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Copper phenylacetylide (PACu) is a promising photocatalyst due to its unique copper ladder (CL) electron transport channel, which facilitates efficient charge transfer. However, the structure–activity relationship between the CL spacing and its catalytic performance has yet to be revealed. In this study, we skillfully selected multiple substituents to regulate the CL spacing of the PACu photocatalyst. Our findings indicate that reducing the CL spacing significantly enhances carrier separation and transport efficiency, leading to improved oxygen adsorption and activation. Specifically, PACu-F demonstrated superior photocatalytic activity, achieving a 99% conversion rate in benzylamine oxidation and maintaining an excellent stability over multiple cycles. This study confirms the feasibility of tuning the CL spacing in PACu using donor/acceptor substituents to achieve a high-efficiency photocatalytic performance, offering crucial insights into the rational design of advanced photocatalysts.

Energetic carriers generated by localized surface plasmon resonance (LSPR) provide an efficient way to drive chemical reactions. However, their dynamics and impact on surface reactions remain unknown due to the challenge in observing hot holes. This makes it difficult to correlate the reduction and oxidation half-reactions involving hot electrons and holes, respectively. Here we detect hot holes in their chemical form, Ag(I), on a Ag surface using surface-enhanced Raman scattering (SERS) of SO32–as a hole-specific label. It allows us to determine the dynamic correlations of hot electrons and holes. We find that the equilibrium of holes is the key factor of the surface chemistry, and the wavelength-dependent plasmonic chemical anode refilling (PCAR) effect plays an important role, in addition to the LSPR, in promoting the electron transfer. This method paves the way for visualizing hot holes with nanoscale spatial resolution toward the rational design of a plasmonic catalytic platform.

Given the extensive role of lipids in cancer development, there is substantial clinical interest in developing therapies that target lipid metabolism. In this study, we identified one cyclometalated iridium complex (Ir2) that exhibits potent antiproliferation activity in MIA PaCa-2 cells by regulating fatty acid metabolism and sphingolipid metabolism simultaneously. Ir2also efficiently overcomes cisplatin resistance in vitro. Satisfyingly, the generated Ir2@F127 carriers, as a temperature-sensitive in situgelling system of Ir2, showed effective cancer treatment with minimal side effects in an in vivoxenograft study. To the best of our knowledge, Ir2is the first reported cyclometalated iridium complex that exerts anticancer activity in MIA PaCa-2 cells by intervening in lipid metabolism, which provides an alternative pathway for the anticancer mechanism of cyclometalated iridium complexes.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and STAT3 has emerged as an effective drug target for TNBC treatment. Herein, we employed a scaffold-hopping strategy of natural products to develop a series of naphthoquinone-furopiperidine derivatives as novel STAT3 inhibitors. The in vitroassay showed that compound 10gpossessed higher antiproliferative activity than Cryptotanshinone and Napabucasin against TNBC cell lines, along with lower toxicity and potent antitumor activity in a TNBC xenograft model. Mechanistically, 10gcould inhibit the phosphorylation of STAT3 and the binding affinity was determined by the SPR assay (KD= 8.30 μM). Molecule docking studies suggested a plausible binding mode between 10gand the SH2 domain, in which the piperidine fragment and the terminal hydroxy group of 10gplayed an important role in demonstrating the success of this evolution strategy. These findings provide a natural product-inspired novel STAT3 inhibitor for TNBC treatment.

The zinc metallic anode encounters significant challenges stemming from vigorous and prolonged water-induced side reactions along with uncontrolled dendritic Zn growth, resulting in a notable decrease in the Coulombic efficiency (CE) and longevity of aqueous zinc batteries (AZBs). To mitigate the negative impacts of water’s reactivity, a robust aluminum-centered metal–organic framework (MOF) is utilized. This MOF features a water pocket embedded within its subnano channels, serving to safeguard the Zn anode. The water pocket within narrow channels captures water molecules originating from solvated Zn2+, facilitating gradual desolvation, thus eliminating water-induced corrosion and side reactions. Essentially, the modified subnano channels act as ion rectifiers, aiding swift and even Zn2+transfer, thereby averting dendrite creation on the Zn metal. Consequently, the shielded Zn metal exhibits unparalleled cycling stability, surpassing 2000 cycles, and an exceptionally high average CE of 99.47%. Moreover, the modified MOF@Zn|KVOH (KV12O30-y·nH2O) cell demonstrates a consistent cycling performance, achieving 6000 cycles at 5 A g–1. This groundbreaking approach for enhancing the reversibility of Zn metal anodes could drive the practical adoption of AZBs.

