HIV (PHIVs) in the UK.The aim of the study was to investigate circumstances surrounding perinatal transmissions of HIV in Pregnancy and Childhood conducts comprehensive surveillance of all pregnancies in women diagnosed with HIV infection and their infants in the UK; reports of all HIV‐diagnosed children are also sought, regardless of country of birth. Children with PHIV born in 2006–2013 and reported by 2014 were included in an audit, with additional data collection via telephone interviews with clinicians involved in each case. Contributing factors for each transmission were identified, and cases described according to main likely contributing factor, by maternal diagnosis timing.The National Study of PHIVs were identified. Of the 41 (38%) infants whose mothers were diagnosed before delivery, it is probable that most were infected in utero, around 20% intrapartum and 20% through breastfeeding. Timing of transmission was unknown for most children of undiagnosed mothers. For infants born to diagnosed women, the most common contributing factors for transmission were difficulties with engagement and/or antiretroviral therapy (ART) adherence in pregnancy (14 of 41) and late antenatal booking (nine of 41); for the 67 children with undiagnosed mothers, these were decline of HIV testing (28 of 67) and seroconversion (23 of 67). Adverse social circumstances around the time of pregnancy were reported for 53% of women, including uncertain immigration status, housing problems and intimate partner violence. Eight children died, all born to undiagnosed mothers.A total of 108 ART adherence and facilitating better engagement in care, with attention to addressing the health inequalities and adverse social situations faced by these women.Priority areas requiring improvement include reducing incident infections, improving  There are around 35 000 women living with HIV in the UK and every year around 1200 become pregnant. Pregnant women access antenatal and HIV services through the publicly funded National Health Service, which are free at the point of care for residents. National standards and guidelines for antenatal screening, obstetric care and management of HIV infection in pregnancy are provided by different organizations The design of the UK and Ireland National Study of HIV in Pregnancy and Childhood (NSHPC) incorporates data collection on both groups of infants with PHIV. Our previous audit of PHIV in England in 2002–2005 highlighted failures in communication, failure to act on suboptimal virological response to ART, and adverse social circumstances which acted as barriers to prevention of mother‐to‐child transmission (MTCT) in utero HIV exposure; in addition, reports of all children diagnosed with HIV infection (< 16 years old) are sought, regardless of country of birth. The core mechanisms are two confidential active reporting schemes: (1) for HIV infection in pregnant women, through a quarterly reporting system with named respondents in every maternity unit; (2) for HIV‐exposed and ‐infected children, through monthly reporting via the British Paediatric Surveillance Unit or directly to the NSHPC from some large clinics with a high case load The NSHPC seeks comprehensive reporting of all pregnancies in women diagnosed with HIV infection prior to or during their current pregnancy in the UK or Ireland and all infants with All children with PHIV, born in the UK between 1 January 2006 and 31 December 2013 and reported by 31 March 2014, were included. The term ‘case’ is used to describe one mother–child pair.Two main data sources were utilized: variables collected within routine NSHPC surveillance and already included in the study database (described above) and additional data collected via interviews with clinicians involved in the care of each case. Interviews were conducted by two of the authors (LB and HP). The paediatrician who had reported the case was initially contacted and interviewed. Obstetric respondents were interviewed in all cases where the mother had been diagnosed with HIV infection by delivery, but otherwise the obstetric unit was only contacted if they were aware of the case, or once the paediatric team had informed them [as recommended by the Children's HIV Association (CHIVA)] Interviews sought information about factors that might have facilitated PHIV transmission. Interviewees were also asked about the presence of uncertain immigration status, housing problems, diagnosed mental health problems, drug/alcohol use, and intimate partner violence, and any other adverse social circumstances or complicating issues. Following the interviews, each case was summarized.An expert review panel was convened to assess anonymized case summaries, including likely timing of transmission, missed opportunities and common patterns, and to make recommendations to strengthen national PMTCT policy. The panel included clinicians with expertise in managing HIV infection in pregnant women and children , the Infectious Disease Screening Programme (IDPS) programme director, IDPS clinical advisor, members of the NSHPC and community representatives . The contributing factors identified are defined in Table The expert review panel found that in most cases only one contributing factor was evident; where multiple factors were identified, one was assigned as the likely Women were classified as ‘diagnosed’ if they had been diagnosed with HIV infection at any time up to delivery and ‘undiagnosed’ if diagnosed after delivery. Baseline viral load (VL) was at diagnosis, or first result reported in pregnancy.Late booking for antenatal care was defined as first antenatal visit at ≥ 24 weeks of gestation, reflecting British HIV Association (BHIVA) guidelines in utero transmission; infants with a negative PCR within 3 days, then a positive PCR within 6 weeks were classified as having likely intrapartum transmission; infants with a negative PCR after 6 weeks and a positive PCR thereafter were classified as having likely postnatal transmission, according to generally accepted definitions Infants with a positive polymerase chain reaction (PCR) within 3 days of birth were classified as having likely access 2010 and excel 2013 , and analysed using stata version 12.1 . Categorical variables were compared using χ2 tests or Fisher's exact tests, and medians using Kruskal–Wallis tests. Mortality rates were calculated using the time ‘at risk’ from the child's birthdate to the end of the data collection period or date of death.Data were managed in Approximately 9200 live births to HIV‐diagnosed women in the UK in 2006–2013 were reported by April 2014, and 41 of their children were reported to have PHIV. A further 67 children born in the UK during this period, whose mothers had not been diagnosed with HIV infection by delivery, were diagnosed and reported with PHIV.Median maternal age at delivery was 29 years [interquartile range (IQR) 26–34 years]. Overall, nearly 90% 94 of 108) of the mothers were born abroad (mainly in Africa), with 39% (22 of 57) having lived in the UK for > 5 years , nearly 20% intrapartum (seven of 41) and 20% in the postnatal period through breast feeding (seven of 41) (timing unclear for four of 41).Among these 41 infants, 26 were born to mothers diagnosed during the current pregnancy . Half were diagnosed before pregnancy. One woman had acquired HIV from her mother. Most (11 of 14) had at least one complicating issue reported, including diagnosed mental health problems (four cases), insecure housing (four) and involvement of social services (three). Reported problems faced by these women included non‐acceptance of diagnosis, concerns over disclosure, treatment side effects and issues swallowing tablets. Interventions ranging from additional support with adherence (from a multidisciplinary approach) to directly observed therapy were provided in all but two cases, where the women (both diagnosed during pregnancy) disengaged from care after diagnosis. All 14 women were prescribed protease inhibitor‐based combination antiretroviral therapy (cART); eight had a VL > 10 000 HIV‐1 RNA copies/mL at delivery. Ten of the 14 infants were delivered by caesarean section (CS); 11 of 14 were probably infected in utero, and one intrapartum (timing unknown for two).The most common factor contributing to perinatal infection among these 41 cases was problems with engagement and/or ART adherence in pregnancy (recognized in 19 cases). This was the main contributing factor in nine cases. Of these nine, all women were diagnosed antenatally, booking at a median of 36 weeks of gestation . Five were known to have been travelling or living abroad until late pregnancy (> 30 weeks); four were known to have language communication barriers. Complicating issues among these women included uncertain immigration status (four women), intimate partner violence (two) and insecure housing (two). Eight of these nine cases were classified as in utero transmissions (timing unclear in one).Late presentation for antenatal care (documented in 11 cases) was identified as the main contributing factor in seven of 41 cases. In three of seven cases, ART was known to have stopped after delivery, consistent with guidelines at the time. In the remaining four cases, there were adherence issues in pregnancy, with either loss to follow‐up or continuing adherence issues postnatally; in all four cases there were known concerns about disclosure of HIV status to family or friends. All seven women had additional issues reported, including insecure housing (four women), uncertain immigration status (three), social services involvement (one) and mental health issues (one).Postnatal transmission, probably as a result of undisclosed breastfeeding, was the main contributing factor in three of 41 cases: in these, cART was started at a median of 24 weeks, and two delivered at < 32 weeks and one at 32–36 weeks. All three pre‐term deliveries were emergency CS, with VL at delivery of 50–399 copies/mL in two and 1000–10 000 copies/mL in the third. One case was a likely in utero transmission, and two were likely intrapartum transmissions.Pre‐term delivery was the in utero in these cases. In one other case, the main contributing factor was a delayed test result (and subsequent initiation of treatment); this transmission probably occurred in utero.Two further women were diagnosed with primary HIV infection late in pregnancy  following a negative test at booking, and transmissions were likely to have been In the five remaining cases, despite information being available from clinicians, it was not possible to establish a clear contributing factor. In four of the five cases, transmission probably occurred intrapartum: all had undetectable VL and were on cART before delivery .Of the 67 children born to undiagnosed women, 42 were diagnosed with HIV infection as a consequence of their mother or other family member being diagnosed. The remaining 25 children presented with symptoms at a median of 6 months , although in a third of cases (eight of 25) an HIV test was not offered at first presentation despite the child having an HIV indicator