Development and Evolution of Neural Networks in an Artificial Chemistry

We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and cell stimulation. We demonstrate the model's power with a hand-written genome that leads to the growth of a simple network which performs classical conditioning. To evolve more complex structures, we implemented a platform-independent, asynchronous, distributed Genetic Algorithm (GA) that allows users to participate in evolutionary experiments via the World Wide Web.Comment: 8 pages LaTeX, style file included, 8 embedded postscript figures. To be published in Proc. of 3rd German Workshop on Artificial Life (GWAL

Association of Mild to Moderate Chronic Kidney Disease With Venous Thromboembolism Pooled Analysis of Five Prospective General Population Cohorts

BACKGROUND: Recent findings suggest that chronic kidney disease (CKD) may be associated with increased risk of venous thromboembolism (VTE). Given the high prevalence of mild-to-moderate CKD in the general population, in depth analysis of this association is warranted. METHODS AND RESULTS: We pooled individual participant data from five community-based cohorts from Europe (HUNT2, PREVEND and Tromsø study) and United States (ARIC and CHS study) to assess the association of estimated glomerular filtration rate (eGFR), albuminuria and CKD with objectively verified VTE. To estimate adjusted hazard ratios (HRs) for VTE, categorical and continuous spline models were fit using Cox regression with shared-frailty or random-effect meta-analysis. A total of 1,178 VTE events occurred over 599,453 person-years follow-up. Relative to eGFR 100 mL/min/1.73m(2), HRs for VTE were 1.29 (95%CI, 1.04-1.59) for eGFR 75, 1.31 (1.00-1.71) for 60, 1.82 (1.27-2.60) for 45 and 1.95 (1.26-3.01) for 30 mL/min/1.73m(2). Compared with albumin-creatinine ratio (ACR) of 5.0 mg/g, the HRs for VTE were 1.34 (1.04-1.72) for 30 mg/g, 1.60 (1.08-2.36) for 300 mg/g and 1.92 (1.19-3.09) for 1000 mg/g. There was no interaction between clinical categories of eGFR and ACR (P=0.20). The adjusted HR for CKD defined as eGFR <60 mL/min/1.73m(2) or albuminuria ≥30 mg/g (vs. no CKD) was 1.54 (95%CI, 1.15-2.06). Associations were consistent in subgroups according to age, gender, and comorbidities as well as for unprovoked versus provoked VTE. CONCLUSIONS: Both eGFR and ACR are independently associated with increased risk of VTE in the general population, even across the normal eGFR and ACR ranges

Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein.

Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes

Association of Mild to Moderate Chronic Kidney Disease With Venous Thromboembolism

BACKGROUND: Recent findings suggest that chronic kidney disease (CKD) may be associated with increased risk of venous thromboembolism (VTE). Given the high prevalence of mild-to-moderate CKD in the general population, in depth analysis of this association is warranted. METHODS AND RESULTS: We pooled individual participant data from five community-based cohorts from Europe (HUNT2, PREVEND and Tromsø study) and United States (ARIC and CHS study) to assess the association of estimated glomerular filtration rate (eGFR), albuminuria and CKD with objectively verified VTE. To estimate adjusted hazard ratios (HRs) for VTE, categorical and continuous spline models were fit using Cox regression with shared-frailty or random-effect meta-analysis. A total of 1,178 VTE events occurred over 599,453 person-years follow-up. Relative to eGFR 100 mL/min/1.73m(2), HRs for VTE were 1.29 (95%CI, 1.04-1.59) for eGFR 75, 1.31 (1.00-1.71) for 60, 1.82 (1.27-2.60) for 45 and 1.95 (1.26-3.01) for 30 mL/min/1.73m(2). Compared with albumin-creatinine ratio (ACR) of 5.0 mg/g, the HRs for VTE were 1.34 (1.04-1.72) for 30 mg/g, 1.60 (1.08-2.36) for 300 mg/g and 1.92 (1.19-3.09) for 1000 mg/g. There was no interaction between clinical categories of eGFR and ACR (P=0.20). The adjusted HR for CKD defined as eGFR <60 mL/min/1.73m(2) or albuminuria ≥30 mg/g (vs. no CKD) was 1.54 (95%CI, 1.15-2.06). Associations were consistent in subgroups according to age, gender, and comorbidities as well as for unprovoked versus provoked VTE. CONCLUSIONS: Both eGFR and ACR are independently associated with increased risk of VTE in the general population, even across the normal eGFR and ACR ranges

Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein

Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes