Online Hemodiafiltration Inhibits Inflammation-Related Endothelial Dysfunction and Vascular Calcification of Uremic Patients Modulating miR-223 Expression in Plasma Extracellular Vesicles.

Decreased inflammation and cardiovascular mortality are evident in patients with end-stage chronic kidney disease treated by online hemodiafiltration. Extracellular vesicles (EV) are mediators of cell-to-cell communication and contain different RNA types. This study investigated whether mixed online hemodiafiltration (mOL-HDF) beneficial effects associate with changes in the RNA content of plasma EV in chronic kidney disease patients. Thirty bicarbonate hemodialysis (BHD) patients were randomized 1:1 to continue BHD or switch to mOL-HDF. Concentration, size, and microRNA content of plasma EV were evaluated for 9 mo; we then studied EV effects on inflammation, angiogenesis, and apoptosis of endothelial cells (HUVEC) and on osteoblast mineralization of vascular smooth muscle cells (VSMC). mOL-HDF treatment reduced different inflammatory markers, including circulating CRP, IL-6, and NGAL. All hemodialysis patients showed higher plasma levels of endothelial-derived EV than healthy subjects, with no significant differences between BHD and mOL-HDF. However, BHD-derived EV had an increased expression of the proatherogenic miR-223 with respect to healthy subjects or mOL-HDF. Compared with EV from healthy subjects, those from hemodialysis patients reduced angiogenesis and increased HUVEC apoptosis and VSMC calcification; however, all these detrimental effects were reduced with mOL-HDF with respect to BHD. Cell transfection with miR-223 mimic or antagomiR proved the role of this microRNA in EV-induced HUVEC and VSMC dysfunction. The switch from BHD to mOL-HDF significantly reduced systemic inflammation and miR-223 expression in plasma EV, thus improving HUVEC angiogenesis and reducing VSMC calcification.

Lifetime reproductive success, selection on lifespan, and multiple sexual ornaments in male European barn swallows.

Natural and sexual selection arise when individual fitness varies according to focal traits. Extra-pair paternities (EPPs) can affect the intensity of selection by influencing variance in fitness among individuals. Studies of selection require that individual fitness is estimated using proxies of lifetime reproductive success (LRS). However, estimating LRS is difficult in large, open populations where EPPs cause reallocation of biological paternity. Here, we used extensive field sampling to estimate LRS in a population of barn swallows (Hirundo rustica) to estimate selection on lifespan and ornamental traits of males. We found selection on lifespan mediated both by within- and extra-pair fertilization success and selection on tail length mediated by within- but not extra-pair fertilization success. In addition, we found selection on tail white spots via extra-pair fertilization success after controlling for selection on other traits. These results were not confounded by factors that hamper studies of LRS, including nonexhaustive sampling of offspring and biased sampling of males. Hence, natural and sexual selection mediated by LRS operates on lifespan, tail length, and size of the tail white spots in barn swallows.

Migration phenology and breeding success are predicted by methylation of a photoperiodic gene in the barn swallow.

Individuals often considerably differ in the timing of their life-cycle events, with major consequences for individual fitness, and, ultimately, for population dynamics. Phenological variation can arise from genetic effects but also from epigenetic modifications in DNA expression and translation. Here, we tested if CpG methylation at the poly-Q and 5'-UTR loci of the photoperiodic Clock gene predicted migration and breeding phenology of long-distance migratory barn swallows (Hirundo rustica) that were tracked year-round using light-level geolocators. Increasing methylation at Clock poly-Q was associated with earlier spring departure from the African wintering area, arrival date at the European breeding site, and breeding date. Higher methylation levels also predicted increased breeding success. Thus, we showed for the first time in any species that CpG methylation at a candidate gene may affect phenology and breeding performance. Methylation at Clock may be a candidate mechanism mediating phenological responses of migratory birds to ongoing climate change.

Candidate genes have sex-specific effects on timing of spring migration and moult speed in a long-distance migratory bird.

