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.

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.

Maternal Brain TNF-α Programs Innate Fear in the Offspring

Tumor necrosis factor alpha (TNF-α) is a cytokine that not only coordinates local and systemic immune responses [1, 2] but also regulates neuronal functions. Most prominently, glia-derived TNF-α has been shown to regulate homeostatic synaptic scaling [3-6], but TNF-α-null mice exhibited no apparent cognitive or emotional abnormalities. Instead, we found a TNF-α-dependent intergenerational effect, as mothers with a deficit in TNF-α programmed their offspring to exhibit low innate fear. Cross-fostering and conditional knockout experiments indicated that a TNF-α deficit in the maternal brain, rather than in the hematopoietic system, and during gestation was responsible for the low-fear offspring phenotype. The level of innate fear governs the balance between exploration/foraging and avoidance of predators and is thus fundamentally important in adaptation, fitness, and survival [7]. Because maternal exercise and activity are known to reduce both brain TNF-α [8] and offspring innate fear [9], whereas maternal stress has been reported to increase brain TNF-α [10] and offspring fear and anxiety [11, 12], maternal brain TNF-α may report environmental conditions to promote offspring behavioral adaptation to their anticipated postnatal environment.

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, PEX11β, 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 β-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.

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Molecular function of Pex11, a protein with roles in metabolism and disease, is unknown.

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Genome-wide screening determined subcellular localization of Pex11-GFP in yeast.

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Mutants defective in components of the ERMES complex show altered Pex11 localization.

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Pex11 physically interacts with the ERMES complex component Mdm34.

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ERMES complex and Pex11 mediate interaction between mitochondria and peroxisomes.

KCNQ1, KCNE2, and Na+-coupled solute transporters form reciprocally regulating complexes that affect neuronal excitability

Na(+)-coupled solute transport is crucial for the uptake of nutrients and metabolic precursors, such as myo-inositol, an important osmolyte and precursor for various cell signaling molecules. We found that various solute transporters and potassium channel subunits formed complexes and reciprocally regulated each other in vitro and in vivo. Global metabolite profiling revealed that mice lacking KCNE2, a K(+) channel β subunit, showed a reduction in myo-inositol concentration in cerebrospinal fluid (CSF) but not in serum. Increased behavioral responsiveness to stress and seizure susceptibility in Kcne2(-/-) mice were alleviated by injections of myo-inositol. Suspecting a defect in myo-inositol transport, we found that KCNE2 and KCNQ1, a voltage-gated potassium channel α subunit, colocalized and coimmunoprecipitated with SMIT1, a Na(+)-coupled myo-inositol transporter, in the choroid plexus epithelium. Heterologous coexpression demonstrated that myo-inositol transport by SMIT1 was augmented by coexpression of KCNQ1 but was inhibited by coexpression of both KCNQ1 and KCNE2, which form a constitutively active, heteromeric K(+) channel. SMIT1 and the related transporter SMIT2 were also inhibited by a constitutively active mutant form of KCNQ1. The activities of KCNQ1 and KCNQ1-KCNE2 were augmented by SMIT1 and the glucose transporter SGLT1 but were suppressed by SMIT2. Channel-transporter signaling complexes may be a widespread mechanism to facilitate solute transport and electrochemical crosstalk.

Maternal hematopoietic TNF, via milk chemokines, programs hippocampal development and memory

Tumor necrosis factor α (TNF) is a proinflammatory cytokine with established roles in host defense and immune system organogenesis. We studied TNF function and found a previously unidentified physiological function that extends its effect beyond the host into the developing offspring. A partial or complete maternal TNF deficit, specifically in hematopoietic cells, resulted in reduced milk levels of the chemokines IP-10, MCP-1, MCP-3, MCP-5 and MIP-1β, which in turn augmented offspring postnatal hippocampal proliferation, leading to improved adult spatial memory in mice. These effects were reproduced by the postpartum administration of a clinically used anti-TNF agent. Chemokines, fed to suckling pups of TNF-deficient mothers, restored both postnatal proliferation and spatial memory to normal levels. Our results identify a TNF-dependent 'lactrocrine' pathway that programs offspring hippocampal development and memory. The level of ambient TNF is known to be downregulated by physical activity, exercise and adaptive stress. We propose that the maternal TNF-milk chemokine pathway evolved to promote offspring adaptation to post-weaning environmental challenges and competition.

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.

