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The patch with a superlubricated surface shows great potential for the prevention of postoperative adhesion during soft tissue repair. However, the existing patches suffer from the destruction of topography during superlubrication coating and lack of pro-healing capability. Herein, we demonstrate a facile and versatile strategy to develop a Janus nanofibrous patch (J-NFP) with antiadhesion and reactive oxygen species (ROS) scavenging functions. Specifically, sequential electrospinning is performed with initiators and CeO2nanoparticles (CeNPs) embedded on the different sides, followed by subsurface-initiated atom transfer radical polymerization for grafting zwitterionic polymer brushes, introducing superlubricated skin on the surface of single nanofibers. The poly(sulfobetaine methacrylate) brush-grafted patch retains fibrous topography and shows a coefficient of friction of around 0.12, which is reduced by 77% compared with the pristine fibrous patch. Additionally, a significant reduction in protein, platelet, bacteria, and cell adhesion is observed. More importantly, the CeNPs-embedded patch enables ROS scavenging as well as inhibits pro-inflammatory cytokine secretion and promotes anti-inflammatory cytokine levels. Furthermore, the J-NFP can inhibit tissue adhesion and promote repair of both rat skin wounds and intrauterine injuries. The present strategy for developing the Janus patch exhibits enormous prospects for facilitating soft tissue repair.

Self-expanding cryogels hold unique prospects for treating uncontrollable hemorrhages. However, development of a mechanically robust, tissue-adhesive, and bioactive self-expanding cryogel enabling effective hemostasis and tissue repair has remained a great challenge. Herein, we report a superelastic cellular-structured bioactive glass nanofibrous cryogel (BGNC) composed of highly flexible BG nanofibers and citric acid-cross-linked poly(vinyl alcohol). These BGNCs exhibit high absorption capacity (3169%), fast self-expanding ability, near zero Poisson’s ratio, injectability, high compressive recovery at a strain of 80%, robust fatigue resistance (almost no plastic deformation after 800 cycles at a strain of 60%), and good adhesion with diverse tissues. The BGNCs provide sustained release of Ca, Si, and P ions. Moreover, the BGNCs present better blood clotting and blood cell adhesion ability and superior hemostatic capacity in rabbit liver and femoral artery hemorrhage models as compared with commercial gelatin hemostatic sponges. In addition, BGNCs are able to stop bleeding in rat cardiac puncture injury in about 1 min. Furthermore, the BGNCs are capable of promoting rat full-thickness skin wound healing. The development of self-expanding BGNCs with superelasticity and bioadhesion provides a promising strategy for exploring multifunctional hemostatic and wound repair materials.

In this article, a calculation method for critical current of high-temperature superconducting (HTS) excitation coils made by Bi-2223 tapes is proposed, which can provide a reference for the design and manufacture of HTS excitation coils. Based on the test data provided by the manufacturer, the suppression effect of the applied parallel and perpendicular magnetic field on the critical current density is separately described by the proposed Jc-B model of the Bi-2223 tapes. Then, taking a 10 kW double-stator HTS machine (DS-HTSM) as an example, the target excitation magnetomotive force (MMF) required to meet its designed electromagnetic properties is obtained. After then, the turns number and the critical current of the HTS excitation coils required to meet the target MMF are calculated by the finite element analysis (FEA). Further, the FEA model of the DS-HTSM will be built by using the homogenization modeling method of HTS coils, and the influence of the armature reaction magnetic field on the critical current of the HTS excitation coils is analyzed. Finally, the correctness and effectiveness of the proposed improved critical current calculation method is verified by fabricating the 10 kW DS-HTSM prototype and conducting experimental test.

Precise recognition and delineation of tumors play a pivotal role in the radiotherapy of non-small cell lung cancer (NSCLC). The current manual delineation techniques used in clinical practice are both time-consuming and labor-intensive. While some studies have made progress in automating the segmentation of lung cancer targets, there is still room for further improvement in their effectiveness. The present study introduces a unique two-stage deep learning (DL) method. Computed tomography (CT) images of two hundred NSCLC cases are collected to train the network, forty cases' images are collected for validation and sixty cases’ images are collected for testing. A coarse network of segmentation is included in the first step, with a primary objective to detect the harsh region of the lesion. In this network, a competing manner between lesions and normal tissues is established based on the distribution of tissue structure, which will improve the ratio of the distinguished lesion overlapping the ground truth (GT) and decrease misidentification. The second step involves the utilization of two distinct segmentation networks for the two categories of images containing large-sized and small-sized tumors. These two networks are trained separately using the respective datasets, ultimately leading to the successful accomplishment of precise tumor delineation. Finally, the suggested approach produced a significantly higher dice similarity coefficient (DSC) of 0.80 ± 0.13 than other methods, achieving the highest TPR, the lowest FPR and HD95. In addition, our two-step method has greatly improved in both CT images with large and small GTVs. Among them, the performance is significantly advanced on CT images with small GTVs, which have poor performance in other methods. The study demonstrates that the suggested approach can precisely segment NSCLC tumors and have the potential to improve the radiotherapy efficiency.