In this article, a neural adaptive intermittent output feedback control is investigated for autonomous underwater vehicles (AUVs) with full-state quantitative designs (FSQDs). To achieve the prespecified tracking performance determined by quantitative indices (e.g., overshoot, convergence time, steady-state accuracy, and maximum deviation) at both kinematic and kinetic levels, FSQDs are designed by transforming constrained AUV model into an unconstrained model via one-sided hyperbolic cosecant boundaries and nonlinear mapping functions. An intermittent sampling-based neural estimator (ISNE) is devised to reconstruct the matched and mismatched lumped disturbances as well as immeasurable velocity states of transformed AUV model, where only system outputs after intermittent sampling are required. Using the estimations of ISNE and the system outputs after triggering, an intermittent output feedback control law incorporated with hybrid threshold event-triggered mechanism (HTETM) is designed to achieve ultimately uniformly bounded (UUB) results. Simulation results are provided and analyzed to validate the effectiveness of the studied control strategy with application to an omnidirectional intelligent navigator (ODIN).

The prolonged mechanical performance degradation and failure of elastic components in mechanical systems can pose significant safety risks and disrupt related operations. While helical compression springs' mechanical behavior and failure mechanisms at high temperatures have been well-studied, their mechanical properties and microstructural deformation mechanisms at extreme cryogenic temperatures remain largely unexplored. This paper conducts experiments to investigate the detailed mechanical properties and microstructural evolution of Ti-6Al-4V helix springs under such conditions. Initially, we examined the mechanical properties at cryogenic temperatures and compared them with those at high temperatures. It was observed that the logarithmic constitutive model applicable at high temperatures does not suitably describe the spring's mechanical behavior at cryogenic temperatures. At extremely low temperatures, the mechanical properties of the spring were enhanced. Furthermore, we characterized the microstructural deformation mechanisms using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM), including twinning, slip activation, and dislocation evolution. The findings suggest that the deformation mechanism in Ti-6Al-4V springs under cryogenic conditions is predominantly governed by twinning and basal and prismatic slip. Due to the significant increase in the critical resolved shear stress (CRSS) of the pyramidal slip systems at cryogenic temperatures, these slip systems may not be activated during the deformation process. Additionally, the volume fraction of twinning shows a significant positive correlation with both cryogenic temperature and mechanical stress.

Fine particulate matter such as PM2.5 threatens significantly to the environment and human health, so it is essential to design a reliable long-term prediction method for PM2.5 concentrations. Existing long-term PM2.5 prediction models inadequately utilize urban spatial features, fail to consider the role of meteorological factors in PM2.5 levels, and overlook the interaction between PM2.5 concentrations in different cities. To tackle this issue, we propose two new models and integrate them. Firstly, we develop a spatial feature model (ECGAT) for extracting PM2.5 concentration among regions based on Graph Neural Networks (GNN), edge-channel mechanisms, and Graph Attention Convolution (GATConv). This model utilizes GNN to extract urban adjacency relationships and meteorological features, employs edge-channel mechanisms to recalculate weights for interactions between cities, and outputs spatial correlations through GATConv. Secondly, we propose Gating Closed-form Continuous-time Neural Networks (GCFC) as a temporal model to extract the PM2.5 concentration's temporal features. The fusion of these two models, named ECGAT-GCFC (EGCFC), enhances the model's capability to capture spatiotemporal features and improves performance in PM2.5 long-term predictions. Results from real-world data analysis show that the proposed algorithm outperforms state-of-the-art existing prediction models in predicting PM2.5 levels over long durations. Compared to baseline models, EGCFC reduces RMSE by an average of 3.39 %, decreases MAE by 4.83 %, increases R2 by 4.89 %, CSI by 3.13 %, and lowers FAR by 11.39 %. These indicate that EGCFC is an effective method for predicting trends in urban PM2.5 concentrations.

