This report summarizes the spread of a raccoon rabies epizootic into New York in the 1990s, the species of animals affected, and human postexposure treatments (PET). A total of 57,008 specimens were submitted to the state laboratory from 1993 to 1998; 8,858 (16%) animals were confirmed rabid, with raccoons the most common species (75%). After exposure to 11,769 animals, 18,238 (45%) persons received PET, mostly because of contact with saliva or nervous tissue. We analyzed expenditure reports to estimate the cost of rabies prevention activities. An estimated $13.9 million was spent in New York State to prevent rabies from 1993 to 1998. Traditional prevention methods such as vaccinating pets, avoiding wildlife, and verifying an animal’s rabies status must be continued to reduce costly PET. To reduce rabid animals, exposures, and costs, oral vaccination of wildlife should also be considered. The incidence of human rabies is high in developing countries, and most cases of the illness occur in humans with untreated dog bites ,2. In deNationwide, the number of reported rabies cases in animals increased from 6,972 in 1991 to 9,495 in 1993, but decreased to 8,224 in 1994, 8,509 in 1997, and 7,961 in 1998 . Wild an The exposure of humans and domestic animals to rabid animals has resulted in an estimated 16,000–39,000 persons per year receiving postexposure prophylaxis treatment (PET) in the United States New York State has passed a legislative appropriation for rabies prevention and PET. Reimbursement of PET costs not covered by third-party payers was first established more than 50 years ago in response to concerns about potential human deaths from fox rabies in those who could not afford treatment. Since the New York State Department of Health (NYSDOH) disburses these funds, this agency can provide accurate estimates of the cost of postexposure rabies treatments in the state. In addition, NYSDOH’s active rabies laboratory conducts all diagnostic work in the state, excluding New York City, which has its own laboratory .Initial analyses of rabies treatments for four New York counties in 1993 and 1994 have been previously published In New York State, public health law requires health-care providers with knowledge of a person exposed to an animal suspected of having rabies infection to report the incident to the local health unit (LHU). LHUs are required to have comprehensive rabies control protocols that provide 24-hour availability of county staff to manage possible exposures, including 10-day confinement and observation of apparently healthy dogs and cats responsible for exposures; collection, preparation, and submission of animal specimens to the rabies laboratory for prompt rabies examination; authorization of human PET; and provision of pet vaccination clinics. Annually, LHUs must submit to NYSDOH a detailed expenditure report for state-reimbursed costs including PET, laboratory specimen preparation, and pet vaccination clinics. We used fiscal year data (April–March) from 1993 to 1998 to estimate the overall cost of human PETs in New York.A rabies specimen history form accompanies each animal specimen submitted to the New York State Wadsworth Center rabies laboratory for testing. Using this form, we gathered information specific to the specimen regarding species, location of capture, nature of human and animal contacts, and rabies testing results. A rabies surveillance report form is completed by the LHU for each animal exposure that resulted in human postexposure treatment and for each rabid animal. These surveillance forms are forwarded to the NYSDOH Bureau of Communicable Disease Control for data entry and analysis. Data collected on these reports include animal species, location, type of exposure, and number of humans exposed to the suspected animal.We matched data from the surveillance reports with data from rabies laboratory specimen history reports. Positive test results with missing surveillance information were actively followed up with LHUs to assure the completeness of exposure and treatment data. The data from laboratory and human exposure reports have been computerized for the years 1993–1998 and are analyzed in this report. To map New York’s counties and the year raccoon rabies was first confirmed in each county, we included data from 1991 to 1997. From 1993 to 1998, a total of 56,947 animal and 61 human specimens were submitted for rabies testing, with the highest number of tested animals in 1993  and the lowest in 1995  . The ove The geographic movement of raccoon variant of rabies is shown in  From 1993 to 1998, a total of 18,071 animal rabies surveillance reports were received from local health departments . Of thesA total of 8,858 rabies surveillance reports were received on animal specimens with laboratory-confirmed rabies , with 6,A total of 11,552 persons received PET for exposure to 8,762 animals with specimens unavailable for testing or not testable because of specimen condition . In addiAcross all categories of rabies status for the animal, most postexposure treatments were provided because of possible contact with saliva or nervous tissue (44.5%), followed by bite (34.9%) and scratch (5.8%) exposures . When th Two fatal human rabies cases related to bat exposure occurred in New York in 1993 and 1995  resulting in treatment of 55 and 48 persons, respectively, who had contact with the cases either at home or in the hospital. Although bats represented only 4.6% of the rabid animals in New York, exposure to bats accounted for 25.8% of the PETs, with a total of 4,706 persons receiving PET after exposure to bats in the state. Fifty-one percent of the bat-related PETs were classified as “unknown” in regard to exposure, and 28% were provided because of reported contact with saliva or nervous tissue. The total expenditure for PETs, laboratory specimen preparation, and pet vaccination clinics increased in New York from $1.8 million in the 1993–1994 fiscal year to $2.9 million in the 1998–1999 fiscal year . The est The public health impact of the reemergence of rabies in New York resulting from the spread of raccoon variant in the 1990s was profound in terms of the number of rabid animals diagnosed, humans exposed and treated, and PET costs. Despite the decreasing number of rabid animals during the study period, the increasing number of humans receiving treatment for rabies from 1993 to 1998 appeared to be a result of the high number of suspected rabid animals (untested) and the high number of reported bat exposures following publicity surrounding two bat rabies–related human deaths. The high proportion of PETs associated with exposures other than bites  in our review indicates the degree of human fear about possible rabies and the difficulties in interpreting definitions of exposure ,18. This A few studies suggest that >$1 billion per year has been spent recently to prevent rabies in the United States  Seventy-five percent (24/32) of the human rabies cases in the United States since 1990 have been attributed to bat variants –23. The  The persistence and spread of rabies in raccoons and domestic animal exposure to this variant continue to be an important issue for public health officials. The reemergence of wildlife rabies in areas like New York (after the fox variant had moved out of the state) as a result of the unimpeded northward spread of the raccoon variant into the state and increased recognition of the importance of bat variants has led to a large number of rabies cases both in domestic and wildlife species and a corresponding number of human rabies PETs. Traditional public health methods of surveillance, public and provider education to avoid exposure to potentially rabid animals, appropriate postexposure prophylaxis, and emphasis on verifying the negative rabies status of suspect animals to avoid unnecessary treatments will remain important methods for rabies control. However, the major impact of raccoon rabies in human exposure and treatments may also need to be addressed with new wildlife rabies control methods such as oral rabies vaccine –29.


The crystal structure consists of two crystallographically independent [TiF6]2− anions, two fluoride anions and two triply-protonated tris(2-aminoethyl)amine cations. The Ti atoms are coordinated by six F atoms within slightly distorted octahedra. The anions and cations are connected by intermolecular N—H⋯F hydrogen bonds.The title compound, (C DOI: 10.1107/S1600536808009781/nc2095Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:


Ischemic brain injury due to stroke and/or cardiac arrest is a major health issue in modern society requiring urgent development of new effective therapies. The use of appropriate animal models is essential to study the mechanisms of ischemia-induced injury and neuroprotection. The goal of our study was to establish a reliable and reproducible model of brain ischemia in pigs  for further research.in vivo SDF videomicroscopy) and histological structure (light microscopy) of brain tissue in healthy control animals and after 3 hours of brain ischemia (three different models).Eighteen pigs (18 to 22 kg) were anesthetized and randomly assigned to the one of the following groups: 1 - control, 2 - unilateral carotid occlusion, 3 - bilateral carotid occlusion, 4 - bilateral carotid occlusion + hypotension (MAP 40 to 50 mmHg). In order to investigate the effects and mechanisms of cerebral ischemia, we assessed the mitochondrial respiration (high-resolution respirometry), microcirculation . Mitochondrial respiration rates after addition of atractyloside and cytochrome c were the same in all experimental groups, suggesting that intactness of mitochondrial outer membrane was not affected by cerebral ischemia. Microcirculatory and histological alterations also demonstrated increasing derangement and reversible structural changes after bilateral carotid occlusion and vascular occlusion combined with systemic hypotension.LEAK respiration  was not affected by ischemia in any model. The OXPHOS capacity with pyruvate + malate as substrates decreased by 20% and 79% compared to the control level after bilateral carotid artery occlusion and bilateral carotid occlusion + hypotension, respectively, resulting in the decrease of RCI (ADP/PM) by 14% and 73%. The OXPHOS capacity with succinate as substrate remained constant after unilateral carotid artery occlusion but decreased by 53% after bilateral carotid artery occlusion and hypotension compared to the control level .


