Bdellovibrio and like organisms (BALOs) also contribute to bacterial mortality and, in some cases, more so than bacteriophages. A full understanding of the ecologic function of the microbial food web requires recognition of all major predators and the magnitude of each predator’s contribution. Here we investigated the contribution of Halobacteriovorax, one of the BALOs, and bacteriophages when incubated with their common prey, Vibrio vulnificus, in a seawater microcosm. We observed that Halobacteriovorax was the greatest responder to the prey, increasing 18-fold with a simultaneous 4.4-log-unit reduction of V. vulnificus at 40 h, whereas the bacteriophage population showed no significant increase. In subsequent experiments to formulate a medium that would support the predatory activities and replication of both predators, low-nutrient media favored the predation and replication of the Halobacteriovorax, whereas higher-nutrient media enhanced phage growth. The greatest prey reduction and replication of both Halobacteriovorax and phage were observed in media with moderate nutrient levels. Additional experiments show that the predatory activities of both predators were influenced by environmental conditions, specifically, temperature and salinity. The two predators combined exerted greater control on V. vulnificus, a synergism that may be exploited for practical applications to reduce bacterial populations. These findings suggest that along with bacteriophage and protists, Halobacteriovorax has the potential to have a prominent role in bacterial mortality and cycling of nutrients, two vital ecologic functions.The role of protists and bacteriophages in bacterial predation in the microbial food web has been well studied. There is mounting evidence that  Bdellovibrio-like predators has largely been ignored, posing a major gap in understanding food web processes. A complete scenario of the microbial food web cannot be developed until the roles of all major micropredators and the magnitude of their contributions to bacterial mortality, structuring of microbial communities, and cycling of nutrients are assessed. Here we show compelling evidence that Halobacteriovorax, a predatory bacterium, is a significant contributor to bacterial death and, in some cases, may rival viruses as agents of bacterial mortality. These results advance current understanding of the microbial loop and top-down control on the bacterial community.Although much has been reported about the marine microbial food web and the role of micropredators, specifically viruses and protists, the contribution of  Halobacteriovorax (Bdellovibrio and like organisms (BALOs), attacks and lyses many Gram-negative bacteria and is ubiquitous in saltwater environments. Up to 85% of cultivable bacteria in estuarine systems have been reported to be susceptible to these predators  higher than that of BALOs, which is reported to be between 10s per ml , 16. Phas per ml , 18, whes per ml –21, althreported . Second,reported .Also, phages attack rapidly growing and dominant bacterial strains in aquatic ecosystems , 25, wheHalobacteriovorax in environmental water samples to an influx of Vibrio parahaemolyticus  and the phage control microcosm (phages+Vv). The test microcosm consisted of both predators and prey (HBx+phages+Vv).The objective of the initial, foundation experiment was to show the predator-prey dynamics of Halobacteriovorax and phages reduced the abundance of the prey significantly (P < 0.01 by t test) over 0 to 40 h. However, at 40 h, prey reduction was much greater by the Halobacteriovorax (P < 0.001 by t test) than by the phage , which suggests that if there were any interactions between Halobacteriovorax and phages in the latter microcosm, they did not affect the results.Correlated to its rapid reduction of prey, s growth . The facP < 0.05) predation rates (the difference between the V. vulnificus growth rates in the control microcosm without predators and the V. vulnificus net growth rates in the presence of predators in the test microcosm) only in the case of the HBx+Vv control microcosm. In that case, the Halobacteriovorax predation rate was 8.4 × 10−10 ml h−1HBx−1; Vo, the threshold prey concentration below which predation ceased, was 1.4 × 104V. vulnificus ml−1; and the natural V. vulnificus mortality rate (Mv) was 0.26 h−1.The results of the predator-prey modeling exercise (see 10.1128/mBio.01202-18.1FIG S1 Vv+HBx microcosm. (a) Logarithms of the Vibrio vulnificus concentrations. The interpolated values were used to calculate the rate of change of the logarithms. (b) Rates of change estimated from panel a (measured) with the values calculated from a multiple linear regression model (V. vulnificus concentration at 40 h was significantly (P < 0.05) lower than the threshold V. vulnificus concentration, only the data from 0 to 20 h were included in the multiple linear regression analysis. The lack of statistical significance in the cases of the other two microcosms reflects scatter in the data and, in particular, the small number of degrees of freedom associated with the multiple linear regression model. In the case of the Vv+HBx microcosm, there were only five time points included in the analysis, and the model contained three coefficients. Hence, there were only two degrees of freedom and the fact that the model parameters were all statistically significant reflects the excellent fit to the data , except for the Vv+phage experiment, which was calculated for all 40 h.Note that the net rate of change of Halobacteriovorax growth. This could be due to the possibility that optimal phage replication requires actively growing prey, which was not the case with the V. vulnificus in the artificial seawater (ASW) medium used. To test this scenario and to find a culture medium formulation that would promote optimal growth of both phage and Halobacteriovorax for future studies, Halobacteriovorax, phage, and prey combinations were grown in ASW medium supplemented with a range of nutrient concentrations. The results show that nutrient concentrations did impact predation and growth of the predators and prey. Irrespective of the nutrient concentration in the medium, the predation activity by phage and Halobacteriovorax on V. vulnificus led to significant decreases in the V. vulnificus population (P < 0.01 by t test) compared to the control microcosm without predators , DNB 1:100 (full-strength NB diluted 100 times), and DNB 1:10 (full-strength NB diluted 10 times), the predators reduced the population of V. vulnificus (P < 0.05 by t test). Because the maximum reduction (3.17 log units) occurred in the microcosm with DNB 1:10 at 48 h . The greatest differences between the growth of the two predators were observed in ASW, in which the Halobacteriovorax grew at its maximum and phage at its minimum, and in NB, in which the phage increase was at its maximum and the Halobacteriovorax at its minimum (Both 10.1128/mBio.01202-18.2FIG S2 Vibrio vulnificus (Vv), Halobacteriovorax (HBx), and phage, respectively, versus time in the microcosms during the 40-h duration of the experiments. The predation rates in panel B are the differences between the growth rates in panel A and the net rates of change of the V. vulnificus in the predator-prey experiments. The latter were equated to the slopes of straight lines fit to the logarithms of the numbers of V. vulnificus versus time in the predator-prey experiments. Download FIG S2, TIF file, 0.05 MB.Growth and predation rates as a function of nutrient enrichment. The growth rates in panels A, C, and D were equated to the slopes of straight lines fit to the logarithms of the numbers of Copyright © 2018 Chen et al.2018Chen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Halobacteriovorax was similar in the three media with low-to-moderate nutrient levels, ASW, DNB 1:100, and DNB 1:10   (The growth of [ANOVA]) . Halobacy ANOVA) .V. vulnificus in ASW, consistent with the results of the previous experiment, they were highly active in the other three media with higher nutrient concentrations, reaching 1010 to 1011 PFU ml−1 , Halobacteriovorax (HBx), and phage and predation rates on V. vulnificus as a function of the salt concentration in DNB 1:10 medium (full-strength NB diluted 10 times) with various salt concentrations. Rates were calculated in the same way as in FIG S3, TIF file, 0.1 MB.Growth rates of Copyright © 2018 Chen et al.2018Chen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the V. vulnificus by predators at 48 h were significant  for all temperatures tested, 10°C, 25°C, 30°C, and 37°C. The levels of prey reduction were similar at 25°C, 30°C, and 37°C but substantially lower at 10°C .Temperature also showed an effect on predation. The reductions of  at 10°C . V. vulnHalobacteriovorax at 25°C, 30°C, and 37°C was not significantly different (P > 0.05 by ANOVA). Phage production was higher as temperature increased. The growth of both Halobacteriovorax and phage was substantially slower at 10°C. The abundance of Halobacteriovorax remained stable in the first 24 h at 10°C and then increased slightly (0.6 log unit) after 48 h of incubation , a group consisting of several different families, genera, and phylogenetic clusters that vary in physical and physiological characteristics, prey range, and habitat.Bacterial predators have been recognized and acknowledged to have critical roles in nature in controlling and shaping the structure of bacterial communities, and global biogeochemical cycling. Yet, other than bacterial viruses and protist grazers, little is known about predators of bacteria and, in particular, bacteria that prey on other bacteria. More than a dozen predatory bacteria have been reported in the literature, and perhaps many more exist –42. Thes–Halobacteriovorax and other BALOs. The findings reported here show Halobacteriovorax to be active predators with the potential for regulating and structuring bacterial communities and nutrient cycling in the environment. This is supported by results from other studies  as reported in our previous work  using the Pp20 double agar overlay technique.s FLA042  was seles tested , 51, 52.MO6-24/O , was useV. vulnificus grown on Luria-Bertani (LB) agar and suspending the colonies in the liquid. The resulting prey suspensions were transferred into sterile tubes for subsequent inoculation into flasks of artificial seawater to establish microcosms for comparing predation rates between phages and Halobacteriovorax. The number of prey bacteria in the prey suspension was determined by spread plating 0.1 ml of serial 10-fold diluted samples onto LB agar plates in triplicate. These plates were incubated at 37°C for 2 days, and CFUs were counted and recorded.Suspensions of the prey were prepared by adding 5 ml of 70% artificial seawater (ASW)   to culture plates (Difco) of an overnight culture of Halobacteriovorax cultures were grown in ASW-V. vulnificus broth and transferred weekly. To obtain a pure suspension of Halobacteriovorax for the predation experiments, the culture lysates of 24- to 48-h cultures were filtered consecutively through 0.45- and 0.22-µm syringe filters to remove any remaining prey. One liter of the filtrate containing the Halobacteriovorax cells (as determined by fluorescence microscopy) was centrifuged at 27,485 × g for 30 min. The pellet of predator cells was then resuspended in 6 ml of ASW. To test whether the concentrated Halobacteriovorax suspension was free of prey cell contamination, 0.1-ml aliquots were spread plated onto LB agar and incubated at 37°C for 2 days. The number of Halobacteriovorax in the suspension was