dataesr/scientific-paragraphs-categorization huggingface dataset:
The assemblies were binned into metagenome assembled genomes (MAGs) using metabat2  (Kang et al. , 2019)  and maxbin2  (Wu et al. , 2016) , and the resulting MAGs were compared and dereplicated using dRep  (Olm et al. , 2017)  with a pairwise ANI cutoff at 99%, % completion > 75% and contamination < 25%. The resulting collection of MAGs were combined into one dataset; reads were mapped back to the MAGs using bowtie2 v 2. 4. 2 to obtain coverage information and visualized in Anvi'o. Taxonomic classification was assigned to each MAG using the GTDB-TK tool kit which compares the genomes to the genome taxonomy database  (Chaumeil et al. , 2020). Taxonomic composition of metagenomic reads based on rRNA reads extracted from the libraries and classified to order level was performed using the PhyloFlash v.
3. 4 (Gruber-Vodicka et al. , 2020) software and the SILVA v. 138 database. Prediction of open reading frames, CAZyme families, PULs as well as taxonomic assignment of the predicted CAZymes were performed on the Abyss-assembly and visualized in accordance to the methodology described in  Wong et al. , 2017. Count of identified sequences in a given CAZyme family was normalized by the number of open reading frames. Normalized count of predicted plant polysaccharide-active CAZyme families from the earlier beaver dropping and moose rumen metagenomes  (Wong et al. , 2017)  were combined with the current dataset for hierarchical clustering (correlation clustering and average linkage) and PCA using R statistics in ClustVis  (Metsalu and Vilo, 2015). The identified genes encoding CAZymes were mapped to the MAGs using BLASTN.
Metagenome sequencing and assembly The cellulose-fed and pretreated poplar-fed enrichments exhibited highest biogas yields compared to the other enrichments generated herein and so corresponding metagenomic DNA was collected for sequencing and assembly. The metagenomic DNA from cellulose-fed and pretreated poplar-fed enrichments yielded 64 and 68 million high quality reads, respectively. The taxonomic composition of the metagenomes was assessed by extracting rRNA genes and comparing them to the SILVA database. The taxonomic composition at order level was similar to the community structure obtained from the amplicon data (Figure  S2 ), with Clostridiales and Bacteroidales representing > 75% of the microbial community in the cellulose-fed enrichments and ~30% in those fed with pretreated poplar.
The 139 dereplicated MAGs from the AG-C and AG-P metagenomes are visualized using the Anvi'o software  (Eren et al. , 2021). The phylogenetic tree at the centre of the figure is a maximum likelihood tree based on concatenated ribosomal proteins calculated using FastTree  (Price et al. , 2010). From inner to outer ring: The first ring shows the % completion of each MAG calculated based on single-copy core genes (SCGs) with maximum set to 100%. The second ring indicates the level of redundancy (or possible contamination) also based on SCGs with maximum set to 30%. The green and yellow bar for each MAG shows the mean coverage of each MAG in each sample, with maximum at 1284 and 587 for AG-C and AG-P, respectively. The AG-C CAZY and AG-P CAZY bar shows number of blast matches to carbohydrate-active enzyme sequences predicted to act on plantpolysaccharides, where the maximum number in each case is 79 and 80, respectively (pairwise sequence identity was 99%-100% belonging to the Bathyarchaeota were identified in the metagenome sequences assembled herein; however, only one MAG (TGPidbaud_mb_bin.
Funding This work was funded by the  Government of Ontario  for the project " Forest FAB: Applied Genomics for Functionalized Fibre and Biochemicals"  ( ORF-RE-05-005 ), the  Natural Sciences and Engineering Research Council of Canada  for the  Strategic Network Grant "Industrial Biocatalysis Network ", a  European Union's Seventh Framework Program  ( FP/2007/2013 )/ European Research Council (ERC)  Grant Agreement  322820  to BH and an  ERC Consolidator Grant  to EM ( BHIVE -648925 ). MW was funded by a  University of Toronto Fellowship  and  Professor William  F. Graydon Memorial Graduate Fellowship. MC was funded by a  Marie Curie International Outgoing Fellowship  within the  7th European Community Framework Program.
