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Object detection in unmanned aerial vehicle (UAV) images presents challenges such as high altitudes, small object sizes, and complex backgrounds. Additionally, many deep learning object detection algorithms require substantial computational resources, making them difficult to deploy on embedded devices with limited memory and processing power, which affects the effectiveness of drones in task execution. To tackle these issues, we propose the LightUAV-YOLO algorithm which is a lightweight object detection algorithm for UAVs based on YOLOv8n. We modified the neck structure of YOLOv8, enhancing the network’s capability to detect small objects. To further optimize features fusion at different scales, we designed the orthogonal feature enhancement module (OFEM) which replaces simple concatenation for better feature representation. We also designed the local attention module (LAM) to effectively filter out irrelevant interference. The module enables our model better focus on important areas and further enhancing the model’s robustness. Results demonstrate that our proposed LightUAV-YOLO algorithm achieves a 6.4 and 3.9% improvement in mAP50 and mAP50:95, respectively, on the VisDrone test dataset compared to the YOLOv8-nano. Meanwhile, the model maintains a low parameter count and computational complexity. Furthermore, we conducted extensive experiments on the UAVDT dataset, and our method consistently exhibited favorable results. This model not only meets accuracy requirements but also considers the lightweight requirements. [ABSTRACT FROM AUTHOR]

Object detection in unmanned aerial vehicle (UAV) images presents challenges such as high altitudes, small object sizes, and complex backgrounds. Additionally, many deep learning object detection algorithms require substantial computational resources, making them difficult to deploy on embedded devices with limited memory and processing power, which affects the effectiveness of drones in task execution. To tackle these issues, we propose the LightUAV-YOLO algorithm which is a lightweight object detection algorithm for UAVs based on YOLOv8n. We modified the neck structure of YOLOv8, enhancing the network’s capability to detect small objects. To further optimize features fusion at different scales, we designed the orthogonal feature enhancement module (OFEM) which replaces simple concatenation for better feature representation. We also designed the local attention module (LAM) to effectively filter out irrelevant interference. The module enables our model better focus on important areas and further enhancing the model’s robustness. Results demonstrate that our proposed LightUAV-YOLO algorithm achieves a 6.4 and 3.9% improvement in mAP50 and mAP50:95, respectively, on the VisDrone test dataset compared to the YOLOv8-nano. Meanwhile, the model maintains a low parameter count and computational complexity. Furthermore, we conducted extensive experiments on the UAVDT dataset, and our method consistently exhibited favorable results. This model not only meets accuracy requirements but also considers the lightweight requirements. [ABSTRACT FROM AUTHOR]

The rise of DNA nanotechnology has driven the development of DNA-based molecular machines, which are capable of performing specific operations and tasks at the nanoscale. Benefitting from the programmability of DNA molecules and the predictability of DNA hybridization and strand displacement, DNA-based molecular machines can be designed with various structures and dynamic behaviors and have been implemented for wide applications in the field of biosensing due to their unique advantages. This review summarizes the reported controlling mechanisms of DNA-based molecular machines and introduces biosensing applications of DNA-based molecular machines in amplified detection, multiplex detection, real-time monitoring, spatial recognition detection, and single-molecule detection of biomarkers. The challenges and future directions of DNA-based molecular machines in biosensing are also discussed. [ABSTRACT FROM AUTHOR]

Life‐like hierarchical architecture shows great potential for advancing intelligent biosensing, but modular expansion of its sensitivity and functionality remains a challenge. Drawing inspiration from intracellular liquid‐liquid phase separation, we discovered that a DNA‐encoded artificial cell with a liquid core (LAC) can enhance peroxidase‐like activity of Hemin and its DNA G‐quadruplex aptamer complex (DGAH) without substrate‐selectivity, unlike its gelled core (GAC) counterpart. The LAC is easily engineered as an ultrasensitive biosensing system, benefiting from DNA′s high programmability and unique signal amplification capability mediated by liquid‐liquid phase separation. As proof of concept, its versatility was successfully demonstrated by coupling with two molecular recognition elements to monitor tumor‐related microRNA and profile cancer cell phenotypes. This scalable design philosophy offers new insights into the design of next generation of artificial cells‐based biosensors. [ABSTRACT FROM AUTHOR]

Life‐like hierarchical architecture shows great potential for advancing intelligent biosensing, but modular expansion of its sensitivity and functionality remains a challenge. Drawing inspiration from intracellular liquid‐liquid phase separation, we discovered that a DNA‐encoded artificial cell with a liquid core (LAC) can enhance peroxidase‐like activity of Hemin and its DNA G‐quadruplex aptamer complex (DGAH) without substrate‐selectivity, unlike its gelled core (GAC) counterpart. The LAC is easily engineered as an ultrasensitive biosensing system, benefiting from DNA′s high programmability and unique signal amplification capability mediated by liquid‐liquid phase separation. As proof of concept, its versatility was successfully demonstrated by coupling with two molecular recognition elements to monitor tumor‐related microRNA and profile cancer cell phenotypes. This scalable design philosophy offers new insights into the design of next generation of artificial cells‐based biosensors. [ABSTRACT FROM AUTHOR]

