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This study was designed to explore differences in the competitive effects of wandering spiders under different cicada–spider ratios in tea plantations, and to provide a basis for the use of predatory wandering spiders for rational biological control. The population dynamics of Empoasca onukii and its natural enemies, the wandering spiders, were investigated in plantations of six tea cultivars, namely Anjibaicha, Huangshandayezhong, Longjing 43, Nongkangzao, Pingyangtezao and Wuniuzao, on the peak day of E. onukii emergence. Competition among wandering spiders under different cicada–spider ratios was analyzed using the gray correlation method and the competition coefficient method, and the results were then combined to derive a competition intensity index, which was then used to verify the accuracy of the results obtained. The results showed that the competition coefficients and competition intensity indices for low cicada–spider ratios were greater than those for high cicada–spider ratios on the peak day of E. onukii feeding, the differences between the two were significant at the t0.20 level, and the most competitive wandering spiders at low and high ratios were Clubiona japonicola and Clubiona reichlini, respectively, indicating that the cicada–spider ratio had some influence on the competitive effect of the wandering spiders. The results provide a scientific basis for the study of competition between species of wandering spiders and the utilization of competitive wandering spiders against E. onukii for the protection of tea crops. [ABSTRACT FROM AUTHOR]

In order to clarify the prediction accuracy of eight models for predicting the peak occurrence of the first generation larvae of Dendrolimus punctatus and provide basis for the pest control, a catastrophe prediction model was established based on the peak occurrence of the first generation larvae of Dendrolimus punctatus in Qianshan City, Anhui Province from 1983 to 2016, and compared with other seven prediction models. Comparing the forecasting results in 2015 and 2016 with actual value and taking 1 head/plant as the error standard, the errors of multiple regression models with six factors as independent variables, namely, peak occurrence of pupae in overwintering generation, peak occurrence of eggs in the first generation, cumulative population in overwintering generation, peak occurrence of adults in overwintering generation, rainfall in early April and parasitic rate of Trichogrammatid in the first generation eggs of Dendrolimus punctatus, were 0.21 heads/plant and 0.23 heads/plant with accuracy rate of 100%. The errors of stepwise regression model with the same six factors were 0.23 head/plant and 0.29 head/plant. The prediction accuracy of artificial BP neural network model, Markov chain model, contingency table model, stationary time series model, and fuzzy comprehensive evaluation model was 100%, but variance period extrapolation model had an accuracy rate of 88%. The accuracy of catastrophe prediction model was related to the selection of catastrophe threshold. Comprehensive comparison of the above eight models, multiple regression, stepwise regression, artificial BP neural network, Markov chain model, stationary time series model, and catastrophe prediction model were more accurate. [ABSTRACT FROM AUTHOR]

Empoasca onukii Matsuda is an important pest widely distributed in tea areas in China, which greatly affects tea production and quality. The long-term use of chemical control measures will cause environmental pollution. To better utilize wandering spiders that have strong competitive effects on the predation of E. onukii, we conducted a path analysis between the populations of E. onukii and wandering spiders in 3 tea plantations in 2020 and 2021. The wandering spider species that had the greatest direct and indirect effects on the population of E. onukii were analyzed by the magnitude of the path coefficient. Then, a gray system analysis was conducted to determine the closeness of the populations of different wandering spiders to E. onukii by the magnitude of gray correlation coefficient. Finally, the competition coefficients were calculated to determine the competitiveness of wandering spiders. In addition, considering the influence of the number of E. onukii on the interspecific competition of wandering spiders, the gray correlation coefficient and competition coefficient were combined to derive the competition intensity index, which was used to analyze the competitiveness of wandering spiders in a comprehensive manner. The highest competition coefficients in 2020 and 2021 were found for Ebrechtella tricuspidata Fabricius (Araneae: Thomisidae) (X2, 0.5329) and Clubiona reichlini Fabricius (Araneae: Clubionidae) (X4, 0.8475), respectively. The magnitude of the competition intensity index showed that the most competitive wandering spider in 2020 and 2021 was E. tricuspidata (X2, 0.5692) and C. reichlini (X4, 0.8892), respectively. The least competitive spider in both years was Plexippus setipes Karsch (Araneae: Salticidae) (X7). The more competitive the wandering spider is, the closer it is to E. onukii in terms of numbers, and the more dominant it is in feeding on E. onukii. By reasonably protecting and utilizing the competitive E. tricuspidata (X2) and C. reichlini (X4), we can achieve sustainable and effective control of E. onukii. [ABSTRACT FROM AUTHOR]

