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Abstract The development of efficient and stable electrocatalysts for water oxidation in acidic media is vital for the commercialization of the proton exchange membrane electrolyzers. In this work, we successfully construct Ru–O–Ir atomic interfaces for acidic oxygen evolution reaction (OER). The catalysts achieve overpotentials as low as 167, 300, and 390 mV at 10, 500, and 1500 mA cm−2 in 0.5 M H2SO4, respectively, with the electrocatalyst showing robust stability for >1000 h of operation at 10 mA cm−2 and negligible degradation after 200,000 cyclic voltammetry cycles. Operando spectroelectrochemical measurements together with theoretical investigations reveal that the OER pathway over the Ru–O–Ir active site is near-optimal, where the bridging oxygen site of Ir–OBRI serves as the proton acceptor to accelerate proton transfer on an adjacent Ru centre, breaking the typical adsorption-dissociation linear scaling relationship on a single Ru site and thus enhancing OER activity. Here, we show that rational design of multiple active sites can break the activity/stability trade-off commonly encountered for OER catalysts, offering good approaches towards high-performance acidic OER catalysts.

Abstract Osteomyelitis (OM) is an inflammatory disease of bone infection and destruction characterized by dysregulation of bone homeostasis. Staphylococcus aureus (SA) has been reported to be the most common pathogen causing infectious OM. Recent studies have demonstrated that N6-methyladenosine (m6A) regulators are associated with the development of OM. However, the molecular mechanism of m6A modifications in OM remains unclear. Here, we investigated the function of methyltransferase-like 3 (METTL3)-mediated m6A modification in OM development. In this study, human bone mesenchymal stem cells (hBMSCs) were treated with staphylococcal protein A (SpA), a vital virulence factor of SA, to construct cell models of OM. Firstly, we found that METTL3 was upregulated in OM patients and SpA-induced hBMSCs, and SpA treatment suppressed osteogenic differentiation and induced oxidative stress and inflammatory injury in hBMSCs. Functional experiments showed that METTL3 knockdown alleviated the inhibition of osteogenic differentiation and the promotion of oxidative stress and inflammation in SpA-treated hBMSCs. Furthermore, METTL3-mediated m6A modification upregulated miR-320a expression by promoting pri-miR-320a maturation, and the mitigating effects of METTL3 knockdown on SpA-mediated osteogenic differentiation, oxidative stress and inflammatory responses can be reversed by miR-320 mimic. In addition, we demonstrated that phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) was a downstream target of miR-320a, upregulation of PIK3CA alleviated miR-320a-induced inhibition of osteogenic differentiation, and upregulation of oxidative stress and inflammatory responses during SpA infection. Finally, we found that silencing METTL3 alleviated OM development by regulating the miR-320a/PIK3CA axis. Taken together, our data demonstrated that the METTL3/m6A/miR-320a/PIK3CA axis regulated SpA-mediated osteogenic differentiation, oxidative stress, and inflammatory responses in OM, which may provide a new therapeutic strategy for OM patients.

INTRODUCTION: The aim of this meta-analysis was to investigate the effect of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on blood glucose and weight in adolescents with overweight/obesity and/or type 2 diabetes mellitus (T2DM) aged

This paper presents the case of a 45-year-old woman who developed aquaporin-4 (AQP4) antibody-positive neuromyelitis optica spectrum disorder (NMOSD) approximately 6 weeks after a herpes zoster infection. Her initial symptoms included area postrema syndrome, which was marked by persistent nausea, vomiting, and belching. This report also provides a summary of the demographic and clinical features, disease progression, magnetic resonance imaging findings, cerebrospinal fluid analysis, treatment plans, and recovery outcomes of 11 patients—including this case and 10 others reported in the literature since 2008—who developed AQP4 antibody-positive NMOSD following herpes zoster infection. This review aims to improve clinicians’ understanding of the characteristics, treatment, and prognosis of this disease. It also highlights the importance for pain management specialists to consider central pain and use the MIDNIGHTS or VINDψCATE mnemonic devices to systematically consider the differential diagnoses of a patient’s new pain symptoms.

Abstract Designing integrated overall water‐splitting catalysts that maintain high efficiency and stability under various conditions is an important trend for future development, yet it remains a significant challenge. Herein, novel nanoflower‐like tri‐metallic Ni–Ru–Mo phosphide catalyst ((Ni–Ru–Mo)P@F‐CDs), integrated with F‐doped carbon dots (F‐CDs), were synthesized via a straightforward hydrothermal process and subsequent phosphatization. Attributable to precise interface engineering and electronic structure optimization, (Ni–Ru–Mo)P@F‐CDs exhibit exceptional bi‐functional catalytic activity in alkaline conditions, achieving remarkably low overpotentials of 231 and 123 mV for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), respectively, at a current density of 100 mA cm−2. Industrially, only 1.426 V is needed for the same efficacy. Additionally, the catalyst requires merely 1.508 and 1.564 V for overall water splitting in 1 M KOH and simulated seawater, respectively, at 100 mA cm−2. The catalyst also shows excellent stability, with minimal performance decline over 100 h within 100–200 mA cm−2. Density functional theory calculations indicate that the interface structure synergistically optimizes Gibbs free energy for H* and O* intermediates during HER and OER, respectively, accelerating electrochemical water‐splitting kinetics.

The surgical navigation system enhances surgical safety and accuracy by providing precise guidance. However, traditional pose estimation algorithms lack real-time performance and accuracy. To address this issue, a multi-average Long Short Term Memory (LSTM) prediction network is designed to maintain sensitivity in estimating the position of surgical instruments and track their random motion trends. Additionally, the spatial coordinates of positioning markers are applied back to the imaging plane, reducing the recognition range and improving algorithm running speed. Experimental results show that the average time of estimation is less than 1ms while ensuring the prediction effect.

Electrocatalytic water splitting through the electrolyzer is the most promising strategy for hydrogen production. Recently, water electrolysis is mainly based on high‐purity freshwater, which not only consumes a large number of freshwater resources but also improves the overall cost due to the extra water purification system. Hence, direct electrolysis of seawater is more desirable for large‐scale hydrogen generation. As is known, the dominant rate‐determining step of overall water splitting is the anodic oxygen evolution reaction (OER), which involves four‐electron transfer and owns a much larger overpotential than cathodic hydrogen evolution reaction. The large challenge for the design of OER catalysts in the seawater media is the competition reaction between OER and chloride oxidation reaction, which greatly influences energy efficiency. Hence, except for the activity and stability, selectivity is another key point for seawater splitting. Herein, after a brief introduction of two half reactions for water splitting, the latest metal hydr(oxide) electrocatalysts with different crystalline structures are summarized according to the previous reports. Moreover, the advantages and disadvantages of three common water electrolyzers are compared. Finally, the perspectives of seawater electrolysis for hydrogen production are outlined for practical applications.

