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Intermittent fasting (IF) is a widely used dietary strategy that has shown several advantageous impacts on general health and aging. IF has recently been linked to the control of neurogenesis, a crucial process for emotional control, memory, and learning, in the hippocampus. Nevertheless, there is little knowledge about the sex-specific impacts of IF on hippocampal neurogenesis and the related mechanisms, which were investigated in this study among both male and female rats, together with analyzing the involvement of the flora-gut-brain axis in facilitating these effects. Our findings show that IF favorably affects hippocampus neurogenesis in female mice relative to male mice, suggesting a sex-specific mechanism. In addition, IF influenced the diversity of the gut microbiota and decreased the synthesis of fructose-1-phosphate (F-1-P), which is believed together with fructose metabolism to be linked to neurological damage and cognitive decline. Collectively, these data indicate that the connection between the flora-gut-brain axis and hippocampus neurogenesis is significant.

Anisotropic hydrogels have found widespread applications in biomedical engineering, particularly as scaffolds for tissue engineering. However, it remains a challenge to produce them using conventional fabrication methods, without specialized synthesis or equipment, such as 3D printing and unidirectional stretching. In this study, we explore the self-assembly behaviors of polyethylene glycol diacrylate (PEGDA), using disodium cromoglycate (DSCG), a lyotropic chromonic liquid crystal, as a removable template. The affinity between short-chain PEGDA (Mn = 250) and DSCG allows polymerization to take place at the DSCG surface, thereby forming anisotropic hydrogel networks with fibrin-like morphologies. This process requires considerable finesse as the phase behaviors of DSCG depend on a multitude of factors, including the weight percentage of PEGDA and DSCG, the chain length of PEGDA, and the concentration of ionic species. The key to modulating the microstructures of the all-PEG hydrogel networks is through precise control of the DSCG concentration, resulting in anisotropic mechanical properties. Using these anisotropic hydrogel networks, we demonstrate that human dermal fibroblasts are particularly sensitive to the alignment order. We find that cells exhibit a density-dependent activation pattern of a Yes-associated protein, a mechanotransducer, corroborating its role in enabling cells to translate external mechanical and morphological patterns to specific behaviors. The flexibility of modulating microstructure, along with PEG hydrogels' biocompatibility and biodegradability, underscores their potential use for tissue engineering to create functional structures with physiological morphologies.

In a clinical setting, the acquisition of certain medical image modality is often unavailable due to various considerations such as cost, radiation, etc. Therefore, unpaired cross-modality translation techniques, which involve training on the unpaired data and synthesizing the target modality with the guidance of the acquired source modality, are of great interest. Previous methods for synthesizing target medical images are to establish one-shot mapping through generative adversarial networks (GANs). As promising alternatives to GANs, diffusion models have recently received wide interests in generative tasks. In this paper, we propose a target-guided diffusion model (TGDM) for unpaired cross-modality medical image translation. For training, to encourage our diffusion model to learn more visual concepts, we adopted a perception prioritized weight scheme (P2W) to the training objectives. For sampling, a pre-trained classifier is adopted in the reverse process to relieve modality-specific remnants from source data. Experiments on both brain MRI-CT and prostate MRI-US datasets demonstrate that the proposed method achieves a visually realistic result that mimics a vivid anatomical section of the target organ. In addition, we have also conducted a subjective assessment based on the synthesized samples to further validate the clinical value of TGDM.

The dynamic wetting behavior of droplets has been of wide concern due to the hazards of accretion/icing of supercooled droplets on engineering components/systems served in low temperature freezing rain environment; thus, it is urgent to establish the relationship between droplet depinning/removing behaviors and surface characteristics. In this article, the actual rotation conditions of moving components such as wind turbine blades are simulated. The self-cleaning hydrophobic coating surface(S1) and bionic superhydrophobic coating surface(S2) show outstanding droplet removal performance compared to hydrophilic bare steel surface(S0), and the average speed of the droplet removal is increased by 400-500%. The "creeping-sliding" behavior of droplets on self-cleaning coatings is investigated by the change of droplet displacement( ΔD ). The effect of the energy storage caused by the droplet creeping process provides initial kinetic energy for the droplet removal. Combined with the experimental data and theoretical model, the critical depinning resistance is calculated. The difference of the wetting interface free energy( ΔE x ) during the dynamic wetting process of the droplets on the bionic superhydrophobic self-cleaning surface is researched. And the influence mechanism of the droplet embedded depth( x ) on the creeping/sliding behavior in the nanotexture is clarified. Thus, the mechanical criterion of droplet depinning is proposed (the error is about 10%). The results can provide a theoretical basis for the design principle of antifreezing rain coatings on moving components.

