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Objective: Analytical studies of risk factor assessment using machine learning have recently been reported. We performed an exploratory detection study of asthma exacerbation-related factors using health insurance claims data and machine learning to explore risk factors that have high generalizability and can be easily obtained in daily practice. Methods: A dataset of asthma patients during May 2014-April 2019 from the Japanese insurance claims database, MediScope® (DB) was used. Patient characteristics and disease information were extracted, and association with occurrence of asthma exacerbation was evaluated to comprehensively search for exacerbation risk factors. Asthma exacerbations were defined as the co-occurrence of emergency medical procedures, such as emergency transport and intravenous steroid injections, with asthma claims, which were recorded in the database. Results: In total, 5,844 (13.7%) subjects had exacerbations in 42,685 eligible cases from the DB. Information on approximately 3,300 diseases was subjected to a machine learning, and 25 variables were extracted as variable importance and targeted for risk assessment. As a result, sex, days without exacerbation from cohort entry date at look-back period, Charlson Comorbidity Index, allergic rhinitis, chronic sinusitis, acute airway disease (upper airway), acute airway disease (lower airways), Chronic obstructive pulmonary disease/chronic bronchitis, gastroesophageal reflux disease, and hypertension were significantly associated with exacerbation. Dyslipidemia and periodontitis were detected as associated factors of reduced exacerbation risk. Conclusions: A comprehensive analysis of claims data using machine learning showed asthma exacerbation risk factors mostly consistent with those in previous studies. Further examination in other fields is warranted. Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.1923740 .

Amino acid sequence alignment of Lepidopteran enolases. Active site residues are marked with asterisks, and metal-binding residues are labeled with arrows. Red asterisks and arrows indicate amino acid residues that differ among B. mori enolases (BmEnos). The concervation of amino acid residues among the various enolase sequences is graphically shown below the sequence. The residues in the alignment are colored according to the Rasmol color scheme ( http://life.nthu.edu.tw/~fmhsu/rasframe/COLORS.HTM#aminocolors ).

Domain structure of B. mori enolases. Amino acid sequences of B. mori enolases (BmEnos) were analyzed by SMART ( http://smart.embl-heidelberg.de/ ). The enolase_N (blue colored) or enolase_C (red colored) domain is shown by squares. The upper number is the amino acid number.

This study is a pioneer report on the development of an environmentally safe in vitro regeneration protocol for Curcuma , Kaempferia and Zingiber . The germplasm of the species was collected from Myanmar, a Southeast Asian country, rich in unexplored Zingiberaceae genetic resources. Rhizome buds were directly regenerated on the Murashige and Skoog medium containing a growth regulator, 6-benzyladenine and a commercial fungicide, Benlate (50% of Benomyl). The pre-treatment protocol did not contain HgCl2, a toxic pollutant for Curcuma amada, Curcuma longa, Zingiber barbatum and Kaempferia galanga. Plantlets were regenerated from the buds without any intervention of the callus phase. The contamination free survival of the bud explants from Curcuma, Zingiber and Kaempferia was more than 75, 57 and 53%, respectively. Buds from immature rhizomes were difficult to regenerate on the media, as well as resulted in higher contamination percentages while the buds from mature rhizomes efficiently regenerated with very few contamination percentages. The contamination was in the range of 0 to 39% among the different accessions. This was also the first report of direct in vitro regeneration of plantlets from Z. barbatum bud explants. The protocol was cost-beneficial, time saving and effective for the conservation of Zingiberaceae genetic resources. Key words : Conservation, regeneration, Zingiberaceae , tissue culture, Curcuma, Zingiber , Myanmar.

Urban green spaces have been arisen growing concern responded to the social and environmental costs of urban sprawl. A wide range of planning and policies has been and/or will be designed to protect urban green spaces and optimize their spatial pattern. A better design or planning of urban green space can make a major contribution to quality of environment and urban life, and furthermore can decide whether we can have a sustainable development in the urban area. Information about the status quo of urban green spaces can help planners design more effectively. However, how to quantify and capture such information will be the essential question we face. In this paper, to quantify the urban green space, a new method comprising gradient analysis, landscape metrics and GIS was developed through a case of Jinan City. The results demonstrate; 1) the gradient analysis is a valid and reliable instrument to quantify the urban green space spatial pattern precisely; 2) using moving window, explicit landscape metrics were spatially realized. Compared with quantifying metrics in the entire landscape, it would be better to link pattern with process and establish an important basis for analyzing the ecological and socioeconomic functions of green spaces.