Dynamics of adventitious rooting in mini-cuttings of Eucalyptus benthamii x Eucalyptus dunnii

It is possible to determine the optimum time for permanence of vegetative propagules (mini-cuttings) inside a greenhouse for rooting, and this value can be used to optimize the structure of the nursery. The aim of this study was to determine the dynamics of adventitious rooting in mini-cuttings of three clones of Eucalyptus benthamii x Eucalyptus dunnii. Sprouts of H12, H19 and H20 clones were collected from mini-stumps that were planted in gutters containing sand and grown in a semi-hydroponic system. The basal region of the mini-cuttings was immersed in 2,000 mg L-1 indole-3-butyric acid (IBA) solution for 10 seconds. The rooting percentage of the mini-cuttings, the total length of the root system and the rooting rate per mini-cutting were also evaluated at 0 (time of planting), 7, 14, 21, 28, 35, 42, 49 and 56 days. We used logistic and exponential regression to mathematically model the speed of rhizogenesis. The rooting percentage was best represented as a logistic model, and the total length of the root system was best represented as an exponential model. The clones had different speeds of adventitious rooting. The optimum time for permanence of the mini-cuttings inside the greenhouse for rooting was between 35 and 42 days, and varied depending on the genetic material.

Risk factors for hepatitis C virus infection in Inland Brazil: An analysis of pooled epidemiological sectional studies

In order to assess the contribution of different parenteral routes as risk exposure to the hepatitis C virus (HCV), samples from nine surveys or cross-sectional studies conducted in two Brazilian inland regions were pooled, including a total of 3,910 subjects. Heterogeneity among the study results for different risk factors was tested and the results were shown to be homogeneous. Anti-HCV antibodies were observed in 241 individuals, of which 146 (3.7%, 95% CI?=?3.24.4) had HCV exposure confirmed by immunoblot analysis or PCR test. After adjustment for relevant variables, a correlation between confirmed HCV exposure and injection drug use, tattooing, and advance age was observed. In a second logistic model that included exposures not searched in all nine studies, a smaller sample was analyzed, revealing an independent HCV association with past history of surgery and males who have sex with other males, in addition to repeated injection drug use. Overall, these analyses corroborate the finding that injection drug use is the main risk factor for HCV exposure and spread, in addition to other parenteral routes. J. Med. Virol. 84:756762, 2012. (C) 2012 Wiley Periodicals, Inc.

Spatial clusters of suicide in the municipality of Sao Paulo 1996-2005: an ecological study

Background: In a classical study, Durkheim mapped suicide rates, wealth, and low family density and realized that they clustered in northern France. Assessing others variables, such as religious society, he constructed a framework for the analysis of the suicide, which still allows international comparisons using the same basic methodology. The present study aims to identify possible significantly clusters of suicide in the city of Sao Paulo, and then, verify their statistical associations with socio-economic and cultural characteristics. Methods: A spatial scan statistical test was performed to analyze the geographical pattern of suicide deaths of residents in the city of Sao Paulo by Administrative District, from 1996 to 2005. Relative risks and high and/or low clusters were calculated accounting for gender and age as co-variates, were analyzed using spatial scan statistics to identify geographical patterns. Logistic regression was used to estimate associations with socioeconomic variables, considering, the spatial cluster of high suicide rates as the response variable. Drawing from Durkheim's original work, current World Health Organization (WHO) reports and recent reviews, the following independent variables were considered: marital status, income, education, religion, and migration. Results: The mean suicide rate was 4.1/100,000 inhabitant-years. Against this baseline, two clusters were identified: the first, of increased risk (RR = 1.66), comprising 18 districts in the central region; the second, of decreased risk (RR = 0.78), including 14 districts in the southern region. The downtown area toward the southwestern region of the city displayed the highest risk for suicide, and though the overall risk may be considered low, the rate climbs up to an intermediate level in this region. One logistic regression analysis contrasted the risk cluster (18 districts) against the other remaining 78 districts, testing the effects of socioeconomic-cultural variables. The following categories of proportion of persons within the clusters were identified as risk factors: singles (OR = 2.36), migrants (OR = 1.50), Catholics (OR = 1.37) and higher income (OR = 1.06). In a second logistic model, likewise conceived, the following categories of proportion of persons were identified as protective factors: married (OR = 0.49) and Evangelical (OR = 0.60). Conclusions: This risk/ protection profile is in accordance with the interpretation that, as a social phenomenon, suicide is related to social isolation. Thus, the classical framework put forward by Durkheim seems to still hold, even though its categorical expression requires re-interpretation.

Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

Noxious stimulation response index: a novel anesthetic state index based on hypnotic-opioid interaction

The noxious stimulation response index (NSRI) is a novel anesthetic depth index ranging between 100 and 0, computed from hypnotic and opioid effect-site concentrations using a hierarchical interaction model. The authors validated the NSRI on previously published data.

Variation in partner notification outcomes for chlamydia in UK genitourinary medicine clinics: multilevel study

Objectives To compare different ways of measuring partner notification (PN) outcomes with published audit standards, examine variability between clinics and examine factors contributing to variation in PN outcomes in genitourinary medicine (GUM) clinics in the UK. Methods Reanalysis of the 2007 BASHH national chlamydia audit. The primary outcome was the number of partners per index case tested for chlamydia, as verified by a healthcare worker or, if missing, reported by the patient. Control charts were used to examine variation between clinics considering missing values as zero or excluding missing values. Hierarchical logistic regression was used to investigate factors contributing to variation in outcomes. Results Data from 4616 individuals in 169 genitourinary medicine clinics were analysed. There was no information about the primary outcome in 41% of records. The mean number of partners tested for chlamydia ranged from 0 to 1.5 per index case per clinic. The median across all clinics was 0.47 when missing values were assumed to be zero and 0.92 per index case when missing values were excluded. Men who have sex with men were less likely than heterosexual men and patients with symptoms (4-week look-back period) were less likely than asymptomatic patients (6-month look-back) to report having one or more partners tested for chlamydia. There was no association between the primary outcome and the type of the health professional giving the PN advice. Conclusions The completeness of PN outcomes recorded in clinical notes needs to improve. Further research is needed to identify auditable measures that are associated with successful PN that prevents repeated chlamydia in index cases.

Comparison of the effect of a CIDR-Select synch versus a long-term CIDR based AI protocol on reproductive performance in multiparous dairy cows in Swiss dairy farms

Background Synchronization programs have become standard in the dairy industry in many countries. In Switzerland, these programs are not routinely used for groups of cows, but predominantly as a therapy for individual problem cows. The objective of this study was to compare the effect of a CIDR-Select Synch and a 12-d CIDR protocol on the pregnancy rate in healthy, multiparous dairy cows in Swiss dairy farms. Methods Cows (N = 508) were randomly assigned to CIDR-Select Synch (N = 262) or 12-d CIDR (N = 246) protocols. Cows in the CIDR-Select Synch group received a CIDR and 2.5 ml of buserelin i.m. on d 0. On d 7, the CIDR insert was removed and 5 ml of dinoprost was administered i.m.. Cows in the 12-d CIDR group received the CIDR on d 0 and it was removed on d 12 (the routine CIDR protocol in Swiss dairies). On d 0 a milk sample for progesterone analysis was taken. Cows were inseminated upon observed estrus. Pregnancy was determined at or more than 35 days after artificial insemination. As a first step, the two groups were compared as to indication for treatment, breed, stud book, stall, pasture, and farmer's business using chi square tests or Fisher's exact test. Furthermore, groups were compared as to age, DIM, number of AI's, number of cows per farm, and yearly milk yield per cow using nonparametric ANOVA. A multiple logistic model was used to relate the success of the protocols to all of the available factors; in particular treatment (CIDR-Select Synch/12-d CIDR), milk progesterone value, age, DIM, previous treatment of the uterus, previous gynecological treatment, and number of preceding inseminations. Results The pregnancy rate was higher in cows following the CIDR-Select Synch compared to the 12-d CIDR protocol (50.4% vs. 22.4%; P < 0.0001). Conclusion The CIDR-Select Synch protocol may be highly recommended for multiparous dairy cows. The reduced time span of the progesterone insert decreased the number of days open, improved the pregnancy rate compared to the 12-d CIDR protocol and the cows did not to have to be handled more often.