). Genes in PULSs corresponds to the number of CAZymes encoded in MAGs that are predicted to act on plant polysaccharides and be localized in polysaccharide utilization loci (PULs). The outer circle shows the taxonomic domain by color, predicted by Anvio using the genome taxonomy database (gtdb, https://gtdb. ecogenomic. org/). Finally, the best taxonomic classification of each MAG is provided. A 'p' in front of the taxon name indicates this is a phylum classification, a 'c' indicates classification to class, 'f' indicates family level classification, 'o' indicates order level classification, 'g' indicates classification to genus and 's' indicates species level classification. The bars on the right-side shows percent and total number of reads mapped to the MAGs from each metagenome. SNV denotes single nucleotide variants and shows the level of diversity in all the MAGs when reads from the two metagenomes are mapped back to the best MAG.
Detection is performed using the photutils  (Bradley et al. 2022  ) software package, identifying sources with contiguous regions of the SNR mosaic with signal > 3 σ and five or more contiguous pixels. We also use photutils to perform circular aperture photometry with filterdependent aperture corrections based on empirical point-spreadfunctions measured from stars in the mosaic. The object flux within a range of aperture sizes is measured, ranging from r = 0. 1 to r = 0. 5 MNRAS 523, 5468-5486 (2023) as well as the PSF 80 per cent encircled energy radius in each filter. Kron ( 1980 )  photometry is also performed using Kron parameters of 1. 2 and 2. 5. Details of the JADES source catalogue generation and photometry will be presented in  Robertson et al. , (in preparation).
The properties of these galaxies are discussed in previous literature  (Guseva et al. 2020 ; Ramambason et al. 2020  ) but of main importance to this work are their high [O III ]/H β ratios, and their low metallicities (mean metallicity, 12 + log(O/H) = 7. 97 + 0. 14 -0. 17 ). MNRAS 523, 5468-5486 (2023) We then use the version 17. 03 of the photoionization code CLOUDY  (Ferland et al. 2017  ), combined with stellar population models from the Binary Population and Spectral Synthesis version 2. 2. 1 (BPASS;  Eldridge et al. 2017 ). We run a grid of simple CLOUDY photoionization models to convergence, using BPASSv2. 2. 1 models with binary interactions. The initial mass function (IMF) only slightly changes the results, therefore, we choose to use a Salpeter IMF ( α = -2. 35, with M * , max = 100 M ).
2006  ). The star-forming SDSS sample is plotted as grey dots. These galaxies populate virtually the same region of the BPT diagram that is expected of galaxies at z > 6, that is, log U > -3. 2023 ), making them appropriate lowredshift analogues to galaxies at the EoR. The relation between the [N II ]/H α intensities ratio and log U is shown for our CLOUDY models in the middle panel of Fig. 2  , and are described by the following cubic equations [N II ] H α Z= 0. 006 = -0. 020 x 3 -0. 045 x 2 -0. 028 x -0. 001 (1) and [N II ] H α Z= 0. 001 = -0. 004 x 3 + 0. 048 x 2 + 0. 092 x + 0. 037 , ( 2 ) where the subscripts represent the intensity ratios for the CLOUDY models corresponding to metallicities Z = 0. 001 (dashed curve, 12 + log(O/H) = 7. 3) and Z = 0. 006 (filled curve, 12 + log(O/H) = 8.
LAEs have been shown to be LyC leakers  Gazagnes et al. ( 2020 ) ;  Nakajima et al. ( 2020 ) ;  Izotov et al. ( 2022 ) , making them interesting objects to analyse. Our sample is not necessarily representative of the entire galaxy population at the EoR; this is a first exploratory work to showcase the science that can be performed using JEMS. Ho we ver, as pre viously mentioned, there is increasing evidence that LAEs might be ke y driv ers of the reionization of the universe. A follow-up study in the future will focus on a more complete sample using the entire JADES data base. Of particular interest, a recent study by  Ning et al. ( 2023 )  focuses on the same class of objects at z ∼ 6, shown as black arrows in Fig. 5. Their sample was constructed by selecting spectroscopically confirmed LAEs observed with the fiberfed, multi-object spectrograph Michigan/Magellan Fiber System (M2FS;  Mateo et al.
AC K N OW L E D G E M E N T S These observations are associated with JWST Cycle 1 GO program number 1963. Support for program JWST-GO-1963 was provided in part by NASA through a grant from the Space Telescope Science Institute, which is operated by the Associations of Universities for Research in Astronomy, Incorporated, under NASA contract number NAS 5-26555. The authors acknowledge the FRESCO team led by PI Pascal Oesch for developing their observing program with a zero-e xclusiv e-access period. The authors acknowledge use of the lux supercomputer at UC Santa Cruz, funded by NSF MRI grant AST 1828315. The research of CCW is supported by NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.