Fmr-1 as an offspring genetic and a maternal environmental factor in neurodevelopmental disease

Since fragile X syndrome (FXS) is a typical X-linked mendelian disorder, the protein product associated with the disease (FMRP) is absent or reduced not only in the affected individuals but, in case of full mutation, also in their mothers. Here, by using the mouse model of the disease, we provide evidence that hyperactivity, a typical symptom of FXS, is not wholly induced by the lack of Fmrp in mice but also occurs as a result of its reduced expression in their mother. Genetically wild-type offspring of mutant mothers also had hyperactivity, albeit less pronounced than the mutant offspring. However, other features of FXS reproduced in the mouse model, such as sensory hyperreactivity and seizure susceptibility, were exclusively associated with the absence of Fmrp in the offspring. These data indicate that fmr-1, the gene encoding Fmrp, can be both an offspring genetic and a maternal environmental factor in producing a neurodevelopmental condition.

Behavioral characterization of cereblon forebrain-specific conditional null mice: a model for human non-syndromic intellectual disability

A nonsense mutation in the human cereblon gene (CRBN) causes a mild type of autosomal recessive non-syndromic intellectual disability (ID). Animal studies show that crbn is a cytosolic protein with abundant expression in the hippocampus (HPC) and neocortex (CTX). Its diverse functions include the developmental regulation of ion channels at the neuronal synapse, the mediation of developmental programs by ubiquitination, and a target for herpes simplex type I virus in HPC neurons. To test the hypothesis that anomalous CRBN expression leads to HPC-mediated memory and learning deficits, we generated germ-line crbn knock-out mice (crbn(-/-)). We also inactivated crbn in forebrain neurons in conditional knock-out mice in which crbn exons 3 and 4 are deleted by cre recombinase under the direction of the Ca(2+)/calmodulin-dependent protein kinase II alpha promoter (CamKII(cre/+), crbn(-/-)). crbn mRNA levels were negligible in the HPC, CTX, and cerebellum (CRBM) of the crbn(-/-) mice. In contrast, crbn mRNA levels were reduced 3- to 4-fold in the HPC, CTX but not in the CRBM in CamKII(cre/+), crbn(-/-) mice as compared to wild type (CamKII(cre/+), crbn(+/+)). Contextual fear conditioning showed a significant decrease in the percentage of freezing time in CamKII(cre/+), crbn(-/-) and crbn(-/-) mice while motor function, exploratory motivation, and anxiety-related behaviors were normal. These findings suggest that CamKII(cre/+), crbn(-/-) mice exhibit selective HPC-dependent deficits in associative learning and supports the use of these mice as in vivo models to study the functional consequences of CRBN aberrations on memory and learning in humans.

Maternal genetic mutations as gestational and early life influences in producing psychiatric disease-like phenotypes in mice

Risk factors for psychiatric disorders have traditionally been classified as genetic or environmental. Risk (candidate) genes, although typically possessing small effects, represent a clear starting point to elucidate downstream cellular/molecular pathways of disease. Environmental effects, especially during development, can also lead to altered behavior and increased risk for disease. An important environmental factor is the mother, demonstrated by the negative effects elicited by maternal gestational stress and altered maternal care. These maternal effects can also have a genetic basis (e.g., maternal genetic variability and mutations). The focus of this review is "maternal genotype effects" that influence the emotional development of the offspring resulting in life-long psychiatric disease-like phenotypes. We have recently found that genetic inactivation of the serotonin 1A receptor (5-HT1AR) and the fmr1 gene (encoding the fragile X mental retardation protein) in mouse dams results in psychiatric disease-like phenotypes in their genetically unaffected offspring. 5-HT1AR deficiency in dams results in anxiety and increased stress responsiveness in their offspring. Offspring of 5-HT1AR deficient dams display altered development of the hippocampus, which could be linked to their anxiety-like phenotype. Maternal inactivation of fmr1, like its inactivation in the offspring, results in a hyperactivity-like condition and is associated with receptor alterations in the striatum. These data indicate a high sensitivity of the offspring to maternal mutations and suggest that maternal genotype effects can increase the impact of genetic risk factors in a population by increasing the risk of the genetically normal offspring as well as by enhancing the effects of offspring mutations.

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.

