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The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high-performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor-provided resources, to enable the user community to adapt this for their own needs, and to identify specific areas of potential optimization with respect to storage and compute architectures.

Abstract Background Thyroid cancer (THCA) is the most common endocrine malignancy and incidence is increasing. There is an urgent need to better understand the molecular differences between THCA tumors at different pathologic stages so appropriate diagnostic, prognostic, and treatment strategies can be applied. Transcriptome State Perturbation Generator (TSPG) is a tool created to identify the changes in gene expression necessary to transform the transcriptional state of a source sample to mimic that of a target. Methods We used TSPG to perturb the bulk RNA expression data from various THCA tumor samples at progressive stages towards the transcriptional pattern of normal thyroid tissue. The perturbations produced were analyzed to determine if there are consistently up- or down-regulated genes or functions in certain stages of tumors. Results Some genes of particular interest were investigated further in previous research. SLC6A15 was found to be down-regulated in all stage 1–3 samples. This gene has previously been identified as a tumor suppressor. The up-regulation of PLA2G12B in all samples was notable because the protein encoded by this gene belongs to the PLA2 superfamily, which is involved in metabolism, a major function of the thyroid gland. REN was up-regulated in all stage 3 and 4 samples. The enzyme renin encoded by this gene, has a role in the renin-angiotensin system; this system regulates angiogenesis and may have a role in cancer development and progression. This is supported by the consistent up-regulation of REN only in later stage tumor samples. Functional enrichment analysis showed that olfactory receptor activities and similar terms were enriched for the up-regulated genes which supports previous research concluding that abundance and stimulation of olfactory receptors is linked to cancer. Conclusions TSPG can be a useful tool in exploring large gene expression datasets and extracting the meaningful differences between distinct classes of data. We identified genes that were characteristically perturbed in certain sample types, including only late-stage THCA tumors. Additionally, we provided evidence for potential transcriptional signatures of each stage of thyroid cancer. These are potentially relevant targets for future investigation into THCA tumorigenesis.

Abstract Background Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility. Processing of larger and deeper RNA-seq experiments will become more common as sequencing technology matures. Results GEMmaker, is a nf-core compliant, Nextflow workflow, that quantifies gene expression from small to massive RNA-seq datasets. GEMmaker ensures results are highly reproducible through the use of versioned containerized software that can be executed on a single workstation, institutional compute cluster, Kubernetes platform or the cloud. GEMmaker supports popular alignment and quantification tools providing results in raw and normalized formats. GEMmaker is unique in that it can scale to process thousands of local or remote stored samples without exceeding available data storage. Conclusions Workflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of access to CPUs. GEMmaker provides these benefits and adds the ability to scale despite low data storage infrastructure. This allows users to process hundreds to thousands of RNA-seq samples even when data storage resources are limited. GEMmaker is freely available and fully documented with step-by-step setup and execution instructions.

Abstract Background Periprosthetic joint infection (PJI) causes significant morbidity. Methicillin sensitive Staphylococcus aureus (MSSA) is the most frequent organism, and the majority are endogenous. Decolonisation reduces PJIs but there is a paucity of evidence comparing treatments. Aims; compare 3 nasal decolonisation treatments at (1) achieving MSSA decolonisation, (2) preventing PJI. Methods Our hospital prospectively collected data on our MSSA decolonisation programme since 2013, including; all MSSA carriers, treatment received, MSSA status at time of surgery and all PJIs. Prior to 2017 MSSA carriers received nasal mupirocin or neomycin, from August 2017 until August 2019 nasal octenidine was used. Results During the study period 15,958 primary hip and knee replacements were performed. 3200 (20.1%) were MSSA positive at preoperative screening and received decolonisation treatment, 698 mupirocin, 1210 neomycin and 1221 octenidine. Mupirocin (89.1%) and neomycin (90.9%) were more effective at decolonisation than octenidine (50.0%, P

