Method of statistical filtering

Minimal formula for bounding the cross correlation between a random forcing function and the state error when this correlation is unknown is used in optimal linear filter theory applications. Use of the bound results in overestimation of the estimation-error covariance

A Network-Scale Modeling Framework for Stream Metabolism, Ecosystem Efficiency, and Their Response to Climate Change

Climate change and the predicted warmer temperatures and more extreme hydrological regimes could affect freshwater ecosystems and their energy pathways. To appreciate the complex spatial and temporal interactions of carbon cycling in flowing waters, ecosystem metabolism (gross primary production [GPP] and ecosystem respiration [ER]) must be resolved at the scale of an entire river network. Here, we propose a meta-ecosystem framework that couples light and temperature regimes with a reach-scale ecosystem model and integrates network structure, catchment land cover, and the hydrologic regime. The model simulates the distributed functioning of dissolved and particulate organic carbon, autotrophic biomass, and thus ecosystem metabolism, and reproduces fairly well the metabolic regimes observed in 12 reaches of the Ybbs River network, Austria. Results show that the annual network-scale metabolism was heterotrophic, yet with a clear peak of autotrophy in spring. Autochthonous energy sources contributed 43% of the total ER. We further investigated the effect of altered thermal and hydrologic regimes on metabolism and ecosystem efficiency. We predicted that an increase of 2.5? in average stream water temperature could boost ER and GPP by 31% (24%-57%) and 28% (5%-57%), respectively. The effect of flashier hydrologic regimes is more complex and depends on autotrophic biomass density. The analysis shows the complex interactions between environmental conditions and biota in shaping stream metabolism and highlights the existing knowledge gaps for reliable predictions of the effects of climate change in these ecosystems

Excellent palliative care as the standard, physician-assisted dying as a last resort

Journal ArticleTo understand the role of physician-assisted death as a last-resort option restricted to dying patients for whom palliative care or hospice has become ineffective or unacceptable, one must understand how frequently and under what circumstances that occurs. If all such cases are the result of inadequately delivered palliative care, then the best answer would be to improve the standard of care and make the problem disappear. Most experts in pain management believe that 95 to 98 percent of pain among those who are terminally ill can be adequately relieved using modern pain management,1 which is a remarkable track record?unless you are unfortunate enough to be in the 2 to 5 percent for whom it is unsuccessful. However, among hospice patients who were asked about their pain level one week before their death, 5 to 35 percent rated their pain as "severe" or "unbearable."2 An additional 25 percent reported their shortness of breath to be "unbearable" one week before death.3 This says nothing of the physical symptoms that are harder to relieve, such as nausea, vomiting, confusion, and open wounds, including pressure sores, which many patients experience.

False dichotomy versus genuine choice the argument over physician-assisted dying

Journal ArticleDespite a growing consensus that palliative care should be a core part of the treatment offered to all severely ill patients who potentially face death,1 challenging questions remain. How broad a choice should patients have in guiding the course of their own dying? What limitations should be placed on the physician's obligation to address patients' suffering? Physician-assisted death (also called physician-assisted suicide or physician aid in dying) has long been the focal point of ethical and political debate-a divisive, hot button issue in a domain in which there is otherwise considerable agreement

Patients' understanding and use of advance directives

Journal ArticleThe Patient Self-Determination Act was implemented in December 1991. Before and after its implementation, we used a structured interview of 302 randomly selected patients to determine their awareness, understanding, and use of advance directives. Implementation of the Act did not have a major effect on these. Although more than 90% of patients were aware of the living will, only about a third selected the correct definition or the correct circumstances in which it applied, and less than 20% of patients had completed one. About a third of patients were aware of a Durable Power of Attorney for Health Care and chose the correct definition, and about half identified the correct circumstances in which it applies; less than 10% had completed such a document. Surprisingly, patients who said they had completed advance directives did not demonstrate better understanding of these documents. Our results indicate that many patients, including some who have completed advance directives, do not fully understand them. It may be unwise to regard these documents as carefully considered, compelling statements of patients' preferences. Appropriate responses to our findings include increased public education, revising state statutes to bring them into congruence with public perception, and expanding the dialogue between physicians and patients

Microprogram scheme for automatic recovery from computer error

Microprogram scheme enables computer to recover from failure in one of its two central processing units during time duration of instruction in which failure occurs. Microprogram advantages include - /1/ built-in interpretive capability, /2/ selection of processing interrupts by priority, and /3/ economical use of bootstrap sequence