Investigating the data quality The MS/MS results were aligned and all possible peaks were detected. These data were then assigned to unique alignment IDs with their retention times and mass-to-charge ratios in both ESI+ and ESI-modes, respectively. In total, 2139 features were detected in ESI + mode and 880 in ESI-mode. All features with their raw peak areas were imported to SIMCA software. Here a PCA were performed to examine the species separation and to evaluate the quality of the dataset. The PCA plots were colored according to the sample type (sample or QC). A total of 25 QC samples are well clustered together in both modes (Fig. 1 ), even though the first two components of the PCA explain a comparatively low percentage of the variance for both modes (ESI+: PC1 = 8. 27%, PC2 = 8. 14%; ESI-: PC1 = 18.
9%, PC2 = 8. 56%). Overlapped chromatograms of all QC samples are displayed in Supplementary Fig. S1. Histograms of the distributions of coefficient of variances (CVs) of detected features are shown in Supplementary Fig. S2. 42% and 60% of the features in ESI+ and ESI-mode, respectively, from the QC samples have a less than 30% CV  (Kaiser et al. , 2011). Altogether, the datasets can be considered as of good quality and are reliable for further processing.
Lipid profiles of 39 bryophyte species with a focus on AA and EPA To annotate lipid features the in silico library LipidBlast was used. A total of 602 out of 2139 features was annotated in ESI+ mode, and 322 out of 880 features in ESI-mode, respectively (Supplementary Data 2). A total of 15 lipid classes were characterized in the whole dataset, and show the fatty acid distribution of six species (R. lanuginosum 39, R. ericoides 46, B. pseudotriquetrum 55-2, P. ellipticum 12, H. splendens 47, R. triquetrus 60) that show high levels of AA (20:4) and EPA (20:5) (Fig. 5 ). Since lipids from different lipid classes have different ion efficiency, and IS for glycerolipids quantification is lacking, the appearances of fatty acids in each lipid class was counted instead of using the ion intensities  (Tsugawa et al.
Low abundance compound can also be quantified with high precision, even though the compound annotation might be challenging  (Tsugawa et al. , 2019b). In this study, only 28% and 36% of the detected features were successfully identified in ESI+ and ESI-mode, respectively. The rest of the lipophilic and semi-lipophilic features remain unannotated here. Future studies could potentially identify the remaining unknown features by searching the obtained spectra in other databases such as MassBank, Metlin, and GNPS  (Horai et al. , 2010; Smith et al. , 2005; Wang et al. , 2016) , and/or by using advanced in silico data analysis methodologies such as Met-Family, SIRIUS, and MS-FINDER  (Dührkop et al. , 2019; Peters et al. , 2019; Tsugawa et al. , 2019a).
The following linear gradient elution was applied: 0-10 min, 40-100% B; 10-12 min, 100% B; 12-14. 5 min, reconditioning to 40% B. Flow rate was 0. 4 mL/min and column temperature was 55 • C. Sample manager temperature was maintained at 10. 0 • C. Mass spectrometry (MS) was operated in V mode. MS Data was collected in both positive and negative ionization mode (ESI+ and ESI-). Injection volume was 2 μL in ESI + mode and 5 μL in ESI-mode. Mass was scanned in a range of 100-1500 Da. Data independent MS/MS acquisition (DIA) mode was applied. Collision energy was 10 eV for the MS1 acquisition and the MS2 acquisition had a collision energy ramp of 20-45 eV. Leucine encephalin (1 ng/μL) was used as reference lock mass calibrant ([M + H]+ ion of 556. 2771 and [M-H]-ion of 554. 2615). Other MS conditions are described in our previous study  (Lu et al.
Lipidomics data processing Raw data from UPLC-ESI_QTOF-MS were converted to. abf format by using ABF Converter  (Tsugawa et al. , 2013)  (https://www. reifycs. com/ AbfConverter/), then imported to MS-DIAL, version 4. 24 (http://prime. psc. riken. jp/compms/msdial/main. html) for performing peak detection, deconvolution, compound identification and alignment. The import parameters are listed in Supplementary data S1.
