dataesr/scientific-paragraphs-categorization huggingface dataset:
The different parts of the imaging set-up (back-light, plate support, and camera) can be positioned along a horizontal double-rail to control the field of view of the camera and accurate lighting. In addition, the camera can be moved vertically. ChronoRoot allows image acquisition at a high temporal resolution (a set of pictures every minute). The use of an IR back-light (850 nm) and optional long-pass IR filters (>830 nm) allow images of the same quality to be acquired independently from the light conditions required for the experiment, during day and night. Each module is connected to the network either by Wi-Fi or Ethernet cable. A web interface allows the control of the device offering live feed of the cameras for field of view and focus setup. The user can program the activation of cameras and IR backlight, starting and ending dates, the time basis for picture acquisition, and finally follow the progression of the experiment.
Root imaging systems of intermediate complexity, like the one proposed in  [22] , serve to address the temporal phenotyping of plant roots using vertical plates for plant growth in agarized medium. However, such a system still requires a single-axis mobile robot, which implies more expensive electronics and plant chamber space devoted to the equipment set-up. In contrast, low-cost ChronoRoot modules can be located easily in already existing facilities without major modifications or permanent movement. The number of modules to be built and used will only depend on the available space and the experimental design (e. g. , a few modules for the characterization of given genotypes vs multiple units for genome-wide association studies [GWAS] approaches using tens to hundreds of plant accessions).
It also allows the hardware set-up to be scaled according to available funding and experi-Downloaded from https://academic. oup. com/gigascience/article/10/7/giab052/6324285 by guest on 23 August 2021 ment requirements progressively. In parallel, the advancement of the do-it-yourself movement has promoted the development of a growing number of low-cost phenotyping devices combining 3D-printed, laser-cut, captor, and microcontroller coming from open-source and open-hardware communities such as Arduino  [23]  or Raspberry Pi  [24]. Successful inexpensive devices have allowed plant leaves to be monitored  [25] , including Phenotiki, an affordable open software and hardware platform for imagebased phenotyping of plant aerial organs  [26]. More recently, the Phenotiki sensor interface was used to characterize cotton RSA on soil-containing big Rhizoboxes, allowing the determination of basic architectural parameters  [27]  (e.
g. , total root area, convex hull area, total root length). In contrast, ChronoRoot allows for a more fine-grained high-throughput temporal phenotyping, e. g. , making it possible to distinguish between MR and LRs. Finally, modular rhizotrons of more sophisticated designs (including more expensive cameras and light flashes, precluding observations during the night) also served for RSA characterization of crops  [28]. In the past few years, images of the root system from different plant species have been acquired manually using a flat-bed scanner or a camera positioned in front of vertical petri dishes. Root phenotyping is generally performed upon single-timepoint images or using several images acquired during growth in time-lapse sequences. More recently, semi-automatic devices and softwares have also helped to increase the efficiency of image acquisition and associated analyses  [29] [30] [31] [32] [33] [34].
Moreover, 3D RSA reconstruction is possible using X-ray computed tomography  [38]  or magnetic resonance imaging  [39]. Nearly all the reported tools need human input to be operated and retrieve precise numbers. RootTrace  [40, 41] , for example, which also focuses on high-throughput analyses of root growth, uses traditional image processing and tracking techniques, resulting in a program that can only extract MR length and count the number of emerged LRs. On the contrary, our model relies on deep networks producing a detailed segmentation of the RSA, which is then classified into MR and LR, allowing for finegrained measurements such as the total length of the LRs, which is not provided by RootTrace. Other tools such as GiaRoots  [34]  and EZ Rhizo  [31]  use simple threshold strategies for root segmentation.
The previously discussed methods are mostly based on conventional image processing approaches and extract a limited number of RSA features. Advances in machine learning applied to image analysis allowed these limitations to be partially overcome. For example, deep learning techniques have been used to improve the consistency of classic approaches, enhancing the quality of root segmentations  [44, 45]. Closest to our work is the recent RootNav 2  [46] , which is also based on deep learning models and provides fine-grained metrics distinguishing between MR and LRs. However, RootNav 2 does not exploit the redundancy provided by the temporal resolution and follows a different architectural design, which makes ground truth annotations more difficult to obtain and prevents us from training the model with our dataset.
