Learning-Based IRS-Assisted Secure Transmission for Mine IoTs.

Mine Internet of Things (MIoT) devices in intelligent mines often face substantial signal attenuation due to challenging operating conditions. The openness of wireless communication also makes it susceptible to smart attackers, such as active eavesdroppers. The attackers can disrupt equipment operations, compromise production safety, and exfiltrate sensitive environmental data. To address these challenges, we propose an intelligent reflecting surface (IRS)-assisted secure transmission system for an MIoT device which enhances the security and reliability of wireless communication in challenging mining environments. We develop a joint optimization problem for the IRS phase shifts and transmit power, with the goal of enhancing legitimate transmission while suppressing eavesdropping. To accommodate time-varying channel conditions, we propose a reinforcement learning (RL)-based IRS-assisted secure transmission scheme that enables MIoT device to optimize both the IRS reflecting coefficients and transmit power for optimal transmission policy in dynamic environments. We adopt the deep deterministic policy gradient (DDPG) algorithm to explore the optimal transmission policy in continuous space. This can reduce the discretization error caused by traditional RL methods. The simulation results indicate that our proposed scheme achieves superior system utility compared with both the IRS-free (IF) scheme and the IRS randomly configured (IRC) scheme. These results demonstrate the effectiveness and practical relevance of our contributions, proving that implementing IRS in MIoT wireless communication can enhance safety, security, and efficiency in the mining industry.

Metabolic activity via 18F-FDG PET/CT is predictive of microsatellite instability status in colorectal cancer.

PURPOSE: Identification of microsatellite instability high (MSI-H) colorectal cancer (CRC) is crucial for screening patients most likely to benefit from immunotherapy. We aim to investigate whether the metabolic characteristics is related to MSI status and can be used to predict the MSI-H CRC. METHODS: A retrospective analysis was conducted on 420 CRC patients who were identified via [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography(CT) prior to therapy. Maximum standardized uptake (SUVmax), mean standardized uptake (SUVmean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary tumor were calculated and compared between MSI-H and microsatellite stability (MSS). Predictive factors of MSI status were selected from metabolic parameters and clinicopathological profiles via a multivariate analysis. RESULTS: Of 420 colorectal cancers, 44 exhibited a high incidence of MSI. Both MTV and TLG were significantly higher in MSI-H group compared with the MSS group (P = 0.004 and P = 0.010, respectively). Logistic regression analysis indicated that CRC with MSI-H were related to younger age (P = 0.013), primary lesion located at right hemi-colon (P < 0.001) and larger MTV on PET/CT imaging (P = 0.019). MTV more than 32.19 of colorectal cancer was linked to the presence of MSI (P = 0.019). CONCLUSION: Tumor metabolic burden were higher in MSI-H CRC which may be useful for predicting the MSI status of CRC patient and thus aid in determination of immunotherapy for patients with CRC.

A 18FDG PET/CT-based volume parameter is a predictor of overall survival in patients with local advanced gastric cancer.

OBJECTIVE: The present study investigated the prognosis value of preoperative fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in patients with local advanced gastric cancer (LAGC). METHODS: In total, 144 patients [median age 63 (range: 48-80) years old] with LAGC underwent18F-FDG PET/CT prior to any treatment. The maximum standardized uptake values (SUVmax), mean standardized uptake values (SUVmean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary lesion were measured on PET/CT and correlated with clinicopathological features and survival. RESULTS: Significant differences in SUVmean, SUVmax, MTV and TLG were found according to Lauren's classification, histologic grade and T category (P<0.05). During the 26.5-month follow-up, 51 (35.4%) patients died and 70 (48.6%) exhibited disease progression. The optimal thresholds of MTV and TLG were 15.1 cm3 and 47.3 cm3, respectively. The 3-year progression-free survival (PFS) and overall survival (OS) for patients with high TLG values were 30% and 38% compared to 38% and 47% for low TLG values, respectively (P<0.05). Univariate and multifactor analyses demonstrated that lymph node metastasis and T stage were independent prognostic factors for PFS; T stage, histologic grade and TLG were independent prognostic factors for OS (P<0.05). Molecular markers had no relationship with patient's outcomes. CONCLUSIONS: Metabolic activity of primary gastric tumors from 18F-FDG PET/CT is a prognostic factor in patients with LAGC.

