The importance of regeneration processes on forest biodiversity in old-growth forests in the Pacific Northwest.

Forest diversity is the outcome of multiple species-specific processes and tolerances, from regeneration, growth, competition and mortality of trees. Predicting diversity thus requires a comprehensive understanding of those processes. Regeneration processes have traditionally been overlooked, due to high stochasticity and assumptions that recruitment is not limiting for forests. Thus, we investigated the importance of seed production and seedling survival on forest diversity in the Pacific Northwest (PNW) using a forest gap model (ForClim). Equations for regeneration processes were fit to empirical data and added into the model, followed by simulations where regeneration processes and parameter values varied. Adding regeneration processes into ForClim improved the simulation of species composition, compared to Forest Inventory Analysis data. We also found that seed production was not as important as seedling survival, and the time it took for seedlings to grow into saplings was a critical recruitment parameter for accurately capturing tree species diversity in PNW forest stands. However, our simulations considered historical climate only. Due to the sensitivity of seed production and seedling survival to weather, future climate change may alter seed production or seedling survival and future climate change simulations should include these regeneration processes to predict future forest dynamics in the PNW. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

External validation of the GAP model in Chinese patients with idiopathic pulmonary fibrosis.

INTRODUCTION: The GAP model was widely used as a simple risk "screening" method for patients with idiopathic pulmonary fibrosis (IPF). OBJECTIVES: We sought to validate the GAP model in Chinese patients with IPF to evaluate whether it can accurately predict the risk for mortality. METHODS: A total of 212 patients with IPF diagnosed at China-Japan Friendship Hospital from 2015 to 2019 were enrolled. The latest follow-up ended in September 2022. Cumulative mortality of each GAP stage was calculated and compared based on Fine-Gray models for survival, and lung transplantation was treated as a competing risk. The performance of the model was evaluated in terms of both discrimination and calibration. RESULTS: The cumulative mortality in patients with GAP stage III was significantly higher than that in those with GAP stage I or II (Gray's test p < 0.0001). The Harrell c-index for the GAP calculator was 0.736 (95% CI: 0.667-0.864). The discrimination for the GAP staging system were similar with that for the GAP calculator. The GAP model overestimated the mortality rate at 1- and 2-year in patients classified as GAP stage I (6.90% vs. 1.77% for 1-year, 14.20% vs. 6.78% for 2-year). CONCLUSIONS: Our findings indicated that the GAP model overestimated the mortality rate in mild group.

Accuracy, realism and general applicability of European forest models.

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.

Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population.

BACKGROUND: Combined pulmonary fibrosis and emphysema (CPFE) is a novel clinical entity with a poor prognosis. This study aimed to develop a clinical nomogram model to predict the 1-, 2- and 3-year mortality of patients with CPFE by using the machine learning approach, and to validate the predictive ability of the interstitial lung disease-gender-age-lung physiology (ILD-GAP) model in CPFE. METHODS: The data of CPFE patients from January 2015 to October 2021 who met the inclusion criteria were retrospectively collected. We utilized LASSO regression and multivariable Cox regression analysis to identify the variables associated with the prognosis of CPFE and generate a nomogram. The Harrell's C index, the calibration curve and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the nomogram. Then, we performed likelihood ratio test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) to compare the performance of the nomogram with that of the ILD-GAP model. RESULTS: A total of 184 patients with CPFE were enrolled. During the follow-up, 90 patients died. After screening out, diffusing lung capacity for carbon monoxide (DLCO), right ventricular diameter (RVD), C-reactive protein (CRP), and globulin were found to be associated with the prognosis of CPFE. The nomogram was then developed by incorporating the above five variables, and it showed a good performance, with a Harrell's C index of 0.757 and an AUC of 0.800 (95% CI 0.736-0.863). Moreover, the calibration plot of the nomogram showed good concordance between the prediction probabilities and the actual observations. The nomogram also improved the discrimination ability of the ILD-GAP model compared to that of the ILD-GAP model alone, and this was substantiated by the likelihood ratio test, NRI and IDI. The significant clinical utility of the nomogram was demonstrated by DCA. CONCLUSION: Age, DLCO, RVD, CRP and globulin were identified as being significantly associated with the prognosis of CPFE in our cohort. The nomogram incorporating the 5 variables showed good performance in predicting the mortality of CPFE. In addition, although the nomogram was superior to the ILD-GAP model in the present cohort, further validation is needed to determine the clinical utility of the nomogram.

