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Silicon, which has been widely studied by virtue of its extremely high theoretical capacity and abundance, is recognized as one of the most promising anode materials for the next generation of lithium-ion batteries. However, silicon undergoes tremendous volume change during cycling, which leads to the destruction of the electrode structure and irreversible capacity loss, so the promotion of silicon materials in commercial applications is greatly hampered. In recent years, many strategies have been proposed to address these shortcomings of silicon. This Review focused on different coatings materials (e. g., carbon-based materials, metals, oxides, conducting polymers, etc.) for silicon materials. The role of different types of materials in the modification of silicon-based material encapsulation structure was reviewed to confirm the feasibility of the protective layer strategy. Finally, the future research direction of the silicon-based material coating structure design for the next-generation lithium-ion battery was summarized.

In recent years, deep learning has been widely used in breast cancer diagnosis, and many high-performance models have emerged. However, most of the existing deep learning models are mainly based on static breast ultrasound (US) images. In actual diagnostic process, contrast-enhanced ultrasound (CEUS) is a commonly used technique by radiologists. Compared with static breast US images, CEUS videos can provide more detailed blood supply information of tumors, and therefore can help radiologists make a more accurate diagnosis. In this paper, we propose a novel diagnosis model based on CEUS videos. The backbone of the model is a 3D convolutional neural network. More specifically, we notice that radiologists generally follow two specific patterns when browsing CEUS videos. One pattern is that they focus on specific time slots, and the other is that they pay attention to the differences between the CEUS frames and the corresponding US images. To incorporate these two patterns into our deep learning model, we design a domain-knowledge-guided temporal attention module and a channel attention module. We validate our model on our Breast-CEUS dataset composed of 221 cases. The result shows that our model can achieve a sensitivity of 97.2% and an accuracy of 86.3%. In particular, the incorporation of domain knowledge leads to a 3.5% improvement in sensitivity and a 6.0% improvement in specificity. Finally, we also prove the validity of two domain knowledge modules in the 3D convolutional neural network (C3D) and the 3D ResNet (R3D).

Canals are constructed worldwidely to divert water from rich to arid areas to mitigate water shortages. Since water resource is fairly limited, it is essential to perform canal control efficiently to improve water-delivery performance. A promising solution is to leverage model predictive control (MPC), which calculates the desired canal action at each time step via reliable predictions of the model. However, the predictive model dependence degrades the practicability and the iterative calculation incurs intensive computations, especially for large-scale canals with high-dimensional state and action spaces (curse of dimensionality). This article presents a new canal control model named efficient model-free canal control (EMCC) that obtains control policies in a model-free way via deep reinforcement learning (DRL) and alleviates the curse of dimensionality via domain knowledge (control experience). EMCC adopts the hidden Markov model with Gaussian mixture densities (GMM-HMM) to model canal system dynamics with control experience, and initializes it according to the actual operation data. Besides, we design a reward generator collaborated with GMM-HMM to supervise the reinforcement learning around control experiences to obtain more efficient control policies. We evaluate EMCC via numerical simulations on Chinese largest water-delivery project (SNWTP). Experimental results show that EMCC leads to significant convergence performances compared with crude applications of DRL on large-scale canals, and achieves desired objectives more satisfactorily than MPC and control-experience under two typical water-delivery tasks.

Radiation-induced pulmonary fibrosis (RIPF) is a general and fatal side effect of radiotherapy, while the pathogenesis has not been entirely understood yet. By now, there is still no effective clinical intervention available for treatment of RIPF. Recent studies revealed mesenchymal stromal cells (MSCs) as a promising therapy treatment due to their homing and differentiation ability, paracrine effects, immunomodulatory effects, and MSCs-derived exosomes. Nevertheless, problems and challenges in applying MSCs still need to be taken seriously. Herein, we reviewed the mechanisms and challenges in the applications of MSCs in treating RIPF.

