Nitrogen and Nod factor signaling determine Lotus japonicus root exudate composition and bacterial assembly.

Symbiosis with soil-dwelling bacteria that fix atmospheric nitrogen allows legume plants to grow in nitrogen-depleted soil. Symbiosis impacts the assembly of root microbiota, but it is unknown how the interaction between the legume host and rhizobia impacts the remaining microbiota and whether it depends on nitrogen nutrition. Here, we use plant and bacterial mutants to address the role of Nod factor signaling on Lotus japonicus root microbiota assembly. We find that Nod factors are produced by symbionts to activate Nod factor signaling in the host and that this modulates the root exudate profile and the assembly of a symbiotic root microbiota. Lotus plants with different symbiotic abilities, grown in unfertilized or nitrate-supplemented soils, display three nitrogen-dependent nutritional states: starved, symbiotic, or inorganic. We find that root and rhizosphere microbiomes associated with these states differ in composition and connectivity, demonstrating that symbiosis and inorganic nitrogen impact the legume root microbiota differently. Finally, we demonstrate that selected bacterial genera characterizing state-dependent microbiomes have a high level of accurate prediction.

Predicting Tissue Loads in Running from Inertial Measurement Units.

BACKGROUND: Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries. METHODS: We investigate a combined physics-based simulation and data-based method. A total of 285 running trials from 76 real runners are subjected to physics-based simulation to recover forces in the Achilles tendon and patella ligament, and the collected data are used to train and test a data-based model using elastic net and gradient boosting methods. RESULTS: Correlations of up to 0.95 and 0.71 for the patella ligament and Achilles tendon forces, respectively, are obtained, but no single best predictive algorithm can be identified. CONCLUSIONS: Prediction of tissues loads based on body-mounted IMUs appears promising but requires further investigation before deployment as a general option for users of running watches to reduce running-related injuries.

How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?

Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm.

Second-order quasi-likelihood for spatial point processes.

Applications of spatial point processes for large and complex data sets with inhomogeneities as encountered, example, in tropical rain forest ecology call for estimation methods that are both statistically and computationally efficient. We propose a novel second-order quasi-likelihood procedure to estimate the parameters for a second-order intensity reweighted stationary spatial point process. Our approach is to derive first- and second-order estimating functions and then combine them linearly using appropriate weight functions. In the stationary case, we argue that the asymptotically optimal weight functions are respectively a constant and a function of lags between distinct locations in the observation window. This leads to a considerable gain in computational efficiency. We further exploit this simplification in the nonstationary case. Simulations show that, when compared with several existing approaches, our method can achieve significant gains in statistical efficiency. An application to a tropical rain forest data set further illustrates the advantages of our procedure.

TOWARD BAYESIAN INFERENCE OF THE SPATIAL DISTRIBUTION OF PROTEINS FROM THREE-CUBE FÖRSTER RESONANCE ENERGY TRANSFER DATA.

Förster resonance energy transfer (FRET) is a quantum-physical phenomenon where energy may be transferred from one molecule to a neighbor molecule if the molecules are close enough. Using fluorophore molecule marking of proteins in a cell, it is possible to measure in microscopic images to what extent FRET takes place between the fluorophores. This provides indirect information of the spatial distribution of the proteins. Questions of particular interest are whether (and if so to which extent) proteins of possibly different types interact or whether they appear independently of each other. In this paper we propose a new likelihood-based approach to statistical inference for FRET microscopic data. The likelihood function is obtained from a detailed modeling of the FRET data-generating mechanism conditional on a protein configuration. We next follow a Bayesian approach and introduce a spatial point process prior model for the protein configurations depending on hyperparameters quantifying the intensity of the point process. Posterior distributions are evaluated using Markov chain Monte Carlo. We propose to infer microscope-related parameters in an initial step from reference data without interaction between the proteins. The new methodology is applied to simulated and real datasets.

Analysis of shape and spatial interaction of synaptic vesicles using data from focused ion beam scanning electron microscopy (FIB-SEM).