BackgroundAcute cadmium (Cd) exposure can cause damage to multiple tissues, with the kidney being the primary target organ. The development of Cd-induced acute kidney injury involves complex mechanisms, in which autophagy and oxidative stress play crucial roles.ObjectiveTo investigate the effect of 10-hydroxy-2-decenoic acid (10-HDA) on kidney injury in mice exposed to cadmium, and provide experimental basis for studying the pathogenesis and prevention of Cd poisoning.MethodsThirty-five male C57BL/6 mice were divided into 7 groups (each of 5 mice): control group (normal saline, intraperitoneal injection), CdCl2 group (4 mg·kg−1, intraperitoneal injection), intervention groups ( 4 mg·kg−1 CdCl2, intraperitoneal Injection + 50, 100, 150, or 200 mg·kg−1 10-HDA, oral gavage), and 10-HDA group (150 mg·kg−1, oral gavage). All treatments were given for 14 d. Twenty-four hours after the last infection, physiological indicators [blood urea nitrogen (BUN), creatinine (CRE), malondialdehyde (MDA), and superoxide dismutase (SOD)], histopathological indicators, autophagy-related proteins (Atg7, Atg5, Beclin-1, and LC3), and mitochondrial autophagy-related proteins (PINK1 and Parkin) were detected to examine the effect of 10-HDA on kidney injury caused by CdCl2.ResultsCompared with the control group, the body weight of mice in the CdCl2 group was significantly reduced (P

In neural machine translation (NMT), the sophistication of word embeddings plays a pivotal role in the model’s ability to render accurate and contextually relevant translations. However, conventional models with single granularity of word segmentation cannot fully embed complex languages like Chinese, where the granularity of segmentation significantly impacts understanding and translation fidelity. Addressing these challenges, our study introduces the Dynamic Multi-Granularity Translation System (DMGTS), an innovative approach that enhances the Transformer model by incorporating multi-granularity position encoding and multi-granularity self-attention mechanisms. Leveraging a Directed Acyclic Graph (DAG), the DMGTS utilizes four levels of word segmentation for multi-granularity position encoding. Dynamic word embeddings are also introduced to enhance the lexical representation by incorporating multi-granularity features. Multi-granularity self-attention mechanisms are applied to replace the conventional self-attention layers. We evaluate the DMGTS on multiple datasets, where our system demonstrates marked improvements. Notably, it achieves significant enhancements in translation quality, evidenced by increases of 1.16 and 1.55 in Bilingual Evaluation Understudy (BLEU) scores over traditional static embedding methods. These results underscore the efficacy of the DMGTS in refining NMT performance.

In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region.

Safflower (Carthamus tinctorius L.), a member of the Asteraceae family, is widely used in traditional herbal medicine. This review summarized agronomic conditions, genetic diversity, clinical application, and phytochemicals and pharmacological properties of safflower. The genetic diversity of the plant is rich. Abundant in secondary metabolites like flavonoids, phenols, alkaloids, polysaccharides, fatty acids, polyacetylene, and other bioactive components, the medicinal plant is effective for treating cardiovascular diseases, neurodegenerative diseases, and respiratory diseases. Especially, Hydroxysafflor yellow A (HYSA) has a variety of pharmacological effects. In terms of treatment and prevention of some space sickness in space travel, safflower could be a potential therapeutic agent. Further studies are still required to support the development of safflower in medicine. Our review indicates that safflower is an important medicinal plant and research prospects regarding safflower are very broad and worthy of further investigation.

Abstract Natural language processing (NLP) based on deep learning provides a positive performance for generative dialogue system, and the transformer model is a new boost in NLP after the advent of word vectors. In this paper, a Chinese generative dialogue system based on transformer is designed, which only uses a multi-layer transformer decoder to build the system and uses the design of an incomplete mask to realize one-way language generation. That is, questions can perceive context information in both directions, while reply sentences can only output one-way autoregressive. The above system improvements make the one-way generation of dialogue tasks more logical and reasonable, and the performance is better than the traditional dialogue system scheme. In consideration of the long-distance information weakness of absolute position coding, we put forward the improvement of relative position coding in theory, and verify it in subsequent experiments. In the transformer module, the calculation formula of self-attention is modified, and the relative position information is added to replace the absolute position coding of the position embedding layer. The performance of the modified model in BLEU, embedding average, grammatical and semantic coherence is ideal, to enhance long-distance attention.

t(1;19)(q23;p13) is one of the most common translocation genes in childhood acute lymphoblastic leukemia (ALL) and is also present in acute myeloid leukemia (AML) and mixed-phenotype acute leukemia (MPAL). This translocation results in the formation of the oncogenic E2A-PBX1 fusion protein, which contains a trans-activating domain from E2A and a DNA-binding homologous domain from PBX1. Despite its clear oncogenic potential, the pathogenesis of E2A-PBX1 fusion protein is not fully understood (especially in leukemias other than ALL), and effective targeted clinical therapies have not been developed. To address this, we established a stable and heritable zebrafish line expressing human E2A-PBX1 (hE2APBX1) for high-throughput drug screening. Blood phenotype analysis showed that hE2APBX1 expression induced myeloid hyperplasia by increasing myeloid differentiation propensity of hematopoietic stem cells (HSPCs) and myeloid proliferation in larvae, and progressed to AML in adults. Mechanistic studies revealed that hE2A-PBX1 activated the TNF/IL-17/MAPK signaling pathway in blood cells and induced myeloid hyperplasia by upregulating the expression of the runx1. Interestingly, through high-throughput drug screening, three small molecules targeting the TNF/IL-17/MAPK signaling pathway were identified, including OUL35, KJ-Pyr-9, and CID44216842, which not only alleviated the hE2A-PBX1- induced myeloid hyperplasia in zebrafish but also inhibited the growth and oncogenicity of human pre-B ALL cells with E2A-PBX1. Overall, this study provides a novel hE2A-PBX1 transgenic zebrafish leukemia model and identifies potential targeted therapeutic drugs, which may offer new insights into the treatment of E2A-PBX1 leukemia.

In recent years, the image feature extraction method based on Transformer has become a research hotspot. However, when using Transformer for image feature extraction, the model’s complexity increases quadratically with the number of tokens entered. The quadratic complexity prevents vision transformer-based backbone networks from modelling high-resolution images and is computationally expensive. To address this issue, this study proposes two approaches to speed up Transformer models. Firstly, the self-attention mechanism’s quadratic complexity is reduced to linear, enhancing the model’s internal processing speed. Next, a parameter-less lightweight pruning method is introduced, which adaptively samples input images to filter out unimportant tokens, effectively reducing irrelevant input. Finally, these two methods are combined to create an efficient attention mechanism. Experimental results demonstrate that the combined methods can reduce the computation of the original Transformer model by 30%–50%, while the efficient attention mechanism achieves an impressive 60%–70% reduction in computation.