condition.in utero transmissions; these infants were tested soon after birth following postnatal maternal diagnosis.Timing of HIV acquisition could not be estimated for nearly 90% of these 67 children. There was one likely intrapartum and two likely main contributing factor in 28 of 31 cases. These 28 women all delivered in 2006–2010, most in 2006–2007. The reason for the decline was available in 12 cases, and included needle phobia, not feeling at risk of HIV infection, confidentiality concerns, wanting to discuss with partner, and feeling unable to cope with a positive result. In six of the 28 cases there was documentation of a re‐offer; three of the six involved an experienced clinician. In two cases it transpired that the woman had not disclosed a previous positive HIV test. In the majority of these cases (20 of 28), at least one complicating issue was reported.In nearly half (31 of 67) of all perinatal infections where the mother was undiagnosed in pregnancy, antenatal HIV screening had been declined, with this identified as the Of the 23 of 67 women who acquired HIV infection after testing negative early in pregnancy, 16 delivered in 2006–2009 and seven in 2010–2013. Median gestation at antenatal test was 12 weeks (IQR 9–17 weeks); the median interval to the mother's first positive HIV test was 6.2 months after delivery (IQR 2.6–22 months). In this group, one current male partner tested negative and 17 partners were known to be HIV positive or subsequently tested positive. Of the four men known to have HIV infection at the time of the pregnancy, two had not disclosed their status.main contributing factor for transmission was late antenatal booking (after 30 weeks of gestation), with two not arriving in the UK until late pregnancy; two were diagnosed in the delivery suite. All three were reported to have uncertain immigration status, along with other complicating issues.In eight of the 67 undiagnosed women, there was a problem in processing the HIV test; all these deliveries occurred before 2009. For a further three women, the In nine cases, the relevant antenatal unit was unaware of the maternal diagnosis, so could not be contacted (in three cases the child or mother had died). In one case, the antenatal unit could not identify the mother, and archived antenatal notes were inaccessible in another. Antenatal notes were unavailable to the interviewee for 18 of 53 women where the main contributing factor was declined antenatal testing or seroconversion.Overall, eight children  were known to have died by the end of the study period: one from complications following pre‐term delivery and seven of HIV‐related causes; six died under the age of 6 months, and two at 18–24 months. The proportion of children who died by age 2 years was 8% overall, and 12% in those born to undiagnosed women. The crude mortality rate was 1.4 deaths per 100 child‐years overall ; 2.2 per 100 child‐years for children born to undiagnosed women .in utero. Our audit demonstrated that at least half of women were experiencing adverse social circumstances, and in nearly a third of cases multiple factors were reported. This is a minimum estimate as information was limited in about a fifth of cases. At least one key factor likely to have contributed directly to HIV transmission was identified in the vast majority of cases. The most common were decline of HIV testing in pregnancy  and seroconversion (around a quarter). Other factors compromising prevention of mother‐to‐child transmission (PMTCT) included engagement/adherence to ART, late antenatal booking, breastfeeding and pre‐term delivery, as has been found among perinatal transmissions in diagnosed women occurring elsewhere across Europe We identified 108 children with PHIV born in the UK between 2006 and 2013 by April 2014, of whom around 60% were born to mothers undiagnosed at delivery; this is slightly lower than reported in our earlier audit The number of children with PHIV born each year declined from 31 children in 2006 to six in 2012 and one in 2013. This is consistent with the declining MTCT rate in utero. The mechanisms of in utero transmission are incompletely understood, but high maternal VL and delayed ART initiation increase risk In untreated non‐breastfeeding women, most transmissions occur intrapartum A fifth of infants born to diagnosed women probably acquired HIV infection postnatally through undisclosed breastfeeding. Although women with HIV infection in the UK are still recommended to avoid breastfeeding, clinicians are now advised to support women who choose to breastfeed with close monitoring to minimize the risk of transmission The 2010 and 2016, national antenatal screening standards recommended re‐offering testing to women declining an HIV test at booking Seroconversions in pregnancy or postnatally after an earlier negative antenatal HIV test occurred in around a quarter of women, including some with a partner with known HIV infection, similar to the previous audit People living with HIV face many potential barriers to optimal ART adherence, such as fear of disclosure, complex regimens, and work and family responsibilities main factor contributing to transmission was late antenatal booking, this being the main factor in nearly a quarter of these women. Pregnant women with HIV infection in the UK are accessing antenatal and HIV care earlier than in the past, but 36% booked after 13 weeks of gestation in 2012–2014, with women from sub‐Saharan Africa and parous women at increased risk Among diagnosed women, the In five cases it was not possible to identify any factor contributing to the transmission. Although for women who achieve an undetectable VL by delivery the risk of MTCT is very low A strength of this study was the near ‘complete’ picture of PHIV in UK‐born infants The MTCT rate in the UK in diagnosed women is at an all‐time low and reports of PHIV in infants born to undiagnosed women have also declined. This audit provides important insights into contemporary cases of PHIV transmission in the UK that could inform future policy and practice, with areas of clinical practice requiring improvement identified. Priorities include promoting earlier booking in key obstetric populations, reducing incident infections in pregnancy and during breastfeeding and improving adherence and service engagement in pregnancy and postpartum, including addressing the health inequalities and adverse social situations that these women face. Our findings highlight the importance of multidisciplinary care and peer support for all women. Further research, including qualitative studies, is needed for example to explore how partner testing within antenatal services and/or PrEP use in pregnancy could be improved, to investigate barriers to care and to monitor outcomes in women with HIV infection who choose to breastfeed.


During the first 12 months of an emergency meningococcal ACWY vaccination program for teenagers in England, coverage among persons who left school in 2015, the first cohort to be vaccinated, was 36.6%. There were 69% fewer group W meningococcal cases than predicted by trend analysis and no cases in vaccinated teenagers. Neisseria meningitides belonging to sequence type 11 (ST11) clonal complex (CC) and associated with severe disease and a high case-fatality rate  disease, caused by a hypervirulent strain of <25 years of age. Compared with persons of the same age who do not attend university, new university entrants have a higher risk for IMD, likely because of social factors that increase meningococcal transmission  for adolescents was started in August 2015  (3). VacStudents in the target group who left secondary school in the summer of 2015 and were 18 years of age before September 2015 were the first cohort offered the vaccine through general medical practices, starting in August 2015. Nearly one third of this cohort were accepted into universities in 2015  medical practices on a university campus or recommended by the university on their website and non–university-affiliated  general medical practices for persons who left school in 2015.To estimate vaccine impact, we compared confirmed MenW, MenY, and MenB cases in persons who left school in 2015 with projected cases for the first academic year (September 2015–August 2016) after program introduction. To estimate projected case numbers for 2015–16 in the absence of vaccination, we fitted a Poisson regression model with age and time-trend parameters for case-patients 19–24 years of age during 2010–11 and 2015–16 who were not in vaccine-targeted cohorts. We used this model to estimate case projections and incidence rate ratios, which are presented as percentage decrease (1 – incidence rate ratio). MenC cases were excluded because of successful MenC vaccination programs for persons 14–16 years of age and new university entrants available since September 2013.We assessed vaccine effectiveness among persons who left school during the 2015–16 academic year. Vaccine effectiveness was estimated by using the screening method  were ST11 CC. Case numbers and incidence increased in every age group except persons 15–19 years of age  and infants <1 year of age  . Six  .During the first 12 months of the MenACWY vaccination program for teenagers, there were 6 confirmed MenW cases among ≈650,000 persons who left school compared with a projected 19.4 cases , . Five ofOne case each of MenY and MenC disease were diagnosed in persons (both not vaccinated) who left school during 2015–16 compared with 3 of each in 2014–15. MenB cases increased from 10 in 2014–15 to 17 in 2015–16. Of the 17 case-patients in 2015–16, six (35%) had received MenACWY vaccine, consistent with national vaccine coverage.We found a major reduction (69%) in observed MenW cases compared with predicted MenW cases among the first cohort in England to be offered MenACWY conjugate vaccine after the first year of an emergency vaccination program for teenagers, even with a small number of cases. This decrease occurred despite national vaccine coverage of only 36.6% for this cohort. All case-patients who left school in 2015 and had confirmed MenW disease were not vaccinated; the only university case was in an overseas student who was not vaccinated. Higher vaccine coverage among university-affiliated general medical practices suggests that persons who left school and were enrolled in universities were more likely to be vaccinated than age-matched peers who did not seek higher education. Some universities have actively vaccinated new entrants and achieved high uptake rates  was added to the UK national infant vaccination program for infants in September 2015  than for persons who left school and were vaccinated through general medical practices (By the autumn of 2017, MenACWY vaccine will have been offered to all targeted teenagers in the United Kingdom. Because this group also has the highest meningococcal carriage rates (


