A retrospective analysis of patients referred for implant placement to a specialty clinic: indications, surgical procedures, and early failures

Purpose: This retrospective study analyzed the pool of patients referred for treatment with dental implants over a 3-year period in a referral specialty clinic. Materials and Methods: All patients receiving dental implants between 2002 and 2004 in the Department of Oral Surgery and Stomatology, University of Bern, were included in this retrospective study. Patients were analyzed according to age, gender, indications for implant therapy, location of implants, and type and length of implants placed. A cumulative logistic regression analysis was performed to identify and analyze potential risk factors for complications or failures. Results: A total of 1,206 patients received 1,817 dental implants. The group comprised 573 men and 633 women with a mean age of 55.2 years. Almost 60% of patients were age 50 or older. The most frequent indication for implant therapy was single-tooth replacement in the maxilla (522 implants or 28.7%). A total of 726 implants (40%) were inserted in the esthetically demanding region of the anterior maxilla. For 939 implants (51.7%), additional bone-augmentation procedures were required. Of these, ridge augmentation with guided bone regeneration was performed more frequently than sinus grafting. Thirteen complications leading to early failures were recorded, resulting in an early failure rate of 0.7%. The regression analysis failed to identify statistically significant failure etiologies for the variables assessed. Conclusions: From this study it can be concluded that patients referred to a specialty clinic for implant placement were more likely to be partially edentulous and over 50 years old. Single-tooth replacement was the most frequent indication (> 50%). Similarly, additional bone augmentation was indicated in more than 50% of cases. Adhering to strict patient selection criteria and a standardized surgical protocol, an early failure rate of 0.7% was experienced in this study population

Analysis of atmospheric transport and the effects of seasonal and interannual transport variability on measurements made at the Pico Mountain observatory

A non-hierarchical K-means algorithm is used to cluster 47 years (1960–2006) of 10-day HYSPLIT backward trajectories to the Pico Mountain (PM) observatory on a seasonal basis. The resulting cluster centers identify the major transport pathways and collectively comprise a long-term climatology of transport to the observatory. The transport climatology improves our ability to interpret the observations made there and our understanding of pollution source regions to the station and the central North Atlantic region. I determine which pathways dominate transport to the observatory and examine the impacts of these transport patterns on the O3, NOy, NOx, and CO measurements made there during 2001–2006. Transport from the U.S., Canada, and the Atlantic most frequently reaches the station, but Europe, east Africa, and the Pacific can also contribute significantly depending on the season. Transport from Canada was correlated with the North Atlantic Oscillation (NAO) in spring and winter, and transport from the Pacific was uncorrelated with the NAO. The highest CO and O3 are observed during spring. Summer is also characterized by high CO and O3 and the highest NOy and NOx of any season. Previous studies at the station attributed the summer time high CO and O3 to transport of boreal wildfire emissions (for 2002–2004), and boreal fires continued to affect the station during 2005 and 2006. The particle dispersion model FLEXPART was used to calculate anthropogenic and biomass-burning CO tracer values at the station in an attempt to identify the regions responsible for the high CO and O3 observations during spring and biomass-burning impacts in summer.

Long-term immunologic response to antiretroviral therapy in low-income countries: a collaborative analysis of prospective studies