This paper mainly studies the colorization of near-infrared (NIR) images. Image colorization methods cannot be extended to NIR image colorization since the wavelength band of the NIR image exceeds the visible light spectral range and it is often linearly independent of the luminance of the RGB image. Furthermore, a symmetric codec, which cannot guarantee the ability of the encoder to extract features, is often used as the main frame of the network in both CNN-based colorization networks and CycleGAN-based colorization networks. In order to deal with the investigated problem, we propose a novel NIR colorization method using asymmetric codec (ACD) and pixel-level fusion. ACD is designed to improve the feature extraction ability of the encoder by allowing the information to enter deeper into the model and learning more non-redundant information. In addition, the global and local feature fusion networks (GLFFNet) are embedded between the encoder and the decoder to improve the prediction of the subtle color information of the image. The ACD and GLFFNet together constitute the colorization network (ColorNet) in this paper. Bilateral filtering and weighted least squares filtering (BFWLS) are used to fuse the pixel-level information of the input NIR image into the raw output image of the ColorNet. Finally, an intensive comparison analysis based on common datasets is conducted to verify superiority over existing methods in qualitative and quantitative visual assessments.

Conditionally reprogrammed cell (CRC) technique is a promising model for biomedical and toxicological research. In the present study, our data first demonstrated an increased level of PARP-1 in conditionally reprogrammed human foreskin keratinocytes (CR-HFKs). We then found that PARP inhibitor ABT-888 (ABT), reactive oxygen species (ROS) scavenger N-acetyl-l-cysteine (NAC), or combination (ABT + NAC) were able to inhibit cell proliferation, ROS, PARP-1, and ROS related protein, NRF2, and NOX1. Interestingly, knockdown of endogenous PARP-1 significantly inhibited cell proliferation, indicating that the increased PARP-1 expression was critical for CR. Importantly, we found that a moderate level of ROS contributed the cell proliferation and increased PARP-1 since knockdown of PARP-1 also inhibited the ROS. The similar inhibition of cell proliferation, ROS, and expression of PARP-1 and NRF2 proteins was observed when CR-HFKs were treated with hydroquinone (HQ), a key component from skin-lightening products. Moreover, the treatment of HQ plus treatment of ABT, NAC, or combination can further inhibit cell proliferation, ROS, expression of PARP-1, and NRF2 proteins. PARP-1 knockdown inhibited the population doubling (PDL) and treatment of HQ inhibited the PDL further, as well as the change of ROS. Finally, we discovered that pathways including cyclin D1, NRF2, Rb and pRb, CHK2, and p53, were involved in cell proliferation inhibition with HQ. Taken together, our findings demonstrated that crosstalk between ROS and PARP-1 involves in the cell proliferation in CR-HFKs, and that inhibition of CR-HFK proliferation with HQ is through modulating G1 cell cycle arrest.

Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. The existing object detection models do not work well underwater. This paper improves the structure of EfficientDet detector and proposes the EfficientDet-Revised (EDR), which is a new marine organism object detection model. Specifically, the MBConvBlock is reconstructed by adding the Channel Shuffle module to enable the exchange of information between the channels of the feature layer. The fully connected layer of the attention module is removed and convolution is used to cut down the amount of network parameters. The Enhanced Feature Extraction module is constructed for multi-scale feature fusion to enhance the feature extraction ability of the network to different objects. The results of experiments demonstrate that the mean average precision (mAP) of the proposed method reaches 91.67% and 92.81% on the URPC dataset and the Kaggle dataset, respectively, which is better than other object detection models. At the same time, the processing speed reaches 37.5 frame per second (FPS) on the URPC dataset, which can meet the real-time requirements. It can provide a useful reference for underwater robots to perform tasks such as intelligent grasping.

In biomedical imaging, photoacoustic computed tomography (PACT) has recently gained increased interest as this imaging technique has good optical contrast and depth of acoustic penetration. However, a spinning blur will be introduced during the image reconstruction process due to the limited size of the ultrasonic transducers (UT) and a discontinuous measurement process. In this study, a damping UT and adaptive back-projection co-optimization (CODA) method is developed to improve the lateral spatial resolution of PACT. In our PACT system, a damping aperture UT controls the size of the receiving area, which suppresses image blur at the signal acquisition stage. Then, an innovative adaptive back-projection algorithm is developed, which corrects the undesirable artifacts. The proposed method was evaluated using agar phantom and ex-vivo experiments. The results show that the CODA method can effectively compensate for the spinning blur and eliminate unwanted artifacts in PACT. The proposed method can significantly improve the lateral spatial resolution and image quality of reconstructed images, making it more appealing for wider clinical applications of PACT as a novel, cost-effective modality.

