dataesr/scientific-paragraphs-categorization huggingface dataset:
This confirms our observation that the structural scores are strongly correlated, while the SSL-RBM brings another view of the data. We also trained Logistic Regressions with cross validation using the different scores and computed the Area Under the Curve for their ability to predict functional variants. While the RBM energy alone had an AUC of 0. 86 (see on The classifier score (pseudo-likelihood score for the recognition of the NGG motif) correlates well with the activity and is complementary to the RBM energy, see Fig 8b. Let us, however, stress that we focused in this work on the NGG PAM only. The reason to do so is twofold. First, the NGG motif is well represented in our initial labelled data. Second, this motif is recognized by our reference enzyme (SpyCas9) as well as by the large majority of close natural  variants present in our dataset, as seen in the phylogenetic tree provided in supplementary (S5 File).
Discussions Structural biology has been a field of major breakthroughs these last years with the increasing success of Deep Learning models for secondary structure prediction  [46, 47] , ligand or protein binding site prediction  [48, 49]  and of course tertiary structure prediction with frameworks such as AlphaFold2  [21] , RoseTTAFold  [22]  or ESM Fold  [50]. Older frameworks like Rosetta  [20] , Amber  [51]  or FoldX  [19]  have been developed around an empirical force field to encompass molecular and atomistic interactions. These frameworks rely on strong physicochemical priors designed to tackle the general lack of structural data. Even today, while hundreds of millions of protein sequences are available in public databases, only tens of thousands of structures are available since their determination is complex and expensive.
Force fields can Green signifies regions where the deleterious impact of mutations is accurately predicted. Conversely, red indicates regions where predictions miss the mark. Areas with insufficient data (less than five variant with a mutation at this position) are marked in gray. Notably, the RBM energy offers precise predictions at the DNA-binding interface, in contrast to AlphaFold2 RMSD and ΔΔG, which tend to be less accurate at these sites. https://doi. org/10. 1371/journal. pcbi. 1011621. g009 evaluate the compatibility between an amino acid sequence and a given structure, and are generally more robust to over-fitting than data-based models. Machine learning methods have also been applied to the modeling of proteins based on their primary sequence information alone. Such efforts range simple models, like the RBM employed here, trained on single protein families, to large language models trained on hundreds of million of diverse protein sequences with impressive successes in various tasks  [12, 13, [52] [53] [54].
Our method yielded functional Cas9-PAM Interacting domain variants differing by more than 50 amino-acids from any known natural sequence. The data generated highlighted how evolution-based modeling and structural modeling capture different information, and how the combination of both can advantageously be used to identify functional variants with high confidence. AlphaFold2 and FoldX can predict the protein structure but miss some information about the functionality of the sequence that can be inferred directly from the first and second order statistics of the Multiple Sequence Alignment. This observation could drive further theoretical works to better understand and circumvent the blind spots of structural modeling. One possibility is that RBMs capture information related to the protein biological function, such as its ability to undergo conformational changes or to interact with other molecular substrates, that are reflected in the sequence statistics of the protein family.
Such information might, however, be missed by AlphaFold2 or FoldX if it is not directly relevant to the stability of the protein fold. In this work, we also demonstrated how scarce functional data about the protein family can be used to improve the model using semi-supervision. We used data on the PAM binding sequence of the Cas9 PID available for 14% of our dataset. The use of this data during RBM training improved both the ability of our model to predict PAM sequences based on the PID sequence, and more interestingly contributed to improving the accuracy of our model in predicting the activity of Cas9 variants. Surprisingly, our model generated Cas9 variants that were more active than SpyCas9 for the task of gene silencing, even if this was not an explicit goal of our design. A possible explanation is that the constraints we placed on FoldX energies led to the generation of proteins that bind the target DNA more stably and are therefore more active in the gene silencing task.
