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The scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients' survival with benign end-stage liver diseases (BESLD) by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD) and a general-illness severity model (the sequential organ failure assessment score, or SOFA score).
Methodology/Principal Findings
With retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP) network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na+; presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, with c-statistic of 0.91 (P<0.001) in one-year and 0.88 (P<0.001) in two-year prediction. The performances of SOFA and MELD were fairly poor in prognostic assessment, with c-statistics of 0.70 and 0.66, respectively, in one-year prediction, and 0.67 and 0.65 in two-year prediction.
The posttransplant prognosis is a multidimensional nonlinear problem, and the MLP can achieve significantly high accuracy than SOFA and MELD scores in posttransplant survival prediction. The pattern recognition methodologies like MLP hold promise for solving posttransplant outcome prediction.
Citation: Zhang M, Yin F, Chen B, Li YP, Yan LN, Wen TF, et al. (2012) Pretransplant Prediction of Posttransplant Survival for Liver Recipients with Benign End-Stage Liver Diseases: A Nonlinear Model. PLoS ONE 7(3): e31256. https://doi.org/10.1371/journal.pone.0031256
Editor: Kwan Man, The University of Hong Kong, Hong Kong
Received: September 20, 2011; Accepted: January 5, 2012; Published: March 1, 2012
Copyright: © 2012 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from the National Basic Research Program of China (also called the 973 Program) (No. 2009CB522401), the Ph.D. Programs Foundation of the Ministry of Education of China (No. 20110181120037), and the China Postdoctoral Science Foundation (No. 20090461343). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Orthotopic Liver transplantation (OLT) has become an established treatment approach for patients with benign end-stage liver diseases (BESLD, i.e. non-neoplastic diseases), but the growing scarcity of grafts compared to numbers of waiting patients, coupled with the high cost of this procedure, make it imperative to make difficult decisions about how to distribute such scarce organs [1]–[3], and highlight the need to identify patients likely to have relatively good outcomes after transplantation [4]–[6]. This need is particularly acute in the Asia-Pacific region, where the carrier rate of hepatitis B virus (HBV) is estimated at 20%–30% [7], [8] and large numbers of BESLD patients with HBV-related cirrhosis and severe hepatitis B need OLT. Under such circumstances, the ideal allocation system would allocate livers to candidates who are most likely to die without a transplant, but who also have a high probability of survival after OLT. The balanced application of a model for liver transplant outcome estimation, in concert with a model for end-stage liver disease (MELD) estimating disease severity, would improve transplant outcomes and maximize patients' benefit from OLT [9].
In order to incorporate likely posttransplant prognosis into decisions about grafts allocation, and to facilitate informed decision-making by potential transplant recipients and their relatives [10]–[12], it is necessary to accurately assess the likelihood of posttransplant survival based on information that is available before transplantation.
Although there have been some attempts to develop a model that meets this requirement, most lacked sufficient discriminating accuracy or simply stratified the prognostic risk [4], [6], [9], [11]–[14]. One major reason for this is inappropriate choice of modeling method [13]. Survival prognosis is a complex nonlinear relationship affected by many interactive factors, especially for a complicated organ transplantation procedure; however, most current models were developed by linear methods, such as multiple regression.
Artificial neural network (ANN) is a computer-based nonlinear data mining mode that can recognize relationships between a series of independent variables and the corresponding dependent variable. It is more successful than traditional linear methods when the prognostic effect of a variable is influenced by other variables in a complex multidimensional nonlinear function, or when the importance of a given prognostic variable is expressed as a complex unknown function of the value of the variable [15], [16]. Thus, ANN is particularly suited to modeling complex multidimensional patterns [17], [18], and has had remarkable success in many medical problems that are too complicated for linear models [15], [19], [20]. To date, there have been a few attempts to use ANN for outcome prediction after organ transplantation [17], [21], [22], but no reliable ANN model had been developed specifically for BESLD recipients.
We investigated the feasibility of using multi-layer perceptron (MLP), arguably one of the most efficient ANN for prognostic research [22], [23], to develop a prognostic model to predict individualized survival probability after deceased donor OLT in recipients with BESLD, employing typically available, objective preoperative characteristics. Furthermore, we evaluated and compared the predictive accuracy of this MLP network with a BESLD-specific prognostic model (MELD) and a general-illness severity prognostic model (the sequential organ failure assessment score, or SOFA score).
Data source
Between February 1999 and August 2009, 386 adults with BESLD received deceased-donor (either no heartbeat or brain dead) liver transplants at the 4300-bed West China Hospital of Sichuan University. We excluded 15 recipients with combined organ transplants or partial organs and 11 recipients with incomplete follow-up records. The remaining 360 transplants were involved in this study and followed up by August 31, 2010. Maintenance immunosuppression initially consisted of a triple-drug regimen that included either tacrolimus or cyclosporine, mycophenolate, and prednisone; and that recipients were eventually weaned to dual or single agent.
We extracted demographic characteristics of donors and recipients, pretransplant clinical records (Tables 1 and Table S1), and recipients' follow-up information form the electronic database of the liver transplantation center at West China Hospital. Surgical and some donor factors were not included in the model development, since they could not have been known when recipients decided whether to undergo OLT and were ranked on the waiting list. All included data were taken from the most recent examinations prior to transplantation, since they reflected the current medical condition of the candidate at time of transplantation.
All organ donations recorded in the electronic database were contributed voluntarily, and no grafts were obtained from executed prisoners or other institutionalized persons. All of the donors or their families had provided written, valid informed consent for donation before the organs were procured. Each liver donation and transplantation in our center was approved by the Medical Ethics Committee of West China Hospital, Sichuan University, and the study protocol was carried out in accordance with the Declaration of Helsinki.
Dataset division
A data-splitting approach was used in this study. The recipients were randomly divided into a modeling set (80% of the total sample, 290 recipients) used to construct the MLP network, and a validation set (20% of the total sample, 70 recipients) used to assess the models' predictive accuracy; the validation samples would not be involved in the model development. The modeling set was randomly re-divided into a general training set (80% of the modeling set, 232 recipients) and a cross validation set (20% of the modeling set, 58 recipients) to perform the internal cross validation in MLP training.
Statistical analysis
Continuous variables were reported as mean ± standard deviation and compared using Student's t test; categorical variables were reported as numbers and percentages, modeled as dummy variables, and compared using the chi-square test. A value of P<0.05 was considered significant in all the analyses. All analyses, except the MLP development, were carried out using SAS 8.0.
MELD and SOFA scores calculation
The BESLD-specific illness severity was evaluated by the MELD and MELD-Na+ scores, which were calculated according to the following formulas: MELD = 3.78×loge TB (mg/dl)+11.20×loge INR+9.57×loge Crea (mg/dl)+6.4 [24], MELD-Na+ = MELD - Na+−(0.025×MELD×(140−Na+))+140 [25].
The general illness severity was assessed by the SOFA score, which is composed of scores from six organ systems (respiratory, coagulation, liver, cardiovascular, renal, and neurological) graded from 0 to 4 points according to normal function or the degree of dysfunction [26] (Table 2).
The MLP network development
An MLP consists of a densely interconnected set of units. In this study, we developed a three-layer network which not only can approximate any reasonable function to any degree of required precision as long as the hidden layer is large enough, but also has an advantage in computing speed compared to multiple hidden layer networks [27]. The concept of a neuron is a high-level abstraction that encompasses both certain values and a set of operations that are performed on those values, and neurons are tied together with weighted connections. The MLP was developed using STATISTICA 8.0.
Determination of input neurons.
We performed the forwards stepwise selection algorithm to screen and identify the input feature variables from the candidate variables (Table 1 and Table S1), in which quantitative variables were assigned one-to-one to the neurons and each sub-category of every categorical variable was defined as an input neuron. All input quantitative variables were scaled linearly between 0 and 1.0 using the following transformation formula, where min{xij} and max{xij} were the minimum and maximum values of the variable. The input categorical variables were entered as dummy variables.
Determination of output neuron.
The probability of survival at posttransplant one year and two years was entered as continuous output on the interval 0–1, in which 0 represents death and 1 represents survival, so the MLP output values represent the probability of posttransplant recipient survival. Survival was chosen as the outcome endpoint because it is the most reliable and unbiased variable in the prognostic research [28].
Determination of hidden neurons and network transfer function.
The hidden neurons calculate the weighted sum of inputs from the input neurons and produce the output result through an activation algorithm (i.e. transfer function). The weights are adjusted based on the training data in order to minimize the error estimate function [29]. Therefore, the approximate number of hidden neurons and the corresponding transfer function are closely related to the predictive accuracy of the network. In this study, the number of hidden neurons varied from two to 35, and the alternative transfer functions included identity, logistic, tanh, exponential, gaussian and softmax. We applied the enumerative combinatory method to exhaustively evaluate all possible combinations of hidden neuron numbers and transfer functions, then identified the combination with the best predictive accuracy.
Experiments have verified that the predictive accuracy of an MLP initially increases with the number of training iterations, but starts deteriorating after a critical point, because the network becomes over-fitted to recognize specific cases rather than learning general characteristics [27]. One effective and widely-accepted way to prevent this over-fitting is to use cross-validation to stop the training at the point of maximum generalization.
Network training process.
The training rule used in this MLP was supervised, feedforward, back-propagation of error, which could adjust the internal parameters of the network over repeated training iterations to improve the overall accuracy, by modifying the weight of the connections between neurons. In detail, once an input variable is applied as a stimulus to the input layer, it is propagated through hidden layer until an output is generated; this output is then compared with the desired output and an error signal is calculated; this error signal is then transmitted backwards across the net and the weight of the connections between neurons is updated to decrease the overall error of the network; as training proceeds, the difference between the network output and the desired output decreases to a minimum [30].
Model Validation
The performances of the MLP, SOFA score, and MELD score in predicting survival at posttransplant one year and two years were assessed in a validation set by measuring both calibration and discrimination ability [31]. We chose these two intervals because outcome at posttransplant one year could reflect surgical and perioperative risk [4], and outcome at two years could also capture mortality associated with most transplant complications, such as rejection and biliary stricture. Calibration refers to the degree of correspondence between predicted and actual survival probabilities. In this study, we used goodness-of-fit testing to evaluate calibration by the Hosmer-Lemeshow test [32], in which the χ2 statistic is the sum of the squared differences between actual and predicted survival probability. Discrimination is usually assessed by the area under a receiver operating characteristic (ROC) curve [33], which is equal to the index of concordance (i.e., c-statistic). The ROC analysis was also performed to measure the sensitivity, specificity, positive predictive value, negative predictive value, and the total accuracy of these three predictive models.
