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Data on toxic effects are at large missing the prevailing understanding of the risks of industrial chemicals. Thyroid hormone (TH) system disruption includes interferences of the life cycle of the thyroid hormones and may occur in various organs. In the current study, high-throughput screening data available for 14 putative molecular initiating events of adverse outcome pathways, related to disruption of the TH system, were used to develop 19 in silico models for identification of potential thyroid hormone system-disrupting chemicals. The conformal prediction framework with the underlying Random Forest was used as a wrapper for the models allowing for setting the desired confidence level and controlling the error rate of predictions. The trained models were then applied to two different databases: (i) an in-house database comprising xenobiotics identified in human blood and ii) currently used chemicals registered in the Swedish Product Register, which have been predicted to have a high exposure index to consumers. The application of these models showed that among currently used chemicals, fewer were overall predicted as active compared to chemicals identified in human blood. Chemicals of specific concern for TH disruption were identified from both databases based on their predicted activity.

Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skellefteå during 2014–2018 in order to identify important spatial covariates and discern hotspot regions. Our large-scale multivariate data point pattern of call events consists of spatial locations and marks containing the associated priority levels and sex labels. The covariates used are related to road network coverage, population density, and socio-economic status. For each marginal point pattern, we model the associated intensity function by means of a log-linear function of the covariates and their interaction terms, in combination with lasso-like elastic-net regularized composite/Poisson process likelihood estimation. This enables variable selection and collinearity adjustment as well as reduction of variance inflation from overfitting and bias from underfitting. To incorporate mobility adjustment, reflecting people’s movement patterns, we also include a nonparametric (kernel) intensity estimate as an additional covariate. The kernel intensity estimation performed here exploits a new heuristic bandwidth selection algorithm. We discover that hotspot regions occur along dense parts of the road network. A mean absolute error evaluation of the fitted model indicates that it is suitable for designing prehospital resource dispatching strategies. Supplementary materials accompanying this paper appear online.

Exposing sires to various environmental manipulations has demonstrated that paternal effects can be non-trivial also in species where male investment in offspring is almost exclusively limited to sperm. Whether paternal effects also have a genetic component (i.e. paternal indirect genetic effects (PIGEs)) in such species is however largely unknown, primarily because of methodological difficulties separating indirect from direct effects of genes. PIGEs may nevertheless be important since they have the capacity to contribute to evolutionary change. Here we use

Statistical methods are commonly used to monitor quality in manufacturing processes. We consider a set of problems where the probability that a unit is defect, i.e., the defect probability, is influenced by a large number of sub-processes, and where the overall aim is to monitor the defect probability and link abnormalities in quality to observed production variables. We developed a simulation framework for studying the performances of statistical methods used to solve the considered problem. Fourteen prediction procedures were obtained by combining six prediction methods (linear regression, logistic regression, LASSO, penalized logistic regression, support vector machines, gradient boosting decision trees) and two pre-processing procedures. These prediction procedures were evaluated on four types of simulated datasets with different relationships between the explanatory variables and the defect probabilities. Additionally, two established methods for variable selection were compared to a novel method called mixed moments selection (MMS). MMS was more robust than the other methods, performed well on all dataset types, and can easily be combined with any type of prediction method. Additionally, it was shown that it can be advantageous to complement the original explanatory variables with their squared values prior to analyzing the data. Overall, a procedure combining MMS, including additional quadratic terms and using PLR had the best performance. The proposed framework can be applied to evaluate any type of prediction procedure for the general problem we consider. This would increase the understanding of different procedures and facilitate the selection of procedures for a specific problem.

