This study delved on the role of semi-outdoor spaces (SOS), as form-based strategies, in providing enhanced, thermally comfortable environments in highly dense urban contexts. A sample of sixtythree (63) SOS was studied, within four different mid-rise and high-rise buildings located in the warm-humid tropical city of Singapore. It was found: (i) that SOS may act as thermal buffer spaces; (ii) that microclimate creation in SOS is linked to form, specifically to geometrical variables such as void-to-solid ratio, height, height-to-depth ratio, height from ground level, green plot ratio and open space ratio, which influence significantly the environmental factors of air temperature, mean radiant temperature, air velocity and relative humidity; (iii) that some aforementioned geometrical variables (height-to-depth ratio and open space ratio) are linked to thermal comfort when estimated with SET* and PMV* thermal indices; (iv) and that thermal comfort (between-1 and +1 PMV*) can be achieved in SOS considering a typical Singaporean outdoor CLO of 0.3, especially for 1 MET (85.7% of SOS). In the context of Singapore, this study demonstrates that incorporating SOS to mid-rise and high-rise building forms promotes the creation of thermally comfortable microclimates suitable for human activity, even during the hottest hours. (C) 2020 Elsevier B.V. All rights reserved.

Several common general purpose optimization algorithms are compared for finding A- and D-optimal designs for different types of statistical models of varying complexity, including high dimensional models with five and more factors. The algorithms of interest include exact methods, such as the interior point method, the Nelder Mead method, the active set method, the sequential quadratic programming, and metaheuristic algorithms, such as particle swarm optimization, simulated annealing and genetic algorithms. Several simulations are performed, which provide general recommendations on the utility and performance of each method, including hybridized versions of metaheuristic algorithms for finding optimal experimental designs. A key result is that general-purpose optimization algorithms, both exact methods and metaheuristic algorithms, perform well for finding optimal approximate experimental designs. (C) 2019 Elsevier B.V. All rights reserved.

Background: Delirium in advanced cancer inpatient ranges between 13% and 85%. Reasons for this variability on the reported data could be related to the setting where they are admitted. Methods: This is an observational, comparative, prospective study on delirium diagnosis and delirium course of advanced cancer inpatients in two different palliative care settings. Hospice (C1) versus palliative care supportive team (C2). Differences between delirium precipitants, delirium treatment, and delirium survival were observed. Results: From 582 consecutive admissions, 494 from C1 and 88 from C2, finally 227 patients met inclusion criteria, were entered in the study. Total population delirium rate at admission, if we add both centers, was 57 patients (25%), 46 (26%) from C1 and 11 (22%) from C2; no statistically significant differences between delirium rate at admission between the two centers were found (chi(2)). When delirium course between delirious patients admitted in C1 and C2 was analyzed, a significantly higher rate of delirium reversibility was found in C2 [11/14 (78%)] versus [9/65 (14%)] in C1 (chi(2) p <= 0.001). Conclusion: The frequency of delirium at admission and during the hospitalization in advanced cancer patients does not seem to be related to the setting, what seems to be related is the delirium course.

Background Major adverse cardiovascular events are the main cause of morbidity and mortality over the long term in patients undergoing carotid endarterectomy. There are few reports assessing the prognostic value of markers of inflammation in relation to the risk of cardiovascular disease after carotid endarterectomy. Here, we aimed to determine whether matrix metalloproteinases (MMP-1, MMP-2, MMP-7, MMP-9 and MMP-10), tissue inhibitor of MMPs (TIMP-1) and in vivo inflammation studied by F-18-FDG-PET/CT predict recurrent cardiovascular events in patients with carotid stenosis who underwent endarterectomy. Methods This prospective cohort study was carried out on 31 consecutive patients with symptomatic (23/31) or asymptomatic (8/31) severe (> 70%) carotid stenosis who were scheduled for carotid endarterectomy between July 2013 and March 2016. In addition, 26 healthy controls were included in the study. Plasma and serum samples were collected 2 days prior to surgery and tested for MMP-1, MMP-2, MMP-7, MMP-9, MMP-10, TIMP-1, high-density lipoprotein, low-density lipoprotein, high-sensitivity C-reactive protein and erythrocyte sedimentation rate. F-18-FDG-PET/CT focusing on several territories' vascular wall metabolism was performed on 29 of the patients because of no presurgical availability in 2 symptomatic patients. Histological and immunohistochemical studies were performed with antibodies targeting MMP-10, MMP-9, TIMP-1 and CD68. Results The patients with carotid stenosis had significantly more circulating MMP-1, MMP-7 and MMP-10 than the healthy controls. Intraplaque TIMP-1 was correlated with its plasma level (r = 0.42 P = .02) and with F-18-FDG uptake (r = 0.38 P = .05). We did not find any correlation between circulating MMPs and in vivo carotid plaque metabolism assessed by F-18-FDG-PET. After a median follow-up of 1077 days, 4 cerebrovascular, 7 cardiovascular and 11 peripheral vascular events requiring hospitalization were registered. Circulating MMP-7 was capable of predicting events over and above the traditional risk factors (HR = 1.15 P = .006). When the model was associated with the variables of interest, the risk predicted by F-18-FDG-PET was not significant. Conclusions Circulating MMP-7 may represent a novel marker for recurrent cardiovascular events in patients with moderate to severe carotid stenosis. MMP-7 may reflect the atherosclerotic burden but not plaque inflammation in this specific vascular territory.