Our aim was to study the close relationship between the number of Empoasca onukii and its natural enemies in tea plantations to provide a scientific basis for the control of E. onukii. The relationship between the number of E. onukii and its main natural enemies in tea plantations in Hefei, China, were compared by gray relational analysis, and then the relationships between seven meteorological factors and the populations of E. onukii were analyzed by the method of path analysis. The results showed that among the top six natural enemies most closely related to the population of E. onukii, two natural enemies were the same in the three years of study and four natural enemies were the same in any two years of the study, and that the natural enemy species closely related to the population of E. onukii varied greatly among years. The direct effect of monthly average relative humidity was the greatest effect in 2020 and the direct effect of monthly average temperature was the greatest effect in 2021, with both being negative in terms of their effect on the population size of E. onukii. The direct effect of the number of days of rainfall was the greatest effect in both 2021 and 2022, and the indirect effect of monthly sunshine hours through rainfall days was the greatest effect on the population of E. onukii. The highest temperatures in early and mid‐August 2022 were higher than in previous years, which had an impact on the growth and development of subsequent E. onukii. [ABSTRACT FROM AUTHOR]

Honey bees (Apis cerana Fabricius) were collected from 195 colonies at seven different localities spanning the main beekeeping areas in Huangshan. Morphometric methods were used to measure seven standard morphometric characters, and these bees were compared to samples from the Henan, Shandong, and Yunnan provinces. Principal component analysis of the total Huangshan database yielded two clusters: bees from Jinxian and Jixixian, and those from other localities. Within the latter cluster, discriminant and hierarchical cluster analyses revealed overlapping regional sub-clusters: bees from Huangshanqu, Qimenxian, Huizhouqu, and Shexian, and those from Yixian. Significant differences between the means of the three clusters were demonstrated using Wilks’ lambda statistic. Morphocluster separation was related to altitude differences. Moreover, we noted some regions with high intercolonial variance, suggesting introgression among these defined honeybee populations.

This paper aims to analyse how to effectively protect and scientifically utilize the natural enemies of Pentatomidae in tea plantations in the peak period. In this paper, the spatial, quantitative and temporal relationships between Pentatomidae and its natural enemies in "Pingyangtezao", "Wuniuzao", "Huangshandayezhong", "Anjibaicha","Longjing 43″ and "Nongkangzao" tea gardens in Hefei, China were comprehensively compared and analyzed with the methods of geostatistical analysis, gray system analysis and temporal ecotone overlap index analysis. According to the size of the closeness index, the top three natural enemies with the largest sum of spatial, quantitative, and temporal closeness indexes in the six tea gardens were Clubiona japonicola (X7, 16.514), Oxyopes sertatus (X5, 16.390) and Xysticus ephippiafus (X2, 16.270). The size of the ratio between the number of Pentatomidae individuals and natural enemies largely determines whether the natural enemies follow Pentatomidae pests closely or not. The better the growth of tea plantations and the richer the diversity of pest species, the more accurately the predation preference of natural enemies can be reflected. In the control of Pentatomidae, specific analysis should be made according to the occurrence characteristics of target pests in different tea plantations and the basic attributes of tea plantations (species, growth, population characteristics of natural enemies and pests, etc.) in order to scientifically utilize its natural enemies. [ABSTRACT FROM AUTHOR]

