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Abstract The East China Sea (ECS) has experienced severe harmful algal blooms (HABs) that have deleterious ecological effects on marine organisms. Recent studies indicated that deploying of a second geostationary ocean color imager (GOCI-II) can significantly improve ocean monitoring. This study systematically assessed GOCI-II and its ability to detect HABs and distinguish between dinoflagellates and diatoms in the ECS. First, the remote-sensing reflectance ( $${R}_{rs}\left(\lambda \right),$$ R rs λ , $$\lambda$$ λ represents the wavelength) obtained from GOCI-II was compared to the local measurement data. Compared to the bands at 412 and 443 nm, the bands at 490, 510, and 620 nm exhibited excellent consistency, which is important for HAB detection. Second, four different methods were employed to extract bloom areas in the ECS: red tide index (RI), spectral shape (SS), red band line height ratio (LHR), and algal bloom ratio ( $${R}_{AB}$$ R AB ). The SS (510) algorithm was the most applicable for detecting blooms from GOCI-II imagery. Finally, the classification capability of GOCI-II for dinoflagellates and diatoms was evaluated using three existing algorithms: the bloom index (BI), combined $$Prorocentrum donghaiens$$ Prorocentrumdonghaiens index (PDI) and diatom index (DI), and the spectral slope ( $${R}_{\_slope}$$ R _ s l o p e ). The BI algorithm yielded more satisfactory results than the other algorithms.

Abstract Background l-ascorbic acid (Asc) plays a pivotal role in regulating various biological processes, including somatic cell reprogramming, through multiple pathways. However, it remains unclear whether Asc regulates reprogramming directly or functions through its metabolites. Results Asc exhibited dual capabilities in promoting reprogramming through both 2,3-diketo-l-gulonic acid (DKG), a key metabolite during Asc degradation, dependent and independent routes. On the one hand, Asc facilitated reprogramming by promoting cell proliferation and inducing the conversion from pre-induced pluripotent stem cells (pre-iPSCs) to iPSCs through DKG-independent pathways. Additionally, Asc triggered mesenchymal-epithelial transition (MET) and activated glycolysis via DKG-dependent mechanisms. Notably, DKG alone activated a non-canonical tricarboxylic acid cycle characterized by increased succinate, fumarate, and malate. Consequently, this shift redirected oxidative phosphorylation toward glycolysis and induced MET. Moreover, owing to its antioxidant capabilities, Asc directly inhibited glycolysis, thereby preventing positive feedback between glycolysis and epithelial-mesenchymal transition, ultimately resulting in a higher level of MET. Conclusion These findings unveil the intricate functions of Asc in the context of reprogramming. This study sheds light on the DKG-dependent and -independent activities of Asc during reprogramming, offering novel insights that may extend the application of Asc to other biological processes.

Abstract Mutation-induced malfunction of ten-eleven translocation methylcytosine dioxygenase 2 (TET2) is widely reported in haematological malignancies. However, the role of TET2 in solid cancers, including colorectal cancer (CRC), is unclear. Here, we found that TET2 malfunction in CRC is mostly due to decreased nuclear localization and that nuclear localization of TET2 is correlated with better survival of patients. To explore the underlying mechanisms, 14 immortalized solid tumour cell lines and 12 primary CRC cell lines were used. TET2 was mostly detected in the nucleus, and it induced significant DNA demethylation and suppressed cell growth by demethylating RORA and SPARC in cell lines like SW480. While in cell lines like SW620, TET2 was observed in the cytosol and did not affect DNA methylation or cell growth. Further examination with immunoprecipitation–mass spectrometry illustrated that β-catenin activation was indispensable for the nuclear localization and tumour suppression effects of TET2. In addition, the β-catenin pathway activator IM12 and the TET2 activator vitamin C were used simultaneously to enhance the effects of TET2 under low-expression conditions, and synergistic inhibitory effects on the growth of cancer were observed both in vitro and in vivo. Collectively, these data suggest that β-catenin-mediated nuclear localization of TET2 is an important therapeutic target for solid tumours.

