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We propose a framework to generate customized summarizations of visual data collections, such as collections of images, materials, 3D shapes, and 3D scenes. We assume that the elements in the visual data collections can be mapped to a set of vectors in a feature space, in which a fitness score for each element can be defined, and we pose the problem of customized summarizations as selecting a subset of these elements. We first describe the design choices a user should be able to specify for modeling customized summarizations and propose a corresponding user interface. We then formulate the problem as a constrained optimization problem with binary variables and propose a practical and fast algorithm based on the alternating direction method of multipliers (ADMM). Our results show that our problem formulation enables a wide variety of customized summarizations, and that our solver is both significantly faster than state‐of‐the‐art commercial integer programming solvers and produces better solutions than fast relaxation‐based solvers. [ABSTRACT FROM AUTHOR]

Similarity graph learning is the most key technique for multiview spectral clustering. However, existing methods fail when applied to uncertain data contaminated with various types of noise in an open environment. Due to the damaged structure by noise, unreliable similar relationships are learned, which extends similarity inconsistency among views. Moreover, the high-order correlation hidden in graphs are ignored generally. To address these problems, we propose a reliable similarity learning scheme for multiview clustering on uncertain data. This method can significantly improve spectral clustering performance in a noisy environment, and the contributions of our scheme include the following three aspects: 1) Uncertain data subspace reconstruction and adaptive graph learning are combined to construct a view-specific graph from high-quality recovered data, thus improving robustness. 2) A low-rank tensor constraint is utilized to facilitate multiview fusion, where the latent high-order correlation among view graphs will be fully explored when learning the consensus graph structure. 3) Data recovery, view-specific graphs, and latent consensus tensor structure are assembled into a unified framework, to be optimized jointly for mutual benefit. Our study also develops an efficient algorithm for obtaining overall solutions. The experimental results on several datasets demonstrate that our proposed approach shows significant improvements in robustness and evaluation metrics over the comparison methods. [ABSTRACT FROM AUTHOR]

Rational design of high efficient and low cost oxygen evolution reaction (OER) electrodes is critical for electrochemical water splitting. Herein, we report Reevesite Ni−Fe layered double hydroxide (NiFe‐(CO32−)‐LDH) nanosheet arrays as novel OER catalysts in alkaline electrolyte. These self‐supported nanosheet arrays with a composition of Ni6Fe2(CO3)(OH)16 ⋅ 4H2O on Ni foam (NF) were synthesized by a one‐pot hydrothermal method, in which NF served as both the conductive substrate and Ni source. The morphology and OER activity can be tuned by iron concentration. In 1 M KOH, the optimized NiFe‐(CO32−)‐LDH catalyst only required overpotentials of 228 and 264 mV to deliver current densities of 10 and 100 mA cm−2, respectively, with a small Tafel slope of 37 mV dec−1. Remarkably, this catalyst also showed outstanding durability by long‐term operation for 20 h with 96 % retention. Our work provides attractive cost‐effective 3D catalysts for efficient and robust water oxidation at alkaline media. [ABSTRACT FROM AUTHOR]

Inspired by the knowledge that a nonchiral metal nanocrystal could be bestowed with chirality when the metal nanocrystal and the chiral molecules form complexes, in this contribution, a two-step pyrolytic route to obtain chiroptical carbon quantum dots (CQDs) was attempted through the optically active surface passivation procedure with enantiomers. Here, tartaric acid (TA) and citric acid (CA), one being a chiral molecule and the other being an achiral molecule, were used as carbon sources to produce a carbon core, respectively, and D / L -penicillamine (D / L -Pen) molecules were utilized as chiral ligands to further passivate it in the two-step pyrolytic route. The strategy is applicative no matter whether the carbon source is from chiral or achiral molecules during the first carbonization step. Also, the second pyrolytic reaction temperature, lower than the melting point of the surface passivation enantiomeric ligand, is critical to preserve the chiral information and to generate the chiral centers of the CQDs. Based on this simple approach, it is possible to envision the design and fabrication of new types of more sophisticated optically active photoluminescence carbon nanodots, which may be applied in chiral catalysis, chiral recognition and sensing. [ABSTRACT FROM AUTHOR]

