Sources of Variation in Assessing Canopy Reflectance of Processing Tomato by Means of Multispectral Radiometry.

Canopy reflectance sensors are a viable technology to optimize the fertilization management of crops. In this research, canopy reflectance was measured through a passive sensor to evaluate the effects of either crop features (N fertilization, soil mulching, appearance of red fruits, and cultivars) or sampling methods (sampling size, sensor position, and hour of sampling) on the reliability of vegetation indices (VIs). Sixteen VIs were derived, including seven simple wavelength reflectance ratios (NIR/R460, NIR/R510, NIR/R560, NIR/R610, NIR/R660, NIR/R710, NIR/R760), seven normalized indices (NDVI, G-NDVI, MCARISAVI, OSAVI, TSAVI, TCARI), and two combined indices (TCARI/OSAVI; MCARI/OSAVI). NIR/560 and G-NDVI (Normalized Difference Vegetation Index on Greenness) were the most reliable in discriminating among fertilization rates, with results unaffected by the appearance of maturing fruits, and the most stable in response to different cultivars. Black mulching film did not affect NIR/560 and G-NDVI behavior at the beginning of the growing season, when the crop is more responsive to N management. Due to a moderate variability of NIR/560 and G-NDVI, a small sample size (5-10 observations) is sufficient to obtain reliable measurements. Performing the measurements at 11:00 and 14:00 and maintaining a greater distance (1.8 m) between plants and instrument enhanced measurement consistency. Accordingly, NIR/560 and G-NDVI resulted in the most reliable VIs.

A modification of the PHYLIP program: A solution for the redundant cluster problem, and an implementation of an automatic bootstrapping on trees inferred from original data.

Felsenstein's PHYLIP package of molecular phylogeny tools has been used globally since 1980. The programs are receiving renewed attention because of their character-based user interface, which has the advantage of being scriptable for use with large-scale data studies based on super-computers or massively parallel computing clusters. However, occasionally we found, the PHYLIP Consense program output text file displays two or more divided bootstrap values for the same cluster in its result table, and when this happens the output Newick tree file incorrectly assigns only the last value to that cluster that disturbs correct estimation of a consensus tree. We ascertained the cause of this aberrant behavior in the bootstrapping calculation. Our rewrite of the Consense program source code outputs bootstrap values, without redundancy, in its result table, and a Newick tree file with appropriate, corresponding bootstrap values. Furthermore, we developed an add-on program and shell script, add_bootstrap.pl and fasta2tre_bs.bsh, to generate a Newick tree containing the topology and branch lengths inferred from the original data along with valid bootstrap values, and to actualize the automated inference of a phylogenetic tree containing the originally inferred topology and branch lengths with bootstrap values, from multiple unaligned sequences, respectively. These programs can be downloaded at: https://github.com/ShimadaMK/PHYLIP_enhance/.

Comparison of Dissolution Similarity Assessment Methods for Products with Large Variations: f2 Statistics and Model-Independent Multivariate Confidence Region Procedure for Dissolution Profiles of Multiple Oral Products.

The current Japanese Ministry of Health Labour and Welfare (MHLW)'s Guideline for Bioequivalence Studies of Generic Products uses averaged dissolution rates for the assessment of dissolution similarity between test and reference formulations. This study clarifies how the application of model-independent multivariate confidence region procedure (Method B), described in the European Medical Agency and U.S. Food and Drug Administration guidelines, affects similarity outcomes obtained empirically from dissolution profiles with large variations in individual dissolution rates. Sixty-one datasets of dissolution profiles for immediate release, oral generic, and corresponding innovator products that showed large variation in individual dissolution rates in generic products were assessed on their similarity by using the f2 statistics defined in the MHLW guidelines (MHLW f2 method) and two different Method B procedures, including a bootstrap method applied with f2 statistics (BS method) and a multivariate analysis method using the Mahalanobis distance (MV method). The MHLW f2 and BS methods provided similar dissolution similarities between reference and generic products. Although a small difference in the similarity assessment may be due to the decrease in the lower confidence interval for expected f2 values derived from the large variation in individual dissolution rates, the MV method provided results different from those obtained through MHLW f2 and BS methods. Analysis of actual dissolution data for products with large individual variations would provide valuable information towards an enhanced understanding of these methods and their possible incorporation in the MHLW guidelines.

