Modularization of Regional Electronic Structure over Defect-Rich CeO2 Rods for Enhancing Photogenerated Charge Transfer and CO2 Activation.

Oxygen vacancy (OV) engineering has been widely applied in different types of metal oxide-based photocatalytic reactions. Our study has shown that the redistributed OVs resulting from voids in CeO2 rods lead to significant differences in the band structure in space. The flat energy band within the highly crystallized bulk region hinders the recombination of photogenerated carrier pairs during the transfer process. The downward curved energy band in the surface region enhances the activation of the absorbents. Therefore, the localization of the band structure through crystal structure regionalization renders V-CeO2 capable of achieving efficient utilization of photogenerated carriers. Practically, the V-CeO2 rod shows a remarkable turnover number of 190.58 µmol g-1 h-1 in CO2 photoreduction, which is ∼9.4 times higher than that of D-CeO2 (20.46 µmol g-1 h-1). The designed modularization structure in our work is expected to provide important inspiration and guidance in coordinating the kinetic behavior of carriers in OV defect-rich photocatalysts.

Automated Restarting Fast Proximal Gradient Descent Method for Single-View Cone-Beam X-ray Luminescence Computed Tomography Based on Depth Compensation.

Single-view cone-beam X-ray luminescence computed tomography (CB-XLCT) has recently gained attention as a highly promising imaging technique that allows for the efficient and rapid three-dimensional visualization of nanophosphor (NP) distributions in small animals. However, the reconstruction performance is hindered by the ill-posed nature of the inverse problem and the effects of depth variation as only a single view is acquired. To tackle this issue, we present a methodology that integrates an automated restarting strategy with depth compensation to achieve reconstruction. The present study employs a fast proximal gradient descent (FPGD) method, incorporating L0 norm regularization, to achieve efficient reconstruction with accelerated convergence. The proposed approach offers the benefit of retrieving neighboring multitarget distributions without the need for CT priors. Additionally, the automated restarting strategy ensures reliable reconstructions without the need for manual intervention. Numerical simulations and physical phantom experiments were conducted using a custom CB-XLCT system to demonstrate the accuracy of the proposed method in resolving adjacent NPs. The results showed that this method had the lowest relative error compared to other few-view techniques. This study signifies a significant progression in the development of practical single-view CB-XLCT for high-resolution 3-D biomedical imaging.

An edge cloud and Fibonacci-Diffie-Hellman encryption scheme for secure printer data transmission.

Network printers face increasing security threats from network attacks that can lead to sensitive information leakage and data tampering. To address these risks, we propose a novel Fibonacci-Diffie-Hellman (FIB-DH) encryption scheme using edge cloud collaboration. Our approach utilizes properties of third-order Fibonacci matrices combined with the Diffie-Hellman key exchange to encrypt printer data transmissions. The encrypted data is transmitted via edge cloud servers and verified by the receiver using inverse Fibonacci transforms. Our experiments demonstrate that the FIB-DH scheme can effectively improve printer data transmission security against common attacks compared to conventional methods. The results show reduced vulnerabilities to leakage and tampering attacks in our approach. This work provides an innovative application of cryptographic techniques to strengthen security for network printer communications.

Large-scale effective connectivity analysis reveals the existence of two mutual inhibitory systems in patients with major depression.

It is posited that cognitive and affective dysfunction in patients with major depression disorder (MDD) may be caused by dysfunctional signal propagation in the brain. By leveraging dynamic causal modeling, we investigated large-scale directed signal propagation (effective connectivity) among distributed large-scale brain networks with 43 MDD patients and 56 healthy controls. The results revealed the existence of two mutual inhibitory systems: the anterior default mode network, auditory network, sensorimotor network, salience network and visual networks formed an "emotional" brain, while the posterior default mode network, central executive networks, cerebellum and dorsal attention network formed a "rational brain". These two networks exhibited excitatory intra-system connectivity and inhibitory inter-system connectivity. Patients were characterized by potentiated intra-system connections within the "emotional/sensory brain", as well as over-inhibition of the "rational brain" by the "emotional/sensory brain". The hierarchical architecture of the large-scale effective connectivity networks was then analyzed using a PageRank algorithm which revealed a shift of the controlling role of the "rational brain" to the "emotional/sensory brain" in the patients. These findings inform basic organization of distributed large-scale brain networks and furnish a better characterization of the neural mechanisms of depression, which may facilitate effective treatment.

