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AbstractContext Liuwei Dihuang pill (LWDH) has been used to treat postmenopausal osteoporosis (PMOP).Objective To explore the effects and mechanisms of action of LWDH in PMOP.Materials and methods Forty-eight female Sprague-Dawley rats were divided into four groups: sham-operated (SHAM), ovariectomized (OVX), LWDH high dose (LWDH-H, 1.6 g/kg/d) and LWDH low dose (LWDH-L, 0.8 g/kg/d); the doses were administered after ovariectomy via gavage for eight weeks. After eight weeks, the bone microarchitecture was evaluated. The effect of LWDH on the differentiation of bone marrow mesenchymal stem cells (BMSCs) was assessed via osteogenesis- and lipogenesis-induced BMSC differentiation. The senescence-related biological indices were also detected using senescence staining, cell cycle analysis, quantitative real-time polymerase chain reaction and western blotting. Finally, the expression levels of autophagy-related proteins and Yes-associated protein (YAP) were evaluated.Results LWDH-L and LWDH-H significantly modified OVX-induced bone loss. LWDH promoted osteogenesis and inhibited adipogenesis in OVX-BMSCs. Additionally, LWDH decreased the positive ratio of senescence OVX-BMSCs and improved cell viability, cell cycle, and the mRNA and protein levels of p53 and p21. LWDH upregulated the expression of autophagy-related proteins, LC3, Beclin1 and YAP, in OVX-BMSCs and downregulated the expression of p62.Discussion and conclusions LWDH improves osteoporosis by delaying the BMSC senescence through the YAP-autophagy axis.

Pyroptosis is a form of programmed cell death that is crucial in the development of various diseases, including autoimmune diseases, atherosclerotic diseases, cancer, and pregnancy complications. In recent years, it has gained significant attention in national and international research due to its association with inflammatory immune overactivation and its involvement in pregnancy complications such as miscarriage and preeclampsia (PE). The mechanisms discussed include the canonical pyroptosis pathway of gasdermin activation and pore formation (caspase-1-dependent pyroptosis) and the non-canonical pyroptosis pathway (cysteoaspartic enzymes other than caspase-1). These pathways work on various cellular and factorial levels to influence normal pregnancy. This review aims to summarize and analyze the pyroptosis pathways associated with abnormal pregnancies and pregnancy complications. The objective is to enhance pregnancy outcomes by identifying various targets to prevent the onset of pyroptosis.

Abstract Objectives The purpose of this review is to investigate the relationship between gastrointestinal microbiome, obesity, and gestational diabetes mellitus (GDM) in an objective manner. Methods We conducted a thorough and comprehensive search of the English language literatures published in PubMed, Web of Science, and the Cochrane Library from the establishment of the library until 12 December 2023. Our search strategy included both keywords and free words searches, and we strictly applied inclusion and exclusion criteria. Meta-analyses and systematic reviews were prepared. Results Six high-quality literature sources were identified for meta-analysis. However, after detailed study and analysis, a certain degree of heterogeneity was found, and the credibility of the combined analysis results was limited. Therefore, descriptive analyses were conducted. The dysbiosis of intestinal microbiome, specifically the ratio of Firmicutes/Bacteroides, is a significant factor in the development of metabolic diseases such as obesity and gestational diabetes. Patients with intestinal dysbiosis and obesity are at a higher risk of developing GDM. Conclusions During pregnancy, gastrointestinal microbiome disorders and obesity may contribute to the development of GDM, with all three factors influencing each other. This finding could aid in the diagnosis and management of patients with GDM through further research on their gastrointestinal microbiome.

The operational trajectory of a skilled operator holds significant guiding implications for the motion control of unmanned excavators. This study collects and analyzes the operational trajectories of experienced operators under specific working conditions, acquires standardized data, and proposes fluctuation judgment data in terms of joint motion speed and angle values at specific stages. Utilizing standardized data, a motion control trajectory is generated and combined with the Lagrange function and MPC algorithm to achieve decoupling of the four composite motions of excavators. Simulation verification and experimental comparison are conducted using the MPC algorithm. By adopting MPC motion control without altering the structure, hydraulic system, or electrical system of the experimental object, operating efficiency reaches 108.1% compared to that of skilled operators while significantly reducing fluctuations in operation trajectory. The average reduction in relative standard deviation and maximum fluctuation value, by 29.3% and 3.14deg/s respectively, signifies enhancements in velocity stability and position stability of over 36.2% and 12.85deg respectively, thereby presenting a novel approach for controlling work devices on unmanned excavators.

