Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models.

Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors' knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model

Plackett–Burman randomization method for Bacterial Ghosts preparation form E. coli JM109

AbstractPlackett–Burman randomization method is a conventional tool for variables randomization aiming at optimization. Bacterial Ghosts (BGs) preparation has been recently established using methods other than the E lysis gene. The protocol has been based mainly on using critical concentrations from chemical compounds able to convert viable cells to BGs. The Minimum Inhibition Concentration (MIC) and the Minimum Growth Concentration (MGC) were the main guide for the BGs preparation. In this study, Escherichia coli JM109 DEC has been used to produce the BGs following the original protocol. The study contained a detail protocol for BGs preparation that could be used as a guide

A Robust UWSN Handover Prediction System Using Ensemble Learning.

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical

3,5-Dimethyl-1-(4-nitro­phen­yl)-1H-pyrazole

In the title pyrazole derivative, C11H11N3O2, the benzene ring is twisted [dihedral angle = 31.38 (12)°] with respect to the pyrazole ring (r.m.s. deviation = 0.009 Å). The nitro group is effectively coplanar with the benzene ring to which it is attached [O—N—C—C torsion angle = −6.5 (3)°]. Supra­molecular chains along the b axis are formed owing to π–π inter­actions [3.8653 (2) Å] between translationally related mol­ecules involving both the five- and six-membered rings

Humoral and cellular immune responses to modified hepatitis B plasmid DNA vaccine in mice

Purpose: To evaluate the immunogenicity and types of immune response of a quality-controlled modified recombinant hepatitis B surface antigen (HBsAg) plasmid encoding HBsAg in mice.Methods: The characterized plasmid DNA was used in the immunization of Balb/c mice. Three groups of mice were intramuscularly injected with three different concentrations (50, 25 and 10 μg/100 μL) of the modified plasmid. Humoral immune response was monitored by enzyme-linked immunosorbent assay (ELISA), while cellular immune response was investigated by analysis of spleen cytokine profile (TNFα, IFN γ and IL2) as well as CD69 expression level in CD4 and CD8 positive cells.Results: In general, the activated CD4 cells showing intracellular cytokines were higher than CD8 positive population of cells (p < 0.05). These findings indicate that the vaccine induced both a humoral and cellular immunity. Cytokine profile also showed high levels of TNFα, IFN γ and IL2 and CD69 expression in the group of animals immunized at a dose of 10 μg when compared to control group (p < 0.05).Conclusion: A 10 μg dose intramuscular injection of the modified DNA-based vaccine encoding HBsAg in mice induces both high humoral and cellular immune responses.Keywords: Hepatitis B virus, Plasmid DNA, Vaccine, Spleen cytokines, Humoral and cellular immune response

Sponge-Like: A New Protocol for Preparing Bacterial Ghosts

Bacterial Ghosts (BGs) received an increasing interest in the recent years for their promising medicinal and pharmaceutical applications. In this study, for the first time we introduce a new protocol for BGs production. E. coli BL21 (DE3) pLysS (Promega) was used as a model to establish a general protocol for BGs preparation. The protocol is based on using active chemical compounds in concentrations less than the Minimum Inhibition Concentration (MIC). Those chemical compounds are SDS, NaOH, and H2O2. Plackett-Burman experimental design was used to map the best conditions for BGs production. Normal and electronic microscopes were used to evaluate the BGs quality (BGQ). Spectrophotometer was used to evaluate the amount of the released protein and DNA. Agarose gel electrophoresis was used to determine the existence of any residue of DNA after each BGs preparation. Viable cells, which existed after running this protocol, were subjected to lysis by inducing the lysozyme gene carried on pLysS plasmid. This protocol is able to produce BGs that can be used in different biotechnological applications

Inhibition of growth of Leishmania donovani promastigotes by newly synthesized 1,3,4-thiadiazole analogs

AbstractLeishmania donovani, the causative agent of visceral leishmaniasis, is transmitted by sand flies and replicates intracellularly in their mammalian host cells. The emergence of drug-resistant strains has hampered efforts to control the spread of the disease worldwide. Forty-four 1,3,4-thiadiazole derivatives and related compounds were tested in vitro for possible anti-leishmanial activity against the promastigotes of L. donovani. Micromolar concentrations of these agents were used to study the inhibition of multiplication of L. donovani promastigotes. Seven compounds were identified with potential antigrowth agents of the parasite. Compound 4a was the most active at 50μM followed by compound 3a. These compounds could prove useful as a future alternative for the control of visceral leishmaniasis