Molecular imaging is a non-invasive method to image and analyze the concentration and activity of functional biomolecules in cells or in vivo at molecular level, and plays an increasing role in deep understanding of biological processes, early and accurate diagnosis of diseases, and evaluation of treatment. Nowadays, numerous novel molecular imaging probes have been developed, involving every biomedical imaging modality, such as optical imaging, photoacoustic imaging, magnetic resonance imaging, single-photon-emission computed tomography, and positron emission tomography. In this review, we summarize the development of current state-of-the-art molecular imaging probes. We introduce the design strategies of molecular probes and detailed imaging modalities, and highlight the properties of probes and biomedical imaging applications in cells and in vivo, including disease diagnosis, drug tracking, and imaging-guided surgery. Then we discuss the perspectives and challenges in this emerging field. We expect this review could inspire more effective molecular imaging probes to be developed, achieving the goal towards clinical practices. [ABSTRACT FROM AUTHOR]

Cell‐specific aptamers offer a powerful tool to study membrane receptors at the single‐molecule level. Most target receptors of aptamers are highly expressed on the cell surface, but difficult to analyze in situ because of dense distribution and fast velocity. Therefore, we herein propose a random sampling‐based analysis strategy termed ligand dilution analysis (LDA) for easily implemented aptamer‐based receptor study. Receptor density on the cell surface can be calculated based on a regression model. By using a synergistic ligand dilution design, colocalization and differentiation of aptamer and monoclonal antibody (mAb) binding on a single receptor can be realized. Once this is accomplished, precise binding site and detailed aptamer‐receptor binding mode can be further determined using molecular docking and molecular dynamics simulation. The ligand dilution strategy also sets the stage for an aptamer‐based dynamics analysis of two‐ and three‐dimensional motion and fluctuation of highly expressed receptors on the live cell membrane. [ABSTRACT FROM AUTHOR]

Cell‐specific aptamers offer a powerful tool to study membrane receptors at the single‐molecule level. Most target receptors of aptamers are highly expressed on the cell surface, but difficult to analyze in situ because of dense distribution and fast velocity. Therefore, we herein propose a random sampling‐based analysis strategy termed ligand dilution analysis (LDA) for easily implemented aptamer‐based receptor study. Receptor density on the cell surface can be calculated based on a regression model. By using a synergistic ligand dilution design, colocalization and differentiation of aptamer and monoclonal antibody (mAb) binding on a single receptor can be realized. Once this is accomplished, precise binding site and detailed aptamer‐receptor binding mode can be further determined using molecular docking and molecular dynamics simulation. The ligand dilution strategy also sets the stage for an aptamer‐based dynamics analysis of two‐ and three‐dimensional motion and fluctuation of highly expressed receptors on the live cell membrane. [ABSTRACT FROM AUTHOR]

Afterglow luminescence is an internal luminescence pathway that occurs after photo-excitation, holds great promise for non-background molecular imaging in vivo, but suffer from poor quantitative ability owing to luminescent attenuation over time. Moreover, the inert structure and insufficient reactive sites of current afterglow materials make it hard to design activatable afterglow probes for specific detection. Here, we report a ratiometric afterglow luminescent nanoplatform to customize various activatable afterglow probes for reliable quantification and molecular imaging of specific analytes, such as NO, ONOO− or pH. Notably, these afterglow probes can not only address the attenuation of afterglow intensity and eliminate the interference of factors (e.g., laser power, irradiation time, and exposure time), but also significantly improve the imaging reliability in vivo and signal-to-background ratios (~1200-fold), both of which enable more reliable quantitative analysis in biological systems. Moreover, as a proof-of-concept, we successfully design an NO-responsive ratiometric afterglow nanoprobe, RAN1. This nanoprobe can monitor the fluctuations of intratumoral NO, as a biomarker of macrophage polarization, making it possible to real-time dynamically evaluate the degree cancer immunotherapy, which provides a reliable parameter to predict the immunotherapeutic effect. Afterglow luminescence is promising for non-background molecular imaging in vivo. Here the authors report a ratiometric afterglow luminescent nanoplatform to generate activatable afterglow probes for quantification of specific analytes including NO. [ABSTRACT FROM AUTHOR]