This study used GOCI-II data to systematically evaluate the feasibility of Rayleigh-corrected reflectance (Rrc) to detect algal blooms in the complex optical environment of the East China Sea (ECS). Based on long-term in situ remote sensing reflectance (Rrs), Rrc spectra demonstrated the similar capability of reflecting the water condition under various atmospheric conditions, and the baseline indices (BLIs) derived from Rrc and Rrs showed good consistency (R2 > 0.98). The effectiveness of five Rrc-based BLIs (SS490, CI, DI, FLH, and MCI) for algal bloom detection was assessed, among which SS490 and MCI showed better performances. A synthetic bloom detection algorithm based on the BLIs of Rrc was then developed to avoid the impact of turbid water. The validation of the BLI algorithm was carried out based on the in situ algal abundance data from 2021 to 2023. Specifically, SS490 showed the best bloom detection result (F-measure coefficient, FM = 0.97), followed by MCI (FM = 0.88). Since the 709 nm bands used in MCI were missing in many ocean color satellites, the SS490 algorithm was more useful in application. Compared to Rrs based bloom detection algorithms, synthetical Rrc BLI proposed in this paper provides more effective observation results and even better algal bloom detection performance. In conclusion, the study confirmed the feasibility of utilizing Rrc for algal bloom detection in the coastal areas of the ECS, and recognized the satisfactory performance of synthetical SS490 by comparing with the other BLIs.

This paper presents a study to evoke rat cortical functional activities, including hemodynamic and neural tissue signal changes, by monopulse electrical stimulation with a microelectrode array using functional optical coherence tomography (fOCT). Based on the principal component analysis and fuzzy clustering method (PCA-FCM), the hemodynamic response of different size blood vessels in rat cortex are analyzed, showing that the hemodynamic response of the superficial large blood vessels is more concentrated. In the regions of neural tissue where blood vessels are removed, positive significant pixels (the intensity of the pixel for five consecutive frames is greater than the average value plus triple standard deviation) and negative significant pixels (the intensity of the pixel for five consecutive frames is less than the average value minus triple standard deviation) exist, and the averaged intensity signal responds rapidly with an onset time of ~20.8 ms. Furthermore, the hemodynamic response was delayed by ~3.5 s from the neural tissue response. fOCT can provide a label-free, large-scale and depth-resolved map of cortical neurovascular activation, which is a promising technology to monitor cortical small-scale neurovascular activities.

An artificial intelligent neural network improved particle swarm optimization algorithm is proposed for the inverse design of semiconductor optical amplifier. Seven input parameters, current-gain curve and saturation output power curve are selected to form the data set based on the physical model of semiconductor optical amplifier. The effectiveness of forecasting performance is improved by contrasting two back propagation neural network techniques (Scaled Conjugate Gradient and Levenberg-Marquardt) and operational settings (Central Processing Unit and Graphics Processing Unit). Higher accuracy is achieved through feedback analysis of neuron number optimization and test error. The addition of a unique backpropagation neural network can make the fitness of particle swarm algorithm mostly converge below $2\times 10^{-4}$. The relative difference between original performances and inverse predictions is close to 0%, which proves the effectiveness of parameter extraction. This method can take advantage of neural networks to improve accuracy and speed of particle swarm optimization algorithms for efficient semiconductor optical amplifier inverse design and multi-solution analysis.

The application of the stereoscopic parking robot makes it possible to relieve the pressure of urban traffic efficiently. With the aggravation of energy and environmental problems, the energy consumption of the stereoscopic parking robot is mainly concerned. In robot design, the kinematic performance reflects the motion transfer capability of the mechanism. A reasonable size of the mechanism can make energy transfer more efficient. This paper proposes a 2-DOF (Degree of Freedom) parallel lifting mechanism of the stereoscopic parking robot. Its branch contains double-constrained triangles that can perform symmetrical movements. Forward kinematics, inverse kinematics, workspace, and singular configuration are studied and analyzed. In this scheme, depending on the characteristics of the mechanism, the evaluation indices of motion consistency, workspace, and singularity performance are proposed. The energy-saving optimization design method based on the multiple kinematic performance indexes is used to find the optimum combination of structural parameters in the feasible region. Finally, the parallel lifting mechanism prototype is constructed, and the dynamic verification experiments are performed at driving speeds of 15.625, 12.5, and 10.42 mm/s. The results provide helpful guidance for the choice of driving speed to achieve optimal movement transfer capacity and stability of the mechanism. The multiple performance index design method can also be applied to other parallel mechanisms with low mobility, which require energy savings and steady motion.

This paper presents an experimental study on the box girder of a low-tower cable-stayed railway bridge with a W-shaped section that consists of prestressed concrete diagonal braces. A 1:6 scale test model was designed and constructed for the experiment. The mechanical behavior of the test model was investigated under two loading conditions: a double-track train symmetrical load and a single-track train unsymmetrical load. The experimental results were validated against a finite element model. Furthermore, the torsional performance of the box girder section was analyzed and discussed.

Soft robots have received a lot of attention because of their great human-robot interaction and environmental adaptability. Most soft robots are currently limited in their applications due to wired drives. Photoresponsive soft robotics is one of the most effective ways to promote wireless soft drives. Among the many soft robotics materials, photoresponsive hydrogels have received a lot of attention due to their good biocompatibility, ductility, and excellent photoresponse properties. This paper visualizes and analyzes the research hotspots in the field of hydrogels using the literature analysis tool Citespace, demonstrating that photoresponsive hydrogel technology is currently a key research direction. Therefore, this paper summarizes the current state of research on photoresponsive hydrogels in terms of photochemical and photothermal response mechanisms. The progress of the application of photoresponsive hydrogels in soft robots is highlighted based on bilayer, gradient, orientation, and patterned structures. Finally, the main factors influencing its application at this stage are discussed, including the development directions and insights. Advancement in photoresponsive hydrogel technology is crucial for its application in the field of soft robotics. The advantages and disadvantages of different preparation methods and structures should be considered in different application scenarios to select the best design scheme.

In coal and rock recognition technology, the acquisition of sound signals is affected by background noise. It is challenging to extract cutting features and accurately identify cutting patterns effectively. Therefore, this paper proposes an approach for combined noise reduction of the cutting sound signal based on the improved adaptive noise complete ensemble empirical mode decomposition (ICEEMDAN) and a singular value decomposition (SVD). First, the method used the ICEEMDAN method to decompose the noisy signal into several intrinsic mode functions (IMF). It calculated the correlation coefficient between the IMF component and the noisy signal and then selected the noisy IMF components based on the threshold formula. Meanwhile, this method constructed a Hankel matrix of the noisy IMF component signals. It used SVD technology to obtain the singular values. According to the singular value standard energy spectrum curve, the paper determined the order of the effective singular value and removed the noise component in the signal. Then, the denoised IMF and noiseless IMF components are superimposed and reconstructed to obtain the noise-reduced cutting sound signal. Finally, it applied simulation signal and simulated shearer cutting experiment to verify the performance of the method. The results show that the proposed method can effectively remove the influence of background noise in the signal and retain the characteristic frequencies of the original cutting sound signal. Compared with traditional noise reduction methods, the ICEEMDAN-SVD combined noise reduction method performs better in noise reduction evaluation standards of signal-noise ratio and root mean square error. It achieved a better noise reduction effect, which could help coal and rock recognition technology based on sound signals.

The coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager (SGLI) conducted by Global Change Observation Mission-Climate (GCOM-C) (Japan), the accuracy of satellite measurements was initially validated using matched pairs of satellite and ground data relating to the ECS. Additionally, using SGLI data from the coast of the ECS, we compared the applicability of three bloom extraction methods: spectral shape, red tide index, and algal bloom ratio. With an RMSE of less than 25%, satellite data at 490 nm, 565 nm, and 670 nm showed good consistency with locally measured remote sensing reflectance data. However, there was unexpected overestimation at 443 nm of SGLI data. By using a linear correction method, the RMSE at 443 nm was decreased from 27% to 17%. Based on the linear corrected SGLI data, the spectral shape at 490 nm was found to provide the most satisfactory results in separating bloom and non-bloom waters among the three bloom detection methods. In addition, the capability in harmful algae distinguished using SGLI data was discussed. Both of the Bloom Index method and the green-red Spectral Slope method were found to be applicable for phytoplankton classification using SGLI data. Overall, the SGLI data provided by GCOM-C are consistent with local data and can be used to identify bloom water bodies in the ECS, thereby providing new satellite data to support monitoring of bloom changes in the ECS.

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm.

Abstract Thermoelectrics converting heat and electricity directly attract broad attentions. To enhance the thermoelectric figure of merit, zT, one of the key points is to decouple the carrier-phonon transport. Here, we propose an entropy engineering strategy to realize the carrier-phonon decoupling in the typical SrTiO3-based perovskite thermoelectrics. By high-entropy design, the lattice thermal conductivity could be reduced nearly to the amorphous limit, 1.25 W m−1 K−1. Simultaneously, entropy engineering can tune the Ti displacement, improving the weighted mobility to 65 cm2 V−1 s−1. Such carrier-phonon decoupling behaviors enable the greatly enhanced μ W/κ L of ~5.2 × 103 cm3 K J−1 V−1. The measured maximum zT of 0.24 at 488 K and the estimated zT of ~0.8 at 1173 K in (Sr0.2Ba0.2Ca0.2Pb0.2La0.2)TiO3 film are among the best of n-type thermoelectric oxides. These results reveal that the entropy engineering may be a promising strategy to decouple the carrier-phonon transport and achieve higher zT in thermoelectrics.