Compared to ChronoRoot, RootNav 2 uses a more complex neural network architecture with 2 output paths: the first one is used to predict root segmentation masks (differentiating between MR and LRs) while the second one produces heat maps associated with root tips. This design choice requires the ground truth annotations to be composed of 3 parts: (i) MR pixel-level annotations, (ii) LR pixel-level annotations, and (iii) root tip annotations. Conversely, ChronoRoot just requires binary segmentation maps (background vs foreground root) for training because the MR and LR labelling is performed after segmentation following a depth-first search approach on the skeletonized binary segmentation. Thus, our dataset is just composed of images with foreground/background pixel-level annotations, which is not enough for training the RootNav 2 model.
Potential Implications ChronoRoot expands the possibilities for high-throughput root phenotyping, which is of major importance for natural variation and GWAS, as well as mutant characterization and screening. Notably, it has been shown that clock-related mutants exhibit a differential oscillating MR growth under alternative conditions  [15, 16, 47]. ChronoRoot offers an ideal platform for the identification of genotypes associated with altered clock traits, based on the analysis of spectral features extracted from temporal signals. Note that the specification of all ChronoRoot hardware components (e. g. , camera) is released in this article. Thus, for anybody installing the system and using the same imaging set-up, our software for A. thaliana analysis should work without further retraining.
Hardware description An automated imaging set-up was designed and built in the shape of an independent module of 62 × 36 × 20 cm (Fig. 1 ). It is aimed at imaging ≤4 vertical plates either in color or in nearinfrared (NIR) lightning. Each module consists of a single board computer (Raspberry Pi) controlling 4 cameras through a multiplexer module and an array of NIR LED illumination through a relay. The main support of each module is a 620 × 36 × 5 mm acrylic sheet cut using a laser cutter to allow to screw the different parts or let pass strips connecting the camera to the camera multiplexer. Several 3D pieces were designed and printed to place the different components of the module. Each module is separated in 4 subparts, each of them along a double aluminum axis. This axis allows adjustment of the distance of the different parts of the imaging set-up: NIR illumination, plate support, camera.
Computational methods We evaluated different state-of-the-art architectures for image segmentation, and proposed new variants that achieved a good compromise between processing time, model complexity, and accuracy, as discussed in the Results section. This segmentation module is followed by several post-processing stages including a CRF post-processing to enhance label homogenity, a temporal consistency refinement step, skeletonization, graph construction, and node tracking. ChronoRoot outputs a labelled graph per image indicating which nodes correspond to the seed, MR, LRs, bifurcations, and the root tips. For each time step, the complete RSA is saved following the RSML format  [49].
Deep learning models for root segmentation CNNs are representation learning methods with multiple abstraction levels, which compose simple but nonlinear modules transforming representations at one level into a representation at a higher, slightly more abstract level  [50]. These models are specially suited for computer vision tasks, in particular for image segmentation  [51]. We explored 6 different convolutional neural network architectures to perform plant root segmentation. Four of them are state-of-the art existing architectures, while the other 2 were proposed in this work. In what follows, we first present a brief description of the state-of-the-art architectures (namely, the UNet  [6] , ResUNet  [52] , SegNet  [7] , and DeepLab  [8] ) and then discuss the 2 models proposed in this work.
Ensemble Our final segmentation method is an ensemble model. The idea of ensembling is that we can create higher performing models by combining multiple predictors using an aggregation function. One of the most common strategies to implement ensemble models is bagging  [55] , where the same classifier is trained multiple times using different samples of the training set, and the final output is obtained as the average of the independent predictions. In this work, we followed a different principle that had been successfully applied in the context of medical image segmentation, where instead of combining several instances of the same model trained with different training samples, we combined different models and architectures trained with the same datasets  [11]. The idea is to average out the bias infused by individual model configurations, to approximate more reliably the true posterior distribution.