In the field of healthcare, the acquisition of sample is usually restricted by multiple considerations, including cost, labor- intensive annotation, privacy concerns, and radiation hazards, therefore, synthesizing images-of-interest is an important tool to data augmentation. Diffusion models have recently attained state-of-the-art results in various synthesis tasks, and embedding energy functions has been proved that can effectively guide the pre-trained model to synthesize target samples. However, we notice that current method development and validation are still limited to improving indicators, such as Fréchet Inception Distance score (FID) and Inception Score (IS), and have not provided deeper investigations on downstream tasks, like disease grading and diagnosis. Moreover, existing classifier guidance which can be regarded as a special case of energy function can only has a singular effect on altering the distribution of the synthetic dataset. This may contribute to in-distribution synthetic sample that has limited help to downstream model optimization. All these limitations remind that we still have a long way to go to achieve controllable generation. In this work, we first conducted an analysis on previous guidance as well as its contributions on further applications from the perspective of data distribution. To synthesize samples which can help downstream applications, we then introduce uncertainty guidance in each sampling step and design an uncertainty-guided diffusion models. Extensive experiments on four medical datasets, with ten classic networks trained on the augmented sample sets provided a comprehensive evaluation on the practical contributions of our methodology. Furthermore, we provide a theoretical guarantee for general gradient guidance in diffusion models, which would benefit future research on investigating other forms of measurement guidance for specific generative tasks. Codes and models are available at: https://github.com/yangqy1110/MGDM.

The linkages among carbon, renewable energy, and electricity markets are gradually strengthening. In order to prevent risk transmission among markets, this paper uses the TVP-VAR-DY (Time-Varying Parameter-Vector Auto Regression-Dynamic) model to analyze the dynamic risk spillover effects and network structure of risk transmission among carbon, renewable energy, and electricity markets. The empirical results show that there are significant asymmetric spillover effects among carbon, renewable energy, and electricity markets. The total spillover index shows that spillover effects among carbon, renewable energy, and electricity markets are time-varying, especially during unexpected events. Besides, the net spillover index indicates that the spillover effects are bidirectional, asymmetric, and time-varying. Finally, under the influence of unexpected events, the network structures of risk transmission among carbon, renewable energy, and electricity markets are heterogeneous. Compared to the Russia-Ukraine conflict, the COVID-19 pandemic has a more significant impact on these markets., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Rapid screening of bacteria by low-cost and eco-friendly material-based approaches is still a major challenge. Herein, a colorimetric biosensor was designed for the ultrasensitive and rapid detection of Gram-positive bacteria. The biosensor exploited polydopamine and polyethyleneimine (PDA-PEI)-modified papers for separating bacteria and carbon dots (CDs) for selective colorimetric detection of Gram-positive bacteria. Noble metal-free CDs can target Gram-positive bacteria by binding with peptidoglycan and possess peroxidase-like activity. Thus, they can avert the step of modifying recognition probes, facilitating biosensor fabrication, and reducing the cost. This biosensor can detect S. aureus as low as 1 cfu mL -1 , L. monocytogenes as low as 5 cfu mL -1 , and B. subtilis as low as 9 cfu mL -1 within 55 min. In addition, a portable device was constructed to enable convenient and on-site quantitative detection of Gram-positive bacteria. The feasibility of the biosensor was verified by detecting Gram-positive bacteria in eggshell and sausage samples with recoveries ranging from 91.2% to 110%., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Non-equilibrium processing of aqueous polyelectrolyte complex (PEC) coacervates is critical to many applications. In particular, many coacervate-forming systems are known to become trapped in out-of-equilibrium states ( e.g. , precipitation). The mechanism and conditions under which these states form, and whether they age, is not clearly understood. Here, we elucidate the influence of processing on the PEC coarsening mechanism as it varies with flow during mixing for a model system of poly(allylamine hydrochloride) and poly(acrylic acid sodium salt) in water. We demonstrate that flow conditions can be used to toggle the formation of rough, precipitate-like aggregates of micron-scale PEC structures. These structures form at compositions with viscous-dominant equilibrium rheology, and observations of their formation via optical microscopy suggest that they comprise colloidal aggregates of PEC coacervate droplets. We further show that these aggregates exhibit micron-scale coarsening, with a mixing time-dependent characteristic aging time scale. The results show that the formation of precipitate-like structures is not solely determined by composition, but is instead highly sensitive to mass transport and colloidal instability effects. Our observations suggest that the details of mixing flow can provide non-equilibrium structural control of a broad range of PEC coacervate materials orthogonally to structure-property inspired polymeric design. We anticipate that these findings will open the door for future studies on the control of non-equilibrium PEC formation and structure.

The ability of cells to reorganize in response to external stimuli is important in areas ranging from morphogenesis to tissue engineering. While nematic order is common in biological tissues, it typically only extends to small regions of cells interacting via steric repulsion. On isotropic substrates, elongated cells can co-align due to steric effects, forming ordered but randomly oriented finite-size domains. However, we have discovered that flat substrates with nematic order can induce global nematic alignment of dense, spindle-like cells, thereby influencing cell organization and collective motion and driving alignment on the scale of the entire tissue. Remarkably, single cells are not sensitive to the substrate's anisotropy. Rather, the emergence of global nematic order is a collective phenomenon that requires both steric effects and molecular-scale anisotropy of the substrate. To quantify the rich set of behaviours afforded by this system, we analyse velocity, positional and orientational correlations for several thousand cells over days. The establishment of global order is facilitated by enhanced cell division along the substrate's nematic axis, and associated extensile stresses that restructure the cells' actomyosin networks. Our work provides a new understanding of the dynamics of cellular remodelling and organization among weakly interacting cells.

