dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional annotation of methanogens MGEs To characterize the functional potential of the identi ed MGEs and their interactions with methanogenic hosts, we proceeded with their annotation. Proteins encoded in MGEs were clustered using vContact2 80 and annotated using the arCOGs database 86 , the NCBI conserved domains database 87 and the phrogs database  68. Overall, most proteins from (pro)viruses and plasmids were assigned to the "function unknown" and "mobilome" arCOG categories (Supplementary Figure  S3 ). We found a signi cant difference in arCOG classi cation between the two types of MGEs, with plasmids encoding a larger fraction of proteins involved in replication, cell wall biogenesis, and protein modi cation and turnover than (pro)viruses (p-value < 0. 001, chi-square test). Also, when compared to the recently identi ed Borgs, the gene content of the newly predicted plasmids is less enriched in metabolism-related proteins and contains more mobilome-speci c arCOGs (Supplementary Figure  S3 ), suggesting fundamentally different modes of propagation for Borgs as compared to plasmids with much smaller genomes.
However, we observed that gut-speci c Methanobacteriales viruses (Families 1,2,3) encode signi cantly more Ig-like domains than environmental viruses (p-value 0. 003, t-test) (Fig. 4 , Fig. 5A ). The Flg_new (/List_Bact; PF09479) domain is a potential adhesin domain structurally akin (betasandwich) to the Ig-like domain and has been shown to be speci cally enriched in some host-associated bacteria  113  as well as gut-associated Methanomassiliicoccales 113 and Methanosarcinales 114. Four Flg_new repeats are present in the Pil3A protein of Streptococcus gallolyticus, playing an essential role in the adhesion of this bacterial pathogen to human gut mucosa 115. We found 85 viral proteins which contain the Flg_new domain (Supplementary Table  S10 ). Interestingly, the large majority (83) belong to head-tailed and pleolipo-like viruses of gut-associated Methanomassiliicoccales (Supplementary Table  S10 ) and are therefore also enriched with respect to environmental viruses (Fig.
All target proteins have the same domain architecture: 1 to 5 beta-sandwich Ig-like domains or Flg_new domains followed by a Ctype lectin fold (CLec-fold) ligand-binding domain. Depending on the DGR, 11-20 diversi ed amino acids are located on the surface of the CLec-fold domain, accommodating up to ~ 10 16 protein sequence variants, consistent with previous estimates for archaeal viruses  (10 18  )  78. According to the domain architecture and genomic location, we annotated the target proteins of these DGRs as tail bers for headtailed viruses, and capsid spike proteins for icosahedral tailless viruses (Fig. 5C ). Again, we found that DGRs are more prevalent in viruses of gut-associated methanogens than in viruses of environmental methanogenic archaea (Fig. 5D ). Interestingly, clustering of the RT proteins based on sequence similarity using the CLANS program  126  shows that DGR systems of viruses of methanogenic archaea are not related to the previously reported archaeal viral DGR systems 127.
Acknowledgements This work was supported by the  French National Agency for Research Grants Viromet  ( ANR-20-CE20-009-01 ) and  Methevol  ( ANR-19-CE02-0005-01 ), and by the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence " Integrative Biology of Emerging Infectious Diseases"  (grant  n°ANR-10-LABX-62-IBEID ). We thank the computational and storage services (Maestro cluster) provided by the  IT department at Institut Pasteur, Paris.
Liquid Chromatography Coupled with Mass Spectrometry in Tandem (LC/MS-MS) and Molecular Network (MN) of the Fungal Endophytic Crude Extracts Our identification and annotation of chemical compounds is based on the comparison of commercial standards, the GNPS (Global Natural Products Social Molecular Networking) libraries and data from the lit. GNPS organizes and visualizes MS/MS data based on spectral similarity based on the presence of homologous MS/MS fragments or homologous neutral loss. MolNetEnhancer Workflow is used in the description for chemical class annotation of molecular networks, and Cytoscape software 3. 8. 0 (U. S. National Institute of General Medical Sciences, Bethesda, MD, USA)  [23]  is used for their visualization. MolNetEnhancer reveals the chemical type of nodes in the GNPS molecular network and provides chemical class annotations.
