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Nanodrug delivery systems (NDDS) have been proposed to improve the targeting and bioavailability of chemotherapy drugs. The approach of drug loading via physical adsorption is facile to operate; however, there exists a risk of premature leakage. Coupling the drug molecules with the carrier through chemical reactions can guarantee the stability of the drug delivery process, yet the preparation procedure is relatively intricate. In this research, a kind of Prussian blue nanocage (PB Cage) was fabricated, and the phase change material, 1-pentadecanol, was used as the gating material to solidify 5-fluorouracil (5-FU) inside the nanocage. Upon irradiation with near-infrared (NIR) light, the temperature of the PB Cage can rise rapidly. When the temperature exceeds 43 °C, 1-pentadecanol undergoes a solid-liquid phase transition and subsequently releases 5-FU to inhibit DNA synthesis. Meanwhile, the photothermal therapy (PTT) mediated by the PB Cage is also capable of ablating tumor cells. The NDDS constructed based on PB has achieved the precise release of 5-FU triggered by NIR light, which may avoid side effects on normal tissues. Moreover, the combination of chemotherapy and photothermal therapy can efficaciously suppress the proliferation of tumor cells.
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Background: The Da Vinci Surgical System (DVSS) has the advantages of minimal invasion, rapid recovery, safety, and reliability. Although the DVSS has been widely used in various abdominal surgeries, descriptions of its use in robot-assisted retroperitoneal tumor resection (RRTR) are limited to case reports; large-sample systematic studies are lacking. The present study was performed to analyze the data of RRTR in our center, summarize our experience, and provide a reference for other retroperitoneal tumor centers. Methods: We retrospectively analyzed the clinical data of 105 patients who underwent RRTR at the Affiliated Hospital of Qingdao University from January 2015 to December 2022. Logistic univariate and multivariate analyses were performed to identify independent risk factors affecting RRTR. A receiver operating characteristic curve was used to find the cut-off value, which was then included in the logistic multivariate analysis for verification. Results: Among the 105 patients, 87 successfully underwent RRTR (DVSS group) and 18 underwent conversion to open surgery (conversion group). There was no significant difference in sex, age, body mass index, history of abdominal surgery, or tumor location between the two groups (P > 0.05). The maximum tumor diameter [odds ratio (OR), 1.041; 95% confidence interval (CI), 1.015-1.067; P = 0.002] and pathological property (OR, 8.646; 95% CI, 2.370-31.544; P = 0.001) were independent risk factors for conversion to open surgery. Further analysis confirmed that the success rate of RRTR was higher for tumors with a maximum diameter of ≤64 mm and benign tumors. Based on our experience and statistical results, we believe that retroperitoneal tumors that meet the following criteria have a higher success rate of DVSS resection: maximum tumor diameter of ≤64 mm, benign tumors, the tumor has relatively clear boundary, no obvious invasion of surrounding tissues and organs, and no need for combined organ resection. Conclusions: RRTR is safe and effective in the treatment of RPT, and the clinical prognosis is similar to that of open surgery. The success rate of RRTR in patients with appropriate surgical indications for this procedure is higher.
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DNA N6-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. However, experimental methods for detecting 6mA sites are high-priced and time-consuming. In this study, we propose a novel computational method, called Ense-i6mA, to predict 6mA sites. Firstly, five encoding schemes, i.e., one-hot encoding, gcContent, Z-Curve, K-mer nucleotide frequency, and K-mer nucleotide frequency with gap, are employed to extract DNA sequence features. Secondly, to our knowledge, it is the first time that eXtreme gradient boosting coupled with recursive feature elimination is applied to 6mA sites prediction domain to remove noisy features for avoiding over-fitting, reducing computing time and complexity. Then, the best subset of features is fed into base-classifiers composed of Extra Trees, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and Support Vector Machine. Finally, to minimize generalization errors, the prediction probabilities of the base-classifiers are aggregated by averaging for inferring the final 6mA sites results. We conduct experiments on two species, i.e., Arabidopsis thaliana and Drosophila melanogaster, to compare the performance of Ense-i6mA against the recent 6mA sites prediction methods. The experimental results demonstrate that the proposed Ense-i6mA achieves area under the receiver operating characteristic curve values of 0.967 and 0.968, accuracies of 91.4% and 92.0%, and Mathew's correlation coefficient values of 0.829 and 0.842 on two benchmark datasets, respectively, and outperforms several existing state-of-the-art methods.
