Digital technologies witnessed rapid advancements in many domains such as computing, programming, cloud computing, data storing, networking, satellite broadcasting, mobile technologies, but increasingly also in the natural sciences, such as medicine, biology, chemistry, pharmacy, etc. These advancements lead to a massive explosion in data volumes which expected to exceed 175ZB (175x1021 Byte) by end of the year 2025. When data size exceeds normal manageable storable sizes, it will be called “big data”. Organizing and analyzing this “big data” with appropriate tools would produce quite valuable results helping decisions and policymakers. Big data also can assist significantly improve the life quality of much of the world’s population. The United Nations, governments, non-profitable and other organizations are utilizing big data to assist achieving the UN’s Sustainable Development Goals (SDG), a set of 17 targets pertinent to protect the natural environment, fighting hunger and poverty, reducing inequality, improving health outcomes and other things that will make life better around the world. Big data analysis could be used in many ways to enhance the understanding of the progress towards the SDGs, specify how best to meet SDGs targets, and ensure accountability. The UN has set up a task team to explore how to use big data to help achieve the SDGs. A survey by the task team found that big data projects most frequently focused on the “no poverty” goal, it was found that mobile phone data was the most common data source.

This study used the fractional B-spline collocation technique to obtain the numerical solution of fractal-fractional differential equations. The technique was considered to solve the fractal-fractional differential equations (FFDEs) with (). In this suggested technique, the B-spline of fractional order was utilised in the collocation technique. The scheme was easily attained, efficient, and relatively precise with reduced computational work numerical findings. Via the proposed technique, FFDEs can be reduced for solving a system of linear algebraic equations using an appropriate numerical approach. The verified numerical illustrative experiments were presented will show the effectiveness of the technique proposed in this study in solving FFDEs in three cases of nonlocal integral and differential operators namely power law kernel, when the kernels are exponential and the generalization of Mittag-Leffler kernel. The approximate solution is very good and accurate to the exact solution.

This study considers the diagenetic processes and the depositional settings of Lower Jurassic gypsum of the Bi'r Elghanem Formation in the northwestern of Libya. The paleo-environment study has revealed a variety of depositional environment ranging from lagoonal to fluvial deposit and evaporitic basins, which become intensely saline as a result of evaporation due to (semi-) arid environments. Samples were analyzed using elemental analysis, statistical evaluation such as Multiple Correlations, Principal Component Analysis, and mineralogical evidence to determine their mode of environmental deposition, mineralogical and geochemical composition. The geochemical results indicate that gypsum has evidence of mineral substitutions and displacement, suggesting a homologous mechanism for lithofacies and subsequent textural change. The development of gypsum minerals in a variety of lithologies and textures is aided by eustatic fluctuations in lake water level due to regional tectonism and climate.

Abstract:

Nepeta cyrenaica Quézel & Zaffran (Lamiaceae), an endemic species of Libyan flora, is here characterized for the first time for its phytochemical composition and biological activities. Phenolic composition, antioxidant and enzyme inhibiting effects of extracts, prepared from five-week-old in vitro propagated N. cyrenaica shoots, were evaluated. Extraction was performed using dichloromethane, methanol, 96% ethanol or hot distilled water. LC-MS analysis showed that the methanol extract contained the highest amount of phenolic components, especially ferulic and rosmarinic acids (1300.73 mg/kg and 528.88 mg/kg, respectively), and epigallocatechin gallate (719.05 mg/kg). The strongest antioxidant activity was recorded for aqueous extract in DPPH assay (66.91%) and dichloromethane extract in β-carotene bleaching assay (81.06%), both tested at the concentration of 2 mg/mL. Concerning α-glucosidase inhibition, dichloromethane extract was shown to possess a higher inhibition capacity than acarbose at the concentration of 2 mg/mL (95.33% vs. 88.29%). The aqueous extract exhibited higher acetylcholinesterase inhibition than the other tested extracts, which was lower compared to the positive control, galantamine. Although methanol extract contained the highest amount of polyphenolics, dichloromethane and aqueous extracts were shown to be more suitable for the extraction of bioactive components. In conclusion, endemic N. cyrenaica could be efficiently propagated through in vitro propagation protocols as a polyphenolic-rich plant with valuable medicinal potential.

