dataesr/scientific-paragraphs-categorization huggingface dataset:
Preprocessing of forcing data When modelling the snow evolution, we covered a period (5 May to 30 Nov 2017) extending outside the snow season to allow a five day spin-up phase to enable the model to reach a physical equilibrium with the applied forcing. Since continuous data are required for both snow models, preprocessing was necessary to fill the gaps in the TA and RH data sets (23 June 11:00 and 31 October 10:00 due to sensor failure) and to correct the wind-induced undercatch in the precipitation data. Therefore, TA and RH data was interpolated based on lapse rates from nearby AWSs (Agua Negra (4774 m a. s. l. ), Llano de las Liebres (3565 m a. s. l. ) and La Laguna (3209 m a. s. l. ; Fig. 1c ). For TA, a daily moist adiabatic lapse rate was calculated using TA measured at La Laguna and Paso Agua Negra AWSs (1565 m elevation difference) between 2014 and 2017.
We fitted a sinusoidal trend over these lapse rates for the 4-year period and found daily lapse rates with a maximum of -6. 9 • C km -1 in winter and a minimum of -8. 0 • C km -1 in summer. These daily lapse rates were subsequently applied to TA observations of Llano de las Liebres AWS which is the only AWS that covers the entire period of missing data in 2017. For RH a similar approach was applied using the lapse rate of the daily dew point temperature between the Paso Agua Negra and La Laguna AWSs and applying it to data measured at the Llano de las Liebres AWS. Dew point temperature was converted to RH following  Liston and Elder (2006a). Evaluation of this lapse rate interpolation, based on 1638 overlapping observations at Tapado, shows an uncertainty (i. e. RMSE) of 2. 8 • C and 9. 97% for TA and RH respectively.
SNOWPACK SNOWPACK was developed by the Swiss Federal Institute (SLF) for Snow and Avalanche Research  (Bartelt and Lehning, 2002; Lehning et al. , 2002a, b). It is a one-dimensional model, but can be implemented in the spatially distributed, threedimensional snow cover and earth surface model Alpine3D  (Lehning et al. , 2006). SNOWPACK includes a MeteoIO preprocessing library for meteorological data  (Bavay and Egger, 2014)  which was not used, as we implemented a homogeneous preprocessing approach for both models (see Sect. 2. 3). SNOWPACK is a physically-based model which has the ability to simulate snow physical properties (e. g. snowpack temperature, layer thickness, snow microstructure and density) and snow processes (e. g. refreezing, sublimation, melt, evaporation) for multiple layers, which are merged if layers become too thin.
Acknowledgements. The authors thank  Glen E. Liston  for providing the code of SnowModel. We are also grateful to CEAZAmet and the  CEAZA glaciology group  for maintaining the Tapado AWS and data centre. Marion Réveillet  and  Shelley MacDonell  were supported by  CONICYT-Programa Regional   R16A10003 , and the  Coquimbo regional government FIC-R ( 2015 )  BIP 30403127-0. Marion Réveillet  was also supported by the  ANR program :  ANR-16-CE01-0006  EBONI. 21 https://doi. org/10. 5194/tc-2021-9Preprint. Discussion started: 26 January 2021 c Author(s)  2021. CC BY 4. 0 License.
Most commonly, co-expressed genes are identified using Pearson correlation, Spearman correlation  [11] , and mutual information  [12, 13]. Another popular approach is to first construct models that predict the expression of one gene, using that of all other genes or potential regulators, and then apply variable selection to identify the dependencies between these genes  [14, 15]. Both types of approaches aim at identifying associations between genes based on their expression levels across all samples in a dataset. Therefore, as with standard gene expression analysis, it is essential to preprocess the expression data that is used as input for co-expression analysis  [16]. To correct for technical variability across samples, various RNA-Sequencing (RNA-Seq) normalization methods have been developed  [17, 18].