Progesterone concentration is lower in women with subsequent sPTB. Conversely, high concentrations of progesterone are implicated in the pathogenesis of hyperemesis gravidarum (HG). We hypothesized that women at increased risk of sPTB or spontaneous late miscarriage would be less likely to have a diagnosis of HG. To explore this hypothesis, we compared the incidence of HG in women at increased risk of sPTB and women with no identifiable risk factors.Progesterone administration prevents spontaneous preterm birth (sPTB) in women at increased riskWomen at increased risk of sPTB were identified from a specialist Preterm Birth Clinic (PTBC) database where criteria for PTBC attendance are previous cervical surgery, previous sPTB <34 weeks, previous spontaneous late miscarriage, incidental sonographic cervical shortening, and uterine anomaly. Hospital antenatal booking and coding records for the same time period were examined to identify HG admissions. Women with multiple gestations, trophoblastic disease, or pre‐existing abnormal thyroid function were excluded. The incidence of HG among PTBC (n=394) and non‐PTBC attendees (n=4762) was calculated.P=.049).The incidence of HG was lower in women at increased risk of sPTB  compared with women with no identifiable risk factor for sPTB (3.33%, n=159; Hospital admission for HG is reduced in women with risk factors for sPTB compared with those without risk factors. Exploration of the pathogenesis of HG may improve understanding of the mechanisms underlying sPTB. Antenatal administration of progesterone prevents sPTB in women at increased risk.Studies investigating the relationship between HG and sPTB have thus far been contradictory; some indicate that HG is a risk factor for sPTB,We hypothesized that women at increased risk of sPTB or spontaneous late miscarriage would be less likely to have a diagnosis of HG. While the term HG is used relatively loosely in clinical practice, the NICE guidance defines HG as “prolonged and severe nausea and vomiting, dehydration, electrolyte imbalance, ketosis, the need for admission to hospital, and body weight loss of more than 5% of pre‐pregnancy weight.”2Electronic records for all women booking at University College London Hospital (UCLH) between April 2012 and January 2014 were identified by the Medical Coding and Information Systems departments. Women booking in this period who were referred to the Preterm Birth Clinic (PTBC) were identified from the prospectively collected PTBC database; Table Hospital admissions for HG were retrieved from the Medical Coding using ICD‐10 codes O21.0 (mild HG) and O21.1 (HG with metabolic disturbance); n=6 for women attending the PTBC and n=159 for the women with no risk factor for sPTB. Figure http://vassarstats.net/) was used to calculate the significance of the difference between the proportions of women with HG in the two groups ; none were admitted with HG. Fourteen of the 394 women included in the study took part in the OPPTIMUM randomized control trial, which investigates the use of progesterone in preventing sPTB, taking either progesterone or placebo after 20 weeks of gestationP = .037). When excluding women treated with progesterone from analysis, the difference in proportions remained significant  in the PTBC cohort were admitted with HG, compared with 159 out of 4762 women (3.33%) in the non‐PTBC cohort  when analyzing the Medical Birth Registry of Norway.The results of this study are in keeping with the concept that women who deliver spontaneously preterm have an altered hormonal response to pregnancy, which may in turn be associated with abnormal placentation. The known association between HG persisting into the second trimester and later placental dysfunction disorders, such as pre‐eclampsia and placental abruption,Additional work is needed to explore the difference in admission rates for HG between women with risk factors for sPTB and those without these risk factors. A prospective study comparing serum or salivary progesterone levels and pregnancy outcomes in the two groups may be useful in this respect.5This analysis is limited by the sample sizes of the subgroups within the PTB HG group; these are insufficient to allow subgroup analysis, which prevents us from studying whether there is a link between HG and gestational age at delivery. Furthermore, complete outcome data are not available for non‐PTBC women not admitted with HG; it is therefore impossible to infer what proportion of the non‐PTBC cohort actually delivered spontaneously preterm. Although this limits the conclusions that can reasonably be drawn from this study, it does not compromise the observation that women with risk factors for sPTB are less likely to be admitted for HG than women without these risk factors.Notably, the incidence of HG in the non‐PTBC cohort (3.33%) is higher than figures quoted elsewhere: 0.8%,We excluded women who had been treated with progesterone from the analysis on the grounds that this may have altered their risk of HG. The PTBC practice followed the RCOG statement at the time: to only prescribe progesterone prophylaxis for preterm birth within the confines of a clinical trial. The PTBC was recruiting to the OPPTIMUM randomized controlled trial.Finally, although we excluded women with certain key individual risk factors for HG—including multiple pregnancy, presence of trophoblastic disease, and thyroid disease6In summary, this study found that hospital admission for HG in women at increased risk of sPTB was reduced compared with women with no identifiable risk factors for sPTB. This supports our hypothesis that HG and sPTB are clinical manifestations of opposite extremes of a biochemical spectrum, and suggests that further exploration of the pathogenesis of HG may facilitate our understanding of the mechanisms underlying sPTB.


In the radiation treatment of moving targets with external surrogates, information on tumor position in real time can be extracted by using accurate correlation models. A fuzzy environment is proposed here to correlate input surrogate data with tumor motion estimates in real time. In this study, two different data clustering approaches were analyzed due to their substantial effects on the fuzzy modeler performance. Moreover, a comparative investigation was performed on two fuzzy‐based and one neuro‐fuzzy–based inference systems with respect to state‐of‐the‐art models. Finally, due to the intrinsic interpatient variability in fuzzy models' performance, a model selectivity algorithm was proposed to select an adaptive fuzzy modeler on a case‐by‐case basis. The performance of multiple and adaptive fuzzy logic models were retrospectively tested in 20 patients treated with CyberKnife real‐time tumor tracking. Final results show that activating adequate model selection of our fuzzy‐based modeler can significantly reduce tumor tracking errors.PACS number: 87 Various strategies have been developed which can be applied to compensate the effects of intrafractional motion, including breath holding, respiratory gating, and tumor tracking.(–) In this latter case, the exact information of tumor position in real time is required during treatment. The accuracy of real time tumor motion localization strongly depends on breathing motion variability during consecutive breathing cycles.(–) There are several strategies to obtain tumor position information over time, ranging from fluoroscopic imaging(–) to the use of external surrogates.(–) In fluoroscopy‐based approaches, although the exact tumor location can be captured at each frame, some concerns arise regarding the additional imaging dose received by patients. In radiotherapy with external respiratory surrogates, tumor motion is tracked using a consistent correlation between external surrogates and internal implanted clips, as detected by stereoscopic X‐ray imaging. In this way, tumor position is estimated by means of a correlation model, even though there are uncertainties in generating a consistent correspondence between external/internal data to infer tumor motion as a function of time.In external beam radiotherapy the final purpose is to produce 3D dose coverage onto the tumor volume while minimizing the dose to the surrounding healthy tissues. When tumor motion is an issue, the delivered dose does not match with the planned treatment, resulting in some over‐ and underdosage in the tumor volume, as a function of motion magnitude and frequency.1 Vari Some of these models were taken into account as a comparative study in a recent work. In these models, tumor motion (model output) is correlated with rib cage/abdomen motion, provided as model input. In this study, we further investigate correlation models based on fuzzy logic.(–) Fuzzy logic‐based correlation was selected due to the reduced dependency to precise input information versus conventional mathematical models. Recently, the improved performance of fuzzy logic‐based systems in data analysis causes arising applications of fuzzy logic theory in many fields.(–) Since the uncertainty in patient breathing results in a huge variability in terms of input/output dataset, a fuzzy environment may be optimal to correlate input data with tumor motion estimation.Recently, several external/internal correlation models have been developed.,–22(–) Data clustering partitions the dataset into several groups in order to create homogeneous clusters for better data analysis. Our fuzzy model consists of two different data clustering algorithms: 1) subtractive‐based, and 2) fuzzy C‐means (FCM)‐based.(–) In the subtractive clustering algorithm, each data point of the dataset can potentially be candidate as cluster center proportionally to the density of surrounding data points, whereas the FCM algorithm uses fuzzy grouping properties such that the given data point in a dataset belongs to several clusters with different membership degrees. Fuzzy logic models can also be combined with neural networks in a mixed adaptive neuro‐fuzzy inference system (ANFIS).In the construction of the fuzzy inference systems, data clustering plays an important role.