[Reversal of resistance to adriamycin in human breast cancer cell line MCF-7/ADM by silencing AEG-1 gene and its mechanism].

The aim of this study was to investigate the effects of AEG-1 gene silencing on the chemoresistance of human breast cancer cell line MCF-7/ADM and its possible mechanism. MCF-7/ADM cells were incubated in the medium containing adriamycin (ADM). The recombinant pLKO.1-shAEG-1 plasmid was constructed to silence AEG-1 expression in human breast cancer MCF-7/ADM cells. MTT assay was employed to detect the anti-tumor effect of ADM on MCF-7/ADM cells, and IC50 value of ADM was calculated according to MTT. Flow cytometry was used to determine the apoptosis. Western blot was used to analyze the expression levels of AEG-1, p-Akt, p-MDM2, p-Bad, p53 and MDR1. The result showed MCF-7/ADM had a significantly higher expression level of AEG-1 compared with that of MCF-7 (P < 0.05), however, the expression of AEG-1 was decreased after AEG-1 gene silencing. The IC50 value of ADM in shAEG-1 group was significantly lower than that in shcontrol group. AEG-1 gene silencing induced cell apoptosis and enhanced the pro-apoptotic effect of ADM on MCF-7/ADM cells. After AEG-1 gene silencing, the phosphorylation of Akt, MDM2 and Bad was inhibited (P < 0.05), the protein levels of p53 and MDR1 were up-regulated (P < 0.05) and down-regulated (P < 0.05) respectively, compared with control. In conclusion, the results suggest that AEG-1 gene silencing can reverse the ADM resistance in human breast cancer cell line MCF-7/ADM by means of inducing apoptosis and down-regulating the protein level of MDR1.

Tumor and metastatic lymph nodes metabolic activity on 18F-FDG-PET/CT to predict progression-free survival in locally advanced cervical cancer.

OBJECTIVE: The present study investigated the predictive diseases progression value of preoperative fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in patients with local advanced cervical cancer (LACC). METHODS: In total, 267 patients [median age 58 (range: 27-85) years old] with LACC underwent 18F-FDG PET/CT prior to any treatment. The maximum standardized uptake values (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary lesion and metastatic lymph nodes were measured on PET/CT and correlated with clinicopathological features and progression-free survival (PFS). RESULTS: The median follow-up was 36.52 (range: 3.09-61.29) months. During the observation period, 80 (30.0%) patients exhibited disease progression. Univariate analysis showed that FIGO stage, concurrent chemoradiotherapy (CRT), serum level of carcinoembryonic antigen (CEA) and squamous cell carcinoma antigen (SCC-Ag), primary tumor MTV (pMTV) and TLG (pTLG), lymph nodes SUVmax (nSUVmax) and TLG (nTLG), and total metabolic activity (sMTV, sTLG) were associated with PFS. nSUVmax ≥ 5.29, CEA ≥ 7.11 ng/ml and deficiency of concurrent CRT were independent risk factor for PFS (p = 0.006, p = 0.008, p = 0.014). The 3-year PFS for patients with high nSUVmax were 42.2% compared to 56.3% for low nSUVmax values. CONCLUSION: Pretreatment cervical and lymph nodes metabolic parameters were associated with PFS in patients with LACC.

Progression of radio-labeled molecular imaging probes targeting chemokine receptors.