Outcomes of the entire series of recipients
Of the 360 DDLT recipients, the mean time on the waiting list was 9.16±3.56 months, and the median follow-up period was 56.23±26.46 months. The overall 6-month, 1-, 2-, 3- and 5- year survival rates were 89.6%, 86.1%, 82.9%, 78.2% and 73.1%, respectively. Of the 360 recipients, 89 recipients (24.7%) died during the 5-year follow-up period. Of these, 23 (6.4%) died within the first 3 months after transplantation of various perioperative causes, including severe fungal infection or sepsis (n = 6), multiple organ failure (n = 4), hepatic artery thrombosis (n = 3), acute rejection (n = 3), primary graft dysfunction (n = 2), upper gastrointestinal bleeding (n = 2), graft versus host disease (n = 2), and subarachnoid hemorrhage (n = 1). 57 (15.8%) recipients died for chronic graft dysfunction with different causes, such as the HBV or HCV recurrence, biliary complications, pathologically-proven chronic rejection, and hepatic vein stenosis, etc. The remaining 9 recipients (2.5%) died of other causes in long-term follow-up, including severe fungus infection or sepsis (n = 3), de novo cancers (n = 2), multi-organ failure (n = 2), respiratory failure (n = 1), cerebral hemorrhage (n = 1).
Recipients' baseline characteristics
Table 1 and table S1 showed the baseline characteristics of the modeling set and validation set. Most of the characteristics between the two sets have no differences, but we also observed significant differences in the percentage of HBV-DNA level, as well as in the mean values of ALB and INR between the modeling and validation set.
MLP input features selection
Two donor factors and ten recipient factors were identified as optimal input features by the forwards stepwise selection algorithm: donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na+; presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically-proven sepsis. As each sub-category of every categorical variable is an input neuron, there are 18 input neurons in the MLP network.
Training and development of the MLP network
By enumerative combinatory method and making many iterations of training and cross-validation in each combination, we identified 12 hidden neurons that optimally delineated the network and produced the best performance in both one- and two-year intervals. The most appropriate transfer functions were Logistic, Gaussian for one-year network, and Exponential, Identity for two-year network (Fig. 1.).
The network consisted of 18 input neurons, 12 hidden neurons, and 1 output neuron.
Taking one input variable, HB as an example, Figure 2 represents the relationships between HB and other variables, and the output prognosis of the trained MLP network. In every subgraph, HB, another variable, and the output prognosis (ie., the MLP target) composed a simulated 3-D rendering; the output prognosis of the network is plotted versus HB and another variable, and the curved surface represents the relationship between HB, the other variable, and the output prognosis. In such a simulated 3-D rendering composed of only two input variables (HB and another variable) and the output prognosis, there is a nonlinear relationship between HB, other variables, and the output prognosis. The relationships between multi-variables and the output prognosis would undoubtedly be even much more complex in corresponding multidimensional space.
(2A): The one-year network. The x-axis represents input variable HB (x1), while the y-axis represents another variable: donor BMI (x2), TB (x3), or ALB (x4). The z-axis represents the output prognosis (ie., the MLP target). (2B): The two-year network. The x-axis represents HB (x1), and the y-axis represents another variable: Crea (x5), INR (x6), or Na+ (x7). The z-axis represents the output prognosis.
Model validation
With the Hosmer-Lemeshow test, a P-value greater than 0.05 and close to 1.0 is considered to indicate better calibration, and the smaller the χ2 value, the better the calibration ability of a model [34]. The MLP's calibration ability (χ2 = 1.56, P = 0.82 in one-year prediction; χ2 = 1.74, P = 0.78 in two-year prediction) was higher than that of the SOFA and MELD in both intervals' prediction (Table 3).
Table 4 and Figure 3 show the discrimination of the MLP, SOFA score, and MELD score for predicting posttransplant 1-year and 2-year survival probability. The c-statistic values range from 0 to 1, with 0.5 corresponding to what is expected by chance alone and 1.0 to perfect discrimination. For a prognostic model, a c-statistic below 0.7 generally suggests poor prediction, while a c-statistic above 0.7 indicates a useful model, and a c-statistic greater than 0.8 indicates excellent predictive accuracy [24]. The MLP had c-statistics of 0.91 (P<0.001) and 0.88 (P<0.001) in one-year and two-year prediction, respectively (Table 4 and Fig. 3). The c-statistics of the SOFA were 0.70 (one-year) and 0.67 (two-year). MELD yielded the least accurate predictions (Table 4 and Fig. 3).
(3A): Posttransplant one-year prediction. (3B): Posttransplant two-year prediction.
The large disparity between patient demand and donated organs is a pressing problem for all transplant surgeons, especially in the Asia-Pacific region. The best solution to this problem is still in dispute, as there are two sometimes-contradictory principles of organ allocation: urgency of patient need, and efficiency of organ use [35]. Unfortunately, prioritizing extremely sick patients make it likely that patients who are not as sick "will be forced to wait until their condition worsens and their chances for success are also diminished" [36], and patients who are very sick may have worse posttransplant outcomes than healthier patients [37]. Thus, the optimal system would offer grafts to those who are sufficiently sick to justify the transplantation but not too sick to benefit from it [38], that is, the urgency of need should be jointly optimized with the likelihood of satisfactory outcomes so as to avoid "futile transplantation".
Furthermore, OLT ranks among the most expensive medical interventions [39], so the urgency-based principle has contributed to rising healthcare costs [37], [40]. An accurate prognostic model could also help potential transplant recipients and their families make informed decisions by providing them with information on the patient's posttransplant survival probability [11], [13].
With the aforementioned goals, a newly-adopted lung allocation score in the United States has incorporated likelihood of posttransplant survival in addition to lung disease severity [41]. The liver transplantation field would also benefit from a continuously optimized allocation system that prioritizes patients who need grafts most, without sacrificing the overall utility of this scarce resource. Such a system necessitates a strong prognostic model that can identify potential recipients with satisfactory survival prospects.
Over the past decade, MELD [42] has proved to be an excellent marker of BESLD-specific illness severity and corresponding pretransplant mortality risk, but many studies have also shown its poor accuracy in predicting posttransplant survival [43], [44], which is consistent with our results. The SOFA score was originally developed to quantitatively describe the degree of organ dysfunction in six organ systems and to evaluate morbidity in intensive care unit septic patients [26], but later studies found that it could be applied equally well in non-septic critically ill patients to measure individual or aggregate organ dysfunction and to describe morbidity risk [45]. Since its introduction, the SOFA score has also been widely applied to prognostic mortality assessment in critically ill patients with good results [46], although it was not developed for this purpose. In recent years, some investigations have applied the SOFA to critically ill cirrhotic patients and have also proven its validity in mortality risk assessment for BESLD patients [47]–[49]. We believe that because BESLD patients usually display multiple-organ damage or dysfunction, such as the renal failure, coagulopathy, and encephalopathy, the SOFA is an excellent scoring model for assessing BESLD patients' illness severity and mortality risk. Additionally, several studies have analyzed the predictive power of SOFA on post-liver transplant mortality; although these achieved some encouraging results in short-term prognosis assessment [50], [51], its value in long-term outcome prediction still requires study. In this study, SOFA achieved good calibration abilities in both intervals and satisfactory discrimination power in one-year prediction, which is consistent with other studies [50], [51], but its accuracy was poor in two-year prediction. Although SOFA encopasses the functions of multiple systems including respiratory, hemostastics, hepatic, circulatory, and brain and kidney, it is not specific enough to BESLD patients and is not tailored to posttransplant outcome prediction. Lack of these specificities may account for its discriminative and calibration inferiority to the MLP network.
Although there have been many attempts to develop a specific model to assess posttransplant prognosis, to date, they have not achieved sufficient accuracy, or have simply categorized the patients into various risk groups [4], [11]; even with some of the most comprehensive efforts, the predictive accuracy of these models has always been reported in the 60–70% range [4], [9], [11]–[14] with no single model being more accurate than any other. We believe there are several possible explanations for this. First, the effect of prognostic factors depends on the underlying liver disease [11]–[13]. Thus, effort would be better spent developing disease-specific models targeted to BESLD patients or cancer patients. Second, Existing studies rely heavily on a few specific variables derived from linear regression analyses, rather than from data mining. The omission of many variables may hinder the discovery of underlying relationships between prognosis and related factors, and the interactions among factors. Third, transplant recipients represent a very complex biological system where the relationship between pretransplant variables and posttransplant prognosis is multidimensional and nonlinear (as shown in Fig. 2) [17], [23], so linear methods are inadequate in predicting regression coefficients and constructing risk factor models.
With the development of artificial intelligence in recent years, ANN has been a superior data-mining solution for complex prognostic problems [17], [20], and MLP has been proven to perform better than other architectures such as radial basis function, recurrent neural network, and self-organizing map [22]. MLP is a computation system that uses a large number of simple units to process information in parallel, so it is capable of learning arbitrarily complex nonlinear functions to arbitrary accuracy levels [22]. Furthermore, MLP allows a certain degree of flexibility when it comes to handling noise [18]. Most importantly, MLP is a nonparametric dynamic model, which can automatically self-training and readjust the internal parameters by back-propagation when more transplants enter the network [52], thus yielding more accurate responses and becoming progressively more dependable over time; this is what the linear models could not achieve.
In this study, although three characteristics of the recipients in the validation set differed from the training set, the MLP still achieved good calibration ability and high discrimination power in posttransplant survival prediction, with c-statistics around 0.9 and satisfactory sensitivity and specificity in both intervals, as well as the small χ2 statistics and associated P-values around 0.8 in both intervals. These results were not only superior to that of the linear regression models reported in previous studies [4], [9], [12], [13], but also outstripped the performances of SOFA and MELD in this study. We believe that several factors may account for the MLP's outstanding performance. First, the MLP network, employing 12 variables to make predictions, included more comprehensive information associated with the posttransplant prognosis. Second, the input features of our MLP included not only donor factors and measurements of disease severity, but also some well-recognized variables reflecting the complications and comorbidities (such as sepsis and diabetes) in BESLD patients. Meanwhile, it should be noted that we decided not to include some subjective variables (such as encephalopathy or ascites) in our model development because their classifications are subjective and could therefore be arbitrary. Third, being computer-based, the MLP can process more information about the survival process and model much more complex nonlinear multidimensional relationship, thus yielding more accurate prognostic estimations.
In this study, donor age and BMI were identified as input features. These two factors could be obtained before transplantation, and have been proved to be associated with graft quality [53], [54] and recipient outcomes [9], [14]. Although some other donor factors (such as the graft steatosis and ischemia times) may directly reflect graft quality and contribute to posttransplant prognosis, they would have been difficult or impossible to know when clinicians and patients make transplant acceptance decisions and when candidates are ranked on a waiting list. This problem would seem to be an inherent difficulty in pretransplant prediction. Therefore, in order to maximize the practical applicability of a pretransplant model, we believe that it must be constructed in accordance with actual clinical conditions, and enhancing the model's performance based on the variables available is the most important goal. Thus, we decided not to include this kind of characteristics in our pretransplant model development.
Meanwhile, we chose posttransplant one-year and two-year as the study endpoints in this study because outcomes within this timeframe could reflect surgical and perioperative risk [4] and mortality associated with most early complications. However, as we know, the recipient's long-term survival would be affected by not only the pretransplant characteristics, but also many intraoperative and posttransplant factors, such as the graft cold-ischemia time and biliary complications. Thus, in our view, once the appropriate modeling method is identified, development of sequential correction models according to the different variable acquisition phases may be a reasonable way to meet the evaluation requirement in different phases. When certain donor characteristics, operative parameters, and even some posttransplant variables could be available after operation, another posttransplant predictive model that incorporated above features should be developed and used to perform a further corrective assessment. We believe the two kinds of model can provide more comprehensive perioperative evaluation information at different variable acquisition phases, and, most importantly, they are consistent with actual clinical conditions.
In this study, we clarified the complex multidimensional and nonlinear relationship between transplant variables and posttransplant outcomes, and identified the value of MLP in solving this complex prognostic problem. We believe this methodological result is the key point of this study, and is more important than the specific factors and specific study intervals included in the presented model.
We believe that this kind of pretransplant model would provide patients and clinicians with important reference information about their early posttransplant prospects during the initial counseling and evaluation phases of referral [4], [11], [13]. If used alongside the MELD system, the pretransplant model can also help predict early outcome with and without transplantation. This provides clinicians with a combined tool to identify patients likely to benefit most from transplantation [9].
Meanwhile, how to ethically balance medical urgency with posttransplant survival prospects is an important issue. For instance, it could be argued that the patient with the highest combined MELD score and survival prospects should be given priority. But we expect that in practice, scientifically combining the two conflicting determinants would not be so simple, just as the use of MELD to guide graft allocation has sparked a wealth of studies and discussion. Therefore, we believe that comprehensively considering and weighing urgency and survival prospects will require further evidence-based research. Whatever shape the final system takes, however, it will undoubtedly include a prognostic model with high predictive accuracy as an important component.
Although this MLP model was more sophisticated than conventional linear models, in practical application, its software implementation allowed the creation of a new interface that can be incorporated into a website and be easily used by everyone, as in the UNOS website, where an interface was created for MELD calculation. Thus, we believe the model's complexity should not present a problem in clinical practice.
Despite our encouraging results, our study has some potential limitations. First, it was developed using data from a single center; we did not validate our model externally with data from different sources. Indeed, we divided our dataset into training and validation sets, and the validation samples were not used in model development. Thus, the proposed MLP network should be further verified with data at other major centers. Fortunately, the dynamic nature of the MLP makes it capable of continuously and automatically adjusting its internal parameters and improving as more transplant data from other centers enter the network [52]. Second, the patient population had a high proportion of HBV infection; therefore, this MLP network may have limited applicability to typical North American and European patients, who tend to have a lower rates of HBV but higher rates of hepatitis C and alcoholism than do Chinese BESLD patients.
In summary, artificial intelligence methodologies such as MLP offer significant advantages over conventional statistical techniques in variable selection and dealing with restrictive assumptions of normality and linearity, and thus hold promise for solving posttransplant outcome prediction. Therefore, in future research we plan to use MLP to develop a posttransplant multi-interval sequential correction model, a step toward establishing a balanced system that considers both pretransplant mortality and expected posttransplant survival.
Supporting Information
Table S1.
Baseline categorical characteristics of the training set and validation set.
The authors thank Shawna Williams for her editing assistance in the preparation of this manuscript.
Author Contributions
Conceived and designed the experiments: MZ FY YPL BL. Performed the experiments: MZ FY BC LNY TFW BL. Analyzed the data: MZ FY BC BL. Contributed reagents/materials/analysis tools: MZ BC LNY TFW BL. Wrote the paper: MZ FY.
- 1. Merion RM, Schaubel DE, Dykstra DM, Freeman RB, Port FK, et al. (2005) The survival benefit of liver transplantation. Am J Transplant 5: 307–313.RM MerionDE SchaubelDM DykstraRB FreemanFK Port2005The survival benefit of liver transplantation.Am J Transplant5307313
- 2. Biggins SW (2007) Beyond the numbers: Rational and ethical application of outcome models for organ allocation in liver transplantation. Liver Transplant 13: 1080–1083.SW Biggins2007Beyond the numbers: Rational and ethical application of outcome models for organ allocation in liver transplantation.Liver Transplant1310801083
- 3. Wiesner RH (2005) Patient selection in an era of donor liver shortage: current US policy. Nature Clin Pract Gastroenterol Hepatol 2: 24–30.RH Wiesner2005Patient selection in an era of donor liver shortage: current US policy.Nature Clin Pract Gastroenterol Hepatol22430
- 4. Burroughs AK, Sabin CA, Rolles K, Delvart V, Karam V, et al. (2006) 3-month and 12-month mortality after first liver transplant in adults in Europe: predictive models for outcome. Lancet 367: 225–232.AK BurroughsCA SabinK. RollesV. DelvartV. Karam20063-month and 12-month mortality after first liver transplant in adults in Europe: predictive models for outcome.Lancet367225232
- 5. Schaubel DE, Sima CS, Goodrich NP, Feng S, Merion RM (2008) The survival benefit of deceased donor liver transplantation as a function of candidate disease severity and donor quality. Am J Transplant 8: 419–425.DE SchaubelCS SimaNP GoodrichS. FengRM Merion2008The survival benefit of deceased donor liver transplantation as a function of candidate disease severity and donor quality.Am J Transplant8419425
- 6. Schaubel DE, Guidinger MK, Biggins SW, Kalbfleisch JD, Pomfret EA, et al. (2009) Survival benefit-based deceased donor liver allocation. Am J Transplant 9: 970–981.DE SchaubelMK GuidingerSW BigginsJD KalbfleischEA Pomfret2009Survival benefit-based deceased donor liver allocation.Am J Transplant9970981
- 7. Huang JF (2007) Ethical and Legislative Perspectives on Liver Transplantation in the People's Republic of China. Liver Transplant 13: 193–196.JF Huang2007Ethical and Legislative Perspectives on Liver Transplantation in the People's Republic of China.Liver Transplant13193196
- 8. Rakela J, Fung JJ (2007) Liver Transplantation in China. Liver Transplant 13: 182.J. RakelaJJ Fung2007Liver Transplantation in China.Liver Transplant13182
- 9. Ghobrial RM, Gornbein J, Steadman R, Danino N, Markmann JF, et al. (2002) Pretransplant model to predict posttransplant survival in liver transplant patients. Ann Surg 236: 315–322.RM GhobrialJ. GornbeinR. SteadmanN. DaninoJF Markmann2002Pretransplant model to predict posttransplant survival in liver transplant patients.Ann Surg236315322
- 10. Freeman RB (2007) Predicting the Future? Liver Transpl 13: 1503–1505.RB Freeman2007Predicting the Future?Liver Transpl1315031505
- 11. Jacob M, Lewsey JD, Sharpin C, Gimson A, Rela M, et al. (2005) Systematic review and validation of prognostic models in liver transplantation. Liver Transplant 11: 814–825.M. JacobJD LewseyC. SharpinA. GimsonM. Rela2005Systematic review and validation of prognostic models in liver transplantation.Liver Transplant11814825
- 12. Ioannou G (2006) Development and validation of a model predicting graft survival after liver transplantation. Liver Transplant 12: 1594–1606.G. Ioannou2006Development and validation of a model predicting graft survival after liver transplantation.Liver Transplant1215941606
- 13. Lewsey JD, Dawwas M, Copley LP, Gimson A, Van der Meulen JH (2006) Developing a prognostic model for 90-day mortality after liver transplantation based on pretransplant recipient factors. Transplantation 82: 898–907.JD LewseyM. DawwasLP CopleyA. GimsonJH Van der Meulen2006Developing a prognostic model for 90-day mortality after liver transplantation based on pretransplant recipient factors.Transplantation82898907
