dataesr/scientific-paragraphs-categorization huggingface dataset:
Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation. Deconvolution is widely used to restore fine details of 3D biological samples. Unfortunately, dealing with deconvolution tools is not straightforward. Among others, end users have to select the appropriate algorithm, calibration and parametrization, while potentially facing demanding computational tasks. To make deconvolution more accessible, we have developed a practical platform for deconvolution microscopy called DeconvolutionLab. Freely distributed, DeconvolutionLab hosts standard algorithms for 3D microscopy deconvolution and drives them through a user-oriented interface. In this paper, we take advantage of the release of DeconvolutionLab2 to provide a complete description of the software package and its built-in deconvolution algorithms.
We examine several standard algorithms used in deconvolution microscopy, notably: Regularized inverse filter, Tikhonov regularization, Landweber, Tikhonov-Miller, Richardson-Lucy, and fast iterative shrinkage-thresholding. We evaluate these methods over large 3D microscopy images using simulated datasets and real experimental images. We distinguish the algorithms in terms of image quality, performance, usability and computational requirements. Our presentation is completed with a discussion of recent trends in deconvolution, inspired by the results of the Grand Challenge on deconvolution microscopy that was recently organized.
Among such possible degradations, the most notable ones are noise amplification, ringing (known as Gibbs or Runge phenomenon) and aliasing (both spatial and spectral). The deconvolution of micrographs was first investigated by Agard and Sedat  [2]. Many variations and improvements have been proposed since then  [3] [4] [5] [6] [7]. Some of these ''deconvolution microscopy" methods led to various commercial and open-source software implementations  [8, 9]. The typical cost of a commercial package varies between USD 5000 and USD 10,000. At the time of writing this paper, the most popular ones are: Huygens (Scientific Volume Imaging); DeltaVision Deconvolution (Applied Precision, GE Healthcare Life Science); and AutoQuant (MediaCybernetics). Some of these commercial solutions (e. g. , Huygens) specialize in the processing of large data and are capable of running unattended deconvolution in batch mode  [10].
Meanwhile, several open-source deconvolution solutions exist too, often taking the form of an ImageJ 1 plugin. One of the first such platform that was made available is the popular Deconvolu-tionLab software developed at the Biomedical Imaging Group (EPFL) and detailed in the present paper. Freely distributed, Decon-volutionLab hosts various algorithms for 3D microscopy deconvolution and drives them through a user-oriented interface. Other open-source softwares also exist, including Nick Linnenbrügger's DeconvolutionJ, Bob Dougerthy's Iterative Deconvolve 3D 2 which implements a deconvolution approach for the mapping of acoustic sources, Piotr Wendykier's Parallel Iterative Deconvolution  3  which proposes four iterative algorithms, and the MiTiV 4  project that proposes blind deconvolution software.
Among others, the required skills address the selection of parameters, the control of computational and memory costs, and the recognition of restoration artifacts. In this paper, we take advantage of the release of Deconvolu-tionLab2, the revamped sequel of DeconvolutionLab, to provide a complete description of the software and its built-in deconvolution algorithms. In regards to the aforementioned pedagogical aspects, the present paper equally intends as a step toward the education of inexperienced users.
DeconvolutionLab2: A Java open-source software package Although microscope manufacturers may sometimes propose well-integrated software packages, their solutions are often mere black boxes. This situation prevents users to make an informed choice on which commercial deconvolution software is the most appropriate for their task at hand. Conversely, many deconvolution methods have been described in the scientific literature over the past twenty years, sometimes accompanied by open-source implementations. But even then, end users who do not master the underlying principles of deconvolution might face difficulties in selecting the method best suited to their needs. Moreover, academic packages meant to investigate some aspects of an algorithm are usually poorly designed in terms of user interface and applicable only to a specific class of signals.
At the Biomedical Imaging Group (EPFL), we have taken upon ourselves to develop the freely available software package Decon-volutionLab  5  to experiment with 3D deconvolution microscopy. DeconvolutionLab is a software platform that hosts various algorithms and drives them through a unified and user-friendly interface. After ten years of experience with this package, we have revamped it and renamed it DeconvolutionLab2. This second version keeps the same key ingredients that made the success of the first version: Java source code, efficient FFT (fast Fourier transform), pluggable algorithms and an accommodating user interface.