Chemokine receptors are significantly expressed in the surface of most inflammatory cells and tumor cells. Guided by chemokines, inflammatory cells which express the relevant chemokine receptors migrate to inflammatory lesions and participate in the evolution of inflammation diseases. Similarly, driven by chemokines, immune cells infiltrate into tumor lesions not only induces alterations in the tumor microenvironment, disrupting the efficacy of tumor therapies, but also has the potential to selectively target tumoral cells and diminish tumor progression. Chemokine receptors, which are significantly expressed on the surface of tumor cell membranes, are regulated by chemokines and initiate tumor-associated signaling pathways within tumor cells, playing a complex role in tumor progression. Based on the antagonists targeting chemokine receptors, radionuclide-labeled molecular imaging probes have been developed for the emerging application of molecular imaging in diseases such as tumors and inflammation. The value and limitations of molecular probes in disease imaging are worth reviewing.

Novel polystyrene microspheres functionalized by imidazolium and the electrocatalytic activity towards H2O2 of its Prussian blue composite.

Copolymerization of styrene (St) and 1-vinyl-3-ethylimidazolium bromide (VEIB), novel poly(St-co-VEIB) microspheres were generated. Owing to the presence of imidazolium groups, such microspheres having an average diameter of 125 nm, behave electropositively when dispersed in aqueous solution. Furthermore, due to the presence of imidazolium groups, having a capacity of ion-exchange and weak reducibility on the surface of the PS microspheres, [Fe(CN)6]³â» was absorbed on the surface of poly(St-co-VEIB) microspheres, and simultaneously, Fe³âº was reduced to Fe²âº. Thus, in situ growth of Prussian blue (PB) nanoparticles could occur on the surface of poly(St-co-VEIB) microspheres without the addition of any other reducing agent. This methodology, utilizing the ion-exchange and weak reducibility properties of the imidazolium groups on the surface of micro-/nanostructures is a novel general method for assembling hierarchical nanostructured materials. Finally, the electrochemical property of the strawberry-like PS/PB composite microspheres was also investigated by applying a glassy carbon electrode. A good repeatability of the cyclic voltammetry responses, having a good linearity and sensitivity, for the electrocatalytic reduction of H2O2 was obtained.

[Inhibitory effect of exogenous insulin-like growth factor binding protein 7 on proliferation of human breast cancer cell line MDA-MB-453 and its mechanism].

The present study was to investigate the effects of exogenous insulin-like growth factor binding protein 7 (IGFBP7) on the proliferation of human breast cancer cell line MDA-MB-453 and its possible mechanism. By means of MTT method in vitro, the results showed exogenous IGFBP7 inhibited the growth of MDA-MB-453 cells (IC50 of IGFBP7 = 8.49 µg/mL) in time- and concentration-dependent manner. SB203580, p38(MAPK) inhibitor, blocked the anti-proliferative effect of exogenous IGFBP7. The flow cytometry assay showed that exogenous IGFBP7 remarkably induced G0/G1 arrest in MDA-MB-453 cells. The Western blot showed that exogenous IGFBP7 promoted phosphorylation of p38(MAPK), up-regulated expression of p21(CIP1/WAF1), and inhibited phosphorylation of Rb. SB203580 restrained exogenous IGFBP7-induced regulation of p21(CIP1/WAF1) and p-Rb in MDA-MB-453 cells. In conclusion, the present study suggests that exogenous IGFBP7 could activate the p38(MAPK) signaling pathway, upregulate p21(CIP1/WAF1) expression, inhibit phosphorylation of Rb, and finally induce G0/G1 arrest in MDA-MB-453 cells.

Nanoarchitectonics on residual carbon from gasification fine slag upon two step low temperature activation for application in supercapacitors.

In this paper, the carbon electrode materials were prepared by the KOH-HNO3 low-temperature activation technique using cheap residual carbon from gasification fine slag (CK) as raw materials. The results showed that the prepared material (CKN-2) which obtained by dry-wet sequential activation at 500°C for 1.5 h at carbon to KOH ratio of 1:2 and further at 80°C for 1 h in 2 mol/L HNO3 solution. The specific capacitance of CKN-2 reached 142 F/g at a current density of 0.5 A/g. CKN-2 was used to assemble a symmetrical (CKN-2//CKN-2) supercapacitor, which exhibited an energy density of 6.80 Wh/kg at a power density of 244.8 W/kg. The CKN-2//CKN-2 capacitor was tested for stability after 10,000 cycles, with a capacitance retention rate of 97%. These results demonstrate that residual carbon from gasification fine slag can be effectively used to produce high-performance carbon electrode materials for supercapacitors using the KOH-HNO3 low-temperature sequential co-activation technique.