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Recently, acoustic communication employing orbital angular momentum (OAM) opens another avenue for efficient data transmission in aquatic environments. Current topological charge (TC) detection of OAM beams relies on the orthogonality among different-order OAM beams. However, such strategy requires measurements of the complete azimuthal acoustic pressure, which inevitably reduces the efficiency and increases the bit error rate (BER). To address these challenges, this study proposes a modified dynamic modal decomposition (DMD) method by partially sampling the acoustic field for precise TC detection. Numerical simulations confirm the accuracy of this approach in extracting single or multiple TCs magnitudes within a partially sampled acoustic field. We theoretically compare the performance of the modified DMD approach with conventional orthogonal decoding method. Simulation results indicate that our modified DMD scheme exhibits lower BER under the same noise interference and is more robust to the array misalignment. This research introduces an efficient demodulation solution for acoustic OAM communication, offering potential benefits for simplifying receiver array design and enhancing long-distance underwater data transmission.
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Dynamically manipulating the spectra and polarization properties of thermal radiation is the key to counter an infrared polarization imaging system (IPIS) under the different background environments. In this Letter, we propose a phase-change metasurface thermal emitter (PCMTE) composed of vanadium dioxide (VO2) dipole antenna arrays to dynamically manipulate polarized radiation spectra in the long-wave infrared (LWIR) region of 8-14â µm. During the thermally induced and reversible insulator-to-metal transition (IMT) in VO2, by simulating the LWIR images at different polarization angles for the PCMTE and background plates, the PCMTE can realize dynamically tunable LWIR camouflage; then, their degree of linear polarization (DoLP) can be calculated, which can demonstrate that the PCMTE can also achieve dynamically tunable LWIR polarization camouflage at the specific radiation angles and backgrounds. Our proposed PCMTE provides an effective scheme for adaptive IR polarization camouflage.
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High-level detection of weak targets under bright light has always been an important yet challenging task. In this paper, a method of effectively fusing intensity and polarization information has been proposed to tackle this issue. Specifically, an attention-guided dual-discriminator generative adversarial network (GAN) has been designed for image fusion of these two sources, in which the fusion results can maintain rich background information in intensity images while significantly completing target information from polarization images. The framework consists of a generator and two discriminators, which retain the texture and salient information as much as possible from the source images. Furthermore, attention mechanism is introduced to focus on contextual semantic information and enhance long-term dependency. For preserving salient information, a suitable loss function has been introduced to constrain the pixel-level distribution between the result and the original image. Moreover, the real scene dataset of weak targets under bright light has been built and the effects of fusion between polarization and intensity information on different weak targets have been investigated and discussed. The results demonstrate that the proposed method outperforms other methods both in subjective evaluations and objective indexes, which prove the effectiveness of achieving accurate detection of weak targets in bright light background.
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Vortex beams carrying orbital angular momentum (OAM) provide a new degree of freedom for light waves in addition to the traditional degrees of freedom, such as intensity, phase, frequency, time, and polarization. Due to the theoretically unlimited orthogonal states, the physical dimension of OAM is capable of addressing the problem of low information capacity. With the advancement of the OAM optical communication technology, OAM router devices (OAM-RDs) have played a key role in significantly improving the flexibility and practicability of communication systems. In this review, major breakthroughs in the OAM-RDs are summarized, and the latest technological standing is examined. Additionally, a detailed account of the recent works published on techniques related to the OAM-RDs has been categorized into five areas: channel multicasting, channel switching, channel filtering, channel hopping, and channel adding/extracting. Meanwhile, the principles, research methods, advantages, and disadvantages are discussed and summarized in depth while analyzing the future development trends and prospects of the OAM-RDs.
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Deep learning has broad applications in imaging through scattering media. Polarization, as a distinctive characteristic of light, exhibits superior stability compared to light intensity within scattering media. Consequently, the de-scattering network trained using polarization is expected to achieve enhanced performance and generalization. For getting optimal outcomes in diverse scattering conditions, it makes sense to train expert networks tailored for each corresponding condition. Nonetheless, it is often unfeasible to acquire the corresponding data for every possible condition. And, due to the uniqueness of polarization, different polarization information representation methods have different sensitivity to different environments. As another of the most direct approaches, a generalist network can be trained with a range of polarization data from various scattering situations, however, it requires a larger network to capture the diversity of the data and a larger training set to prevent overfitting. Here, in order to achieve flexible adaptation to diverse environmental conditions and facilitate the selection of optimal polarization characteristics, we introduce a dynamic learning framework. This framework dynamically adjusts the weights assigned to different polarization components, thus effectively accommodating a wide range of scattering conditions. The proposed architecture incorporates a Gating Network (GTN) that efficiently integrates multiple polarization features and dynamically determines the suitable polarization information for various scenarios. Experimental result demonstrates that the network exhibits robust generalization capabilities across continuous scattering conditions.
