Clinical performance of wide-body implants with a sandblasted and acid-etched (SLA) surface: results of a 3-year follow-up study in a referral clinic

PURPOSE: The aim of this study was to evaluate the 3-year success rates of wide-body implants with a regular- or wide-neck configuration and a sandblasted, large grit, acid-etched (SLA) surface. MATERIALS AND METHODS: A total of 151 implants were consecutively placed in posterior sites of 116 partially edentulous patients in a referral clinic at the School of Dental Medicine, University of Bern. All implants were restored with cemented crowns or fixed partial dentures after a healing period of 6 to 8 weeks (for implants placed without simultaneous bone augmentation) or 10 to 14 weeks (for implants with simultaneous bone augmentation). All patients were recalled 36 months following implant placement for a clinical and radiographic examination. RESULTS: One implant failed to integrate during healing, and 11 implants were lost to follow-up and considered dropouts. The remaining 139 implants showed favorable clinical and radiographic findings and were considered successfully integrated at the 3-year examination. This resulted in a 3-year success rate of 99.3%. Radiographic evaluation of 134 implants indicated stability of the crestal bone levels: During the study period, the crestal bone level changed less than 0.5 mm for 129 implants. CONCLUSION: Successful tissue integration was achieved with wide-body implants with a regular or a wide-neck configuration and an SLA surface with high predictability. This successful tissue integration was well maintained for up to 3 years of follow-up.

Immediate loading of single SLA implants: drilling vs. osteotomes for the preparation of the implant site

OBJECTIVES: To evaluate whether or not preparation of the implant site with osteotomes instead of drilling may improve peri-implant bone density and/or osseointegration, and whether or not this further improves the predictability of immediate loading of SLA implants. MATERIAL AND METHODS: The second, third, and fourth premolars were extracted in both sides of the mandible in six dogs, and after at least 3 months four SLA implants were inserted into each side of the jaw. In three animals, the implant sites were prepared by means of osteotomes, while standard stepwise drilling was used in the remaining animals. In each side of the jaw, two non-adjacent implants were restored with single crowns 4 days after installation, while the remaining two implants were left without crowns to serve as non-loaded controls. After 2, 4, or 12 weeks of loading, specimens including the implants and surrounding tissues were obtained and processed for histologic analysis of undecalcified sections. RESULTS: All implants placed with osteotomes were lost (five before delivery of the crowns and the rest during the first week after loading). None of the conventionally inserted implants, however, was lost, and histomorphometrical analysis revealed similar soft- and hard peri-implant tissue characteristics at immediately loaded and non-loaded implants at all observation times. Average bone-to-implant contact was 59-72% at immediately loaded implants vs. 60-63% at non-loaded ones. CONCLUSION: Preparation of the implant site by means of osteotomes had a deleterious effect on osseointegration, while immediate loading of single, free-standing, SLA implants following a conventional surgical protocol did not jeopardize their osseointegration.

A feasibility study on model-based evaluation of kidney perfusion measured by means of FAIR prepared true-FISP arterial spin labeling (ASL) on a 3-T MR scanner

RATIONALE AND OBJECTIVES: A feasibility study on measuring kidney perfusion by a contrast-free magnetic resonance (MR) imaging technique is presented. MATERIALS AND METHODS: A flow-sensitive alternating inversion recovery (FAIR) prepared true fast imaging with steady-state precession (TrueFISP) arterial spin labeling sequence was used on a 3.0-T MR-scanner. The basis for quantification is a two-compartment exchange model proposed by Parkes that corrects for diverse assumptions in single-compartment standard models. RESULTS: Eleven healthy volunteers (mean age, 42.3 years; range 24-55) were examined. The calculated mean renal blood flow values for the exchange model (109 +/- 5 [medulla] and 245 +/- 11 [cortex] ml/min - 100 g) are in good agreement with the literature. Most important, the two-compartment exchange model exhibits a stabilizing effect on the evaluation of perfusion values if the finite permeability of the vessel wall and the venous outflow (fast solution) are considered: the values for the one-compartment standard model were 93 +/- 18 (medulla) and 208 +/- 37 (cortex) ml/min - 100 g. CONCLUSION: This improvement will increase the accuracy of contrast-free imaging of kidney perfusion in treatment renovascular disease.