The timing of major life-history events, such as migration and moult, is set by endogenous circadian and circannual clocks, that have been well characterized at the molecular level. Conversely, the genetic sources of variation in phenology and in other behavioral traits have been sparsely addressed. It has been proposed that inter-individual variability in the timing of seasonal events may arise from allelic polymorphism at phenological candidate genes involved in the signaling cascade of the endogenous clocks. In this study of a long-distance migratory passerine bird, the willow warbler Phylloscopus trochilus, we investigated whether allelic variation at 5 polymorphic loci of 4 candidate genes (Adcyap1, Clock, Creb1, and Npas2), predicted 2 major components of the annual schedule, namely timing of spring migration across the central Mediterranean sea and moult speed, the latter gauged from ptilochronological analyses of tail feathers moulted in the African winter quarters. We identified a novel Clock gene locus (Clock region 3) showing polyQ polymorphism, which was however not significantly associated with any phenotypic trait. Npas2 allele size predicted male (but not female) spring migration date, with males bearing longer alleles migrating significantly earlier than those bearing shorter alleles. Creb1 allele size significantly predicted male (but not female) moult speed, longer alleles being associated with faster moult. All other genotype-phenotype associations were statistically non-significant. These findings provide new evidence for a role of candidate genes in modulating the phenology of different circannual activities in long-distance migratory birds, and for the occurrence of sex-specific candidate gene effects.

Clock gene polymorphism, migratory behaviour and geographic distribution: a comparative study of trans-Saharan migratory birds.

Migratory behaviour is controlled by endogenous circannual rhythms that are synchronized by external cues, such as photoperiod. Investigations on the genetic basis of circannual rhythmicity in vertebrates have highlighted that variation at candidate 'circadian clock' genes may play a major role in regulating photoperiodic responses and timing of life cycle events, such as reproduction and migration. In this comparative study of 23 trans-Saharan migratory bird species, we investigated the relationships between species-level genetic variation at two candidate genes, Clock and Adcyap1, and species' traits related to migration and geographic distribution, including timing of spring migration across the Mediterranean Sea, migration distance and breeding latitude. Consistently with previous evidence showing latitudinal clines in 'circadian clock' genotype frequencies, Clock allele size increased with breeding latitude across species. However, early- and late-migrating species had similar Clock allele size. Species migrating over longer distances, showing delayed spring migration and smaller phenotypic variance in spring migration timing, had significantly reduced Clock (but not Adcyap1) gene diversity. Phylogenetic confirmatory path analysis suggested that migration date and distance were the most important variables directly affecting Clock gene diversity. Hence, our study supports the hypothesis that Clock allele size increases poleward as a consequence of adaptation to the photoperiodic regime of the breeding areas. Moreover, we show that long-distance migration is associated with lower Clock diversity, coherently with strong stabilizing selection acting on timing of life cycle events in long-distance migratory species, likely resulting from the time constraints imposed by late spring migration.

Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients.

Anemia management, based on erythropoiesis stimulating agents (ESA) and iron supplementation, has become an increasingly challenging problem in hemodialysis patients. Maintaining hemodialysis patients within narrow hemoglobin targets, preventing cycling outside target, and reducing ESA dosing to prevent adverse outcomes requires considerable attention from caregivers. Anticipation of the long-term response (i.e. at 3 months) to the ESA/iron therapy would be of fundamental importance for planning a successful treatment strategy. To this end, we developed a predictive model designed to support decision-making regarding anemia management in hemodialysis (HD) patients treated in center. An Artificial Neural Network (ANN) algorithm for predicting hemoglobin concentrations three months into the future was developed and evaluated in a retrospective study on a sample population of 1558 HD patients treated with intravenous (IV) darbepoetin alfa, and IV iron (sucrose or gluconate). Model inputs were the last 90 days of patients' medical history and the subsequent 90 days of darbepoetin/iron prescription. Our model was able to predict individual variation of hemoglobin concentration 3 months in the future with a Mean Absolute Error (MAE) of 0.75 g/dL. Error analysis showed a narrow Gaussian distribution centered in 0 g/dL; a root cause analysis identified intercurrent and/or unpredictable events associated with hospitalization, blood transfusion, and laboratory error or misreported hemoglobin values as the main reasons for large discrepancy between predicted versus observed hemoglobin values. Our ANN predictive model offers a simple and reliable tool applicable in daily clinical practice for predicting the long-term response to ESA/iron therapy of HD patients.