Chiral recognition of enantiomers has always been a thorny issue since they exhibit the same properties under an achiral environment. Herein, polydopamine-functionalized magnetic particles (MP@PDA) were synthesized to immobilize the genetically engineered bacterium Escherichia coli DH5α (MP@PDA-E. coli). L-tryptophan (Trp) instead of D-Trp can be stereo-specifically degraded by tryptophanase in E. coli. The degradation product indole reacts with 4-dimethylaminobenzaldehyde to generate a rose-red adduct. Thus, MP@PDA-E. coli was employed to fabricate a chiral colorimetric method for chiral recognition and determination of L-Trp. The method averts the purification of tryptophanase. More importantly, tryptophanase demonstrates excellent enantioselective ability for L-Trp. The method can not only quantitatively detect L-Trp but also realize the measurement of the enantiomer percentage in the enantiomeric mixture. The feasibility was verified by detecting L-Trp in millet samples from different origins. Furthermore, a portable device was fabricated to make the method more convenient., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This publisher's note contains a correction to Opt. Lett.47, 3780 (2022)10.1364/OL.464020.

Topological defects on colloids rotating in nematic liquid crystals form far-from-equilibrium structures that perform complex swim strokes in which the defects periodically extend, depin, and contract. These defect dynamics propel the colloid, generating translation from rotation. The swimmer's speed and direction are determined by the topological defect's polarity and extent of elongation. Defect elongation is controlled by a rotating external magnetic field, allowing control over particle trajectories. The swimmers' translational motion relies on broken symmetries associated with lubrication forces between the colloid and the bounding surfaces, line tensions associated with the elongated defect, and anisotropic viscosities associated with the defect elongation adjacent to the colloid. The scattering or effective pair interaction of these swimmers is highly anisotropic, with polarization-dependent dimer stability and motion that depend strongly on entanglement and sharing of their extended defect structures. This research introduces transient, far-from-equilibrium topological defects as a class of virtual functional structures that generate modalities of motion and interaction.

A quasi-distributed acoustic sensor using in-line weak reflectors and a low coherence light source is presented. The dynamic strain is retrieved from the phase change of the two interfering light beams reflected by the same weak reflector. In the experiments, two vibrations at different channels along a weak reflector array are successfully detected simultaneously. A strain resolution of 50 pɛ/ H z with 20-m interval is achieved in experiments, and no cross talk is observed. With simple system configuration and low cost, this approach provides a new, to the best of our knowledge, solution for quasi-distributed acoustic sensing.

Research on shear thickening colloidal suspensions demonstrates that measurements of the microstructure can elucidate the source of the rheological material properties in the shear thickened state as well as critically test simulations and theory based on a variety of mechanisms such as enhanced lubrication hydrodynamics, elastohydrodynamics, and contact friction. Prior work on continuous shear thickening dispersions with a well-defined shear thickened state identified the formation of hydroclusters as characteristic of this state, determined the anisotropy in the nearest neighbor distribution, and used this information to test prevailing theories and simulations. However, important questions remain about the mesoscale ( i.e. , particle cluster scale) microstructure of the shear thickened state. Here we employ neutron scattering methods applied to shearing colloidal dispersions of spherical particles with two extremes of friction and lubrication surface properties to resolve the longer-length scale microstructure in the shear thickened state. Hydroclusters are shown to be highly localized, in agreement with prior neutron scattering and direct optical measurements, but in disagreement with the most recent simulations that predict a longer-range structure formation. These results combined with prior measurements provide experimental evidence about the length scale of microstructure formation in continuous shear thickening suspensions necessary to improve our understanding of the phenomenon as well as guide theoretical investigations that quantitatively link nanoscale forces to macroscopic properties in the shear thickened state.

Stroke is a type of cerebrovascular disease that significantly endangers human health and lowers quality of life. This understandably places a heavy burden on society and families. In recent years, intestinal flora has attracted increasing attention from scholars worldwide, and its association with ischemic stroke is becoming a hot topic of research amongst researchers in field of stroke. After suffering from a stroke, intestinal microbial dysbiosis leads to increased intestinal permeability and activation of the intestinal immune system, which in turn leads to ectopic intestinal bacteria and pro-inflammatory cells that enter brain tissue through the damaged blood-brain barrier. This exacerbates ischemia-reperfusion injury. Interestingly, after a stroke, some metabolites produced by the intestinal flora attenuate ischemia-reperfusion injury by suppressing the post-stroke inflammatory response and promotes the repair of neurological function. Here we elucidate the changes in gut flora after occurrence of a stroke and highlight the immunomodulatory processes of the post-stroke gut flora., (© 2022. The Author(s).)