A future direction of this work will be to employ our models to generate Cas9 variants with modified PAM specificity while maintaining wild-type level efficiency or even improving on it. Altogether, our results highlight the possibility to further improve our protein modeling capabilities by incorporating, structural, evolutionary but also functional information into our models and protein design process. Supervision data for the SSL-RBM consisted in the PAM recognition sequence of 154 PID variants obtained from Vink et al. [41]. This dataset contains PAM sequences inferred from the alignment of regions adjacent to target sequences of natural CRISPR-Cas systems. 4. 1. 2 engineering. To build a validation set, sequences of the PF16595 PFAM family were clustered using MMSEQS2 with 90% similarity threshold  [59].
The other term, however, is trickier: @ log ZðY RBM Þ @Y RBM ¼ 1 ZðY RBM Þ @ @Y RBM X x e À E RBM ðxjY RBM Þ ð10Þ ¼ À X x @E RBM ðxjY RBM Þ @Y RBM PðxjY RBM Þ ð11Þ where P Y RBM the distribution of the RBM model, denoted by M. We then have E x�T @pðxjY RBM Þ @Y RBM � � ¼ E x�M @E RBM ðxjY RBM Þ @Y RBM � � À E x�T @E RBM ðxjY RBM Þ @Y RBM � � ð12Þ While sampling from the training set is easy, sampling from the model distribution is harder. For this, we mainly rely on Persistent Contrastive Divergence  [39]. We implemented our own RBM in PyTorch (available on https://github. com/CyrilMa/torchpgm-standalone) running on GPU, and we used the AdamW  [61]  optimizer as it yielded a more robust training. The number of neurons in the hidden layer (200) was fixed arbitrarily at an early stage by a quick exploration of the number of Gaussian units.
For each variant, we build the list of mutations to the wild-type and used the BuildModel function to evaluate stability. You can see these stability estimates on    [62]  (GenBank: MT412099. 1), a p15a plasmid with a chloramphenicol resistance gene carrying dCas9 expressed under the control of the DAPG inducible pPhlF promoter and a guide RNA under the control of a constitutive promoter. To obtain plasmid pWR5, the PID domain of dCas9 was replaced by a ccdB gene framed by BsaI restriction sites, enabling to easily swap in new PID sequences. In addition, an operon consisting of the mCherry gene followed by the sacB gene under the control of a constitutive promoter was added to the plasmid downstream of the dCas9 gene. Finally, the plasmid contains a guide RNA targeting the promoter of the mCherry-sacB operon.
Funding: SC and RM were supported by the  Agence Nationale de la Recherche  grant numbers  ANR-17-CE30-0021 RBMPro  and  ANR-19-CE30-0021  Decrypted. CM is recipient of a PhD funding from  AMX program , E  ´cole polytechnique  and benefits from financial support from the  Centre de Recherche Interdisciplinary (CRI)  through " E ´cole Doctorale Frontieres de l'Innovation en Recherche et Education -Programme Bettencourt ". DB, WR and FD were supported by  European Research Council  [ 677823 ],  European Research Council  [ 101044479 ],  Agence Nationale de la Recherche  [ ANR-10-LABX-62-IBEID ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Providing energy from fusion and finding ways to scale up the fusion process to commercial proportions in an efficient, economical, and environmentally benign way is one of the grand challenges for engineering. Controlling the burning plasma in real-time is one of the critical issues that need to be addressed. Plasma Position Reflectometry (PPR) is expected to have an important role in next-generation fusion machines, such as DEMO, as a diagnostic to monitor the position and shape of the plasma continuously, complementing magnetic diagnostics. The reflectometry diagnostic uses radar science methods in the microwave and millimetre wave frequency ranges and is envisaged to measure the radial edge density profile at several poloidal angles providing data for the feedback control of the plasma position and shape.
While significant steps have already been given to accomplish that goal, with proof of concept tested first in ASDEX-Upgrade and afterward in COMPASS, important, ground-breaking work is still ongoing. The Divertor Test Tokamak (DTT) facility presents itself as the appropriate future fusion device to implement, develop, and test a PPR system, thus contributing to building a knowledge database in plasma position reflectometry required for its application in DEMO. At DEMO, the PPR diagnostic's in-vessel antennas and waveguides, as well as the magnetic diagnostics, may be exposed to neutron irradiation fluences 5 to 50 times greater than those experienced by ITER. In the event of failure of either the magnetic or microwave diagnostics, the equilibrium control of the DEMO plasma may be jeopardized.
