dataesr/scientific-paragraphs-categorization huggingface dataset:
The Bland-Altman plots for the air temperature data are shown in Figure  15. The X-axis shows the average of the two signals. The red lines represent the 95% confidence limits of the differences between the two networks. A regression line is shown that indicates that the mean differences between the networks are nearly zero. The difference of means between the signals of air temperature of Node 2 of the WiFi and ZigBee networks is 4. 753, with agreement limits of -1. 303 and 3. 45. The agreement limits are narrow due to the strong correlation and the nearby location of the sensor nodes. The difference of means between the air temperature signals of Node 1 and 3 of the DigiMesh network is 21. 44, with limits of agreement -8. 744 and 12. 5. The agreement limits are wide due to the weak correlation and the distant location of the sensor nodes.
Discussion and Conclusions We studied the signal dynamics of environmental variables CO 2 , air relative humidity, luminosity, solar radiation, UV radiation, air temperature, wind speed, and wind direction, in two tomato greenhouses located in the Ecuadorian region of the Andes. The measurements were acquired during four days through several Sensor Nodes of three WSNs; two networks used ZigBee technology (star topology, and mesh denoted as DigiMesh), and a WiFi technology (access point topology). The sampling rate was constant for the ZigBee and DigiMesh networks at approximately 0. 333 samples per second, and variable for the WiFi network at around 0. 125 and 2. 083 × 10 -3 samples per second. The data were stored in databases compatible with Matlab TM. The dynamics were analyzed from two viewpoints.
Through the experimentation phase, we identified two factors that significantly affected the accuracy of the model adjustment. The first was the instability or persistence in some signal sections, particularly in the air relative humidity of Node 1 of the DigiMesh network. A further aspect was the high variability of the samples amplitudes between days, combined with the presence of significant peaks or drops in the signal, this being the most common cause of mismatch, and it was present in the CO 2 , solar radiation, UV radiation, wind speed, and wind direction measurements. Based on this experience, we concluded that the data acquisition period for these variables should be longer than the one used in this work, to optimize the learning of the analysis tool, and to increase the modeling accuracy on a long-term basis.
Much more information could be retrieved for describing the environmental variable dynamics that have been addressed here. Nevertheless, in the present study, we constrained ourselves to analyze their short-term and middle-term dynamics, as, in doing so, we were able, for instance, to analyze with detail the impact of sampling rate starting from high rates. The extension of the analysis to longer periods and to different climate conditions is strongly desirable, especially in terms of frost conditions, which is the main problem for the tomato cultivation in Ecuador. This research can also be complemented through the design and implementation of a frost early warning system, described as a climatic phenomenon that causes serious crop losses, principally in the Ecuadorian Andes. The data will be acquired at a steady sampling rate for a time span not shorter than one year, through a wireless network of high-precision weather stations located in strategic areas.
Funding: This research received no external funding. Acknowledgments: This work was supported in part by  Research Grants PRINCIPIAS and FINALE  ( TEC2013-48439-C4-1-R  and  TEC2016-75161-C2-1-R ) from  Spanish Government  and by  Research Grant PRICAM  ( S2013/ICE-2933 ) from  Comunidad de Madrid ; in part by the  Universidad de las Fuerzas Armadas ESPE  under  Research Grant Energy Efficiency in Wireless Sensor Networks  ( 2016-PIC-043  and  2015-PIT-004 ); and in part by the  Thematic Network RiegoNets  ( CYTED  project  514RT0486 ), and  PLATANO  project from  Universidad Politécnica Salesiana, Cuenca, Ecuador.