- 14. Rana A, Hardy MA, Halazun KJ, Woodland DC, Ratner LE, et al. (2008) Survival outcomes following liver transplantation (SOFT) score: A novel method to predict patient survival following liver transplantation. Am J Transplant 8: 2537–2546.A. RanaMA HardyKJ HalazunDC WoodlandLE Ratner2008Survival outcomes following liver transplantation (SOFT) score: A novel method to predict patient survival following liver transplantation.Am J Transplant825372546
- 15. Banerjee R, Das A, Ghoshal UC, Sinha M (2003) Predicting mortality in patients with cirrhosis of liver with application of neural network technology. J of Gastro Hepato 18: 1054–1060.R. BanerjeeA. DasUC GhoshalM. Sinha2003Predicting mortality in patients with cirrhosis of liver with application of neural network technology.J of Gastro Hepato1810541060
- 16. Lapuerta P, Rajan S, Bonacini M (1997) Neural networks as predictors of outcomes in alcoholic patients with severe liver disease. Hepatology 25: 302–307.P. LapuertaS. RajanM. Bonacini1997Neural networks as predictors of outcomes in alcoholic patients with severe liver disease.Hepatology25302307
- 17. Kaplan B, Schold J (2009) Transplantation: neural networks for predicting graft survival. Nat Rev Nephrol 5: 190–192.B. KaplanJ. Schold2009Transplantation: neural networks for predicting graft survival.Nat Rev Nephrol5190192
- 18. Cross SS, Harrison RF, Kennedy RL (1995) Introduction to neural networks. Lancet 346: 1075–1079.SS CrossRF HarrisonRL Kennedy1995Introduction to neural networks.Lancet34610751079
- 19. Logeswaran R (2009) Cholangiocarcinoma - An automated preliminary detection system using MLP. J Med Syst 33: 413–421.R. Logeswaran2009Cholangiocarcinoma - An automated preliminary detection system using MLP.J Med Syst33413421
- 20. Catto JW, Linkens DA, Abbod MF, Chen M, Burton JL, et al. (2003) Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks. Clinical Cancer Research 9: 4172–4177.JW CattoDA LinkensMF AbbodM. ChenJL Burton2003Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.Clinical Cancer Research941724177
- 21. Akl A, Ismail AM, Ghoneim M (2008) Prediction of Graft Survival of Living-Donor Kidney Transplantation: Nomograms or Artificial Neural Networks? Transplantation 86: 1401–1406.A. AklAM IsmailM. Ghoneim2008Prediction of Graft Survival of Living-Donor Kidney Transplantation: Nomograms or Artificial Neural Networks?Transplantation8614011406
- 22. Oztekin A, Delen D, Kong ZY (2009) Predicting the graft survival for heart-lung transplantation patients: An integrated data mining methodology. Inter J Med Informatics 78: e84–e96.A. OztekinD. DelenZY Kong2009Predicting the graft survival for heart-lung transplantation patients: An integrated data mining methodology.Inter J Med Informatics78e84e96
- 23. Cucchetti A, Vivarelli M, Heaton ND, Phillips S, Piscaglia F, et al. (2007) Artificial neural network is superior to MELD in Predicting mortality of patients with end-stage liver disease. Gut 56: 253–258.A. CucchettiM. VivarelliND HeatonS. PhillipsF. Piscaglia2007Artificial neural network is superior to MELD in Predicting mortality of patients with end-stage liver disease.Gut56253258
- 24. Kamath PS, Wiesner RH, Malinchoc M, Kremers W, Therneau TM, et al. (2001) A model to predict survival in patients with end-stage liver disease. Hepatology 33: 464–470.PS KamathRH WiesnerM. MalinchocW. KremersTM Therneau2001A model to predict survival in patients with end-stage liver disease.Hepatology33464470
- 25. Kim WR, Biggins SW, Kremers WK, Wiesner RH, Kamath PS, et al. (2008) Hyponatremia and Mortality among Patients on the Liver-Transplant Waiting List. N Engl J Med 359: 1018–1026.WR KimSW BigginsWK KremersRH WiesnerPS Kamath2008Hyponatremia and Mortality among Patients on the Liver-Transplant Waiting List.N Engl J Med35910181026
- 26. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonca A, et al. (1996) The SOFA score to describe organ dysfunction/failure. Intensive Care Med 22: 707–710.JL VincentR. MorenoJ. TakalaS. WillattsA. De Mendonca1996The SOFA score to describe organ dysfunction/failure.Intensive Care Med22707710
- 27. Basher IA, Hajmeer M (2000) Artificial neural network fundamentals, computing, design and application. J Microb Methods 43: 3–31.IA BasherM. Hajmeer2000Artificial neural network fundamentals, computing, design and application.J Microb Methods43331
- 28. Llovet JM, Di Bisceglie AM, Bruix J, Kramer BS, Lencioni R, et al. (2008) Design and endpoints of clinical trials in hepatocellular carcinoma. J Nat Cancer Inst 100: 698–711.JM LlovetAM Di BisceglieJ. BruixBS KramerR. Lencioni2008Design and endpoints of clinical trials in hepatocellular carcinoma.J Nat Cancer Inst100698711
- 29. Lin RS, Horn SD, Hurdle JF, Goldfarb-Rumyantzev AS (2008) Single and multiple-time prediction models in kidney transplant outcomes. J Biomedical Informatics 41: 944–952.RS LinSD HornJF HurdleAS Goldfarb-Rumyantzev2008Single and multiple-time prediction models in kidney transplant outcomes.J Biomedical Informatics41944952
- 30. Rumelhart DE, Hinton GE, Williams RL (1986) Learning representation by backpropagating errors. Nature 323: 533–536.DE RumelhartGE HintonRL Williams1986Learning representation by backpropagating errors.Nature323533536
- 31. Ruttimann UE (1994) Statistical approaches to development and validation of predictive instruments. Crit Care Clin 10: 19–35.UE Ruttimann1994Statistical approaches to development and validation of predictive instruments.Crit Care Clin101935
- 32. Rosenberg AL (2002) Recent innovations in intensive care unit risk-prediction models. Curr Opin Crit Care 8: 321–330.AL Rosenberg2002Recent innovations in intensive care unit risk-prediction models.Curr Opin Crit Care8321330
- 33. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29–36.JA HanleyBJ McNeil1982The meaning and use of the area under a receiver operating characteristic (ROC) curve.Radiology1432936
- 34. Lemeshow S, Hosmer DW Jr (1982) A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol 115: 92–106.S. LemeshowDW Hosmer Jr1982A review of goodness of fit statistics for use in the development of logistic regression models.Am J Epidemiol11592106
- 35. Bronsther O, Fung JJ, Izakis A, Van Thiel D, Starzl TE (1994) Prioritization and organ distribution for liver transplantation. JAMA 271: 140–143.O. BronstherJJ FungA. IzakisD. Van ThielTE Starzl1994Prioritization and organ distribution for liver transplantation.JAMA271140143
- 36.
UNOSRationale for Objectives of Equitable Organ Allocation. UNOSRationale for Objectives of Equitable Organ Allocation.Available at: http://www.unos.org/resources/bioethics.asp?index=10, Accessed August 15, 2011. Available at: http://www.unos.org/resources/bioethics.asp?index=10, Accessed August 15, 2011.
- 37. Neuberger J, Gimson A, Davies M, Akyol M, O'Grady J, et al. (2008) Selection of patients for liver transplantation and allocation of donated livers in the UK. Gut 57: 252–257.J. NeubergerA. GimsonM. DaviesM. AkyolJ. O'Grady2008Selection of patients for liver transplantation and allocation of donated livers in the UK.Gut57252257
- 38. Dawwas MF, Gimson AE (2009) Candidate selection and organ allocation in liver transplantation. Semin Liver Dis 29: 40–52.MF DawwasAE Gimson2009Candidate selection and organ allocation in liver transplantation.Semin Liver Dis294052
- 39. Gilbert JR, Pascual M, Schoenfeld DA, Rubin RH, Delmonico FL, et al. (1999) Evolving trends in liver transplantation: an outcome and charge analysis. Transplantation 67: 246–253.JR GilbertM. PascualDA SchoenfeldRH RubinFL Delmonico1999Evolving trends in liver transplantation: an outcome and charge analysis.Transplantation67246253
- 40. Trotter JF, Osgood MJ (2004) MELD scores of liver transplant recipients according to size of waiting list:Impact of organ allocation and patient outcomes. JAMA 291: 1871–1874.JF TrotterMJ Osgood2004MELD scores of liver transplant recipients according to size of waiting list:Impact of organ allocation and patient outcomes.JAMA29118711874
- 41. Hachem RR, Trulock EP (2008) The new lung allocation system and its impact on waitlist characteristics and post-transplant outcomes. Semin Thorac Cardiovasc Surg 20: 139–142.RR HachemEP Trulock2008The new lung allocation system and its impact on waitlist characteristics and post-transplant outcomes.Semin Thorac Cardiovasc Surg20139142
- 42. Giannini E, Botta F, Testa R (2003) Utility of the MELD score for assessing 3-month survival in patients with liver cirrhosis: one more positive answer. Gastroenterology 125: 993–994.E. GianniniF. BottaR. Testa2003Utility of the MELD score for assessing 3-month survival in patients with liver cirrhosis: one more positive answer.Gastroenterology125993994
- 43. Hayashi PH, Forman L, Steinberg T, Bak T, Wachs M, et al. (2003) Model for End-Stage Liver Disease score does not predict patient or graft survival in living donor liver transplant recipients. Liver Transpl 9: 737–740.PH HayashiL. FormanT. SteinbergT. BakM. Wachs2003Model for End-Stage Liver Disease score does not predict patient or graft survival in living donor liver transplant recipients.Liver Transpl9737740
- 44. Desai NM, Mange KC, Crawford MD, Abt PL, Frank AM, et al. (2004) Predicting outcome after liver transplantation: utility of the Model for End-Stage Liver Disease and a newly derived discrimination function. Transplantation 77: 99–106.NM DesaiKC MangeMD CrawfordPL AbtAM Frank2004Predicting outcome after liver transplantation: utility of the Model for End-Stage Liver Disease and a newly derived discrimination function.Transplantation7799106
- 45. Vincent J, Ferreira F, Moreno R (2000) Scoring systems for assessing organ dysfunction and survival. Crit Care Clin 16: 353–366.J. VincentF. FerreiraR. Moreno2000Scoring systems for assessing organ dysfunction and survival.Crit Care Clin16353366
- 46. Minne L, Abu-Hanna A, de Jonge E (2008) Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review. Critical Care 12: R161.L. MinneA. Abu-HannaE. de Jonge2008Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review.Critical Care12R161