Both group-specific and global properties of the expression distribution are then used to correct for technical variability. This method, called smooth quantile normalization, or qsmooth, yields better preservation of global shifts in expression as well as adequate control over the within-group variability  [20]. While qsmooth was only recently developed, it has already been used in several analyses with large heterogeneous RNA-Seq datasets  [1, 21, 22, 23]. Here, we show that quantile-based normalization methods, and in particular smooth quantile normalization, can introduce false-positive associations, or false-positive co-expression values, between genes. We found that this can particularly occur in datasets that have large differences in the library size across samples. To correct for these false-positives, we developed SNAIL, or Smooth-quantile Normalization Adaptation for Inference of co-expression Links.
Preparation of GTEx data We downloaded RNA-Seq count data from the GTEx Consortium V8 release  [24]. We followed the procedure conducted by Paulson et al. ,  [26]  to merge tissues with similar expression profiles. We selected those tissues previously reported to have the largest number of genes that deviate the most when comparing the expression in the tissue-of-interest with the median expression across all tissues-testis, kidney -cortex, brain -other, breast -mammary tissue, and whole blood )  [1]. Note that the brain -other tissue consists of several merged brain regions, as described in Paulson et al. ,  [26]. The resulting datasets consisted of 1,575 samples and 55,878 genes. Combined with the tissue information (biosample name) in the meta data, we then used the Bioconductor package qsmooth (version 1.
Preparation of ENCODE data For the validation datasets of our normalization approach, we downloaded bulk polyA plus RNA-seq count data consisting of twelve tissues (embryonic facial prominence, forebrain, heart, hindbrain, intestine, kidney, limb, liver, lung, midbrain, neural tube, stomach) from the Mouse ENCODE database using Bioconductor package ENCODExplorer (version 2. 14. 0, download date September 11, 2020)  [27]. Among all the available experiments, we extracted those for which External RNA Controls Consortium (ERCC)-only spike-ins (accession: ENCSR884LPM) information was available. The resulting datasets consisted of 126 samples and 43,346 genes. To establish the validation dataset, we normalized the read counts by the expression of 96 spike-in genes (Supplementary Materials 1, Section 3).
Evaluation of SNAIL For both the GTEx and ENCODE datasets, we extracted the genes that were exclusively expressed in one tissue, denoted as tissue-exclusive genes. We define tissue-exclusitivity using the following two criteria:  (1)  the median ground truth expression of the gene is higher than or equal to 10 across all samples from the tissue-of-interest, and (2) the median ground truth expression is lower than or equal to 1 across samples from all other tissues. Note that we used these criteria to facilitate the visualization of the problem. We did not particularly look into the biological insights of these genes in this study. To showcase the false-positive associations introduced by smooth quantile normalization, we compared the Spearman's rank correlation coefficients for these tissue-exclusive genes based on qsmooth-normalized and SNAIL-normalized expression levels.
Since the number of tissue-exclusive genes can show large differences across different tissues, we excluded tissue-exclusive genes of tissues with less than five or more than 1,000 tissue-exclusive genes in total from the visualization (specifically, genes exclusively expressed in testis for the GTEx dataset, and embryonic facial prominence, limb, neural tube, and forebrain for the ENCODE dataset). Note that the exclusion of these genes is not required when applying SNAIL. The number of tissue-exclusive genes for the two datasets are shown in Supplementary Materials 1 Section 4. To evaluate the performance of SNAIL, we defined two genes to be associated when the absolute value of 4 their Spearman's rank correlation coefficient, based on the ground truth expression in the ENCODE dataset, was higher than or equal to a specific value.
We ranged this value from 0. 2 to 0. 8. In addition, to evaluate whether the false-positive associations introduced by quantile normalization-based methods can propagate through downstream network analysis, we constructed sample-specific networks using Bioconductor package lionessR (version 1. 2. 0-0)  [28]  with Spearman's rank correlation coefficients as the network reconstruction function. We modeled these networks using the qsmooth-normalized expression as well as using the SNAILnormalized expression data. We then used Bioconductor package limma (version 3. 44. 1) [29]  to identify significant differences of the distributions of edge weights measured across all sample-specific networks constructed from the ground truth co-expression networks. We excluded 27 tissue-exclusive genes that did not have gene symbol annotations in biomaRt (version 2.