Lipid identification In silico library LipidBlast was used for identification as a part of the data processing procedure in MS-DIAL  (Kind et al. , 2013). The following lipid classes were investigated in this study: phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylserine (PS), phasphatidic acid (PA), monogalactosyldiacylglycerol (MGDG), digalactosyl diacylglycerol (DGDG), sulfoquinovosyldiacylglyceride (SQDG), diacylglyceryltrimethylhomoserine (DGTS), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), lysodiacylglyceryltrimethylhomoserine (LDGTS), diacylglycerol (DG), triacylglycerol (TG), N-acyl glycine (NAGly), ceramide (Cer), fatty acid (FA). Since DIA method provides all fragment ions from all precursors in MS/MS spectra, it might be difficult to obtain "clean" spectra purified from other co-eluting compounds.
scipy. org/) with Ward's cluster analysis. The chemotaxonomic relationships were revealed with aid of the Hierarchical clustering analysis (HCA) function in SIMCA 17. The distance was calculated with Ward method and sorted by index  (Ward, 1963). Barplots were generated using R package "ggplot2"  (Wickham, 2009). Heatmap was created by using package "complexheatmap" in R software (version 4. 1. 0)  (Gu et al. , 2016) , log-transformed relative concentrations of lipid molecular species were used for generating the heatmap, the order of the bryophyte species was manually set to follow the phylogenetic order.
Acknowledgements We acknowledge  Dr. Maonian Xu ,  University of Iceland , for the help of grinding plant materials in liquid nitrogen. Mingzhou Bai ,  BGI Shenzhen , is acknowledged for preparing phylogeny tree figures. This study is funded by  Marie Sklodowska-Curie Actions ,  Innovative Training Networks  under  European Union Horizon 2020 programme  under grant agreement No. 765115-MossTech.
Participants and procedures We conducted a longitudinal, mailed survey of HCPs in New York State (NYS). We collected pre-campaign data from October 2017-January 2018 and post-campaign data from July 2018-September 2018 (2 months post-campaign). We obtained a list of NYS licensed physicians, physician assistants, and nurse practitioners from the Fig. 1. Example Campaign Print Ad Encouraging TDT. NYSDOH ran a paid provider-focused media campaign digitally (e. g. , display banners on provider-related websites), on social media (e. g. , LinkedIn, Facebook), in print (e. g. , trade journals such as the New England Journal of Medicine), and via email marketing (e. g. , AdFire). University at Albany, State University of New York's Center for Health Workforce Studies. We selected a stratified random sample of 6,000 HCPs (2,202 physicians, 1,999 physician assistants, and 1,799 nurse practitioners) to participate in a written, mailed survey.
Phase recognition has received a lot of attention and is a very active area of research in the medical computer vision community  [8] -  [12]. Alongside phases, there has been substantial research focusing on fine-grained activities such as robotic gestures  [13] -  [19] , action triplets  [20] , and instrument detection and tracking  [11] ,  [21] ,  [22]. Recently, there has been a aof research works focusing particularly on step recognition  [6] ,  [7] ,  [23]. While steps define a surgical workflow at a more finegrained level than phases, the time required to annotate a dataset with steps is significantly higher than with phase annotations. For example, in Laparoscopic Roux-en-Y gastric bypass (LRYGB) procedures, the workflow consists of 44 steps and 11 phases (Table  II ). Precisely defining and annotating all the steps requires a considerably higher time of experts due to the number of steps and more importantly lower interclass variances between steps.
Since recent works in surgical phase/step recognition employ deep learning models, they rely on the availability of large-scale annotated datasets. Curation of these annotated datasets is difficult and time-consuming as these tasks require domain-specific medical knowledge. To address this issue, a few works  [24] -  [27]  have proposed methods based on semi-supervision. These approaches involve either pre-training the model on proxy tasks or training on synthetic labels generated by a teacher model trained on a small subset for phase recognition. Unlike these works, inspired by  [22]  and  [28] , we address the annotation scarcity issue by proposing a weakly supervised learning approach utilizing relatively economical annotations. The main contributions of our work are summarized as follows: 1) We propose a weakly supervised learning method for surgical workflow analysis to tackle the problem of fine-grained surgical activity (step) recognition.
