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Single image-based 3D shape retrieval (IBSR) has attracted appealing academic interests recently, which aims to find the corresponding 3D shape from a shape repository for a given single 2D image. However, state-of-the-art methods neglect the discrepancy in the image domain due to unavoidable occlusion. The occluded image representations acting as noise, may perturb the alignment of the normal 2D representations with the 3D representations, resulting in occlusion-sensitive image-shape retrieval. To tackle this crucial challenge, in this paper, we propose a novel Occlusion-invariant PErception Network (OPEN) to learn occlusion-invariant image representations and image-shape correspondence. Specifically, we propose a hard occlusion example mining strategy to sample a hard image pair. Hereafter, to enforce the consistency between normal and occluded 2D images, we propose an Occlusion-invariant Image Consistency (OIC) based on hard image pairs, which gathers 2D image representations of the same instance while pushing away other 2D image representations. In addition, to prevent the 3D representations from perturbation by the occluded 2D representations, we design an Occlusion-invariant Correspondence Consistency (OCC) based on hard image pairs, which pulls the image-specific 3D shape embedding derived by attention mechanism close to the other 2D image representation of the same instance. The combination of OIC and OCC leads to accurate 2D-3D shape matching in challenging occluded scenarios. Our OPEN outperforms state-of-the-art methods by $6\%\sim 11\%$ in terms of Top-1 retrieval accuracy on several representative benchmark datasets.

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc. We in this work study the problem of synthesizing instantiations of a user's own concepts in a never-ending manner, i.e., create your world, where the new concepts from user are quickly learned with a few examples. To achieve this goal, we propose a Lifelong text-to-image Diffusion Model (L $^{2}$2 DM), which intends to overcome knowledge “catastrophic forgetting” for the past encountered concepts, and semantic “catastrophic neglecting” for one or more concepts in the text prompt. In respect of knowledge “catastrophic forgetting”, our L $^{2}$2 DM framework devises a task-aware memory enhancement module and an elastic-concept distillation module, which could respectively safeguard the knowledge of both prior concepts and each past personalized concept. When generating images with a user text prompt, the solution to semantic “catastrophic neglecting” is that a concept attention artist module can alleviate the semantic neglecting from concept aspect, and an orthogonal attention module can reduce the semantic binding from attribute aspect. To the end, our model can generate more faithful image across a range of continual text prompts in terms of both qualitative and quantitative metrics, when comparing with the related state-of-the-art models.

The visual perception systems aim to autonomously collect consecutive visual data and perceive the relevant information online like human beings. In comparison with the classical static visual systems focusing on fixed tasks (e.g., face recognition for visual surveillance), the real-world visual systems (e.g., the robot visual system) often need to handle unpredicted tasks and dynamically changed environments, which need to imitate human-like intelligence with open-ended online learning ability. Therefore, we provide a comprehensive analysis of open-ended online learning problems for autonomous visual perception in this survey. Based on “what to online learn” among visual perception scenarios, we classify the open-ended online learning methods into five categories: instance incremental learning to handle data attributes changing, feature evolution learning for incremental and decremental features with the feature dimension changed dynamically, class incremental learning and task incremental learning aiming at online adding new coming classes/tasks, and parallel and distributed learning for large-scale data to reveal the computational and storage advantages. We discuss the characteristic of each method and introduce several representative works as well. Finally, we introduce some representative visual perception applications to show the enhanced performance when using various open-ended online learning models, followed by a discussion of several future directions.

Continual learning algorithms which keep the parameters of new tasks close to that of previous tasks, are popular in preventing catastrophic forgetting in sequential task learning settings. However, 1) the performance for the new continual learner will be degraded without distinguishing the contributions of previously learned tasks; 2) the computational cost will be greatly increased with the number of tasks, since most existing algorithms need to regularize all previous tasks when learning new tasks. To address the above challenges, we propose a self-paced Weight Consolidation (spWC) framework to attain robust continual learning via evaluating the discriminative contributions of previous tasks. To be specific, we develop a self-paced regularization to reflect the priorities of past tasks via measuring difficulty based on key performance indicator (i.e., accuracy). When encountering a new task, all previous tasks are sorted from “difficult” to “easy” based on the priorities. Then the parameters of the new continual learner will be learned via selectively maintaining the knowledge amongst more difficult past tasks, which could well overcome catastrophic forgetting with less computational cost. We adopt an alternative convex search to iteratively update the model parameters and priority weights in the bi-convex formulation. The proposed spWC framework is plug-and-play, which is applicable to most continual learning algorithms (e.g., EWC, MAS and RCIL) in different directions (e.g., classification and segmentation). Experimental results on several public benchmark datasets demonstrate that our proposed framework can effectively improve performance when compared with other popular continual learning algorithms.

Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients. However, most FL methods unreasonably assume data categories of FL framework are known and fixed in advance. Moreover, some new local clients that collect novel categories unseen by other clients may be introduced to FL training irregularly. These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories. To tackle the above issues, we propose a novel Local-Global Anti-forgetting (LGA) model. It ensures no local clients are left behind as they learn new classes continually, by addressing local and global catastrophic forgetting. Specifically, considering tackling class imbalance of local client to surmount local forgetting, we develop a category-balanced gradient-adaptive compensation loss and a category gradient-induced semantic distillation loss. They can balance heterogeneous forgetting speeds of hard-to-forget and easy-to-forget old categories, while ensure consistent class-relations within different tasks. Moreover, a proxy server is designed to tackle global forgetting caused by Non-IID class imbalance between different clients. It augments perturbed prototype images of new categories collected from local clients via self-supervised prototype augmentation, thus improving robustness to choose the best old global model for local-side semantic distillation loss. Experiments on representative datasets verify superior performance of our model against comparison methods.