Context: Palliative care is gaining ground globally and is endorsed in high level policy commitments, but service provision, supporting policies, education and funding are incommensurate with rapidly growing need. Objectives: To describe current levels of global palliative care development and report on changes since 2006. Methods: An online survey of experts in 198 countries generated 2017 data on 10 indicators of palliative care provision, fitted to six categories of development. Factor analysis and discriminant analysis showed the validity of the categorization. Spearman correlation analyses assessed the relationship with World Bank Income Level (WBIL), Human Development Index (HDI) and Universal Health Coverage (UHC). Results: Numbers (percentages) of countries in each development category were: 1) no known palliative care activity 47 (24%); 2) capacity-building 13 (7%); 3a) isolated provision 65 (33%); 3b) generalized provision 22 (11%); 4a) preliminary integration into mainstream provision 21 (11%); 4b) advanced integration 30 (15%). Development levels were significantly associated with WBIL (rS= 0·4785), UHC (rS=0·5558) and HDI (rS=0·5426) with p < 0.001. Net improvement between 2006-2017 saw 32 fewer countries in categories 1/2, 16 more countries in 3a/3b, and 17 more countries in 4a/4b. Conclusion: Palliative care at the highest level of provision is available for only 14% of the global population and is concentrated in European countries. An 87% global increase in serious health related suffering amenable to palliative care interventions is predicted by 2060. With need rising, palliative care is not reaching the levels required by at least half the global population.

In this paper, the tools provided by the theory of Optimal Experimental Design are applied to a nonlinear calibration model. This is motivated by the need of estimating radiation doses using radiochromic films for radiotherapy purposes. The calibration model is in this case nonlinear and the explanatory variable cannot be worked out explicitly from the model. In this case an experimental design has to be found on the dependent variable. For that, the inverse function theorem will be used to obtain an information matrix to be optimized. Optimal designs on the response variable are computed from two different perspectives, first for fitting the model and estimating each of the parameters and then for predicting the proper dose. While the first is a common point of view in a general context of the Optimal Experimental Design, the latter is actually the main objective of the calibration problem for the practitioners and algorithms for computing these optimal designs are also provided. The optimal designs obtained have just three different points in their support, but practitioners usually demand for more support points. Thus, a methodology for computing space-filling designs is also provided when the support points are forced to follow some mathematical rule, such as arithmetic or geometric sequences. Cross efficiencies of all these designs are computed in order to show their ability for different goals.

Purpose:To introduce optimal experimental design techniques in the cytogenetic biological dosimetry practice. This includes the development of a new optimality criterion for the calibration of radiation doses. Materials and methods:The most typical optimal design criterion and the one developed in this research are introduced and applied in an example from the literature. In another example from the literature, a simulation study has been performed to compare the standard error of the dose estimation using different experimental designs. An RStudio project and a GitHub project have been developed to reproduce these results. Results:In the paper, it is observed that the application of optimal experimental design techniques can reduce the standard error of biodosimetric dose estimations. Conclusions:Optimal experimental design techniques jointly with practitioners' requirements may be applied. This practice would not involve an additional laboratory work.

Background: Service provision is a key domain to assess national-level palliative care development. Three editions of the European Association for Palliative Care (EAPC) Atlas of Palliative Care monitored the changes in service provision across Europe since 2005. Aim: To study European trends of specialized service provision at home care teams, hospital support teams, and inpatient palliative care services between 2005 and 2019. Design: Secondary analysis was conducted drawing from databases on the number of specialized services in 2005, 2012, and 2019. Ratios of services per 100,000 inhabitants and increase rates on number of services for three periods were calculated. Analysis of variance (ANOVA) analyses were conducted to determine significant changes and chi-square to identify countries accounting for the variance. Income-level and sub-regional ANOVA analysis were undertaken. Setting: 51 countries. Results: Forty-two countries (82%) increased the number of specialized services between 2005 and 2019 with changes for home care teams (104% increase-rate), inpatient services (82%), and hospital support teams (48%). High-income countries showed significant increase in all types of services (p <¿0.001), while low-to-middle-income countries showed significant increase only for inpatient services. Central¿Eastern European countries showed significant improvement in home care teams and inpatient services, while Western countries showed significant improvement in hospital support a

Background: Obesity is a major public health problem, which continues to be diagnosed and classified by BMI, excluding the most elemental concepts of the precision medicine approach. Obesity does not equally affect males and females, even with the same BMI. Microalbuminuria is a risk marker of cardiovascular disease closely related to obesity. The aim of this study was to evaluate the gender-dependent differences in the development of early obesity-related disease, focusing on pathologic microalbuminuria (PMA). Material and methods: We developed a single-centre cross-sectional study including 1068 consecutive adults from May 2016 to January 2018, divided into two groups: one including the first 787 patients attended, evaluated as a description population; the second group included 281 subjects analysed as an external validation population. Collected data included medical history, anthropometric measures, abdominal bioimpedance and routine laboratory tests. Results: First, we confirmed the lack of accuracy of classic obesity measures in predicting microalbuminuria. Second, we tested the utility of a tailored evaluation to predict PMA, with an area under the ROC curve of 0.78 for females and 0.82 for males. We also confirmed the different physiology of visceral adiposity for males when compared to females, in which small variations of fat mass entail major changes in the clinical repercussion. Third, we performed an external validation of our results, achieving a 77% accuracy rate. Conclusions: Our findings support that there is an individual threshold of fat amount necessary to develop obesity-dependent PMA and that gender plays a major role in the interplay between PMA and adiposity.