Testing the Integration of the Antennas of Gaps 4 and 6 between Blanket Modules To optimize the design of the antennas for gaps 4 and 6, extensive simulations were performed using the REFMUL Finite-Difference Time-Domain (FDTD) code [23-Error! Reference source not found. ], which is able to consider the geometric characteristics of the space between BMs where the antennas would be installed. From these simulations,.
The utilization of FDTD codes enables a detailed description of the reflectometers, which includes aspects such as propagation in realistic plasmas, the specific location within the vacuum vessel, and access to the plasma. Additionally, the results of these simulations can be used to test new signal-processing techniques. To predict the behaviour and capabilities of the proposed reflectometry system for DTT, three synthetic diagnostics (SD) were prepared. These SDs correspond to three O-mode reflectometers located on the LFS, with one on the midplane and two others located away from the midplane  [43]. The simulations were performed using the 2D FDTD full-wave code REFMULF in two plasma scenarios envisioned for DTT, namely the 5 MA single null (SN) and double null (DN) configurations during the start-of-flat (SOF) phase of the discharge.
The neutronics and thermo-mechanical studies provide an insight into what needs to be optimized in the design to meet certain requirements, while the EM studies provide an understanding of the impact of the thermo-mechanical loads on the ability of the systems to perform measurements. Some of the developments achieved so far have strongly benefited from the progress made for the REFMUL suite of codes developed at IPFN for reflectometry simulation. The suite includes REFMUL, a 2D O-mode simulation code, and REFMULX/REFMULXp, a 2D X-mode (serial/parallel) simulation code. Recently, two new codes were added: REFMULF and REFMUL3. REFMULF is a 2D code able to cope with full polarisation waves, treating all components of the electric and magnetic fields of the wave  [50]. REFMUL3, which recently entered production, is a 3D full-wave code.
Additional tools were developed to automate the simulation and data analysis of a generic PPR system and make it adaptable to different devices. These techniques can be used to optimize a given PPR system for a given equilibrium scenario and study its stability. Furthermore, an effort has been devoted to EM modelling to further understand how edge turbulence impacts density profile reconstruction using O-mode reflectometry  [51]. The ITER PPR system would allow testing the use of this diagnostic for control of burning plasma, assess its performance under harsh conditions, and evaluate the design decisions taken and how those would affect the performance of the diagnostic. The R&D and design of the in-vessel components progressed according to schedule. Unfortunately, ITER terminated the PPR project due to concerns that captive components (waveguides and supports needed to install the system between the ports and the diagnostic hall) would not be procured and delivered in time.
Experimental Conditions The model described in Sec. 3 was implemented in Java 17. The code is available upon request. The experiments were performed on a machine using a Intel(R) Xeon(R) W-2175 CPU @ 2. 50GHz with 256 GB of RAM and running under Ubuntu 18. 04. The sentence selection task was performed with models without any fine-tuning by using either OpenAI GPT-2  [Brown et al. , 2020]  for English sentences or a French trained GPT-2  [Simoulin and Crabbé, 2021]. These models are available from the huggingface library  [Wolf et al. , 2020].
and because Wikipedia is an encyclopedia. These sentences have not been considered well suited for a vision test. Another issue was found when we tested a subpart of Google n-grams corpus  [Michel et al. , 2011]. The Google n-grams represent a large part of the text written in several languages with a wide variety of sources (Google books database). This creates problems because some sentences will lead to the production of n-grams which are not acceptable for our use (e. g. , bad n-grams of end of sentences). For instance : "-You. you're not allowed. Dad told you that you shouldn't do ma. magic. ". Ad-hoc and non-obvious filters must be defined and applied to a considerable amount of data (terabytes). The task is much more complex than it seems. For all of these reasons, we chose to remain on books for the moment.