People with higher socio-economic status (SES) are generally in better health. Less is known about when these socio-economic health differences set in during childhood and how they develop over time. The goal of this study was to prospectively study the development of socio-economic health differences in the Netherlands, and to investigate possible explanations for socio-economic variation in childhood health.Data from the Dutch Prevention and Incidence of Asthma and Mite Allergy (PIAMA) birth cohort study were used for the analyses. The PIAMA study followed 3,963 Dutch children during their first eight years of life. Common childhood health problems  were assessed annually using questionnaires. Maternal educational level was used to indicate SES. Possible explanatory lifestyle determinants  and biological determinants  were analysed using generalized estimating equations.This study shows that socio-economic differences in a broad range of health problems are already present early in life, and persist during childhood. Children from families with low socio-economic backgrounds experience more asthma symptoms (odds ratio (OR) 1.27; 95% Confidence Interval (CI) 1.08-1.49), poorer general health , more frequent respiratory infections , more overweight , and more obesity . The most important contributors to the observed childhood socio-economic health disparities are socio-economic differences in maternal age at birth, breastfeeding, and day-care centre attendance.Socio-economic health disparities already occur very early in life. Socio-economic disadvantage takes its toll on child health before birth, and continues to do so during childhood. Therefore, action to reduce health disparities needs to start very early in life, and should also address socio-economic differences in maternal age at birth, breastfeeding habits, and day-care centre attendance. Those who are socio-economically better off are generally in better health . This asThe family environment is the first and most intimate level that influences the early development and health of children . The famFamily size also seems to be relevant when it comes to the development of childhood health. Having siblings was found to protect children against hay fever, and, in several studies, against asthma and eczema as well . It has This paper describes the development of socio-economic health differences early in life in the Netherlands. We studied the impact of maternal educational level (as an indicator of the socio-economic background of the child) on the development of several health indicators among a birth cohort until the children are eight years old. The health outcomes studied are common childhood health problems: eczema, asthma symptoms, general health, frequent respiratory infections, overweight, and obesity. We explored when socio-economic differences in health occur during childhood, and whether these health disparities change as children grow older. Furthermore, we investigated explanations for socio-economic disparities in children's health during the first eight years of their lives Figure .Data were obtained from the Dutch longitudinal Prevention and Incidence of Asthma and Mite Allergy (PIAMA) study, which gathered health information on 3,963 children born in 1996 or 1997. The study collected health data during the first eight years of their lives, and is described in more detail elsewhere . The stuThe parents filled in questionnaires during pregnancy, when the child was three months old, and subsequently, every year until the child was eight years old. Questionnaires were sent to the parents once a year in the month the child celebrated his or her birthday.A child was classified as having eczema if an itchy rash had been present at one or more of the specified locations  during the previous 12 months. The question about eczema was asked every year at ages one to eight.Children were categorized as having asthma symptoms if they had one or more symptoms: a wheeze, shortness of breath (dyspnoea) or if a medical doctor had prescribed inhaled steroids for respiratory or lung problems in the preceding 12 months. Asthma symptoms were reported annually starting at three years of age.Frequent respiratory infections were measured annually from ages one to eight. Frequent respiratory infections were defined as having suffered from 3 or more of the following infections during the previous 12 months: bronchitis, pneumonia, middle ear infection, sinusitis, throat infection, or flu or a serious cold.A Dutch version of the 'RAND General Health Rating Index' for children was used to assess general health . The 'RAOverweight and obesity (severe overweight) were defined using weight and height information collected from the questionnaires at ages three to eight. Parents were asked to report their child's body weight (in kg) and height (in cm) from the last time the child was weighed and measured by a medical doctor or nurse if this had been done within the previous three months. If these measures were not available, the parents were asked to weigh and measure their child without shoes and heavy clothes. Age- and sex-specific cut-off points defined by Cole et al.  were useMaternal educational level was used to indicate the family's socio-economic status (SES). Education is considered to be a good indicator of adult SES in the Netherlands and elsewhere ,18. It iThis study examined how biological and lifestyle factors may affect the development of health inequalities early in life. Determinants present at birth and in the child's first year were chosen to examine their influence on the development of health inequalities from birth onward. The biological and lifestyle determinants used in the explanatory analyses were selected on theoretical grounds. The research group included those determinants they considered most plausible and explanatory for SES differences in childhood health. The biological determinants were the presence of older siblings (yes/no), maternal age at birth (continuous), and the child's birthweight (continuous), and were reported in the questionnaire administered at three months. Lifestyle variables were reported in the child's first year: maternal smoking (during at least the first four weeks of the pregnancy), smoking by anyone in the home (more than once a week), breastfeeding, including any form of breastfeeding, also partial , and day-care centre attendance during the first year. Day-care centre attendance was dichotomized into 'none or fewer than 4 hours per week' and '4 hours per week or more'.We used generalized estimating equations (GEE) to analyse the development of socio-economic health disparities during the first eight years of the children's lives. GEE models take into account the correlation between repeated measurements in the same individual. An autoregressive correlation was chosen to fit the data. All analyses used the most highly educated group as reference category, and were adjusted for the sex of the child.First, we estimated the association between maternal educational level and the health outcomes for the whole period by fitting a GEE model with the child's age, sex and maternal educational level. Second, to find out whether educational differences in children's health varied during the period studied, we included the interaction between the child's age and maternal educational level in the model. Third, we studied which variables longitudinally predicted childhood health by fitting GEE models containing the child's sex, age, and one potential determinant. Variables were considered predictors of the health outcome if they were significantly associated with the health outcome (p < 0.05). Next, for those determinants significantly related to one or more health conditions, we studied the distribution of the predictor according to the educational level of the mother. Finally, if the predictors are also significantly related to the educational level of the mother (p < 0.05), we added the predictor to the basic GEE model  in an attempt to explain the association between maternal educational level and childhood health. Time-dependent relationships between determinants and health outcomes were included in the model when significant, and we calculated the contribution of all significant determinants together to the educational differences in children's health. Determinants were excluded from the total model if we found no reduction in the odds ratios (ORs) and/or in the 95% confidence intervals (CIs) of the ORs of the different educational groups.) was used to perform Proc Genmod analyses and PROC MIANALYSE to pool the outcomes from the five datasets. All analyses described above were performed using both the original dataset and the imputed datasets. In this article, we report results from the analyses of the imputed datasets. The characteristics of the study population of the imputed dataset did not differ from the original dataset  of the 3,963 children included at baseline were lost to follow-up. In addition, not every parent always filled in all of the questions every year, which resulted in missing data. Particularly affected were the health outcomes, with missing data ranging from 15% for frequent respiratory infections to 35% for overweight and obesity. If data are not 'missing completely at random' (MCAR), complete case analysis may lead to biased results ,20. To o0 of the The prevalence of asthma symptoms decreases from 23% at the age of three to 13% at the age of eight. The prevalence of frequent respiratory infections decreases from 14% to 4% between the ages of three and eight. As the children grow older, more children become overweight or obese. At three years of age, 8% of the children are overweight and fewer than 1% are obese. At eight years of age, 12% are overweight and 2% are obese. The prevalence of poor general health and eczema fluctuated . The repeated data collection over a period of eight years (starting at birth) allows for longitudinal analyses to study the development of children's health. The PIAMA study contains a broad range of different health outcomes that enables a more thorough study of the relationship between a family's socio-economic background and the health development of their children.One of the study's limitations is that all of the information on health is self-reported by the parents. Self-reported information is prone to reporting bias, which could distort our findings, assuming this bias is related to SES. Another limitation is the selective availability of variables for studying explanations for educational differences in childhood health. We could only study the contribution of the available determinants to the educational health inequalities. These determinants were gathered with the intention of unravelling the aetiology of asthma and allergies . FurtherMaternal educational level is used in this study as an indicator of family socio-economic background. The educational level of the mother is a good proxy for social status in the Netherlands, and is supported by the literature ,18. It iOur study reports a consistent pattern of poorer health outcomes among children from families with low socio-economic