Bactrian camel (Camelus bactrianus), dromedary (Camelus dromedarius) and alpaca (Vicugna pacos) are economically important livestock. Although the Bactrian camel and dromedary are large, typically arid-desert-adapted mammals, alpacas are adapted to plateaus. Here we present high-quality genome sequences of these three species. Our analysis reveals the demographic history of these species since the Tortonian Stage of the Miocene and uncovers a striking correlation between large fluctuations in population size and geological time boundaries. Comparative genomic analysis reveals complex features related to desert adaptations, including fat and water metabolism, stress responses to heat, aridity, intense ultraviolet radiation and choking dust. Transcriptomic analysis of Bactrian camels further reveals unique osmoregulation, osmoprotection and compensatory mechanisms for water reservation underpinned by high blood glucose levels. We hypothesize that these physiological mechanisms represent kidney evolutionary adaptations to the desert environment. This study advances our understanding of camelid evolution and the adaptation of camels to arid-desert environments.

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Klebsiella pneumoniae (K. pneumoniae) is involved in several hospital and community-acquired infections. The prevalence of K. pneumoniae-producing-carbapenemase (KPC) resistance genes rapidly increases and threatens public health worldwide. This study aimed to assess the antibiotic resistance level of K. pneumoniae isolates from Makkah Province, Saudi Arabia, during the Islamic ‘Umrah’ ritual and to identify the plasmid types, presence of genes associated with carbapenem hydrolyzing enzymes, and virulence factors. The phenotypic and genotypic analyses based on the minimum inhibitory concentration (MIC), biofilm formation, PCR, and characterization of KPC-encoding plasmids based on the replicon typing technique (PBRT) were explored. The results showed that most isolates were resistant to carbapenem antibiotics and other antibiotics classes. This study identified sixteen different replicons of plasmids in the isolates and multiple genes encoding carbapenem factors, with blaVIM and blaOXA-48 being the most prevalent genes identified in the isolates. However, none of the isolates exhibited positivity for the KPC production activity. In addition, this study also identified six virulence-related genes, including kfu, wabG, uge, rmpA, fimH, and a capsular polysaccharide (CPS). Together, the data reported in this study indicate that the isolated K. pneumoniae during the pilgrimage in Makkah were all resistant to carbapenem antibiotics. Although the isolates lacked KPC production activity, they carried multiple carbapenem-resistant genes and virulence factors, which could drive their resistant phenotype. The need for specialized methods for KPC detection, monitoring the possibility of nosocomial transmission, and diverse therapeutic alternatives are necessary for controlling the spreading of KPC. This study can serve as a reference for clinicians and researchers on types of K. pneumoniae commonly found during religious gathering seasons in Saudi Arabia.

Bacterial classification is a vital step in medical diagnosis. This procedure normally has several stages. An early stage involves inspecting the morphology of the bacterial colonies. Traditionally, a bacterial colony expert inspects the sample to determine the type of bacteria through visual inspection or molecular biology techniques. With advances in image processing, specifically, the use of deep and transfer learning techniques, and the wide availability of cameras, we applied deep and transfer learning techniques to address this task without requiring expert knowledge or sample shipping. We used a convolutional neural network (CNN) to identify different bacterial colonies based on their appearance in images captured by cell phone cameras. In this paper, we collected a dataset that contains images of different bacteria taken by cell phone cameras with various settings. Thus, images of two classes of bacterial colonies were obtained in King Abdulaziz City for Science and Technology. The dataset contains 8,043 images. The experimental results show that our application has high accuracy without requiring expert inspections.