289, m/z 407. 272 and m/z 447. 268 as [M + H] + , corresponding to lovastatin-related compounds according MS/MS spectra. In the clusters 8B, 8D and 10D, phenols/1-hydroxy-2-unsubstituted benzenoids were identified. In these networks, we observed the ion corresponding to Aspulvinone E, a yellow pigment found in Aspergillus, with peaks ions m/z 297. 071 as [M + H] + which matched the molecular formula of C 17 Workflow is used in the description for chemical class annotation of molecular netw and Cytoscape software 3. 8. 0 (U. S. National Institute of General Medical Science thesda, MD, USA)  [23]  is used for their visualization. MolNetEnhancer reveals the c ical type of nodes in the GNPS molecular network and provides chemical class an tions. In particular, MolNetEnhancer can integrate the output of several metabolome ing and annotation tools, including GNPS molecular networking and in silico anno tools, as well as identify molecular families, subfamilies and subtle structural differ between family members.
Gas Chromatography Coupled to Mass Spectrometer (GC-MS): Equipment and Method The GC-MS analyzes were carried out using a QP2010-Shimadzu equipment equipped with a quadrupole mass detector and operating in electron ionization mode at 70 eV. An apolar "DB-5 MS Agilent" column (30 m × 0. 25 mm × 0. 25 µm) was used with a temperature program of 36 min at 60-320 • C at 5 • C/min. The temperature of the injector was 250 • C; the flow rate of carrier gas (helium) was 1 mL. min -1. The injection was made in split mode with a division ratio of 1/5. The quantity of extracts injected was 1 µL. The identification of the compounds was carried out by comparing the measured data with those of the NIST library (V2. 0-19/05/2011). The extracts were dissolved in dichloromethane at a concentration of 20 mg/mL.
Liquid Chromatography Coupled to Mass Spectrometer (LC-MS): Equipment, Method and Molecular Networking The LC system consisted of a U3000-Dionex apparatus with an injector comprising a 1 µL loop. The LC analytical column used was a Hypersil Gold (100 mm × 2. 1 mm, Thermo Scientific, Bellefonte, PA, USA) and eluted at a flow rate of 200 µL/min using a gradient 0 mn 5%B/5 mn 5%B/40 mn 99%B/45 mn 99%B/50 mn 5%B/55 mn 5%B. Solvent A consisted of water/2% of formic acid (HCOOH), and solvent B consisted of acetonitrile (ACN). The oven temperature was set at 40. 00 • C, and 2 µL was injected. The LC-MS analysis was performed using a micrOTOF Q TM apparatus (Bruker Daltonics, Bruker, Bremen, Germany), and the MS/MS data are obtained using Electrospray Ionization-High Resolution Mass Spectrometry (ESIHRMS).
A mass range of 50-1000 m/z and collision energy of 20 eV was used. The raw data were converted using Bruker DataAnalysis; each data point was calibrated with sodium formate. All MS/MS data were converted in a mascot generic file (. mgf) file. The analyses were performed in mass spectrometry of the L2CM laboratory at the University of Lorraine, France. The. mgf file was sent to the GNPS website  [33]. A molecular network was created using the online workflow on the GNPS platform (http://gnps. ucsd. edu (accessed on 26 June 2023)). The data were filtered by removing all MS/MS fragment ions within ±17 Da of the precursor m/z. MS/MS spectra were window-filtered by choosing only the top 6 fragment ions in the ±50 Da window throughout the spectrum. The precursor ion mass tolerance was set to 2.
To enhance chemical structural information within the molecular network, information from in silico structure annotations from GNPS Library Search were incorporated into the network using the GNPS MolNetEnhancer workflow (https://ccms-ucsd. github. io/ GNPSDocumentation/molnetenhancer/, (accessed on 26 June 2023)) on the GNPS website (http://gnps. ucsd. edu (accessed on 26 June 2023)). Chemical class annotations were performed using the ClassyFire chemical ontology  [33, 62, 63]. For the visualization of molecular networking, the software Cytoscape ® (version 3. 8. 2) was used  [23]. A similar protocol was described in the article Elmi et al. [64]  4. 9. Isolation of Pure Compounds from Extract A, from Alternaria alternata Endophyte 4. 9. 1. General Experimental Procedures Column chromatography (CC) was carried out on an open silica gel column or on Sephadex gel filtration (Sephadex TM LH-20, GE Healthcare Bio-Sciences AB, Uppsala, Sweden).