Unsupervised domain adaptation without accessing expensive annotation processes of target data has achieved remarkable successes in semantic segmentation. However, most existing state-of-the-art methods cannot explore whether semantic representations across domains are transferable or not, which may result in the negative transfer brought by irrelevant knowledge. To tackle this challenge, in this paper, we develop a novel Knowledge Aggregation-induced Transferability Perception (KATP) module for unsupervised domain adaptation, which is a pioneering attempt to distinguish transferable or untransferable knowledge across domains. Specifically, the KATP module is designed to quantify which semantic knowledge across domains is transferable, by incorporating the transferability information propagation from constructed global category-wise prototypes. Based on KATP, we design a novel KATP Adaptation Network (KATPAN) to determine where and how to transfer. The KATPAN contains a transferable appearance translation module $\mathcal {T}_A(\cdot)$TA(·) and a transferable representation augmentation module $\mathcal {T}_R(\cdot)$TR(·), where both modules construct a virtuous circle of performance promotion. $\mathcal {T}_A(\cdot)$TA(·) develops a transferability-aware information bottleneck to highlight where to adapt transferable visual characterizations and modality information; $\mathcal {T}_R(\cdot)$TR(·) explores how to augment transferable representations while abandoning untransferable information, and promotes the translation performance of $\mathcal {T}_A(\cdot)$TA(·) in return. Comprehensive experiments on several representative benchmark datasets and a medical dataset support the state-of-the-art performance of our model.

Three-dimensional (3-D) object recognition has achieved satisfied achievement in both academia and industry. However, most traditional 3-D object classification methods implicitly assume that there are abundant training data from a static distribution. To relax the assumption, we target on a more challenging and realistic setting: few-shot incremental 3-D object learning (FSI3DL), which intends to incrementally classify the new coming 3-D objects with few training data. In order to achieve this, two key challenges need to be concerned: 1) the catastrophic forgetting issue caused by incremental 3-D data with irregular and redundant topological structures and 2) the overfitting issue caused by few-shot training data. To address the first challenge, we use Laplacian spectral analysis based on 3-D meshes to design an embedding network that consists of super-vertex graph convolution (SVGC) module and topology-aware graph attention (TAGA) module. The SVGC is designed to construct the discriminative local topological characteristics for representing the irregular 3-D meshes better. The TAGA is designed to identify redundant topological characteristics. To address the second challenge, a fine-tuning strategy with model alignment regularization is investigated. Furthermore, an embedding space selection and fusion (ESSF) strategy is proposed in the inference phase to mitigate catastrophic forgetting and overfitting further. Combining SVGC, TAGA, and alignment regularization with ESSF strategy, a novel topology-aware graph convolution network (TopGCN) is proposed to address the FSI3DL. Experiments on representative 3-D classification datasets validate the superiority of TopGCN.

To investigate the association between plant-based diet indices and sleep quality in Chinese middle-aged and older adults.

3-D object recognition has successfully become an appealing research topic in the real world. However, most existing recognition models unreasonably assume that the categories of 3-D objects cannot change over time in the real world. This unrealistic assumption may result in significant performance degradation for them to learn new classes of 3-D objects consecutively due to the catastrophic forgetting on old learned classes. Moreover, they cannot explore which 3-D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3-D objects. To tackle the above challenges, we develop a novel Incremental 3-D Object Recognition Network (i.e., InOR-Net), which could recognize new classes of 3-D objects continuously by overcoming the catastrophic forgetting on old classes. Specifically, category-guided geometric reasoning is proposed to reason local geometric structures with distinctive 3-D characteristics of each class by leveraging intrinsic category information. We then propose a novel critic-induced geometric attention mechanism to distinguish which 3-D geometric characteristics within each class are beneficial to overcome the catastrophic forgetting on old classes of 3-D objects while preventing the negative influence of useless 3-D characteristics. In addition, a dual adaptive fairness compensations’ strategy is designed to overcome the forgetting brought by class imbalance by compensating biased weights and predictions of the classifier. Comparison experiments verify the state-of-the-art performance of the proposed InOR-Net model on several public point cloud datasets.

As an ensemble average result, vibrational spectrum simulation can be time-consuming with high accuracy methods. We present a machine learning approach based on the range-corrected deep potential (DPRc) model to improve the computing efficiency. The DPRc method divides the system into “probe region” and “solvent region”; “solvent–solvent” interactions are not counted in the neural network. We applied the approach to two systems: formic acid C═O stretching and MeCN C≡N stretching vibrational frequency shifts in water. All data sets were prepared using the quantum vibration perturbation approach. Effects of different region divisions, one-body correction, cut range, and training data size were tested. The model with a single-molecule “probe region” showed stable accuracy; it ran roughly 10 times faster than regular deep potential and reduced the training time by about four. The approach is efficient, easy to apply, and extendable to calculating various spectra.

This work presents a novel visual-tactile fused clustering framework, called Lifelong Visual-Tactile Spectral Clustering (i.e., LVTSC), to effectively learn consecutive object clustering tasks for robotic perception. Lifelong learning has become an important and hot topic in recent studies on machine learning, aiming to imitate “human learning” and reduce the computational cost when consecutively learning new tasks. Our proposed LVTSC model explores the knowledge transfer and representation correlation from a local modality-invariant perspective under modality-consistent constraint guidance. For the modality-invariant part, we design a set of modality-invariant basis libraries to capture the latent clustering centers of each modality and a set of modality-invariant feature libraries to forcibly embed the manifold information of each modality. A modal-consistent constraint reinforces the correlation between visual and tactile modalities by maximizing the feature manifold correspondences. When the object clustering task comes continuously, the overall objective is optimized by an effective alternating direction method with guaranteed convergence. Our proposed LVTSC framework has been extensively validated for its effectiveness and efficiency on the three challenging real-world robotic object perception datasets.