Context. International consensus on indicators is necessary to standardize the global assessment of palliative care (PC) development. Objectives. To identify the best indicators to assess current national-level PC development. Methods. Experts in PC development were invited to rate 45 indicators organized by domains of the World Health Organization Public Health Strategy in a two-round RAND/UCLA-modified Delphi process. In the first round, experts rated indicators by relevance, measurability, and feasibility (1-9). Ratings were used to calculate a global score (1-9). Indicators scoring >7 proceeded to the second round for fine-tuning of global scores. Median, confidence interval, Content Validity Index, and Disagreement Index were calculated. Indicators scoring a lower limit 95% confidence interval of >7 and a Content Validity Index of >= 0.30 were selected. Results. 24 experts representing five continents and several organizations completed the study. 25 indicators showed a high content validity and level of agreement. Policy indicators (n = 8) included the existence of designated staff in the National Ministry of Health and the inclusion of PC services in the basic health package and in the primary care level list of services. Education indicators (n = 4) focused on processes of official specialization for physicians, inclusion of teaching at the undergraduate level, and PC professorship. Use of medicines indicators (n = 4) consisted of opioid consumption, availability, and prescription requirements. Services indicators (n = 6) included number and type of services for adults and children. Additional indicators for professional activity (n = 3) were identified. Conclusion. The first list including 25 of the best indicators to evaluate PC development at a national level has been identified. (C) 2019 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

CONTEXT: Demoralization is a state of existential distress in patients with advanced illness, typically with coping difficulties, feelings of loss of sense, and purpose in life and despair, among other things. The New Demoralization Scale (DS-II) is an evaluation tool for this syndrome, which has recently been reformulated on a shorter scale. OBJECTIVES: The objective of this study was to obtain a Spanish version of the DS-II and to assess its psychometric properties in advanced cancer patients in Spain and a number of Latin American countries. METHODS: Following a translation-back translation process, a validation study and a confirmatory analysis using structural equation models with their corresponding latent constructs were undertaken. Patients completed the DS-II in Spanish (DS-II (es)), the Hospital Anxiety and Depression Scale, and the Edmonton Symptom Assessment System-revised. Reliability was studied according to internal consistency; construct validity and concurrent validity with the Hospital Anxiety and Depression Scale and the Edmonton Symptom Assessment System-revised; discriminant validity using the Karnofsky Performance Status scale; and feasibility, with response ratio and required time. Cutoff points were established, and sensitivity and specificity were studied. RESULTS: The DS-II (es) was obtained. One hundred fifty patients completed the validation study. The confirmatory analysis showed coherence, and all items correlated positively with their subscales and with the overall scale. Cronbach's alpha for the DS-II (es) was 0.88, for the sense and purpose subscale 0.83, and for the coping ability 0.79. Demoralization correlated significantly with emotional distress (rho = 0.73, P < 0.001). The tool distinguished between patients with diverse functional levels (rho = -0.319, P = 0.001). Cutoff points at 10 and 20 out of 32 were established. The scale showed high sensitivity (81.97%) and specificity (80.90%). The prevalence of demoralization was 33% in our sample. CONCLUSION: The Spanish version of the new Kissane DS-II demoralization scale has shown to be valid, reliable, and feasible with adequate psychometric properties.

Bioassays are applied to workers and other people exposed to radiation through routine or accidental intake of radioactive isotopes. The quantity of the isotope intake by an individual can be estimated from the measured quantities in the bioassays using the corresponding retention or excretion function. A retention (excretion) function represents the predicted activity of a radionuclide in the body, organ, or tissue or in a 24-h excreta at a particular time after the intake. The International Commission on Radiological Protection provides these biokinetic models. We have used these functions to compute optimal experimental designs for estimating the intake quantity in workers following radionuclide inhalation or ingestion. In particular, we have considered the case where the individual is exposed to a constant intake during some periods followed by other periods without intakes, which we called multiple constant intakes. The main aim of this work is finding the best times where the bioassay samples should be obtained in order to estimate optimally the actual intake of the last period. The problem is also extended to compute the next two or more bioassays to be performed. Measurements on the same worker are modeled through a correlation structure. The outline of the problem is established for a general case and results are given for a typical case as a real example. The methodology given in the paper can be applied to other cases with multiple constant intakes, e.g. in medical treatments where the patient is exposed to successive doses during particular periods.

Quality medical education, centred on patient¿s needs, is crucial to develop the health professionals that our society requires. Research suggests a strong contribution of palliative care education to professionalism. The aim of this study was to design and validate a self-report inventory to measure student¿s professional development. Method: Sequential exploratory strategy mix method. Inventory was built based on the themes that emerged from the analysis of four qualitative studies about nurse and medical students¿ perceptions related to palliative care teaching interventions (Ballesteros et al 2014, Centeno et al 2014&2017, Rojí et al 2017). The structure and psychometrics of the inventory obtained has been tested in two different surveys with two different groups of medical students. Inventory reliability and construct validity were tested in the first survey group. To verify the inventory structure a confirmatory factor analysis was performed in a second survey group. Results: the has 33-items and seven dimensions: holistic approach, caring for and understanding the patient, personal growth, teamwork, decision-making, patient evaluation, and being a health care professional. Cronbach¿s-alpha was 0.73-0,84 in all seven domains, ICC: 0.95. The confirmatory factor analysis CFI was 1 with a Standardized Root Mean Square index 0,088(SRMR) and obtained a 0,99 goodness-of-fit R-square coefficient. Conclusions: This inventory is valid to assess student¿s professional development

Building Energy Models (BEMs) are a key element of the Energy Performance of Buildings Directive (EPBD), and they are at the basis of Energy Performance Certificates (EPCs). The main goal of BEMs is to provide information for building stakeholders; they can be a powerful market tool to increase demand for energy efficiency solutions in buildings without affecting the comfort of users, as well as providing other benefits. The next generation of BEMs should value buildings in a holistic and cost-effective manner across several complementary dimensions: envelope performances, system performances, and controlling the ability of buildings to offer flexible services to the grid by optimizing energy consumption, distributed generation, and storage. SABINA is a European project that aims to look for flexibility to the grid, targeting the most economic source possible: existing thermal inertia in buildings. In doing so, SABINA works with a new generation of BEMs that tend to mimic the thermal behavior of real buildings and therefore requires an accurate methodology to choose the model that complies with the requirements of the system. This paper details our novel extensive research on which statistical indices should be chosen in order to identify the best model offered by the calibration process developed by Fernandez et al. in a previous paper and therefore is a continuation of that work.