BACKGROUND: Few data are available on the long-term immunologic response to antiretroviral therapy (ART) in resource-limited settings, where ART is being rapidly scaled up using a public health approach, with a limited repertoire of drugs. OBJECTIVES: To describe immunologic response to ART among ART patients in a network of cohorts from sub-Saharan Africa, Latin America, and Asia. STUDY POPULATION/METHODS: Treatment-naive patients aged 15 and older from 27 treatment programs were eligible. Multilevel, linear mixed models were used to assess associations between predictor variables and CD4 cell count trajectories following ART initiation. RESULTS: Of 29 175 patients initiating ART, 8933 (31%) were excluded due to insufficient follow-up time and early lost to follow-up or death. The remaining 19 967 patients contributed 39 200 person-years on ART and 71 067 CD4 cell count measurements. The median baseline CD4 cell count was 114 cells/microl, with 35% having less than 100 cells/microl. Substantial intersite variation in baseline CD4 cell count was observed (range 61-181 cells/microl). Women had higher median baseline CD4 cell counts than men (121 vs. 104 cells/microl). The median CD4 cell count increased from 114 cells/microl at ART initiation to 230 [interquartile range (IQR) 144-338] at 6 months, 263 (IQR 175-376) at 1 year, 336 (IQR 224-472) at 2 years, 372 (IQR 242-537) at 3 years, 377 (IQR 221-561) at 4 years, and 395 (IQR 240-592) at 5 years. In multivariable models, baseline CD4 cell count was the most important determinant of subsequent CD4 cell count trajectories. CONCLUSION: These data demonstrate robust and sustained CD4 response to ART among patients remaining on therapy. Public health and programmatic interventions leading to earlier HIV diagnosis and initiation of ART could substantially improve patient outcomes in resource-limited settings.

Prognostic importance of anaemia in HIV type-1-infected patients starting antiretroviral therapy: collaborative analysis of prospective cohort studies

BACKGROUND: In HIV type-1-infected patients starting highly active antiretroviral therapy (HAART), the prognostic value of haemoglobin when starting HAART, and of changes in haemoglobin levels, are not well defined. METHODS: We combined data from 10 prospective studies of 12,100 previously untreated individuals (25% women). A total of 4,222 patients (35%) were anaemic: 131 patients (1.1%) had severe (<8.0 g/dl), 1,120 (9%) had moderate (male 8.0-<11.0 g/dl and female 8.0- < 10.0 g/dl) and 2,971 (25%) had mild (male 11.0- < 13.0 g/ dl and female 10.0- < 12.0 g/dl) anaemia. We separately analysed progression to AIDS or death from baseline and from 6 months using Weibull models, adjusting for CD4+ T-cell count, age, sex and other variables. RESULTS: During 48,420 person-years of follow-up 1,448 patients developed at least one AIDS event and 857 patients died. Anaemia at baseline was independently associated with higher mortality: the adjusted hazard ratio (95% confidence interval) for mild anaemia was 1.42 (1.17-1.73), for moderate anaemia 2.56 (2.07-3.18) and for severe anaemia 5.26 (3.55-7.81). Corresponding figures for progression to AIDS were 1.60 (1.37-1.86), 2.00 (1.66-2.40) and 2.24 (1.46-3.42). At 6 months the prevalence of anaemia declined to 26%. Baseline anaemia continued to predict mortality (and to a lesser extent progression to AIDS) in patients with normal haemoglobin or mild anaemia at 6 months. CONCLUSIONS: Anaemia at the start of HAART is an important factor for short- and long-term prognosis, including in patients whose haemoglobin levels improved or normalized during the first 6 months of HAART.

Spatial and temporal analysis of land cover and landscape structure change in Zagros Forests

The Zagros oak forests in Western Iran are critically important to the sustainability of the region. These forests have undergone dramatic declines in recent decades. We evaluated the utility of the non-parametric Random Forest classification algorithm for land cover classification of Zagros landscapes, and selected the best spatial and spectral predictive variables. The algorithm resulted in high overall classification accuracies (>85%) and also equivalent classification accuracies for the datasets from the three different sensors. We evaluated the associations between trends in forest area and structure with trends in socioeconomic and climatic conditions, to identify the most likely driving forces creating deforestation and landscape structure change. We used available socioeconomic (urban and rural population, and rural income), and climatic (mean annual rainfall and mean annual temperature) data for two provinces in northern Zagros. The most correlated driving force of forest area loss was urban population, and climatic variables to a lesser extent. Landscape structure changes were more closely associated with rural population. We examined the effects of scale changes on the results from spatial pattern analysis. We assessed the impacts of eight years of protection in a protected area in northern Zagros at two different scales (both grain and extent). The effects of protection on the amount and structure of forests was scale dependent. We evaluated the nature and magnitude of changes in forest area and structure over the entire Zagros region from 1972 to 2009. We divided the Zagros region in 167 Landscape Units and developed two measures— Deforestation Sensitivity (DS) and Connectivity Sensitivity (CS) — for each landscape unit as the percent of the time steps that forest area and ECA experienced a decrease of greater than 10% in either measure. A considerable loss in forest area and connectivity was detected, but no sudden (nonlinear) changes were detected at the spatial and temporal scale of the study. Connectivity loss occurred more rapidly than forest loss due to the loss of connecting patches. More connectivity was lost in southern Zagros due to climatic differences and different forms of traditional land use.

