Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Fuzzy logic and deep Q learning based control for traffic lights

Traffic congestion is a major concern for many metropolises. Although it is difficult to regulate traffic flow because of numerous complexities and uncertainties, the traffic congestion problem must be mitigated in order to reduce the environmental problems related to traffic and the time lost on the roads in big cities. Intelligent traffic control methods, the use of which is increasing with the development of new methods, as opposed to conventional methods, and provide more efficient solutions, especially in traffic intersections with high traffic density. In this paper, we propose a new agent-based Fuzzy Logic assisted traffic light signal timing for traffic intersections. Deep Q-Learning algorithms and Fuzzy Logic Control (FLC) are used together in the proposed method. In this study, the proposed method and many traffic light control methods in the literature were simulated. In order to demonstrate the effectiveness of the proposed method, some of the important metrics of evaluation such as traffic congestion, air pollution, and waiting time were used in the assessment of the simulation results. In addition, with the proposed method, it has been shown that the stability and robustness of the system are increased

A Theoretical Model Proposal: Personal Innovativeness and User Involvement as Antecedents of Unified Theory of Acceptance and Use of Technology

An emerging stream of work on technology acceptance and innovation diffusion complements a large body of literature that points to users' technology acceptance behavior. In this paper, we argue theoretically that technology acceptance should be integrated into diffusion of innovation theory, so both concepts should be explained at the same framework. On the other hand, acceptance behavior is explained many other constructs, such as user satisfaction, user involvement. Unlike the previous research, we propose an overall framework to explain acceptance behavior and system implementation success. Hence we use unified theory of acceptance and use of technology. After that we try to create a linkage with this theory and personal innovativeness and user involvement as antecedents by providing some propositions. We will test these propositions in a field research for future research

Autonomic Fall Detection System

Internet of Things (IoT) and cloud computing have produced major breakthroughs in information services and applications. Internet provided the required connections to countless devices or things that have led to a significant improvement in the quality of life. In addition, using wearable devices and smart sensors has facilitated the deployment of pervasive healthcare services that provided better healthcare, and reduced life threatening risks without affecting one's privacy. In this paper, we present an architectural approach for autonomic healthcare management system and show how to use this system for autonomic fall detection, where we leverage wearable technologies, IoT, and cloud computing

Fibromyalgia frequency and course in renal transplantation patients

AbstractBackground/Aims: Musculoskeletal complications such as fibromyalgia syndrome may occur following renal transplantation. However, frequency and the course of this condition have not been deeply assessed in this group of patients. This study aimed to investigate the frequency of fibromyalgia syndrome and identify the factors associated with its occurrence in patients who underwent renal transplantation for end-stage renal disease. Methods: The demographic, clinical, and laboratory characteristics were recorded. All patients were assessed in outpatient setting for the presence of fibromyalgia and classified into three groups in the following order: Group 1, normal patients; Group 2, possible fibromyalgia; Group 3, fibromyalgia syndrome. In addition, Fibromyalgia Impact Questionnaire was administered.Results: A total of 128 patients who underwent renal transplantation were included. Three groups did not differ with regard to the age, marital status, smoking history, etiology of the renal failure and laboratory findings (p>0.05). However, female gender was more frequent among patients with fibromyalgia. Fatigue, sleep disturbance, irritable colon syndrome, depression and antidepressant use were also more common and Fibromyalgia Impact Questionnaire score was higher in patients with fibromyalgia. A multivariate logistic regression analysis identified Fibromyalgia Impact Questionnaire score and antidepressant use as significant independent predictors of fibromyalgia syndrome and possible fibromyalgia.Conclusion: Fibromyalgia syndrome shows a tendency to occur more commonly in renal transplant patients than in normal population, where depression is accompanied by these conditions. Fibromyalgia Impact Questionnaire scoring system is a useful tool to assess the presence of fibromyalgia syndrome or possible fibromyalgia syndrome in this group of patients