In the context of image segmentation, given a dataset L = (x, y) i , where x is an intensity image and y the corresponding ground-truth segmentation, we aim at learning the underlying conditional distribution P(y|x), which maps input images x into segmentation maps y. This is commonly approximated by a model P(y|x; θ m ), which has trainable parameters, determined in our case by the neural network architecture. These parameters were learnt so that they minimize a particular loss function (see next section for more details in the loss functions used in our work) using the dataset. Given different architectures, we obtained independent estimates and combined them following  [11] , approximating the posterior P(y|x) as: P (y|x) ≈ 1 M M m P (y|x, θ m ). (1) We implemented this ensemble of multiple models and architectures by averaging the predictions of the 5 previous models (UNet, ResUNet, DSResUNet, SegNet, and DeepLab v3), obtaining a more robust and accurate segmentation method that significantly outperforms the independent instances.
Training details All the CNN models were trained using binary cross-entropy as the loss function, Adam optimizer with default parameters, learning rate of 0. 0001, and weight decay = 1e-8 for UNet-like models, 1e-9 for DeepLab, and 1e-10 for SegNet. The hyperparameters were chosen by grid search using the validation data. All models were implemented in TensorFlow 1, and the source code is publicly available. The training was done on a standard workstation with Intel R Core TM i7-8700 CPU, 64 GB RAM, and a NVIDIA Titan X GPU. Because we are dealing with a relatively small dataset, data augmentation was crucial to achieve good segmentation performance. We implemented online data augmentation through a variety of patch-based augmentation procedures including addition of Gaussian noise, random Gamma corrections to simulate different lighting conditions, artificial blur, and horizontal flipping.
Note that we visualize the graphs using a simplified version where only nodes corresponding to seed, bifurcation, and tips are shown and connected. However, it is important to highlight that MR and LR length are computed considering the real length along the labelled skeleton, which are stored as an edge attribute in the simplified graph for optimization reasons. cation equipment. We thank Jean-Paul Bares and Ma ël Jeuffrard from IPS2 for support and assembling of the ChronoRoot modules. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp used for this research.
Funding This work was supported by grants from  French State  (Saclay Plant Sciences, reference No. ANR-17-EUR-0007 , EUR SPS-GSR) managed by the  French National Research Agency  under an  Investments  for the  Future program  (reference No. ANR-11-IDEX-0003-02 ) to V. D. , T. R. , M. C. , and T. B. ; CNRS through the  MITI interdisciplinary programs  to T. B. ;  AXA Research Fund, AN-PCyT  ( PICT2018-3907 ) and  UNL  ( CAI+D 50220140100084LI  and  50620190100145LI ) to E. F. ;  ANPCyT  ( PICT2019-04137 ) to F. A. ;  AN-PCyT  ( PICT 2018-3384 ) to DM. ; and  CNRS (Laboratoire International Associ é NOCOSYM)  to M. C. and F. A.
Data Availability All data gathered and reported in this study are available as supplementary material. The 2 datasets of images and annotations described in the Datasets section, as well as the 3D printing and laser cutting files, are publicly available at https: //github. com/ThomasBlein/ChronoRootModuleHardware under the CERN Open Hardware License Version 2-Strongly Reciprocal licence. Supplementary figures and tables referenced in this work, as well as a detailed description of the hardware system, are available in the supplementary file. Snapshots of our code and other data further supporting this work are openly available in the GigaScience Repository, GigaDB  [61].
Three protein families including different sub-families were used to train AAE models. The protein functional annotations of these families were used to analyze the clustered sequences. The three different protein families selected were the sulfatases, the HUP (HIGH-signature proteins, UspA, and PP-ATPase) and the TPP (Thiamin diphosphate (ThDP)-binding fold, Pyr/PP domains) families. The sulfatases are a group of proteins acting on sulfated biomolecules. This family have been manually curated into sub-family with specific functions according to substrate specificity  (Barbeyron et al. , 2016). They are found in various protein family databases, such as in Pfam (PF00884). The SulfAtlas database  (Barbeyron et al. , 2016)  is a collection of curated sulfatases centered on the classification of their substrate specificity.