determined by both plate counts and 4′,6-diamidino-2-phenylindole (DAPI) direct cell counts. Counts by the two methods were not significantly different (P > 0.05 by t test). Microscopic counts were used to enumerate and establish the desired ratio of predator and prey cells at the beginning of the predation experiments. The plate counts were recorded to assess the number of infective Halobacteriovorax and phage in the samples.Active Halobacteriovorax plate counts were determined using the double agar overlay method described by Williams and Falker . Three control microcosms were established: one for monitoring the growth in dual cultures of V. vulnificus and Halobacteriovorax , a second for monitoring the growth in dual cultures of V. vulnificus and phages (F3: phages+Vv), and a third for monitoring the growth of V. vulnificus prey only, without the predator amendments . Microcosms were incubated while shaking at 25°C for 40 h. Test and control microcosms were monitored by measurements of optical density (OD) values every 4 h. Aliquots of samples were removed at 0, 12, 20, and 40 h to obtain viable counts of Halobacteriovorax, phages, and V. vulnificus by the plating methods described above. This experiment was repeated three times.Microcosms were established to monitor the population dynamics between predator and prey and their respective abundances at selected time intervals. For the initial investigation, equal numbers of active HBx+Vv microcosm, we assumed that the dynamics of the V. vulnificus were described by the equationG is the predation rate (in milliliters per hour per HBx), Vo is the concentration of V. vulnificus below which predation by Halobacteriovorax ceases , and Mv is the mortality rate (per hour) of V. vulnificus due to factors other than predation by Halobacteriovorax. We assumed a threshold predation concentration based on the work of Fenton et al. . The right-hand side consists of three terms, a constant term (−Mv), a term proportional to the concentration of Halobacteriovorax (−G), and a term proportional to the ratio of Halobacteriovorax to V. vulnificus (G ⋅ Vo). To estimate the rate of change of the logarithm, we interpolated the logarithms of the V. vulnificus concentrations at 1-h intervals with a shape-preserving piecewise cubic interpolation using MatLab software. The rates of change of the logarithms were then estimated using one-sided finite difference equations at times 0 and 40 h and time-centered finite differences at 4, 12, 16, and 20 h. The parameters −Mv, −G, and G ⋅ Vo were then estimated by multiple linear regression analysis of the rate of change of the logarithm of V. vulnificus with a constant term and Halobacteriovorax and Halobacteriovorax/V. vulnificus as the independent variables. The data included in the analysis consisted of data at time points where the observed V. vulnificus concentration was significantly (P < 0.05) greater than the calculated Vo. A similar approach was used in the case of the Vv+phage microcosm, but in that case, we assumed that there was no threshold V. vulnificus concentration below which predation ceased. The two equations were combined in the case of the Vv+HBx+phage microcosm:The left-hand side of Halobacteriovorax. Such a medium, although perhaps not representative of natural waters, would be an important advance in the capability to conduct experimental comparative studies on these two predators, including the testing of their responses to various parameters. We were especially interested in examining the Halobacteriovorax and phage predation activity under different environmental conditions.On the basis of the results showing low predation and growth rates of bacteriophage compared to the BALOs, we considered that this could be due to the low-nutrient medium used (ASW plus prey) in the experiments. Although the ASW may be comparable to the natural environmental water, it did not support an actively growing prey bacterial population suggested as being necessary for optimal bacteriophage replication . This beHalobacteriovorax and phage. The nutrient concentrations included full-strength nutrient broth (NB) (Difco) and diluted nutrient broth (DNB) preparations (full-strength nutrient broth diluted 10 times [DNB 1:10] and diluted 100 times [DNB 1:100]) in ASW, and ASW with no added nutrients. For all medium preparations, the salt concentration and pH were held constant at 21 ppt and pH 8, respectively. A test microcosm of each of the four nutrient medium formulations was established. Harvested V. vulnificus culture suspensions were inoculated into each microcosm flask to an OD of 0.3, corresponding to ca. 2 × 107 CFU ml−1. Active Halobacteriovorax and phage cultures were inoculated into the four test microcosms simultaneously to yield a final concentration of ca. 2 × 106 PFU ml−1. Four additional microcosms of the respective medium formulations containing only prey served as controls. Microcosms were incubated at 25°C while shaking (130 rpm). At intervals of 0, 4, 10, 24, and 48 h, aliquots were aseptically removed from each microcosm for OD measurements. Halobacteriovorax and V. vulnificus in the respective microcosms were enumerated by quantitative real-time PCR (qPCR)  (5′-TCG ACT GTG AGC GTT TTG TC-3′) and VvhA1795 [5′-TGC CT(AG) GAT GTT TAT GGT GAG AAC-3′] were used to target the V. vulnificus cytolysin/hemolysin gene . Phage numbers were determined by the plating method described above.ASW, supplemented with different concentrations of nutrient, was tested for growth of R (qPCR)  using thsin gene . Each saHalobacteriovorax and phages to bacterial cell death at various temperature and salinity conditions were conducted. DNB 1:10 was selected as the culture medium for these experiments as it was found to support the optimum growth of both Halobacteriovorax and phage when tested against other medium formulations. We also wanted to assess whether the range of predation was expanded under various environmental conditions by the presence of the two predators versus only one predator. As in the previous experiments, qPCR was used to quantify Halobacteriovorax and prey in the mixed culture. The virus was quantitated by direct plating as described above. Sampling time intervals of the latter experiments were also adjusted based on the results of the experiment with the seawater microcosm.Experiments testing the relative contributions of Halobacteriovorax on the population of V. vulnificus, we used a basal medium of DNB 1:10 supplemented with 3 mM CaCl2 and 2 mM MgCl2  was incubated on shakers (130 rpm) at temperatures set at 10°C, 15°C, 25°C, 30°C, and 37°C. Aliquots of the samples from each microcosm were removed aseptically at 0, 4, 24, and 48 h for OD measurements. Bacterial and phage counts were measured as described above and recorded.The impact of temperature on phage and t test was used to compare two groups of treatments when normality and equal variance tests were passed. These statistical analyses were performed using the Sigmastat, version 3.5, software package. The growth rates (per hour) of the V. vulnificus, Halobacteriovorax, and phage as functions of temperature and concentrations of nutrients and salt were estimated from the slopes of straight lines fit to the logarithms of the organism concentrations versus time. Reported error bounds are 95% confidence intervals assuming a normal distribution of errors.Analysis of variance (ANOVA) was used followed by Holm-Sidak test to detect significant differences among the numbers (log transformed) of microbes in the various microcosm treatments. A


Escherichia coli isolates circulating in sympatric human (n = 321) and livestock (n = 633) samples from 99 households across Nairobi, Kenya. E. coli isolates were tested for susceptibility to 13 antimicrobial drugs representing nine antibiotic classes. High rates of AMR were detected, with 47.6% and 21.1% of isolates displaying resistance to three or more and five or more antibiotic classes, respectively. Human isolates showed higher levels of resistance to sulfonamides, trimethoprim, aminoglycosides and penicillins compared with livestock (P<0.01), while poultry isolates were more resistant to tetracyclines (P = 0.01) compared with humans. The most common co-resistant phenotype observed was to tetracyclines, streptomycin and trimethoprim (30.5%). At the household level, AMR carriage in humans was associated with human density (P<0.01) and the presence of livestock manure (P = 0.03), but keeping livestock had no influence on human AMR carriage (P>0.05). These findings revealed a high prevalence of AMR E. coli circulating in healthy humans and livestock in Nairobi, with no evidence to suggest that keeping livestock, when treated as a single risk factor, contributed significantly to the burden of AMR in humans, although the presence of livestock waste was significant. These results provide an understanding of the broader epidemiology of AMR in complex and interconnected urban environments.There are substantial limitations in understanding of the distribution of antimicrobial resistance (AMR) in humans and livestock in developing countries. This papers present the results of an epidemiological study examining patterns of AMR in  Notably, none of these studies considered urban livestock, which are of increasing importance, particularly in LMIC settings To date, studies investigating the epidemiology of AMR have tended to focus on either human or livestock populations without making comparisons of resistance between the two populations. A recent systematic review E. coli is an ideal organism to study the spread of AMR in this complex environment, as it is a ubiquitous commensal in both livestock and humans but with a wide range of resistance phenotypes.This study focused on the role of keeping livestock as a potentially high-risk urban interface for AMR transmission between humans and livestock in urban Nairobi. Nairobi is a rapidly growing city where livestock are commonly kept within household compounds, bringing them into close contact with people. E. coli from cohabiting human and livestock populations in a low-resource urban setting. At the scale of individual households, the role of livestock is explored as a risk factor for AMR carriage in humans, hence providing insight into the pathways of AMR transfer.This paper reports the results from the first study to characterize the patterns and epidemiology of antibiotic-resistant 22.1A cross-sectional study targeting sympatric human and livestock populations in Nairobi, Kenya was carried out from August 2015 to October 2016 as part of the Urban Zoo Project In total, 99 households were involved in the study . The des2.2E. coli by biochemical testing using triple sugar iron agar, Simmon's citrate agar and motility-indole-lysine media.In each household, a questionnaire was used to collect data on household composition, socio-economic variables, livestock ownership, food consumption and medical history. Human and animal faecal samples were collected and transported on ice to one of two laboratories  within 5 h of collection. Samples were enriched in buffered peptone water for 24 h, and thereafter plated on to eosin methylene blue agar (EMBA) and incubated for 24 h at 37 °C. One colony from each plate was selected and subcultured for a further 24 h on a second round of EMBA. Subsequently, one purified colony from each plate was selected at random (hereafter referred to as an ‘isolate’), and confirmed as E. coli ATCC 25922 was used as a quality control of the susceptibility tests.Antimicrobial susceptibility testing for 13 antibiotics – ampicillin (10 µg/mL), amoxicillin-clavulanic acid (30 µg/mL), cefepime (30 µg/mL), cefotaxime (30 µg/mL), ceftazidime (30 µg/mL), chloramphenicol (30 µg/mL), ciprofloxacin (5 µg/mL), gentamicin (10 µg/mL), nalidixic acid (30 µg/mL), streptomycin (25 µg/mL), sulfamethoxazole (30 µg/mL), tetracycline (30 µg/mL) and trimethoprim (2.5 µg/mL) – that are frequently used in either/both veterinary and/or human medicine in Kenya was undertaken using the Kirby–Bauer disc diffusion method . Standardized protocols were used, in which antibiotic discs were dispensed on to bacteria-containing agar plates and incubated for a maximum of 18 h at 35°C. Clinical and Laboratory Standards Institute interpretive criteria for Enterobacteriaceae 2.3post hoc for pairwise comparisons between groups, and P-values <0.05 were considered significant.The distribution of resistance phenotypes between hosts was calculated using Chi-squared tests (humans and livestock) and a one-way analysis of variance . Tukey's multiple-comparison test was performed P-value (adjusted for multiple testing using Bonferroni's correction) was <0.05.Generalized linear mixed models (GLMMs), implemented in R package ‘lme4’ To investigate finer scale household-level risk factors for AMR carriage in humans, a Poisson-distributed GLMM was fitted with the counts of resistance phenotypes (antibiogram length) as the response variable. Model explanatory variables were human density (count of people in a household as a function of household area) and types of livestock kept by the household . Additionally, for households that kept livestock, a separate Poisson-distributed GLMM was fitted to investigate the effect of human density and manure disposal practises (manure disposed in the household compound or outside) on human antibiogram length. Separate models were fitted for the most prevalent AMR phenotypes .n = 99), sublocation (n = 33) and wealth category (n = 7) were included as random factors. Further details of data exploration and statistical models are given in the online supplementary material.To account for the nested  nature of the sampling design, household site  were to sulfonamides, trimethoprim, tetracyclines and aminoglycosides. A smaller percentage of isolates (<10%) were resistant to amoxicillin/clavulanic acid, cephalosporins, phenicols and fluoroquinolones . The disP<0.01, Chi-squared test; When analysed by host, human isolates were more commonly resistant to each of the individual antibiotic classes than those of animal origin. Of 321 human isolates, >40% were resistant to sulfonamides, trimethoprim, aminoglycosides and tetracyclines. Of 633 livestock isolates, >40% of isolates were resistant to sulfonamides, tetracyclines and trimethoprim. For both human and livestock isolates, <10% of isolates were resistant to phenicols, fluoroquinolones, cephalosporins and beta-lactams. Resistance to penicillins, aminoglycosides, sulfonamides and trimethoprim was significantly more common in humans than in livestock . Conversely, poultry were more likely to carry isolates resistant to tetracyclines than humans . Humans were more likely to carry n = 217/633, 34.3%) than human isolates  . Of the 217 pan-susceptible livestock isolates, 22% of poultry isolates (n = 76), 51.6% of bovine isolates (n = 33), 33.3% of pig isolates (n = 17), 54.6% of goat isolates (n = 72) and 46.3% of rabbit isolates (n = 19) were pan-susceptible. Across both human and livestock isolates, 404 (47.6%) and 201 (21.1%) isolates were resistant to three or more and five or more antibiotic classes, respectively. Eight isolates (0.8%) showed resistance to seven or more antibiotic classes tested; five (1.6%) from humans and three (0.9%) from poultry  isolates were susceptible to all 13 antibiotics tested (nine antibiotic classes). The proportion of pan-susceptible isolates was significantly higher among livestock isolates  but significantly higher than those from bovines, goats and rabbits   was significantly higher in humans than in livestock [odds ratio (OR) = 1.14, 95% confidence interval (CI) 0.68–0.81, 1, GLMM) .Table 3RE. coli antibiograms from human and livestock isolates revealed 84 distinct profiles: 30 in livestock, 19 in humans and 35 common to both . Using a co-occurrence analysis based on a statistically significant (P<0.05) correlation coefficient (ρ>0.5), a tetracycline-sulfonamide-trimethoprim cluster was identified . Further, denoting multi-resistance, this cluster was commonly associated with resistance to aminoglycoside and penicillins.Examination of the similarity of entified . This co3.2P = 0.24; small livestock OR = 1.04, 95% CI 0.82–1.30, P = 0.94, marginal R2 = 0.3, GLMM)    . However3, GLMM) . The imp5, GLMM) . These r4E. coli isolates collected systematically from sympatric human and livestock populations in the rapidly developing urban landscape of Nairobi, Kenya.This study applied ecological and epidemiological approaches to characterize the epidemiology of AMR Resistance to aminoglycosides, sulfonamides, tetracyclines, trimethoprim and penicillins was high in both humans and livestock, while resistance to cephalosporins and fluoroquinolones was low. These results are consistent with previous studies When analysed by host, human isolates appeared to have a higher prevalence of AMR carriage compared with livestock isolates, with the exception of tetracyclines. In particular, the observed prevalence was significantly higher in four clinically relevant antibiotic classes: penicillins, sulphonamides, trimethoprim and aminoglycosides. A possible explanation for this variation in AMR carriage is that it relates to variation in antibiotic use between these populations. Although antibiotics are used extensively in both human and livestock populations, previous studies have shown that frequency of use of antibiotics is higher in human medicine than in livestock medicine, especially in resource-poor settings Although the use of chloramphenicol in food animals has been banned in Kenya At the household level, there is evidence of an intricate interplay between humans and livestock in relation to the development and transmission of AMR. This analysis revealed that human AMR carriage increased with number of occupants in a household, and that keeping manure inside the household compound was also significantly associated with AMR carriage in humans. In urban Nairobi, people live in a continuum of urban spaces with varying human and animal population densities, with the majority (>60%) of people living in slums These results suggest that, at the household level, keeping livestock in and of itself does not add to the risk of acquisition or carriage of AMR bacteria in humans. However, given the multiple pathways of AMR exchange between humans and livestock E. coli population in both human and livestock populations. AMR genes conferring resistance to tetracycline, sulfonamide and trimethoprim antibiotic classes are commonly associated with mobile genetic elements E. coli isolates. Genetic data are required to validate the existence of mobile genetic elements, and determine whether AMR genes are being transferred across them.It is important to note that, while this analysis was not designed to address transmission of AMR bacteria and their AMR determinants, it is also plausible that clonal expansion could have played a role in the observed AMR patterns. The finding of 35 common AMR profiles in both human and livestock bacterial populations may, in part, reflect overlapping antibiotic usage patterns, acquisition of AMR from a shared source or clonal expansion. It is hypothesized that the finding that 30.5% (340/954) of all isolates contain a tetracycline-sulfonamide-trimethoprim cluster phenotype and that the pairwise correlations between these three antibiotic classes were very high is suggestive of a conjugative MDR plasmid circulating within the Distinguishing molecular transmission of AMR from selection for AMR due to antibiotic use is challenging 5E. coli carriage in livestock and humans outside the clinical setting across a developing country urban landscape, with no evidence that direct contact with livestock contributes to the burden of human AMR, but that indirect contact between livestock and humans does play a role. In LMIC urban ecosystems, the elevated prevalence of AMR in both human and livestock populations could be attributed to unregulated access to antibiotics, poor hygiene and sanitation, and waste management, which encourage the evolution and spread of AMR bacteria. These findings highlight a need for targeted surveillance strategies across various sectors, and for actors to address and design effective measures to curb AMR in these populations, both in Nairobi and in other similar urban landscapes. Further work is required to understand the ecology of genetic determinants of resistance, particularly the extent of the role that plasmids play in the dissemination and evolution of resistance traits in these human and livestock populations.This rigorously structured epidemiological study found a high prevalence of AMR


KS distributions and the analysis of intragenomic colinearity. Despite the increased interest in WGDs and the acknowledgment of their evolutionary importance, user-friendly and comprehensive tools for their analysis are lacking. Here, we present an easy to use command-line tool for KS distribution construction named wgd. The wgd suite provides commonly used KS and colinearity analysis workflows together with tools for modeling and visualization, rendering these analyses accessible to genomics researchers in a convenient manner.Ancient whole-genome duplications (WGDs) have been uncovered in almost all major lineages of life on Earth and the search for traces or remnants of such events has become standard practice in most genome analyses. This is especially true for plants, where ancient WGDs are abundant. Common approaches to find evidence for ancient WGDs include the construction of https://github.com/arzwa/wgd.wgd is free and open source software implemented in Python and is available at Bioinformatics online. KS distributions  and loss of duplicated copies not under selection, a whole paranome KS distribution is expected to show an exponential decay of the number of retained duplicates in function of age  events have been uncovered across the eukaryotic tree of life  for fitting kernel density estimates (KDEs). Importantly, we apply a correction for boundary effects, which are often neglected but may lead to artificial peaks in low KS regions. As peaks derived from WGDs are expected to be approximately log-normally distributed, Gaussian mixture models (GMMs) have also been used frequently to analyze KS distributions. We provide tools (‘wgd mix’) for fitting mixtures of log-normal components using different inference algorithms, implemented using the scikit-learn python library  visualization of histograms and KDEs in ‘wgd viz’ . These tKS and colinearity based analysis of WGDs in an easy to use and freely available package named wgd. We hope that, besides being a useful tool for researchers, it will also aid in preventing common pitfalls and misinterpretations when analyzing putative WGDs in genomic data.We provide, to our knowledge, the first comprehensive toolshed for This work was supported by the European Union Seventh Framework Programme (FP7/2007-2013) under European Research Council Advanced Grant Agreement 322739—DOUBLEUP [to Y.V.d.P]; and a PhD Fellowship of the Research Foundation—Flanders (FWO) [to A.Z.].Conflict of Interest: none declared.bty915_Supplementary_DataClick here for additional data file.


