Search

The safety and efficacy of the lipid nanoparticle (LNP) delivery system are crucial for the successful development of messenger RNA vaccines. We designed and synthesized a series of ketal ester lipids (KELs), featuring a biodegradable ketal moiety in the linker and ester segments in the tail. Through iterative optimization of the head and tail groups of KELs, we tuned the pKa and molecular shapes, and identified (4S)‐KEL12 as a safe and efficient ionizable lipid for mRNA delivery. (4S)‐KEL12 LNP showed significantly higher delivery efficacy and lower toxicity than the DLin‐MC3‐DMA LNP. In comparison to SM‐102 LNP, (4S)‐KEL12 LNP exhibited better spleen tropism, reduced liver tropism, and hepatotoxicity. Additionally, (4S)‐KEL12 demonstrated good biodegradability following intramuscular or intravenous injection. Notably, (4S)‐KEL12 LNP encapsulated with a therapeutic mRNA cancer vaccine elicited robust cellular immune responses leading to substantial tumor regression along with prolonged survival in tumor‐bearing mice. Our results suggest that (4S)‐KEL12 LNP holds great promise for mRNA vaccine delivery. The comprehensive analysis of the structure‐activity relationship, toxicity, biodegradability, distribution, expression, efficacy, and stereochemistry of these LNPs will greatly contribute to the rational design and discovery of novel lipid‐based delivery systems. [ABSTRACT FROM AUTHOR]

Accurately synthesizing coordination‐driven metal‐organic cages with customized shape and cavity remains a great challenge for chemists. In this work, a bottom‐up step‐wise coordination‐driven self‐assembly approach was put forward. Employing this strategy, three terpyridyl heterometallic‐organic truncated tetrahedral cages with different sizes and cavity were precisely synthesized. Firstly, the coordination of tripodal organic ligands with Ru2+ afforded dendritic metal‐organic ligands L1‐L3. Then the Ru building blocks complexed with Fe2+ and shrunk to form the desired heterometallic‐organic cages (C1‐C3). These discrete heterometallic‐organic supramolecular cages were fully characterized and displayed the large and open cavities varied from 7205 Å3 to 9384 Å3. Notably, these cages could not be directly constructed by single‐step assembly process using initial organic ligands or dimeric metal‐organic ligands, indicative of the irreplaceability of a bottom‐up step‐wise assembly strategy for size‐customized architectures. This work paves a new way for precisely constructing metal‐organic cages with well‐defined cavities. [ABSTRACT FROM AUTHOR]

Electrocatalysts are extensively employed to suppress the shuttling effect in lithium-sulfur (Li-S) batteries. However, it remains challenging to probe the sulfur redox reactions and mechanism at the electrocatalyst/LiPS interface after the active sites are covered by the solid discharge products Li2S/Li2S2. Here, we demonstrate the intrinsic autocatalytic activity of the Li2S (100) plane towards lithium polysulfides on single-atom nickel (SANi) electrocatalysts. Guided by theoretical models and experimental data, it is concluded that LiPS dissociates into Li2S2 and short-chain LiPS on the Li2S (100) plane. Subsequently, Li2S2 undergoes further lithiation to Li2S on the Li2S (100) surface, generating a new Li2S (100) layer, thus enabling the autocatalytic formation of a new Li2S (100) surface. Benefiting from the autocatalytic growth of Li2S, the concentration of LiPS in the electrolyte remains at a lower level, enabling Li-S batteries under high loading and low electrolyte conditions to exhibit superior electrochemical performance. This study reveals the autocatalytic growth of Li2S crystals at the solid-liquid interface in lithium-sulfur batteries enabling good electrochemical performance under high loading and low electrolyte conditions. [ABSTRACT FROM AUTHOR]