The control precision of the working device has always been a challenging aspect in unmanned excavator research due to the adoption of a triangular drive mode and a complex hydraulic system in the working mechanism. The article focuses on the research of autonomous control for the downward motion of a robotic arm in an unmanned excavator equipped with a regeneration valve. The study aims to achieve precise tracking of fast movement trajectories during operator manipulation, utilizing Model Predictive Control (MPC). Furthermore, the exceptional disturbance rejection capability of the MPC algorithm is demonstrated through interference application. A comprehensive model considering mechanical, hydraulic, and electrical factors is established for the excavator boom. The MPC algorithm is applied to achieve precise control over the boom descent process, providing a foundation for motion control in unmanned excavators. This article presents a theoretical analysis to elucidate the robustness principle of MPC in the descent control of uncertain dynamic arms. By incorporating real parameters, we successfully track predetermined planned paths at different speeds and validate them on a 20-ton hydraulic excavator. The results demonstrate that the MPC control algorithm accurately manipulates the boom descent motion while exhibiting excellent disturbance rejection performance. Compared to PID control algorithms, MPC offers wider target adaptability range and better disturbance rejection performance, making it suitable for rapid application in controlling working devices of unmanned excavators.

With the intersection and convergence of multiple disciplines and technologies, more and more researchers are actively exploring interdisciplinary cooperation outside their main research fields. Facing a new research field, researchers often hope to quickly learn what is being studied in this field, which research points are receiving high attention, which researchers are studying these research points, and then consider the possibility of collaborating with core researchers on these research points. In addition, students who are preparing for academic further education usually conduct research on mentors and mentors’ research platforms, including academic connections, employment opportunities, etc. In order to satisfy these requirements, we (1) design a research point state map based on a science map to help researchers and students understand the development state of a new research field; (2) design a bar-link author-affiliation information graph to help researchers and students clarify academic networks of scholars and find suitable collaborators or mentors; (3) designs citation pattern histogram to quickly discover research achievements with high research value, such as the Sleeping Beauty papers, recently hot papers, classic papers and so on. Finally, an interactive analytical system named PubExplorer was implemented with IEEE VIS publication data, and its effectiveness is verified through case studies.

The motion control accuracy of the excavator manipulator is the primary guarantee for autonomous excavators to complete work tasks. This paper studies the motion control strategy of the manipulator of a certain autonomous excavator. By establishing a dynamic model of a 3-degree of freedom of excavator manipulator, the gravity term, inertia term, and centripetal force term in the model are equivalent to external disturbances for online compensation, which improves the motion control accuracy of the excavator manipulator. Based on the dynamic model, a control method of the bucket tooth tip trajectory of an autonomous excavator is proposed. The simulation and test results show that the maximum tracking errors of the joint angles of the boom, the bucket, and the bucket are reduced by 43.14, 26.56, and 51.05% respectively, and the average errors of the whole trajectory are reduced by 56.41, 61.33, and 64.26% respectively. The simulation and test results show that the proposed motion control strategy improves the operation accuracy of the excavator, and can effectively improve the operation accuracy and efficiency of the autonomous excavator.