backgrounds. Although this relationship is found in other studies, several studies have discussed a reverse SES effect . Chen etOur study shows that breastfeeding plays an important role in the development of childhood socio-economic differences for asthma symptoms and frequent respiratory infections. Mothers from lower socio-economic backgrounds are less likely to breastfeed their children , whereasIn this study, day-care centre attendance during the first year is related to socio-economic differences in children's poor general health and weight problems, but not to respiratory infections or asthma. On the one hand, day-care centre attendance is considered a threat to children's health (in particular with regard to respiratory infections) because of the close contacts between children in day-care centres ,25. RepoMaternal age at birth is an important factor for childhood socio-economic differences in asthma symptoms, poor general health, and frequent respiratory infections. Younger mothers report poorer general health, more asthma symptoms, and more respiratory infections than older mothers. The reason for this might be that younger mothers are more worried than older mothers . On the In our study, mothers from low socio-economic groups smoke more often during pregnancy, which is associated with asthma and weight problems in childhood. For asthma, similar results were found in other studies ,36. SmokThis study confirms that socio-economic health disparities already occur very early in life. Socio-economic disadvantage takes its toll before birth and continues to do so during childhood. Therefore, action to reduce health disparities needs to start very early on in life. These findings fit in with the trend of intervening in the preconception phase, for instance, with health promotion activities to reduce risky behaviour among prospective parents ,41. MoreThe authors declare that they have no competing interests.AR performed the analyses and the writing of the manuscript. MD conceived the idea for this article, supervised the analyses and preparation of the manuscript, and reviewed the drafts of the manuscript. AHW was involved in the analyses and interpretation of the findings and reviewed the drafts of the manuscript. MK, GHK and JAS participated in the realization of the PIAMA birth cohort study and reviewed the final draft of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/11/225/prepub


S. pneumoniae as a major human pathogen is largely due to its remarkable genomic plasticity, allowing efficient escape from antimicrobials action and host immune response. Natural transformation, or the active uptake and chromosomal integration of exogenous DNA during the transitory differentiated state competence, is the main mechanism for horizontal gene transfer and genomic makeover in pneumococci. Although transforming DNA has been proposed to be captured by Type 4 pili (T4P) in Gram-negative bacteria, and a competence-inducible comG operon encoding proteins homologous to T4P-biogenesis components is present in transformable Gram-positive bacteria, a prevailing hypothesis has been that S. pneumoniae assembles only short pseudopili to destabilize the cell wall for DNA entry. We recently identified a micrometer-sized T4P-like pilus on competent pneumococci, which likely serves as initial DNA receptor. A subsequent study, however, visualized a different structure - short, ‘plaited’ polymers - released in the medium of competent S. pneumoniae. Biochemical observation of concurrent pilin secretion led the authors to propose that the ‘plaited’ structures correspond to transformation pili acting as peptidoglycan drills that leave DNA entry pores upon secretion. Here we show that the ‘plaited’ filaments are not related to natural transformation as they are released by non-competent pneumococci, as well as by cells with disrupted pilus biogenesis components. Combining electron microscopy visualization with structural, biochemical and proteomic analyses, we further identify the ‘plaited’ polymers as spirosomes: macromolecular assemblies of the fermentative acetaldehyde-alcohol dehydrogenase enzyme AdhE that is well conserved in a broad range of Gram-positive and Gram-negative bacteria.The success of  Streptococcus pneumoniae often escapes prevention and treatment through rapid horizontal gene transfer via natural transformation. Uptake of exogenous DNA requires expression of a transformation pilus but two markedly different models for pilus assembly and function have been proposed. We previously reported a long, Type 4 pilus-like appendage on the surface of competent pneumococci that binds extracellular DNA as initial receptor, while a separate study proposed that secreted short, ‘plaited’ transformation pili act simply as peptidoglycan drills to open DNA gateways. Here we show that the ‘plaited’ structures are not competence-specific or related to transformation. We further demonstrate that these are macromolecular assemblies of the metabolic enzyme acetaldehyde-alcohol dehydrogenase—or spirosomes—broadly conserved across the bacterial kingdom. Streptococcus pneumoniae remains a leading mortality cause worldwide . . 10]. AmS. pneumoniae . . 18]. Honterpart E and 2Filaments .adhE-null Streptococcus pneumoniae R6 mutant (ΔadhE) and examined its transformation efficiency for uptake of resistance-encoding DNA cassette under challenge with the corresponding antibiotic. While the ΔadhE mutant shows slightly decreased transformation efficiency (~ 2-fold), this change is negligible compared to typical results under comGC disruption (~ 10 000-fold) and can be due to reduced metabolic fitness under the microaerobic conditions of the experiment G.thermogirosomes ,21.S. pneumoniae, we observed spirosome release in cultures of Clostridium difficile, Streptococcus sanguinis, and E. coli . Also, although with slightly different parameters in terms of helical width and pitch, the ‘plaited’ filaments were structurally similar to both the coiled structure of a eukaryotic RecA homolog (B) [in vitro reconstituted RecAS.pneumoniae nucleofilaments (C). Nevertheless, release of the characteristic polymers persisted in a ΔrecA strain (D) and we were unable to detect the protein in the filament-enriched fractions following purification  , as wellilaments  panel C.A strain  panel D fication .E. coli spirosomes shown here underscores the fact that limited-view, low-resolution morphology imaging and bulk biochemical experiments alone are often insufficient to deduce the nature of macromolecular assemblies. Rather, a combination of orthogonal approaches that spans the different resolution levels and integrates genetic, biochemical and structural data in a meaningful way is generally warranted to avoid false-positive or otherwise erroneous results.It is therefore important to note that macromolecular organization in helical filaments is not uncommon among proteins from both the bacterial and other taxa. These include but are not limited to nucleic acid-binding proteins, cytoskeletal elements, building blocks of cell surface appendages and phage capsid subunits ,42–44. TV. cholerae, which shares many features with the pneumococcal transformation pilus: competence-induced expression, prerequisite for DNA uptake, and roughly a single copy per cell [Taken together, our data rule out the existence of short ‘plaited’ transformation pili in competent pneumococci and reassert the expression of a long, 5–6 nanometer wide appendage, structurally and compositionally similar to T4P in Gram-negative bacteria . This fiper cell .Apart from morphology alone, however, it is interesting to discuss the probable mechanism through which the transformation pili secure DNA entry into competent pneumococci. Although expression of any type of pilus would require overcoming the physical barriers of cell-wall peptidoglycan and overlaying capsule—and thus possibly facilitate DNA entry—the similarities among transformation pili of Gram-positive and Gram-negative bacteria suggest that naturally transformable species might have evolved a conserved and more sophisticated mechanism of pilus function than simple cell-wall destabilization.S. pneumoniae, thus opening gateways in the cell wall peptidoglycan for passive exogenous DNA entry [S. pneumoniae strains after centrifugation correlates with the peak of transformation efficiency [In agreement with a long-standing ‘pseudo-pilus’ hypothesis, Balaban and colleagues proposed a model in which the transformation pili self-secrete in the medium of competent NA entry ,9. Theirficiency . Since wficiency . Howeverficiency ,12. Suchficiency .As we have conducted only single time point visualization experiments, it is theoretically possible that the expresses transformation pili eventually detach from the cell to open entry pores for transforming DNA . HoweverS. pneumoniae and other transformable Gram-positive bacteria, pneumococci lack homologous retraction ATPase and are likely to utilize a distinct mechanism for DNA entry. In addition, transforming DNA uptake occurs at much lower speeds in Gram-positive bacteria than Gram-negative T4P retraction [While no mechanistic or quantitative data on DNA binding by the pilus are available, electron microscopy showed extensive contact interfaces between the long transformation pili and DNA chains . It is ttraction ,52.Many sequence-specific DNA binding proteins can scan DNA for their target sites at speeds several orders of magnitude higher than the upper limit for a three-dimensional diffusion-controlled process . This caStreptococcus pneumoniae spirosomes were observed in both competent and non-competent cells. For competence induction cells were grown in microaerobic conditions, without agitation, at 37°C in Casamino Acid-Tryptone (CAT) medium supplemented with 0.2% glucose, 15mM dipotassium phosphate, 3mM sodium hydroxide and 1mM calcium chloride and adjusted to pH 7.8. Competence was triggered by the addition of Competence Stimulating Peptide (CSP) at OD600 = 0.15 for 10–30 min. Non-competent pneumococci and Escherichia coli cells were grown similarly in LB to OD600 = 0.3 and OD600 = 0.6, respectively. Clostridium difficile cells were grown at 37°C under strict anaerobic conditions on Tryptone-Yeast extract-Glucose (TYG) plates supplemented with 0.1% thioglycolate. Streptococcus sanguinis cells were grown anaerobically, without agitation, at 37°C in CAT medium to OD600 = 0.3. For spirosome visualization cells were scraped off the plates or pelleted by centrifugation and resuspended in TBS  at ~ 5 μl TBS per milliliter of culture at OD600 = 0.3. 