The spatial interactions of synaptic vesicles in synapses were assessed after a detailed characterization of size, shape, and orientation of the synaptic vesicles. We hypothesized that shape and orientation of the synaptic vesicles are influenced by their movement toward the active zone causing deviations from spherical shape and systematic trends in their orientation. We studied three-dimensional representations of synapses obtained by manual annotation of focused ion beam scanning electron microscopy (FIB-SEM) images of male mouse brain. The configurations of synaptic vesicles were regarded as marked point patterns, where the points are the centers of the vesicles, and the mark of a vesicle is given by its size, shape, and orientation characteristics. Statistics for marked point processes were employed to study spatial interactions between vesicles. We found that the synaptic vesicles in excitatory synapses appeared to be of oblate ellipsoid shape and in inhibitory synapses appeared to be of cigar ellipsoid shape, and followed a systematic pattern regarding their orientation toward the active zone. Moreover, there was strong evidence of spatial alignment in the orientations of pairs of synaptic vesicles, and of repulsion between them only in excitatory synapses, beyond that caused by their physical extent.

Multivariate product-shot-noise Cox point process models.

We introduce a new multivariate product-shot-noise Cox process which is useful for modeling multi-species spatial point patterns with clustering intra-specific interactions and neutral, negative, or positive inter-specific interactions. The auto- and cross-pair correlation functions of the process can be obtained in closed analytical forms and approximate simulation of the process is straightforward. We use the proposed process to model interactions within and among five tree species in the Barro Colorado Island plot.

Quasi-likelihood for Spatial Point Processes.

Fitting regression models for intensity functions of spatial point processes is of great interest in ecological and epidemiological studies of association between spatially referenced events and geographical or environmental covariates. When Cox or cluster process models are used to accommodate clustering not accounted for by the available covariates, likelihood based inference becomes computationally cumbersome due to the complicated nature of the likelihood function and the associated score function. It is therefore of interest to consider alternative more easily computable estimating functions. We derive the optimal estimating function in a class of first-order estimating functions. The optimal estimating function depends on the solution of a certain Fredholm integral equation which in practise is solved numerically. The derivation of the optimal estimating function has close similarities to the derivation of quasi-likelihood for standard data sets. The approximate solution is further equivalent to a quasi-likelihood score for binary spatial data. We therefore use the term quasi-likelihood for our optimal estimating function approach. We demonstrate in a simulation study and a data example that our quasi-likelihood method for spatial point processes is both statistically and computationally efficient.

Misclassification of iodine intake level from morning spot urine samples with high iodine excretion among Inuit and non-Inuit in Greenland.

Iodine nutrition is commonly assessed from iodine excretion in urine. A 24 h urine sample is ideal, but it is cumbersome and inconvenient. Hence, spot urine samples with creatinine to adjust for differences in void volume are widely used. Still, the importance of ethnicity and the timing of spot urine samples need to be settled. We, thus, collected 104 early morning spot urine samples and 24 h urine samples from Inuit and non-Inuit living in Greenland. Diet was assessed by a FFQ. Demographic data were collected from the national registry and by questionnaires. Iodine was measured using the Sandell-Kolthoff reaction, creatinine using the Jaffe method and para-amino benzoic acid by the HPLC method for the estimation of completeness of urine sampling and compensation of incomplete urine samples to 24 h excretion. A population-based recruitment was done from the capital city, a major town and a settlement (n 36/48/20). Participants were seventy-eight Inuit and twenty-six non-Inuit. The median 24 h iodine excretion was 138 (25th-75th percentile 89-225) µg/97 (25th-75th percentile 72-124) µg in Inuit/non-Inuit (P= 0.030), and 153 (25th-75th percentile 97-251) µg/102 (25th-75th percentile 73-138) µg (P= 0.026) when including compensated iodine excretion. Iodine excretion in 24 h urine samples increased with a rising intake of traditional Inuit foods (P= 0.005). Iodine excretion was lower in morning spot urine samples than in 24 h urine samples (P< 0.001). This difference was associated with iodine intake levels (P< 0.001), and was statistically significant when the iodine excretion level was above 150 µg/24 h. In conclusion, the iodine intake level was underestimated from morning spot urine samples if iodine excretion was above the recommended level.

