dataesr/scientific-paragraphs-categorization huggingface dataset:
Our response: We have limited our analysis here on only very few time-points for easier understanding of the tool and also to provide small test data files. Indeed, Mfuzz clustering will especially be useful to analyse larger time-series. However, this will inevitably lead to large test data files, as well as large figures. As an example, for our dataset on flight muscle development published in (doi: 10. 7554/eLife. 34058), we have created 40 clusters from 8 time-points using Mfuzz. Using only three time-points makes the demonstration of the software simply easier. We would also like to note that many researchers will only have two conditions to compare using the differential expression analysis function, which is equally useful in RNfuzzyApp. 4. The enrichment analysis is currently only allowing 'Only annotated genes' or 'All known genes' as statistical domain scope (i.
Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? NoIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? PartlyAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Bioinformatics, Systems Biology, Epigenetics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
24derlying dataGene Expression Omnibus (GEO): Tabula Muris Senis: Bulk sequencing. Accession number GSE132040; https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE132040. 15GeneExpressionOmnibus(GEO):Muscle-typespecific transcriptomic expression patterns in Drosophila. Accession number GSE143430; https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE143430. 24ExtendeddataDryad:Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis. https://doi. org/10. 5061/dryad. 8pk0p2nnd. Haering_etal_extendedDatatable_1f_Tabulamurissenis_12vs27m_gpofiler. txt:gprofilerresults of Tabula muris senis project (GSE132040), DEA, limb muscle, 3 vs 12 months.
4. 4 Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? NoIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? PartlyAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Bioinformatics, Systems Biology, Epigenetics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
•Table2a: Haering_etal_extendedDatatable_2a_Tabulamurissenis_cluster1_gpofiler. txt: gprofiler results of hierachical clustering of Tabula muris senis project (GSE132040), limb muscle, cluster 1. • Table 2b: Haering_etal_extendedDatatable_2b_Tabulamurissenis_cluster2_gpofiler. txt: gprofiler results of hierachical clustering of Tabula muris senis project (GSE132040), limb muscle, cluster 2. • Table 2c: Haering_etal_extendedDatatable_2c_Tabulamurissenis_cluster3_gpofiler. txt: gprofiler results of hier- achical clustering of Tabula muris senis project (GSE132040), limb muscle, cluster 3. This project contains the following extended data:• Table 1a: Haering_etal_extendedDatatable_1a_Tabulamurissenis_3vs12m_DEA. txt: results of differential expression analysis (DEA) of Tabula muris senis project (GSE132040), limb muscle, 3 vs 12 months.
•Table1b:Haering_etal_extendedDatatable_1b_Tabulamurissenis_3vs27m_DEA. txt:results of DEA of Tabula muris senis project (GSE132040), limb muscle, 3 vs 27 months. •Table1c:Haering_etal_extendedDatatable_1c_Tabulamurissenis_12vs27m_DEA. txt:results of DEA of Tabula muris senis project (GSE132040), limb muscle, 12 vs 27 months. •Table1d:Haering_etal_extendedDatatable_1d_Tabulamurissenis_3vs12m_gpofiler. txt:gprofilerresults of Tabula muris senis project (GSE132040), DEA, limb muscle, 3 vs 12 months. •Table1e: Haering_etal_extendedDatatable_1e_Tabulamurissenis_3vs27m_gpofiler. txt: gprofiler results of Tabula muris senis project (GSE132040), DEA, limb muscle, 3 vs 12 months. • • Table 2d: Haering_etal_extendedDatatable_2d_Tabulamurissenis_cluster4_gpofiler. txt: gprofiler results of hierachical clustering of Tabula muris senis project (GSE132040), limb muscle, cluster 4.
With the development of affordable 3D printers, we believe Malbert Charles-Henri, Revision 3, Page 4 that all of these limitations can be overcome using open-source hardware and software dedicated to porcine brain surgery. The availability of these tools is likely to promote porcine brain surgery in a similar way as our freely accessible porcine atlas, which is used far beyond in-house research  (20) , (  21 ), (  9 ),  (22). This study aimed to develop and validate an open-source set of tools combining software and hardware components suitable for stereotaxic placement of catheters or electrodes within the brains of adult and growing pigs, irrespective of their weight. The system used a commercially available 3D localizer infrared (IR) camera for precise positioning. The localizer was the only piece of hardware that was not open-source or 3D printable.