Robot manipulation, for example, pick-and-place manipulation, is broadly used for intelligent manufacturing with industrial robots, ocean engineering with underwater robots, service robots, or even healthcare with medical robots. Most traditional robot manipulations adopt 2-D vision systems with plane hypotheses and can only generate 3-DOF (degrees of freedom) pose accordingly. To mimic human intelligence and endow the robot with more flexible working capabilities, 3-D vision-based robot manipulation has been studied. However, this task is still challenging in the open world especially for general object recognition and pose estimation with occlusion in cluttered backgrounds and human-like flexible manipulation. In this article, we propose a comprehensive analysis of recent progress about the 3-D vision for robot manipulation, including 3-D data acquisition and representation, robot-vision calibration, 3-D object detection/recognition, 6-DOF pose estimation, grasping estimation, and motion planning. We then present some public datasets, evaluation criteria, comparisons, and challenges. Finally, the related application domains of robot manipulation are given, and some future directions and open problems are studied as well.

Traditional 3D point cloud classification tasks focus on training a classifier in the closed-set scenario, where training and test data have the same label set and the same data distribution. In this work, we focus on a more challenging and realistic scenario in 3D point cloud classification task: universal domain adaptation (UniDA), where 1) data distributions for training and test data are different; and 2) for given label sets of training data and test data, they may have the shared classes and keep the private classes respectively, introducing an extra label set discrepancy. To solve UniDA problem, researchers have designed many methods based on 2D image datasets. However, due to the difficulty in capturing discriminative local geometric structures brought by the unordered and irregular 3D point cloud data, we cannot directly deploy the existing methods based on 2D image datasets to the 3D scenarios. To address UniDA in 3D scenarios, we develop a 3D universal domain adaptation framework, which consists of three modules: Self-Constructed Geometric (SCG) module, Local-to-Global Hypersphere Reasoning (LGHR) module and Self-Supervised Boundary Adaptation (SBA) module. SCG and LGHR generate the discriminative representation, which is used to acquire domain-invariant knowledge for training and test data. SBA is designed to automatically recognize whether a given label is from the shared label set or private label set, and adapts training and test data from the shared label set. To our best knowledge, this work is the first exploration of UniDA for 3D scenarios. Extensive experiments on public 3D point cloud datasets verify that the proposed method outperforms the existing UniDA methods.

Hydrogel-based wearable flexible pressure sensors have great promise in human health and motion monitoring. However, it remains a great challenge to significantly improve the toughness, sensitivity and stability of hydrogel sensors. Here, we demonstrate the fabrication of hierarchically structured hydrogel sensors by 3D printing microgel-reinforced double network (MRDN) hydrogels to achieve both very high sensitivity and mechanical toughness. Polyelectrolyte microgels are used as building blocks, which are interpenetrated with a second network, to construct super tough hydrogels. The obtained hydrogels show a tensile strength of 1.61 MPa, and a fracture toughness of 5.08 MJ m−3with high water content. The MRDN hydrogel precursors exhibit reversible gel–sol transitions, and serve as ideal inks for 3D printing microstructured sensor arrays with high fidelity and precision. The microstructured hydrogel sensors show an ultra-high sensitivity of 0.925 kPa−1, more than 50 times that of plain hydrogel sensors. The hydrogel sensors are assembled as an array onto a shoe-pad to monitor foot biomechanics during gaiting. Moreover, a sensor array with a well-arranged spatial distribution of sensor pixels with different microstructures and sensitivities is fabricated to track the trajectory of a crawling tortoise. Such hydrogel sensors have promising application in flexible wearable electronic devices.

Hydrogels have emerged as multifunctional interface materials between implantable bioelectronic devices and biotissues. The soft and wet materials with low and alterable mechanical properties can match the mechanical, chemical, electrical, and biological properties of biotissues and thus diminish the mechanical and electrical mismatch. Interactions at the hydrogel–biotissue and hydrogel–device interfaces have attracted broad research interest. Great efforts have been devoted to establishing instant, strong, and conformal adhesion at the interface by chemical bonding, electrostatic interaction, hydrogen bonding, supramolecular recognition, hydrophobic association, and even topological entanglements at the interfaces. This Perspective provides a brief account of representative progress on the hydrogel–tissue adhesive that forms seamless and conformal interface adhesion and applications in implantable devices for physiological, cardiac, and neuronal signal collection and electrical stimulation. Major challenges such as wet adhesion and the stability of the adhesive hydrogel–tissue interface are identified for examination in future investigations.

To explore the clinical value of contrast‐enhanced ultrasound (CEUS) in the differential diagnosis of benign and malignant subpleural pulmonary lesions (SPLs). Among 959 patients with SPLs who were scheduled to undergo ultrasound‐guided puncture in our department between January 2019 and June 2019, 506 patients were included and their B‐mode ultrasound and CEUS features, including the lesion's location, size, margin, echo, perfusion pattern of ultrasound contrast agent, degree of enhancement, homogeneity, vascular signs, and necrosis, were retrospectively investigated. All malignant cases were diagnosed by pathology, while benign cases were diagnosed by two respiratory physicians after comprehensive analysis of pathology, etiology, imaging, and clinical symptoms. Statistical differences in these features between the benign and malignant groups were then analyzed. There were 506 cases in this study, including 219 benign cases and 287 malignant cases. Among them, 351 were males and 155 were females, with an average age of 59 ± 16 years. There were statistically significant differences between benign and malignant groups in the perfusion pattern, the degree of enhancement, and vascular signs. The features of the malignant group included local‐to‐whole perfusion pattern, hypo‐enhancement, and curly hair sign, while those of the benign group included a centrifugal perfusion pattern, iso‐enhancement and hyper‐enhancement, and dendritic sign. There was no statistically significant difference between the two groups in homogeneity and necrosis. CEUS enhancement mode is different between benign and malignant SPLs, which can provide supplementary information for the differential diagnosis of SPLs in the existing imaging diagnosis.

Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are of prescribed size and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL3). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our FCL3 model firstly transfers knowledge from these libraries to encode the new task, i.e., effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then: 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time; or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multitask data sets, and the experimental results demonstrate that our FCL3 model can achieve better performance than most lifelong learning frameworks, even batch clustered multitask learning models. [ABSTRACT FROM AUTHOR]

The arrival time difference ratio, an analysis of pulmonary artery and bronchial artery contrast-enhanced US, can provide an accurate method to distinguish benign subpleural pulmonary lesions from malignant ones.