In order to reasonably protect and utilize natural enemies for comprehensive control of Frankliniella intonsa, a systematic investigation was conducted on F. intonsa and natural enemies in tea gardens in Hefei, Anhui Province, and the spatial following effect of natural enemies on F. intonsa was studied. The semi‐variogram of natural enemies and thrips was obtained by the geostatistics method, and the correlation degree between them was analyzed by the grey correlation degree analysis method. The higher the correlation degree between natural enemies and F. intonsa, the closer the relationship between natural enemies and F. intonsa was. In this study, we analyzed the relationship between 11 natural enemies of F. intonsa in tea gardens from October 28, 2020 to November 20, 2020 and from August 22, 2021 to November 19, 2021. The top five natural enemies with the largest closeness index to F. intonsa in Huangshan Dayezhong tea garden are Oxyopes sertatus, Theridion octomaculatum, Plexippus setipes, Xysticus ephippiafus and Erigonidium graminicolum. The top five natural enemies with the largest closeness index to F. intonsa in Pingyang Tezao tea garden are Erigonidium graminicolum, Plexippus setipes, Xysticus ephippiafus, Oxyopes sertatus and Clubiona japonicola. Among the top five natural enemies in the two tea gardens, those in common are Oxyopes sertatus, Erigonidium graminicolum, Xysticus ephippiafus and Plexippus setipes. According to the summation of the closeness index and the sum of the serial numbers, the top five natural enemies closely related to the spatial following of F. intonsa in the tea gardens were Oxyopes sertatus, Plexippus setipes, Erigonidium graminicolum, Xysticus ephippiafus and Theridion octomaculatum. One of the factors closely related to the space of F. intonsa in the same tea garden was the ratio of F. intonsa to a certain natural enemy. The smaller the ratio was, the closer the following relationship was. The results of this study provide scientific basis for biological control and natural enemy protection of F. intonsa. [ABSTRACT FROM AUTHOR]

To identify the natural enemy species which are close to Ricanidae in spatial relationship and to provide scientific basis for biological control and reasonable protection of natural enemies, geostatistics and the angular cosine coefficient method were used to analyze the population of Ricanidae in their prime and their natural enemies in the Anji white tea garden, the Longjing43 tea garden, the Nongkangzao tea garden, the Pingyangtezao tea garden and the Wuniuzao tea garden. The spatial relationship between six natural enemies and Ricanidae was also studied. The angular cosine coefficients were normalized to obtain the intimacy index. According to the sum of the intimacy index and the serial number of the intimacy index of each natural enemy in five tea gardens, the following conclusions could be drawn: the top three natural enemies closely related to Ricanidae in spatial relationship were Clubiona reichlini, Clubiona japonicola and Misumenops tricuspidatus; at least two species of the top three natural enemies in each tea garden were the same as the top three natural enemies in the comprehensive analysis of the five tea gardens; one of the factors that determined the spatial relationship between natural enemies and Ricanidae was the ratio of the number of Ricanidae and natural enemies. The results of this study identified the spider species of natural enemies which should be rationally used and protected in the five tea gardens. [ABSTRACT FROM AUTHOR]

To improve the accuracy of forecasting the peak period of Dendrolimus punctatus, fuzzy comprehensive evaluation method, contingency table analysis and BP neural network were used to predict peak period of D. punctatus for the first generation egg from 1983 to 2016 in Qianshan County. The forecasted values for the three methods were in 2017 and 2018 were consistent with the actual results. The historical coincidence rate for fuzzy comprehensive evaluation method from 1983 to 2016 was 93.94% and for contingency table analysis was 100%. When taking into consideration the standard error of BP neural network was 4 d, the historical coincidence rate was 100%. Among three methods, The screening predictors closely related to forecasted quantity and the classifcation standard were the key for improving the accuracy of forecast. [ABSTRACT FROM AUTHOR]

The purpose of this study was to comprehensively evaluate the dominant natural enemies of Breuipalpus oboyats in autumn and winter tea gardens. The relationship between natural enemies and B.oboyats in time and space was studied by niche analysis and geostatistics combined with angular cosine coefficient method. According to the comparison of the sum of closeness index, it is found that the top three natural enemies (from large to small) that are most closely related to B.oboyats in the "Nongkangzao" tea garden are Plexippus paykulli, Clubiona japonicola, and Xysticus ephippiafus; "Pingyangtezao" tea garden are X.ephippiafus, Plexippus setipet, and Oxyopes sertatus. In front of the two tea gardens, one of the three natural enemies is the same. The evaluation of the comprehensive closeness index of the two tea gardens shows that the top three natural enemies most closely related to B.oboyats are X.ephippiafus, P.paykulli, and O.sertatus. X.ephippiafus is the most important natural enemy of B.oboyats in tea gardens in autumn and winter; the dominant natural enemies of the same pests in different varieties of tea gardens in the same area are often different. [ABSTRACT FROM AUTHOR]

