Molecular characterization of two microalgal strains in Egypt and investigation of the antimicrobial activity of their extracts

The emergence of new pathogens and the increasing drug-resistance of recognized ones pose a difficult challenge. One way that this challenge is being addressed is through the discovery of new cost-effective drug resources in the form of bioactive compounds. Algae represent a promising source of bioactive compounds in this regard. In the present research, we used molecular and phylogenetic analysis to isolate and identify two microalgal strains. We found that one strain belonged to the phylum chrysophyta and the other to the cyanobacteria. We also investigated the antimicrobial activity of some of the lipophilic extracts of the two microalgal strains. Several fractions showed high individual antimicrobial bioactivity against multidrug-resistant Salmonella sp., Citrobacter sp., Aspergillus niger and Aspergillus flavus. Fraction III from Poterioochromonas malhamensis showed the highest level of activity against two multidrug-resistant bacterial pathogens. The inhibition zone diameter was 1.4 cm for Salmonella and 1.4 cm for Citrobacter. Meanwhile, another lipophilic fraction from the cyanobacterium Synechocystis salina showed broad-spectrum bioactivity (inhibition zone diameter of 0.9 cm for Aspergillus niger, 1 cm for Citrobacter and 0.9 cm for Salmonella). One lipophilic fraction from Aphanizomenon showed antifungal bioactivity against Aspergillus niger and Aspergillus flavus, where the inhibition zone diameter was 1.1 cm and 1.0 cm, respectively. The study highlights the antimicrobial bioactivity of extracts from local microalgae and emphasizes the importance of carrying out screening programs for those microorganisms

The homotopy analysis method for q-difference equations

AbstractThe q-difference equations are kind of important problems in q-calculus and applied mathematics. In this paper, the homotopy analysis method is extended to find approximate solution for some of q-differential equations. The q-diffusion equation and some examples are analytically investigated. The series solutions obtained by the proposed method are checked by reducing the solutions of q-calculus problems to h-calculus approximate solutions when q→1

Anticancer activity of Cyanothece sp. strain extracts from Egypt: First record

AbstractObjectiveTo assess the anticancer activity of eight cyanobacterial hydrophilic extracts on Ehrlich ascites carcinoma cell line.MethodsThe cyanobacterial strains used in the investigation were collected from diverse habitats in Egypt. The initial cytotoxicity test of cyanobacterial hydrophilic extracts was carried out by MTT assay. The in vitro anticancer activity of the four most active extracts was performed on MCF-7 cells using sulforhodamine B assay. Morphological and molecular techniques were used to characterise identity of the isolate from which the most potent cytotoxic extract was obtained.ResultsExtracts from four cyanobacterial strains had higher cytotoxic activities scoring 76.68%, 77.70%, 76.70% and 74.45%, respectively. A considerable anticancer effect was only detected when the concentrated extracts were used. One cyanobacterial extract gave the highest anticancer activity on human breast adenocarcinoma cell line (57.6% of inhibition) as compared to control. The isolate was best-matched to Cyanothece sp. with sequence resemblance 98% to Cyanothece sp. strain PCC7564 and the phylogenetic analysis confirmed its close identity to the Cyanothece genus.ConclusionsThis is the first study to report the anticancer effect of aqueous extracts derived from the unicellular Cyanothece sp. from Egypt and its potential as a plausible candidate for future mass biotechnological applications

Evaluation of Mapping Accuracy of High-Resolution Stereoscopic Satellite Images

High resolution satellite images is still used in large scale mapping due to the need to produce fast products. High resolution stereoscopic satellite images present good enough 3d products that include the benefits of large-scale coverage and low-cost products. A stereopair of IKONOS satellite is used in this research that covers a part of North Sudan country. The study handles the 3d mapping accuracy of using stereoscopic satellite images. The study gives a spotlight on the accuracy in X, Y, Z and the space vector R. Another view of this study the N, E and elevation is indicated. The research environment is mainly ENVI software due to its capabilities of topographic processing module. Some distributed set of ground points (control and tie) was determined on the images and then observed using GPS surveying. Several experiments have been performed to evaluate the resulted mapping product

Microalgal culture in photo-bioreactor for biodiesel production: case studies from Egypt