Quantile-based normalization methods can introduce false-positive associations in large-scale heterogeneous datasets In this section, we demonstrate how quantile-based normalization-and in specific smooth quantile normalizationcan introduce false-positive associations between genes. To do so, we will present a case study on co-expression analysis between genes that are exclusively expressed in a specific tissue. We used RNA-Seq data from the Genotype Tissue-Expression (GTEx) project  [24]  and selected the tissues with high levels of tissue-specific gene expression (see Methods 3. 3). We performed smooth quantile normalization to remove the technical variability presented in the dataset, while preserving the global expression differences between the different tissues. Thereafter, we extracted the tissue-exclusive genes for each tissue (see Methods 3.
To understand how these false-positive associations arise, we dissected both quantile normalization (Supplementary Materials 1 Section 1, 2) and the smooth quantile normalization algorithm as it is implemented in the Bioconductor package qsmooth  [20]. As we found the problem more prone to arise with smooth quantile normalization, we focus on this methodology in the remainder of this manuscript. The qsmooth implementation of smooth quantile normalization computes the average expression level in each quantile, considering only the samples within a given user-defined group-the group reference distribution-as well as the average expression level in each quantile, considering all samples-the background reference distribution. The method then estimates the empirical reference distribution to be the weighted average of the background reference distribution and the group reference distribution, where the weight coefficient is computed based on the proportion of explained variability in the group quantile distribution.
Since qsmooth uses the average to derive the quantile distributions, the values corresponding to small quantiles could be non-zero despite most of the values that those quantiles are based on are zeros (see also Supplementary Materials 1 Section 1, 2). Another important detail of the implementation of quantile-based normalization is the ranking method used to process genes that have the same read count (tied counts) in each sample. The quantiles corresponding to these genes are dependent on the number of genes that have the same read count in that specific sample (Supplementary Materials 1, Section 1, 2). Therefore, even if a gene would have the exact same read count in two different samples, the corresponding quantiles can be drastically different. Especially for zero-inflated RNA-Seq data in heterogeneous datasets that have large differences between the smallest and the largest number of non-expressed genes across samples, lowly expressed genes could, for example, share the same quantile with non-expressed genes in different samples.
SNAIL reduces false-positive associations We applied SNAIL to normalize gene expression levels in the GTEx data and then repeated the co-expression analysis described in the previous section (see also Methods 3. 3), using the same gene set and tissues. Comparing the Spearman's rank correlation coefficients obtained in the qsmooth-and SNAIL-normalized data, we found that SNAIL is capable of removing false-positive associations, while modeling tissue-exclusive biological variability similarly to smooth quantile normalization (Figure  2b ). With the above-mentioned threshold of ρ=0. 3 to define co-expression, SNAIL reduces the number of such false-positive associations from 3,442 (8. 6%) to 231 (0. 58%). To provide more evidence supporting the capability of SNAIL to reduce these false-positive associations, we applied the normalization method to RNA-Seq data from the Mouse ENCODE database, which includes spike-ins (Methods 3.
2). We used the expression of spike-in genes to normalize the read count (Supplementary Materials 1 Section 3), creating the ground truth expression dataset. Comparing Spearman's rank correlation coefficients obtained with qsmooth and SNAIL with those derived from the ground truth expression dataset for each gene pair, we observed that the root mean square error (RMSE) between the correlation coefficients decreases from 0. 389 to 0. 015 after applying SNAIL. We next defined two genes to be associated if the ground truth Spearman's rank correlation coefficients exceeded a certain threshold, which we ranged from 0. 2 to 0. 8. We conducted receiver-operator curve and precision-recall curve analyses and reported the area under the two curves. We found that SNAIL can reduce the false-discovery rate in co-expression analysis, regardless of the strength of the correlation signal (Figure  3 ).
