Low prevalence of active COVID-19 in Slovenia: a nationwide population study of a probability-based sample.

OBJECTIVES: Accurate population-level assessment of the coronavirus disease 2019 (COVID-19) burden is fundamental for navigating the path forward during the ongoing pandemic, but current knowledge is scant. We conducted the first nationwide population study using a probability-based sample to assess active severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, combined with a longitudinal follow-up of the entire cohort over the next 6 months. Baseline SARS-CoV-2 RNA testing results and the first 3-week follow-up results are presented. METHODS: A probability-based sample of the Slovenian population comprising data from 2.1 million people was selected from the Central Population Register (n = 3000). SARS-CoV-2 RNA was detected in nasopharyngeal samples using the cobas 6800 SARS-CoV-2 assay. Each participant filled in a detailed baseline questionnaire with basic sociodemographic data and detailed medical history compatible with COVID-19. After 3 weeks, participants were interviewed for the presence of COVID-19-compatible clinical symptoms and signs, including in household members, and offered immediate testing for SARS-CoV-2 RNA if indicated. RESULTS: A total of 1368 individuals (46%) consented to participate and completed the questionnaire. Two of 1366 participants tested positive for SARS-CoV-2 RNA (prevalence 0.15%; posterior mean 0.18%, 95% Bayesian confidence interval 0.03-0.47; 95% highest density region (HDR) 0.01-0.41). No newly diagnosed infections occurred in the cohort during the first 3-week follow-up round. CONCLUSIONS: The low prevalence of active COVID-19 infections found in this study accurately predicted the dynamics of the epidemic in Slovenia over the subsequent month. Properly designed and timely executed studies using probability-based samples combined with routine target-testing figures provide reliable data that can be used to make informed decisions on relaxing or strengthening disease mitigation strategies.

Programming social behavior by the maternal fragile X protein.

The developing fetus and neonate are highly sensitive to maternal environment. Besides the well-documented effects of maternal stress, nutrition and infections, maternal mutations, by altering the fetal, perinatal and/or early postnatal environment, can impact the behavior of genetically normal offspring. Mutation/premutation in the X-linked FMR1 (encoding the translational regulator FMRP) in females, although primarily responsible for causing fragile X syndrome (FXS) in their children, may also elicit such maternal effects. We showed that a deficit in maternal FMRP in mice results in hyperactivity in the genetically normal offspring. To test if maternal FMRP has a broader intergenerational effect, we measured social behavior, a core dimension of neurodevelopmental disorders, in offspring of FMRP-deficient dams. We found that male offspring of Fmr1(+/-) mothers, independent of their own Fmr1 genotype, exhibit increased approach and reduced avoidance toward conspecific strangers, reminiscent of 'indiscriminate friendliness' or the lack of stranger anxiety, diagnosed in neglected children and in patients with Asperger's and Williams syndrome. Furthermore, social interaction failed to activate mesolimbic/amygdala regions, encoding social aversion, in these mice, providing a neurobiological basis for the behavioral abnormality. This work identifies a novel role for FMRP that extends its function beyond the well-established genetic function into intergenerational non-genetic inheritance/programming of social behavior and the corresponding neuronal circuit. As FXS premutation and some psychiatric conditions that can be associated with reduced FMRP expression are more prevalent in mothers than full FMR1 mutation, our findings potentially broaden the significance of FMRP-dependent programming of social behavior beyond the FXS population.

Genome-Wide Localization Study of Yeast Pex11 Identifies Peroxisome-Mitochondria Interactions through the ERMES Complex.

Pex11 is a peroxin that regulates the number of peroxisomes in eukaryotic cells. Recently, it was found that a mutation in one of the three mammalian paralogs, PEX11ß, results in a neurological disorder. The molecular function of Pex11, however, is not known. Saccharomyces cerevisiae Pex11 has been shown to recruit to peroxisomes the mitochondrial fission machinery, thus enabling proliferation of peroxisomes. This process is essential for efficient fatty acid ß-oxidation. In this study, we used high-content microscopy on a genome-wide scale to determine the subcellular localization pattern of yeast Pex11 in all non-essential gene deletion mutants, as well as in temperature-sensitive essential gene mutants. Pex11 localization and morphology of peroxisomes was profoundly affected by mutations in 104 different genes that were functionally classified. A group of genes encompassing MDM10, MDM12 and MDM34 that encode the mitochondrial and cytosolic components of the ERMES complex was analyzed in greater detail. Deletion of these genes caused a specifically altered Pex11 localization pattern, whereas deletion of MMM1, the gene encoding the fourth, endoplasmic-reticulum-associated component of the complex, did not result in an altered Pex11 localization or peroxisome morphology phenotype. Moreover, we found that Pex11 and Mdm34 physically interact and that Pex11 plays a role in establishing the contact sites between peroxisomes and mitochondria through the ERMES complex. Based on these results, we propose that the mitochondrial/cytosolic components of the ERMES complex establish a direct interaction between mitochondria and peroxisomes through Pex11.

