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In recent years, the exploration on large-scale cyber-physical systems (CPSs) has become a fertile research field of significant impact. Large-scale CPS applications cover not only manufacturing and production areas but also daily living domains. Traditional solutions dedicated for large-scale CPSs mainly concentrate on the service latency or reliability optimization, but neglect the resultant negative impact on system lifetime. In this paper, we conduct the first study on jointly optimizing the service latency and system lifetime subject to the constraints of reliability, energy consumption, and schedulability for large-scale CPSs. We propose an edge intelligent solution composed of offline and online phases. At offline phase, the long short-term memory (LSTM) technique is leveraged to predict task offloading rates at individual user groups. Afterwards, multi-objective evolutionary algorithm with dual local search (DLS-MOEA) is exploited to determine optimal system static settings of computation offloading mapping and task replication number. At online phase, an affinity-driven scheme incurring minimal system dynamic overheads is designed to deal with the inherent mobility of terminal users. We also build an algorithm validation platform upon which extensive simulation experiments are carried out. Experimental results show that our offline and online schemes outperform the state-of-the-art benchmarking methods by 27.1% and 43.5%, respectively.

Humidity is a critical environmental factor affecting the epidemic of plant diseases. However, it is still unclear how ambient humidity affects the occurrence of diseases in plants. In this study, we show that high ambient humidity enhanced blast development in rice plants under laboratory conditions. Furthermore, we found that high ambient humidity enhanced the virulence of Magnaporthe oryzae by promoting conidial germination and appressorium formation. In addition, the results of RNA-sequencing analysis and the ethylene content assessment revealed that high ambient humidity suppressed the accumulation of ethylene and the activation of ethylene signaling pathway induced by M. oryzae in rice. Knock out of ethylene signaling genes OsEIL1 and OsEIN2 or exogenous application of 1-methylcyclopropene (ethylene inhibitor) and ethephon (ethylene analogues) eliminated the difference of blast resistance between the 70% and 90% relative humidity conditions, suggesting that the activation of ethylene signaling contributes to humidity-modulated basal resistance against M. oryzae in rice. In conclusion, our results demonstrated that high ambient humidity enhances the virulence of M. oryzae and compromises basal resistance by reducing the activation of ethylene biosynthesis and signaling in rice. Results from this study provide cues for novel strategies to control rice blast under global environmental changes.

An ensemble of three-dimensional ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS). Basic software building blocks are reused from previously presented deterministic 3DEnVar functionality, and combined with a formal experimental workflow manager in MPAS-Workflow. En-3DEnVar is used to produce an 80-member ensemble of analyses, which are cycled with ensemble forecasts in a 1-month experiment. The ensemble forecasts approximate a purely flow-dependent background error covariance (BEC) at each analysis time. The En-3DEnVar BECs and prior ensemble mean forecast errors are compared to those produced by a similar experiment that uses the Data Assimilation Research Testbed (DART) Ensemble Adjustment Kalman Filter (EAKF). The experiment using En-3DEnVar produces similar ensemble spread to and slightly smaller errors than the EAKF. The ensemble forecasts initialized from En-3DEnVar and EAKF analyses are used as BECs in deterministic cycling 3DEnVar experiments, which are compared to a control experiment that uses 20-member MPAS-A forecasts initialized from Global Ensemble Forecast System (GEFS) initial conditions. The experimental ensembles achieve mostly equivalent or better performance than the off-the-shelf ensemble system in this deterministic cycling setting; although, there are many obvious differences in configuration between GEFS and the two MPAS ensemble systems. An additional experiment that uses hybrid 3DEnVar, which combines the En-3DEnVar ensemble BEC with a climatological BEC, increases tropospheric forecast quality compared to the corresponding pure 3DEnVar experiment. The JEDI-MPAS En-3DEnVar is technically working and useful for future research studies. Tuning of observation errors and spread is needed to improve performance and several algorithmic advancements are needed to improve computational efficiency for larger-scale applications.

CRISPR-guided DNA base editors (BEs) are potent genome editing tools in biotechnology and medicine. However, conventional cytosine and adenine BEs can only induce base transitions (C-to-T and A-to-G) and cannot induce base transversions. Recently, several C-to-G base editors (CGBEs) were generated and applied in human cells. By comparing them, we found that engineered deaminases rather than additional base excision repair proteins significantly improved the C-to-G efficiency. In addition, significant increase in C-to-G transversions in the GC context were determined by using rationally engineered eAID deaminase. The genome-targeting scope of CGBEs were further expanded by using SpRY Cas9 variant, which then successfully induced stop codon (TAC to TAG) to disrupt

To improve the wind speed forecasts at turbine locations and at hub height, this study develops the WRFDA system to assimilate the wind speed observations measured on the nacelle of turbines (hereafter referred as turbine wind speed observations) with both 3DVAR and 4DVAR algorithms. Results exhibit that the developed data assimilation (DA) system helps in greatly improving the analysis and the forecast of wind turbine speed. Among three experiments with no cycling DA, with 2-h cycling DA, and with 4-h cycling DA, the last experiment generates the best analysis, improving the averaged forecasts (from T + 9 to T + 24) of wind speed over all wind farms by 32.5% in the bias and 6.3% in the RMSE. After processing the turbine wind speed observations into superobs, even bigger improvements are revealed when validating against either the original turbine wind speed observations or the superobs. Taken the results validated against the superobs as an example, the bias and RMSE of the forecasts (from T + 9 to T + 24) averaged over all wind farms are reduced by 38.8% and 12.0%, respectively. Compared to the best-performed 3DVAR experiment (4-h cycling and superobs), the experiment following the same DA strategy but using 4DVAR algorithm exhibits further improvements, especially for the averaged bias in the forecasts of all wind farms, and the changing amount in the forecasts of the enhanced wind farms. Compared to the control experiment, the 4DVAR experiment reduces the bias and RMSE in the forecasts (from T + 9 to T + 24) by 54.6% (0.66 m s−1) and 12.7% (0.34 m s−1).