Interaction Between Nitrous Oxide, Sevoflurane, and Opioids: A Response Surface Approach

BACKGROUND:: The interaction of sevoflurane and opioids can be described by response surface modeling using the hierarchical model. We expanded this for combined administration of sevoflurane, opioids, and 66 vol.% nitrous oxide (N2O), using historical data on the motor and hemodynamic responsiveness to incision, the minimal alveolar concentration, and minimal alveolar concentration to block autonomic reflexes to nociceptive stimuli, respectively. METHODS:: Four potential actions of 66 vol.% N2O were postulated: (1) N2O is equivalent to A ng/ml of fentanyl (additive); (2) N2O reduces C50 of fentanyl by factor B; (3) N2O is equivalent to X vol.% of sevoflurane (additive); (4) N2O reduces C50 of sevoflurane by factor Y. These four actions, and all combinations, were fitted on the data using NONMEM (version VI, Icon Development Solutions, Ellicott City, MD), assuming identical interaction parameters (A, B, X, Y) for movement and sympathetic responses. RESULTS:: Sixty-six volume percentage nitrous oxide evokes an additive effect corresponding to 0.27 ng/ml fentanyl (A) with an additive effect corresponding to 0.54 vol.% sevoflurane (X). Parameters B and Y did not improve the fit. CONCLUSION:: The effect of nitrous oxide can be incorporated into the hierarchical interaction model with a simple extension. The model can be used to predict the probability of movement and sympathetic responses during sevoflurane anesthesia taking into account interactions with opioids and 66 vol.% N2O.

Evolutionary forces shaping genomic islands of population differentiation in humans

Background Levels of differentiation among populations depend both on demographic and selective factors: genetic drift and local adaptation increase population differentiation, which is eroded by gene flow and balancing selection. We describe here the genomic distribution and the properties of genomic regions with unusually high and low levels of population differentiation in humans to assess the influence of selective and neutral processes on human genetic structure. Methods Individual SNPs of the Human Genome Diversity Panel (HGDP) showing significantly high or low levels of population differentiation were detected under a hierarchical-island model (HIM). A Hidden Markov Model allowed us to detect genomic regions or islands of high or low population differentiation. Results Under the HIM, only 1.5% of all SNPs are significant at the 1% level, but their genomic spatial distribution is significantly non-random. We find evidence that local adaptation shaped high-differentiation islands, as they are enriched for non-synonymous SNPs and overlap with previously identified candidate regions for positive selection. Moreover there is a negative relationship between the size of islands and recombination rate, which is stronger for islands overlapping with genes. Gene ontology analysis supports the role of diet as a major selective pressure in those highly differentiated islands. Low-differentiation islands are also enriched for non-synonymous SNPs, and contain an overly high proportion of genes belonging to the 'Oncogenesis' biological process. Conclusions Even though selection seems to be acting in shaping islands of high population differentiation, neutral demographic processes might have promoted the appearance of some genomic islands since i) as much as 20% of islands are in non-genic regions ii) these non-genic islands are on average two times shorter than genic islands, suggesting a more rapid erosion by recombination, and iii) most loci are strongly differentiated between Africans and non-Africans, a result consistent with known human demographic history.

FAST ADAPTIVE PENALIZED SPLINES

This paper proposes a numerically simple routine for locally adaptive smoothing. The locally heterogeneous regression function is modelled as a penalized spline with a smoothly varying smoothing parameter modelled as another penalized spline. This is being formulated as hierarchical mixed model, with spline coe±cients following a normal distribution, which by itself has a smooth structure over the variances. The modelling exercise is in line with Baladandayuthapani, Mallick & Carroll (2005) or Crainiceanu, Ruppert & Carroll (2006). But in contrast to these papers Laplace's method is used for estimation based on the marginal likelihood. This is numerically simple and fast and provides satisfactory results quickly. We also extend the idea to spatial smoothing and smoothing in the presence of non normal response.