The closeness of the spatial relationship between natural enemies and target pests directly affects the control effect of natural enemies on target pests. Therefore, we here use both geostatistics and cluster sample variance analysis methods to investigate the six kinds of natural enemies and pests in Huangshan large‐leaf tea garden at fall and winter of 2020. In this study, we firstly use the geostatistics method to calculate the geostatistical semivariogram variation range and then conduct the Grey correlation analysis on six main natural enemies of Empoasca onukii Matsuda. Our results indicate that the larger the correlation value is, the closer the natural enemy is to the target pest in space, and the top three natural enemies Clubiona japonicola, Oxyopes sertatus and Tetragnatha squamata are more closely related to Empoasca onukii Matsuda in space through the comprehensive judgment. Secondly, we use the cluster sample variance analysis method to calculate the basic sample square number and then perform the Grey correlation analysis on them. Our findings demonstrate that the top three natural enemies O. sertatus, T. squamata and Erigonidium graminicolum are more closely related to Empoasca onukii Matsuda in space via the comprehensive judgment. Remarkably, both O. sertatus and T. squamata have been revealed to be vital enemies of the Empoasca onukii Matsuda in tea garden. Overall, our study not only uncovers the spatial relationship between natural enemies and Empoasca onukii Matsuda, but also provides a scientific basis for further protecting and utilizing natural enemies to control pests in the tea garden. [ABSTRACT FROM AUTHOR]

To improve the accuracy of forecasting the peak period of Dendrolimus punctatus, stationary time series, periodic distance method, stepwise regression model and the Bayes discriminant analysis were used. RSME value, kappa coefficient and accuracy were used as evaluation criteria to predict the peak period for the second generation egg of D. punctatus with over 33 years from 1983 to 2016 in Qianshan County, Anhui Province. The predictions of these models were verified in 2017 and 2018. The prediction of the stationary time model and Bayes discriminant analysis for 2017 was one level lower than the actual result and for 2018 was one level higher than the actual result, while the prediction of the periodic distance method was identical to the actual result for 2017 and greatly different from the actual result for 2018. The accuracy for stationary time series (RMSE = 0.92 kappa = 0.76) and periodic distance method (RMSE = 2.96, kappa = 0.81) from 1983 to 2018 were 87.88% and 85.71%, respectively. When taking into consideration the standard error was based on differential, the accuracy for the prediction of stepwise regression model (RMSE = 0.25, kappa = 1.00) from 1983 to 2018 was 100%. The accuracy of Bayes discriminant method (RMSE = 0.71, kappa = 0.96) was 97.14%. Comparatively speaking, the stepwise regression model and Bayes discriminant analysis method were better than the stationary time series and periodic distance method in RMSE value, kappa coefficient and accuracy. So they were relatively ideal forecast methods. [ABSTRACT FROM AUTHOR]

To improve the accuracy of forecasting the peak occurrence of Dendrolimus punctatus Walker, we here used the Bayes discriminant analysis to predict this peak occurrence for the first and second generation of Dendrolimus punctatus larvae based on these data from 1983 to 2016 in Qianshan County, Anhui Province. Our present results showed that this discriminant equation for the first generation was as follows: f(1) = −3.2588‐6.2700x1 + 1.2870x2 + 0.7920x3 + 0.4152x4; f(2) = −14.5215‐8.5710x1 + 2.9790x2 + 2.0280x3 + 0.5031x4; f(3) = −3.5264; f(4) = −66.8312‐12.5216x1 + 5.1740x2 + 4.7162x3 + 0.6033x4. And that the prediction accuracy for the first generation was 97.22%. Whilst this discriminant equation for the second generation was as follows: f(1) = −3.536‐1.192x5 + 1.338x6 + 0.638x7−0.025x8; f(2) = −7.317‐1.337x5 + 4.240x6 + 1.010x7−0.295x8; f(3) = −16.488‐3.192x5 + 4.955x6 + 1.900x7–0.411x8; f(4) = −34.502‐4.184x5 + 7.484x6 + 2.583x7–0.443x8. The prediction accuracy for the second generation was 85.71%. Overall, our findings revealed that the Bayes discriminant analysis could screen out key factors to significantly improve the prediction accuracy of peak occurrence of Dendrolimus punctatus larvae. [ABSTRACT FROM AUTHOR]