In this work, the phase equilibrium solubility of 5-fluorocytosine (5-FC) in dimethyl sulfoxide (DMSO), N,N-dimethylformamide (DMF), 1-butanol, acetonitrile, 1-propanol, 1,4-dioxane, acetone, ethyl acetate, 2-propanol, 2-butanone, and mixture systems (DMSO + 2-propanol and DMF + 2-propanol) at a series of temperatures (283.15–323.15 K) is determined, and model correlation, solvent effect, and preferential solvation analysis are researched. The solubility of 5-FC is related to temperature, which decreases with decreasing temperature. For monosolvent systems, the mole solubility order is DMSO > DMF > 1-butanol > 1-propanol > 2-propanol > acetone > 2-butanone > 1,4-dioxane > acetonitrile > ethyl acetate. The solvent effect has been studied in monosolvents, hydrogen-bonding acceptor (HBA) interactions and nonspecific dipolarity/polarizability interactions favor dissolution, and solvent self-cohesiveness is not beneficial to dissolution. For two mixture systems, the mole solubility of 5-FC in DMSO + 2-propanol solvent systems is higher than that in DMF + 2-propanol solvent systems. A dimensionless parameter (δ) is defined to observe the deviation from ideality of mixed solvents whose values are all greater than zero. The equilibrium constant of the solvent exchange process (Kps) can be used to evaluate the preferential solvation of 5-FC. DMSO or DMF dissolves 5-FC in the local region around 5-FC preferentially. The equilibrium solubility data is correlated by four correlation models (modified Apelblat, λh, CNIBS/R-K, and Jouyban–Acree models). The greatest relative average deviation (RAD) and root-mean-square deviation (RMSD) values are 2.01% and 11.87 × 10–5in pure solvent systems and 2.20% and 5.99 × 10–5in mixture systems, respectively. These four models show excellent correlation with the experimental solubility data.

Cilostazol is a neutral molecule with poor solubility. In this work, the solubility data of cilostazol in 10 pure and 1,4-dioxane + ethanol binary solvents was measured using the isothermal saturation method within the temperature range from 273.15 to 313.15 K at atmospheric pressure. In the 10 pure solvents, the results indicated that the maximum and minimum solubility of cilostazol appeared in 1,4-dioxane and isopropanol, respectively. The sequence of solubility was 1,4-dioxane > 2-butanone > acetone > 1-butanol > acetonitrile > methanol > n-propanol > ethanol > ethyl acetate > isopropanol. In a mixture of 1,4-dioxane + ethanol, it first increased and then decreased with increasing mass fraction of 1,4-dioxane in 1,4-dioxane + ethanol mixtures at all selected temperatures. The dissolution process of cilostazol was analyzed by the solvent properties. Moreover, the solubility data of cilostazol in the 10 pure solvents was correlated with the modified Apelblat and λhequations. The result in binary mixture at various temperatures was evaluated by the Jouyban–Acree model. In a pure solvent, the RAD values between the experimental and calculated data with modified Apelblat and λhequations were all no larger than 3.57 × 10–2and 11.40 × 10–2, respectively. In 1,4-dioxane + ethanol binary mixtures, the RAD data of the Jouyban–Acree model was 6.22 × 10–2. The study of the dissolution process for cilostazol is an important aspect of its purification and recrystallization.

Adhesive hydrogels have broad applications in tissue adhesives, hemostatic agents, and biomedical sensors. Various bio‐inspired glues and synthetic adhesives are clinically used as conventional hemostatic agents and auxiliary tools for wound closure. Medical adhesives are needed to effectively and quickly control bleeding, thereby reducing the risk of complications caused by severe blood loss. Medical sensors need to have excellent skin compliance, mechanical properties, sensitivity, and biological safety. This review focuses on recent progress in adhesive hydrogel systems, their structures, adhesion mechanisms, construction strategies, and emerging applications in the biomedical field.

The state-of-the-art multitask multiview (MTMV) learning tackles a scenario where multiple tasks are related to each other via multiple shared feature views. However, in many real-world scenarios where a sequence of the multiview task comes, the higher storage requirement and computational cost of retraining previous tasks with MTMV models have presented a formidable challenge for this lifelong learning scenario. To address this challenge, in this article, we propose a new continual multiview task learning model that integrates deep matrix factorization and sparse subspace learning in a unified framework, which is termed deep continual multiview task learning (DCMvTL). More specifically, as a new multiview task arrives, DCMvTL first adopts a deep matrix factorization technique to capture hidden and hierarchical representations for this new coming multiview task while accumulating the fresh multiview knowledge in a layerwise manner. Then, a sparse subspace learning model is employed for the extracted factors at each layer and further reveals cross-view correlations via a self-expressive constraint. For model optimization, we derive a general multiview learning formulation when a new multiview task comes and apply an alternating minimization strategy to achieve lifelong learning. Extensive experiments on benchmark data sets demonstrate the effectiveness of our proposed DCMvTL model compared with the existing state-of-the-art MTMV and lifelong multiview task learning models. [ABSTRACT FROM AUTHOR]

Ion-conductive hydrogel sensors have attracted great research interests for applications in wearable devices, electronic skins, and implantable sensors, but most such sensors are fragile, with low conductivity and sensitivity. This study reports on novel ion-conductive double network hydrogels with a cross-linked helical structure, hydrophobic association, and metal-ion coordination. The helical κ-carrageenan first network and the second network cross-linked by Pluronic F127 diacrylate micelles and tridentate Fe3+–COO–coordination work synergistically to show the tensile strength of 2.7 MPa, fracture strain of 1400%, and tensile toughness of 9.82 MJ m–3and fatigue resistance against cyclic loadings with high strains. The hydrogels show an ion conductivity of 1.15 S m–1, a strain sensitivity of up to 2.8, and a pressure sensitivity of 0.33 kPa–1. Sensor arrays fabricated from the conductive hydrogels provide an in-plane detection of pressures less than 200 Pa. Such hydrogel sensors have potential applications to electron skins and implantable sensors.