Compositional changes in the early-life gut microbiota have been implicated in IgE-associated allergic diseases, but there is lack of longitudinal studies. We examined gut microbiota development from infancy to school age in relation to onset of IgE-associated allergic diseases. At 8 years of age, we also examined the relationship between gut microbiota and T-cell regulation, estimated as responses to polyclonal T-cell activation.Stool samples were collected from 93 children at 4, 6, 13 months, and 8 years of age. The gut microbiota was profiled using 16S rRNA gene sequencing. Peripheral blood was drawn from all children, and mononuclear cells were polyclonally activated. Levels of IL-10 and FOXP3 mRNA copies were determined using real-time quantitative reverse transcriptase-PCR.At 8 years of age, 21 children were diagnosed with IgE-associated allergic disease and 90% displayed allergic comorbidity. Seventy-two children were nonallergic and nonsensitized. Statistical tests with multiple testing corrections demonstrated temporal underrepresentation of Ruminococcus and consistent underrepresentation of Bacteroides, Prevotella, and Coprococcus in allergic compared to nonallergic children from infancy to school age. The gut microbiota of the allergic 8-year-olds was enriched in Bifidobacterium and depleted of Lactobacillus, Enterococcus, and Lachnospira. In allergic 8-year-olds, Faecalibacterium correlated with IL-10 mRNA levels (rWe identified both temporal and long-term variation in the differential abundance of specific bacterial genera in children developing IgE-associated allergic disease. Improved dietary interventions aiming at expanding immune-modulatory taxa could be studied for prevention of allergic disease.

Francisella tularensis causes the severe disease tularemia. In the present study, the aim was to identify correlates of protection in the rat co-culture model by investigating the immune responses using two vaccine candidates conferring distinct degrees of protection in rat and mouse models. The immune responses were characterized by use of splenocytes from na&iuml, ve or Live vaccine strain- (LVS) or ∆clpB/∆wbtC-immunized Fischer 344 rats as effectors and bone marrow-derived macrophages infected with the highly virulent strain SCHU S4. A complex immune response was elicited, resulting in cytokine secretion, nitric oxide production, and efficient control of the intracellular bacterial growth. Addition of LVS-immune splenocytes elicited a significantly better control of bacterial growth than ∆clpB/∆wbtC splenocytes. This mirrored the efficacy of the vaccine candidates in the rat model. Lower levels of IFN-&gamma, TNF, fractalkine, IL-2, and nitrite were present in the co-cultures with ∆clpB/∆wbtC splenocytes than in those with splenocytes from LVS-immunized rats. Nitric oxide was found to be a correlate of protection, since the levels inversely correlated to the degree of protection and inhibition of nitric oxide production completely reversed the growth inhibition of SCHU S4. Overall, the results demonstrate that the co-culture assay with rat-derived cells is a suitable model to identify correlates of protection against highly virulent strains of F. tularensis

SUMMARY Bacterial pathogens often employ RNA regulatory elements located in the 5′ untranslated regions (UTRs) to control gene expression. Using a comparative structural analysis, we examine the structure of 5′ UTRs at a global scale in the pathogenic bacterium Listeria monocytogenes under different conditions. In addition to discovering an RNA thermoswitch and detecting simultaneous interaction of ribosomes and small RNAs with mRNA, we identify structural changes in the 5′ UTR of an mRNA encoding the post-translocation chaperone PrsA2 during infection conditions. We demonstrate that the 5′ UTR of the prsA2 mRNA base pairs with the 3′ UTR of the full-length hly mRNA encoding listeriolysin O, thus preventing RNase J1-mediated degradation of the prsA2 transcript. Mutants lacking the hly-prsA2 interaction exhibit reduced virulence properties. This work highlights an additional level of RNA regulation, where the mRNA encoding a chaperone is stabilized by the mRNA encoding its substrate., In Brief By using a comparative structural analysis method targeting 5′ untranslated regions, Ignatov et al. identify different RNA-based regulatory mechanisms such as an RNA thermoswitch, a small RNA (sRNA)-mRNA interaction, and strikingly, an mRNA-mRNA interaction., Graphical Abstract