Gender and Age Analyses of NIRS/STAI Pearson Correlation Coefficients at Resting State.

According to the valence asymmetry hypothesis, the left/right asymmetry of PFC activity is correlated with specific emotional responses to mental stress and personality traits. In a previous study we measured spontaneous oscillation of oxy-Hb concentrations in the bilateral PFC at rest in normal adults employing two-channel portable NIRS and computed the laterality index at rest (LIR). We investigated the Pearson correlation coefficient between the LIR and anxiety levels evaluated by the State-Trait Anxiety Inventory (STAI) test. We found that subjects with right-dominant activity at rest showed higher STAI scores, while those with left dominant oxy-Hb changes at rest showed lower STAI scores such that the Pearson correlation coefficient between LIR and STAI was positive. This study performed Bootstrap analysis on the data and showed the following statistics of the target correlation coefficient: mean=0.4925 and lower confidence limit=0.177 with confidence level 0.05. Using the KS-test, we demonstrated that the correlation did not depend on age, whereas it did depend on gender.

Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds.

This study compared the accuracy of genome-enabled prediction models using individual single nucleotide polymorphisms (SNP) or haplotype blocks as covariates when using either a single breed or a combined population of Nordic Red cattle. The main objective was to compare predictions of breeding values of complex traits using a combined training population with haplotype blocks, with predictions using a single breed as training population and individual SNP as predictors. To compare the prediction reliabilities, bootstrap samples were taken from the test data set. With the bootstrapped samples of prediction reliabilities, we built and graphed confidence ellipses to allow comparisons. Finally, measures of statistical distances were used to calculate the gain in predictive ability. Our analyses are innovative in the context of assessment of predictive models, allowing a better understanding of prediction reliabilities and providing a statistical basis to effectively calibrate whether one prediction scenario is indeed more accurate than another. An ANOVA indicated that use of haplotype blocks produced significant gains mainly when Bayesian mixture models were used but not when Bayesian BLUP was fitted to the data. Furthermore, when haplotype blocks were used to train prediction models in a combined Nordic Red cattle population, we obtained up to a statistically significant 5.5% average gain in prediction accuracy, over predictions using individual SNP and training the model with a single breed.

Prognostic implication of TSC1 and mTOR expression in gastric carcinoma.

BACKGROUND: Gastric adenocarcinoma is the sixth most common and third most lethal cancer in the world. Except for HER2-targeted therapy, targeted agents against specific molecules participating in gastric carcinogenesis, including those in the mechanistic target of rapamycin (serine/threonine kinase) (mTOR) pathway have not been proved to be effective. However, some studies have suggested that dysfunction of TSC1 may augment mTOR inhibitor activity. METHODS: We studied p-mTOR and TSC1 status by immunohistochemical analysis of gastric carcinoma samples using a tissue microarray method and expression values adopted from The Cancer Genome Atlas. RESULTS: High p-mTOR and low TSC1 expression status is associated with adverse clinicopathologic parameters. Patients with high p-mTOR levels showed poor survival. Patients with low TSC1 levels showed unfavorable survival status in the overall patients group. The combination of p-mTOR status and TSC1 status provided more strong survival information than using each parameter alone. CONCLUSIONS: In gastric cancer, high p-mTOR expression level is a statistically significant parameter in multivariate and Kaplan-Meier analyses (log-rank test). In addition to p-mTOR, TSC1 expression provided additional information to predict survival. We therefore suggest that evaluation of both p-mTOR and TSC1 status may be helpful in clinical trials related to mTOR inhibitors.

bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

Spatial association of Mycobacterium bovis infection in cattle and badgers at the pasture interface in an endemic area in France.