ABSTRACTDocetaxel is an important anti-microtubule agent used to treat a variety of solid tumors, including breast cancer; notably, docetaxel-containing regimens improve outcomes for patients in metastatic, adjuvant, and neoadjuvant settings. However, the effectiveness of docetaxel in clinical practice can be compromised by suboptimal management of side effects. Here, we report two cases of docetaxel-based chemotherapy regimens in patients who exhibited invasive ductal breast cancer and underwent two different clinical treatment approaches. A 58-year-old postmenopausal female received salvage treatment with 8 cycles of docetaxel (67 mg/m2), and a 74-year-old female received 1 cycle of docetaxel (100 mg/m2). The two patients exhibited considerable hearing loss two days later. Of note, both patients had no hearing loss symptoms prior to docetaxel. Thus, ototoxicity may be a side effect of docetaxel that should be considered during treatment.

Biotissue adhesives and antibacterial materials have great potential applications in wound dressing, implantable devices, and bioelectronics. In this study, stretchable tissue adhesive hydrogels with intrinsic antibacterial properties have been demonstrated by copolymerizing zwitterionic monomers with ionic monomers. The hydrogels are stretchable to about 900% strain and show a modulus of 4–9 kPa. The zwitterionic moieties provide strong dipole–dipole interaction, electrostatic interaction, and hydrogen bonding with the skin surface, and thus show adhesion strength values of 1–4 kPa to skin. Meanwhile, the copolymerized cationic or anionic monomers break the intrinsic electrostatic stoichiometry of the zwitterionic units and thus mediate the electrostatic interactions and the adhesion strength with the surface. The stretchable hydrogels form a robust and compliant (due to low modulus and stretchability) adhesive to skin, rubber, glass, and plastics, and could be repeatedly peeled-off and readhered to the skin. Moreover, the abundant quaternary ammonium (QA) groups in the zwitterionic moieties and the added QA groups endow it outstanding antibacterial properties (>99%). These stretchable tissue adhesive antibacterial hydrogels are promising for wound dressings and implantable devices.

The solubility of antofloxacin hydrochloride in deep eutectic solvent (DES) aqueous solutions between choline chloride (ChCl) and p-toluenesulfonic acid (PTS), (ethanol + water), and (ethanol + acetonitrile) cosolvent mixtures were investigated by means of the isothermal dissolution equilibrium method at 273.15–318.15 K under pressure condition of 101.3 kPa. Experimental solubility was strongly increased more than about 560-fold in aqueous ChCl/PTS system with the increasing temperature and mass fraction of DES, and it is 404 times in ethanol + water and 60 times in ethanol + acetonitrile. The solubility maximum 0.01226 (318.15 K, in mole fraction) was obtained in wDES= 0.6 of the aqueous ChCl/PTS DES. X-ray power diffraction (XPRD) was employed to examine the equilibrium solid phase samples of antofloxacin hydrochloride in three systems resulting in no polymorphic transformation, solvate formation or crystal transition compared to the raw sample. The modified Apelblat equation was exclusively used to correlate the equilibrium solubility. Results show that the highest relative average deviation (RAD) and root-mean-square deviation (RMSD) were 2.35 × 10–2and 3.45 × 10–5, respectively. The modified Apelblat equation could be used to correlate the results. Data obtained elucidated that ChCl/PTS DES is an effective additive for enhancing the antofloxacin hydrochloride solubility.

The aim of this study was to assess local response to radiotherapy (RT) in a quantitative manner by evaluating the bone density of metastases. Spinal and pelvic bone metastases in 44 patients with breast cancer who were treated between May 2010 and December 2016 were retrospectively assessed. Bone density values of irradiated and unirradiated bone metastases before, 1-3 months after, 4-6 months after, and 7-9 months after RT were compared. At each time point, mean bone density ± standard deviation values were measured in Hounsfield units (HU) from computed tomography (CT) scans. Student's t-test was used for statistical analyses of the differences in bone density and for univariate analysis of the prognostic factors for differences in bone density at various time points after RT. Mean bone densities in irradiated and unirradiated bone metastases before RT were 297.31 ± 211.93 HU and 326.29 ± 228.61 HU, respectively. At the subsequent three time points examined, the mean bone density values in the irradiated and unirradiated bone metastases were: 61.97 ± 78.58 HU (P = 0.000) and 36.93 ± 52.49 HU (P = 0.001); 149.07 ± 133.27 HU (P = 0.000) and 68.40 ± 101.10 HU (P = 0.000); and 183.94 ± 168.30 HU (P = 0.000) and 88.21 ± 159.49 HU (P = 0.004), respectively, in each case. Patients receiving bisphosphonates exhibited greater increases in bone density in their metastases 1-3 months after RT (83.04 ± 82.18 HU vs 26.86 ± 60.55 HU, respectively; P = 0.044), whereas chemotherapy before RT was associated with significantly lower increases in bone density at the subsequent three time points [(37.53 ± 67.66 HU vs 93.63 ± 80.36 HU, P = 0.027), (99.30 ± 107.92 HU vs 180.24 ± 127.85 HU, P = 0.030), and (126.07 ± 141.77 HU vs 236.28 ± 158.22 HU, P = 0.024), respectively, in each case]. Comparing bone density values determined from CT scans appears to be a practicable and reproducible method for assessing local response to RT for bone metastasis of breast cancer. Increased bone density was also observed in the irradiated bone metastases.

Peptidyl arginine deiminases (PADs) are important enzymes in many diseases, especially those involving inflammation and autoimmunity. Despite many years of effort, developing isoform-specific inhibitors has been a challenge. We describe herein the discovery of a potent, noncovalent PAD2 inhibitor, with selectivity over PAD3 and PAD4, from a DNA-encoded library. The biochemical and biophysical characterization of this inhibitor and two noninhibitory binders indicated a novel, Ca2+competitive mechanism of inhibition. This was confirmed via X-ray crystallographic analysis. Finally, we demonstrate that this inhibitor selectively inhibits PAD2 in a cellular context.