Proteins are a kind of macromolecules and the main component of a cell, and thus it is the most essential and versatile material of life. The research of protein functions is of great significance in decoding the secret of life. In recent years, researchers have introduced multi-label supervised topic model such as Labeled Latent Dirichlet Allocation (Labeled-LDA) into protein function prediction, which can obtain more accurate and explanatory prediction. However, the topic-label corresponding way of Labeled-LDA is associating each label (GO term) with a corresponding topic directly, which makes the latent topics to be completely degenerated, and ignores the differences between labels and latent topics.To achieve more accurate probabilistic modeling of function label, we propose a Partially Function-to-Topic Prediction (PFTP) model for introducing the local topics subset corresponding to each function label. Meanwhile, PFTP not only supports latent topics subset within a given function label but also a background topic corresponding to a ‘fake’ function label, which represents common semantic of protein function. Related definitions and the topic modeling process of PFTP are described in this paper. In a 5-fold cross validation experiment on yeast and human datasets, PFTP significantly outperforms five widely adopted methods for protein function prediction. Meanwhile, the impact of model parameters on prediction performance and the latent topics discovered by PFTP are also discussed in this paper.All of the experimental results provide evidence that PFTP is effective and have potential value for predicting protein function. Based on its ability of discovering more-refined latent sub-structure of function label, we can anticipate that PFTP is a potential method to reveal a deeper biological explanation for protein functions. Proteins are the main component of a cell, which explain the basic activity of cellular life. The research of protein functions is of great significance in elucidating the phenomena of life . AlthougThe earlier computational approach for predicting protein function is to utilize the protein sequence or structure similarity to transfer functional information, such as BLAST. With the In classification-based approaches, since each protein is annotated with several functions, various multi-label classifiers can be adopted. Yu et.al  proposedIn the topic modeling process of reference , each prInspired by the application of multi-label topic model in protein function prediction and PLDA model, we introduce a Partially Function-to-Topic Prediction model . Firstly, we describe the related definitions by contrasting text data and protein function data. Then the topic modeling process of PFTP is described in detail, including the generative process and parameter estimation of PFTP. In a 5-fold cross validation experiment on predicting protein function, PFTP significantly outperforms five algorithms compared. All of the experimental results provide evidence that PFTP is effective and have potential value for predicting protein function.To better understand related objects of topic model, the corresponding relationship between protein function prediction and multi-label classification of text is first depicted in Fig. Several topic modeling concepts of protein function data and text data are displayed in Fig. As the input for topic model, the bag of words (BoW) is constructed by computing the word-document matrix, where matrix element is obtained by counting the times of word in each document. As an instance, the word ‘table’ appears two times in document D1. Likewise for protein function data, an amino acid block - protein sequence matrix is computed for the construction of protein BoW. As an example, the amino acid block ‘MS’ appears one times in protein P1. Besides, the fixed amino acid blocks set or words set is also called ‘vocabulary’.For topic model, a ‘topic’ is viewed as a probability distribution over a fixed vocabulary. Taking the text data as an example, the probabilities of word ‘table’ over ‘topic 1’ are 0.05. For the protein data, the probabilities of amino acid block ‘MS’ over ‘topics 1’ are 0.21. Obviously, topics are latent and needed to be inferred by topic modeling. Finally, in order to establish the connection between labels and topics, the latent topics discovered by our PFTP are divided into several non-overlapping subsets, each of which associates a label. As can be seen in Fig. D proteins in the protein set which compose the protein space W is the size of vocabulary. The topic space including Ktopics is represented by Suppose there are L groups without overlap, and each group corresponds to a subspace of topic In PFTP model, the global topic space lB.In this case, the label space is expanded to L + 1 dimensions and expressed as θk obeys a Dirichlet prior distribution of hyper parameter l is represented as a multinomial distribution of parameter πlk is the probabilities of topic k among topic subspace l. Suppose πl obeys a Dirichlet prior distribution of hyper parameter α.Then, each of labels is assigned a subspace of topic mentclass2pt{minimmentclass2pt{minimΛd to map global label space We utilize a binary vector d, L + 1 = 1 illustrates that latent background label lB is assigned to each protein d. Then, the probabilities of d is represented by a weight of protein-label ψd obeys a Dirichlet prior distribution of hyper parameter βd constrained by β and Λd:ΛIn this paper, the shared parameters of whole protein sets is called global parameter in this paper, and the parameter facing one protein is called local parameter.Based on above expression, the process of PFTP topic modeling is divided into three steps: BoW construction, the description of model (the generative process or graphic model) and parameter estimation (model training and predicting).These steps are depicted in Fig. As shown in Fig. entclass1pt{minimaentclass1pt{minimaThe second and third steps are discussed in next sections. It is worth noting that the third step includes two sub-steps for realizing function prediction: model training and predicting. Both of these two sub-steps need adopt learning and inference algorithm to estimate parameters of model, and are described with more detail as follows.πl, θk and ψd. The local hidden variables include the label number and topic number of each word sample. The unknown parameter and local hidden variables can be estimated by inferring algorithm in model training.PFTP takes a training protein set with known function as an input of training model. The unknown parameter includesψd and hidden variables are updating by constraining the global parameter πl and θk. Then, the label probabilities over protein are obtained.For unannotated proteins, based on the estimated parameters and local hidden variables, unknown local parameter x is composed by protein set, where Nd word samples in protein d, xdn represents one word sample. At this point, word sample xdn not only associates a word number wdnxdn,i.ldn of xdn from Ld dimensions multinomial distribution of parameter ψd:Sample label number For each word sample ii.zdn of xdn from K dimensions multinomial distribution of parameterSample topic number iii.wdn of xdn from W dimensions multinomial distribution of parameter Sample word number Λd, word samples In PFTP model, the unknown parameters to be estimated are the global label multinomial parameters n in Eq. :11\docump, the posterior distribution of unknown model parameters and hidden variables. In this paper, we use the Collapsed Gibbs sampling (CGS) to train a PFTP model. By marginalizing the model parameters  from the joint distribution  is obtained. The collapsed inference is as follows.Based on the joint distribution, several parameter estimations can be obtained, including ribution , the colψd only appears in p and p(Ld| ψd):In the joint distribution Eq. , functioNdl is the number of samples assigned to observed label d; C1 is the constant of multinomial distribution coefficient:ψd in Eq.  and p.By the same way, in the joint distribution Eq. , global Nlk represents the number of samples assigned to topic k of global label l; C2 is the constant of multinomial distribution coefficient:π in Eq.  is obtained by doing the integral of  in Eqs.  an anL, Z,  in Eqs. .24\documTo simplify computation, the Dirichlet prior distributions are symmetric Dirichlet distributions:d to Eq. :26\documzdn and ldncan be computed from that collapsed joint distribution as a transition probability of state space in the Markov chain. Through Gibbs Sampling iteration, Markov chain converges to the target stationary distribution after the burn-in time. Finally, collecting sufficient statistic samples from the converged Markov chain state space and averaging among the samples, we can get a posteriori estimates of corresponding parameters.Then, the prediction probability distribution of hidden variable wof topic k in label l for sample xdn is:Deducing from Eqs. , 21) an an21) anTo investigate the performance of the proposed method, we utilize two types of datasets. The first one is S.cerevisiae dataset (S.C) proposed in , and theIn S.C dataset, there are several sub datasets that constructed from different characteristics of yeast genome. Meanwhile, each sub dataset use two kinds of function annotation standard, FunCat and GO. We mainly use the sub dataset that depends on the amino acid sequence of protein and GO. What’s more, to compare the performance of PFTP between difference label numbers, we construct a dataset named S.C-CC from S.C, which only includes GO terms belonging to cellular component. Then, there are two datasets constructed from S.C.The human dataset is constructed from the Universal Protein Resource (UniProt) databank  and consL’ represents the number of GO terms after BMD; ‘D’ denotes the number of proteins in each dataset; ‘W’ denotes the size of vocabulary.Due to the large number of GO terms in protein function dataset, we adopted a label space dimension reduction (LSDR) method to overcome the classification difficulty of classifiers. Boolean Matrix Decomposition (BMD) has been studied for LSDR recently, which can recovery the label space after classification conveniently. Therefore, a BMD method proposed in reference  has condα, λ and K. α and λ are the parameters of two Dirichlet distribution, where the larger the value of λ, the more balanced the probabilistic of word in a topic. According to the experience, we set α = 50/K,λ = 200/W. The settings and impact of K value are explained later.PFTP model involves three parameters: In the Gibbs sampling process of model training, we set the number of Markov chain as 1, the maximum number of iterations as 2000 times, where the number of iteration of burn-in time is set to 1000. We record the state space at intervals of 50 times on converged Markov chain, and 20 times of record is conducted. In the process of model predicting, we set the number of iterations as 1000 times. After 500 times of iterations for burn-in time, we record the state space at intervals of 50 times.In all of our experiments, we use three representative multi-label learning evaluation criteria, including Hamming loss(HL), Average precision(AP) and One Error. Besides, we also use three kinds of area under Precision-Recall curve proposed in reference , includiK denotes the number of global topics. The analysis about impact of K on model performance is discussed in this section. According to the description of Section 2, as PFTP allocates one or more