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Background: Machine learning (ML) is increasingly used to predict the prognosis of numerous diseases. This retrospective analysis aimed to develop a prediction model using ML algorithms and to identify predictors associated with the recurrence of hallux valgus (HV) following surgery., Methods: A total of 198 symptomatic feet that underwent chevron osteotomy combined with a distal soft tissue procedure were enrolled and analyzed from 2 independent medical centers. The feet were grouped according to nonrecurrence or recurrence based on 1-year follow-up outcomes. Preoperative weightbearing radiographs and immediate postoperative nonweightbearing radiographs were obtained for each HV foot. Radiographic measurements (eg, HV angle and intermetatarsal angle) were acquired and used for ML model training. A total of 9 commonly used ML models were trained on the data obtained from one institute (108 feet), and tested on the other data set from another independent institute (90 feet) for external validation. Optimal feature sets for each model were identified based on a 2000-resample bootstrap-based internal validation via an exhaustive search. The performance of each model was then tested on the external validation set. The area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model were calculated to evaluate the performance of each model., Results: The support vector machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.88 and an accuracy of 75.6%. Preoperative hallux valgus angle, tibial sesamoid position, postoperative intermetatarsal angle, and postoperative tibial sesamoid position were identified as the most selected features by several ML models., Conclusion: ML classifiers such as SVM could predict the recurrence of HV (an HVA >20 degrees) at a 1-year follow-up while identifying associated predictors in a multivariate manner. This study holds the potential for foot and ankle surgeons to effectively identify individuals at higher risk of HV recurrence postsurgery., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Disclosure forms for all authors are available online.

In the application of classical graph theory, there always are various indeterministic factors. This study studies the indeterministic factors in the connected graph by employing the uncertainty theory. First, this study puts forward two concepts: generalized uncertain graph and its connectivity index. Second, it presents a new algorithm to compute the connectivity index of an uncertain graph and generalized uncertain graph and verify this algorithm with typical examples. In addition, it proposes the definition and algorithm of α -connectivity index of generalized uncertain graph and verifies the stability and efficiency of this new algorithm by employing numerical experiments., Competing Interests: The authors state that this article has no conflicts of interest., (Copyright © 2022 Jinchan Wang et al.)

The basic principle of the static polarization wind imaging interferometer (SPWII) is expounded in this paper. By using trigonometric function and complex amplitude methods, the complex vibration amplitude of each polarization device with deviation from its ideal direction is calculated. The variations of the fringe visibility and optical throughput with deviation angles are given analytically and simulated numerically. According to the design parameters of the SPWII, the air-equivalent length L is equal to 16.14 cm and the total transmissivity is greater than 0.4. When the polarization directions of each polarization device are all in the ideal directions, the total optical throughput can be maintained at about 16.4% of the incident optical intensity. When the polarization directions of each polarization device are all 2° deviated from the ideal positions, the total optical throughput is decreased by 0.08%. This work would be useful for the realization and data processing of the SPWII.

Tetrapod zinc oxide (T-ZnO), being a kind of nano-material, has large specific surface area and surface binding energy, which will make it sensitive to the ambient gas condition. So the field emission properties will be influenced by the gas adsorption when being applied as the cathode materials of field emission devices. Carbon monoxide is the main residual gas in T-ZnO field emission devices. In this paper, carbon monoxide was introduced into a field emission device with T-ZnO emitters. The field emission currents of tetrapod ZnO were compared before and after exposure to CO.

A total of 124 fecal specimens were collected from four deer farms in Zhengzhou City, China and examined for Cryptosporidium by Sheather's sugar flotation technique. Cryptosporidim oocysts were detected in two 1-year-old sika deer, and one of the two specimens was genotyped by sequence and phylogenetic analyses of the small subunit ribosomal RNA (rRNA) (18S rRNA), 70-kDa heat shock protein (HSP70), actin, and Cryptosporidium oocyst wall protein (COWP) genes. Results obtained suggested that the Cryptosporidium studied belonged to Cryptosporidium cervine genotype, although slight sequence differences were noticed at the three loci. The similarities between this isolate and other Cryptosporidium cervine genotype isolates were 99.1-99.8%, 9.8%, 99.7%, and 100% at the 18S rRNA, HSP70, actin, and COWP loci, respectively. This study is the first report of Cryptosporidium infection in sika deer in China.