Abstract Anion exchange membrane fuel cells (AEMFCs) have been hailed as a promising hydrogen energy technology due to high energy conversion efficiency, zero carbon emission and the potential independence on scare and expensive noble metal electrocatalysts. A variety of platinum group metal (PGM)‐free catalysts has been developed with superior catalytic performance to noble metal benchmarks toward cathodic oxygen reduction reactions (ORR). However, PGM electrocatalysts still dominate the anodic catalyst research because the kinetics of hydrogen oxidation reaction (HOR) are two or three orders of magnitude slower than in that acidic media. Therefore, it is urgently desirable to improve noble metal utilization efficiency and/or develop high‐performance PGM‐free electrocatalysts for HOR, thus promoting the real‐world implementation of AEMFCs. In this review, the current research progress of electrocatalysts for HOR in alkaline media is summarized. We start with the discussion on the current HOR reaction mechanisms and existing controversies. Then, methodologies to improve the HOR performance are reviewed. Following these principles, the recently developed HOR electrocatalysts including PGM and PGM‐free HOR electrocatalysts in alkaline media are systematically introduced. Finally, we put forward the challenges and prospects in the field of HOR catalysis.

Abstract The limited durability of metal-nitrogen-carbon electrocatalysts severely restricts their applicability for the oxygen reduction reaction in proton exchange membrane fuel cells. In this study, we employ the chemical vapor modification method to alter the configuration of active sites from FeN4 to the stable monosymmetric FeN2+N’2, along with enhancing the degree of graphitization in the carbon substrate. This improvement effectively addresses the challenges associated with Fe active center leaching caused by N-group protonation and free radicals attack due to the 2-electron oxygen reduction reaction. The electrocatalyst with neoteric active site exhibited excellent durability. During accelerated aging test, the electrocatalyst exhibited negligible decline in its half-wave potential even after undergoing 200,000 potential cycles. Furthermore, when subjected to operational conditions representative of fuel cell systems, the electrocatalyst displayed remarkable durability, sustaining stable performance for a duration exceeding 248 h. The significant improvement in durability provides highly valuable insights for the practical application of metal-nitrogen-carbon electrocatalysts.

The usage of cheap crude H2 in proton-exchange membrane fuel cells (PEMFCs) is still unrealistic to date, due to the suffering of the current Pt based nano-catalysts from impurities such as CO in anode. Recently, synergistic active sites between single atom (SA) and nanoparticle (NP) have been found to be promising for overcoming the poisoning problem. However, lengthening the nanoparticle-single atom (SA–NP) interface, i.e., constructing high density synergistic active sites, remains highly challenging. Herein, we present a new strategy based on molecular fusion strategy to create abundant SA–NP interfaces, with high density SA–NP interfaces created on a two dimensional nitrogen doped carbon nanosheets (Ir-SACs&NPs/NC). Owing to the abundance of SA–NP interface sites, the catalyst was empowered with a high tolerance towards up to 1000 ​ppm CO in H2 feed. These findings provide guidelines for the design and construction of active and anti-poisoning catalysts for PEMFC anode.

Abstract Background Deep learning (DL), a specialized form of machine learning (ML), is valuable for forecasting survival in various diseases. Its clinical applicability in real-world patients with gastric cancer (GC) has yet to be extensively validated. Methods A combined cohort of 11,414 GC patients from the Surveillance, Epidemiology and End Results (SEER) database and 2,846 patients from a Chinese dataset were utilized. The internal validation of different algorithms, including DL model, traditional ML models, and American Joint Committee on Cancer (AJCC) stage model, was conducted by training and testing sets on the SEER database, followed by external validation on the Chinese dataset. The performance of the algorithms was assessed using the area under the receiver operating characteristic curve, decision curve, and calibration curve. Results DL model demonstrated superior performance in terms of the area under the curve (AUC) at 1, 3, and, 5 years post-surgery across both datasets, surpassing other ML models and AJCC stage model, with AUCs of 0.77, 0.80, and 0.82 in the SEER dataset and 0.77, 0.76, and 0.75 in the Chinese dataset, respectively. Furthermore, decision curve analysis revealed that the DL model yielded greater net gains at 3 years than other ML models and AJCC stage model, and calibration plots at 3 years indicated a favorable level of consistency between the ML and actual observations during external validation. Conclusions DL-based model was established to accurately predict the survival rate of postoperative patients with GC.