In this paper, the dynamical behaviors for a five-dimensional virus infection model with Latently Infected Cells and Beddington–DeAngelis incidence are investigated. In the model, four delays which denote the latently infected delay, the intracellular delay, virus production period and CTL response delay are considered. We define the basic reproductive number and the CTL immune reproductive number. By using Lyapunov functionals, LaSalle's invariance principle and linearization method, the threshold conditions on the stability of each equilibrium are established. It is proved that when the basic reproductive number is less than or equal to unity, the infection-free equilibrium is globally asymptotically stable; when the CTL immune reproductive number is less than or equal to unity and the basic reproductive number is greater than unity, the immune-free infection equilibrium is globally asymptotically stable; when the CTL immune reproductive number is greater than unity and immune response delay is equal to zero, the immune infection equilibrium is globally asymptotically stable. The results show that immune response delay may destabilize the steady state of infection and lead to Hopf bifurcation. The existence of the Hopf bifurcation is discussed by using immune response delay as a bifurcation parameter. Numerical simulations are carried out to justify the analytical results. [ABSTRACT FROM AUTHOR]

In order to provide scientific basis for the comprehensive pest management in tea garden, the status of the main natural enemies, Tetragnatha squamata and T. maxillosa in their prey on neutral insects, mosquitoes and mayflies in tea garden from spring to summer was verified. The quantity, temporal and spatial relationships of mosquitoes, mayflies, five main pests and T. squamata , T. maxillosa were analyzed by angle cosine coefficient, temporal niche similarity coefficient and the ranges of spatial dependence of theoretical model of semivariogram in geostatistics. The assay was conducted in Huangshan large-leafed tea gardens in 2013 and 2014, Anji white tea gardens in 2014 and 2015, and, Wuniuzao and Baihaozao tea gardens. The results showed that the top three preys, Monolepta hieroglyhpica , thrips and mosquitoes, had a close relationship with two types of natural enemies. The top three preys had a close relationship with T. squamata , mayflies were the sixth. The top three prey M. hieroglyhpica , thrips and mosquitoes had a close relationship with T. maxillosa. Neutral insects, mosquitoes were the main prey of the potential natural enemies, T. squamata and T. maxillosa from March to May in tea gardens, mayflies were the fifth to sixth. There was no significant difference in the individuals of mosquitoes in different tea gardens. Unlabelled Image • Monolepta. hieroglyhpica , thrips and mosquitoes were related to the natural enemies. • Neutral insects, and mosquitoes were the main prey of the main natural enemies. • There was no great difference in the number of mosquitoes in different tea gardens. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A) modification, as one of the commonest post-transcription modifications in RNAs, has been reported to be highly related to many biological processes. Over the past decade, several tools for m6A sites prediction of Saccharomyces cerevisiae have been developed and are freely available online. However, the quality of predictions by these tools is difficult to quantify and compare. In this study, an independent dataset M6Atest6540 was compiled to systematically evaluate nine publicly available m6A prediction tools for S. cerevisiae. The experimental results indicate that RAM-ESVM achieved the best performance on M6Atest6540; however, most models performed substantially worse than their performances reported in the original papers. The benchmark dataset Met2614, which was used as the training dataset for the nine methods, were further analyzed by using a position bias index. The results demonstrated the significantly different bias of dataset Met2614 compared with the RNA segments around m6A sites recorded in RMBase. Moreover, newMet2614 was collected by randomly selecting RNA segments from non-redundant data recorded in RMBase, and three different kinds of features were extracted. The performances of the models built on Met2614 and newMet2614 with the features were compared, which shows the better generalization of models built on newMet2614. Our results also indicate the position-specific propensity-based features outperform other features, although they are also easily over-fitted on a biased dataset. [ABSTRACT FROM AUTHOR]

Tea is one of the most popular non-alcoholic beverages consumed worldwide. Numerous bioactive constituents of tea were confirmed to possess healthy benefits via the mechanisms of regulating gene expressions or protein activities. However, a complete interacting profile between tea bioactive compounds (TBCs) and their target genes is lacking, which put an obstacle in the study of healthy function of tea. To fill this gap, we developed a database of target genes of TBCs (TBC2target, http://camellia.ahau.edu.cn/TBC2target) based on a pharmacophore mapping approach. In TBC2target, 6,226 interactions between 240 TBCs and 673 target genes were documented. TBC2target contains detailed information about each interacting entry, such as TBC, CAS number, PubChem CID, source of compound (e.g., green, black), compound type, target gene(s) of TBC, gene symbol, gene ID, ENSEMBL ID, PDB ID, TBC bioactivity and the reference. Using the TBC-target associations, we constructed a bipartite network and provided users the global network and local sub-network visualization and topological analyses. The entire database is free for online browsing, searching and downloading. In addition, TBC2target provides a BLAST search function to facilitate use of the database. The particular strengths of TBC2target are the inclusion of the comprehensive TBC-target interactions, and the capacity to visualize and analyze the interacting networks, which may help uncovering the beneficial effects of tea on human health as a central resource in tea health community. [ABSTRACT FROM AUTHOR]

