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

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

Progress on lead-free metal halide perovskites for photovoltaic applications: a review

ABSTRACT: Metal halide perovskites have revolutionized the field of solution-processable photovoltaics. Within just a few years, the power conversion efficiencies of perovskite-based solar cells have been improved significantly to over 20%, which makes them now already comparably efficient to silicon-based photovoltaics. This breakthrough in solution-based photovoltaics, however, has the drawback that these high efficiencies can only be obtained with lead-based perovskites and this will arguably be a substantial hurdle for various applications of perovskite-based photovoltaics and their acceptance in society, even though the amounts of lead in the solar cells are low. This fact opened up a new research field on lead-free metal halide perovskites, which is currently remarkably vivid. We took this as incentive to review this emerging research field and discuss possible alternative elements to replace lead in metal halide perovskites and the properties of the corresponding perovskite materials based on recent theoretical and experimental studies. Up to now, tin-based perovskites turned out to be most promising in terms of power conversion efficiency; however, also the toxicity of these tin-based perovskites is argued. In the focus of the research community are other elements as well including germanium, copper, antimony, or bismuth, and the corresponding perovskite compounds are already showing promising properties. GRAPHICAL ABSTRACT: [Image: see text

Modern Mashrabiyas with High-tech Daylight Responsive Systems

The environmental and social role of closed oriental balconies (Mashrabiyas) remains a significant vernacular aspect of Middle Eastern architecture. However, nowadays this traditional Islamic window element with its characteristic latticework is used to cover entire buildings as an oriental ornament, providing local identity and a sun-shading device for cooling. In fact, designers have reinvented this vernacular Islamic wooden structure into high-tech responsive daylight systems – often on a massive scale and using computer technology – not only to cover tall buildings as an oriental ornament, but also as a major responsive daylight system.It is possible to use the traditional architectural Islamic elements of the Middle East for problem solving design solutions in present-day architecture. The potential for achieving these solutions lies in the effective combination of the design concepts of the traditional elements with new smart materials and technologies. Hence, modern mashrabiyas could be a major responsive daylight system. Contextual information drawn from relevant theory, ethnography and practice is used to form a methodological framework for the modern mashrabiyas with high-tech responsive daylight systems. The main results set boundaries for the viability of computer technology to produce mashrabiyas and promote a sustainable way of reviving their  use within Middle Eastern buildings

Multidrug-Resistant Bacterial Pathogens and Public Health: The Antimicrobial Effect of Cyanobacterial-Biosynthesized Silver Nanoparticles

Background: Cyanobacteria are considered as green nano-factories. Manipulation of the size of biogenic silver nanoparticles is needed to produce particles that suit the different applications such as the use as antibacterial agents. The present study attempts to manipulate the size of biosynthesized silver nanoparticles produced by cyanobacteria and to test the different-sized nanoparticles against pathogenic clinical bacteria. Methods: Cyanothece-like. coccoid unicellular cyanobacterium was tested for its ability to biosynthesize nanosilver particles of different sizes. A stock solution of silver nitrate was prepared from which three different concentrations were added to cyanobacterial culture. UV-visible spectroscopy and FTIR were conducted to characterize the silver nanoparticles produced in the cell free filtrate. Dynamic Light Scattering (DLS) was performed to determine the size of the nanoparticles produced at each concentration. The antimicrobial bioassays were conducted on broad host methicillin-resistant Staphylococcus aureus (MRSA), and Streptococcus sp., was conducted to detect the nanoparticle size that was most efficient as an antimicrobial agent. Results. The UV-Visible spectra showed excellent congruence of the plasmon peak characteristic of nanosilver at 450 nm for all three different concentrations, varying peak heights were recorded according to the concentration used. The FTIR of the three solutions revealed the absence of characteristic functional groups in the solution. All three concentrations showed spectra at 1636 and 2050–2290 nm indicating uniformity of composition. Moreover, DLS analysis revealed that the silver nanoparticles produced with lowest concentration of precursor AgNO3 had smallest size followed by those resulting from the higher precursor concentration. The nanoparticles resulting from highest concentration of precursor AgNO3 were the biggest in size and tending to agglomerate when their size was above 100 nm. The three types of differently-sized silver nanoparticles were used against two bacterial pathogenic strains with broad host range; MRSA-(Methicillin-resistant Staphylococcus aureus) and Streptococcus sp. The three types of nanoparticles showed antimicrobial effects with the smallest nanoparticles being the most efficient in inhibiting bacterial growth. Discussion: Nanosilver particles biosynthesized by Cyanothece-like cyanobacterium can serve as antibacterial agent against pathogens including multi-drug resistant strains. The most appropriate nanoparticle size for efficient antimicrobial activity had to be identified. Hence, size-manipulation experiment was conducted to find the most effective size of nanosilver particles. This size manipulation was achieved by controlling the amount of starting precursor. Excessive precursor material resulted in the agglomeration of the silver nanoparticles to a size greater than 100 nm. Thereby decreasing their ability to penetrate into the inner vicinity of microbial cells and consequently decreasing their antibacterial potency. Conclusion: Antibacterial nanosilver particles can be biosynthesized and their size manipulated by green synthesis. The use of biogenic nanosilver particles as small as possible is recommended to obtain effective antibacterial agents

Laser Treatment Increases the Antimicrobial Efficacy of Cyanobacterial Extracts against Staphylococcusaureus (SA) and Methicillin-resistantStaphylococcus aureus (MRSA)

Staphylococcus aureus (SA) and Methicillin-resistant Staphylococcus aureus (MRSA) are multidrug-resistant bacterial pathogens. A novel approach needs to be followed to combat these pathogens in an ecofriendly manner. Cyanobacterial extracts were previously proven to be affective as antimicrobial agents. To capitalize on this, laser treatments were used to increase their antimicrobial efficacy. Two cyanobacterial strains isolated from Al-Ahsa were identified using molecular methods. Their aqueous extracts were used in the antimicrobial bioassay for these two bacterial pathogens. The first group of aqueous extracts were exposed directly to laser treatment and used in antibacterial bioassay. In parallel, the cyanobacterial biomass of the two isolates was exposed to the laser, then aqueous extracts were prepared. The third group of extracts were not exposed to the laser and were used as a control. Time and distance were the factors tested as they affected the dose of the laser, both individually and in combination. In addition, accessory pigment estimation in extracts before and after laser exposure of extracts was also determined. The two cyanobacterial strains were identified as Thermoleptolyngbya sp. and Leptolyngbya sp. and the molecular analysis also confirmed the identity of pathogenic bacteria. The untreated cyanobacterial aqueous extracts had little effect against the two bacterial strains. In contrast, the extract directly exposed to the laser was significantly more effective, with an inhibition zone of 22.0 mm in the case of a time of 32 min and distance of 10 cm against S. aureus. Accessory pigment composition increased in extracts directly exposed to the laser. This is the first case report on the effect of lasers on enhancing the antimicrobial profile of cyanobacterial extracts against SA and MRSA bacterial pathogens, as well as enhancing accessory pigment content. The laser dose that was most effective was that of 32 min time and 10 cm distance of Thermoleptolyngbya sp. extract directly exposed to the laser, which highlights the importance of time for increasing the laser dose and consequently increasing its antimicrobial impact