Regioselective Synthesis of Functionalized Dihydropyrones via the Petasis Reaction.

A novel synthesis methodology for the construction of functionalized dihydropyrones has been developed with amines, glyoxylic acid, and 4-substituted-1,2-oxaborol-2(5H)-ols from the Petasis reaction. Mechanistic investigation indicated the intermolecular SN2 cyclization to provide 3,6-dihydro-2H-pyran-2-ones (3,6-DHP) and 5,6-dihydro-2H-pyran-2-ones (5,6-DHP) in one step with moderate to excellent yields.

Multi-Target Joint Detection and Estimation Error Bound for the Sensor with Clutter and Missed Detection.

The error bound is a typical measure of the limiting performance of all filters for the given sensor measurement setting. This is of practical importance in guiding the design and management of sensors to improve target tracking performance. Within the random finite set (RFS) framework, an error bound for joint detection and estimation (JDE) of multiple targets using a single sensor with clutter and missed detection is developed by using multi-Bernoulli or Poisson approximation to multi-target Bayes recursion. Here, JDE refers to jointly estimating the number and states of targets from a sequence of sensor measurements. In order to obtain the results of this paper, all detectors and estimators are restricted to maximum a posteriori (MAP) detectors and unbiased estimators, and the second-order optimal sub-pattern assignment (OSPA) distance is used to measure the error metric between the true and estimated state sets. The simulation results show that clutter density and detection probability have significant impact on the error bound, and the effectiveness of the proposed bound is verified by indicating the performance limitations of the single-sensor probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters for various clutter densities and detection probabilities.

[Study on the inclusion compound of avermectin by infrared spectroscopy].

This study was designed to investigate the formation and effect of inclusion complex of Avermectin-beta-cyclodextrin based on the accommodation property of beta-cyclodextrin's molecular cavity. The inclusion complex of Averrnectin-beta-cyclodextrin was prepared using saturated solution method and high performance liquid chromatography (HPLC) was employed to determine its entraping efficiency. The formation of Avermectin-beta-cyclodextrin inclusion complex was also demonstrated by infrared spectroscopy(IR). The change of chemical structure produced by photocatalysis of Abamectin was analyzed and the effect of inclusion complex to strengthen the photolysis stability of Abamectin's chemical structure was studied. The results show that the entraping efficiency of the inclusion complex was 40. 5%. The IR analysis presents that the intermolecular hydrogen bond was formed in the Avermectin-beta-cyclodextrin inclusion complex, indicating the composition effect was different from physical mixture. The lactones structure of Avermectin Bla can be photodecomposed and disrupted. After decomposition, the infrared stretching vibration peak of C-O-C structure disappeared and the lactone bond was significantly broken. The lactones structure of avermectin Bla was covered by the inclusion molecular loci in beta-cyclodextrin after the formation of avermectin-beta-cyclodextrin inclusion complex, providing a good photophobic protection for C-O-C structure in the macrocyclic lactone structure of avermectin Bla and improving the photostability of avermectin Bla molecule. The innovation of this study is that the structure and the characters of the prepared avermectin-beta-cyclodextrin inclusion complex were analyzed using spectrum methods. This inclusion complex is expected to be the ideal intermediate in the construction of protective controlled release formulation of avermectin.

Diabetic retinopathy identification based on multi-source-free domain adaptation.

AIM: To address the challenges of data labeling difficulties, data privacy, and necessary large amount of labeled data for deep learning methods in diabetic retinopathy (DR) identification, the aim of this study is to develop a source-free domain adaptation (SFDA) method for efficient and effective DR identification from unlabeled data. METHODS: A multi-SFDA method was proposed for DR identification. This method integrates multiple source models, which are trained from the same source domain, to generate synthetic pseudo labels for the unlabeled target domain. Besides, a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances. Validation is performed using three color fundus photograph datasets (APTOS2019, DDR, and EyePACS). RESULTS: The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks. It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains. CONCLUSION: The multi-SFDA method provides an effective approach to overcome the challenges in DR identification. The method not only addresses difficulties in data labeling and privacy issues, but also reduces the need for large amounts of labeled data required by deep learning methods, making it a practical tool for early detection and preservation of vision in diabetic patients.