Multiparametric MRI-based VI-RADS: can it predict 1- to 5-year recurrence of bladder cancer?

OBJECTIVES: To evaluate whether Vesical Imaging-Reporting And Data System (VI-RADS) scores based on multiparametric MRI (mp-MRI) can predict bladder cancer (BCa) recurrence. METHODS: In this retrospective study, 284 patients with pathologically confirmed bladder neoplasms from November 2011 to October 2020 were included. Two radiologists blindly and independently scored mp-MRI scans according to VI-RADS. Scoring inconsistency was resolved in consensus. The latest follow-up was completed in December 2022. Pearson's correlation analyses, independent-sample t-tests, and receiver operating characteristic analyses were performed to assess the efficacy of VI-RADS score for the 1- to 5-year recurrence prognostication. RESULTS: Based on the latest follow-up, 37 (of 284, 13.0%), 69 (of 284, 24.3%), 70 (of 234, 29.9%), 72 (of 190, 37.9%), and 63 (of 135, 46.7%) patients had cancer recurrence at 1- to 5-year follow-up, respectively. VI-RADS scores showed significantly intergroup differences between recurrent and nonrecurrent cases during 1- to 4-year surveillance (p < 0.05). The recurrence-free survival was significantly higher in patients with VI-RADS scores of 1 or 2, compared to those with scores of 3, 4, or 5 (p < 0.05). Areas under the receiver operating characteristic curves for 1- to 5-year recurrence prediction were 0.744, 0.686, 0.656, 0.595, and 0.536, respectively. VI-RADS score of 3 or more was the threshold for 1-year recurrence assessment, and VI-RADS more than 3 was the cutoff for 2-year recurrence prediction. CONCLUSION: VI-RADS score has potential in preoperative prognostication of BCa recurrence, but its predictive power decreases over time. CLINICAL RELEVANCE STATEMENT: VI-RADS has potential in bladder cancer recurrence assessment, but its prognostic value decreases over time. Patients with VI-RADS ≥ 3 may be more likely to recur in 1 or 2 years postoperatively, thus should be performed with intensive surveillances. KEY POINTS: • VI-RADS scores had significant differences in 1- to 4-year recurrent and nonrecurrent patient groups. • Patients with VI-RADS scores of ≤ 2 showed more favorable recurrence-free survival outcomes. • The prognostic value of VI-RADS score decreased over time for bladder cancer recurrence prediction.

Predicting minty compounds binary mixtures' pleasantness by odor intensity in aqueous solutions.

The aroma of mint is well-liked by the public, and key flavor odorants in mint aroma had been found, but how these molecules interact and form a satisfying odor remains a challenge. Quality, intensity, and pleasantness are our most basic perceptions of aromas; both intensity and pleasantness can be quantified. However, compared to intensity, research on pleasantness was lacking. Pleasantness was one of the most important indicators for formulating a satisfying mint flavor, and the study of binary mixtures was fundamental to our understanding of more complex mixtures. Therefore, the purpose of this study was to explore the characteristics of pleasantness as a function of concentration and, at the same time, to investigate the relationship between intensity and pleasantness in binary mixtures. Thirty sensory evaluation volunteers participated in the evaluation of the intensity and pleasantness of six key flavor odorants of mint and five binary mixtures. The results showed that the pleasantness increased first and then decreased or stabilized with the rising of concentration; even though the interactions in binary mixtures were not the same, their pleasantness could be predicted using the intensities of the components by Response Surface Design of Experiments, and the goodness of fit was greater than 0.92, indicating that the models had the great predictive ability. PRACTICAL APPLICATION: Whether blending flavors or evaluating them, a great deal of experience is required, yet the acquisition of this experience is a long process. Performing these tasks is difficult for the novice, and it helps to quantify the feeling for the flavor and build some mathematical models.