Reinforcement learning (RL) has been used to implement the perception-action cycle of multi-input–multi-output (MIMO) cognitive radar. This allows for the adaptive optimization of the radar's beampattern, which is guided by information from echoes and an appropriate reward signal. However, the present approaches rely on a greedy DOA estimation to select candidate angles, which means that decisions are primarily based on detection results from the last pulse. If the system misses a target, it can be time-consuming to recapture it, and this can limit the detection performance. To address this issue, we develop a data-driven method for executing DOA estimation by utilizing the Bayesian inference to evaluate the likelihood of an angle containing a target based on historical detection information. Furthermore, to accommodate dynamic scenarios, a decay method for historical experience is proposed, allowing the system to adapt to environmental changes dynamically. The simulation results show that the RL-based MIMO radar with our refined DOA estimation module outperforms existing RL-based detector, providing the SOTA detection performance by focusing more frequently on essential angles, even in small-scale system setups.

The accurate prediction of a lithium-ion battery’s State of Health is of critical importance for efficient battery health management. Existing data-driven estimation methodologies grapple with issues such as high model complexity and a dearth of guidance from prior knowledge, which impose constraints on their efficacy. This work introduces a novel cross-modal distillation network for battery State of Health estimation, structured around a TransformerEncoder as the teacher network and a Convolutional Neural Network as the student network. Initially, the teacher model is pre-trained offline using State of Health degradation data to learn the degradation patterns. The directly measurable feature data (such as voltage, temperature, and current) is subsequently fed into the student network for online training and computation of a hard loss. the student network’s output is then directed into the pre-trained the teacher network to compute a soft loss, thereby offering prior knowledge of degradation laws and steering the optimization process of the student network. Rigorous experiments are conducted utilizing various datasets, with the outcomes validating the superior estimation accuracy and degradation rule adherence of the model. Notably, among five different models, this model demonstrates the best performance on almost all datasets, achieving an RMSE of 0.0097 and an MAE of 0.0065 on Cell1 of the Oxford dataset. Moreover, the model also demonstrates robust performance across different usage scenarios, inclusive of multi-battery estimation. Furthermore, this paper also introduces a fine tuning method for State of Health predictions only using the first half of the data. Comparative analysis with other models underscores the competitiveness of the proposed model, showcasing its potential for broader application.

Long interspersed nuclear element-1 (LINE-1 or L1) is a retrotransposon group that constitutes 17% of the human genome and shows variable expression across cell types. However, the control of L1 expression and its function in gene regulation are incompletely understood. Here we show that L1 transcription activates long-range gene expression. Genome-wide CRISPR–Cas9 screening using a reporter driven by the L1 5′ UTR in human cells identifies functionally diverse genes affecting L1 expression. Unexpectedly, altering L1 expression by knockout of regulatory genes impacts distant gene expression. L1s can physically contact their distal target genes, with these interactions becoming stronger upon L1 activation and weaker when L1 is silenced. Remarkably, L1s contact and activate genes essential for zygotic genome activation (ZGA), and L1 knockdown impairs ZGA, leading to developmental arrest in mouse embryos. These results characterize the regulation and function of L1 in long-range gene activation and reveal its importance in mammalian ZGA.

Methamphetamine (METH) is a widely abused amphetamine-type psychoactive drug that causes serious health problems. Previous studies have demonstrated that METH can induce neuron autophagy and apoptosis in vivo and in vitro. However, the molecular mechanisms underlying METH-induced neuron autophagy and apoptosis remain poorly understood. Stromal interacting molecule 1 (STIM1) was hypothesized to be involved in METH-induced neuron autophagy and apoptosis. Therefore, the expression of STIM1 protein was measured and the effect of blocking STIM1 expression with siRNA was investigated in cultured neuronal cells, and the hippocampus and striatum of mice exposed to METH. Furthermore, intracellular calcium concentration and endoplasmic reticulum (ER) stress-related proteins were determined in vitro and in vivo in cells treated with METH. The results suggested that STIM1 mediates METH-induced neuron autophagy by activating the p-Akt/p-mTOR pathway. METH exposure also resulted in increased expression of Orai1, which was reversed after STIM1 silencing. Moreover, the disruption of intracellular calcium homeostasis induced ER stress and up-regulated the expression of pro-apoptotic protein CCAAT/enhancer-binding protein homologous protein (CHOP), resulting in classic mitochondria apoptosis. METH exposure can cause neuronal autophagy and apoptosis by increasing the expression of STIM1 protein; thus, STIM1 may be a potential gene target for therapeutics in METH-caused neurotoxicity.

