Human monoclonal islet specific autoantibodies share features of islet cell and 64 kDa antibodies

The first human monoclonal islet cell antibodies of the IgG class (MICA 1-6) obtained from an individual with Type 1 (insulin-dependent) diabetes mellitus were cytoplasmic islet cell antibodies selected by the indirect immunofluorescence test on pancreas sections. Surprisingly, they all recognized the 64 kDa autoantigen glutamate decarboxylase. In this study we investigated which typical features of cytoplasmic islet cell antibodies are represented by these monoclonals. We show by double immunofluorescence testing that MICA 1-6 stain pancreatic beta cells which is in agreement with the beta-cell specific expression of glutamate decarboxylase. In contrast an islet-reactive IgM monoclonal antibody obtained from a pre-diabetic individual stained all islet cells but lacked the tissue specificity of MICA 1-6 and must therefore be considered as a polyreactive IgM-antibody. We further demonstrate that MICA 1-6 revealed typical features of epitope sensitivity to biochemical treatment of the target tissue which has been demonstrated for islet cell antibodies, and which has been used to argue for a lipid rather than a protein nature of target antigens. Our results provide direct evidence that the epitopes recognized by the MICA are destroyed by methanol/chloroform treatment but reveal a high stability to Pronase digestion compared to proinsulin epitopes. Conformational protein epitopes in glutamate decarboxylase therefore show a sensitivity to biochemical treatment of sections such as ganglioside epitopes. MICA 1-6 share typical features of islet cell and 64 kDa antibodies and reveal that glutamate decarboxylase-reactive islet cell antibodies represent a subgroup of islet cell antibodies present in islet cell antibody-positive sera

A Human Development Framework for CO2 Reductions

Although developing countries are called to participate in CO2 emission reduction efforts to avoid dangerous climate change, the implications of proposed reduction schemes in human development standards of developing countries remain a matter of debate. We show the existence of a positive and time-dependent correlation between the Human Development Index (HDI) and per capita CO2 emissions from fossil fuel combustion. Employing this empirical relation, extrapolating the HDI, and using three population scenarios, the cumulative CO2 emissions necessary for developing countries to achieve particular HDI thresholds are assessed following a Development As Usual approach (DAU). If current demographic and development trends are maintained, we estimate that by 2050 around 85% of the world's population will live in countries with high HDI (above 0.8). In particular, 300Gt of cumulative CO2 emissions between 2000 and 2050 are estimated to be necessary for the development of 104 developing countries in the year 2000. This value represents between 20% to 30% of previously calculated CO2 budgets limiting global warming to 2{\deg}C. These constraints and results are incorporated into a CO2 reduction framework involving four domains of climate action for individual countries. The framework reserves a fair emission path for developing countries to proceed with their development by indexing country-dependent reduction rates proportional to the HDI in order to preserve the 2{\deg}C target after a particular development threshold is reached. Under this approach, global cumulative emissions by 2050 are estimated to range from 850 up to 1100Gt of CO2. These values are within the uncertainty range of emissions to limit global temperatures to 2{\deg}C.Comment: 14 pages, 7 figures, 1 tabl

Gad65 is recognized by t-cells, but not by antibodies from nod-mice

Since the 64kDa-protein glutamic acid decarboxylase (GAD) is one of the major autoantigens in T-cell mediated Type 1 diabetes, its relevance as a T-cell antigen needs to be clarified. After isolation of splenic T-cells from non-obese diabetic (NOD) mice, a useful model for human Type 1 diabetes, we found that these T-cells proliferate spontaneously when incubated with human GAD65, but only marginally after incubation with GAD67, both recombinated in the baculovirus system. No effect was observed with non-diabetic NOD mice or with T-cells from H-2 identical NON-NOD-H-2g7 control mice. It has been published previously that NOD mice develop autoantibodies against a 64kDa protein detected with mouse beta cells. In immunoprecipitation experiments with sera from the same NOD mice and 33S-methionine-labelled GAD, no autoantibody binding could be detected. We conclude firstly that GAD65 is an important T-cell antigen which is relevant early in the development of Type 1 diabetes and secondly that there is an antigenic epitope in the human GAD65 molecule recognized by NOD T-cells, but not by NOD autoantibodies precipitating conformational epitopes. Our results therefore provide further evidence that GAD65 is a T-cell antigen in NOD mice, being possibly also involved in very early processes leading to the development of human Type 1 diabetes

Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO2, O3, PM10 and PM2.5 for 2001-2010

This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km  ×  5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model–measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2. 5, and PM10 (daily maximum 8 h running mean for O3). The comparison was temporally and spatially comprehensive, covering a 10-year period (2 years for PM2. 5) and all non-roadside measurement data from the UK national reference monitor network, which applies consistent operational and QA/QC procedures for each pollutant (44, 47, 24, and 30 sites for NO2, O3, PM2. 5, and PM10, respectively). Two important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – together with root mean square error, were evaluated by site type, year, month, and day-of-week. Model–measurement statistics were generally better than, or comparable to, values that allow for realistic magnitudes of measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2, and PM2. 5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2. 5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at rural background sites (median normalized mean bias (NMB) values for daily O3 and NO2 of 8 and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB  =  −29 %) and PM2. 5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km  ×  5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions (e.g. closer to traffic sources) than the grid average. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. some components of wind-blown dust. There were instances of monthly and weekday/weekend variations in the extent of model–measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model–measurement differences (aside from grid versus monitor spatial representivity) was inaccuracy of model emissions – both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals – rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology

The impact of predation by marine mammals on Patagonian toothfish longline fisheries

Predatory interaction of marine mammals with longline fisheries is observed globally, leading to partial or complete loss of the catch and in some parts of the world to considerable financial loss. Depredation can also create additional unrecorded fishing mortality of a stock and has the potential to introduce bias to stock assessments. Here we aim to characterise depredation in the Patagonian toothfish (Dissostichus eleginoides) fishery around South Georgia focusing on the spatio-temporal component of these interactions. Antarctic fur seals (Arctocephalus gazella), sperm whales (Physeter macrocephalus), and orcas (Orcinus orca) frequently feed on fish hooked on longlines around South Georgia. A third of longlines encounter sperm whales, but loss of catch due to sperm whales is insignificant when compared to that due to orcas, which interact with only 5% of longlines but can take more than half of the catch in some cases. Orca depredation around South Georgia is spatially limited and focused in areas of putative migration routes, and the impact is compounded as a result of the fishery also concentrating in those areas at those times. Understanding the seasonal behaviour of orcas and the spatial and temporal distribution of “depredation hot spots” can reduce marine mammal interactions, will improve assessment and management of the stock and contribute to increased operational efficiency of the fishery. Such information is valuable in the effort to resolve the human-mammal conflict for resources

Closing the Loop: Dell Technologies and the Future of E-Waste Management

The exponential growth of electronic waste—exceeding 50 million metric tons annually—poses a formidable challenge at the intersection of environmental sustainability, global equity, and corporate responsibility. This study critically examines Dell Technologies’ approach to circular economy implementation, with a focus on product modularity, ethical supply chain management, and reverse logistics infrastructure. Anchored in a comparative framework alongside Apple, HP, and Lenovo, the analysis evaluates the scalability, transparency, and material impact of Dell’s initiatives, including the Concept Luna prototype and the Dell Reconnect program. Findings underscore the structural shortcomings of voluntary corporate action in the absence of robust global regulation, consistent product traceability, and enforceable extended producer responsibility. The paper advances a set of recommendations for sector-wide reform: the institutionalization of modular design, investment in repair and recycling capacity in the Global South, and the establishment of unified international standards. Without systemic transformation, the digital economy risks deepening global disparities while accelerating ecological degradation

The Limits of Military Officers’ Duty to Obey Civilian Orders: A Neo-Classical Perspective

This monograph offers a neo-classically republican perspective on a perennial problem of civilian/military relations: limitations on military officers’ obligation to obey civilian authorities. All commentators agree that military officers are generally obliged—morally, professionally, and legally—to obey civilian orders, even as they agree that this rule of obedience must admit of exceptions. Commentators tend to differ, however, on the basis and breadth of these exceptions. Following Samuel Huntington’s classic analysis in The Soldier and the State, this monograph shows that disagreement about the breadth of the exceptions tends to assume that their bases—moral, professional, and legal—are incommensurable. It suggests, to the contrary, that all defensible exceptions to the rule of military obedience, like that rule itself, derive from a single neo-classical, Huntingtonian standard, binding on civilian authorities and military officers alike: the common good. This perspective promises significantly to reduce the range of disagreement over the limits of military obedience both in theory and in practice.https://press.armywarcollege.edu/monographs/1447/thumbnail.jp

Statistical modeling of ground motion relations for seismic hazard analysis

We introduce a new approach for ground motion relations (GMR) in the probabilistic seismic hazard analysis (PSHA), being influenced by the extreme value theory of mathematical statistics. Therein, we understand a GMR as a random function. We derive mathematically the principle of area-equivalence; wherein two alternative GMRs have an equivalent influence on the hazard if these GMRs have equivalent area functions. This includes local biases. An interpretation of the difference between these GMRs (an actual and a modeled one) as a random component leads to a general overestimation of residual variance and hazard. Beside this, we discuss important aspects of classical approaches and discover discrepancies with the state of the art of stochastics and statistics (model selection and significance, test of distribution assumptions, extreme value statistics). We criticize especially the assumption of logarithmic normally distributed residuals of maxima like the peak ground acceleration (PGA). The natural distribution of its individual random component (equivalent to exp(epsilon_0) of Joyner and Boore 1993) is the generalized extreme value. We show by numerical researches that the actual distribution can be hidden and a wrong distribution assumption can influence the PSHA negatively as the negligence of area equivalence does. Finally, we suggest an estimation concept for GMRs of PSHA with a regression-free variance estimation of the individual random component. We demonstrate the advantages of event-specific GMRs by analyzing data sets from the PEER strong motion database and estimate event-specific GMRs. Therein, the majority of the best models base on an anisotropic point source approach. The residual variance of logarithmized PGA is significantly smaller than in previous models. We validate the estimations for the event with the largest sample by empirical area functions. etc