As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-beat interval can be identified. However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple BCG signals. Methods. This paper develops a deep learning framework based on ResNet and bidirectional long short-term memory (BiLSTM) to conduct beat-to-beat detection of BCG signals. Unlike the existing methods, the proposed network takes multiscale features of BCG signals as the input and, thus, can enjoy the complementary advantages of both morphological features of one BCG signal and rhythm features of multiple BCG signals. Different time scales of multiscale features for the proposed model are validated and analyzed through experiments. Results. The BCG signals recorded from 21 healthy subjects are conducted to verify the performance of the proposed heartbeat detection scheme using leave-one-out cross-validation. The impact of different time scales on the detection performance and the performance of the proposed model for different sleep postures are examined. Numerical results demonstrate that the proposed multiscale model performs robust to sleep postures and achieves an averaged absolute error E abs and an averaged relative error E rel of the heartbeat interval relative to the R-R interval of 9.92 ms and 2.67 ms, respectively, which are superior to those of the state-of-the-art detection protocol. Conclusion. In this work, a multiscale deep-learning model for heartbeat detection using BCG signals is designed. We demonstrate through the experiment that the detection with multiscale features of BCG signals can provide a superior performance to the existing works. Further study will examine the ultimate performance of the multiscale model in practical scenarios, i.e., detection for patients suffering from cardiovascular disorders with night-sleep monitoring. [ABSTRACT FROM AUTHOR]

A power and area efficient two-step hybrid voltage–time ADC achieves a 4-GS/s conversion speed and 39.9-dB SNDR in 28-nm CMOS. Two pipelined time-based converters (TBCs) with a thermometer capacitive DAC (CDAC) in the ADC lead to a high-speed and low-power operation. The pipelined architecture splits the full ADC resolution, thus relaxing the TBC complexity. The TBC consists of a voltage-domain comparator, a current-source-based voltage-to-time converter (VTC), and a ring oscillator (RO)-based time-to-digital converter (TDC) with feedforward and 2 $\times $ interpolation that achieves high conversion speed and good linearity simultaneously. The prototype ADC is fabricated in a standard 28-nm CMOS process with an active area of only 0.017mm2. The measured SNDR and SFDR are 39.9 and 47.8 dB with a Nyquist input at 4 GS/s. The FoMW and FoMS are 39.3 fJ/conv-step and 152.2 dB, respectively. [ABSTRACT FROM AUTHOR]

With the recent development of material science, high strength steel (HSS) has become a practical solution for landmark buildings and major projects. The current codes for design of bearing-type bolted connections of steel constructions were established based on the research of conventional steels. Since the mechanical properties of HSS are different from those of conventional steels, more works should be done to develop the appropriate approach for the design of bearing-type bolted connections in HSS. A review of the research carried out on bearing-type bolted connections fabricated from conventional steel and HSS is presented. The up-to-date tests conducted at Tongji University on four connection types fabricated from three grades of HSS with nominal yield strengths of 550, 690, and 890 MPa are presented. The previous research on failure modes, bearing resistance and the design with consideration of bolt hole elongation are summarized. It is found that the behavior of bolted connections in HSS have no drastic difference compared to that of conventional steel connections. Although the ductility is reduced, plastic deformation capacity of HSS is sufficient to ensure the load redistribution between different bolts with normal construction tolerances. It is also found that behavior of each bolt of multi-bolt connections arranged in perpendicular to load direction is almost identical to that of a single-bolt connection with the same end distance. For connections with bolts arranged in parallel to load direction, the deformation capacity of the whole connection depends on the minimum value between the end distance and the spacing distances in load direction. The comparison with existing design codes shows that Eurocode3 and Chinese GB50017-2017 are conservative for the design of bolted connections in HSS while AISC 360-16 may overestimate the bearing resistance of bolted connections. [ABSTRACT FROM AUTHOR]

In order to maintain tissue homeostasis, cells communicate with the outside environment by receiving molecular signals, transmitting them, and responding accordingly with signaling pathways. Thus, one key challenge in engineering molecular signaling systems involves the design and construction of different modules into a rationally integrated system that mimics the cascade of molecular events. Herein, we rationally design a DNA-based artificial molecular signaling system that uses the confined microenvironment of a giant vesicle, derived from a living cell. This system consists of two main components. First, we build an adenosine triphosphate (ATP)-driven DNA nanogatekeeper. Second, we encapsulate a signaling network in the biomimetic vesicle, consisting of distinct modules, able to sequentially initiate a series of downstream reactions playing the roles of reception, transduction and response. Operationally, in the presence of ATP, nanogatekeeper switches from the closed to open state. The open state then triggers the sequential activation of confined downstream signaling modules. Cells communicate with the outside world to maintain homeostasis. Here the authors design a synthetic biology DNA-based signalling system AMSsys that responds to the presence of ATP. [ABSTRACT FROM AUTHOR]