Disease risk estimation by combining case-control data with aggregated information on the population at risk.

We propose a novel statistical framework by supplementing case-control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case-control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation. The proposed method is computationally simple and more efficient than standard approaches based on case-control data alone. We also establish asymptotic properties of the resulting estimator, and investigate its finite-sample performance through simulation. As a substantive application, we apply the proposed method to investigate risk factors for endometrial cancer, by using data from a recently completed population-based case-control study and summary statistics from the Behavioral Risk Factor Surveillance System, the Population Estimates Program of the US Census Bureau, and the Connecticut Department of Transportation.

A new estimation approach for combining epidemiological data from multiple sources.

We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. In the first step we derive all possible unbiased estimating functions based on a group of cases and a group of controls each time. In the second step, we combine these estimating functions efficiently in order to make full use of the information contained in data. Our approach is computationally simple and flexible. We illustrate its efficacy through simulation and apply it to investigate pancreatic cancer risks based on data obtained from the Connecticut Tumor Registry, a population-based case-control study, and the Behavioral Risk Factor Surveillance System which is a state-based system of health surveys.

Reproducible probe-level analysis of the Affymetrix Exon 1.0 ST array with R/Bioconductor.

The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge owing to the vast amounts of data and the large variety of preprocessing and filtering steps used before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results, it is crucial to ensure that all analyses are completely reproducible for other researchers. We here give a detailed workflow on how to perform reproducible analysis of the GeneChip®Human Exon 1.0 ST Array at probe and probeset level solely in R/Bioconductor, choosing packages based on their simplicity of use. To exemplify the use of the proposed workflow, we analyse differential splicing and differential gene expression in a publicly available dataset using various statistical methods. We believe this study will provide other researchers with an easy way of accessing gene expression data at different annotation levels and with the sufficient details needed for developing their own tools for reproducible analysis of the GeneChip®Human Exon 1.0 ST Array.

Interrater reliability of diagnostic methods in traditional Indian ayurvedic medicine.

This study assesses the interrater reliability of Ayurvedic pulse (nadi), tongue (jivha), and body constitution (prakriti) assessments. Fifteen registered Ayurvedic doctors with 3-15 years of experience independently examined twenty healthy subjects. Subjects completed self-assessment questionnaires and software analyses for prakriti assessment. Weighted kappa statistics for all 105 pairs of doctors were computed for the pulse, tongue, and prakriti data sets. According to the Landis-Koch scale, the pairwise kappas ranged from poor to slight, slight to fair, and fair to moderate for pulse, tongue, and prakriti assessments, respectively. The average pairwise kappa for pulse, tongue, and prakriti was 0.07, 0.17, and 0.28, respectively. For each data set and pair of doctors, the null hypothesis of random rating was rejected for just twelve pairs of doctors for prakriti, one pair of doctors for pulse examination, and no pairs of doctors for tongue assessment. Thus, the results demonstrate a low level of reliability for all types of assessment made by doctors. There was significant evidence against random rating by software and questionnaire use and by the diagnosis preferred by the majority of doctors. Prakriti assessment appears reliable when questionnaire and software assessment are used, while other diagnostic methods have room for improvement.

Reliability studies of diagnostic methods in Indian traditional Ayurveda medicine: An overview.

Recently, a need to develop supportive new scientific evidence for contemporary Ayurveda has emerged. One of the research objectives is an assessment of the reliability of diagnoses and treatment. Reliability is a quantitative measure of consistency. It is a crucial issue in classification (such as prakriti classification), method development (pulse diagnosis), quality assurance for diagnosis and treatment and in the conduct of clinical studies. Several reliability studies are conducted in western medicine. The investigation of the reliability of traditional Chinese, Japanese and Sasang medicine diagnoses is in the formative stage. However, reliability studies in Ayurveda are in the preliminary stage. In this paper, examples are provided to illustrate relevant concepts of reliability studies of diagnostic methods and their implication in practice, education, and training. An introduction to reliability estimates and different study designs and statistical analysis is given for future studies in Ayurveda.