Trajectory planning plays a crucial role in achieving unmanned excavator operations. The quality of trajectory planning results heavily relies on the level of rules extracted from objects such as scenes and optimization objectives, using traditional theoretical methods. To address this issue, this study focuses on professional operators and employs machine learning methods for job trajectory planning, thereby obtaining planned trajectories which exhibit excellent characteristics similar to those of professional operators. Under typical working conditions, data collection and analysis are conducted on the job trajectories of professional operators, with key points being extracted. Machine learning is then utilized to train models under different parameters in order to obtain the optimal model. To ensure sufficient samples for machine learning training, the bootstrap method is employed to adequately expand the sample size. Compared with the traditional spline curve method, the trajectories generated by machine learning models reduce the maximum speeds of excavator boom arm, dipper stick, bucket, and swing joint by 8.64 deg/s, 10.24 deg/s, 18.33 deg/s, and 1.6 deg/s, respectively; moreover, the error does not exceed 2.99 deg when compared with curves drawn by professional operators; and, finally, the trajectories generated by this model are continuously differentiable without position or velocity discontinuities, and their overall performance surpasses that of those generated by the traditional spline curve method. This paper proposes a trajectory generation method that combines excellent operators with machine learning and establishes a machine learning-based trajectory-planning model that eliminates the need for manually establishing complex rules. It is applicable to motion path planning in various working conditions of unmanned excavators.

The foamed polypropylene (PP) composites were prepared by injection molding process. Fourier’s law and software were used to calculate and simulate the internal temperature distribution of PP composites, respectively, and the influence of the temperature distribution on the foaming quality of foamed PP composites was further analyzed. The result showed that the calculative and simulated results of temperature distribution in different thermal transfer directions had great reproducibility. In different isothermal planes, the temperature from the nozzle to the dynamic mold gradually decreased. The isothermal plane with a temperature of 370.36 K had a better foaming quality, average diameter of cell and cell density were 28.46 µm and 3.7 × 1010 cells·cm−3, respectively. In different regions of the same isothermal plane, the temperature gradually decreased from the center to the edge. The foaming quality in the region (c) at a temperature of 335.86 K was ideal, and the average diameter of cell and the cell density were 26.5 µm and 2.39 × 1010 cells·cm−3, respectively. This work could provide prediction for improving the foaming quality of foamed polyolefin composites.

A matrix AA is said to have the displacement structure if the rank of the Sylvester displacement AU−VAAU-VA or the Stein displacement A−VAUA-VAU is much smaller than the rank of AA. In this article, we study the displacement structure of the DMP inverse Ad,†{A}^{d,\dagger }. Estimations for the Sylvester displacement rank of DMP inverse are presented under some restrictions. The generalized displacement is also discussed. The general results are applied to the core inverse.

Objective To study the influence of exogenous high-mobility group box 1(HMGB1) on the mitigative effect of tanshinone ⅡA (TSA) to myocardial ischemia-reperfusion (I/R) injury. Methods Rats were divided into sham, I/R (ligation of left anterior descending coronary artery), TSA (I/R + TSA intravenous injection) and TSA+HMGB1 (I/R+TSA intravenous injection + recombination HMGB1 intraperitoneal injection) groups. Serum CK-MB was measured by ELISA. Myocardial infarct area was measured by TCC. The expression of inflammatory indicators were measured by RT-qPCR and Western blot. Results Compared with sham group, CK-MB, myocardial infarct area, transcription level of IL-6, IL-1β and TNF-α, expression of HMGB1, NLRP3 and caspase-1 all increased in I/R group(P＜0.05). Compared with I/R group, CK-MB, myocardial infarct area, transcription levels of IL-6, IL-1β and TNF-α, expressions of HMGB1, NLRP3, caspase-1 and NF-κB decreased in TSA group (P＜0.05). However, compared with TSA group, CK-MB, myocardial infarct area, transcription levels of IL-6, IL-1β and TNF-α, expressions of HMGB1, NLRP3, caspase-1 and NF-κB increased in TSA+HMGB1 group(P＜0.05). Conclusions Exogenous HMGB1 attenuates the mitigative effect of tanshinone ⅡA to rat myocardial I/R injury through up-regulation the expressions of inflammatory factors.

