A Polynomial Time Algorithm for Deciding Branching Bisimilarity on Totally Normed BPA

Strong bisimilarity on normed BPA is polynomial-time decidable, while weak bisimilarity on totally normed BPA is NP-hard. It is natural to ask where the computational complexity of branching bisimilarity on totally normed BPA lies. This paper confirms that this problem is polynomial-time decidable. To our knowledge, in the presence of silent transitions, this is the first bisimilarity checking algorithm on infinite state systems which runs in polynomial time. This result spots an instance in which branching bisimilarity and weak bisimilarity are both decidable but lie in different complexity classes (unless NP=P), which is not known before. The algorithm takes the partition refinement approach and the final implementation can be thought of as a generalization of the previous algorithm of Czerwi\'{n}ski and Lasota. However, unexpectedly, the correctness of the algorithm cannot be directly generalized from previous works, and the correctness proof turns out to be subtle. The proof depends on the existence of a carefully defined refinement operation fitted for our algorithm and the proposal of elaborately developed techniques, which are quite different from previous works.Comment: 32 page

Branching Bisimilarity on Normed BPA Is EXPTIME-complete

We put forward an exponential-time algorithm for deciding branching bisimilarity on normed BPA (Bacis Process Algebra) systems. The decidability of branching (or weak) bisimilarity on normed BPA was once a long standing open problem which was closed by Yuxi Fu. The EXPTIME-hardness is an inference of a slight modification of the reduction presented by Richard Mayr. Our result claims that this problem is EXPTIME-complete.Comment: We correct many typing errors, add several remarks and an interesting toy exampl

Blocking Nuclear Factor-Kappa B Protects against Diet-Induced Hepatic Steatosis and Insulin Resistance in Mice

Inflammation critically contributes to the development of various metabolic diseases. However, the effects of inhibiting inflammatory signaling on hepatic steatosis and insulin resistance, as well as the underlying mechanisms remain obscure. In the current study, male C57BL/6J mice were fed a chow diet or high-fat diet (HFD) for 8 weeks. HFD-fed mice were respectively treated with p65 siRNA, non-silence control siRNA or vehicle every 4th day for the last 4 weeks. Vehicle-treated (HF) and non-silence siRNA-treated (HFNS) mice displayed overt inflammation, hepatic steatosis and insulin resistance compared with chow-diet-fed (NC) mice. Upon treatment with NF-κB p65 siRNA, HFD-fed (HFPS) mice were protected from hepatic steatosis and insulin resistance. Furthermore, Atg7 and Beclin1 expressions and p-AMPK were increased while p-mTOR was decreased in livers of HFPS mice in relative to HF and HFNS mice. These results suggest a crosslink between NF-κB signaling pathway and liver AMPK/mTOR/autophagy axis in the context of hepatic steatosis and insulin resistance

Nesting and foraging behavior of Xylocopa valga in the Ejina Oasis, China.

Xylocopa valga is extinct in Latvia and Lithuania and is critically endangered in Poland, and its distribution in the Ejina Oasis, China, is currently unknown. Studies on the biology of X. valga are scarce, and thus, conservation efforts for this species are currently limited. Here, we investigated the morphological characteristics, nest architecture, nest structure and food type of offspring in the nest cells of X. valga. This research was conducted in the Populus euphratica forest reserve in the Ejina Oasis, China, between July 2014 and June 2019. The primary investigation methods included visual inspection, photography, observation and measurements of nest anatomy, and examination of pollen stores by microscopy. We found that in the Ejina Oasis, China, X. valga builds its nests in the dead wood of P. euphratica. X. valga is univoltine. Its lifestyle varies from solitude to symbiosis. When many females nest near each other, several females may share a single nest entrance, based on which they build their own cells. The nests are branched. According to our results, there is a significant difference between the thickness of the inner cell partition and that of the outermost cell partition in the branched tunnel. In the P. euphratica forest area, the food for the progeny of X. valga is mainly composed of the pollen and nectar of Sophora alopecuroide and Populus euphratica. Therefore, X. valga and S. alopecuroides exhibit close ecological interactions in the P. euphratica forest ecosystem

