First report of Alternaria alternata causing leaf spot on Hibiscus mutabilis in China.

Hibiscus mutabilis L. is a deciduous shrub native to China. Because of its ornamental value and ecological value, it has been widely cultivated in many provinces of China (Shang et al. 2020). In October 2021, leaf spot on Cotton rose with about 80% disease incidence was observed in Jinan (116.9408° N, 36.6688° E), Shandong, China. Symptoms first appear on leaves with small dark brown spots surrounded by yellow halos, then become irregular necrotic spots with yellow halos. The diseased leaf samples were packed in paper bags and transferred to the laboratory for isolation. The infected leaves were firstly surface-sterilized for 45 seconds in 75% ethanol, 1 min in 1% sodium hypochlorite, and 1 min in 75% ethanol, then rinsed for 2 min in distilled water and blotted on dry sterile filter paper. Then samples were cut into 5 × 5 mm pieces using a double-edge blade, and transferred onto the surface of potato dextrose agar (PDA; 200 g potatoes, 20 g dextrose, 20 g agar per L) and malt extract agar (MEA; 30 g malt extract, 5 g mycological peptone, 15 g agar per L), and incubated at 25 ◦C to obtain the pure culture. After 7 days of incubation, greyish fungal colonies appeared on PDA. Single-spore isolation method was employed to recover the pure cultures for six isolates. The colonies initially produce light gray aerial hyphae, which turn dark gray as they mature. Conidiophores (n=50) single or in small groups, straight or curved, sometime geniculate, 20-50 nm long, with scars. Conidia (n=50) were obclavate to pyriform and measured 15 to 60 µm long, 4 to 16 µm wide with 0 to 3 longitudinal, and 1 to 6 transverse septa with short beak (2-30 µm). The morphological characters matched those of Alternaria alternata (Simmons 2007). DNA was extracted from the fungal colonies using a Ezup Column Fungi Genomic DNA Purification Kit. The internal transcribed spacer (ITS) region, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and translation elongation factor 1-alpha (tef1) were amplified with the primers ITS1/ITS4 (White et al. 1990), gpd1/gpd2 (Berbee et al. 1999) and EF1-728F/EF1-986R (Carbone & Kohn 1999). The obtained sequences were deposited in the GenBank (ITS: OM759881 and OM759882, GAPDH: ON376732 and ON376733, tef1: ON376730 and ON376731). The morphological characteristics and molecular analyses of the isolate matched the descriptin of A. alternata. To perform pathogenicity test, The seedlings of twenty 2-year-old potted H. mutabilis plants were inoculated by spraying conidial suspension at the concentration of 1 × 106 conidia/ml on both sides of leaves and ten plants sprayed with sterile water served as control. The test was repeated three times. All plants were covered with polyethylene covers and kept under the greenhouse at 26 ± 1 ℃. After six days, the inoculated plants showed the same symptoms as the original diseased plants and the controls remained asymptomatic. The fungal pathogen was reisolated from the artificially infected plants and confirmed as A. alternata based on morphocultural characteristics and PCR assays. The results indicated that A. alternata is a causal agent of the disease. The leaf spot disease of cotton rose caused by Nigrospora oryzae has already been reported from Sichuan, China (Han et al. 2021). To our knowledge, this is the first report on the presence of A. alternata affecting H. mutabilis plants. The identification could provide relevant information for adopting appropriate management strategies to control the disease.

Discovery of first active breeding den of Chinese mountain cat ( Felis bieti).

In mid-September 2018, during a field survey in Chiat'ung, Sanjiangyuan (Three-River-Source) Region, Tibetan Plateau, China, we discovered the first active breeding den of the Chinese mountain cat (Felis bieti), inhabited by one adult female and two kittens. Based on fieldwork over the following months, five breeding dens were discovered, and 33 sightings were recorded. In addition, at least five individuals were confirmed to inhabit this overlooked region, and much previously unknown information concerning this cat species and its ecology was revealed for the first time.

The application of machine learning algorithms in understanding the effect of core/shell technique on improving powder compactability.

This study systemically investigated the application of core/shell technique to improve powder compactability. A 28-run Design-of-Experiment (DoE) was conducted to evaluate the effects of the type of core and shell materials and their concentrations on tensile strength and brittleness index. Six machine learning algorithms were used to model the relationships of product profile outputs and raw material attribute inputs: response surface methodology (RSM), Support Vector Machine (SVM), and four different types of artificial neural networks (ANN), namely, Backpropagation Neural Network (BPNN), Genetic Algorithm Based BPNN (GA-BPNN), Mind Evolutionary Algorithm Based BPNN (MEA-BPNN), and Extreme Learning Machine (ELM). Their predictive and generalization performance were compared with the training dataset as well as an external dataset. The results indicated that the core/shell technique significantly improved powder compactability over the physical mixture. All machine learning algorithms being evaluated provided acceptable predictability and capability of generalization; furthermore, the ANN algorithms were shown to be more capable of handling convoluted and non-linear patterns of dataset (i.e. the DoE dataset in this study). Using these models, the relationship of product profile outputs and raw material attribute inputs were disclosed and visualized.