Fungus‐growing termites are among the most successful herbivorous animals and improve crop productivity and soil fertility. A range of symbiotic organisms can be found inside their nests. However, interactions of termites with these symbionts are poorly understood. This review provides detailed information on the role of multipartite symbioses (between termitophiles, termites, fungi, and bacteria) in fungus‐growing termites for lignocellulose degradation. The specific functions of each component in the symbiotic system are also discussed. Based on previous studies, we argue that the enzymatic contribution from the host, fungus, and bacteria greatly facilitates the decomposition of complex polysaccharide plant materials. The host–termitophile interaction protects the termite nest from natural enemies and maintains the stability of the microenvironment inside the colony.
Termitomyces species are wild edible mushrooms that possess high nutritional value and a wide range of medicinal properties. However, the cultivation of these mushrooms is very difficult because of their symbiotic association with termites. In this study, we aimed to examine the differences in physicochemical indices and microbial communities between combs with Termitomyces basidiomes (CF) and combs without Termitomyces basidiomes (CNF). High-performance liquid chromatography (HPLC), inductively coupled plasma optical emission spectrometry (ICP-OES), gas chromatography equipped with a flame ionization detector (GC-FID), some commercial kits, high-throughput sequencing of the 16s RNA, and internal transcribed spacer (ITS) were used. Humidity, pH, and elements, i.e., Al, Ba, Fe, Mn, Ni, S, Ca, and Mg were higher while amino acids particularly alanine, tyrosine, and isoleucine were lower in CF as compared to CNF. The average contents of fatty acids were not significantly different between the two comb categories. The bacterial genera Alistipes, Burkholderia, Sediminibacterium, and Thermus were dominant in all combs. Brevibacterium, Brevundimonas, and Sediminibacterium were significantly more abundant in CF. Basidiomycota and Ascomycota were also identified in combs. Termitomyces clypeatus, Termitomyces sp. Group3, and Termitomyces sp. were the most dominant species in combs. However, any single Termitomyces species was abundantly present in an individual comb.
Contemporarily, Long-Short Term Memory Model is becoming a widely applied machine learning models in time series data prediction. This article introduces background of LSTM Models and its concept with an application example to propose some perspectives and expectations on cryptocurrency price prediction (specifically, bitcoin in this paper). According to the results, the prediction results tally well with the real price, indicating the feasibility for the implementation of such a state-of-art machine learning models in cryptocurrency pricing. These results shed light on future development of price prediction based on machine learning for investors.
Globalization is one of the typical features of today's world development, and most of the countries in the world have a high degree of recognition for the expansion of openness, fueled by the strategic positioning of open development and the "liberal theory". Globalization creates an expanded global market and allows sources to be allocated efficiently while create pollution and increase the wealth gap at the same times. This article provides two positive and negative aspects of globalization to better understand what impacts globalization brought to human beings.
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