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To effectively contain antimicrobial-resistant (AMR) infections, we must better understand the social determinates of health that contribute to transmission and spread of infections.We used clinical data from patients attending primary healthcare clinics across three jurisdictions of Australia (2007-2019). Escherichia coli (E. coli), Klebsiella pneumoniae (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) isolates and their corresponding antibiotic susceptibilities were included. Using multivariable logistic regression analysis, we assessed associations between AMR prevalence and indices of social disadvantage as reported by the Australian Bureau of Statistics (i.e., remoteness, socio-economic disadvantage and average person per household).This study reports 12 years of longitudinal data from 43 448 isolates from a high-burden low-resource setting in Australia. Access to health and social services (as measured by remoteness index) was a risk factor for increased prevalence of third-generation cephalosporin-resistant (3GC) E. coli (odds ratio 5.05; 95% confidence interval 3.19, 8.04) and methicillin-resistant S. aureus (MRSA) (odds ratio 5.72; 95% confidence interval 5.02, 6.54). We did not find a positive correlation of AMR and socio-economic disadvantage or average person per household indices.Remoteness is a risk factor for increased prevalence of 3GC-resistant E. coli and MRSA. We demonstrate that traditional disease surveillance systems can be repurposed to capture the broader social drivers of AMR. Access to pathogen-specific and social data early and within the local regional context will fill a significant gap in disease prevention and the global spread of AMR.

Late antique and medieval cotton and wool textiles found in the middle Nile Valley (Nubia, northern Sudan) were analysed for their technical characteristics and strontium (Sr) isotope composition. All wool textiles exhibit Sr isotope signatures consistent with the isotopic background of the region studied and are considered to be of local origin. However, a medieval wool kilim from Meinarti shows technical and aesthetic features suggesting its foreign Maghreb provenance. As this fabric dates back to the occupation of Meinarti by the Beni Ikrima tribe, it is suggested that the kilim was woven by the Beni Ikrima people from local Nubian raw material. The cotton samples tested come from abroad and document trade with the oases of the Egyptian Western Desert, the west coast of India, and perhaps also with the Arabian Peninsula or Pakistan.

File contains description of supplementary methods used for sciatic nerve immunofluorescence data generation and quantification with Supplementary Fig 1 showing how raw fluorescence intensity values were normalized in order to compare levels of acetylated alpha tubulin from samples across treatment groups. Supplementary Figs 2 thru 4 show DRG and sciatic nerve morphology/morphometrics for chemotherapies other than PACLI. Supplementary Table 1 adds perspective to how MTD used to compare the chemotherapies in this study relates to their pharmacokinetic and IC50 properties.

Chemotherapy-induced peripheral neuropathy (CIPN) is a major cause of disability in cancer survivors. CIPN investigations in preclinical model systems have focused on either behaviors or acute changes in nerve conduction velocity (NCV) and amplitude, but greater understanding of the underlying nature of axonal injury and its long-term processes is needed as cancer patients live longer. In this study, we used multiple independent endpoints to systematically characterize CIPN recovery in mice exposed to the antitubulin cancer drugs eribulin, ixabepilone, paclitaxel, or vinorelbine at MTDs. All of the drugs ablated intraepidermal nerve fibers and produced axonopathy, with a secondary disruption in myelin structure within 2 weeks of drug administration. In addition, all of the drugs reduced sensory NCV and amplitude, with greater deficits after paclitaxel and lesser deficits after ixabepilone. These effects correlated with degeneration in dorsal root ganglia (DRG) and sciatic nerve and abundance of Schwann cells. Although most injuries were fully reversible after 3–6 months after administration of eribulin, vinorelbine, and ixabepilone, we observed delayed recovery after paclitaxel that produced a more severe, pervasive, and prolonged neurotoxicity. Compared with other agents, paclitaxel also displayed a unique prolonged exposure in sciatic nerve and DRG. The most sensitive indicator of toxicity was axonopathy and secondary myelin changes accompanied by a reduction in intraepidermal nerve fiber density. Taken together, our findings suggest that intraepidermal nerve fiber density and changes in NCV and amplitude might provide measures of axonal injury to guide clinical practice.Significance: This detailed preclinical study of the long-term effects of widely used antitubulin cancer drugs on the peripheral nervous system may help guide clinical evaluations to improve personalized care in limiting neurotoxicity in cancer survivors. Cancer Res; 78(3); 817–29. ©2017 AACR.

Supplementary Figures 1-2 from Comparison of Neuropathy-Inducing Effects of Eribulin Mesylate, Paclitaxel, and Ixabepilone in Mice

Supplementary Figure Legends 1-2 from Comparison of Neuropathy-Inducing Effects of Eribulin Mesylate, Paclitaxel, and Ixabepilone in Mice