5 μl drops of each suspension were then placed directly on glow discharged carbon coated grids  for 1 minute. The grids were then blot-dried on filter paper, washed on a drop of ultrapure water, and negatively stained with 2% uranyl acetate in water. Specimens were examined on an FEI Tecnai T12 BioTWIN LaB6 electron microscope operating at 120 kV at nominal magnifications of 18500–68000 and 1–3 μm defocus. Images were recorded on a Gatan Ultrascan 4000 CCD camera.adhE deletion (strain AD001) was introduced in the R1501 genetic background by transformation with a DNA cassette carrying a kanamycin resistance gene inserted between two ~1000 base pair fragments corresponding to the S. pneumoniae genomic regions flanking adhE. Briefly, the genomic region upstream from the AdhE open reading frame was amplified using forward and reverse primers 5’-ACA TGG CAA TCC GAT TCA TAA GGG G-3’ and 5’-GCC ATC TAT GTG TCG GAA CGA TAT CCT TTG TTA ATT TTT TCA CAA GTT TAT TAT AAC G-3’, respectively, while the genomic region downstream of the adhE gene was amplified the following primer pair 5’-AAA ATG TGT TTT TCT TTG TTT TGT TTA TCA GTC TAG AAG CAA GAC AAA AAC TCA A-3’ and 5’-TTG CTA TTT ATG CAT GCA GAA GAC CAA ATG-3’. A third pcr reaction was used to amplify a kanamycin-resistance gene using the pR411 plasmid as template DNA [adhE-upstream and 5’-end of the adhE-downstream fragments, respectively. The three pcr products were then assembled using overlap extension PCR and the purified DNA cassette was used for transformation of competence-induced S. pneumoniae R1501 cells. adhE-null mutants (strain AD001) were positively selected by growth in the presence of kanamycin (60 μg/ml) and adhE deletion was confirmed independently by DNA sequencing and western blot detection using an anti-AdhE antibody. For all transformation experiments, competence was triggered as above at OD600 = 0.15 for 10 minutes, followed by DNA addition and 20 minute incubation at 30°C. Transformants were selected on Columbia Agar supplemented with 5% horse blood and appropriate antibiotics. For the transformation efficiency assays, cells were transformed with 100 ng of a DNA cassette, amplified from S. pneumoniae R304 genomic DNA and containing the streptomycin resistance gene str41. Bacteria were plated in the presence and absence of streptomycin (100 μg/ml) and incubated at 37°C overnight before colony counting.An late DNA  and forwS. pneumoniae culture grown in LB to OD600 = 0.3 or 4L of E. coli culture grown to OD600 = 0.6 were pelleted by centrifugation (20 min at 5000 g) and resuspended in 6 ml of cold TBS, vortexed briefly and centrifuged to remove the bulk of intact cells and debris (10 min at 12000 g followed by 15 min at 50000 g). Triton x-100 was added to the supernatant at final concentration of 0.25%. Following 30 minute agitation for solubilization of remaining membrane fragments, the samples were filtered through a 0.45 μm cellulose acetate filter (Corning) and centrifuged for 1h at 125000 g for spirosome pelleting. After careful removal of the supernatant, the pellet was resuspended in 50 μl TBS, re-filtered and loaded on a Superose 6 3.2/300 size exclusion column . Spirosome enriched fractions were found to elute with the void volume. Sample preparation for electron microscopy was performed as above.All steps of the purification protocol were performed at 4°C. 8L of Streptococcus pneumoniae (strain ATCC BAA-255 / R6) (Taxonomy 171101–1947 proteins) or Escherichia coli (strain K12 / MC4100 / BW2952) (Taxonomy 595496–4043 proteins) database. A false-discovery rate of 1% was used for both peptide and protein identification. Reverse and contaminant proteins were excluded and only proteins identified with a minimum of 2 peptides were considered.Trypsin digestion was performed as described previously  and the S. pneumoniae spirosomes  and 555 particles for the E. coli spirosomes . Normalization, centering, multi-reference alignment, multi-statistical analysis, and classification (15–40 particles per class) were done in IMAGIC-4D . IMAGIC-4D was also used for generation of two-dimensional reprojections of the T2SS pilus and the G. thermoglucosidasius spirosome.Spirosomes and transformation pili were visualized by EM as above at nominal magnifications of 49000 and 68000, respectively. The contrast transfer function parameters were assessed using CTFFIND3 , and theS.pneumoniae was cloned in-frame in a pET21a vector for heterologous expression of C-terminally hexahistidine-tagged protein in E. coli. Briefly, the AdhES.pneumoniae open reading frame was PCR-amplified from S. pneumoniae R1501 genomic DNA using forward and reverse primers 5’-CAT ATG AAA GCT ATG GAG GAA AAT ATG GCT G-3’ and 5’-GCG GCC GCT TTA CGG CGT CCT GGT CTT TCT TTG-3’, respectively. The pET21a vector was pcr-amplified using forward and reverse primers 5’-GGA CGC CGT AAA GCG GCC GCA CTC GAG CAC CAC CAC-3’ and 5’-CAT ATT TTC CTC CAT AGC TTT CAT ATG TAT ATC TCC TTC TTA-3’, respectively. 90 ng of linearized vector DNA were incubated in 1:1 molar ratio with the AdhE pcr product in an In-Fusion cloning reaction (Clontech) and transformed into Top10 cells (Invitrogen). Plasmid DNA was purified from individual clones, verified for AdhE coding region insertion and used for transformation in BL21 (DE3) cells (Invitrogen). For expression, transformed BL21 (DE3) cells were grown under agitation at 37°C in LB to OD600 = 0.6 and expression was induced with 0.7 mM IPTG for 2h. Cells were pelleted by centrifugation (4500 g for 20 min), resuspended in TBS supplemented with cOmplete Protease Inhibitor Cocktail (Roche) and disrupted by sonication. Cellular debris were removed by centrifugation (20 000 g for 30 min) and the lysates were incubated with batch Talon metal affinity resin (Clontech) for 30 min at room temperature. The resin was extensively washed  and bound protein was eluted with TBS supplemented with 140 mM Imidazole. For EM observation eluted protein was immediately applied on glow-discharged continuous carbon grids, stained, and imaged as above.The coding sequence for AdhEadhE gene was constructed. Briefly, the adhE gene was PCR-amplified using S. pneumoniae R1501 genomic DNA as template and primer pair 5’-GAG GAA GAA ACC ATG TTG AAA GCT ATG GAG GAA AAT ATG GCT GAT AAA AAA AC-3’ and 5’-AAA ATC AAA CGG ATC TTA CTT GTC ATC GTC ATC CTT GTA ATC TTT ACG GCG TCC TGG TCT TTC TTT G-3’, the latter designed to add a C-terminal FLAG tag to the encoded protein. In parallel, pCEPx vector DNA [adhE-FLAG pcr product in an In-Fusion cloning reaction (Clontech) and transformed into Top10 E. coli cells (Invitrogen). Plasmid DNA was purified from individual clones, verified for AdhE-FLAG coding region insertion, and transformed into competence-induced R1501 cells, followed by selection with kanamycin (Kan). The resulting SO007 strain was sequence-verified for the pCEPx-derived adhE-FLAG–KanR cassette recombination [600 = 0.15. CSP-induced and non-induced cells were pelleted by centrifugation and resuspended in TBS by brief vortexing in the presence of millimeter-sized glass beads for increased cell lysis. Cells were pelleted again, and the supernatants were incubated with anti-FLAG M2 affinity resin (Sigma-Aldrich A2220) for 1h at RT and under agitation. After washing with TBS, the resin-bound fraction was eluted by the addition of 100 μg/mL 3X FLAG peptide (Sigma Aldrich F4799) and mixing for additional 15 min at 4°C. The samples were then subjected to SDS-PAGE and EM analyses.For AdhE spirosome pull-down, a strain carrying an additional competence-inducible FLAG-tagged ectopic copy of the ctor DNA  was digebination  and cultS. pneumoniae was incubated with 50 mM KCl, 0.5 mM DTT, 10 mM Tris-HCl pH 7.5, 2 mM magnesium acetate, and 2 mM ATP-γ-S for 15 minutes at 37°C. RecA nucleofilament formation was induced by the addition of a 54-nucleotide-long single-stranded DNA fragment or single-stranded M13mp18 DNA (New England Biolabs) at nucleotide:RecA monomer ratio of 3:1. After additional 15 minutes at 37°C, the samples were prepared for EM observation as above.2.5 μM purified RecAS1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(A) Western blot detection of RecA release in the medium of competence-induced S. pneumoniae cells. (B) Representative class average of S. pneumoniae spirosomes from culture supernatant compared to the reprojected nucleofilament structure of RecA homolog hDmc1 [(C)In vitro reconstituted RecAS. pneumoniae—ssDNA nucleofilament (left) compared to a spirosome from culture supernatant (right). Scale bars 10 nm. (D) Unabolished spirosome release in ΔrecA S. pneumoniae culture.og hDmc1 . (C)In (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN),  An important task in drug design is to guide the synthesis, purchase, and testing of compounds based on their predicted properties. Proper prediction of properties can save time and resources, but also generate compounds not available beforehand. There are several methods to compare a real or virtual compound to known collection of compounds, from topological similarity, fingerprints, molecular features, among others   with several targets, such as is the case seen in clinic-approved kinase inhibitors, while at the same time avoiding off- or anti-targets that may be responsible for side-effects  method for the visualization and the representation of chemical space is implemented for the first time, and the use of different machine learning methods from decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k-nearest neighbors (k-NN), and logistic regression models, separately and together, to form a new ensemble learning method called AL Boost for separation of drugs and nondrugs. Good models can also be achieved for disease categories such as antineoplastic compounds, cardiovascular system drugs, and nervous system diseases.The full data set contains 762 compounds; compounds were classified into two classes: drug (366 compounds) and non-drug (396 compounds). The compounds were obtained from previous work ; Wiener index; molecular surface area (MSA); polar surface area (PSA); apolar surface area (apolarSA); hydrogen bond donor count; hydrogen bond acceptor count; rotatable bond count; atom count; hydrogen count; number of heavy atoms (NHA); molecular polarizability; aliphatic ring count; aromatic ring count; aromatic atom count; Balaban index; Harary index; bond count; hyperWiener index; Platt index; Randic index; ring count; Szeged index; Wiener polarity; and the ligand efficiencies  Data visualization using the t-SNE method; (2) Classification models, which starts by dividing the data into training and test sets, followed by model generation with seven classification methods and model validation.The t-Distributed Stochastic Neighbor Embedding (t-SNE)  divergence, i.e., how one probability distribution diverges from a second expected probability distribution) between a Gaussian distribution in the high-dimensional space and a In order to evaluate the ability of the low dimensional representation to preserve the high-dimensional data and structure, we used the trust measure , and a test set . Similar proportions (20%) of drug (73 compounds) and non-drug (79 compounds) compounds were selected for the test sets by applying independent selection procedures of a representativeness function  and logistic regression (LR), and one newly boosting method named AL Boost, were used to build the classification models. In each case, a classification model was built using the training set and subsequently used to predict the activities (drug status) of the test set compounds for validation. The six models were generated with algorithms implemented in the WEKA version 3.9.1 , support vector machine (SVM), artificial neural network (ANN), The decision tree algorithm Quinlan,  