Note that when the true association is more strictly defined (correlation coefficient above 0. 7), the small number of positive associations (less than 30 positive associations when the threshold is above 0. 70) causes a fluctuation in the AUPRC. In addition to these analyses, we compared SNAIL's performance to that of other commonly used normalization methods, such as relative log expression (RLE) and, transcripts per million (TMM), (Supplementary Materials 1 Section 5). Compared to qsmooth, SNAIL effectively removes false-positive associations, while reaching a similar performance in detecting correct associations between genes as RLE and TMM (Supplementary Figure  6 ). Note however, that the performance of these methods can not be directly comparable, as SNAIL and smooth quantile normalization explicitly model the global differences across different biological groups and show better control for the within-group variability (Supplementary Figure  7 ).
Discussion Here, we showed that the use of quantile-based normalization approaches-and in specific smooth quantile normalization-to RNA-Seq data can introduce false-positive associations between genes, and that this can propagate to and affect downstream network analyses. We found that false-positive associations particularly arise when there is a large difference between the smallest and the largest number of non-expressed genes across the samples in the dataset. This can for example occur when dealing with RNA-Seq datasets collected from large-scale projects that include heterogeneous data, such as data from The Cancer Genome Atlas (TCGA)  [36] , ENCODE, and GTEx.
10 A frequently applied strategy that attempts to remove potential false-positive associations is filtering out genes with low read counts across a certain number of samples. However, thresholds used for filtering are often chosen arbitrarily, and can remove genes that are specifically expressed in a subset of samples, such as the tissue-exclusive genes that we described in our example of network analysis in data from GTEx. Therefore, arbitrary filtering is not ideal if one aims to compare gene associations or networks derived from different subgroups of samples. Moreover, it would be ideal to include all genes in large-scale network analysis, as certain network reconstruction algorithms make use of the entire distribution of gene expression and thus filtering out genes may remove some signal from the input dataset  [6].
To address this issue, we developed SNAIL, an adaptation of smooth quantile normalization, which retains global expression differences in heterogeneous datasets through the use of both group-specific and global properties. By using the trimmed mean to infer the reference quantile distribution as well as median aggregation for genes with the same read count, SNAIL avoids the formation of false-positive associations. Importantly, as the method does not require gene filtering, it allows for direct comparison of networks modeled on heterogeneous datasets. While we specifically focused our examples on modeling co-expression across different tissues in this work, false-positive associations can also arise when comparing other biological conditions that show large differences in expression profiles under certain experimental settings, such as when comparing networks for males and females  [34].
We also envision that other methods that are based on correlations, such as eQTL studies, could potentially include quantile-based normalization-introduced false-positives, and could benefit from normalization with SNAIL. In general, we would like to raise awareness of implementing tools designed for gene expression data in existing correlation-based approaches or pipelines. Most of the published evaluations of normalization methods are based on comparing differences between ground truth and normalized expression levels. However, the impact of normalization on correlation-based measures is often neglected. Heterogeneous datasets with increasing numbers of samples and conditions will likely be published in the near future, and new methods for combining data from different studies  [37]  will result in the emergence of even larger and more heterogeneous datasets.
In September 2018, an in-flight campaign was performed over Corsica (France) in the framework of the EXAEDRE (EXploiting new Atmospheric Electricity Data for Research and the Environment) project to investigate the electrical activity in thunderstorms. During this campaign, eight scientific flights were done with a Falcon 20 (F20) of SAFIRE (the French facility for airborne research) inside or in the vicinity of thunderstorms. During the flight of the 8 of October 2018, the aircraft was struck by lightning at an altitude of 8400 m. The purpose of this paper is to present the AMPERA system and the atmospheric electrostatic field recorded during EXAEDRE flights and then discuss a method to compare the atmospheric electrostatic field values measured by C160 and F20 during lightning strikes.