X-ray excited luminescent nanoparticles for deep photodynamic therapy.

Photodynamic therapy (PDT), as a non-invasive treatment, has received wide attention because of its high selectivity and low side effects. However, traditional PDT is influenced by the excitation light source and the light penetration depth is limited, which can only be used for superficial epidermal tumor treatment, and it is still a great challenge for deep tumor treatment. In recent years, X-ray excitation photodynamic therapy (X-PDT) using penetrating X-rays as an external excitation source and X-ray excited luminescent nanoparticles (XLNP) as an energy transfer medium to indirectly excite photosensitizer (PS) has solved the problem of insufficient penetration depth in tissues and become a research hotspot in the field of deep tumor treatment. In this review, the recent research progress of nanoparticles for efficient X-PDT, listing different types of XLNP and luminescence enhancement strategies. The loading method of PS is highlighted to achieve efficient energy transfer by regulating the intermolecular distance between both XLNP/PS. In addition, the water-soluble modification of XLNP surface is discussed and different hydrophilic modification methods are proposed to provide reference ideas for improving the dispersibility and biocompatibility of XLNP in aqueous solution. Finally, the therapeutic effects about X-PDT are discussed, and the current challenges and future perspectives for its clinical applications are presented.

Additive Manufacturing for Bioinspired Structures: Experimental Study to Improve the Multimaterial Adhesion Between Soft and Stiff Materials.

The fabrication of bioinspired structures has recently gained an increasing popularity: mimicking the way in which nature develops structures is a vital prerequisite in soft robotics to achieve multiple benefits. Stiff structures connected by soft joints (recalling, for instance, human bones connected by cartilage) are highly appealing: several prototypes have been manufactured and tested, demonstrating their full potential. In the present research, the material extrusion (MEX) additive manufacturing technology has been used to manufacture stiff-soft bioinspired structures activated by shape memory alloy (SMA) actuators. First, three commercially available stiff composite plastic materials were investigated and linked to different 3D printing infills. Surprisingly, we found that the "gyroid" infill was correlated to the mechanical properties, demonstrating that it produces better results in terms of Young's modulus and ultimate tensile strength (UTS) than the widely studied "lines" infill. The primary focus of the research is an experimental study aimed at improving the adhesion at the interface between stiff and soft materials using an inexpensive method (i.e., MEX). Three different variables that have significant effects on the interface bonding were studied: (1) the interface geometry between stiff and soft parts, (2) the mesh overlapping process parameter, and (3) the annealing post-treatment. By optimizing the three variables, a Young's modulus of 48.8 MPa and a UTS of 3.8 MPa were achieved, when nylon+glass fiber (a stiff material) and thermoplastic polyurethane (a soft material) were 3D printed together. In particular, the 3.8 MPa UTS is 48% higher than the highest adhesion between the soft and stiff material (thermoplastic polyurethane [TPU] and acrylonitrile butadiene styrene) reported in literature. Finally, taking advantage of the improved stiff-soft adhesion, a bioinspired robotic finger has been fabricated and tested using an SMA actuator, showing an enormous potential for the proposed additive manufacturing approach in realizing bioinspired systems.

Structure-preserving low-dose computed tomography image denoising using a deep residual adaptive global context attention network.

