bluuebunny/crossref_metadata_2025_split huggingface dataset:
External evaluation of published population pharmacokinetic models of tacrolimus in adult renal transplant recipients
Authors: Zhao Chen‐Yan , Jiao Zheng , Mao Jun‐Jun , Qiu Xiao‐Yan 
Published: November 2015

AimSeveral tacrolimus population pharmacokinetic models in adult renal transplant recipients have been established to facilitate dose individualization. However, their applicability when extrapolated to other clinical centres is not clear. This study aimed to (1) evaluate model external predictability and (2) analyze potential influencing factors.MethodsPublished models were screened from the literature and were evaluated using an external dataset with 52 patients (609 trough samples) collected by postoperative day 90 via methods that included (1) prediction‐based prediction error (PE%), (2) simulation‐based prediction‐ and variability‐corrected visual predictive check (pvcVPC) and normalized prediction distribution error (NPDE) tests and (3) Bayesian forecasting to assess the influence of prior observations on model predictability. The factors influencing model predictability, particularly the impact of structural models, were evaluated.ResultsSixteen published models were evaluated. In prediction‐based diagnostics, the PE% within ±30% was less than 50% in all models, indicating unsatisfactory predictability. In simulation‐based diagnostics, both the pvcVPC and the NPDE indicated model misspecification. Bayesian forecasting improved model predictability significantly with prior 2–3 observations. The various factors influencing model extrapolation included bioassays, the covariates involved (CYP3A5*3 polymorphism, postoperative time and haematocrit) and whether non‐linear kinetics were used.ConclusionsThe published models were unsatisfactory in prediction‐ and simulation‐based diagnostics, thus inappropriate for direct extrapolation correspondingly. However Bayesian forecasting could improve the predictability considerably with priors. The incorporation of non‐linear pharmacokinetics in modelling might be a promising approach to improving model predictability.
Marginal Regression Models for Clustered Count Data Based on Zero-Inflated Conway–Maxwell–Poisson Distribution With Applications
Authors: Choo-Wosoba Hyoyoung , Levy Steven M. , Datta Somnath 
Published: November 2015

Summary
Community water fluoridation is an important public health measure to prevent dental caries, but it continues to be somewhat controversial. The Iowa Fluoride Study (IFS) is a longitudinal study on a cohort of Iowa children that began in 1991. The main purposes of this study (http://www.dentistry.uiowa.edu/preventive-fluoride-study) were to quantify fluoride exposures from both dietary and nondietary sources and to associate longitudinal fluoride exposures with dental fluorosis (spots on teeth) and dental caries (cavities). We analyze a subset of the IFS data by a marginal regression model with a zero-inflated version of the Conway–Maxwell–Poisson distribution for count data exhibiting excessive zeros and a wide range of dispersion patterns. In general, we introduce two estimation methods for fitting a ZICMP marginal regression model. Finite sample behaviors of the estimators and the resulting confidence intervals are studied using extensive simulation studies. We apply our methodologies to the dental caries data. Our novel modeling incorporating zero inflation, clustering, and overdispersion sheds some new light on the effect of community water fluoridation and other factors. We also include a second application of our methodology to a genomic (next-generation sequencing) dataset that exhibits underdispersion.
Testing and Estimation in Marker-Set Association Study Using Semiparametric Quantile Regression Kernel Machine
Authors: Kong Dehan , Maity Arnab , Hsu Fang-Chi , Tzeng Jung-Ying 
Published: November 2015

Summary
We consider quantile regression for partially linear models where an outcome of interest is related to covariates and a marker set (e.g., gene or pathway). The covariate effects are modeled parametrically and the marker set effect of multiple loci is modeled using kernel machine. We propose an efficient algorithm to solve the corresponding optimization problem for estimating the effects of covariates and also introduce a powerful test for detecting the overall effect of the marker set. Our test is motivated by traditional score test, and borrows the idea of permutation test. Our estimation and testing procedures are evaluated numerically and applied to assess genetic association of change in fasting homocysteine level using the Vitamin Intervention for Stroke Prevention Trial data.
Bayes and Empirical Bayes Methods for Reduced Rank Regression Models in Matched Case-Control Studies
Authors: Satagopan Jaya M. , Sen Ananda , Zhou Qin , Lan Qing , Rothman Nathaniel , Langseth Hilde , Engel Lawrence S. 
Published: November 2015