Abstract Oxygen evolution reaction catalysts capable of working efficiently in acidic media are highly demanded for the commercialization of proton exchange membrane water electrolysis. Herein, we report a Zn-doped RuO2 nanowire array electrocatalyst with outstanding catalytic performance for the oxygen evolution reaction under acidic conditions. Overpotentials as low as 173, 304, and 373 mV are achieved at 10, 500, and 1000 mA cm−2, respectively, with robust stability reaching to 1000 h at 10 mA cm−2. Experimental and theoretical investigations establish a clear synergistic effect of Zn dopants and oxygen vacancies on regulating the binding configurations of oxygenated adsorbates on the active centers, which then enables an alternative Ru−Zn dual-site oxide path of the reaction. Due to the change of reaction pathways, the energy barrier of rate-determining step is reduced, and the over-oxidation of Ru active sites is alleviated. As a result, the catalytic activity and stability are significantly enhanced.

Abstract The Visual Question Answering (VQA) system is the process of finding useful information from images related to the question to answer the question correctly. It can be widely used in the fields of visual assistance, automated security surveillance, and intelligent interaction between robots and humans. However, the accuracy of VQA has not been ideal, and the main difficulty in its research is that the image features cannot well represent the scene and object information, and the text information cannot be fully represented. This paper used multi-scale feature extraction and fusion methods in the image feature characterization and text information representation sections of the VQA system, respectively to improve its accuracy. Firstly, aiming at the image feature representation problem, multi-scale feature extraction and fusion method were adopted, and the image features output of different network layers were extracted by a pre-trained deep neural network, and the optimal scheme of feature fusion method was found through experiments. Secondly, for the representation of sentences, a multi-scale feature method was introduced to characterize and fuse the word-level, phrase-level, and sentence-level features of sentences. Finally, the VQA model was improved using the multi-scale feature extraction and fusion method. The results show that the addition of multi-scale feature extraction and fusion improves the accuracy of the VQA model.

With the continuous development of information technology and the rapid increase in new users of social networking sites, recommendation technology is becoming more and more important. After research, it was found that the behavior of users on social networking sites has a great correlation with their personalities. The five characteristics of the OCEAN personality model can cover all aspects of a user’s personality. In this research, a micro-directional propagation model based on the OCEAN personality model and a Stacked Denoising Auto Encoder (SDAE) was built through the application of deep learning to a collaborative filtering technique. Firstly, the dimension of the user and item feature matrices was lowered using SDAE in order to extract deeper information. The user OCEAN personality model matrix and the reduced user feature matrix were integrated to create a new user feature matrix. Finally, the multiple linear regression approach was used to predict user-unrated goods and generate recommendations. This approach allowed us to leverage the relationships between various factors to deliver personalized recommendations. This experiment evaluated the RMSE and MAE of the model. The evaluation results show that the stacked denoising auto encoder collaborative filtering algorithm can improve the accuracy of recommendations, and the user’s OCEAN personality model improves the accuracy of the model to a certain extent.

The electrochemical oxygen reduction reaction (ORR) is pivotal in energy conversion via a 4e− ORR pathway and green hydrogen peroxide production via 2e− ORR pathway. Transition metal single atom catalysts (TM SACs) have attracted intense attention in recent years for ORR due to their high activity and near maximum metal atom utilization. The future development of TM SACs for ORR requires improved understanding of reaction pathways, since currently the true origin of activity remains contentious owing to the lack of qualitative/quantitative information about active sites. Knowledge-guided design is imperative for the optimization of TM SACs performance in terms of activity and selectivity. This review focuses on the latest progress in the design of TM SACs for ORR, placing particular attention on efforts to elucidate reaction mechanisms. Experimental evidence based on in-situ/operando characterization measurements, along with theoretical predictions, are summarized to deepen understanding of the structure-performance relationships at both atomic and molecular level. Finally, some perspectives are offered relating to the fundamental science needed for TM SACs to find practical application in energy storage and conversion devices. We hope this review will inspire the development of new synthetic routes towards high-performance ORR electrocatalysts for the energy sector.

In arid oasis regions, water resources are severely scarce, with agricultural water usage far exceeding that of more humid areas. As a result, assessing non-productive water losses in farmland becomes crucial for estimating the water requirements reliant on irrigation for oasis cultivation. From April 2018 to October 2021, we established an observation system in the Minqin Oasis farmland. By utilizing hydrogen and oxygen isotopes in precipitation, soil water, and maize stem xylem water, we quantified the non-productive water loss (FE) and soil water infiltration rate (fe) in maize fields. Our findings indicate: (1) The average non-productive losses from rainfall and irrigation in arid maize fields were 39 %, with peak losses reaching 58 %; (2) Non-productive losses due to evapotranspiration were mainly observed in June and July, while losses from infiltration predominantly occurred in April-May and August-September; (3) Crop evapotranspiration capacity emerged as a significant factor influencing evaporation losses, while individual irrigation amounts and rainfall determined soil infiltration losses. We believe that judiciously managing irrigation volume and adjusting irrigation strategies in arid farmlands could unlock substantial potential for conserving water resources.

Computer vision technology has broad application prospects in the field of intelligent fire detection, which has the benefits of accuracy, timeliness, visibility, adjustability, and multi-scene adaptability. Traditional computer vision algorithm flaws include erroneous detection, detection gaps, poor precision, and slow detection speed. In this paper, the efficient and lightweight YOLOv5s model is used to detect fire flames and smoke. The attention mechanism is embedded into the C3 module to enhance the backbone network and maximize the algorithm's suppression of invalid feature data. Alpha CIOU is adopted to improve the positioning function and detection target. At the same time, the concept of transfer learning is used to realize semi-automatic data annotation, which reduces training expenses in terms of manpower and time. The comparative experiments of six distinct fire detection algorithms (YOLOv5 and five optimization algorithms) are carried out. The results indicate that the self-attention mechanism based on the Transformer structure has a substantial impact on enhancing target detection precision. The improved location function based on Alpha CIOU aids in enhancing the detection recall rate. The average recall rate of fire detection of the YOlOv5+TR+αCIOU algorithm is the highest, which is 68.5%, clearly outperforming other algorithms. Based on the surveillance video, this optimization algorithm is utilized to detect a fire in a factory, and the fire is detected in the 9th second when it starts to appear. The results demonstrate the algorithm's viability for real-time fire detection.