Decomposition of Variance for Spatial Cox Processes.

Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive or log linear random intensity functions. We moreover consider a new and flexible class of pair correlation function models given in terms of normal variance mixture covariance functions. The proposed methodology is applied to point pattern data sets of locations of tropical rain forest trees.

Quantifying effects of habitat heterogeneity and other clustering processes on spatial distributions of tree species.

Spatially explicit consideration of species distribution can significantly add to our understanding of species coexistence. In this paper, we evaluated the relative importance of habitat heterogeneity and other clustering processes (e.g., dispersal limitation, collectively called the non-habitat clustering process) in explaining the spatial distribution patterns of 341 tree species in three stem-mapped 25-50 ha plots of tropical, subtropical, and temperate forests. Their relative importance was estimated by a method that can take one mechanism into account when estimating the effects of the other mechanism and vice versa. Our results demonstrated that habitat heterogeneity was less important in explaining the observed species patterns than other clustering processes in plots with flat topography but was more important in one of the three plots that had a complex topography. Meanwhile, both types of clustering mechanisms (habitat or non-habitat) were pervasive among species at the 50-ha scale across the studied plots. Our analyses also revealed considerable variation among species in the relative importance of the two types of mechanism within each plot and showed that this species-level variation can be partially explained by differences in dispersal mode and growth form of species in a highly heterogeneous environment. Our findings provide new perspectives on the formation of species clustering. One important finding is that a significant species-habitat association does not necessarily mean that the habitat heterogeneity has a decisive influence on species distribution. The second insight is that the large species-level variation in the relative importance of the two types of clustering mechanisms should not be ignored. Non-habitat clustering processes can play an important role on species distribution.

Intrarater and interrater reliability of pulse examination in traditional Indian Ayurvedic medicine.

BACKGROUND: In Ayurveda, pulse examination (nadipariksha) is an important tool to assess the status of three doshas: vata, pitta, and kapha. Long historical use has been seen as a documentation of its efficacy; however, there is a lack of a quantitative measure of the reliability of the pulse examination method. The objective of this study was to test the intrarater and interrater reliability of pulse examination in Ayurveda. METHODS: Fifteen registered Ayurvedic doctors with 3-15 years of experience examined the pulse of 20 healthy volunteers twice, for a total of 600 examinations. The examinations were performed blind and in a random order. Only the current status of dosha-specific methods of pulse examination were considered. Cohen's weighted κ statistic was used as a measure of intrarater and interrater reliability, and a hypothesis of homogeneous diagnosis (random rating) was tested. Following this, we tested whether proportions of ratings were equal between doctors. RESULTS: According to the Landis and Koch scale, the level of reliability ranged from poor to moderate. It was observed that the doctors more frequently diagnosed a combination of two doshas than a single dosha. The κ values were generally larger for experienced doctors (p = 0.04). CONCLUSION: Experience and proper training have important roles in pulse examination.

Repeatability of Pulse Diagnosis and Body Constitution Diagnosis in Traditional Indian Ayurveda Medicine.

In Ayurveda, pulse diagnosis and body constitution diagnosis have a long historical use; still, there is lack of quantitative measure of the reliability of these diagnostic methods. Reliability means consistency of information. Consistent diagnosis leads to consistent treatment and is important for clinical practice, education, and research. The objective of this study is to study the methodology to evaluate the test-retest reliability (repeatability) of pulse diagnosis and body constitution diagnosis. A double-blinded, controlled, clinical trial was conducted in Copenhagen. The same doctor, an expert in Ayurvedic pulse diagnosis, examined the pulse and body constitution of 17 healthy participants twice, in random order without seeing them. A metric on pulse and body constitution variables was developed. Cohen's weighted kappa statistic was used as a measure of intra-rater reliability. Permutation tests were used to test the hypothesis of homogeneous diagnosis (ie, the doctor's diagnosis does not depend on the subject). The hypothesis of homogeneous classification was rejected on the 5% significance level (P values of .02 and .001, respectively, for pulse and body constitution diagnosis). According to the Landis and Koch scale, values of the weighted kappa for pulse diagnosis (P = .42) and body constitution diagnosis (P = .65) correspond to "moderate" and "substantial" agreement, respectively. There was a reasonable level of consistency between 2 pulse and body constitution diagnoses. Further studies are required to quantify inter-subject and intra-subject agreement for greater understanding of reliability of pulse and body constitution diagnosis.