Abstract Aqueous redox flow batteries, by using redox‐active molecules dissolved in nonflammable water solutions as electrolytes, are a promising technology for grid‐scale energy storage. Organic redox‐active materials offer a new opportunity for the construction of advanced flow batteries due to their advantages of potentially low cost, extensive structural diversity, tunable electrochemical properties, and high natural abundance. In this review, we present the emergence and development of organic redox‐active materials for aqueous organic redox flow batteries (AORFBs), in particular, molecular engineering concepts and strategies of organic redox‐active molecules. The typical design strategies based on organic redox species for high‐capacity, high‐stability, and high‐voltage AORFBs are outlined and discussed. Molecular engineering of organic redox‐active molecules for high aqueous solubility, high chemical/electrochemical stability, and multiple electron numbers as well as satisfactory redox potential gap between the redox pair is essential to realizing high‐performance AORFBs. Beyond molecular engineering, the redox‐targeting strategy is an effective way to obtain high‐capacity AORFBs. We further discuss and analyze the redox reaction mechanisms of organic redox species based on a series of electrochemical and spectroscopic approaches, and succinctly summarize the capacity degradation mechanisms of AORFBs. Furthermore, the current challenges, opportunities, and future directions of organic redox‐active materials for AORFBs are presented in detail.

Milk fat globule （MFG） is secreted by mammalian glands and composed of a triacylglycerol core enclosed by a complex triple membrane structure, referred as the milk fat globule membrane （MFGM）. MFGM contains complex lipids and proteins, with nutritional, immunological, neurological and digestive functions. However, the relationship between functions and gastrointestinal digestion and the factors that influence the gastrointestinal digestion of milk fat have not been fully studied. In this paper, the structure composition, functional characteristics, gastrointestinal digestion and the factors affecting the digestion and absorption of milk fat were reviewed in order to provide an insight for the research on the digestion of infant formula.

IntroductionOcular abnormalities and the development of retinal vasculature may cause postnatal retinopathy. In the past decade, tremendous progress has been made in identifying the mechanisms that regulate retina vasculature. However, the means of regulating embryonic hyaloid vasculature development is largely unknown. This study aims to determine whether and how andrographolide regulates embryonic hyaloid vasculature development.MethodsMurine embryonic retinas were used in this study. Whole mount isolectin B4 (IB4) staining, hematoxylin and eosin (H&E) staining, immunohistochemistry (IHC), and immunofluorescence staining (IF) were performed to determine whether andrographolide is critical for embryonic hyaloid vasculature development. BrdU incorporation assay, Boyden chamber migration assay, spheroid sprouting assay, and Matrigel-based tube formation assay were performed to evaluate whether andrographolide regulates the proliferation and migration of vascular endothelial cells. Molecular docking simulation and Co-immunoprecipitation assay were used to observe protein interaction.ResultsHypoxia conditions exist in murine embryonic retinas. Hypoxia induces HIF-1a expression; high-expressed HIF-1a interacts with VEGFR2, resulting in the activation of the VEGF signaling pathway. Andrographolide suppresses hypoxia-induced HIF-1a expression and, at least in part, interrupts the interaction between HIF-1a and VEGFR2, causing inhibiting endothelial proliferation and migration, eventually inhibiting embryonic hyaloid vasculature development.ConclusionOur data demonstrated that andrographolide plays a critical role in regulating embryonic hyaloid vasculature development.

BackgroundGou Qi Zi (Lycium barbarum) is a traditional herbal medicine with antioxidative effects. Although Gou Qi Zi has been used to prevent premature aging and in the treatment of non-small cell lung cancer (NSCLC), its mechanism of action in NSCLC remains unclear. The present study utilized network pharmacology to assess the potential mechanism of action of Gou Qi Zi in the treatment of NSCLC.MethodsThe TCMSP, TCMID, SwissTargetPrediction, DrugBank, DisGeNET, GeneCards, OMIM and TTD databases were searched for the active components of Gou Qi Zi and their potential therapeutic targets in NSCLC. Protein-protein interaction networks were identified and the interactions of target proteins were analyzed. Involved pathways were determined by GO enrichment and KEGG pathway analyses using the Metascape database, and molecular docking technology was used to study the interactions between active compounds and potential targets. These results were verified by cell counting kit-8 assays, BrdU labeling, flow cytometry, immunohistochemistry, western blotting, and qRT-PCR.ResultsDatabase searches identified 33 active components in Gou Qi Zi, 199 predicted biological targets and 113 NSCLC-related targets. A network of targets of traditional Chinese medicine compounds and potential targets of Gou Qi Zi in NSCLC was constructed. GO enrichment analysis showed that Gou Qi Zi targeting of NSCLC was mainly due to the effect of its associated lipopolysaccharide. KEGG pathway analysis showed that Gou Qi Zi acted mainly through the PI3K/AKT1 signaling pathway in the treatment of NSCLC. Molecular docking experiments showed that the bioactive compounds of Gou Qi Zi could bind to AKT1, C-MYC and TP53. These results were verified by experimental assays.ConclusionGou Qi Zi induces apoptosis and inhibits proliferation of NSCLC in vitro and in vivo by inhibiting the PI3K/AKT1 signaling pathway.