Each of the tools supporting four reflective markers is capable of submillimeter accuracy. The system takes advantage of CustusX software  (23) , an opensource software designed by the Norwegian National Competence Center for ultrasound and image-guided therapy. The software processes online data from the infrared localizer and overlaps them with the models describing the porcine brain and stereotaxic supports. Our system was primarily oriented for the placement of ventricular catheters and pushpull cannulas that do not require accurate positioning. It was further challenged by targeting a smaller zone, which requires precise navigation capabilities. The subthalamic nucleus (STN) was chosen because it is difficult to access and exhibits a specific electrophysiological signature even in anesthetized animals  (4).
Localizer tools The localizer tools were tracked by a Polaris Spectra (NDI, Waterloo, Canada) that was located approximately 1. 5 m above the surgical plane and attached to a Macintosh computer running CustusX software. Three localizer tools were designed with two reference frames. The schematic for the distance and angle between the reflective markers was obtained from the Apple library created under an open-source license by Brown et al. (27). This library allowed several localizers to simultaneously track whether their ROM definitions (the file format used by the NDI localizer) were inserted into the navigation software. One tool (the cannula tool) consisted of a stainless steel rod (2 mm OD; Phymep, Paris, France) attached to a localizer frame. The diameter of the rod was selected such that it could be inserted into a 2.
85 mm OD stainless steel tube that served for ventricular access or to attach an additional smaller tube to create a push-pull cannula. The rod outer end was tightly inserted in Luer to 1/4 28 female peek adapters (Idex Health and Science, Oak Harbor, USA) to allow connection with a Luer-tipped syringe. The inner end, designed to be inserted into the brain, was carefully polished with a rotating felt brush under binocular conditions. Another tool (the coagulator tool) accommodated a coagulation electrode (Dixi Medical, Chaudefontaine, France), and the last localizer was designed to be attached to the robotic targeting device. For each tool, a ROM definition was constructed from scratch using NDI Architect software (V6; NDI, Waterloo, Canada) together with the required. xml files to be inserted into the CustusX Malbert Charles-Henri, Revision 3, Page 8 navigation software.
The two reference frames had identical distances and angles between the reflective markers and were attached to dissimilar supports. One frame was used during the initial navigation steps and was placed under the chin of the animal. The second frame could be adjusted in any direction and was designed such that its end was fixed directly on the skull. As the references were virtually identical for the navigation software, it was possible to exchange them during surgery to optimize their IR camera visibility. NDI tracker software (NDI V5. 001, Waterloo, Canada) was used as the first step of the surgical procedure to confirm the visibility of the tools and optimize the position of the NDI camera.
Software customization CustusX software (https://www. custusx. org) was customized to allow 3D localization of the coagulator, cannula, and motorized targeting tools. Several files including ROM definitions have been designed and are available in the "Tools definition for Custus X" folder of Supplemental Material 1. Once detected by the IR camera, the tool was displayed on a real-time screen using a simplified 3D representation (included with each tool). While the ROM files were immediately usable, it is advisable to "pivot" the tool using the pivoting procedure described in the NDI Architect software to update the ROM file; this compensates for small variations in the length of the bar/coagulator. VTK and STL models of the pig brain ventricles and each of the tools plus the swivel base are provided in the Supplemental Material.
The biological models were obtained from the conversion in VTK format, using 3D Slicer software (V4. 10. 2, https://www. slicer. org) (28) of the pig brain atlas developed in our laboratory  (5). The software models were required to be uploaded to CustusX before the registration procedure for a comprehensive representation of the surgical scene (Figure  2 ). animal -the first one after the placement of the external references on the skin, and the second one once the targeting was completed. The first scan also served to calculate the skull bone thickness and was part of a failsafe procedure to position a drill guard (details of construction are described in Supplemental Material 1) on the burr-hole drill. The second scan confirmed the placement of the cannula in the lateral ventricle after the injection of 2 ml radio-opaque medium (Hexabrix 320, Guerbet, Villepinte, France).
Two supports dedicated to the reference frame were designed in an attempt to minimize this issue. Nevertheless, the surgeon must take care when performing the actual acquisition of both the active and reference frames, especially during the descent of the cannula. Fortunately, flags present on the main screen of the CustusX software consistently indicated the effectiveness of the tracking. The physical balance of the tools in the surgeon's hand is another important issue for the simplicity of the procedure. While several attachment possibilities were incorporated in the design by Sudhakar et al. (18) , the most suitable for adequate balance was selected. Care must be taken when assembling the tools to match the correct holes because the ROM files are designed for a specific attachment only.
