Changing Incidence and Risk Factors for Kaposi Sarcoma by Time Since Starting Antiretroviral Therapy: Collaborative Analysis of 21 European Cohort Studies.

BACKGROUND:  Kaposi sarcoma (KS) remains a frequent cancer in human immunodeficiency virus (HIV)-positive patients starting combination antiretroviral therapy (cART). We examined incidence rates and risk factors for developing KS in different periods after starting cART in patients from European observational HIV cohorts. METHODS:  We included HIV-positive adults starting cART after 1 January 1996. We analyzed incidence rates and risk factors for developing KS up to 90 and 180 days and 1, 2, 5, and 8 years after cART start and fitted univariable and multivariable Cox regression models. RESULTS:  We included 109 461 patients from 21 prospective clinical cohorts in Europe with 916 incident KS cases. The incidence rate per 100 000 person-years was highest 6 months after starting cART, at 953 (95% confidence interval, 866-1048), declining to 82 (68-100) after 5-8 years. In multivariable analyses adjusted for exposure group, origin, age, type of first-line regimen, and calendar year, low current CD4 cell counts increased the risk of developing KS throughout all observation periods after cART initiation. Lack of viral control was not associated with the hazard of developing KS in the first year after cART initiation, but was over time since starting cART increasingly positively associated (P < .001 for interaction). CONCLUSION:  In patients initiating cART, both incidence and risk factors for KS change with time since starting cART. Whereas soon after starting cART low CD4 cell count is the dominant risk factor, detectable HIV-1 RNA viral load becomes an increasingly important risk factor in patients who started cART several years earlier, independently of immunodeficiency

The Pediatric and Young Adult Choroidal and Ciliary Body Melanoma Genetic Study, A Survey by the European Ophthalmic Oncology Group

PURPOSE:To explore the genetic background of choroidal and ciliary body melanoma among children and young adults, with special focus on BAP1 germline variants in this age group. METHODS:Patients under the age of 25 and with confirmed choroidal or ciliary body melanoma were included in this retrospective, multicenter observational study. Nuclear BAP1 immunopositivity was used to evaluate the presence of functional BAP1 in the tumor. Next-generation sequencing using Ion Torrent platform was used to determine pathogenic variants of BAP1, EIF1AX, SF3B1, GNAQ and GNA11 and chromosome 3 status in the tumor or in DNA extracted from blood or saliva. Survival was analyzed using Kaplan-Meier estimates. RESULTS:The mean age at diagnosis was 17 years (range 5.0–24.8). A germline BAP1 pathogenic variant was identified in an 18-year-old patient, and a somatic variant, based mainly on immunohistochemistry, in 13 (42%) of 31 available specimens. One tumor had a somatic SF3B1 pathogenic variant. Disomy 3 and the absence of a BAP1 pathogenic variant in the tumor predicted the longest metastasis-free survival. Males showed longer metastasis-free survival than females (P = 0.018). CONCLUSIONS:We did not find a stronger-than-average BAP1 germline predisposition for choroidal and ciliary body melanoma among children and young adults compared to adults. Males had a more favorable survival and disomy 3, and the absence of a BAP1 mutation in the tumor tissue predicted the most favorable metastasis-free survival. A BAP1 germline pathogenic variant was identified in one patient (1%), and a somatic variant based mainly on immunohistochemistry in 13 (42%).</p

The Pediatric and Young Adult Choroidal and Ciliary Body Melanoma Genetic Study, A Survey by the European Ophthalmic Oncology Group

PURPOSE:To explore the genetic background of choroidal and ciliary body melanoma among children and young adults, with special focus on BAP1 germline variants in this age group. METHODS:Patients under the age of 25 and with confirmed choroidal or ciliary body melanoma were included in this retrospective, multicenter observational study. Nuclear BAP1 immunopositivity was used to evaluate the presence of functional BAP1 in the tumor. Next-generation sequencing using Ion Torrent platform was used to determine pathogenic variants of BAP1, EIF1AX, SF3B1, GNAQ and GNA11 and chromosome 3 status in the tumor or in DNA extracted from blood or saliva. Survival was analyzed using Kaplan-Meier estimates. RESULTS:The mean age at diagnosis was 17 years (range 5.0–24.8). A germline BAP1 pathogenic variant was identified in an 18-year-old patient, and a somatic variant, based mainly on immunohistochemistry, in 13 (42%) of 31 available specimens. One tumor had a somatic SF3B1 pathogenic variant. Disomy 3 and the absence of a BAP1 pathogenic variant in the tumor predicted the longest metastasis-free survival. Males showed longer metastasis-free survival than females (P = 0.018). CONCLUSIONS:We did not find a stronger-than-average BAP1 germline predisposition for choroidal and ciliary body melanoma among children and young adults compared to adults. Males had a more favorable survival and disomy 3, and the absence of a BAP1 mutation in the tumor tissue predicted the most favorable metastasis-free survival. A BAP1 germline pathogenic variant was identified in one patient (1%), and a somatic variant based mainly on immunohistochemistry in 13 (42%).</p

Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences

Abstract: Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs

Design and management of biomass-for-bioenergy supply chains: Towards a comprehensive spatio-temporal optimisation approach

The depletion of fossil fuel reserves and the negative environmental impacts associated with their use are the driving forces towards the biobased economy. In analogy to today's oil refineries, biorefineries can process renewable biological resources into a range of value-added bioproducts (e.g., bioplastics, paper, transport fuels, electricity, heat). However, the discontinuous (in time) and geographically fragmented (in space) availability of biomass and the relatively high maintenance and logistics costs still compromise the economic viability of biobased products for large scale production and commercialisation. Besides the economical side, also environmental sustainability, energy efficiency and social acceptance are important concerns for the development of a sustainable biobased sector. Poor planning can hurt the environment, damage the image of biobased products, and limit available resources. Comprehensive planning for the supply chain must start prior to or in combination with the expansion of the biobased sector. So, the role that biomass will play in the future will depend upon the extent to which the constraining factors inhibiting trade as well as a sustainable and efficient production of biobased products can be overcome. This dissertation presents the design of a generator (prototype) to create decision support systems to address strategic (e.g., design) and tactical (e.g., inventory and fleet management) decisions in all kinds of biomass-for-bioenergy supply chains with a view to optimise (a combination of) different objectives. This generator encompasses four modules: (1) a database module, (2) a query module, (3) the decision module and (4) a user interface. The database module holds a reference data model which can be used to specify all kinds of biomass-for-bioenergy supply chains. The roots of the reference data model in a generic cradle-to-gate analysis ensures that all kinds of biomass sources, all kinds of biomass destinations and all kinds of handling techniques can be classified into one of the 6 key object types of the data model: (1) biomass production, (2) harvest, (3) collection, (4) pre-treatment, (5) storage and (6) conversion. The database module is connected to the query module to organise and pre-process the initial spatial information and to visualise and post-process the optimisation results. The decision module encompasses the mixed integer linear programming models, OPTIMASS and t-OPTIMASS, to define the strategic and tactical decisions in the supply chain in order to meet the maximum energy output, the maximum profit and/or the minimum environmental impact. OPTIMASS takes into account the geographical fragmentation of biomass to define the optimal supply chain configuration considering one time period, the re-injection of by-products from the conversion process and the changes in biomass characteristics (e.g., moisture content, particle size) due to treatment operations. t-OPTIMASS, the spatio-temporal optimisation model, adds the consideration of temporal variations (e.g., seasonal, regrowth) in biomass availability and energy demand. t-OPTIMASS supports the use of biomass as a sustainable, renewable source by balancing the seasonal availability and regeneration of biomass and the required year-round constant supply of biomass at the conversion facilities. The development of the user interface and the automation of the linkages between the modules are not part of this dissertation. The generator has been implemented to create specific decision support systems for three case studies: (1) the biomass supply chain based on low input high diversity (LIHD) biomass systems in the province of Limburg (Belgium), (2) the municipal wastewater sludge processing chain in Flanders and (3) the Jatropha-to-electricity chain in Mali. The specific DSSs are used to address strategic questions (i.e. new dryer?) as well as tactical questions (i.e. allocation?). The specific DSSs are used to investigate the effect of changes in biomass availability, energy demand, etc. on the supply chain configuration. In general, the results highlight that the requirements imposed to the biomass mixture at the conversion facilities are the main drivers in the decision process. So, OPTIMASS and t-OPTIMASS define the harvesting moment and introduce treatment operations to make sure that biomass is delivered with characteristics that fit best these requirements. In addition, the analyses indicate that storage facilities are indispensable to deal with the temporal availability of biomass, the conflicting temporal (energy) demand and the required constant feeding of the conversion facilities. Additionally, the biomass is preferably grown in the areas where it is also consumed to ensure long term sustainability and to guarantee biomass supply. Directly, nearby biomass availability reduces the transport costs to the conversion facility. Indirectly, the proximity of biomass leads to the reduction of the negative side effects of an uncontrolled biomass demand on tropical forest cover, to the support of landscape functions such as biodiversity, recreation, hydrological and erosion buffering, etc. Unfortunately, the biomass to be converted in Flanders is often grown in areas with low marginal cost and shipped (often imported) for valorisation. We believe that the generator prototype is an inspiring tool for a variety of stakeholders working with or having a large impact on the biomass sector and mostly interested in a macro-analysis (e.g. governments, government institutions, consultancy agents, etc.). Stakeholders are able to get insight in evolutions of biomass flows and the development of the biomass network through the simulation of the consequences of e.g. political decisions, import restrictions, introducing toll, etc. The tool supports the evaluation of the biomass potential, the feasibility of new operation facilities and the definition of the optimal type and location of facilities from a set of potential facilities proposed by the user. Also, guidance can be provided to stakeholders (e.g. biomass suppliers, owners and operators of storage and conversion facilities) ones the supply chain is operational to evaluate the impact of e.g. the shortage in biomass supply, disappearance of a neighbouring facility, potential new biomass sites, etc. Furthermore, a range of scenarios can be run and serve as the basis for a dialogue between the various stakeholders working with biomass to help resolve bottlenecks hampering the optimal use of biomass. Being able to optimise (and somehow simulate) the biomass flows within the biomass network considering different kinds of criteria, this can be an attractive tool that can lead to successful networking between stakeholders in the biomass sector.nrpages: 264status: publishe