Despite control and surveillance programmes, Mycobacterium bovis, the main aetiologic agent of bovine tuberculosis (bTB), is still detected on cattle farms and in wildlife populations in France, especially in badgers in the French Côte-d'Or département. The aim of our study was to find out if infected badgers were trapped significantly closer to pastures of infected farms than non-infected badgers and, if so, to determine the most efficient distance around those pastures for badger trapping, particularly for surveillance purposes. We studied two subareas (southern and northern), chosen based on natural barriers to badger movements and according to the presence of pastures belonging to infected farms (POIFs) and infected or non-infected badgers. In each subarea, we computed the shortest distances D0 and D between badgers trapped a given year n between 2015 and 2019 (n = 59 infected and n = 1535 non-infected badgers for D0; n = 53 infected and n = 1476 non-infected badgers for D) and POIFs designated as infected between the year n - 4 and n + 1 (respectively n = 373 and n = 388 POIFs). D0 was calculated without considering spoligotypes, while D was calculated considering the possible epidemiological link between infected badgers and POIFs by using bTB spoligotype information. Then, we computed the observed mean and median of the D0 and D distances and used a bootstrap analysis to test if infected badgers were found significantly closer to POIFs than non-infected badgers. We observed that infection of badgers was not independent of distance from POIF in both subareas but distances (D0 or D) were different between the northern and southern subarea. In the northern subarea, which displays a mosaic landscape (mean and median D distances were respectively 612 m and 303 m for infected badgers), infected badgers indeed were trapped closer to POIFs, considering D0 and D. In the southern subarea, predominantly forested, infected badgers were significantly closer to POIFs than non-infected badgers when considering D0 but not for D (mean and median D distances were respectively 7148 m and 4831 m for infected badgers). These results will help to determine the most efficient distance from POIFs to trap badgers to determine their infection status in countryside landscapes. They also highlight the need to better understand the epidemiological systems at play in more forested landscapes where badgers may behave differently or other susceptible sympatric wild species might play a more important role in the circulation of M. bovis, both phenomena contributing to badger infection at greater distances from POIFs.

Stressful Life Events, Unhealthy Eating Behaviors and Obesity among Chinese Government Employees: A Follow-Up Study.

Background: The underlying mechanisms of the relationship between stressful life events and obesity among Chinese workers are unclear. Objective: This study aimed to understand the processes and mechanisms involved in stressful life events, unhealthy eating behavior, and obesity among Chinese workers. Methods: From January 2018 to December 2019, a total of 15,921 government employees were included at baseline and they were followed-up until May 2021. Stressful life events were assessed using the Life Events Scale, and unhealthy eating behavior was assessed using four items. BMI was calculated as weight (kg) divided by height (m2) using physically measured data. Results: Overeating at each mealtime (OR = 2.21, 95%CI: 1.78-2.71) at baseline led to reports of higher risk of obesity at follow up. Eating before going to bed at night sometimes (OR = 1.51, 95%CI: 1.31-1.73) or often (OR = 3.04, 95%CI: 2.28-4.05) at baseline led to reports of higher risk of obesity at follow-up. Eating out sometimes (OR = 1.74, 95%CI: 1.47-2.07) or often (OR = 1.59, 95%CI: 1.07-2.36) at baseline led to reports of higher risk of obesity at follow-up. Stressful life events were not directly associated with obesity, but unhealthy eating behaviors, including overeating at each mealtime (ß = 0.010, 95%CI: 0.007-0.014; ß = 0.002, 95%CI: 0.001-0.004, respectively) and irregular meal timing (ß = -0.011, 95%CI: -0.015--0.008; ß = -0.004, 95%CI: -0.006--0.001, respectively), significantly mediated the associations between stressful life events at baseline and obesity at both baseline and follow-up. Conclusions: Unhealthy eating behaviors mediated the relationship between stressful life events and obesity. Interventions should be provided to workers who have experienced stressful life events and unhealthy eating behaviors.