SummaryMatched case-control studies are popular designs used in epidemiology for assessing the effects of exposures on binary traits. Modern studies increasingly enjoy the ability to examine a large number of exposures in a comprehensive manner. However, several risk factors often tend to be related in a nontrivial way, undermining efforts to identify the risk factors using standard analytic methods due to inflated type-I errors and possible masking of effects. Epidemiologists often use data reduction techniques by grouping the prognostic factors using a thematic approach, with themes deriving from biological considerations. We propose shrinkage-type estimators based on Bayesian penalization methods to estimate the effects of the risk factors using these themes. The properties of the estimators are examined using extensive simulations. The methodology is illustrated using data from a matched case-control study of polychlorinated biphenyls in relation to the etiology of non-Hodgkin's lymphoma.
Bayesian Predictive Modeling for Genomic Based Personalized Treatment Selection
Authors: Ma Junsheng , Stingo Francesco C. , Hobbs Brian P. 
Published: November 2015

Summary
Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this article, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches.
In vivo single human sweat gland activity monitoring using coherent anti-Stokes Raman scattering and two-photon excited autofluorescence microscopy
Authors: Chen X. , Gasecka P. , Formanek F. , Galey J.-B. , Rigneault H. 
Published: November 2015

Summary

Background
Eccrine sweat secretion is of central importance for control of body temperature. Although the incidence of sweat gland dysfunction might appear of minor importance, it can be a real concern for people with either hypohidrosis or hyperhidrosis. However, sweat gland function remains relatively poorly explored.

Objectives
To investigate the function of single human sweat glands.

Methods
We describe a new approach for noninvasive imaging of single sweat gland activity in human palms in vivo up to a depth of 100 μm, based on nonlinear two-photon excited autofluorescence (TPEF) and coherent anti-Stokes Raman scattering (CARS).

Results
These techniques appear to be useful compared with approaches already described for imaging single sweat gland activity, as they allow better three-dimensional spatial resolution of sweat pore inner morphology and real-time monitoring of individual sweat events. By filling the sweat pore with oil and tuning the CARS contrast at 2845 cm−1, we imaged the ejection of sweat droplets from a single sweat gland when oil is pushed out by sweat flow. On average, sweat events lasted for about 30 s every 3 min under the conditions studied. On the other hand, about 20% of sweat glands were found inactive. TPEF and CARS were also used to study, at the single pore level, the antiperspirant action of aluminium chlorohydrate (ACH) and to reveal, for the first time in vivo, the formation of a plug at the pore entrance, in agreement with reported ACH antiperspirant mechanisms.

Conclusions
Although data were acquired on human palms, these techniques show great promise for a better understanding of sweat secretion physiology and should be helpful to improve the efficacy of antiperspirant formulations.
Trends in utilisation, perioperative outcomes, and costs of nephroureterectomies in the management of upper tract urothelial carcinoma: a 10‐year population‐based analysis
Authors: Tinay Ilker , Gelpi‐Hammerschmidt Francisco , Leow Jeffrey J. , Allard Christopher B. , Rodriguez Dayron , Wang Ye , Chung Benjamin I. , Chang Steven L. 
Published: November 2015

ObjectiveTo perform a population‐based study to evaluate contemporary utilisation trends, morbidity, and costs associated with nephroureterectomies (NUs), as contemporary data for NUs are largely derived from single academic institution series describing the experience of high‐volume surgeons and it is unclear if the same favourable results occur at a national level.Patients and MethodsUsing the Premier Hospital Database, we captured patients undergoing a NU with diagnoses of renal pelvis or ureteric neoplasms from 2004 to 2013. We fitted regression models, adjusting for clustering by hospitals and survey weighting to evaluate 90‐day postoperative complications, operating‐room time (OT), prolonged length of stay (pLOS), and direct hospital costs among open (ONU), laparoscopic (LNU) and robotic (RNU) approaches.ResultsAfter applying sampling and propensity weights, we derived a final study cohort of 17 254 ONUs, 13 317 LNUs and 3774 RNUs for upper tract urothelial carcinoma (UTUC) in the USA between 2004 and 2013. During that period, minimally invasive NU (miNU) increased from 36% to 54%, while the total number of NUs decreased by nearly 20%. No differences were noted in perioperative outcomes between the three surgical approaches, including when the analysis was restricted to the highest‐volume hospitals and highest‐volume surgeons. The OT was longer for LNU and RNU (P < 0.001), while the pLOS rates were decreased (P < 0.001). Adjusted 90‐day median direct hospital costs were higher for LNU and RNU (P < 0.001), which disappeared when adjusting for the highest‐volume groups, except for RNUs performed by high‐volume surgeons.ConclusionsDuring this contemporary 10‐year study, miNU has been replacing ONU for UTUC with a recent surge in RNU, along with a concurrent reduction in total NUs performed. Despite not being associated with a clinically significant improvement in perioperative outcomes, the costs for miNUs were consistently higher. However, higher hospital volumes suggest a potential cost containment strategy when performing miNUs.
Clinical significance of preoperative C‐reactive protein and squamous cell carcinoma antigen levels in patients with penile squamous cell carcinoma
Authors: Li Zai‐Shang , Yao Kai , Li Yong‐Hong , Chen Jie‐Ping , Deng Chuang‐Zhong , Zhao Qi , Chen Peng , Wang Bin , Mi Qi‐Wu , Liu Zhuo‐Wei , Qin Zi‐Ke , Han Hui , Zhou Fang‐Jian 
Published: November 2015

ObjectiveTo evaluate the relevance of C‐reactive protein (CRP) and squamous cell carcinoma antigen (SCC‐Ag) levels in relation to clinicopathological factors and prognosis in penile cancer.Patients and MethodsA total of 124 Chinese patients with penile squamous cell carcinoma (SCC), treated between November 2007 and October 2014, were analysed retrospectively. Receiver‐operating characteristic curves were used to identify the combination of markers with the best sensitivity and specificity for prognosis prediction. Statistical data analysis was performed using a non‐parametric method, and survival analysis was performed using the log‐rank test and Cox proportional hazard model.ResultsLevels of CRP ≥4.5 mg/L and SCC‐Ag ≥1.4 ng/mL were both significantly associated with lymph node metastasis (LNM) laterality (chi‐squared trend test, P = 0.041), extranodal extension (chi‐squared trend test, P < 0.001), pelvic LNM (chi‐squared trend test, P = 0.024), pathological tumour status (chi‐squared trend test, P = 0.002), pathological nodal status (chi‐squared trend test, P < 0.001), and disease‐specific survival (DSS; log‐rank test, P < 0.001). Moreover, the influence of CRP and SCC‐Ag levels on DSS (P = 0.033, hazard ratio 3.390, 95% confidence interval 1.104–10.411) remained after adjusting for smoking history, phimosis, tumour status, tumour cell differentiation and nodal status.ConclusionsThe present study shows that the combined measurement of preoperative CRP and SCC‐Ag levels may serve as an independent biomarker for LNM, advanced tumour stage and DSS in patients with penile SCC.
What Ecological and Anthropogenic Factors Affect Group Size in White‐lipped Peccaries (Tayassu pecari)?
Authors: Reyna‐Hurtado Rafael , Beck Harald , Altrichter Mariana , Chapman Colin A. , Bonnell Tyler R. , Keuroghlian Alexine , Desbiez Arnaud L. , Moreira‐Ramírez Jose F. , O'Farrill Georgina , Fragoso Jose , Naranjo Eduardo J. 
Published: November 2015

AbstractGroup living among ungulates has evolved mainly in species living in open habitats, such as grasslands and savannas, whereas in the forest, few ungulate species form groups and these tend to be small. Therefore, the white‐lipped peccary (Tayassu pecari), a Neotropical ungulate listed as Vulnerable by theIUCN, represents an almost unique social occurrence as it lives in large and cohesive groups, yet it inhabits dense tropical forests. Large variations in group sizes have been observed throughout the species range, with reports of herds with less than 10 to around 300 individuals. We examined factors that might cause variation in group size in white‐lipped peccary, including ecological and anthropogenic variables. We conducted an extensive literature review and used our original data to compile information on white‐lipped peccary's group size across its range. We built models to quantitate generalizations for group sizes distinguishing data from areas with high human influence, and areas that have not been significantly disturbed by humans for at least the last 20 years. We found that white‐lipped peccary's group size for all sites was most strongly predicted by a combination of the distances to the nearest human settlement and rainfall and its seasonality. Results from the undisturbed sites indicated that group size is positively influenced by rainfall. Our results contribute to understand why group size varies in different environments that are subjected to different ecological and human conditions. Information on these relationships is a key to advance our understanding of the socio‐ecological strategies of animal species living in groups.
The State‐of‐the‐Art of Set Visualization
Authors: Alsallakh Bilal , Micallef Luana , Aigner Wolfgang , Hauser Helwig , Miksch Silvia , Rodgers Peter 
Published: November 2015