operatesRandom forests (RF) Breiman, , as deveSupport vector machine (SVM) Vapnik,  is an alArtificial neural network (ANN) Hassoun,  is a nonk-Nearest Neighbor (k-NN)   together into one predictive model in order to improve the predictive force and decrease the bias. This method takes the predictions of each classification (learners) and combines them using a weighted majority voting function to determine the prediction of each compound. Each learner is assigned a weight according to its corrected classification rate error, given that poor learners get lower weights. For each compound, two functions are calculated as follows:AL Boost: is a new chemoinformatics ensemble learning classification method which combines all the models obtained in this work  of learner i, and δi is 1 if the learner is predicted as active class , or 0 if predicted as inactive class , for Equation (2).where i is 1 if the learner is predicted as inactive class  or 0 if it is predicted as active class .For Equation (3), δThe majority vote between Equations (2) and (3) determines the prediction for the compound.P) of a compound with certain features belonging to either class are computed, and that compound is assigned to the class for which the highest P is obtained.The last classification method detailed in this paper is Naïve Bayesian classifiers. This method was used in a previous publication , accuracy, Matthews correlation coefficient (MCC), sensitivity, specificity, and the variance between the sensitivity and the specificity (Equations 5–10), where sensitivity is the percentage of truly active  compounds being predicted from the model (Equation 8), and specificity is the percentage of truly inactive  compounds being predicted from the model (Equation 9).TN and TP represent the number of true negative  and true positive  predictions, respectively. NN and NP represent the total number of the two activity classes, and FN and FP represent the number of false negative and false positive predictions, respectively. μ represents the mean of the sensitivity and specificity.where vs. non-drugs, but drugs of one DC against another DC. In this work, we focus on the three largest DCs, namely cardiovascular, anti-neoplastic, and nervous systems. These three groups were evaluated against each other preforming three sub data sets Cardiovascular drugs vs. Anti-neoplastic agents, Cardiovascular drugs vs. Nervous system, and Anti-neoplastic agents vs. Nervous system. The same data mining workflow as before was applied. The number of compounds and the number of training and test sets (similar procedure and proportions as in the drug/non-drug database) for each DC are presented in Table Further in the analysis of the drug/non-drug database, the drugs in the data set were characterized into their different anatomic therapeutical classifications, also called disease or organ category (DC). Here the comparison was not drugs The properties of the compounds include widely used metrics such as size, weight, polarity, as well as topological indices, and ligand efficiencies. An important consideration for the data set construction was the curation of binding free energies of similar magnitude between drugs and non-drugs (bioactive compounds). Ligand efficiencies can normalize the binding energy of a compound according to other properties of a compound, such as size, lipophilicity, etc., and have a pragmatic use in developing series of compounds in order to improve or maintain binding strength while also improving their profile in other dimensions.The first step of the data mining workflow is data visualization; the resulting 35 dimensional data of the drug/non-drug database was reduced into a 3D representation using t-SNE. The resulting trust measure of the low dimensional embedding was found to be 63%. A comparison to the common dimension reduction technique PCA, found that for a 3D representation using PCA, a trust measure of only 42% was obtained. This result indicates a good preservation of the structure and of the local information of the high dimensional data in the low embedding for the t-SNE. The distribution and the chemical space of the drug/non-drug compounds in the resulting t-SNE 3D space are presented in Figure From Figure These results show that the separation between classes is good, and comparable to those found in other studies . These results show a low bias and a well-balanced model with a variance of 0.01%P, ΔGbind_MSA, and Balaban Index were found to be features that separated drug and nondrug in REF . First, we visualize the feature space using t-SNE for each sub dataset. The resulting 3D representation of Anti-Neoplastic-Nervous system, Anti-Neoplastic-Cardiovascular, and Cardiovascular-Nervous system can be seen in Figures Figure Cardiovascular drugs were also majorly separated from cancer drugs Figure , locatedThis separation between groups was not the case for the Cardiovascular vs. Nervous system drug plots, since both groups are very similar, perhaps only a cluster of cardiovascular drugs can be seen on the middle right.7, respectively.Having visualized the data, the next step is to build classification models using the six classification methods and the ensemble learning AL Boost. The results for the Anti-Neoplastic vs. Nervous system, Anti-neoplastic vs. Cardiovascular, and Cardiovascular vs. Nervous System are presented in Tables 2 and 4.73%2 on the training and test set, respectively. The accuracy for the AL Boost method was high, 0.91 for the training set, and 0.96 for the test set. These values are better when compared to those obtained previously using naïve Bayesian classifiers for the same data sets, 0.88 for training set, and 0.90 for the test set  with an average CCR of 0.69 and 0.80 for the training set and test set, respectively. While the average CCR results for the four models of the AL Boost are 0.74 and 0.82 for the training set and test set, respectively. An independent sample t-test was performed among the individual CCR models results and the AL Boost CCR results which found no statically significant difference for the training set and not for the test set results. In summary, the average results for the AL Boost were found to overcome the average results for the individual classification models but weren't found to be significantly higher.Summary of the classification results for the four databases presented here : a total of 24 results obtained for the individual classification models  for prediction.To the best of our knowledge, this is the first example of the usage of t-SNE for the visualization and representation of the chemical space and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. Clear and good separations were obtained with the dimension reduction and machine learning approaches to distinguish drugs and non-drugs, as well as three major classes of drug compounds. The ability to use such tools for the identification of interesting trends, opens up new opportunities for understanding the factors affecting drugs performances and for designing new drugs. Considerations such as drug likeness and drug target, organ, and/or system class are thus made possible, providing another route for designing specificity into ligands and drugs. Clearly, this research should be conducted in close collaboration with experts in the medicinal/pharmaceutical chemistry field to both provide a chemistry-based explanation to the observed trends, as well as to capitalize on the results. We expect that the tools and methods implemented in this work will further be used in medicinal chemistry and drug design research.AY applied the t-SNE and models and AL Boost algorithms, analyzed results, and wrote the manuscript. RG revised the manuscript. AG-S designed the research project, collected and curated the data, analyzed the results, and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We present a novel quantitative approach to assess cardiac structure and function by extending the classical traction force microscopy technique to tissue-level preparations. Using this system, we investigated the relationship between contractile proficiency and metabolism in neonate rat ventricular myocytes (NRVM) cultured on gels with stiffness mimicking soft immature (1 kPa), normal healthy (13 kPa), and stiff diseased (90 kPa) cardiac microenvironments. We found that tissues engineered on the softest gels generated the least amount of stress and had the smallest work output. Conversely, cardiomyocytes in tissues engineered on healthy- and disease-mimicking gels generated significantly higher stresses, with the maximal contractile work measured in NRVM engineered on gels of normal stiffness. Interestingly, although tissues on soft gels exhibited poor stress generation and work production, their basal metabolic respiration rate was significantly more elevated than in other groups, suggesting a highly ineffective coupling between energy production and contractile work output. Our novel platform can thus be utilized to quantitatively assess the mechanotransduction pathways that initiate tissue-level structural and functional remodeling in response to substrate stiffness.Cardiac tissue development and pathology have been shown to depend sensitively on microenvironmental mechanical factors, such as extracellular matrix stiffness, in both  Alternatively, the spatial complexity of the heart musculature may be reconstructed in-vitro using cardiac microphysiological systems (MPS)[in-vitro remains limited.In the heart, striated cardiomyocytes seamlessly merge into two-dimensional layers that wrap around three-dimensional atria and ventricles. When single myocytes contract, the muscular layers compress and twist the heart chambers pumping blood into the body ,2. This ems (MPS),4. In thems (MPS)–9. Insteems (MPS)–14. UnfoAdditionally, the coupling of MPS-based structural-functional characterizations with metabolic assessments has remained challenging. We previously utilized traction force microscopy (TFM) to correlate contractile structure and function of isolated cardiomyocytes , as wellHere, we present a mini tissue (mTissue) TFM platform to measure tractional forces in engineered cardiac tissues and asked whether changes in substrate stiffness affect energy production and utilization in primary cardiomyocytes. To achieve this, we microcontact printed mm-scale fibronectin islands on polyacrylamide (PA) gels with embedded fluorescent beads. We prepared the gels to mimic the stiffness of fetal (~1 kPa), adult physiological (~13 kPa) and adult pathological (~90 kPa) myocardium ,8. We thin-vitro, gelatin, alginate, and PDMS have been used, but it is difficult to independently control the mechanical properties of the substrate and the geometry of cells using these materials [i) a connected grid with horizontal and vertical spacing of 1 mm and 0.5 mm, respectively; and ii) isolated diamond-shaped islands  in the gel and used a confocal microscope to image bead displacement immediately below the tissue during several consecutive contraction cycles . From thA number of important phenotypic differences occur during cardiomyocytes maturation, including the expression of different transcription