Beyond the Margins: Analyzing Social Exclusion with a Homeless Client Dataset

A great share of literature on social exclusion has been based mainly on the analysis of official survey data. Whereas these efforts have provided insights into the characteristics and conditions of those people living at the margins of mainstream social relations, they have however failed to encompass those who live beyond these very margins. Meanwhile, research on these hidden subpopulations, such as homeless and other vulnerable groups, remains generally less abundant and is significantly detached from the theoretical core of the debate on social exclusion. The concern about these shortcomings lies at the heart of our research. We seek to bring some light to the area by using data made available by an organization that provides services to people experiencing homelessness in Barcelona (Spain). The data sample contains clients in early stages of exclusion and others in chronic situations. Thus, we attempt to identify some of the variables that operate in preventing the "chronification" of those individuals in situation of social exclusion. Our findings suggest that certain variables such as educational level, income and housing type, which are considered to be central predictors in the analysis of poverty, behave differently when analyzing differences between stages of social exclusion. Although these results cannot be extrapolated to the whole Spanish or European reality, they could provide useful insight for future investigations on this topic.

Circumcision and HIV infection among men who have sex with men in Britain: the insertive sexual role

The objective was to examine the association between circumcision status and self-reported HIV infection among men who have sex with men (MSM) in Britain who predominantly or exclusively engaged in insertive anal intercourse. In 2007-2008, a convenience sample of MSM living in Britain was recruited through websites, in sexual health clinics, bars, clubs, and other venues. Men completed an online survey which included questions on circumcision status, HIV testing, HIV status, sexual risk behavior, and sexual role for anal sex. The analysis was restricted to 1,521 white British MSM who reported unprotected anal intercourse in the previous 3 months and who said they only or mostly took the insertive role during anal sex. Of these men, 254 (16.7 %) were circumcised. Among men who had had a previous HIV test (n = 1,097), self-reported HIV seropositivity was 8.6 % for circumcised men (17/197) and 8.9 % for uncircumcised men (80/900) (unadjusted odds ratio [OR], 0.97; 95 % confidence interval [95 % CI], 0.56, 1.67). In a multivariable logistic model adjusted for known risk factors for HIV infection, there was no evidence of an association between HIV seropositivity and circumcision status (adjusted OR, 0.79; 95 % CI, 0.43, 1.44), even among the 400 MSM who engaged exclusively in insertive anal sex (adjusted OR, 0.84; 95 % CI, 0.25, 2.81). Our study provides further evidence that circumcision is unlikely to be an effective strategy for HIV prevention among MSM in Britain.

Expanding the cone location and magnitude index to include corneal thickness and posterior surface information for the detection of keratoconus

PURPOSE To extend the capabilities of the Cone Location and Magnitude Index algorithm to include a combination of topographic information from the anterior and posterior corneal surfaces and corneal thickness measurements to further improve our ability to correctly identify keratoconus using this new index: ConeLocationMagnitudeIndex_X. DESIGN Retrospective case-control study. METHODS Three independent data sets were analyzed: 1 development and 2 validation. The AnteriorCornealPower index was calculated to stratify the keratoconus data from mild to severe. The ConeLocationMagnitudeIndex algorithm was applied to all tomography data collected using a dual Scheimpflug-Placido-based tomographer. The ConeLocationMagnitudeIndex_X formula, resulting from analysis of the Development set, was used to determine the logistic regression model that best separates keratoconus from normal and was applied to all data sets to calculate PercentProbabilityKeratoconus_X. The sensitivity/specificity of PercentProbabilityKeratoconus_X was compared with the original PercentProbabilityKeratoconus, which only uses anterior axial data. RESULTS The AnteriorCornealPower severity distribution for the combined data sets are 136 mild, 12 moderate, and 7 severe. The logistic regression model generated for ConeLocationMagnitudeIndex_X produces complete separation for the Development set. Validation Set 1 has 1 false-negative and Validation Set 2 has 1 false-positive. The overall sensitivity/specificity results for the logistic model produced using the ConeLocationMagnitudeIndex_X algorithm are 99.4% and 99.6%, respectively. The overall sensitivity/specificity results for using the original ConeLocationMagnitudeIndex algorithm are 89.2% and 98.8%, respectively. CONCLUSIONS ConeLocationMagnitudeIndex_X provides a robust index that can detect the presence or absence of a keratoconic pattern in corneal tomography maps with improved sensitivity/specificity from the original anterior surface-only ConeLocationMagnitudeIndex algorithm.

