dataesr/scientific-paragraphs-categorization huggingface dataset:
Oncogenic fusion proteins represent novel combinations of functions driving tumorigenesis The analysis of COSMIC annotations clearly highlighted that LLPS scaffolds primarily contribute to cancer through forming oncogenic fusion proteins (OFPs). Therefore, we performed a systematic analysis of the known OFPs of COSMIC Census proteins. Of the 450 unique fusion gene pairs identified for COSMIC Census genes, 303 in-frame-fused chimeric OFPs could be obtained wherein fusion boundaries could be precisely defined on the protein level (Table  S11 ). In our dataset, each pair of fusion genes is represented by a single OFP. We derived the sequences of these OFPs from the fusion breakpoint that most frequently occurs in cancer samples, at least where such information was available (mainly cases from COSMIC).
In such fusions, RTKs lose their N-terminally encoded extracellular ligand-binding domains and commonly their transmembrane segments too, while the fusion partners replacing those can form homodimers or homo-oligomers. This leads to the dimerization, cross-phosphorylation and constitutive activation of the tyrosine kinase domains and relocalization to the cytoplasm or nucleus (depending on the partner). Consequently, this pathogenic process results in uncontrolled, ligandindependent phosphorylation of their downstream target proteins  [58, 59]. Although this association has been long recognized, to our knowledge, it has never been confirmed systematically on statistical terms. Our dataset reveals over 50 OFPs showing an association of these two functions (an overlap coefficient (OC) of 0. 62 on a 0 -1 scale for OFPs) while it does not occur in wild-type proteins (OC is 0.
In the other 11 fusions with the same association, including PML-RARA, NPM1-RARA, BCOR-RARA, NUMA1-RARA, STAT5B-RARA, ZBTB16-RARA, NPM1-MLF1, NONO-TFE3, SFPQ-TFE3, PAX5-ELN and PAX5-PML, which are mainly associated with acute leukemias, an oligomerization-prone subregion of an LLPS-driver or any other protein is combined with a TF (Figure  6B ). (Since retinoic receptor alpha (RARα) is an LLPS scaffold and it combines LLPS-prone disordered regions with a DNA-binding domain in itself  [64] , all its fusions are a part of the dataset. ) The LLPS propensity of the TFs is likely increased in their oligomerizationprone fusions due to increased multivalency. At the same time, homo-oligomerization through an extraneous domain can compromise certain functional modalities of the incorporated TFs (as seen for PML-RARA  [67, 68]  and NONO-TFE3  [69] ).
In contrast, in the context of fusions TFs are complemented by potent LLPS-driver regions or at least by homo-oligomerization domains and display elevated expression levels due to the exchange of their gene regulatory regions, which both favor condensate formation. Therefore, such fusions resolve the dependencies of the incorporated TFs and form ectopic condensates along the DNA even at genes which are not normally regulated by the TF  [73, 99, 100]. Such condensates act as potent transcriptional activators or repressors by efficiently recruiting diverse chromatin remodeling complexes  [68, [73] [74] [75] 101 -107] (or even RNA polymerase II itself  [83, 108] ), leading to aberrant gene expression patterns  [40, 109]. The pathomechanisms of many fusions in our dataset (see Figure  6B  for examples and Table  S11  for a full list of fusion constructs) have been studied individually, however, our results underscore that they represent a much larger group of LLPS-prone OFPs that combine similar functions and thus likely rely on similar underlying molecular principles.
Furthermore, cancer-driving LLPS scaffolds are often multifunctional proteins in the hallmark space, thus their mutations can contribute to several hallmarks at once, driving tumorigenesis and cancer progression more efficiently. The aggressive dominant cellular effect of fusions created by LLPS scaffolds is further exacerbated by the fact that the resulting OFPs tend to be modular, large and largely disordered  [57] , meaning that finding a single compound to inhibit them is likely to be challenging. In reality, none of the cancer-driving LLPS scaffolds in our dataset has any FDA-approved drug (Figure  4A ), in line with previous studies of LLPS-prone OFPs  [68]. When targeting LLPS-prone proteins or OFPs, many factors need to be considered, for instance, that the partitioning, concentration and activity of cancer drugs may be influenced by the physicochemical attributes of the MLOs  [114].