Supporting regenerative medicine by integrative dimensionality reduction.

OBJECTIVE: The assessment of the developmental potential of stem cells is a crucial step towards their clinical application in regenerative medicine. It has been demonstrated that genome-wide expression profiles can predict the cellular differentiation stage by means of dimensionality reduction methods. Here we show that these techniques can be further strengthened to support decision making with i) a novel strategy for gene selection; ii) methods for combining the evidence from multiple data sets. METHODS: We propose to exploit dimensionality reduction methods for the selection of genes specifically activated in different stages of differentiation. To obtain an integrated predictive model, the expression values of the selected genes from multiple data sets are combined. We investigated distinct approaches that either aggregate data sets or use learning ensembles. RESULTS: We analyzed the performance of the proposed methods on six publicly available data sets. The selection procedure identified a reduced subset of genes whose expression values gave rise to an accurate stage prediction. The assessment of predictive accuracy demonstrated a high quality of predictions for most of the data integration methods presented. CONCLUSION: The experimental results highlighted the main potentials of proposed approaches. These include the ability to predict the true staging by combining multiple training data sets when this could not be inferred from a single data source, and to focus the analysis on a reduced list of genes of similar predictive performance.

The serotonin1A receptor gene as a genetic and prenatal maternal environmental factor in anxiety.

Low serotonin(1A) receptor (5-HT(1A)R) binding is a risk factor for anxiety and depression, and deletion of the 5-HT(1A)R results in anxiety-like behavior in mice. Here we show that anxiety-like behavior in mice also can be caused, independently of the offspring's own 5-HT(1A)R genotype, by a receptor deficit in the mother: a nongenetic transmission of a genetic defect. Some of the nongenetically transmitted anxiety manifestations were acquired prenatally and linked to a delay in dentate gyrus maturation in the ventral hippocampus of the offspring. Both the developmental delay and the anxiety-like phenotype were phenocopied by the genetic inactivation of p16(ink4a) encoding a cyclin-dependent kinase inhibitor implicated in neuronal precursor differentiation. No maternal 5-HT(1A)R genotype-dependent anxiety developed when the strain background was switched from Swiss Webster to C57BL/6, consistent with the increased resilience of this strain to early adverse environment. Instead, all anxiety manifestations were caused by the offspring's own receptor deficiency, indicating that the genetic and nongenetic effects converge to common anxiety manifestations. We propose that 5-HT(1A)R deficit represents a dual risk for anxiety and that vulnerability to anxiety associated with genetic 5-HT(1A)R deficiency can be transmitted by both genetic and nongenetic mechanisms in a population. Thus, the overall effect of risk alleles can be higher than estimated by traditional genetic assays and may contribute to the relatively high heritability of anxiety and psychiatric disorders in general.

Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection.

Within this study, we have used a set of computational techniques to relate the genotypes and phenotypes of natural populations of Saccharomyces cerevisiae, using allelic information from 11 microsatellite loci and results from 24 phenotypic tests. A group of 103 strains was obtained from a larger S. cerevisiae winemaking strain collection by clustering with self-organizing maps. These strains were further characterized regarding their allelic combinations for 11 microsatellites and analysed in phenotypic screens that included taxonomic criteria (carbon and nitrogen assimilation tests, growth at different temperatures) and tests with biotechnological relevance (ethanol resistance, H(2)S or aromatic precursors formation). Phenotypic variability was rather high and each strain showed a unique phenotypic profile. The results, expressed as optical density (A(640)) after 22 h of growth, were in agreement with taxonomic data, although with some exceptions, since few strains were capable of consuming arabinose and ribose to a small extent. Based on microsatellite allelic information, naïve Bayesian classifier correctly assigned (AUC = 0.81, p < 10(-8)) most of the strains to the vineyard from where they were isolated, despite their close location (50-100 km). We also identified subgroups of strains with similar values of a phenotypic feature and microsatellite allelic pattern (AUC > 0.75). Subgroups were found for strains with low ethanol resistance, growth at 30 degrees C and growth in media containing galactose, raffinose or urea. The results demonstrate that computational approaches can be used to establish genotype-phenotype relations and to make predictions about a strain's biotechnological potential.

Rule-based clustering for gene promoter structure discovery.