Chemotherapy-induced neurotoxicity is a significant problem associated with successful treatment of many cancers. Tubulin is a well-established target of antineoplastic therapy; however, tubulin-targeting agents, such as paclitaxel and the newer epothilones, induce significant neurotoxicity. Eribulin mesylate, a novel microtubule-targeting analogue of the marine natural product halichondrin B, has recently shown antineoplastic activity, with relatively low incidence and severity of neuropathy, in metastatic breast cancer patients. The mechanism of chemotherapy-induced neuropathy is not well understood. One of the main underlying reasons is incomplete characterization of pathology of peripheral nerves from treated subjects, either from patients or preclinically from animals. The current study was conducted to directly compare, in mice, the neuropathy-inducing propensity of three drugs: paclitaxel, ixabepilone, and eribulin mesylate. Because these drugs have different potencies and pharmacokinetics, we compared them on the basis of a maximum tolerated dose (MTD). Effects of each drug on caudal and digital nerve conduction velocity, nerve amplitude, and sciatic nerve and dorsal root ganglion morphology at 0.25 × MTD, 0.5 × MTD, 0.75 × MTD, and MTD were compared. Paclitaxel and ixabepilone, at their respective MTDs, produced significant deficits in caudal nerve conduction velocity, caudal amplitude and digital nerve amplitudes, as well as moderate to severe degenerative pathologic changes in dorsal root ganglia and sciatic nerve. In contrast, eribulin mesylate produced no significant deleterious effects on any nerve conduction parameter measured and caused milder, less frequent effects on morphology. Overall, our findings indicate that eribulin mesylate induces less neuropathy in mice than paclitaxel or ixabepilone at equivalent MTD-based doses. Cancer Res; 71(11); 3952–62. ©2011 AACR.

SLC15A4 is an endolysosome-resident transporter that is intimately linked with autoinflammation and autoimmunity. Specifically, SLC15A4 is critical for Toll-like receptor (TLR) 7, 8, and 9 as well as the nucleotide-binding oligomerization domain-containing protein (NOD) 2 signaling in several immune cell subsets. Notably, SLC15A4 is essential for the development of systemic lupus erythematosus in murine models and is associated with autoimmune conditions in humans. Despite its therapeutic potential, to our knowledge no pharmacological tools have been developed that target SLC15A4. Here, we use an integrated chemical proteomics approach to develop a suite of chemical tools, including first-in-class functional inhibitors, for SLC15A4. We demonstrate SLC15A4 inhibitors suppress endosomal TLR and NOD functions in a variety of human and mouse immune cells and provide early evidence of their ability to suppress inflammation in vivo and in clinical settings. Our findings establish SLC15A4 as a druggable target for the treatment of autoimmune/autoinflammatory conditions.One-Sentence SummaryDiscovery and characterization of SLC15A4 inhibitors with anti-inflammatory activity.

Human milk oligosaccharides (HMOs) are a family of unconjugated soluble glycans found in human breast milk that exhibit a myriad of biological activity. While recent studies have uncovered numerous biological functions for HMOs (antimicrobial, anti-inflammatoryprobiotic properties), the receptors and protein binding partners involved in these processes are not well characterized. This can be attributed largely in part to the low affinity and transient nature of soluble glycan-protein interactions, precluding the use of traditional characterization techniques to survey binding partners in live cells. Here, we present the use of synthetic photoactivatable HMO probes to capture, enrich and identify HMO protein targets in live cells using mass spectrometry-based chemoproteomics. Following initial validation studies using purified lectins, we profiled the targets of HMO probes in live mouse macrophages. Using this strategy, we mapped hundreds of HMO binding partners across multiple cellular compartments, including many known glycan-binding proteins as well as numerous proteins previously not known to bind glycans. We expect our findings to inform future investigations of the diverse roles of how HMOs may regulate protein function.

A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.

Lassa virus (LASV), a Risk Group-4 zoonotic haemorrhagic fever virus, affects sub-Saharan African countries. Lassa fever, caused by LASV, results in thousands of annual deaths. Although decades have elapsed since the identification of the Natal multimammate mouse (Mastomys natalensis) as a natural reservoir of LASV, little effort has been made to characterize LASV infection in its reservoir. The natural route of infection and transmission of LASV within M. natalensis remains unknown, and the clinical impact of LASV in M. natalensis is mostly undescribed. Herein, using an outbred colony of M. natalensis, we investigate the replication and dissemination dynamics of LASV in this reservoir following various inoculation routes. Inoculation with LASV, regardless of route, resulted in a systemic infection and accumulation of abundant LASV-RNA in many tissues. LASV infection in the Natal multimammate mice was subclinical, however, clinical chemistry values were transiently altered and immune infiltrates were observed histologically in lungs, spleens and livers, indicating a minor disease with coordinated immune responses are elicited, controlling infection. Intranasal infection resulted in unique virus tissue dissemination dynamics and heightened LASV shedding, compared to subcutaneous inoculation. Our study provides important insights into LASV infection in its natural reservoir using a contemporary infection system, demonstrating that specific inoculation routes result in disparate dissemination outcomes, suggesting intranasal inoculation is important in the maintenance of LASV in the natural reservoir, and emphasizes that selection of the appropriate inoculation route is necessary to examine aspects of viral replication, transmission and responses to zoonotic viruses in their natural reservoirs.