LLPS-related proteins LLPS scaffold proteins were taken as the consolidated dataset of 87 proteins from  [7] , and were extended with 54 manually curated proteins totalling 141 high-confidence human LLPS scaffolds. 344 human LLPS regulators were derived from DrLLPS  [21]  and 3,503 clients from various data resources (PhaSepDB v1. 3  [22] , DrLLPS  [21] , MSGP [120], RNP granule DB [121], MiCroKiTS  [122] ) that provide information on the localizations of proteins to MLOs (see Table  S1 ). Additionally, another larger set of less confident LLPS scaffolds were retrieved from a newer version of PhaSepDB (version 2. 1) [123] presenting a dataset of 859 proteins from low throughput experiments. The obtained proteins were filtered for unique human proteins and the resulting set of 271 proteins (Table  S4 ) (which contains 56 COSMIC Census proteins) was used as an independent alternative of our high-confidence scaffold dataset.
Germline cancer-related proteins Germline cancer-related genes with mutations in protein coding regions were obtained from ClinVar  [125]  and HUMSAVAR (https://www. uniprot. org/docs/humsavar) (both downloaded in September 2021) and limited to records with the cancer-related disease terms available in Supplementary data file 1. ClinVar classifies phenotype-genotype relationships into groups ranging from less reliable to definite links. To achieve high confidence, entries with "limited", "disputed", "no known disease relationship", "refuted" relationships were not accepted. HUMSAVAR genotype-phenotype records were filtered for the ones accompanied by OMIM phenotype identifiers. The resulting dataset of proteins is available as Table  S2. ClinVar [125]  and HUMSAVAR (https://www. uniprot. org/docs/humsavar) were downloaded in September 2021, and genes that have neurodegenerative disease-linked mutations in protein coding regions were selected.
To achieve this, the dataset was filtered for a curated list of expressions related to neurodegenerative diseases -the precise search terms are available in Supplementary data file 1, while the resulting dataset is available as Table  S2. Entries with the four least confident phenotype-genotype relationship categories were excluded again, as explained previously in Data and Methods section 1. 3. Finally, the remaining mutations that did not match either germline cancer-or neurodegenerative disease-associated terms were included into the "other hereditary diseases" category.
We generated 1000 sets of randomly selected proteins from the human proteome that have the same number of proteins, all with a UniProt annotation score of 5, and the same subcellular localization distribution as the set of proteins we are assessing, for instance, COSMIC Census proteins. This procedure was applied to gain unbiased randomized background sets for all disease protein sets, separately (see Supplementary data files 2-5), as well as for oncogenes and tumor suppressors (Supplementary data files 6-7).
Oncogenic fusions 1. 8. 1. Assembling data on the oncogenic fusion proteins of all COSMIC Census proteins We performed a comprehensive, protein-level manual curation of the OFPs of all COSMIC Census proteins, where fusion was provided as a dominant mutation type. For these proteins, each of their fusion partners listed either by COSMIC or UniProt were collected, and the corresponding fusion gene pairs annotated, totaling 450 unique gene pairs. Fusions of the same two genes that only differ in their fusion breakpoints were considered as variants of the same fusion and not as distinct fusions, so only one representative was annotated. Only in-frame fusions of two different genes were considered, where the resulting fusion protein contained in-frame portions of any size of both encoded proteins, including those cases where a short non-coding (usually intronic) segment separates two in-frame protein-coding regions.
For the fusion gene pairs for which at least one COSMIC sample and fusion identifier was available, we selected the most frequently occurring fusion setup/breakpoint (the one indicated with the most samples) and made an attempt to annotate the exact protein boundaries based on that. A preference was given to fusion identifiers/transcript-level descriptions, wherein the transcript boundaries were well-defined (lacking +/-and ? characters marking lack of confidence in fusion boundaries). The UniProt isoforms corresponding to the indicated Ensemble transcript identifiers could be unambiguously identified in each case. For the N-terminal fusion partner we obtained the protein boundary by taking the indicated fusion breakpoint of the transcript, subtracting the length of the 5'UTR and dividing the remaining number (length of CDS before the breakpoint) by 3.