Clock gene polymorphism and scheduling of migration: a geolocator study of the barn swallow Hirundo rustica.

Circannual rhythms often rely on endogenous seasonal photoperiodic timers involving 'clock' genes, and Clock gene polymorphism has been associated to variation in phenology in some bird species. In the long-distance migratory barn swallow Hirundo rustica, individuals bearing the rare Clock allele with the largest number of C-terminal polyglutamine repeats found in this species (Q8) show a delayed reproduction and moult later. We explored the association between Clock polymorphism and migration scheduling, as gauged by light-level geolocators, in two barn swallow populations (Switzerland; Po Plain, Italy). Genetic polymorphism was low: 91% of the 64 individuals tracked year-round were Q7/Q7 homozygotes. We compared the phenology of the rare genotypes with the phenotypic distribution of Q7/Q7 homozygotes within each population. In Switzerland, compared to Q7/Q7, two Q6/Q7 males departed earlier from the wintering grounds and arrived earlier to their colony in spring, while a single Q7/Q8 female was delayed for both phenophases. On the other hand, in the Po Plain, three Q6/Q7 individuals had a similar phenology compared to Q7/Q7. The Swiss data are suggestive for a role of genetic polymorphism at a candidate phenological gene in shaping migration traits, and support the idea that Clock polymorphism underlies phenological variation in birds.

Optimal convection volume for improving patient outcomes in an international incident dialysis cohort treated with online hemodiafiltration.

Online hemodiafiltration (OL-HDF), the most efficient renal replacement therapy, enables enhanced removal of small and large uremic toxins by combining diffusive and convective solute transport. Randomized controlled trials on prevalent chronic kidney disease (CKD) patients showed improved patient survival with high-volume OL-HDF, underlining the effect of convection volume (CV). This retrospective international study was conducted in a large cohort of incident CKD patients to determine the CV threshold and range associated with survival advantage. Data were extracted from a cohort of adult CKD patients treated by post-dilution OL-HDF over a 101-month period. In total, 2293 patients with a minimum of 2 years of follow-up were analyzed using advanced statistical tools, including cubic spline analyses for determination of the CV range over which a survival increase was observed. The relative survival rate of OL-HDF patients, adjusted for age, gender, comorbidities, vascular access, albumin, C-reactive protein, and dialysis dose, was found to increase at about 55 l/week of CV and to stay increased up to about 75 l/week. Similar analysis of pre-dialysis ß2-microglobin (marker of middle-molecule uremic toxins) concentrations found a nearly linear decrease in marker concentration as CV increased from 40 to 75 l/week. Analysis of log C-reactive protein levels showed a decrease over the same CV range. Thus, a convection dose target based on convection volume should be considered and needs to be confirmed by prospective trials as a new determinant of dialysis adequacy.

A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis.

Chronic Kidney Disease (CKD) anemia is one of the main common comorbidities in patients undergoing End Stage Renal Disease (ESRD). Iron supplement and especially Erythropoiesis Stimulating Agents (ESA) have become the treatment of choice for that anemia. However, it is very complicated to find an adequate treatment for every patient in each particular situation since dosage guidelines are based on average behaviors, and thus, they do not take into account the particular response to those drugs by different patients, although that response may vary enormously from one patient to another and even for the same patient in different stages of the anemia. This work proposes an advance with respect to previous works that have faced this problem using different methodologies (Machine Learning (ML), among others), since the diversity of the CKD population has been explicitly taken into account in order to produce a general and reliable model for the prediction of ESA/Iron therapy response. Furthermore, the ML model makes use of both human physiology and drug pharmacology to produce a model that outperforms previous approaches, yielding Mean Absolute Errors (MAE) of the Hemoglobin (Hb) prediction around or lower than 0.6 g/dl in the three countries analyzed in the study, namely, Spain, Italy and Portugal.

