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Background: The incidence of inborn errors of metabolism varies widely across countries. Very few studies have analyzed the incidence of these disorders in Mainland China. We aimed to estimate the overall and disease-specific incidences of inborn errors of metabolism in Chinese newborns and investigate the geographical distribution of these disorders. Methods: A national cross-sectional survey was conducted to investigate newborn inborn errors of metabolism screening by tandem mass spectroscopy in Mainland China between 2016 and 2017. A total of 246 newborn screening centers were surveyed using a standardized questionnaire. We examined the cumulative and disease-specific incidences of inborn errors of metabolism in Mainland China as a whole and in different geographical locations. Results: Over 7 million newborns were screened and 2747 were diagnosed with inborn errors of metabolism, yielding an overall incidence of 38.69 per 100,000 births (95% confidence interval: 37.27–40.17). The most common disorders were amino acid disorders (17.14 per 100,000 births, 95% confidence interval: 16.21–18.13), followed by organic acid disorders (12.39 per 100,000 births, 95% confidence interval: 11.60–13.24) and fatty acid oxidation disorders (9.16 per 100,000 births, 95% confidence interval: 8.48–9.89). The overall and disease-specific incidence rates differed significantly across geographical locations (P < 0.001). Conclusions: The overall incidence of inborn errors of metabolism in Chinese newborns is relatively high. It is urgent to establish the recommended uniform screening panel for inborn errors of metabolism to guide the national and regional tandem mass spectroscopy newborn screening programs. [ABSTRACT FROM AUTHOR]

A microporous MOF based on a heterotopic ligand was successfully synthesized under solvothermal conditions and fully characterized using single‐crystal and powder X‐ray diffraction, TGA, FTIR, and microanalyses. Single‐crystal X‐ray diffraction analyses showed that the title MOF contains rich inorganic nitrate ions decorating the channel surface that can serve as C2H2 recognition sites. Its selective gas adsorption properties towards C2H2 over CO2 and CH4 were evaluated by single‐component equilibrium adsorption measurements and IAST calculations, establishing its promising potential for C2H2 separation and purification. At ambient conditions, the C2H2 uptake capacity reaches 92.2 cm3 (STP) g–1, while the IAST‐predicted adsorption selectivities are up to 30.6 and 4.5 for the equimolar C2H2‐CH4 and C2H2‐CO2 binary gas mixtures, respectively. Furthermore, DFT computation studies revealed that the inorganic nitrate ion plays a very important role for preferential adsorption of C2H2 over CO2 and CH4. [ABSTRACT FROM AUTHOR]

With the continuous development of new technologies, the scale of training data is also expanding. Machine learning algorithms are gradually beginning to be studied and applied in places where the scale of data is relatively large. Because the current structure of learning algorithms only focus on the identification of dependencies and ignores the direction of dependencies, it causes multiple labeled samples not to identify categories. Multiple labels need to be classified using techniques such as machine learning and then applied to solve the problem. In the environment of more training data, it is very meaningful to explore the structure extension to identify the dependencies between attributes and take into account the direction of dependencies. In this article, Bayesian network structure learning, analysis of the shortcomings of traditional algorithms, and binary evolutionary algorithm are applied to the randomized algorithm to generate the initial population. In the optimization process of the algorithm, it uses a Bayesian network to do a local search and uses a depth-first algorithm to break the loop. Finally, it finds a higher score for the network structure. In the simulation experiment, the classic data sets, ALARM and INSURANCE, are introduced to verify the effectiveness of the algorithm. Compared with NOTEARS and the Expectation-Maximization (EM) algorithm, the weight evaluation index of this article was 4.5% and 7.3% better than other schemes. The clustering effect was improved by 13.5% and 15.2%. The smallest error and the highest accuracy are also better than other schemes. The discussion of Bayesian reasoning in this article has very important theoretical and practical significance. This article further improves the Bayesian network structure and optimizes the performance of the classifier, which plays a very important role in promoting the expansion of the network structure and provides innovative thinking., Competing Interests: The author declares that there are no competing interests., (©2023 Yao.)