Retrospective Analysis of the Predictive Value of 18F-FDG PET/CT Metabolic Parameters for PD-L1 Expression in Cervical Cancer.

BACKGROUND: Immunotherapy targeting PD-1/PD-L1 has been proven to be effective for cervical cancer treatment. To explore non-invasive examinations for assessing the PD-L1 status in cervical cancer, we performed a retrospective study to investigate the predictive value of 18F-FDG PET/CT. METHODS: The correlations between PD-L1 expression, clinicopathological characteristics and 18F-FDG PET/CT metabolic parameters were evaluated in 74 cervical cancer patients. The clinicopathological characteristics included age, histologic type, tumor differentiation, FIGO stage and tumor size. The metabolic parameters included maximum standard uptake (SUVmax), mean standard uptake (SUVmean), total lesion glycolysis (TLG) and tumor metabolic volume (MTV). RESULTS: In univariate analysis, SUVmax, SUVmean, TLG, tumor size and tumor differentiation were obviously associated with PD-L1 status. SUVmax (rs = 0.42) and SUVmean (rs = 0.40) were moderately positively correlated with the combined positive score (CPS) for PD-L1 in Spearman correlation analysis. The results of multivariable analysis showed that the higher SUVmax (odds ratio = 2.849) and the lower degree of differentiation (Odds Ratio = 0.168), the greater probability of being PD-L1 positive. The ROC curve analysis demonstrated that when the cut-off values of SUVmax, SUVmean and TLG were 10.45, 6.75 and 143.4, respectively, the highest accuracy for predicting PD-L1 expression was 77.0%, 71.6% and 62.2%, respectively. The comprehensive predictive ability of PD-L1 expression, assessed by combining SUVmax with tumor differentiation, showed that the PD-L1-negative rate was 100% in the low probability group, whereas the PD-L1-positive rate was 84.6% in the high probability group. In addition, we also found that the H-score of HIF-1α was moderately positively correlated with PD-L1 CPS (rs = 0.51). CONCLUSIONS: The SUVmax and differentiation of the primary lesion were the optimum predictors for PD-L1 expression in cervical cancer. There was a great potential for 18F-FDG PET/CT in predicting PD-L1 status and selecting cervical cancer candidates for PD1/PD-L1 immune checkpoint therapy.

Prediction of product yields using fusion model from Co-pyrolysis of biomass and coal.

This study aimed to establish a self-corrective machine learning model base on co-pyrolysis data of biomass and coal. Proximate and ultimate analysis of raw materials were chosen as input parameters. Radial basis function (RBF), support vector machine (SVM), and random forest (RF) were used to build the base regression models for the fusion (FU) model. 96 sets of the experimental data were applied to train and test the base models. A learning weight were then determined by the predicted performance of base models. Based on the learning weight method, FU model spontaneously regulated and controlled the weight of base models to output the predicted result of co-pyrolysis products. The coefficient of determination (R2) was more than 0.99 and the root-mean-squared error (RMSE) was lower than 0.88%. The results suggested that FU model was more accurately adequate to forecast the yields of co-pyrolysis products than any of the base models.

Catalytic Pyrolysis of Sawdust with Desulfurized Fly Ash for Pyrolysis Gas Upgrading.