Insect pests reduce yield and cause economic losses, which are major problems in agriculture. Parasitic wasps are the natural enemies of many agricultural pests and thus have been widely used as biological control agents. Plants, phytophagous insects and parasitic wasps form a tritrophic food chain. Understanding the interactions in this tritrophic system should be helpful for developing parasitic wasps for pest control and deciphering the mechanisms of parasitism. However, the genomic resources for this tritrophic system are not well organized. Here, we describe the WaspBase, a new database that contains 573 transcriptomes of 35 parasitic wasps and the genomes of 12 parasitic wasps, 5 insect hosts and 8 plants. In addition, we identified long non-coding RNA, untranslated regions and 25 widely studied gene families from the genome and transcriptome data of these species. WaspBase provides conventional web services such as Basic Local Alignment Search Tool, search and download, together with several widely used tools such as profile hidden Markov model, Multiple Alignment using Fast Fourier Transform, automated alignment trimming and JBrowse. We also present a collection of active researchers in the field of parasitic wasps, which should be useful for constructing scientific networks in this field. [ABSTRACT FROM AUTHOR]

• Estimation of biomass by random forest combined with Monte Carlo simulations. • Using Monte Carlo method to reduce the error caused by residual variability. • Quantifying the effect of different variable combinations on model uncertainty. Estimates of aboveground biomass (AGB) in forests and the associated uncertainty are important pieces of information outlined in the United Nations Framework Convention on Climate Change. Traditional integrated surveying methods require high inputs of manpower and material resources, whereas remote sensing technology can be used to improve the efficiency of forest resources surveying at a lower measurement cost. Previous studies have primarily utilized spectral information and textural features as the main modeling factors for AGB prediction models, however, few studies have examined how different modeling factors affect prediction accuracy in detail. At the same time, uncertainties in AGB are usually determine by a data set producing a single prediction result, ignoring the model uncertainty due by residual variability in the prediction results. To solve the above problems, this study focuses on Milin County of the Tibet Autonomous Region of China as the research area and uses data from the Landsat Operational Land Imager, digital elevation models (DEM), and the national forest inventory (NFI) to extract and analyze spectral information, textural features, and terrain factors. Four regression models based on different combinations of variables were established using a combination of random forest and Monte Carlo simulation methods (RF-MC), and the uncertainties of each model were determined. The purpose of this paper is to study the influence of different types of variable factors on AGB inversion and to measure the model uncertainty resulting from changes in the variable factors. The coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the fit and accuracy of the model, while the relative root mean square error (rRMSE) was used to quantify the uncertainty. The results show that the degree of fitting and accuracy are greatest for the RF-MC model based on three kinds of variables (spectrum and plot data + DEM + texture characteristics) the best (R2 = 0.85, RMSE = 11.76 t /ha, MAE = 8.49 t /ha, rRMSE = 5.97 %), which performed better than the RF-MC model based on two variables (spectrum and plot data + texture characteristics, R2 = 0.78, RMSE = 13.93 t /ha, MAE = 10.01 t /ha, rRMSE = 7.07 % or spectrum and plot data + DEM, R2 = 0.69, RMSE = 16.75 t /ha, MAE = 14.62 t /ha, rRMSE = 8.49 %) and the RF-MC model based on a single variable (spectrum and plot data, R2 = 0.49, RMSE = 21.39 t /ha, MAE = 19.84 t /ha, rRMSE = 10.86 %). The range of the predicted model value is 150.6–227.8 t /ha, which is close to the actual value. The RF-MC method based on multifeature is demonstrated to be effective in the practical application of AGB inversion, and different types of modeling variables have a certain impact on the accuracy of AGB inversion. This study provides a theoretical basis for improving the accuracy of AGB estimates and the selection of modeling variables, while also contributing to continued improvements in remote sensing-based systems for monitoring forest resources. [ABSTRACT FROM AUTHOR]