Amplification, sequencing and characterization of pectin methyl esterase inhibitor 51 gene in Tectona grandis L.f.

Tectona grandis L.f. (Teak), a very important source of incomparable timber, withstands a wide range of tropical deciduous conditions. We achieved partial amplification of pectin methylesterase inhibitor 51 (PMEI) gene in teak by E. pilularis cinnamoyl Co-A reductase (CCR) gene specific primer. The amplified teak gene was of 750 bp, 79% identity and 97% query cover with PMEI of Sesamum indicum. The phylogenetic tree clustered the amplified gene with PMEI of database plant species, Erythranthe guttata and Sesamum indicum (87% bootstrap value). On conversion to amino acid sequence, the obtained protein comprised 237 amino acids. However, PMEI region spanned from 24 to 171 amino acids, 15.94 kDa molecular weight, 8.97 pI value and C697H1117N199O211S9 molecular formula with four conserved cysteine residues as disulfide bridges. 25.9 % protein residues were hydrophilic, 42.7% hydrophobic and 31.2% neutral. Teak 3D PMEI protein structure corresponded well with Arabidopsis thaliana and Actinidia deliciosa PMEIs. The gene maintains integrity of pectin component of middle lamella of primary cell wall and confers tolerance against various kinds of stresses. Teak conferred with overexpression of PMEI may secure a wide adaptability as well as luxuriant timber productivity and quality in adverse/ fluctuating/ scarce climatic and environmental conditions of tropical forests.

Analysis of metabolomic PCA data using tree diagrams.

Large amounts of data from high-throughput metabolomic experiments are commonly visualized using a principal component analysis (PCA) two-dimensional scores plot. The question of the similarity or difference between multiple metabolic states then becomes a question of the degree of overlap between their respective data point clusters in principal component (PC) scores space. A qualitative visual inspection of the clustering pattern in PCA scores plots is a common protocol. This article describes the application of tree diagrams and bootstrapping techniques for an improved quantitative analysis of metabolic PCA data clustering. Our PCAtoTree program creates a distance matrix with 100 bootstrap steps that describes the separation of all clusters in a metabolic data set. Using accepted phylogenetic software, the distance matrix resulting from the various metabolic states is organized into a phylogenetic-like tree format, where bootstrap values 50 indicate a statistically relevant branch separation. PCAtoTree analysis of two previously published data sets demonstrates the improved resolution of metabolic state differences using tree diagrams. In addition, for metabolomic studies of large numbers of different metabolic states, the tree format provides a better description of similarities and differences between each metabolic state. The approach is also tolerant of sample size variations between different metabolic states.

Four distinct types of E.C. 1.2.1.30 enzymes can catalyze the reduction of carboxylic acids to aldehydes.

Increasing demand for chemicals from renewable resources calls for the development of new biotechnological methods for the reduction of oxidized bio-based compounds. Enzymatic carboxylate reduction is highly selective, both in terms of chemo- and product selectivity, but not many carboxylate reductase enzymes (CARs) have been identified on the sequence level to date. Thus far, their phylogeny is unexplored and very little is known about their structure-function-relationship. CARs minimally contain an adenylation domain, a phosphopantetheinylation domain and a reductase domain. We have recently identified new enzymes of fungal origin, using similarity searches against genomic sequences from organisms in which aldehydes were detected upon incubation with carboxylic acids. Analysis of sequences with known CAR functionality and CAR enzymes recently identified in our laboratory suggests that the three-domain architecture mentioned above is modular. The construction of a distance tree with a subsequent 1000-replicate bootstrap analysis showed that the CAR sequences included in our study fall into four distinct subgroups (one of bacterial origin and three of fungal origin, respectively), each with a bootstrap value of 100%. The multiple sequence alignment of all experimentally confirmed CAR protein sequences revealed fingerprint sequences of residues which are likely to be involved in substrate and co-substrate binding and one of the three catalytic substeps, respectively. The fingerprint sequences broaden our understanding of the amino acids that might be essential for the reduction of organic acids to the corresponding aldehydes in CAR proteins.