BACKGROUND: The genetic cellular response to internal and external changes is determined by the sequence and structure of gene-regulatory promoter regions. OBJECTIVES: Using data on gene-regulatory elements (i.e., either putative or known transcription factor binding sites) and data on gene expression profiles we can discover structural elements in promoter regions and infer the underlying programs of gene regulation. Such hypotheses obtained in silico can greatly assist us in experiment planning. The principal obstacle for such approaches is the combinatorial explosion in different combinations of promoter elements to be examined. METHODS: Stemming from several state-of-the-art machine learning approaches we here propose a heuristic, rule-based clustering method that uses gene expression similarity to guide the search for informative structures in promoters, thus exploring only the most promising parts of the vast and expressively rich rule-space. RESULTS: We present the utility of the method in the analysis of gene expression data on budding yeast S. cerevisiae where cells were induced to proliferate peroxisomes. CONCLUSIONS: We demonstrate that the proposed approach is able to infer informative relations uncovering relatively complex structures in gene promoter regions that regulate gene expression.

Polyketide synthase genes and the natural products potential of Dictyostelium discoideum.

MOTIVATION: The genome of the social amoeba Dictyostelium discoideum contains an unusually large number of polyketide synthase (PKS) genes. An analysis of the genes is a first step towards understanding the biological roles of their products and exploiting novel products. RESULTS: A total of 45 Type I iterative PKS genes were found, 5 of which are probably pseudogenes. Catalytic domains that are homologous with known PKS sequences as well as possible novel domains were identified. The genes often occurred in clusters of 2-5 genes, where members of the cluster had very similar sequences. The D.discoideum PKS genes formed a clade distinct from fungal and bacterial genes. All nine genes examined by RT-PCR were expressed, although at different developmental stages. The promoters of PKS genes were much more divergent than the structural genes, although we have identified motifs that are unique to some PKS gene promoters.

Clustering gene expression data with temporal abstractions.

This paper describes a new technique for clustering short time series coming from gene expression data. The technique is based on the labelling of the time series through temporal trend abstractions and a consequent clustering of the series on the basis of their labels. Clustering is performed at three different levels of aggregation of the original time series, so that the results are organized and visualized as a three-levels hierarchical tree. Results on simulated and on yeast data are shown. The technique appears robust and efficient and the results obtained are easy to be interpreted.

Decisions at hand: a decision support system on handhelds.

One of the applications of clinical information systems is decision support. Although the advantages of utilizing such aids have never been theoretically disputed, they have been rarely used in practice. The factor that probably often limits the utility of clinical decision support systems is the need for computing power at the very site of decision making--at the place where the patient is interviewed, in discussion rooms, etc. The paper reports on a possible solution to this problem. A decision-support shell LogReg is presented, which runs on a handheld computer. A general schema for handheld-based decision support is also proposed, where decision models are developed on personal computers/workstations, encoded in XML and then transferred to handhelds, where the models are used within a decision support shell. A use case where LogReg has been applied to clinical outcome prediction in crush injury is presented.

GenePath: a computer program for genetic pathway discovery from mutant data.

The sequencing of the human genome and the genomes of several model organisms is the first step toward the long-term objective of genetic research: the identification of all genes, and the discovery of their functions and mutual interactions. This article presents a methodology and a computer program called GenePath to support the discovery of gene function. GenePath uses mutant data and available genetic knowledge to identify potential genetic pathways. Several pilot applications based on experimental results from Dictyostelium and C. elegans confirmed the usefulness of the proposed schema. Our results suggest that GenePath is a valuable tool that can be used as an intelligent assistant to support genetic reasoning.

Feature mining and predictive model construction from severe trauma patient's data.

In management of severe trauma patients, trauma surgeons need to decide which patients are eligible for damage control. Such decision may be supported by utilizing models that predict the patient's outcome. The study described in this paper investigates the possibility to construct patient outcome prediction models from retrospective patient's data at the end of initial damage control surgery by using feature mining and machine learning techniques. As the data used comprises rather excessive number of features, special attention was paid to the problem of selecting only the most relevant features. We show that a small subset of features may carry enough information to construct reasonably accurate prognostic models. Furthermore, the techniques used in our study identified two factors, namely the pH value when admitted to ICU and the worst partial active thromboplastin time, to be of highest importance for prediction. This finding is pathophysiologically reasonable and represents two of three major problems with severe trauma patients, metabolic acidosis, hypothermia, and coagulopathy.

Predicting patient's long-term clinical status after hip arthroplasty using hierarchical decision modelling and data mining.