Paradoxical anxiogenic response of juvenile mice to fluoxetine

Depression, anxiety, and conduct disorders are common in children and adolescents, and selective serotonin reuptake inhibitors (SSRIs) are often used to treat these conditions. Fluoxetine (Prozac) is the first approved SSRI for the treatment of depression in this population. Although it is believed that overall, fluoxetine is effective in child and adolescent psychiatry, there have been reports of specific adverse drug effects, most prominently, suicidality and psychiatric symptoms such as agitation, worsening of depression, and anxiety. Chronic fluoxetine substantially increases brain extracellular 5-HT concentrations, and the juvenile developing brain may respond to supraphysiological 5-HT levels with specific adverse effects not seen or less prominent in adult brain. Using novelty-induced hypophagia, as well as open-field and elevated plus maze tests, we show that both Swiss Webster and C57Bl/6 mice, receiving fluoxetine in a clinically relevant dose and during their juvenile age corresponding to child-adolescent periods in humans, exhibit a paradoxical anxiogenic response. The adverse effects of juvenile fluoxetine disappeared upon drug discontinuation and no long-term behavioral consequences were apparent. No adverse effect to chronic fluoxetine was seen in adult mice and a dose-dependent anxiolytic effect developed. These data show that the age of the mice, independently of the strains and tests used in this study, is the determining factor of whether the response to chronic fluoxetine is anxiolytic or anxiogenic. Taken together, the response of the juvenile and adult brain to fluoxetine could be fundamentally different and the juvenile fluoxetine administration mouse model described here may help to identify the mechanism underlying this difference.

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.

Wild-type male offspring of fmr-1+/- mothers exhibit characteristics of the fragile X phenotype

Fragile X syndrome is an X-linked disorder caused by the inactivation of the FMR-1 gene with symptoms ranging from impaired cognitive functions to seizures, anxiety, sensory abnormalities, and hyperactivity. Males are more severely affected than heterozygote (H) females, who, as carriers, have a 50% chance of transmitting the mutated allele in each pregnancy. fmr-1 knockout (KO) mice reproduce fragile X symptoms, including hyperactivity, seizures, and abnormal sensory processing. In contrast to the expectation that wild-type (WT) males born to H (fmr-1(+/-)) mothers (H>WT) are behaviorally normal and indistinguishable from WT males born to WT mothers (WT>WT); here, we show that H>WT offspring are more active than WT>WT offspring and that their hyperactivity is similar to male KO mice born to H or KO (fmr-1(-/-)) mothers (H>KO/KO>KO). H>WT mice, however, do not exhibit seizures or abnormal sensory processing. Consistent with their hyperactivity, the effect of the D2 agonist quinpirole is reduced in H>WT as well as in H>KO and KO>KO mice compared to WT>WT offspring, suggesting a diminished feedback inhibition of dopamine release. Our data indicate that some aspects of hyperactivity and associated dopaminergic changes in 'fragile X' mice are a maternal fmr-1 genotype rather than an offspring fmr-1 genotype effect.

Inactivation of the maternal fragile X gene results in sensitization of GABAB receptor function in the offspring

Fragile X syndrome is an X-linked disorder caused by the inactivation of the FMR1 gene, with symptoms ranging from impaired cognitive functions to seizures, anxiety, sensory abnormalities, and hyperactivity. Although fragile X syndrome is considered a typical Mendelian disorder, we have recently reported that the environment, specifically the fmr1(+/-) or fmr1(-/-) [H or knockout (KO)] maternal environment, elicits on its own a partial fragile X-like phenotype and can contribute to the overall phenotype of fmr1(-/0) (KO) male offspring. Genetically fmr1(+/0) (WT) males born to H females (H(maternal) > WT(offspring)), similar to KO male offspring born to H and KO mothers (H > KO and KO > KO), exhibit locomotor hyperactivity. These mice also showed reduced D(2) autoreceptor function, indicating a possible diminished feedback inhibition of dopamine (DA) release in the nigrostriatal and mesolimbic systems. The GABAergic system also regulates DA release, in part via presynaptic GABA(B) receptors (Rs) located on midbrain dopaminergic neurons. Here, we show that the locomotor inhibitory effect of the GABA(B)R agonist baclofen [4-amino-3-(4-chlorophenyl)-butanoic acid] is enhanced in all progeny of mutant mothers (H > WT, H > KO, and KO > KO) compared with WT > WT mice, irrespective of their own genotype. However, increased sensitivity to baclofen was selective and limited to the locomotor response because the muscle-relaxant and sedative effects of the drug were not altered by the maternal environment. These data show that GABA(B)R sensitization, traditionally induced pharmacologically, can also be elicited by the fmr1-deficient maternal environment.