- 47. Tu KH, Jenq CC, Tsai MH, Hsu HH, Chang MY, et al. (2011) Outcome Scoring Systems for Short-term Prognosis in Critically ill Cirrhotic Patients. Shock 36: 445–450.KH TuCC JenqMH TsaiHH HsuMY Chang2011Outcome Scoring Systems for Short-term Prognosis in Critically ill Cirrhotic Patients.Shock36445450
- 48. Cholongitas E, Betrosian A, Senzolo M, Shaw S, Patch D, et al. (2008) Prognostic models in cirrhotics admitted to intensive care units better predict outcome when assessed at 48 h after admission. J Gastro Hepatol 23: 1223–1227.E. CholongitasA. BetrosianM. SenzoloS. ShawD. Patch2008Prognostic models in cirrhotics admitted to intensive care units better predict outcome when assessed at 48 h after admission.J Gastro Hepatol2312231227
- 49. Cholongitas E, Senzolo M, Patch D, Shaw S, Hui C, et al. (2006) Review article: scoring systems for assessing prognosis in critically ill adult cirrhotics. Aliment Pharmacol Ther 24: 453–464.E. CholongitasM. SenzoloD. PatchS. ShawC. Hui2006Review article: scoring systems for assessing prognosis in critically ill adult cirrhotics.Aliment Pharmacol Ther24453464
- 50. Wong CS, Lee WC, Jenq CC, Tian YC, Chang MY, et al. (2010) Scoring short-term mortality after liver transplantation. Liver Transpl 16: 138–146.CS WongWC LeeCC JenqYC TianMY Chang2010Scoring short-term mortality after liver transplantation.Liver Transpl16138146
- 51. Yuan JZ, Ye QF, Zhao LL, Ming YZ, Sun H, et al. (2006) Preoperative risk factor analysis in orthotopic liver transplantation with pretransplant artificial liver support therapy. World J Gastroenterol 12: 5055–5059.JZ YuanQF YeLL ZhaoYZ MingH. Sun2006Preoperative risk factor analysis in orthotopic liver transplantation with pretransplant artificial liver support therapy.World J Gastroenterol1250555059
- 52. Sinha M, Kennedy C, Ramundo M (2001) Artificial neural network in predicting CT abnormalities in pediatric patients with closed head injury. J Trauma 50: 308–312.M. SinhaC. KennedyM. Ramundo2001Artificial neural network in predicting CT abnormalities in pediatric patients with closed head injury.J Trauma50308312
- 53. Cucchetti A, Vivarelli M, Ravaioli M, Cescon M, Ercolani G, et al. (2009) Assessment of donor steatosis in liver transplantation: is it possible without liver biopsy? Clinical Transplant 23: 519–524.A. CucchettiM. VivarelliM. RavaioliM. CesconG. Ercolani2009Assessment of donor steatosis in liver transplantation: is it possible without liver biopsy?Clinical Transplant23519524
- 54. Liu ZJ, Gong JP, Yan LN (2009) Quantitative Estimation of the Degree of Hepatic Macrovesicular Steatosis in a Disease-Free Population: A Single-Center Experience in Mainland China. Liver Transpl 15: 1605–1612.ZJ LiuJP GongLN Yan2009Quantitative Estimation of the Degree of Hepatic Macrovesicular Steatosis in a Disease-Free Population: A Single-Center Experience in Mainland China.Liver Transpl1516051612


text:
KK's Family History
BAKER William Henry, IIAbt 1827 - 1863 (~ 36 years)
Name BAKER William Henry Suffix II Born Abt 1827 , , , Tennessee, USA Gender Male 1830 U.S. Census 1 Jun 1830 , , Knox County, Tennessee, USA [1] Age: > 5y
- Members of this household:
Henry Baker, age 20 thru 29
1 male under 5 (William Henry II)
1 male age 5-9
2 females under 5
1 female age 20-29 (Rhoda Heavin Baker, his wife)
1830 U.S. census, Tennessee, Knox County [BAKER, Henry I] 1860 U.S. Census 1 Jun 1860 , Watkins Twp., Dent County, Missouri, USA [2] Age: 33y
- He was recorded as the head of household, and was 33 years old at the time of this census. His occupation was recorded as "farmer", and Tennessee was his reported birthplace.
1860 U.S. census, Missouri, Dent County, Watkins [BAKER] Military Service Between 1862 and 1863 , , , Missouri, USA Letter-Correspondence 20 Jul 1862 , Alton, Madison County, Illinois, USA
- Transcription:
Alton, Illinois July 20th 1862
My Dear Loving companion I take
pleasure in addressing you a few lines
to inform you that I am yet alive and well
I have had good health the most of the time
since I left home I was unwell awhile
but I am all right again I was taken
prisoner on the 20th of may as you have
probably heard I am now in Alton Ill
I have been here three weeks I have wrote
one letter since I left home but have
received no answer MW Province landed at
this place last night and had a letter he got
from home it stated you was all well that
is the only word I have heard from you
any way since I left now I want you
to write to me soon and often don't neglect
it write all about the children and how you
are getting along with the farming business do
the best you can give you self no trouble
about me I am doing very well I don't
know how long I shall remain here I
hope it will not be long direct you letter
to the Alton prison Ill I am limited
I can write no more at present
Wm Baker
Letter from William Baker II to his wife, Keziah (Arthur) Baker, 20 July 1862 Letter-Correspondence 10 Aug 1862 , Alton, Madison County, Illinois, USA
- Transcription:
Military prison Alton Ill Aug 10th 1862
Dear loving wife and children I again take my pen in
hand to inform you that I am still alive and well I
am in as good health at this time as ever I was in my
life for which blessing I feel thankful to the great donor
of all good I have nothing more to write that would interest you I received your letter yesterday which gave me much
delight to hear that you and the children was all well as
I can't see you I would like to hear from you oftener you
write that you will come to see me in 6 weeks I would
be glad to see you of course but at the same time I advise
you not to come from the fact that I have no idea of
being here at that time I expect to be in dixie before
then and if I was here when you came you would not get
to see me you said you would bring me clothing I have
plenty of good clothing I am well treated and
well furnished here as a prisoner you want to know about
the wheat sowing I advise you to sow all you can on
the old place and not trouble Haises? corn I have heard
bad news from your country but not from your letter stay
at home keep in good spirits do the best you can write often to me when
you know where I am
MW Province is not well today I am
your affectionate husband until death
Wm Baker to Kezia Baker
Letter from William Baker II to his wife, Keziah (Arthur) Baker, 10 August 1862 Occupation farmer Occupation 1863 U.S. Military Civil War Veteran (Confederate) Died 1863
- There is a question as to the date and place of William's death, as extensive research done by John Harris (email of 3 Oct 2009 to author) indicates that a different William Henry Baker died on 1 May 1863 of "fever" in Arkansas.
Death (Alternate) 1 May 1863 , Van Buren, Crawford County, Arkansas, USA [3] Cause: "fever" during the Civil War Veterans Schedule 1890 , Watkins Twp., Dent County, Missouri, USA [4]
- William was a 1st Lieutenant in the Missouri Infantry and served from 1 Jun 1861 to 8 Sep 1963 for 2 yrs, 3 mos, and 8 days. Even though this is supposed to be a "Union" veterans census, William served in the Confederate Missouri Infantry.
1890 U.S. Veterans Schedule, Missouri, Dent County, Watkins Twp. [BAKER, William H.] Notes
- William Henry Baker fought as a Confederate in the Civil War. Believe to have been exchanged for Federal prisoner. Supposedly wrote wife Keziah that he was going back South. Belonged to same unit as Great-Grandfather Joseph Warren Case and during probate of William Henry Baker estate, Joseph Warren Case listed as Administrator and took inventory.
Taken prisoner by the Union Army, 5/20/1863. Held in Military Prison, Alton, IL.
Person ID I4163 Kult-Lawhorn-Case-Collier Family Tree | Collier Branch Last Modified 18 Jul 2016
Father BAKER William Henry, I, b. Cal 1804, , , , Virginia, USA Mother HEAVIN Rhoda, b. Cal 1801, , , Montgomery County, Virginia, USA Marriage Bond 24 Jan 1823 , , Knox County, Tennessee, USA [5]
- The Bond was betwwen Henry Baker, Richard Havin, and the William Carroll, the Governor of Tennessee. Richard Havin was Rhoda's father.
Marriage Bond for William Henry Baker and Rhoda Heavin, pg. 1 Marriage Bond for William Henry Baker and Rhoda Heavin, pg. 2 Marriage License 24 Jan 1823 , Knoxville, Knox County, Tennessee, USA [6] Marriage License for William Henry Baker and Rhoda Heavin, pg. 1 Marriage License for William Henry Baker and Rhoda Heavin, pg. 2 Married 6 Feb 1823 [7]
- married by John Goss, J.P.
Marriage Record for Henry BAKER and Rhoda HEAVIN Family ID F1391 Group Sheet | Family Chart
Family ARTHUR Kezia, b. 29 Nov 1831, , , Crawford [later Pulaski then Phelps] County, Missouri, USA , d. 3 Aug 1900, , , Dent County, Missouri, USA (Age 68 years) Married Est 1853 [8] Children + 1. BAKER Rhoda Catherine, b. 25 Nov 1854, , Corn Creek, Pulaski [later Phelps] County, Missouri, USA , d. 23 Dec 1932, , Yancy Mills, Phelps County, Missouri, USA (Age 78 years) 2. BAKER Sarah Jane, b. 1855, , , , Missouri, USA , d. Yes, date unknown + 3. BAKER Marshall Custus, b. 30 Nov 1856, On Corn Creek, Newburg, Pulaski [later Phelps] County, Missouri, USA , d. 3 Jul 1939, , Anutt, Dent County, Missouri, USA (Age 82 years) + 4. BAKER William Henry, III, b. 21 Mar 1858, , Edgar Springs, Phelps County, Missouri, USA , d. 22 Aug 1926 (Age 68 years) + 5. BAKER Mary Elizabeth, b. 29 Sep 1860, , , , Missouri, USA , d. 24 Mar 1931 (Age 70 years) Photos Home of BAKER in Yancy Mills, Missouri
SOURCE: Phelps County Missouri Heritage, Volume 1, page 14
Last Modified 30 Dec 2012 Family ID F157 Group Sheet | Family Chart
- Members of this household:
Event Map = Link to Google Earth Pin Legend : Address : Location : City/Town : County/Shire : State/Province : Country : Not Set
- [S1185] Tennessee. Knox County. 1830 U.S. census. Digital images. Ancestry.com. http://www.ancestry.com : 2016, Census, U.S., 1830: BAKER, William I. (Reliability: 3).
- [S183] Missouri. Dent County. 1860 U.S. census, population schedule. Digital images. Ancestry.com. http://www.ancestry.com : 2007, CENSUS-1860-BAKER-WilliamHenry. (Reliability: 3).