Chimeric antigen receptor (CAR) T cells have made a groundbreaking advancement in personalized immunotherapy and achieved widespread success in hematological malignancies. As CAR technology continues to evolve, numerous studies have unveiled its potential far beyond the realm of oncology. This review focuses on the current applications of CAR-based cellular platforms in non-neoplastic indications, such as autoimmune, infectious, fibrotic, and cellular senescence-associated diseases. Furthermore, we delve into the utilization of CARs in non-T cell populations such as natural killer (NK) cells and macrophages, highlighting their therapeutic potential in non-neoplastic conditions and offering the potential for targeted, personalized therapies to improve patient outcomes and enhanced quality of life.

Dynamic epigenomic reprogramming occurs during mammalian oocyte maturation and early development. However, the underlying transcription circuitry remains poorly characterized. By mapping cis-regulatory elements using H3K27ac, we identified putative enhancers in mouse oocytes and early embryos distinct from those in adult tissues, enabling global transitions of regulatory landscapes around fertilization and implantation. Gene deserts harbour prevalent putative enhancers in fully grown oocytes linked to oocyte-specific genes and repeat activation. Embryo-specific enhancers are primed before zygotic genome activation and are restricted by oocyte-inherited H3K27me3. Putative enhancers in oocytes often manifest H3K4me3, bidirectional transcription, Pol II binding and can drive transcription in STARR-seq and a reporter assay. Finally, motif analysis of these elements identified crucial regulators of oogenesis, TCF3 and TCF12, the deficiency of which impairs activation of key oocyte genes and folliculogenesis. These data reveal distinctive regulatory landscapes and their interacting transcription factors that underpin the development of mammalian oocytes and early embryos.

7-Xylosyl-10-deacetyl paclitaxel is a crucial semisynthetic precursor of paclitaxel, which could prevent cancer cells from dividing and growing. In this study, twin-column recycling chromatography with a step solvent gradient was applied to the isolation and purification of 7-xylosyl-10-deacetyl paclitaxel. By introduction of a weak solvent between the two columns, band broadening was counteracted to facilitate the separation process. Also the effects of various variables including the magnitude of solvent gradient, feeding volume, switch number, and interval were comprehensively investigated, and the operating conditions were systematically optimized. In the results, 7-xylosyl-10-deacetyl paclitaxel was successfully refined from 23.90% to almost 100%, with a remarkable yield of over 95%. This study demonstrates the distinct advantage of utilizing a new chromatography system in the separation of natural products.

The hypersonic shock-shock interaction flow field at double-wedge geometries controlled by plasma synthetic jet actuator is experimentally studied in a Ma = 8 high-enthalpy shock tunnel with the purpose of exploring a novel technique for reducing surface heat flux in a real flight environment. The results demonstrate that increasing the discharge energy is advantageous in eliminating the shock wave, shifting the shock wave interaction point, and shortening the control response time. The oblique shock wave can be completely removed when the actuator's discharge energy grows from 0.4 J to 11.5 J, and the displacement of the shock wave interaction point increases by 124.56%, while the controlled response time is shortened by 30 μs. Besides, the reduction in diameter of the jet exit is firstly proved to have a negative impact on energy deposition in a working environment with incoming flow, which reduces the discharge energy and hence decreases the control effect. The shock wave control response time lengthens when the jet exits away from the second wedge. Along with comparing the change in wall heat flux at the second wedge over time, the control effect of plasma synthetic jet actuator with and without inflation is also analyzed. When plasma synthetic jet works in inflatable mode, both the ability to eliminate shock waves and the shifting effect of the shock wave interaction point are increased significantly, and the wall heat flux is also reduced.