The integer portion of the result was accepted as the protein level fusion breakpoint for the N-terminal gene. If the remainder after dividing by 3 was not zero, we made a note of that to see if the fusion is inframe or not. For the transcript of the C-terminal gene, based on the first nucleotide indicated by COSMIC to be part of the fusion, we calculated the length of the CDS that was missing from the fusion and divided it by 3 to see how many residues were missing from the N-terminus of the protein. The remainder (if any) was compared to the previously noted remainder of the N-terminal gene. If the two remainders were equal, that means that the N-terminal portion of the first gene could substitute for the N-terminal portion of the second gene in a way that the reading frame was preserved, so the fusion was accepted to be an in-frame fusion.
Both were quantified by fold enrichment values and the p-values of Fisher's exact tests (Table  S10 ). Pfam [133]  (downloaded on February 24, 2023) and InterPro [134] (version 5. 56) annotations were used to scan both cancer driver proteins, as well as OFPs. UniProt region annotations were taken from the UniProt database, downloaded on October 7, 2022. UniProt regions were assigned to OFPs if the fusion construct contained at least 10% of the residues in the original region. While this cutoff is low, for structured regions (such as domains, DNA-binding regions, etc), it is virtually always the case that the fusion product either contains all of the region or none of it  [57]. Fractions of UniProt regions only show up in fusion constructs where the region denotes a region without well-defined tertiary structure or if the region is repetitive in nature, such as coiled coils.
For simplicity, the GO term "protein homooligomerization" (GO:0051260) was assigned to it as a proxy. InterPro and Pfam domains listed in the ELM database [140] (downloaded on September 28, 2022) as binding partners for any motif were annotated with the 'GO:0019904, protein domain specific binding' term. The InterPro/Pfam/UniProt -GO associations were then used to attach GO annotations to cancer proteins (Table  S15 ) and OFPs (Table  S16 ). These GO terms were then mapped to a GO subset (GO Slim) that represents biologically relevant, fairly specific yet high level processes and functions (Table  S17 ).
Large-scale prediction of LLPS propensity DeePhase [141], Droppler [142], PSPredictor  [143]  and GraPES [144] were benchmarked on our LLPS scaffold set. GraPES predictions were accessed from the online database, and the MaGS Z-scores were converted using the (z+1)/3 formula and capped at [0,1]. The rest of the predictors gave numbers within [0,1]. While DeePhase and PSPredictor are parameter-free and only take the sequence as input, Doppler had to be run by setting the experimental conditions for which we chose the default parameters (T=37°C, c=10uM, pH=7, I=0. 01M, crowder=None). The LLPS propensity scores for each protein were compiled as distributions for each predictor, and evaluated as quartiles and upper/lower interquartile ranges * 1. 5 (IQR*1. 5) visualized as box-whisker plots (python 3.
10; using package 'matplotlib' version 2. 6. 3). DeePhase was selected for proteome-wide LLPS propensity prediction as its propensity distribution including the data points ranging from the upper to the lower whisker were best overlapping with the 0. 5-1. 0 normalized LLPS propensity range, while other predictors more often predicted lower LLPS propensities (<0. 5) for experimentally validated LLPS scaffolds. Proteome-wide LLPS propensity prediction (Table  S22 ) was performed on the UniProtassembled human reference proteome (UP000005640) downloaded in May 2022 (Table  S3 ). From this dataset two selections were made: a subset for the LLPS scaffolds and a subset for the cancer drivers from COSMIC Census (Supplementary data file 8). Lastly, LLPS propensities were also predicted (Table  S23 ) by DeePhase for reconstituted sequences of oncogenic fusion proteins (see Data and Methods subsection 1.
