dataesr/scientific-paragraphs-categorization huggingface dataset:
Given that CLPB deficiency is an autosomal recessive condition, we broadened our variant search using relaxed filters. We searched for any rare variant (<0. 001 MAF) in CLPB including the intronic regions. This revealed a second CLPB variant, c. 1257+5G>A (Fig. 1B ). This variant was intronic but fell near the donor splice site for the exon 11/12 junction. The variant is absent in public databases, such as gnomAD, and the affected nucleotide is moderately conserved. The variant was maternally inherited, consistent with compound heterozygosity. At the time of variant discovery, all reported cases of CLPB deficiency presented with urinary 3-MGA-uria. 3-MGA is a nonspecific hallmark of many syndromes with compromised mitochondrial function  (30). In contrast to the previously reported patients with CLPB deficiency, no increased urinary excretion of 3-MGA was detected in the patient of this study.
3C ). CLPB in patient lymphoblasts appears to be migrating slightly slower than in control samples; however, the cause of this shift is not clear. There was no evidence of truncated CLPB, likely due to nonsense-mediated decay of the PTC-containing mRNA and sensitivity limitations of the CLPB antibody. To investigate the biological consequence of CLPB disruption, comparative analysis between patient and control lymphoblast protein abundance was performed. The mitochondrial complement was filtered from the whole cell proteomic dataset and normalized independently. To discern those proteins significantly enriched or depleted from patient replicates, a two-sided t test was utilized, and results were visually represented as volcano plots (Fig. 3D ). CLPB is significantly reduced in both whole cell and mitochondrial analyses, with a Log 2 fold difference of 1.
Funding This work was supported by an  National Health and Medical Research Council (NHMRC)  program grant ( 1074258  to A. H. S. ),  NHMRC  grant ( 1140906  to D. A. S. ),  NHMRC  fellowships (1054432 to E. J. T. , 1126995 to R. S. , 2009732 to D. A. S. , 1062854 to A. H. S. ), a  Suzi Carp postdoctoral scholarship  (to E. J. T. ), a  CHU Rennes grant  (Appel à Projets Innovations 2019 to S. J. ), an  Australian   Government Research Training Program scholarship  (to M. J. B. ), the  Australian Mito Foundation  for provision of instrumentation, incubator grants (to E. J. T. and D. S. ), a  Booster grant  (to D. S. ) and PhD Top-up scholarships (to M. J. B. and D. H. H. ), and a  Medical Research Council (UK)   Clinician Scientist Fellowship  ( MR/S002065/1  to R. D. S. P. ). S. B. W. was funded by ERAPERMED2019-310-Personalized Mitochondrial Medicine (PerMiM): Optimizing diagnostics and treatment for patients with mitochondrial diseases (FWF 4704-B).
Regarding climate projections, the Intergovernmental Panel on Climate Change (IPCC) concluded in its fifth assessment report (AR5)  (6)  that "Future [human-induced] warming trends cannot be predicted precisely, especially at local scales". In the IPCC AR6  (7) , a new generation of climate models  (8)  has been used to provide a range of projections in response to different socioeconomic scenarios  (9). On the basis of this new dataset, various studies have recently shown that uncertainty in global mean warming can be considerably reduced using the information provided by recent observed warming trends via so-called "constraint" methods  (10) (11) (12) (13). These studies consistently point toward a downward revision of the expected warming in all emission scenarios  (10, 12) , with a decrease in model uncertainty of nearly 40% for end of century projections  (11) , and even more at shorter lead times.
First, the local signal-to-noise ratio can be small. This may happen if local internal variability or measurement uncertainty is large (i. e. , local observations provide little insight on the externally forced response). Second, the global and local responses can be highly correlated with each other so that they partly provide the same information, leading to a limited impact of local observations on uncertainty ranges. In both cases, the model uncertainty will be only marginally reduced by local historical data (see Materials and Methods). The application of the Local + GMST constraint to all grid points worldwide results in a projected warming pattern, which remains quite close to the GMST-only case (Fig. 3, C  and E ) but with regional differences. On the one hand, for several regions over the Arctic, the warming is revised downward compared to the unconstrained case, making the projections more consistent with recent observations and implying a reduced warming compared to the GMST-only case.