Background: Low-dose computed tomography (LDCT) scans can effectively reduce the radiation damage to patients, but this is highly detrimental to CT image quality. Deep convolutional neural networks (CNNs) have shown their potential in improving LDCT image quality. However, the conventional CNN-based approaches rely fundamentally on the convolution operations, which are ineffective for modeling the correlations among nonlocal similar structures and the regionally distinct statistical properties in CT images. This modeling deficiency hampers the denoising performance for CT images derived in this manner. Methods: In this paper, we propose an adaptive global context (AGC) modeling scheme to describe the nonlocal correlations and the regionally distinct statistics in CT images with negligible computation load. We further propose an AGC-based long-short residual encoder-decoder (AGC-LSRED) network for efficient LDCT image noise artifact-suppression tasks. Specifically, stacks of residual AGC attention blocks (RAGCBs) with long and short skip connections are constructed in the AGC-LSRED network, which allows valuable structural and positional information to be bypassed through these identity-based skip connections and thus eases the training of the deep denoising network. For training the AGC-LSRED network, we propose a compound loss that combines the L1 loss, adversarial loss, and self-supervised multi-scale perceptual loss. Results: Quantitative and qualitative experimental studies were performed to verify and validate the effectiveness of the proposed method. The simulation experiments demonstrated the proposed method exhibits the best result in terms of noise suppression [root-mean-square error (RMSE) =9.02; peak signal-to-noise ratio (PSNR) =33.17] and fine structure preservation [structural similarity index (SSIM) =0.925] compared with other competitive CNN-based methods. The experiments on real data illustrated that the proposed method has advantages over other methods in terms of radiologists' subjective assessment scores (averaged scores =4.34). Conclusions: With the use of the AGC modeling scheme to characterize the structural information in CT images and of residual AGC-attention blocks with long and short skip connections to ease the network training, the proposed AGC-LSRED method achieves satisfactory results in preserving fine anatomical structures and suppressing noise in LDCT images.

Radiomics-based T-staging of hollow organ cancers.

Cancer growing in hollow organs has become a serious threat to human health. The accurate T-staging of hollow organ cancers is a major concern in the clinic. With the rapid development of medical imaging technologies, radiomics has become a reliable tool of T-staging. Due to similar growth characteristics of hollow organ cancers, radiomics studies of these cancers can be used as a common reference. In radiomics, feature-based and deep learning-based methods are two critical research focuses. Therefore, we review feature-based and deep learning-based T-staging methods in this paper. In conclusion, existing radiomics studies may underestimate the hollow organ wall during segmentation and the depth of invasion in staging. It is expected that this survey could provide promising directions for following research in this realm.

UniVisNet: A Unified Visualization and Classification Network for accurate grading of gliomas from MRI.

Accurate grading of brain tumors plays a crucial role in the diagnosis and treatment of glioma. While convolutional neural networks (CNNs) have shown promising performance in this task, their clinical applicability is still constrained by the interpretability and robustness of the models. In the conventional framework, the classification model is trained first, and then visual explanations are generated. However, this approach often leads to models that prioritize classification performance or complexity, making it difficult to achieve a precise visual explanation. Motivated by these challenges, we propose the Unified Visualization and Classification Network (UniVisNet), a novel framework that aims to improve both the classification performance and the generation of high-resolution visual explanations. UniVisNet addresses attention misalignment by introducing a subregion-based attention mechanism, which replaces traditional down-sampling operations. Additionally, multiscale feature maps are fused to achieve higher resolution, enabling the generation of detailed visual explanations. To streamline the process, we introduce the Unified Visualization and Classification head (UniVisHead), which directly generates visual explanations without the need for additional separation steps. Through extensive experiments, our proposed UniVisNet consistently outperforms strong baseline classification models and prevalent visualization methods. Notably, UniVisNet achieves remarkable results on the glioma grading task, including an AUC of 94.7%, an accuracy of 89.3%, a sensitivity of 90.4%, and a specificity of 85.3%. Moreover, UniVisNet provides visually interpretable explanations that surpass existing approaches. In conclusion, UniVisNet innovatively generates visual explanations in brain tumor grading by simultaneously improving the classification performance and generating high-resolution visual explanations. This work contributes to the clinical application of deep learning, empowering clinicians with comprehensive insights into the spatial heterogeneity of glioma.