In this study, the catalytic effects of desulfurized fly ash (DFA) on the gaseous products of sawdust (SD) pyrolysis were investigated in a tubular furnace. The results indicated that DFA catalyzed the process of SD decomposition to improve the hydrogen content and the calorific value of pyrolysis gas. As to its effect on pyrolysis products, DFA increased the non-oxide content of CH4, C3H4, and H2 in pyrolysis gas by 1.4-, 1.8-, and 2.3-fold, respectively. Meanwhile, the catalytic effect of DFA reduced the CO and CO2 yields during DFA/SD pyrolysis. Based on the model compound method, CaSO3 and Ca(OH)2 in DFA was proved to have quite different catalytic effects on pyrolysis gas components. Ca(OH)2 accelerated the formation of CH4 and H2 through the cracking of methoxyl during lignin and cellulose degradation, while CaSO3 favored the generation of CO and CO2 due to the carbonyl and carboxyl of lignin in SD. CaSO3 also catalyzed SD pyrolysis to promote the C3H4 yield in pyrolysis gas. Overall, the catalytic pyrolysis of SD with DFA yielded negative-carbon emission, which upgraded the quality of the pyrolysis gas.

Water Quality Prediction Based on SSA-MIC-SMBO-ESN.

Water pollution threatens the safety of human production and life. To quickly respond to water pollution, it is important for water management staff to predict water quality changes in advance. Drawing on the temporality of water quality data, the leaky integrator echo state network (ESN) was introduced to construct the water quality prediction models for dissolved oxygen (DO), permanganate index (CODMn), and total phosphorus (TP), respectively. First, the missing values were filled by the linear trend method of adjacent points, and the outliers were detected and corrected by the Z-score method and the linear trend method. Second, the singular spectrum analysis (SSA) was performed to denoise the original monitoring data, such that the predicted data catch up with the real data, and the model accuracy is not affected by the hidden noise in the data. Third, the correlation between water quality indices was measured by the maximum information coefficient (MIC), and the strongly correlated indices were imported to the prediction model. Finally, according to these strong correlation indicators, the water quality prediction models based on multiple features were constructed, respectively, using the offline and online learning algorithms of the ESN. The hyperparameters of the models were optimized through the sequential model-based optimization (SMBO). Experimental results show that the proposed water quality prediction models, namely, SSA-MIC-SMBO-Offline ESN and SSA-MIC-SMBO-Online ESN, predicted DO, CODMn, and TP accurately, providing suitable tools for practical applications.

Ultra-thin graphene cube framework@TiO2 heterojunction as high-performance anode materials for lithium ion batteries.

Here, we proposed a new strategy to build the integrated graphene cube (Gr) framework@TiO2 composite to improve the ion transport kinetics and electrical conductivity of TiO2 as a long-life and high-capacity anode for lithium ion batteries. Combined with the salt template method for ultra-thin framework, the distinct structure of Gr@TiO2 shows an excellent electrochemical performance, e.g., initial coulombic efficiency (ICE), rate performance and specific capacity, due to the increased kinetics of lithium ions. Through this method, the integrity is dramatically improved and the pulverization and agglomeration of the anode after long-term cycles are restrained. The optimized Gr@TiO2 displays a high stable reversible capacity of 179.5 mAh g-1 after 4000 cycles at 1 A g-1, excellent rate performance (125.5 mAh g-1 at 5 A g-1). Kinetic studies through Electrochemical Impedance Spectra, Galvanostatic Intermittent Titration Technique and Linear Sweep Voltammetry confirm that the electrical conductivity and ion transport kinetics are dramatically improved through the ultra-thin graphene cube framework as a heterojunction structure of Gr@TiO2.

Transmission of SARS-CoV-2 on Cold-Chain Food: Precautions Can Effectively Reduce the Risk.

The COVID-19 pandemic has generated a new era in the world, also in the food safety. Up to now, there is no evidence to suggest that people can infect COVID-19 via food contaminated by SARS-CoV-2. Here, we analyzed the results of regular SARS-CoV-2 nucleic acid testing of considerable cold-chain food practitioners, cold-chain food surfaces, and their internal or external packaging as well as their associated environments, aiming to explore the risk of cold-chain food being contaminated by SARS-CoV-2 and the probability of people infecting COVID-19 through contaminated cold-chain food in the context of COVID-19 epidemic. This study found that only two batches of cold-chain food were contaminated by SARS-CoV-2, none of the cold-chain food handler were infected due to effective regulatory measures for cold-chain food. Therefore, effective supervision and preventive methods could effectively reduce the transmission risk of SARS-CoV-2 on cold-chain food.