Equilibrium solubility of hesperidin in aqueous solutions of ethanol, isopropanol, propylene glycol, and n-propanol was determined by static technique at temperatures ranging from 293.15 to 333.15 K under p= 101.1 kPa. The hesperidin solubility increased monotonously with the increase in mass fraction of ethanol isopropanol, propylene glycol, and n-propanol and temperature. At the same composition of alcohol and temperature, the mole fraction solubility of hesperidin was largest in propylene glycol + water among the four cosolvent mixtures. The solubilities of hesperidin were correlated with Jouyban–Acree model, Apelblat–Jouyban–Acree model, and van’t Hoff–Jouyban–Acree model. The obtained average relative deviations were less than 2.38%.

In this work, nine ternary phase diagrams and three quaternary phase diagrams of different systems at 298.15, 308.15, and 318.15 K were constructed. The ternary solid–liquid phase equilibrium of (uric acid + adenine + water), (uric acid + guanine + water), and (adenine + guanine + water) and quaternary solid–liquid phase equilibrium of (uric acid + adenine + guanine + water) were determined at atmospheric pressure (p= 101.3 kPa). The binary solubility of uric acid, adenine, and guanine in water at different temperatures was determined and correlated using the modified Apelblat model, and the mole solubility of adenine was about 30 and 180 times that of uric acid and guanine, respectively. The relative average deviation and root-mean-square deviation values were no more than 1.73 × 10–2and 0.102 × 10–5, respectively. For a ternary system, each phase diagram consisted of one cosaturated point, two cosaturated solubility curves, and three solid-phase regions (two single solid-phase regions and one double solid-phase region). The mutual solubility of uric acid (adenine or guanine) in the systems of (uric acid + adenine + water), (uric acid + guanine + water), or (adenine + guanine + water) increased with increasing temperature. The unsaturated regions enlarged, and the single or double solid-phase regions diminished. For a quaternary system, each Janeck projection of the (uric acid + adenine + guanine + water) system consisted of one cosaturated point, three cosaturated solubility curves, and three solid-phase regions. At the same temperature, the solid-phase regions of three compounds were ranked as guanine > uric acid > adenine. The construction of binary, ternary, and quaternary phase diagrams of uric acid, adenine, and guanine in water was of great significance for studying the deposition of uric acid, and this can also provide reference and basic phase equilibrium data for further study of gout treatment and effects of uric acid on the cardiovascular system.

Museum exhibition halls are one of the most visually demanding buildings. However, unique lighting methods, luminance contrast, display cabinet reflections lead to complex spatial luminance distribution and intense glare, thus seriously affecting the visual effect. Modeling accurate glare calculations is an effective way to reduce the risk of visual discomfort. In this study, we obtain the real glare perception in the lighting scenes of museum exhibition halls through small-sample evaluation experiments, and then propose an improved model on the basis of testing the accuracy rate of UGR to be 41.67%, which contains key parameters characterizing the effects caused by multiple glares. Then we optimized the coefficients and power indices according to the real glare perception, and verified that the accuracy rate of glare calculation in museum exhibition halls was improved by 36.46% compared with that of UGR, and gave the glare rating corresponding to the calculated value of the model.

Biocompatible conductive hydrogels with intrinsic flexibility, high sensitivity, linearity and outstanding reliability are highly demanded for wearable devices or implantable sensors. Here we report novel tough conductive hydrogels composed of interpenetrating polyaniline (PANI) and poly(acrylamide-co-hydroxyethyl methyl acrylate) (P(AAm-co-HEMA)) networks. Intrinsic interactions between the conductive PANI network and the flexible P(AAm-co-HEMA) endowed hydrogels with outstanding strength and toughness to cyclic loadings. The conductive hydrogels show very high sensitivity (gauge factor 11) and outstanding linear dependence of sensitivity on strain. Strain sensors based on the conductive hydrogels demonstrate reliable detection of repeated large strains and subtle vibrations, including the movements of various human joints, pulses and voiceprints. Moreover, a prototype 2D sensor array is fabricated to sense strains or pressures in the two dimensions, which is promising for electronic skin, touchpads, biosensors, human-machine interfaces, biomedical implants, wearable electronic devices and so on.

The solubilities of 3-nitrobenzonitrile in solvent mixtures of ethyl acetate (1) + methanol (ethanol, n-propanol or isopropyl alcohol) (2) determined over the temperature range from 278.15 to 318.15 K under atmospheric pressure (101.1 kPa) with the isothermal dissolution equilibrium method were reported. They increased with a rise of temperature and mass fraction of ethyl acetate, and the largest solubility value was observed in neat ethyl acetate for all the binary mixtures investigated. The temperature and solvent composition dependence of 3-nitrobenzonitrile solubility was analyzed through the Jouyban–Acree, van’t Hoff–Jouyban–Acree, and Apelblat–Jouyban–Acree models acquiring average relative deviations lower than 1.57% and root-mean-square deviation lower than 11.52 × 10–4for correlative investigations. In addition, the preferential solvation parameters (δx1,3) of 3-nitrobenzonitrile by ethyl acetate were determined from experimental solubility values by using the inverse Kirkwood–Buff integrals. It was found that alcohol preferentially solvated 3-nitrobenzonitrile in alcohol-rich mixtures while ethyl acetate forms local solvation shells in compositions from intermediate composition up to neat ethyl acetate. The former case was possibly due to the ordered structure of alcohol molecules around the apolar group of 3-nitrobenzonitrile, which was formed via hydrophobic hydration in alcohol-rich solutions.

