Data Transformation Algorithm for Reliable Bacterial Concentration Detection Using the Impedance Method

Author(s):  
Marco Grossi Massimo Lanzoni
2020 ◽  
Vol 139 ◽  
pp. 213-221
Author(s):  
C Birkett ◽  
R Lipscomb ◽  
T Moreland ◽  
T Leeds ◽  
JP Evenhuis

Flavobacterium columnare immersion challenges are affected by water-related environmental parameters and thus are difficult to reproduce. Whereas these challenges are typically conducted using flow-through systems, use of a recirculating challenge system to control environmental parameters may improve reproducibility. We compared mortality, bacterial concentration, and environmental parameters between flow-through and recirculating immersion challenge systems under laboratory conditions using 20 rainbow trout families. Despite identical dose concentration (1:75 dilution), duration of challenge, lot of fish, and temperature, average mortality in the recirculating system (42%) was lower (p < 0.01) compared to the flow-through system (77%), and there was low correlation (r = 0.24) of family mortality. Mean days to death (3.25 vs. 2.99 d) and aquaria-to-aquaria variation (9.6 vs. 10.4%) in the recirculating and flow-through systems, respectively, did not differ (p ≥ 0.30). Despite 10-fold lower water replacement rate in the recirculating (0.4 exchanges h-1) compared to flow-through system (4 exchanges h-1), differences in bacterial concentration between the 2 systems were modest (≤0.6 orders of magnitude) and inconsistent throughout the 21 d challenge. Compared to the flow-through system, dissolved oxygen during the 1 h exposure and pH were greater (p ≤ 0.02), and calcium and hardness were lower (p ≤ 0.03), in the recirculating system. Although this study was not designed to test effects of specific environmental parameters on mortality, it demonstrates that the cumulative effects of these parameters result in poor reproducibility. A recirculating immersion challenge model may be warranted to empirically identify and control environmental parameters affecting mortality and thus may serve as a more repeatable laboratory challenge model.


Author(s):  
Iago Smanio Saad ◽  
Gilmar Guimaraes ◽  
CLEUDMAR ARAÚJO ◽  
Gabriela Lima Menegaz

2020 ◽  
Author(s):  
Ramachandro Majji

BACKGROUND Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. OBJECTIVE Propose an automatic prediction system for classifying cancer to malignant or benign. METHODS This paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is the data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer-based on the reduced dimension features to produce a satisfactory result. RESULTS The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, the maximal sensitivity of 95.95%, and the maximal specificity of 96.96%. CONCLUSIONS The resulted output of the proposed JayaALO-based DeepRNN is used for cancer classification.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sayar Singh Shekhawat ◽  
Harish Sharma ◽  
Sandeep Kumar ◽  
Anand Nayyar ◽  
Basit Qureshi

Antibiotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 194
Author(s):  
Andrea Miró-Canturri ◽  
Rafael Ayerbe-Algaba ◽  
Manuel Enrique Jiménez-Mejías ◽  
Jerónimo Pachón ◽  
Younes Smani

The stimulation of the immune response to prevent the progression of an infection may be an adjuvant to antimicrobial treatment. Here, we aimed to evaluate the efficacy of lysophosphatidylcholine (LPC) treatment in combination with colistin in murine experimental models of severe infections by Acinetobacter baumannii. We used the A. baumannii Ab9 strain, susceptible to colistin and most of the antibiotics used in clinical settings, and the A. baumannii Ab186 strain, susceptible to colistin but presenting a multidrug-resistant (MDR) pattern. The therapeutic efficacies of one and two LPC doses (25 mg/kg/d) and colistin (20 mg/kg/8 h), alone or in combination, were assessed against Ab9 and Ab186 in murine peritoneal sepsis and pneumonia models. One and two LPC doses combined with colistin and colistin monotherapy enhanced Ab9 and Ab186 clearance from spleen, lungs and blood and reduced mice mortality compared with those of the non-treated mice group in both experimental models. Moreover, one and two LPC doses reduced the bacterial concentration in tissues and blood in both models and increased mice survival in the peritoneal sepsis model for both strains compared with those of the colistin monotherapy group. LPC used as an adjuvant of colistin treatment may be helpful to reduce the severity and the resolution of the MDR A. baumannii infection.


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