Evolution of composition, rheology, and morphology during phase separation in complex fluids is highly coupled to rheological and mass transport processes within the emerging phases, and understanding this coupling is critical for materials design of multiphase complex fluids. Characterizing these dependencies typically requires careful measurement of a large number of equilibrium and transport properties that are difficult to measure in situ as phase separation proceeds. Here, we propose and demonstrate a high-throughput microscopy platform to achieve simultaneous, in situ mapping of time-evolving morphology and microrheology in phase separating complex fluids over a large compositional space. The method was applied to a canonical example of polyelectrolyte complex coacervation, whereby mixing of oppositely charged species leads to liquid-liquid phase separation into distinct solute-dense and dilute phases. Morphology and rheology were measured simultaneously and kinetically after mixing to track the progression of phase separation. Once equilibrated, the dense phase viscosity was determined to high compositional accuracy using passive probe microrheology, and the results were used to derive empirical relationships between the composition and viscosity. These relationships were inverted to reconstruct the dense phase boundary itself, and further extended to other mixture compositions. The resulting predictions were validated by independent equilibrium compositional measurements. This platform paves the way for rapid screening and formulation of complex fluids and (bio)macromolecular materials, and serves as a critical link between formulation and rheology for multi-phase material discovery.

Purpose: Reducing X-ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional neural networks (CNNs) have shown improvements in the quality of low-dose X-ray fluoroscopy images but may compromise clinically important details required by cardiologists., Methods: In order to obtain denoised X-ray fluoroscopy images whilst preserving details, we propose a novel deep-learning-based denoising framework, namely edge-enhancement densenet (EEDN), in which an attention-awareness edge-enhancement module is designed to increase edge sharpness. In this framework, a CNN-based denoiser is first used to generate an initial denoising result. Contours representing edge information are then extracted using an attention block and a group of interacted ultra-dense blocks for edge feature representation. Finally, the initial denoising result and enhanced edges are combined to generate the final X-ray image. The proposed denoising framework was tested on a total of 3262 clinical images taken from 100 low-dose X-ray sequences acquired from 20 patients. The performance was assessed by pairwise voting from five cardiologists as well as quantitative indicators. Furthermore, we evaluated our technique's effect on catheter detection using 416 images containing coronary sinus catheters in order to examine its influence as a pre-processing tool., Results: The average signal-to-noise ratio of X-ray images denoised with EEDN was 24.5, which was 2.2 times higher than that of the original images. The accuracy of catheter detection from EEDN denoised sequences showed no significant difference compared with their original counterparts. Moreover, EEDN received the highest average votes in our clinician assessment when compared to our existing technique and the original images., Conclusion: The proposed deep learning-based framework shows promising capability for denoising interventional X-ray fluoroscopy images. The results from the catheter detection show that the network does not affect the results of such an algorithm when used as a pre-processing step. The extensive qualitative and quantitative evaluations suggest that the network may be of benefit to reduce radiation dose when applied in real time in the catheter laboratory., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

A green and efficient visible-light induced functionalization of anilines under mild conditions has been reported. Utilizing nontoxic, cost-effective, and water-soluble diacetyl as photosensitizer and acetylating reagent, and water as the solvent, a variety of anilines were converted into the corresponding aryl ketones, iodides, and bromides. With advantages of environmentally friendly conditions, simple operation, broad substrate scope, and functional group tolerance, this reaction represents a valuable method in organic synthesis.

Reducing radiation dose in cardiac catheter-based X-ray procedures increases safety but also image noise and artifacts. Excessive noise and artifacts can compromise vital image information, which can affect clinical decision-making. Developing more effective X-ray denoising methodologies will be beneficial to both patients and healthcare professionals by allowing imaging at lower radiation dose without compromising image information. This paper proposes a framework based on a convolutional neural network (CNN), namely Ultra-Dense Denoising Network (UDDN), for low-dose X-ray image denoising. To promote feature extraction, we designed a novel residual block which establishes a solid correlation among multiple-path neural units via abundant cross connections in its representation enhancement section. Experiments on synthetic additive noise X-ray data show that the UDDN achieves statistically significant higher peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) than other comparative methods. We enhanced the clinical adaptability of our framework by training using normally-distributed noise and tested on clinical data taken from procedures at St. Thomas' hospital in London. The performance was assessed by using local SNR and by clinical voting using ten cardiologists. The results show that the UDDN outperforms the other comparative methods and is a promising solution to this challenging but clinically impactful task.