–33 DThree different fuzzy‐based external/internal correlation models based on (i) subtractive clustering, (ii) FCM clustering, and (iii) ANFIS systems were implemented and comparatively tested. The generated models can be updated automatically, using new arrived training data points during treatment. Each correlation model is able to track tumor motion independently, and different motion estimations are calculated according to each modeler characteristics and also to the degree of variability of external/internal motion data.Since each patient has a unique breathing pattern and may also be affected by a different degree of variability, a model selectivity option is proposed here in order to select the best correlation model in the training step on a case‐by‐case basis.The implemented strategies were compared together and also with a typical CyberKnife Synchrony module in terms of residuals targeting errors in 20 patients.,–15 The external signals are obtained by monitoring three external markers placed on a vest, using an infrared tracking system (at ~25 Hz). Internal signals depicting tumor location are extracted through orthogonal X‐ray imaging, where implanted clips are automatically segmented. The correlation model is built at the beginning of each irradiation session and updated as needed over the course of treatment with intermittent X‐ray imaging. The accuracy of Cyberknife Synchrony measured in real patient treatments is reported (1 SD) within 1.9 mm (SI), 1.9 mm (LR), and 2.5 mm (AP) in terms of pure external/internal correlation errors. The Synchrony module models instantaneous tumor motion as a function of the external marker coordinates, and predicts tumor motion in the near future to account for the system lag (~115−192.5 ms). Retrospective clinical studies show that the uncertainties related to prediction in the future are of smaller magnitude than the ones due to external/internal correlation modeling. As the treatment proceeds, relevant data including tumor motion model and prediction, the coordinates of external markers, and intermittent clip localization, are logged and stored in ASCII format.Among the external radiotherapy devices, the Synchrony respiratory tracking system integrated with the CyberKnife robotic linear accelerator  is an implementation of real time tumor tracking. In this system, tumor motion is tracked by using a correlation model generated between the synchronized signals coming from external surface markers and internal implanted clips near or inside the tumor region.14 The was used for the analysis: 10 worst cases and 10 control cases, randomly selected among the population. Case selection was carried out in order to include both critical cases in terms of residual correlation error (worst group) and examples that are easily handled by current clinical solutions (control group).The performance of Synchrony respiratory tracking was analyzed in terms of residual tracking errors measured with X‐ray imaging during treatment. The same group of patients that was selected in our previous work23 was(–) Basically in fuzzy logic, linguistic variables are used to represent operating parameters in order to apply a more human‐like way of thinking. Fuzzy logic incorporates a simple IF–THEN rule‐based approach to solve a problem, rather than attempting to model a system mathematically, and this property plays a central role in most of fuzzy logic applications. More recently, fuzzy logic has been highly recommended to generate solutions for problems based on qualitative, incomplete or imprecise information, where rigorous, analytical solutions do not exist. The main idea of fuzzy systems is to extend the classical two‐valued modeling of concepts and attributes in a sense of gradual truth. It is currently used in the fields of business, systems control, electronics, traffic engineering, and weather forecasting, to yield superior results compared to conventional algorithms.The concept of Fuzzy Logic was proposed in 1965 by Lotfi A. Zadeh.–28 B(–) for membership function generation, as a requirement for the fuzzy inference system section Alternatively, fuzzy logic‐based systems require membership functions as a representation of the magnitude of participation for each input. The proposed fuzzy correlation model involves data clustering–34 fIn the implemented fuzzy logic algorithm, data from all three external markers were used in an input matrix with nine columns as input, where columns represent the x(t), y(t), and z(t) of each marker.The fuzzy correlation model was implemented in MATLAB  using the fuzzy logic toolbox. The model is built at the beginning of each irradiation session using the same training data considered by the Synchrony module  Data clustering analysis is the organization of a collection of datasets into clusters based on similarity. Two of the most representative techniques of data clustering were utilized in our model: subtractive clustering and fuzzy C‐means clustering.As seen in Fig. –33 DIn the training step, two fuzzy inference systems based on the above clustering approaches and also on an adaptive neuro‐fuzzy inference system are configured for motion prediction during treatment. The properties and the implementation of these inference systems are detailed in the following paragraphs. was considered in initial data grouping and membership function generation. In subtractive clustering, each data point of the dataset can potentially be candidate as cluster center, proportionally to the density of neighboring data points. Therefore, based on the following equation, a calculation is performed to define the cluster center:xi is the ith measured data point, cj is the center of the cluster, and r is the neighborhood radius or influence range.In this model, the subtractive clustering algorithm36 wasIn this algorithm, a small influence range yields many small clusters in the dataset and specifying large influence range results in few large clusters. In the implemented model, the value of such influence range was set to one‐third of the width of the training data space, according to the same approach followed in our previous work.23By the end of data clustering, a set of fuzzy rules and membership functions are extracted. Each cluster represents a rule. Since the membership functions used for our fuzzy inference system are in Gaussian shape, the cluster centers and the distance between them can be used as required parameter for membership function generation over the input and output training dataset.n groups using the subtractive clustering algorithm, as mentioned above.The fuzzy C‐means algorithm uses fuzzy partitioning for data clustering, such that each data point of the dataset belongs to several groups with specific membership grades between 0 and 1. The membership grade represents the relationship of a data point at each cluster, and its magnitude depends on the distance of given data point to the cluster centers. It should be noted that in this way, before employing the FCM technique, the training dataset is clustered into m is any real number greater than 1, uij is the grade of membership of x in cluster j, xi is the ith measured data point, and c is the center of the cluster. At first, FCM assumes the mean location of each cluster as initial cluster center. Next, the FCM algorithm assigns every data point a membership degree for each cluster, and then iteratively moves the cluster centers c and updates the membership degrees uij:From the mathematical point of view, membership functions in FCM clustering are obtained by minimization of the following metric:|U(k+1)−U(k)|<ε, where ε is a termination criterion between 0 and 1, U is the matrix of [uij] and k is the number of iterations.This iteration will stop when  Since this model is computationally more efficient and gives a faster response, its implementation is proper here for our real‐time motion estimation.The structure of the two above fuzzy inference systems used for our predication model is based on the Sugeno (or Takagi‐Sugeno‐Kang) model.39 SinANFIS is an adaptive network which uses neural network learning algorithms and fuzzy inference system to associate the external marker motions as input to the tumor motion as output. In ANFIS, the membership function parameters are determined using subtractive clustering in the fuzzy section, and then adjusted using either a back‐propagation algorithm alone or in combination with a least squares type method.pi, qi, and ri (i=1 or 2) are linear parameters, and Ai, Bi are linguistic labels characterized by appropriate membership functions. The Gaussian and Bell‐shape membership functions are increasingly popular for specifying fuzzy sets due to the smoothness and concise notation. The architecture of ANFIS consists of five layers, and the number of neurons at each layer equals to the number of rules. Layer 1 consists of input nodes. Each node generates membership grades using membership functions. In Layer 2 which includes rule nodes, the AND operator is used in order to product all incoming signals that represents the firing strength for that rule (wi). In the third layer, the purpose is to calculate the ratio of each ith rule's firing strength to the sum of all rules' firing strength. Thus the output is called normalized firing strength (wi). The fourth layer computes the contribution of each rule toward the total output. In layer 5, the single output node calculates the overall output by summing all incoming signals:As a brief description about the ANFIS structure, if x and y are assumed as input variables for single‐output fuzzy inference system, the output variable is obtained by applying fuzzy rules to a fuzzy set of input variables. The rule set contains two fuzzy if‐then rules for the first order Sugeno fuzzy model as follows:ai, bi, and ci) associated with the membership functions are tuned through the learning process in an iterative fashion. The parameter tuning process is controlled proportionally to the error value of the fuzzy inference system section in correlating the input/output dataset.In ANFIS, the nonlinear parameters  was exploited to check the accuracy of each modeler. The optimal modeler for each patient was selected relying on the preliminary check performed on 25% of the training dataset, assuming that optimal performance during training resulted as optimal during the rest of the treatment. Figure SUB−FIS=SUBtractive−based Fuzzy Inference System, and FCM−FIS=Fuzzy C‐Means‐based Fuzzy Inference System.Figures The statistical analysis (Kolmogorov‐Smirnov test) revealed that 3D targeting errors are not normally distributed at 99% confidence: hence, nonparametric statistical tests were used. The median and interquartile range of total 3D targeting errors overall 10 patients in control and worst groups are shown in Table <1%). A posthoc analysis based on ranks highlighted a significantly worse performance for SUB‐FIS and ANFIS if compared to the other strategies; conversely, the mean ranks for FCM‐FIS and CyberKnife can be considered comparable.Paired wise statistical comparison (Friedman test) showed no significant difference among the different algorithms for the control group, whereas substantial variations were confirmed for the worst group . As depicted for the worst group in Fig. Results show a significant interpatient variability in both groups, as confirmed by the Kruskal‐Wallis test  and average (control patients) working conditions for external/internal correlation algorithms. As inputs are received by the model, the number of clustering is determined; then, the required membership functions and rules are extracted, which have strong effects on the proved performance of the model.Three fuzzy correlation models were investigated to correlate external breathing motion as input data with tumor motion estimation: subtractive‐based fuzzy correlation model, FCM‐based fuzzy correlation model, and adaptive neuro‐fuzzy inference systemThe two former correlation models were selected in order to investigate the effect of data clustering techniques on membership function generation, that have an effective role on final modeler performance. On the other hand, a comparative investigation was performed considering adaptive neuro‐fuzzy