La medicina ayurvédica lleva mucho tiempo utilizando el diagnóstico mediante el pulso y la constitución corporal; no obstante, se sigue careciendo de medidas cuantitativas de la fiabilidad de estos métodos diagnósticos, la cual implica uniformidad de la información. Un diagnóstico coherente lleva a un tratamiento uniforme y resulta de gran importancia para la práctica clínica, la educación y la investigación.El objetivo de este estudio, un ensayo clínico controlado doble ciego llevado a cabo en Copenhagen, es analizar la metodología para evaluar la fiabilidad (repetibilidad) del diagnóstico mediante el pulso y la constitución corporal. El mismo médico, un experto en el diagnóstico ayurvédico mediante el pulso, examinó en dos ocasiones el pulso y la constitución corporal de 17 participantes sanos, en orden aleatorio y sin verlos. Se desarrolló una escala de medida de las variables de pulso y constitución corporal. Como método de la fiabilidad en el mismo evaluador se usó el valor estadístico kappa ponderada de Cohen y se usaron pruebas de permutación para probar la hipótesis de la homo-geneidad del diagnóstico (es decir, el diagnóstico del médico no depende del paciente).La hipótesis de la homogeneidad de la clasificación se rechazó al nivel de significación del 5 % (P valor de 0,02 y 0,001, respectivamente, para el diagnóstico medi-ante el pulso y la constitución corporal). Conforme a las escalas de Landis y Koch, los valores de kappa ponderada para el diagnóstico mediante el pulso (P = 0,42) y medi-ante la constitución corporal (P = 0,65) se corresponden con una concordancia "moderada" y "sustancial", respectivamente.Se produjo un nivel de uniformidad razonable entre los dos diagnósticos mediante constitución corporal y pulso, aunque se requieren más estudios para cuantificar la concordancia inter e intrapacientes con el objetivo de obtener una mayor comprensión de la fiabilidad.

Selection for environmental variation: a statistical analysis and power calculations to detect response.

Data from uterine capacity in rabbits (litter size) were analyzed to determine whether the environmental variance was partly genetically determined. The fit of a classical homogeneous variance mixed linear (HOM) model and that of a genetically structured heterogeneous variance mixed linear (HET) model were compared. Various methods to assess the quality of fit favor the HET model. The posterior mean (95% posterior interval) of the additive genetic variance affecting the environmental variance was 0.16 (0.10; 0.25) and the corresponding number for the coefficient of correlation between genes affecting mean and variance was -0.74 (-0.90;-0.52). It is argued that stronger support for the HET model than that derived from statistical analysis of data would be provided by a successful selection experiment designed to modify the environmental variance. A simple selection criterion is suggested (average squared deviation from the mean of repeated records within individuals) and its predicted response and variance under the HET model are derived. This is used to determine the appropriate size and length of a selection experiment designed to change the environmental variance. Results from the analytical expressions are compared with those obtained using simulation. There is good agreement provided selection intensity is not intense.

A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics.

In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity. A particular challenge for MCMC applications in quantitative genetics is to obtain efficient updates of the high-dimensional vectors of genetic random effects and the associated covariance parameters. We discuss various strategies to approach this problem including reparameterization, Langevin-Hastings updates, and updates based on normal approximations. The methods are compared in applications to Bayesian inference for three data sets using a model with genetically structured variance heterogeneity.