DeconvolutionLab: The original ImageJ deconvolution tool DeconvolutionLab was initially developed for educational purposes at EPFL. For over a decade it has been allowing students to conduct deconvolution experiments with the most representative classical algorithms, as well as with some more recent ones such as fast iterative soft-thresholding  [11] , Richardson-Lucy total variation  [12] , and thresholded Landweber  [13]. Nowadays, we still train students with the help of DeconvolutionLab. We have made DeconvolutionLab freely available since its release as an ImageJ plugin. As ImageJ is the de facto standard software tool of biological imaging, most biologists know how to install DeconvolutionLab on their own and can rapidly experiment with it. The package permits the deconvolution of large biological images at least as efficiently as commercial software packages  [9].
DeconvolutionLab2: The remasterized Java deconvolution tool The present paper focuses on the complete description of DeconvolutionLab2, the sequel to DeconvolutionLab. It is a freely accessible and open-source software package running on Windows, Linux, and Mac OS operating systems. The package can be linked to well-known imaging software platforms. The backbone of the software architecture is a library that contains the number-crunching elements of the deconvolution task. The current list of built-in algorithms includes: Naive inverse filtering (NIF, Section 3. 2); Tikhonov regularization (TR, Section 3. 3); Regularized inverse filtering (RIF, Section 3. 4); Landweber (LW, Section 3. 5); Tikhonov-Miller (TM, Section 3. 6); Fast iterative soft-thresholding (FISTA, Section 3. 7); Richardson-Lucy (RL, Section 3.
8); Richardson-Lucy with total-variation regularization (RL-TV, Section 3. 9). New algorithms are easily pluggable into the framework of DeconvolutionLab2. The source code is written in Java 1. 6, as closely as possible to the text-book definition of the algorithms. Inquisitive minds inclined to peruse the code will find it fosters the understanding of deconvolution. Our goal with DeconvolutionLab2 is to make deconvolution broadly accessible to the community of all those who show interest in this technique. To achieve such a goal, we provide a userfriendly front-end interface that also accommodates non-experts. Our software package is able to process large volumes on a midrange desktop computer, or even on a laptop computer. To experiment with the software, we share test data on the DeconvolutionLab2 website.
These data include synthetic and real cases to help benchmarking algorithms. DeconvolutionLab2 can act not only as a didactic tool equipped with a simulator (convolution and noise generator), but also as a validation module that gives access to the signal-to-noise ratio between a ground-truth image and the output of every algorithm. Like DeconvolutionLab, DeconvolutionLab2 is able to process data relevant to real biological applications. However, and contrarily to the commercial software packages, our tools are restricted to deconvolution alone. We intentionally apply neither pre-processing nor postprocessing. Compared to Deconvolu-tionLab, DeconvolutionLab2 includes new fast Fourier transform (FFT) libraries (see Appendix A. 1), a recordable macro for ImageJ, new apodization functions, new padding schemes, and new switchable constraints in the space domain.
Practical details DeconvolutionLab2 is delivered as a plugin for ImageJ  [20] , for Fiji  [21] , and for the new bio-imaging platform Icy  [22]. Since it is a Java class, it is also callable from the MATLAB command line and runnable as a standalone application through a Java Virtual Machine. For batch processing, we recommend calling Deconvo-lutionLab2 from an ImageJ macro. This key feature enables one to handle time-lapse images or multiple channels, which need to be processed individually, in sequence. Deconvolution is a heavy computational task in terms of running time and memory usage. In DeconvolutionLab2, we tried to find the best tradeoff between computational efficiency and code readability. The deconvolution is implemented in the discrete Fourier domain, so that the most time-consuming task is the FFT.