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This paper presents the design of a vision-based automated robotic microinjection system for batch injection of both zebrafish embryos and larvae. A novel visual recognition algorithm based on an automatic threshold and excessive dilatation is introduced to accurately identify the center of zebrafish embryos and larval yolks. A corresponding software system is developed using the producer-consumer model as the framework structure, and a friendly user interface is designed to allow operators to choose from a range of desired functions according to their different needs. In addition, a novel microstructural agarose device is designed and fabricated to simultaneously immobilize mixed batches of embryos and larvae. Moreover, a prototype microinjection system is fabricated by integrating hardware devices with visual algorithms. An experimental study is conducted to verify the performance of the robotic microinjection system. The results show that the reported system can accurately identify zebrafish embryos and larvae and efficiently complete batch microinjection tasks of the mixtures with an injection success rate of 92.05% in 13.88 s per sample. Compared with manual and existing microinjection systems, the proposed system demonstrates the merits of versatility, excellent efficiency, high success rate, high survival rate, and sufficient stability.
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The multi-focus metalens can couple the light into multiple channels in optical interconnections, which is beneficial to the development of planar, miniaturized, and integrated components. We propose broadband photonic spin Hall effect (PSHE) driven multi-focus metalenses, in which each nanobrick plays a positive role for all focal points. Three PSHE driven metalenses with four, six, and eight focal points have been designed and investigated, respectively. Under the incidences of left-/right-handed circularly polarized (LCP/RCP) light, these metalenses can generate regularly distributed two, three, and four RCP/LCP focal points, respectively. The uniformity of the focusing intensity has been investigated in detail by designing an additional four six-focus metalenses with different focus distributions. The uniqueness of these metalenses makes this design philosophy very attractive for applications in spin photonics, compact polarization detection, multi-imaging systems, and information processing systems.
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Polarization imaging, which provides multidimensional information beyond traditional intensity imaging, has prominent advantages for complex imaging tasks, particularly in scattering environments. By introducing deep learning (DL) into computational imaging and sensing, polarization scattering imaging (PSI) has obtained impressive progresses, however, it remains a challenging but long-standing puzzle due to the fact that scattering medium can result in significant degradation of the object information. Herein, we explore the relationship between multiple polarization feature learning strategy and the PSI performances, and propose a new multi-polarization driven multi-pipeline (MPDMP) framework to extract rich hierarchical representations from multiple independent polarization feature maps. Based on the MPDMP framework, we introduce a well-designed three-stage multi-pipeline networks (TSMPN) architecture to achieve the PSI, named TSMPN-PSI. The proposed TSMPN-PSI comprises three stages: pre-processing polarization image for de-speckling, multiple polarization feature learning, and target information reconstruction. Furthermore, we establish a real-world polarization scattering imaging system under active light illumination to acquire a dataset of real-life scenarios for training the model. Both qualitative and quantitative experimental results show that the proposed TSMPN-PSI achieves higher generalization performance than other methods on three testing data sets refer to imaging distances, target structures, and target materials and their background materials. We believe that our work presents a new framework for the PSI and paves the way to its pragmatic applications.
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Orbital angular momentum (OAM) has recently obtained tremendous research interest in free-space optical communications (FSO). During signal transmission within the free-space link, atmospheric turbulence (AT) poses a significant challenge as it diminishes the signal strength and introduce intermodal crosstalk, significantly reducing OAM mode detection accuracy. This issue directly impacts the performance of OAM-based communication systems and leads to a reduction in received information. To address this critical bottleneck of low mode recognition accuracy in OAM-based FSO-communications, a deep learning method based on vision transformers (ViT) is proposed for what we believe is for the first time. Designed carefully by numerous experts, the advanced self-attention mechanism of ViT captures more global information from the input image. To train the model, pretraining on a large dataset, named IMAGENET is conducted. Subsequently, we performed fine-tuning on our specific dataset, consisting of OAM beams that have undergone varying AT strengths. The computer simulation shows that based on ViT method, the multiple OAM modes can be recognized with a high accuracy (nearly 100%) under weak-to-moderate turbulence and with almost 98% accuracy even under long transmission distance with strong turbulence (C N2=1×10-14). Our findings highlight that leveraging ViT enables robust detection of complex OAM beams, mitigating the adverse effects caused by atmospheric turbulence.