latent topics to each GO term, then the value of K should range from Lto infinity in theory. Specifically, if we allocate only one topic to each GO term (K = L), then the model reduces to Labeled-LDA. Obviously, setting K < Lmakes our PFTP have no ability to discover the sub-structure of function. In our experiment, each function is assigned exactly the same number of topics for the simplicity of computation. For example, we set K = 3L, then each GO term corresponds to a topic set with three topics. In view of above reason, the lower bounded of K value is set to 2L. On the other hand, although theory insists that the larger K value equals to the more refined sub-structure of label, incorporating more latent topics per function will increase the computational load. In reference [K value on the effectiveness of PLDA model has been discussed in several texts collections. Along with the growth of topic size, the performance of PLDA model approaches a fixed value which was obtained by a non-parametric model. In other words, the infinitely larger size of topics doesn’t equal to an infinitely greater performance, but an unbearable running time. Therefore, we set the upper bound of K value as 5Lbased on our empirical experience and the acceptable level of time overhead. In sum, the Kvalue should be set to an integer between 2L and5L. Then, the performance of PFTP under different Kvalue is shown in Fig. eference , the impKis set to2L~4L. Nonetheless, when Kvalue is greater than 4L, the values of AP,K, the value of Hamming loss and One Error slowly increase with the increase of K. These results suggest that the optimum value range of K is 2L to4L. This was due to that the lower K value makes the fewer topics allocated to each label, and the higher K value makes the small difference of word distribution between topics. What’s more, the problem of huge labels is particularly obvious in protein function dataset, even if a BMD method has applied to reduce the label dimension. Therefore, we set K as 3L in our experiment.As shown in Fig. Firstly, we compare PFTP with Labeled-LDA  and multAs shown in Fig. For Human1 dataset, PFTP obtain a better performance in all evaluation criteria. On HL, PTPF achieves 9.7 and 2% improvements over Labeled-LDA and MLKNN. On One-Error, PTPF achieves 80 and 99% improvements over Labeled-LDA and MLKNN. On AP, For Human2 dataset, PFTP obtain a better performance in four evaluation criteria except For S.C dataset, PFTP obtain a better performance in four evaluation criteria except HL and One-Error. On AP, For S.C-CC dataset, PFTP obtain a better performance on AP, Besides, we compare PFTP with three hierarchal multi-label classification (HMC) algorithm based on decision tree, namely HMC/SC (single-label classification)/HSC  . These tOn The greatest strength of our protein function topic modeling is that, it can not only provide the function label probability distribution over proteins as an output, but also each function label can be explained as a probability distribution over topic subset, where each topic is represented as the probability distribution over amino acid blocks. To better understand this topic modeling process, we take GO term ‘GO0016020’ as an example, whose corresponding topics are shown in Table As shown in Table For PFTP model, each GO term is a partition of global topics set. Such as for S.C-CC dataset, the number of function label is 319, while the number of global topics is three times that of the labels, that’s a total of 958(including a background topic). Therefore, each GO term corresponds with four topics . The topic number 863,864,865 and 1 are the four topics corresponded by ‘GO0016020’, where the number 1 is a background topic. Likewise, the top 20 words of these four topics are listed from large to small order.The results in Figs. Compared with traditional multi-label classifiers(non-topic model), our method can further improve the accuracy of protein function prediction by introducing topics subset into supervised topic model, which can discover the topic that represents common semantic of documents and reflect the differences between labels and latent topics. Especially for CLUS-HMC/SC/HSC, our method exhibit the dominant advantage on Compared with Labeled-LDA, PFTP is able to discovery more-refined latent sub-structure of function label than Labeled-LDA. By introducing topic subset for each label in PTPF, the relationship between functions and variety words, labels and topics were disclosed. Therefore, we can anticipate that PFTP is a potential method to reveal a deeper biological explanation for protein functions.K value doesn’t lead to great impact on prediction effect. In the comparison of S.C and S.C-CC dataset, we find that the value of AP, Meanwhile, the performance comparison of different dataset is also shown in Fig. In this paper, we introduced an improved multi-label supervised topic model for predicting protein function. In our previous study, a multi-label supervised topic model Labeled-LDA has been applied to protein function prediction, which associates each label (GO term) with a corresponding topic directly. This way makes the latent topics to be completely degenerated, and ignores the differences between labels and latent topics. To address the faultiness, we proposed a Partially Function-to-Topic Prediction model for introducing the local topic subset corresponding to each function label. PFTP not only supports latent topics subsets within given function labels but also a background topic corresponding to a ‘fake’ function label. In a 5-fold cross validation experiment on predicting protein function, PFTP significantly outperforms compared methods. Due to the more-refined way of function label modeling, PFTP shows the effectiveness and potential value in predicting protein function through experimental studies. Meanwhile, there are several problems in topic modeling of protein function prediction to be improved, such as the introduction of protein extra features and hierarchical function label structure. However, multi-label topic model is a potential method in many applications of bioinformatics.


Extension to metal–organic frameworks (MOFs), metallocycles or supramolecules, such as catenanes, would be straightforward. Through integration with third party codes, stk offers the user the opportunity to explore the potential energy landscape of the assembled supramolecule and then calculate the supramolecule's structural features and properties. stk provides support for high‐throughput screening of large batches of supramolecules at a time. The source code of the program can be found at https://github.com/supramolecular-toolkit/stk. © 2018 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.A tool for the automated assembly, molecular optimization and property calculation of supramolecular materials is presented.  In recent years, computational modeling has received increasing interest as a tool for both supporting and accelerating materials discovery. Molecular simulations have long been employed to offer insights by supplementing or rationalizing experimental data. However, computational screening is increasingly becoming a choice for the initial screening of large sets of materials, before any attempts at synthetic realization.Supramolecular materials result from the self‐assembly of a number of smaller molecular components, and can include molecular systems up to a large degree of complexity, such as macrocycles, organic cages, catenanes, rotaxanes, knots and molecular machines. Increasing the number of dimensions of these organic materials leads to linear polymers , and periodic materials such as covalent organic frameworks (COFs), which can be two‐, or three‐dimensional or amorphous polymeric systems . The inclusion of metal atoms in the materials leads to additional systems, such as metallocycles, metal–organic polyhedra (MOPs) and the highly topical metal–organic frameworks (MOFs). Beyond the inherent beauty of many of these structures, supramolecular materials have shown potential in a broad range of applications,supramolecular toolkit (stk), a Python library for general supramolecular assembly.Nowadays, thanks to the multitude of freely available computational chemistry packages, such as ORCA,stk currently provides for the construction of complex supramolecules including molecular cages, linear polymers and covalent organic frameworks (COFs). Due to its modular design, it could be easily extended to support further, more exotic, supramolecular structures and even to structures containing metals, such as metallocycles, MOPs and MOFs. The key design principles of stk emphasize modularity and object‐oriented programing. Within stk, the underlying molecular representation is provided by RDKit,stk, a multitude of operations are available to apply on the structure, for example, geometry optimization, conformer generation, molecular dynamics simulation, energy calculation or centroid calculation. The current stk version already directly interfaces with codes such as RDKit, Schrödinger's Macromodelstk can be readily extended to interface with other molecular simulation packages to allow for further desired operations to be carried out on the supramolecules. Finally, stk provides a simple API for the assembly, structure optimization and property calculation of molecules in batches and in parallel across available CPUs.stk, then discuss the implementation via stk objects. Next, we will give an overview of how the supramolecular assembly from building blocks is conducted, before showcasing examples for the four supramolecular classes that are currently covered by stk; molecular cages . This is because building blocks are placed on a shape which corresponds to an idealized structure of the supramolecule, as defined by the assembly function. For example, the building blocks of a linear polymer are placed in a straight chain, even though the minimum energy structure may be twisted, bent or otherwise nonlinear. By defining more complex assembly functions, structures closer to the minimum of the PES may be generated. However, this can often result in duplicated effort, as structure optimization tools for many supramolecules already exist. As a result, structure optimization by stk does not force any particular global minimization method onto an assembled supramolecule. This is particularly important as different supramolecules will require vastly different procedures to generate realistic structures. Forcing an approach would result in loss of generality. For example, in our work with cage molecules, we found that first minimizing the distance between the bonds added during assembly, while keeping the remaining bond distances in the structure fixed, was key to producing realistic structures. This step is followed by an unconstrained geometry optimization, which in turn is followed by a conformer search, performed by simulated annealing. Each sampled structure is geometry optimized and the lowest energy conformer is used as the final cage structure. With simpler systems, stk can use RDKit's fast ETKDG algorithmstk, as integration with other third party software such as MOPAC is also available.An advantage of our approach, is that stk molecule object is generated, methods for the calculation of a broad range of molecular properties become available. Due to the integration with RDKit, any functionality provided by this library is automatically included. This includes tools for drawing molecules, substructure searching, fingerprinting, molecular similarity and descriptor calculation. In addition to these tools, stk provides functions to calculate physical and electronic properties as well as perform energy calculations. Similar to the structure manipulation functions, property calculation functions may invoke third party software when necessary. However, regardless of how these functions are implemented, a key benefit of stk is that it allows the user to always remain in a Python environment.Once an stk objects can also be stored as JSON files, which can be reloaded to restore an stk object in a new Python session.In addition to the returned physical and electronic properties, the constructed molecule objects of supramolecules can be saved in a variety of formats, including MDL, Macromodel structure files, PDB, SMILES and InChI. stk provides support for high‐throughput screening by assembling, optimizing and analyzing large batches of supramolecules at a time via Population objects. Given a set of building blocks, a population of all possible supramolecules can be created by considering every combination. Alternatively, a random set of supramolecules can be produced by selecting building blocks at random. Finally, stk supports the generation of a “chemically diverse” population. In this case, half of the supramolecules in the population are generated by randomly selecting building blocks from the set. Each random supramolecule is then used to produce a “chemically diverse” analog. This is achieved by finding the building blocks in the set which are least similar to those making up the random supramolecule. The chemical similarity is calculated using Dice similarity on the Morgan fingerprints generated by RDKit.stk and their interactions. A summary is shown in Figure In this section, we provide an overview of the classes which make up stk provides a hierarchy of classes, as outlined in Figure Molecule. It is inherited by all classes which describe molecules, be it building blocks or supramolecules. It includes methods for the calculation of structural properties, such as molecular size and centre of mass, and methods for manipulating a molecule's position, such as applying rotations and translations and aligning any arbitrary component of the molecule with any arbitrary axis. Finally, Molecule includes tools for the calculation of electronic properties, such as energy, ionization potential and electron affinity. For a complete list of properties that can be calculated, the reader is referred to the stk documentation.To work with molecules, StructUnit, represents the building blocks of the supramolecules. While supporting all operations of the Molecule class, additional operations which may be necessary for constructing supramolecules are added, for example, the method “bonder_centroid” returns the centroid of all the atoms in the building block which form new bonds during the supramolecular assembly process. This class is extended by the StructUnit2 and StructUnit3 subclasses. StructUnit2 adds operations for the alignment of building blocks with 2 functional groups (di‐topic) when assembling a supramolecule. Equivalently, StructUnit3 adds operations for 3 (tri‐topic) or more functional groups. The user can specify which building block conformers to use during construction, to provide a greater degree of control over the final structure of the assembled supramolecule.The second basic class, MacroMolecule class represents assembled supramolecules; as with the Molecule class, this class serves as an abstract base class and is not used to initialize objects directly. Instead, a new subclass is made for each supramolecule, holding any additional operations which that supramolecule may require. For example, the Polymer, Periodic and Cage classes represent finite size polymers (and short oligomers), periodic materials and covalent organic cages, respectively. As stk supports storing multiple conformers for each supramolecule, the user can specify which conformer needs to be used when calculating molecular properties. This allows the user to easily evaluate how molecular properties change between conformations, for example when searching for the structure which best matches experimental properties.The 4Di6Tri, or cubic, 8Di12Tri, topology, among many others. Similarly, a polymer may be linear or branched. The class Topology is used in stk to assemble the supramolecules from constituent building blocks into a defined topology. Whenever a supramolecule object is created, a topology object must be provided so that a molecule with the correct structure is formed. For example, the Linear topology defines a linear polymer and so an instance of the Linear class will define the repeating unit as well as the number of repeating units. A small number of repeating units can generate a short oligomer. This instance is then provided to the initializer when constructing a Polymer object. stk can be extended with new topology classes to add support for the construction of new types supramolecules, such as MOFs or rotaxanes, or new topologies of existing supramolecules. Though there is no inherent limit to the classes of molecules which can be added, more complex supramolecules will require contributors to provide more complex assembly functions.The topology of a supramolecule defines the underlying connectivity of the building blocks in the final supramolecule, and is a feature that does not change upon deformation. For example, a molecular cage can have a tetrahedral, stk, the assembly process requires two independent pieces of information. First, the building blocks need to be supplied, and these can be either molecules or molecular fragments. As mentioned in the software overview, the building blocks may be provided in a variety of input formats. Second, the topology needs to be defined, as this establishes how the building blocks are connected together to form the supramolecule. At a minimum, the topology must be defined as a child class of Topology and contain the assembly function as a method. Topologies must be defined ahead of time, and then when the user runs stk, they need only to state which topology to use and the appropriate function will be executed. Assembly can also be performed in parallelized batches by providing the stkPopulation class with groups of building blocks and topologies.Next we will give a descriptive overview of the general procedure that is carried out to take molecular building blocks and arrange them into supramolecular structures; in the following section, we will then illustrate how this is applied to four specific classes of supramolecular materials. When assembling supramolecules in StructUnit instances, stk identifies the atoms that will be directly involved in bond formation during assembly. To do this, stk has a database of functional groups. A “functional group” in stk consists of a group of atoms in a building block that are known to be directly involved in the assembly process. Functional groups in stk may match real, chemical functional groups, such as the —NH2 groups in amines and the —C(H)=O groups in aldehydes, but this is not a requirement. Thus, stk can form bonds between any arbitrary, user defined, set of atoms within the building blocks. The functional group identification for an example pair of building blocks is shown in Figure stk will attempt to deduce it from the file name by checking if the name of a functional group is present. This framework allows users to mimic real chemical reactions during supramolecular assembly, such as imine condensation, which is beneficial when access to databases of chemical precursors is available, as building blocks can be used directly without any modifications. However, because stk is not restricted to chemical functional groups, this approach integrates seamlessly with molecular fragments and can be used to produce any arbitrary bonding between building blocks.After the building blocks have been loaded into stk defines a set of vectors passing through each building block, as shown in Figure To easily position and align building blocks on the topology of a supramolecule, stk. Currently supported techniques for structural optimization include forcefield optimization, conformational searching using a simulated annealing procedure with molecular dynamics simulations, or the ETKDG method implemented in RDKit.The procedure for the geometric arrangement of the building blocks into the supramolecule of desired topology is best understood through the elucidation of the specific material examples in the next section. However, in brief the procedure can be summarized as: (i) each building block is placed and aligned at the appropriate position and orientation for the given topology; (ii) bonds are formed between the bonder atoms, as specified by the topology function; (iii) the deleter atoms, if any, are removed and (iv) the structure is geometry optimized. This is performed by third party software integrated into stk has the potential to construct a broad range of supramolecules in user defined topologies. The program already supports molecular cages and COFs in a range of topologies, linear polymers and short oligomers, including dimers. These materials, which differ substantially in their underlying construction, are now discussed as demonstrations of stk. Each of these four materials is of significant research interest for a range of applications including molecular separations, encapsulation, sensing, (photo)catalysis and electronic devices, as discussed in multiple reviews.stk will be extended to support more supramolecular material classes in the future, as one of its key design objectives is simple extensibility.Porous organic cages belong to a class of porous materials that lack an extended three‐dimensional network of covalent bonds.Organic cages are often the product of reversible reactions via dynamic covalent chemistry (DCC) between a pair of building blocks. Among the most commonly applied reactions to date is imine condensation reaction between aldehyde and amine precursors.XpmYn, where X and Y correspond to building blocks constituting a cage and will be Di for ditopic, Tri for tritopic and Tet for tetratopic building blocks. X and Y should be ordered such that the number of reactive end groups in X is always greater than or equal to Y. The subscripts m and n identify the number of times each building block can be found within the cage. The subscript p identifies how many building blocks of type Y connect two building blocks of type X. When p = 1, this subscript is optional. Using this nomenclature, cage topologies can be divided into four families, shown in Figures Tri + Di), (Tet + Di), (Tri + Tri) and (Tet + Tri) families.To precisely identify a cage topology, a nomenclature was recently developed, which identifies the number of each building blocks incorporated into a cage and the number of functional groups per building block.stk as a simple, general molecular assembly tool. While porous organic cages have many diverse topologies, they can be divided into four families, which are determined by the number of reactive end groups in the selected building block pair. In each family, construction in stk is implemented slightly differently, however the user input required to construct any cage is always the same, as only the building blocks and name of the topology need to be provided.Due to the complex nature of predicting whether an organic cage will form from a set of monomers, discovery of these materials has been relatively slow. The assembly