Biological thiols play a key role in biological processes and are involved in a variety of diseases. The discriminative detection of biological thiols is still challenging. In this contribution, a platform, an energy transfer-based quenching system composed of nitrogen and sulphur co-doped carbon dots (N, S-CDs) and gold nanoparticles (AuNPs), was established to discriminate glutathione (GSH) from other competitive biothiols including cysteine (Cys) and homocysteine (Hcy) based on a photoluminescence (PL) “switch-on” signal readout. The presence of GSH can encapsulate AuNPs in priority because of the strong affinity towards AuNPs and the steric hindrance effect of GSH, leaving little chance for the N, S-CDs binding on the surface of AuNPs and thus resulting in the PL recovery of N, S-CDs. Compared with the nitrogen-doped carbon dots (N-CDs), the N, S-CDs can enhance 10 times sensitivity for the designed PL “switch-on” sensing strategy. The proposed method has a detection limit of 3.6 nM and can be successfully applied for the detection of GSH in human serum. [ABSTRACT FROM AUTHOR]

To investigate in patients with retinoblastoma (RB). We collected 0.1 ml AH of 35 children with RB and 20 patients with congenital cataracts as controls. Multiplex enzyme-linked immunosorbent assays (ELISAs) and Luminex xMAP technology were used to assess 45 cytokines/chemokines, matrix metalloproteinases (MMPs), and acute-phase proteins in the identification cohort. The concentrations of IL-6, IL-7, IL-8, IFN-γ, PIGF-1, VEGF-A, β-NGF, HGF, EGF and FGF-2 were significantly higher in the AH of patients with RB than those in the control group (P<0.05). The study showed that the higher levels of IP-10, IL-6, IL-7, IL-8, IFN-γ, PIGF-1, VEGF-A, β-NGF, HGF, EGF and FGF-2 in AH may be associated with RB. Our findings may facilitate a better understanding of the molecular pathways of tumors and solid molecular targets for new strategies for therapy and the earlier diagnosis of RB. [ABSTRACT FROM AUTHOR]

The bilateral filter (BF) has showed great effectiveness for a variety of problems. However, its brute‐force implementation is time consuming. One way of accelerating a BF is to approximate the nonlinear range kernel of the BF by a set of linear time shiftable kernels. To achieve this goal, only finite values of the kernel of the BF have been used to perform smoothing due to the quantisation of digital images. Thus, the filtering results are not changed by substituting the range kernel with the function having the same values at finite discrete points. The Lagrange interpolation polynomial can exactly pass through predefined points and therefore can be employed to replace original kernels for accurate by accelerating the BF. To speed up the BF at the cost of small approximation error, two approximation methods are proposed to obtain the optimal fitting polynomial. The performance of the proposed method is validated by extensive experiments. [ABSTRACT FROM AUTHOR]

The bilateral filter (BF) as an edge‐preserving lowpass filter is a valuable tool in various image processing tasks, including noise reduction and dynamic range compression. However, its computational cost is too high to apply in the real‐time processing tasks as the range kernel, which acts on the pixel intensities, making the averaging process nonlinear and computationally intensive, particularly when the spatial filter is large. Using the well‐known Hermite polynomials, a BF accelerating method is proposed, which reduces the computational complexity from O(r2n) to O(n), where r denotes the filter size of a BF and n is the total number of pixels in an image. [ABSTRACT FROM AUTHOR]

Arsenic (As), a potent toxicant, is known to be a hepatotoxicant. Although As induced liver apoptosis and autophagy, the relationship between apoptosis and autophagy of hepatocytes caused by As remains largely unknown. 3-methyladenine (3-MA) and rapamycin can inhibit and promote autophagy of AML-12 cells, respectively. Hence, in this study, AML-12 cells were treated with different concentrations (0, 2, 4, 6, 8, 10 and 12 μmol/L) of As 2 O 3 , and 5 mmol/L 3-MA or 100 nmol/L rapamycin were applied to distinguish the effect of autophagy on apoptosis in AML-12. Results showed that exposure to As induced cell apoptosis and autophagy, which were mediated by the significantly altered expression levels of autophagy markers (mTOR, LC3, PI3K and P62), and apoptosis markers (Bcl-2 and caspase-3). Further analysis indicated that a certain dosage of 3-MA and rapamycin decreased apoptosis and the caspase-3 expression, which suggested that As-induced autophagy regulated AML-12 cells apoptosis through the expressions of PI3K, mTOR, P62 and Bcl-2. • Arsenic enhanced autophagy and apoptosis in AML-12 cells. • Arsenic-induced autophagy modulated apoptosis in AML-12 cells. • Arsenic regulated AML-12 cells apoptosis through the expressions of PI3K, mTOR, P62 and Bcl-2. [ABSTRACT FROM AUTHOR]