CONTEXT: To date, there is no study comparing palliative care (PC) development among African countries. OBJECTIVE: To analyze comparatively PC development in African countries based on region-specific indicators. METHODS: Data were obtained from the APCA Atlas of PC in Africa and a comparative analysis conducted. Nineteen indicators were developed and defined through qualitative interviews with African PC experts and a two-round modified Delphi consensus process with international experts on global PC indicators. Indicators were grouped by the WHO public health strategy for PC dimensions. These indicators were then sent as a survey to key informants in 52/54 African countries. Through an expert weighting process and ratings from the modified Delphi, weights were assigned to each indicator. RESULTS: Surveys were received from 89% (48/54) of African countries. The top three countries in overall PC development were, in order, Uganda, South Africa, and Kenya. Variability existed by dimension. The top three countries in specialized services were Uganda, South Africa, and Nigeria; in policies, it was Botswana followed by parity among Ethiopia, Rwanda, and Swaziland; in medicines, it was Swaziland, South Africa, then Malawi; in education, it was equivalent between Uganda and Kenya, then Ghana and Zambia. CONCLUSION: Uganda, South Africa, and Kenya are the highest performing countries and were the only ones with composite scores greater than 0.5 (50%). However, not one country universally supersedes all others across all four PC dimensions. The breakdown of rankings by dimension highlights where even high-performing African countries can focus their efforts to further PC development.

BACKGROUND AND OBJECTIVE: Binary response models are used in many real applications. For these models the Fisher information matrix (FIM) is proportional to the FIM of a weighted simple linear regression model. The same is also true when the weight function has a finite integral. Thus, optimal designs for one binary model are also optimal for the corresponding weighted linear regression model. The main objective of this paper is to provide a tool for the construction of MV-optimal designs, minimizing the maximum of the variances of the estimates, for a general design space. METHODS: MV-optimality is a potentially difficult criterion because of its nondifferentiability at equal variance designs. A methodology for obtaining MV-optimal designs where the design space is a compact interval [a, b] will be given for several standard weight functions. RESULTS: The methodology will allow us to build a user-friendly computer tool based on Mathematica to compute MV-optimal designs. Some illustrative examples will show a representation of MV-optimal designs in the Euclidean plane, taking a and b as the axes. The applet will be explained using two relevant models. In the first one the case of a weighted linear regression model is considered, where the weight function is directly chosen from a typical family. In the second example a binary response model is assumed, where the probability of the outcome is given by a typical probability distribution. CONCLUSIONS: Practitioners can use the provided applet to identify the solution and to know the exact support points and design weights.

The objective is to improve the fatigue characterisation process based on the concept of optimal experimental design. This is carried out through a probabilistic model, previously developed, which takes into account the experimentally observed loading frequency effect on the fatigue life in plain and fibre-reinforced concrete. The Fisher Information Matrix is first obtained for the simplified fatigue model. The optimal design is found to be located at the minimum values allowed for both the maximum stress and stress ratio, whereas the two loading frequencies are the minimum and maximum values in the defined range. Next, the FIM is derived for the extended fatigue model. The previously carried out experimental plan is 65% efficient compared to the optimum. Even though it has been developed for the specific chosen fatigue model, the current procedure can be applied to any other fatigue model to significantly improve the fatigue characterisation process of any material.

Background: Palliative care (PC) research in Africa has been proposed as a fifth dimension of the World Health Organization PC Public Health Strategy. We conducted a scoping review of published articles (2005-2016) on palliative care development (PCD) in African countries. Forty-seven articles were found across 26 countries. Objective: To study whether the number of published articles on PCD in countries in Africa can be used as an indicator of PCD. Design: This is a secondary analysis of a completed scoping review. Measurements: Spearman correlations were applied to the number of published articles ("published articles") and the number of published articles with a coauthor from a high-income country (HIC) ("HIC published articles") with level of PCD using Lynch et al's updated world map (PC World Map) as a proxy. A subanalysis was undertaken for Anglophone versus non-Anglophone countries. Results: There were positive Spearman correlations (r) between the PC World Map's levels and published articles (r¿=¿0.73; p¿<¿0.001), and with HIC published articles (r¿=¿0.68; p¿<¿0.001). For Anglophone countries, the r was statistically significant (p¿<¿0.001) at 0.69 and 0.70, versus 0.58 and 0.45 for non-Anglophone countries for published articles and HIC published articles, respectively. Kruskal-Wallis test showed a statistically significant difference between Anglophone and non-Anglophone countries for both published articles and HIC published articles (p¿<¿0.01). Conclusion: Publish

PURPOSE: To assess the diagnostic impact of 18F-FDG-PET/CT in patients suspected of paraneoplastic neurological syndrome (PNS) based on our own pre-test risk classification (PRC). METHODS: A multicenter retrospective longitudinal study was conducted from 2006 to 2014. We designed a seven-point scoring system using the clinical syndrome characteristics [classical (CS) and non-classical syndromes (NCS)] and its location (central, peripheral, in the neuromuscular junction or combined), onconeural antibodies and tumor markers. Patients were classified as low (score 0-2), intermediate (3-4) and high (5-7) pre-test risk of PNS. FDG-PET/CT was classified as negative or positive. Final diagnosis according Graus' criteria (definite, possible or no PNS) was established. Relations between clinical and metabolic variables with the final diagnosis were studied. RESULTS: 73 patients were included, with a follow-up time of 33 months. Eleven (15 %) patients were finally diagnosed with neoplasm (8 invasive cancers). Ultimately, 13 (18 %) and 24 (33 %) subjects were diagnosed as definite or possible PNS. All the patients with final diagnosis of neoplasm had a CS (p = 0.005). PET/CT was helpful to diagnose 6/8 (75 %) invasive cancers. PET/CT findings were associated with the final diagnosis of neoplasm (p = 0.003) and the diagnosis of PNS attending to Graus' criteria (p = 0.019). PRC showed significant association with the final diagnosis of neoplasm and PET/CT results. A majority of patients (10/11) diagnosed of neoplasm had intermediate/high-risk. CONCLUSIONS: Our PRC seems to be a valid tool to select candidates for PET/CT imaging in this setting. PET/CT detected malignancy in a significant proportion of patients with invasive cancer.