Results Patient data sampled over time and space. We analyzed samples from two time points for each patient, separated by a mean of 79 months (range 73-91) for non-progressors and 30 months (range 3-74) for progressors. Throughout these results, progressors are indicated by -P and non-progressors by -NP appended to the patient number. For all patients, we analyzed three endoscopic biopsies at the first time point. For non-progressors, an additional three biopsies were analyzed at the second time point. For progressors, eight biopsies were excised and analyzed from surgical resection specimens (see Fig. 1  for full description). The epithelium was purified by treating the biopsies with EDTA and then separating the epithelium from the stroma. This yielded a total of 48 crypts and 6 whole biopsy epitheliums (hereafter referred to as biopsies) from non-progressors, as well as 88 crypts and 11 biopsies from progressors.
All samples were assayed with Illumina 2. 5 M SNP arrays. The data from single crypt samples were noisier than data from whole biopsies. This limited us to reliably detecting lesions that were at least 1 Mb and, after quality control and further filtering of the data, copy number profiles were produced for a total of 358/612 samples (9-72 per patient, Supplementary Figs. 1 2 3 4 5 6 7 8 ). Between 75 and 174 segments were reported per patient, of size varying from 1 to 138 Mb (mean: 22 Mb; median: 14 Mb; Supplementary Fig. 9 ). Table  1  reports the data collection and clinical characteristics of the eight patients. The quality control and segmentation procedures are available in the Supplementary Methods.
In two patients the data were inadequate to assess this: patient 437-NP had only three usable crypt samples (two from the same biopsy) and patient 911-NP had only two lesions (one found in all samples and one in a single crypt). In five out of the six remaining patients, genetic distances between crypts from different biopsies were significantly higher than those between crypts from the same biopsy. This included all four progressors and patient 451-NP (p < 0. 05, Wilcoxon rank-sum test, Supplementary Fig. 45 ). Evolutionary distances between biopsies in patient 437-NP were higher than in the other non-progressors (p = 0. 001, Wilcoxon rank-sum test) and, after further investigation of the clinical database, it was found that this patient had undergone esophagectomy with a diagnosis of high-grade dysplasia at a different hospital, suggesting subsequent progression.
Reassuringly however, we find that genetic diversity at the crypt level is well reflected at the biopsy level, implying that the multiple biopsy approach efficiently measures genetic diversity. Our data thus indicate that prognostication efforts based on genetic diversity, rather than the presence of a particular genetic change, are likely to be more robust to confounding introduced by incomplete spatial sampling. The eight patients assayed in this study are from a tertiary referral cohort and presented more advanced lesions, with low-or high-grade dysplasia at baseline, than the general BE population. Most patients with BE will never develop even lowgrade dysplasia. However, a recent study of a large cohort of Barrett's patients without dysplasia found that diversity measures at baseline predicted progression  19.
Methods Patient cohort. Samples were obtained from the biorepository of the Seattle Barrett's Esophagus Program (SBEP). All research participants contributing clinical data and samples for genetic analysis to this study provided written informed consent, subject to oversight by the Fred Hutchinson Cancer Research Center IRB Committee C (Reg ID 5619). Four patients who progressed to EAC during surveillance and four patients who did not progress over at least 6 years' surveillance were selected. Criteria for patient selection for progressors were availability of three biopsies from an endoscopy before detection of cancer and availability of the surgical resection specimen of the cancer and adjacent BE segment. Criteria for non-progressors were availability of three biopsies from each of two endoscopies at least 6 years apart.
Following DNA extraction and preparation, each sample was separately analyzed on an Illumina 2. 5 M OmniQuad beadchip SNP array. Gastric samples representing the normal constitutive genome were analyzed for each patient and were prepared using Puregene DNA Isolation Kits (Gentra Systems, Inc. ) and quantitated with Picogreen (Quant-iT dsDNA Assay; Invitrogen)  11. For six of eight patients, these gastric samples were obtained from the initial endoscopy. For patient 740-P the gastric sample was from the surgical resection and for patient 391-P the gastric sample was from a surveillance endoscopy taken before surgery. Table  1  reports the data collection and clinical characteristics of the eight patients. DNA content flow cytometric ploidy data were obtained for five endoscopic biopsies and seven surgical biopsies adjacent to the biopsies used for the array assays.