Observational dataset and models The temperature observations are from the HadCRUT5 (48) dataset over the 1850-2021 period. The temperature field comes from a blending of near-surface temperature and sea surface temperature using land sea mask and sea ice concentration. The measurement uncertainty of the HadCRUT5 dataset is estimated from an ensemble of 200 equiprobable realizations. Most of the other observational products are included in the temperature range estimated by this ensemble, which confirms our choice to consider the HadCRUT5 dataset as a reference. CMIP6 models are selected according to the availability of the following data: at least 200 years of a preindustrial control (piControl) simulation; at least one member of a historical simulation and one member of a projection simulation for the SSP5-8.
Σ meas. is estimated as the sample covariance matrix over the 200-member ensemble of the HadCRUT5 dataset  (48). Σ iv is estimated using observed annual time series of global and local temperature over the 1850-2021 period. First, we compute the global observational residuals by subtracting the CMIP6 response to all external forcings  glo (1,…,n y ) from the observations y glo. Similarly, we derive local residuals by subtracting  loc (1,…,n y ) from y loc. These residuals constitute a first estimate of global and local internal variability, noted ˆ  iv,loc,1 , ˆ  iv,glo,1 , respectively. We define Σ iv as a matrix of size 2n y × 2n y of the following form ∑ iv = [ Σ iv,loc Σ iv,dep ′ Σ iv,dep Σ iv,glo ] (8) where Σ iv,loc and Σ iv,glo are the covariance matrices of size n y × n y modeling local and global internal variability within y loc and y glo , respectively.
, 2013)  and hosts supraglacial lakes each summer  (Arthur et al. , 2020b). We use a machine learning approach to assess the inter-and intra-annual variability in the location of satellite-observed surface melt and compare our results with surface topography and patterns of climate variables such as surface air temperatures and albedo to identify the influence of localised controls on the occurrence of surface melt. Our approach therefore goes beyond the metrics used in previous remote sensing studies and begins to bridge the gap between spatiotemporal descriptions of melt variability derived from satellite observations and the process-level understanding of melt from SEB studies that are more detailed but limited in time and space. The paper is structured as follows. In Sect. 2, we provide an overview of the Shackleton Ice Shelf and the passive microwave datasets.
In Sect. 3, we describe the self-organising map methodology. In Sect. 4, we present the results, which we then discuss in Sect. 5; particular attention is given to understanding the results in relation to the local geographic setting of the shelf (e. g. surface topography, albedo, and winds) and its role in controlling surface melt. Finally, in Sect. 6, we briefly conclude the work. 2 Study region and datasets.
Satellite datasets We use daily passive microwave observations of surface brightness temperature (T B ) at 19 GHz from two successive versions of the Advanced Microwave Scanning Radiometer (AMSR), called AMSR-E (May 2002-October 2011) and AMSR-2 (May 2012-present). The 19 GHz observations have an underlying footprint of ∼ 14-16 km × 22-27 km but are pre-processed to a regular 12. 5 km southern stereographic polar grid using the drop-in-the-bucket method with daily averaged T B  (Meier et al. , 2018). The overpass time, which is important for climatic studies of surface melt  (Picard and Fily, 2006) , has remained approximately constant through the sensors' lifespans, with the equatorial crossing time of the ascending pass being ∼ 13:30 local solar time each day (REMSS, 2022). To assess whether the time series can be extended back in time, we further utilise observations from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) sensors: F13 (May 1995-December 2007), F17  (December 2006-present), and F18 (January 2017-present).
These observations, collec-   (Bindschadler et al. , 2008)  overlain on REMA topography  (Howat et al. , 2019) ; the shelf boundary is taken from the MEaSURES dataset  (Mouginot et al. , 2017). tively hereafter SSMIS, were pre-processed in the same way as the AMSR datasets but gridded at 25 km due to their coarser underlying footprint (∼ 70 km × 45 km at 19 GHz)  (Meier et al. , 2021). Overpass times vary between the three SSMIS sensors, with their ascending passes observing the Shackleton Ice Shelf between ∼ 16:30 and ∼ 20:00 local solar time each day.