Due to the heavy computation cost, the bilateral filter cannot be applied to real‐time applications. To deal with this problem, many range kernel approximation based acceleration schemes have been proposed. However, all these methods use predefined basis function and assign the same penalisation for all range values to fulfil range kernel approximation. These constraints block from further improving the filtering accuracy and speed. Employing weighted variable projection, a new acceleration method which achieves state‐of‐the‐art performance is proposed. This is done by: (i) unleashing the constraint of using fixed basis function; (ii) exploiting the colour distribution information of the input image to perform a weighted approximation of the range kernel. Experiments demonstrate the superiority of the proposed method in gaining more accurate filtering results efficiently. [ABSTRACT FROM AUTHOR]

We propose a minimax path-based depth interpolation method. The algorithm computes for each target pixel varying contributions from reliable depth seeds, and weighted averaging is used to interpolate missing depths. Compared with state-of-the-art joint geodesic upsampling method which selects the K nearest seeds to interpolate missing depths with O ( Kn ) complexity, our method does not need to limit the number of seeds to K and reduces the computational complexity to O ( n ). In addition, the minimax path chooses a path with the smallest maximum immediate pairwise pixel difference on it, so it tends to preserve sharp depth discontinuities better. In contrast to the results of previous depth upsampling algorithms, our approach provides accurate depths with fewer artifacts. [ABSTRACT FROM AUTHOR]

Developing efficient and nonprecious electrocatalysts for oxygen evolution reaction (OER) is crucial for the hydrogen generation via water splitting. Here we report the direct growth of heterostructural Ni 2 P–Fe 2 P hybrid electrocatalysts on NiFe foams. The redox engineered NiFe foam (rNF) significantly enhances the OER performance. Among prepared catalysts, Ni 2 P–Fe 2 P/rNF performs the highest OER activity, driving the current densities of 10 and 100 mA cm−2 at low overpotentials of 218 and 270 mV in 1 M KOH, respectively. Meanwhile, it can maintain 100 mA cm−2 for 24 h without obvious degradation. The morphology and composition of NFP/rNF after OER test were carefully studied. This work suggests that self-supported bimetal phosphides of NFP/rNF is an efficient and durable electrode material for water oxidation. Heterostructural Ni2P-Fe2P hybrid electrocatalysts were direct grown on NiFe foams, which show excellent oxygen evolution activity and stability. Image 1 • The formation of micro-sized pores on the skeleton of NiFe foam (NF) substrate can enlarge surface area. • The heterogeneous Ni2P-Fe2P structure provides plentiful active sites for OER. • The Ni2P-Fe2P/ rNF foam exhibits an extraordinary electro catalytic performance. [ABSTRACT FROM AUTHOR]