Differential dynamic microscopy (DDM) is a form of video image analysis that combines the sensitivity of scattering and the direct visualization benefits of microscopy. DDM is broadly useful in determining dynamical properties including the intermediate scattering function for many spatiotemporally correlated systems. Despite its straightforward analysis, DDM has not been fully adopted as a routine characterization tool, largely due to computational cost and lack of algorithmic robustness. We present statistical analysis that quantifies the noise, reduces the computational order, and enhances the robustness of DDM analysis. We propagate the image noise through the Fourier analysis, which allows us to comprehensively study the bias in different estimators of model parameters, and we derive a different way to detect whether the bias is negligible. Furthermore, through use of Gaussian process regression (GPR), we find that predictive samples of the image structure function require only around 0.5%-5% of the Fourier transforms of the observed quantities. This vastly reduces computational cost, while preserving information of the quantities of interest, such as quantiles of the image scattering function, for subsequent analysis. The approach, which we call DDM with uncertainty quantification (DDM-UQ), is validated using both simulations and experiments with respect to accuracy and computational efficiency, as compared with conventional DDM and multiple particle tracking. Overall, we propose that DDM-UQ lays the foundation for important new applications of DDM, as well as to high-throughput characterization.

Anti-icing materials have become increasingly urgent for many fields such as power transmission, aviation, energy, telecommunications, and so on. Bionic lotus hydrophobic surfaces with hierarchical micro-/nanostructures show good potential of delaying ice formation; however, their icephobicity (deicing ability) has been controversial. It is mainly attributed to lack of deep understanding of the correlation between micro-/nanoscale structures, wettability, and icephobicity, as well as effective methods for evaluating the deicing ability close to natural environments. In this article, the natural deicing ability is innovatively proposed on the basis of ice adhesion and the influence of microscale structure evolution on dynamic wetting and deicing ability (both ice adhesion strength and natural deicing time) was systematically investigated. Interestingly, different modes (sticky or slippery) were found in natural deicing of hierarchical hydrophobic surfaces, although their ice adhesion strength was higher than that of smooth surfaces. The mechanism was analyzed from three aspects: mechanics, heat transfer, and dynamic wetting. It is highlighted that the sliding of melted interface is not equal to water droplet sliding (dynamic wetting) before freezing or after deicing but significantly depends on the microscale structure. The fundamental understanding on natural deicing of bionic hydrophobic surfaces will open up a new window for developing new anti-icing materials and technology.

Hepatic stellate cell (HSC) line LX-2 is activated by liver cancer stem-like cells (LCSLCs) and produces various cytokines that make up most of the hepatocellular carcinoma (HCC) microenvironment. The new genistein derivative, 7-difluoromethoxyl-5,4'-di-n-octylgenistein (DFOG), shows anticancer effects in multiple malignancies by controlling forkhead box M1 (FOXM1). In this study, we aimed to assess whether DFOG disrupts the crosstalk between human HSC LX-2 cells and LCSLCs. Distinct generations of MHCC97H-derived spheres were obtained with the second generation considered as LCSLCs which displayed enhanced self-renewal ability and elevated expression levels of CD133, CD44, and EpCAM proteins, as well as tumorigenicity, as revealed by colony formation assay in vitro and tumorigenicity assay in vivo. LX-2 and MHCC97H cells were co-cultured with/without DFOG (1, 5, and 10 μM, respectively) using the transwell system. FOXM1 overexpression and/or knockdown were employed for mechanistic investigations. Our results suggested that Co-CM promoted LX-2 cell transformation into liver cancer-associated HSCs. Meanwhile, FOXM1 was up-regulated and the level of hepatocyte growth factor (HGF) was increased in LX-2 cells and in the supernatant after Co-CM stimulation. Sphere and colony formation abilities in MHCC97H cells, and protein levels of CD133, CD44, and EpCAM, were also markedly elevated. DFOG dose-dependently inhibited the above effects, similar to FOXM1 knockdown in LX-2 cells. FOXM1 overexpression reversed the inhibitory effects of DFOG or FOXM1 knockdown or both on LX-2 cell activation and LCSLC feature induction in MHCC97H cells by LCSLC/LX-2 co-culture. This study demonstrated that DFOG disrupts the crosstalk between HSCs and LCSLCs to suppress LCSLC features via down-regulating FOXM1 expression and reducing HGF secretion in HSCs., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Convolutional neural networks (CNNs) have wide applications in pattern recognition and image processing. Despite recent advances, much remains to be done for CNNs to learn a better representation of image samples. Therefore, constant optimizations should be provided on CNNs. To achieve a good performance on classification, intuitively, samples' interclass separability, or intraclass compactness should be simultaneously maximized. Accordingly, in this paper, we propose a new network, named separability and compactness network (SCNet) to rectify this problem. SCNet minimizes the softmax loss and the distance between features of samples from the same class under a jointly supervised framework, resulting in simultaneous maximization of interclass separability and intraclass compactness of samples. Furthermore, considering the convenience and the efficiency of the cosine similarity in face recognition tasks, we incorporate it into SCNet's distance metric to enable sample features from the same class to line up in the same direction and those from different classes to have a large angle of separation. We apply SCNet to three different tasks: visual classification, face recognition, and image superresolution. Experiments on both public data sets and real-world satellite images validate the effectiveness of our SCNet.