AbstractSets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non‐trivial problem due to the large number of possible relations between them. We provide a systematic overview of state‐of‐the‐art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net.
Dynamics of a low‐density tiger population in Southeast Asia in the context of improved law enforcement
Authors: Duangchantrasiri Somphot , Umponjan Mayuree , Simcharoen Saksit , Pattanavibool Anak , Chaiwattana Soontorn , Maneerat Sompoch , Kumar N. Samba , Jathanna Devcharan , Srivathsa Arjun , Karanth K. Ullas 
Published: November 2015

AbstractRecovering small populations of threatened species is an important global conservation strategy. Monitoring the anticipated recovery, however, often relies on uncertain abundance indices rather than on rigorous demographic estimates. To counter the severe threat from poaching of wild tigers (Panthera tigris), the Government of Thailand established an intensive patrolling system in 2005 to protect and recover its largest source population in Huai Kha Khaeng Wildlife Sanctuary. Concurrently, we assessed the dynamics of this tiger population over the next 8 years with rigorous photographic capture‐recapture methods. From 2006 to 2012, we sampled across 624–1026 km2with 137–200 camera traps. Cameras deployed for 21,359 trap days yielded photographic records of 90 distinct individuals. We used closed model Bayesian spatial capture‐recapture methods to estimate tiger abundances annually. Abundance estimates were integrated with likelihood‐based open model analyses to estimate rates of annual and overall rates of survival, recruitment, and changes in abundance. Estimates of demographic parameters fluctuated widely: annual density ranged from 1.25 to 2.01 tigers/100 km2, abundance from 35 to 58 tigers, survival from 79.6% to 95.5%, and annual recruitment from 0 to 25 tigers. The number of distinct individuals photographed demonstrates the value of photographic capture–recapture methods for assessments of population dynamics in rare and elusive species that are identifiable from natural markings. Possibly because of poaching pressure, overall tiger densities at Huai Kha Khaeng were 82–90% lower than in ecologically comparable sites in India. However, intensified patrolling after 2006 appeared to reduce poaching and was correlated with marginal improvement in tiger survival and recruitment. Our results suggest that population recovery of low‐density tiger populations may be slower than anticipated by current global strategies aimed at doubling the number of wild tigers in a decade.
Community‐Driven Research Agenda to Reduce Health Disparities
Authors: McElfish Pearl A. , Kohler Peter , Smith Chris , Warmack Scott , Buron Bill , Hudson Jonell , Bridges Melissa , Purvis Rachel , Rubon‐Chutaro Jellesen 
Published: November 2015

AbstractThis paper describes how a new regional campus of an academic health center engaged in a community‐based participatory research (CBPR) process to set a community‐driven research agenda to address health disparities. The campus is situated among growing Marshallese and Hispanic populations that face significant health disparities. In 2013, with support from the Translational Research Institute, the University of Arkansas for Medical Sciences Northwest began building its research capacity in the region with the goal of developing a community‐driven research agenda for the campus. While many researchers engage in some form of community‐engaged research, using a CBPR process to set the research agenda for an entire campus is unique. Utilizing multiple levels of engagement, three research areas were chosen by the community: (1) chronic disease management and prevention; (2) obesity and physical activity; and (3) access to culturally appropriate healthcare. In only 18 months, the CBPR collaboration had dramatic results. Ten grants and five scholarly articles were collaboratively written and 25 community publications and presentations were disseminated. Nine research projects and health programs were initiated. In addition, many interprofessional educational and service learning objectives were aligned with the community‐driven agenda resulting in practical action to address the needs identified.
Transitive inference of social dominance by human infants
Authors: Gazes Regina Paxton , Hampton Robert R. , Lourenco Stella F. 
Published: November 2015

AbstractIt is surprising that there are inconsistent findings of transitive inference (TI) in young infants given that non‐linguistic species succeed on TI tests. To conclusively test for TI in infants, we developed a task within the social domain, with which infants are known to show sophistication. We familiarized 10‐ to 13‐month‐olds (M = 11.53 months) to a video of two dominance interactions between three puppets (bear > elephant; hippo > bear) consistent with a dominance hierarchy (hippo > bear > elephant; where ‘>’ denotes greater dominance). Infants then viewed interactions between the two puppets that had not interacted during familiarization. These interactions were either congruent (hippo > elephant) or incongruent (elephant > hippo) with the inferred hierarchy. Consistent with TI, infants looked longer to incongruent than congruent displays. Control conditions ruled out the possibility that infants’ expectations were based on stable behaviors specific to individual puppets rather than their inferred transitive dominance relations. We suggest that TI may be supported by phylogenetically ancient mechanisms of ordinal representation and visuospatial processing that come online early in human development.