Functionally distinct tree species support long-term productivity in extreme environments.

Despite evidence of a positive effect of functional diversity on ecosystem productivity, the importance of functionally distinct species (i.e. species that display an original combination of traits) is poorly understood. To investigate how distinct species affect ecosystem productivity, we used a forest-gap model to simulate realistic temperate forest successions along an environmental gradient and measured ecosystem productivity at the end of the successional trajectories. We performed 10 560 simulations with different sets and numbers of species, bearing either distinct or indistinct functional traits, and compared them to random assemblages, to mimic the consequences of a regional loss of species. Long-term ecosystem productivity dropped when distinct species were lost first from the regional pool of species, under the harshest environmental conditions. On the contrary, productivity was more dependent on ordinary species in milder environments. Our findings show that species functional distinctiveness, integrating multiple trait dimensions, can capture species-specific effects on ecosystem productivity. In a context of an environmentally changing world, they highlight the need to investigate the role of distinct species in sustaining ecosystem processes, particularly in extreme environmental conditions.

The evolution, complexity and diversity of models of long-term forest dynamics.

To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies.We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis.Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes.Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the 'prevailing spirit of the epoch'. The lack of firm theories, laws and 'first principles' in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity-with the exception of global models-can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.

Determinants of Risk Disparity Due to Infrastructure Service Losses in Disasters: A Household Service Gap Model.

The objective of this article is to systematically assess and identify factors affecting risk disparity due to infrastructure service disruptions in extreme weather events. We propose a household service gap model that characterizes societal risks at the household level by examining service disruptions as threats, level of tolerance of households to disruptions as susceptibility, and experienced hardship as an indicator for the realized impacts of risk. The concept of "zone of tolerance" for the service disruptions was encapsulated to account for different capabilities of the households to endure the adverse impacts. The model was tested and validated in the context of power outages through survey data from the residents of Harris County in the aftermath of Hurricane Harvey in 2017. The results show that households' need for utility service, preparedness level, the existence of substitutes, possession of social capital, previous experience with disasters, and risk communication affect the zone of tolerance within which households cope with service outages. In addition, sociodemographic characteristics, such as race and residence type, are shown to influence the zone of tolerance, and hence the level of hardship experienced by the affected households. The results reveal that population subgroups show variations in the tolerance level of service disruptions. The findings highlight the importance of integrating social dimensions into the resilience planning of infrastructure systems. The proposed model and results enable human-centric hazards mitigation and resilience planning to effectively reduce the risk disparity of vulnerable populations to service disruptions in disasters.

Stand-scale climate change impacts on forests over large areas: transient responses and projection uncertainties.

The increasing impacts of climate change on forest ecosystems have triggered multiple model-based impact assessments for the future, which typically focused either on a small number of stand-scale case studies or on large scale analyses (i.e., continental to global). Therefore, substantial uncertainty remains regarding the local impacts over large areas (i.e., regions to countries), which is particularly problematic for forest management. We provide a comprehensive, high-resolution assessment of the climate change sensitivity of managed Swiss forests (~10,000 km2 ), which cover a wide range of environmental conditions. We used a dynamic vegetation model to project the development of typical forest stands derived from a stratification of the Third National Forest Inventory until the end of the 22nd century. Two types of simulations were conducted: one limited to using the extant local species, the other enabling immigration of potentially more climate-adapted species. Moreover, to assess the robustness of our projections, we quantified and decomposed the uncertainty in model projections resulting from the following sources: (1) climate change scenarios, (2) local site conditions, and (3) the dynamic vegetation model itself (i.e., represented by a set of model versions), an aspect hitherto rarely taken into account. The simulations showed substantial changes in basal area and species composition, with dissimilar sensitivity to climate change across and within elevation zones. Higher-elevation stands generally profited from increased temperature, but soil conditions strongly modulated this response. Low-elevation stands were increasingly subject to drought, with strong negative impacts on forest growth. Furthermore, current stand structure had a strong effect on the simulated response. The admixture of drought-tolerant species was found advisable across all elevations to mitigate future adverse climate-induced effects. The largest uncertainty in model projections was associated with climate change scenarios. Uncertainty induced by the model version was generally largest where overall simulated climate change impacts were small, thus corroborating the utility of the model for making projections into the future. Yet, the large influence of both site conditions and the model version on some of the projections indicates that uncertainty sources other than climate change scenarios need to be considered in climate change impact assessments.