Flow-cytometric ploidy was assessed using previously published methods  39, 40. Pre-processing and quality control. Standard quality control was performed using the Illumina GenomeStudio software. Two hundred and sixteen samples did not pass the quality control (0/1 column in Supplementary Data 1) and were excluded from further analysis. logR values were corrected for GC content bias using the genomic wave correction tool of the pennCNV software suite  41. Bioinformatics and phylogenetics analyses. The bioinformatics procedures and statistical tools to analyze the data are described in more detail in the Supplementary Methods (Supplementary Figs. 60-83, Supplementary Tables  1 2 3 4 5 6 , and Supplementary Note 1). Briefly, as the amount of DNA per crypt sample was marginal for SNP array analysis, results were post-processed to remove areas of noisy signal shared across samples from different patients.
Segments were jointly segmented based on the copynumber package  42  and the ASCAT  43  software was used for genotyping. The phangorn  44  and BEAST  45  packages were used for phylogenetic and evolutionary analyses. In order to estimate SCA mutation rates we developed a new phylogenetic method (PISCA) implemented as a BEAST 1. 8 plugin (available at https://github. com/adamallo/PISCA).
Acknowledgements Rumen Kostadinov  shared his previous modifications to BEAST, which guided our own modifications. Joe Felsenstein  and  Jon Yamato  assisted in developing the substitution model. This work was primarily supported by  National Cancer Institute  grants  R01 CA140657  and  P01 CA091955  (T. G. P. , X. L. , C. A. S. , B. J. R. , M. K. K. , and C. C. M. ). This work was also supported in part by  NIH  grants  R01 CA149566 ,  R01 CA170595 , and  R01 CA185138 , as well as CDMRP  Breast Cancer Research Program  Award  BC132057. T. A. G. was supported by  Cancer Research UK  ( A19771 ). The findings, opinions, and recommendations expressed here are those of the authors and not necessarily those of the universities where the research was performed or the National Institutes of Health.
Background: Growth rates, interactions between community members, stochasticity, and immigration are important drivers of microbial community dynamics. In sequencing data analysis, such as network construction and community model parameterization, we make implicit assumptions about the nature of these drivers and thereby restrict model outcome. Despite apparent risk of methodological bias, the validity of the assumptions is rarely tested, as comprehensive procedures are lacking. Here, we propose a classification scheme to determine the processes that gave rise to the observed time series and to enable better model selection. Results: We implemented a three-step classification scheme in R that first determines whether dependence between successive time steps (temporal structure) is present in the time series and then assesses with a recently developed neutrality test whether interactions between species are required for the dynamics.
If the first and second tests confirm the presence of temporal structure and interactions, then parameters for interaction models are estimated. To quantify the importance of temporal structure, we compute the noise-type profile of the community, which ranges from black in case of strong dependency to white in the absence of any dependency. We applied this scheme to simulated time series generated with the Dirichlet-multinomial (DM) distribution, Hubbell's neutral model, the generalized Lotka-Volterra model and its discrete variant (the Ricker model), and a selforganized instability model, as well as to human stool microbiota time series. The noise-type profiles for all but DM data clearly indicated distinctive structures. The neutrality test correctly classified all but DM and neutral time series as non-neutral.
These deterministic models in turn can move towards the stochastic end of the gradient by integrating intrinsic noise or random environmental fluctuations of increasing strength. In this work, we aim to provide guidelines that help distinguish the different generating processes directly from the time series, thereby complementing and guiding standard model fitting procedures. More precisely, our goal is to determine whether the inference of a microbial network from a time series data set is meaningful. Our proposed classification scheme consists of three steps: (i) test for temporal structure, (ii) test for neutrality, (iii) fit an interaction model (Fig. 2 ). This classification scheme is complementary to the one proposed by Gibbons and colleagues, which does not test for the presence of temporal structure or neutrality  [29].
