dataesr/scientific-paragraphs-categorization huggingface dataset:
One AltProt (AltLOC101927356) translated from the transcript LOC101927356, which is specifically identified in the activated EMT condition, interacts with the proteins of the tropomyosin cytoskeleton (TPM), highlighting the relation of the protein to the cytoskeleton and potential function associated with cell morphology. On the other hand, the AltLINC00624 protein derived from the ncRNA LINC00624 was found to potentially play a role in the regulation of protein expression which is in line with previous findings on proteins encoded from ncRNAs. In another proteomic study of epigenetic modifications (histone methylation) induced by the tumor on tumor-associated macrophages (TAMs), 10 AltProts have been identified in TAMs as significantly involved in the epigenetic modulation. Among the AltProts identified, there is IP_1295370 encoded by the ncRNA AC106932.
1  (Cardon et al. , 2020c). Further work is needed to characterize its function; however, for the first time AltProt translated from ncRNA has been associated with epigenetics. Moreover, the study of AltProts in extracellular vesicles (EVs) produced by rat GBM cells identifies the presence of an AltProt IP_2659453 translated from the ncRNA LOC103695286  (Murgoci et al. , 2020). This AltProt which is involved in the communication of glioma cells may play a role in the proliferation and the cell. The application of large-scale MS-based methods is much more versatile than targeted strategies. They can be easily used to search data present in the public repositories or implemented in commonly used proteomic workflows. In 2017, an exploratory work aiming at grade III glioma classification and based on spatially resolved proteomics guided by mass spectrometry imaging (MSI) led to identification of 22 AltProts (Le Rhun et al.
, 2020). One of these FIGURE 4 | Study of the phylogeny of AltProt (IP_652563 identified in the clinical study of GBM). Based on its sequence homology with the known and predicted proteome of all the mammalian species present on BlastP, the AltProt sequence is only found in primates. In blue, the nearby human genes that can encode this sequence, and in green, those of other primates. AltProts (IP_652563) has been correlated with the patient overall survival, and its expression is related to poorer survival. IP_652563 is translated from the ncRNA Z99774. 1. Very interestingly, this transcript might not be a fixed allele in the entire population, making it a major predisposition marker if correlated with a lower survival rate of GBM patients. This gene would be a de novo gene encoding a protein derived from an ancestral noncoding transcript.
The phylogenetic alignment of the gene shows a strong homology only with primate (Figure  4 ), a sign of a recent appearance of this gene during the evolution. Notably, among the identified AltProts, 13 are translated from eight ncRNAs previously described in GBM as significant variants (RMST, HMMR-AS1, HOTAIR, MALAT1, ST7-AS1, DDX11-AS1, LBX2-AS1, RNF219-AS1). From these eight ncRNAs found in a previous study, 100 AltProts are predictable though 13 have been found in the reanalysis of the MS data, of which 11 are also present in the Ribo-Seq data used to cross-validate the OpenProt database (Table  1 ). AltProts which have remained hidden until recently are now revealed by proteogenomic strategies and, as supported by all these preliminary works, will undoubtedly be one of the keys for better understanding GBM and finding new treatment leads.
Mutations in AltProts: A Deep Impact A key question is to determine if AltProts carry mutations as well as the impact of these mutations. In 2021, Erady et al. have predicted posttranscriptional modifications (PTMs) and mutations on AltProts in cancer, thanks to the combination of genome-wide association (GWAS), variants, and mutations in the Catalogue of Somatic Mutations in Cancer (COSMIC), Human Gene Mutation database (HGMD), and AltProt databases  (Erady et al. , 2021). Some of these mutations are described as impacting the regulatory region of genes implicated in cancer. However, the consequences of these mutations on the cell function and the impact on the pathology have not been described yet and are only explored in very few studies (Figure  5 ). However, as an example, it has recently been shown in endometrial cancer the presence of a mutation (C > T, COSM8898738) on the transcript encoding the RefProt GNL1 which has been described as a synonymous mutation with a silent impact on the protein.
cancer cells. However, the transcript encoding this AltProt (ENST00000360351. 8) bears 33 mutations which are localized in the region overlapping the coding DNA sequence (CDS) of MAP2 and the part encoding AltMAP2. Eight have been described to be silent mutations for the RefProts, although the analysis in the frame translating for the AltProt reveals that it is missense in that case (Table  2 ). Likewise, the mutations carried by the ncRNAs are considered to be silent because no proteins are expected to be translated from these RNAs. However, the AltProts resulting from these ncRNAs will necessarily be impacted by these mutations. For instance, from the MALAT1 transcript (accession: ENST00000534336. 1) (Gutschner et al. , 2013) , 19 AltProts are predicted. In addition, in the TCGA mutation database, this transcript is described to be mutated for GBM (CPTAC-3 project) in squamous cell neoplasms, adenomas, and adenocarcinomas.