FIGURES 1 – 8 in A new phytophagous eulophid wasp (Hymenoptera: Chalcidoidea: Eulophidae) that feeds within leaf buds and cones of Pinus massoniana

FIGURES 1 – 8. Aprostocetus pinus sp. nov.: 1, ♀ head, front view; 2, ♀ head, dorsal view; 3, ♀ mesosoma; 4, ♂ mesosoma; 5, ♀ propodeum; 6, ♂ propodeum; 7, ♀ metasoma; 8, ♂ metasoma

Foraging Behavior of Honeybees (Apis Mellifera L.) and Ground Bumblebees (Bombus Terrestris L.) and its Influence on Seed Yield and Oil Quality of Oil Tree Peony Cultivar ‘Fengdan’ (Paeonia Ostii T. Hong et J. X. Zhang)

Oil peony (Paeonia spp.) is a new type of woody oil crop in China with a large cultivation area. Inadequate pollination is one of the main reasons for low seed yield. A pollination net room was built at an oil tree peony base, the numbers of honeybees (Apis mellifera L.) and ground bumblebees (Bombus terrestris L.) were artificially increased and the foraging behaviors and daily activities of the two bees on the plants were observed. Four different pollination methods (honeybee pollination, ground bumblebee pollination, natural field pollination and pollination without insects) were applied. The visit interval, single-flower visit time, number of single-flower visits, number of flowers visited per minute and number of stigma contacts were compared. Meanwhile, the effects of honeybee and bumblebee pollination on the oil yield and quality of peony seeds were compared. There were noticeable differences in daily activity between honeybees and ground bumblebees. Significant differences in the single-flower visit time, visit interval and visit frequency were also observed; honeybee and ground bumblebee pollination increased the seed yield of oil tree peony by 78.74% and 31.88%, respectively. Therefore, both honeybees and ground bumblebees are effective pollinators of oil tree peony. These results provide a theoretical basis for further utilization of bee resources for oil tree peony pollination

Effects of Probiotics and Gut Microbiota on Bone Metabolism in Chickens: A Review

Broiler leg diseases are a common abnormal bone metabolism issue that leads to poor leg health in growing poultry. Bone metabolism is a complicated regulatory process controlled by genetic, nutritional, feeding management, environmental, or other influencing factors. The gut microbiota constitutes the largest micro-ecosystem in animals and is closely related to many metabolic disorders, including bone disease, by affecting the absorption of nutrients and the barrier function of the gastrointestinal tract and regulating the immune system and even the brain–gut–bone axis. Recently, probiotic-based dietary supplementation has emerged as an emerging strategy to improve bone health in chickens by regulating bone metabolism based on the gut–bone axis. This review aims to summarize the regulatory mechanisms of probiotics in the gut microbiota on bone metabolism and to provide new insights for the prevention and treatment of bone diseases in broiler chickens

Improvement of hepatic steatosis and liver insulin signaling.

<p>(A) H&E staining of liver sections. Magnification: 200×. (B) Quantification of the degree of lipid droplets. (C) Liver triglyceride contents (n = 4–6). (D) Relative hepatic mRNA levels of genes related to lipid metabolism (n = 4–6). (E)Western blotting of insulin-stimulated Akt phosphorylation in liver. Representative western blot images and graphs representing the ratio of the (insulin-stimulated phospho-) protein of interest on Akt as measured by densitometry analysis are shown. *p<0.05, **p<0.01 HF, HFNS and HFPS versus NC. ^#p<0.05, ^##p<0.01 HFPS versus HF. ^$p<0.05, ^$ $p<0.01, HFPS versus HFNS. All data are presented as mean ± SE.</p