Due to the smaller data sizes, we chose to apply Fisher's exact test using the 713 COSMIC cancer genes as background in statistical analyses of the association between LLPS and cancer hallmarks or other characteristics including molecular toolkits. In the case of molecular toolkits, the overrepresentation of cancer drivers among the proteins of the human proteome belonging to each toolkit was evaluated by comparisons to a background consisting of 100 randomized datasets with an equivalent number of proteins. The overrepresentation values of cancer drivers served as a baseline to evaluate the extent and significance of toolkit enrichments of the cancer-associated proteins of the 3 LLPS groups. Table  S12 : GO annotations assigned to Pfam domains Table  S13 : GO annotations assigned to InterPro domains Table  S14 : GO annotations assigned to UniProt regions Table  S15 : GO annotations for cancer proteins Table  S16 : GO annotations for OFPs Table  S17 : Term definitions of applied GO Slim, number of cancer proteins/OFPs belonging to each term Table  S18 : Overlap coefficients between the occurrences of pairs of GO terms in cancer proteins Table  S19 : Overlap coefficients between the occurrences of pairs of GO terms in OFPs Table  S20 : Overlap coefficient differences between the occurrences of pairs of GO in cancer proteins and OFPs Table  S21 : Minimal LLPS driver region boundaries for the cancer fusion-associated LLPS scaffolds Table  S22 : LLPS propensity prediction by DeePhase for the human proteome Table  S23 : LLPS propensity prediction by DeePhase for oncogenic fusions of cancer driver proteins.
schemes granted to R. P. R. P is a holder of the  János Bolyai Research Fellowship  of the  Hungarian Academy of Sciences  ( BO/00174/22 ). This work was supported by a  VUB Spearhead grant   SRP51  (to P. T. ) and an  EC H2020-WIDESPREAD-2020-5   Twinning grant  ' PhasAge ' (no. 952334  to P. T. ). N. F is a PhD fellow supported by an  FWO fellowship in fundamental research  ( FWOTM1124 ). T. L. is a postdoctoral innovation mandate holder ( HBC. 2022. 0194 ) of the  Flanders Innovation & Entrepreneurship Agency (VLAIO). B. M. thanks  ALSAC  for funding and support for his research.
This paper presents PrognosEase; a software that provides an easier way to produce different types of run-to-failure data mimicking real-world conditions to simplify prognosis studies in terms of data collection and improvement in ML degradation modelling process. Different types of degradation types made available to meet different types of applications. Besides, some primary ML tests were performed to ensure that complexity patterns of real systems could be observed in the training/testing predictions attitude. This paper also presents the impacts, limitations and potential improvements of the data generator.
Illustrative Examples In this study, PrognosEase is used to generate a dataset that combines a mixture of sensor measurements types with a higher level of complexity, similar to the work done in the literature in terms of data generation. 10 features, 5 lifecycles for training and 2 cycles for testing are generated with cycles that are 1000 samples long. The training and testing lifecycles are showcased in Figure  6. The data visualization addresses that the constructed features space includes different types of complex samples with higher-level non-stationarity. In fact, these samples are supposed to be flown from the same devices under different conditions. This means that all data generation parameters are fixed when reconstructing the data set while retaining only the noise generation and distortion that changes the conditions.
Conclusions This paper presented PrognosEase; a software for generating run-to-failure data for Data-driven prognosis studies. PrognosEase mimics real conditions of different systems by generating similar measurements to their real degradation trends and working conditions. Data visualization for feature spaces generated by PrognosEase and Some ML experiments shows that the software is able to address prediction complexity for unseen samples as in real samples. This paper also presented impact of PrognosEase in PHM filed besides to some future prospects on its potential improvements.
Statistical analysis The data are presented as frequencies and percentages for categorical variables. Normally distributed data are expressed as means (standard deviations) and non-normally distributed data are expressed as medians (interquartile range [IQR]). Linked samples were compared using the two-tailed nonparametric Wilcoxon test because a standardized normal distribution could not be assumed due to the small sample size. Chi square and Fisher's exact tests were used to analyse qualitative data. P <0. 05 was considered to indicate statistical significance. No specific institutional review board or written consent were needed for this study. Patients are included prospectively in a maintained database for ESD cases (registered NCT 04592003).
Methodology and Analysis We applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method  [17]  (Figure  1 ). Specifically, the literature search of peer-reviewed articles in English was based on expert definitions of 49 keywords divided into three topics: "Landsat", "monitoring", and "wetlands" (Table  S1 ). The period of the literature search ranged from 2009 (i. e. , one year after the free release of Landsat images) to 1 March 2022. It was conducted globally to consider all wetland types. Only studies that monitored wetlands for at least 20 years were considered. Keywords in the title, abstract, or keyword fields were sought using queries of the Institute for Scientific Information Web of Science (WOS) and Elsevier Scopus databases. As a result, 401 and 459 articles were retrieved from WOS and Scopus, respectively.