Protein families The sulfatase family An initial seed protein multiple sequence alignment (MSA) was computed from sequences of the protein structures of SulfAtlas  (Barbeyron et al. , 2016)  database sub-families one to 12. This seed was used to search for homologous sequences on the UniRef90  (Suzek et al. , 2014)  protein sequence database using hmmsearch  (Eddy, 2011)  with reporting and inclusion e-values set at 1e -3. A label was assigned to each retrieved protein if the protein belonged to one of the 12 known sub-families. The MSA computed with hmmsearch was filtered to remove columns and sequences with more than 90% and 75% gap characters respectively. Proteins with multiple hits on different parts of their sequences were also merged into a single entry. From 105181 initial protein sequences retrieved by hmmsearch, the filtering steps led to a final set of 41,901 proteins.
HUP and TPP protein families A similar protocol was followed for the HUP and TPP protein families. Instead of using an initial seed alignment made of sequences with known 3D structures, the CATH protein domain HMM  (Orengo et al. , 1997; Sillitoe et al. , 2018)  was used to search for homologous sequences in the UniRef90 database. CATH models 3. 40. 50. 620 and 3. 40. 50. 970 correspond to the HUP and TPP protein families, respectively. A sequence filtering pipeline identical to the one used for the sulfatase family was applied to each of the resulting MSAs. The final numbers of proteins in each dataset were: 25041 for the HUP family (32,590 proteins before filtering) and 33,693 for the TPP family (13,3701 before filtering).
The sequence similarities were also computed inside a sub-family, between sub-families, and between generated sequences. The distributions of sequence similarities allow to explore the abilities of the latent space arithmetic operations and of the decoder to produce meaningful intermediate protein sequences from data points not corresponding to biological sequences. These data points correspond to an uncharted sequence space. Protein structural models were computed using the structures of the initial sub-families as templates for MODELLER  (Webb & Sali, 2014)  and evaluated using the DOPE score  (Shen & Sali, 2006). Models were computed using the generated sequences by latent space arithmetic on template structures from their source and query sub-families. The DOPE energies of the modeled structures were compared to structural models computed as references.
RESULTS A structurally constrained MSA was computed using Expresso from T-Coffee webserver  (Armougom et al. , 2006; Di Tommaso et al. , 2011)  between sequences of S1 sulfatases structures. This MSA was processed into a Hidden Markov Model and hmmsearch was used to retrieve aligned sequence matches against the UniRef90 sequence database. A total of 76,427 protein sequence hits were found to match the sulfatase HMM in UniRef90. The sequences were filtered to remove columns and hits with more than 90% and 75%, respectively, of gap characters. The final MSA comprised 41,901 sequences. The sulfatases protein dataset was separated into a training, a validation, and a test sets with a split ratio of: 0. 8, 0. 1, and 0. 1. The three different AAE architectures (see "Method" section) were trained on the training set and evaluated on the validation set.
The protein is projected (yellow dot corresponding to the S1-11 sub-family) at coordinates (0. 733,-1. 289), inside the space of the S1-4 family (red). This may look like an error but a closer inspection shows that this protein is part of both the S1-4 and S1-11 sub-families of the SulfAtlas database. Projections of sequences into latent spaces using AAE with two dimensions were also tested on the HUP and TPP families. The AAE projections can be visualized on Fig. S3. There are fewer functional annotations for these two families than for the sulfatase family. A strong separation can however clearly be observed between the major functions of the two families. Latent spaces were evaluated for each protein family based on enzyme classification (EC) and taxonomic homogeneity. Given a set of protein sequences, the encoded sequences in a latent space of a 100 dimensions were clustered using HDBSCAN.