A 3D-2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification.

As one of the fast evolution of remote sensing and spectral imagery techniques, hyperspectral image (HSI) classification has attracted considerable attention in various fields, including land survey, resource monitoring, and among others. Nonetheless, due to a lack of distinctiveness in the hyperspectral pixels of separate classes, there is a recurrent inseparability obstacle in the primary space. Additionally, an open challenge stems from examining efficient techniques that can speedily classify and interpret the spectral-spatial data bands within a more precise computational time. Hence, in this work, we propose a 3D-2D convolutional neural network and transfer learning model where the early layers of the model exploit 3D convolutions to modeling spectral-spatial information. On top of it are 2D convolutional layers to handle semantic abstraction mainly. Toward simplicity and a highly modularized network for image classification, we leverage the ResNeXt-50 block for our model. Furthermore, improving the separability among classes and balance of the interclass and intraclass criteria, we engaged principal component analysis (PCA) for the best orthogonal vectors for representing information from HSIs before feeding to the network. The experimental result shows that our model can efficiently improve the hyperspectral imagery classification, including an instantaneous representation of the spectral-spatial information. Our model evaluation on five publicly available hyperspectral datasets, Indian Pines (IP), Pavia University Scene (PU), Salinas Scene (SA), Botswana (BS), and Kennedy Space Center (KSC), was performed with a high classification accuracy of 99.85%, 99.98%, 100%, 99.82%, and 99.71%, respectively. Quantitative results demonstrated that it outperformed several state-of-the-arts (SOTA), deep neural network-based approaches, and standard classifiers. Thus, it has provided more insight into hyperspectral image classification.

Pseudotext Injection and Advance Filtering of Low-Resource Corpus for Neural Machine Translation.

Scaling natural language processing (NLP) to low-resourced languages to improve machine translation (MT) performance remains enigmatic. This research contributes to the domain on a low-resource English-Twi translation based on filtered synthetic-parallel corpora. It is often perplexing to learn and understand what a good-quality corpus looks like in low-resource conditions, mainly where the target corpus is the only sample text of the parallel language. To improve the MT performance in such low-resource language pairs, we propose to expand the training data by injecting synthetic-parallel corpus obtained by translating a monolingual corpus from the target language based on bootstrapping with different parameter settings. Furthermore, we performed unsupervised measurements on each sentence pair engaging squared Mahalanobis distances, a filtering technique that predicts sentence parallelism. Additionally, we extensively use three different sentence-level similarity metrics after round-trip translation. Experimental results on a diverse amount of available parallel corpus demonstrate that injecting pseudoparallel corpus and extensive filtering with sentence-level similarity metrics significantly improves the original out-of-the-box MT systems for low-resource language pairs. Compared with existing improvements on the same original framework under the same structure, our approach exhibits tremendous developments in BLEU and TER scores.

The correlation between molecular pathological profiles and metabolic parameters of 18F-FDG PET/CT in patients with gastroesophageal junction cancer.

