Third international challenge to model the medium- to long-range transport of radioxenon to four Comprehensive Nuclear-Test-Ban Treaty monitoring stations.

In 2015 and 2016, atmospheric transport modeling challenges were conducted in the context of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) verification, however, with a more limited scope with respect to emission inventories, simulation period and number of relevant samples (i.e., those above the Minimum Detectable Concentration (MDC)) involved. Therefore, a more comprehensive atmospheric transport modeling challenge was organized in 2019. Stack release data of Xe-133 were provided by the Institut National des Radioéléments/IRE (Belgium) and the Canadian Nuclear Laboratories/CNL (Canada) and accounted for in the simulations over a three (mandatory) or six (optional) months period. Best estimate emissions of additional facilities (radiopharmaceutical production and nuclear research facilities, commercial reactors or relevant research reactors) of the Northern Hemisphere were included as well. Model results were compared with observed atmospheric activity concentrations at four International Monitoring System (IMS) stations located in Europe and North America with overall considerable influence of IRE and/or CNL emissions for evaluation of the participants' runs. Participants were prompted to work with controlled and harmonized model set-ups to make runs more comparable, but also to increase diversity. It was found that using the stack emissions of IRE and CNL with daily resolution does not lead to better results than disaggregating annual emissions of these two facilities taken from the literature if an overall score for all stations covering all valid observed samples is considered. A moderate benefit of roughly 10% is visible in statistical scores for samples influenced by IRE and/or CNL to at least 50% and there can be considerable benefit for individual samples. Effects of transport errors, not properly characterized remaining emitters and long IMS sampling times (12-24 h) undoubtedly are in contrast to and reduce the benefit of high-quality IRE and CNL stack data. Complementary best estimates for remaining emitters push the scores up by 18% compared to just considering IRE and CNL emissions alone. Despite the efforts undertaken the full multi-model ensemble built is highly redundant. An ensemble based on a few arbitrary runs is sufficient to model the Xe-133 background at the stations investigated. The effective ensemble size is below five. An optimized ensemble at each station has on average slightly higher skill compared to the full ensemble. However, the improvement (maximum of 20% and minimum of 3% in RMSE) in skill is likely being too small for being exploited for an independent period.

Acute mania following COVID-19 in a woman with no past psychiatric history case report.

BACKGROUND: The COVID-19 pandemic that began in late 2019 is caused by infection with the severe acute respiratory syndrome coronavirus-2. Since that time, many neuropsychiatric sequelae including psychosis, neurocognitive disorders, and mood disorders have been observed. The mechanism underlying these effects are currently unknown, however several mechanisms have been proposed. CASE PRESENTATION: A 47-year-old woman with past medical history including hypertension and premenstrual syndrome but no psychiatric history presented to the psychiatric hospital with new onset mania. She had developed symptoms of COVID-19 and was later diagnosed with COVID pneumonia. During quarantine, she reported high levels of stress, grief, and anxiety. Seventeen days into her illness, she developed altered mental status, sleeplessness, elevated mood, talkativeness, and preoccupations. Her spouse was concerned for her safety and contacted emergency medical services who brought her to the psychiatric hospital. She had not slept for five days prior to her arrival and exhibited flight of ideas, talkativeness, and grandiose ideas. She reported a family history of bipolar disorder but no past manic or depressive episodes. She was diagnosed with acute mania and stabilized using antipsychotics, a mood stabilizer, and a short course of a benzodiazepine. Many of her symptoms improved, including her elevated mood, increased activity level, and flight of ideas though she continued to have decreased need for sleep as her benzodiazepine was tapered. She and her partner were agreeable to transitioning to outpatient care after her mood stabilized. CONCLUSIONS: This report emphasizes the link between COVID-19 and neuropsychiatric symptoms. Acute mania has no recognized association with COVID-19, but similar presentations have been reported. The patient's age and time to onset of psychiatric symptoms is consistent with previous reports. Given the growing body of evidence, this association warrants further investigation. Severe acute respiratory syndrome coronavirus-2 causes systemic inflammation and has been shown to be neurotropic. In addition, patients undergoing quarantine experience anxiety related to the disease in addition to social isolation. Psychiatric practitioners should be aware of these effects and advocate for psychiatric evaluation following COVID-19 infection. Understanding the sequelae of infectious disease is crucial for responding to future pandemics.

Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection.

BACKGROUND: The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information. RESULTS: To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches. CONCLUSIONS: The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .

Germline contamination and leakage in whole genome somatic single nucleotide variant detection.

BACKGROUND: The clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. RESULTS: The median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases. CONCLUSIONS: The potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.

Long-range transport of Xe-133 emissions under convective and non-convective conditions.

To investigate the transport of xenon emissions, the Provisional Technical Secretariat (PTS) operates an Atmospheric Transport Modelling (ATM) system based on the Lagrangian Particle Dispersion Model FLEXPART. The air mass trajectory ideally provides a "link" between a radionuclide release and a detection confirmed by radionuclide measurements. This paper investigates the long-range transport of Xe-133 emissions under convective and non-convective conditions, with special emphasis on evaluating the changes in the simulated activity concentration values due to the inclusion of the convective transport in the ATM simulations. For that purpose a series of 14 day forward simulations, with and without convective transport, released daily in the period from 1 January 2011 to 30 June 2013, were analysed. The release point was at the ANSTO facility in Australia. The simulated activity concentrations for the period January 2011 to February 2012 were calculated using the daily emission values provided by the ANSTO facility; outside the aforementioned period, the median daily emission value was used. In the simulations the analysed meteorological input data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) were used with the spatial resolution of 0.5°. It was found that the long-range transport of Xe-133 emissions under convective conditions, where convection was included in the ATM simulation, led to a small decrease in the activity concentration, as compared to transport without convection. In special cases related to deep convection, the opposite effect was observed. Availability of both daily emission values and measured Xe-133 activity concentration values was an opportunity to validate the simulations. Based on the paired t-test, a 95% confidence interval for the true mean difference between simulations without convective transport and measurements was constructed. It was estimated that the overall uncertainty lies between 0.08 and 0.25 mBq/m3. The uncertainty for the simulations with the convective transport included is slighted shifted to the lower values and is in the range between 0.06 and 0.20 mBq/m3.

Variation in functional pelvic tilt in patients undergoing total hip arthroplasty.

AIMS: The pelvis rotates in the sagittal plane during daily activities. These rotations have a direct effect on the functional orientation of the acetabulum. The aim of this study was to quantify changes in pelvic tilt between different functional positions. PATIENTS AND METHODS: Pre-operatively, pelvic tilt was measured in 1517 patients undergoing total hip arthroplasty (THA) in three functional positions - supine, standing and flexed seated (the moment when patients initiate rising from a seated position). Supine pelvic tilt was measured from CT scans, standing and flexed seated pelvic tilts were measured from standardised lateral radiographs. Anterior pelvic tilt was assigned a positive value. RESULTS: The mean pelvic tilt was 4.2° (-20.5° to 24.5°), -1.3° (-30.2° to 27.9°) and 0.6° (-42.0° to 41.3°) in the three positions, respectively. The mean sagittal pelvic rotation from supine to standing was -5.5° (-21.8° to 8.4°), from supine to flexed seated was -3.7° (-48.3° to 38.6°) and from standing to flexed seated was 1.8° (-51.8° to 39.5°). In 259 patients (17%), the extent of sagittal pelvic rotation could lead to functional malorientation of the acetabular component. Factoring in an intra-operative delivery error of ± 5° extends this risk to 51% of patients. CONCLUSION: Planning and measurement of the intended position of the acetabular component in the supine position may fail to predict clinically significant changes in its orientation during functional activities, as a consequence of individual pelvic kinematics. Optimal orientation is patient-specific and requires an evaluation of functional pelvic tilt pre-operatively. Cite this article: Bone Joint J 2017;99-B:184-91.

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

BACKGROUND: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. METHODS: Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. FINDINGS: 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. INTERPRETATION: Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer. FUNDING: Sanofi US Services, Project Data Sphere.

Patient-specific instrumentation improves the accuracy of acetabular component placement in total hip arthroplasty.