Accurate and stable estimation of the state of health (SOH), which is one of the critical indicators to characterize the ability of lithium-ion (Li-ion) batteries to store and release energy, is critical in the stable driving of electric vehicles. In this paper, a novel SOH estimation method based on the aging factors of battery, which combines convolutional neural network (CNN), wavelet neural network (WNN), and wavelet long short-term memory (WLSTM) named CNN–WNN–WLSTM, is designed. The proposed CNN–WNN–WLSTM estimation scheme inherits both the fast convergence and robust stability of the WNN, as well as the ability of long short-term memory neural network (LSTM) to extract the time series features of the data; moreover, using CNN can make the proposed algorithm extract the data features from the original battery data automatically, and the WNN–WLSTM is then adopted to produce the final SOH estimation by exploiting the features from the CNN. To further speed and achieve global optimization, the RMSprop optimizer, instead of the usually used Adagrad optimizer, is chosen as the solver of the CNN–WNN–WLSTM network. Experimental results on data set from the NASA Ames Prognostics Center of Excellence show that the proposed algorithm can be commendably used for Li-ion battery health management by quantitative comparison with other commonly used machine learning methods, such as back-propagation neural network, WNN, LSTM, WLSTM, convolutional neural network–long short-term memory neural network (CNN–LSTM), and Gaussian process regression.

In this study, a developed easy-operation method was adopted to generate ultralight and compressible graphene aerogels by using graphene oxide and ammonia. By changing the process parameters, such as reduction temperature, reducing agent content and ammonia concentration, the variation laws of the density and pore size of aerogels were obtained, which aided in realizing the controllable preparation of aerogel structures. The prepared graphene aerogel has good compressive performance, and its density can reach 5.26 mg/cm3. Although it was repeatedly compressed 200 times under a load that was 4000 times as large as its own weight, it maintained its structural integrity and mechanical properties. An ideal model of three-dimensional graphene aerogel was constructed, and the electromagnetic wave absorption performance was simulated using the Computer Simulation Technology (CST) Microwave Studio software. The results showed that when the thickness, pore size and height of the sheet were 1.4, 5 and 14 mm, respectively, an optimal electromagnetic wave absorption effect of −31.08 dB could be obtained. Furthermore, the effects of thickness, pore size and height of the sheet on the electromagnetic wave absorption performance are revealed, which provide a reference for the structural design of aerogels with both compressibility and electromagnetic wave absorption performance.

Upgrading to the CHINA 7 standard is crucial for managing air pollution from passenger vehicles in China. Meanwhile, China aims to achieve carbon neutrality by 2060, which necessitates large-scale replacement of gasoline vehicles with electric vehicles in the future. Consequently, the public might view upgrading gasoline vehicles to the CHINA 7 standard as redundant. However, the emission reduction benefits of upgrading standards in the context of uncertain electrification ambitions have not received adequate attention. Here, we show that upgrading standards will compensate for the absence of emissions reductions due to hindered electrification efforts. In the best scenario, China’s CO2emissions can be reduced to 0.047 Gt and NOxto 8.2 × 103t in 2050. In nonextreme electrification scenarios with CHINA 7 standard, the emission intensity reduction will remain the main driver for emission reductions, outweighing the electrification contribution. In extreme electrification scenarios, upgrading standards will tackle the increased emissions from plug-in hybrid electric vehicles. Our fleet-level results advocate for early standards upgrades to enhance resilience against air pollution risks arising from uncertainties in electrification. Our evidence from China, with one of the most stringent emission standards, can provide a reference point for the world on the upgrading passenger vehicle emission standard issue.

Catalytic pyrolysis of biomass is an important approach to upgrade the quality of bio-oil. The development of catalyst with high activity and low cost is important for the development of catalytic pyrolysis technology. Red mud (RM), an industrial solid waste from alumina production, was used for the in situ catalytic pyrolysis of cellulose. The effect of the ratio of RM and cellulose was investigated, and the upgraded products were analyzed. The results showed that levoglucosan (LG) from pyrolysis of cellulose could be further catalytically converted into small oxygen-containing compounds. When cellulose was mixed with 40%RM, the production of furan increases significantly by 255%. Moreover, the obtained biochar mixed with RM shows the micro-mesoporous structures. The specific surface area decreases with the increase of RM content.