Abstract Background Osteonecrosis of the femoral head (ONFH) can lead to pain and loss of function of the hip joint, which places a great burden on patients and society. Surgery is the main treatment for osteonecrosis of the femoral head, and quadratus femoris muscle pedicle bone grafting has a definite therapeutic effect as one method of surgery for the treatment of ONFH. However, the posterior superior retinacular artery is often injured during quadratus femoris muscle pedicle bone graft surgery. There is evidence that this artery is extremely important to the femoral head, as injury to this artery will seriously affect the blood supply of the femoral head. Therefore, this situation restricts the clinical application of quadratus femoris muscle pedicle bone grafts. We aimed to explore a new surgical method of quadratus femoris muscle pedicle bone grafting that can preserve the integrity of the posterior superior retinacular artery. Methods We modified the traditional quadratus femoris muscle pedicle bone graft and preserved the integrity of the posterior superior retinacular artery. To explore the safety and feasibility of the operation, we simulated the operation on 6 fresh frozen cadavers (12 hips) and measured the related data. We also tried this modified surgical method in the clinic and collected detailed data from the patients. Results By simulating the modified quadratus femoris muscle pedicle bone graft on the hip joints of fresh frozen cadavers, we found that the posterior superior retinacular artery existed in all cadaver specimens and that the sources may be different (MFCA or IGA). In the modified operation, the joint capsule did not need to be cut during the operation; therefore, the integrity of the posterior superior retinacular artery was preserved. The quadratus femoris muscle was exposed via the posterior approach of the hip joint, and then the quadratus femoris muscle pedicle bone flap was chiseled. After the pedicle of the quadratus femoris muscle was loosened properly, the migration distance of the quadratus femoris muscle pedicle bone flap reached 5.89 ± 0.45 (χ ± s) cm. The bone flap was trimmed properly and placed on one side. Next, we drilled a bone tunnel from the external intertrochanteric aspect of the capsule of the hip joint, and the bone tunnel broke through the sclerosing zone and proceeded straight to the necrotic area of the femoral head. Next, the necrotic bone was removed with a ring saw and arc bone knife, autogenous bone or allogeneic bone was filled into the bone groove according to the situation, and the cancellous bone in the bone groove was tamped by percussion. Then, the bone flap was inserted into the bone groove, and appropriate pressurization was performed. The depth of the bone groove was determined by the location of ONFH. We found that the furthest distance between the bone groove and the femoral head was 4.76 ± 0.07 (χ ± s) cm and that the length of the bone flap was (4.91 ± 0.23) (χ ± s) cm. This means that when the depth of the bone groove reached the area of ONFH, the quadratus femoris muscle pedicle bone flap had a sufficient length and migration distance to be embedded in the area of ONFH and firmly fixed, and the quadratus femoris did not have much tension. The closest distance between the posterior superior retinacular artery and the bone groove was (1.11 ± 0.96) (χ ± s) cm. When the bone groove was created in this area, the edge of the bone groove had a safe distance of at least 1 cm from the posterior superior retinacular artery of the femoral head. We attempted to implement this modified operation clinically. During the procedure, the quadratus femoris muscle pedicle bone flap was embedded into the drilled bone groove and fixed with a magnesium nail. There was no sliding of the bone flap after the operation, and the posterior superior retinacular artery was intact. We followed the patient for 3 months and found that the patient recovered well with no weight-bearing by the affected limb. The duration of the modified operation was shorter than that of the traditional quadratus femoris muscle pedicle bone graft, the amount of bleeding was significantly reduced, the postoperative pain was lessened, and no special discomfort was reported. Postoperative imaging examination showed that the collapse of the femoral head had been partially corrected and that the bone flap had gradually fused with the surrounding bone. Conclusions Through this experimental study, we confirmed the feasibility of the modified method for quadratus femoris muscle pedicle bone grafting with preservation of the posterior superior retinacular artery. This modified operation not only retains the integrity of the posterior superior retinacular artery of the femoral head but also reduces the difficulty of the operation and shortens the surgical time, which is of great clinical significance.

As the battery cycles between charging and discharging, the working conditions or improper operations such as overcharge and over discharge will aggravate the negative reaction inside the battery, generate irreversible chemical substances, and reduce the number of active substances involved in the electrochemical reaction, resulting in a decrease in battery capacity. Batteries that lose 20% of their capacity can be considered to have failed. A failed battery shows that the battery capacity and power decay faster, and the electrical characteristics, stability, and safety of the battery will drop significantly. As a means of improving the machine learning model’s accuracy and generalization for RUL prediction of zinc-ion batteries, this paper mainly discusses about the design of the encoder–decoder model structure and the application of optimization methods. Then, the method of neural network hyperparameter optimization is studied. Finally, the validity of the research work done in this paper is verified by a series of comparative experiments.

Abstract The discovery of low‐cost and high‐performance bifunctional oxygen electrocatalysts is vital to the future commercialization of rechargeable zinc‐air batteries (ZABs). Herein, a Fe single atom seed‐mediated strategy is reported for the fabrication of Fe3C species closely surrounded by FeN4C active sites with strong electronic interactions built between them and more importantly, creating optimized coordination environment, via subtly adjusting their ratio, for favorable adsorption energies of oxygen intermediates formed during oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Concretely, the voltage difference (ΔE) between the ORR half‐wave and OER potential at a current density of 10 mA cm−2 for the compositionally‐optimized FeNC/Fe3C‐op electrocatalyst is only 0.668 V, endowing itself one of the best bifunctional OER/ORR benchmarks. As a demo, ZABs assembled with FeNC/Fe3C‐op as the air cathode deliver a remarkable specific capacity (818.1 mAh gZn−1) and a power density (1013.9 mWh gZn−1), along with excellent long‐term durability (>450 h). This work extends the methodology to modulate the activity of FeN4C atomic site, undoubtedly inspiring wide explorations on the precise design of bifunctional oxygen electrocatalysts.

Visual Question Answering (VQA) is a significant cross-disciplinary issue in the fields of computer vision and natural language processing that requires a computer to output a natural language answer based on pictures and questions posed based on the pictures. This requires simultaneous processing of multimodal fusion of text features and visual features, and the key task that can ensure its success is the attention mechanism. Bringing in attention mechanisms makes it better to integrate text features and image features into a compact multi-modal representation. Therefore, it is necessary to clarify the development status of attention mechanism, understand the most advanced attention mechanism methods, and look forward to its future development direction. In this article, we first conduct a bibliometric analysis of the correlation through CiteSpace, then we find and reasonably speculate that the attention mechanism has great development potential in cross-modal retrieval. Secondly, we discuss the classification and application of existing attention mechanisms in VQA tasks, analysis their shortcomings, and summarize current improvement methods. Finally, through the continuous exploration of attention mechanisms, we believe that VQA will evolve in a smarter and more human direction.

Abstract Carbon dots (CDs) are widely utilized in sensing, energy storage, and catalysis due to their excellent optical, electrical and semiconducting properties. However, attempts to optimize their optoelectronic performance through high‐order manipulation have met with little success to date. In this study, through efficient packing of individual CDs in two‐dimensions, the synthesis of flexible CDs ribbons is demonstrated technically. Electron microscopies and molecular dynamics simulations, show the assembly of CDs into ribbons results from the tripartite balance of π–π attractions, hydrogen bonding, and halogen bonding forces provided by the superficial ligands. The obtained ribbons are flexible and show excellent stability against UV irradiation and heating. CDs ribbons offer outstanding performance as active layer material in transparent flexible memristors, with the developed devices providing excellent data storage, retention capabilities, and fast optoelectronic responses. A memristor device with a thickness of 8 µm shows good data retention capability even after 104 cycles of bending. Furthermore, the device functions effectively as a neuromorphic computing system with integrated storage and computation capabilities, with the response speed of the device being less than 5.5 ns. These properties create an optoelectronic memristor with rapid Chinese character learning capability. This work lays the foundation for wearable artificial intelligence.