Electrocatalytic carbon dioxide (CO2) reduction is considered as an economical and environmentally friendly approach to neutralizing and recycling greenhouse gas CO2. However, the design of preeminent and robust electrocatalysts for CO2 electroreduction is still challenging. Herein, we report the in-situ growth of dense CuOx nanowire forest on 3D porous Cu foam (CuOx-NWF@Cu-F), which can be directly applied as a freestanding and binder-free working electrode for highly effective electrocatalytic CO2 reduction. By adjusting the surface morphology and chemical composition of CuOx nanowires via surface reconstruction, large electrochemically active surface area and abundant Cu(+1) sites were generated, leading to remarkable activity for CO2 electroreduction. The as-prepared hierarchical conductive electrode exhibited an enhanced Faradaic efficiency of 15.0% for ethanol formation (FEC2H5OH) and a total Faradaic efficiency of 69.4% for all carbonaceous compounds (FEC-total) at a mild applied potential of –0.45 V vs. RHE in 0.1 M KHCO3 electrolyte. It achieved a 4-fold increase in FEC-total than that of Cu nanowire forest supported on 3D porous Cu foam (Cu-NWF@Cu-F) obtained by in-situ reduction of the CuOx-NWF@Cu-F via annealing at H2 atmosphere, and thereby effectively suppressed the hydrogen evolution side-reaction.

Aging is associated with the increased risk of most age-related diseases in humans. Complanatoside A (CA) is a flavonoid compound isolated from the herbal medicine Semen Astragali Complanati. CA was reported to have potential anti-inflammatory and anti-oxidative activities. In this study, we investigated whether CA could increase the stress resistance capability and life span of Caenorhabditis elegans. Our results showed that CA could extend the longevity of C. elegans in a dosage-dependent manner, while 50 μM of CA has the best effect and increased the life span of C. elegans by about 16.87%. CA also improved the physiological functions in aging worms, such as enhanced locomotor capacity, and reduced the accumulation of the aging pigment. CA could also reduce the accumulation of toxic proteins (α-synuclein and β-amyloid) and delay the onset of neurodegenerative disorders, such as Alzheimer’s disease and Parkinson’s disease, in models of C. elegans. Further investigation has revealed that CA requires DAF-16/FOXO, SKN-1, and HSF-1 to extend the life span of C. elegans. CA could increase the antioxidation and detoxification activities regulated by transcription factor SKN-1 and the heat resistance by activating HSF-1 that mediated the expression of the chaperone heat shock proteins. Our results suggest that CA is a potential antiaging agent worth further research for its pharmacological mechanism and development for pharmaceutical applications.

Abstract The chemical transformation of carbon dioxide (CO2) has been considered as a promising strategy to utilize and further upgrade it to value‐added chemicals, aiming at alleviating global warming. In this regard, sustainable driving forces (i.e., electricity and sunlight) have been introduced to convert CO2 into various chemical feedstocks. Electrocatalytic CO2 reduction reaction (CO2RR) can generate carbonaceous molecules (e.g., formate, CO, hydrocarbons, and alcohols) via multiple‐electron transfer. With the assistance of extra light energy, photoelectrocatalysis effectively improve the kinetics of CO2 conversion, which not only decreases the overpotentials for CO2RR but also enhances the lifespan of photo‐induced carriers for the consecutive catalytic process. Recently, rational‐designed catalysts and advanced characterization techniques have emerged in these fields, which make CO2‐to‐chemicals conversion in a clean and highly‐efficient manner. Herein, this review timely and thoroughly discusses the recent advancements in the practical conversion of CO2 through electro‐ and photoelectrocatalytic technologies in the past 5 years. Furthermore, the recent studies of operando analysis and theoretical calculations are highlighted to gain systematic insights into CO2RR. Finally, the challenges and perspectives in the fields of CO2 (photo)electrocatalysis are outlined for their further development.