In order to optimally utilise the resources of a country’s prehospital care system, i.e. ambulance service(s),it is crucial that one is able to spatio-temporally forecast hot-spots, i.e. spatial regions and periods with anincreased risk of seeing a call to the emergency number 112 which results in the dispatch of an ambulance.Such forecasts allow the dispatcher to make strategic decisions regarding e.g. the fleet size and where todirect unoccupied ambulances. In addition, simulations based on forecasts may serve as the startingpoint for different optimal routing strategies. Although the associated data typically comes in the form ofspatio-temporal point patterns, point process based modelling attempts in the literature has been scarce.In this paper, we study a unique set of Swedish spatio-temporal ambulance call data, which consists ofthe spatial (gps) locations of the dispatch addresses and the associated days of occurrence of the calls.The spatial study region is given by the four northernmost regions of Sweden and the study period isJanuary 1, 2014 to December 31, 2018. Motivated by the non-infectious disease nature of the data, wehere employ log-Gaussian Cox processes (LGCPs) for the spatio-temporal modelling and forecasting ofthe calls. To this end, we propose a K-means based bandwidth selection method for the kernel estimationof the spatial component of the separable spatio-temporal intensity function. The temporal componentof the intensity function is modelled by means of Poisson regression, using different calendar covariates,and the spatio-temporal random field component of the random intensity of the LGCP is fitted usingsimulation via the Metropolis-adjusted Langevin algorithm. A study of the spatio-temporal dynamics ofthe data shows that a hot-spot can be found in the south eastern part of the study region, where mostpeople in the region live and our fitted model/forecasts manage to capture this behaviour quite well. Thefitted temporal component of the intensity functions reveals that there is a significant association betweenthe expected number of calls and the day of the week as well as the season of the year. In addition,non-parametric second-order spatio-temporal summary statistic estimates indicate that LGCPs seem tobe reasonable models for the data. Finally, we find that the fitted forecasts generate simulated futurespatial event patterns which quite well resemble the actual future data.

Gene co-expression networks (GCNs) can be prepared using a variety of mathematical approaches based on data sampled across diverse developmental processes, tissue types, pathologies, mutant backgrounds, and stress conditions. These networks are used to identify genes with similar expression dynamics but are prone to introducing false-positive and false-negative relationships, especially in the instance of large and heterogenous datasets. With the aim of optimizing the relevance of edges in GCNs and enhancing global biological insight, we propose a novel approach that involves a data-centering step performed simultaneously per gene and per sub-experiment, called centralization within sub-experiments (CSE). Using a gene set encoding the plant mitochondrial proteome as a case study, our results show that all CSE-based GCNs assessed had significantly more edges within the majority of the considered functional sub-networks, such as the mitochondrial electron transport chain and its complexes, than GCNs not using CSE; thus demonstrating that CSE-based GCNs are efficient at predicting canonical functions and associated pathways, here referred to as the core gene network. Furthermore, we show that correlation analyses using CSE-processed data can be used to fine-tune prediction of the function of uncharacterized genes; while its use in combination with analyses based on non-CSE data can augment conventional stress analyses with the innate connections underpinning the dynamic system being examined. Therefore, CSE is an effective alternative method to conventional batch correction approaches, particularly when dealing with large and heterogenous datasets. The method is easy to implement into a pre-existing GCN analysis pipeline and can provide enhanced biological relevance to conventional GCNs by allowing users to delineate a core gene network. Author summary Gene co-expression networks (GCNs) are the product of a variety of mathematical approaches that identify causal relationships in gene expression dynamics but are prone to the misdiagnoses of false-positives and false-negatives, especially in the instance of large and heterogenous datasets. In light of the burgeoning output of next-generation sequencing projects performed on a variety of species, and developmental or clinical conditions; the statistical power and complexity of these networks will undoubtedly increase, while their biological relevance will be fiercely challenged. Here, we propose a novel approach to generate a "core" GCN with enhanced biological relevance. Our method involves a data-centering step that effectively removes all primary treatment/tissue effects, which is simple to employ and can be easily implemented into pre-existing GCN analysis pipelines. The gain in biological relevance resulting from the adoption of this approach was assessed using a plant mitochondrial case study.

Summary: The culture negative peritonitis in Sudan 2010 was 46% exceeding 20% of the recommended ISPD (International Society for Peritoneal Dialysis) guidelines. This study reports an update after applying the standard ISPD protocol. The routine method was replaced by ISPD protocol. The culture negative rate using the ISPD guidelines dropped from 46% in the year 2010, to 39% in the year 2011, to 5% in the 2012 and to zero percent in the year 2013. Bacterial and fungal species represent (86.76%) and (13.23%) of infection and most isolates showed low resistance rate to antibiotics. Touch contamination added significantly (p = 0.0006) to the risk of contracting Peritonitis. The risk of contracting Peritonitis was 1.53 times higher in the group exposed by touch contamination. None of the other risk factors contributed significantly to Peritonitis. The study highlights the importance of implementing high hygiene practice. Keywords: Sudan, Culture negative, Peritonitis, CAPD