Landweber The LW algorithm minimizes the same least-squares cost function than NIF. But, instead of expressing the solution through direct inversion, it resorts to an iterative gradient-descent approach  [28]. In DeconvolutionLab2, we take advantage of the iterative nature of LW to impose a nonnegativity constraint at each iteration. Each update indexed by k can be written as x ðkþ1Þ ¼ P ðR þ Þ K x ðkÞ þ cH T y À Hx ðkÞ À Á n o ; ð10Þ where c is a step size parameter and P ðR þ Þ K fxg ¼ maxðx; 0Þ is the component-wise projection of x onto the set ðR þ Þ K. Minimizing the energy (4) only enforces data fidelity of the result. The consequence is that the solution at convergence of iterations  (10)  tends to produce an over-fitting of the noise in the input data. However, one may obtain a satisfactory tradeoff between deconvolution and noise amplification if the algorithm is stopped early.
At the same time, ringing artifacts will reveal themselves as waves in the background and as Gibbs phenomena in the high-contrast areas. To attenuate these artifacts, we have implemented two countermeasures in DeconvolutionLab2: apodization and zero-padding. Apodization consists in multiplying the input data by a window function that gradually sets the signal to zero near the borders of the image. Depending on the window specifics, the central part of the data may or may not remain pristine. In DeconvolutionLab2, we have made available the five classical apodization functions referred to as Cosine, Hamming, Hann, Tukey, and Welch. They can be applied independently along the axial and the lateral directions. As shown in Fig. 3(B) , apodization succeeds in cancelling the ghost object, but also reduces the intensity of the data.
While it modifies the data, apodization proceeds without a change in the image dimensions. Conversely, zero-padding maintains the data intact but modifies the dimensions of the image by extending its border with zero values. For practical reasons related to the computational efficiency of the FFT, the width of the extension is generally chosen such that the size of the extended image is highly decomposable as a product of small prime numbers. To facilitate adherence to this constraint, DeconvolutionLab2 automatically proposes extensions to the next even number, to the next multiple of 2 and 3, to the next multiple of 2, 3, and 5, and to the next power of 2, independently in the axial and the lat-eral directions. As shown in Fig. 3 (C), zero-padding succeeds in cancelling the ghost object, but does so at an increased computational cost compared to apodization.
Widefield data Finally, we briefly illustrate how DeconvolutionLab2 may be used in a practical application to efficiently deconvolve real biomicroscopy data. We work with a 3D visualization of a C. elegans embryo which was acquired on a standard wide-field microscope (k ¼ 542 nm;NA ¼ 1:4); the lateral pixelsize is 64. 5 nm and the stepsize in the axial direction is 160 nm. As shown in Fig. 7 , our 3D measurement displays some non-desirable visual features, such as a relatively poor contrast or an indistinguishability of certain neighboring centrosomes. To enhance the visual condition of this measurement, we apply three distinct deconvolution algorithms (TR, LW, RL) to it. The results after deconvolution are shown in Fig. 7. Globally, we observe similar effects than with the previous data sets.
The ''Memory Ratio" corresponds to the ratio between the required memory and the number of voxels of the input dataset. The deconvolution tasks were performed on a Mac OS X 2 Â 3:06 GHz 6-Core Intel Xeon for all algorithms except for the Huygens software that was run on a These three topics are shared by all inverse problems. Deconvolution microscopy can benefit from every improvements in this currently very active field of research. The priors introduced by the regularizer must be chosen carefully to retain usefulness while avoiding the pitfall of overfitting. During the last decade, the compressive-sensing and the sparsity theories gave theoretical grounds to the observation that the ' 1based regularizers of (  8 ) and (  19 ) in Sections 3. 7 and 3. 9, respectively, always perform better than the ' 2 -based regularizers of Eqs.
A 9. 6-meter-high EC flux tower was installed in the center of the 2. 45 km 2 area watershed. Sensible and latent heat fluxes data collected from 2010 to 2013 were quality controlled. The software REddyProc was used to gap-fill the fluxes at hourly timescale. To account for the combined effects of wind direction and topography, REddyProc was applied after separating the two dominant wind directions, which notably improved the estimated fluxes. Aggregating the hourly λE estimates at daily and monthly timescales allowed analyzing the temporal variability of ETa over the seasons and between years.