We describe and demonstrate a novel experimental approach to measure broadband, amplitude- and phase-resolved scattering spectra of single nanoparticles with 10-nm spatial resolution. Nanofocusing of surface plasmon polaritons (SPPs) propagating along the shaft of a conical gold taper is used to create a spatially isolated, spectrally broad nanoscale light source at its very apex. The interference between these incident SPPs and SPPs that are backpropagating from the apex leads to the formation of an inherently phase-stable interferogram, which we detect in the far field by partially scattering SPPs off a small protrusion on the taper shaft. We show that these interferograms allow the reconstruction of both the amplitude and phase of the local optical near fields around individual nanoparticles optically coupled to the taper apex. We extract local light scattering spectra of particles and quantify line broadenings and spectral shifts induced by tip-sample coupling. Our experimental findings are supported by corresponding finite-difference time-domain and coupled dipole simulations and show that, in the limit of weak tip-sample coupling, the measurements directly probe the projected local density of optical states of the plasmonic system. The combination of a highly stable inline interferometer with the inherent optical background suppression through nanofocusing makes it a promising tool for the locally resolved study of the spectral and temporal optical response of coupled hybrid nanosystems.

In this paper, a face spoofing detection method called the Fully Convolutional Network with Domain Adaptation and Lossless Size Adaptation (FCN-DA-LSA) is proposed. As its name suggests, the FCN-DA-LSA includes a lossless size adaptation preprocessor followed by an FCN based pixel-level classifier embedded with a domain adaptation layer. The FCN local classifier makes full use of the basic properties of face spoof distortion namely ubiquitous and repetitive. The domain adaptation (DA) layer improves generalization across different domains. The lossless size adaptation (LSA) preserves the high-frequent spoof clues caused by the face recapture process. The ablation study shows that both DA and the LSA are necessary for high-accuracy face spoofing detection. The FCN-LSA obtains competitive performance among the state-of-the-art methods. With the help of small-sample external data in the target domain (2/50, 2/50, and 1/20 subjects for CASIA-FASD, Replay-Attack, and OULU-NPU respectively), the FCN-DA-LSA further improves the performance and outperforms the existing methods.

Inspired by the role of proteins in regulating eggshell mineralization, here Tao, Liu and colleagues apply trifluoroethanol modified eggshell membrane to combat lithium dendrite. Cryo-electron microscopy reveals that the growth along the most favored crystallographic direction is suppressed.

IntroductionPathological vascular remodeling is a hallmark of various vascular diseases. Smooth muscle cell (SMC) phenotypic switching plays a pivotal role during pathological vascular remodeling. The mechanism of how to regulate SMC phenotypic switching still needs to be defined. This study aims to investigate the effect of Andrographolide, a key principle isolated from Andrographis paniculate, on pathological vascular remodeling and its underlying mechanism.MethodsA C57/BL6 mouse left carotid artery complete ligation model and rat SMCs were used to determine whether Andrographolide is critical in regulating SMC phenotypic switching. Quantitative real-time PCR, a CCK8 cell proliferation assay, BRDU incorporation assay, Boyden chamber migration assay, and spheroid sprouting assay were performed to evaluate whether Andrographolide suppresses SMC proliferation and migration. Immunohistochemistry staining, immunofluorescence staining, and protein co-immunoprecipitation were used to observe the interaction between EDNRA, EDNRB, and Myocardin-SRF.ResultsAndrographolide inhibits neointimal hyperplasia in the left carotid artery complete ligation model. Andrographolide regulates SMC phenotypic switching characterized by suppressing proliferation and migration. Andrographolide activates the endothelin signaling pathway exhibited by dramatically inducing EDNRA and EDNRB expression. The interaction between EDNRA/EDNRB and Myocardin-SRF resulted in promoting SMC differentiation marker gene expression.ConclusionAndrographolide plays a critical role in regulating pathological vascular remodeling.