factors, ion channels, and contractile proteins; the appearance of physiologically-relevant structures such as t-tubules; and the progressive assembly of the contractile cytoskeleton . SpecifiFurther, we measured bead displacement in the mini tissue TFM platform and calculated contractile stress and work output in NRVM tissues engineered on soft , panel iHeart failure with preserved ejection fraction, valve disease, diabetes, and even normal aging have all been associated with stiffer myocardium –24. Impo2) that can be addressed individually along the diagonal of the field of view of a typical 20x objective. A limitation worth mentioning is that, consistent with our experience with TFM in single cells and cell pairs, stamping of gels with stiffness less than five kPa remains difficult. Therefore, a better stamping technique might be needed to obtain the larger sample sizes needed to tease apart smaller contractile differences in cells seeded on substrates with stiffness values in the fetal range .We presented an alternative quantitative method to assess contractile structure and function at the tissue level in cardiac preparations. Our platform offers important advantages over existing alternatives. First, our technique makes it possible to use the same assessment technique, namely traction force microscopy, to study the contractile properties of cardiomyocytes in preparations ranging from single cells, to cell-pairs, to tissues , 7, 8. TAs a proof-of-concept study, we combined our mTissue TFM platform with an industry-standard high-throughput metabolic flux analyzers to investigate the coupling between mechanotransduction, contractility, and metabolism. We speculate  that myoIn conclusion, the tissue-level assay presented here completes a consistent suite of TFM-based assays that can enable thorough multi-scale studies of the coupling between metabolism, mechanotransduction, and contractility in primary and stem cell-derived myocytes –8. Two r2 in Medium 199 supplemented with 10% heat-inactivated fetal bovine serum , 10 mM HEPES, 0.1 mM MEM non-essential amino acids, 20 mM glucose, 2 mM L-glutamine, 1.5 μM vitamin B-12, and 50 U/mL penicillin for 1 day. The serum concentration was reduced to 2% after 24 hr to minimize proliferation of the small pool of fibroblast present in the culture. Cells were cultured for four days before conducting the experiments.All animal procedures conducted in this study were approved by the Harvard University Animal Care and Use Committee. Cardiac myocytes were extracted from neonatal rat ventricles using a previously described method , 8. LeftPolyacrylamide gel substrates were fabricated and characterized as previously described in details , 8. SpecPhoto- and soft-lithography were conducted as previously described in details , 8, 10. 2, 5 mM glucose, 5 mM HEPES, 1 mM MgCl2, 5.4 mM KCl, 135 mM NaCl, 0.33 mM NaH2PO4, pH 7.4) were imaged on an environmental controlled line scanning confocal microscope (Zeiss LSM510) using a 40X air objective (with a 0.5x zoom) or a 20x objective to ensure the full diamond-shaped tissue fit within the diagonal of the field of view. Movies of contracting myocytes and bead displacement were imaged at 33 Hz with both brightfield, and 488 laser excitation and recordings were performed over multiple [Traction force microscopy experiments were conducted as previously described , 8, 10. multiple –10 contrThe methods used to acquire displacement and traction stress vectors from images of bead displacement have been previously described , 18. BriImage analysis and processing were performed as previously described . After t2 incubator. After two hours, 500 mL of media was added to each well and media exchanges were performed as previously described above. After four days in culture, culture media was replaced with XF Assay Medium  supplemented with 20 mM glucose and plates were incubated at a 37 C for 1 h. The proprietary hydrated cartridge was loaded with 2 mM oligomycin, 1 mM carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP), and 1 mM antimycin A and 1 mM rotenone from the XF Cell Mito Stress Test Kit . The cartridge and cell plate were then inserted into the Seahorse XFe 24 Extracellular Flux Analyzer , which made three measurements of baseline oxygen consumption rate (OCR) and three OCR measurement after injection of each of the drugs listed above. Data from multiple wells across three different neonatal rat ventricular myocyte harvest cycles were collected and used for analysis.Cellular metabolism was measured using a Seahorse Bioscience XFe 24 Extracellular Flux Analyzer . Polyacrylamide gels were pipetted into the wells (10 mL/well) of a standard XF24 microplate  and allowed to crosslink before rinsing with PBS. Plates were stored at 4 °C until cell seeding with neonatal rat ventricular myocytes (100 000 cells/100 mL/well). After seeding, plates were left in the laminar flow hood for 30 min to minimize edge effects before transfer to a 37 °C, 5% COp-value smaller than 0.05.Through the text, our results are presented as mean ± standard error of the mean (s.e.m.). After verifying that the data points were normally distributed (Shapiro-Wilkinson test), statistical comparisons were conducted among the various group using 1-way ANOVA test followed by Tukey pairwise comparisons. All analyses were conducted using SigmaPlot . The symbol * was used to denote statistically significant difference characterized by a S1 Checklist(PDF)Click here for additional data file.


We prove haptic rendering plausibility on a very high number of test paths. Important errors are below just noticeable differences for the hand-arm system.This work presents an evaluation study using a force feedback evaluation framework for a novel direct needle force volume rendering concept in the context of liver puncture simulation. PTC/PTCD puncture interventions targeting the bile ducts have been selected to illustrate this concept. The haptic algorithms of the simulator system are based on (1) partially segmented patient image data and (2) a non-linear spring model effective at organ borders. The primary aim is to quantitatively evaluate force errors caused by our patient modeling approach, in comparison to haptic force output obtained from using gold-standard, completely manually-segmented data. The evaluation of the force algorithms compared to a force output from fully manually segmented gold-standard patient models, yields a low mean of 0.12 N root mean squared force error and up to 1.6 N for systematic maximum absolute errors. Force errors were evaluated on 31,222 preplanned test paths from 10 patients. Only twelve percent of the emitted forces along these paths were affected by errors. This is the first study evaluating haptic algorithms with deformable virtual patients  A major part of such simulation systems, namely the force feedback during needle insertion, has been surveyed6. The downside of most systems is their inability to easily simulate an intervention on new patient data (patient-specific) with little manual segmentation preparation time and effort, for which an overview was recently given by ref. 8. In addition, these simulators usually require a fully segmented 3D patient, which leads to high segmentation efforts and avoids the use of VR simulators in a clinical setting. This fact and missing patient motion dynamics are the major drawback of state of the art simulators. Our works using 3D and 4D CT image data10 remedy this situation.Virtual reality (VR) surgery simulation with needle insertion into blood vessels12. Cholestasis can be caused by i.e. gallstones or tumors in the common bile duct (CHD). To reach the target , the needle has to be inserted between the ribs  and into the liver. Modern intervention techniques use an ultrasound (US) probe with an attached needle guide , tissue deformation and non-axial forces16 are also relevant for realistic, real-time, visuo-haptic simulations. However, these topics are beyond the scope of this work.Regarding haptics, needle insertion procedure simulations must mimic stiffness, cutting and friction forces at the needle tip and shaftin vitro/vivo gold-standard force measurements, we use reference forces based on previously evaluated fully segmented patient data (in silico)13. Qualitatively, the haptic parameters determining the force output using our new system regarding liver structures, were tuned by two medical experts experienced in PTCD liver punctures9. Here, we present the quantitative haptics evaluation counterpart. We think the design of the presented evaluation experiment is a practicable way to bridge the gap between qualitative and still missing quantitative evaluations of virtual haptic simulations.Currently, haptic rendering in these visuo-haptic simulations raises the need for evaluation methods and detailed studies of the force output at the handle regarding needle insertion Fig. . A preliThe aim of this study is to compare axial force errors of simulated needle insertion force feedback of a full patient segmentation, against a partial segmentation based force calculation algorithm. We also characterize the differences in terms of the magnitude and location of the errors found. The errors observed here are the quantitative marks of our virtual training simulator based on our virtual patient modeling approach, where only certain key structures need reviewed segmentations.The article is organized as follows: First, quantitative results using the evaluation methods on page 12 of our haptic sub-system for PTC(D) are given in section “Results” on the current page and discussed on the following page. The methods on page 6 start with an overview of AcusVR-4D and its hardware and software components. Then, the concept of our virtual patient modeling is summarized on page 7. Details of our method for the haptic simulation of axial forces during needle insertion, are presented in the following section dealing with haptic rendering on page 9. The evaluation methods section on page 12, shows our general framework for evaluation and interpretation of our direct needle force rendering quality.We applied our methods to 10 patient (#1-#10) clinical CT data sets and compared the new haptics method using “partial segmentation masks augmented by our patient-specific transfer functions,” against the haptic algorithm based on gold-standard fully manually-segmented data.18 vs. manually driven full volume segmentation, is the significantly shorter time frame (<10x) needed to provide a virtual patient model. This is to a large extent achieved by using the threshold-based (t0 … t2) transfer functions dependent on the voxel position19. The transfer functions cover the voluminous, but less relevant and easily segmentable tissues, such as skin, fat, bone and air cavities inside the body. In this study, we introduce and report (1) segmentation errors using new local metrics, i.e. well-known measures are evaluated only on the planned puncture paths, rather than the full volume; (2) we show the axial force errors caused by these segmentation errors.The benefit of our patient modelingRegarding haptics, this is the first study in which a direct haptic volume rendering