3 ). While structured community models (gLV, Ricker, Hubbell, and SOI) generate time series with no or only a small percentage of white noise species, the DM distribution gives rise to mostly white noise species, as expected for unstructured data. Deterministic models such as gLV and Ricker with low intrinsic noise are dominated by black noise species, whereas Hubbell time series are mostly composed of brown noise species and SOI time series of pink noise species. However, we found that longer intervals and shorter time series increase the number of white noise species in the stool data and SOI time series, that higher connectance increases the proportion of black taxa at the cost of pink ones in Ricker but not SOI time series (Additional file 4: Figure  S2  and Additional file 5: Figure  S3 ), that high mortality rates favors pink instead of brown noise in the Hubbell time series (Additional file 6: Figure  S4 ), and that increasing intrinsic noise increases the percentage of pink and brown noise in Ricker time series.
Neutrality test distinguishes neutral from non-neutral dynamics To distinguish neutral from non-neutral structured models, we applied a test previously developed to recognize neutrality in time series data  [30]. Neutrality, in this test, is defined as a general feature in which demographic rates and performance are independent of species identities. The idea of the neutrality test is to test for group invariance, which means that time series properties are not affected by summing species into groups that represent higher-level taxa such as genera or families. In particular, the group invariance of inter-species quadratic covariation is tested; thus, not only group invariance of covariance is tested, but also its correspondence to a type of covariance common to all neutral processes. When validating this test on our community time series, the DM and Hubbell time series are classified as neutral (p values > 0.
For high levels of external noise or too large sampling intervals, the neutrality test may yield a false positive outcome. In neutral communities, variation can be suitably analyzed within stochastic frameworks which is in agreement with our knowledge of the numerous cross-feeding and competition interactions taking place between gut organisms  [31]. A number of algorithms have been developed to parameterize the gLV or Ricker model directly from time series data  [14, 18, 32] , thereby inferring ecological interactions. Here, we employ LIMITS  [14]  to test this parameterization step. We applied it to the top 60 most abundant species in our test time series and measured inference accuracy as the mean correlation between the known and the inferred interaction matrix. The LIMITS algorithm was found to perform well on time series data with known interaction matrices (Fig.
Conclusion In conclusion, we have demonstrated that knowledge about the fundamental properties of microbial community dynamics is required for unbiased community study and model inference. We have proposed a classification scheme for microbial sequencing time series that first differentiates between unstructured and structured models, then tests for neutrality and finally determines the goodness of fit to a deterministic model. To make these tests accessible, we have implemented a new R package called seqtime. While several hurdles still have to be overcome, this classification scheme is a first step towards model identification from time series data.
Stool time series data The stool data consist of two metagenomic time series of fecal samples that were collected almost daily by two individuals  [3]. We rarefied the counts to 10,000 reads per sample and omitted the last time point from individual B, since there was a gap of 66 days between it and the previous sample. We then interpolated the data with function stineman in the stinepack R package  [51]  to ensure equidistant time intervals. A few small negative values introduced by the interpolation were set to zero. After interpolation, the data set from individual A included 365 time points and the data set from individual B 253 time points. Finally, we selected the 100 top-abundant OTUs, ranked by their sum across time points.
Neutrality test and LIMITS Neutrality test The neutrality test  [30]  tests the per-capita equivalence of species by determining whether or not the covariances between species are invariant to grouping. The test relies on a constant-volatility transformation that stabilizes the volatility of a two-group (two-species) neutral community irrespective of how species are grouped. Neutrality was tested on relative abundances using 500 randomly drawn constant-volatility transformations through the function NeutralCovTest in the Wright-Fisher R package (https://github. com/reptalex/Wright-Fisher) with method logitnorm. p values produced from the neutral covariance test were used as a measure of the incompatibility of the data with the neutral model.