Results We developed a Python-coded image analysis script, called SeptoSympto, in which deep learning models based on the Unet and YOLO architectures were used to quantify necrosis and pycnidia, respectively. Small datasets of different sizes (containing 50, 100, 200, and 300 leaves) were trained to create deep learning models and to facilitate the transferability of the tool, and ve different datasets were tested to develop a robust tool for the accurate analysis of STB symptoms. The results revealed that (i) the amount of annotated data does not in uence the good performance of the models, (ii) the outputs of SeptoSympto are highly correlated with those of the experts, with a similar magnitude to the correlations between experts, and that (iii) the accuracy of SeptoSympto allows precise and rapid quanti cation of necrosis and pycnidia on both durum and bread wheat leaves inoculated with different strains of the pathogen, scanned with different scanners and grown under different conditions.
Among the tools available for phenotyping wheat disease symptoms, only few methods are available to evaluate symptoms related to STB  ([16] ). Some are based on hyperspectral remote sensing, they are available for eld evaluation but do not allow an evaluation of pycnidia production  [17, 18]. A batch processing macro in ImageJ image analysis software has been developed to automatically measure lesions and pycnidia  [19, 20]. Lesions are detected by color thresholding in RGB color space to measure the green leaf area, and pycnidia are identi ed using the 'Find Maxima' function which produces a binary mask. Another method for detecting pycnidia has been developed in Python and is based on determining contours of constant brightness in the vicinity of each pycnidia  [21]. More recently, a tool has been developed to detect the yellow halo present in lesion perimeters, that also detects lesions based on a transformation of the original image into six color spaces, a random forest classi er for thresholding followed by a post-processed lter  [22].
Surprisingly deep learning methods have not been yet developed for a quanti cation of STB symptoms despite the creation of a tool to classify the principal foliar diseases of wheat  [23, 24]. However, despite efforts to create tools to detect STB symptoms, the most preferred solution remains a visual analysis of symptoms by experts. This is mainly due to the perception of these tools as more time consuming, di culty of transferring these technologies or their possible lack of accuracy in new lab environment. Indeed, the tools developed to detect STB severity seem to be too speci c to the training sets used to develop them and therefore unsuitable or di cult to use with other datasets obtained under different conditions. Therefore, it is critical to develop a method for quantifying STB severity that is user-friendly, high-throughput, accurate, unbiased, and easily adaptable.
Bacterial spot disease in pepper plants was detected using YOLOv5 and provided higher accuracy and speed compared to previous versions of YOLO  [35]. It was also used to detect tomato diseases and pests and provided better accuracy and speed of detection compared to the results obtained with other architectures such as Faster R-CNN, Mask R-CNN and SSD  [36]. The U-net and YOLOv5 architectures could therefore allow more accurate quanti cation of objects such as pycnidia and polygons such as necrosis respectively. In this work, we tested if deep learning techniques allow to accurately distinguish necrosis caused by STB from leaf senescence symptoms and to precisely quantify STB necrosis and pycnidia on various datasets. We developed an image analysis script, named SeptoSympto (https://github.
Material Each leaf is taken from a plant, pasted to an A4 sticky sheet, placed in a clear pouch and scanned directly afterwards. All the scanned images were taken at 1200 dpi and in TIFF format to run correctly the SeptoSympto script. The images selected for the datasets were chosen to represent a maximum diversity of symptoms and an equal distribution of each wheat line in the datasets. Different datasets were used to train and evaluate the models (Table  1 ). In order to represent the maximum of possible parameters in uencing symptom development, the datasets originated from three laboratories, four growth conditions, two wheat species and a large set of Z. tritici genotypes.
In order to detect necrosis, we used semantic segmentation, which is an image analysis method that classi es each pixel of the image according to the object or class of objects to which it belongs. In this case, the U-net architecture was chosen. The U-net architecture is speci cally designed for biomedical image segmentation and is known for its ability to produce accurate results with a limited number of training samples. Unlike pycnidia, necroses cannot be approximated by rectangles. Semantic segmentation therefore allows for the identi cation of more complex shapes in the form of polygons. All the training was conducted using Python as a programming language on a computer equipped with an Intel Core i5 processor and two T4 graphics processing units (GPU), each with 16Go of memory. Necrosis models are using a U-Net implementation for semantic segmentation, implemented with the Tensor ow library (https://github.