The effect of defect distribution and pitch length of carbon nanosprings fabricated by different construction methods on their tensile and compressive mechanical properties is comprehensively studied. Twelve examples of nanosprings with various defect distribution are constructed according to three widely used methods based on two parent toroidal carbon nanotubes (TCNTs). Their mechanical performances are studied by using molecular dynamic (MD) simulations through tensile and compressive tests. The results reveal that the spring constant, ductility, strength, gravimetric energy density and deformation process obviously depend on the defect distribution, indicating that the mechanical properties of nanosprings can be enhanced greatly by skillfully introducing defect distribution for specific purposes. Remarkably, the tensile stiffness (1.89–68.13 nN/nm) and ductility (84%–794.8%) can be adjusted by changing the defect positions and geometries of nanosprings. The force-strain curves of nanosprings under tensile loads show sawtooth-like patterns and the oscillations of force-strain curves can be controlled by the defect distribution to produce the high energy absorbing capacity. Furthermore, all nanosprings show excellent reversibility under compressive loads, and energy storage densities ranging from 53.31 to 859.67 J/g are achieved by adjusting the defect distribution and pitch length. The findings are meaningful for scientists to design carbon nanosprings with high performance.

The simultaneous control of crystal phase stability and one-dimensional growth has always been the major challenge for the preparation of tetragonal zirconia whiskers. In this study, we report a facile molten salt processing that utilizes CaF2 as a stabilizer and adsorbent to meet the phase and morphology requirements. The processes of stabilization, phase transition, ion adsorption and formation of special morphology involved in zirconia growth were investigated by XRD, IR, SEM, DTA-TG and TEM. By controlling the amount of CaF2 (15 wt.%) added in the reaction precursor, tetragonal zirconia whiskers with a desired aspect ratio (>16) can be synthesized, which show single crystalline nature and [001] preferred direction. Ca ions doped at low temperature inhibit the occurrence of phase transition, which allows the tetragonal structure to be retained to room temperature. The inducement and pattern of whisker growth are supposed to be F-adsorption and screw dislocation elongation, respectively. The specific selectivity of surface adsorbed ions to certain zirconia crystal planes reduces or even inhibits the epitaxial growth rate in this direction, offering a viable avenue for the morphology modulation of inorganic nanomaterials.

An assemblage of eubrontid-type theropod tracks, representing terrestrial progression and turtle track swim tracks representing subaqueous progression characterises a newly reported site in the Mid...

Large-scale canals with cascaded pools are constructed wordwide to divert water from rich to arid areas to mitigate water shortages. Efficient control of canals is essential to improve water-diversion performance. Numerous model-based approaches have been proposed and made great progress for canal control. However, when the predictive model is unavailable or unpromising for long time step predictions, model-free approaches could be considered as a possible way to achieve efficient control. Since most existing model-free approaches are focused on control of small canals or reservoirs, this article proposes a new control approach named policy and action reinforcement learning (PARL) for large-scale canals. We leverage the idea of “divide and conquer” to decompose the control task of large-scale canals into policy learning and action learning subtasks, and develop PARL by means of hierarchical reinforcement learning. Extensive experiments are conducted via numerical simulation on the case study of Chinese South to North Water Transfer Project, and experimental results show that PARL can achieve desirable performance improvements over other model-free learning approaches.