Body composition and survival in dialysis patients: results from an international cohort study.

BACKGROUND AND OBJECTIVES: High body mass index appears protective in hemodialysis patients, but uncertainty prevails regarding which components of body composition, fat or lean body mass, are primarily associated with survival. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data between April 2006 and December 2012 were extracted from the Fresenius Medical Care Europe subset of the international MONitoring Dialysis Outcomes initiative. Fresenius Medical Care Europe archives a unique repository of predialysis body composition measurements determined by multifrequency bioimpedance (BCM Body Composition Monitor). The BCM Body Composition Monitor reports lean tissue indices (LTIs) and fat tissue indices (FTIs), which are the respective tissue masses normalized to height squared, relative to an age- and sex-matched healthy population. The relationship between LTI and FTI and all-cause mortality was studied by Kaplan-Meier analysis, multivariate Cox regression, and smoothing spline ANOVA logistic regression. RESULTS: In 37,345 hemodialysis patients, median (25th-75th percentile) LTI and FTI were 12.2 (10.3-14.5) and 9.8 (6.6-12.4) kg/m(2), respectively. Median (25th-75th percentile) follow-up time was 266 (132-379) days; 3458 (9.2%) patients died during follow-up. Mortality was lowest with both LTI and FTI in the 10th-90th percentile (reference group) and significantly higher at the lower LTI and FTI extreme (hazard ratio [HR], 3.37; 95% confidence interval [95% CI], 2.94 to 3.87; P<0.001). Survival was best with LTI between 15 and 20 kg/m(2) and FTI between 4 and 15 kg/m(2) (probability of death during follow-up: <5%). When taking the relation between both compartments into account, the interaction was significant (P=0.01). Higher FTI appeared protective in patients with low LTI (HR, 3.37; 95% CI, 2.94 to 3.87; P<0.001 at low LTI-low FTI, decreasing to HR, 1.79; 95% CI, 1.47 to 2.17; P<0.001 at low LTI-high FTI). CONCLUSIONS: This large international study indicates best survival in patients with both LTI and FTI in the 10th-90th percentiles of a healthy population. In analyses of body composition, both lean tissue and fat tissue compartments and also their relationship should be considered.

Polymorphism at the Clock gene predicts phenology of long-distance migration in birds.

Dissecting phenotypic variance in life history traits into its genetic and environmental components is at the focus of evolutionary studies and of pivotal importance to identify the mechanisms and predict the consequences of human-driven environmental change. The timing of recurrent life history events (phenology) is under strong selection, but the study of the genes that control potential environmental canalization in phenological traits is at its infancy. Candidate genes for circadian behaviour entrained by photoperiod have been screened as potential controllers of phenological variation of breeding and moult in birds, with inconsistent results. Despite photoperiodic control of migration is well established, no study has reported on migration phenology in relation to polymorphism at candidate genes in birds. We analysed variation in spring migration dates within four trans-Saharan migratory species (Luscinia megarhynchos; Ficedula hypoleuca; Anthus trivialis; Saxicola rubetra) at a Mediterranean island in relation to Clock and Adcyap1 polymorphism. Individuals with larger number of glutamine residues in the poly-Q region of Clock gene migrated significantly later in one or, respectively, two species depending on sex and whether the within-individual mean length or the length of the longer Clock allele was considered. The results hinted at dominance of the longer Clock allele. No significant evidence for migration date to covary with Adcyap1 polymorphism emerged. This is the first evidence that migration phenology is associated with Clock in birds. This finding is important for evolutionary studies of migration and sheds light on the mechanisms that drive bird phenological changes and population trends in response to climate change.

Improved survival of incident patients with high-volume haemodiafiltration: a propensity-matched cohort study with inverse probability of censoring weighting.