One-dimensional and two-dimensional homonuclear (COSY) and heteronuclear (HSQC, HMBC) NMR experiments were performed using a Bruker Avance III 400 spectrometer (Bruker BioSpin, Rheinstetten, Germany), operating at a frequency of 400. 13 MHz at a temperature of 26 • C using a BBFO probe and a Bruker autosampler. The spectra thus obtained were reprocessed with the MestreNova software (version 14. 3. 1) and Bruker TopSpin software (version 4. 0. 6). The chemical shifts (δ) are expressed in parts per million (ppm) and the coupling constants (J) in Hertz (Hz).
Acknowledgments: The authors acknowledge: the support of " LAE UMR 1121 " and " L2CM UMR   7053 " and by the " Impact Biomolecules"  project of the " Lorraine Université d'Excellence " ( Investissements d'avenir-ANR ); we thank the mass spectrometry MassLor platform for GC-MS and LC-MS facilities and SynBioN platform for analytical and preparative HPLC facilities of Lorraine University. The authors gratefully acknowledge the  CPM NMR facility of Lorraine University.
Funding: This work was supported by " Impact Biomolecules " project of the " Lorraine Université d'Excellence ", funded by the  ANR "Investissements d'avenir " ( ANR-15-IDEX-04-LUE ) concerning E-info emerging project. The study was funded by project " Interdisciplinarité " (number:  INTER-2019-029 ) of the  University of Lorraine. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
We deploy artificial neural networks to unfold neutron spectra from measured energy-integrated quantities. These neutron spectra represent an important parameter allowing to compute the absorbed dose and the kerma to serve radiation protection in addition to nuclear safety. The built architectures are inspired from convolutional neural networks. The first architecture is made up of residual transposed convolution's blocks while the second is a modified version of the U-net architecture. A large and balanced dataset is simulated following "realistic" physical constraints to train the architectures in an efficient way. Results show a high accuracy prediction of neutron spectra ranging from thermal up to fast spectrum. The dataset processing, the attention paid to performances' metrics and the hyperoptimization are behind the architectures' robustness.
To get more accurate neutron spectra unfolding without depending on a priori knowledge of the measured neutrons, alternatives using artificial neural networks (ANNs) have been proposed  [5, 6]. An ANN can learn and model complex and non-linear relationships between inputs and outputs. It is based on a training process, which overcomes the requirement of any prior knowledge of the neutron spectrum. However, a large and balanced training dataset is mandatory to deploy a robust ANN. Besides, the design of these architectures is rather challenging, and special attention should be paid to model choice, training dataset processing (feature engineering, data augmentation, scaling, …), performance metrics, loss functions, and hyper-parameters tuning. Different unfolding ANN architectures have been proposed in recent years.
Training dataset To perform efficient and robust solutions for neutron spectra reconstruction, a large and balanced dataset is necessary for the neural network training. This dataset should include neutron spectra and their reaction rates over a detector. The term "balanced" refers to an equiprobable proportion of the different types of neutron spectra. The dataset is based on neutronic simulation, since the neutron spectrum is data that cannot be measured directly. The simulations are based on a simple physical model (neutron source, material's geometry, and detector) enabling to generate as many samples as needed. This dataset generation is original and should cover large range of neutron spectrum that are representative of the real system neutron spectra shapes. The representativity of the spectrum in the dataset compared to real systems is an essential information to keep in mind when using the trained networks.
For instance, the generated dataset is limited to fission neutron sources, that excludes its application to fusion systems. The set of the neutron spectra, (flux distributed over 1000 energy bins), with the shape of (rows ¼ number of samples, columns ¼ 1000) corresponds to the neural network output. It contains 19 000 samples where each one is a neutron spectrum generated via a Monte-Carlo based SERPENT software (http:// montecarlo. vtt. fi/download/Serpent_manual. pdf). The generated default SERPENT spectra are normalized by the total neutron flux. The considered geometry is described in Fig. 1. It is about a central sphere s1 of a radius R1 and two spherical "shells" s2 and s3 surrounding it and having respectively radii R2 and R3. For the spectra generation, the radii R1, R2 and R3 are randomly sampled within a given range.