Construction of a prognostic model is presented for the long-term outcome after femoral neck fracture treatment with implantation of hip endoprosthesis. While the model is induced from the follow-up data, we show that the use of additional expert knowledge is absolutely crucial to obtain good predictive accuracy. A schema is proposed where domain knowledge is encoded as a hierarchical decision model of which only a part is induced from the data while the rest is specified by the expert. Although applied to hip endoprosthesis domain, the proposed schema is general and can be used for the construction of other prognostic models where both follow-up data and human expertise is available.

Applications of qualitative multi-attribute decision models in health care.

Hierarchical decision models are a general decision support methodology aimed at the classification or evaluation of options that occur in decision-making processes. They are also important for the analysis, simulation and explanation of options. Decision models are typically developed through the decomposition of complex decision problems into smaller and less complex subproblems; the result of such decomposition is a hierarchical structure that consists of attributes and utility functions. This article presents an approach to the development and application of qualitative hierarchical decision models that is based on DEX, an expert system shell for multi-attribute decision support. The distinguishing characteristics of DEX are the use of qualitative (symbolic) attributes, and 'if-then' decision rules. Also, DEX provides a number of methods for the analysis of models and options, such as selective explanation and what-if analysis. We demonstrate the applicability and flexibility of the approach presenting four real-life applications of DEX in health care: assessment of breast cancer risk, assessment of basic living activities in community nursing, risk assessment in diabetic foot care, and technical analysis of radiogram errors. In particular, we highlight and justify the importance of knowledge presentation and option analysis methods for practical decision-making. We further show that, using a recently developed data mining method called HINT, such hierarchical decision models can be discovered from retrospective patient data.

Machine learning for survival analysis: a case study on recurrence of prostate cancer.

Machine learning techniques have recently received considerable attention, especially when used for the construction of prediction models from data. Despite their potential advantages over standard statistical methods, like their ability to model non-linear relationships and construct symbolic and interpretable models, their applications to survival analysis are at best rare, primarily because of the difficulty to appropriately handle censored data. In this paper we propose a schema that enables the use of classification methods--including machine learning classifiers--for survival analysis. To appropriately consider the follow-up time and censoring, we propose a technique that, for the patients for which the event did not occur and have short follow-up times, estimates their probability of event and assigns them a distribution of outcome accordingly. Since most machine learning techniques do not deal with outcome distributions, the schema is implemented using weighted examples. To show the utility of the proposed technique, we investigate a particular problem of building prognostic models for prostate cancer recurrence, where the sole prediction of the probability of event (and not its probability dependency on time) is of interest. A case study on preoperative and postoperative prostate cancer recurrence prediction shows that by incorporating this weighting technique the machine learning tools stand beside modern statistical methods and may, by inducing symbolic recurrence models, provide further insight to relationships within the modeled data.

Predictive model for survival at the conclusion of a damage control laparotomy.

BACKGROUND: We employed modern statistical and data mining methods to model survival based on preoperative and intraoperative parameters for patients undergoing damage control surgery. METHODS: One hundred seventy-four parameters were collected from 68 damage control patients in prehospital, emergency center, operating room, and intensive care unit (ICU) settings. Data were analyzed with logistic regression and data mining. Outcomes were survival and death after the initial operation. RESULTS: Overall mortality was 66.2%. Logistic regression identified pH at initial ICU admission (odds ratio: 4.4) and worst partial thromboplastin time from hospital admission to ICU admission (odds ratio: 9.4) as significant. Data mining selected the same factors, and generated a simple algorithm for patient classification. Model accuracy was 83%. CONCLUSION: Inability to correct pH at the conclusion of initial damage-control laparotomy and the worst PTT can be predictive of death. These factors may be useful to identify patients with a high risk of mortality.

Slovenian national health insurance card: the next step.

The Slovenian national health insurance company started a full-scale deployment of the insurance smart card that is at the present used for insurance data and identification purpose only. There is ample capacity on the cards that were selected, to contain much more data than needed for the purely administrative and charging purposes. There are plans to include some basic medical information, donor information, etc. On the other hand, there are no firm plans to use the security infrastructure and the extensive network, connecting the insurance company with the more than 200 self service terminals positioned at the medical facilities through the country to build an integrated medical information system that would be very beneficial to the patients and the medical community. This paper is proposing some possible future developments and further discusses on the security issues involved with such countrywide medical information system.

Naive Bayesian-based nomogram for prediction of prostate cancer recurrence.

This paper introduces a schema with naive-Bayesian classifier and patient weighting technique to develop a prostate cancer recurrence prediction model from patient data. We propose the graphical presentation of naive-Bayesian classifier with a nomogram, which can be used both for prediction or can provide means to data analysis. The resulting model was experimentally evaluated; the results were favorable both in terms of interpretability and predictive accuracy.