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Metasurfaces have been extensively demonstrated in engineering and detection of polarization of light from the visible to terahertz regions. However, most of the previous metasurfaces for polarization detection are spatially divided into different parts, and each of the parts focuses on different polarization components, resulting in large metasurface size and hindering their integration development. In this paper, a compact all-dielectric metasurface is proposed and numerically demonstrated to achieve full polarization detection at the long-wavelength infrared region (LIR). First, we design the metasurface at a wavelength of 10 µm, which can converge incident beams to specific positions corresponding to different polarization states. In this design, the metasurface is based on an oblique alternant double-phase modulation method, which arranges meta-atoms with the ability to control as many as possible different polarizations in a limited region, ensuring the high efficiency of polarization detection while giving more freedom and flexibility to the metasurface. Second, the intensity distributions of the electric field of different polarization components are simulated at wavelengths of 9.4 µm and 10.5 µm, verifying the broadband performance of the proposed metasurface. The proposed method has potential applications in integrated multifunctional devices and multispectral polarization imaging.
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Visible-infrared compatible camouflage is important to increase the counter-detection ability of a target due to the fast development of detection systems. However, most of the previously reported visible-infrared compatible camouflage structures are not suitable when the temperature of targets and type of background environment change. In this paper, we propose a tunable infrared emitter composed of ZnS/Ge/Ag/Ge2Sb2Te5/Ag films and numerically demonstrate visible-infrared compatible camouflage and radiation heat dissipation. Firstly, the proposed infrared emitter can produce different structural colors as the thickness of the ZnS film changes, which can be applied to visible camouflage. Secondly, the crystallization fraction of the Ge2Sb2Te5 (GST) layer could help to engineer the average emissivity of the proposed infrared emitter, achieving tunable mid-infrared (MIR) camouflage, radiation heat dissipation, and long-infrared (LIR) camouflage in wavelength ranges of 3-5 µm, 5-8 µm, and 8-14 µm, respectively. Finally, we numerically demonstrate the visible camouflage and infrared camouflage for different application scenarios by using the simulated visible and infrared images. This work has promising application potential in visible-infrared compatible camouflage technology.
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In this study, a novel heteropolysaccharide (ASPA80-1) with an average molecular weight of 5.48 × 104 Da was isolated and structurally elucidated from custard apple pulp (Annona squamosa) through DEAE-cellulose, Sephadex G-100 and Sephacryl S-300 HR chromatography and spectral analysis. ASPA80-1 is a water-soluble polysaccharide and it is a polymer consisting of predominant amounts of (1 â 3)-linked-L-arabinose (Ara) residues, small amounts of (1 â 6)-linked-D-galactose (Gal), (1 â 3,5)-linked-L-arabinose (Ara) residues and terminal linked-L-arabinose (Ara) residues, trace amount of (1 â 4)-linked-D-glucose (Glc) residues and (1 â 2)-linked-L-rhamnose (Rham) residues. ASPA80-1 showed significant effect on antigen-presenting cells (APCs) activation. On the one hand, ASPA80-1 activated RAW264.7 macrophage cells by inducing morphology change, enhancing phagocytic ability, increasing nitric oxide (NO) secretion and promoting expression of major histocompatibility complex class II (MHC II) and cluster of differentiation 86 (CD 86). On the other hand, ASPA80-1 promoted the maturation of dendritic cells (DCs) by inducing longer dendrites, decreasing phagocytic ability and increasing MHC II and CD86 expression. Furthermore, mitogen-activated protein kinases (MAPKs) and nuclear factor kappa B (NF-κB) signaling pathways were activated after the intervention of ASPA80-1 on RAW264.7 cells or DCs. Thus, the novel heteropolysaccharide ASPA80-1 has the potential to be used as an immunoenhancing component in functional foods.