BACKGROUND: Haemodiafiltration (HDF) is the preferred dialysis modality in many countries. The aim of the study was to compare the survival of incident patients on high-volume HDF (HV-HDF) with high-flux haemodialysis (HD) in a large-scale European dialysis population. METHODS: The study population was extracted from 47,979 patients in 369 NephroCare centres throughout 12 countries. Baseline was six months after dialysis initiation; maximum follow-up was 5 years. Patients were either on HV-HDF (defined as with ≥21 litres substitution fluid volume per session) or on HD if on that treatment for ≥75% of the 3 months before baseline. The main predictor was treatment modality. Other parameters included country, age, gender, BMI, haemoglobin, albumin and Charlson comorbidity index. Propensity score matching and Inverse Probability of Censoring Weighting (IPCW) were applied to reduce bias by indication and consider modality crossover, respectively. RESULTS: After propensity score matching, 1,590 incident patients remained. Kaplan-Meier and proportional Cox regression analyses revealed no significant survival advantage of HV-HDF. Results were biased by modality crossover: during the 5-year study period, 7% of HV-HDF patients switched to HD, and 55% of HD patients switched to HV-HDF. IPCW uncovered a statistically significant survival advantage of HV-HDF (OR 0.501; CI 0.366-0.684; p < 0.001). A higher benefit of HV-HDF for some subgroups was revealed, for example, non-diabetics, patients 65-74 years, patients with obesity or high blood pressure. CONCLUSIONS: This large-scale study supports the generalizability of previous RCT findings regarding the survival benefit of HV-HDF. Sub-group analysis showed that some sub-cohorts appear to benefit more from HV-HDF than others.

A simulation model to estimate cost-offsets for a disease-management program for chronic kidney disease.

AIM: The aim of this paper is to develop a simulation model that analyzes cost-offsets of a hypothetical disease management program (DMP) for patients with chronic kidney disease (CKD) in Germany compared to no such program. METHODS: A lifetime Markov model with simulated 65-year-old patients with CKD was developed using published data on costs and health status and simulating the progression to end-stage renal disease (ESRD), cardiovascular disease and death. A statutory health insurance perspective was adopted. RESULTS: This modeling study shows considerable potential for cost-offsets from a DMP for patients with CKD. The potential for cost-offsets increases with relative risk reduction by the DMP and baseline glomerular filtration rate. Results are most sensitive to the cost of dialysis treatment. CONCLUSION: This paper presents a general 'prototype' simulation model for the prevention of ESRD. The model allows for further modification and adaptation in future applications.

The case for treating refractory congestive heart failure with ultrafiltration.

Extracellular fluid retention and congestion is a fundamental manifestation of heart failure (HF) and cardiorenal syndrome (CRS). Patients are normally hospitalized and treated with diuretics, but their outcomes are often poor as severe congestion and diuretics resistance is the primary cause of HF-related hospital admissions and readmissions. Isolated ultrafiltration (UF), which can be considered as a 'mechanical diuretic and natriuretic' tool, offers promise in achieving safe and effective fluid volume removal in HF patients with CRS who are resistant to stepwise guided diuretic therapy. This paper outlines the rationale for machine-driven isolated UF in CRS and the available clinical evidence regarding its use in patients with HF. In addition, this article summarizes some future clinical perspectives for expanding the use of UF therapy in HF patients in order to improve outcomes.

Optimization of anemia treatment in hemodialysis patients via reinforcement learning.

OBJECTIVE: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. METHODS: RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDPs). Computing optimal drug administration strategies for chronic diseases is a sequential decision-making problem in which the goal is to find the best sequence of drug doses. MDPs are particularly suitable for modeling these problems due to their ability to capture the uncertainty associated with the outcome of the treatment and the stochastic nature of the underlying process. The RL algorithm employed in the proposed methodology is fitted Q iteration, which stands out for its ability to make an efficient use of data. RESULTS: The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level. The performance of the proposed method is evaluated and compared with the well-known Q-learning algorithm and with a standard protocol. Simulation results show that the performance of Q-learning is substantially lower than FQI and the protocol. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. CONCLUSION: Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols.

Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques.