[Research Progress in Preoperative Evaluation of Lymph Node Metastasis of Bladder Cancer].

Bladder cancer is a common malignant tumor of the urinary system.The prognosis of patients with positive lymph nodes is worse than that of patients with negative lymph nodes.An accurate assessment of preoperative lymph node statushelps to make treatmentdecisions,such as the extent of pelvic lymphadenectomy and the use of neoadjuvant chemotherapy.Imaging examination and pathological examination are the primary methods used to assess the lymph node status of bladder cancer patients before surgery.However,these methods have low sensitivity and may lead to inaccuate staging of patients.We reviewed the research progress and made an outlook on the application of clinical diagnosis,imaging techniques,radiomics,and genomics in the preoperative evaluation of lymph node metastasis in bladder cancer patients at different stages.

Cdk5 mediates rotational force-induced brain injury.

Millions of traumatic brain injuries (TBIs) occur annually. TBIs commonly result from falls, traffic accidents, and sports-related injuries, all of which involve rotational acceleration/deceleration of the brain. During these injuries, the brain endures a multitude of primary insults including compression of brain tissue, damaged vasculature, and diffuse axonal injury. All of these deleterious effects can contribute to secondary brain ischemia, cellular death, and neuroinflammation that progress for weeks, months, and lifetime after injury. While the linear effects of head trauma have been extensively modeled, less is known about how rotational injuries mediate neuronal damage following injury. Here, we developed a new model of repetitive rotational head trauma in rodents and demonstrated acute and prolonged pathological, behavioral, and electrophysiological effects of rotational TBI (rTBI). We identify aberrant Cyclin-dependent kinase 5 (Cdk5) activity as a principal mediator of rTBI. We utilized Cdk5-enriched phosphoproteomics to uncover potential downstream mediators of rTBI and show pharmacological inhibition of Cdk5 reduces the cognitive and pathological consequences of injury. These studies contribute meaningfully to our understanding of the mechanisms of rTBI and how they may be effectively treated.

Full-fieldin vivoimaging of nanoparticles using benchtop cone-beam XFCT system with pixelated photon counting detector.

Objective.X-ray fluorescence computed tomography (XFCT) is a promising noninvasive technique forin vivoimaging of high-Z elements (e.g. gadolinium (Gd) or gold (Au)). In this study we upgraded our experimental XFCT system using a flat panel photon counting detector with redesigned pinhole collimation in order to achieve 3D XFCT images during one scan.Approach.Aiming at the characteristics of pinhole-collimated cone-beam XFCT imaging, a new scatter correction algorithm was proposed to estimate the normalized spectrum of scatter background based on K-N formula and realize correction by a weighted least squares method. Then, images were quantitatively reconstructed by a maximum likelihood iterative algorithm with the attenuation correction.Main results.The potential on full-fieldin vivoXFCT imaging of this new system was investigated. An imaging experiment of a PMMA phantom with the diameter of 35 mm was carried out for quantitative evaluation of the system performance. Results show that 2 mg ml-1Gd solutions can be successfully reconstructed with a 45 min cone-beam XFCT scan.In vivoXFCT imaging experiments of mice with injection of Gd nanoparticles (GdNPs) were also performed and demonstrated in this paper. A mouse was injected through the tail vein with 20 mg ml-1NaGdF4 solution and then anesthetized with isoflurane during the cone-beam XFCT scan.Significance.The distribution of the GdNPs inside the mouse can be well reconstructed so that the deposition of NPsin vivocan be clearly observed, which indicates the feasibility of the proposed system for full-field XFCT of small animals and further potential in relevantin vivoresearch.

Cone-beam X-ray luminescence computed tomography based on MLEM with adaptive FISTA initial image.