Each cell of this geometry (s1, s2 and s3) is made up of a mixture of 16 basic materials that absorb or slow down the neutrons with random fractions. The list of the materials is the following: H 2 O, D 2 O, graphite, B, B 4 C, CH 2 , concrete, steel, UO 2 , U 8 , Cd, Pb, Gd, AgeIneCd, Xe and Hf. Vacuum is also added to the list to enable the modification of the materials' densities. These fractions are found by deploying a genetic algorithm guaranteeing a complete exploration of the sampling spaces and a conception of a balanced dataset. Finally, the neutrons source is generated in the center of s1 and randomly chosen from the fission neutron distribution of typical isotopes. The obtained neutron flux at the external surface of s3 is scored in 1000 energy bins between 1. 10 À9 MeV and 20 MeV with equal lethargy.
Using this approach, we have the possibility to re-use the neutron spectrum dataset, which generation requested some CPU and analysis investment, to other detectors, by simply replacing the response functions. R j ¼ X n i¼1 RF ji *f i (1) Equation (  1 ) assumes that the detector response is linear, allowing the response function matrix and generated spectra to convolute in order to generate the reaction rates. Such an assumption is valid in the simulation case. For experimental data and depending on the detector and the intensity of the incident flux, this assumption should be re-evaluated. In the present case of SNAC2 detector, we have checked that no saturation effect was observed as well as no detection limits. The total number of resulting neutron spectra is 16 240. A feature engineering is performed by computing the ratios between all the reactions rates pairs.
In this paper, we propose two varieties of CNNs: ResConvT which includes residual blocks of transposed convolutions  [11]  and UnetUnfold which is an adapted version of the original U-net architecture  [12]. To develop both architectures, we are based on Python scientific libraries for dataset processing and on Tensorflow (version 2. 11) (Releases $ tensorflow/tensorflow (github. com) libraries for training and evaluation processes. We use the callback TensorBoard (version 2. 11. 2) (https://pypi. python. org/pypi/tensorflowtensorboard) to track the training in real time, and we use the mlflow (version 2. 0) (https://learn. microsoft. com/en-us/azure/ machine-learning/how-to-track-experiments-mlflow) library to easily save all our results.
CNN2: UnetUnfold Another way to reconstruct the neutron spectra efficiently is to propose an adapted version of a novel architecture named Une-tUnfold. This architecture is a CNN developed for biomedical image segmentation at the Computer Science Department of University of Freiburg  [12]. U-net has several advantages such as better handling of data noise, a more precise output estimation thanks to its layers' combination and preservation of the structural integrity of the input data. Fig. 4  shows the detailed structure of the adapted U-net version that we propose. U-Net architecture looks like a 'U' justifying its name. It is composed of a contracting path (encoder) and an expansive path (decoder). Both paths are connected through a bottleneck section. These fundamental sections are kept in the proposed adapted version of the architecture but with key modifications in the choice of layers, data dimensions, and filter numbers and dimensions.
Results and discussion To adapt the data to the architectures, scaling the input and the output is mandatory. We have tested all the possible combinations of the predefined scalers called MaxScaler, MinMaxScaler and StandardScaler from scikit learn library respectively for the neutron spectra and reaction rates. The best scaling combination is applying MinMaxScaler to neutron spectra and StandardScaler to reaction rates. In fact, MinMaxScaler translates each sample individually to a given range (between zero and one in this case). StandardScaler consists of scaling each input vector separately while subtracting the mean (called centering) and dividing by the standard deviation to shift the distribution. Then, the mean and the standard deviation will be respectively equal to zero and one. We consider 75% of the dataset for training and the other 25% for validation.
The algorithm behind optuna  [15]  uses a Bayesian approach and is called Tree-structured Parzen Estimator. optuna is implemented in the ML script and adapted to the project context. We detail in Table  1  the values of the batch size and the chosen regularization fixed after the hyper-optimization. These two hyper-parameters have a significant impact on the predicted spectra smoothness. For example, in the UnetUnfold case; decreasing the batch size from 256 to 128 makes the relative error between the predicted and the real spectra decrease from an interval of [À5. 2%, 5. 9%] to an interval of [À2%, 2%] for Spectrum 1. Besides, applying a dropout of 0. 2; in the ResConvT case; reduces the relative error from an interval of [À3. 8%, 4. 4%] to an interval of [À2%, 1. 5%] for the same spectrum.