AIMS: Accurate placement of the acetabular component during total hip arthroplasty (THA) is an important factor in the success of the procedure. However, the reported accuracy varies greatly and is dependent upon whether free hand or navigated techniques are used. The aim of this study was to assess the accuracy of an instrument system that incorporates 3D printed, patient-specific guides designed to optimise the placement of the acetabular component. PATIENTS AND METHODS: A total of 100 consecutive patients were prospectively enrolled and the accuracy of placement of the acetabular component was measured using post-operative CT scans. PATIENTS AND METHODS: A total of 100 consecutive patients were prospectively enrolled and the accuracy of placement of the acetabular component was measured using post-operative CT scans. CONCLUSION: Accurate placement of the acetabular component can be achieved using patient-specific guides and is superior to free hand techniques and comparable to navigated and robotic techniques. Cite this article: Bone Joint J 2016;98-B:1342-6.

Comparative performance of isolation methods using Preston broth, Bolton broth and their modifications for the detection of Campylobacter spp. from naturally contaminated fresh and frozen raw poultry meat.

The performance of different isolation methods was evaluated for the detection of Campylobacter from naturally contaminated raw poultry meat. Therefore, fresh and frozen poultry meat samples were analysed using the standard procedure (ISO 10272-1:2006), enrichment in Preston broth, and enrichment in modified Bolton broth (supplemented with (i) potassium clavulanate (C-BB), (ii) triclosan (T-BB), (iii) polymyxin B (P-BB)). The enrichment cultures were streaked onto both modified charcoal cefoperazone deoxycholate agar (mCCDA) and RAPID'Campylobacter agar (RCA). Moreover, direct plating on mCCDA and RCA was performed to quantify Campylobacter. In total, 33 out of 59 fresh retail meat samples (55.9%) were Campylobacter positive. For both fresh and frozen poultry meat samples, enrichment in Bolton broth (ISO 10272-1:2006) resulted in a higher number of positive samples than enrichment in Preston broth. Supplementation of Bolton broth with potassium clavulanate (C-BB) and triclosan (T-BB) enhanced the Campylobacter recovery from fresh poultry meat compared to non-supplemented Bolton broth, although the use of C-BB was less applicable than T-BB for Campylobacter recovery from frozen samples. Additionally, the use of RCA resulted in a higher isolation rate compared to mCCDA. The present study demonstrates the impact of culture medium on the recovery of Campylobacter from fresh and frozen naturally contaminated poultry meat samples and can support laboratories in choosing the most appropriate culturing method to detect Campylobacter.

Functional orientation of the acetabular component in ceramic-on-ceramic total hip arthroplasty and its relevance to squeaking.

AIMS: Long-term clinical outcomes for ceramic-on-ceramic (CoC) bearings are encouraging. However, there is a risk of squeaking. Guidelines for the orientation of the acetabular component are defined from static imaging, but the position of the pelvis and thus the acetabular component during activities associated with edge-loading are likely to be very different from those measured when the patient is supine. We assessed the functional orientation of the acetabular component. PATIENTS AND METHODS: A total of 18 patients with reproducible squeaking in their CoC hips during deep flexion were investigated with a control group of 36 non-squeaking CoC hips. The two groups were matched for the type of implant, the orientation of the acetabular component when supine, the size of the femoral head, ligament laxity, maximum hip flexion and body mass index. RESULTS: The mean functional anteversion of the acetabular component at the point when patients initiated rising from a seated position was significantly less in the squeaking group than in the control group, 8.1° (-10.5° to 36.0°) and 21.1° (-1.9° to 38.4°) respectively (p = 0.002). CONCLUSION: The functional orientation of the acetabular component during activities associated with posterior edge-loading are different from those measured when supine due to patient-specific pelvic kinematics. Individuals with a large anterior pelvic tilt during deep flexion might be more susceptible to posterior edge-loading and squeaking as a consequence of a significant decrease in the functional anteversion of the acetabular component. Cite this article: Bone Joint J 2016;98-B:910-16.

Influence of consciousness, muscle action and activity on medial condyle translation after Oxford unicompartmental knee replacement.