The combination of the Landsat archive and data derived from other remote sensing sensors was shown in 34. 7% of the reviewed studies. Various topographic information derived from DEM data can be combined with spectral data to capture additional wetland characteristics not captured by spectral data alone  [141]. More precisely, 17. 4% of the reviewed studies used DEMs derived from remote sensing data, such as SRTM, e. g. ,  [122] ; ASTER, e. g. , [51]; TanDEM-X, e. g. ,  [63] ; ALOS PALSAR, e. g. ,  [63] ; or LiDAR, e. g. ,  [142] , to characterize topography, vegetation, or water height. In 19. 7% of the reviewed studies, optical or radar variables from other sensors such as MODIS, e. g. ,  [119] ; AVHRR  [25] ; ALOS AVNIR, e. g. ,  [143] ; IRS-LISS II/III/IV, e. g. ,  [42] ; SPOT 2/4/5, e.
9. More recently, Berhane et al. [149]  used the random forest algorithm to monitor LULC in the wetlands of the Midwestern USA. They classified 1749 Landsat data with an overall classification accuracy of 81-89%. The studies also used unsupervised classification methods. For example, Dewidar  [150]  classified Landsat MSS, TM, and ETM+ data by using the ISODATA algorithm to monitor temporal changes in the area of water in lagoons in Egypt from 1972 to 2007, with an overall accuracy of ca. 97. 5%. Fitoka et al. [100]  mapped wetland areas in Greece by combining Sentinel-2 and Landsat 5 TM data from 1986 to 2017. They combined classification trees with expert rules to distinguish 31 wetland classes with an overall accuracy of more than 90% over a total area of more than 2 million ha. Raynolds and Walker  [151]  applied regression methods to Alaska, USA, from 1985 to 2011.
Open-Source Software The Landsat archive has been processed less often using open-source software (13% of the reviewed studies; Figure  6 ). For example, Otero et al. [160]  created maps of changes in mangrove cover in Malaysia from 1988 to 2015 from Landsat TM, ETM+, and OLI using R software. Tin et al. [161]  processed and classified Landsat images using QGIS software to describe historical changes in mangroves in Vietnam from 1973 to 2016. Notably, Schaffer-Smith et al. [162]  investigated dynamics of the area of water in wetlands in California, USA, from 1983 to 2015. They analyzed Landsat 4, 5, 7, and 8 data using Python to pre-process images and R software to classify images and assess accuracy, along with commercial ArcGIS software. Notably, 20% of the studies did not name which software they used.
Cloud Computing Overall, 9. 4% of the reviewed studies used cloud computing for long-term wetland analysis, of which all used the Google Earth Engine (GEE) cloud-computing platform  [163]  (Figure  6 ). Comparing cloud-computing studies to local-computing studies revealed statistically significant differences in several attributes (Table  2 ). Studies that used cloud computing performed more change detections using time series and thus used more intraannual images. Similarly, they combined Landsat TM, ETM+, and OLI/TIRS data more often and at pre-processing Level 2. Cloud-computing studies were conducted more often at regional and continental scales than at the local scale. Regarding study periods, 30-40-year periods were more common for cloud-computing than local-computing studies. Cloud-computing studies did not use more open-source software than local-computing studies did.
Future research should extend the temporal monitoring period to at least 50 years by including not only new Landsat 9 images but also older MSS images, although some wetlands may not be detected and monitored due to the broad spatial resolution of these data. The combination of topographic data with spectral data significantly improves wetland delineation, and several high spatial resolution global DEMs are available in open access  [191]. Moreover, the future monitoring of wetland structure and function could be improved by using new variables such as the global canopy height model derived from GEDI and Landsat data  [192]  or Sentinel-1 interferometric coherence time series  [193]. The large number of long-term wetland monitoring studies in this review from 2009 to 2022, especially in developing countries, shows the positive effect of providing open access Landsat archive data for wetland conservation worldwide, especially in developing countries where managers have limited resources and funds for monitoring highly threatened wetlands  [4].