Nasopharyngeal carcinoma (NPC) is characterised by distinct geographical distribution and is particularly prevalent in Asian countries. But the mechanisms related to the progression of nasopharyngeal carcinoma (NPC) are not completely understood. MiR-124-3p functions as a tumor suppressor in many kinds of human cancers. Here, we explored the effects and mechanism of miR-124-3p on the proliferation and colony formation in NPC. In our study, we reported that miR-124-3p was significantly downregulated in NPC tissues and cell lines. Overexpression miR-124-3p decreased NPC cell proliferation and colony formation abilities. Meanwhile, knockdown miR-124-3p increased proliferation and colony formation abilities. Additionally, dual-luciferase assay showed that miR-124-3p could positively regulated PCDH8 by targeting its 3'-UTR. Overexpression of PCDH8 could partially rescue the proliferation and colony formation role of miR-124-3p inhibitor. Our study indicated that miR-124-3p played a tumor suppressor by directly interacting with PCDH8 and inhibiting the activation of the phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling pathway. Overall, we found that miR-124-3p inhibited the activation of the PI3K/AKT/mTOR signaling pathway in NPC by interacting with PCDH8. Thus, PCDH8 may be a potential molecular target that impeded NPC proliferation and colony formation.

Background CDK4/6 inhibitors combined with endocrine therapy has become the preferred treatment approach for patients with estrogen receptor-positive metastatic breast cancer. However, the predictive biomarkers and mechanisms of innate resistance to CDK4/6 inhibitors remain largely unknown. We sought to elucidate the molecular hallmarks and therapeutically actionable features of patients with resistance to CDK4/6 inhibitors. Methods A total of 36 patients received palbociclib and endocrine therapy were included in this study as the discovery cohort. Next-generation sequencing of circulating tumour DNA in these patients was performed to evaluate somatic alterations associated with innate resistance to palbociclib. Then the candidate biomarker was validated in another independent cohort of 104 patients and publicly available datasets. The resistance was verified in parental MCF-7 and T47D cells, as well as their derivatives with small interfering RNA transfection and lentivirus infection. The relevant mechanism was examined by RNA sequencing, chromatin immunoprecipitation and luciferase assay. Patient-derived organoid and patient-derived xenografts studies were utilized to evaluated the antitumor activity of rational combinations. Results In the discovery cohort, S6K1 amplification (3/35, 9%) was identified as an important reason for innate resistance to CDK4/6 inhibitors. In the independent cohort, S6K1 was overexpressed in 15/104 (14%) patients. In those who had received palbociclib treatment, patients with high-expressed S6K1 had significantly worse progression free survival than those with low S6K1 expression (hazard ratio = 3.0, P = 0.0072). Meta-analysis of public data revealed that patients with S6K1 amplification accounted for 12% of breast cancers. Breast cancer patients with high S6K1 expression had significantly worse relapse-free survival (hazard ratio = 1.31, P Conclusions S6K1 amplification is an important mechanism of innate resistance to palbociclib in breast cancers. Breast cancers with S6K1 amplification could be considered for combinations of CDK4/6 and S6K1 antagonists.

For the linear Lagrange interpolation over a triangular domain, we propose an efficient algorithm to rigorously evaluate the interpolation error constant under the maximum norm by using the finite-element method (FEM). In solving the optimization problem corresponding to the interpolation error constant, the maximum norm in the constraint condition is the most difficult part to process. To handle this difficulty, a novel method is proposed by combining the orthogonality of the space decomposition using the Fujino–Morley FEM space and the convex-hull property of the Bernstein representation of functions in the FEM space. Numerical results for the lower and upper bounds of the interpolation error constant for triangles of various types are presented to verify the efficiency of the proposed method.

A novel AlGaInAs/InP electro-absorption modulated laser (EML) with a simple fabrication process is proposed, in which the electro-absorption modulator (EAM) has a 10 nm blueshift induced by quantum well intermixing (QWI) and is monolithically integrated with a sidewall grating distributed-feedback (DFB) laser working at 1.55 μm wavelength. The extent of the QWI process is characterized by a diffusion length. The quantum confined Stark effect (QCSE) is simulated in terms of extinction ratio (ER) and chirp for bias electric fields from 0 kV/cm to 200 kV/cm and for different amounts of intermixing. The results indicate that for a 150 µm-long EAM with a 10 nm blueshift induced by QWI, an ER of 40 dB is obtained at 2.5 V reverse bias with no penalty in chirp compared to an as-grown quantum well (QW) and the insertion loss at 0 V bias is 0.11 dB for 1.55 µm operation wavelength. The simulated –3 dB bandwidth of the electrical to optical power response is 22 GHz.