This induces the necessity to evaluate the existing gap-filling methods for EC data collected over hilly cropping systems, and to adapt these methods if necessary. In the context of providing long-term observations of ETa, the current study aimed at evaluating a simple, robust, available and widely used gap-filling method, for obtaining complete time series of ETa measurements over hilly cropping systems. We used a nearly four-years long EC measurements dataset, collected from a flux tower within an agricultural watershed. The latter was typified by hilly topography, and the footprint of the flux tower measurements integrated a patchwork of crop fields. The crops were rainfed, which minimized advection processes induced by changes in soil moisture. We evaluated the gap-filling method proposed by  [24] , which is world widely distributed through the REddyProc package.
We evaluated this method on λE for ETa, but also on H that is used to estimate water status indicators (e. g. Bowen ratio, evaporative fraction). We compared the performance of the gap-filling method when used in its original version and when adapted to the conditions of hilly and heterogeneous cropping systems, i. e. by discriminating between wind directions. Finally, daily and monthly values of ETa were documented and analyzed. The paper is structured as following. Section 2 present the experimental strategy (site description, experimental design, data collection and processing, implementation and evaluation of the gap-filling method) as well as the experimental conditions, with a focus on meteorology. Section 3 reports and discusses (1) the comparison of the performances of the original and adapted versions of the gap-filling method, as well as (2) the obtained times series of ETa estimates at the daily and monthly timescales.
Instrumental design The meteorological station is located at 36°52'10. 5" N, 10°52'6. 3" E, 108 m asl. , near the catchment outlet. The measurements of ETa at the watershed scale are collected from an eddy covariance (EC) device set up on a 9. 6 m-height flux tower. Installed in 2010, the flux tower is located at 36°52'39. 0''N, 10°52'36. 4''E, 115 m asl. , close to the center of the watershed (see Figure  1 ). In the current study, we focused on the data collected between March 2010 and August 2013.
Eddy covariance flux tower The eddy covariance (EC) tower was equipped with sensors installed at 9. 6 m above the soil surface for measuring the turbulent fluxes. A three-dimensional sonic anemometer (CSAT3, Campbell Scientific, USA) measured wind speed in the three directions and air temperature at a 20 Hz frequency. The sonic anemometer was vertically set up and oriented relative to North West. An open path gas CO2 / H2O analyzer (LI-7500, Li-Cor Biosciences, USA) measured concentrations of water vapor and CO2 at a 20 Hz-frequency. A thermo-hygrometer HMP45C sensor (Vaisala, Finland) measured air temperature and air humidity at a 1 Hz-frequency. All sensors were connected to a datalogger (CR3000 Campbell SC, USA) and were power supplied by photovoltaic panels connected to batteries. Data from the fast sensors (CSAT3 and LI-7500) were stored at 20 Hz frequency.
Quality control The quality control of the 30-minutes flux data was performed using two standard tests that are routinely employed over flat and sloping terrains, i. e. , the Steady State test and the Integral Turbulence Characteristics test. These tests permitted to ensure that the theoretical requirements for the EC measurements were fulfilled  [27]. On the same site,  [21, 22]  applied these tests over EC datasets collected under conditions of hilly topography, and reported good energy balance closure for the selected data. We kept the high and good quality classes as defined by  [28]  and  [29] , since these two classes are considered as suitable for long-term observations. Before quality control, the rate of missing because of experimental failures (deficient energy supply, sensors malfunctioning) ranged between 20% and 57% for H, and between 28% and 81% for λE (see Table  1 ).
REddyProc gap filling method In this study, the gap filling method proposed by  [24]  was chosen. This choice was motivated by the following reasons. First, this method was tested and proven for a worldwide variety of land surface conditions, in the context of the FLUXNET network. Second, this method does not involve much ancillary information, as it only requires meteorological data. Third, this method is world-widely spread through the R package labeled REddyProc that was developed by  [24]  at the Max Planck Institute for Biogeochemistry Institute. This package relies on a gap-filling technique similar to that proposed by  [11] , and additionally includes (1) the co-variation of fluxes with meteorological variables, and (2) the temporal auto-correlation of the fluxes. In this method, three different conditions are identified one after another  [24]  : • Step 1: all meteorological data of interest are available (solar incoming radiation Rs, air temperature Ta, and vapor pressure deficit VPD).