AIM: To analyze the relationship of clinical variables related to prognosis and tumor burden, with metabolic variables obtained in the staging (18)F-FDG PET/CT, and their value in the prognosis in follicular lymphoma (FL). METHODS: 82 patients with FL, a (18)F-FDG PET/CT at diagnosis and a follow-up for a minimum of 12 months, were retrospectively enrolled in the present study. Clinical variables (Tumor grade, Follicular Lymphoma International Prognostic Index (FLIPI) and Tumor burden) were evaluated. Metabolic variables such as SUVmax in the highest hypermetabolic lesion, extralymphatic locations, number of involved lymph node locations, bone marrow (BM) involvement, PET stage and diameter of the biggest hypermetabolic lesion, were analyzed in order to establish a PET score and classify the studies in low, intermediate and high metabolic risk. Clinical and metabolic variables (included metabolic risk) were compared. The relation among all variables and disease-free survival (DFS) was studied. RESULTS: The 28% of patients had a high-grade tumor. The 30.5% had FLIPI risk low, 29.3% intermediate y 40.2% high. The 42.7% presented a high tumor burden. The PET/CT was positive in 94% of patients. The tumor grade did not show significant relation with metabolic variable. FLIPI risk and tumor burden showed statistical relations with the SUV max and the PET score (p<0.008 and p=0.003 respectively). With respect to DFS, significant differences were detected for the PET stage and FLIPI risk (p=0.015 and p=0.047 respectively). FLIPI risk was the only significant predictor in Cox regression analysis, with a Hazard Ratio of 5.13 between high risk and low risk. CONCLUSION: The present research highlights the significant relation between metabolic variables obtained with FDG PET/CT and clinical variables although their goal as an independent factor of prognosis was not demonstrated in the present work.

After the demonstration of the feasibility of hypernuclear spectroscopy with heavy-ion beams, the HypHI Collaboration will next focus on the study of proton- and neutron-rich hypernuclei. The use of a fragment separator for the production and separation of rare-isotope beams is a crucial aspect to producing hypernuclei far from the stability line. Precise spectroscopy of exotic hypernuclei is planned to be carried out at the GSI and later at the FAIR facility with the FRS and Super-FRS fragment separators. A systematic study and an optimization analysis were performed to determine optimal experimental conditions for producing hypernuclei with high isospin. The optimal conditions are obtained based on theoretical models for the heavy-ion induced reaction and hypernuclei production. Experimental efficiencies for the production of exotic secondary beams were also taken into account via Monte Carlo simulations of the fragment separator. The developed methodology is presented to deduce the expected yields of Be-8(Lambda) and subsequently other proton-rich and neutron-rich hypernuclei.

Aim To explore the relationship between basal 18F-FDG uptake in breast tumors and survival in patients with breast cancer (BC) using a molecular phenotype approach. Material and Methods This prospective and multicentre study included 193 women diagnosed with BC. All patients underwent an 18F-FDG PET/CT prior to treatment. Maximum standardized uptake value (SUVmax) in tumor (T), lymph nodes (N), and the N/T index was obtained in all the cases. Metabolic stage was established. As regards biological prognostic parameters, tumors were classified into molecular sub-types and risk categories. Overall survival (OS) and disease free survival (DFS) were obtained. An analysis was performed on the relationship between semi-quantitative metabolic parameters with molecular phenotypes and risk categories. The effect of molecular sub-type and risk categories in prognosis was analyzed using Kaplan¿Meier and univariate and multivariate tests. Results Statistical differences were found in both SUVT and SUVN, according to the molecular sub-types and risk classifications, with higher semi-quantitative values in more biologically aggressive tumors. No statistical differences were observed with respect to the N/T index. Kaplan¿Meier analysis revealed that risk categories were significantly related to DFS and OS. In the multivariate analysis, metabolic stage and risk phenotype showed a significant association with DFS. Conclusion High-risk phenotype category showed a worst prognosis with respect to the other categories with higher SUVmax in primary tumor and lymph nodes.

The body mass growth of organisms is usually represented in terms of what is known as ontogenetic growth models, which represent the relation of dependence between the mass of the body and time. The paper is concerned with a problem of finding an optimal experimental design for discriminating between two competing mass growth models applied to a beef farm. T-optimality was first introduced for discrimination between models but in this paper, KL-optimality based on the Kullback-Leibler distance is used to deal with correlated obsevations since, in this case, observations on a particular animal are not independent.

In the literature, different optimality criteria have been considered for model identification. Most of the proposals assume the normal distribution for the response variable and thus they provide optimality criteria for discriminating between regression models. In this paper, a max-min approach is followed to discriminate among competing statistical models (i.e., probability distribution families). More specifically, k different statistical models (plausible for the data) are embedded in a more general model, which includes them as particular cases. The proposed optimal design maximizes the minimum KL-efficiency to discriminate between each rival model and the extended one. An equivalence theorem is proved and an algorithm is derived from it, which is useful to compute max-min KL-efficiency designs. Finally, the algorithm is run on two illustrative examples.