BACKGROUND AND OBJECTIVE: As an emerging dual-mode optical molecular imaging, cone-beam X-ray luminescence computed tomography (CB-XLCT) has shown potential in early tumor diagnosis and other applications with increased depth and little autofluorescence. However, due to the low transfer efficiency of PNPs to convert X-ray energy to visible or near-infrared (NIR) light and X-ray dose limitation, the signal to noise ratio of projections is quite low, making the quality of CB-XLCT relatively poor. METHODS: To improve the reconstruction quality of low-counts CB-XLCT imaging, an adaptive reconstruction algorithm (named ADFISTA-MLEM) based on the maximum likelihood expectation estimation (MLEM) framework is proposed for CB-XLCT reconstruction from Poisson distributed projections. In the proposed framework, the image reconstructed by fast iterative shrinkage-thresholding algorithm (FISTA) is used as the initial image for MLEM iterations to improve reconstruction accuracy, in which both the projection noise model and the sparsity constraint of the image could be considered. For relative quantitative imaging, a specific normalization is applied to the projection data and system matrix. To determine the hyperparameter of FISTA, which may be different for different projections, an adaptive strategy (ADFISTA) is then designed for adaptive update of the hyperparameter with reconstructed image in each iteration. RESULTS AND CONCLUSIONS: Results from numerical simulations and phantom experiments indicate that the proposed framework can obtain superior reconstruction accuracy in terms of contrast to noise ratio and shape similarity. In addition, high intensity-concentration linearity between different probe targets indicates its potential for quantitative CB-XLCT imaging.

The Additional Value of Tri-parametric MRI in Identifying Muscle-invasive Status in Bladder Cancer.

RATIONALE AND OBJECTIVES: Identification of muscle-invasive status (MIS) of bladder cancer (BCa) is critical for treatment decisions. The Vesical Imaging-Reporting and Data System (VI-RADS) has been widely used in preoperatively predicting MIS using tri-parametric MR imaging including T2-weighted (T2W), diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences. While the diagnostic values of radiomics features from bi-parametric MRI such as T2W + DW to identification of MIS have been reported, whether the tri-parametric MRI could provide additional diagnostic value to the radiomics prediction task, and how to integrate DCE features into the radiomics model, which is the objectives of this study, remain unknown. MATERIALS AND METHODS: Patients with postoperatively confirmed BCa lesions (150 in non-muscle-invasive BCa and 56 in muscle-invasive BCa groups) were retrospectively included. Their T2W, DW with apparent diffusion coefficient (ADC) maps, and DCE sequences were acquired using a 3.0T MR system. Regions of interest were manually depicted and VI-RADS scores were assessed by three radiologists. Three predictive models were developed by the radiomics features extracted from sequence combinations of T2W + DW (Model one), T2W + DCE (Model two), and T2W + DW + DCE (Model three), respectively, using the least absolute shrinkage and selection operator. The performance of each model was quantitatively assessed on both the training (n = 165) and testing (n = 41) cohorts. Then a 10 times five-fold cross validation was conducted to assess the overall performance. RESULTS: Three models achieved area under the curve of 0.888, 0.869, and 0.901 in the cross validation, respectively. The tri-parametric model performed significantly superior than the two bi-parametric models and VI-RADS. The analysis of feature coefficients derived from least absolute shrinkage and selection operator algorithm showed features from the tri-parametric MRI are effective in MIS discrimination. CONCLUSION: The tri-parametric MRI provides additional value to the radiomics-based identification of MIS.

Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression.

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

Co-MOFs as Emerging Pulse Modulators for Femtosecond Ultrafast Fiber Laser.