Abstract Currently, there is a strong drive to discover alternative materials that exhibit room‐temperature phosphorescence (RTP) for displays, bioimaging, and data security. Ideally, these materials should be nontoxic, cheap, and possess controllable photoluminescent properties. Carbon dots (CDs) possess each of these characteristics, but to date, less attention has been paid to their RTP mechanism. Herein, we synthesized a series of CDs by self‐crosslinking and carbonization of precursor. The resultant CDs were luminescent and exhibited a bright, micro‐second afterglow lifetime. To increase the RTP, a second microwave processing step was used to coat the CDs with polyvinyl alcohol (PVA), polyacrylamide (PAM), or tetraethyl orthosilicate (TEOS), producing CDs@PVA, CDs@PAM, and CDs@TEOS composites. The core–shell structure acted to enhance crosslinking at the surface of the CDs to boost the RTP, creating abundant energy levels for intersystem crossover. In situ X‐ray photoelectron spectroscopy verified electron transfer during luminescence. Finally, we present a design rule that can be used to tune the quantum yields and RTP lifetime of CDs, based on the effective stabilization of triplet excited states through the extent and strength of cross‐linking. This simple strategy provides a flexible route for guiding the further development of CDs with tailored RTP properties for various applications.

Abstract Currently, the most efficient electrocatalyst for the hydrogen evolution reaction (HER) in water dissociation is Pt‐based catalyst. Unfortunately, the high cost and less than perfect efficiency hinder wide‐range industrial/technological applications. Here, by controlling the treatment temperature of tris (2,2‐bipyridine) ruthenium dichloride hexahydrate, synthesis of compounds with novel ruthenium single/dual atoms (Ru S/DAs) mixed with Ru nanoclusters (Ru S/DAs + Ru NCs) and supported by carbon dots is demonstrated. These compounds are shown to be highly efficient and competitive catalysts for hydrogen evolution. Ru S/DAs + Ru NCs exhibit very high activity, with overpotentials of 15 and 40 mV at a current density of 10 mA/cm2 in 1.0 mol/L KOH and 0.5 mol/L H2SO4, respectively. Furthermore, the composites are found to possess outstanding stability and rapid HER kinetics. X ray absorption fine structure analysis, supported by density functional theory calculations, shows charge rearrangement in single‐atomic Ru, and the Ru dual sites promote active hydrogen adsorption and recombination. Ru S/DAs and Ru NCs demonstrate high electroactivity due to the electroactive Ru 4d orbitals. The introduction of Ru NCs activates the carbon support, providing a high electronic conductivity to transfer electrons from Ru NCs to Ru S/DAs, and facilitates water dissociation for the HER process.

Abstract Due to the complex core–shell structure and variety of surface functional groups, the photoluminescence (PL) mechanism of carbon dots (CDs) remain unclear. o-Phenylenediamine (oPD), as one of the most common precursors for preparing red emissive CDs, has been extensively studied. Interestingly, most of the red emission CDs based on oPD have similar PL emission characteristics. Herein, we prepared six different oPD-based CDs and found that they had almost the same PL emission and absorption spectra after purification. Structural and spectral characterization indicated that they had similar carbon core structures but different surface polymer shells. Furthermore, single-molecule PL spectroscopy confirmed that the multi-modal emission of those CDs originated from the transitions of different vibrational energy levels of the same PL center in the carbon core. In addition, the phenomenon of “spectral splitting” of single-particle CDs was observed at low temperature, which confirmed these oPD-based CDs were unique materials with properties of both organic molecules and quantum dots. Finally, theoretical calculations revealed their potential polymerization mode and carbon core structure. Moreover, we proposed the PL mechanism of red-emitting CDs based on oPD precursors; that is, the carbon core regulates the PL emission, and the polymer shell regulates the PL intensity. Our work resolves the controversy on the PL mechanism of oPD-based red CDs. These findings provide a general guide for the mechanism exploration and structural analysis of other types of CDs.

.Sensitive detection of weak acoustic signals at nanometer scale is challenging. Here, the authors present an acoustic detection system based on a single molecule as a probe, where frequency and amplitude of acoustic vibrations can be extracted from its minute variations in distance to the surface of a plasmonic gold nanorod.

In the field of image fusion, the integration of infrared and visible images aims to combine complementary features into a unified representation. However, not all regions within an image bear equal importance. Target objects, often pivotal in subsequent decision-making processes, warrant particular attention. Conventional deep-learning approaches for image fusion primarily focus on optimizing textural detail across the entire image at a pixel level, neglecting the pivotal role of target objects and their relevance to downstream visual tasks. In response to these limitations, TDDFusion, a Target-Driven Dual-Branch Fusion Network, has been introduced. It is explicitly designed to enhance the prominence of target objects within the fused image, thereby bridging the existing performance disparity between pixel-level fusion and downstream object detection tasks. The architecture consists of a parallel, dual-branch feature extraction network, incorporating a Global Semantic Transformer (GST) and a Local Texture Encoder (LTE). During the training phase, a dedicated object detection submodule is integrated to backpropagate semantic loss into the fusion network, enabling task-oriented optimization of the fusion process. A novel loss function is devised, leveraging target positional information to amplify visual contrast and detail specific to target objects. Extensive experimental evaluation on three public datasets demonstrates the model’s superiority in preserving global environmental information and local detail, outperforming state-of-the-art alternatives in balancing pixel intensity and maintaining the texture of target objects. Most importantly, it exhibits significant advantages in downstream object detection tasks.

Abstract Developing super stability, high coulomb efficiency, and long‐span life of sodium‐ion batteries (SIBs) can significantly widen their practical industrial applications. In this study, we report a pine‐derived carbon/SnS2@reduced graphene oxide (PDC/SnS2@rGO) film with fast ion/electron transport micro‐channel used as a SIB anode, which shows ultrahigh stable stability and long‐span life. Functionally, a biomass PDC/SnS2@rGO film with ~30 μm micro carbon channel and ~1.2 μm thick carbon wall can simultaneously provide the fast electron transport path and the Na+ transport channel. Also, the two‐dimensional (2D) layered SnS2 particles attached to the carbon wall of PDC can increase more Na+ contact sites and shorten the Na+ transport path in the NaPF6 electrolyte. To avoid the separation of SnS2 from PDC during the sodiation process, rGO with excellent conductivity and flexibility is wrapped in the SnS2 outer layer as an “electronic garment”. A ~650 mA h g−1 high Na+ storage capacity at 0.1 A g−1 and ~99.8% ultrahigh coulomb efficiency after 800 cycles at 5 A g−1 are obtained when PDC/SnS2@rGO film is used as a SIB anode. Furthermore, a SIB full‐cell is assembled using PDC/SnS2@rGO film (anode) and Na3V2(PO4)3 (cathode), which exhibits a ~163.9 mA h g−1 high reversible capacity and ~99.7% coulomb efficiency performance. Our work provides a reasonable design strategy for the application of biomass‐derived carbon in SIBs, which may inspire more biomass‐derived material studies.