Gene Co-expression Networks (GCNs) are obtained by a variety of mathematical of models commonly derived on data sampled from diverse developmental processes, tissue types, pathologies, mutant backgrounds, and stress conditions. These networks aim to identify genes with similar expression dynamics, but are prone to introduce false-positive and -negative relations, especially in the instance of large and highly complex datasets. With the aim of optimizing the relevance of edges in GCNs and enhancing global biological insight, we propose a novel approach that involves a data-centering step performed simultaneously per gene and per sub-experiment, called centralisation within sub-experiments (CSE).Using a gene set encoding for the plant mitochondrial proteome as a case study, our results show that CSE-based GCNs had significantly more edges within the majority of the considered functional sub-networks, such as the mitochondrial electron transport chain and its sub-complexes, than GCNs not using CSE; thus demonstrating that the CSE-based GCNs are efficient at predicting those canonical functions and associated pathways, also referred to as the “core network”. Furthermore, we show that CSE, in conjunction with conventional correlation analyses can be used to fine-tune the prediction of the function for uncharacterised genes; while in combination with analyses based on non-centralised data can augment those conventional stress analyses with the innate connections underpinning the dynamic system examined.Therefore, CSE appears as an alternative method to conventional batch correction approaches. The method is easy to implement into a pre-existing GCN analysis pipeline and can provide accentuated biological relevance to conventional GCNs by allowing users to delineate a “core” gene network.Author SummaryGene Co-expression networks (GCNs) are the product of a variety of mathematical models that identify causal relationships in gene expression dynamics, but are prone to the misdiagnoses of false-positives and -negatives, especially in the instance of large and highly complex datasets. In light of the burgeoning output of next generation sequencing projects performed on any species, under different developmental or clinical conditions, the statistical power and complexity of these networks will undoubtedly increase, while their biological relevance will be fiercely challenged. Here, we propose a novel approach to primarily generate a “core” GCN with augmented biological relevance. Our method, which involves data-centering steps and thus effectively removes all primary treatment / tissue /patient effects, is simple to employ and can be easily implemented into pre-existing GCN analysis pipelines. The gained biological relevance of such an approach was validated using a subcellular gene set encoding for the plant mitochondrial proteome, and by applying numerous steps to challenge its application.

Bone metastasis is the lethal end‐stage of prostate cancer (PC), but the biology of bone metastases is poorly understood. The overall aim of this study was therefore to explore molecular variability in PC bone metastases of potential importance for therapy. Specifically, genome‐wide expression profiles of bone metastases from untreated patients (n = 12) and patients treated with androgen‐deprivation therapy (ADT, n = 60) were analyzed in relation to patient outcome and to morphological characteristics in metastases and paired primary tumors. Principal component analysis and unsupervised classification were used to identify sample clusters based on mRNA profiles. Clusters were characterized by gene set enrichment analysis and related to histological and clinical parameters using univariate and multivariate statistics. Selected proteins were analyzed by immunohistochemistry in metastases and matched primary tumors (n = 52) and in transurethral resected prostate (TUR‐P) tissue of a separate cohort (n = 59). Three molecular subtypes of bone metastases (MetA‐C) characterized by differences in gene expression pattern, morphology, and clinical behavior were identified. MetA (71% of the cases) showed increased expression of androgen receptor‐regulated genes, including prostate‐specific antigen (PSA), and glandular structures indicating a luminal cell phenotype. MetB (17%) showed expression profiles related to cell cycle activity and DNA damage, and a pronounced cellular atypia. MetC (12%) exhibited enriched stroma–epithelial cell interactions. MetB patients had the lowest serum PSA levels and the poorest prognosis after ADT. Combined analysis of PSA and Ki67 immunoreactivity (proliferation) in bone metastases, paired primary tumors, and TUR‐P samples was able to differentiate MetA‐like (high PSA, low Ki67) from MetB‐like (low PSA, high Ki67) tumors and demonstrate their different prognosis. In conclusion, bone metastases from PC patients are separated based on gene expression profiles into molecular subtypes with different morphology, biology, and clinical outcome. These findings deserve further exploration with the purpose of improving treatment of metastatic PC.