The asymptotic model is applied to analyze the high-frequency electromagnetic coupling problem of the multiconductor transmission lines (MTLs). To efficiently calculate the current of MTLs in the nonuniform electromagnetic field, an extended asymptotic model combined with matrix pencil method (MPM) is proposed in this article. In the proposed method, the complicated electric field is analytically expressed using an MPM signal model first. Then, considering the basic theory of the moment of method (MOM), the previous solving process of the asymptotic model is optimized in this article to adapt to the requirements of multiple calculations. Finally, the asymptotic model is extended to calculate the current excited by the equivalent plane wave component of the signal model with a good efficiency and accuracy. Moreover, calculating the transient response by the inverse fast Fourier transform (IFFT), several numerical examples with typical nonuniform electromagnetic fields are presented to validate the reliability of the extended asymptotic model.

Uncertain environments and inefficient decision analysis restrict the efficient utilization of depletable public resources by multiagents, especially for the scenario involved with multiobjective game dilemmas and weak scalability of decision making. To address the above conundrums, this article proposes a multilayer games framework that integrates cognition, decision making, and countermeasures (CDCs). Through the transformation of agent preference to alliance communication structure, a cooperation-competition topology network (CCTN) model is constructed, which improves the convergence and solution efficiency of the game model. In view of the Gaussian kernel ascending dimension mapping, a game equilibrium particle swarm optimization (GEPSO) algorithm is designed to improve the efficiency of finding equilibrium solutions and solve the nondeterministic polynomial (NP) problem of multiobjective games. To validate the effectiveness and performance of the proposed methodology, a case study of collaborative detection of multivehicle is conducted using the proposed framework and model.

In recent years, an increasing number of map apps have provided route planning services to their users. However, the quality of route planning services relies heavily on having correct data about transportation infrastructure. As many planned subway lines are being built across cities, there are conflicts between the actual conditions and the data provided by map apps for temporary bus stops, which may result in complaints against public transportation operators. However, it is difficult to tackle these complaints, as public transportation operators can obtain only inaccurate information about the locations of temporary bus stops. To resolve these conflicts, crowd bus sensing (CBS) is proposed in this paper. CBS is a new sensing paradigm that takes advantage of the extensive deployment of GPS trackers and prior knowledge about the transportation infrastructures covered by scheduled bus routes. Extensive experimental evaluations on real-world and synthetic datasets show that the proposed CBS system outperforms state-of-the-art methods.

In this study, the effects of Fe on the oxygen-evolution reaction (OER) in the presence of Au with a focus on the identification of distinct OER areas associated with Fe and Au sites are investigated.

ABSTRACTA brief daily body-mind-spirit (BMS) workplace well-being program has been developed for community mental health workers (CMHW). Aiming to evaluate the program’s efficacy, this study adopted a multi-site randomized controlled trial design. Primary outcome measures included work engagement and burnout. The data analysis included 175 participants. ANOVA revealed significant group x time interaction effects on work engagement (η2 = 0.037, p = 0.039) and on one of its sub-scores: absorption (η2 = 0.048, p = 0.014). Regarding burnout, ANOVA revealed that at T4 there were significant group x time effects on burnout total score and all three of its sub-scores, including work-related burnout, client-related burnout and personal burnout. Partial eta squared ranged between 0.028 and 0.071, suggesting a small-to-medium effect size. Overall findings demonstrated the protective effect of the Brief Daily BMS program in preserving work engagement during challenging conditions, and to a lesser extent in reducing burnout among CMHW.

X chromosome inactivation triggers a dramatic reprogramming of transcription and chromosome architecture. However, how the chromatin organization of inactive X chromosome is established de novo in vivo remains elusive. Here, we identified an Xist-separated megadomain structure (X-megadomains) on the inactive X chromosome in mouse extraembryonic lineages and extraembryonic endoderm (XEN) cell lines, and transiently in the embryonic lineages, before Dxz4-delineated megadomain formation at later stages in a strain-specific manner. X-megadomain boundary coincides with strong enhancer activities and cohesin binding in an Xistregulatory region required for proper Xistactivation in early embryos. Xistregulatory region disruption or cohesin degradation impaired X-megadomains in extraembryonic endoderm cells and caused ectopic activation of regulatory elements and genes near Xist, indicating that cohesin loading at regulatory elements promotes X-megadomains and confines local gene activities. These data reveal stepwise X chromosome folding and transcriptional regulation to achieve both essential gene activation and global silencing during the early stages of X chromosome inactivation.

