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Abstract Vascular dysfunction, characterized by changes in anatomy, hemodynamics, and molecular expressions of vasculatures, is closely linked to the onset and development of diseases, emphasizing the importance of its detection. In clinical practice, medical imaging has been utilized as a significant tool in the assessment of vascular dysfunction, however, traditional imaging techniques still lack sufficient resolution for visualizing the complex microvascular systems. Over the past decade, with the rapid advancement of nanotechnology and the emergence of corresponding detection facilities, engineered nanomaterials offer new alternatives to traditional contrast agents. Compared with conventional small molecule counterparts, nanomaterials possess numerous advantages for vascular imaging, holding the potential to significantly advance related technologies. In this review, the latest developments in nanotechnology-assisted vascular imaging research across different imaging modalities, including contrast-enhanced magnetic resonance (MR) angiography, susceptibility-weighted imaging (SWI), and fluorescence imaging in the second near-infrared window (NIR-II) are summarized. Additionally, the advancements of preclinical and clinical studies related to these nanotechnology-enhanced vascular imaging approaches are outlined, with subsequent discussion on the current challenges and future prospects in both basic research and clinical translation. Graphical Abstract

Abstract The good combination of high strength and high toughness is a long‐standing challenge in the design of robust biomaterials. Meanwhile, robust biomaterials hardly perform fast and significant mechanical property changes under the trigger of light at room temperature. These limit the application of biomaterials in some specific areas. Here, photoresponsive alginate fibers are fabricated by using the designed azobenzene‐containing surfactant as flexible contact point for cross‐linking polysaccharide chains of alginate, which gain high mechanics through reinforced plastic strain and photo‐modulating mechanics through isomerization of azobenzene. By transferring molecular motion into macro‐scale mechanical property changes, such alginate fibers achieve reversible photo‐modulations on the mechanics. Their breaking strength and toughness can be photo‐modulated from 732 MPa and 112 MJ m−3 to 299 MPa and 27 MJ m−3, respectively, leading to record high mechanical changes among the developed smart biomaterials. With merits of good tolerance to pH and temperature, fast response to light, and good biocompatibility, the reported fibers will be suitable for working in various application scenarios as new smart biomaterials. This study provides a new design strategy for gaining highly‐strong and highly‐tough photoresponsive biomaterials.

Abstract Elastin-like polypeptides (ELPs) are thermally responsive biopolymers derived from natural elastin. These peptides have a low critical solution temperature phase behavior and can be used to prepare stimuli-responsive biomaterials. Through genetic engineering, biomaterials prepared from ELPs can have unique and customizable properties. By adjusting the amino acid sequence and length of ELPs, nanostructures, such as micelles and nanofibers, can be formed. Correspondingly, ELPs have been used for improving the stability and prolonging drug-release time. Furthermore, ELPs have widespread use in tissue repair due to their biocompatibility and biodegradability. Here, this review summarizes the basic property composition of ELPs and the methods for modulating their phase transition properties, discusses the application of drug delivery system and tissue repair and clarifies the current challenges and future directions of ELPs in applications.

Organic synaptic memristors are of considerable interest owing to their attractive characteristics and potential applications to flexible neuromorphic electronics. In this work, an organic type-II heterojunction consisting of poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) and pentacene was adopted for low-voltage and flexible memristors. The conjugated polymer PEDOT:PSS serves as the flexible resistive switching (RS) layer, while the thin pentacene layer plays the role of barrier adjustment. This heterojunction enabled the memristor device to be triggered with low-energy RS operations (V < ± 1.0 V and I < 9.0 μA), and simultaneously providing high mechanical bending stability (bending radius of ≈2.5 mm, bending times = 1,000). Various synaptic properties have been successfully mimicked. Moreover, the memristors presented good potentiation/depression stability with a low cycle-to-cycle variation (CCV) of less than 8%. The artificial neural network consisting of this flexible memristor exhibited a high accuracy of 89.0% for the learning with MNIST data sets, even after 1,000 tests of 2.5% stress-strain. This study paves the way for developing low-power and flexible synaptic devices utilizing organic heterojunctions.

