scholarly journals Vitek 2 Automated Identification System and Kocuria kristinae

2005 ◽  
Vol 43 (11) ◽  
pp. 5832-5832 ◽  
Author(s):  
M. Boudewijns ◽  
J. Vandeven ◽  
J. Verhaegen ◽  
R. Ben-Ami ◽  
Y. Carmeli
Keyword(s):  
Identification System ◽  
Automated Identification ◽  
Vitek 2 ◽  
Kocuria Kristinae

scholarly journals Marine Network Protocols and Security Risks

2021 ◽  
Vol 1 (2) ◽  
pp. 239-251
Author(s):  
Ky Tran ◽  
Sid Keene ◽  
Erik Fretheim ◽  
Michail Tsikerdekis
Keyword(s):  
Network Protocols ◽  
Network Protocol ◽  
Identification System ◽  
Automated Identification ◽  
Security Risks ◽  
Domain Specific ◽  
Protocol Security ◽  
Challenges And Opportunities

Marine network protocols are domain-specific network protocols that aim to incorporate particular features within the specialized marine context that devices are implemented in. Devices implemented in such vessels involve critical equipment; however, limited research exists for marine network protocol security. In this paper, we provide an analysis of several marine network protocols used in today’s vessels and provide a classification of attack risks. Several protocols involve known security limitations, such as Automated Identification System (AIS) and National Marine Electronic Association (NMEA) 0183, while newer protocols, such as OneNet provide more security hardiness. We further identify several challenges and opportunities for future implementations of such protocols.

scholarly journals Listerial meningitis: Atypical strain and misidentification by the automated identification system (Vitek2)

2019 ◽  
Vol 75 (1) ◽  
pp. 99-101
Author(s):  
Gurpreet Singh Bhalla ◽  
Mahadevan Kumar ◽  
Pooja Mahajan ◽  
Kavita Sahai
Keyword(s):  
Identification System ◽  
Automated Identification

scholarly journals Design and Implementation of an Efficient Rose Leaf Disease Detection using K-Nearest Neighbours

2020 ◽  
Vol 9 (3) ◽  
pp. 21-27
Keyword(s):  
Plant Disease ◽  
Spot Size ◽  
Extraction Process ◽  
Disease Classification ◽  
Identification System ◽  
Automated Identification ◽  
Accurate Identification ◽  
Leaf Disease ◽  
Key Factor ◽  
Agriculture Products

Plants are prone to different diseases caused by multiple reasons like environmental conditions, light, bacteria, and fungus. These diseases always have some physical characteristics on the leaves, stems, and fruit, such as changes in natural appearance, spot, size, etc. Due to similar patterns, distinguishing and identifying category of plant disease is the most challenging task. Therefore, efficient and flawless mechanisms should be discovered earlier so that accurate identification and prevention can be performed to avoid several losses of the entire plant. Therefore, an automated identification system can be a key factor in preventing loss in the cultivation and maintaining high quality of agriculture products. This paper introduces modeling of rose plant leaf disease classification technique using feature extraction process and supervised learning mechanism. The outcome of the proposed study justifies the scope of the proposed system in terms of accuracy towards the classification of different kind of rose plant disease.

scholarly journals The Epidemiological Profile of Candidemia at an Indian Trauma Care Center

2014 ◽  
Vol 6 (02) ◽  
pp. 096-101 ◽  
Author(s):  
Vibhor Tak ◽  
Purva Mathur ◽  
Prince Varghese ◽  
Jacinta Gunjiyal ◽  
Immaculata Xess ◽  
...  
Keyword(s):  
Antifungal Susceptibility ◽  
Fatal Outcome ◽  
Antifungal Drugs ◽  
Identification System ◽  
Trauma Patients ◽  
Candida Spp ◽  
Tetrazolium Chloride ◽  
Blood Samples ◽  
Hospital Infection Control ◽  
Vitek 2

ABSTRACT Purpose: Candida spp. is a common cause of bloodstream infections. Candidemia is a potentially fatal infection that needs urgent intervention to salvage the patients. Trauma patients are relatively young individuals with very few comorbidities, and the epidemiology of candidemia is relatively unknown in this vulnerable and growing population. In this study, we report the epidemiology of candidemia in a tertiary care Trauma Center of India. Materials and Methods: The study was conducted from January 2009 to July 2012. All patients from whose blood samples a Candida spp. was recovered were included in this study. A detailed history and follow up of the patients was done. The isolates of Candida were identified to the species level. The speciation was done by conventional methods, including morphology on Corn Meal Agar, color development on Triphenyl Tetrazolium Chloride Agar and CHROMagar, and germ tube tests. The VITEK 2 YST ID colorometric card, a fully automated identification system was also used. Antifungal susceptibility was performed using the VITEK 2 system. Results: A total of 212 isolates of the Candida species were recovered from blood samples of 157 patients over the study period. Candida tropicalis, 82 (39%), was the most common, followed by C. parapsilosis, 43 (20%), C. albicans, 29 (14%), C. glabrata, 24 (11%), C. rugosa, 20 (9%), C. hemulonii,; 6 (3%), C. guilliermondii, 4 (2%), C. famata, 3 (1.5%), and C. lusitaniae 1 (0.5%). Out of all the candidemia patients, 68 (43%) had a fatal outcome. Fluconazole and Amphotericin B resistance was seen in seven (3.3%) and seven (3.3%) of the isolates, respectively. Conclusion: Candidemia is a significant cause of mortality in trauma patients in our center, with C. tropicalis and C. parapsilosis being the predominant pathogens. Resistance to antifungal drugs is a matter of concern. Better hospital infection control practices and good antibiotic stewardship policies could possibly help in reducing the morbidity and mortality associated with candidemia.