Background Unbiased estimates of the health and economic impacts of health care–associated infections (HAIs) are scarce and focus largely on patients with bloodstream infections (BSIs). We sought to estimate the hospital length of stay (LOS), mortality rate, and costs of HAIs and the differential effects on patients with an antimicrobial-resistant infection. Methods We conducted a multisite, retrospective case-cohort of all acute-care hospital admissions with a positive culture of 1 of the 5 organisms of interest (Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, or Enterococcus faecium) from 1 January 2012 through 30 December 2016. Data linkage was used to generate a data set of statewide hospital admissions and pathology data. Patients with bloodstream, urinary, or respiratory tract infections were included in the analysis and matched to a sample of uninfected patients. We used multistate survival models to generate LOS, and logistic regression to derive mortality estimates. Results We matched 20 390 cases to 75 635 uninfected control patients. The overall incidence of infections due to the 5 studied organisms was 116.9 cases per 100 000 patient days, with E. coli urinary tract infections (UTIs) contributing the largest proportion (51 cases per 100 000 patient days). The impact of a UTI on LOS was moderate across the 5 studied pathogens. Resistance significantly increased LOS for patients with third-generation cephalosporin-resistant K. pneumoniae BSIs (extra 4.6 days) and methicillin-resistant S. aureus BSIs (extra 2.9 days). Consequently, the health-care costs of these infections were higher, compared to corresponding drug-sensitive strains. Conclusions The health burden remains highest for BSIs; however, UTIs and respiratory tract infections contributed most to the health-care system expenditure., In a comprehensive analysis with unbiased estimates of morbidity, mortality, and costs of antimicrobial-resistant and -susceptible infections acquired in Australian hospitals, the health burden remained highest for bloodstream infections. However, urinary and respiratory infections contributed most to the health-care costs.

Background An increasing proportion of antibiotic-resistant infections are community acquired. However, the burden of community-associated infections (CAIs) and the resulting impact due to resistance have not been well described. Methods We conducted a multisite, retrospective case–cohort study of all acute care hospital admissions across 134 hospitals in Australia. Patients admitted with a positive culture of 1 of 5 organisms of interest, namely Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and Enterococcus faecium, from January 1, 2012, through December 30, 2016, were included. Data linkage was used to link hospital admissions and pathology data. Patients with a bloodstream infection (BSI), urinary tract infection (UTI), or respiratory tract infection (RTI) were included in the analysis. We compared patients with a resistant and drug-sensitive infection and used regression analyses to derive the difference in length of hospital stay (LOS) and mortality estimates associated with resistance. Results No statistically significant impact on hospital LOS for patients with resistant CAIs compared with drug-sensitive CAIs was identified. CAI patients with drug-resistant Enterobacteriaceae (E. coli, K. pneumoniae) BSIs were more likely to die in the hospital than those with drug-sensitive Enterobacteriaceae BSIs (odds ratio [OR], 3.28; 95% CI, 1.40–6.92). CAI patients with drug-resistant P. aeruginosa UTIs were more likely to die in the hospital than those with the drug-sensitive counterpart (OR, 2.43; 95% CI, 1.12–4.85). Conclusions The burden of CAI in the hospital is significant, and antibiotic resistance is adding to associated mortality.

Background International travel has been recognized as a risk factor contributing to the spread of antimicrobial resistance (AMR). However, tools focused on AMR in the context of international travel and designed to guide decision-making are limited. We aimed at developing an evidence-based educational tool targeting both healthcare professionals (HCPs) and international travellers to help prevent the spread of AMR. Methods A literature review on 12 antimicrobial-resistant bacteria (ARB) listed as critical and high tiers in the WHO Pathogen Priority List covering four key areas was carried out: AMR surveillance data; epidemiological studies reporting ARB prevalence data on carriage in returning travellers; guidance documents reporting indications on screening for ARB in returning travellers and recommendations for ARB prevention for the public. The evidence, catalogued at country-level, provided the content for a series of visualizations that allow assessment of the risk of AMR acquisition through travel. Results Up to January 2021, the database includes data on: (i) AMR surveillance for 2.018.241 isolates from 86 countries; (ii) ARB prevalence of carriage from 11.679 international travellers and (iii) 15 guidance documents published by major public health agencies. The evidence allowed the development of a consultation scheme for the evaluation of risk factors, prevalence of carriage, proportion and recommendations for screening of AMR. For the public, pre-travel practical measures to minimize the risk of transmission were framed. Conclusions This easy-to-use, annually updated, freely accessible AMR travel tool (https://epi-net.eu/travel-tool/overview/), is the first of its kind to be developed. For HCPs, it can provide a valuable resource for teaching and a repository that facilitates a stepwise assessment of the risk of AMR spread and strengthen implementation of optimized infection control measures. Similarly, for travellers, the tool has the potential to raise awareness of AMR and outlines preventive measures that reduce the risk of AMR acquisition and spread.

Lassa virus (LASV), a Risk Group-4 zoonotic haemorrhagic fever virus, affects sub-Saharan African countries. Lassa fever, caused by LASV, results in thousands of annual deaths. Although decades have elapsed since the identification of the Natal multimammate mouse (Mastomys natalensis) as a natural reservoir of LASV, little effort has been made to characterize LASV infection in its reservoir. The natural route of infection and transmission of LASV within M. natalensis remains unknown, and the clinical impact of LASV in M. natalensis is mostly undescribed. Herein, using an outbred colony of M. natalensis, we investigate the replication and dissemination dynamics of LASV in this reservoir following various inoculation routes. Inoculation with LASV, regardless of route, resulted in a systemic infection and accumulation of abundant LASV-RNA in many tissues. LASV infection in the Natal multimammate mice was subclinical, however, clinical chemistry values were transiently altered and immune infiltrates were observed histologically in lungs, spleens and livers, indicating a minor disease with coordinated immune responses are elicited, controlling infection. Intranasal infection resulted in unique virus tissue dissemination dynamics and heightened LASV shedding, compared to subcutaneous inoculation. Our study provides important insights into LASV infection in its natural reservoir using a contemporary infection system, demonstrating that specific inoculation routes result in disparate dissemination outcomes, suggesting intranasal inoculation is important in the maintenance of LASV in the natural reservoir, and emphasizes that selection of the appropriate inoculation route is necessary to examine aspects of viral replication, transmission and responses to zoonotic viruses in their natural reservoirs.

