GIS-based Spatial Analysis of Population Density in Kuwait, 1957 to 2020

Population density is among the most insightful demographical metrics for urban planners, land developers and researchers in the geography sector. In this article, a Geographical Information System (GIS) framework is designed to study the spatial and temporal population density trends and investigate whether any notable patterns may be attributed to the socioeconomic factors prevalent in each period. The methodology involves collecting spatial population data over time and using GIS to overlay the population density changes against various socioeconomic parameters in Kuwait. The results indicate that the population density is strongly correlated to the national and international economic and political conditions of each respective period. Furthermore, the population tended to form high density clusters. The findings suggest that future development shall aim to address the impacts of high population density, and the effects of the pandemic and global energy and economic turbulence on Kuwait’s labor market and lifestyle over 2020-2022

MERS-CoV transmitted from animal-to-human vs MERSCoV transmitted from human-to-human: Comparison of virulence and therapeutic outcomes in a Saudi hospital

Purpose: To examine virulence (severity of disease and/or symptoms) and response to therapy (medications, supportive measures) between confirmed cases of MERS-CoV animal-to-human transmission compared with cases resulting from human-to-human transmission.Methods: The records for laboratory-confirmed MERS-CoV infections that were diagnosed at King Fahad Hofuf Hospital (Al-Ahsa, Saudi Arabia) from April 1, 2012 to November 30, 2016 were reviewed retrospectively.Results: There were 107 laboratory-confirmed MERS-CoV cases. Transmission of the virus from animal-to-human was less common (21.4 vs 78.6 %). The human-to-human transmission group had a higher mortality rate (53.57 vs 39.13 %). Patients in this group also had higher APACHEE II (11.2 vs 23, p = 0.043), SOFA scores (10.9 vs 12.55, p = 0.076), and higher rates of sepsis (17.39 vs 26.19 %, p = 0.582) and septic shock (13.04 vs 20.23 %, p = 0.555). The infections were more severe in the humanto- human transmission group; patients had increased rates of intensive care unit (ICU) admission (43.47 vs 51.19 %), decreased time from symptom onset until ICU admission, and greater need for mechanical ventilation (8 days vs 4 days, p = 0.041, and 6 days vs 4 days, respectively), longer time to respond to antiviral treatment and resolve the infection (5 days vs 11 days and 7 days vs 13 days, respectively) and a shorter time from the beginning of symptoms until death (11 days vs 5 days, p = 0.048).Conclusion: MERS-CoV transmitted from human-to-human was more virulent, resulted in higher casemortality rates and required more ICU treatment.Keywords: Animal-to-human, Human-to-human, MERS-CoV, Outcomes, Primary infection, Secondary infection, Virulenc

Assessment of tigecycline prescription and patients' outcomes at three different hospitals in Saudi Arabia.

Purpose: To investigate tigecycline prescription and patient outcomes in the Kingdom of Saudi Arabia (KSA). Methods: A retrospective observational study was conducted in three KSA government hospitals, between January, 2013 and May, 2014. The patients were identified from electronic prescription records; data were retrieved by trained researchers. Results: Thirty-seven patients who received tigecycline were included (mean age, 52.5 years; range, 17 92); 51.4 % were female. Tigecycline was prescribed for sepsis (59.5 %), pneumonia (21.6 %), and/or intra-abdominal infections (13.5 %). The majority of the patients (86.5 %) were prescribed tigecycline in intensive care unit (ICU) and the remaining patients were in the general medical ward. APCHE II score at the beginning of treatment was 16.8 ± 4.3, indicating severe disease. Susceptibility testing revealed 22 different bacterial pathogens, most commonly Acinetobacter baumannii (20 patients) and Klebsiella pneumoniae (14 patients). A significant proportion (56.7 %) was polymicrobial and 16.2 % involved suspected resistant pathogens. Sixteen patients recovered (5 on tigecycline alone, 5 with additional antimicrobials, and six switched to alternatives) while 21 patients died (nine on tigecycline alone, 12 with additional antimicrobials). Conclusions: The study revealed that tigecycline prescription was conducted according to marketing authorizations and national guidelines. Infection severity/stage and comorbidities may influence patients’ response, and explain some of the poor outcomes

Capturing wheat phenotypes at the genome level

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence

Supplementary File for Capturing wheat phenotypes at the genome level

Supplementary S1: Yield and related traits in bread wheat. Table S1: Examples of genomic regions, candidate and cloned genes for yield and related traits in bread wheat. Supplementary S2: Drought tolerance. Table S2: Examples of genomic regions and candidate genes for drought tolerance. Supplementary S3: Heat tolerance. Table S3. Examples of genomic regions and candidate genes for heat tolerance. Supplementary S4: salinity tolerance in bread wheat. Table S4. Examples of genomic regions and candidate genes for salinity tolerance in bread wheat. Supplementary S5: Frost tolerance. Supplementary S6: Disease resistance. Table S5. Examples of genomic regions, candidate and cloned genes mapped for disease resistance in wheat species. Supplementary S7 insect and mite resistance. Table S6. Examples of genomic regions and candidate genes mapped for insect and mite resistance. Supplementary S8: Quality traits. Table S7. Examples of genomic regions, candidate and cloned genes for quality traits.Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.Peer reviewe

COMPUTER AIDED DIAGNOSIS SYSTEM FOR AUTOMATIC TWO STAGES CLASSIFICATION OF BREAST MASS IN DIGITAL MAMMOGRAM IMAGES

Breast cancer is the most frequent cancer type that is diagnosed in women. The exact causes of such cancer are still unknown. Early and precise detection of breast cancer using mammogram images or biopsy to provide the required medications can increase the healing percentage. There are much current research efforts to developed a computer aided diagnosis (CAD) system based on mammogram images for detecting and classification of breast masses. In this research, a CAD system is developed for automated segmentation and two-stages classification of breast masses. The first stage includes the classification of the masses into seven classes (normal, calcification, circumscribed, spiculated, ill-defined, architectural distortion, asymmetry), which is done using probabilistic neural network (PNN). The second classification stage is to define the severity of abnormality into two classes (Benign and Malignant) which were done using support vector machine (SVM). The results of applying the proposed method on two mammogram image show that the accuracy of detection and segmentation of the breast mass was 99.8% for mammographic image analysis society database (MIAS-DB) with 322 images and 97.5% for breast cancer digital repository (BCDR), BCDR-F03 and BCDR-DN01 with 936 images, while for the first classification stage has accuracy of 97.08%, sensitivity of 98.30% and specificity of 89.8%, and the second classification stage has an accuracy of 99.18%, sensitivity of 98.42% and specificity of 94.90%

FACE RECOGNITION WITH ILLUMINATION VARYING CONDITIONS AND OCCLUSIONS

Face recognition with illumination varying conditions and occlusions is one of the most important challenges in the field of digital image processing. Despite of the fact that a number of studies (Patel & Yagni, 2013; Azeem, Sharif, Raza & Murtaza, 2014) have improved the accuracy of different techniques by normalizing and compensating the illumination variations using some pre-processing methods, a lot of these methods are still facing many serious challenges with illumination changes and occlusion. In this paper, we suggest the use of tow pre-processing methods that will have a great impact on the performance and the robustness of the recognition procedures in case small sample size (SSS) as the training set