OBJECTIVE: The aim of this study was to evaluate PET/FDG metabolic parameters in locally advanced GEJC and correlate it with molecular pathological profiles. METHODS: We retrospectively analyzed data from 66 patients with a histopathological diagnosis of GEJC who had undergone 18F-FDG PET/CT before surgical resection. Maximum standardized uptake (SUVmax), mean standardized uptake (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary tumor were measured and calculated using the region of interest (ROI) technique. The relationship between metabolic parameters and the Lauren's classification, histologic differentiation, Ki-67 staining and positivity for human epidermal growth factor receptor 2 (HER2), c-Met, and epidermal growth factor receptor (EGFR) were investigated through immunohistochemical (IHC) analyses. RESULTS: Of the total 66 patients, significant differences were observed between intestinal and non-intestinal (mixed and diffuse) adenocarcinomas in SUVmax (8.23 ± 2.83 vs. 6.29 ± 2.41, P = 0.008), SUVmean (4.85 ± 1.47 vs. 3.93 ± 1.22, P = 0.017), MTV (24.96 cm3 vs. 8.90 cm3; P = 0.004), and TLG (97.38 cm3 vs. 37.09 cm3, P = 0.005) values. SUVmax, MTV, and TLG of moderately differentiated adenocarcinomas were significantly higher than those of the poorly differentiated ones. SUVmax was significantly higher in tissues with a higher Ki-67 index or in the c-MET-negative group (P = 0.045, P = 0.036). No significant correlation was found between metabolic parameters and the expression of HER2 or EGFR in GEJC. CONCLUSION: 18F-FDG PET/CT may be useful for predicting the molecular pathological profiles of GEJC and for determining appropriate therapeutic strategy.

Discriminated sgRNAs-Based SurroGate System Greatly Enhances the Screening Efficiency of Plant Base-Edited Cells.

The development of CRISPR/Cas9-mediated base editing has made genomic modification more efficient. However, selection of genetically modified cells from millions of treated cells, especially plant cells, is still challenging. In this study, an efficient surrogate reporter system based on a defective hygromycin resistance gene was established in rice to enrich base-edited cells. After step-by-step optimization, the Discriminated sgRNAs-based SurroGate system (DisSUGs) was established by artificially differentiating the editing abilities of a wild-type single guide RNA (sgRNA) targeting the surrogate reporter gene and an enhanced sgRNA targeting endogenous sites. The DisSUGs enhanced the efficiency of screening base-edited cells by 3- to 5-fold for a PmCDA1-based cytosine-to-tyrosine base editor (PCBE), and 2.5- to 6.5-fold for an adenine base editor (ABE) at endogenous targets. These targets showed editing efficiencies of <25% in the conventional systems. The DisSUGs greatly enhanced the frequency of homozygous substitutions and expanded the activity window slightly for both a PCBE and an ABE. Analyses of the total number of single-nucleotide variants from whole-genome sequencing revealed that, compared with the no-enrichment PCBE strategy, the DisSUGs did not alter the frequency of genome-wide sgRNA-independent off-target mutations, but slightly increased the frequency of target-dependent off-target mutations. Collectively, the DisSUGs developed in this study greatly enhances the efficiency of screening plant base-edited cells and will be a useful system in future applications.

[The research on bidirectional reflectance computer simulation of forest canopy at pixel scale].

Computer simulation is based on computer graphics to generate the realistic 3D structure scene of vegetation, and to simulate the canopy regime using radiosity method. In the present paper, the authors expand the computer simulation model to simulate forest canopy bidirectional reflectance at pixel scale. But usually, the trees are complex structures, which are tall and have many branches. So there is almost a need for hundreds of thousands or even millions of facets to built up the realistic structure scene for the forest It is difficult for the radiosity method to compute so many facets. In order to make the radiosity method to simulate the forest scene at pixel scale, in the authors' research, the authors proposed one idea to simplify the structure of forest crowns, and abstract the crowns to ellipsoids. And based on the optical characteristics of the tree component and the characteristics of the internal energy transmission of photon in real crown, the authors valued the optical characteristics of ellipsoid surface facets. In the computer simulation of the forest, with the idea of geometrical optics model, the gap model is considered to get the forest canopy bidirectional reflectance at pixel scale. Comparing the computer simulation results with the GOMS model, and Multi-angle Imaging SpectroRadiometer (MISR) multi-angle remote sensing data, the simulation results are in agreement with the GOMS simulation result and MISR BRF. But there are also some problems to be solved. So the authors can conclude that the study has important value for the application of multi-angle remote sensing and the inversion of vegetation canopy structure parameters.