ABSTRACT
Objective To investigate the distribution of host animals and the serological distribution and virulence gene of Yersinia eterocolitica in Gaomi City.Methods Sample stools of poultry and domestic and diarrhea patients,as well as flies and chilled (frozen) poultry and domestic animal meats in Gaomi City were preliminarily identified and the suspected strains isolated were sent to Shandong Provincial Center for Disease Control and Prevention for further identification from 2012 to 2015.The bacteria were analyzed by the systematic biochemistry to determine the serotypes,bio-types and virulence genes [Yersinia enterocolitica adhesion aggression site gene (ail),heat-tolerance enterotoxin gene (ystA),organism type 1A enterotoxin gene (ystB),adhesin (yadA),yop regulator transcription activation action factor (virF)].Results Eighty-five stains of Yersinia enterocolitica were detected in 2 860 specimens and the detection rate was 3.0％ (85/2 860).The chicken strains accounted for 50.6％ (43/85),the swine strains accounted for 20.0％ (17/85),and the sheep strains accounted for 10.6％ (9/85).In all the serotypes,the highest was O ∶ 8 (24.7％,21/85),followed by O ∶ 5 (12.9％,11/85),the bacteria were detected in the stools of chickens,duck,and swine etc.,as well as in diarrhea patients and chilled (frozen) poultry and domestic animal meats,biotype is 1A.One strain O ∶ 3/4 was detected from swine stool with ail,ystA,yadA and virF genes.Conclusions The Yersinia enterocolitica is widely distributed and both pathogenic and non-pathogenic strains coexist in Gaomi City;the main serological types are O ∶ 5/1A and O ∶ 8/1A;the pathogenic serotype is O ∶ 3/4,and chicken,swine and sheep are the main host animals.
ABSTRACT
The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.
Subject(s)
Candida , Metabolism , Entropy , Fermentation , Glutathione , Neural Networks, ComputerABSTRACT
BACKGROUND: In China, this laboratory is the first one to report such researches, confirming that strong αo-immunoreactive (IR) appears in the substantia gelatinosa (SG) of spinal cord and lateral spinal nucleus which is similar to the distribution of certain neuropeptides that participate in sensory regulation, which suggests that guanine nucleotide binding protein (G protein) may be related to primary afferent informational transfer. OBJECTIVE: To observe the change of αo-IR in gelatinous substance by the method of transection of unilateral spinal dorsal roots.DESIGN: A randomized controlled experiment on animals.SETTING: Staff Room of Neurobiology, Tongji Medical College, Huazhong University of Science and Technology.MATERIALS: The experiment was conducted at the Staff Room of Neurobiology, Tongji Medical College, Huazhong University of Science and Technology from December 1995 to December 1996. Fifteen healthy adult SD rats were selected and divided into 3 groups: ①normal group with five rats (not dealt with any disposal), ②transected dorsal root group with 10 rats (right side) and ③control group (non-transected left sidedness as control).METHODS: Right lumbar 1-3 spinal neural dorsal roots were cut off under the anesthesia of 100 g/L chloral hydrate (300 mg/kg)through intraperitoneal injection in rats, living for 48-60 hours after operation. The subunit αo of guanine nucleotide-binding protein (rabbit polyclonal antiserum) was demonstrated in the αo-IR of rat spinal cord by immunohisto chemical methods. G protein was oriented, and its change was observed after transection ofneural dorsal roots MAIN OUTCOME MEASURES: ①The αo-IR of Ⅰ to Ⅲ of the dorsal horn and lateral spinal nucleus of the normal rats and control rats. ②The αo-IR of Ⅰ to Ⅲ of the dorsal horn and lateral spinal nucleus of rats in the transected dorsal root group. RESULTS: Data of a total of 15 rats were involved in the result analysis. ①In the normal group and control group, intense αo-IR was presented in rexed lamina ( Ⅰ to Ⅲ ) of the dorsal horn of rats, and the highest αo-IR in second lamina (SG). Lateral spinal nucleus of rat revealed higher density of αo-IR containing fiber networks. Following unilateral transection of dorsal roots in SG, αo-IR was markedly decreased. ②Quantitative analysis of absorbance (A) of αo-IR, it was (0.847±0.081) in the inside of the control group, (0.633±0.073)(t=5.71 ,P ＜ 0.001 ) in the inside of transected dorsal root group. It was (0.823±0.089) in the middle area of the control group,(0.660 4±0.074)(t=6.90,P ＜ 0.001 ) in the middle area of the transected dorsal root group. It was (0.915±0.090) in the lumbar region of the control group, and (0.656±0.077)(t=10.31 ,P ＜ 0.001 ) in the lumbar region of the transected dorsal root group. Average value of the control group was (0.852±0.084), and average value of the transected dorsal root group was (0.639±0.078)(t=10.23 ,P ＜ 0.001 ).CONCLUSION: Part of G protein of end-brush neurons related with the primary afferent noxious stimulation in SG derives from primary sensory neurons, which maybe join the adjustment of primary sensory transfer.