inference system with two former modelers. Finally, a model selectivity option was proposed to select an adaptive modeler for each patient uniquely, relying on training data to identify the best performing strategy. This is meant to take into account that each patient has a unique pattern in breathing and also a different degree of uncontrolled motion (mainly patients with respiratory disorder). Through this algorithm, the best model may be found for each patient on a case‐by‐case basis, relying on a fully automated strategy for the selection of the optimal correlation.Results show that the performance of different algorithms on the control group is not statistically different, thus indicating that the implemented fuzzy models are able to reach an adequate accuracy. Conversely, significant differences were found among the worst cases group, with SUB‐FIS and ANFIS exhibiting degraded performance if compared to other strategies. The cumulative PDF in this case shows that the FCM‐FIS algorithm may ensure a benefit in terms of larger errors reduction Fig. .Among the fuzzy models, the implemented approximately 90% of control and 70% of worst cases were better tracked using FCM‐based fuzzy model than subtractive‐based fuzzy model. This was confirmed when subtractive clustering was used in the fuzzy section of ANFIS, as it operates as an overfitted version of SUB‐FIS due to the neural network capabilities in model learning. It should be considered that ANFIS takes a longer time for model configuration and update in comparison with the two other fuzzy modelers, owing to data processing in its neural network section.Finally, an investigation was done concerning the performance of model selectivity algorithm. As derived from the validity results, the performance of this algorithm was not successful for all patients. This may be due to an insufficient number of cases in the training dataset, which is extremely critical to handle in terms of learning performance. But the decisions for best modeler selection in worst patient group were 80% correct, and final results when the selectivity option is active leads to the best performance overall, both in terms of median error and variability.Three different fuzzy logic‐based correlation models to predict tumor motion were investigated. The final analyzed results show adaptive selection of adequate fuzzy correlation models on a patient specific basis is feasible, despite the limited availability of model training data.The authors acknowledge Sonja Dieterich for providing access to the clinical database. The research leading to these results has received funding from the European Community's Seventh Framework Programme ([FP7/2007‐2013] under grant agreement n° 215840‐2).


This study examined associations between household sanitation and enteric infection – including diarrhoeal‐specific outcomes – in children 0–2 years of age in a low‐income, dense urban neighbourhood.MAL‐ED study, 230 children in a low‐income, urban, Indian neighbourhood provided stool specimens at 14–17 scheduled time points and during diarrhoeal episodes in the first 2 years of life that were analysed for bacterial, parasitic (protozoa and helminths) and viral pathogens. From interviews with caregivers in 100 households, the relationship between the presence (and discharge) of household sanitation facilities and any, pathogen‐specific, and diarrhoea‐specific enteric infection was tested through mixed‐effects Poisson regression models.As part of the RR: 0.91, 95% CI: 0.79–1.06), bacterial infection  and protozoal infection , although not statistically significant, but had no association with diarrhoea  or viral infections . Models also suggested that the relationship between household toilets discharging to drains and enteric infection risk may vary by season.Few study households (33%) reported having toilets, most of which (82%) discharged into open drains. Controlling for season and household socio‐economic status, the presence of a household toilet was associated with lower risks of enteric infection (The presence of a household toilet was associated with lower risk of bacterial and protozoal enteric infections, but not diarrhoea or viral infections, suggesting the health effects of sanitation may be more accurately estimated using outcome measures that account for aetiologic agents. Despite an estimated 1.7 billion cases of diarrhoea annually, most of which are in children, the impact of enteric infections worldwide is underestimated due to high, undetected rates of asymptomatic infection vs. urban) Current knowledge about the associations between WASH and enteric infections is limited by the lack of precision of common WASH outcome measures (e.g. self‐reported diarrhoea) and differences in study locations  The Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and Consequences for Child Health and Development Project (MAL‐ED study); and (ii) the SaniPath Exposure Assessment Tool 2), poor sanitation and high burden of enteric disease Annually, Vellore has a dry season (January–May), a southwest monsoon (June–September) and a northeast monsoon (October–December) Ethical approval for the MAL‐ED study in Vellore was obtained from the CMC Institutional Review Board (IRB) prior to subject recruitment Stool specimens were collected from one study child per household as described in MAL‐ED study protocols Household selection has been previously described in detail Household surveys assessed demographics and sanitation, defecation practices of household members and reported water treatment. The target respondent was the female head of household. GPS points were collected at the time of household survey.Analyses were conducted in R version 3.1.2  using standard packages and the ‘lme4’ package for mixed‐effects models α of 0.05 was used for all tests of significance.Mixed‐effects Poisson regression models with a random effect for the child sampled were used to estimate associations between household sanitation and the risk of enteric infection or diarrhoea. Parasitic aetiologic agents were modelled individually as soil‐transmitted helminths or protozoa based on the potential for differential associations with environmental conditions Given the reversal in timing of exposure and outcome assessment, a sensitivity analysis of the presence of household sanitation, approximated using data collected at three to four time points during the follow‐up period for a ‘use of sanitation facility’ question as part of the socio‐economic metric Characteristics of the household and neighbourhood environments and child exposure behaviours were quantified through surveys with the adult caregiver Table . Overallvs. diarrhoeal) to characterise the infection burden, and by study population  to compare the representativeness of the SaniPath study households to the overall MAL‐ED study. A total of 3754 stool specimens were collected from children during the MAL‐ED study, 1650 of which were collected from the 100 children in SaniPath study households. Approximately 68% of routine and 79% of diarrhoeal stool specimens were positive for enteric infection  The associations between pathogen presence and the risk of diarrhoeal stool were evaluated using mixed‐effects Poisson regression (data not shown). Virus presence was associated with significantly higher risk of diarrhoeal stool (risk ratio (RR) for viral detection: 4.37, 95% confidence interval (CI): 3.39–5.63). Bacterial presence  and protozoal presence  were not significantly associated with risk of diarrhoeal stool.P = 0.01). Risk of bacterial infection, protozoal infection and diarrhoea did not differ significantly by season (data not shown).Seasonality of enteric infection risk was evaluated by mixed‐effects Poisson regression for all stool specimens in the SaniPath study households. Overall, the risk of enteric infection (defined as presence of any pathogen in stool) did not vary significantly by season (data not shown). Viral infections were 40% less likely during the northeast monsoon (October–December) than during the dry season  households had toilets that contained waste onsite. Although not significant, risk of enteric infection (infection with any pathogen) was 9% lower in children in households with toilets compared to those without. Risks of bacterial and protozoal infections for children in households with toilets were 13% and 36% lower , respectively, than for children in households without toilets, although these relationships were not significant. No association was observed between the presence of household toilets and risk of viral infection  or risk of diarrhoea . Although not significant, the associations between household toilets that emptied to open drains and enteric infection risk suggested seasonal variation: the presence of a toilet was associated with a 7% decreased risk overall and a 14% decreased risk during the dry season; however, during the northeast monsoon season – the heaviest period of rain, the presence of a toilet did not suggest any protective effects. Similar trends were also observed for risks of bacterial and protozoal infections.Associations between household sanitation and risk of enteric infection and diarrhoea were compared, controlling for season, household SES  of stool specimens collected may have been misclassified .The goal of this study was to evaluate the associations between household sanitation and paediatric enteric infections and diarrhoea in a low‐income, urban setting, focusing on associations with specific groups of aetiologic agents and diarrhoeal episodes. Household sanitation was generally associated with a lower risk of enteric infection, including lower risk of bacterial and protozoal enteric infections specifically, but was not associated with the risk of viral infections. Household sanitation was not associated with the risk diarrhoea in these children, suggesting that relationships between household toilets and health outcomes may be more appropriately evaluated with a specific focus on the type of enteric infection, rather than more nonspecific symptoms like diarrhoea. Although the presence of a household toilet was largely protective, data from households with toilets that discharged to open drains suggested a potential seasonal variation in the effectiveness of this specific type of toilet.This study is unique in testing associations between urban household sanitation and enteric infection in a paediatric cohort that captured most enteric infections and diarrhoea during the first 2 years of life and comparing risk factors by type of infection. While previous studies have explored risk factors for diarrhoeal disease in urban contexts, few have included laboratory‐confirmed enteric infections or examined infections by specific groups of pathogens in the analysis Although the study children were too young to use a toilet for much of the study period, children's contact with older family members may have connected them to faecal contamination, emphasising the importance of household toilet use by all family members in lieu of open defecation. Beyond