Hongfei Lu

BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass

Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-network-aided dual quintic equation (BP-ANN-QE) model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, ‘Savannah’ and ‘Princess VII’). Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low) temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments). Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R2 values of germination prediction function could be significantly improved from about 0.6940–0.8177 (DQEM approach) to 0.9439–0.9813 (BP-ANN-QE). These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale of China, and suggested the optimum sowing regions and times for them.

Effects of elevated CO2 and temperature on Gynostemma pentaphyllum physiology and bioactive compounds.

Recently, an important topic of research has been how climate change is seriously threatening the sustainability of agricultural production. However, there is surprisingly little experimental data regarding how elevated temperature and CO2 will affect the growth of medicinal plants and production of bioactive compounds. Here, we comprehensively analyzed the effects of elevated CO2 and temperature on the photosynthetic process, biomass, total sugars, antioxidant compounds, antioxidant capacity, and bioactive compounds of Gynostemma pentaphyllum. Two different CO2 concentrations [360 and 720μmolmol(-1)] were imposed on plants grown at two different temperature regimes of 23/18 and 28/23°C (day/night) for 60days. Results show that elevated CO2 and temperature significantly increase the biomass, particularly in proportion to inflorescence total dry weight. The chlorophyll content in leaves increased under the elevated temperature and CO2. Further, electron transport rate (ETR), photochemical quenching (qP), actual photochemical quantum yield (Yield), instantaneous photosynthetic rate (Photo), transpiration rate (Trmmol) and stomatal conductance (Cond) also increased to different degrees under elevated CO2 and temperature. Moreover, elevated CO2 increased the level of total sugars and gypenoside A, but decreased the total antioxidant capacity and main antioxidant compounds in different organs of G. pentaphyllum. Accumulation of total phenolics and flavonoids also decreased in leaves, stems, and inflorescences under elevated CO2 and temperature. Overall, our data indicate that the predicted increase in atmospheric temperature and CO2 could improve the biomass of G. pentaphyllum, but they would reduce its health-promoting properties.

Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China

Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40°C with 5°C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25°C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15°C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy.

Biological characteristics, bioactive components and antineoplastic properties of sporoderm-broken spores from wild Cordyceps cicadae

BACKGROUND Cordyceps cicadae, an entomogenous fungus has been used as a dietary therapeutic in traditional Chinese medicine for several millennia, in the form of powders and decoction. However, wild C. cicadae is notably scarce. To date, there is still a lack of comprehensive and deep studies on the biological characteristics, chemical profiles and antineoplastic mechanisms of C. cicadae, especially its spores. AIM OF THE STUDY This study aimed to identify wild C. cicadae using rDNA-ITS sequences. Active constituents and volatile ingredients of C. cicadae sporoderm-broken spore powders (CCBSP) were elucidated using UPLC-ESI-Q-TOF-MS and GC-MS, respectively. The underlying anti-neoplastic mechanisms of CCBSP were further investigated in A549 lung carcinoma cells. RESULTS Molecular phylogenetic analysis of nuclear rDNA sequences indicated that wild C. cicadae belonged to Paecilomyces cicadae. Eight primary compounds from CCBSP were identified by MS fragmentation ions including nucleosides, cordycepic acid, cordycepin, beauvericin and myriocin. In total, forty-nine volatile components representing 99.56% of CCBSP were clearly identified. CCBSP exhibited antiproliferative effects on A549 cells with IC50 value of 125.54 ± 2.71 µg/ml, blocking the cell cycle in the G2/M phase. The nuclear morphology exhibited typical characteristics of apoptosis by Hoechst fluorescent stain. AnnexinV-FITC/PI staining revealed that the number of apoptotic cells increased after CCBSP treatment. Furthermore, immunofluorescence experiments indicated that CCBSP lowered the expressions of β-catenin and N-cadherin, which was accompanied by repressed Wnt/β-catenin signalling and activation of caspase-mediated apoptosis pathways. CONCLUSIONS rDNA-ITS sequencing enabled molecular identification of wild C. cicadae. Importantly, these findings provide the first evidence regarding the full-scale bioactive components and antineoplastic properties of CCBSP. These data highlight the significance of C. cicadae as a potential antineoplastic agent.

Isaria cicadae conidia possess antiproliferative and inducing apoptosis properties in gynaecological carcinoma cells

ABSTRACT Isaria cicadae is an entomogenous fungus that has been used as a traditional Chinese medicinal materials to treat different diseases, including cancer. However, Isaria cicadae conidia for inhibitory activity against breast cancer cells growth are still not systematically studied. The present aim was to elucidate the phytochemical composition of Isaria cicadae conidia and to explore relevant anti-cancer potential in gynaecological carcinoma MCF-7 and Hela cells. Isaria cicadae conidia were identified by UPLC-ESI-Q-TOF-MS: high performance liquid chromatography-electrospray/quadrupole time of flight tandem mass spectrometry technology. Eight main compounds were identified which are nucleosides, cordycepic acid, cordycepin, beauvericin and myriocin by MS fragmentation ions. The nuclear morphology indicated the typical characteristics of apoptosis by Hoechst staining. Annexin V/PI staining revealed that the number of apoptotic cells was increased by Isaria cicadae conidia treatment. Furthermore, Isaria cicadae conidia also induced the caspase-mediated mitochondrial apoptosis pathway. The findings suggest that the full-scale active ingredients highlight the significance of Isaria cicadae conidia as potential anti-cancer agent in China.