During the discharge of a two-dimensional silo, the flow of grains through an opening is arrested if the size of the outlet is not large enough. In the outpouring of grains, jamming occurs due to the formation of an arch at the outlet. After breaking the arch, the grains fall until a new arch is formed. Several models have been proposed to explain this process. The goal of this work is twofold. First, we developed a comparative study of the models proposed by Janda et al. (Europhys Lett 84(4):44002-1¿44002-6, 2008) and To (Phys Rev E 71(6):060301-1¿060301-4, 2005). We have computed D-optimal and c-optimal designs for the most important parameter of the models. Secondly, using the criterion of KL-optimality developed by López-Fidalgo et al. (J R Stat Soc Ser B 69(2):231¿242, 2007), optimal designs for discriminating between these models have been computed.

This article focuses on analyzing the process of jam formation during the discharge by gravity of granular material stored in a two-dimensional silo. The aim of the article is two-fold. First, optimal experimental designs are computed, in which four approaches are considered: D-optimality, a combination of D-optimality and a cost/gain function, Bayesian D-optimality, and sequential designing. These results reveal that the efficiency of the design used by the experimenters can be improved dramatically. A sensitivity analysis with respect to the most important parameter is also performed. Second, estimation of the unknown parameters is done using least squares, that is, assuming normality, and also via maximum likelihood assuming the exponential distribution. Simulations for the designs considered in this article show that the variance, the mean squared error, and the bias of the estimators using maximum likelihood are in most cases lower than those using least squares.

Aim: To explore the relationship between basal (18)¿F-FDG PET/CT information in breast tumours and survival in locally advanced breast cancer (LABC). Methods: This prospective, multicentre study included 198 women diagnosed with LABC. All patients underwent (18)¿F-FDG PET/CT prior to treatment. The maximum standardized uptake value (SUVmax) in tumor (T), lymph nodes (N) and the N/T ratio was obtained in all cases. Stage according to PET/CT imaging (metabolic stage) and conventional imaging techniques (clinical stage) was established. During follow-up, patient status was established (disease free status or not). The relationship between all the variables and overall survival (OS) and disease-free survival (DFS) was analysed using the Kaplan-Meier and Cox regression methods. A ROC analysis was performed to obtain a cut-off value of SUVmax that was useful in the prediction of outcome. Results: The mean SUVmax¿±¿SD values in the primary tumour, lymph nodes and the SUVmax N/T index were 7.40¿±¿5.57, 4.17¿±¿4.74 and 0.73¿±¿1.20, respectively. Higher semiquantitative metabolic values were found in more advanced metabolic and clinical stages. During follow-up, 78.4 % of patients were free of disease. Significant relationships were observed between SUVT and SUVN and patient status. With respect to OS and DFS, significant differences were detected for the metabolic stage. Kaplan-Meier analysis revealed that using the cut-off values, a primary-tumour SUVmax¿¿¿6.05 or a nodal SUVmax ¿2.25 were significantly correlated with DFS and OS. Conclusion: PET imaging with (18)¿F-FDG offers prognostic information for LABC that can be obtained preoperatively and noninvasively.

The body mass growth of organisms is usually represented in terms of what is known as ontogenetic growth models, which represent the relation of dependence between the mass of the body and time. This paper discusses design issues of West's ontogenetic growth model applied to a Holstein-Friesian dairy farm in the northwest of Spain. D-optimal experimental designs were computed to obtain an optimal fitting of the model. A correlation structure has been included in the statistical model due to the fact that observations on a particular animal are not independent. The choice of a robust correlation structure is an important contribution of this paper; it provides a methodology that can be used for any correlation structure. The experimental designs undertaken provide a tool to control the proper weight of heifers, which will help improve their productivity and, by extension, the competitiveness of the dairy farm.

Some time series applications require data which are either expensive or technically difficult to obtain. In such cases scheduling the points in time at which the information should be collected is of paramount importance in order to optimize the resources available. In this paper time series models are studied from a new perspective, consisting in the use of Optimal Experimental Design setup to obtain the best times to take measurements, with the principal aim of saving costs or discarding useless information. The model and the covariance function are expressed in an explicit form to apply the usual techniques of Optimal Experimental Design. Optimal designs for various approaches are computed and their efficiencies are compared. The methods working in an application of industrial maintenance of a critical piece of equipment at a petrochemical plant are shown. This simple model allows explicit calculations in order to show openly the procedure to find the correlation structure, needed for computing the optimal experimental design. In this sense the techniques used in this paper to compute optimal designs may be transferred to other situations following the ideas of the paper, but taking into account the increasing difficulty of the procedure for more complex models.

Optimal experimental designs are considered for models with simultaneous equations. In particular, a model with two equations is assumed where one of the explanatory variables (exogenous) of the first equation is then the response variable (endogenous) of the second equation. In both equations there is a control variable, which is being designed through the celebrated D-optimality criterion. This work is based on a more restricted approach using just the first equation and assuming the distribution of the exogenous/endogenous variable completely known. Then a conditionally restricted optimal design was computed afterwards. In this paper the conditional model is assumed partially known but it has to be fitted as well. Although both approaches identify different prior knowledge a comparison of the optimal designs for both approaches is made. Since the model is not linear in the usual sense the optimal designs will depend on the parameters and a sensitivity analysis against its choice is performed.

A procedure based on a multiplicative algorithm for computing optimal experimental designs subject to cost constraints in simultaneous equations models is presented. A convex criterion function based on a usual criterion function and an appropriate cost function is considered. A specific L-optimal design problem and a numerical example are taken from Conlisk (J Econ 11:63¿76, 1979) to compare the procedure. The problem would need integer nonlinear programming to obtain exact designs. To avoid this, he solves a continuous nonlinear programming problem and then he rounds off the number of replicates of each experiment. The procedure provided in this paper reduces dramatically the computational efforts in computing optimal approximate designs. It is based on a specific formulation of the asymptotic covariance matrix of the full-information maximum likelihood estimators, which simplifies the calculations. The design obtained for estimating the structural parameters of the numerical example by this procedure is not only easier to compute, but also more efficient than the design provided by Conlisk.