Polymer gels have been effectively employed as a water management material for profile control and water shutoff treatments in low-middle temperature and low-middle salinity reservoirs. However, most polymer gel systems have limitations under high temperature and salinity reservoir conditions, such as short gelation time, poor strength, and long-term instability. Therefore, several researchers have developed enhanced polymer gels to satisfy the water control requirements in high temperature and salinity reservoirs. This work reviews the five main types of enhanced polymer gels that have been developed so far: nano silica-enhanced gel systems, cellulose-enhanced gel systems, graphite-enhanced gel systems, oily sludge-enhanced gel systems, and foam-enhanced polymer gel systems. Further, this article investigates the fundamental properties, strengthening and crosslinking mechanisms, reservoir application conditions, and field applications of several enhanced polymer systems. In this paper, it is found that the addition of strengthening materials can increase the bound water content in the gel network and significantly improve the temperature and salt resistance of polymer gel, so as to cope with the application of profile control and water plugging in high temperature and high salt reservoirs. Moreover, it also offers references and future research directions for enhanced polymer gel systems.

Abstract Carbon dots (CDs) possess outstanding luminescence properties, making them widely used in optical displays, anti‐counterfeiting systems, bioimaging, and sensors. Presently, there is much debate about the classification of CDs, as well as their formation process, structure, and fluorescence mechanisms. Aggregation plays an important role in the formation and fluorescence (e.g., aggregation‐induced emission) of CDs, yet is seldom studied in detail. This review aims fill this knowledge gap, by first exploring how aggregation leads to the formation of different types of CDs (e.g., graphene quantum dots, carbon quantum dots, and carbonized polymer dots), followed by a detailed examination of the effect of aggregation‐induced morphology on the luminescence properties and application of CDs. Finally, opportunities and challenges for the application of CDs in various applications are discussed, with the need for better mechanistic understanding of aggregation‐induced luminescence being an imperative.

Numerous studies have suggested the possibility of explaining the etiology of metabolic syndrome through DNA methylation. DNA methyltransferase 3B (DNMT3B) plays an important role in de novo DNA methylation. There was an alteration in maternal (F0) endometrial function, which might lead to growth and developmental disorder in offspring (F1). In this study, we investigated the effect of maternal endometrial DNMT3B deficiency on the metabolism in offspring. We constructed endometrial DNMT3B conditional knockout female mice (cKO) which were mated with normal C57BL/6 male mice to obtain the F1 generation. Further, to study the development of these offspring, we observed them at three different life stages which included the 6-week-old juvenile, 9-week-old sub-adult and 12-week-old adult. Follow the detection of a range of metabolism-related indicators, we found that in the cKO F1 generation, liver triglyceride level was significantly elevated in 9-week-old female mice, lipid droplet deposition was significantly increased in 9-week-old and 12-week-old mice, and the expression of lipid metabolism key factors in the liver was markedly decreased except of 6-week-old male mice. These results indicate that maternal endometrial DNMT3B conditional knockout leads to imbalance in hepatic metabolism in F1 generation, the mechanism of which requires further discussion.

Change detection of natural lake boundaries is one of the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons in each layer can only be propagated to the upper layer, and the processing of samples is independent at each moment. However, for time-series data with transferability, the learned change information needs to be recorded and utilized. To solve the above problems, we propose a lake boundary change prediction model combining U-Net and LSTM. The ensemble of LSTMs helps to improve the overall accuracy and robustness of the model by capturing the spatial and temporal nuances in the data, resulting in more precise predictions. This study selected Lake Urmia as the research area and used the annual panoramic remote sensing images from 1996 to 2014 (Lat: 37°00′ N to 38°15′ N, Lon: 46°10′ E to 44°50′ E) obtained by Google Earth Professional Edition 7.3 software as the research data set. This model uses the U-Net network to extract multi-level change features and analyze the change trend of lake boundaries. The LSTM module is introduced after U-Net to optimize the predictive model using historical data storage and forgetting as well as current input data. This method enables the model to automatically fit the trend of time series data and mine the deep information of lake boundary changes. Through experimental verification, the model’s prediction accuracy for lake boundary changes after training can reach 89.43%. Comparative experiments with the existing U-Net-STN model show that the U-Net-LSTM model used in this study has higher prediction accuracy and lower mean square error.

The identification of small land targets in remote sensing imagery has emerged as a significant research objective. Despite significant advancements in object detection strategies based on deep learning for visible remote sensing images, the performance of detecting a small and densely distributed number of small targets remains suboptimal. To address this issue, this study introduces an improved model named YOLOV4_CPSBi, based on the YOLOV4 architecture, specifically designed to enhance the detection capability of small land targets in remote sensing imagery. The proposed model enhances the traditional CSPNet by redefining its channel partitioning and integrating this enhanced structure into the neck part of the YOLO network model. Additionally, the conventional pyramid fusion structure used in the traditional BiFPN is removed. By integrating a weight-based bidirectional multi-scale mechanism for feature fusion, the model is capable of effectively reasoning about objects of various sizes, with a particular focus on detecting small land targets, without introducing a significant increase in computational costs. Using the DOTA dataset as research data, this study quantifies the object detection performance of the proposed model. Compared with various baseline models, for the detection of small targets, its AP performance has been improved by nearly 8% compared with YOLOV4. By combining these modifications, the proposed model demonstrates promising results in identifying small land targets in visible remote sensing images.

Text classification has been highlighted as the key process to organize online texts for better communication in the Digital Media Age. Text classification establishes classification rules based on text features, so the accuracy of feature selection is the basis of text classification. Facing fast-increasing Chinese electronic documents in the digital environment, scholars have accumulated quite a few algorithms for the feature selection for the automatic classification of Chinese texts in recent years. However, discussion about how to adapt existing feature selection algorithms for various types of Chinese texts is still inadequate. To address this, this study proposes three improved feature selection algorithms and tests their performance on different types of Chinese texts. These include an enhanced CHI square with mutual information (MI) algorithm, which simultaneously introduces word frequency and term adjustment (CHMI); a term frequency–CHI square (TF–CHI) algorithm, which enhances weight calculation; and a term frequency–inverse document frequency (TF–IDF) algorithm enhanced with the extreme gradient boosting (XGBoost) algorithm, which improves the algorithm’s ability of word filtering (TF–XGBoost). This study randomly chooses 3000 texts from six different categories of the Sogou news corpus to obtain the confusion matrix and evaluate the performance of the new algorithms with precision and the F1-score. Experimental comparisons are conducted on support vector machine (SVM) and naive Bayes (NB) classifiers. The experimental results demonstrate that the feature selection algorithms proposed in this paper improve performance across various news corpora, although the best feature selection schemes for each type of corpus are different. Further studies of the application of the improved feature selection methods in other languages and the improvement in classifiers are suggested.