Conclusion In this paper, we detailed two implemented ANNs for neutron spectra unfolding. Both networks include convolutional layers enabling a precise reconstruction of signals. Compared to other ANN architectures, the CNN, with both proposed versions, allows accurate neutron spectra unfolding even for a high number of energy bins (1000 bins). The spectral quality is significantly high (around 0. 065) and the different types of neutron spectra are efficiently predicted giving a relative loss of an order of 10 À9. ResConvT is less time-consuming than UnetUnfold. Then, it is a better alternative for applications dealing with long-dimension data vectors. It also have slightly better performances metrics. However, UnetUnfold has higher performances than ResConvT for smaller training datasets (same performances metrics with only 70% of the original training dataset).
So, it is an efficient option for applications suffering from a lack of available dataset on which the prediction is based. We built a large dataset including in a balanced way the different types of spectra for robust training. The suitable dataset processing (scaling and feature engineering) is one of the powerful keys to enhance accuracy. The results show that we can rely on ANNs to overcome the limitation of classical unfolding techniques, which is the necessity of the prior information and to get a robust unfolding. The adaptation of the feature engineering to the experimental activation measurements issued from gamma spectrometry is in progress. This step requires an accurate normalization of the input measurement to the simulated training dataset. We have done some preliminary tests with real spectra resulting from CALIBAN, SILENE and GODIVA IV reactors.
The first step of this approach consists in finding a normalization factor using the mean value of gold data in the training dataset divided by the value of gold activation foil in the measurement input part. Since some other deconvolution approaches and simulations have shown that the impact of neutron diffusion has not been highlighted in simulation leading to a nonrealistic computed response function for the backcopper foil, a second step is to replace the back-copper response function and the associated back-copper counting respectively by the front-copper response function and the front copper counting. In complement, we have done tests with different binning (1024 bins, 512 bins and 128 bins) in order to see the impact of this parameter on real spectra unfolding. this approach leads to promising results on case of SILENE, where we can compare with a precise simulation of the reactor and its environment.
However, recent studies diverge substantially in their estimates of how much tropical forest for SEA remains  [14] [15] [16]. For example, the estimates of forest cover in 10 SEA countries range from 2. 085 to 3. 494 million ha  [9, [16] [17] [18] [19] [20] [21] [22]. The differences in these estimates arise from the different mapping approaches used in the assessments, as well as the spatial scale of assessment. Studies mapping the extent of tropical forests typically rely on a single type of remote sensing data, relying entirely on either optical imagery  [9, 23, 24] , synthetic aperture radar (SAR)  [16, 25, 26] , or lidar  [27]. Efforts based on optical sensors have primarily resulted in medium-to coarse-resolution forest cover datasets  [28] , such as the ≥1-km resolution products of the Advanced Very High-Resolution Radiometer (AVHRR;  [29, 30] ), a variety of ≥100-m resolution products developed from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or PROBA-V (  [31, 32] ), and ≥25-m resolution products based on Sentinel-2, Landsat, and/or SPOT  [9, 24].
77-m and semiannual analysis-ready mosaics covering the world's tropics publicly available at no cost (https://www. planet. com/nicfi/). The spatial resolution of these products offers the promise of reducing rounding errors, while the daily frequency helps to increase the number of clear observations within a defined period, thereby minimizing the uncertainty caused by atmospheric contamination  [51]. However, the degree to which such high spatiotemporal resolution optical data can reduce map error has not been fully explored, given that a thorough assessment requires investigation over large spatial extents and across a broad diversity of forest conditions, which has only now become possible with the release of the NICFI data. To answer this question, we developed a machine-learning approach for mapping forest cover within the Google Earth Engine (GEE) platform  [52] , which combines Planet NICFI imagery along with C-band SAR imagery captured by Sentinel-1 C-band at near-high resolution (10 m)  [53] , and used it to map all of SEA's forests at ~5-m resolution.