Neomicrocoleus gen.nov.(Microcoleaceae,Oscillatoriales),a novel cyanobacterial genus from benthic mats in a water channel

A new filamentous cyanobacterial strain(CHAB 4127)was successfully isolated from the algal mat during the field investigation of the open channel from Luanhe River to Tianjin,China.The polyphasic approach combining morphological,ultrastructural,ecological,and molecular features was used to characterize this studied strain.The strain is morphologically similar to the Microcoleus-like cyanobacterial taxa under light microscopy,and the radial arrangement of thylakoids is also consistent with that of Microcoleus-like groups.The phylogenetic position of CHAB 4127 based on 16S rRNA gene sequences,is shown to be clearly clustered into an independent clade with the newly established genus Microcoleusiopsis.The maximum similarity of 16S r RNA gene of the studied strain with other existing related cyanobacterial taxa is 93.97%,and the ITS secondary structures is also obviously different from other members of Microcoleaceae.Based on all the above evidence,we proposed the establishment of this novel cyanobacterial genus as Neomicrocoleus,with its type species,Neomicrocoleus tianjinensis.

Combined data mining techniques based patient data outlier detection for healthcare safety

Purpose–Among the growing number of data mining(DM)techniques,outlier detection has gained importance in many applications and also attracted much attention in recent times.In the past,outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack.However,outliers are not always erroneous.Therefore,the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care,in particular.Design/methodology/approach–It is a combined DM(clustering and the nearest neighbor)technique for outliers’detection,which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety.The outcomes or the knowledge implicit is vitally essential to a proper clinicaldecision-making process.The method isimportant to thesemantic,andthe novel tactic of patients’events and situations prove that play a significant role in the process of patient care safety and medications.Findings–The outcomes of the paper is discussing a novel and integrated methodology,which can be inferring for different biological data analysis.It is discussed as integrated DM techniques to optimize its performancein the field of health and medicalscience.It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors.Based on these facts,outliers are detected as clusters and point events,and novel ideas proposed to empower clinical services in consideration of customers’satisfactions.It is also essential to be a baseline for further healthcare strategic development and research works.Research limitations/implications–This paper mainly focussed on outliers detections.Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch.Therefore,the research can be extended more about the hierarchy of patient problems.Originality/value–DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety.Clinical data based outlier detection is a basic task to achieve healthcare strategy.Therefore,in this paper,the authors focussed on combined DM techniques for a deep analysis of clinical data,which provide an optimal level of clinical decision-making processes.Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services.Therefore,using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers,which could be fundamental to further analysis of healthcare and patient safety situational analysis.