Abstract Understanding the lattice dynamics and heat transport physics in the lead-free halide double perovskites remains an outstanding challenge due to their lattice dynamical instability and strong anharmonicity. In this work, we investigate the microscopic mechanisms of anharmonic lattice dynamics and thermal transport in lead-free halide double perovskite Cs2AgBiBr6 from first principles. We combine self-consistent phonon calculations with bubble diagram correction and a unified theory of lattice thermal transport that considers both the particle-like phonon propagation and wave-like tunnelling of phonons. An ultra-low thermal conductivity at room temperature (~0.21 Wm−1K−1) is predicted with weak temperature dependence( ~ T −0.34), in sharp contrast to the conventional ~T −1 dependence. Particularly, the vibrational properties of Cs2AgBiBr6 are featured by strong anharmonicity and wave-like tunnelling of phonons. Anharmonic phonon renormalization from both the cubic and quartic anharmonicities are found essential in precisely predicting the phase transition temperature in Cs2AgBiBr6 while the negative phonon energy shifts induced by cubic anharmonicity has a significant influence on particle-like phonon propagation. Further, the contribution of the wave-like tunnelling to the total thermal conductivity surpasses that of the particle-like propagation above around 310 K, indicating the breakdown of the phonon gas picture conventionally used in the Peierls-Boltzmann Transport Equation. Importantly, further including four-phonon scatterings is required in achieving the dominance of wave-like tunnelling, as compared to the dominant particle-like propagation channel when considering only three-phonon scatterings. Our work highlights the importance of lattice anharmonicity and wave-like tunnelling of phonons in the thermal transport in lead-free halide double perovskites.

Abstract Although first principles based anharmonic lattice dynamics is one of the most common methods to obtain phonon properties, such method is impractical for high-throughput search of target thermal materials. We develop an elemental spatial density neural network force field as a bottom-up approach to accurately predict atomic forces of ~80,000 cubic crystals spanning 63 elements. The primary advantage of our indirect machine learning model is the accessibility of phonon transport physics at the same level as first principles, allowing simultaneous prediction of comprehensive phonon properties from a single model. Training on 3182 first principles data and screening 77,091 unexplored structures, we identify 13,461 dynamically stable cubic structures with ultralow lattice thermal conductivity below 1 Wm−1K−1, among which 36 structures are validated by first principles calculations. We propose mean square displacement and bonding-antibonding as two low-cost descriptors to ease the demand of expensive first principles calculations for fast screening ultralow thermal conductivity. Our model also quantitatively reveals the correlation between off-diagonal coherence and diagonal populations and identifies the distinct crossover from particle-like to wave-like heat conduction. Our algorithm is promising for accelerating discovery of novel phononic crystals for emerging applications, such as thermoelectrics, superconductivity, and topological phonons for quantum information technology.

The electrochemical carbon dioxide reduction (ECR) to profoundly diminished chemical entities offers a compelling avenue for transforming sporadic energy resources into enduring fuels while forging an enclosed anthropogenic carbon cycle. Metal-organic frameworks (MOFs) has been extensively investigated as a promising multifunctional material for ECR. Notably, two-dimensional (2D) MOFs attract particular research attention due to their specific chemical and structural properties, i.e., enhanced electrical conductivity, increased open sites, improved mass transport and tunable interfacial environments. In this review, the recent progress of 2D MOFs for ECR is summarized. We begin with the introduction of the synthetic strategies of 2D MOFs. Then, we mainly focus on the advanced 2D MOF electrocatalysts for ECR in recent years, which are clarified by the products. The mechanism underlying the conversion of carbon dioxide (CO2) into carbon products, the factors influencing product formation and a summary of selected 2D MOF catalysts and their synthetic methods are presented. By consolidating the potential factors contributing to the products, we anticipate that the review will offer fresh opportunities for further advancements in CO2 reduction with 2D MOF catalysts.

Abstract Exposure to fine particulate matter (PM2.5) is linked to lung cancer incidence and mortality. However, the impact of PM2.5 exposure on lung cancer patients after lobectomy, which remains the primary treatment for early-stage lung cancer, is unknown. Therefore, we investigated the correlation between PM2.5 exposure and the survival of lung cancer patients after lobectomy. This study included 3,327 patients with lung cancer who underwent lobectomy procedures. We converted residential addresses into coordinates and estimated individual patients' daily PM2.5 and O3 exposure levels. A Cox multivariate regression model was used to analyze the specific monthly association between PM2.5 exposure and lung cancer survival. Every 10 μg/m3 increase in monthly PM2.5 concentration in the first and second months after lobectomy increased the risk of death (hazard ratio [HR]: 1.043, 95% confidence interval [CI]: 1.019–1.067 and HR: 1.036, 95% CI: 1.013–1.060, respectively). Non-smokers, younger patients, and patients with longer hospitalization durations had worse survival rates when exposed to greater concentrations of PM2.5. High postoperative PM2.5 exposure immediately after lobectomy reduced the survival of patients with lung cancer. Patients living in areas with high PM2.5 should be offered the opportunity to transfer to areas with better air quality after undergoing lobectomies, to prolong their survival times.