We exploit the hierarchical step-phase relationships and utilize easier-to-annotate weak phase annotations on videos with missing step annotations. 2) We introduce a novel dependency loss to enforce the weak supervision and encode the step-phase hierarchical relationship as a matrix. By optimizing for this loss, it encourages the model to learn possible step sequences and transitions from videos with only phase annotations. 3) We present an end-to-end model consisting of ResNet-50 and Single-Stage Temporal Convolutional Network (SS-TCN) to learn both visual and temporal cues jointly. 4) We extend the CATARACTS 1  dataset (containing step annotations) with phase annotations. These annotations will be released upon acceptance of this manuscript. 5) We extensively evaluate our approach on two surgical video datasets, namely Bypass40  [7]  and CATARACTS  [29] , demonstrating the effectiveness and generalizability of our method.
B. Weak Supervision Weak supervision has seen a great interest in the medical computer vision community to tackle the need for highvolume annotated datasets that are difficult to generate. Some of the interesting applications of weak supervision are seen in surgical tool localization  [22] , tool segmentation  [28] , cancerous tissue segmentation  [35] , and detection of the region of interest in chest X-rays and mammograms  [36]. To reduce the number of labeled videos, most of the recent research works in phase recognition have proposed approaches based on semi-supervised learning. These approaches follow a similar strategy of pre-training the models on different proxy tasks of frame-sorting  [24] , predicting the temporal distance between multiple frames  [25] , and predicting the remaining surgery duration  [26].
B. Weak Supervision: Step-Phase dependency loss Steps and phases are two types of activities describing the surgical workflow that are defined at different levels of granularity and possess an inherent hierarchical relationship  [4] ,  [7]. Steps are defined at a higher level of detail compared to phases. This brings about lower inter-class variances between steps, compared to phases, making it a more complex task to clearly define and distinguish between them. The challenges can be seen in the sample images presented in Fig. 1. For instance, in the Bypass40 dataset, similar actions are performed across different steps belonging to different phases. Dissection is performed in at least 7 steps spread across 3 different phases. Similarly, Stapling is performed in 5 steps across 4 different phases.
Designing and training a deep learning model to distinguish between these similar steps poses a great challenge. Even the state-of-the-art method, MTMS-TCN  [7] , trained on a fully annotated dataset achieves an accuracy of ∼76% with a precision of ∼56%, accentuating the difficulty of the problem. The class imbalance further creates a challenge for training deep learning models that require large datasets with plenty of samples for each class. In the scenario presented in this paper where the number of annotations is scarce, the recognition difficulties increase drastically. To overcome some of the challenges, this work proposes a weakly supervised approach that utilizes labels of less granular activities, i. e. , phases. Phase information alone could help the model in two ways. Firstly, phase information could help the model reduce errors related to recognizing similar looking steps, e.
g. , 'S6: horizontal stapling' and 'S18: gastrojejunal stapling', belonging to two different phases. Secondly, we can gather a smaller subset of probable steps that could occur in a given phase eliminating the rest. For example, given the phase to be 'Phacoemulsification' of cataract surgery, only 5 out of 19 steps are likely to occur (Table  I ). Similarly, a phase such as 'P5: anastomosis test' in the Bypass40 dataset, reduces the possible steps to 7 out of 44 (Table  II ). Here, the phase information provides cues to the model to learn to distinguish between steps belonging to the subset rather than the whole set. Thus we hypothesize that the additional available weak phase information could be very beneficial for step recognition in the low data regime. We propose to represent the relationship as a step-phase mapping matrix M s→p , where the elements m ij of the matrix are binary indicator variables which are 1 if step s i occurs in phase p j.
IV. EXPERIMENTAL SETUP In this section, we discuss the experimental setup of our method. First, we present the datasets used for evaluation. Next, we discuss the experimental study followed by the training setup and evaluation metrics. A. Datasets 1) Bypass40: The Bypass40 dataset  [7]  consists of 40 videos of LRYGB procedures with resolution 854 × 480 or 1920×1080 pixels recorded at 25 fps. Each frame is manually assigned to one of the 11 phases and one of the 44 steps  [7]. For example, steps such as gastric opening, gastric tube placement, horizontal stapling, and vertical stapling occur in gastric pouch creation phase. A detailed list of phases and steps along with their hierarchical relationship is presented in Table  II. For more information, we ask the readers to refer to  [7]. We split the 40 videos into 24, 6, and 10 videos for training, validation, and test sets, respectively, and subsampled them at 1 frame-per-second (fps).