BACKGROUND: Quantification of the in vivo position of the medial condyle throughout flexion is important for knee replacement design, and understanding knee pathology. The influence of consciousness, muscle action, and activity type on condyle translation was examined in patients who had undergone medial unicompartmental knee replacement (UKR) using lateral video fluoroscopy. METHODS: The position of the centre of the femoral component relative to the tibial component was measured for nine patients under different conditions. The following activities were assessed; passive flexion and extension when anaesthetised, passive flexion and extension when conscious, and active flexion, extension and step-up. RESULTS: The position of the centre of the femoral component relative to the tibial component was highly patient dependent. The greatest average translation range (14.9 mm) was observed in anaesthetised patients, and the condyle was significantly more anterior near to extension. Furthermore, when conscious but being moved passively, the femoral condyle translated a greater range (8.9 mm) than when moving actively (5.2mm). When ascending stairs, the femoral condyle was more posterior at 20-30° of flexion than during flexion/extension. CONCLUSIONS: The similarity between these results and published data suggest that knee kinematics following mobile-bearing UKR is relatively normal. The results show that in the normal knee and after UKR, knee kinematics is variable and is influenced by the patient, consciousness, muscle action, and activity type. CLINICAL RELEVANCE: It is therefore essential that all these factors are considered during knee replacement design, if the aim is to achieve more normal knee kinematics.

Prediction of human population responses to toxic compounds by a collaborative competition.

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.

The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.

Contamination of freshly slaughtered pig carcasses with enteropathogenic Yersinia spp.: Distribution, quantification and identification of risk factors.

A cross-sectional survey was undertaken to determine the overall prevalence of enteropathogenic Yersinia spp. in the tonsils, feces and on carcasses of pigs at slaughter. Moreover, factors associated with Yersinia contamination of freshly eviscerated pig carcasses were studied. Yersinia enterocolitica serotype O:3 was isolated from the tonsils and feces of 55.3% and 25.6% of pigs, and Y. pseudotuberculosis from 1.4% and 0.6%, respectively. The pathogens were also recovered from 39.7% of carcass surfaces post-evisceration. The highest prevalence was found at the mandibular region (28.9%), followed by the sternal region (16.4%), pelvic duct (7.8%), and split surface near the sacral vertebrae (6.9%). Regarding the quantification of the pathogen, the median concentration of pathogenic Y. enterocolitica was 4.14l og10 CFU/g in tonsils with countable numbers (n=143) and 2.80 log10 CFU/g for fecal samples with countable numbers (n=26). The quantitative load on the carcass surface was generally low as the majority of the carcass samples (97.0%) had Yersinia concentrations below the detection limit of enumeration (<1.30 log10 CFU/100 cm(2)). The initial presence of Y. enterocolitica in the tonsils and/or feces was significantly associated with carcass contamination at all sampled areas. Other risk factors for carcass contamination are the splitting of the head together with the carcass, and incision of the tonsils during removal of the pluck. Small adaptations in slaughter practices and the training of slaughterhouse personnel to respect basic hygienic instructions may diminish carcass contamination with enteropathogenic Yersinia.

Simulation studies as designed experiments: the comparison of penalized regression models in the "large p, small n" setting.

New algorithms are continuously proposed in computational biology. Performance evaluation of novel methods is important in practice. Nonetheless, the field experiences a lack of rigorous methodology aimed to systematically and objectively evaluate competing approaches. Simulation studies are frequently used to show that a particular method outperforms another. Often times, however, simulation studies are not well designed, and it is hard to characterize the particular conditions under which different methods perform better. In this paper we propose the adoption of well established techniques in the design of computer and physical experiments for developing effective simulation studies. By following best practices in planning of experiments we are better able to understand the strengths and weaknesses of competing algorithms leading to more informed decisions about which method to use for a particular task. We illustrate the application of our proposed simulation framework with a detailed comparison of the ridge-regression, lasso and elastic-net algorithms in a large scale study investigating the effects on predictive performance of sample size, number of features, true model sparsity, signal-to-noise ratio, and feature correlation, in situations where the number of covariates is usually much larger than sample size. Analysis of data sets containing tens of thousands of features but only a few hundred samples is nowadays routine in computational biology, where "omics" features such as gene expression, copy number variation and sequence data are frequently used in the predictive modeling of complex phenotypes such as anticancer drug response. The penalized regression approaches investigated in this study are popular choices in this setting and our simulations corroborate well established results concerning the conditions under which each one of these methods is expected to perform best while providing several novel insights.

Co-occurrence of free-living protozoa and foodborne pathogens on dishcloths: implications for food safety.