Soil organic carbon is very important to increase crop yield. Understanding the composition changes and migration characteristics of SOC under different irrigation conditions in arid oasis areas is of great significance for the sustainable development of agro-ecosystem and the estimation of carbon balance of farmland ecosystem in arid areas. In this paper, classical statistics and geostatistics were used to study the leaching characteristics and migration laws of soil organic carbon under different irrigation conditions in Minqin Oasis, and the leaching amount and loss process and mechanism of SOC under different irrigation conditions were quantified. The research results showed that: (1) Irrigation increased the average 0–100 cm SOC content. After irrigation, SOC content decreased first and then increased with the increase in soil depth, with the maximum value of 8.56 g/kg and the minimum value of 1.82 g/kg. Compared with that before irrigation, SOC content in 0–30 cm surface layer was in leaching state, SOC content in 30–70 cm surface layer was accumulated, and SOC content in 70–100 cm surface layer had no obvious change. (2) Water was an important factor affecting soil carbon. The greater the irrigation amount, the higher the carbon leaching rate and the greater the migration amount. The SOC content in 0–30 cm soil layer was most significantly affected by irrigation, and the migration amount of SOC gradually decreased with the increase in soil depth. The maximum leaching rate of SOC was 36.8%, the minimum leaching rate was 13.5%, and its average leaching rate was 23.4%. (3) Due to the influence of soil infiltration rate under water, SOC content in different irrigation periods showed that the greatest SOC occurred in the 0–70 cm layer, and SOC content in soil was basically the same as that in non-irrigated areas on the fifth day after irrigation. (4) Precipitation, plastic film mulching, soil physical and chemical properties and other environmental factors were important factors affecting the migration and change in SOC content.

Detecting changes in land cover is a critical task in remote sensing image interpretation, with particular significance placed on accurately determining the boundaries of lakes. Lake boundaries are closely tied to land resources, and any alterations can have substantial implications for the surrounding environment and ecosystem. This paper introduces an innovative end-to-end model that combines U-Net and spatial transformation network (STN) to predict changes in lake boundaries and investigate the evolution of the Lake Urmia boundary. The proposed approach involves pre-processing annual panoramic remote sensing images of Lake Urmia, obtained from 1996 to 2014 through Google Earth Pro Version 7.3 software, using image segmentation and grayscale filling techniques. The results of the experiments demonstrate the model’s ability to accurately forecast the evolution of lake boundaries in remote sensing images. Additionally, the model exhibits a high degree of adaptability, effectively learning and adjusting to changing patterns over time. The study also evaluates the influence of varying time series lengths on prediction accuracy and reveals that longer time series provide a larger number of samples, resulting in more precise predictions. The maximum achieved accuracy reaches 89.3%. The findings and methodologies presented in this study offer valuable insights into the utilization of deep learning techniques for investigating and managing lake boundary changes, thereby contributing to the effective management and conservation of this significant ecosystem.

A bilateral adaptive control method based on PEB control structure is designed for a class of time-delay force feedback teleoperation system without external interference and internal friction to study the uncertainty of dynamic parameters and time delay. The stability and tracking performances of the closed-loop constant time delay teleoperation system are analyzed by Lyapunov stability theory. Finally, the controller designed in this paper is successfully applied to the teleoperation system composed of a two-degree of freedom rotating manipulator as the master robot and the slave robot. The simulation is carried out in no operator and environment force or with operator and environment force. The adaptive bilateral control method's control performance is compared with that of the traditional time-delay teleoperation system. Finally, it is verified that the method has good control performance.

In glaciology, snow–firn temperature at 10 m is considered a representation of the mean annual air temperature at the surface (MAAT) of the studied site. Although MAAT is an important parameter in ice-sheet investigations, it has not been widely measured in Antarctica. To measure the 10 m snow–firn temperature in Antarctica, a shallow hot-point drill system is designed. In this simple and lightweight system, a hot-point drill can melt boreholes with a diameter of 34 mm in the snow–firn to a depth of 30 m and a temperature sensors string can measure the borehole temperature precisely. In the 2018/19 field season, 16 boreholes along the Zhongshan–Dome A traverse were drilled, and the borehole temperature was measured. Although certain problems existed pertaining to the hot-point drill, a total depth of ~244 m was successfully drilled at an average penetration rate of ~10 m h−1. After borehole drilling, ~12–15 h were generally required for the borehole to achieve thermal equilibrium with the surroundings. Preliminary results demonstrated that the 10 m snow–firn temperature along the traverse route was affected by the increasing altitude and latitude, and it decreased gradually with an increase in the distance from Zhongshan station.

Stress is a central concept in biology and has now been widely used in psychological, physiological, social, and even environmental fields. However, the concept of stress was cross-utilized to refer to different elements of the stress system including stressful stimulus, stressor, stress response, and stress effect. Here, we summarized the evolution of the concept of stress and the framework of the stress system. We find although the concept of stress is developed from Selye’s “general adaptation syndrome”, it has now expanded and evolved significantly. Stress is now defined as a state of homeostasis being challenged, including both system stress and local stress. A specific stressor may potentially bring about specific local stress, while the intensity of stress beyond a threshold may commonly activate the hypothalamic-pituitary-adrenal axis and result in a systematic stress response. The framework of the stress system indicates that stress includes three types: sustress (inadequate stress), eustress (good stress), and distress (bad stress). Both sustress and distress might impair normal physiological functions and even lead to pathological conditions, while eustress might benefit health through hormesis-induced optimization of homeostasis. Therefore, an optimal stress level is essential for building biological shields to guarantee normal life processes.

Global changes and human activities have significantly altered water cycle processes and water resource patterns in inland river basins in arid zones. New tools are needed to conduct more comprehensive and scientific assessments of basin water cycle processes and water resource patterns. Based on GRACE satellite and Landsat data, this study investigated terrestrial water storage changes and surface water area in the Shiyang River Drainage Basin from 2002 to 2021. It explored the effects of climate change and water conservancy construction on terrestrial water storage changes in the basin. The results of the study show that, although the surface water quantity in the Shiyang River basin has increased in the past 20 years, the overall decreasing trend of terrestrial water storage in the basin of the Shiyang River has an interannual decreasing rate of 0.01 cm/a. The decreasing trend of water storage in the midstream and downstream areas is more prominent. The change in precipitation controls the change in water storage in the Shiyang River Drainage Basin. Artificial water transfer has changed the spatial distribution of water resources in the basin of the Shiyang River. However, it still has not completely reversed the trend of decreasing water storage in the middle and lower reaches of the Shiyang River.