contaminating the local environment, open defecation by older family members increased their own direct and indirect (e.g. through flies) contact with faecal contamination compared with household toilet use 6–108 required by many enteric bacterial pathogens We observed associations between household toilet presence and lower risk of bacterial and protozoal infections, but not viral infections, suggesting that use of toilets (and therefore sequestration of large volumes of faeces from human contact) may have been more effective in preventing bacterial and protozoal infections than infections with viruses. These findings may be due to differences between bacteria or protozoa and viruses in their transmission pathways and infectious doses, which in turn influenced their relationship with household sanitation. Compared to bacterial and protozoal infections, viral infections have more direct person‐to‐person contact and transmission via fomites because of their lower infectious doses and high titre shedding Shigella spp. and heat‐stable enterotoxin‐producing E. coli (ST‐ETEC) There were no observed differences in risk of diarrhoea in children with or without household toilets, suggesting that any changes in exposure to faecal contamination for young children associated with household toilets were not associated with symptomatic infections specifically. Recent reanalysis of data from the Global Enteric Multicenter Study (GEMS) using quantitative molecular methods has shown strong quantity‐dependent associations between detection levels in stool and diarrhoea for only a handful of enteric organisms, specifically Although not significant, we observed evidence that the relationship between household toilets leading to open drains and risk of enteric infection may vary by season. While further research is needed to substantiate this observation, it may reflect the poor containment of faeces from these toilets. Owning and using toilets that discharged to open drains may have been of limited benefit during periods of heavy monsoon or flooding because of the high levels of human‐specific faecal contamination discharged into the immediate environment This study has some notable strengths and limitations in outcome and exposure measurements. Measurement of enteric pathogens in stool specimens collected regularly over the first 2 years of life provided a rich dataset that allowed sensitive assessment of the relationships between household sanitation and multiple health outcomes, including symptomatic and asymptomatic enteric infections vs. use of sanitation facility in socio‐economic assessments). Assessment of household SES was robust, given that household assets, household income, and mother's education were assessed at three to four time points during the follow‐up period and classified into MAL‐ED site‐specific categories Static assumptions were necessary to characterise household sanitation practices because household surveys were conducted at the end of the MAL‐ED study period. Sensitivity analyses suggested low levels of temporal misclassification of household sanitation, although these may have been sufficient to affect study power in determining statistical significance for many estimates. Exact determination of misclassification was hindered slightly by differences in the question administered to the respondent (presence/absence of household toilet in exposure assessment The results of this study are important for mitigation of paediatric enteric infections in under 2‐year‐olds, especially in dense, urban settings. Children in this study were enrolled at birth and selected at random for outcome follow‐up, with robust, comprehensive measures of enteric infections over the entire 2‐year period, when children may be most susceptible to growth‐faltering due to repeat enteric infections ® system, future sanitation studies should consider measures of enteric organisms present in stool to better understand how changes to the immediate household environment cause changes in faecal exposures and infection Mitigation of paediatric enteric infection by household sanitation may have important implications for malnutrition, growth and cognitive development, independent of diarrhoea In this setting, the presence of a household toilet was associated with lower risk of paediatric bacterial and protozoal infections, but not viral infections or diarrhoea. Further evidence suggested that these associations may vary by season, likely due to toilets discharging excreta directly into open drains and subsequent spread of faecal‐contaminated floodwaters during the heaviest rainy season. This study contributes to growing evidence around the importance of measuring enteric infections, and not solely diarrhoea, as the primary health outcome in studies of household sanitation Table S1. STROBE Statement—checklist of items that should be included in reports of observational studies.Click here for additional data file.


The purpose of this work is to extract three‐dimensional (3D) motion trajectories of internal implanted and external skin‐attached markers from kV cone‐beam projections and reduce image artifact from patient motion in cone‐beam computed tomography (CBCT) from on‐board imager. Cone beam radiographic projections were acquired for a mobile phantom and liver patients with internal implanted and external skin‐attached markers. An algorithm was developed to automatically find the positions of the markers in the projections. It uses normalized cross‐correlation between a template image of a metal seed marker and the projections to find the marker position. From these positions and time‐tagged angular views, the marker 3D motion trajectory was obtained over a time interval of nearly one minute, which is the time required for scanning. This marker trajectory was used to remap the pixels of the projections to eliminate motion. Then, the motion‐corrected projections were used to reconstruct CBCT. An algorithm was developed to extract 3D motion trajectories of internal and external markers from cone‐beam projections using a kV monoscopic on‐board imager. This algorithm was tested and validated using a mobile phantom and patients with liver masses that had radio‐markers implanted in the tumor and attached to the skin. The extracted motion trajectories were used to investigate motion correlation between internal and external markers in liver patients. Image artifacts from respiratory motion were reduced in CBCT reconstructed from cone‐beam projections that were preprocessed to remove motion shifts obtained from marker tracking. With this method, motion‐related image artifacts such as blurring and spatial distortion were reduced, and contrast and position resolutions were improved significantly in CBCT reconstructed from motion‐corrected projections. Furthermore, correlated internal and external marker 3D‐motion tracks obtained from the kV projections might be useful for 4DCBCT, beam gating and tumor motion monitoring or tracking.PACS numbers: 87.57.Q, 87.57.C‐ However, organ motion either involuntary such as respiration, gas motion in the rectum, and heart beating or voluntary patient relaxation produces image artifact in kV CBCT.(–) because it takes a longer scanning time. The kV cone‐beam projections used in CBCT reconstruction are acquired over an extended scanning period of about 1 minute as required to perform a full rotation of the linear accelerator gantry.On‐board imaging (OBI),2 ha(–) that includes tumor motion in addition to the volumetric CT anatomical patient data to define PTV for treatment planning. One approach to minimize motion artifacts in CT is fast scanning using shorter scanner rotation time and multislice technology. Several other techniques were advocated to reduce motion artifacts by retrospectively correcting motion in the projections prior to CT reconstruction. Vedam et al. corrected motion in sinogram space prior to CT reconstruction. Some of above techniques has been applied successfully to improve image quality in conventional CT. However, the application of these techniques to CBCT from kV OBI systems is limited where the degradation of image quality by motion is stronger in the later one.Stereotactic body radiation therapy with a large single dose, or hypo‐fractionated doses, and intensity‐modulated radiation therapy techniques represent a challenge because of the need for more accurate patient setup and tumor localization. The delivery of large conformal doses may be limited by organ motion. For example, the treatment margins needed to correct for respiratory motion may require large planning target volumes (PTV) that includes normal tissue and critical structures. Several papers have reported about the employment of 4DCT–13 t(–) and (b) indirect tumor tracking by external marker or respiratory sensor monitoring.There are several techniques to track tumor motion that include: (a) direct tumor tracking using radiographic or fluoroscopic imaging of internal fiducial markers implanted inside the patient,–23 aIn this work, an algorithm was developed to extract 3D motion trajectories of metal markers by tracking the position of seed markers in kV cone‐beam radiographic projections. This algorithm calculates six degrees of freedom required to determine both magnitude and direction of each marker using the monoscopic kV on‐board imager. The motion tracks from the projections were used to investigate correlation between external and internal marker motions in liver patients. Further, the trajectories of the markers were used to reduce motion from the projections, which were then used to reconstruct CBCT.40×30 cm2. Cone‐beam projections are usually acquired with a matrix of 1024×768 pixels, 16 bit‐depth and 7–10 frames per second. The kV, mA, and ms depend on the scanning protocol; for example, the HF and bowtie mode uses 125 kV, 80 mA and 25 ms. Bowtie filters  are used in CBCT both to reduce patient dose as well as to improve image quality by attenuating low‐energy scattered radiation.A kV OBI system mounted on a Varian Trilogy machine  was used to acquire cone‐beam projections. The OBI consists of a diagnostic quality kV X‐ray source and an amorphous‐silicon flat‐panel imager  held by robotic arms mounted on the linac gantry. The source and imager are extended using the robotic arms and are set opposite to each other at 100 cm and 50 cm from isocenter, respectively, during image acquisition. The system rotates nearly 360° with the OBI at right angle with respect to the MV beam and takes nearly one minute to acquire about 650 projections. Two scanning modes are used: (a) full‐fan (FF) with a maximum field of view (FOV) of 17 cm thickness and 25 cm diameter, and (b) half‐fan (HF) with a FOV as large as 15 cm thickness and 50 cm diameter. The flat‐panel imager provides an effective imaging area of nearly We used a commercially available phantom ,T, of either an internal or an external metal marker with an image, I, from the sequence of radiographic projections acquired in a CBCT scan. The template image is obtained by selecting a region‐of‐interest (ROI) from one of the radiographic projections that includes the metal marker. This cross‐correlation algorithm registers the metal marker to similar objects in the projections by optimizing both intensity and shape in the ROI that includes the marker, and then determines the position of the point of maximum overlap. The position of maximum correlation is considered to represent the position of the metal marker in a projection. The error in localization of a marker position is about ± 0.5 pixel, which corresponds to about ± 0.13 mm at isocenter.The motion of internal and external markers was measured by tracking the marker positions in kV cone‐beam projections. The marker tracking is based on a normalized cross‐correlation image registration algorithm. and the corresponding central pixel on the imager is . The patient is assumed to be composed of discrete voxels that have sides with equal distances (0.26 mm at isocenter). The marker position in the K‐direction represents patient's superior‐inferior (Z), and the J‐direction represents patient position in a plane perpendicular to the superior‐inferior direction (X‐Y), as shown in (ρ=rsin(β)=x2+y2 is the radial distance in X‐Y plane r=x2+y2+z2,θ is the projection view angle, α=tan−1(xy) is the angle which depends on the location of the patient voxel projection in X‐Y plane, β=tan−1(x2+y2z) is the angle between r and Z‐direction as shown in  is projected onto a pixel  on a radiographic projection at a particular angular view, θ, as shown in shown in ‐b. Usingshown in , the relshown in ‐b, SAD ic=SADSID, which represents a scaling factor. Using similarity of triangles in From Eq. From Eqs. (2) and (3), the relationship between the patient stationary voxels  and the imager pixels  is given by the following: according to Eq. c=SADSID, as given by Eq. If we assume that the patient is not moving and thus his voxels are stationary, the position of the pixels on cone‐beam projections  changes from one view to another depending on the position of the corresponding voxels relative to the imaging isocenter and the projection view angle. A voxel in the patient that matches with the isocenter will be projected at the center of the imager  from the mobile  tracks, as shown by the simulations in  including patient motion was extracted first from kV projections using normalized cross‐correlations, as explained previously. Then, the stationary track  was found with nonlinear curve fitting of a moving marker track  using the Levenberg‐Marquardt method,In this algorithm, the marker track  from mobile  voxel positions calculated from Eq. Ax,Ay and Az are motion amplitudes, fx,fy and fz are motion frequencies, and δx,δy and δz are phases in patient coordinate system in the X‐, Y‐, and Z‐directions, respectively.The displacements  due to motion in patient coordinate system can be calculated by subtracting the stationary )−z(ks) shows thI′, of the transformed radiographic projection at a certain angular view, θ, is given by the following equation:Position shifts  due to patient motion that are extracted from the sinogram of an internal marker were used as a transformation vector, , to map the position of the external marker at the various angular views for all projections from a CBCT scan. All pixels in each projection are shifted equally with the spatial shifts from the particular internal marker that is being tracked. Marker shifts along J‐axis of a projection removes marker motion in X‐Y plane using patient coordinate system. The marker shifts along K‐direction eliminates motion shifts in the superior‐inferior direction. The resultant 2D‐intensity map, In this work, we have investigated seed marker motion in four patients with liver masses. These patients had two to three seed markers that were implanted in the liver within and around the lesion area. At least one additional external marker was placed on patient skin anterior to the internal makers. These patients were treated with six fractions of 5 Gy per fraction using IMRT. ‐b show a< 25cm diameter), where the region of interest including seed markers shows up in each projection.The external and internal marker motion tracks for patient (1) (as shown in  and (d)) and (d)  this study indicates that the motion of external marker may not correlate fully with internal marker motion. The motion correlation pattern varies from one patient to another and, even within the same patient, the motion amplitudes and phases vary between internal and external markers. For example, the motion tracks of patient 1 shown in  which measures only a noncalibrated anterior‐posterior motion component of an infrared marker attached to the patient skin. In addition, tracking merely an external marker does not guarantee that its motion is correlated with internal tumor motion. The technique presented here can be useful to establish motion correlation between internal and external markers that can be used as a baseline for further patient motion monitoring or tracking. One possible application scenario of this approach is to establish radiographically a correlation of the motion between internal and external makers using cone‐beam projections considering differences in motion amplitudes and phases. Then, the external marker can be tracked using nonradiographic methods such as infraredSimilar to the results of numerous previous works,,–31shown in ‐d demons the approach used in this work uses internal marker motion to eliminate motion in the projections prior to the reconstruction of CBCT. CT images reconstructed from projections sorted at certain respiratory phases have residual motion because a respiratory phase does not represent a stationary state. The approach introduced in this work actually reduces motion artifacts in CBCT where projections are mapped onto a semi‐stationary position in which the motion amplitude is zero, instead of capturing the projections at different phases followed by reconstruction of CT at one phase or the other as it is done in phase‐sorted 4DCT. Another advantage of using projection mapping is that all projections from different angular views are used in the construction of motion‐corrected CBCT. However, 4DCBCT reconstructed from sorted projections include only the projection acquired in a certain respiration phase, which limits its image quality. to correct patient motion, this technique uses the measured specific‐patient motion from implanted seed marker. The track of only one seed marker was used here to correct for patient motion. Furthermore, with this projection mapping technique, an average shift from more than one seed marker can be used to correct patient motion in the projections, or the motion of several markers can be used to map the corresponding ROI's locally in the projections in order to reduce differential motion at various patient parts. These techniques may be interesting for a future investigation to test the ability to perform differential motion correction in the tumor and other regions of interest.In the approach introduced in this work to correct respiratory motion, the whole patient body is assumed to move as a rigid body. Although the whole body may not be moving altogether rigidly, the ROI which includes the seed marker and the surrounding lesion will be moving similar to the marker within. Thus, anatomical mapping will be accurate for the ROI that includes the seed marker and tumor. This technique uses actual motion of a marker implanted in the ROI, while in CT reconstructed from phase‐sorted projections, an external marker is often used which could be out of phase with internal tumor motion. In contrast to other algorithms based on motion simulation or modeling,,38(–) radiographic projections,(–) external skin surface imaging, or respiratory sensor monitoring.(–) a metal marker with high‐contrast resolution and similar features in most angular views works well with automatic tracking algorithms, as we have demonstrated in this work. Furthermore, the motion of internal markers varies depending on the location of the markers within the liver , wherThis technique is limited by imaging blind spots of marker motion in a plane parallel to the CAX line. However, this motion component does not influence image quality and thus is not required to reconstruct motion‐free CBCT using the algorithm developed here. Further, the probability that one or two internal markers will have the motion in this plane is small considering the whole imaging solid angle. If a blind spot takes place in one angular view, motion signal will be recovered from other angular view and thus the marker motion track is reconstructed from the available data. Another limitation of the use of marker motion from cone‐beam projections is the visibility of the markers in all projections. This can be resolved by using full‐fan mode to image only appropriate ROI that includes the tumor and seed markers. Considering the 3D volumetric imaging and motion information by marker tracking from cone‐beam projections, our technique extends the use of the kV OBI to an additional dimension to perform 4DCBCT. Furthermore, the amplitudes of motion extracted in real time from internal markers just prior to dose delivery can be used to define accurate margins for the treatment planning target and perform adaptive radiation therapy.In contrast with fluoroscopy or other radiographic techniques, using cone‐beam projections to obtain tumor motion requires no additional dose to the patient other than that used for CBCT imaging. Furthermore, sagging of the heavy linac gantry during the rotation around the patient in imaging produces shifts that add small displacements to the position of the voxels projected on the imager.In this work, an algorithm to extract 3D internal and external marker motion from kV cone‐beam radiographic projection was developed. Although internal and external marker motion might correlate with each other and have similar motion frequency, the markers were found to have different motion amplitudes and phases in liver patients. 3D‐motion of internal seed markers provides actual tumor position variation due to respiratory motion. Combining this information about tumor motion and 3DCBCT allows not only accurate patient setup for IGRT procedures, but also real‐time prediction of tumor motion trajectory just before starting patient treatment. The marker motion track from cone‐beam projections provides motion information that can be compared with the motion of other external markers such as the RPM infrared signal to test internal and external marker correlation and validation of beam gating or tumor tracking. Internal marker motion can be extracted directly from kV cone‐beam projections and used for online tumor tracking by imaging during treatment in arc therapy. Motion produces strong image artifacts in kV CBCT such as blurring, spatial distortion, poor contrast and position resolutions. The technique developed and tested in this work to track and correct marker motion in cone‐beam projections prior to reconstruction eliminates motion‐related image artifacts.