The thiamphenicol solubility in aqueous cosolvent solutions of ethanol (1), methanol (1), 1,4-dioxane (1), and N,N-dimethylformamide (DMF, 1) was measured via the isothermal dissolution equilibrium method at temperatures ranging from 278.15 to 318.15 K under local pressure (101.1 kPa). At fixed composition of ethanol (methanol, 1,4-dioxane, or DMF) and temperature, the solubility of thiamphenicol was larger in DMF + water mixtures than in the ethanol/methanol/1,4-dioxane mixtures. The local solvent proportions were acquired with the method of inverse Kirkwood–Buff integrals. The absolute value of these preferential solvation parameters were all lower than 1.0 × 10–2for ethanol (1) + water (2) and 1,4-dioxane (1) + water (2) solutions in water-rich compositions and for methanol (1) + water (2) solutions in whole compositions. In the former two cosolvent mixtures in intermediate compositions and cosolvent-rich regions, thiamphenicol was preferentially solvated by cosolvent. However, for the DMF (1) + water (2) solutions, water solvated preferentially thiamphenicol in water-rich compositions and by DMF in intermediate and DMF-rich compositions. This case by cosolvent might be illustrated based on higher basic behavior of water, which interacted with Lewis acidic groups of thiamphenicol. In addition, the solubility of thiamphenicol was described with the van’t Hoff–Jouyban–Acree, Jouyban–Acree, and Apelblat–Jouyban–Acree models. The obtained average relative deviations were no greater than 1.85%. Furthermore, the solvent effect treatment through the KAT-LSER model indicated that the solubility variation was significantly affected by the cavity term.

Iron oxide (Fe2O3) was doped onto fullerene[60] (C60) to form a C60-Fe2O3composite using an easy and scalable impregnation method. The as-prepared C60-Fe2O3samples were characterized by powder X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, high-resolution transmission electron microscopy, UV-vis absorption spectroscopy, Raman spectroscopy, and Fourier transform infrared spectroscopy. The photocatalytic activity of the C60-Fe2O3catalyst was evaluated by examining the degradation of methylene blue (MB), rhodamine B (RhB), methyl orange (MO), and phenol under visible light (λ> 420 nm) in the presence of hydrogen peroxide. The results showed that the catalyst exhibited excellent catalytic properties over a wide pH range 3.06–10.34. Under optimal conditions, 98.9% discoloration and 71% mineralization of MB were achieved in 80 min. Leaching test results indicated that the leaching of iron from the catalyst was negligible and that the catalyst had a high photocatalytic activity after five reaction cycles. The catalyst was also efficient in the degradation of RhB, MO, and phenol. These findings could be attributed to the synergetic effects of C60and Fe2O3. We used active species trapping experiments to determine the main active oxidant in the photocatalytic reaction process and found that hydroxyl radicals played a major role in the entire process.

Farber disease is a rare autosomal recessive disorder caused by acid ceramidase deficiency that usually presents as early-onset progressive visceral and neurologic disease. To understand the neurologic abnormality, we investigated behavioral, biochemical, and cellular abnormalities in the central nervous system of Asah1P361R/P361Rmice, which serve as a model of Farber disease. Behaviorally, the mutant mice had reduced voluntary locomotion and exploration, increased thigmotaxis, abnormal spectra of basic behavioral activities, impaired muscle grip strength, and defects in motor coordination. A few mutant mice developed hydrocephalus. Mass spectrometry revealed elevations of ceramides, hydroxy-ceramides, dihydroceramides, sphingosine, dihexosylceramides, and monosialodihexosylganglioside in the brain. The highest accumulation was in hydroxy-ceramides. Storage compound distribution was analyzed by mass spectrometry imaging and morphologic analyses and revealed involvement of a wide range of central nervous system cell types (eg, neurons, endothelial cells, and choroid plexus cells), most notably microglia and/or macrophages. Coalescing and mostly perivascular granuloma–like accumulations of storage-laden CD68+microglia and/or macrophages were seen as early as 3 weeks of age and located preferentially in white matter, periventricular zones, and meninges. Neurodegeneration was also evident in specific cerebral areas in late disease. Overall, our central nervous system studies in Asah1P361R/P361Rmice substantially extend the understanding of human Farber disease and suggest that this model can be used to advance therapeutic approaches for this currently untreatable disorder.

Graphene-supported BiFeO3(rG-BiFeO3) was synthesized by the hydrothermal method and used for the efficient removal of ammonia under visible light. X-ray diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, and ultraviolet-visible diffuse reflectance spectroscopy were conducted to characterize the rG-BiFeO3. The specific surface area of the rG-BiFeO3catalyst was 48.6 m2/g, larger than that of BiFeO3(21.0 m2/g). When used as a heterogeneous photocatalyst, rG-BiFeO3achieved 91.20% degradation of a NH3-N solution (50 mg/L) at pH = 11 under visible-light irradiation in the absence of hydrogen peroxide. The degradation of ammonia followed pseudo-first-order kinetics, and the catalyst retained high photocatalytic activity after seven reaction cycles. Study of the mechanism showed that the holes, superoxide anion radicals, and hydroxyl radicals, arising from the synergy between graphene and BiFeO3, oxidized NH3directly to N2.

This paper gives a definition of the industrial internet and expounds on the academic connotation of the future industrial internet. From this foundation, we outline the development and current status of the industrial internet in China and globally. Moreover, we detail the avant-garde paradigms encompassed within the National Natural Science Foundation of China (NSFC)’s “Future Industrial Internet Fundamental Theory and Key Technologies” research plan and its corresponding management strategies. This research initiative endeavors to enhance interdisciplinary collaborations, aiming for a synergistic alignment of industry, academia, research, and practical implementations. The primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial internet. It is poised to traverse the “first mile”, encompassing foundational research and pioneering innovations specific to the industrial internet, and seamlessly bridges to the “last mile”, ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market applications. The anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

Remote technology refers to applications that rely on wireless/wired network communication technology to enable the effective interconnection of multiple terminals distributed in different geographical locations, which helps to achieve effective information dissemination and sharing, and thereby improve the convenience and efficiency of remote working and learning. With the rapid development of sensor technology, information and communications technology (ICT), artificial intelligence (AI), and other related technologies, remote technology has made considerable progress since the 1970s and has been widely used in many fields, such as education, medicine. Especially the outbreak of COVID-19, remote technology achieves dramatically development within a short period of time, and leads to big changes in the way of working in many industries and peoples’ lifestyles. Therefore, it was selected as one of the “Top 10 Global Breakthrough Technologies” by MIT Technology Review in 2021.①①Original source in Chinese: Y. Cong, Remote everything, Bulletin of National Natural Science Foundation of China. 35 (3) (2021) 412-413.