Background: RNA-seq data from tumor samples can be used to identify novel cancer subtypes using cluster analysis. The number of features is often large compared to the number of samples and different clusters can appear in different subsets of the feature space. Feature selection techniques are therefore commonly used to reduce the dimension and remove redundant and irrelevant features before performing cluster analysis. An abundance of feature selection methods have been proposed in the literature, but it is unclear how the ability to identify novel subtypes of cancer using cluster analysis is affected by choice of feature selection method. Method: We evaluated 13 feature selection methods on four publicly available cancer data sets from The Cancer Genome Atlas. RNA-seq data and associated clinical data were retrieved from Broad institute GDAC Firehose. Overlap and characterization of the highest ranked features (i.e. genes) were studied for top 100, 1000 and 3000 genes. Performance was measured by comparing known cancer subtypes to partitions obtained using hierarchical clustering with Euclidean distance and Ward´s linkage, based on the selected features. The result was compared to both a random selection (negative control) and a supervised approach (positive control) using adjusted Rand index. Results: The relative performance of the feature selection methods varied heavily depending on number of included genes and the data set used. Based on all data, gene selection using the Dip-test statistic and the Bimodality index generated the overall highest clustering performance. The overlap of selected genes between the Bimodality index, the Dip-test and the supervised approach was relatively low. The Dip-test and Bimodality index tended to favor genes with relatively low expression values. Conclusions: The choice of feature selection method can have a huge impact on the ability to identify cancer subtypes using cluster analysis, and the relative performance is highly dependent on the data. Low overlap of selected genes between the highest ranked methods suggests that the methods identify genes that contain complementary information, and that it might be beneficial to combine two or more feature selection methods. Citation Format: Linda Vidman, David Källberg, Patrik Rydén. Evaluation of feature selection methods used for cluster analysis in identification of novel cancer subtypes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5475.

New vaccines are needed to combat the public health threat posed by M. tuberculosis (M. tb), but no correlates have been defined to aid vaccine development. Using mouse models, we previously developed an in vitro system that measures the ability of M. tb-immune lymphocytes to control bacterial replication during co-culture with M. tb-infected macrophages. We demonstrated that the degree of in vitro growth control by lymphocytes from mice given vaccines of varying efficacy reflected the relative degree of in vivo protection against lethal challenge. Further, using targeted analyses of gene expression in lymphocytes recovered from co-cultures, we found mediators whose relative expression also correlated with in vitro and in vivo outcomes. Here we advanced those findings by employing genome-wide expression analyses. We first screened splenocytes recovered from co-cultures by microarray, revealing additional genes whose expression correlated with protection. After applying pathway analyses to down-select gene candidates, we used both splenocytes and peripheral blood lymphocytes to validate microarray findings by qRT-PCR. We then subjected data from top candidates to rigorous statistical analyses. Resulting correlate candidates, including CXCL9, IFN-γ, and CCL5, significantly predicted protection with high specificity. These findings therefore refine and extend a panel of relevant immune correlates to advance vaccine development.

There are no defined correlates of protection for any intracellular pathogen, including the bacterium Francisella tularensis, which causes tularemia. Evaluating vaccine efficacy against sporadic diseases like tularemia using field trials is problematic, and therefore alternative strategies to test vaccine candidates like the Francisella Live Vaccine Strain (LVS), such as testing in animals and applying correlate measurements, are needed. Recently, we described a promising correlate strategy that predicted the degree of vaccine-induced protection in mice given parenteral challenges, primarily when using an attenuated Francisella strain. Here, we demonstrate that using peripheral blood lymphocytes (PBLs) in this approach predicts LVS-mediated protection against respiratory challenge of Fischer 344 rats with fully virulent F. tularensis, with exceptional sensitivity and specificity. Rats were vaccinated with a panel of LVS-derived vaccines and subsequently given lethal respiratory challenges with Type A F. tularensis. In parallel, PBLs from vaccinated rats were evaluated for their functional ability to control intramacrophage Francisella growth in in vitro co-culture assays. PBLs recovered from co-cultures were also evaluated for relative gene expression using a large panel of genes identified in murine studies. In vitro control of LVS intramacrophage replication reflected the hierarchy of protection. Further, despite variability between individuals, 22 genes were significantly more up-regulated in PBLs from rats vaccinated with LVS compared to those from rats vaccinated with the variant LVS-R or heat killed LVS, which were poorly protective. These genes included IFN-gamma, IL-21, NOS2, LTA, T-bet, IL-12rβ2, and CCL5. Most importantly, combining quantifications of intramacrophage growth control with 5-7 gene expression levels using multivariate analyses discriminated protected from non-protected individuals with greater than 95% sensitivity and specificity. The results therefore support translation of this approach to non-human primates and people to evaluate new vaccines against Francisella and other intracellular pathogens.