Shelf mask To define the boundary of the Shackleton Ice Shelf, we make two small modifications to the shelf mask from the MEa-SURES dataset  (Rignot et al. , 2013; Mouginot et al. , 2017). Firstly, we exclude the front ∼ 25 km of the Denman Glacier tongue, where the pixels become progressively more icecovered each summer owing to the glacier's high flow velocity  (Miles et al. , 2021). Secondly, we exclude the westernmost edge of the shelf, which is often bordered by a polynya  (Nihashi and Ohshima, 2015). Manual inspection of the underlying T B data indicates that the pixels along the western https://doi. org/10. 5194/tc-16-4553-2022 The Cryosphere, 16, 4553-4569, 2022 D. Saunderson et al. :  Surface melt on the Shackleton Ice Shelf edge may have been contaminated by the inclusion of sea ice and open ocean in the sensor footprint and are therefore not suitable for use with our algorithm, which is only designed to differentiate between wet and dry snow.
Climate variables We use monthly averaged climate variables from the RACMO2. 3p3 regional climate model  (van Dalum et al. , 2021 (van Dalum et al. , , 2022)) , which is the latest version of the RACMO model that has been used extensively in Antarctica (e. g. Lenaerts et al. , 2012; van Wessem et al. , 2014a van Wessem et al. , , 2018)). RACMO2. 3p3 includes updates to the albedo scheme and multilayer firn module and allows subsurface penetration of shortwave radiation, which can be important for melt in Antarctica  (Liston and Winther, 2005; Liston et al. , 1999). The model is forced at its lateral boundaries by ERA5 reanalysis data  (Hersbach et al. , 2020).
Self-organising maps A self-organising map (SOM) is a machine learning approach that can simplify multi-dimensional datasets  (Koho-nen, 1990 (Koho-nen, , 2001)). SOMs have been widely used in many fields, including climate science (e. g. Hewitson and Crane, 2002; Sheridan and Lee, 2011; Cassano et al. , 2016; Gibson et al. , 2017; Udy et al. , 2021) , but have not been used to investigate surface melt in Antarctica before. A SOM algorithm produces a set of outputs that represent important variability in the input data. We refer to these outputs as patterns because, for our dataset, each output represents a typical spatial distribution (i. e. "pattern") of melting and non-melting pixels across the shelf, taken at a daily resolution. SOMs work by comparing each input (here a daily melt observation) against a set of self-adaptive reference nodes, which ultimately become the output melt patterns.
Implementation with binary surface melt data We apply the SOM approach using the kohonen package  (Wehrens and Buydens, 2007; Wehrens and Kruisselbrink, 2018)  in R (R Core Team, 2021). Daily maps of binary melt status in the AMSR datasets are flattened to 1D vectors and combined into a matrix in which each row represents a single day, and each column references a specific pixel within the shelf mask. All days between the onset and freeze-up dates of each summer are included. Owing to the binary nature of our input data, we use the "Tanimoto" similarity measure. Within the kohonen package, this setting represents the Hamming distance divided by the total length of the input vector; conceptually, this is the fraction of times in which the input disagrees with the reference pattern. Implementation of the SOM algorithm requires the definition of multiple initiating parameters, the most crucial here being how many output patterns are required.
We run a series of sensitivity experiments for each of the parameters (Figs. S4-S5 ) and find that using nine patterns is the most appropriate for our purposes (Sect. 3. 3). Boxplots showing how well each SOM pattern represents its respective members are shown in the Supplement, along with examples of visual comparisons (Figs. S2-S3 ). Together, these figures show that the SOM patterns are able to capture the dominant fea-tures of spatial variability and simplify the dataset for further analysis.
S4d  and S5 ), the additional patterns are near-replicas of the nine patterns shown in Fig. 2  and would be considered analogous when interpreting the results given the above constraints. Overall, the similarity of the outputs strengthens our confidence in the results, and our interpretations remain unchanged between sensitivity tests. Were the current analysis to be repeated on an alternative shelf, or even on the Shackleton Ice Shelf using a different underlying dataset, there is no expectation that the same settings would produce the most optimal output for that particular case, and sensitivity tests for SOM parameters should be run again.
Sensor comparisons Few previous studies have investigated surface melt on the Shackleton Ice Shelf in detail, but there is some evidence of heterogeneous melt behaviour (see e. g. Fig. 3  of  Liu et al. , 2006; Fig. 2b of Tedesco and Monaghan, 2009; and Fig. 6 of Trusel et al. , 2012). However, results can be sensordependent. For example, a longer melt season is observed in the north of the shelf with an active microwave sensor (AS-CAT), but to the south-east with a passive sensor (AMSR-2) (see Fig. 2d  of  Zheng and Zhou, 2020). The frequency of the sensor also determines how many melt days are observed (see  Figs. 3 and 6 of Leduc-Leballeur et al. , 2020). Whilst it can be tempting to try to establish which of the various datasets is "correct", we advocate building on the approach of Leduc-Leballeur et al.