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Computational ghost imaging (CGI) can reconstruct scene images by two-order correlation between sampling patterns and detected intensities from a bucket detector. By increasing the sampling rates (SRs), imaging quality of CGI can be improved, but it will result in an increasing imaging time. Herein, in order to achieve high-quality CGI under an insufficient SR, we propose two types of novel sampling methods for CGI, to the best of our knowledge, cyclic sinusoidal-pattern-based CGI (CSP-CGI) and half-cyclic sinusoidal-pattern-based CGI (HCSP-CGI), in which CSP-CGI is realized by optimizing the ordered sinusoidal patterns through "cyclic sampling patterns," and HCSP-CGI just uses half of the sinusoidal pattern types of CSP-CGI. Target information mainly exists in the low-frequency region, and high-quality target scenes can be recovered even at an extreme SR of 5%. The proposed methods can significantly reduce the sampling number and real-time ghost imaging possible. The experiments demonstrate the superiority of our method over state-of-the-art methods both qualitatively and quantitatively.
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The recent discovery of numerous DNA N6-methyladenine (6mA) sites has transformed our perception about the roles of 6mA in living organisms. However, our ability to understand them is hampered by our inability to identify 6mA sites rapidly and cost-efficiently by existing experimental methods. Developing a novel method to quickly and accurately identify 6mA sites is critical for speeding up the progress of its function detection and understanding. In this study, we propose a novel computational method, called I-DNAN6mA, to identify 6mA sites and complement experimental methods well, by leveraging the base-pairing rules and a well-designed three-stage deep learning model with pairwise inputs. The performance of our proposed method is benchmarked and evaluated on four species, i.e., Arabidopsis thaliana, Drosophila melanogaster, Rice, and Rosaceae. The experimental results demonstrate that I-DNAN6mA achieves area under the receiver operating characteristic curve values of 0.967, 0.963, 0.947, 0.976, and 0.990, accuracies of 91.5, 92.7, 88.2, 0.938, and 96.2%, and Mathew's correlation coefficient values of 0.855, 0.831, 0.763, 0.877, and 0.924 on five benchmark data sets, respectively, and outperforms several existing state-of-the-art methods. To our knowledge, I-DNAN6mA is the first approach to identify 6mA sites using a novel image-like representation of DNA sequences and a deep learning model with pairwise inputs. I-DNAN6mA is expected to be useful for locating functional regions of DNA.
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Arabidopsis , Aprendizado Profundo , Animais , Metilação de DNA , Drosophila melanogaster , DNA/genéticaRESUMO
Polarization imaging has outstanding advantages in the field of scattering imaging, which still encounters great challenges in heavy scattering media systems even though there are helps from deep learning technology. In this paper, we propose a self-attention module (SAM) in multi-scale improved U-net (SAM-MIU-net) for the polarization scattering imaging, which can extract a new combination of multidimensional information from targets effectively. The proposed SAM-MIU-net can focus on the stable feature carried by polarization characteristics of the target, so as to enhance the expression of the available features, and make it easier to extract polarization features which help to recover the detail of targets for the polarization scattering imaging. Meanwhile, the SAM's effectiveness has been verified in a series of experiments. Based on proposed SAM-MIU-net, we have investigated the generalization abilities for the targets' structures and materials, and the imaging distances between the targets and the ground glass. Experimental results demonstrate that our proposed SAM-MIU-net can achieve high-precision reconstruction of target information under incoherent light conditions for the polarization scattering imaging.
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The obstacle in a channel is a typical scenario for free-space optical (FSO) communications, however, it will destroy the information in channels, especially for the orbital angular momentum (OAM) multiplexing systems and cause performance degradation. Motivated by the feature of predefining intensity profile, here we propose to use frozen wave (FW) carrying OAM for the FSO communications to mitigate the influence of obstacles on the beam propagation. The key idea is to design the longitudinal intensity profile of FW to distribute the beam energy of the location where the obstacle exists over a large region and focus again on the central region after the obstacle for propagation. By analyzing the cases under different sizes, positions, and shapes of the obstacles with on-axis and off-axis scenarios, it has been demonstrated that the detection probability of OAM mode carried by FW can be improved by 0.35 and 0.15 in short-distance and long-distance transmission scenarios, respectively, when compared to that carried by Bessel-Gaussian beam. It demonstrates the FWs have great potential in the OAM-based FSO communications, especially for the obstacle channels.