Optimal design is a crucial issue in Environmental measurement with typical time-space correlated observations. A modified Arrhenius model with a particular correlation structure will be applied to the methane removal in the atmosphere, a very important environmental issue at this moment. We introduce a class of integrated compound criteria for obtaining robust designs. In particular, the paper provides an insight into the relationship of a compound D-optimality criterion for both the trend and covariance parameters, and the Integrated Mean Squared Prediction Error (IMSPE) criterion. In general, if there are two or more approaches of a given problem, e.g. two rival models or two different parts of a model, an integral relationship may be constructed with the aim of finding a suitable compromise between them. The Fisher information matrix (FIM) will be used in both cases. Then the integral compound criterion with respect to a density from a given parametric family of distributions is optimized. We also discuss some general conditions around the behavior of the introduced approach for comparing the FIMs and provide computing methods.

Multiple choice questions (MCQs) are one of the most popular tools to evaluate learning and knowledge in higher education. Nowadays, there are a few indices to measure reliability and validity of these questions, for instance, to check the difficulty of a particular question (item) or the ability to discriminate from less to more knowledge. In this work two new indices have been constructed: (i) the no answer index measures the relationship between the number of errors and the number of no answers; (ii) the homogeneity index measures homogeneity of the wrong responses (distractors). The indices are based on the lack-of-fit statistic, whose distribution is approximated by a chi-square distribution for a large number of errors. An algorithm combining several traditional and new indices has been developed to refine continuously a database of MCQs. The final objective of this work is the classification of MCQs from a large database of items in order to produce an automated-supervised system of generating tests with specific characteristics, such as more or less difficulty or capacity of discriminating knowledge of the topic.

In a follow-up study the time-to-event may be censored either because of dropouts or the end of study is earlier. This situation is frequently modeled through a Cox-proportional hazards model including covariates, some of which are under the control of the experimenter. When the model is to be fitted to n observed times these are known and for each of them it is also known whether that time is censored or not. When the experiment is to be designed neither the observed times nor the information about whether a particular unit will be censored are known. For censoring some additional prior probability distribution has to be assumed. Thus, the design problem faces two sources of imprecision when the experiment is to be scheduled. On the one hand, the censored times are not known. On the other hand, there is uncertainty about occurrence of censoring. A prior probability distribution is needed for this. Moreover, the Cox partial likelihood instead of the full likelihood is usually considered for these models. A partial information matrix is built in this case and optimal designs are computed and compared with the optimal designs for the full likelihood information. The usual tools for computing optimal designs with full likelihood are no longer valid for partial information. Some general results are provided in order to deal with this new approach. An application to a simple case with two possible treatments is used to illustrate it. The partial information matrix depends on the parameters and therefore a sensitivity analysis is conducted in order to check the robustness of the designs for the choice of the nominal values of the parameters.

Proportional Hazards models have been widely used to analyze survival data. In many cases survival data do not verify the assumption of proportional hazards. An alternative to the PH models with more relaxed conditions are Accelerated Failure Time models. These models are fairly commonly used in the field of manufacturing, but they are more and more frequent for modeling clinical trial data. They focus on the direct effect of the explanatory variables on the survival function allowing an easier interpretation of the effect of the corresponding covariates on the survival time. Optimal experimental designs are computed in this framework for Type I and random arrival. The results are applied to clinical models used to prevent tuberculosis in Ugandan adults infected with HIV.

Compound optimal designs are considered where one component of the design criterion is a traditional optimality criterion such as the D-optimality criterion, and the other component accounts for higher efficacy with low toxicity. With reference to the dose-finding problem, we suggest the technique to choose weights for the two components that makes the optimization problem simpler than the traditional penalized design. We allow general bivariate responses for efficacy and toxicity. We then extend the procedure in the presence of nondesignable covariates such as age, sex, or other health conditions. A new breast cancer treatment is considered to illustrate the procedures.

Background: The pathology of the Benign Paroxysmal Positional Vertigo (BPPV) is detected by a clinician through maneuvers consisting of a series of consecutive head turns that trigger the symptoms of vertigo in patient. A statistical model based on a new maneuver has been developed in order to calculate the volume of endolymph displaced after the maneuver. Methods: A simplification of the Navier-Stokes problem from the fluids theory has been used to construct the model. In addition, the same cubic splines that are commonly used in kinematic control of robots were used to obtain an appropriate description of the different maneuvers. Then experimental designs were computed to obtain an optimal estimate of the model. Results: D-optimal and c-optimal designs of experiments have been calculated. These experiments consist of a series of specific head turns of duration Delta t and angle alpha that should be performed by the clinician on the patient. The experimental designs obtained indicate the duration and angle of the maneuver to be performed as well as the corresponding proportion of replicates. Thus, in the D-optimal design for 100 experiments, the maneuver consisting of a positive 30 degrees pitch from the upright position, followed by a positive 30 degrees roll, both with a duration of one and a half seconds is repeated 47 times. Then the maneuver with 60 degrees/60 degrees pitch/roll during half a second is repeated 16 times and themaneuver 90 degrees/90 degrees pitch/roll during half a second is repeated 37 times. Other designs with significant differences are computed and compared. Conclusions: A biomechanical model was derived to provide a quantitative basis for the detection of BPPV. The robustness study for the D-optimal design, with respect to the choice of the nominal values of the parameters, shows high efficiencies for small variations and provides a guide to the researcher. Furthermore, c-optimal designs give valuable assistance to check how efficient the D-optimal design is for the estimation of each of the parameters. The experimental designs provided in this paper allow the physician to validate the model. The authors of the paper have held consultations with an ENT consultant in order to align the outline more closely to practical scenarios.