Oncogene activation and epigenome dysregulation drive tumor initiation and progression, contributing to tumor immune evasion and compromising the clinical response to immunotherapy. Epigenetic immunotherapy represents a promising paradigm in conquering cancer immunosuppression, whereas few relevant drug combination and delivery strategies emerge in the clinic. This study presents a well-designed triune nanomodulator, termed ROCA, which demonstrates robust capabilities in tumor epigenetic modulation and immune microenvironment reprogramming for cancer epigenetic immunotherapy. The nanomodulator is engineered from a nanoscale framework with epigenetic modulation and cascaded catalytic activity, which self-assembles into a nanoaggregate with tumor targeting polypeptide decoration that enables loading of the immunogenic cell death (ICD)-inducing agent. The nanomodulator releases active factors specifically triggered in the tumor microenvironment, represses oncogene expression, and initiates the type 1 T helper (TH1) cell chemokine axis by reversing DNA hypermethylation. This process, together with ICD induction, fundamentally reprograms the tumor microenvironment and significantly enhances the rejuvenation of exhausted cytotoxic T lymphocytes (CTLs, CD8+T cells), which synergizes with the anti-PD-L1 immune checkpoint blockade and results in a boosted antitumor immune response. Furthermore, this strategy establishes long-term immune memory and effectively prevents orthotopic colon cancer relapse. Therefore, the nanomodulator holds promise as a standalone epigenetic immunotherapy agent or as part of a combination therapy with immune checkpoint inhibitors in preclinical cancer models, broadening the array of combinatorial strategies in cancer immunotherapy.

The light reflected into the back focal plane of a microscope objective allows one to gather a great deal of information about the resonant modes excited on a sample. These dips represent modes excited on the sample, which are related to both the material properties and the structure. Automatic identification of these resonances is a vital stage in developing automated machine-learning techniques for high-throughput sample characterization. In previous work, identification of a single isolated mode was demonstrated; here we show how multiple modes can be separately identified using an automated centering procedure in a process we call radial thresholding. Once the center was determined, the radial thresholding process was modified and combined with interpolation to locate the precise modal positions. We show that this method is capable of resolving very closely spaced modes and is sensitive to nanometric changes in sample dimensions. The processing time for the method is sufficiently fast to ensure that it is suited for rapid sample identification.

At present, synthetic dyes have been widely used in several industries such as textiles, rubber, paper, plastic, and leather tanning. The dyes in effluents are among the most aggressive pollutions of all the industrial sectors and can lead to severe water contamination as well as resource waste without proper treatment and recovery. In the present work, magnetic poly(aspartic acid)-poly(acrylic acid) hydrogels (PAsp-PAA/Fe3O4) with semi-interpenetrating networks are successfully prepared. Their physicochemical properties are systematically characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, the Brunauer–Emmett–Teller method, thermogravimetric analysis, and vibrating sample magnetometry. Organic dye methylene blue (MB) and neutral red (NR) adsorption onto the PAsp-PAA/Fe3O4hydrogel are studied for the first time, and the maximum adsorption capacities of MB and NR are calculated by using the Langmuir model as 357.14 and 370.37 mg·g–1, respectively. The adsorption kinetics can be well described by a pseudo-second-order kinetic model. Furthermore, reusability tests indicate that the hydrogel has good reproducibility. In conclusion, the results demonstrate that the PAsp-PAA/Fe3O4hydrogel can used as an excellent adsorbent material for removing dye pollutants from wastewater.

Part II of this paper is focused on the dynamic response of a steel plate of the centrifuge model which is fixed on the container. This plate is 5 cm thick, allowing for non-destructive elastic response to all the 19 blasts. Three accelerators and 10 sets of strain gauges are installed on the plate to monitor the acceleration and deformation histories induced by shock waves and bubble oscillations. This article presents the characteristic values measured for shock waves and bubble oscillations response. The detailed information can be found in a database available on the web. In order to obtain the correct acceleration time history, the cut-off frequency of low-pass filtering by Fourier transformation was applied. The results indicated that the acceleration and strain induced by shock wave was nearly unaffected by the increasing gravity, while the effect of gravity has remarkable influence on those induced by bubble oscillation. A preliminary study of comparing the measured dynamic response with numerical simulations has been carried out using ANSYS LS-DYNA. The results captured the dynamic response in terms of characteristics and trends, but deviated in magnitude due the theoretical and technological limitations involved in both the physical and numerical models. It certainly deserves further studies.