Protein latent space interpolation Interpolation between encoded sequences can be used to "navigate" between proteins of two sub-families. After the selection of a query sub-family, the sequences of this sub-family are projected to the latent space and used as the starting points of the interpolation. The end points of the interpolation correspond to sequences of a target sub-family, different from the query sub-family, and projected into the latent space. Twenty pairs of protein sequences were randomly selected between all combinations of protein subfamilies to test the capacity of the encoded space and 50 intermediates, i. e. interpolated, data points were generated between each query/target pair. The sequence similarities were computed between the generated protein sequences from the interpolated latent space and the biological query and target protein sequences of the sub-families.
The Sulfatases sub-families S1-0, S1-2, S1-3, S1-7, S1-8 and S1-11 were chosen to test this technique. In the following section, the terminology _ S eq. S1-XmY will correspond to a generated sequence using a combination of the mean latent space of the sub-family S1-Y added to the latent space of the sub-family S1-X. The X and Y sub-families will be referred to as the query and source sub-families. First, two Prosite motifs of the Sulfatase family are analyzed from generated and original sequences. Figure  S6  displays logo plots of two regions corresponding to Prosite motifs PS00523 and PS00149 to illustrate the amino acid content of the generated protein sequences by latent space arithmetic. These regions correspond to the most conserved regions of the sulfatase family and have been proposed as signature patterns for all the sulfatases in the Prosite database.
Capitalizing on Nextflow, Conda, Docker, Singularity and the nf-core initiative, we propose a set of best practices along the development life cycle of the bioinformatics pipeline and deployment for production operations which address different expert communities including i) the bioinformaticians and statisticians ii) the software engineers and iii) the data managers and core facility engineers. We implemented Geniac (Automatic Configuration GENerator and Installer for nextflow pipelines) which consists of a toolbox with three components: i) a technical documentation available at https://geniac. readthedocs. io to detail coding guidelines for the bioinformatics pipeline with Nextflow, ii) a linter to check that the code respects the guidelines, and iii) an add-on to generate configuration files, build the containers and deploy the pipeline.
, 2020). Both levels feed each other in a virtuous circle. Whatever the level, the analysis is generally a complex workflow (or pipeline) which involves many steps with different informatics languages and software, whether developed in-house or by third-parties. Coordinating the execution and allowing the scalability of these different steps to benefit from high-performance computing infrastructures to speed-up computation have been simplified with the development of workflow management systems  (Leipzig, 2017; Strozzi et al. , 2019)  in combination with several technologies  (Gruening et al. , 2018; Tanjo et al. , 2021)  including package management systems with Conda or Bioconda  (Grüning et al. , 2018) , and containers with Docker  (Merkel, 2014)  or Singularity  (Kurtzer et al. , 2017).
This obviously requires harmonized guidelines and protocols across the different developers involved in the development life cycle of the pipeline. This way, it is easier to maintain many different pipelines on one hand and it simplifies the skill transfer from one person to another on the other hand. We developed the biogitflow protocols which cover the code versioning using git and GitLab  (Kamoun et al. , 2020). However, these protocols do not address how to write the code itself. While very valuable guidelines have been proposed to avoid pitfalls in the implementation of bioinformatics pipelines using the technological stack with workflows management systems, package management systems and containers  (Gruening et al. , 2018; Reiter et al. , 2021; Tanjo et al. , 2021) , they remain very generic.
Therefore, software engineering best practices and technical protocols for coding are necessary in order to promote software quality in bioinformatics to ensure the usability, maintainability, interoperability, sustainability, portability, reproducibility, scalability and efficiency of the workflows, as highlighted by several authors  (da Veiga Leprevost et al. , 2014; Georgeson et al. , 2019; Lawlor & Walsh, 2015). We acknowledge the nf-core initiative  (Ewels et al. , 2020)  which plays a major role in promoting the software engineering best practices to implement bioinformatics pipelines with Nextflow including code template, linter, code reviewing and continuous integration for better quality. Our Bioinformatics Core Facility decided to capitalize on both Nextflow and the nf-core initiative to provide the end-users with bioinformatics pipelines in the context of production analysis.