Text-based person search aims to retrieve corresponding images of person from a large gallery based on text descriptions. Existing methods strive to bridge the modality gap between images and texts and have made promising progress. However, these approaches disregard the knowledge imbalance between images and texts caused by the reporting bias. To resolve this issue, we present a cross-modal feature fusion-based knowledge transfer network to balance identity information between images and texts. First, we design an identity information emphasis module to enhance person-relevant information and suppress person-irrelevant information. Second, we design an intermediate modal-guided knowledge transfer module to balance the knowledge between images and texts. Experimental results on CUHK-PEDES, ICFG-PEDE, and RSTPReid datasets demonstrate that our method achieves state-of-the-art performance.

In response to the new mechanism of direct vortex melting reduction of vanadium–titanium magnetite, the reaction control mechanism and the migration regularity of valuable components in the process of direct melting reduction were investigated using kinetic empirical equation by fitting and combining with X-ray diffraction, X-ray fluorescence, scanning electron microscopy, energy-dispersive spectrometry, and optical microscopy. The results show that iron reduction is controlled by the mass transfer process of (FeOx) in the slag, while vanadium reduction is controlled by both the mass transfer of (VOx) in the slag and the mass transfer of [V] in the molten iron, and the slag–metal interfacial reaction is the only pathway for vanadium reduction. The reduction of iron and vanadium is an obvious first-order reaction, with activation energy of 101.6051 and 197.416 kJ mol−1, respectively. Increasing the vortex rate and reaction temperature is beneficial to improving the reaction rate and reduction efficiency. The mineral phase variation of iron and vanadium in the slag during the reduction process is Fe2O3→ Fe3O4/FeV2O4→ FeTiO3and FeV2O4→ MgV2O5; titanium in slag is mainly in the form of MgxTi3−xO5(0 ≤ x≤ 1) and CaTiO3. As the reaction time went on, the molar ratio (nTi/nMg) in MgxTi3−xO5(0 ≤ x≤ 1) and the Ti2O3content in the slag gradually went up, while the area proportion of MgxTi3−xO5(0 ≤ x≤ 1) went up and then down, and the porosity of the slag and the grain size of MgxTi3−xO5(0 ≤ x≤ 1) got smaller.

Liver fibrosis is an urgent clinical problem without effective therapies. Here we conducted a high-content screening on a natural Euphorbiaceae diterpenoid library to identify a potent anti-liver fibrosis lead, 12-deoxyphorbol 13-palmitate (DP). Leveraging a photo-affinity labeling approach, apolipoprotein L2 (APOL2), an endoplasmic reticulum (ER)-rich protein, was identified as the direct target of DP. Mechanistically, APOL2 is induced in activated hepatic stellate cells upon transforming growth factor-β1 (TGF-β1) stimulation, which then binds to sarcoplasmic/ER calcium ATPase 2 (SERCA2) to trigger ER stress and elevate its downstream protein kinase R-like ER kinase (PERK)–hairy and enhancer of split 1 (HES1) axis, ultimately promoting liver fibrosis. As a result, targeting APOL2 by DP or ablation of APOL2significantly impairs APOL2–SERCA2–PERK–HES1 signaling and mitigates fibrosis progression. Our findings not only define APOL2 as a novel therapeutic target for liver fibrosis but also highlight DP as a promising lead for treatment of this symptom.

Embedding unified skeletons into unregistered scans is fundamental to finding correspondences, depicting motions, and capturing underlying structures among the articulated objects in the same category. Some existing approaches rely on laborious registration to adapt a predefined LBS model to each input, while others require the input to be set to a canonical pose, e.g., T-pose or A-pose. However, their effectiveness is always influenced by the water-tightness, face topology, and vertex density of the input mesh. At the core of our approach lies a novel unwrapping method, named SUPPLE (Spherical UnwraPping ProfiLEs), which maps a surface into image planes independent of mesh topologies. Based on this lower-dimensional representation, a learning-based framework is further designed to localize and connect skeletal joints with fully convolutional architectures. Experiments demonstrate that our framework yields reliable skeleton extractions across a broad range of articulated categories, from raw scans to online CADs.