- [S207] Phelps County Genealogical Society (Mo.). Phelps County Missouri Heritage, Vol. I, (Rolla, Missouri : Phelps County Genealogical Society, 1992), Book: Phelps County MO Heritage, Vol. I., p. 39 (Baker-Mace article) and p. 40 (William Henry Baker article) (Reliability: 1).
- [S1186] Missouri. Dent County. 1890 U.S. census. "Special Schedule: Surviving Soldiers, Sailors, and Marines, and Widows, etc.," Digital images. Ancestry.com. http://www.ancestry.com : 2016, Census, U.S., 1890 Veterans Schedule: BAKER, William. (Reliability: 3).
- [S1173] "Tennessee State Marriages, 1780-2002." Database and digital images. Ancestry.com, (http://ancestry.com : accessed 2016), Ancestry.com: Tennessee State Marriages, 1780-2002., Knox County, 1823, January, image 1 and 2 of 49 (Reliability: 3).
- [S1173] "Tennessee State Marriages, 1780-2002." Database and digital images. Ancestry.com, (http://ancestry.com : accessed 2016), Ancestry.com: Tennessee State Marriages, 1780-2002., Knox County, 1823, January, image 3 and 4 of 49 (Reliability: 3).
- [S1181] Knox County, Tennessee. Marriage record. "Henry Baker and Rhoda Heavin." 24 Jan 1823 (filed); p. 71, Digital image. Iimage downloaded from Ancestry.com. "Tennessee State Marriages 1780-2002.", Marriage: BAKER, Henry and HEAVIN, Rhoda. (Reliability: 3).
- [S787] Primary Source unavailable.
Phelps County started recording marriages in 1857, therefore no marriage record is available from Phelps County records.
- [S1185] Tennessee. Knox County. 1830 U.S. census. Digital images. Ancestry.com. http://www.ancestry.com : 2016, Census, U.S., 1830: BAKER, William I. (Reliability: 3).

subset: commoncrawl


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Director: Robert Zemeckis
Stars: Tom Hanks, Robin Wright, Gary Sinise, Sally Field
Léon: The Professional (1994)
Mathilda, a 12-year-old girl, is reluctantly taken in by Léon, a professional assassin, after her family is murdered. Léon and Mathilda form an unusual relationship, as she becomes his protégée and learns the assassin's trade. (110 mins.)
Director: Luc Besson
Stars: Jean Reno, Gary Oldman, Natalie Portman, Danny Aiello
Gladiator (2000)
When a Roman general is betrayed and his family murdered by an emperor's corrupt son, he comes to Rome as a gladiator to seek revenge. (155 mins.)
Director: Ridley Scott
The Lives of Others (2006)
In 1984 East Berlin, an agent of the secret police, conducting surveillance on a writer and his lover, finds himself becoming increasingly absorbed by their lives. (137 mins.)
Director: Florian Henckel von Donnersmarck
Stars: Ulrich Mühe, Martina Gedeck, Sebastian Koch, Ulrich Tukur
Snatch (2000)
Unscrupulous boxing promoters, violent bookmakers, a Russian gangster, incompetent amateur robbers, and supposedly Jewish jewelers fight to track down a priceless stolen diamond. (104 mins.)
Director: Guy Ritchie
Stars: Jason Statham, Brad Pitt, Benicio Del Toro, Dennis Farina
The Good, the Bad and the Ugly (1966)
A bounty hunting scam joins two men in an uneasy alliance against a third in a race to find a fortune in gold buried in a remote cemetery. (161 mins.)
Director: Sergio Leone
Stars: Clint Eastwood, Eli Wallach, Lee Van Cleef, Aldo Giuffrè
Spirited Away (2001)
During her family's move to the suburbs, a sullen 10-year-old girl wanders into a world ruled by gods, witches, and spirits, and where humans are changed into beasts. (125 mins.)
Director: Hayao Miyazaki
Stars: Daveigh Chase, Suzanne Pleshette, Miyu Irino, Rumi Hiiragi
A Clockwork Orange (1971)
In future Britain, Alex DeLarge, a charismatic and psychopath delinquent, who likes to practice crimes and ultra-violence with his gang, is jailed and volunteers for an experimental aversion therapy developed by the government in an effort to solve society's crime problem - but not all goes according to plan. (136 mins.)
Director: Stanley Kubrick
The Secret in Their Eyes (2009)
A retired legal counselor writes a novel hoping to find closure for one of his past unresolved homicide cases and for his unreciprocated love with his superior - both of which still haunt him decades later. (129 mins.)
Director: Juan José Campanella
Stars: Ricardo Darín, Soledad Villamil, Pablo Rago, Carla Quevedo
Gran Torino (2008)
Disgruntled Korean War veteran Walt Kowalski sets out to reform his neighbor, a Hmong teenager who tried to steal Kowalski's prized possession: a 1972 Gran Torino. (116 mins.)
Director: Clint Eastwood
Stars: Clint Eastwood, Bee Vang, Christopher Carley, Ahney Her
The Green Mile (1999)
The lives of guards on Death Row are affected by one of their charges: a black man accused of child murder and rape, yet who has a mysterious gift. (189 mins.)
Director: Frank Darabont
Stars: Tom Hanks, Michael Clarke Duncan, David Morse, Bonnie Hunt
Good Will Hunting (1997)
Will Hunting, a janitor at M.I.T., has a gift for mathematics, but needs help from a psychologist to find direction in his life. (126 mins.)
Director: Gus Van Sant
Stars: Robin Williams, Matt Damon, Ben Affleck, Stellan Skarsgård
A Beautiful Mind (2001)
After John Nash, a brilliant but asocial mathematician, accepts secret work in cryptography, his life takes a turn for the nightmarish. (135 mins.)
Director: Ron Howard
Scarface (1983)
In Miami in 1980, a determined Cuban immigrant takes over a drug cartel and succumbs to greed. (170 mins.)
Director: Brian De Palma
Kill Bill: Vol. 1 (2003)
The Bride wakens from a four-year coma. The child she carried in her womb is gone. Now she must wreak vengeance on the team of assassins who betrayed her - a team she was once part of. (111 mins.)
Stars: Uma Thurman, David Carradine, Daryl Hannah, Michael Madsen
Let the Right One In (2008)
Oskar, an overlooked and bullied boy, finds love and revenge through Eli, a beautiful but peculiar girl. (115 mins.)
Director: Tomas Alfredson
Stars: Kåre Hedebrant, Lina Leandersson, Per Ragnar, Henrik Dahl
The Pianist (2002)
A Polish Jewish musician struggles to survive the destruction of the Warsaw ghetto of World War II. (150 mins.)
Director: Roman Polanski
Stars: Adrien Brody, Thomas Kretschmann, Frank Finlay, Emilia Fox
In Bruges (2008)
Guilt-stricken after a job gone wrong, hitman Ray and his partner await orders from their ruthless boss in Bruges, Belgium, the last place in the world Ray wants to be. (107 mins.)
Director: Martin McDonagh
Atonement (2007)
Fledgling writer Briony Tallis, as a thirteen-year-old, irrevocably changes the course of several lives when she accuses her older sister's lover of a crime he did not commit. (123 mins.)
Director: Joe Wright
Sunshine (2007)
A team of international astronauts are sent on a dangerous mission to reignite the dying Sun with a nuclear fission bomb in 2057. (107 mins.)
Director: Danny Boyle
Stars: Cillian Murphy, Rose Byrne, Chris Evans, Cliff Curtis
Fargo (1996)
Jerry Lundegaard's inept crime falls apart due to his and his henchmen's bungling and the persistent police work of the quite pregnant Marge Gunderson. (98 mins.)
Director: Joel Coen
Million Dollar Baby (2004)
A determined woman works with a hardened boxing trainer to become a professional. (132 mins.)
Director: Clint Eastwood
Stars: Hilary Swank, Clint Eastwood, Morgan Freeman, Jay Baruchel
The Machinist (2004)
An industrial worker who hasn't slept in a year begins to doubt his own sanity. (101 mins.)
Director: Brad Anderson
The Butterfly Effect (2004)
Evan Treborn suffers blackouts during significant events of his life. As he grows up, he finds a way to remember these lost memories and a supernatural way to alter his life by reading his journal. (113 mins.)
Director: Eric Bress, J. Mackye Gruber
Stars: Ashton Kutcher, Amy Smart, Melora Walters, Elden Henson
Lucky Number Slevin (2006)
A case of mistaken identity lands Slevin into the middle of a war being plotted by two of the city's most rival crime bosses: The Rabbi and The Boss. Slevin is under constant surveillance by relentless Detective Brikowski as well as the infamous assassin Goodkat and finds himself having to hatch his own ingenious plot to get them before they get him. (110 mins.)
Director: Paul McGuigan
Stars: Josh Hartnett, Ben Kingsley, Morgan Freeman, Lucy Liu
Mulholland Dr. (2001)
After a car wreck on the winding Mulholland Drive renders a woman amnesiac, she and a perky Hollywood-hopeful search for clues and answers across Los Angeles in a twisting venture beyond dreams and reality. (147 mins.)
Director: David Lynch
Stars: Naomi Watts, Laura Harring, Justin Theroux, Jeanne Bates
Vanilla Sky (2001)
A self-indulgent and vain publishing magnate finds his privileged life upended after a vehicular accident with a resentful lover. (136 mins.)
Director: Cameron Crowe
Stars: Tom Cruise, Penélope Cruz, Cameron Diaz, Kurt Russell
The Diving Bell and the Butterfly (2007)
The true story of Elle editor Jean-Dominique Bauby who suffers a stroke and has to live with an almost totally paralyzed body; only his left eye isn't paralyzed. (112 mins.)
Director: Julian Schnabel
The Assassination of Jesse James by the Coward Robert Ford (2007)
Robert Ford, who's idolized Jesse James since childhood, tries hard to join the reforming gang of the Missouri outlaw, but gradually becomes resentful of the bandit leader. (160 mins.)
Director: Andrew Dominik
Stars: Brad Pitt, Casey Affleck, Sam Shepard, Mary-Louise Parker
Jackie Brown (1997)
A middle-aged woman finds herself in the middle of a huge conflict that will either make her a profit or cost her life. (154 mins.)