Pulmonary artery sarcoma (PAS) is a rare and aggressive mesenchymal tumor that often mimics thromboembolic disease. Due to its rare and fatal nature, patients are often underdiagnosed or misdiagnosed. There is still no consensus regarding the diagnosis and treatment of PAS. We present a case of a 63 year old male misdiagnosed with pulmonary thromboembolism who received anticoagulant therapy. 18 FDG positron emission tomography (PET) integrated with computed tomography (PET/CT) and subsequent surgery led to the final diagnosis of PAS. Whole exome sequencing of the tissue identified the genetic alterations profile of PAS: copy number variation (CNV) of KIT and mutations of TP53, PIK3CA, IL7R and  ATR . Treated with chemotherapy followed by anlotinib, the patient's survival time was 8 months after firm diagnosis. To our knowledge, anlotinib used as a treatment for PAS has not been reported., Competing Interests: The authors report no conflicts of interest in this work.

Nematic liquid crystals (NLCs) offer remarkable opportunities to direct colloids to form complex structures. The elastic energy field that dictates colloid interactions is determined by the NLC director field, which is sensitive to and can be controlled by boundaries including vessel walls and colloid surfaces. By molding the director field via liquid-crystal alignment on these surfaces, elastic energy landscapes can be defined to drive structure formation. We focus on colloids in otherwise defect-free director fields formed near undulating walls. Colloids can be driven along prescribed paths and directed to well-defined docking sites on such wavy boundaries. Colloids that impose strong alignment generate topologically required companion defects. Configurations for homeotropic colloids include a dipolar structure formed by the colloid and its companion hedgehog defect or a quadrupolar structure formed by the colloid and its companion Saturn ring. Adjacent to wavy walls with wavelengths larger than the colloid diameter, spherical particles are attracted to locations along the wall with distortions in the nematic director field that complement those from the colloid. This is the basis of lock-and-key interactions. Here, we study ellipsoidal colloids with homeotropic anchoring near complex undulating walls. The walls impose distortions that decay with distance from the wall to a uniform director in the far field. Ellipsoids form dipolar defect configurations with the colloid's major axis aligned with the far field director. Two distinct quadrupolar defect structures also form, stabilized by confinement; these include the Saturn I configuration with the ellipsoid's major axis aligned with the far field director and the Saturn II configuration with the major axis perpendicular to the far field director. The ellipsoid orientation varies only weakly in bulk and near undulating walls. All configurations are attracted to walls with long, shallow waves. However, for walls with wavelengths that are small compared to the colloid length, Saturn II is repelled, allowing selective docking of aligned objects. Deep, narrow wells prompt the insertion of a vertical ellipsoid. By introducing an opening at the bottom of such a deep well, we study colloids within pores that connect two domains. Ellipsoids with different aspect ratios find different equilibrium positions. An ellipsoid of the right dimension and aspect ratio can plug the pore, creating a class of 2D selective membranes.

By confining soft materials within tailored boundaries it is possible to design energy landscapes to address and control colloidal dynamics. This provides unique opportunities to create reconfigurable, hierarchically organized structures, a leading challenge in materials science. Example soft matter systems include liquid crystals. For instance, when nematic liquid crystals (NLCs) are confined in a vessel with undulated boundaries, bend and splay distortions can be used to position particles. Here we confine this system in a twist cell. We also study cholesteric liquid crystals, which have an "intrinsic" twist distortion which adds to the ones imposed by the solid boundaries. The cholesteric pitch competes with the other length scales in the system (colloid radius, vessel thickness, wavelength of boundary undulations), enriching the possible configurations. Depending on the pitch-to-radius and pitch-to-thickness ratios the interaction can be attractive or repulsive. By tuning the pitch (i.e. changing the concentration of the chiral dopant), it is possible to selectively promote or inhibit particle trapping at the docking sites.

The ability to dictate the motion of microscopic objects is an important challenge in fields ranging from materials science to biology. Field-directed assembly drives microparticles along paths defined by energy gradients. Nematic liquid crystals, consisting of rod-like molecules, provide new opportunities in this domain. Deviations of nematic liquid crystal molecules from uniform orientation cost elastic energy, and such deviations can be molded by bounding vessel shape. Here, by placing a wavy wall in a nematic liquid crystal, we impose alternating splay and bend distortions, and define a smoothly varying elastic energy field. A microparticle in this field displays a rich set of behaviors, as this system has multiple stable states, repulsive and attractive loci, and interaction strengths that can be tuned to allow reconfigurable states. Microparticles can transition between defect configurations, move along distinct paths, and select sites for preferred docking. Such tailored landscapes have promise in reconfigurable systems and in microrobotics applications.