Keywords: biological activities; extracts; in vitro propagation; Nepeta cyrenaica

Exact string-matching algorithms have become very supreme in many bioinformatics tools. Despite the abundance and diversity of such algorithms, exposing them to real-time experimental analysis has been critical. This study was conducted to evaluate the efficiency of ten exact-string matching algorithms on large-scale genomic sequences from a runtime perspective. To define the most efficient algorithms are qualified to handle the short alphabet used for nucleic acid coding. The methodology promoted for this study was the factorial experiment with Randomized Complete Block Design (FRCBD). Under influence of four independent parameters, four levels of pattern lengths, four levels of pattern indices, two levels of programming languages, and ten levels of algorithmic architecture. The yield of the tested algorithms was calculated in nanoseconds. One-way ANOVA and Two-way ANOVA tests with post-hoc Games-Howell test were used separately for statistical analysis. In this study two widely accepted programming languages, C# and JAVA were used to speculate the possible effect of programing language on algorithm performance.

One of the unique combinations in the mammalian genome, that revolutionized concepts in the fields of genetics and molecular

pathology is what is termed the CpG islands. However, the accurate and rapid determination of CpG islands for DNA sequences remains

experimentally and computationally challenging. The main goal of this project is to design an offline, cross-platform CpG islands detection

tool. The Algorithm implemented in this study was the traditional sliding window algorithm by using the C++ programming language.

Three datasets were used for evaluating the performance of the application. The ANK1 gene, SPTB gene, and RET gene sequence files

were obtained from NCBI. In this study, the highest CGIs were reported in ANK1 (ankyrin 1) Gene which scored 13 successive islands

whereas the lowest score was reported in RET (ret proto-oncogene) Gene which shows only 6 islands. Generally, the program fulfills the

boundary limits as expected. We strongly recommend for further work, the implementation of other algorithms in addition to the sliding

window algorithm such as the Hidden Markov Model (HMM).

Abstract

Abstract

Introduction:

Coronavirus Disease 2019 (COVID-19) is an infectious res-

piratory disease caused by SARS-CoV-2. The disease was first broke out in

Wuhan City, Hubei Province, China and subsequently spread to all countries

and was considered by WHO as a worldwide pandemic. This study is aimed

to determine the epidemiology of SARS-CoV-2 and the presence of UK va-

riants in Zintan city of Libya taking some risk factors into account. Me-

thods: In a cross-sectional retrospective study, a total of 15486 nasopha-

ryngeal swabs were collected from COVID-19 suspected patients, travelers

and people need disease-free certificates for hospital admission, etc. The

samples were collected during the period from August 2020 to June 2021

and tested using real-time RT -PCR (rRT-PCR) kits for SARS-CoV-2 and

UK variants. Age groups, sex, and monthly weather were considered as risk

factors. Results: The positivity rate of COVID-19 in Zintan city was esti-

mated to be (3891; 25.12%) for the period from August 2020 to June 2021.

Females showed significantly (p < 0.05) higher positivity rate (2100; 54%) as

compared to males (1791; 46%). Out of the 3891 positive cases, 52 were de-

ceased. The Case Fatality Rate (CFR) was 1.33 recorded significantly in cas-

es aged ≥ 65 years which was higher in males (56.66%) than females (43.33%).

The peak of the first wave of infection was recorded in October 2020 (590;

15.15%) whereas the peak of the second wave of infection was recorded in

April 2021 (727; 18.71%). The positivity rate was decreased as the tempera-

ture increased. UK variant is detected firstly in May 2021 with the percen-

tage of 6.2% of tested samples. Conclusions: Health Authorities are en-

crease phase of infection to stop transmission of the virus in the next wave.

Early detection of new variants and studying their genetic characteristics

play a valuable role in prevention and control.

Keywords

SARS

-

CoV

-

2, UK Variant, Epidemiology, Zintan,

Libya, rRT

-

PCR.