Amores Perros (2000)
A horrific car accident connects three stories, each involving characters dealing with loss, regret, and life's harsh realities, all in the name of love. (154 mins.)
Director: Alejandro González Iñárritu
Being John Malkovich (1999)
A puppeteer discovers a portal that leads literally into the head of movie star John Malkovich. (112 mins.)
Director: Spike Jonze
Life of Brian (1979)
Brian is born on the original Christmas, in the stable next door. He spends his life being mistaken for a messiah. (94 mins.)
Director: Terry Jones
Stars: Graham Chapman, John Cleese, Michael Palin, Terry Gilliam
A Scanner Darkly (2006)
An undercover cop in a not-too-distant future becomes involved with a dangerous new drug and begins to lose his own identity as a result. (100 mins.)
Director: Richard Linklater
Adaptation. (2002)
A lovelorn screenwriter becomes desperate as he tries and fails to adapt 'The Orchid Thief' by Susan Orlean for the screen. (114 mins.)
Director: Spike Jonze
Stars: Nicolas Cage, Meryl Streep, Chris Cooper, Tilda Swinton
The Constant Gardener (2005)
A widower is determined to get to the bottom of a potentially explosive secret involving his wife's murder, big business, and corporate corruption. (129 mins.)
Director: Fernando Meirelles
Stars: Ralph Fiennes, Rachel Weisz, Hubert Koundé, Danny Huston
Grave of the Fireflies (1988)
A young boy and his little sister struggle to survive in Japan during World War II. (89 mins.)
Director: Isao Takahata
Michael Clayton (2007)
A law firm brings in its "fixer" to remedy the situation after a lawyer has a breakdown while representing a chemical company that he knows is guilty in a multibillion-dollar class action suit. (119 mins.)
Director: Tony Gilroy
The Jacket (2005)
A Gulf war veteran is wrongly sent to a mental institution for insane criminals, where he becomes the object of a Doctor's experiments, and his life is completely affected by them. (103 mins.)
Director: John Maybury
The Cove (2009 Documentary)
Using state-of-the-art equipment, a group of activists, led by renowned dolphin trainer Ric O'Barry, infiltrate a cove near Taijii, Japan to expose both a shocking instance of animal abuse and a serious threat to human health. (92 mins.)
Director: Louie Psihoyos
Mysterious Skin (2004)
A teenage hustler and a young man obsessed with alien abductions cross paths, together discovering a horrible, liberating truth. (105 mins.)
Director: Gregg Araki
PROT is a patient at a mental hospital who claims to be from a far away planet. His psychiatrist tries to help him, only to begin to doubt his own explanations. (120 mins.)
Director: Iain Softley
Stars: Kevin Spacey, Jeff Bridges, Mary McCormack, Alfre Woodard
Twin Peaks: Fire Walk with Me (1992)
A young FBI agent disappears while investigating a murder miles from Twin Peaks that may be related to the future murder of Laura Palmer; the last week of the life of Laura Palmer is chronicled. (135 mins.)
Director: David Lynch
Stars: Sheryl Lee, Ray Wise, Mädchen Amick, Dana Ashbrook
The Cider House Rules (1999)
A compassionate young man, raised in an orphanage and trained to be a doctor there, decides to leave to see the world. (126 mins.)
Director: Lasse Hallström
The Ape (2009)
A descent into hell, Jesper Ganslandt's disturbing and suspenseful second feature begins with a man waking up in unfamiliar surroundings, only to find the life he knew the day before is gone. (81 mins.)
Director: Jesper Ganslandt
Stars: Olle Sarri, Eva Rexed, Françoise Joyce, Sean Pietrulewicz
Involuntary (2008)
In several unrelated stories, the consequences of putting ones foot down - or failing to do so - are explored. (98 mins.)
Director: Ruben Östlund
Lantana (2001)
The relationships of four couples unravel after the discovery of a young woman's body in Lantana bush in suburban Sydney. (121 mins.)
Director: Ray Lawrence
The Zone (2007)
Residents of an enclosed neighborhood in the middle of Mexico DF are shocked by a violent crime, and for one resident in particular, young Alejandro, the drama is ratcheted up when he encounters the lone kid who escaped the event and is hiding out within the neighborhood's borders. (97 mins.)
Director: Rodrigo Plá
The Triplets of Belleville (2003)
When her grandson is kidnapped during the Tour de France, Madame Souza and her beloved pooch Bruno team up with the Belleville Sisters--an aged song-and-dance team from the days of Fred Astaire--to rescue him. (78 mins.)
Director: Sylvain Chomet
Hotel Chevalier (2007 Short Film)
A prologue of one heart-breaking history of love and the prologue of the travel told in The Darjeeling Limited. (13 mins.)
Director: Wes Anderson
Darling (2007)
A tragic and comic tale about beautiful and self-absorbed Eva, who cheats on her boyfriend, which becomes the starting point of a slow but relentless descent down to the life of ordinary people and a surprising, but doomed friendship. (94 mins.)
Director: Johan Kling
Kenny Begins (2009)
Kenny Starfighter is probably the most hopeless student the Hero academics of the galaxy has ever had... (87 mins.)
Director: Mats Lindberg, Carl Åstrand
The King of Kong: A Fistful of Quarters (2007 Documentary)
Die-hard gamers compete to break world records on classic arcade games. (79 mins.)
Director: Seth Gordon
Stars: Steve Wiebe, Billy Mitchell, Mark Alpiger, Adam Wood
It's All Gone Pete Tong (2004)
Its All Gone Pete Tong is a comedy following the tragic life of legendary Frankie Wilde. The story takes... (90 mins.)
Director: Michael Dowse
Stars: Paul Kaye, Beatriz Batarda, Kate Magowan, Mike Wilmot
Millions (2004)
Ethics, being human and the soul come to the fore when a 7-year old finds a bag of Pounds just days before the currency is switched to Euros and learns what we are really made of. (98 mins.)
Director: Danny Boyle
Stars: Alex Etel, James Nesbitt, Daisy Donovan, Lewis McGibbon
The Animatrix (2003 Video)
The Animatrix is a collection of several animated short films, detailing the backstory of the "Matrix" universe, and the original war between man and machines which led to the creation of the Matrix. (100 mins.)
Director: Peter Chung, Andrew R. Jones
The King of Ping Pong (2008)
Rille, an ostracized and bullied teenager, who only excels in the ping pong room, descends into a life-and-death struggle with his younger, more popular brother when the truth about their father surfaces during their spring break. (112 mins.)
Director: Jens Jonsson
Il Postino (1994)
Simple Italian postman learns to love poetry while delivering mail to a famous poet; he uses this to woo local beauty Beatrice. (108 mins.)
Director: Michael Radford, Massimo Troisi
Limitless (2011)
With the help of a mysterious pill that enables the user to access 100 percent of his brain abilities, a struggling writer becomes a financial wizard, but it also puts him in a new world with lots of dangers. (105 mins.)
Director: Neil Burger
Stars: Bradley Cooper, Anna Friel, Abbie Cornish, Robert De Niro
The Social Network (2010)
Harvard student Mark Zuckerberg creates the social networking site that would become known as Facebook, but is later sued by two brothers who claimed he stole their idea, and the co-founder who was later squeezed out of the business. (120 mins.)
Pulp Fiction (1994)
The lives of two mob hit men, a boxer, a gangster's wife, and a pair of diner bandits intertwine in four tales of violence and redemption. (154 mins.)
The Prestige (2006)
After a tragic accident two stage magicians engage in a battle to create the ultimate illusion whilst sacrificing everything they have to outwit the other. (130 mins.)
Director: Christopher Nolan
Scott Pilgrim vs. the World (2010)
Scott Pilgrim must defeat his new girlfriend's seven evil exes in order to win her heart. (112 mins.)
Director: Edgar Wright
The Departed (2006)
An undercover cop and a mole in the police attempt to identify each other while infiltrating an Irish gang in South Boston. (151 mins.)
Director: Martin Scorsese
Sin City (2005)
A film that explores the dark and miserable town, Basin City, and tells the story of three different people, all caught up in violent corruption. (124 mins.)
Director: Frank Miller, Robert Rodriguez
Stars: Mickey Rourke, Clive Owen, Bruce Willis, Jessica Alba
The Notebook (2004)
A poor yet passionate young man falls in love with a rich young woman, giving her a sense of freedom, but they are soon separated because of their social differences. (123 mins.)
Director: Nick Cassavetes
Stars: Gena Rowlands, James Garner, Rachel McAdams, Ryan Gosling
300 (2006)
King Leonidas of Sparta and a force of 300 men fight the Persians at Thermopylae in 480 B.C. (117 mins.)
Director: Zack Snyder
Stars: Gerard Butler, Lena Headey, David Wenham, Dominic West
Into the Wild (2007)
After graduating from Emory University, top student and athlete Christopher McCandless abandons his possessions, gives his entire $24,000 savings account to charity and hitchhikes to Alaska to live in the wilderness. Along the way, Christopher encounters a series of characters that shape his life. (148 mins.)
Director: Sean Penn
(500) Days of Summer (2009)
An offbeat romantic comedy about a woman who doesn't believe true love exists, and the young man who falls for her. (95 mins.)
Director: Marc Webb
The Big Lebowski (1998)
"The Dude" Lebowski, mistaken for a millionaire Lebowski, seeks restitution for his ruined rug and enlists his bowling buddies to help get it. (117 mins.)
Director: Joel Coen
Stars: Jeff Bridges, John Goodman, Julianne Moore, Steve Buscemi
Kenny Starfighter (1997 TV Series)
Kenny Starfighter, a dim-witted Galaxy superhero, comes to Earth to save his home planet Mylta from the evil lord Dr. Deo. To his aid are four kids, whose school is controlled by a bunch of very weird teachers. (28 mins.)
Director: Mats Lindberg, Pontus Löwenhielm
Taken (2008)
A retired CIA agent travels across Europe and relies on his old skills to save his estranged daughter, who has been kidnapped while on a trip to Paris. (93 mins.)
Director: Pierre Morel
Stars: Liam Neeson, Maggie Grace, Famke Janssen, Leland Orser
The Usual Suspects (1995)
A sole survivor tells of the twisty events leading up to a horrific gun battle on a boat, which began when five criminals met at a seemingly random police lineup. (106 mins.)
