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With the integration of an increasing number of outward-facing components in intelligent and connected vehicles, the open controller area network (CAN) bus environment faces increasingly severe security threats. However, existing security measures remain inadequate, and CAN bus messages lack effective security mechanisms and are vulnerable to malicious attacks. Although encryption algorithms can enhance system security, their high bandwidth consumption negatively impacts the real-time performance of intelligent and connected vehicles. Moreover, the message authentication mechanism of the CAN bus requires lengthy authentication codes, further exacerbating the bandwidth burden. To address these issues, we propose an improved dynamic compression algorithm that achieves higher compression rates and efficiency by optimizing header information processing during data reorganization. Additionally, we have proposed a novel dynamic key management approach, incorporating a dynamic key distribution mechanism, which effectively resolves the challenges associated with key management. Each Electronic Control Unit (ECU) node independently performs compression, encryption, and authentication while periodically updating its keys to enhance system security and strengthen defense capabilities. Experimental results show that the proposed dynamic compression algorithm improves the average compression rate by 2.24% and enhances compression time efficiency by 10% compared to existing solutions. The proposed security protocol effectively defends against four different types of attacks. In hardware tests, using an ECU operating at a frequency of 30 MHz, the computation time for the security algorithm on a single message was 0.85 ms, while at 400 MHz, the computation time was reduced to 0.064 ms. Additionally, for different vehicle models, the average CAN bus load rate was reduced by 8.28%. The proposed security mechanism ensures the security, real-time performance, and freshness of CAN bus messages while reducing bus load, providing a more efficient and reliable solution for the cybersecurity of intelligent and connected vehicles.

Cytidine base editors (CBEs) and adenine base editors (ABEs), composed of a cytidine deaminase or an evolved adenine deaminase fused to Cas9 nickase, enable the conversion of C·G to T·A or A·T to G·C base pair in organisms, respectively. Here, we show that BE3 and ABE7.10 systems can achieve a targeted mutation efficiency of 53–88% and 44–100%, respectively, in both blastocysts and Founder (F0) rabbits. Meanwhile, this strategy can be used to precisely mimic human pathologies by efficiently inducing nonsense or missense mutations as well as RNA mis-splicing in rabbit. In addition, the reduced frequencies of indels with higher product purity are also determined in rabbit blastocysts by BE4-Gam, which is an updated version of the BE3 system. Collectively, this work provides a simple and efficient method for targeted point mutations and generation of disease models in rabbit. Base editors can make targeted changes without inducing a double-stranded break. Here, the authors apply the BE3 and ABE7.10 systems to rabbit to create highly efficient targeted base substitutions and various mutation types, and show reduced frequency of undesired by-products with the updated BE4-Gam system.

Cytosine base editors (CBEs), combining cytidine deaminases with the Cas9 nickase (nCas9), enable targeted C-to-T conversions in genomic DNA and are powerful genome-editing tools used in biotechnology and medicine. However, the overexpression of cytidine deaminases in vivo leads to unexpected potential safety risks, such as Cas9-independent off-target effects. This risk makes the development of deaminase off switches for modulating CBE activity an urgent need. Here, we report the repurpose of four virus-derived anti-deaminases (Ades) that efficiently inhibit APOBEC3 deaminase-CBEs. We demonstrate that they antagonize CBEs by inhibiting the APOBEC3 catalytic domain, relocating the deaminases to the extranuclear region or degrading the whole CBE complex. By rationally engineering the deaminase domain, other frequently used base editors, such as CGBE, A&CBE, A&CGBE, rA1-CBE and ABE8e, can be moderately inhibited by Ades, expanding the scope of their applications. As a proof of concept, the Ades in this study dramatically decrease both Cas9-dependent and Cas9-independent off-target effects of CBEs better than traditional anti-CRISPRs (Acrs). Finally, we report the creation of a cell type-specific CBE-ON switch based on a microRNA-responsive Ade vector, showing its practicality. In summary, these natural deaminase-specific Ades are tools that can be used to regulate the genome-engineering functions of BEs. Anti-deaminases can inhibit APOBEC3, a component of cytosine base editors. Here Zhanjun Li and colleagues repurposed anti-deaminase proteins derived from viruses to inhibit base editors for use in efficient regulation of base editors’ activity in gene modification and therapeutic applications.

Antibiotics are commonly used to prevent and control diseases in aquaculture. However, long-term/overuse of antibiotics not only leaves residues but results in the development of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Antibiotics, ARB, and ARGs are widespread in aquaculture ecosystems. However, their impacts and interaction mechanisms in biotic and abiotic media remain to be clarified. In this paper, we summarized the detection methods, present status, and transfer mechanisms of antibiotics, ARB, and ARGs in water, sediment, and aquaculture organisms. Currently, the dominant methods of detecting antibiotics, ARB, and ARGs are UPLC−MS/MS, 16S rRNA sequencing, and metagenomics, respectively. Tetracyclines, macrolides, fluoroquinolones, and sulfonamides are most frequently detected in aquaculture. Generally, antibiotic concentrations and ARG abundance in sediment are much higher than those in water. Yet, no obvious patterns in the category of antibiotics or ARB are present in organisms or the environment. The key mechanisms of resistance to antibiotics in bacteria include reducing the cell membrane permeability, enhancing antibiotic efflux, and structural changes in antibiotic target proteins. Moreover, horizontal transfer is a major pathway for ARGs transfer, including conjugation, transformation, transduction, and vesiculation. Identifying, quantifying, and summarizing the interactions and transmission mechanisms of antibiotics, ARGs, and ARB would provide useful information for future disease diagnosis and scientific management in aquaculture.