Modeling the coupled dynamics of stream metabolism and microbial biomass

Estimating and interpreting ecosystem metabolism remains an important challenge in stream ecology. Here, we propose a novel approach to model, estimate, and predict multiseasonal patterns of stream metabolism (gross primary production [GPP] and ecosystem respiration [ER]) at the reach scale leveraging on increasingly available long-term, high-frequency measurements of dissolved oxygen (DO). The model uses DO measurements to estimate the parameters of a simple ecosystem model describing the underlying dynamics of stream autotrophic and heterotrophic microbial biomass. The model has been applied to four reaches within the Ybbs river network, Austria. Even if microbial biomasses are not observed, that is, they are treated as latent variables, results show that by accounting for the temporal dynamics of biomass, the model reproduces variability in metabolic fluxes that is not explained by fluctuations of light, temperature, and resources. The model is particularly data-demanding: to estimate the 11 parameters used in this formulation, it requires sufficiently long, for example, annual, time series, and significant scouring events. On the other hand, the approach has the potential to separate ER into its autotrophic and heterotrophic components, estimate a richer set of ecosystem carbon fluxes (i.e., carbon uptake, loss, and scouring), extrapolate metabolism estimates for periods when DO measurements are unavailable, and predict how ecosystem metabolism would respond to variations of the driving forces. The model is seen as a building block to develop tools to fully appreciate multiseasonal patterns of metabolic activity in river networks and to provide reliable estimations of carbon fluxes from land to ocean

The Metabolic Regimes at the Scale of an Entire Stream Network Unveiled Through Sensor Data and Machine Learning

Streams and rivers form dense networks that drain the terrestrial landscape and are relevant for biodiversity dynamics, ecosystem functioning, and transport and transformation of carbon. Yet, resolving in both space and time gross primary production (GPP), ecosystem respiration (ER) and net ecosystem production (NEP) at the scale of entire stream networks has been elusive so far. Here, combining Random Forest (RF) with time series of sensor data in 12 reach sites, we predicted annual regimes of GPP, ER, and NEP in 292 individual stream reaches and disclosed properties emerging from the network they form. We further predicted available light and thermal regimes for the entire network and expanded the library of stream metabolism predictors. We found that the annual network-scale metabolism was heterotrophic yet with a clear peak of autotrophy in spring. In agreement with the River Continuum Concept, small headwaters and larger downstream reaches contributed 16% and 60%, respectively, to the annual network-scale GPP. Our results suggest that ER rather than GPP drives the metabolic stability at the network scale, which is likely attributable to the buffering function of the streambed for ER, while GPP is more susceptible to flow-induced disturbance and fluctuations in light availability. Furthermore, we found large terrestrial subsidies fueling ER, pointing to an unexpectedly high network-scale level of heterotrophy, otherwise masked by simply considering reach-scale NEP estimations. Our machine learning approach sheds new light on the spatiotemporal dynamics of ecosystem metabolism at the network scale, which is a prerequisite to integrate aquatic and terrestrial carbon cycling at relevant scales

Blood pressure measurement at two years in offspring of women randomized to a trial of metformin for GDM: follow up data from the MiG trial

Background: Offspring born following maternal gestational diabetes are at risk of excessive childhood weight gain and Type 2 diabetes in childhood, which in turn is associated with an increased rate of hypertension. We aimed to determine the systolic and diastolic blood pressure at two years of age in a cohort of children exposed to gestational diabetes mellitus using data from the MiG trial of metformin use in gestational diabetes. The secondary aim was to analyze these data by randomization of treatment to insulin or metformin. Methods: The offspring of women who had gestational diabetes and had been assigned to either open treatment with metformin (with supplemental insulin if required) or insulin in the MiG trial were followed up at 2 years of age. Oscillometric measurement of BP in the right arm was performed by a researcher using an appropriately sized cuff. Results: A total of 489 measurement blood pressure measurements were obtained in 170 of the 222 children who were seen at a median (range) age of 29 (22-38) months corrected gestational age. At the time of assessment the mean (SD) weight and height was 13.8(2) kg and 90 (4.2) cm respectively. For the whole group the mean (SD) systolic pressure was 90.9 (9.9) mmHg and mean (SD) diastolic pressure was 55.7 (8.1) mmHg. No difference was found between the metformin and insulin treatment arms. In a regression model, height and weight were only two factors associated with the levels of systolic blood pressure. For each additional kg the systolic blood pressure increased by 1.0 mmHg. For each additional cm of height the systolic blood pressure increased by 0.42 mmHg. Conclusions: Blood pressure data was obtained at approximately two years of age in a substantial cohort of children whose mothers received treatment for GDM. These novel data compare favorably with published norms. Clinical Trials Registry: This study was registered under the Australian New Zealand Clinical Trials Registry ( ACTRN12605000311651 ).Malcolm R Battin, Victor Obolonkin, Elaine Rush, William Hague, Suzette Coat and Janet Rowa

Automated multigravity assist trajectory planning with a modified ant colony algorithm

The paper presents an approach to transcribe a multigravity assist trajectory design problem into an integrated planning and scheduling problem. A modified Ant Colony Optimization (ACO) algorithm is then used to generate optimal plans corresponding to optimal sequences of gravity assists and deep space manoeuvers to reach a given destination. The modified Ant Colony Algorithm is based on a hybridization between standard ACO paradigms and a tabu-based heuristic. The scheduling algorithm is integrated into the trajectory model to provide a fast time-allocation of the events along the trajectory. The approach demonstrated to be very effective on a number of real trajectory design problems