Zaber imaging This system is based around a Zaber MVR motorised inverted microscope with Zeiss optics, and 4x 0. 13NA and 63x 0. 75NA Air objectives. Fluorescence illumination comes via 3 LEDs (385 nm, 473 nm and 568 nm) and a quad bandpass dichroic filter set (Chroma #89402). The system has a small format (Approx 30 mm cube) Ximea (model MQ042MGCM) camera and a Red Pitaya single board computer running as a hardware timing device. Multi channel images were taken by sequentially illuminating with the three different LEDs without changing the quad dichroic filter cube. This system was used to collect the example data for the simulated microscope and test Python script available at https://doi. org/10. 5281/zenodo. 5745648.
The reviewers make a good point that would significantly strengthen the paper. We have written a short script that utilises some of the embedded functionality to find and mark cell nuclei in a typical DAPI stained sample. The code, along with a test image set, config files and detailed instructions on how to set it up are collected into a zenodo repository and available via a doi (10. 5281/zenodo. 5745648) link. In fact, this simulated microscope approach is a very powerful approach to setting up and testing such scripts. We have also added the following text to the manuscript.
org/10. 5281/zenodo. 5745648. For this simple example the parameters are fixed and tuned to the data set used, and select a subset of nuclei within a size range and with defined circularity. The current code also doesn't reliably detect nuclei at the edges between images in the mosaic scan. However, it can easily detect a large number of cell positions to create a point list which could then be run through a multi-site experiment to collect data such as a 3D, multi-channel stack, or time lapse on a large number of cells semi-automatically. Together with python-microscope paper, this paper introduces a new open-source solution for microscope control and imaging acquisition. Both in the introduction and in the discussion, Cockpit is compared to several equivalent commercial and open-source solutions, including the well-established Micro-Manager.
Acknowledgements We are grateful to the original UCSF software team led by  John Sedat , including  Melvin Jones ,  Chris Weisiger  and  Sebastian Haase  for the initial writing of the Python code used to control the OMX and OMX-T microscopes, which was subsequently released as open source software and formed the starting point for this project. We wish to thank many colleagues who have provided helpful suggestions, discussions and testing during development of Cockpit, the  Micron Advanced Bioimaging Unit particularly Mantas Žurauskas  and  Andrew Jefferson ,  Maria Harkiolaki  from the  Diamond Light Source  beamline  B24. Oscar Davis  for help on the interface look and feel. An earlier version of this article can be found on bioRxiv (https://doi. org/10. 1101/2021. 01. 18. 427171 ).
The study population will consist of 1,200 older adults with low socioeconomic status (SES) from the United Kingdom (UK) and 1,200 older adults from Beijing, China, all with 2 or more lifestyle factors at levels associated to an increased dementia risk  (12). In this article, we describe the development of the PRODEMOS mHealth intervention, from general idea to platform design, and from prototype to pilot study. We make specific recommendations on mHealth design for vulnerable populations, based on extensive interactions with the target population and other important stakeholders, including health care professionals, software developers, and researchers.
The PRODEMOS platform also comprises a separate assessor-and researcher portal for data collection and outcome assessment, and a static mobile application with written healthcare advice only and without interactive features, for those randomized to the control condition of the trial. The assessor-and researcher portals and control application have been designed within the research context of the PRODEMOS project, of which the protocol is described in more detail elsewhere  (12). Figure  1  shows the components of the PRODEMOS platform and their interrelationships. All key functionalities of the PRODEMOS platform will be similar in the UK and China. Besides differences in language, certain cultural adaptations will be made to ensure adequate fit of the intervention with the target population.
The functionality of the portal and mobile application was subsequently trialed in sixweek pilot studies in the UK and China. We used qualitative data, gathered through focus groups with pilot participants and participating coaches, and data on user statistics to evaluate usability. User statistics included details on goals, measurements, and chat history and were gathered manually from the platform, as the automated export functionality for user statistics had not been finalized by that stage. Findings from the internal test sessions, thinking aloud session, and pilot evaluation informed the final adaptation phase, in which the portal and mobile application was prepared for use in the full trial. Unless stated otherwise, all qualitative sessions were led by at least one member of the research team and one member of the technical team, following a topic guide.