Myotis myotis and Myotis blythii) at sympatric colonial sites. We tested whether distinct, species‐specific trophic niches and food availability patterns may drive interspecific differences in key life‐history components such as age at first reproduction and survival. We took advantage of a quasi‐experimental setting in which prey availability for the two species varies between years (pulse vs. nonpulse resource years), modeling mark‐recapture data for demographic comparisons. Prey availability dictated both adult survival and age at first reproduction. The bat species facing a more abundant and predictable food supply early in the season started its reproductive life earlier and showed a lower adult survival probability than the species subjected to more limited and less predictable food supply, while lifetime reproductive success was comparable in both species. The observed life‐history trade‐off indicates that temporal patterns in food availability can drive evolutionary divergence in life‐history strategies among sympatric sibling species.Life‐history theory predicts trade‐offs between reproductive and survival traits such that different strategies or environmental constraints may yield comparable lifetime reproductive success among conspecifics. Food availability is one of the most important environmental factors shaping developmental processes. It notably affects key life‐history components such as reproduction and survival prospect. We investigated whether food resource availability could also operate as an ultimate driver of life‐history strategy variation between species. During 13 years, we marked and recaptured young and adult sibling mouse‐eared bats ( CentralEnergy input remains the crux for individual decisions in resource allocation trade‐offs among different life‐history components. As variation in food availability is the main determinant of energy supply for any given organism, it might be considered an ultimate driver of life‐history evolution Lack, . This isSibling species are phylogenetically more related to each other than to any other species. These species share the same evolutionary history, having diverged from a recent common ancestor. The advantage of working with sibling species, at least in areas where they have occurred in sympatry for generations, is that observed interspecific differences are most likely the result of diverging evolutionary trajectories. Their species‐specific morphological, ecological, or behavioral traits can therefore be seen as recent (post‐speciation) adaptations to different niche contexts, unless they were the root cause of speciation by disruptive selection. Whatever the mechanism of speciation, this allows strong inferences to be made about how natural selection drives the evolution of life‐history traits between species.Myotis myotis (Borkhausen 1797) exploits primarily ground‐dwelling invertebrates, mostly carabid beetles  that occur as fully grown adults at a fairly constant rate from early spring until the onset of hibernation  relies on bush crickets . Bush crickets are a profitable prey when they reach adult body size, that is, from late spring or early summer onward, but are not in early spring  from 1989 to 2001 (except in 1994) at two colonial roosts  in the upper Rhône valley . Every captured female was inspected for age. We distinguished two age classes: young  and adults . Note that the age of the individuals marked as adults cannot be exactly known. We also recorded the reproductive status of all captured females as reproducing  and nonreproducing (no embryo detected in abdomen cavity or no milk extractable from mammary glands). Each individual was marked with a forearm ring.Mass occurrence of cockchafers was assessed yearly in May by visiting mouse‐eared bats’ traditional foraging grounds located by radiotracking .2.22.2.1n), breeding dispersal (b), and recapture (p) from the capture–recapture data. This model is similar to the model introduced by Lebreton, Hines, Pradel, Nichols, and Spendelow  and of 4 × 1 vectors of recapture probabilities. As some model parameters change with age and some transitions are restricted to specific age classes, we used age‐specific transition matrices. The model requires that the age at which all bats have started to reproduce is fixed; otherwise some parameters are not estimable :We used a multistate capture–recapture model to estimate the probabilities of apparent survival (ϕ), first reproduction (α), natal dispersal  and the movement between colonies . The subscripts refer to age  and to reproducing status . Note that the individuals marked as adults only provide information about adult survival, breeding dispersal adult recapture probability, while individuals marked as juveniles provide information about all parameters of the model.The superscripts of the model parameters indicate to which colony they are specific  was undergoing renovation. Hence, the interaction colony*time had to be kept in the model. However, we did include models with and without effects of species and reproductive status.To model natal and breeding dispersal between the two nursery roosts (movement), we considered four models. We formulated models where natal and breeding dispersal were both species‐specific; where only one of the two was species‐specific; and where both were the same in the two species. We did not consider models with year effects because we were not interested in such effects on dispersal and due to the sparseness of available data.To model the probability to start reproduction, we considered four different models and only young females with known age were informative about this parameter. We assumed that the probability to start to reproduce was constant over time. This was enforced due to the sparseness of the data, but we will relax this assumption in the second modeling step. The models considered differed as to whether or not effects of species and nursery roosts  were present. Comparison between these models enabled to evaluate whether the probability to start to reproduce depended on the colony of birth (origin) and species.Survival probabilities were modeled with 14 different models. We always considered two age classes: the first referred to the first year of life and the second to all ages beyond the first year. The fitted models differed as to whether or not year‐specific variations occurred in both age classes or in the first age class only, whether survival probabilities differed between species, and whether colony effects were present. Moreover, we included models in which the temporal variation was different between species (interacting models) and models in which temporal variation was the same (additive models and models without species‐specific parameters). Comparing these models enabled us to evaluate whether the two species were similarly sensitive to environmental variation.2.2.3In the following modeling exercise, we evaluated whether survival and age‐specific probability of first reproduction were impacted by cyclic food conditions such as the mass occurrence of cockchafers in some years. The constructed models were based on the most parsimonious structure identified during the preceding modeling. We fitted models in which the target parameters were a function of whether a specific year was linked or not with mass occurrence of cockchafers. We included models where the target parameters of both species were a function of cockchafer years, and where only one species was impacted. This way it was possible to assess whether both species were similarly affected by cyclic food conditions. For the age‐specific probability of first reproduction, we included the effect of cockchafers as a cohort as well as a temporal effect. For the probability of starting to reproduce beyond the first year, this makes a difference: The cohort model tests for long‐lasting effects of cyclic food conditions; that is, it assumes that the probability to start to reproduce is different for individuals born in a cockchafer year vs. a non‐cockchafer year. By contrast, in the temporal model, the year of birth is assumed to only affect the current year. This model therefore simply assumes that the probability to start to reproduce differs depending on whether it occurs in a cockchafer year or not.For all of the models, we considered common cockchafer years across the whole study area. Initially, we also tested for colony‐specific cockchafer years, but these yielded similar results, which is why they are not presented here. From the parameter estimates, we calculated the mean age at first reproduction as3χ2207 = 201.12, p = .60).During the 13‐year study period, we marked 430 and 849 female lesser and greater mouse‐eared bats, respectively, of which 227 and 461, respectively, were individuals of exact known age. The goodness‐of‐fit test of a general multistate model was acceptable  vary more over time . An average lesser mouse‐eared bat can expect to have 3.6 years of reproductive opportunities during its life , whereas a greater mouse‐eared bat will have 3.3 reproductive years at its disposal (age at first reproduction: 2 years). As mouse‐eared bats attempt to breed every year, have a litter size of only one, and face identical preweaning mortality (Arlettaz, As reported for mammals in general (Descamps, Boutin, Berteaux, & Gaillard, The fact that adults move more between colonies than first‐year individuals may result from breeding as closely as possible to crucial foraging grounds—notably where there is sporadic mass prey supply, that is, availability of the pulse resource represented by cockchafers. (Arlettaz, M. blythii on cockchafers in some years makes them engage earlier into reproduction in cockchafer years as a result of an accelerated pregnancy (Arlettaz et al., M. blythii's diet (Arlettaz et al., In contrast to the evidence that major niche differentiation affects survival and age at first reproduction while enabling a stable sympatric coexistence (Arlettaz, We conclude that major patterns in species‐specific prey availability and interaction with a clear‐cut interspecific trophic niche partitioning can result in diverging evolution of life‐history strategies. Beyond contrasted prey preferences, more subtle spatiotemporal fluctuations of prey availability had a less perceptible impact on life‐history components. As our two model species are true sibling species sharing a more recent common ancestor than any other pair of living bat species does, only niche partitioning can drive the major life‐history differences and subtle life‐history adjustments we observed at the interspecific level. Our results show that the classical life‐history trade‐offs typically observed within a species also operate at the interspecific level, showing that evolution of life‐history strategies is eventually governed by species‐specific patterns of trophic resource availability and acquisition during niche differentiation.None declared. Click here for additional data file.