Dynamic Optimization of SLA-Based Services Scaling Rules

Current advanced cloud infrastructure management solutions allow scheduling actions for dynamically changing the number of running virtual machines (VMs). This approach, however, does not guarantee that the scheduled number of VMs will properly handle the actual user generated workload, especially if the user utilization patterns will change. We propose using a dynamically generated scaling model for the VMs containing the services of the distributed applications, which is able to react to the variations in the number of application users. We answer the following question: How to dynamically decide how many services of each type are needed in order to handle a larger workload within the same time constraints? We describe a mechanism for dynamically composing the SLAs for controlling the scaling of distributed services by combining data analysis mechanisms with application benchmarking using multiple VM configurations. Based on processing of multiple application benchmarks generated data sets we discover a set of service monitoring metrics able to predict critical Service Level Agreement (SLA) parameters. By combining this set of predictor metrics with a heuristic for selecting the appropriate scaling-out paths for the services of distributed applications, we show how SLA scaling rules can be inferred and then used for controlling the runtime scale-in and scale-out of distributed services. We validate our architecture and models by performing scaling experiments with a distributed application representative for the enterprise class of information systems. We show how dynamically generated SLAs can be successfully used for controlling the management of distributed services scaling.

Improving Management of Distributed Services Using Correlations and Predictions in SLA-Driven Cloud Computing Systems

Recent advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing environmental conditions and number of users, application performance might suffer, leading to Service Level Agreement (SLA) violations and inefficient use of hardware resources. We introduce a system for controlling the complexity of scaling applications composed of multiple services using mechanisms based on fulfillment of SLAs. We present how service monitoring information can be used in conjunction with service level objectives, predictions, and correlations between performance indicators for optimizing the allocation of services belonging to distributed applications. We validate our models using experiments and simulations involving a distributed enterprise information system. We show how discovering correlations between application performance indicators can be used as a basis for creating refined service level objectives, which can then be used for scaling the application and improving the overall application's performance under similar conditions.

Mutations and polymorphisms in the human argininosuccinate lyase (ASL) gene

Argininosuccinate lyase deficiency (ASLD) is caused by a defect of the urea cycle enzyme argininosuccinate lyase (ASL) encoded by the ASL gene. Patients often present early after birth with hyperammonemia but can also manifest outside the neonatal period mainly triggered by excessive protein catabolism. Clinical courses comprise asymptomatic individuals who only excrete the biochemical marker, argininosuccinic acid, in urine, and patients who succumb to their first hyperammonemic decompensation. Some patients without any hyperammonemia develop severe neurological disease. Here, we are providing an update on the molecular basis of ASLD by collecting all published (n = 67) as well as novel mutations (n = 67) of the ASL gene. We compile data on all 160 different genotypes ever identified in 223 ASLD patients, including clinical courses whenever available. Finally, we are presenting structural considerations focusing on the relevance of mutations for ASL homotetramer formation. ASLD can be considered as a panethnic disease with only single founder mutations identified in the Finnish (c.299T>C, p.Ile100Thr) and Arab (c.1060C>T, p.Gln354*) population. Most mutations are private with only few genotypes recurring in unrelated patients. The majority of mutations are missense changes including some with more frequent occurrence such as p.Arg12Gln, p.Ile100Thr, p.Val178Met, p.Arg186Trp, p.Glu189Gly, p.Gln286Arg, and p.Arg385Cys.

SLA-driven predictive orchestration for distributed cloud-based mobile services

We describe a system for performing SLA-driven management and orchestration of distributed infrastructures composed of services supporting mobile computing use cases. In particular, we focus on a Follow-Me Cloud scenario in which we consider mobile users accessing cloud-enable services. We combine a SLA-driven approach to infrastructure optimization, with forecast-based performance degradation preventive actions and pattern detection for supporting mobile cloud infrastructure management. We present our system's information model and architecture including the algorithmic support and the proposed scenarios for system evaluation.