AMSR-SSMIS comparison We compare our AMSR melt observations against those from the SSMIS sensors and find that the two datasets only agree on the SOM melt pattern ∼ 30 % of the time overall (Fig. 8 ). Because the assigned SOM patterns represent the observed melt well for both datasets , this low correspondence is mainly the result of three discrepancies in the sensors' observations. Firstly, a third of all pattern 9 days in the AMSR dataset are mapped as pattern 8 days in the SSMIS observations. These two patterns are very similar, and the melt observations for each often only differ in the melt status of a couple of pixels. For many purposes, these patterns could therefore be considered equivalent between the sensors, though pattern 9 represents truer shelf-wide melt. Secondly, the sensors disagree on when the melt season begins and ends each summer (i.
e. the melt onset and freezeup dates). Nearly a quarter of all days across the AMSRdefined melt seasons occur beyond the SSMIS-defined dates. Many of these days also have little melt in the AMSR observations (i. e. pattern 1), but, on average, there are 3. 5 d each summer with extensive melt (mainly patterns 2 and 4) on days outside of the SSMIS-defined season. A clear diurnal melt cycle exists in East Antarctica  (Picard and Fily, 2006) , suggesting that the afternoon passes of the AMSR sensors are more likely to observe melt than the evening and morning passes of the SSMIS observations. Because we use a binary definition of melt, the effects of a diurnal cycle are likely to be more pronounced away from the height of the melt season and could thus explain this discrepancy. Thirdly, patterns 3 and 5-7 are rarely observed in the SS-MIS dataset at all and are only observed simultaneously in both datasets on 11 d over the entire study period.
Differences in the spatial resolution of the sensors can become very important for melt observations in less homogeneous areas of an ice shelf, particularly if there are changes in elevation within the footprint  (Johnson et al. , 2020). The rarity of patterns 3, 6, and 7 in the SSMIS dataset is likely because of the sensor's coarser resolution as each of these three patterns show extensive melt along the steep grounding line. Likewise, pattern 7 in the AMSR data often corresponds to pattern 4 in the SSMIS data and thus represents a reduction in melt along the grounding line between the two sensors. However, the grounding line does not remain melt-free in all SSMIS observations, indicating that sufficiently intense melt can compensate for higher-elevation topography within the sensor's underlying footprint.
Together, the different resolutions and overpass times of the sensors can explain why the two datasets do not always agree and suggest that discrepancies between different sensors can potentially provide additional information on the diurnal variability and relative intensity of melt. Future work can build on our SOM-based approach to help identify and understand such differences in further detail and also extend comparisons to include other datasets used in the melt literature, such as ASCAT, SMOS, and Sentinel-1 (e. g. Bevan et al. , 2018; Zhou et al. , 2019; Johnson et al. , 2020; Leduc-Leballeur et al. , 2020; Banwell et al. , 2021; Liang et al. , 2021).
Melt days and melt fluxes In this study, we use the concept of melt days, based on a binary definition of whether liquid water is observable at the shelf surface when the satellite passes overhead. At sufficiently large spatial scales, the number of melt days correlates well with meltwater production  (Trusel et al. , 2012). Visually inspecting maps of averaged AMSR melt season duration (Fig. 6c ) and RACMO melt fluxes (Fig. 6f ) shows a broad general agreement in their respective spatial variability, but correlations between the two are statistically insignificant regardless of the resampling approach used. This discrepancy is in part because of the coarser resolution of the RACMO dataset and also because of differences along the grounding line. Comparisons with QuikSCAT-derived melt fluxes suggest that RACMO overestimates melt along the grounding line by ∼ 25-75 mm w.
e. yr -1 (see Fig. 12d  of  van Dalum et al. , 2022) , but melt observations from both passive and active microwave sensors can be greatly affected by the rock outcrops and blue ice found in and around the Bunger Hills  (Zheng and Zhou, 2020). Because no in situ melt data exist for the Shackleton Ice Shelf, it is neither possible to verify the accuracy of flux estimates across the shelf nor possible to quantify the relationship between melt days and melt fluxes at this finer spatial scale. Although we use melt days in the current work, our SOMbased approach could be adapted to incorporate melt fluxes in future work. In such a case, it is likely that using more than nine melt patterns would become beneficial because meltwater fluxes could help to further differentiate melt behaviour within each of the nine patterns we observe here and therefore help to identify the importance of different processes in driving melt.