In an in-situ workflow, multiple components such as simulation and analysis applications are coupled with streaming data transfers. The multiplicity of possible configurations necessitates an auto-tuner for workflow optimization. Existing auto-tuning approaches are computationally expensive because many configurations must be sampled by running the whole workflow repeatedly in order to train the auto-tuner surrogate model or otherwise explore the configuration space. To reduce these costs, we instead combine the performance models of component applications by exploiting the analytical workflow structure, selectively generating test configurations to measure and guide the training of a machine learning workflow surrogate model. Because the training can focus on well-performing configurations, the resulting surrogate model can achieve high prediction accuracy for good configurations despite training with fewer total configurations. Experiments with real applications demonstrate that our approach can identify significantly better configurations than other approaches for a fixed computer time budget.

The growing spread of antimicrobial resistance (AMR) is accepted as a threat to humans, animals and the environment. This threat is considered to be both country specific and global, with bacteria resistant to antibiotic treatment geographically dispersed. Despite this, we have very few Australian estimates available that use national surveillance data supplemented with measures of risk, to generate reliable and actionable measures of AMR impact. These data are essential to direct policies and programs and support equitable healthcare resource utilisation. Importantly, such data can lead to implementation of programs to improved morbidity and mortality of patients with a resistant infection.Using data from a previous case-cohort study, we estimated the AMR-associated health and economic impact caused by five hospital-associated AMR pathogens (In 2020, there were 1,031 AMR-associated deaths (95% uncertainty interval [UI] 294, 2,615) from the five resistant hospital-associated infections in Australia. The greatest odds of dying were from respiratory infections (ceftazidime-resistantThese are the first Australian estimates of AMR-associated health and economic impact. Country-level estimates of AMR impact are needed to provide local evidence to better inform programs and health policies to reduce morbidity and mortality associated with infection. The burden in hospital is likely an underestimate of the impact of AMR due to community-associated infections where data are limited, and the AMR burden is high. This should now be the focus of future study in this area.TMW was supported by the Australian Partnership for Preparedness Research on Infectious Disease Emergencies (APPRISE) (grant number GNT1116530) Fellowship.

Exquisitely tuned activity of protein kinase C (PKC) isozymes is essential to maintaining cellular homeostasis. Whereas loss-of-function mutations are generally associated with cancer, gain-of-function variants in one isozyme, PKCα, are associated with Alzheimer’s disease (AD). Here we show that the enhanced activity of one variant, PKCα M489V, is sufficient to rewire the brain phosphoproteome, drive synaptic degeneration, and impair cognition in a mouse model. This variant causes a modest 30% increase in catalytic activity without altering on/off activation dynamics or stability, underscoring that enhanced catalytic activity is sufficient to drive the biochemical, cellular, and ultimately cognitive effects observed. Analysis of hippocampal neurons from the PKCα M489V mice reveals enhanced amyloid-β-induced synaptic depression and reduced spine density compared to wild-type mice. Behavioral studies reveal that this mutation alone is sufficient to impair cognition, and, when coupled to a mouse model of AD, further accelerates cognitive decline. The druggability of protein kinases positions PKCα as a new and promising therapeutic target in AD.

Vascular inflammation causes endothelial barrier disruption and tissue edema. Several inflammatory mediators act through G protein–coupled receptors (GPCRs), including protease-activated receptor-1 (PAR1), to elicit inflammatory responses. The activation of PAR1 by its ligand thrombin stimulates proinflammatory, p38 mitogen-activated protein kinase (MAPK) signaling that promotes endothelial barrier disruption. Through mass spectrometry phosphoproteomics, we identified heat shock protein 27 (HSP27), which exists as a large oligomer that binds to actin, as a promising candidate for the p38-mediated regulation of barrier integrity. Depletion of HSP27 by siRNA enhanced endothelial cell barrier permeability and slowed recovery after thrombin stimulation. We further showed that two effector kinases of p38 MAPK, MAPKAPK2 (MK2) and MAPKAPK3 (MK3), differentially phosphorylated HSP27 at Ser(15), Ser(78), and Ser(82). Whereas inhibition of thrombin-stimulated p38 activation blocked HSP27 phosphorylation at all three sites, inhibition of MK2 reduced the phosphorylation of only Ser(15) and Ser(78). Inhibition of both MK2 and MK3 was necessary to attenuate Ser(82) phosphorylation. Thrombin-stimulated p38-MK2-MK3 signaling induced HSP27 oligomer disassembly. However, a phosphorylation-deficient mutant of HSP27 exhibited defective oligomer disassembly and altered the dynamics of barrier recovery after thrombin stimulation. Moreover, blocking HSP27 oligomer reassembly with the small-molecule inhibitor J2 enhanced endothelial barrier permeability in vitro and vascular leakage in vivo in response to PAR1 activation. These studies reveal the distinct regulation of HSP27 phosphorylation and function induced by the GPCR-stimulated p38-MK2-MK3 signaling axis that controls the dynamics of endothelial barrier recovery in vitro and vascular leakage in vivo.