Both arsenic (As) and fluorine (F) are toxic substances widely found in the environment, which threaten to various organs of both human and animals, especially the kidney. In this study, to investigate the individual and combined effects of arsenic (15 mg/L As 2 O 3 (III)) and fluoride (100 mg/L NaF), arsenic (15 mg/L As 2 O 3 (III)) and fluoride-arsenic (15 mg/L As 2 O 3 (III)+100 mg/L NaF) on the renal autophagy during early life, a mouse model of gestationally exposed to As and/or F was established. The results showed that the mRNA expression levels of LC3, LC3I, LC3II, Beclin-1, ULK1, Atg13 and Atg14 were significantly increased with a concomitant decrease in mTOR and Bcl-2 up on individual exposure to As and F rather than in combined (As + F) exposure. In addition, the protein expression levels of LC3-II/LC3-I, Beclin-1, and LAMP1 were significantly increased with a concomitant decrease in mTOR and Bcl-2 in the mice subjected to individual exposure than the combined exposure. Based on the results, it was observed that renal tissue of mice was highly sensitive to F than As. Moreover, the toxicity of the combined (As + F) exposure was significantly lower than that of the individual exposure, which could be attributed due to the antagonism between As and F. • The exposure of fluoride and arsenic in the mother rats induced the autophagy in the kidney of the offspring mice. • Under the experimental conditions, the combination of As and F showed certain antagonistic effects. • The expression of autophagy negative regulator p62 is increased, which may be due to autophagic flow block. [ABSTRACT FROM AUTHOR]

Mesh denoising is a crucial technology that aims to recover a high-fidelity 3D mesh from a noise-corrupted one. Deep learning methods, particularly graph convolutional networks (GCNs) based mesh denoisers, have demonstrated their effectiveness in removing various complex real-world noises while preserving authentic geometry. However, it is still a quite challenging work to faithfully regress uncontaminated normals and vertices on meshes with irregular topology. In this paper, we propose a novel pipeline that incorporates two parallel normal-aware and vertex-aware branches to achieve a balance between smoothness and geometric details while maintaining the flexibility of surface topology. We introduce ResGEM, a new GCN, with multi-scale embedding modules and residual decoding structures to facilitate normal regression and vertex modification for mesh denoising. To effectively extract multi-scale surface features while avoiding the loss of topological information caused by graph pooling or coarsening operations, we encode the noisy normal and vertex graphs using four edge-conditioned embedding modules (EEMs) at different scales. This allows us to obtain favorable feature representations with multiple receptive field sizes. Formulating the denoising problem into a residual learning problem, the decoder incorporates residual blocks to accurately predict true normals and vertex offsets from the embedded feature space. Moreover, we propose novel regularization terms in the loss function that enhance the smoothing and generalization ability of our network by imposing constraints on normal consistency. Comprehensive experiments have been conducted to demonstrate the superiority of our method over the state-of-the-art on both synthetic and real-scanned datasets.

Objective: To investigate the value of confocal microscopy in determining the morphology of the tarsal gland after cataract phacoemulsification., Methods: A total of 74 patients (74 eyes) who underwent phacoemulsification for a monocular cataract and intraocular lens implantation (all were single eye surgeries) in our hospital from May 2018 to October 2018 were recruited as the study cohort, with 43 male patients and 31 female patients, and a mean age of (64.8±12.5) years old. All the patients were followed up for 6 months, of whom 25 cases with MGD were included in the MGD group and 49 cases without MGD were included in the control group. All the patients were examined within 30 days and underwent IVCM inspections of the acinar morphology of the tarsal glands (expansion and atrophy), the infiltration of the inflammatory cells in the tarsal gland tissue, and a classification of the fibrosis in the tarsal gland tissue., Results: The longest and shortest acinar diameters in the MGD patients were significantly greater than they were in the control group, but the acinar areas were smaller than they were in the control group. The meibomian glandular vesicle densities, the average opening diameters, the fibrosis, and the inflammatory cell density in the MGD group were significantly increased., Conclusion: IVCM plays a vital role in the early diagnosis, in the severity grading, and in the evaluation of the clinical effectiveness of MGD-related diseases, by which the morphological changes of the tarsal gland after phacoemulsification can be observed in a timely manner to predict the occurrence of MGD., Competing Interests: None., (AJTR Copyright © 2021.)