Demineralization prevention with a new antibacterial restorative composite containing QASi nanoparticles: an in situ study.

OBJECTIVES: To investigate whether a newly developed dental composite with quaternary ammonium silica dioxide (QASi) nanoparticles incorporated with other fillers into the restorative material demonstrates antibacterial activity by reducing enamel demineralization in an in situ gap model. MATERIALS AND METHODS: Twenty subjects wearing a lower removable partial denture (RPD) with acrylic flanges on both sides of the mouth were recruited into the 4-week in situ study. The gap model consisted of an enamel slab placed next to a composite, separated by a 38-µm space. In the split-mouth design on one side of the RPD, the composite was the Nobio Infinix composite (Nobio Ltd., Kadima, Israel), and the contralateral side used a control composite. Each participant received enamel slabs from one tooth. The gap model was recessed into the RPD buccal flange, allowing microbial plaque to accumulate within the gap. After 4 weeks of continuous wearing, decalcification (∆Z mineral loss) of the enamel slabs adjacent to the gap was determined by cross-sectional microhardness testing in the laboratory. RESULTS: The ∆Z for the antibacterial composite test side was 235±354 (mean±standard deviation [SD]; data reported from 17 participants) and statistically significantly lower compared to ∆Z of the control side (774±556; mean±SD) (paired t-test, P<0.0001; mean of test minus control -539 (SD=392), 95% confidence interval of difference: -741, -338). CONCLUSIONS: This in situ clinical study showed that composites with QASi antibacterial particles significantly reduced demineralization in enamel adjacent to a 38-µm gap over a 4-week period in comparison to a conventional composite. CLINICAL RELEVANCE: Composites with QASi nanoparticle technology have the potential to reduce the occurrence of secondary caries. TRIAL REGISTRATION: ClinicalTrials.gov #NCT04059250.

Recurrent time-to-event models with ordinal outcomes.

A model to accommodate time-to-event ordinal outcomes was proposed by Berridge and Whitehead. Very few studies have adopted this approach, despite its appeal in incorporating several ordered categories of event outcome. More recently, there has been increased interest in utilizing recurrent events to analyze practical endpoints in the study of disease history and to help quantify the changing pattern of disease over time. For example, in studies of heart failure, the analysis of a single fatal event no longer provides sufficient clinical information to manage the disease. Similarly, the grade/frequency/severity of adverse events may be more important than simply prolonged survival in studies of toxic therapies in oncology. We propose an extension of the ordinal time-to-event model to allow for multiple/recurrent events in the case of marginal models (where all subjects are at risk for each recurrence, irrespective of whether they have experienced previous recurrences) and conditional models (subjects are at risk of a recurrence only if they have experienced a previous recurrence). These models rely on marginal and conditional estimates of the instantaneous baseline hazard and provide estimates of the probabilities of an event of each severity for each recurrence over time. We outline how confidence intervals for these probabilities can be constructed and illustrate how to fit these models and provide examples of the methods, together with an interpretation of the results.

A modified GAP model for East-Asian populations with idiopathic pulmonary fibrosis.

BACKGROUND: The easy-to-calculate gender, age, and lung physiology (GAP) model shows good predictive and discriminative performance in the prognosis of idiopathic pulmonary fibrosis (IPF). However, the GAP model was not effective in predicting the prognosis accurately in previous Japanese and Korean IPF cohort studies. Therefore, we developed a modified GAP model for the East-Asian populations by weighing the GAP variables. The validity of the modified GAP model was subsequently evaluated in East-Asian IPF patients. METHODS: The derivation cohort comprised 326 patients with IPF. Weights of the variables were adjusted on the basis of coefficients derived from Cox regression models. The total points were distributed to the three stages of the disease so that the number of patients included in each stage was appropriate. The validity of the modified model was analyzed in another Japanese cohort of 117 patients with IPF and a nationwide cohort of Korean patients with IPF. RESULTS: Predicted survival rates differed significantly in the derivation cohort using the modified GAP model for each stage of IPF (log-rank test: stage I vs. stage II, p < 0.001; stage II vs. stage III, p < 0.001). Model performance improved according to Harrell's C-index (at three years: 0.696 in the original GAP model to 0.738 in the modified model). The performance of the modified model was validated in the Japanese validation and Korean national cohorts. CONCLUSIONS: Our modification of the original GAP model showed improved performance in East-Asian IPF patient populations.