Short-term functional outcome and premorbid adjustment in clinical high-risk patients. Results of the EPOS project

PURPOSE In patients with schizophrenia, premorbid psychosocial adjustment is an important predictor of functional outcome. We studied functional outcome in young clinical high-risk (CHR) patients and how this was predicted by their childhood to adolescence premorbid adjustment. METHODS In all, 245 young help-seeking CHR patients were assessed with the Premorbid Adjustment Scale, the Structured Interview for Prodromal Syndromes (SIPS) and the Schizophrenia Proneness Instrument (SPI-A). The SIPS assesses positive, negative, disorganised, general symptoms, and the Global Assessment of Functioning (GAF), the SPI-A self-experienced basic symptoms; they were carried out at baseline, at 9-month and 18-month follow-up. Transitions to psychosis were identified. In the hierarchical linear model, associations between premorbid adjustment, background data, symptoms, transitions to psychosis and GAF scores were analysed. RESULTS During the 18-month follow-up, GAF scores improved significantly, and the proportion of patients with poor functioning decreased from 74% to 37%. Poor premorbid adjustment, single marital status, poor work status, and symptoms were associated with low baseline GAF scores. Low GAF scores were predicted by poor premorbid adjustment, negative, positive and basic symptoms, and poor baseline work status. The association between premorbid adjustment and follow-up GAF scores remained significant, even when baseline GAF and transition to psychosis were included in the model. CONCLUSION A great majority of help-seeking CHR patients suffer from deficits in their functioning. In CHR patients, premorbid psychosocial adjustment, baseline positive, negative, basic symptoms and poor working/schooling situation predict poor short-term functional outcome. These aspects should be taken into account when acute intervention and long-term rehabilitation for improving outcome in CHR patients are carried out.

Thrombus formation in the left ventricle after large myocardial infarction – assessment with cardiac magnetic resonance imaging

INTRODUCTION Left ventricular thrombus (LVT) formation may worsen the post-infarct outcome as a result of thromboembolic events. It also complicates the use of modern antiplatelet regimens, which are not compatible with long-term oral anticoagulation. The knowledge of the incidence of LVT may therefore be of importance to guide antiplatelet and antithrombotic therapy after acute myocardial infarction (AMI). METHODS In 177 patients with large, mainly anterior AMI, standard cardiac magnetic resonance imaging (CMR) including cine and late gadolinium enhancement (LGE) imaging was performed shortly after AMI as per protocol. CMR images were analysed at an independent core laboratory blinded to the clinical data. Transthoracic echocardiography (TTE) was not mandatory for the trial, but was performed in 64% of the cases following standard of care. In a logistic model, 3 out of 61 parameters were used in a multivariable model to predict LVT. RESULTS LVT was detected by use of CMR in 6.2% (95% confidence interval [CI] 3.1%-10.8%). LGE sequences were best to detect LVT, which may be missed in cine sequences. We identified body mass index (odds ratio 1.18; p = 0.01), baseline platelet count (odds ratio 1.01, p = 0.01) and infarct size as assessed by use of CMR (odds ratio 1.03, p = 0.02) as best predictors for LVT. The agreement between TTE and CMR for the detection of LVT is substantial (kappa = 0.70). DISCUSSION In the current analysis, the incidence of LVT shortly after AMI is relatively low, even in a patient population at high risk. An optimal modality for LVT detection is LGE-CMR but TTE has an acceptable accuracy when LGE-CMR is not available.