Malbert Charles-Henri, Revision 3, Page 14 It was possible to estimate the system's targeting possibilities with specific reference to the target depth and thickness of the skull. Unsurprisingly, the target possibilities increased linearly with the target depth. Nevertheless, deeply located targets are more complex to reach because the system's offsetting capability halves when the skull thickness triples, that is, when a piglet becomes adult. Knowing that the encephalon fit within a box approximately 60 × 40 × 35 mm (5), the entire brain is not accessible with a single burr hole. This explains the importance of surgical planning and the need for the swivel base to have a precise location. The system described here resembles the StealthStation surgical navigation system (Medtronic, Minneapolis, USA); the StealthStation is restricted to use in humans because the navigation software does not allow the insertion of alternative atlases  (30).
The advantages and disadvantages of these systems are listed in Table  1. Table  1  The addition of an electromagnetic localizer at the tip of the cannula represent an exciting addition to the IR localization. However, it is important to balance the system's increased complexity, cost, and easiness for targeting large structures such as the ventricle. While a waterproof stepper may be useful, the price tag for a miniature waterproof stepper is significant. The most relevant improvement is probably a simplification of the CustusX user interface and its dedication to brain navigation only. The open-source code is highly versatile, including hybrid representation with synchronization of CT based images, ultrasound video, and ultrasound 3D reconstruction. Malbert Charles-Henri, Revision 3, Page 15 Several registration methods, which are mandatory for the initial purpose of the software, e.
g. , bronchoscopy, also contributed to the complexity of the interface  (23). The alternative open-source navigation software, 3D Slicer, with the adequate registration plugin, was powerful but equally complex  (28). In conclusion, we describe an open-source 3D printable frameless stereotaxic system that is suitable for both adult and young pigs, irrespective of their weight. This system was not designed to replace the already developed frame-based stereotaxic apparatus, but as an effective substitute for rapid and easy probe placement within sub-millimetric targets. While it was tested for ventricular access and recording of STN activity, the system is suitably flexible for radiofrequency lesioning or excitotoxin injections using an optimal path to the desired target  (34). The technique and instrumentation may benefit future research within this species, and was designed to be easily duplicated with a lowcost 3D printer using the models supplied.
depicted as a conical grey marker, the swivel base and mobile cannula holder are in green, the target is in red, and the cannula path is presented as a thin green line. The animal was placed in a non-symmetrical manner with the head slightly tilted to investigate the capability of the system to work with suboptimal positioning relative to the CT plane. The outlines of the skull were obtained after thresholding, projection of the actual CT images using Osirix, and further importation in Rhino Software. The contours of the brain were obtained from our pig brain atlas (5), and were coregistered using 3D slicer with the actual CT volume of the brain. A small gap between the brain and the skull reconstructions was introduced in the rendering to ease visualization. The diameter of the burr hole corresponded to the largest available drill from the surgical suppliers.
Climate change is transforming marine ecosystems through the expansion and contraction of species' ranges. Sea ice loss and warming temperatures are expected to expand habitat availability for macroalgae along long stretches of Arctic coastlines. To better understand the current distribution of kelp forests in the Eastern Canadian Arctic, kelps were sampled along the coasts for species identifications and percent cover. The sampling effort was supplemented with occurrence records from global biodiversity databases, searches in the literature, and museum records. Environmental information and occurrence records were used to develop ensemble models for predicting habitat suitability and a Random Forest model to predict kelp cover for the dominant kelp species in the region -Agarum clathratum, Alaria esculenta, and Laminariaceae species (Laminaria solidungula and Saccharina latissima).
Kelp Occurrence Occurrence data of kelp species were compiled using museum data records of kelp specimens from the National Herbarium of Canada at the Canadian Museum of Nature (CANA). Each was accompanied by a picture of the specimen, enabling identification if a record needed to be renamed according to the most updated taxonomic nomenclature. We also compiled information from datasets collected from field campaigns that took place between 2011 and 2020 in different regions of the Eastern Canadian Arctic (North Baffin Island, Hudson Strait, Roes Welcome Sound, Foxe Basin, Baffin Bay, Davis Strait, Labrador Sea, and Ellesmere Island). Divers and drop cameras were used to take pictures and/or make videos along transects to identify kelp (see details in Supplementary Table  1 ). To complement this dataset and increase the number of occurrence points, biodiversity databases such as the Global Biodiversity Information Facility (GBIF 1  ) and Ocean Biogeographic Information System (OBIS 2  ) were used.
Additionally, a literature search was done to complete occurrence records in places where there were gaps in the information already gathered (Northeastern and Southwestern Greenland, Svalbard, and some regions of the Barents and White seas)  (Borum et al. , 2002; Hop et al. , 2016; Schoenrock et al. , 2018; Filbee-Dexter et al. , 2019; Ronowicz et al. , 2020). We also contacted kelp experts (e. g. , G. Saunders) who provided occurrence records from their lab databases (Supplementary Figure  1 , but also see Supplementary Table  2  for the complete raw dataset). To limit over-prediction, duplicate records were removed and only a single presence record was retained for each grid cell (see Section "Environmental Data" for details on grid size). Occurrence points were considered at the same resolution as the corresponding environmental layers  (García-Roselló et al.