The impact of assimilating radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) on forecasts of several tropical cyclones (TCs) was studied using the Weather Research and Forecasting Model (WRF) and a limited-area ensemble Kalman filter (EnKF). Analysis/forecast cycling experiments with and without AMSU-A radiance assimilation were performed over the Atlantic Ocean for the period 11 August–13 September 2008, when five named storms formed. For convenience, the radiance forward operators and bias-correction coefficients, along with the majority of quality-control decisions, were computed by a separate, preexisting variational assimilation system. The bias-correction coefficients were obtained from 3-month offline statistics and fixed during the EnKF analysis cycles. The vertical location of each radiance observation, which is required for covariance localization in the EnKF, was taken to be the level at which the AMSU-A channels’ weighting functions peaked. Deterministic 72-h WRF forecasts initialized from the ensemble-mean analyses were evaluated with a focus on TC prediction. Assimilating AMSU-A radiances produced better depictions of the environmental fields when compared to reanalyses and dropwindsonde observations. Radiance assimilation also resulted in substantial improvement of TC track and intensity forecasts with track-error reduction up to 16% for forecasts beyond 36 h. Additionally, assimilating both radiances and satellite winds gave markedly more benefit for TC track forecasts than solely assimilating radiances.

To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (2015–2017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 µm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98–134, 46–101, 32–59 and 19–60 µg m-3, respectively, indicating that the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11–12, 53–96 and 22–40 µg m-3 were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within±5 µg m-3.The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 µg m-3 for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 µg m-3). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 µg m-3, while emission control measures caused a decrease of 8 µg m-3, and the combined effects still showed aPM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol–meteorology feedbacks in the model can generate large uncertainties in the analysis as well.

Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

Antibiotics release into the water environment through sewage discharge is a significant environmental concern. In the present study, we investigated the removal of ciprofloxacin (CIP) in simulated sewage by biological aeration filter (BAF) equipped with Fe3O4-modified zeolite (Fe3O4@ZF). Fe3O4@ZF were prepared with impregnation method, and the Fe3O4 particles were successfully deposited on the surface of ZF in an amorphous form according to the results of XPS and XRD analysis. The modification also increased the specific surface area (from 16.22 m²/g to 22 m²/g) and pore volume (from 0.0047 cm³/g to 0.0063 cm³/g), improving the adsorption efficiency of antibiotics. Fe3O4 modified ZF improved the treatment performance significantly, and the removal efficiency of CIP in BAF-Fe3O4@ZF was 79%±2.4%. At 10ml/L CIP, the BAF-Fe3O4@ZF reduced the relative abundances of antibiotics resistance genes (ARGs) int, mexA, qnrB and qnrS in the effluent by 57.16%, 39.59%, 60.22%, and 20.25%, respectively, which effectively mitigate the dissemination risk of ARGs. The modification of ZF increased CIP-degrading bacteria abundance, such as Rhizobium and Deinococcus-Thermus, and doubled bacterial ATP activity, promoting CIP degradation. This study offers a viable, efficient method to enhance antibiotic treatment and prevent leakage via sewage discharge.

An ensemble Kalman filter data assimilation (DA) system has been developed to improve air quality forecasts using surface measurements of PM10, PM2.5, SO2, NO2, O3, and CO together with an online regional chemical transport model, WRF-Chem (Weather Research and Forecasting with Chemistry). This DA system was applied to simultaneously adjust the chemical initial conditions (ICs) and emission inputs of the species affecting PM10, PM2.5, SO2, NO2, O3, and CO concentrations during an extreme haze episode that occurred in early October 2014 over East Asia. Numerical experimental results indicate that ICs played key roles in PM2.5, PM10 and CO forecasts during the severe haze episode over the North China Plain. The 72 h verification forecasts with the optimized ICs and emissions performed very similarly to the verification forecasts with only optimized ICs and the prescribed emissions. For the first-day forecast, near-perfect verification forecasts results were achieved. However, with longer-range forecasts, the DA impacts decayed quickly. For the SO2 verification forecasts, it was efficient to improve the SO2 forecast via the joint adjustment of SO2 ICs and emissions. Large improvements were achieved for SO2 forecasts with both the optimized ICs and emissions for the whole 72 h forecast range. Similar improvements were achieved for SO2 forecasts with optimized ICs only for the first 3 h, and then the impact of the ICs decayed quickly. For the NO2 verification forecasts, both forecasts performed much worse than the control run without DA. Plus, the 72 h O3 verification forecasts performed worse than the control run during the daytime, due to the worse performance of the NO2 forecasts, even though they performed better at night. However, relatively favorable NO2 and O3 forecast results were achieved for the Yangtze River delta and Pearl River delta regions.

The inevitability of faults arises due to prolonged exposure of photovoltaic (PV) power plants to intricate environmental conditions. Therefore, fault diagnosis of PV power plants is crucial to ensure the continuity and reliability of power generation. This paper proposes a fault diagnosis method that integrates PV power prediction and an exponentially weighted moving average (EWMA) control chart. This method predicts the PV power based on meteorological factors using the adaptive particle swarm algorithm-back propagation neural network (APSO-BPNN) model and takes its error from the actual value as a control quantity for the EWMA control chart. The EWMA control chart then monitors the error values to identify fault types. Finally, it is verified by comparison with the discrete rate (DR) analysis method. The results showed that the coefficient of determination of the prediction model of the proposed method reached 0.98. Although the DR analysis can evaluate the overall performance of the inverter and identify the faults, it often fails to point out the specific location of the faults accurately. In contrast, the EWMA control chart can monitor abnormal states such as open and short circuits and accurately locate the string where the fault occurs.