AbstractIn order to explore the interspecific competition of wandering spiders feeding on Ectropis obliqua in tea plantations, the population dynamics of E. obliqua and its natural enemies in Nongkangzao and Pingyangtezao tea plantations in 2021 were investigated. Gray correlation method, Fuzzy graded method and competition coefficient method were used to analyse the competitive effects of different spiders and to identify the most competitive spiders, and then gray correlation and competition coefficient were combined to derive the concept of competition intensity index to verify the accuracy of the results. The results showed a strong correlation between the number of Clubiona japonicola and E. obliqua among the wandering spiders, with the least average competition coefficient for Oxyopes sertatus and the greatest average competition intensity index for C. japonicola in both tea gardens. Spiders with larger populations were not necessarily more competitive. C. japonicola was the most competitive and O. sertatus was the least competitive when feeding on E. obliqua. The Fuzzy graded frequencies are significantly more concentrated in distribution and have higher data validity than the equal graded frequencies, and pre-processing the data with Fuzzy graded method can improve the accuracy of the data when performing interspecific relationship analysis. The results of the study provided a scientific basis for the rational protection and use of wandering spiders to control E. obliqua. [ABSTRACT FROM AUTHOR]

• The databases, features, algorithms and existing predictors of RNA methylation sites were summarized. • The development of computational models to identify RNA methylation sites will help understand their functional mechanisms. • The features were categorized into three classes whose combinations could improve the performance. • Advanced deep learning algorithms can help develop models to identify different types of methylation in multiple species. Thanks to the tremendous advancement of deep sequencing and large-scale profiling, epitranscriptomics has become a rapidly growing field. As one of the most important parts of epitranscriptomics, ribonucleic acid (RNA) methylation has been focused on for years for its fundamental role in regulating the many aspects of RNA function. Thanks to the big data generated in sequencing, machine learning methods have been developed for efficiently identifying methylation sites. In this review, we comprehensively explore machine learning based approaches for predicting 10 types of methylation of RNA, which include m6A, m5C, m7G, 5hmC, m1A, m5U, m6Am, and so on. Firstly, we reviewed three main aspects of machine learning which are data, features and learning algorithms. Then, we summarized all the methods that have been used to predict the 10 types of methylation. Furthermore, the emergent methods which were designed to predict multiple types of methylation were also reviewed. Finally, we discussed the future perspectives for RNA methylation sites prediction. [ABSTRACT FROM AUTHOR]

Abstract In order to improve the accuracy of forecasting the catastrophe occurrence of Dendrolimus punctatus based on disaster index, the GM (1, 1) method was used to develop catastrophe prediction models of overwintering generation, first generation and second generation based on occurrence data which spanned from 1989 to 2016 in Qianshan City, Anhui Province. The GM (1, 1) catastrophe prediction models for each generation were ω ( 1 ) ( k + 1 ) = 6 .95092 e 0 . 415619 k − 5.95092 , ω ( 1 ) ( k + 1 ) = 5.107843 e 0 . 492974 k − 4 . 107843 and ω ( 1 ) ( k + 1 ) = 1 6.287938 e 0 .248843 k − 1 5.287938 . The years of catastrophe occurrence for each generation were obtained. The t values by t test between the predicted values and the observed values were 0.2873, 0.0335 and 0.1734 respectively, which were all less than t0.05, and the differences were not significant. The mean accuracies of predicted values were 82.50%, 97.88% and 93.16%. The predicted catastrophe about each generation will take place in 2031, 2026 and 2022 respectively. The catastrophe prediction method was simple to calculate and had high accuracy. It was an ideal method for predicting the catastrophe occurrence of Dendrolimus punctatus based on disaster index and provided a reference for other pests to predict catastrophe. [ABSTRACT FROM AUTHOR]