Respiratory reactance in forced oscillation technique reflects disease stage and predicts lung physiology deterioration in idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease. Although pulmonary function test (PFT) is useful for evaluating the progression of IPF, obtaining adequate results in advanced cases can be challenging. Conversely, the forced oscillation technique (FOT) can be noninvasively performed, even in patients with severely deteriorated lung function. In this study, the usefulness of FOT for the evaluation of IPF disease status was investigated. METHODS: We analyzed the PFT and FOT data of 97 patients with IPF. RESULTS: The respiratory reactance (Xrs) components of FOT, especially in the inspiratory phase, correlated with the PFT values. Patients with advanced disease had significantly lower reactance at 5 Hz (X5), higher resonant frequency (Fres) and low-frequency reactance area (ALX). The longitudinal deterioration of Xrs was also observed. Moreover, X5 in the inspiratory phase predicted subsequent lung capacity deterioration. CONCLUSION: The Xrs components of FOT, especially in the inspiratory phase, reflected restrictive ventilatory impairment and disease severity in patients with IPF.

Osseointegrative effect of rhBMP-2 covalently bound on a titan-plasma-spray-surface after modification with chromosulfuric acid in a large animal bone gap-healing model with the Göttingen minipig.

BACKGROUND: Bone morphogenetic proteins play an important role as osseointegrative factors. It is used widely in orthopedic research and surgery to enhance the osseointegrative potential of implants, e.g., in spinal fusion or alveolar socket augmentation. The aim of the present study was to investigate the benefit of rhBMP-2 on a titan plasma spray (TPS) layer after a special modification with chromosulfuric acid (CSA) at different postoperative times, regarding osseoconduction and osseoinduction. METHODS: We allocated 27 Göttinger minipigs into three groups consisting of nine animals each. They received four dumbbell-shaped implants in the metaphyseal parts of the femora. The implants had a TPS surface with (CSA group) and without a CSA treatment (TPS group). The former received an additional layer of BMP-2 (BMP-2 group). For the assessment of osseointegration after healing periods of 4, 8, and 12 weeks, histomorphometry was applied to undecalcified specimens after staining according to Masson-Goldner. An intravital labeling with different fluorochromes was used in the gap model. A multivariable analysis with repeated measurement design was performed for statistical evaluation. RESULTS: We observed several statistical differences in a three-way ANOVA. The comparison between the BMP-2 and the TPS group (two-way ANOVA) showed statistically significant differences in terms of the osseoinduction (osteoid volume), and pronounced for the osseoconduction (bone and osteoid ongrowth), in favor of the BMP-2 group. In the pairwise comparison between BMP-2 and CSA (two-way ANOVA), no statistical significance occurred. The intravital staining with tetracycline, calcein green, and xylenol orange revealed no considerable differences between the groups. CONCLUSION: BMP-2, covalently bound on a CSA-treated TPS surface, has positive effects on the osseointegration in the large animal bone gap-healing model over the observation period of 12 weeks.

Open Screw Placement in a 1.5 mm LCP Over a Fracture Gap Decreases Fatigue Life.