Dynamic SLA management with forecasting using multi-objective optimization

Cost-efficient operation while satisfying performance and availability guarantees in Service Level Agreements (SLAs) is a challenge for Cloud Computing, as these are potentially conflicting objectives. We present a framework for SLA management based on multi-objective optimization. The framework features a forecasting model for determining the best virtual machine-to-host allocation given the need to minimize SLA violations, energy consumption and resource wasting. A comprehensive SLA management solution is proposed that uses event processing for monitoring and enables dynamic provisioning of virtual machines onto the physical infrastructure. We validated our implementation against serveral standard heuristics and were able to show that our approach is significantly better.

SLA-Driven Simulation of Multi-Tenant Scalable Cloud-Distributed Enterprise Information Systems

Cloud Computing is an enabler for delivering large-scale, distributed enterprise applications with strict requirements in terms of performance. It is often the case that such applications have complex scaling and Service Level Agreement (SLA) management requirements. In this paper we present a simulation approach for validating and comparing SLA-aware scaling policies using the CloudSim simulator, using data from an actual Distributed Enterprise Information System (dEIS). We extend CloudSim with concurrent and multi-tenant task simulation capabilities. We then show how different scaling policies can be used for simulating multiple dEIS applications. We present multiple experiments depicting the impact of VM scaling on both datacenter energy consumption and dEIS performance indicators.

Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications

Cloud Computing has evolved to become an enabler for delivering access to large scale distributed applications running on managed network-connected computing systems. This makes possible hosting Distributed Enterprise Information Systems (dEISs) in cloud environments, while enforcing strict performance and quality of service requirements, defined using Service Level Agreements (SLAs). {SLAs} define the performance boundaries of distributed applications, and are enforced by a cloud management system (CMS) dynamically allocating the available computing resources to the cloud services. We present two novel VM-scaling algorithms focused on dEIS systems, which optimally detect most appropriate scaling conditions using performance-models of distributed applications derived from constant-workload benchmarks, together with SLA-specified performance constraints. We simulate the VM-scaling algorithms in a cloud simulator and compare against trace-based performance models of dEISs. We compare a total of three SLA-based VM-scaling algorithms (one using prediction mechanisms) based on a real-world application scenario involving a large variable number of users. Our results show that it is beneficial to use autoregressive predictive SLA-driven scaling algorithms in cloud management systems for guaranteeing performance invariants of distributed cloud applications, as opposed to using only reactive SLA-based VM-scaling algorithms.

Holocene vegetation development in the catchment of Sagistalsee (1935 m asl), a small lake in the Swiss Alps

Pollen and plant macrofossils were analysed at Sägistalsee (1935 m asl), a small lake near timber-line in the Swiss Northern Alps. Open forests with Pinus cembra and Abies alba covered the catchment during the early Holocene (9000–6300 cal. BP), suggesting subcontinental climate conditions. After the expansion of Picea abies between 6300 and 6000 cal. BP the subalpine forest became denser and the tree-line reached its maximum elevation at around 2260 m asl. Charcoal fragments in the macrofossil record indicate the beginning of Late-Neolithic human impact at ca. 4400 cal. BP, followed by a extensive deforestation and lowering of the forest-limit in the catchment of Sägistalsee at 3700 cal. BP (Bronze Age). Continuous human activity, combined with a more oceanic climate during the later Holocene, led to the local extinction of Pinus cembra and Abies alba and favoured the mass expansion of Picea and Alnus viridis in the subalpine area of the Northern Alps. The periods before 6300 and after 3700 cal. BP are characterised by high erosion activity in the lake's catchment, whereas during the phase of dense Picea-Pinus cembra-Abies forests (6300–3700 cal. BP) soils were stable and sediment-accumulation rates in the lake were low. Due to decreasing land-use at higher altitudes during the Roman occupation and the Migration period, forests spread beween ca. 2000 and 1500 cal. BP, before human impact increased again in the early Middle Ages. Recent reforestation due to land-use changes in the 20th century is recorded in the top sediments. Pollen-inferred July temperature and annual precipitation suggest a trend to cooler and more oceanic climate starting at about 5500 cal. BP.