Recalculation of data from 1990 to 2010 on the effects of highly diluted thyroxine on the metamorphosis of highland amphibians

Experiments on amphibian metamorphosis can vary considerably in duration. The authors had set themselves the task of defining a generally applicable pooling method for metamorphosis experiments [1]. Normalization with respect to time was done on the assumption that differences in speed of metamorphosis attributable to treatment would override differences in duration between experiments. The problem of artificial differences in variability when comparing and pooling data from several experiments was approached by normalization with respect to time based on the development of both the test and the control animals. The range from 0% to 100% over which the fraction of four-legged animals progresses in the course of an experiment is divided into 10%-intervals and mapped onto a corresponding relative scale. Each measurement is then assigned to the point on the 10%-scale to which it is closest. In this way each reference point is assigned a value giving the number or percentage of four-legged animals at that point. These values are aggregated over all experiments within the test- and control-group. The results of experiments performed over the course of two decades (1990 - 2010) on highland Rana temporaria treated with a homeopathically prepared high dilution of thyroxine (“30x”) are presented in full detail based on this normalization method[1]. It was found that differences between treatment groups thus calculated were in line with those obtained with other pooling methods [2]. Thyroxine 30x does slow down metamorphosis in inert highland amphibians. This was observed by 5 researchers in 20 sub-experiments, and it seems to be the most reliable bio-assay found in amphibian research on homeopathy so far2. When experiments were performed with highland animals pretreated by hyperstimulation with molecular thyroxine, slowing down of metamorphosis was again observed (by 3 of 4 researchers) in most of 10 sub-experiments.

A New Method for Time Normalization Based on the Continuous Phase: Application to Neck Kinematics

There is growing interest in analyzing human movement data for clinical, sport, and ergonomic applications. Functional Data Analysis (FDA) has emerged as an advanced statistical method for overcoming the shortcomings of traditional analytic methods, because the information about continuous signals can be assessed over time. This paper takes the current literature a step further by presenting a new time scale normalization method, based on the Hilbert transform, for the analysis of functional data and the assessment of the effect on the variability of human movement waveforms. Furthermore, a quantitative comparison of well-known methods for normalizing datasets of temporal biomechanical waveforms using functional data is carried out, including the linear normalization method and nonlinear registration methods of functional data. This is done using an exhaustive database of human neck flexion-extension movements, which encompasses 423 complete cycles of 31 healthy subjects measured in two trials of the experiment on different days. The results show the advantages of the novel method compared to existing techniques in terms of computational cost and the effectiveness of time-scale normalization on the phase differences of curves and on the amplitude of means, which are assessed by Root Mean Square (RMS) values of functional means of angles, angular velocities, and angular accelerations. Additionally, the confidence intervals are obtained through a bootstrapping process.

Normalisation of SARS-CoV-2 concentrations in wastewater: the use of flow, conductivity and CrAssphages

Over the course of the COVID-19 pandemic in 2020-2021, monitoring of SARS-CoV-2 RNA in wastewater has rapidly evolved into a supplementary surveillance instrument for public health. Short term trends (2 weeks) are used as a basis for policy and decision making on measures for dealing with the pandemic. Normalization is required to account for the varying dilution rates of the domestic wastewater, that contains the shedded virus RNA. The dilution rate varies due to runoff, industrial discharges and extraneous waters. Three normalization methods using flow, conductivity and CrAssphage, have been investigated on 9 monitoring locations between Sep 2020 and Aug 2021, rendering 1071 24-hour flow-proportional samples. In addition, 221 stool samples have been analyzed to determine the daily CrAssphage load per person. Results show that flow normalization supported by a quality check using conductivity monitoring is the advocated normalization method in case flow monitoring is or can be made available. Although Crassphage shedding rates per person vary greatly, the CrAssphage loads were very consistent over time and space and direct CrAssphage based normalization can be applied reliably for populations of 5600 and above.

Assessing Methods for Evaluating the Number of Components in Non-Negative Matrix Factorization

Non-negative matrix factorization is a relatively new method of matrix decomposition which factors an m × n data matrix X into an m × k matrix W and a k × n matrix H, so that X ≈ W × H. Importantly, all values in X, W, and H are constrained to be non-negative. NMF can be used for dimensionality reduction, since the k columns of W can be considered components into which X has been decomposed. The question arises: how does one choose k? In this paper, we first assess methods for estimating k in the context of NMF in synthetic data. Second, we examine the effect of normalization on this estimate’s accuracy in empirical data. In synthetic data with orthogonal underlying components, methods based on PCA and Brunet’s Cophenetic Correlation Coefficient achieved the highest accuracy. When evaluated on a well-known real dataset, normalization had an unpredictable effect on the estimate. For any given normalization method, the methods for estimating k gave widely varying results. We conclude that when estimating k, it is best not to apply normalization. If the underlying components are known to be orthogonal, then Velicer’s MAP or Minka’s Laplace-PCA method might be best. However, when the orthogonality of the underlying components is unknown, none of the methods seemed preferable.

RUV-III-NB: Normalization of single cell RNA-seq Data

Motivation: Despite numerous methodological advances, the normalization of single cell RNA-seq (scRNA-seq) data remains a challenging task. The performance of different methods can vary greatly across datasets. Part of the reason for this is the different kinds of unwanted variation, including library size, batch and cell cycle effects, and the association of these with the biology embodied in the cells. A normalization method that does not explicitly take into account cell biology risks removing some of the signal of interest. Here we propose RUV-III-NB, a statistical method that can be used to adjust counts for library size and batch effects. The method uses the concept of pseudo-replicates to ensure that relevant features of the unwanted variation are only inferred from cells with the same biology and return adjusted sequencing count as output. Results: Using five publicly available datasets that encompass different technological platforms, kinds of biology and levels of association between biology and unwanted variation, we show that RUV-III-NB manages to remove library size and batch effects, strengthen biological signals, improve differential expression analyses, and lead to results exhibiting greater concordance with independent datasets of the same kind. The performance of RUV-III-NB is consistent across the five datasets and is not sensitive to the number of factors assumed to contribute to the unwanted variation. It also shows promise for removing other kinds of unwanted variation such as platform effects. Availability: The method is implemented as a publicly available R package available from https://github.com/limfuxing/ruvIIInb. Contact: [email protected], [email protected] Supplementary information: Online Supplementary Methods

A New 3D Shaping Method for Low-Thrust Trajectories between Non-Intersect Orbits

This paper proposes a new shape-based method in spherical coordinates to solve three-dimensional rendezvous problems. Compared with the existing shape-based methods, the proposed method does not need parameter optimization. Moreover, it improves the flexibility of orbit fitting, greatly reduces the velocity increment and maximum thrust acceleration, and ensures the orbit safety to a certain extent. The shaping function can provide the initial estimate for numerical trajectory optimization and improve the convergence rate in a certain range when combined with the normalization method. The superiority of the proposed method over the existing methods is demonstrated by two numerical examples. Its effectiveness at initial estimation generation in the indirect optimization of a low-thrust trajectory is demonstrated by the third example.

MEASURING COMPETITIVENESS IN TOURISM ENTERPRISES USING INTEGRAL INDEX

The study aims to develop the methodology of measuring competitiveness in the context of tourism enterprises. The normalization method was used to construct the integral index of competitiveness which was aggregated by 13 sub indicators. In the process of obtaining sub indicators the methods of survey and face to face interviews were used. This study recommends new method of measuring competitiveness in the context of tourism enterprises uniting human resources, service quality competitiveness, market share, and profit benchmark in one integral index. This index can be used by regional tourism destination management bodies to make better decisions regarding sustainable development of tourism destination.