OBJECTIVE: To investigate the influence of plate and screw hole position on the stability of simulated radial fractures stabilized with a 1.5 mm condylar locking compression plate (LCP). STUDY DESIGN: In vitro mechanical testing of paired cadaveric limbs. SAMPLE POPULATION: Paired radii (n = 7) stabilized with a 1.5 mm condylar LCP with an open screw hole positioned either proximal to (PG), or over (OG), a simulated small fracture gap. METHODS: Constructs were cycled in axial compression at a simulated trot load until failure or a maximum of 200,000 cycles. Specimens that sustained 200,000 cycles without failure were then loaded in axial compression in a single cycle to failure. Construct cyclic axial stiffness and gap strain, fatigue life, and residual strength were evaluated and compared between constructs using analysis of variance. RESULTS: Of pairs that had a failure during cyclic loading, OG constructs survived fewer cycles (54,700 ± 60,600) than PG (116,800 ± 49,300). OG constructs had significantly lower initial stiffness throughout cyclic loading and higher gap strain range within the first 1,000 cycles than PG constructs. Residual strength variables were not significantly different between constructs, however yield loads occurred at loads only marginally higher than approximated trot loads. Fatigue life decreased with increasing body weight. CONCLUSION: Fracture fixation stability is compromised by an open screw hole directly over a fracture gap compared to the open screw hole being buttressed by bone in the model studied. The 1.5 mm condylar LCP may be insufficient stabilization in dogs with appropriate radial geometry but high body weights.

The importance of forest structure to biodiversity-productivity relationships.

While various relationships between productivity and biodiversity are found in forests, the processes underlying these relationships remain unclear and theory struggles to coherently explain them. In this work, we analyse diversity-productivity relationships through an examination of forest structure (described by basal area and tree height heterogeneity). We use a new modelling approach, called 'forest factory', which generates various forest stands and calculates their annual productivity (above-ground wood increment). Analysing approximately 300 000 forest stands, we find that mean forest productivity does not increase with species diversity. Instead forest structure emerges as the key variable. Similar patterns can be observed by analysing 5054 forest plots of the German National Forest Inventory. Furthermore, we group the forest stands into nine forest structure classes, in which we find increasing, decreasing, invariant and even bell-shaped relationships between productivity and diversity. In addition, we introduce a new index, called optimal species distribution, which describes the ratio of realized to the maximal possible productivity (by shuffling species identities). The optimal species distribution and forest structure indices explain the obtained productivity values quite well (R2 between 0.7 and 0.95), whereby the influence of these attributes varies within the nine forest structure classes.

Patterns of interstitial lung disease and mortality in rheumatoid arthritis.

Objective: To characterize a cohort of patients with RA who have interstitial lung disease (ILD) and to assess the utility of previously developed mortality staging systems [gender, age, lung physiology (GAP) and ILD-GAP]. Methods: All patients with RA and ILD seen at the Mayo Clinic from 1998 to 2014 were identified and manually screened for study inclusion. RA disease characteristics and pulmonary findings including high-resolution CT and pulmonary function testing were evaluated. Survival was estimated using Kaplan-Meier methods. GAP and ILD-GAP models were evaluated using c-statistics and standardized incidence ratios. Results: The study included 181 patients with RA-associated ILD (96% Caucasian; 48% females; 37% never-smokers). The mean age at ILD diagnosis was 67.4 years ( s . d . 9.9). The median time from RA diagnosis to ILD was 4.9 years (range -10.9-48.1). The median follow-up was 3.1 years (range 0.01-14.8). Age, RA disease duration and low initial diffusing capacity for carbon monoxide were predictive of premature mortality in multivariate modelling. Sex, smoking status, obstructive lung disease, seropositivity and erosive disease were not associated with mortality. The 5-year survival rate was 59.7% (95% CI 51.5, 69.2). Survival did not differ between usual interstitial pneumonia, non-specific interstitial pneumonia and organizing pneumonia ( P = 0.42). The GAP model performed well in this cohort for both discrimination and calibration (c-statistic 0.71, standardized incidence ratio 0.97). Conclusion: In this large single-centre cohort of patients with RA-ILD, most patients were seropositive and had a history of smoking. ILD most commonly occurred after the RA diagnosis. Mortality was high and did not differ among the types. The GAP model may be useful in assessing mortality risk.

Analysis of the osseointegrative force of a hyperhydrophilic and nanostructured surface refinement for TPS surfaces in a gap healing model with the Göttingen minipig.