In Silico Study of Variable Surface Proteins in Plasmodium Species: Perspectives in Drug Design.

The variable surface proteins expressed by P. falciparum and P. vivax are transported to the surface of infected erythrocyte and are exposed to the host immune system. The possibility of using variable surface proteins as a common drug target has been analyzed in both the Plasmodium species. Sequence analysis of variable surface proteins showed a low-level conservation within as well as between the species. Amino acid composition analysis revealed higher frequency of hydrophilic amino acids as compared with that of hydrophobic residues. In order to gain more insight into their diverse functional role, the three-dimensional structure was predicted using comparative modeling approach. These models were evaluated and validated by checking stereochemistry of underlying amino acids. Structural alignment of variable surface proteins by superimposing them shows less conservation. Due to differences at sequence as well as structural level, the variable surface proteins are expected to show difference in their degree of invasiveness. These differences were also cross-examined by evolutionary study, and the results obtained were in accordance with the aforesaid study. The existence of structural differences noticed in the present study showed that the variable surface proteins could not be used as a common drug target in both the malarial species. Therefore, species-specific strategy may be followed for drug targeting against variable surface proteins of P. falciparum and P. vivax.

The influence of molecular order and microstructure on the R2* and the magnetic susceptibility tensor.

In this work, we demonstrate that in the presence of ordered sub-voxel structure such as tubular organization, biomaterials with molecular isotropy exhibits only apparent R2* anisotropy, while biomaterials with molecular anisotropy exhibit both apparent R2* and susceptibility anisotropy by means of susceptibility tensor imaging (STI). To this end, R2* and STI from gradient echo magnitude and phase data were examined in phantoms made from carbon fiber and Gadolinium (Gd) solutions with and without intrinsic molecular order and sub-voxel structure as well as in the in vivo brain. Confidence in the tensor reconstructions was evaluated with a wild bootstrap analysis. Carbon fiber showed both apparent anisotropy in R2* and anisotropy in STI, while the Gd filled capillary tubes only showed apparent anisotropy on R2*. Similarly, white matter showed anisotropic R2* and magnetic susceptibility with higher confidence, while the cerebral veins displayed only strong apparent R2* tensor anisotropy. Ordered sub-voxel tissue microstructure leads to apparent R2* anisotropy, which can be found in both white matter tracts and cerebral veins. However, additional molecular anisotropy is required for magnetic susceptibility anisotropy, which can be found in white matter tracts but not in cerebral veins.

Pre- and in-therapy predictive score models of adult OSAS patients with poor adherence pattern on nCPAP therapy.