Patients who suffer from chronic renal failure (CRF) tend to suffer from an associated anemia as well. Therefore, it is essential to know the hemoglobin (Hb) levels in these patients. The aim of this paper is to predict the hemoglobin (Hb) value using a database of European hemodialysis patients provided by Fresenius Medical Care (FMC) for improving the treatment of this kind of patients. For the prediction of Hb, both analytical measurements and medication dosage of patients suffering from chronic renal failure (CRF) are used. Two kinds of models were trained, global and local models. In the case of local models, clustering techniques based on hierarchical approaches and the adaptive resonance theory (ART) were used as a first step, and then, a different predictor was used for each obtained cluster. Different global models have been applied to the dataset such as Linear Models, Artificial Neural Networks (ANNs), Support Vector Machines (SVM) and Regression Trees among others. Also a relevance analysis has been carried out for each predictor model, thus finding those features that are most relevant for the given prediction.

Effects of fluid overload on heart rate variability in chronic kidney disease patients on hemodialysis.

BACKGROUND: While fluid overload (FO) and alterations in the autonomic nervous system (ANS) such as hypersympathetic activity, are known risk factors for cardiovascular morbidity and mortality in patients on chronic hemodialysis (HD), their relationship has not been thoroughly studied. METHODS: In this observational study involving 69 patients on chronic HD, FO was assessed by whole body bioimpedance measurements before the midweek HD session and ANS activity reflected by Heart Rate Variability (HRV) was measured using 24-hour Holter electrocardiogram recordings starting before the same HD treatment. In total, 13 different HRV indices were analyzed, comprising a mixture of time domain, frequency domain and complexity parameters. A correlation analysis was performed between the HRV indices and hydration status indices. Successively, patients were retrospectively assigned to a high FO (H, FO > 2.5 L) or low FO (L, FO ≤ 2.5 L) group and these were further compared also after stratification by diabetes mellitus. Finally, a small number of patients without diabetes with significant and persistent FO were followed up for 3 months post-study to investigate how normalization of fluid status affects HRV. RESULTS: SDANN, VLF, LZC and HF% parameters significantly correlate with FO (correlation coefficients were respectively r = -0.40, r = -0.37, r = -0.28 and r = 0.26, p-value < 0.05). Furthermore, LF% and LF/HF were inversely correlated with hydration status (correlation coefficients were respectively r = -0.31 and r = -0.33, p-value < 0.05). These results indicate an association between FO and reduced HRV, higher parasympathetic activation and reduced sympathetic response to the HD session. Indeed, group H tended to have lower values of SDANN, VLF and LZC, and higher values of HF% than patients in the L group. Finally, there was a trend towards lower LF% measured during the last 30 minutes of HD for the H group versus the L group. Reduction in FO achieved over 3 months by implementation of a strict fluid management plan resulted in an increase of HRV. CONCLUSIONS: Our results suggest that depressed HRV is associated with fluid overload and that normalization of hydration status is accompanied by improved HRV.

Computational intelligence for the Balanced Scorecard: studying performance trends of hemodialysis clinics.

OBJECTIVES: The Balanced Scorecard (BSC) is a general, widely employed instrument for enterprise performance monitoring based on the periodic assessment of strategic Key Performance Indicators that are scored against preset targets. The BSC is currently employed as an effective management support tool within Fresenius Medical Care (FME) and is routinely analyzed via standard statistical methods. More recently, the application of computational intelligence techniques (namely, self-organizing maps) to BSC data has been proposed as a way to enhance the quantity and quality of information that can be extracted from it. In this work, additional methods are presented to analyze the evolution of clinic performance over time. METHODS: Performance evolution is studied at the single-clinic level by computing two complementary indexes that measure the proportion of time spent within performance clusters and improving/worsening trends. Self-organizing maps are used in conjunction with these indexes to identify the specific drivers of the observed performance. The performance evolution for groups of clinics is modeled under a probabilistic framework by resorting to Markov chain properties. These allow a study of the probability of transitioning between performance clusters as time progresses for the identification of the performance level that is expected to become dominant over time. RESULTS: We show the potential of the proposed methods through illustrative results derived from the analysis of BSC data of 109 FME clinics in three countries. We were able to identify the performance drivers for specific groups of clinics and to distinguish between countries whose performances are likely to improve from those where a decline in performance might be expected. According to the stationary distribution of the Markov chain, the expected trend is best in Turkey (where the highest performance cluster has the highest probability, P=0.46), followed by Portugal (where the second best performance cluster dominates, with P=0.50), and finally Italy (where the second best performance cluster has P=0.34). CONCLUSION: These results highlight the ability of the proposed methods to extract insights about performance trends that cannot be easily extrapolated using standard analyses and that are valuable in directing management strategies within a continuous quality improvement policy.