Summary: We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcriptome perturbations from RNA-sequencing data required to shift from a source to a target class. We apply TSPG as an effective method of detecting biologically relevant alternate expression patterns between normal and tumor human tissue samples. We demonstrate that the application of TSPG to expression data obtained from a biopsy sample of a patient's kidney cancer can identify patient-specific differentially expressed genes between their individual tumor sample and a target class of healthy kidney gene expression. By utilizing TSPG in a precision medicine application in which the patient sample is not replicated (i.e., n=1), we present a novel technique of determining significant transcriptional aberrations that can be used to help identify potential targeted therapies. The Bigger Picture: Deep learning has shown tremendous success in image and natural language processing; however, attempts to apply the tools of machine learning to better understanding biological systems are still in the stage of early adoption. We propose a novel deep-learning tool that can be used to process samples of RNA-sequencing data. By applying the Transcriptome State Perturbation Generator to human samples, we show that deep learning derives insight into the gene expression shifts required for transition between two biological conditions (e.g., normal versus tumor). RNA-sequencing data derived from a single patient's tumor were analyzed using this tool to determine gene expression aberrations specific to that patient's tumor. As medicine shifts from cohort-based population studies to individual-based precision treatments, our example demonstrates that deep learning is a powerful ally in the quest to understand how complex biological systems have shifted for a single patient.

In this research, we used a gravity model to investigate whether water scarcity variables influence agricultural trade of cereal crops for Saudi Arabia. We compare the OLS, Fixed effects, Random effects, and Poisson Pseudo-Maximum Likelihood (PPML) estimators to determine the best model. The AIC, and multicollinearity, heteroskedasticity, and autocorrelation tests assist in determining estimation procedures and the final model. We cluster the errors by distance to improve the specific country effect variables, such as economic mass. We find that water-related variables influence virtual water imports of cereals, millet, corn, barely, and sesame.

Objectives: The incidence of fractured neck of femur (FNOF) is increasing yearly. Many of these patients undergo hip hemiarthroplasty. High dose dual-antibiotic cement (HDDAC) has been shown to reduce rates of deep surgical site infection (SSI) when compared to the current standard low dose single-antibiotic cement (LDSAC) in a quasi-randomised controlled trial. Some concerns exist regarding the use of HDDAC and the development of antibiotic resistance. We reviewed cases of infection in LDSAC and HDDAC bone cement with regard to causative organism and resistance profile.Methods: A retrospective analysis was undertaken of all hemiarthroplasties within our trust from April 2008 to December 2014. We identified all patients in this time period who acquired a deep SSI. The infecting organisms and susceptibility patterns were collated for each cement.Results: We identified 1941 hemiarthroplasties. There were 38 deep surgical site infections representing an infection rate of 3.4% in LDSAC patients and 1.2% in HDDAC patients. The majority of infections were polymicrobial. Staphylococcus epidermidis was the most commonly isolated organism. It accounted for a larger proportion of HDDAC than LDSAC infections (pCorynebacterium species and S. aureus, including MRSA, was eradicated completely with the use of HDDAC. There was no significant change in the proportion of Gram negative and Gram positive infections between the two cements. In Gram positive organisms, there was no significant change in resistance to most antibiotics. Although fewer resistant infections overall, there were significant increases in the proportion of resistance to ciprofloxacin and clindamycin with HDDAC. We observed no resistance to daptomycin or linezolid in either cement and levels of resistance remained low to rifampicin and teicoplanin. In Gram negative organisms, no significant change in resistance was observed.Conclusions: We observed a significantly lower infection rate with the use of HDDAC compared to LDSAC. Such was this reduced infection rate that there was a trend to a lower rate of resistance with the use of HDDAC. However, there were increases in the proportion of resistant cases, most notably to clindamycin and ciprofloxacin in Gram positive organisms, possibly reflecting the higher number of S. epidermidis in the HDDAC group. Whilst the differences in our study were not found to be statistically significant, it is reassuring for teams using HDDAC to prevent SSI in hip hemiarthroplasty.