of these materials, even for computational studies, is a time consuming process. This is due to the relative complexity and diversity of their topologies, as well as the large number of possible structural isomers some cages can form. As a result, they are an ideal candidate for demonstrating the effectiveness of stk has three groupings of topologies, the construction of each of which involves a slightly different procedure. The first grouping is for topologies that contain a di‐topic linker; the (Tri + Di) and (Tet + Di) families, Figures stk from the same set of building blocks for each family. An example of the construction of a 4Di6Tri cage from this grouping, both in the code and visually, is shown in Figure Topology object. For each building block placed at a vertex, the user can specify which bonder atom is to bond with a di‐topic linker on which edge (there will be three connection options for a tri‐topic building block and four for a tetra‐topic building block). The building block will then be rotated so that the distance between the two atoms to be bonded is minimized. The remaining bonder atoms then each bond to the di‐topic linkers on their nearest edges  family, which involves two tri‐topic building blocks . These topologies are shown in Figure stk. For this group, the two tri‐topic building blocks are placed on vertices and directly connected without any building block on the “edge” of the topology. Finally, the third grouping of cages is the Tet + Tri family, which has only a single member, the 6Tri8Tet topology, as shown in Figure The second grouping of cage topologies is the (Cage initializer. By default stk will select a building block with the correct topicity to place on a vertex or edge at random, however the user can also specify on which vertex or edge each building block is placed during initialization of the Cage object. An example of a multi‐component cage constructed by stk from 4 tritopic and 6 ditopic building blocks can be seen in Figure In addition to structural isomers, all cage types support the construction of multi‐component cages. Multi‐component cages, for example those of Klotzbach and Beuerle,Cage object produced after construction can be used for further studies in a number of ways. In addition to the properties mentioned earlier, stk allows users to directly calculate properties specific to cages, such as cavity and window size, the latter of which is calculated using the pyWindow library.The Linear polymers are employed in a range of applications, which may be simple, such as food packaging, or complex, such as hydrogen evolution through photocatalytic water‐splitting,stk, only a list of building blocks corresponding to the monomers, a string identifying the polymer repeat unit and the number of repeat units is required as input. To construct the model, stk will use the repeat unit string to place the building blocks in a line in the correct order. As each building block encodes a bonder–bonder direction vector, they can be placed parallel or anti‐parallel to the chain. Alternatively, the choice can be made by stk at random at each point along the chain, where the probability of switching from parallel to anti‐parallel can be specified by the user for each monomer. This gives the user full control over the possible sequence isomerism of the polymer, in that a completely head‐to‐tail or completely head‐to‐head chain can assembled, as well as a chain with an arbitrary preference of head‐to‐head over head‐to‐tail. The latter is achieved by specifying a probability for a monomer to flip from parallel to anti‐parallel, as opposed to guaranteeing a parallel or anti‐parallel orientation in the first two cases. An example of the code and the resulting assembled polymer can be seen in Figure stk optimizes a population of objects.When considering the connectivity between building blocks, linear polymers are a relatively simple class of molecule from a structural perspective. For the construction of finite size linear polymer models in stk provides great freedom in how the optimization is performed, how long the chain is, as well as its isomerism, the final structure can be tuned for a variety of studies. stk already allows users to calculate properties which may be useful when examining polymeric systems, such as the distance between the two furthest atoms in a polymer. Finally, because stk provides tools for translating and rotating molecules, a polymer chain can be used to prepare a multi‐chain simulation box, by repeatedly modifying the position of the chain.The assembled polymer chain can be used as input for further calculations. Because Polymer class, stk creates new Polymer objects and as a result all methods available for these molecules can be used.The polymer API can also be used to construct small molecules such as dimers and other oligomers. For dimers, the user needs to provide two building blocks and set the number of repeating units to 1, everything remains identical to the polymer case. For other oligomers, additional building blocks can be provided. These assemblies can be seen in Figure COFs are crystalline organic structures composed of carbon and other light elements such as boron, nitrogen, oxygen and silicon.stk. Second, there are 3D COFs, which feature a three‐dimensional network of covalent bonds, often via an sp3 carbon or silicon atom.stk will be a useful tool for researchers interested in these materials.COFs can be categorized into two broad types of topologies. The first, and most common, type are 2D COFs, which consist of stacked two‐dimensional, non‐covalently bonded sheets. These COFs have been synthesized in four distinct topologies: hexagonal, honeycomb, square and kagome, all of which have been implemented within hcb, hxl, sql and kgm, respectively. Each of these topologies consists of two building blocks, one ditopic and one with three or more functional groups, referred to as multi‐topic in the following discussion. The multi‐topic building block in the hexagonal topology has three functional groups, the square and kagome building blocks have four functional groups and the hexagonal multi‐topic building block has six functional groups. The construction process for each of these topologies is identical, with only the number and connectivity of the vertices and edges changing. Examples of assembled COFs can be seen in Figure xy plane and can be rotated along the z‐axis to generate different structural isomers in cases where the building block is not symmetrical. Similarly, each di‐topic building block can be aligned parallel or anti‐parallel with the edge it is placed on. To join the building blocks, bonds are created between bonder atoms on the di‐topic building blocks with the bonder atoms on the multi‐topic building blocks. The created bonds may be direct or periodic, depending on which building blocks are being joined.The current implementation supports the construction of the four synthesized 2D‐COF topologies, but is easily extendable to other 3D topologies. The currently supported topologies are honeycomb, hexagonal, square and Kagome, with underlying net types of stk further provides the option for producing molecular “islands” of arbitrary size. Molecular islands are finite size cut outs of the periodic material, with terminating atoms used in place of periodic bonds. Future extensions to stk can include the calculation of COF features such as pore size and surface area, or to run calculations such as Grand Canonical Monte Carlo simulations to determine gas sorption properties.Due to the construction process placing building block molecules on an idealized geometry, represented by the vertices and edges of a perfect unit cell, the bonds added during assembly will have unrealistic lengths. To correct this and find a realistic structure an optimization and conformer search should be performed using an optimization function. The constructed COF model can be written as a periodic unit cell into a GULPstk is a Python library designed to assemble, optimize and calculate the properties of supramolecules. It aims to provide a modular, extensible framework that can be applied to a range of different supramolecules. In general, during the assembly process building block molecules are placed onto a predefined topology and bonds are formed between them to generate a supramolecule. Currently supported molecules include linear polymers, small molecule oligomers, organic cages and COFs. Furthermore, stk allows a range of calculations, which can be performed with molecules loaded directly into the library or those assembled by it. The library provides tools to use these molecules in input files for further calculations and in high‐throughput screening. In the future, we believe that stk will be extended to other complex materials such as metal organic frameworks, metallocycles and rotaxanes.


Kawasaki disease (KD) is a vasculitis with formation of coronary artery aneurysms (CAAs) that can lead to myocardial ischemia. Echocardiography is the primary imaging modality for the coronary arteries despite limited visualization. Coronary angiography (CAG) is the gold standard yet invasive with high-radiation exposure. To date however, state-of-the-art CT scanners enable high-quality low-dose coronary computed tomographic angiography (cCTA) imaging. The aim of our study in KD is to report (i) the diagnostic yield of cCTA compared to echocardiography, and (ii) the radiation dose.Z score > 3, in 22 patients) were not detected by echocardiography. In addition, the left circumflex (aneurysmatic in 6 patients) was always visible upon cCTA and not detected upon echocardiography. Calcifications, plaques, and/or thrombi were visualized by cCTA in 25 coronary arteries (15 patients). Calcifications were seen as early as 2.7 years after onset of disease. In 5 patients, the cCTA findings resulted in an immediate change of treatment. The median effective dose (ED) in millisievert differed significantly (p < 0.01) between third-generation dual-source and other CT scanners (1.5 [0.3–9.4] (n = 56) vs 3.8 [1.7–20.0] (n = 14)).We collected data of KD patients who underwent cCTA. cCTA findings were compared with echocardiography results. In 70 KD patients , the cCTA identified 61 CAAs, of which 34 (56%, with a The diagnostic yield of third-generation dual-source cCTA combined with reduced radiation exposure makes cCTA a favorable diagnostic modality to complete the diagnosis and long-term treatment indications for KD.cCTA is a favorable diagnostic modality to complete the diagnosis and long-term treatment indications for Kawasaki disease.• Kawasaki disease patients with proven coronary artery involvement on echocardiography require additional imaging.