COMPUTATIONAL ANALYSIS OF THE AEROELASTIC DYNAMICS OF A WIND TURBINE BLADE WITH VARIABLE-SPEED STALL CONTROL

The accuracy of simulating the aerodynamics and structural properties of the blades is crucial in the wind-turbine technology. Hence the models used to implement these features need to be very precise and their level of detailing needs to be high. With the variety of blade designs being developed the models should be versatile enough to adapt to the changes required by every design. We are going to implement a combination of numerical models which are associated with the structural and the aerodynamic part of the simulation using the computational power of a parallel HPC cluster. The structural part models the heterogeneous internal structure of the beam based on a novel implementation of the Generalized Timoshenko Beam Model Technique.. Using this technique the 3-D structure of the blade is reduced into a 1-D beam which is asymptotically equivalent. This reduces the computational cost of the model without compromising its accuracy. This structural model interacts with the Flow model which is a modified version of the Blade Element Momentum Theory. The modified version of the BEM accounts for the large deflections of the blade and also considers the pre-defined structure of the blade. The coning, sweeping of the blade, tilt of the nacelle and the twist of the sections along the blade length are all computed by the model which aren’t considered in the classical BEM theory. Each of these two models provides feedback to the other and the interactive computations lead to more accurate outputs. We successfully implemented the computational models to analyze and simulate the structural and aerodynamic aspects of the blades. The interactive nature of these models and their ability to recompute data using the feedback from each other makes this code more efficient than the commercial codes available. In this thesis we start off with the verification of these models by testing it on the well-known benchmark blade for the NREL-5MW Reference Wind Turbine, an alternative fixed-speed stall-controlled blade design proposed by Delft University, and a novel alternative design that we proposed for a variable-speed stall-controlled turbine, which offers the potential for more uniform power control and improved annual energy production.. To optimize the power output of the stall-controlled blade we modify the existing designs and study their behavior using the aforementioned aero elastic model.

Structural analysis of B-Box 2 from MuRF1: identification of a novel self-association pattern in a RING-like fold

The B-box motif is the defining feature of the TRIM family of proteins, characterized by a RING finger-B-box-coiled coil tripartite fold. We have elucidated the crystal structure of B-box 2 (B2) from MuRF1, a TRIM protein that supports a wide variety of protein interactions in the sarcomere and regulates the trophic state of striated muscle tissue. MuRF1 B2 coordinates two zinc ions through a cross-brace alpha/beta-topology typical of members of the RING finger superfamily. However, it self-associates into dimers with high affinity. The dimerization pattern is mediated by the helical component of this fold and is unique among RING-like folds. This B2 reveals a long shallow groove that encircles the C-terminal metal binding site ZnII and appears as the defining protein-protein interaction feature of this domain. A cluster of conserved hydrophobic residues in this groove and, in particular, a highly conserved aromatic residue (Y133 in MuRF1 B2) is likely to be central to this role. We expect these findings to aid the future exploration of the cellular function and therapeutic potential of MuRF1.