Abstract: The specific binding ability of DNA–lipid micelles (DLMs) can be increased by the introduction of an aptamer. However, supramolecular micellar structures based on self‐assemblies of amphiphilic DLMs are expected to demonstrate low stability when interacting with cell membranes under certain conditions, which could lead to a reduction in selectivity for targeting cancer cells. We herein report a straightforward cross‐linking strategy that relies on a methacrylamide branch to link aptamer and lipid segments. By an efficient photoinduced polymerization process, covalently linked aptamer–lipid units help stabilize the micelle structure and enhance aptamer probe stability, further improving the targeting ability of the resulting nanoassembly. Besides the development of a facile cross‐linking method, this study clarifies the relationship between aptamer–lipid concentration and the corresponding binding ability. [ABSTRACT FROM AUTHOR]

Abstract: The specific binding ability of DNA–lipid micelles (DLMs) can be increased by the introduction of an aptamer. However, supramolecular micellar structures based on self‐assemblies of amphiphilic DLMs are expected to demonstrate low stability when interacting with cell membranes under certain conditions, which could lead to a reduction in selectivity for targeting cancer cells. We herein report a straightforward cross‐linking strategy that relies on a methacrylamide branch to link aptamer and lipid segments. By an efficient photoinduced polymerization process, covalently linked aptamer–lipid units help stabilize the micelle structure and enhance aptamer probe stability, further improving the targeting ability of the resulting nanoassembly. Besides the development of a facile cross‐linking method, this study clarifies the relationship between aptamer–lipid concentration and the corresponding binding ability. [ABSTRACT FROM AUTHOR]

Aptamer binding characterization, including receptor density calculation, single-molecule colocalization, binding site determination, motion and fluctuation analysis, was performed with minimal probe and instrument requirements. Aptamers, Binding Site, Dilution, Fluorescent Probes, Motion Analysis Keywords: Aptamers; Binding Site; Dilution; Fluorescent Probes; Motion Analysis EN Aptamers Binding Site Dilution Fluorescent Probes Motion Analysis 1 1 1 02/22/23 20230301 NES 230301 B A membrane-receptor-analysis method b termed ligand dilution analysis, which combines the concepts of single-molecule localization and random sampling, is reported by Yifan Lyu, Jianhui Jiang, Weihong Tan et al. in their Research Article (e202215387). [Extracted from the article]

Aptamer binding characterization, including receptor density calculation, single-molecule colocalization, binding site determination, motion and fluctuation analysis, was performed with minimal probe and instrument requirements. Back Cover: Ligand Dilution Analysis Facilitates Aptamer Binding Characterization at the Single-Molecule Level (Angew. Keywords: Aptamers; Binding Site; Dilution; Fluorescent Probes; Motion Analysis EN Aptamers Binding Site Dilution Fluorescent Probes Motion Analysis 1 1 1 02/22/23 20230301 NES 230301 B A membrane-receptor-analysis method b termed ligand dilution analysis, which combines the concepts of single-molecule localization and random sampling, is reported by Yifan Lyu, Jianhui Jiang, Weihong Tan et al. in their Research Article (e202215387). [Extracted from the article]

A highly efficient nanozyme system, termed hollow multipod Cu(OH)2 superstructure (HMPS), has been developed via direct conversion from irregular nanoparticles. The HMPS displayed a body size around 150 nm and branch lengths in the range of 150–250 nm. Based on the excellent catalytic properties of HMPS, we developed a simple and highly sensitive colorimetric assay to detect urine glucose, and the results are in good agreement with hospital examination reports. [ABSTRACT FROM AUTHOR]

In recent years, live webcast classes have been increasingly used in China as an approach to alleviating educational poverty through equal access to high-quality education. Many schools in impoverished areas have managed to increase their proportions of students entering college by introducing the new model. While celebrating improved learning outcomes of a small percentage of students, educators should also be concerned about the overall academic wellbeing and sustainable development of less successful students. In the present study, academic wellbeing was conceptualized as a multidimensional construct covering seven dimensions, namely Empathy, Support, Responsiveness, Reliability, Tangibility, Self-efficacy and Buoyancy. Data were collected from 136 twelfth-grade students who had studied in live webcast classes. The results show that the overall academic wellbeing in live webcast classes was consistent among students of different academic performance levels, but the specific dimensions of academic wellbeing that they think mostly need improvement varied among different student groups. The findings of this study suggest that learner wellbeing and sustainability can be enhanced by closer collaboration between live webcast instructors and local teachers in instructional materials design, exercise and test questions' compilation, as well as students' self-study facilitation. The degree to which a local teacher should be involved in classroom teaching depends on the students' academic level and learning needs. [ABSTRACT FROM AUTHOR]