Director: Bryan Singer


text:
Conference Venue:
Vancouver Convention Centre in Downtown Vancouver
"Under the Sails" (East Building of the Convention Centre)
999 Canada Place, Vancouver
British Columbia Canada V6C 3E1
Enter Vancouver Convention Centre East through the lobby doors located to the west of the Pan Pacific Hotel entrance.
The Ballrooms and Exhibition Halls are at G or street level. Plenary sessions are held on this level.
The Meeting Rooms for the Children's Programs and Seminars are at M level, up one floor from the main entrance and may be reached via elevators or escalators.
Conference 2018 Host Hotel (for overnight accommodations)
Pan Pacific Vancouver
Special Features: The hotel will provide complimentary High Speed LITE internet access in the guestrooms over the contracted group dates – January 27th – 29th, 2017. You are able to remain on-site, accessing your hotel room directly from the conference rooms in the Convention Centre without going outside. Just go up to your room from the convention centre lobby!
Missions Fest has negotiated the best possible price at the Pan Pacific for our exhibitors and attendees.
Missions Fest Conference Rate – $189 for a Deluxe Room and $219 for a Deluxe Habour View.
Address: 999 Canada Place Vancouver BC Canada V6C 3B5 Phone: 1-800-663-1515
Cancellation of individual reservations may be made up to 24 hours prior to arrival or 72 hours prior to arrival for suites, otherwise a charge of one night's stay at the confirmed rate will be applied.
Our guestrooms are being held until December 28, 2017. The Reservations are being made on an individual basis, please contact the Reservations Office to book:
Reservations ID: MISS0217 (Please quote to the Reservations Agent)
Reservations Direct Phone: 604-662-3223
Reservations Direct Fax: 604-895-2469
Toll-Free Numbers: 1-800-663-1515 (Canada)/ 1-800-937-1515 (USA)
E-mail: email@example.com
Missions Fest has NOT contracted any third party hotel organizer such as CONVENTION or EXHIBITOR HOUSING SERVICES to approach our exhibitors or participants. Please do not respond to their unauthorized solicitations.
Public Paid Parking
VINCI Park – under the Convention Centre (most convenient but most expensive) Tel: (604) 684-2251
Book online: http://www.vinciparkcanadaplace.ca/reserve-a-space.html
If you choose to just drive in without a parking reservation, you need to use your phone to book a parking spot online.
Save the confirmation email to show it to the parking lot attendant.
It should be $23 per day (for 24 hours starting 0:01 AM until 12:00 midnight).
Enter at the north end of Howe Street, obtain a ticket from the dispenser and proceed to P1 or P2.
Use Convention Centre/Hotel elevators (not World Trade Centre elevators).
For Convention Street Level: press "G" for Ballrooms and Exhibition Halls.
Plenary sessions and the Film Fest are held on G level.
For Meeting Rooms: press "M" for the Children's Programs and Seminars and may be reached via elevators or escalators.
Additonal Public Parking
DPC Parking: 900 Cordova Street – underground access to the convention centre during mall open hours via the food fair. Weekend rates approx. $8/day. Be careful of closing hours or you may not be able to access your car until the next day.
Commercial Place: 400 Burrard St.
Waterfront Centre: 200 Burrard St.
Imperial Parking: 200 Granville St.
Station East: 555 Cordova St.
EasyPark: 1095 Waterfront Rd.


text:
|Half Baths
|Property type
|Minimum Stay
|2 nights
This is the perfect place to stay while in Chapel Hill. Our three bedroom house is located in Southern village. Southern Village is conveniently located in Chapel Hill; renowned for its quality of life, excellent schools, and proximity to all that the Triangle area has to offer. Located within the award winning new urbanist community of Southern Village, our home is just footsteps away from fine dining and shopping venues on Market Street, the new Southern Village park and minutes away from UNC and the RTP, interstate I-40, and the Raleigh-Durham International Airport, your portal to major destinations around the world.
With a park and ride bus lot located in the development, there is no need to drive to down town Chapel Hill for your sporting event or graduation.
Keywords: House, on Bus Line, 1 mile from UNC, Football, Basketball, Graduation
Close to University (1 Mile), On bus line with special event busses for sporting events and graduation, Town center in housing development with grocery store/movie theater/3 restaurants and many other retail stores.
It was fine. It is in Southern Village which is quite a way from Franklin Street. It had the bare essentials, but in a safe neighborhood and the small patio in the back was pleasant. It took a couple of days to figure out the parking. TV has local stations only, (no ESPN, no CNN...). Owner was immediately responsive to my requests although we could not control the fan in the room so it was either on with the light or both completely off, however, that would be an easy fix with the remote control. In terms of any changes, a guest book would have been lovely and quite useful
Great location convenient to downtown Chapel Hill and surrounding area, walkable neighborhood, comfy beds and plenty of towels.
We had an excellent stay in this property. The house is in a great location in Chapel Hill. Minutes from Franklin Street and campus. The property was extremely clean and spacious. Thanks!
This was a great clean house to rent for 3 couples visiting chapel hill. It was in a great neighborhood, minutes from downtown. Very spacious and had a great patio.
The kitchen didn't offer much for cooking supplies but we ate out most of the time.
Owner was always available for any questions. We noticed Fox News was not on the cable line up. Upon asking the owner, he immediately had it turned on. Very nice neighbors and neighborhood.
Once again our friends, a couple with two children, enjoyed their stay and the spaciousness of the house, especially the master suite.
|Rate Period
|Weekend Night
|Monthly *
My Standard Rate
2 night minimum stay
|Notes: Rates are based on 6 guests; add $25 per night, per additional guest
|Cleaning Fee
|Additional Guest Fee
|$25 per person per night
|Pet Fee
|Damage Deposit
Pet fee is refundable
High occupancy rate of $25/person over 6
* Approximate monthly rate. Actual rate will depend on the days of the month you stay.


text:
Are you as productive as you'd like to be?
It is such a common complaint: How can I get more done? The true challenge with this, is that time is static. You can't stretch, or bend time. An hour is still an hour, no matter how you look at it. Certain tasks also take a finite amount of time. How will you ever "make time" for all that needs to get done?Some suggestions:
- Have less to-do's on your list. Are there things on your list that you may never get to? Cross them off now, as they're taking up space in your brain. Mental clutter inhibits mental clarity.
- Know your "best times": What time of day do you thrive? If you are an entrepreneur, and have some flexibility in how you create your schedule, give yourself the advantage of doing tasks best suited to the specific time of day. If you are a morning person, tackle the "hard stuff" first thing in your day. If your creativity blossoms in the wee hours, take that time to write, or create.
- Try single-tasking. For years, we have recognized multi-tasking as the most productive way to be. Zen Habits blogger Leo Baubata proposes this, as doing a lot of things at one time does not get them done as effectively, or as efficiently. Focus, and limit your distractions when you need to get something done, and give it your full attention. There is an even a computer app for this, called "Now Do This," to train you in single-tasking.
- Move around. Staying at a desk, or indoors all day makes for a dull entrepreneur. Do you what you can to move around, get your circulation moving, and get your heart pumping. Take a walk. Dance in your living room for 5 minutes. Do yoga. You will find that when you come back to your work, you feel energized and refreshed.
- Practice the 90-minute solution proposed by Tony Scwhartz, President and CEO of The Energy Project. Set a timer. Work in 90-minute increments, and hit it hard for that entire 90-minutes. Stay on one project, focus, and then take a short break after the 90 minutes.
- Celebrate your successes, no matter how small. When you work for yourself, no one may be giving you the accolades or the "thank you's" along the way. It is up to you to celebrate yourself, and your productivity, which will fuel you into your next project. Reward your good behavior with something that is good for you, and cherish your down-time. A balanced life inspires productivity.
If you are looking for support with your productivity, and want to be held accountable, consider hiring a Life Coach. If you need help getting the little things off your plate, or are stuck with your social media, technology, website, or writing, contact me here. I love supporting Entrepreneurs, Life Coaches, Consultants, and Speakers in doing what they do best.


text:
School says farewell to sophomore secretary Joyce Weis
Samantha Gaddis, Staff writer
April 13, 2017 • 1,551 views
Filed under news, showcase
After 19 years of working at West, sophomore secretary Joyce Weis plans to retire with her sixth class in May. After receiving her job four years ago, Sophomore Assistant Principal Jenn Sebold met with Weis to introduce herself. "She had this motherly stern look and said, 'If you can remember ...
Returning to life after chemo
Kristin Priest, Staff Writer
March 22, 2017 • 1,949 views
Filed under editorials, showcase
I still have good and bad days. After all, my body is still fully recovering from the harsh treatment of my medical endeavours. I routinely see my integrated medicine doctor, continually take my vitamins and follow a diet of fresh and healthy foods. Supplying my body with the appropriate fuel is critical...
We're Really Clef-er; Valentine's Day playlists for all
Dani Fischer and Justin Cupps
February 14, 2017 • 359 views
Filed under columns, We're Really Clef-er
Playlists for the people hopelessly in love and those that only have love for Netflix this Valentine's Day.Read more »
Spending secrets of Valentine's Day
Lily Briscoe, Staff Writer
February 12, 2015 • 883 views
Filed under news
Americans are starting to use this holiday as an excuse just to spend their money.Read more »


text:
Cystic Fibrosis is a genetic illness that causes the glands that secrete body fluids to produce abnormal secretions that can cause problems including lung infections and reduced food digestion.
We have a specialist team, including nurses and physiotherapists, who are experts in cystic fibrosis care. This team will work with you and your loved ones on both an individual basis and alongside our general staff, to make sure your health needs are met.
Our nurses can assist you by:
- providing assessment, monitoring and support
- liaising closely with your hospital care team
- providing technical care when it is required at home i.e. portacath and central lines
- caring for you before and after a hospital admission
If you have a child requiring care, our nurses can also:
- visit children at home, crèche, kindergarten or school to provide care and educate child care workers and teachers
- provide information at the time of diagnosis and ongoing support
- visit at the most suitable times to accommodate family time commitments
- advise you on physiotherapy, diet and medication issues.
How can I access this service?
Call us on 1300 33 44 55 and speak to one of our qualified staff. This line is open 24 hours a day, 7 days a week.