STUDY OF SPARK IGNITION ENGINE COMBUSTION MODEL FOR THE ANALYSIS OF CYCLIC VARIATION AND COMBUSTION STABILITY AT LEAN OPERATING CONDITIONS

A fundamental combustion model for spark-ignition engine is studied in this report. The model is implemented in SIMULINK to simulate engine outputs (mass fraction burn and in-cylinder pressure) under various engine operation conditions. The combustion model includes a turbulent propagation and eddy burning processes based on literature [1]. The turbulence propagation and eddy burning processes are simulated by zero-dimensional method and the flame is assumed as sphere. To predict pressure, temperature and other in-cylinder variables, a two-zone thermodynamic model is used. The predicted results of this model match well with the engine test data under various engine speeds, loads, spark ignition timings and air fuel mass ratios. The developed model is used to study cyclic variation and combustion stability at lean (or diluted) combustion conditions. Several variation sources are introduced into the combustion model to simulate engine performance observed in experimental data. The relations between combustion stability and the introduced variation amount are analyzed at various lean combustion levels.

A Combinatorial Approach to the Variable Selection in Multiple Linear Regression: Analysis of Selwood et al Data set -A Case Study.

A combinatorial protocol (CP) is introduced here to interface it with the multiple linear regression (MLR) for variable selection. The efficiency of CP-MLR is primarily based on the restriction of entry of correlated variables to the model development stage. It has been used for the analysis of Selwood et al data set [16], and the obtained models are compared with those reported from GFA [8] and MUSEUM [9] approaches. For this data set CP-MLR could identify three highly independent models (27, 28 and 31) with Q2 value in the range of 0.632-0.518. Also, these models are divergent and unique. Even though, the present study does not share any models with GFA [8], and MUSEUM [9] results, there are several descriptors common to all these studies, including the present one. Also a simulation is carried out on the same data set to explain the model formation in CP-MLR. The results demonstrate that the proposed method should be able to offer solutions to data sets with 50 to 60 descriptors in reasonable time frame. By carefully selecting the inter-parameter correlation cutoff values in CP-MLR one can identify divergent models and handle data sets larger than the present one without involving excessive computer time.

Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: an Artificial Neural Network perspective

Clinical studies indicate that exaggerated postprandial lipemia is linked to the progression of atherosclerosis, leading cause of Cardiovascular Diseases (CVD). CVD is a multi-factorial disease with complex etiology and according to the literature postprandial Triglycerides (TG) can be used as an independent CVD risk factor. Aim of the current study is to construct an Artificial Neural Network (ANN) based system for the identification of the most important gene-gene and/or gene-environmental interactions that contribute to a fast or slow postprandial metabolism of TG in blood and consequently to investigate the causality of postprandial TG response. The design and development of the system is based on a dataset of 213 subjects who underwent a two meals fatty prandial protocol. For each of the subjects a total of 30 input variables corresponding to genetic variations, sex, age and fasting levels of clinical measurements were known. Those variables provide input to the system, which is based on the combined use of Parameter Decreasing Method (PDM) and an ANN. The system was able to identify the ten (10) most informative variables and achieve a mean accuracy equal to 85.21%.

Deprivation, Violence, and Conflict: An Analysis of “Naxalite” Activity in the Districts of India

This paper asks: is it a fact that there is more violence in districts affected by Naxalite (Maoist) activity compared to those which are free of Naxalite activity? And can the existence of Naxalite activity in some districts of India, but not in others, be explained by differences in economic and social conditions? This study identifies districts in India in which there was significant Naxalite activity and correlating the findings with district-level economic, social, and crime indicators. The econometric results show that, after controlling for other variables, Naxalite activity in a district had, if anything, a dampening effect on its level of violent crime and crimes against women. Furthermore, even after controlling for other variables, the probability of a district being Naxalite-affected rose with an increase in its poverty rate and fell with a rise in its literacy rate. So, one prong in an anti-Naxalite strategy would be to address the twin issues of poverty and illiteracy in India.

Long-term patterns of subjective wellbeing in schizophrenia: cluster, predictors of cluster affiliation, and their relation to recovery criteria in 2842 patients followed over 3 years

To study the longitudinal patterns of subjective wellbeing in schizophrenia using cluster analysis and their relation to recovery criteria, further to examine predictors for cluster affiliation, and to evaluate the sensitivity and specificity of baseline subjective wellbeing cut-offs for cluster affiliation.