method is tested on 10 patients and 31,222 test paths (i.e. ca. 3000 paths per patient). Exemplary test path planning results can be seen in Fig. We report the segmentation results, shown in Figs N and MAE errors less than 1.0 N, except for some displaced outliers around 1.5 N. Systematic maximal force error medians of 0.7 N and rare sporadic peak errors, around 1.4 N occur in some patients  force threshold, elaborated on page 13, of 0.145 N Fig. .N on average, can be observed in terms of MAE. These and the outliers of ca. 1.4 N prominent in some patients for the change of segmentation experiment, are clarified in the discussion. In total, we achieve on average 88% of exactly identical emitted forces  are below just noticeable thresholds, except for some (HSD) outliers of the liver. While small, almost imperceptible, RMS force errors occur, a systematic bias of about 0.7 9 for needle insertion simulation, has been evaluated quantitatively in terms of haptic rendering plausibility, with positive results for the influence of usual segmentation errors. The conducted “change of segmentation model” experiment  in absence of in vivo and in vitro reference measurement data, which are also difficult to supply. Needle steering can not be assessed by axial forces and deformations24 alone, and non-axial needle-tissue interaction forces are found along the entire embedded needle length. However, in our situation  the forces from breathing motion are mainly directed orthogonally to the needle insertion direction, so that they do not contribute saliently during needle advance. Thus, regarding axial force we evaluate the relevant haptic aspect of the simulation compared to gold-standard virtual patients and reference force output approved by medical experts.Our qualitatively accepted VR system, AcusVR-4Dent Figs  and 7 vaN at the skin surface is not severe in our context, as the needle enters the body through a small ski n incision made by the surgeon.With regards to skin, soft-tissue and bile segmentation, the spatial errors  are small. We often observed that the manual skin segmentation is detected slightly later on a needle path than the skin found by the threshold. The resulting systematic maximum force error of 0.7 N in the MAE metric can be located, explained and well justified by the following: (1) at the skin a maximum 0.7 N difference is calculated and (2) immediately above the fascial layer, a somewhat differently classified voxel (group) can cause phase 1 (Pre-X) to begin from a different force level resulting in misaligned puncture peaks (cf. Sec. on page 9). This results in ca. 1.6 N maximal error. Thus, for the change of the segmentation model, errors are caused by slight differences in the manual expert segmentations and the segmentations resulting from our patient modeling approach. As long as the sequence of events is guaranteed to be the same, these outliers are haptically irrelevant. The slight spatial difference of haptic events can hardly be felt by the user comparing two independent puncture attempts along the same path. For example, the slightly displaced detection of the skin or differently detected tissues around the fascial layer, causes out of sync force emission with potentially high MAE errors (max. 1.6 N) in the pre-puncture phase of the haptic algorithm near to the fascial layer (p. 10). The segmentation of the surrounding tissue of the fascial layers can be slightly different, causing misaligned force incline start levels of the pre-puncture phase on the needle trajectory. Again, more important is the correct sequence of events and accurately reaching the puncture goal. This is guaranteed by low segmentation errors and operator reviewed segmentation of the structures, i.e. especially the bile ducts. This argument is supported by currently published, experimentally determined, spatial JNDs for the hand-arm system26. The elaborated JND thresholds are undercut despite some outliers at the liver fringe in the outlier HSD metric  or in vitro (cadaver) experiments. For our haptics research, we only use already available CT images acquired from routine clinical data. We are grateful for the guiding work of other groups producing axial force measurements with real tissues28.Our algorithm for axial force rendering and the haptic parameters for deformable virtual patients, have not been published in this level of detail and in this scale of a study with a high number of test paths from 10 test patients before. In addition, our evaluation methodology has been clearly refined, e.g. using new per test path spatial error metrics and the incorporation of spatial and force JNDs. We thoroughly show in this study set-up, using 31,222 test paths, that the generated force output using the partially segmented patient image data is robust. It is shown by example that the force evaluation framework is a valid means to prove force output of haptic needle insertion simulations, without ethics approval and the experimental hassle of specimen to image registration during needle insertion. We completely circumvent 18 and better force rendering as we propose a non-linear incline at tissue borders (Pre-X). Other proxy-based methods use linear force inclines for direct haptic volume rendering31.Finally, with regards to the potential time savings in patient modeling important in clinical settings, we can fully support the use the of partially segmented data combined with the non-linear spring force output. This method offers faster patient preparation by a factor of 129. The simulation concept could easily be transfered to other liver site intervention fields in addition to PTC or PTCD, simply by replacing the target structures (with e.g. tumors).The system setup, experimental design, observed haptic errors and interpretation lines could be helpful for other haptic algorithm developersOur extended framework and study showed, that our methods, i.e. the use of partially segmented patient image data and haptic rendering techniques featuring a non-linear spring model, are usable for virtual needle insertion simulation. The evaluated system allows the user to experience and become familiar with the visual presentation and needle insertion forces during liver needle punctures.Furthermore, our evaluation of surgical needle insertion simulator haptics identifies, quantifies, and interprets errors, which is important for algorithm development.26 and very similar in force amplitude to the puncture event using gold-standard label data. The force errors are also below the force JND threshold adapted to our context27. Bone is rendered impenetrable and would cause our high force haptic device to exert 22 N. The encountered MAE force outliers in this study are clearly unrelated to puncturing false-positive bone voxels, in which case we would see 22 N MAE errors. Our customizable non-linear spring model gives the user a more elastic feeling of tissue borders and thus is more realistic. It can be easily adapted to in vivo/vitro measured realistic force curves6 by model fitting.In summary, the force errors of the presented force rendering algorithms using partially segmented patient data are small. In 12% of the path positions the detected errors are haptically irrelevant for the user experience. This is due to the fact that the puncture incident mismatch is mostly below JND-thresholds in terms of insertion depthFrom a developer perspective, our evaluation framework and interpretation cues help to sort out very sensitively noticeable errors, to improve virtual patient models and haptic algorithms.in vivo force measurements15. In future studies, regarding axial force curve shape, we would like to investigate refined force feedback algorithms, taking into account real force measurement curves and puncture events due to different bevel tips. This way, our first haptic rendering phase could be sub-divided in two subphases: (a) initial puncture and (b) transition from tip bevel front to shaft. In reality, there are more haptic micro-events overlayed to our 4-phase curve to be included. These effects could be modeled as superposed micro processes as well. For a more granular feeling of the tissue, the CT values could be used to infer material characteristics for micro processes. The currently constant phase 3 would be more realistically modelled by a flat, non-linear incline depending on the insertion depth.Our work on force output modeling is guided by comprehensive surveys featuring 32, work for which haptics have to be continued and evaluated accordingly. Dealing with new patient image data could be improved by researching new real-time voxel classification methods at the needle tip that for example, reliably detect the liver and its vessels. In this case, some currently necessary semi-automatic segmentation steps could become obsolete18. This could be achieved using high dimensional feature spaces and random forest classification19.Further work will be in dynamic patient effects. More complex surgical instruments and irregular respiratory motion are currently included into the haptic simulation9 . When a risk structure is struck by the needle, the user is notified by turning the simulation window background red, a successful insertion is indicated by a green background in the main viewport  can be used to assign scores to each training attempt along bookmarked reference paths defined by medical experts, so that the trainee can monitor his improvements during the experience with the virtual patient model.18.Here, we provide background and summarize the time-saving virtual patient modeling approach detailed in ref. Our start point is a fully manually-segmented and reviewed reference patient (CT and label data) to define a general virtual patient model. For fully segmented patients, we can estimate a tissue label using Alg. 1, step 1. Each new virtual patient model then consists of the same parameter attributes inherited from the reference patient model. After the automatic intensity data adaptation, only the segmentation masks for the key structures are segmented and reviewed anew specifically for the new patient model by organ-specific, known, semi-automatic segmentation methods  with cutting force threshold [N], friction force [N] and material stiffness [N/mm] for each label in a fully segmented patient  and bone are modeled. For these large volume tissue classes we determine the intersections  and bile ducts (target) located inside the liver segmentation can be masked out and are ready to be addressed. We use a vessel highlighting and selection method based on multi-scale vesselness filtering21 and auto-seeded region growing followed by morphological post-processing38.The liver and hepatic target structures are segmented by organ-specific approaches accompanied by typical segmentation errors. We use a multi-atlas and GraphCut segmentation concept to address the liverThe following patient modeling summary items consist of two paragraphs each: First, we describe the concept for the reference patient model based on a reference patient. Second, the step for its generalization to new patient models is briefly presentedFinally, during simulation we can estimate a label and hence