In the present study, the occurrence of free-living protozoa (FLP) and foodborne bacterial pathogens on dishcloths was investigated. Dishcloths form a potentially important source of cross-contamination with FLP and foodborne pathogens in food-related environments. First various protocols for recovering and quantifying FLP from dishcloths were assessed. The stomacher technique is recommended to recover flagellates and amoebae from dishcloths. Ciliates, however, were more efficiently recovered using centrifugation. For enumeration of free-living protozoa on dishcloths, the Most Probable Number method is a convenient method. Enrichment was used to assess FLP diversity on dishcloths (n=38). FLP were found on 89% of the examined dishcloths; 100% of these tested positive for amoebae, 71% for flagellates and 47% for ciliates. Diversity was dominated by amoebae: vahlkampfiids, vannellids, Acanthamoeba spp., Hyperamoeba sp. and Vermamoeba vermiformis were most common. The ciliate genus Colpoda was especially abundant on dishcloths while heterotrophic nanoflagellates mainly belonged to the genus Bodo, the glissomonads and cercomonads. The total number of FLP in used dishcloths ranged from 10 to 10(4) MPN/cm(2). Flagellates were the most abundant group, and ciliates the least abundant. Detergent use was identified as a prime determinant of FLP concentrations on used dishcloths. Bacterial load on dishcloths was high, with a mean total of aerobic bacteria of 7.47 log 10 cfu/cm(2). Escherichia coli was detected in 68% (26/38) of the used dishcloths, with concentrations up to 4 log 10 cfu/cm(2). Foodborne pathogens including Staphylococcus aureus (19/38), Arcobacter butzleri (5/38) and Salmonella enterica subsp. enterica ser. Halle (1/38) were also present. This study showed for the first time that FLP, including some opportunistic pathogens, are a common and diverse group on dishcloths. Moreover, important foodborne pathogens are also regularly recovered. This simultaneous occurrence makes dishcloths a potential risk factor for cross-contamination and a microbial niche for bacteria-FLP interactions.

Network portal: a database for storage, analysis and visualization of biological networks.

The ease of generating high-throughput data has enabled investigations into organismal complexity at the systems level through the inference of networks of interactions among the various cellular components (genes, RNAs, proteins and metabolites). The wider scientific community, however, currently has limited access to tools for network inference, visualization and analysis because these tasks often require advanced computational knowledge and expensive computing resources. We have designed the network portal (http://networks.systemsbiology.net) to serve as a modular database for the integration of user uploaded and public data, with inference algorithms and tools for the storage, visualization and analysis of biological networks. The portal is fully integrated into the Gaggle framework to seamlessly exchange data with desktop and web applications and to allow the user to create, save and modify workspaces, and it includes social networking capabilities for collaborative projects. While the current release of the database contains networks for 13 prokaryotic organisms from diverse phylogenetic clades (4678 co-regulated gene modules, 3466 regulators and 9291 cis-regulatory motifs), it will be rapidly populated with prokaryotic and eukaryotic organisms as relevant data become available in public repositories and through user input. The modular architecture, simple data formats and open API support community development of the portal.

Behavior of Yersinia enterocolitica in the presence of the bacterivorous Acanthamoeba castellanii.

Free-living protozoa play an important role in the ecology and epidemiology of human-pathogenic bacteria. In the present study, the interaction between Yersinia enterocolitica, an important food-borne pathogen, and the free-living amoeba Acanthamoeba castellanii was studied. Several cocultivation assays were set up to assess the resistance of Y. enterocolitica to A. castellanii predation and the impact of environmental factors and bacterial strain-specific characteristics. Results showed that all Y. enterocolitica strains persist in association with A. castellanii for at least 14 days, and associations with A. castellanii enhanced survival of Yersinia under nutrient-rich conditions at 25°C and under nutrient-poor conditions at 37°C. Amoebae cultivated in the supernatant of one Yersinia strain showed temperature- and time-dependent permeabilization. Intraprotozoan survival of Y. enterocolitica depended on nutrient availability and temperature, with up to 2.8 log CFU/ml bacteria displaying intracellular survival at 7°C for at least 4 days in nutrient-rich medium. Transmission electron microscopy was performed to locate the Yersinia cells inside the amoebae. As Yersinia and Acanthamoeba share similar ecological niches, this interaction identifies a role of free-living protozoa in the ecology and epidemiology of Y. enterocolitica.