Synoptic Analysis of the New York March 1888 Blizzard

The meteorological circumstances that led to the Blizzard of March 1888 that hit New York are analysed in Version 2 of the “Twentieth Century Reanalysis” (20CR). The potential of this data set for studying historical extreme events has not yet been fully explored. A detailed analysis of 20CR data alongside other data sources (including historical instrumental data and weather maps) for historical extremes such as the March 1888 blizzard may give insights into the limitations of 20CR. We find that 20CR reproduces the circulation pattern as well as the temperature development very well. Regarding the absolute values of variables such as snow fall or minimum and maximum surface pressure, there is anunderestimation of the observed extremes, which may be due to the low spatial resolution of 20CR and the fact that only the ensemble mean is considered. Despite this drawback, the dataset allows us to gain new information due to its complete spatial and temporal coverage.

Clinical and pathological analysis of epidural inflammation in intervertebral disk extrusion in dogs

BACKGROUND Little is known about the pathologic changes in the epidural space after intervertebral disk (IVD) extrusion in the dog. OBJECTIVES To analyze the pathology of the epidural inflammatory response, and to search for correlations between this process and clinical findings. METHODS Clinical data from 105 chondrodystrophic (CD) and nonchondrodystrophic (NCD) dogs with IVD extrusion were recorded. Epidural material from these dogs was examined histopathologically and immunohistochemically. Using statistical analysis, we searched for correlations between severity of epidural inflammation and various clinical and pathologic variables. RESULTS Most dogs exhibited an epidural inflammatory response, ranging from acute invasion of neutrophils to formation of chronic granulation tissue. The mononuclear inflammatory infiltrates consisted mostly of monocytes and macrophages and only few T and B cells. Surprisingly, chronic inflammatory patterns also were found in animals with an acute clinical history. Severity of the epidural inflammation correlated with degree of the epidural hemorrhage and nucleus pulposus calcification (P = .003 and .040), but not with age, chondrodystrophic phenotype, neurologic grade, back pain, pretreatment, or duration. The degree of inflammation was statistically (P = .021) inversely correlated with the ability to regain ambulation. CONCLUSION AND CLINICAL IMPORTANCE Epidural inflammation occurs in the majority of dogs with IVD extrusion and may develop long before the onset of clinical signs. Presence of calcified IVD material and hemorrhage in the epidural space may be the triggers of this lesion rather than an adaptive immune response to the nucleus pulposus as suggested in previous studies. Because epidural inflammation may affect outcome, further research is warranted.

Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies

Low self-esteem and depression are strongly related, but there is not yet consistent evidence on the nature of the relation. Whereas the vulnerability model states that low self-esteem contributes to depression, the scar model states that depression erodes self-esteem. Furthermore, it is unknown whether the models are specific for depression or whether they are also valid for anxiety. We evaluated the vulnerability and scar models of low self-esteem and depression, and low self-esteem and anxiety, by meta-analyzing the available longitudinal data (covering 77 studies on depression and 18 studies on anxiety). The mean age of the samples ranged from childhood to old age. In the analyses, we used a random-effects model and examined prospective effects between the variables, controlling for prior levels of the predicted variables. For depression, the findings supported the vulnerability model: The effect of self-esteem on depression (β = -.16) was significantly stronger than the effect of depression on self-esteem (β = -.08). In contrast, the effects between low self-esteem and anxiety were relatively balanced: Self-esteem predicted anxiety with β = -.10, and anxiety predicted self-esteem with β = -.08. Moderator analyses were conducted for the effect of low self-esteem on depression; these suggested that the effect is not significantly influenced by gender, age, measures of self-esteem and depression, or time lag between assessments. If future research supports the hypothesized causality of the vulnerability effect of low self-esteem on depression, interventions aimed at increasing self-esteem might be useful in reducing the risk of depression.