The poor stability of ruthenium-based catalysts has greatly hampered their application in polymer electrolyte membrane water electrolysis. Here, the authors uncover the decisive role of reaction route on catalytic performance, which enables the screening of efficient ruthenium-based water oxidation catalysts.

Abstract Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness and precision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55 elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learning and data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralow lattice thermal conductivity (

Abstract Background Fusobacterium nucleatum (F. nucleatum) often colonizes cancerous gastric tissues and is characterized by the promotion of platelet aggregation and the development of visceral thrombosis. Venous thromboembolism (VTE) leads to a significant increase in the mortality of gastric cancer (GC) patients. However, the relationship between the colonization of F. nucleatum and the prognosis of GC patients is still unknown. Aim The aim of this study was to explore whether the colonization of F. nucleatum is related to the prognosis of GC patients complicated with VTE and to explore other potential risk factors. Methods From 2017–2021, the data of 304 patients with new VTEs during the treatment of GC at the Affiliated Cancer Hospital of Zhengzhou University were collected. Fluorescence in situ hybridization of F. nucleatum was performed on pathological sections of cancer tissues from the patients. Survival analysis methods, including the Kaplan‒Meier method and Cox proportional hazard model, were performed. Results F. nucleatum colonization was significantly associated with splanchnic vein thrombosis, higher platelet-lymphocyte ratio (PLR), and lower absolute lymphocyte count. In the multivariable Cox model, F. nucleatum colonization was found to be an independent risk factor for the prognosis of GC, with an adjusted HR of 1.77 (95% CI, 1.17 to 2.69 [P = 0.007]). In addition, patients with high PLR (HR: 2.65, P = 0.004) or VTE occurring during four cycles of chemotherapy (HR: 2.32, P = 0.012) exhibited shorter survival. Conversely, those experiencing VTE later (HR per month from diagnosis of GC: 0.95, P = 0.006) or using IVC filters (HR: 0.27, P = 0.011) had longer survival. Conclusion Colonization of F. nucleatum in GC tissues was associated with lower absolute lymphocyte count and higher PLR in GC patients with VTE. F. nucleatum colonization also appeared to be associated with the development of VTE in specific sites, in particular the splanchnic vein. Colonization of F. nucleatum may potentially represent an independent predictor of poor prognosis in GC patients. Additional research is necessary to validate these findings.

Abstract The long-wavelength behavior of vibrational modes plays a central role in carrier transport, phonon-assisted optical properties, superconductivity, and thermomechanical and thermoelectric properties of materials. Here, we present general invariance and equilibrium conditions of the lattice potential; these allow to recover the quadratic dispersions of flexural phonons in low-dimensional materials, in agreement with the phenomenological model for long-wavelength bending modes. We also prove that for any low-dimensional material the bending modes can have a purely out-of-plane polarization in the vacuum direction and a quadratic dispersion in the long-wavelength limit. In addition, we propose an effective approach to treat invariance conditions in crystals with non-vanishing Born effective charges where the long-range dipole-dipole interactions induce a contribution to the lattice potential and stress tensor. Our approach is successfully applied to the phonon dispersions of 158 two-dimensional materials, highlighting its critical relevance in the study of phonon-mediated properties of low-dimensional materials.

Abstract The Jahn-Teller (JT) effect involves the ions M with a degenerate electronic state distorting the corner-sharing MO6 octahedra to lift the degeneracy, inducing strong coupling of electrons to lattice, and mediating the exotic properties in perovskite oxides. Conversely, the anti-Jahn–Teller (AJT) effect refers to the deformation against the Jahn-Teller-distorted MO6 octahedra. However, it is difficult to experimentally execute both effects descending from the fine-tuning of crystal structures. We propose the AJT can be introduced by THz laser illumination at 11.71 THz in a candidate superconducting perovskite material BaBiO3 near room temperature. The illumination coherently drives the infrared-active phonon that excites the Raman breathing mode through the quadratic-linear nonlinear interaction. The process is characterized by the emergence of an AJT effect, accompanied by an insulator-to-metal transition occurring on the picosecond timescale. This study underlines the important role of crystal structure engineering by coherent phonon excitation in designing optoelectronic devices.

