Modulational Instability of Trapped Two-Component Bose-Einstein Condensates

The modulational instability of two-component Bose-Einstein condensates(BECs)under an external parabolic potential is discussed.Based on the trapped two-component Gross-Pitaevskill equations,a time-dependent dispersion relation is obtained analytically by means of the modified lens-type transformation and linear stability analysis.It is shown that a modulational unstable time scale exists for trapped two-component BECs.The modulational properties-which are determined by the wave number,external trapping parameter,intraand inter-species atomic interactions-are modified significantly.The analytical results are confirmed by direct numerical simulation.Our results provide a criterion for judging the occurrence of instability of the trapped two-component BECs in experiment.

Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study

BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system,ranking sixth in incidence and fourth in mortality worldwide.Since 42.5%of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type,the application of imaging diagnosis is restricted.AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning(ML)algorithms and to evaluate their pre-dictive performance in clinical practice.METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Depart-ment of General Surgery of Affiliated Hospital of Xuzhou Medical University(Xuzhou,China)from March 2016 to November 2019 were collected and retro-spectively analyzed as the training group.In addition,data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People’s Hospital(Jining,China)were collected and analyzed as the verifi-cation group.Seven ML models,including decision tree,random forest,support vector machine(SVM),gradient boosting machine,naive Bayes,neural network,and logistic regression,were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer.The ML models were established fo-llowing ten cross-validation iterations using the training dataset,and subsequently,each model was assessed using the test dataset.The models’performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.RESULTS Among the seven ML models,except for SVM,the other ones exhibited higher accuracy and reliability,and the influences of various risk factors on the models are intuitive.CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer,which can aid in personalized clinical diagnosis and treatment.

Joining mechanism of Mg alloy/steel butt joints with Cu-Zn interlayer by hybrid laser-TIG welding source

Hybrid laser-TIG （tungsten inert gas） welding technology was used to join the Mg to steel with Cu-Zn interlayer. The effect of Al content in Mg alloy on the interface bonding of AZ31BMg/Q235 steel dissimilar butt joints was investigated. For comparison, ZK60 Mg alloy with no Al addition and AZ31 Mg alloy were utilized. The results showed that AZ31/Q235 butt joints with Cu-Zn interlayer, of which the fractures occurred in the Mg weld seam, showed quite sound and reliable interface bonding. The obvious concentration of element Al occurred along the Mg/steel interface. The results indicated that the addition of Cu-Zn interlayer, especially Zn, could significantly decrease the reaction temperature of Fe-Al at the Mg/ steel interface and promote the formation of reaction layer along the interface. The diffusion of Al element on the Mg-Fe interface was increased by the Cu-Zn interlayer. For ZK60 Mg alloy, Mg and Zn approaching the Mg-Fe interface were evaporated. The intense vaporizing could inhibit the direct contact between steel and Mg weld pool, even destroying the possible formation of reaction layer. The intimate interface bonding of AZ31/Q235 butt joints was attributed to the synergistic effect of element AI and Cu.

Multiple genotyping based on multiplex PCR and microarray

The genetic variability has obtained more and more attention in the process of diagnosis and treatment of tumors. Herein, we have described a multiple genotyping method based on magnetic enrichment- multiplex PCR （MEM-PCR） and microarray technology. Monodisperse magnetic beads were fabricated and modified with streptavidin. Four loci on two genes （M235T and A-6G loci on AGT gene, A1298C and C677T loci on MTHFR gene） were selected to study single nucleotide polymorphisms （SNP）. Target sequences of these SNP loci were amplified using Cy3-1abeled primers through multiplex PCR in one tube after the templates were enriched and purified by functional magnetic beads （MB）. Four pairs of NH2- labeled probes, corresponding to each locus, were fixed on CHO-modified glass slide by covalent binding. Hybridization between target sequences and probes was performed under suitable conditions. The spotting locations on microarray and the ratio of fluorescence intensity, produced by different loci, were used to distinguish the SNP genotypes. Finally, three of gastric cancer samples were collected and genotvping analysis for these four SNP loci was carried out successfully simultaneously by this method.