IntelliEppi: Intelligent reaction monitoring and holistic data management system for the molecular biology lab

AbstractDaily alterations of routines and protocols create high, yet so far unmet demands for intelligent reaction monitoring, quality control and data management in molecular biology laboratories. To meet such needs, the “internet of things” is implemented here. We propose an approach which combines direct tracking of lab tubes, reactions and racks with a comprehensive data management system. Reagent tubes in this system are tagged with 2D data matrices or imprinted RFID-chips using a unique identification number. For each tube, individual content and all relevant information based on conducted experimental procedures are stored in an experimental data management system. This information is managed automatically but allow scientists to engage and interfere via user-friendly graphical interface. Tagged tubes are used in connection with a detectable RFID-tagged rack. We show that reaction protocols, HTS storage and complex reactions are easily planned and controlled.

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IntelliEppi: Intelligent Reaction Monitoring and Holistic Data Management System for the Molecular Biology Lab

Lecture Notes in Networks and Systems - Advances in Information and Communication ◽

10.1007/978-3-030-12388-8_28 ◽

2019 ◽

pp. 392-407 ◽

Cited By ~ 1

Author(s):

Arthur Neuberger ◽

Zeeshan Ahmed ◽

Thomas Dandekar

Keyword(s):

Molecular Biology ◽

Data Management ◽

Management System ◽

Data Management System ◽

Reaction Monitoring ◽

Biology Lab

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Development of a customised data management system for a COVID-19-adapted colorectal cancer pathway

10.1101/2020.10.05.20206920 ◽

2020 ◽

Author(s):

Frances Gunn ◽

Janice Miller ◽

Malcolm G Dunlop ◽

Farhat V N Din ◽

Yasuko Maeda

Keyword(s):

Colorectal Cancer ◽

Data Management ◽

Management System ◽

Data Entry ◽

Large Data ◽

Data Monitoring ◽

Data Management System ◽

Volume Data ◽

Direct Data ◽

User Friendly

AbstractPurposeThe COVID-19 pandemic posed an unprecedented challenge to healthcare systems around the world. To mitigate the risks of those referred with possible colorectal cancer during the pandemic we implemented a clinical pathway which required a customised data management system for robust operation. Here, we describe the principal concepts and evaluation of the performance of a spreadsheet-based data management system.MethodsA system was developed using Microsoft Excel® 2007 aiming to retain the spreadsheets inherent intuitiveness of direct data entry. Data was itemised limiting entry errors. Visual Basic for Applications (VBA) was used to construct a user-friendly interface to enhance efficiency of data entry and segregate the data required for operational tasks. This was done with built-in loop-back data entry. Finally data derivation and analysis was performed to facilitate pathway monitoring.ResultsFor a pathway which required rapid implementation and development of a customised data management system, the use of a spreadsheet was advantageous due to its user-friendly direct data entry capability. Its function was enhanced by UserForm and large data handling by data segregation using VBA macros. Data validation and conditional formatting minimised data entry errors. Computation by the COUNT function facilitated live data monitoring on a dashboard. During the three months the pathway ran for, the system processed 36 nodal data points for each of the included 837 patients. Data monitoring confirmed its accuracy.ConclusionLarge volume data management using a spreadsheet system is possible with appropriate data definition and VBA programmed data segregation. Clinicians’ regular input and optimisation made the system adaptable for rapid implementation.

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Bringing introspection into BlobSeer: Towards a self-adaptive distributed data management system

International Journal of Applied Mathematics and Computer Science ◽

10.2478/v10006-011-0017-y ◽

2011 ◽

Vol 21 (2) ◽

pp. 229-242 ◽

Cited By ~ 3

Author(s):

Alexandra Carpen-Amarie ◽

Alexandru Costan ◽

Jing Cai ◽

Gabriel Antoniu ◽

Luc Bougé

Keyword(s):

Data Management ◽

Management System ◽

Large Scale ◽

Data Access ◽

Relevant Information ◽

Data Management System ◽

Specific Information ◽

Distributed Data ◽

Distributed Data Management ◽

Self Adaptive

Bringing introspection into BlobSeer: Towards a self-adaptive distributed data management system Introspection is the prerequisite of autonomic behavior, the first step towards performance improvement and resource usage optimization for large-scale distributed systems. In grid environments, the task of observing the application behavior is assigned to monitoring systems. However, most of them are designed to provide general resource information and do not consider specific information for higher-level services. More precisely, in the context of data-intensive applications, a specific introspection layer is required to collect data about the usage of storage resources, data access patterns, etc. This paper discusses the requirements for an introspection layer in a data management system for large-scale distributed infrastructures. We focus on the case of BlobSeer, a large-scale distributed system for storing massive data. The paper explains why and how to enhance BlobSeer with introspective capabilities and proposes a three-layered architecture relying on the MonALISA monitoring framework. We illustrate the autonomic behavior of BlobSeer with a self-configuration component aiming to provide storage elasticity by dynamically scaling the number of data providers. Then we propose a preliminary approach for enabling self-protection for the BlobSeer system, through a malicious client detection component. The introspective architecture has been evaluated on the Grid'5000 testbed, with experiments that prove the feasibility of generating relevant information related to the state and behavior of the system.

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Risk Management on Information Security in ABC Company

ACMIT Proceedings ◽

10.33555/acmit.v4i1.60 ◽

2017 ◽

Vol 4 (1) ◽

pp. 62-66

Author(s):

Luyen Ha Nam

Keyword(s):

Risk Management ◽

Information Security ◽

Data Management ◽

Management System ◽

Risk Mitigation ◽

Data Management System ◽

Data Centre ◽

Long Time ◽

Mitigation Action ◽

Better Than

From long, long time ago until nowadays information still takes a serious position for all aspect of life, fromindividual to organization. In ABC company information is somewhat very sensitive, very important. But how wekeep our information safe, well we have many ways to do that: in hard drive, removable disc etc. with otherorganizations they even have data centre to save their information. The objective of information security is to keep information safe from unwanted access. We applied Risk Mitigation Action framework on our data management system and after several months we have a result far better than before we use it: information more secure, quickly detect incidents, improve internal and external collaboration etc.

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