This paper considers optimal experimental designs for models with correlated observations through a covariance function depending on the magnitude of the responses. This suggests the use of stochastic processes whose covariance structure is a function of the mean. Covariance functions must be positive definite. This fact is nontrivial in this context and constitutes one of the challenges of the present paper. We show that there exists a huge class of functions that, composed with the mean of the process in some way, preserves positive definiteness and can be used for the purposes of modeling and computing optimal designs in more realistic situations. We offer some examples for an easy construction of such covariances and then study the problem of locally D-optimal designs through an illustrative example as well as a real radiation retention model in the human body.

Two nested pharmacokinetic models are considered in this article. Several observations are taken on the same subject so they are correlated. The covariance function assumed is an exponential covariance function. Optimal exact designs are computed with different criteria, both for discriminating between models and estimating parameters. Compound criteria to estimate the parameters and nonlinear functions of the parameters are used. An iterative algorithm based on T-optimality and an algorithm from Brimkulov et al. (1986) are combined in order to compute T-optimal designs with correlated observations. Finally, compound designs to discriminate between the models and estimate the nonlinear functions are considered. A test power study is performed to adjust the compound parameter.

We briefly review and discuss design issues for population growth and decline models. We then use a flexible growth and decline model as an illustrative example and apply optimal design theory to find optimal sampling times for estimating model parameters, specific parameters and interesting functions of the model parameters for the model with two real applications. Robustness properties of the optimal designs are investigated when nominal values or the model is mis-specified, and also under a different optimality criterion. To facilitate use of optimal design ideas in practice, we also introduce a website for generating a variety of optimal designs for popular models from different disciplines.

Background: Allergic symptoms are commonly related to atmospheric pollen counts in sensitized allergic individuals. However, concordance between symptoms, pollen counts, and aeroallergen concentrations is not always good. Objectives: To determine the correlation between olive pollen counts, aeroallergen levels, and clinical symptoms in patients with allergic asthma or rhinitis in Ciudad Real (Spain). Methods: Two types of samplers were used to determine pollen exposure: a Burkard spore trap to collect pollen grains and a high-volume air sampler to collect airborne particles. A total of 366 air filters were collected. After extraction, they were analyzed by specific immunoglobulin E enzyme-linked immunosorbent assay inhibition using a serum pool containing high titers of olive-specific immunoglobulin E. Twenty olive-pollen monosensitized patients were asked to record their daily symptoms before, during, and after the olive pollen season. Results: Olive pollen was detected between April 21 and June 30, 2004. Symptoms showed positive and significant correlations with pollen counts (r = 0.700, P < .001) and aeroallergen levels (r = 0.803, P < .001). Using a Poisson regression model, relative changes in aeroallergen concentrations and pollen counts were found to be similar and significant. Threshold levels for the induction of symptoms were 162 olive pollen grains/m(3) and 22.7 ng of olive pollen allergen/m(3) (equivalent to 0.9 ng/m(3) of Ole e 1). Conclusions: Olive aeroallergen concentrations and pollen counts are positively associated with symptoms of rhinitis and asthma in olive-allergic patients. Both data may be used in the clinical follow-up of these patients.

This paper considers the problem of constructing optimal approximate designs when an independent variable might be censored. The problem is which design should be applied in practice to obtain the best approximate design when a censoring distribution is assumed known in advance. The approach for finite or continuous design spaces deserves different attention. In both cases, equivalent theorems and algorithms are provided in order to calculate optimal designs. Some examples illustrate this approach for D-optimality.

Pectinases are used in Enology for some different utilities. Enzymatic preparations from moulds are a mixed of different enzymes with strong and unspe-cific activities. Some Saccharomyces cerevisiae pro-duce pectinases which can be used instead of com-mercial preparations. The objectives of this work were to study the enzyme secretion by one Saccharo-myces cerevisiae (CECT 11783) for growing on grape skin (industry oenological by-product) as carbon source. Preliminary experiments showed that the strain produced pectinases for growing on grape skin without any other carbon source. Statistical treat-ment (factorial design 25) was applied to evaluate the influences of related factors (agitation, temperature, presence of peptone and detergent in the medium and time of growth) Variables with the most significant interactions for pectinase production were agitation and nitrogen source concentration. Response surface methodology showed that a first order model was not adequate for results. Nevertheless, the built of a sec-ond order model offered a polynomial equation which surface predicted a maximum of activity (52.68 enzymatic units) for specific values of the studied variables (147.8 rpm of agitation and 15.9 g of pep-tone/ L culture medium).

The Bayesian KL-optimality criterion is useful for discriminating between any two statistical models in the presence of prior information. If the rival models are not nested then, depending on which model is true, two different Kullback-Leibler distances may be defined. The Bayesian KL-optimality criterion is a convex combination of the expected values of these two possible Kullback-Leibler distances between the competing models. These expectations are taken over the prior distributions of the parameters and the weights of the convex combination are given by the prior probabilities of the models. Concavity of the Bayesian KL-optimality criterion is proved, thus classical results of Optimal Design Theory can be applied. A standardized version of the proposed criterion is also given in order to take into account possible different magnitudes of the two Kullback-Leibler distances. Some illustrative examples are provided.

The model that describes the retention in lungs of radioisotope particles is studied in this paper, considering the situation of an accident in facilities that handle radioactive materials. Optimal times to make the bioassays are computed for D- and c-optimality, and efficiencies for the computed designs are provided and compared. Moreover, the test power is checked by means of simulations and replications. After that the inverse of the Fisher information matrix is compared to an estimation of the covariance matrix of the parameters. Finally, a study taking into consideration the randomness of the designs space is performed.