Abstract CO impurity‐induced catalyst deactivation has long been one of the biggest challenges in proton‐exchange membrane fuel cells, with the poisoning phenomenon mainly attributed to the overly strong adsorption on the catalytic site. Here, we present a mechanistic study that overturns this understanding by using Rh‐based single‐atom catalysis centers as model catalysts. We precisely modulated the chelation structure of the Rh catalyst by coordinating Rh with C or N atoms, and probed the reaction mechanism by surface‐enhanced Raman spectroscopy. Direct spectroscopic evidence for intermediates indicates that the reactivity of adsorbed OH*, rather than the adsorption strength of CO*, dictates the CO electrocatalytic oxidation behavior. The RhN4 sites, which adsorb the OH* intermediate more weakly than RhC4 sites, showed prominent CO oxidation activity that not only far exceeded the traditional Pt/C but also the RhC4 sites with similar CO adsorption strength. From this study, it is clear that a paradigm shift in future research should be considered to rationally design high‐performance CO electro‐oxidation reaction catalysts by sufficiently considering the water‐related reaction intermediate during catalysis.

Proton exchange membrane fuel cells (PEMFCs), which can directly convert chemical energy into electrical energy with high efficiency and zero carbon emission, have attracted extensive attention. Unfortunately, the sluggish kinetics of oxygen reduction reaction (ORR) on the cathode leads to considerable overpotential and thus severely lowering its operational energy conversion efficiency. Although Pt-based catalysts have been developed as the most efficient catalyst towards ORR, however, their stability is far from the application requirements, which hinders the large-scale application of PEMFCs to a certain extent. Thus, improving the stability of Pt-based catalysts is urgently desirable to advance the widespread commercialization of fuel cells. This review focuses on the stability of Pt-based ORR catalysts in PEMFCs, from the perspectives of catalyst degradation mechanism and stability improvement strategies. It is aimed at providing research directions for the development of stable Pt-based catalysts. Firstly, degradation of metal nanoparticles (dissolution, migration, agglomeration, Ostwald ripening, etc.) and corrosion of carbon supports are introduced. To conquer the two attenuation mechanisms, stability improvement strategies such as constructing intermetallic compounds, enhancing metal-support interaction and the modification of carbon support, are summarized in detail. In addition, some typical stability characterization techniques are outlined. Finally, we discuss the challenges and possible research directions in the future. We hope this review can help readers gain insights into the stability issues of Pt-based ORR nanocatalysts and encourage research that will enable the commercialization of PEMFCs.

ObjectiveTo investigate the possible impact of lockdown policies on the diagnosis and treatment of cancer patients in Henan, China.Design, Setting, and ParticipantsWe collected data from the Henan Cancer Hospital, affiliated with Zhengzhou University. The monthly numbers of inpatient admissions from January 2014 to December 2019 were used to forecast the number of inpatient admissions in 2020, which was then compared to the actual number of patients admitted during the pandemic to evaluate how the actual number diverges from this forecast. We conducted an interrupted time series analysis using the autoregressive integrated moving average (ARIMA) model.Main Outcomes and MeasuresFor specific diagnoses, treatment modalities, and age groups, we compared the changes in monthly admissions after the pandemic with the forecasted changes from the model.ResultsThe observed overall monthly number of inpatient admissions decreased by 20.2% [95% confidence interval (CI), 11.7–27.2%], 78.9% (95% CI, 77.3–80.4%), and 40.9% (95% CI, 35.6–45.5%) in January, February, and March 2020, respectively, as compared with those predicted using the ARIMA model. After the lockdown, visits for all treatment modalities decreased sharply. However, apparent compensation and recovery of the backlog appeared in later surgeries. As a result, the number of patients who underwent surgery in 2020 (30,478) was close to the number forecasted by the ARIMA model (30,185). In the same period, patients who received other treatments or underwent examinations were 106,074 and 36,968, respectively; the respective numbers that were forecasted by ARIMA were 127,775 and 60,025, respectively. These findings depict a decrease of 16.9 and 38.4% in patients who received other treatments or underwent examinations only, respectively. Regarding diagnosis, the reported incidence of various cancers decreased dramatically in February, with varying extent and speed of recovery.Conclusion and RelevanceThe COVID-19 pandemic has significantly delayed the diagnosis and treatment of cancer in Henan, China. Long-term research should be conducted to assess the future effects of lockdown policies.