Both mesenchymal stromal cells (MSCs) and myeloid-derived suppressor cells (MDSCs) have immunosuppressive properties, and their presence may confer a worse prognosis upon cancer patients. However, whether MSCs can enhance the immunosuppressive effects of MDSCs in MM remains unknown. We evaluated the influence of MSCs on MDSCs growth, apoptosis, and functions. Our results show that MSCs promote proliferation and inhibit apoptosis in MDSCs. Additionally, MSCs enhance the ability of MDSCs by inhibiting T-cell proliferation and IFN-γ production. Furthermore, both the mRNA and protein levels of Arg1 and NOS2 were upregulated in MDSCs. These results suggest that MSCs may exert immunomodulatory effects on MDSCs by upregulating Arg1 and NOS2.

Signal transducer and activator of transcription 3 (STAT3) is a member of the family of latent cytoplasmic transcriptional factors that could regulate cell proliferation, survival, and development. It has been reported that Twist is a target gene of STAT3, and STAT3/Twist signaling plays an important role in regulating cancer progress. Here, to explore whether 8-bromo-7-methoxychrysin (BrMC) inhibits liver cancer stem-like cell (LCSLC) properties via disrupting STAT3/Twist signaling, we cultured SMMC-7721 cells in vitro, and evaluated the effects of BrMC on the stemness of spheroids by determining the sphere-forming capability and migration. The sphere formation assay results showed a concentration-dependent decrease of sphere-forming capacity in LCSLCs (P < 0.05) treated with different concentrations of BrMC. Wound-healing assays results demonstrated a concentration-dependent decline in cell migration of LCSLCs treated with different concentrations of BrMC. In addition, CD133, CD44, and ALDH1 levels were decreased in LCSLCs treated with BrMC. Treatment with different concentrations of BrMC also reduced the expressions of p-STAT3 and Twist1 proteins. The effect of BrMC was substantially enhanced by co-treatment with JSI-124, a specific inhibitor of STAT3. Ectopic expression of Twist1 attenuated the inhibitory effects of BrMC on sphere formation, migration, and expression of the markers in LCSLCs. However, it had no affect on p-STAT3 expression in LCSLCs. These results demonstrated that BrMC inhibits the stemness of LCSLCs originated from SMMC-7721 cell line by inhibiting STAT3/Twist signal axis., (© The Author 2017. Published by Oxford University Press on behalf of the Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Anti-icing surfaces/interfaces are of considerable importance in various engineering fields under natural freezing environment. Although superhydrophobic self-cleaning surfaces show good anti-icing potentials, promotion of these surfaces in engineering applications seems to enter a "bottleneck" stage. One of the key issues is the intrinsic relationship between superhydrophobicity and icephobicity is unclear, and the dynamic action mechanism of "air cushion" (a key internal factor for superhydrophobicity) on icing suppression was largely ignored. Here we report that icing inhibition (i.e., icing-delay) of self-cleaning surfaces is mainly ascribed to air cushion and its convection. We experimentally found air cushion on the porous self-cleaning coating under vacuum environments and on the water/ice-coating interface at low temperatures. The icing-delay performances of porous self-cleaning surfaces compared with bare substrate, up to 10-40 min under 0 to ∼-4 °C environments close to freezing rain, have been accurately real-time recorded by a novel synergy method including high-speed photography and strain sensing voltage. Based on the experimental results, we innovatively propose a physical model of "air cushion convection inhibiting icing", which envisages both the static action of trapped air pocket without air flow and dynamic action of air cushion convection. Gibbs free energy of water droplets increased with the entropy of air derived from heat and mass transfer between warmer air underneath water droplets and colder surrounding air, resulting in remarkable ice nucleation delay. Only when air cushion convection disappears can ice nucleation be triggered on suitable Gibbs free energy conditions. The fundamental understanding of air cushion on anti-icing is an important step toward designing optimal anti-icing surfaces for practical engineering application.

The ability to control the movement and assembly of particles in liquid crystals is not only an important route to design functional materials, but also sheds light on the mechanisms of colloidal interactions. In this study we place micron-sized particles with "Saturn ring" defects near a wall with hills and dales that impose perpendicular (homeotropic) molecular anchoring. The strong splay distortion at the wall interacts with the distortion around the particles in the near field and favors their migration towards the dales via the so-called "lock-and-key" mechanism. We demonstrate experimentally that the lock-and-key mechanism can robustly localize a particle at specific topographical features. We observe the complex trajectories traced by the particles as they dock on the dales, estimate the binding energy, and explore a range of parameters that favor or disfavor the docking event, thus exploiting the capabilities of our experimental system. We extend the study to colloids with homeotropic anchoring but with an associated point defect instead of a Saturn ring and show that they find a different preferred location, i.e. we can place otherwise identical particles at well defined sites according to their topological defect structure. Finally, for deep enough wells, confinement drives topological transitions of Saturn rings to dipoles. This ability to tailor wall geometry to guide colloids to well defined sites within nematic liquid crystals represents an important new tool in directed assembly.