Exquisitely tuned activity of protein kinase C (PKC) isozymes is essential to maintaining cellular homeostasis. Whereas loss-of-function mutations are generally associated with cancer, gain-of-function variants in one isozyme, PKCα, are associated with Alzheimer’s disease (AD). Here we show that the enhanced activity of one variant, PKCα M489V, is sufficient to rewire the brain phosphoproteome, drive synaptic degeneration, and impair cognition in a mouse model. This variant causes a modest 30% increase in catalytic activity without altering on/off activation dynamics or stability, underscoring that enhanced catalytic activity is sufficient to drive the biochemical, cellular, and ultimately cognitive effects observed. Analysis of hippocampal neurons from PKCα M489V mice reveals enhanced amyloid-β-induced synaptic depression and reduced spine density compared to wild-type mice. Behavioral studies reveal that this mutation alone is sufficient to impair cognition, and, when coupled to a mouse model of AD, further accelerates cognitive decline. The druggability of protein kinases positions PKCα as a promising therapeutic target in AD.

Protein acetylation is a central event in orchestrating diverse cellular processes. However, current strategies to investigate protein acetylation in cells are often non-specific or lack temporal and magnitude control. Here, we developed an acetylation tagging system, AceTAG, to induce acetylation of targeted proteins. The AceTAG system utilizes bifunctional molecules to direct the lysine acetyltransferase p300/CBP to proteins fused with the small protein tag FKBP12F36V, resulting in their induced acetylation. Using AceTAG, we induced targeted acetylation of a diverse array of proteins in cells, specifically histone H3.3, the NF-κB subunit p65/RelA, and the tumor suppressor p53. We demonstrate that targeted acetylation with the AceTAG system is rapid, selective, reversible, and can be controlled in a dose-dependent fashion. AceTAG represents a useful strategy to modulate protein acetylation and will enable the exploration of targeted acetylation in basic biological and therapeutic contexts.Abstract Figure

Background Urinary tract infections are common and are increasingly resistant to antibiotic therapy. Northern Australia is a sparsely populated region with limited access to healthcare, a relatively high burden of disease, a substantial regional and remote population, and high rates of antibiotic resistance in skin pathogens. Objectives To explore trends in antibiotic resistance for common uropathogens Escherichia coli and Klebsiella pneumoniae in northern Australia, and how these relate to current treatment guidelines in the community and hospital settings. Methods We used data from an antibiotic resistance surveillance system. We calculated the monthly and yearly percentage of isolates that were resistant in each antibiotic class, by bacterium. We analysed resistance proportions geographically and temporally, stratifying by healthcare setting. Using simple linear regression, we investigated longitudinal trends in monthly resistance proportions and correlation between community and hospital isolates. Results Our analysis included 177 223 urinary isolates from four pathology providers between 2007 and 2020. Resistance to most studied antibiotics remained Conclusions Antibiotic resistance in uropathogens is increasing in northern Australia, but treatment guidelines generally remain appropriate for empirical therapy of patients with suspected infection (except trimethoprim in some settings). Our findings demonstrate the importance of local surveillance data (HOTspots) to inform clinical decision making and guidelines.

While the use of workflows for HPC is growing, MPI interoperability remains a challenge for workflow management systems. The MPI standard and/or its implementations provide a number of ways to build multiple-programs-multiple-data (MPMD) applications. These methods present limitations related to fault tolerance, and are not easy to use. In this paper, we advocate for a novel MPI_Comm_launch function acting as the parallel counterpart of a system(3) call. MPI_Comm_launch allows a child MPI application to be launched inside the resources originally held by processes of a parent MPI application. Two important aspects of MPI_Comm_launch is that it pauses the calling process, and runs the child processes on the parent’s CPU cores, but in an isolated manner with respect to memory. This function makes it easier to build MPMD applications with well-decoupled subtasks. We show how this feature can provide better flexibility and better fault tolerance in ensemble simulations and HPC workflows. We report results showing 2 × throughput improvement for application workflows with faults, and scaling results for challenging workloads up to 256 nodes.

IMPORTANCE: Hypotension is common during tracheal intubation of critically ill adults and increases the risk of cardiac arrest and death. Whether administering an intravenous fluid bolus to critically ill adults undergoing tracheal intubation prevents severe hypotension, cardiac arrest, or death remains uncertain. OBJECTIVE: To determine the effect of fluid bolus administration on the incidence of severe hypotension, cardiac arrest, and death. DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial enrolled 1067 critically ill adults undergoing tracheal intubation with sedation and positive pressure ventilation at 11 intensive care units in the US between February 1, 2019, and May 24, 2021. The date of final follow-up was June 21, 2021. INTERVENTIONS: Patients were randomly assigned to receive either a 500-mL intravenous fluid bolus (n = 538) or no fluid bolus (n = 527). MAIN OUTCOMES AND MEASURES: The primary outcome was cardiovascular collapse (defined as new or increased receipt of vasopressors or a systolic blood pressure

This papers presents the results of analysis done on a compression-ignition engine supplied with methyl ester of rapeseed oil (Yellow Grease), methyl ester of goose fat (Brown Grease) and pure diesel. The analysis included the engine characteristics, emissions and fuel consumption. Results also include chromatographic analysis for all of the three fuels. Additional evaluation was done on a vehicle idling and under load.