The local electronic configuration of active sites in molybdenum disulfide (MoS2) could significantly alter the intrinsic activity towards hydrogen evolution reaction (HER). Herein, we report that the HER performance of MoS2 electrocatalysts was boosted via bismuth and palladium co-doping. The introduction of heteroatom Bi successfully altered the local electronic configuration of S atoms in the MoS2 inert basal plane to form an electron-enriched environment, conducive to boost active site density and proton adsorption, as experimentally verified by X-ray photoelectron spectroscopy (XPS) and ultraviolet photoelectron spectroscopy (UPS). Furthermore, after activating the MoS2 via Pd doping, a phase transition to stable 1 T phase occurred which was verified by Raman. In acidic media, the as-prepared Bi/Pd-MoS2 catalyst exhibited excellent electrochemical HER performance in terms of a low overpotential of -114 mV at a current density of 10 mA cm−2, a small Tafel slope of 65 mV dec−1, and outstanding long-term stability.

For large-scale and sustainable water electrolysis, it is of great significance to develop cheap and efficient electrocatalysts that can replace platinum. Currently, it is difficult for most catalysts to combine high activity and stability. To solve this problem, we use cobalt to regulate the electronic structure of ruthenium to achieve high activity, and use carbon matrix to protect alloy nanoparticles to achieve high stability. Herein, based on the zeolitic imidazolate frameworks (ZIFs), a novel hybrid composed of RuCo alloy nano-particles and N-doped carbon was prepared via a facile pyrolysis-displacement-sintering strategy. Due to the unique porous structure and multi-component synergy, the optimal RuCo500@NC750 material in both acidic and alkaline media exhibited eminent HER catalytic activity. Notably, the 3-RuCo500@NC750 obtained a current density of 10 mA cm−2 at 22 mV and 31 mV in 0.5 M H2SO4 and 1.0 M KOH, respectively, comparable to that of the reference Pt/C catalyst. Furthermore, the Tafel slopes of the catalyst are 52 mV Dec−1 and 47 mV Dec−1, respectively, under acid and alkali conditions, and the catalyst has good stability, indicating that it has broad application prospects in practical electrolytic systems. This work contributes to understanding the role of carbon-supported polymetallic alloy in the electrocatalytic hydrogen evolution process, and provides some inspiration for the development of a high efficiency hydrogen evolution catalyst.

Objective: Field trials were conducted in 13 citrus production regions including Hunan and Hubei provinces to evaluate the safety of the application of acetamiprid and pyriproxyfen on citrus. The residue, dissipation dynamics and dietary risk assessment of acetamiprid and pyriproxyfen in citrus fruit were investigated. Methods: The samples were extracted with 1% acetic acid in acetonitrile, purified using a mixture of primary secondary amine and graphitized carbon black, and detected by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Results: Excellent linearity (R2 > 0.99) was observed between the response peak area and the concentration of acetamiprid and pyriproxyfen in the range of 0.001–0.5 mg/L. In whole citrus fruit and flesh spiked at 0.01, 0.5 and 2.5 mg/kg, the recoveries of acetamiprid and pyriproxyfen ranged from 83.0% to 104.3% and from 74.8% to 98.7% with relative standard deviation (RSDs) of 3.8% to 10.0% and 2.0 to 6.0%, respectively. The limits of quantification (LOQs) for the analytes were both 0.01 mg/kg. The dissipation dynamics of acetamiprid and pyriproxyfen in whole citrus fruit was fitted to a first-order kinetics model. The degradation half-lives of acetamiprid and pyriproxyfen in whole citrus fruit were 5.1–15.8 days and 4.5–21.2 days, respectively. Twenty-seven percent acetamiprid-pyriproxyfen dispersible concentrate was sprayed twice on citrus at the recommended dosage of 500 g/hm2 (2 000 × dilution). At a 7-day safety interval, the residues of acetamiprid and pyriproxyfen in citrus fruit ranged from less than 0.01 mg/kg to 0.45 mg/kg and to 0.68 mg/kg, respectively. At a 10-day safety interval, the residues of acetamiprid and pyriproxyfen in citrus fruit ranged from less than 0.01 mg/kg to 0.40 mg/kg and to 0.56 mg/kg, respectively and were both lower than the maximum residue limits (MRLs) for acetamiprid (0.5 mg/kg) and pyriproxyfen (2.0 mg/kg) according to the Chinese national standard. The risk assessment results showed that the acute and chronic dietary exposure risk of acetamiprid and pyriproxyfen ranged from 0.0% to 53.0% (much lower than 100%) for people in different age groups, which was at an acceptable level. Conclusion: This study can provide guidance for the safety evaluation and rational application of acetamiprid and pyriproxyfen in citrus.