To understand physiological processes, it is vital to disclose the conformation of biological molecules inside living cells and correlate their structure with their function. Here, we describe a method, FUSE (5ʹ UTR Structure Elucidation), that we developed to study how the structures of 5ʹ UTRs change in response to different conditions inside the human bacterial pathogen Listeria monocytogenes. We discovered a novel RNA thermoswitch and could calculate the ratio of the two conformations that this thermoswitch assumes at different conditions. FUSE also identified the site of base-pairing between a small RNA and mRNA with a single-nucleotide resolution. Strikingly, FUSE discovered a functional mRNA-mRNA interaction, where the stability of an mRNA encoding the PrsA2 chaperone was increased by the direct binding of an mRNA encoding its substrate, the main virulence factor listeriolysin O. Specifically targeting the 5ʹ UTRs, FUSE can be applied in any living organism or virus.

Geographic distribution of cases was correlated with the locations of lakes and rivers., The zoonotic disease tularemia is endemic in large areas of the Northern Hemisphere, but research is lacking on patterns of spatial distribution and connections with ecologic factors. To describe the spatial epidemiology of and identify ecologic risk factors for tularemia incidence in Sweden, we analyzed surveillance data collected over 29 years (1984–2012). A total of 4,830 cases were notified, of which 3,524 met all study inclusion criteria. From the first to the second half of the study period, mean incidence increased 10-fold, from 0.26/100,000 persons during 1984–1998 to 2.47/100,000 persons during 1999–2012 (p

A 2-year prospective intervention on the prescription of trimethoprim reduced the use by 85% in a health care region with 178,000 inhabitants. Here, we performed before-and-after analyses of the within-population distribution of trimethoprim resistance in Escherichia coli . Phylogenetic and population genetic methods were applied to multilocus sequence typing data of 548 consecutively collected E. coli isolates from clinical urinary specimens. Results were analyzed in relation to antibiotic susceptibility and the presence and genomic location of different trimethoprim resistance gene classes. A total of 163 E. coli sequence types (STs) were identified, of which 68 were previously undescribed. The isolates fell into one of three distinct genetic clusters designated BAPS 1 ( E. coli phylogroup B2), BAPS 2 (phylogroup A and B1), and BAPS 3 (phylogroup D), each with a similar frequency before and after the intervention. BAPS 2 and BAPS 3 were positively and BAPS 1 was negatively associated with trimethoprim resistance (odds ratios of 1.97, 3.17, and 0.26, respectively). In before-and-after analyses, trimethoprim resistance frequency increased in BAPS 1 and decreased in BAPS 2. Resistance to antibiotics other than trimethoprim increased in BAPS 2. Analysis of the genomic location of different trimethoprim resistance genes in isolates of ST69, ST58, and ST73 identified multiple independent acquisition events in isolates of the same ST. The results show that despite a stable overall resistance frequency in E. coli before and after the intervention, marked within-population changes occurred. A decrease of resistance in one major genetic cluster was masked by a reciprocal increase in another major cluster.

We introduce a novel approach to estimate the parameters of a mixture of two distributions. The method combines a grid approach with the method of moments and can be applied to a wide range of two- ...