The CRISPR/Cas9 system has been used in a wide range of applications in the production of gene-edited animals and plants. Most efforts to insert genes have relied on homology-directed repair (HDR)-mediated integration, but this strategy remains inefficient for the production of gene-edited livestock, especially monotocous species such as cattle. Although efforts have been made to improve HDR efficiency, other strategies have also been proposed to circumvent these challenges. Here we demonstrate that a homology-mediated end-joining (HMEJ)-based method can be used to create gene-edited cattle that displays precise integration of a functional gene at the ROSA26 locus. We found that the HMEJ-based method increased the knock-in efficiency of reporter genes by eightfold relative to the traditional HDR-based method in bovine fetal fibroblasts. Moreover, we identified the bovine homology of the mouse Rosa26 locus that is an accepted genomic safe harbor and produced three live-born gene-edited cattle with higher rates of pregnancy and birth, compared with previous work. These gene-edited cattle exhibited predictable expression of the functional gene natural resistance-associated macrophage protein-1 (NRAMP1), a metal ion transporter that should and, in our experiments does, increase resistance to bovine tuberculosis, one of the most detrimental zoonotic diseases. This research contributes to the establishment of a safe and efficient genome editing system and provides insights for gene-edited animal breeding., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The guided filter (GF) is a widely used smoothing tool in computer vision and image processing. However, to the best of our knowledge, few papers investigate the mathematical connection between this filter and the least-squares optimization. In this paper, we first interpret the guided filter as the cyclic coordinate descent (CCD) solver of a least-squares objective function. This discovery implies an extension approach to generalize the guided filter since we can change the least-squares objective function and define new filters as the first pass iteration of the CCD solver of modified objective functions. In addition, referring to the iterative minimizing procedure of the CCD, we can derive new rolling filtering schemes. So, we are reasonable to say that our discovery not only reveals an approach to design new GF-like filters adapting to specific requirements of applications but also offers thorough explanations for two rolling filtering schemes of the guided filter as well as the method to extend them. Experiments prove our new proposed filters and rolling filtering schemes could produce state-of-the-art results.

Accumulation of mitochondrial reactive oxygen species (mROS) has been implicated in the pathogenesis of fluorosis. As the main mitochondrial deacetylase, SIRT3 is closely associated with oxidative stress. To investigate the role of SIRT3 in response to sodium fluoride (NaF)-induced nephrotoxicity. Our results showed that NaF treatment impaired mitochondrial ultrastructure, decreased cell viability and increased apoptosis in TCMK-1 cells. Oxidative stress, detected by mROS and 8-Hydroxy-2'-deoxyguanosine (8-OHdG) were higher in NaF-treated cells, accompanied by decreased level of reduced glutathione (GSH). NaF reduces manganese superoxide dismutase (SOD2) expression through SIRT3-mediated DNA-binding activity of FoxO3a and decrease SOD2 activity by inhibiting SIRT3-mediated deacetylation. These effects were ameliorated by overexpression of SIRT3. Peroxisome proliferator-activated receptor-coactivator 1a (PGC-1α) interacted with nuclear factor erythroid 2 (NF-E2)-related factor 2 (NRF2) that bound to SIRT3 promoter to regulate SIRT3 expression. The study provides new insights into a critical NRF2/PGC-1α-SIRT3 pathway in response to NaF-induced nephritic oxidative injury. In vivo treatment of SIRT3-expressing adenovirus protects against NaF-induced nephritic injury in mice. Moreover, mechanistic study revealed that ERK1/2 activation was associated with increased apoptosis induced by NaF. In conclusion, these data shedding light on new approaches for treatment of NaF-induced nephrotoxicity.

Computational complexity of the brute-force implementation of the bilateral filter (BF) depends on its filter kernel size. To achieve the constant-time BF whose complexity is irrelevant to the kernel size, many techniques have been proposed, such as 2D box filtering, dimension promotion, and shiftability property. Although each of the above techniques suffers from accuracy and efficiency problems, previous algorithm designers were used to take only one of them to assemble fast implementations due to the hardness of combining them together. Hence, no joint exploitation of these techniques has been proposed to construct a new cutting edge implementation that solves these problems. Jointly employing five techniques: kernel truncation, best N-term approximation as well as previous 2D box filtering, dimension promotion, and shiftability property, we propose a unified framework to transform BF with arbitrary spatial and range kernels into a set of 3D box filters that can be computed in linear time. To the best of our knowledge, our algorithm is the first method that can integrate all these acceleration techniques and, therefore, can draw upon one another's strong point to overcome deficiencies. The strength of our method has been corroborated by several carefully designed experiments. In particular, the filtering accuracy is significantly improved without sacrificing the efficiency at running time.