Adapting the REddyProc method to hilly cropping systems REddyProc was applied to fill gaps within incomplete times series of H and λE measurements, labeled HORI and λEORI hereafter, by using the meteorological data collected at the local meteorological station: global radiation, air temperature and vapour pressure deficit. The time series of H and λE measurements to be completed were quality controlled data (see section 2. 3. 2). • First, REddyProc was applied in its original version without discriminating the two dominant wind directions (classical way). The obtained gap-filled data were labeled HREP and λEREP. • Second, REddyProc was applied after discriminating the collected data under conditions of north-west (NW) and south (S) winds. We split the complete time series in two datasets. The NW (respectively S) dataset included the HORI and λEORI data collected under NW (respectively S) wind conditions.
05, intercept = -0. 5), with a small dispersion (RMSE = 12. 2 W. m -2 ), whereas under S winds, λEREP overestimated λERNS (slope = 0. 81, intercept = 3. 8), again with a slightly larger dispersion (RMSE = 18. 9 W. m -2 ). These results clearly demonstrate the need of accounting for the wind direction when gap-filling land surface fluxes at hourly time scale, over hilly conditions. Under NW winds, REddyProc without (REP) or with (RNS) discriminating between wind directions leaded to very similar estimates of the fluxes H and λE. As the NW wind sectors were more frequent (66% in our dataset), the classical way of applying REddyProc (REP) was likely to often replace missing flux data under S winds, by flux data obtained under NW winds. Hourly flux data were integrated at the daily time scale.
Conclusions This study aimed at obtaining continuous time series of actual evapotranspiration from EC measurement collected during nearly four years, within an hilly watershed covered by rainfed crops, in a Mediterranean context. This agricultural watershed was typified by the existence of two dominant wind directions, that might interact with the hilly topography. The REddyProc method was choosen to gap-fill the missing flux data that are inherent to the eddy-covariance method, but was adapted to our particular conditions by separating the flux dataset between the two dominant wind directions. It was demonstrated that at hourly timescale, it was necessary to discriminate between wind directions. Conversely, the fluxes obtained with or without discriminating wind directions were very similar at daily and monthly timescales.
Acknowledgments: The author's express their thanks to (1) the  Environmental Research Observatory OMERE  (http://www. obs-omere. org) and the data used are available (https://zenodo. org/record/821527#. WVZcssbpORs) according to its data policy; (2) the  MISTRALS/SICMED program ; (3)  French National Research Agency (ANR) TRANSMED program  through the  ALMIRA project  (contract  ANR-12-TMED-0003 ) for their financial support provided for this study; (4) the technical staff from  INRGREF  and  UMR LISAH particulary Rim Louati  and  François Garnier.
The impact of these algorithmic developments on developmental biology has become particularly visible in recent years, notably thanks to the latest advances in computing architectures able to handle and process massive & 2017 The Author(s) Published by the Royal Society. All rights reserved. multi-dimensional datasets, together with the progressively systematic release of computer codes through open collaborative software platforms  [9]. This review discusses the problems, solutions and remaining challenges in the quest for quantitative tissue analytics from the point of view of bioimage informatics, with a particular focus on the primordial tasks of cell segmentation and tracking, for they are crucial first steps to decipher the mechanisms by which cells form and shape tissue throughout development.
Commonly used pre-processing methods for tissue imaging can be grouped into one of four categories: (1) Fusion methods combine data from multiple acquisitions of the same sample, thereby reducing the background noise or the impact of light scattering through the tissue. These methods range from simple line or frame averaging to more complex multi-view fusion of varying sample poses, notably in SPIM  [4,7,12 -14]   [18,19,21 -24]. Image pre-processing can be of crucial importance to facilitate the subsequent analysis steps, so long as the chosen method is adapted to the specimen and optical device at hand. Nevertheless, it is worth stressing that these techniques apply some transformation that directly affects the pixel values (and thereby the dynamic range) of the original dataset. In some instances, such pre-processed datasets may no longer be usable to extract biological information that directly depends on carefully calibrated intensity values (e.