Acknowledgements We would like to thank  Lawrence A. David  for sharing stool time series data and  Charles K. Fisher  and  Pankaj Mehta  for sharing their LIMITS code. We would also like to thank  Thomas Rattei  and  Florian Goldenberg  for providing access to and maintaining the  Life Science Compute Cluster at the Division of Computational Systems Biology, University of Vienna. Finally, we are grateful for the insightful comments of the reviewers.
This framework called the Science Portal (hereafter Portal) was developed in the context the Dark Energy Survey (DES) to facilitate scientific analysis. In this paper, we show how the Portal can provide a reliable environment to access vast data sets, provide validation algorithms and metrics, even in the case of multiple photo-zs methods. It is possible to maintain the provenance between the steps of a chain of workflows while ensuring reproducibility of the results. We illustrate how the Portal can be used to provide photo-z estimates using the DES first year (Y1A1) data. While the DES collaboration is still developing techniques to obtain more precise photo-zs, having a structured framework like the one presented here is critical for the systematic vetting of DES algorithmic improvements and the consistent production of photo-zs in future DES releases.
, 2015). The primary goal of DES is to constrain the nature of dark energy through the combination of four observational probes, namely baryon acoustic oscillations, counts of galaxy clusters, weak gravitational lensing, and determination of distances of SNe. Besides, many other fields of astrophysics benefit from the large data set generated by the survey, as detailed by  DES et al. (2016). The constraining power of cosmological results provided by DES will strongly depend on the ability to estimate reliable photometric redshifts (photo-z, e. g. ,  Huterer et al. , 2004; Ma et al. , 2006; Lima and Hu, 2007; Ma and Bernstein, 2008; Hearin et al. , 2010; Cunha et al. , 2014; Georgakakis et al. , 2014). In fact, the computation of accurate photo-zs has been one of the major concerns of the collaboration, which has spurred the implementation and testing of several algorithms.
For instance,  Sánchez et al. (2014)  addressed the performance of several codes when applied to the DES science verification data (SVA1), while  Banerji et al. (2015)  discussed the effect of using infrared data. More recently,  Bonnett et al. (2016)  examined the impact of four photo-z algorithms on the conclusions of the first DES cosmological analysis based on weak lensing discussed by  Abbott et al. (2016). Photo-z estimation will only get more challenging for the next DES releases and future photometric surveys. The reason is that we are sampling magnitudes beyond the reach of most spectroscopic surveys and therefore, traditional photo-z validations are not realistic. This issue has inspired the implementation of new ideas in the collaboration, such as the calibration of photo-zs with cross-correlations  (Newman, 2008; Davis et al.
, 2017; Gatti et al. , 2018) , the training and validation of photo-z codes with simulations (data-augmentation)  (Hoyle et al. , 2015)  and validation of photo-zs with multi-band photometric samples  (Hoyle et al. , 2017). Techniques for assignment and validation of photo-zs for DES are under continuous development. There are a large number of methods and algorithms available in the literature to compute and validate photo-zs. Thus, it is useful to work in an integrated environment where one can perform repeated tests and compare the results, while keeping the history well documented. Such an environment should provide the necessary hardware and software infrastructure to make feasible the comparison of different methods applied to large datasets. Besides dealing with big data, another remarkable aspect of current and near-future surveys is a large number of people working collaboratively.
The computational methods are developed jointly by groups of people, commonly located in different countries. Therefore, it is useful to share a development environment that organizes software with version control, keeps the history, and ensures it is possible to reproduce results at any time. Other web-based interfaces for astrophysical data mining and analysis are also being developed (e. g. , the DAMEWERE environment by  Brescia et al. , 2014)  aiming at the exploitation of large datasets. The DES collaboration proposed, along with the Data Management system (DESDM 1  ,  Mohr et al. , 2012) , the creation of a dedicated portal to solve some of the problems associated with the data processing. This concept became the DES Science Portal, hereafter "the Portal". During the early days of the DES project, the Portal was conceived as an "end-to-end" (E2E) process where the data flowed through a chain of tasks to prepare science-ready catalogs and perform scientific analyses.