Biodiesel production from three local microalgae from Egypt was investigated. These microalgae strains differ in their growth pattern as one of the cyanobacterial strainsis filamentous mat-forming  Phormidium sp. whereas the other strain is coccoid colony-forming  Microcystis sp. The third is coccoid yellow-green Botrydiopsis sp. The mass productivity for the strains in a photobioreactor usingsemi-continuous culture was arranged as: Microcystissp.>  Botrydiopsis sp.> Phormidium sp. The mass productivity can be increased by increasing the illumination periodin case of  Botrydiopsis sp. and Microcystis sp. The lipid content was determined by using different solvents for lipid extraction. The  Botrydiopsis sp. gave the highest lipidcontent (48%) for  Botrydiopsis sp. cultured in Oscillatoria medium. Microcystis sp. had (28%) lipid content while the Phormidium  sp. had the lowest lipid content (15%). The major components of the fatty acid compositions in different algal species studied were linoleic, palmitic, oleicand stearic. In conclusion, the cultivation of microalgae inphoto-bioreactor has given high biomass productivity by applying semi-continuous feeding technique. The highest mass productivity doesn’t mean the highest lipid content. The Gas chromatography analysis showed that the algae oils have the suitable fatty acid composition for biodiesel production

Variables Affecting the Mothers Access to Quality Care during Childbirth using the Neural Networks and Logistic Regression Models

Quality pregnancy and birth care is crucial in reducing maternal and child mortality in Egypt, requiring both supply and demand interventions. Using data from the Egypt Demographic Health Survey 2014, a neural networks and logistic regression models were built to determine demographic, social, and economic determinants affecting mothers access to care during childbirth. The study found that mothers working status had a significant impact on access to care, with an inverse relationship. Logistic regression outperformed neural networks in analyzing the relationship between explanatory variables and mothers access to care during childbirth

Deep Learning-Based Prediction of Alzheimer’s Disease Using Microarray Gene Expression Data

Alzheimer’s disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer’s disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer’s disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA–CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD–CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent genes. Integrating gene selection methodologies with deep learning architectures presents a promising framework for elevating AD prediction and promoting precision medicine in neurodegenerative disorders. Ongoing research endeavors aim to generalize this approach for diverse applications, explore alternative gene selection techniques, and investigate a variety of deep learning architectures

Using Voice Technologies to Support Disabled People

In recent years, significant strides have been made in speech and speaker recognition systems, owing to the rapid evolution of data processing capabilities. Utilizing a speech recognition system facilitates straightforward and efficient interaction, especially for individuals with disabilities. This article introduces an automatic speech recognition (ASR) system designed for seamless adaptation across diverse platforms. The model is meticulously described, emphasizing clarity and detail to ensure reproducibility for researchers advancing in this field. The model’s architecture encompasses four stages: data acquisition, preprocessing, feature extraction, and pattern recognition. Comprehensive insights into the system’s functionality are provided in the Experiments and Results section. In this study, an ASR system is introduced as a valuable addition to the advancement of educational platforms, enhancing accessibility for individuals with visual disabilities. While the achieved recognition accuracy levels are promising, they may not match those of certain commercial systems. Nevertheless, the proposed model offers a cost-effective solution with low computational requirements. It seamlessly integrates with various platforms, facilitates straightforward modifications for developers, and can be tailored to the specific needs of individual users. Additionally, the system allows for the effortless inclusion of new words in its database through a single recording process

Improving sentiment classification using a RoBERTa-based hybrid model

IntroductionSeveral attempts have been made to enhance text-based sentiment analysis’s performance. The classifiers and word embedding models have been among the most prominent attempts. This work aims to develop a hybrid deep learning approach that combines the advantages of transformer models and sequence models with the elimination of sequence models’ shortcomings.MethodsIn this paper, we present a hybrid model based on the transformer model and deep learning models to enhance sentiment classification process. Robustly optimized BERT (RoBERTa) was selected for the representative vectors of the input sentences and the Long Short-Term Memory (LSTM) model in conjunction with the Convolutional Neural Networks (CNN) model was used to improve the suggested model’s ability to comprehend the semantics and context of each input sentence. We tested the proposed model with two datasets with different topics. The first dataset is a Twitter review of US airlines and the second is the IMDb movie reviews dataset. We propose using word embeddings in conjunction with the SMOTE technique to overcome the challenge of imbalanced classes of the Twitter dataset.ResultsWith an accuracy of 96.28% on the IMDb reviews dataset and 94.2% on the Twitter reviews dataset, the hybrid model that has been suggested outperforms the standard methods.DiscussionIt is clear from these results that the proposed hybrid RoBERTa–(CNN+ LSTM) method is an effective model in sentiment classification