Centrifuge is a promising tool for underwater explosion (UNDEX) research as both Mach and Froude similitudes could be satisfied with hyper-gravity in models, which would result in similarities in both shock wave and bubble oscillation. Scaling laws for UNDEX in centrifuge have been proposed based on dimensional analysis. Two dimensionless numbers, i.e., π3and π4, are used to characterize shock wave and bubble oscillation, respectively. To validate scaling laws, 17 UNDEX tests are designed by varying accelerations or explosive weights and positions in centrifuge. The tests are classified into different groups to validate scaling laws as well as calibrate coefficients in empirical formulae for both shock wave and bubble oscillation. The results show that changes of gravity acceleration or hydrodynamic pressure almost has no influence on shock wave peak pressure and time constant as long as π3is constant. The dimensionless bubble period and maximum radius agreed with each other when π4is constant. Based on the research, an example is exhibited to suggest method for the computation of initial loading conditions for a submerged obstacle subjected to UNDEX.

In this report we present our study on the assembly process of both the 8 inches substrate assembling as well as the single LCOS module. After the process parameter optimization, 6 um±0.3 um thickness variation were obtained for the 8 inches substrate which is suitable for the WSS based application. Assembly process of single LCOS with 5.8±0.1 um thickness was finally obtained with the optimized process.

In this article, we propose an switchable anti‐spy display device, which has two modes. And we creatively propose to use a brightness enhancement film with polarization function to improve the brightness of the vertical view. In the anti‐spy mode, the display content cannot be clearly recognized by human eyes on the left and right side, but it can be recognized in other directions. In the sharing mode, the display contents can be seen clearly from all perspectives. The anti‐spy display device includes an anti‐spy module and a display module, which is used to display the information, and the anti‐ spy module is used for switching between the sharing mode and the anti‐spy mode. This anti‐spy display device can protect personal privacy and share information with friends or colleagues.

Three novel fluorescent compounds were synthesized and employed as fluorescent probes for detecting different metal ions selectively. The naphthol-based probe (I) exhibited weak fluorescence in water, which was enhanced in the presence of Al3+ions due to the complexation between Iand Al3+. In addition, the coumarin-based probe (II) and quinolone-based probe (III) could selectively detect Cu2+ions and Zn2+ions, respectively. Unlike the changes in the fluorescence spectra of Iand IIIupon Al3+ion and Zn2+ion addition, the fluorescence intensity of IIdecreased dramatically after Cu2+ions were added into the aqueous solution. This is due to the fact that different mechanisms occurred in the recognition process for metal ions. Colorimetric monitoring of these processes was also feasible.

Combination therapy is extensively developed for cancer treatment in recent years due to its high efficiency. Herein, we constructed a nanocomposite based on gold nanorods (GNRs) and drug-loaded tetrahedral DNA nanostructures (TDN) for chemo-photothermal combinational therapy. Anti-tumor drug doxorubicin (DOX) was loaded via the insertion within GC base pairs of TDN. The aptamer AS1411 was attached to the apex of TDN (ATDN) to target tumor cells. The DOX-loaded DNA tetrahedron (ATDN-DOX) was compressed by the GNRs coated with PEI (GNRs@ATDN-DOX) to realize the photothermal function and lysosome escape. GNRs under the illumination of 808 nm infrared laser showed high photothermal conversion and stability due to the protection of PEI layer. The drug-loading capacity of ATDN-DOX was as high as 314 DOX molecules in per ATDN. The positive charge of PEI in GNRs@ATDN-DOX nanocomposites was utilized to achieve excellent cell penetration and induce proton sponge effect for lysosomal escape. The nanocomposites presented HeLa and 4T1 cells targeting and resulted in efficient anticancer activity.

A novel, sensitive, and highly selective fluorescent probe which contains 2-(2-hydroxyphenyl)benzimidazole receptor groups has been prepared for detecting glutathione (GSH) that operates under aqueous conditions. The complex of the fluorescent compound with copper(II) ion was selectively responsive to GSH and induced a recovery of blue fluorescence.

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.