Conclusions We use a self-organising map and daily passive microwave data from the AMSR-E and AMSR-2 sensors to identify nine representative patterns of surface melt on the Shackleton Ice Shelf over the past 2 decades (2002/03-2020/21). The nine melt patterns show that the occurrence, extent, and duration of surface melt are determined by both largerscale temperature trends and local controls, specifically surface topography, albedo, and winds. Our results support the importance of katabatic winds in driving melt on East Antarctic ice shelves and suggest that the feedbacks between katabatic winds and surface albedo can initiate and prolong the melt season along the grounding line even when the rest of the shelf remains frozen. Nevertheless, strong correlations between the number of days of shelf-wide melt each summer, the cumulative melting surface (CMS), and the average summer (DJ) air temperature point to the importance of the larger-scale climate as a control on the interannual variability in surface melt on the Shackleton Ice Shelf.
Acknowledgements. We would like to thank the editor and reviewers for their time and insightful comments, which have helped make the manuscript stronger. We also thank  Christiaan van Dalum  for providing the RACMO2. 3p3 wind vectors used in Fig. 7e. support. Dominic Saunderson  was supported by the  Monash Graduate Scholarship (MGS)  and  Monash International Tuition Scholarship (MITS). This research was carried out as part of the  Australian Research Council (ARC)   SRIEAS  grant  SR200100005 ,  Securing Antarctica 's  Environmental Future. Felicity McCormack  and  Richard Selwyn Jones  were also respectively supported by  ARC   Discovery Early Career Research  awards  DE210101433  and  DE210101923. Ghislain Picard  was supported by  ESA/AO/1-9570/18/I-DT -4DAntarctica. Review statement.
Code and data availability. The AMSR-E, AMSR-2, and SSMIS binary melt data can be accessed online at https://doi. org/10. 18709/perscido. 2022. 09. ds376  (Picard, 2022). The R code used in this analysis can be accessed online at https://doi. org/10. 26180/21132934. v1  (Saunderson, 2022). RACMO2. 3p3 model output  (van Dalum et al. , 2021)  is publicly available online at https://doi. org/10. 5281/zenodo. 5512077. LIMA  (Bindschadler et al. , 2008)  and MEaSURES  (Mouginot et al. , 2017)  are publicly available online and also accessible within Quantarctica  (Matsuoka et al. , 2021). REMA (Howat et al. , 2019)   is available online at https://www. pgc. umn. edu/data/rema (Polar  Geospatial Center, 2022). Supplement. The supplement related to this article is available online at: https://doi.
| Mapping The raw reads were demultiplexed using the process_radtag function of stacks version. 2. 53  (Catchen et al. , 2013). We trimmed low-quality bases and adaptor sequences using fastp  (Chen et al. , 2018)  with the following setting: a minimum value of 15 for qualified base (-q 15), a maximum of 50% unqualified bases allowed per read (-u 50) and a sliding window size of 5 bases (-W 5). We further trimmed all clean reads to a uniform length of 91 bp in order to remove multiple bases of low quality at the 3′ end. The final pruned reads were mapped to a female harbour seal reference nuclear genome (NCBI GenBank accession: GCA_004348235. 1) using bwa mem version 0. 7. 17 with default settings  (Li, 2013). This reference genome is assembled on the scaffold level and consists of  2,363,596,144 bp over 5,541 scaffolds (N50: 41,024,070).
| Data quality filtering To remove low-quality data before performing population genetic analyses, we used an iterative approach. First, we removed samples that were misidentified during sampling or storage, or were sequenced at too low depth. We then used the raw data from all the remaining samples to compile a set of filters that allowed us to discard low-quality regions and loci in the genome. Next, using these filters we performed additional sample quality filtering where we removed samples that were either duplicated or closely related. Finally, we updated the quality filters for genomic regions and loci, so they were based only on the samples that passed all the sample quality filters.