Methylphenidate administration to juvenile rats alters brain areas involved in cognition, motivated behaviors, appetite, and stress

Thousands of children receive methylphenidate (MPH; Ritalin) for attention deficit/hyperactivity disorder (ADHD), yet the long-term neurochemical consequences of MPH treatment are unknown. To mimic clinical Ritalin treatment in children, male rats were injected with MPH (5 mg/kg) or vehicle twice daily from postnatal day 7 (PND7)-PND35. At the end of administration (PND35) or in adulthood (PND135), brain sections from littermate pairs were immunocytochemically labeled for neurotransmitters and cytological markers in 16 regions implicated in MPH effects and/or ADHD etiology. At PND35, the medial prefrontal cortex (mPFC) of rats given MPH showed 55% greater immunoreactivity (-ir) for the catecholamine marker tyrosine hydroxylase (TH), 60% more Nissl-stained cells, and 40% less norepinephrine transporter (NET)-ir density. In hippocampal dentate gyrus, MPH-receiving rats showed a 51% decrease in NET-ir density and a 61% expanded distribution of the new-cell marker PSA-NCAM (polysialylated form of neural cell adhesion molecule). In medial striatum, TH-ir decreased by 21%, and in hypothalamus neuropeptide Y-ir increased by 10% in MPH-exposed rats. At PND135, MPH-exposed rats exhibited decreased anxiety in the elevated plus-maze and a trend for decreased TH-ir in the mPFC. Neither PND35 nor PND135 rats showed major structural differences with MPH exposure. These findings suggest that developmental exposure to high therapeutic doses of MPH has short-term effects on select neurotransmitters in brain regions involved in motivated behaviors, cognition, appetite, and stress. Although the observed neuroanatomical changes largely resolve with time, chronic modulation of young brains with MPH may exert effects on brain neurochemistry that modify some behaviors even in adulthood.

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.

Temporal Control of Conditioned Responding in Goldfish

The peak procedure was used to characterize response timing during acquisition and maintenance of conditioned responding in goldfish. Subjects received light-shock pairings with a 5- or 15-s interstimulus interval. On interspersed peak trials, the conditioned stimulus light was presented for 45 s and no shock was delivered. Peaks in the conditioned response, general activity, occurred at about the time of the expected unconditioned stimulus, and variability in the activity distribution was scalar. Modeling of the changes in the activity distributions over sessions revealed that the temporal features of the conditioned response changed very little during acquisition. The data suggest that times are learned early in training, and, contrary to I. P. Pavlov's (1927/1960) concept of "inhibition of delay," that timing is learning when to respond rather than learning when not to respond.

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.

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.

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.

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.

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.

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.

Hierarchical multi-attribute decision models and their application in health care

Hierarchical decision models are developed through decomposition of complex decision problems into smaller and less complex subproblems. They are aimed at the classification or evaluation of options and can be used for analysis, simulation and explanation. This paper presents a set of methods for the construction and application of qualitative hierarchical decision models in health care. We present the results of four ongoing projects in oncology, radiology, community nursing and diabetic foot treatment.

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.

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.

Acquiring background knowledge for machine learning using function decomposition: a case study in rheumatology

Domain or background knowledge is often needed in order to solve difficult problems of learning medical diagnostic rules. Earlier experiments have demonstrated the utility of background knowledge when learning rules for early diagnosis of rheumatic diseases. A particular form of background knowledge comprising typical co-occurrences of several groups of attributes was provided by a medical expert. This paper explores the possibility of automating the process of acquiring background knowledge of this kind and studies the utility of such methods in the problem domain of rheumatic diseases. A method based on function decomposition is proposed that identifies typical co-occurrences for a given set of attributes. The method is evaluated by comparing the typical co-occurrences it identifies as well as their contribution to the performance of machine learning algorithms, to the ones provided by a medical expert.

Relating clinical and neurophysiological assessment of spasticity by machine learning

Spasticity following spinal cord injury (SCI) is most often assessed clinically using a five-point Ashworth score (AS). A more objective assessment of altered motor control may be achieved by using a comprehensive protocol based on a surface electromyographic (sEMG) activity recorded from thigh and leg muscles. However, the relationship between the clinical and neurophysiological assessments is still unknown. In this paper we employ three different classification methods to investigate this relationship. The experimental results indicate that, if the appropriate set of sEMG features is used, the neurophysiological assessment is related to clinical findings and can be used to predict the AS. A comprehensive sEMG assessment may be proven useful as an objective method of evaluating the effectiveness of various interventions and for follow-up of SCI patients.

Cognitive vulnerability in children at risk for depression

Cognitive, developmental, and psychodynamic theories all hypothesize that negative self-concepts acquired in childhood may induce vulnerability to depression. Children at risk because of maternal major affective disorder, compared with children of medically ill and normal mothers, were examined for evidence of negative cognitions about themselves, and were found to have more negative self-concept, less positive self-schemas, and more negative attributional style. It was further predicted that negative cognitions about the self would be related to maternal depression and chronic stress, and to the quality of perceived and actual interactions with the mother. In general, the predicted associations were obtained, supporting speculations about how maternal affective disorder is associated with stress and with relatively negative and unsupportive relationships with children that in turn diminish children's self-regard.