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It is easy for today's students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of 'Big Data' has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology. [ABSTRACT FROM AUTHOR]

A comparative genomic analysis was conducted for 171 Salmonella isolates recovered from raw inshell almonds and raw almond kernels between 2001 and 2013 and for 30 Salmonella Enteritidis phage type (PT) 30 isolates recovered between 2001 and 2006 from a 2001 salmonellosis outbreak-associated almond orchard. Whole genome sequencing was used to measure the genetic distance among isolates by single nucleotide polymorphism (SNP) analyses and to predict the presence of plasmid DNA and of antimicrobial resistance (AMR) and virulence genes. Isolates were classified by serovars with Parsnp, a fast core-genome multi aligner, before being analyzed with the CFSAN SNP Pipeline (U.S. Food and Drug Administration Center for Food Safety and Applied Nutrition). Genetically similar (≤18 SNPs) Salmonella isolates were identified among several serovars isolated years apart. Almond isolates of Salmonella Montevideo (2001 to 2013) and Salmonella Newport (2003 to 2010) differed by ≤9 SNPs. Salmonella Enteritidis PT 30 isolated between 2001 and 2013 from survey, orchard, outbreak, and clinical samples differed by ≤18 SNPs. One to seven plasmids were found in 106 (62%) of the Salmonella isolates. Of the 27 plasmid families that were identified, IncFII and IncFIB plasmids were the most predominant. AMR genes were identified in 16 (9%) of the survey isolates and were plasmid encoded in 11 of 16 cases; 12 isolates (7%) had putative resistance to at least one antibiotic in three or more drug classes. A total of 303 virulence genes were detected among the assembled genomes; a plasmid that harbored a combination of pef, rck, and spv virulence genes was identified in 23% of the isolates. These data provide evidence of long-term survival (years) of Salmonella in agricultural environments. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the relationship between protest and ethical and political theory, and social and political philosophy.

• We present a new method for generating realistic simulations of twophoton calcium imaging data. • Two-photon imaging simulations are used to evaluate the performance of automated segmentation algorithms. • Datasets for a variety of customized two-photon microscopy techniques and sample conditions are generated and analyzed. The past decade has seen a multitude of new in vivo functional imaging methodologies. However, the lack of ground-truth comparisons or evaluation metrics makes the large-scale, systematic validation vital to the continued development and use of optical microscopy impossible. We provide a new framework for evaluating two-photon microscopy methods via in silico Neural Anatomy and Optical Microscopy (NAOMi) simulation. Our computationally efficient model generates large anatomical volumes of mouse cortex, simulates neural activity, and incorporates optical propagation and scanning to create realistic calcium imaging datasets. We verify NAOMi simulations against in vivo two-photon recordings from mouse cortex. We leverage this in silico ground truth to directly compare different segmentation algorithms and optical designs. We find modern segmentation algorithms extract strong neural time-courses comparable to estimation using oracle spatial information, but with an increase in the false positive rate. Comparison between optical setups demonstrate improved resilience to motion artifacts in sparsely labeled samples using Bessel beams, increased signal-to-noise ratio and cell-count using low numerical aperture Gaussian beams and nuclear GCaMP, and more uniform spatial sampling with temporal focusing versus multi-plane imaging. NAOMi is a first-of-its kind framework for assessing optical imaging modalities. Existing methods are either anatomical simulations or do not address functional imaging. Thus there is no competing method for simulating realistic functional optical microscopy data. By leveraging the rich accumulated knowledge of neural anatomy and optical physics, we provide a powerful new tool to assess and develop important methods in neural imaging. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which discusses various reports published within the issue including one on the democratic governance's failure on genetically-modified organism (GMO) and another on the influence of the growing urban farming on the marginalization of rural knowledge.

An introduction is presented in which the editor discusses various reports within the issue on topics including an epistemological justification of democracy, an examination of the theme of civic virtue, and the conflict between virtues of civility and righteous demands for civil and political rights.

Driven in part by its metabolic versatility, high intrinsic antibiotic resistance, and a large repertoire of virulence factors, Pseudomonas aeruginosa is expertly adapted to thrive in a wide variety of environments, and in the process, making it a notorious opportunistic pathogen. Apart from the extensively studied chronic infection in the lungs of people with cystic fibrosis (CF), P. aeruginosa also causes multiple serious infections encompassing essentially all organs of the human body, among others, lung infection in patients with chronic obstructive pulmonary disease, primary ciliary dyskinesia and ventilator-associated pneumonia; bacteremia and sepsis; soft tissue infection in burns, open wounds and postsurgery patients; urinary tract infection; diabetic foot ulcers; chronic suppurative otitis media and otitis externa; and keratitis associated with extended contact lens use. Although well characterized in the context of CF, pathogenic processes mediated by various P. aeruginosa virulence factors in other organ systems remain poorly understood. In this review, we use an organ system-based approach to provide a synopsis of disease mechanisms exerted by P. aeruginosa virulence determinants that contribute to its success as a versatile pathogen. [ABSTRACT FROM AUTHOR]

Driven in part by its metabolic versatility, high intrinsic antibiotic resistance, and a large repertoire of virulence factors, Pseudomonas aeruginosa is expertly adapted to thrive in a wide variety of environments, and in the process, making it a notorious opportunistic pathogen. Apart from the extensively studied chronic infection in the lungs of people with cystic fibrosis (CF), P. aeruginosa also causes multiple serious infections encompassing essentially all organs of the human body, among others, lung infection in patients with chronic obstructive pulmonary disease, primary ciliary dyskinesia and ventilator-associated pneumonia; bacteremia and sepsis; soft tissue infection in burns, open wounds and postsurgery patients; urinary tract infection; diabetic foot ulcers; chronic suppurative otitis media and otitis externa; and keratitis associated with extended contact lens use. Although well characterized in the context of CF, pathogenic processes mediated by various P. aeruginosa virulence factors in other organ systems remain poorly understood. In this review, we use an organ system-based approach to provide a synopsis of disease mechanisms exerted by P. aeruginosa virulence determinants that contribute to its success as a versatile pathogen. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses report within the issue on topics including a key tension that exists in Catholic universities between a right to sectarian exclusivity on the one hand and theologies of love and compassion that require inclusivity on the other, the problems of knowledge and justice, and the nature of experiential knowledge claims.

Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1–5 days post-hospital admission) and late (6–20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs. [ABSTRACT FROM AUTHOR]