The metal organic framework (MOF) has attracted more and more attention due to its unique morphology, functional linkers, and orderly network structure. Zeolitio imidazolata frameworks (ZIFs), which are formed by bivalent transition metals (Zn, Co, etc.) and nitrogen-containing heterocyclic imidazole or purine organic ligands, are a very attractive subclass of MOFs. ZIF-67, obtained by the nucleation growth of dimethylimidazole and Co 2p, has been developed as a precursor for porous nanostructured cobalt-based metal oxides. During material preparation we add rGO because it can be used as a basic element to construct macroscopic three-dimensional carbon structural materials, which self-assemble into a 3D network structure with ZIF-67 through simple van der Waals forces or hydrogen bonds, and some samples contain specific functional groups that are added to the precursor. In this paper, we employ liquid-phase synthesis to generate rGO-ZIF-67 and calcine it at the temperature of 350 °C to obtain rGO-Co3O4. Then we fabricate rGO-Co3O4 and rGO-ZIF-67 modulators based on microfibers and test their nonlinear optical absorption in 1.5 µm range. The modulation depths of rGO-Co3O4 and rGO-ZIF-67 are measured as 10.41% and 6.61%, respectively. By using microfiber-based rGO-Co3O4 modulator, we have obtained a conventional soliton and a soliton molecule in Er3+-doped fiber lasers. The conventional soliton has a pulse width of 793.4 fs and a spectral width of 3.3 at 1558.9 nm, respectively. The obtained soliton molecule has a spectral modulation period of 1.65 nm and temporal separation of 4.94 ps at 1563.2 nm. By employing a microfiber-based rGO-ZIF-67 modulator, we obtain conventional solitons with a spectral width of 1.9 nm at the central wavelength of 1529.8 nm. Our research may expand the MOF-based materials for ultrafast photonics, blazing a new path for fiber laser, optical communications, and optoelectronics, etc.

Polyimide aerogels for ballistic impact protection.

The ballistic performance of edge-clamped monolithic polyimide aerogel blocks (12 mm thickness) has been studied through a series of impact tests using a helium-filled gas gun connected to a vacuum chamber and a spherical steel projectile (approximately 3 mm diameter) with an impact velocity range of 150-1300 m s-1. The aerogels had an average bulk density of 0.17 g cm-3 with high porosity of approximately 88%. The ballistic limit velocity of the aerogels was estimated to be in the range of 175-179 m s-1. Moreover, the aerogels showed a robust ballistic energy absorption performance (e.g., at the impact velocity of 1283 m s-1 at least 18% of the impact energy was absorbed). At low impact velocities, the aerogels failed by ductile hole enlargement followed by a tensile failure. By contrast, at high impact velocities, the aerogels failed through an adiabatic shearing process. Given the substantially robust ballistic performance, the polyimide aerogels have a potential to combat multiple constraints such as cost, weight, and volume restrictions in aeronautical and aerospace applications with high blast resistance and ballistic performance requirements such as in stuffed Whipple shields for orbital debris containment application.

Ultra-Small and Metabolizable Near-Infrared Au/Gd Nanoclusters for Targeted FL/MRI Imaging and Cancer Theranostics.

Tumor accurate imaging can effectively guide tumor resection and accurate follow-up targeted therapy. The development of imaging-stable, safe, and metabolizable contrast agents is key to accurate tumor imaging. Herein, ultra-small and metabolizable dual-mode imaging probe Au/Gd@FA NCs is rationally engineered by a simple hydrothermal method to achieve accurate FL/MRI imaging of tumors. The probes exhibit ultra-small size (2.5-3.0 nm), near-infrared fluorescence (690 nm), high quantum yield (4.4%), and a better T1 nuclear magnetic signal compared to commercial MRI contrast agents. By modifying the folic acid (FA) molecules, the uptake and targeting of the probes are effectively improved, enabling specific fluorescence imaging of breast cancer. Au/Gd@FA NCs with good biosafety were found to be excreted in the feces after imaging without affecting the normal physiological metabolism of mice. Intracellular reactive oxygen species (ROS) increased significantly after incubation of Au/Gd@FA NCs with tumor cells under 660 nm laser irradiation, indicating that Au/Gd@FA NCs can promote intracellular ROS production and effectively induce cell apoptosis. Thus, metabolizable Au/Gd@FA NCs provide a potential candidate probe for multimodal imaging and tumor diagnosis in clinical basic research. Meanwhile, Au/Gd@FA NCs mediated excessive intracellular production of ROS that could help promote tumor cell death.