OBJECTIVES: To identify patterns of adherence to nasal continuous positive airway pressure (nCPAP) use in the first 3 months of therapy among newly diagnosed adult patients with obstructive sleep apnea/hypopnea syndrome (OSAS) and their predictors. To develop pretherapy and in-therapy scores to predict adherence pattern. METHODS: Newly diagnosed adult OSAS patients were consecutively recruited from March to August 2013. Baseline clinical information and measures such as Epworth Sleepiness Scale (ESS), Fatigue Severity Scale (FSS), Zung's Self-Rating Depression Scale (SDS), and The Pittsburgh Sleep Quality Index (PSQI) at baseline and at the end of 3rd-week therapy were collected. Twelve weeks' adherence data were collected from the nCPAP memory card, and K-means cluster analysis was used to explore adherence patterns. Predictive scores were developed from the coefficients of cumulative logit models of adherence patterns using variables available at baseline and after 3 weeks of therapy. Performance of the score was validated using 500 bootstrap resamples. RESULTS: Seventy six patients completed a 12-week follow-up. Three patterns were revealed. Patients were identified as developing an adherence pattern that was poor (n=14, mean ± SD, 2.3±0.9 hours per night), moderate (n=19, 5.3±0.6 hours per night), or good (n=43, 6.8±0.3 hours per night). Cumulative logit regression models (good → moderate → poor) revealed independent baseline predictors to be ESS (per unit increase) (OR [95% CI], 0.763 [0.651, 0.893]), SDS (1.461 [1.238, 1.724]), and PSQI (2.261 [1.427, 3.584]); and 3-week therapy predictors to be ESS (0.554 [0.331, 0.926]), PSQI (2.548 [1.454, 4.465]), and the changes (3rd week-baseline data) in ESS (0.459 [0.243, 0.868]), FSS (3.556 [1.788, 7.070]), and PSQI (2.937 [1.273, 6.773]). Two predictive score formulas for poor adherence were developed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curves for baseline and 3-week formulas were 0.989 and 0.999, respectively. Bootstrap analysis indicated positive predictive values of baseline and 3-week predictive scores in our patient population of 0.82 (95% CI [0.82, 0.83]) and 0.94 (95% CI [0.93, 0.94]), respectively. CONCLUSION: A high level of prediction of poor adherence pattern is possible both before and at the first 3 weeks of therapy. The predictive scores should be further evaluated for external validity.

Robust high-throughput batch screening method in 384-well format with optical in-line resin quantification.

High-throughput batch screening technologies have become an important tool in downstream process development. Although continuative miniaturization saves time and sample consumption, there is yet no screening process described in the 384-well microplate format. Several processes are established in the 96-well dimension to investigate protein-adsorbent interactions, utilizing between 6.8 and 50 µL resin per well. However, as sample consumption scales with resin volumes and throughput scales with experiments per microplate, they are limited in costs and saved time. In this work, a new method for in-well resin quantification by optical means, applicable in the 384-well format, and resin volumes as small as 0.1 µL is introduced. A HTS batch isotherm process is described, utilizing this new method in combination with optical sample volume quantification for screening of isotherm parameters in 384-well microplates. Results are qualified by confidence bounds determined by bootstrap analysis and a comprehensive Monte Carlo study of error propagation. This new approach opens the door to a variety of screening processes in the 384-well format on HTS stations, higher quality screening data and an increase in throughput.

Analysis and modeling of ensemble recordings from respiratory pre-motor neurons indicate changes in functional network architecture after acute hypoxia.

We have combined neurophysiologic recording, statistical analysis, and computational modeling to investigate the dynamics of the respiratory network in the brainstem. Using a multielectrode array, we recorded ensembles of respiratory neurons in perfused in situ rat preparations that produce spontaneous breathing patterns, focusing on inspiratory pre-motor neurons. We compared firing rates and neuronal synchronization among these neurons before and after a brief hypoxic stimulus. We observed a significant decrease in the number of spikes after stimulation, in part due to a transient slowing of the respiratory pattern. However, the median interspike interval did not change, suggesting that the firing threshold of the neurons was not affected but rather the synaptic input was. A bootstrap analysis of synchrony between spike trains revealed that both before and after brief hypoxia, up to 45% (but typically less than 5%) of coincident spikes across neuronal pairs was not explained by chance. Most likely, this synchrony resulted from common synaptic input to the pre-motor population, an example of stochastic synchronization. After brief hypoxia most pairs were less synchronized, although some were more, suggesting that the respiratory network was transiently "rewired" after the stimulus. To investigate this hypothesis, we created a simple computational model with feed-forward divergent connections along the inspiratory pathway. Assuming that (1) the number of divergent projections was not the same for all presynaptic cells, but rather spanned a wide range and (2) that the stimulus increased inhibition at the top of the network; this model reproduced the reduction in firing rate and bootstrap-corrected synchrony subsequent to hypoxic stimulation observed in our experimental data.