Image analysis Necrosis and pycnidia are detected thanks to the SeptoSympto script, which cuts each leaf to be able to analyze them with deep learning models (Fig. 1B ). To run the image analysis, a folder must contain les called "images_input", "csv_input", "models", "outputs", "tools" and the script SeptoSympto available on https://github. com/maximereder/septo-sympto. The images in TIFF format and scanned at 1200 dpi are either directly usable by SeptoSympto if there are only horizontally scanned leaves on the image, or pre-cut and renamed with the XnView software if there are writings on the scanned images in order to have only horizontal leaves present on the input images. These images are stored in the le called "images_input". A le in csv format containing information for each image and the name of each image in the rst column can be added in the le called "csv_input".
The deep learning-based models used for necrosis and pycnidia detection, respectively necrosis-model-375. h5 and pycnidia-model. pt, are stored in "models" le. They are implemented using the OpenCV and TensorFlow libraries. The "tools" le contains a python script called "metrics. py" to calculate metrics for segmentation models and can also store other optional scripts. The rst function, in SeptoSympto script, detects each leaf that has a minimum area of 50000 pixels and a color range between [0,  35, 65 ] and [255, 255, 255] using the function **cv2. ndContours**. The detected leaves are then cut with a rectangle encompassing the whole leaf, resized to a xed size of 304 x 3072, renamed with the le name and the leaf number of the image and saved in a le called "cropped". The second function, **predict_necrosis_mask**, predicts the probability of each pixel to be in necrosis class.
Model evaluation Before implementing models in SeptoSympto script, several models trained with different sized datasets were tested and compared using metrics and expert observations. The metrics used are precision, recall and F1 for necrosis and pycnidia models: with TP: true positive, FP: false positive and FN: false negative The results obtained from best models were then compared to expert evaluations using Spearman correlation. Expert assessments were made by visually measuring the area of necrosis present on the total leaf surface and the area of necrosis containing pycnidia.
Script conception and model validation Before implementing the nal models (one for necrosis and one for pycnidia) in the SeptoSympto script, four models for necrosis and three for pycnidia were trained from different size subsets of dataset 1. Three metrics were used to evaluate the models: precision, recall and F1 score. High precision indicates minimal false positive detection, suggesting that the and minimum false negatives, suggesting that the model misses a minimum of true positives. F1 is the harmonic mean of recall and precision, the higher it is, the better the model performs. The model metrics obtained with training datasets remained similar regardless of the size of datasets used for training (Table  2 ). The model trainings were carried out on a small number of leaves and epochs.
The resulting models were evaluated using different metrics: precision, recall and F1. The N3 model, which is used in the SeptoSympto script for detecting necrosis, has a precision of 0. 95, a recall of 0. 85 and a F1 score of 0. 90 (Table  2 ), suggesting that our model is adapted to detect necrosis. After only 20 training cycles (epochs), the loss function, which is the summation of the errors made by the model for each example in the training set, does not decrease anymore but has reached a low value (Additional le 1). This demonstrates that the model makes few errors when comparing the expert annotations with the model predictions and that there was therefore no need for further training. For the P2 model, which is the one implemented in the SeptoSympto script for detecting pycnidia, precision is 0.
56, recall is 0. 25, and F1 is 0. 34 (Table  2 ). Due to the large number of pycnidia that can be detected on leaves, the metrics remain quite low even with 183 training cycles. Indeed, it is much more di cult to have metrics with high values if they are calculated on a large number of detected objects. However, the loss function curves for the training and validation sets are strongly decreasing (Additional le 1). This shows that the model makes fewer errors at each iteration. The metrics analyzed show therefore very little variation between the models tested with different sizes of training datasets, suggesting that the number of annotated leaves used to create the models could be lower. Nevertheless, the trained models with more annotated leaves (models N3 and P2) were chosen for the SeptoSympto script because they allow a better representation of the diversity of observable symptoms and thus a better generalization of the models for interlaboratory use.