Considerable studies demonstrate that plant transcription factors (TFs) play key regulatory roles in abiotic/biotic stress conditions, such as drought and pathogen attack. However, there is no effort dedicated to curate experimentally validated stress-TF regulatory relationships from these individual reports into a central database, which put an obstacle in the exploration of stress-TF regulations in plants. To address this issue, we presented a literature-curated database ‘Stress2TF’ that currently documented 1533 regulatory relationships between 71 abiotic/biotic stresses and 558 TFs in 47 plant species. Each entry in Stress2TF contains detailed information about a stress-TF relationship such as plant name, stress name, TF and brief description of stress-TF relationship. Stress2TF provided a user-friendly interface for entry browse, search and download. In addition, a submission page and several useful tools (e.g., BLAST, network visualization) were integrated. Stress2TF may be a valuable resource for the research of stress-TF regulatory mechanisms in plants. Stress2TF is available at http://csgenomics.ahau.edu.cn/Stress2TF . [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A) is one of the most important RNA modifications, which is involved in many biological activities. Computational methods have been developed to detect m6A sites due to their high efficiency and low costs. As one of the most widely utilized model organisms, many methods have been developed for predicting m6A sites of Saccharomyces cerevisiae . However, the generalization of these methods was hampered by the limited size of the benchmark datasets. On the other hand, over 60,000 low resolution m6A sites and more than 10,000 base resolution m6A sites of Saccharomyces cerevisiae are recorded in RMBase and m6A-Atlas, respectively. The base resolution m6A sites are often obtained from low resolution results by post calibration. In view of these, we proposed a two-stage deep learning method, named MTDeepM6A-2S, to predict RNA m6A sites of Saccharomyces cerevisiae based on RNA sequence information. In the first stage, a multi-task model with convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) deep framework was built to not only detect the low resolution m6A sites but also assign a reasonable probability for the predicted site. In the second stage, a transfer-learning strategy was used to build the model to predict the base resolution m6A sites from those low resolution m6A sites. The effectiveness of our model was validated on both training and independent test sets. The results show that our model outperforms other state-of-the-art models on the independent test set, which indicates that our model holds high potential to become a useful tool for epitranscriptomics analysis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Zhao, Cheng, Bi and Zhu.)

As one of the most important posttranslational modifications (PTMs), protein lysine glycation changes the characteristics of the proteins and leads to the dysfunction of the proteins, which may cause diseases. Accurately detecting the glycation sites is of great benefit for understanding the biological function and potential mechanism of glycation in the treatment of diseases. However, experimental methods are expensive and time-consuming for lysine glycation site identification. Instead, computational methods, with their higher efficiency and lower cost, could be an important supplement to the experimental methods. In this study, we proposed a novel predictor, BERT-Kgly, for protein lysine glycation site prediction, which was developed by extracting embedding features of protein segments from pretrained Bidirectional Encoder Representations from Transformers (BERT) models. Three pretrained BERT models were explored to get the embeddings with optimal representability, and three downstream deep networks were employed to build our models. Our results showed that the model based on embeddings extracted from the BERT model pretrained on 556,603 protein sequences of UniProt outperforms other models. In addition, an independent test set was used to evaluate and compare our model with other existing methods, which indicated that our model was superior to other existing models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Liu, Wang, Cheng, Bi and Zhu.)

Insect pests reduce yield and cause economic losses, which are major problems in agriculture. Parasitic wasps are the natural enemies of many agricultural pests and thus have been widely used as biological control agents. Plants, phytophagous insects and parasitic wasps form a tritrophic food chain. Understanding the interactions in this tritrophic system should be helpful for developing parasitic wasps for pest control and deciphering the mechanisms of parasitism. However, the genomic resources for this tritrophic system are not well organized. Here, we describe the WaspBase, a new database that contains 573 transcriptomes of 35 parasitic wasps and the genomes of 12 parasitic wasps, 5 insect hosts and 8 plants. In addition, we identified long non-coding RNA, untranslated regions and 25 widely studied gene families from the genome and transcriptome data of these species. WaspBase provides conventional web services such as Basic Local Alignment Search Tool, search and download, together with several widely used tools such as profile hidden Markov model, Multiple Alignment using Fast Fourier Transform, automated alignment trimming and JBrowse. We also present a collection of active researchers in the field of parasitic wasps, which should be useful for constructing scientific networks in this field.

Background: The Mount Huang eastern honey bees ( Apis cerana ) are an endemic population, which is well adapted to the local agricultural and ecological environment. In this study, the genetic structure of seven eastern honey bees ( A. cerana ) populations from Mount Huang in China were analyzed by SSR (simple sequence repeat) markers., Results: The results revealed that 16 pairs of primers used amplified a total of 143 alleles. The number of alleles per locus ranged from 6 to 13, with a mean value of 8.94 alleles per locus. Observed and expected heterozygosities showed mean values of 0.446 and 0.831 respectively. UPGMA cluster analysis grouped seven eastern honey bees in three groups., Conclusion: The results obtained show a high genetic diversity in the honey bee populations studied in Mount Huang, and high differentiation among all the populations, suggesting that scarce exchange of honey bee species happened in Mount Huang. Our study demonstrated that the Mount Huang honey bee populations still have a natural genome worth being protected for conservation.