BACKGROUND: A lot of advantages can result in a high wettability as well as a nanostructure at a titanium surface on bone implants. Thus, the aim of this study was to evaluate the osseointegrative potential of a titan plasma-sprayed (TPS) surface refinement by acid-etching with chromosulfuric acid. This results in a hyperhydrophilic surface with a nanostructure and an extreme high wetting rate. METHODS: In total, 72 dumbbell shape titan implants were inserted in the spongy bone of the femora of 18 Göttingen minipigs in a conservative gap model. Thirty-six titan implants were coated with a standard TPS surface and 36 with the hyperhydrophilic chromosulfuric acid (CSA) surface. After a healing period of 4, 8, and 12 weeks, the animals were killed. The chronological healing process was histomorphometrically analyzed. RESULTS: The de novo bone formation, represented by the bone area (BA), is increased by approximately 1.5 times after 12 weeks with little additional benefit by use of the CSA surface. The bone-to-implant contact (BIC), which represents osseoconductive forces, shows results with a highly increased osteoid production in the CSA implants beginning at 8 and 12 weeks compared to TPS. This culminates in a 17-fold increase in BIC after a healing period of 12 weeks. After 4 weeks, significantly more osteoid was seen in the gap as de novo formation in the CSA group (p = 0.0062). Osteoid was also found more frequently after 12 weeks at the CSA-treated surface (p = 0.0355). The site of implantation, intertrochanteric or intercondylar, may influence on the de novo bone formation in the gap. CONCLUSIONS: There is a benefit by the CSA surface treatment of the TPS layer for osseointegration over an observation time up to 12 weeks. Significant differences were able to be shown in two direct comparisons between the CSA and the TPS surface for osteoid formation in the gap model. Further trials may reveal the benefit of the CSA treatment of the TPS layer involving mechanical tests if possible.

Applying revised gap analysis model in measuring hotel service quality.

INTRODUCTION: With the number of tourists coming to Taiwan growing by 10-20 % since 2010, the number has increased due to an increasing number of foreign tourists, particularly after deregulation allowed admitting tourist groups, followed later on by foreign individual tourists, from mainland China. The purpose of this study is to propose a revised gap model to evaluate and improve service quality in Taiwanese hotel industry. Thus, service quality could be clearly measured through gap analysis, which was more effective for offering direction in developing and improving service quality. CASE DESCRIPTION: The HOLSERV instrument was used to identify and analyze service gaps from the perceptions of internal and external customers. The sample for this study included three main categories of respondents: tourists, employees, and managers. DISCUSSION AND EVALUATION: The results show that five gaps influenced tourists' evaluations of service quality. In particular, the study revealed that Gap 1 (management perceptions vs. customer expectations) and Gap 9 (service provider perceptions of management perceptions vs. service delivery) were more critical than the others in affecting perceived service quality, making service delivery the main area of improvement. CONCLUSION: This study contributes toward an evaluation of the service quality of the Taiwanese hotel industry from the perspectives of customers, service providers, and managers, which is considerably valuable for hotel managers. It was the aim of this study to explore all of these together in order to better understand the possible gaps in the hotel industry in Taiwan.

Validation of the Japanese disease severity classification and the GAP model in Japanese patients with idiopathic pulmonary fibrosis.

BACKGROUND: The clinical course of idiopathic pulmonary fibrosis (IPF) shows great inter-individual differences. It is important to standardize the severity classification to accurately evaluate each patient׳s prognosis. In Japan, an original severity classification (the Japanese disease severity classification, JSC) is used. In the United States, the new multidimensional index and staging system (the GAP model) has been proposed. The objective of this study was to evaluate the model performance for the prediction of mortality risk of the JSC and GAP models using a large cohort of Japanese patients with IPF. METHODS: This is a retrospective cohort study including 326 patients with IPF in the Hokkaido prefecture from 2003 to 2007. We obtained the survival curves of each stage of the GAP and JSC models to perform a comparison. In the GAP model, the prognostic value for mortality risk of Japanese patients was also evaluated. RESULTS: In the JSC, patient prognoses were roughly divided into two groups, mild cases (Stages I and II) and severe cases (Stages III and IV). In the GAP model, there was no significant difference in survival between Stages II and III, and the mortality rates in the patients classified into the GAP Stages I and II were underestimated. CONCLUSIONS: It is difficult to predict accurate prognosis of IPF using the JSC and the GAP models. A re-examination of the variables from the two models is required, as well as an evaluation of the prognostic value to revise the severity classification for Japanese patients with IPF.