Urinary tract infections (UTIs) are among the most common bacterial infections in men and urine culture is gold standard for diagnosis. Considering the high prevalence of culture-negative specimens, any method that identifies such specimens is of interest. The aim was to evaluate a new screening concept for flow cytometry analysis (FCA). The outcomes were evaluated against urine culture, uropathogen species and three conventional screening methods. A prospective, consecutive study examined 1,312 urine specimens, collected during January and February 2012. The specimens were analyzed using the Sysmex UF1000i FCA. Based on the FCA data culture negative specimens were identified in a new model by use of linear discriminant analysis (FCA-LDA). In total 1,312 patients were included. In- and outpatients represented 19.6% and 79.4%, respectively; 68.3% of the specimens originated from women. Of the 610 culture-positive specimens, Escherichia coli represented 64%, enterococci 8% and Klebsiella spp. 7%. Screening with FCA-LDA at 95% sensitivity identified 42% (552/1312) as culture negative specimens when UTI was defined according to European guidelines. The proposed screening method was either superior or similar in comparison to the three conventional screening methods. In conclusion, the proposed/suggested and new FCA-LDA screening method was superior or similar to three conventional screening methods. We recommend the proposed screening method to be used in clinic to exclude culture negative specimens, to reduce workload, costs and the turnaround time. In addition, the FCA data may add information that enhance handling and support diagnosis of patients with suspected UTI pending urine culture. Errata European Journal of Clinical Microbiology & Infectious Diseases (2017) 36: 1705. https://doi.org/10.1007/s10096-017-3059-8

Autosomal recessive osteopetrosis (ARO) is a heterogeneous disorder, characterized by defective osteoclastic resorption of bone that results in increased bone density. We have studied nine individuals with an intermediate form of ARO, from the county of Västerbotten in Northern Sweden. All afflicted individuals had an onset in early infancy with optic atrophy, and in four patients anemia was present at diagnosis. Tonsillar herniation, foramen magnum stenosis, and severe osteomyelitis of the jaw were common clinical features. Whole exome sequencing, verified by Sanger sequencing, identified a splice site mutation c.212 + 1 G > T in the SNX10 gene encoding sorting nexin 10. Sequence analysis of the SNX10 transcript in patients revealed activation of a cryptic splice site in intron 4 resulting in a frame shift and a premature stop (p.S66Nfs * 15). Haplotype analysis showed that all cases originated from a single mutational event, and the age of the mutation was estimated to be approximately 950 years. Functional analysis of osteoclast progenitors isolated from peripheral blood of patients revealed that stimulation with receptor activator of nuclear factor kappa-B ligand (RANKL) resulted in a robust formation of large, multinucleated osteoclasts which generated sealing zones; however these osteoclasts exhibited defective ruffled borders and were unable to resorb bone in vitro.

PURPOSE: Clinical test batteries for evaluation of knee function after injury to the Anterior Cruciate Ligament (ACL) should be valid and feasible, while reliably capturing the outcome of rehabilitation. There is currently a lack of consensus as to which of the many available assessment tools for knee function that should be included. The present aim was to use a statistical approach to investigate the contribution of frequently used tests to avoid redundancy, and filter them down to a proposed comprehensive and yet feasible test battery for long-term evaluation after ACL injury. METHODS: In total 48 outcome variables related to knee function, all potentially relevant for a long-term follow-up, were included from a cross-sectional study where 70 ACL-injured (17-28 years post injury) individuals were compared to 33 controls. Cluster analysis and logistic regression were used to group variables and identify an optimal test battery, from which a summarized estimator of knee function representing various functional aspects was derived. RESULTS: As expected, several variables were strongly correlated, and the variables also fell into logical clusters with higher within-correlation (max ρ = 0.61) than between clusters (max ρ = 0.19). An extracted test battery with just four variables assessing one-leg balance, isokinetic knee extension strength and hop performance (one-leg hop, side hop) were mathematically combined to an estimator of knee function, which acceptably classified ACL-injured individuals and controls. This estimator, derived from objective measures, correlated significantly with self-reported function, e.g. Lysholm score (ρ = 0.66; p