The kinase complex mTORC2 is widely accepted as controlling phosphorylation of the hydrophobic motif, a key regulatory switch in the C-terminal tail of protein kinase C (PKC), Akt, and other AGC kinases. Yet the biochemical mechanism by which it controls this site and whether mTOR is the direct hydrophobic motif kinase remain controversial. Here we identify a distinct mTOR-mediated phosphorylation site we term the TOR-Interaction Motif (TIM; F-x(3)-F-pT), which controls hydrophobic motif phosphorylation and activity of PKC and Akt. The TIM is invariant in all mTOR-dependent kinases, is evolutionarily conserved, and co-evolved with mTORC2 components. Mutation of this motif alone in Akt1 (Thr(443)) or together with the turn motif in PKCβII (Thr(634)/Thr(641)) abolishes cellular kinase activity by impairing activation loop and hydrophobic motif phosphorylation. mTORC2 directly phosphorylates the PKC TIM in vitro, and its phosphorylation is detected in mouse brain by mass spectrometry. Overexpression of PDK1 in cells lacking mTORC2 rescues hydrophobic motif phosphorylation of PKC and Akt by a mechanism that depends on their intrinsic catalytic, revealing that mTORC2 facilitates the PDK1 phosphorylation step, which in turn permits autophosphorylation. Analysis of a previously reported PKCβII crystal structure reveals a PKC homodimer driven by a helix containing the TIM. Biophysical proximity assays show that unphosphorylated PKC, but not phosphorylated PKC, dynamically dimerizes in cells. Furthermore, disruption of the dimer interface by stapled peptides promotes hydrophobic motif phosphorylation. Our data support a model in which mTORC2 relieves nascent PKC dimerization through TIM phosphorylation, recruiting PDK1 to phosphorylate the activation loop, and triggering intramolecular hydrophobic motif autophosphorylation. Identification of TIM phosphorylation and its role in the regulation of PKC provides the basis for AGC kinase regulation by mTORC2.

The drive to withstand environmental stresses and defend against invasion is a universal trait extant in all forms of life. While numerous canonical signaling cascades have been characterized in detail, it remains unclear how these pathways interface to generate coordinated responses to diverse stimuli. To dissect these connections, we followed heparanase (HPSE), a protein best known for its endoglycosidic activity at the extracellular matrix but recently recognized to drive various forms of late-stage disease through unknown mechanisms. Using herpes simplex virus-1 (HSV-1) infection as a model cellular perturbation, we demonstrate that HPSE acts beyond its established enzymatic role to restrict multiple forms of cell-intrinsic defense and facilitate host cell reprogramming by the invading pathogen. We reveal that cells devoid of HPSE are innately resistant to infection and counteract viral takeover through multiple amplified defense mechanisms. With a unique grasp of the fundamental processes of transcriptional regulation and cell death, HPSE represents a potent cellular intersection with broad therapeutic potential.

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new, unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses we found that classifier performance in this system was organelle-dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine (SVM) learning for mitochondrial and ER proteins, but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, BANDLE, on SVM-classified data. Comparative BANDLE analysis of cells induced to express the wild-type viral genome vs. cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as TCR signaling components and coatomer complex. Lastly, we found that SVM classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.

The rate for Higgs ( H ) bosons production in association with either one ( tH ) or two ( tt¯H ) top quarks is measured in final states containing multiple electrons, muons, or tau leptons decaying to hadrons and a neutrino, using proton-proton collisions recorded at a center-of-mass energy of 13 TeV by the CMS experiment. The analyzed data correspond to an integrated luminosity of 137 fb-1 . The analysis is aimed at events that contain H → WW , H → ττ , or H → ZZ decays and each of the top quark(s) decays either to lepton+jets or all-jet channels. Sensitivity to signal is maximized by including ten signatures in the analysis, depending on the lepton multiplicity. The separation among tH , tt¯H , and the backgrounds is enhanced through machine-learning techniques and matrix-element methods. The measured production rates for the tt¯H and tH signals correspond to 0.92 ± 0.19 (stat)-0.13+0.17 (syst) and 5.7 ± 2.7 (stat) ± 3.0 (syst) of their respective standard model (SM) expectations. The corresponding observed (expected) significance amounts to 4.7 (5.2) standard deviations for tt¯H , and to 1.4 (0.3) for tH production. Assuming that the Higgs boson coupling to the tau lepton is equal in strength to its expectation in the SM, the coupling yt of the Higgs boson to the top quark divided by its SM expectation, κt = yt/ytSM , is constrained to be within - 0.9 {\textless} κt {\textless} - 0.7 or 0.7 {\textless} κt {\textless} 1.1 , at 95\% confidence level. This result is the most sensitive measurement of the tt¯H production rate to date.

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross validation within a single study to assess model accuracy. While an essential first step, cross validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI. Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies, and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening., Comment: Accepted by Briefings in Bioinformatics

Laboratory-controlled physiological data for the multimammate rat (Mastomys natalensis) are scarce, despite this species being a known reservoir and vector for zoonotic viruses, including the highly pathogenic Lassa virus, as well as other arenaviruses and many species of bacteria. For this reason, M. natalensis is an important rodent for the study of host-virus interactions within laboratory settings. Herein, we provide basic blood parameters for age- and sex-distributed animals in regards to blood counts, cell phenotypes and serum chemistry of a specific-pathogen-monitored M. natalensis breeding colony, to facilitate scientific insight into this important and widespread rodent species.

Machine learning in biomedicine is reliant on the availability of large, high-quality data sets. These corpora are used for training statistical or deep learning-based models that can be validated against other data sets and ultimately used to guide decisions. The quality of these data sets is an essential component of the quality of the models and their decisions. Thus, identifying and inspecting outlier data is critical for evaluating, curating, and using biomedical data sets. Many techniques are available to look for outlier data, but it is not clear how to evaluate the impact on highly complex deep learning methods. In this paper, we use deep learning ensembles and workflows to construct a system for automatically identifying data subsets that have a large impact on the trained models. These effects can be quantified and presented to the user for further inspection, which could improve data quality overall. We then present results from running this method on the near-exascale Summit supercomputer.