For these occurrences, any that were up to two grid cells inland were moved to the closest sea grid using the Near Proximity tool in ArcMap v10. 2. 2. Any points further inland were removed from the database. All retained occurrence points were then clipped to only include those that were from Arctic environments (using the Arctic region delimited by the Arctic Monitoring and Assessment Programme, AMAP) (Figure  1A  and Supplementary Figure  1 ) to ensure that the data used for modeling was focused on coldadapted kelp populations. Populations that are distributed on the extreme ends of their temperature ranges can have local adaptations to marginal climatic conditions present in those places, so this approach excluded occurrence data from southern limits of these species that could skew the model  (Angilletta et al.
Kelp Cover Previous and recent research campaigns across the Eastern Canadian Arctic  (Filbee-Dexter et al. , 2021)  collected kelp cover data that allow habitat suitability and percent cover of kelp species to be modeled and predicted throughout this region. Abundance of kelp can be measured in several ways, with biomass per area, number of individuals per area (i. e. , density) and percent cover being the three most common. Although biomass per area provides the best measure of algal standing stock, most of the data combined in the present study and used to estimate kelp abundance were derived from videos or photographs of the kelp canopy and seafloor. Because it is not possible to extract either biomass or density reliably from photographs, percent cover was chosen to maximize the number of sites for which data could be obtained.
Environmental Data Environmental data layers were used as input for modeling habitat suitability and cover of kelp in the Eastern Canadian Arctic. These layers were downloaded at a global scale from Bio-ORACLE v2. 1 3   (Assis et al. , 2018b)  and then cropped to the Arctic region. Bio-ORACLE layers are created from pre-processed global ocean re-analyses of a combination of satellite and in situ observations to calculate monthly averages for present conditions (roughly 2000-2014)  (Assis et al. , 2018b). The resolution of these environmental layers was 5 arcmin (approximately 9. 2 km at the equator). A set of 9 environmental layers were initially included to model the present conditions in the Arctic and were composed of long-term mean values of minimum and maximum records per year (except for surface temperature -see explanation below) of variables at the maximum depth of the grid cell and/or sea surface values.
Five replicates of 1,000 random pseudo-absence points were generated to build several sets of pseudo-absences to prevent sampling bias  (Guisan et al. , 2017). The use of a smaller numbers of pseudo-absences is suggested when there are limited numbers of presence points in a species' database  (Barbet-Massin et al. , 2012). The five models used to build the final ensemble model were run with default setting options. A complete set of default options can be found in the biomod2 package under the function 'Print_Default_ModelingOptions()'. Replicate model runs were done five times, and the average was used as the final outcome of the models, which was then converted to binary values of suitable and unsuitable habitat. This binary classification was based on the maximum training sensitivity plus specificity threshold since it is known to produce the most accurate predictions  (Jiménez-Valverde and Lobo, 2007; Liu et al.
Future Projections After running, testing, and validating predictive models of current kelp habitat suitability and cover, these predictions were projected into the future under global change scenarios. Environmental layers representing future global change were obtained from Bio-ORACLE v2. 1 for the most extreme emission scenario (RCP 8. 5) for the years 2050 and 2100. Under this extreme emission future scenario without climate policies, increasing greenhouse gas emissions are projected to create a median temperature anomaly of 4. 9 • C relative to pre-industrial levels by the end of the century  (Riahi et al. , 2007; Moss et al. , 2010)  and an ice-free Arctic during the summer season by 2050  (Hwang et al. , 2020). All of the variables that were used for running the models for present conditions were also used for models that were projected into the future, with the caveat that only temperature, salinity, ice thickness and current velocity data layers changed for projected future scenarios (Supplementary Figure  2 ).
In general, by comparing percent cover results and model accuracy for all species, we observed that the Random Forest models underperformed at predicting areas with cover higher than 30%, and almost never predicted model covers greater than 60% even though observed values of 100% cover exist in the dataset (see summary of Supplementary Figure  4B ). Thus, actual percent cover throughout the Eastern Canadian Arctic is likely much higher than the results shown here. As seen in Supplementary Figure  5, S. latissima was the one with higher values of standard errors given the proportion of models that had <0. 7 TSS values (Supplementary Figure  4A ). For the Laminariaceae there is near perfect agreement within the ensemble models about the habitat suitability in the deeper waters of the Labrador Sea and Baffin Bay.