Arsenophonus Interacts with Buchnera to Improve Growth Performance of Aphids under Amino Acid Stress.

Amino acids play a crucial role in the growth and development of insects. Aphids cannot ingest enough amino acids in plant phloem to meet their requirements, and therefore, they are mainly dependent on the obligate symbiont Buchnera aphidicola to synthesize essential amino acids. Besides Buchnera, aphids may harbor another facultative symbiont, Arsenophonus, which alters the requirement of the cotton-melon aphid Aphis gossypii for amino acid. However, it is unclear how Arsenophonus regulates the requirement. Here, we found that Arsenophonus ameliorated growth performance of A. gossypii on an amino acid-deficient diet. A deficiency in lysine (Lys) or methionine (Met) led to changes in the abundance of Arsenophonus. Arsenophonus suppressed the abundance of Buchnera when aphids were fed a normal amino acid diet, but this suppression was eliminated or reversed when aphids were on a Lys- or Met-deficient diet. The relative abundance of Arsenophonus was positively correlated with that of Buchnera, but neither of them was correlated with the body weight of aphids. The relative expression levels of Lys and Met synthase genes of Buchnera were affected by the interaction between Arsenophonus infections and Buchnera abundance, especially in aphids reared on a Lys- or Met-deficient diet. Arsenophonus coexisted with Buchnera in bacteriocytes, which strengthens the interaction. IMPORTANCE The obligate symbiont Buchnera can synthesize amino acids for aphids. In this study, we found that a facultative symbiont, Arsenophonus, can help improve aphids' growth performance under amino acid deficiency stress by changing the relative abundance of Buchnera and the expression levels of amino acid synthase genes. This study highlights the interaction between Arsenophonus and Buchnera to ameliorate aphid growth under amino acid stress.

[Research on the application of improved M-P neural network to the determination of lead and zinc ore element contents by energy disperse X-ray fluorescence analysis].

Because of different constraints (such as different kinds of measurable elements, characteristic X-ray energy, changes in matrix composition, etc.), usually it's not easy to get accurate information of elements, resulting in mistakes in later data analysis of energy disperse X-ray fluorescence measurement. The method is based on McCulloch-Pitts neural network (M-P neural network), according to matrix effect, to establish a new neural network model for quantitative forecasting of Zn by taking the data of X-ray fluorescence measurements of Cu, Fe, Pb, etc in lead-zinc mine in western Tianshan as the training sample. The relative error between predicted value and measured value is less than 5%. This method can be more accurate and rapid for X-ray fluorescence; it provides a new approach to correcting information of X-ray fluorescence.