Upper and lower limb rehabilitation training is essential for restoring patients' physical movement ability and enhancing muscle strength and coordination. However, traditional rehabilitation training methods have limitations, such as high costs, low patient participation, and lack of real-time feedback. The purpose of this study is to design and implement a rehabilitation training evaluation system based on virtual reality to improve the quality of patients' rehabilitation training. This paper proposes an upper and lower limb rehabilitation training evaluation system based on virtual reality technology, aiming to solve the problems existing in traditional rehabilitation training. The system provides patients with an immersive and interactive rehabilitation training environment through virtual reality technology, aiming to improve patients' participation and rehabilitation effects. This study used Kinect 2.0 and Leap Motion sensors to capture patients' motion data and transmit them to virtual training scenes. The system designed multiple virtual scenes specifically for different upper and lower limbs, with a focus on hand function training. Through these scenes, patients can perform various movement training, and the system will provide real-time feedback based on the accuracy of the patient's movements. The experimental results show that patients using the system show higher participation and better rehabilitation training effects. Compared with patients receiving traditional rehabilitation training, patients using the virtual reality system have significantly improved movement accuracy and training participation. The virtual reality rehabilitation training evaluation system developed in this study improves the quality of patients' rehabilitation and provides personalized treatment information to medical personnel through data collection and analysis, promoting the systematization and personalization of rehabilitation training. This system is innovative and has broad application potential in the field of rehabilitation medicine. [ABSTRACT FROM AUTHOR]

【Objective】 Soil temperature affects crop growth and biogeochemical processes in terrestrial systems, and understanding its variation is hence important to evaluate the impact of global warming on agricultural production and soil gas emissions. The aim of this paper is to analyze the change in soil temperature over the past 50 years at Wudaogou Hydrological Station in Anhui province, China. 【Method】 The analysis was based on data measured from 1971 to 2020. Monthly average temperature at different soil depths from 0 to 320 cm, as well as their relationship with ambient air temperature was analyzed, using linear trend estimation, M-K test and Morlet wavelet method. 【Result】 ①The temperature at different soil layers has changed. Except in the period from June to September, soil temperature in other months has been in increase, with the largest increase seen in March and the least increase in October. Temperature in the depths of 0~160 cm has been increasing, with the increasing rate decreasing along the soil depth. In contrast, the temperature at the depth of 320cm has been dropping. ②The wavelet analysis showed two dominant periods in the monthly-average temperature: 3~6, 8~12 a, and a further period of 15~25 a. ③The M-K mutation test showed that the temperature at depths from 0 to 320 cm had undergone abrupt changes in 2012, 2017, 2016, 2019 and 1972. ④The temperature at different soil depths was correlated with ambient air temperature differently, with the temperature in the depths of 0~80 cm being more sensitive to air temperature. No correlation was found between the air temperature and soil temperature at the depth of 320 cm. 【Conclusion】 The monthly average temperature at different soil layers has been in increase over the past centuries, likely due to global warming, though the increasing rate and its correlation with air temperature varied with soil depth.

Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.

Regulating the transformation of sulfur species is the key to improving the electrochemical performance of lithium‐sulfur (Li−S) batteries, in particular, to accelerate the reversible conversion between solid phase Li2S2 and Li2S. Herein, we introduced Spidroin, which is a main protein in spider silk, as a dual functional separator coating in Li−S batteries to effectively adsorb polysulfides via the sequence of amino acids in its primary structure and regulate Li+ flux through the β‐sheet of its secondary structure, thus accelerating the reversible transformation between Li2S2 and Li2S. Spidroin‐based Li−S cells exhibited an exceptional electrochemical performance with a high specific capacity of 744.1 mAh g−1 at 5 C and a high areal capacity of 7.5 mAh cm−2 at a low electrolyte‐to‐sulfur (E/S) ratio of 6 μL mgs−1 and a sulfur loading of 8.6 mgs cm−2. [ABSTRACT FROM AUTHOR]

In structural health monitoring, because the number of sensors used is far lower than the number of degrees of freedom of the structure being monitored, the optimization problem of the location and number of sensors in the structures is becoming more and more prominent. However, spatial grid structures are complex and diverse, and their dynamic characteristics are complex. It is difficult to accurately measure their vibration information. Therefore, an appropriate optimization method must be used to determine the optimal positioning of sensor placement. Aiming at the problem that spatial grid structures have many degrees of freedom and the fact that it is difficult to obtain complete vibration information, this paper analyzed the typical EI method, MKE method, and EI-MKE method in the arrangement of the measuring points, and it was verified that the EI method was more suitable for the vibration detection of spatial grid structures through the example of a plane truss and spatial grid structures. Measuring points under the assumption of structural damage were explored, and it was proposed that there might have been a stable number of measuring points that could cover the possible vibration mode changes in the structures. At the same time, combined with the three-level improved Guyan recursive technique, in order to obtain better complete modal parameters, the influence of the number of measuring points on the complete vibration mode information was studied. It was concluded that MACd was better than MACn as the quantitative target. [ABSTRACT FROM AUTHOR]