Considering biomass growth and regeneration in the optimisation of biomass supply chains

© 2015 Elsevier Ltd. This paper presents t-OPTIMASS, a multi-period mixed integer linear programming model to optimise strategic and tactical decisions in all kinds of biomass supply chains taking into account the geographical fragmentation and temporal availability of biomass and changing biomass characteristics due to handling operations. Unlike existing models, t-OPTIMASS considers the growth and regeneration of biomass to determine the optimal harvesting moment(s). t-OPTIMASS is demonstrated based on the use of grass from nature reserves and road verges to substitute maize in the digestion mixture converted in the currently available wet anaerobic digesters or potential dry anaerobic digesters in Limburg (Belgium).The results highlight that the decision process is driven by the requirements imposed to the characteristics of the biomass to be converted at the conversion facility. The harvesting moment is defined and pre-treatment operations are introduced to make sure that biomass is delivered with characteristics that fit best these requirements. The analyses indicate that storage facilities are indispensable to deal with the temporal availability of biomass, the conflicting temporal demand and the required constant feeding of the digesters. t-OPTIMASS allows users, interested in macro-analysis, to define biomass potentials, to support policy decisions, to evaluate the feasibility of new facilities, etc.status: publishe

A generic mathematical model to optimise strategic and tactical decisions in biomass-based supply chains (OPTIMASS)

High logistics and handling costs prevent the bioenergy industry from making a greater contribution to the present energy market. Therefore, a mathematical model, OPTIMASS, is presented to optimise strategic (e.g. facility location and type) and tactical (e.g. allocation) decisions in all kinds of biomass-based supply chains. In addition to existing models, OPTIMASS evaluates changes in biomass characteristics due to handling operations which is needed to meet the requirements set to biomass products delivered at a conversion facility. Also, OPTIMASS considers the re-injection of by-products from conversion facilities which can play a decisive role in the determination of a sustainable supply chain. The scenario analysis illustrates the functionalities of OPTIMASS in the optimisation of an existing supply chain, the definition of the optimal location of new conversion facilities and the definition of the optimal configuration of a supply chain. OPTIMASS, as a deterministic model, does not consider variability related to e.g. seasonal changes which can be a major obstacle. However, a thorough sensitivity analysis of influencing factors must give insight in the induced changes in the supply chain. The sensitivity analysis in this paper investigates the influence of uncertainty in biomass production, energy demand and of changes in transport distance. The analysis demonstrate that OPTIMASS can be used as an inspiring tool to investigate the possible effects of governmental decisions, of considering new biomass material, new facilities, of technology changes, etc. The coupling with GIS allows characterisation and visualisation of problems in advance and visualisation of results in an interpretative way.status: publishe