Abstract Background Osteosarcoma is the most common primary bone cancer occurring in young adults and the 5-year survival rate of patients with metastatic osteosarcoma is less than 30% due to high metastatic recurrence and drug resistance. Notch is a highly conserved cell to cell signaling pathway in evolution, and Jagged1 is an important ligand of Notch. Although some studies have found that Notch receptors and ligands including Jagged1 were highly expressed in osteosarcoma tissues and osteosarcoma cells, the role of Jagged1 in osteosarcoma progression and metastasis are still not clear. Methods Tumor tissues were collected from 68 patients and immunohistochemical staining was employed to group these patients by expression of Jagged1. Real-time quantitative PCR and Western blotting were used to detect the expression of Jagged1. We used siRNA to knockdown the expression of Jagged1 in F5M2 cells. Colony formation assay and MTT were employed to detect and analyze the proliferation of F5M2 cells with or without knockdown of Jagged1. Transwell assay were used to detect the migration and invasion of F5M2 cells. Results In this study, we found that the high expression of Jagged1 is closely related to the metastasis and recurrence of osteosarcoma in 68 clinical specimens. The expression of Jagged1 in F5M2 cells with high metastasis was significantly higher than that in F4 cells with low metastasis. Knockdown of Jagged1 led to lower ability of proliferation, migration, and invasion in F5M2 cells. Conclusion The high expression of Jagged1 is closely related to the metastasis and recurrence of osteosarcoma. Knockdown of Jagged1 significantly reduced the proliferation, migration, and invasion of osteosarcoma cells. Our results suggested that knockdown of Jagged1 may be a potentially effective treatment for metastatic osteosarcoma.

Carbon dots (CDs), comprising crystalline graphitized carbon cores and polymer surface groups, are currently attracting a lot of interest in biological fields owing to their fluorescent properties, high photostability, biocompatibility and low toxicity. In addition, the easy preparation and functionalization of CDs stimulate the development of CDs‐based composite materials with specific functions. Presently, the biological applications of CDs are growing at a remarkable speed, justifying the need for up‐to‐date review articles that capture recent progress in this blossoming field. In this review, breakthroughs in the synthesis, modification, optical properties, toxicology and biocatalytic platforms of CDs are described. Further, recent research related to bioimaging, biosensing, drug delivery, antibacterial, anticancer (photothermal therapy, photodynamic therapy and synergistic therapy) and antiviral therapies involving CDs are discussed in detail. Finally, a perspective on the prospects and challenges of CDs in the fields of biomedicine and biotechnology is provided.

Highlights The formation of manganese oxide induces self-assembly of block copolymers to form polymeric vesicles. The polymeric vesicles possessed strong stability and high drug loading capacity. The drug-loaded polymeric vesicles have been demonstrated, especially in in vivo studies, to exhibit a higher efficacy of tumor suppression without known cardiotoxicity. Abstract Molecular self-assembly is crucially fundamental to nature. However, the aqueous self-assembly of polymers is still a challenge. To achieve self-assembly of block copolymers [(polyacrylic acid–block–polyethylene glycol–block–polyacrylic acid (PAA68–b–PEG86–b–PAA68)] in an aqueous phase, manganese oxide (MnO2) is first generated to drive phase separation of the PAA block to form the PAA68–b–PEG86–b–PAA68/MnO2 polymeric assembly that exhibits a stable structure in a physiological medium. The polymeric assembly exhibits vesicular morphology with a diameter of approximately 30 nm and high doxorubicin (DOX) loading capacity of approximately 94%. The transformation from MnO2 to Mn2+ caused by endogenous glutathione (GSH) facilitates the disassembly of PAA68–b–PEG86–b–PAA68/MnO2 to enable its drug delivery at the tumor sites. The toxicity of DOX-loaded PAA68–b–PEG86–b–PAA68/MnO2 to tumor cells has been verified in vitro and in vivo. Notably, drug-loaded polymeric vesicles have been demonstrated, especially in in vivo studies, to overcome the cardiotoxicity of DOX. We expect this work to encourage the potential application of polymer self-assembly.

Pathogenic microorganisms pose great challenges to public health, which is constantly urgent to develop extra strategies for the fast staining and efficient treatments. In addition, once bacteria form stubborn biofilm, extracellular polymeric substance (EPS) within biofilm can act as protective barriers to prevent external damage and inward diffusion of traditional antibiotics, which makes it frequently develop drug-resistant ones and even hard to treat. Therefore, it is imperative to develop more efficient methods for the imaging/detection and efficient inhibition of pathogenic microorganisms. Here, a water-soluble aggregation-induced emission (AIE)-active photosensitizer TPA-PyOH was employed for fast imaging and photodynamic treatment of several typical pathogens, such as S. aureus, methicillin-resistant Staphylococcus aureus, L. monocytogenes, C. albicans, and E. coli. TPA-PyOH was non-fluorescent in water, upon incubation with pathogen, positively charged TPA-PyOH rapidly adhered to pathogenic membrane, thus the molecular motion of TPA-PyOH was restricted to exhibit AIE-active fluorescence for turn-on imaging with minimal background. Upon further white light irradiation, efficient reactive oxygen species (ROS) was in-situ generated to damage the membrane and inhibit the pathogen eventually. Furthermore, S. aureus biofilm could be suppressed in vitro. Thus, water-soluble TPA-PyOH was a potent AIE-active photosensitizer for fast fluorescent imaging with minimal background and photodynamic inhibition of pathogenic microorganisms.

As the secondary metabolites of cyanobacterial harmful algal blooms (Cyano-HABs), microcystins (MCs) were generated under various environmental and cellular conditions. The understanding of the causes of MCs generation is of great interest in the field of water treatment and environmental science. In this work, we studied how Microcystis aeruginosa (FACHB-905) cell densities affect the MCs synthetase genes (mcy) expression, microcystin-LR (MC-LR) and quorum sensing molecules (Acyl-homoserine lactones (AHLs)) production. An electrochemical sensor was developed here for sensitive and quantitative detection of MC-LR that cultured at different cell densities. The results showed that mcy expression and MC-LR concentration started to increase when the cell density reached ca. 22 × 106 cells/mL, and was significantly increased with increasing cell densities. Moreover, the up-regulation of AHLs with increasing cell densities revealed that MC-LR is quorum sensing-mediated. Our results undoubtedly confirmed that MC-LR was produced in a cell density-dependent way that mimics quorum sensing, and the minimum cell density (ca. 22 × 106 cells/mL) that was required to produce MC-LR was provided and offered a reference standard for the prevention and control of MCs pollution in the actual water environment.

Nitrate (NO3-) pollution has become increasingly prominent due to industry and agriculture. Electrochemical reduction can convert NO3- into high value-added ammonia (NH3) and remove NO3- pollution. This review focuses on the latest research progress in the field of electrochemical nitrate reduction reaction (NO3-RR) to NH3. The mechanism of NO3-RR is briefly discussed. Catalysts, as well as qualitative and quantitative methods for the detection of NH3 are also summarized. Finally, the challenges and prospects in this field are discussed. We hope this mini review aids researchers in the design and development of advanced research strategies for electrochemical NO3--to-NH3 processes.

This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. For this reason, we propose a domain transformation semi-supervised weighted kernel extreme learning machine (DTSWKELM) algorithm, which converts the data through the domain and uses SWKELM algorithmic classification to transform the semi-supervised classification problem of different domain data into a semi-supervised classification problem of the same domain data.