Geophysical subsurface characterization plays a key role in the success of geologic carbon sequestration (GCS). While deterministic inversion methods are commonly used due to their computational efficiency, they often fail to adequately quantify the model uncertainty, which is essential for informed decision‐making and risk mitigation in GCS projects. In this study, we propose the SVGD‐AE method, a novel geostatistical inversion approach that integrates geophysical data with prior geological knowledge to estimate subsurface properties. SVGD‐AE combines Stein Variational Gradient Descent (SVGD) for sampling high‐dimensional distributions with an autoencoder (AE) neural network for re‐parameterizing reservoir models, aiming to accurately preserve geologic characteristics of reservoir models derived from prior knowledge. Through a synthetic example of pre‐stack seismic inversion, we demonstrate that the SVGD‐AE method outperforms traditional probabilistic methods, particularly in inverse problems with complex posterior distributions. Then, we apply the SVGD‐AE method to the Illinois Basin—Decatur Project (IBDP), a large‐scale CO2 storage initiative in Decatur, Illinois, USA. The resulting petrophysical models with quantified uncertainty enhance our understanding of subsurface properties and have broad implications for the feasibility, decision making, and long‐term safety of CO2 storage at the IBDP. Plain Language Summary: Geologic carbon sequestration (GCS) provides a promising solution to mitigate the adverse effects of human‐generated CO2 emissions on the climate. GCS involves capturing CO2 emitted from industrial activities, such as those from coal‐fired power plants, and storing it deep underground. This procedure effectively prevents gas from entering the atmosphere and contributing to global warming. To ensure the success and safety of GCS projects, it is crucial to have a comprehensive understanding of the subsurface, including rock and fluid properties. In this research, we present an innovative probabilistic approach for quantifying subsurface properties using geophysical data. The proposed methodology has been successfully applied to the Illinois Basin—Decatur Project (IBDP), which is a large‐scale CO2 storage initiative in Decatur, Illinois, USA. Our study enhances the comprehension of subsurface attributes and supports informed decision‐making regarding the long‐term safety of CO2 storage at the IBDP. Key Points: A geostatistical inversion method is developed by integrating Stein variational gradient descent with autoencoderThe developed method provides an efficient approach for accurate subsurface characterization with uncertainty quantificationAn integrated reservoir characterization of the Mount Simon Sandstone at the Illinois Basin—Decatur Project is presented [ABSTRACT FROM AUTHOR]

Aims: Reactive oxygen species (ROS) play a vital role in conveying the cytotoxicity and resistance of most chemotherapy drugs. Therefore, gaining a comprehensive understanding of the intricate activities against oxidative stress in cancer cells may provide valuable insights into the discovery of common mechanisms underlying chemoresistance. Results: We identified a novel long noncoding RNA (lncRNA), designated fluorouracil-associated transcript-1 (FUAT1), as a key nongenetic player involved in ROS-mediated intrinsic chemoresistance by employing a unique screening strategy based on transcriptome sequencing (RNA-Seq) technology. To investigate the precise role of the FUAT1 regulatory axis in chemoresistance, we conducted a series of in vitro and in vivo assays including gain/loss-of-function and rescue experiments. Mechanistically, our findings revealed that FUAT1 upregulates Tensin 4 (TNS4) by sponging miR-140-5p, which allows gastric cancer cells to survive chemotherapy by inhibiting ROS-mediated apoptosis. Clinically, we observed that the FUAT1/TNS4 regulatory axis is negatively associated with overall survival and progression-free survival among gastric and colon cancer patients treated with 5-fluorouracil adjuvant chemotherapy. Innovation: We devised a novel screening strategy distinct from conventional approaches using drug-resistant strains. Through this approach, we identified the previously unrecognized lncRNA FUAT1/TNS4 axis that plays a critical role in ROS-mediated intrinsic chemoresistance. Conclusions: Our findings shed light on fundamental adaptive mechanisms employed by cancer cells to respond to chemotherapy and provide new insights into developing strategies aiming at overcoming chemoresistance. [ABSTRACT FROM AUTHOR]