Redox flow batteries are promising for large-scale energy storage, but are hindered by cost, stability, and safety issues. Here the authors construct an all-polymer particulate slurry battery to bypass solubility limits and apply insoluble redox-active materials.

Salient object detection can identify the most distinctive objects in a scene. In this study, a novel graph‐based approach is proposed to detect a salient object via reliable boundary seeds and saliency refinement. A natural image is firstly mapped to a graph with superpixels as nodes. Saliency information is then diffused over the graph using seeds. For the reason that the boundary nodes may contain salient nodes, it is not appropriate to use all boundary nodes as the background seeds. Therefore, a boundary saliency measurement is proposed to obtain more accurate background seeds. After that, the information of background seeds is diffused by a two‐stage scheme. A background‐based map and a foreground‐based map are generated based on the two‐stage scheme. Furthermore, in order to enhance the detection accuracy, a refinement model is presented to fuse the information of background‐based and foreground‐based maps. Experiments on seven public datasets show the proposed algorithm out‐performs the state‐of‐the‐art salient object detection algorithms.

The search for peroxidase-like as well as other enzyme-like nanozymes mainly relies on trial-and-error strategies, due to the lack of predictive descriptors. Here, the authors fill this gap by investigating the occupancy of e g orbitals as a possible descriptor for the peroxidase-like activity of transition metal oxide nanozymes

Summary: The emergence of all-inorganic halide perovskites has shown great potential in photovoltaic and optoelectronic devices. However, the photo-induced phase segregation in lead mixed-halide perovskites has severely limited their application. Herein, by real-time monitoring the photoluminescence (PL) spectra of metal mixed-halide perovskites under light irradiation, we found that the photo-induced phase transition can be significantly inhibited by B-site doping. For pristine mixed-halide perovskites, an intermediate phase of CsPbBrxI3-x can only be stabilized under low excitation power. After introducing Sn2+ ions, such intermediate phase can be stabilized in nitrogen atmosphere under high excitation power and phase segregation can be started after the exposure in oxygen due to oxidization of Sn2+. Replacing Sn2+ by Mn2+ can further improve the intermediate phase's tolerance to oxygen proving that B-site doping in perovskites structure by Sn2+ or Mn2+ could effectively minimize the light-induced phase segregation and promote them to serve as promising candidates in photovoltaic and light-emitting devices.

Let A be a {0, 1}-matrix and r(A) denotes its rank. The complement matrix of A is defined and denoted by Ac = J − A, where J is the matrix with each entry being 1. In particular, when A is a square {0, 1}-matrix with each diagonal entry being 0, another kind of complement matrix of A is defined and denoted by A = J − I − A, where I is the identity matrix. We determine the possible values of r(A) ± r(Ac) and r(A) ± r(A) in the general case and in the symmetric case. Our proof is constructive.

In many applications, data organized in tensor form contains noise and missing entries. In this paper, the goal is to complete a tensor from its partial noisy observations. Specifically, we consider low-tubal-rank tensors sampled by element-wise Bernoulli sampling with additive sub-exponential noise. Algorithmically, a soft-impute-like algorithm, namely iterative singular tube thresholding (ISTT), is proposed. Statistically, bound on the estimation error of ISTT is explored. First, the estimation error is upper bounded non-asymptotically. Then, the minimax optimal lower bound of the estimation error for tensors with limited tubal-rank and bounded l∞-norm is established, indicating that the proposed upper bound is order optimal up to a logarithm factor. Numerical simulations show that the proposed upper bound can precisely predict the scaling behavior of the estimation error of ISTT. Compared with several state-of-the-art convex algorithms, the effectiveness of ISTT is demonstrated through experiments on real-world data sets.