International audience; Training modern deep neural network (DNN) models involves complex workflows triggered by model exploration, sensitivity analysis, explainability, etc. A key primitive in this context is the ability to clone a model training instance, i.e. "fork" the training process in a potentially different direction, which enables comparisons of different evolution paths using variations of training data and model parameters. However, in a quest improve the training throughput, a mix of data parallel, model parallel, pipeline parallel and layer-wise parallel approaches are making the problem of cloning highly complex. In this paper, we explore the problem of efficient cloning under such circumstances. To this end, we leverage several properties of data-parallel training and layer-wise parallelism to design DeepClone, a cloning approach based on augmenting the execution graph to gain direct access to tensors, which are then sharded and reconstructed asynchronously in order to minimize runtime overhead, standby duration, readiness duration. Compared with state-of-art approaches, DeepClone shows orders of magnitude improvement for several classes of DNN models.

The drive to withstand environmental stresses and defend against invasion is a universal trait extant in all forms of life. While numerous canonical signaling cascades have been characterized in detail, it remains unclear how these pathways interface to generate coordinated responses to diverse stimuli. To dissect these connections, we follow heparanase (HPSE), a protein best known for its endoglycosidic activity at the extracellular matrix but recently recognized to drive various forms of late stage disease through unknown mechanisms. Using herpes simplex virus-1 (HSV-1) infection as a model cellular perturbation, we demonstrate that HPSE acts beyond its established enzymatic role to restrict multiple forms of cell-intrinsic defense and facilitate host cell reprogramming by the invading pathogen. We reveal that cells devoid of HPSE are innately resistant to infection and counteract viral takeover through multiple amplified defense mechanisms. With a unique grasp of the fundamental processes of transcriptional regulation and cell death, HPSE represents a potent cellular intersection with broad therapeutic potential.

Lassa fever may cause severe disease in humans, in particular in areas of endemicity like Sierra Leone and Nigeria. Despite its public health importance, the pathophysiology of Lassa fever in humans is poorly understood. Here, we present clinical immunology data obtained in the field during the 2018 Lassa fever outbreak in Nigeria indicating that severe Lassa fever is associated with activation of T cells antigenically unrelated to Lassa virus and poor Lassa virus-specific effector T-cell responses. Mechanistically, we show that these bystander T cells express defined tissue homing signatures that suggest their recruitment to inflamed tissues and a putative role of these T cells in immunopathology. These findings open a window of opportunity to consider T-cell targeting as a potential postexposure therapeutic strategy against severe Lassa fever, a hypothesis that could be tested in relevant animal models, such as nonhuman primates., Lassa fever (LF) is a zoonotic viral hemorrhagic fever caused by Lassa virus (LASV), which is endemic to West African countries. Previous studies have suggested an important role for T-cell-mediated immunopathology in LF pathogenesis, but the mechanisms by which T cells influence disease severity and outcome are not well understood. Here, we present a multiparametric analysis of clinical immunology data collected during the 2017–2018 Lassa fever outbreak in Nigeria. During the acute phase of LF, we observed robust activation of the polyclonal T-cell repertoire, which included LASV-specific and antigenically unrelated T cells. However, severe and fatal LF cases were characterized by poor LASV-specific effector T-cell responses. Severe LF was also characterized by the presence of circulating T cells with homing capacity to inflamed tissues, including the gut mucosa. These findings in LF patients were recapitulated in a mouse model of LASV infection, in which mucosal exposure resulted in remarkably high lethality compared to skin exposure. Taken together, our findings indicate that poor LASV-specific T-cell responses and activation of nonspecific T cells with homing capacity to inflamed tissues are associated with severe LF. IMPORTANCE Lassa fever may cause severe disease in humans, in particular in areas of endemicity like Sierra Leone and Nigeria. Despite its public health importance, the pathophysiology of Lassa fever in humans is poorly understood. Here, we present clinical immunology data obtained in the field during the 2018 Lassa fever outbreak in Nigeria indicating that severe Lassa fever is associated with activation of T cells antigenically unrelated to Lassa virus and poor Lassa virus-specific effector T-cell responses. Mechanistically, we show that these bystander T cells express defined tissue homing signatures that suggest their recruitment to inflamed tissues and a putative role of these T cells in immunopathology. These findings open a window of opportunity to consider T-cell targeting as a potential postexposure therapeutic strategy against severe Lassa fever, a hypothesis that could be tested in relevant animal models, such as nonhuman primates.

Background Mentoring is a critical component of career development and job satisfaction leading to a healthier workforce and more productive outputs. However, there are limited data on mentorship models in regional areas and in particular for women aspiring to leadership positions. Mentorship programs that leverage off experienced mentors from diverse disciplines have the potential to foster the transfer of knowledge and to positively influence job satisfaction and build capacity within the context of workforce shortage. Methods This study describes a dual-mentorship model of professional development for women working in health in regional and rural Australia. We present the framework and describe the evaluation findings from a 12-month pilot program. Results Both academic and corporate mentors provided diverse perspectives to the mentees during the 12-month period. On average, corporate mentors met with mentees more often, and focused these discussions on strategy and leadership skills whilst academic mentors provided more technical advice regarding academic growth. Mentees reported an improvement in workplace interconnectedness and confidence at the completion of the program. Conclusion We developed a framework for establishing a professional mentorship program that matches women working in regional health with mentors from diverse sectors including business, government, philanthropy and health, to provide a holistic approach to improving career satisfaction, institutional productivity and supporting a diverse workforce in regional or resource-poor settings.