We study colloids suspended in nematic liquid crystal in grooves with homeotropic anchoring. We observe "eyelashes", topological dipole chains that follow the local, curved director field. These beget wires that connect the groove corners to topographical features on the cell lid to yield oriented, curvilinear colloidal wires spanning the cell, formed in a nonsingular director field. As the groove aspect ratio changes, we find different ground states and corroborate our observation with numerics. Our results rely upon on the scale of topographical features, the sharpness of edges, and the colloid-sourced distortions; all these elements can be exploited to guide the formation of reconfigurable structures in nematics.

Coaxial cables for data transmission are ubiquitous in telecommunications, aerospace, automotive, and robotics industries. Yet, the metals used to make commercial cables are unsuitably heavy and stiff. These undesirable traits are particularly problematic in aerospace applications, where weight is at a premium and flexibility is necessary to conform with the distributed layout of electronic components in satellites and aircraft. The cable outer conductor (OC) is usually the heaviest component of modern data cables; therefore, exchanging the conventional metallic OC for lower weight materials with comparable transmission characteristics is highly desirable. Carbon nanotubes (CNTs) have recently been proposed to replace the metal components in coaxial cables; however, signal attenuation was too high in prototypes produced so far. Here, we fabricate the OC of coaxial data cables by directly coating a solution of CNTs in chlorosulfonic acid (CSA) onto the cable inner dielectric. This coating has an electrical conductivity that is approximately 2 orders of magnitude greater than the best CNT OC reported in the literature to date. This high conductivity makes CNT coaxial cables an attractive alternative to commercial cables with a metal (tin-coated copper) OC, providing comparable cable attenuation and mechanical durability with a 97% lower component mass.

Focal conic domains (FCDs) form in smectic-A liquid crystal films with hybrid anchoring conditions with eccentricity and size distribution that depend strongly on interface curvature. Assemblies of FCDs can be exploited in settings ranging from optics to material assembly. Here, using micropost arrays with different shapes and arrangement, we assemble arrays of smectic flower patterns, revealing their internal structure as well as defect size, location, and distribution as a function of interface curvature, by imposing positive, negative, or zero Gaussian curvature at the free surface. We characterize these structures, relating free surface topography, substrate anchoring strength, and FCD distribution. Whereas the largest FCDs are located in the thickest regions of the films, the distribution of sizes is not trivially related to height, due to Apollonian tiling. Finally, we mold FCDs around microposts of complex shape and find that FCD arrangements are perturbed near the posts, but are qualitatively similar far from the posts where the details of the confining walls and associated curvature fields decay. This ability to mold FCD defects into a variety of hierarchical assemblies by manipulating the interface curvature paves the way to create new optical devices, such as compound eyes, via a directed assembly scheme.

We report a facile and general strategy for enhancing the photostability of organic fluorophores for bioimaging applications. As a proof of concept, bright and robust fluorescence was observed in solid states of a well-defined synthetic polymer polycaprolactone consisting of di(thiophene-2-yl)-diketopyrrolopyrrole covalently linked in the middle of the polymer chain as a biocompatible and bioresorbable matrix. The nanoparticles prepared through a nanoprecipitation process of these polymers could be internalized by both tumor cells and stem cells with little cytotoxicity. Moreover, these highly fluorescent nanoparticles exhibited significantly enhanced photostability compared to commercial quantum dots or physical blends of dye/polymer complexes in cell imaging and long-term tracing.

Bionic self-cleaning surfaces with well-ordered polymer nano-fibers are firstly fabricated by disturbing crystallization during one-step coating-curing process. Orderly thin (100 nm) and long (5-10 μm) polymer nano-fibers with a certain direction are fabricated by external macroscopic force (F blow) interference introduced by H2 gas flow, leading to superior superhydrophobicity with a water contact angle (WCA) of 170° and a water sliding angle (WSA) of 0-1°. In contrast, nano-wires and nano-bridges (1-8 μm in length/10-80 nm in width) are generated by "spinning/stretching" under internal microscopic force (F T) interference due to significant temperature difference in the non-uniform cooling medium. The findings provide a novel theoretical basis for controllable polymer "bionic lotus" surface and will further promote practical application in many engineering fields such as drag-reduction and anti-icing.

Locomotion mode recognition plays an important role in the control of powered lower-limb prostheses. In this paper, we present a non-contact capacitance sensing system (C-Sens) to measure the interfacial signals between the residual limb and the prosthetic socket. The system includes sensing front-ends, a sensing circuit, a control circuit and foot pressure insoles. In the proposed system, the electrodes are fixed on the inner surface of the socket, which couple with the human body forming capacitors. The foot pressure insoles are built for detecting gait phases. The data sequence is controlled by the control circuit. To evaluate the capacitance sensing system, experiments with a transtibial amputee are carried out and seven kinds of locomotion modes are recorded. With the continuous phase dependent classification method and the quadratic discriminant analysis (QDA) classifier, the average recognition accuracies are 93.8% and 95.0% for the stance phase and the swing phase respectively. The results show the potential of the proposed system for the control of powered lower-limb prostheses.