Clock gene variation is associated with breeding phenology and maybe under directional selection in the migratory barn swallow.

BACKGROUND: In diverse taxa, photoperiodic responses that cause seasonal physiological and behavioural shifts are controlled by genes, including the vertebrate Clock orthologues, that encode for circadian oscillator mechanisms. While the genetic network behind circadian rhythms is well described, relatively few reports exist of the phenological consequences of and selection on Clock genes in the wild. Here, we investigated variation in breeding phenology in relation to Clock genetic diversity in a long-distance migratory bird, the barn swallow (Hirundo rustica). METHODOLOGY/PRINCIPAL FINDINGS: In a sample of 922 adult barn swallows from a single population breeding in Italy we found one very common (Q(7)) and three rare (Q(5), Q(6), Q(8)) length variants of a functionally significant polyglutamine repeat. Rare (2.9%) Q(7)/Q(8) heterozygous females, but not males, bred significantly later than common (91.5%) Q(7)/Q(7) females, consistent with the expectation that 'long' alleles cause late breeding, as observed in a resident population of another bird species. Because breeding date depends on arrival date from migration, present results suggest that the association between breeding date and Clock might be mediated by migration phenology. In addition, fecundity selection appears to be operating against Q(7)/Q(8) because late migrating/breeding swallows have fewer clutches per season, and late breeding has additional negative selection effects via reduced offspring longevity. Genotype frequencies varied marginally non-significantly with age, as Q(7)/Q(8) frequency showed a 4-fold reduction in old individuals. This result suggests negative viability selection against Q(7)/Q(8), possibly mediated by costs of late breeding. CONCLUSIONS/SIGNIFICANCE: This is the first study of migratory birds showing an association between breeding phenology and Clock genotype and suggesting that negative selection occurs on a phenologically deviant genotype. Low polymorphism at Clock may constrain microevolutionary phenological response to changing climate, and may thus contribute to the decline of barn swallow populations.

Use of Self-Organizing Maps for Balanced Scorecard analysis to monitor the performance of dialysis clinic chains.

The Balanced Scorecard (BSC) is a validated tool to monitor enterprise performances against specific objectives. Through the choice and the evaluation of strategic Key Performance Indicators (KPIs), it provides a measure of the past company's outcome and allows planning future managerial strategies. The Fresenius Medical Care (FME) BSC makes use of 30 KPIs for a continuous quality improvement strategy within its dialysis clinics. Each KPI is monthly associated to a score that summarizes the clinic efficiency for that month. Standard statistical methods are currently used to analyze the BSC data and to give a comprehensive view of the corporate improvements to the top management. We herein propose the Self-Organizing Maps (SOMs) as an innovative approach to extrapolate information from the FME BSC data and to present it in an easy-readable informative form. A SOM is a computational technique that allows projecting high-dimensional datasets to a two-dimensional space (map), thus providing a compressed representation. The SOM unsupervised (self-organizing) training procedure results in a map that preserves similarity relations existing in the original dataset; in this way, the information contained in the high-dimensional space can be more easily visualized and understood. The present work demonstrates the effectiveness of the SOM approach in extracting useful information from the 30-dimensional BSC dataset: indeed, SOMs enabled both to highlight expected relationships between the KPIs and to uncover results not predictable with traditional analyses. Hence we suggest SOMs as a reliable complementary approach to the standard methods for BSC interpretation.