Ship instance segmentation in synthetic aperture radar (SAR) images is a hard and challenging task, which not only locates ships but also obtains their shapes with pixel-level masks. However, in ocean SAR images, because of the consistent reflective intensities of ships, the appearances of different ships are similar, thus making it far too difficult to distinguish ships when they are in densely packed groups. Especially when ships have incline directions and large aspect ratios, the horizontal bounding boxes (HB-Boxes) used by all the instance-segmentation networks that we know so far inevitably contain redundant backgrounds, docks, and even other ships, which mislead the following segmentation. To solve this problem, a novel ship instance-segmentation network, called SRNet, is proposed with rotated bounding boxes (RB-Boxes), which are taken as the foundation of segmentation. Along the directions of ships, the RB-Boxes can surround the ships tightly, but a minor deviation will corrupt the integrity of the ships’ masks. To improve the performance of the RB-Boxes, a dual feature alignment module (DAM) was designed to obtain the representative features with the direction and shape information of ships. On account of the difference between the classification task and regression task, two different sampling location calculation strategies were used in two convolutional kernels of the DAM, making these locations distributed dynamically on the ships’ bodies and along the ships’ boundaries. Moreover, to improve the effectiveness of training, a new adaptive Intersection-over-Union threshold (AIoU) was proposed based on the aspect-ratio information of ships to raise positive samples. To obtain the masks in the RB-Boxes, a new Mask-segmentation Head (MaskHead) with the twice sampling processes was explored. In experiments to evaluate the RB-Boxes, the accuracy of the RB-Boxes output from the Detection Head (DetHead) of SRNet outperformed eight rotated object-detection networks. In experiments to evaluate the final segmentation masks, compared with several classic and state-of-the-art instance-segmentation networks, our proposed SRNet achieved more accurate ship instance masks in SAR images. The ablation studies demonstrated the effectiveness of the DAM in the SRNet and the AIoU for our network training.

In-band relay nodes can be utilized to enhance the coverage of heterogeneous networks in a cost-effective way. However, the wireless backhaul links of in-band relay nodes also consume the limited spectrum resources. Appropriate spectrum resource management/cooperation is necessary to ensure the balanced resource usages between the macro base stations and the relay nodes. In this paper, we derive an optimal spectrum reuse strategy in a heterogeneous network with in-band relay nodes to maximize the overall proportional fairness metric. We first provide one distributed resource allocation algorithm based on the alternating direction method of multipliers for the heterogeneous network with in-band relay nodes. Even though the number of feasible spectrum reuse patterns increases exponentially with the total number of base stations and relay nodes, we further prove that we can achieve the optimal network performance by only activating a limited number of spectrum reuse patterns, which is just in the order of the total number of mobile stations and relay nodes. According to this finding, we put forth an algorithm to refine the set of active reuse patterns in a soft manner by employing the re-weighted $\ell _{1}$ -norm algorithm. Numerical simulations demonstrate the superiority and effectiveness of our proposed algorithms.

Visible light communication (VLC) is regarded as a promising technology for indoor communications due to its safety, availability, and efficiency. By deploying multiple light-emitting diodes (LEDs) at the transmitter, high data rate can be achieved. On the other hand, the dimming control is necessary in VLC systems to meet the lighting requirements, and puts an additional constraint to the system design. Thus, providing required communication quality while preserving the maximum dimming control capability becomes the baseline design philosophy for VLC systems. In this paper, we focus on multi-user multiple-input multiple-output VLC systems and propose a joint beamforming and direct current bias design. The proposed scheme can be formulated as an optimization problem and finds the minimum dimming level subject to the transmission rate requirement as well as the lighting constraints. Semi-definite program method and concave-convex procedure are exploited to solve the optimization problem efficiently. The impacts of locations of LEDs and users to the dimming control are also studied with numerical results.

The detection of myoglobin (Myo), cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), and b-type natriuretic peptide (BNP) plays a vital role in diagnosing cardiovascular diseases. Here we present single site-specific polyaniline (PANI) nanowire biosensors that can detect cardiac biomarkers such as Myo, cTnI, CK-MB, and BNP with ultra-high sensitivity and good specificity. Using single PANI nanowire-based biosensors integrated with microfluidic channels, very low concentrations of Myo (100 pg/mL), cTnI (250 fg/mL), CK-MB (150 fg/mL), and BNP (50 fg/mL) were detected. The single PANI nanowire-based biosensors displayed linear sensing profiles for concentrations ranging from hundreds (fg/mL) to tens (ng/mL). In addition, devices showed a fast (few minutes) response satisfying respective reference conditions for Myo, cTnI, CK-MB, and BNP diagnosis of heart failure and for determining the stage of the disease. This single PANI nanowire-based biosensor demonstrated superior biosensing reliability with the feasibility of label free detection and improved processing cost efficiency due to good biocompatibility of PANI to monoclonal antibodies (mAbs). Therefore, this development of single PANI nanowire-based biosensors can be applied to other biosensors for cancer or other diseases.

As low power, low cost, and longevity of transceivers are major requirements in wireless sensor networks, optimizing their design under energy constraints is of paramount importance. To this end, we develop quantizers under strict energy constraints to effect optimal reconstruction at the fusion center. Propagation, modulation, as well as transmitter and receiver structures are jointly accounted for using a binary symmetric channel model. We first optimize quantization for reconstructing a single sensor's measurement, and deriving the optimal number of quantization levels as well as the optimal energy allocation across bits. The constraints take into account not only the transmission energy but also the energy consumed by the transceiver's circuitry. Furthermore, we consider multiple sensors collaborating to estimate a deterministic parameter in noise. Similarly, optimum energy allocation and optimum number of quantization bits are derived and tested with simulated examples. Finally, we study the effect of channel coding on the reconstruction performance under strict energy constraints and jointly optimize the number of quantization levels as well as the number of channel uses.