Staphylococcus aureus bacteremia (SaB) causes significant disease in humans, carrying mortality rates of ~25%. The ability to rapidly predict SaB patient responses and guide personalized treatment regimens could reduce mortality. Here, we present a resource of SaB prognostic biomarkers. Integrating proteomic and metabolomic techniques enabled the identification of >10,000 features from >200 serum samples collected upon clinical presentation. We interrogated the complexity of serum using multiple computational strategies, which provided a comprehensive view of the early host response to infection. Our biomarkers exceed the predictive capabilities of those previously reported, particularly when used in combination. Lastly, we validated the biological contribution of mortality-associated pathways using a murine model of SaB. Our findings represent a starting point for the development of a prognostic test for identifying high-risk patients at a time early enough to trigger intensive monitoring and interventions.

Thrombin, a procoagulant protease, cleaves and activates protease-activated receptor-1 (PAR1) to promote inflammatory responses and endothelial dysfunction. In contrast, activated protein C (APC), an anticoagulant protease, activates PAR1 through a distinct cleavage site and promotes anti-inflammatory responses, prosurvival, and endothelial barrier stabilization. The distinct tethered ligands formed through cleavage of PAR1 by thrombin versus APC result in unique active receptor conformations that bias PAR1 signaling. Despite progress in understanding PAR1 biased signaling, the proteins and pathways utilized by thrombin versus APC signaling to induce opposing cellular functions are largely unknown. Here, we report the global phosphoproteome induced by thrombin and APC signaling in endothelial cells with the quantification of 11,266 unique phosphopeptides using multiplexed quantitative mass spectrometry. Our results reveal unique dynamic phosphoproteome profiles of thrombin and APC signaling, an enrichment of associated biological functions, including key modulators of endothelial barrier function, regulators of gene transcription, and specific kinases predicted to mediate PAR1 biased signaling. Using small interfering RNA to deplete a subset of phosphorylated proteins not previously linked to thrombin or APC signaling, a function for afadin and adducin-1 actin binding proteins in thrombin-induced endothelial barrier disruption is unveiled. Afadin depletion resulted in enhanced thrombin-promoted barrier permeability, whereas adducin-1 depletion completely ablated thrombin-induced barrier disruption without compromising p38 signaling. However, loss of adducin-1 blocked APC-induced Akt signaling. These studies define distinct thrombin and APC dynamic signaling profiles and a rich array of proteins and biological pathways that engender PAR1 biased signaling in endothelial cells.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by progressive weight loss and nutritional deterioration. Several cytokines, such as activin A and myostatin, ligands of the transforming growth factor-β superfamily, have been shown to influence the pathogenesis of muscle wasting and tumor progression. The aim of our study was to assess the clinical significance of these cytokines in patients with different stages of PDAC. The study included 93 patients: 73 with newly diagnosed PDAC and 20 healthy volunteers as the control group. PDAC patients included 42 diagnosed with non-metastatic pancreatic cancer (stage I - III) and 31 patients with metastatic cancer (stage IV). The peripheral venous blood samples were collected from each patients at the time of cancer diagnosis and plasma concentrations of activin A and myostatin have been measured with an enzyme-linked immunoassay. Forty five patients (61.6%) presented weight loss5%, including 24 (57.1%) with stage I - II and 21 (67.7%) with metastatic PDAC (P0.05). Plasma levels of activing A were significantly higher in metastatic PDAC patients compared with stage I - III PDAC patients and control group (P0.01). The relationship between higher activin A levels and weight loss was also observed (P0.05). On the other hand, myostatin was not associated with weight loss in analysed group of patients. In conclusion, the current study demonstrates that high activin A plasma levels at the time of PDAC diagnosis is associated with unintentional weight loss and may be an useful biomarker for identifying patients with metastatic disease. However, further prospective studies are needed to fully explore the clinical significance of myostatin in pathogenesis of progressive weight loss in PDAC patients.

Connections between the microbiome and health are rapidly emerging in a wide range of diseases. However, a detailed mechanistic understanding of how different microbial communities are influencing their hosts is often lacking. One method researchers have used to understand these effects are germ-free (GF) mouse models. Differences found within the organ systems of these model organisms may highlight generalizable mechanisms that microbiome dysbioses have throughout the host. Here, we applied multiplexed, quantitative proteomics on the brains, spleens, hearts, small intestines, and colons of conventionally raised and GF mice, identifying associations to colonization state in over 7000 proteins. Highly ranked associations were constructed into protein–protein interaction networks and visualized onto an interactive 3D mouse model for user-guided exploration. These results act as a resource for microbiome researchers hoping to identify host effects of microbiome colonization on a given organ of interest. Our results include validation of previously reported effects in xenobiotic metabolism, the innate immune system, and glutamate-associated proteins while simultaneously providing organism-wide context. We highlight organism-wide differences in mitochondrial proteins including consistent increases in NNT, a mitochondrial protein with essential roles in influencing levels of NADH and NADPH, in all analyzed organs of conventional mice. Our networks also reveal new associations for further exploration, including protease responses in the spleen, high-density lipoproteins in the heart, and glutamatergic signaling in the brain. In total, our study provides a resource for microbiome researchers through detailed tables and visualization of the protein-level effects of microbial colonization on several organ systems.