

It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.

Photoinduced C(sp3 )-N Bond Cleavage Leading to the Stereoselective Syntheses of Alkenes.

Herein we report a versatile Mizoroki-Heck-type photoinduced C(sp3 )-N bond cleavage reaction. Under visible-light irradiation (455 nm, blue LEDs) at room temperature, alkyl Katritzky salts react smoothly with alkenes in a 1:1 molar ratio in the presence of 1.0 mol % of commercially available photoredox catalyst without the need for any base, affording the corresponding alkyl-substituted alkenes in good yields with broad functional-group compatibility. Notably, the E/Z-selectivity of the alkene products can be controlled by an appropriate choice of photoredox catalyst.

Cross-coupling polycondensation via C-O or C-N bond cleavage.

π-Conjugated polymers are widely used in optoelectronics for fabrication of organic photovoltaic devices, organic light-emitting diodes, organic field effect transistors, and so on. Here we describe the protocol for polycondensation of bifunctional aryl ethers or aryl ammonium salts with aromatic dimetallic compounds through cleavage of inert C-O/C-N bonds. This reaction proceeds smoothly in the presence of commercially available Ni/Pd catalyst under mild conditions, affording the corresponding π-conjugated polymers with high molecular weight. The method is applicable to monomers that are unreactive in other currently employed polymerization procedures, and opens up the possibility of transforming a range of naturally abundant chemicals into useful functional compounds/polymers.

From Anilines to Aryl Ethers: A Facile, Efficient, and Versatile Synthetic Method Employing Mild Conditions.

We have developed a simple and direct method for the synthesis of aryl ethers by reacting alcohols/phenols (ROH) with aryl ammonium salts (ArNMe3+ ), which are readily prepared from anilines (ArNR'2 , R'=H or Me). This reaction proceeds smoothly and rapidly (within a few hours) at room temperature in the presence of a commercially available base, such as KOt Bu or KHMDS, and has a broad substrate scope with respect to both ROH and ArNR'2 . It is scalable and compatible with a wide range of functional groups.