the haptic parameters and force output for every voxel as shown in Alg. 1 (steps 1 and 2) in real time (≥1000 Hz).10.For the axial forces at the needle tool handle, we use a proxy-based computation divided into four phases: (1) pre-puncture, (2) post-puncture, (3) pass and (4) transition Fig. . In the t16. Regarding tissue deformation by needle interaction we define a deformation (and force) vector d = |p − x| between two points as follows: First, the needle tip x lies at the same tissue related position in the undeformed and deformed spaces. Second, in the deformed state we introduce a proxy position p to correspond to the needle tip 16. At the same time it is used as a force vector for a spring mounted between x and p. We determine the haptic parameters at the proxy p as we follow our assumption that the spatial relation of tip and proxy defines the deformation of the tissue9. Thus, retrieving the parameters at the proxy position in the undeformed image data (x), is equivalent to obtaining the parameters in a deformed state of the image (p). Our visual deformation algorithms are described elsewhere32. In this paper, the focus lies on the details of the axial force rendering algorithms,which work in the undeformed state of the image.The deformation of the tissue caused by instruments or breathing, is described by a smooth (inverted) deformation field d = |d| according to Hooke’s law, the simplest spring model has a linear characteristic when tip and proxy diverge at a tissue border. Another variant for computation of needle forces is a non-linear spring force. Facing organ surfaces we use a second degree polynomial15:d being the displacement from the undeformed surface. With Eq.  as proposed in our previous work13. Congruent to this spring and to preserve the main haptic characteristics, the start force level a0, the pre-puncture phase design parameter a1 ∈  and consequently a2 = k · (k − a1)/|TN − a0| yield a non-linear spring, which punctures the surface at the same displacement as the linear spring to achieve similar behavior to our evaluated reference system13. However, when the underlying gold standard and our segmentation differ only in detail, certain force errors are caused , R(p), k(p)) at every proxy position p in the deformed patient data.Using Table Our force output algorithm  is already located in the internal organ structures behind the organ membrane.In this pre-puncture phase, by manipulation of the position of the tip behind the tissue border, 3D-forces are exerted. Simulation of the resistance of tissue surfaces to needle puncture is achieved by fixing the proxy pph1 and jumps into the inner organ structures towards x (R ≤ TN):We decided to model this phase such that after the cut instance, the proxy is released from being held back at R maintained in phase 3. This provides the user with a salient feeling of cutting the membrane of an organ.As the proxy is a virtual object only, this motivates the quick decay of the output force to the force sustain level k(p) [N/mm] and the friction force R(p) [N] (threshold) to define a maximum length for a spring moving behind the tip x that mimics advancing the needle through a viscous material:Advancing continuously inside an organ the modeling of viscosity comes into play. Here, we use the stiffness parameter In contrast to the membrane puncturing, a standard linear Hook formulation is used to render the forces of the moving spring:k as a function with additional dependence on a displacement parameter, however, this is left to a future work. The spring movement is implemented by moving the proxy pph3, if the following condition is violated: |fph3| ≥ R or rather |pph3 − x| ≥ lmax:max at maximum. This way when advancing or retracting the needle, the proxy moves in constant distance behind or before the tip, resulting in a ductile feeling.The force emission could be improved by modelling R from phase 3 is greater than the new structure’s shell force threshold TN, the surface is cut immediately and we skip phase 1 of the new material. This results in a decline of the force as no cutting forces are due at this instant. This case is implemented as force drop to fph2 (phase 2). If the needle tip meets a harder tissue (TN > R), we start with phase 1 of the new material resulting in a force incline. The trivial but uncommon case in our scenario is exiting the body with a force decline towards 0 (piercing).This phase serves as a link between cycles or exit, depending on the haptic parameters of a newly encountered tissue. Exiting an organ means to encounter a border again. When the force R is the same as the cut force level TN (0.7 N)  Table .We establish ground truth on the 10 CT test data sets spanning the lower thorax and upper abdomen. The spacing between the voxels in the data sets is between 0.775–0.925 mm in the x and y directions and 0.825–1 mm in z the direction. CT images were acquired in portal venous phase. The necessary full volume manual expert segmentations used as gold-standard were carried out by a team of three experts.Trajectory (“reference path”) planning specific to the gold-standard virtual patient. This step produces paths in proximity to the usual access area between the 6th and 7th rib on the right side of the patient,Simulated robotic needle steering to accurately follow the trajectories and 1D force signal sampling on these paths using a reference  and a new algorithm ,Structure border lag analysis, force signal comparison and statistical analysis.For axial force output assessment we measure and compare forces along preplanned test paths hitting the target. Instead of curved insertion paths, we use straight paths planned in the undeformed reference space Fig. , left. TExtensive quantitative evaluations take place for force output of the haptic algorithm. We use many target (center line of bile ducts) hitting paths from a surgery planning step for quantitative evaluation of force feedback. Automatic needle steering along approximately 3000 paths from each of the ten test patients (“reference paths”) yields reproducible force outputs for the algorithms to be compared in ten patients  visibility of a target structure voxel from the skin and (2) needle insertion length (9 cm). Individually rated soft constraints yield a path score in a weighted averaging manner.Visibility checks serve to generate a high number of test paths. Ray casting on the GPU is used for planningC1: The visibility hard constraint dismisses all trajectories that are blocked on their way from the skin to the target through a risk structure such as bones or blood vessels. Failure is indicated by a value of infinity, success by 0.C2: Distance to target. The target must be reached in 9 cm path length. Failure is indicated by a value of infinity, success by 0.C3: The minimal distance to critical structures soft constraint scores a trajectory by calculation of the smallest distance to a risk structure on the path.C4: The target hit soft criterion counts the voxels along the needle shaft. This counting is conducted inside a cut target structure (bile duct) only and tests for a certain number of voxels being visited. A higher number indicates a favorable penetration depth and insertion angle in line with a bile duct.We use the following criteria for candidate paths starting from every skin voxel:N is a normalized average of the criterion scores:The final score for a path C3,4 are normalized to 1. Weights QN = ∞ are dismissed right away. For each skin voxel we keep the best path from the candidate set as reference path.The criterion scores QN < 0.4. Also, falsely detected bone voxels from the transfer function classification could occur in very close proximity to the ribs, e.g. due to partial volume effects. These medical and technical arguments motivate the removal of these low score paths from the candidate set , where fine nerves and vessels are located causing pain if struck. Blood vessels should also not be close to the needle. Hence, nearby bone and blood vessel paths are removed from the candidate set by a quality threshold, i.e. To sum up, we test our simulator for successful trainee experience, i.e. starting from the skin paths hit the target with respect to the hard and quality soft constraints. Paths from the skin to the target (bile ducts) colliding with or aligned in close proximity to risk structures are dismissed. Soft constraints are used to score the candidate path set and to reduce it further.Finally, we test our haptics on 10 patients with a total of 31,222 test paths, i.e. ca. 3.000 paths per patient.18 using the same non-linear spring model.The selected paths from ten test patients are now used for force output evaluation. While steering the needle along the paths, two forces are calculated and compared. We compare forces based on the fully manually vs. partially segmented dataUsing about 1,000 force pair samples per planned path for insertion, the worse case for sample spacing of the currently steered path can be given as 0.09 mm, which is sub-voxel resolution. Thus, generous oversampling of ≥10× for a full length path of 90 mm (worst case) is used.σ, (2) account for top force outliers higher than 2.7 · σ and (3) use box plots in terms of MAE to better show systematic errors. The analysis of the higher MAE outliers can be important for method investigations during haptic algorithm development. For the evaluation of the segmentation errors caused by out of sync force rendering  and “maximum absolute error” (MAE) are used to assess the axial force differences from the experiment. We use (1) mean and standard deviation ing Fig.  the meanS and a just perceivable relative stimulus change ΔS27:26. Note, that empirically measured JNDs for distances with the finger span method26 are twice as low as for the upper limb movements25. The upper limb movement is more relevant in our context. A 2 mm lower threshold corresponds to a conservative JND of 8% distance related to an average reference length of 28 mm. This corresponds to the most important sub-distances for our needle to bypass, i.e. the liver to bile duct border and skin to fascia border path segments. The distance of 3 mm corresponds to a JND of 11% distance. This value is justified by ref. 26. Generally, JNDs for decreasing reference measures are higher above average in small scale domains.The so-called Weber fraction given in reference force or distance magnitude % with a perceived reference stimulus 27. Regarding those human force JNDs we relate a hand-arm JND of 18.1% force magnitude  to low (0.5 N) reference forcesN:To further assess the path position fraction where the segmentation errors cause force errors, we also asses the strict measure of the percentage of exactly identical emitted forces for every path In summary, in the conducted experiment the segmentation fundament is varied: For the reference forces we use the full manual expert segmentation, for the test forces the transfer functions augmented by partially segmented data are used. Using these metrics and study design, we mainly aim to evaluate the influence of the segmentation errors caused by our patient modeling strategy on the force rendering in our haptic simulation.