The low bioavailability of most phytochemicals limits their anticancer effects in humans. The present study was designed to test whether combining arctigenin (Arc), a lignan mainly from the seed of Arctium lappa, with green tea (GT) and quercetin (Q) enhances the chemopreventive effect on prostate cancer. We performed in vitro proliferation studies on different cell lines. We observed a strong synergistic anti-proliferative effect of GT+Q+Arc in exposing androgen-sensitive human prostate cancer LNCaP cells. The pre-malignant WPE1-NA22 cell line was more sensitive to this combination. No cytotoxicity was observed in normal prostate epithelial PrEC cells. For an in vivo study, 3-week-old, prostate-specific PTEN (phosphatase and tensin homolog) knockout mice were treated with GT+Q, Arc, GT+Q+Arc, or the control daily until 16 weeks of age. In vivo imaging using prostate-specific membrane antigen (PSMA) probes demonstrated that the prostate tumorigenesis was significantly inhibited by 40% (GT+Q), 60% (Arc at 30 mg/kg bw), and 90% (GT+Q+Arc) compared to the control. A pathological examination showed that all control mice developed invasive prostate adenocarcinoma. In contrast, the primary lesion in the GT+Q and Arc alone groups was high-grade prostatic intraepithelial neoplasia (PIN), with low-grade PIN in the GT+Q+Arc group. The combined effect of GT+Q+Arc was associated with an increased inhibition of the androgen receptor, the PI3K/Akt pathway, Ki67 expression, and angiogenesis. This study demonstrates that combining Arc with GT and Q was highly effective in prostate cancer chemoprevention. These results warrant clinical trials to confirm the efficacy of this combination in humans.

Object detection plays a critical role for automatic video analysis in many vision applications. Background subtraction has been the mainstream in the field of moving objects detection. However, most of state‐of‐the‐art techniques of background subtraction operate on each pixel independently ignoring the global features of images. A motion detection method based on subspace update of background is proposed in this study. This method uses a subspace spanned by the principal components of background sequence to characterise the background and integrates the regional continuity of objects to segment the foreground. To deal with changes in the background geometry, a learning factor is introduced into the authors’ model to update the subspace timely. Additionally, to reduce computational complexity, they use two‐dimension principal component analysis (PCA) rather than traditional PCA to obtain the principal components of background. Experiments demonstrate that the update policy is effective and in most cases this proposed method can achieve better results than others compared in this study.

An astronomical increase in microbial genome data in recent years has led to strong demand for bioinformatic tools for pan-genome analysis within and across species. Here, we present PGAweb, a user-friendly, web-based tool for bacterial pan-genome analysis, which is composed of two main pan-genome analysis modules, PGAP and PGAP-X. PGAweb provides key interactive and customizable functions that include orthologous clustering, pan-genome profiling, sequence variation and evolution analysis, and functional classification. PGAweb presents features of genomic structural dynamics and sequence diversity with different visualization methods that are helpful for intuitively understanding the dynamics and evolution of bacterial genomes. PGAweb has an intuitive interface with one-click setting of parameters and is freely available at http://PGAweb.vlcc.cn/.

In computer vision and pattern recognition applications, there are usually a vast number of unlabelled data whereas the labelled data are very limited. Active learning is a kind of method that selects the most representative or informative examples for labelling and training; thus, the best prediction accuracy can be achieved. A novel active learning algorithm is proposed here based on one‐versus‐one strategy support vector machine (SVM) to solve multi‐class image classification. A new uncertainty measure is proposed based on some binary SVM classifiers and some of the most uncertain examples are selected from SVM output. To ensure that the selected examples are diverse from each other, Gaussian kernel is adopted to measure the similarity between any two examples. From the previous selected examples, a batch of diverse and uncertain examples are selected by the dynamic programming method for labelling. The experimental results on two datasets demonstrate the effectiveness of the proposed algorithm.

It has important meaning for Synthetic Aperture Radar (SAR) interpretation to estimate parameters from measured SAR data and visualize the SAR imagery. For the linearity of IFFT, single scattering center can be individually imaged adaptively to form an overall image. Thus, the side effects of side lobes and windowing can be decreased and the quality of the SAR imagery can be improved. The efficiency of the method is confirmed by the test based on the simulated data.