Phase-only (constant modulus) beamforming synthesis is a key issue in radar, communication and sonar systems. Due to the constant modulus constraint (CMC), the problem is non-convex and NP-hard. Most existing methods either indirectly solve the problem by relaxing with performance degrading, or by directly solve the problem in the high dimensional space with huge computational complexity. To address this problem, the low-complexity Accelerated Coordinate Descent (ACD) based method is proposed by directly solving the problem in the low dimensional space. First, the Coordinate Descent (CD) based method is proposed, where the high-dimensional problem is divided into multiple one-dimensional problems, to which the optimal solutions are found. Then, the ACD method is derived by the square iterative technique, which needs less computational complexity due to it only uses linear-complexity storage. Both the continuous and discrete phases are designed in the case of the uniform and random linear arrays. Compared with the existing methods, the proposed method achieves 4 dB beampattern sidelobe reduction and 1.5 magnitude complexity decreasing. [ABSTRACT FROM AUTHOR]

Scientific community is still lacking of research in sound field characteristics of the Chinese traditional architecture courtyard currently, the author chose three outdoor courtyards sound field in Shenyang Palace Museum as the research object, and firstly used the sound cutting method to do system testing of the important acoustic indicators reverberation time T10 in the Palace courtyard, then placed a dodecahedron sound player as a continuous sound source in the courtyard, test the sound pressure level on the adjacent courtyard, the purpose is to analyze the sound privacy degree of the inner courtyard of the Palace. The results may help to understand the sound field characteristics of the palace courtyard, and lay a solid foundation for the future research.

In this study, the authors propose a robust and high accurate pose estimation algorithm to solve the perspective-N-point problem in real time. This algorithm does away with the distinction between coplanar and non-coplanar point configurations, and provides a unified formulation for the configurations. Based on the inverse projection ray, an efficient collinearity model in object–space is proposed as the cost function. The principle depth and the relative depth of reference points are introduced to remove the residual error of the cost function and to improve the robustness and the accuracy of the authors pose estimation method. The authors solve the pose information and the depth of the points iteratively by minimising the cost function, and then reconstruct their coordinates in camera coordinate system. In the following, the optimal absolute orientation solution gives the relative pose information between the estimated three-dimensional (3D) point set and the 3D mode point set. This procedure with the above two steps is repeated until the result converges. The experimental results on simulated and real data show that the superior performance of the proposed algorithm: its accuracy is higher than the state-of-the-art algorithms, and has best anti-noise property and least deviation by the influence of outlier among the tested algorithms.

In this paper, we proposed a faulty insulator diagnosis method for insulator faulty detection based on repetitiveness feature from UAV video sequence. The repetitiveness feature which describes the relationship of each structural element of insulator is employed for the insulator faulty diagnosis, and  is robust to noise, camera motivation  and complex backgrounds. Base on the repetition of structural element, our method can not only deal with single insulator with multi-faults, but also work on double insulator strings. The recognition results on insulator dataset and UAV video show the effectiveness of our method.

Porous and hollow porous microspheres prepared by a modified double emulsion method show potential to control the release of proteins and demonstrate to be efficient in promoting cell compatibility of hydrogels.

Abstract: We present biologically motivated simple models to study phyllotaxis patterns. A simplified MaxMin-principle is applied for determining new-born primordium appearance. We propose a new repulsion pressure model to interpret the angle movement of each primordium influenced by the repulsion from all other primordia or from the nearest left and right neighbors. For a large range of growing velocities, both mechanisms can generate uniformly packed patterns under suitable settings of repulsion parameters. Measurement of the pattern uniformity and demonstration of robustness are also provided. [Copyright &y& Elsevier]

BOS-013 actinomycete strain was obtained by separating difference soil of areas from ChangBai Mountain. The antifungal antibiotic produced by Streptomyces Hygroscopicus BOS-013 was firstly purified by means of macro-porous adsorbent resin and thin-layer chromatography. Separation and Purification of the antifungal antibiotic from its fermentation broth of streptomyces hygroscopicus BOS-013 strain were further carried out by Simulated Moving Bed Chromatography and then the crystal of the antibiotic with high purity was got. In this paper, the methods of purification by adsorbing of micro-porous adsorbent resin and detection by high performance liquid chromatography were established. The exact structure of the antibiotic was identified by mass spectrometry and NMR spectra.

In a high-speed rotor system, vibration control is necessary to ensure steady operation of the system. Magnetic damper is one of the effective means to control the vibration. This paper is concerned with the optimal design of a passive magnetic damper’s system parameter, including its geometric and electrical parameters. The relation between the damper’s system parameter and its damping coefficient is derived. A mathematical model that maximizes the damping coefficient is built, with the constraints of the geometric and electrical parameters. The optimization program is based on the algorithm of sequential quadratic programming method. The program yields the maximum damping coefficient under the given condition of the rotor’s diameter.

AbstractModeling genetic regulatory networks is an important problem in genomic research. Boolean Networks (BNs) and their extensions Probabilistic Boolean Networks (PBNs) have been proposed for modeling genetic regulatory interactions. In a PBN, its steady-state distribution gives very important information about the long-run behavior of the whole network. However, one is also interested in system synthesis which requires the construction of networks. The inverse problem is ill-posed and challenging, as there may be many networks or no network having the given properties, and the size of the problem is huge. The construction of PBNs from a given transition-probability matrix and a given set of BNs is an inverse problem of huge size. We propose a maximum entropy approach for the above problem. Newton's method in conjunction with the Conjugate Gradient (CG) method is then applied to solving the inverse problem. We investigate the convergence rate of the proposed method. Numerical examples are also given to demonstrate the effectiveness of our proposed method.