Dune Morphology Classification and Dataset Construction Method Based on Unmanned Aerial Vehicle Orthoimagery.

Dunes are the primary geomorphological type in deserts, and the distribution of dune morphologies is of significant importance for studying regional characteristics, formation mechanisms, and evolutionary processes. Traditional dune morphology classification methods rely on visual interpretation by humans, which is not only time-consuming and inefficient but also subjective in classification judgment. These issues have impeded the intelligent development of dune morphology classification. However, convolutional neural network (CNN) models exhibit robust feature representation capabilities for images and have achieved excellent results in image classification, providing a new method for studying dune morphology classification. Therefore, this paper summarizes five typical dune morphologies in the deserts of western Inner Mongolia, which can be used to define and describe most of the dune types in Chinese deserts. Subsequently, field surveys and the experimental collection of unmanned aerial vehicle (UAV) orthoimages for different dune types were conducted. Five different types of dune morphology datasets were constructed through manual segmentation, automatic rule segmentation, random screening, and data augmentation. Finally, the classification of dune morphologies and the exploration of dataset construction methods were conducted using the VGG16 and VGG19 CNN models. The classification results of dune morphologies were comprehensively analyzed using different evaluation metrics. The experimental results indicate that when the regular segmentation scale of UAV orthoimages is 1024 × 1024 pixels with an overlap of 100 pixels, the classification accuracy, precision, recall, and F1-Score of the VGG16 model reached 97.05%, 96.91%, 96.76%, and 96.82%, respectively. The method for constructing a dune morphology dataset from automatically segmented UAV orthoimages provides a reference value for the study of large-scale dune morphology classification.

[Deep Learning-Based Artificial Intelligence Model for Automatic Carotid Plaque Identification].

This study aims at developing a dataset for determining the presence of carotid artery plaques in ultrasound images, composed of 1761 ultrasound images from 1165 participants. A deep learning architecture that combines bilinear convolutional neural networks with residual neural networks, known as the single-input BCNN-ResNet model, was utilized to aid clinical doctors in diagnosing plaques using carotid ultrasound images. Following training, internal validation, and external validation, the model yielded an ROC AUC of 0.99 (95% confidence interval: 0.91 to 0.84) in internal validation and 0.95 (95% confidence interval: 0.96 to 0.94) in external validation, surpassing the ResNet-34 network model, which achieved an AUC of 0.98 (95% confidence interval: 0.99 to 0.95) in internal validation and 0.94 (95% confidence interval: 0.95 to 0.92) in external validation. Consequently, the single-input BCNN-ResNet network model has shown remarkable diagnostic capabilities and offers an innovative solution for the automatic detection of carotid artery plaques.

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.

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.

[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.

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.

Fabrication of "Plug and Play" Channels with Dual Responses by Host-Guest Interactions.

The "Plug and Play" template can be individually or successively grafted by dual-responsive molecules on the α-CD modified channels by host-guest interactions and can be peeled off by UV irradiation. The artificial channels present six kinds of responses cycling among four states responding to three environment stimuli, as light, pH, and temperature.

Ethereal C-O Bond Cleavage Mediated by Ni(0)-Ate Complex: A DFT Study.

Density functional theory calculations were performed to explore the mechanism of Ni-catalyzed cross-coupling reactions involving organo-lithium and -zinc reagents through ethereal C-O bond cleavage. Based on this work, together with our previous mechanistic study on etheric Kumada-Tamao reaction, we identify and characterize a novel catalytic cycle for cross-coupling mediated by Ni(0)-ate complex.

Cross-Coupling of Organolithium with Ethers or Aryl Ammonium Salts by C-O or C-N Bond Cleavage.

Various aryl-, alkenyl-, and/or alkyllithium species reacted smoothly with aryl and/or benzyl ethers with cleavage of the inert C-O bond to afford cross-coupled products, catalyzed by commercially available [Ni(cod)2 ] (cod=1,5-cyclooctadiene) catalysts with N-heterocyclic carbene (NHC) ligands. Furthermore, the coupling reaction between the aryllithium compounds and aryl ammonium salts proceeded under mild conditions with C-N bond cleavage in the presence of a [Pd(PPh3 )2 Cl2 ] catalyst. These methods enable selective sequential functionalizations of arenes having both C-N and C-O bonds in one pot.

Regulation of DNA self-assembly and DNA hybridization by chiral molecules with corresponding biosensor applications.

Chirality is one of the fundamental biochemical properties in a living system, and a lot of biological and physiological processes are greatly influenced by the chirality of molecules. Inspired by this phenomenon, we study the covalent assembly of DNA on chiral molecule modified surfaces and further discuss the hybridization of DNA on chiral surfaces with nucleic acids. Take methylene blue (MB) modified DNA as a model molecule, we show that the peak current of the L-NIBC (NIBC, N-isobutyryl-L(D)-cysteine) modified gold surface (L-surface) is larger than the D-surface because of a stronger interaction between short-chain DNA and the L-surface; however, the D-surface has a higher hybridization efficiency than the L-surface. Moreover, we apply this result to actual application by choosing an electrochemical DNA (E-DNA) sensor as a potential platform. Furthermore, we further amplify the difference of hybridization efficiency using the supersandwich assay. More importantly, our findings are successfully employed to program the sensitivity and limit of detection.

Nanopore-based DNA-probe sequence-evolution method unveiling characteristics of protein-DNA binding phenomena in a nanoscale confined space.

Almost all of the important functions of DNA are realized by proteins which interact with specific DNA, which actually happens in a limited space. However, most of the studies about the protein-DNA binding are in an unconfined space. Here, we propose a new method, nanopore-based DNA-probe sequence-evolution (NDPSE), which includes up to 6 different DNA-probe systems successively designed in a nanoscale confined space which unveil the more realistic characteristics of protein-DNA binding phenomena. There are several features; for example, first, the edge-hindrance and core-hindrance contribute differently for the binding events, and second, there is an equilibrium between protein-DNA binding and DNA-DNA hybridization.

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.

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.

Traditional Chinese Medicine (TCM)-Inspired Fully Printed Soft Pressure Sensor Array with Self-Adaptive Pressurization for Highly Reliable Individualized Long-Term Pulse Diagnostics.

Reliable, non-invasive, continuous monitoring of pulse and blood pressure is essential for the prevention and diagnosis of cardiovascular diseases. However, the pulse wave varies drastically among individuals or even over time in the same individual, presenting significant challenges for the existing pulse sensing systems. Inspired by pulse diagnosis methods in traditional Chinese medicine (TCM), this work reports a self-adaptive pressure sensing platform (PSP) that combines the fully printed flexible pressure sensor array with an adaptive wristband-style pressure system can identify the optimal pulse signal. Besides the detected pulse rate/width/length, "Cun, Guan, Chi" position, and "floating, moderate, sinking" pulse features, the PSP combined with a machine learning-based linear regression model can also accurately predict blood pressure such as systolic, diastolic, and mean arterial pressure values. The developed diagnostic platform is demonstrated for highly reliable long-term monitoring and analysis of pulse and blood pressure across multiple human subjects over time. The design concept and proof-of-the-concept demonstrations also pave the way for the future developments of flexible sensing devices/systems for adaptive individualized monitoring in the complex practical environments for personalized medicine, along with the support for the development of digital TCM.

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.

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.

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.

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.

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.