scholarly journals Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3782 ◽  
Author(s):  
Julius Venskus ◽  
Povilas Treigys ◽  
Jolita Bernatavičienė ◽  
Gintautas Tamulevičius ◽  
Viktor Medvedev
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Processing Time ◽  
Heavy Traffic ◽  
Sensor Data ◽  
Identification System ◽  
Automated Identification ◽  
Maritime Traffic ◽  
Abnormal Movements ◽  
The Neural Network

The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel’s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly.

scholarly journals Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

2019 ◽  
Vol 90 (1) ◽  
pp. 69-76 ◽  
Author(s):  
Hye-Won Hwang ◽  
Ji-Hoon Park ◽  
Jun-Ho Moon ◽  
Youngsung Yu ◽  
Hansuk Kim ◽  
...  
Keyword(s):  
Reference Points ◽  
Identification System ◽  
Learning Method ◽  
Detection Error ◽  
Hard Tissue ◽  
Automated Identification ◽  
Viable Option ◽  
The Mean ◽  
Clinically Significant ◽  
Better Than

ABSTRACT Objectives To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners. Materials and Methods The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified. On the 283 test images, the same 80 landmarks were identified by AI and human examiners twice. Statistical analyses were conducted to detect whether any significant differences between AI and human examiners existed. Influence of image factors on those differences was also investigated. Results Upon repeated trials, AI always detected identical positions on each landmark, while the human intraexaminer variability of repeated manual detections demonstrated a detection error of 0.97 ± 1.03 mm. The mean detection error between AI and human was 1.46 ± 2.97 mm. The mean difference between human examiners was 1.50 ± 1.48 mm. In general, comparisons in the detection errors between AI and human examiners were less than 0.9 mm, which did not seem to be clinically significant. Conclusions AI showed as accurate an identification of cephalometric landmarks as did human examiners. AI might be a viable option for repeatedly identifying multiple cephalometric landmarks.

scholarly journals Identification of Candida auris by Use of the Updated Vitek 2 Yeast Identification System, Version 8.01: a Multilaboratory Evaluation Study

2019 ◽  
Vol 57 (11) ◽  
Author(s):  
Georges Ambaraghassi ◽  
Philippe J. Dufresne ◽  
Simon F. Dufresne ◽  
Émilie Vallières ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Evaluation Study ◽  
Multidrug Resistant ◽  
Identification System ◽  
Correct Identification ◽  
South American ◽  
The South ◽  
Candida Auris ◽  
Yeast Identification ◽  
Content Type ◽  
Vitek 2

ABSTRACT Candida auris is an emerging multidrug-resistant yeast that has been systematically incorrectly identified by phenotypic methods in clinical microbiology laboratories. The Vitek 2 automated identification system (bioMérieux) recently included C. auris in its database (version 8.01). We evaluated the performance of the Vitek 2 YST ID card to identify C. auris and related species. A panel of 44 isolates of Candida species (C. auris, n = 35; Candida haemulonii, n = 5; Candida duobushaemulonii, n = 4) were tested by three different hospital-based microbiology laboratories. Among 35 isolates of C. auris, Vitek 2 yielded correct identification in an average of 52% of tested samples. Low-discrimination (LD) results with an inability to distinguish between C. auris, C. duobushaemulonii, and Candida famata were obtained in an average of 27% of samples. Incorrect identification results were obtained in an average of 21% of samples, the majority (91%) of which were reported as C. duobushaemulonii and the remaining 9% of which were reported as Candida lusitaniae/C. duobushaemulonii. The proportion of correct identification was not statistically different across different centers (P = 0.78). Stratification by genetic clades demonstrated that 100% (n = 8) of the strains of the South American clade were correctly identified compared to 7% (n = 10) and 0% (n = 4) from the African and East Asian clades, respectively. None of the non-auris Candida strains (n = 9) were incorrectly identified as C. auris. Our results show that the Vitek 2 (version 8.01) yeast identification system has a limited ability to correctly identify C. auris. These data suggest that an identification result for C. duobushaemulonii should warrant further testing to rule out C. auris. The overall performance of the Vitek 2 seems to differ according to C. auris genetic clade, with the South American isolates yielding the most accurate results.

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