•  Z score (coronary artery diameter corrected for body surface area). The maximal Z score combined with the evolution over time (based on echocardiographic findings) has predictive value for the development of myocardial ischemia and infarction. Echocardiography is considered the ideal non-invasive imaging modality for coronary artery assessment. However, even with increased spatial and temporal resolution offered by the newer machines, echocardiography can only detect the proximal sections of the coronary artery tree due to limited ultrasound windows. Notwithstanding this limitation, echocardiography remains the primary imaging modality in the AHA guidelines of 2017, due to its great ease of application in the acute phase of the disease and long-term follow-up in young children.Kawasaki disease (KD) is the most common acquired pediatric heart disease in Western society, which mainly occurs in young children. It is an acute vasculitis where the medium- and small-sized arteries become inflamed. The most precarious complication of this inflammation is the formation of coronary artery aneurysms (CAAs) that can lead to myocardial ischemia and infarction. Identification of patients at risk for ischemia and infarction is essential for treatment optimization and to reduce the morbidity and mortality rate. The American Heart Association (AHA) guidelines recommend risk stratification according to Z score > 2.5), but is not routinely applied in clinical practice.By recognizing the limitation of echocardiography, the importance of additional imaging for complete coronary artery assessment is emphasized. The most recent AHA guidelines of 2017 suggest that besides echocardiography, angiography  should be considered in every patient with CAA , 70 consecutive KD patients met the diagnostic criteria of the AHA for KD and underwent cCTA. The cCTA findings were compared to echocardiography Fig. . Clinican = 2], 128-slice CT scanner [n = 2] and 320-slice CT scanner [n = 1] and the third-generation dual-source CT scanner [n = 2]) were available for comparison with echocardiography data from our own institution.From November 2015, a third-generation dual-source 2 × 192-slice multidetector CT scanner (Siemens SOMATOM Force) was used for cCTA. A prospective ECG-triggered high-pitch spiral scan sequence was used for patients in sinus rhythm and a heart rate below 70 bpm. A prospective ECG-triggered sequential step and shoot sequence was used for patients with an irregular heart rate or heart rate above 70 bpm. The lowest feasible tube voltage was selected by using care-kV, a software tool from Siemens (range 70 to 120 kVp). Intravenous iodine-based contrast medium  was administered with an injection speed of 0.3–4.0 ml/s depending on body weight (0.5–44 kg) at the antecubital fossa. Scans were analyzed and reported by an experienced cardiovascular radiologist. The results were discussed in a multi-disciplinary team including a radiologist, cardiologist, pediatric cardiologist, and pediatric immunologist (with expertise in KD). Before the introduction of the third-generation CT scanner in 2015, cCTA images were acquired using a 64-slice CT scanner  in 9 patients using a prospective ECG-triggered sequential step and shoot sequence and low-pitch spiral scan. In 7 patients, cCTA data from a referring hospital  cardiovascular ultrasound system. A trained technician and pediatric cardiologist read all images. Discrepancies were directly compared to avoid misinterpretations in Z score was reported according to the McCrindle/Boston model; aneurysms were defined as a Z score ≥ 3.0. For the circumflex (Cx), Z scores are not available. Aneurysms in the Cx were defined when the luminal diameter was ≥ 4.0 mm ), patients were analyzed in 2 groups: third-generation dual-source cCTA and other CT scanners . The distinction between the 2 groups was made based on the ability to apply low tube voltages in the range of 70–100 kV. The conversion factors to calculate ED from the dose length product (DLP) were based on pediatric organ and tissue conversion factors from the Commission on Radiological Protection publication 103, available for the ages 0, 1, 5, and 10 years and for adults . In ordeThe radiation exposure in both groups was a non-normal distribution. Therefore, the Mann-Whitney test was performed .Z score ≥ 10) in the acute phase of KD (based on echocardiography results). The median time between the date-of-onset of acute disease and cCTA (ΔTime) was 11.7 years and the median age in years at the time of cCTA was 15.1  had a giant aneurysm  and static phase (n = 61), with a respective median time lag (between echocardiography and cCTA) of 31 and 92 days. Without exception, the cCTA acquired images of high-end quality, the images were all of diagnostic quality without disturbing motion artifacts and with adequate luminal attenuation.In total, 70 cCTAs were performed in the dynamic (n = 10), RCA (n = 14), and LAD (n = 10) , RCA (n = 3), and LAD (n = 1).The results were compared to findings obtained by echocardiography performed prior to cCTA. One patient was excluded for the comparative analysis because of prior coronary artery bypass grafting (CABG) and unreliable echocardiography at the time of cCTA. We found discrepant results between echocardiography and cCTA. In total, we observed 61 CAAs with the cCTA, whereas only 27 CAAs had been visualized by echocardiography. Echocardiography was unable to detect 34 CAAs (56%), in the LMCA (10) Fig. , Table 2Z score ≥ 3 as abnormal), while defined shortly before as “normal” upon echocardiography (median time between echocardiography and cCTA was 73.5 days)  of the abovementioned 34 “missed CAAs,” the proximal segments of the coronary arteries were correctly identified by both imaging methods, but a difference in the luminal dimension of the CAA measurement became apparent. cCTA defined these lesions as abnormal by size and hence falling in the category of CAA (taking a ys) Fig. .As expected, the echocardiography was prone to miss CAAs in the distal segments that were difficult or impossible to visualize. In 19 (55.8%) of the before-mentioned 34 “missed CAAs,” the CAAs were missed due to the distal location. In the most commonly affected coronary arteries, i.e., the RCA and LAD , we founZ scores are based on echocardiography findings, there are no normal values for the luminal diameter of the Cx available. Despite this drawback, in 6 patients (8.6%), the Cx was considered to be enlarged with a luminal diameter ≥ 4 mm by cCTA or an internal diameter of a segment measuring 1.5 to 4 times that of an adjacent segment at the age of ≥ 5 years, including 1 giant CAA  was not detected by echocardiography, whereas the Cx was visualized in all patients by cCTA. Because AA Table .Table 3An = 13) depicted in Fig. n = 6), thrombosis (n = 5), and plaque formation (n = 4) were identified (Table Z score (between 12.3 and 39.6) during the dynamic phase of the disease at the same location in these coronary arteries, accentuating the severe course of the coronary disease during their acute stage. In 2 of these 15 patients, a relevant change in clinical management was based on the cCTA findings: i.e., invasive CAG with dilation of the stenosis (n = 1) and the start of cholesterol-lowering medication due to the combination of early calcification, obesity, and hyperlipidemia (n = 1).In 15 patients, additional pathological features consisting of calcification (tion n =  were iden = 56), whereas in other CT scanners, the ED was 3.8 mSv ; this difference is statistically significant (p < 0.01). The wide range in ED of the third-generation dual-source cCTA group was due to several reasons, i.e., (1) additional scan for calcium scoring in 6 cases , (2) adaptation of scan mode from high-pitch spiral to sequential because of an elevated and/or irregular heart rate in 16 cases, and (3) a second/additional acquisition needed in 2 cases because of a suboptimal contrast timing or motion artifacts. The radiation exposure (ED) used for the scan that acquired the images of the coronary arteries in relation to the tube voltage (kVp) is depicted in Fig. Of the 70 cCTAs performed and analyzed in the study, a total of 56 scans had been executed with a third-generation dual-source CT scanner. The median cumulative ED (in millisievert [mSv]) for this CT scanner was 1.5 mSv , the cCTA findings were relevant for CAA classification, i.e., no CAA, small CAA, medium CAA, or giant CAA. This led in only 3 patients to (re)start treatment whereas in the remaining patients oral therapy was simply advised to continue; this as a result of our awareness of the limitations of echocardiography and our precautious approach in translating echocardiography results into clinical practice. Furthermore, 15 patients had additional coronary artery pathology upon cCTA that was not detected by echocardiography, including thrombosis, stenosis, plaques, and calcification. In 2 of these 15 patients, a change in clinical management was based on these cCTA findings.Z score < 3 changed to small/medium-sized CAA-positive [n = 11] and giant CAA [n = 2]), and a reinterpretation of a medium-sized to a giant CAA (Z score > 10 [n = 1]). Of the 11 patients that changed from “no CAA” to “small/medium-sized CAA” 2 were performed in the dynamic phase, which still need further follow-up.Some of the consequences of the cCTA findings in the patients with a CAA that had not been detected by echocardiography at all  are to be considered. First, it resulted in clear re-categorization of their CAA status . There were no isolated CAAs in the distal segments without involvement of the proximal segment, but the diameter of the distal CAA could exceed the diameter of the proximal CAA. In fact, in 14 out of the 69 patients (20%), despite multiple CAAs, the cCTA findings were of relevance for the CAA scoring as such  adults, which are much more difficult if not impossible to undergo routine coronary echocardiography. Secondly, the Cx is not visualized by echocardiography. The absence of Z scores for the Cx does not imply that there are no aneurysms in the Cx. In fact, we found in 6 patients an enlarged Cx (≥ 4 mm), based on the Japanese Circulation Society (JCS) that existed before the Z scores were defined. Based on these criteria, one of the aberrant Cx would be classified as a giant aneurysm with a luminal diameter of 9.5 mm  [Echocardiography remains the primary imaging modality. neurysm) . As a reThese results emphasize the necessity of additional imaging besides echocardiography. Whereas before the MRI was considered a feasible and accurate method to identify coronary artery pathology in addition to echocardiography , studiesThe timing to perform early cCTA following acute disease will strongly depend on the clinical presentation but seems justified to be performed after 2–3 months when increasing diameter has become less likely and before regression occurs .This is a retrospective study with inherent limitations, of which selection bias seems the most obvious limitation. Our study population is not a mere representation of the normal KD population due to the fact that the patients were selected because of the suggested necessity of imaging (often with CAAs at present or in the past) in addition to standard echocardiography according to the AHA 2004 and 2017 guidelines. For that reason, the study population does not reflect the epidemiological patterns in the general KD population.Z score ≥ 3—and clearly adds to the interpretation of the coronary status.Third-generation dual-source cCTA has a good diagnostic yield compared to echocardiography. In 22 (32%) out of 69 individuals with KD in the past, additional coronary lesions were observed, which had not been detected by echocardiography. In addition, 6 aneurysms in the left circumflex were found and coronary artery changes occurred much earlier than previously has been reported. With the third-generation dual-source CT scanners, radiation dose is significantly reduced. This lower radiation exposure makes the benefits counterbalance the potential harm and thereby enables accurate coronary artery assessment in KD by cCTA in a routine clinical setting. The third-generation dual-source cCTA is a safe and accurate standard complementary imaging modality in addition to echocardiography in all KD patients—in particular, those with CAA