We have previously described gene expression changes during spontaneous immortalization of T-cells, thereby identifying cellular processes important for cell growth crisis escape and unlimited proliferation. Here, we analyze the same model to investigate the role of genome-wide methylation in the immortalization process at different time points pre-crisis and post-crisis using high-resolution arrays. We show that over time in culture there is an overall accumulation of methylation alterations, with preferential increased methylation close to transcription start sites (TSSs), islands, and shore regions. Methylation and gene expression alterations did not correlate for the majority of genes, but for the fraction that correlated, gain of methylation close to TSS was associated with decreased gene expression. Interestingly, the pattern of CpG site methylation observed in immortal T-cell cultures was similar to clinical T-cell acute lymphoblastic leukemia (T-ALL) samples classified as CpG island methylator phenotype positive. These sites were highly overrepresented by polycomb target genes and involved in developmental, cell adhesion, and cell signaling processes. The presence of non-random methylation events in in vitro immortalized T-cell cultures and diagnostic T-ALL samples indicates altered methylation of CpG sites with a possible role in malignant hematopoiesis.

SUMMARYSweden reports large and variable numbers of human tularemia cases, but the high-risk regions are anecdotally defined and factors explaining annual variations are poorly understood. Here, high-risk regions were identified by spatial cluster analysis on disease surveillance data for 1984–2012. Negative binomial regression with five previously validated predictors (including predicted mosquito abundance and predictors based on local weather data) was used to model the annual number of tularemia cases within the high-risk regions. Seven high-risk regions were identified with annual incidences of 3·8–44 cases/100 000 inhabitants, accounting for 56·4% of the tularemia cases but only 9·3% of Sweden's population. For all high-risk regions, most cases occurred between July and September. The regression models explained the annual variation of tularemia cases within most high-risk regions and discriminated between years with and without outbreaks. In conclusion, tularemia in Sweden is concentrated in a few high-risk regions and shows high annual and seasonal variations. We present reproducible methods for identifying tularemia high-risk regions and modelling tularemia cases within these regions. The results may help health authorities to target populations at risk and lay the foundation for developing an early warning system for outbreaks.

Francisella tularensis subspecies tularensis is an extremely virulent facultative intracellular bacterial pathogen capable of causing significant mortality in humans when inhaled. Consequently, subspecies tularensis was developed as a biological weapon more than 50 years ago. To counter this threat the US Army empirically developed a live vaccine strain, F. tularensis LVS, from the less virulent holarctica subspecies. In human experiments LVS afforded substantial protection against transdermal challenge with clinical subspecies tularensis strain, SCHU S4, but lesser protection against infection initiated by inhalation of the pathogen. Several regulatory and clinical issues remain unresolved for this vaccine, including the absence of a robust correlate of protection. To try to address this, we have developed several defined gene deletion mutants of SCHU S4 that elicit varying degrees of protection in a mouse dermal or respiratory challenge model. In the present study, we have examined whether host immune responses to immunization with such live vaccine candidates can serve as correlates of protection. Antibody responses were unable to distinguish between effective and ineffective vaccine strains. However, several cytokine responses to vaccination showed some promise. Especially, serum levels of TNFα, IFNγ, and MCP-1 between days 4 and 7 after vaccination appear to correlate with protection against respiratory challenge. © 2012.

The purpose is to review recent studies examining the role of gut microbiota in allergic diseases and asthma.Work in experimental models gives further evidence that a disturbed gut microbiota influences the propensity to develop allergic manifestations, and that changing the gut microbiota by dietary means (high fiber/acetate or prebiotics) in pregnancy may reduce the risk of allergic airways disease and food allergy in the offspring, respectively. The gut microbiome in established allergic disease and prior to disease onset has also been assessed in clinical trials. One study demonstrated a strong association between high abundance of Faecalibacterium prausnitzii and decreased levels of butyrate and propionate, and established eczema. Lower relative abundance of Ruminococcaceae appears to be implicated in food sensitization and to precede the development of atopic eczema. Decreased relative abundance of Lachnospira, Veillonella, Faecalibacterium, and Rothia in early infancy was reported to be associated with increased asthma risk. Inoculation of germ-free mice with these genera decreased airway inflammation in their offspring thereby proposing a causal role of bacteria in preventing allergic airways disease.Gut microbiome research is an actively developing field. Although candidate bacterial taxa have been reported it still remains unclear which bacteria (or other microbes), in which numbers and combinations, and when during the gut colonization process may prevent allergic diseases and asthma. There is still a call for standardized approaches that will enable direct comparison of different studies.