AltProt alternative Proteins circRNA circular RNA COSMIC Catalogue of Somatic Mutations in Cancer DDX11-AS1 DEAD box 11 antisense RNA1 EMT epithelial mesenchymal transition ENCODE Encyclopedia of DNA elements endo-siRNA Endogenous short interfering RNAs GBM Glioblastoma GWAS genome-wide association HGMD Human Gene Mutation database HMMR-AS1 Hyaluronan-mediated motility receptor antisense RNA1 HOTAIR HOX Antisense Intergenic RNA HRMS high resolution mass spectrometry IDH isocitrate dehydrogenase IRES internal ribosome entry site ITAF IRES transacting factor LBX2-AS1 ladybird homeobox 2 antisense RNA1 lncRNA long non-coding RNA m6A methylation of the N6 group position of the adenosine MALAT1 Metastasis associated lung adenocarcinoma transcript 1 miPEP micropeptides miPEPs miRNA-encoded peptides miRNA microRNAs MSI mass spectrometry imaging ncRNA non-coding ribonucleic acid OBI1-AS1 ORC ubiquitin ligase 1 antisense RNA 1 ORF Open Reading Frame piRNA Piwi-interacting RNA POU4F1-AS1 POU class 4 homeobox 1 antisense RNA 1 PPI protein-protein interaction PTMs post translational modifications RefProt reference proteins Ribo-seq ribosome profiling RNC-seq ribosome-nascent chain complex sequencing rRNA ribosomal RNA sdRNA Self-deliverable siRNA SEPs sORF-encoded peptides smORF small Open Reading Frame smProt small or short proteins sncRNA short non-coding RNA snoRNA small nucleolar RNAs snRNA small nuclear RNA TCGA The Cancer Genome Atlas tRNA transfer RNA WHO World Health Organization XL-MS crosslink coupled to mass spectrometry.
How can we detect fraudulent lockstep behavior in large-scale multi-aspect data (i. e. , tensors)? Can we detect it when data are too large to fit in memory or even on a disk? Past studies have shown that dense subtensors in real-world tensors (e. g. , social media, Wikipedia, TCP dumps, etc. ) signal anomalous or fraudulent behavior such as retweet boosting, bot activities, and network attacks. Thus, various approaches, including tensor decomposition and search, have been proposed for detecting dense subtensors rapidly and accurately. However, existing methods suffer from low accuracy, or they assume that tensors are small enough to fit in main memory, which is unrealistic in many real-world applications such as social media and web. To overcome these limitations, we propose D-Cube, a disk-based dense-subtensor detection method, which also can run in a distributed manner across multiple machines.
CrossSpot, M-Zoom, M-Biz, and DenseAlert require all tuples of relations to be loaded into memory at once and to be randomly accessed, which limit their applicability to large-scale datasets. DenseStream maintains only the tuples created within a time window, and thus it can find a dense subtensor only within the window. Dense-subtensor detection in tensors has been found useful for detecting retweet boosting  [18] , network attacks  [36, 38, 26] , bot activities  [36] , and vandalism on Wikipedia  [38] , and also for genetics applications  [33, 26]. Large-Scale Tensor Decomposition. Tensor decomposition such as HOSVD and CP decomposition  [23]  can be used to spot dense subtensors  [26]. Scalable algorithms for tensor decomposition have been developed, including disk-based algorithms  [29, 39] , distributed algorithms  [20, 39, 17] , and approximate algorithms based on sampling  [30]  and count-min sketch  [41].
Proposed Method In this section, we propose D-Cube, a disk-based dense-subtensor detection method. We first describe D-Cube in Section 3. 1. Then, we prove its theoretical properties in Section 3. 2. Lastly, we present our MapReduce implementation of D-Cube in Section 3. 3. Throughout these subsections, we assume that the entries of tensors (i. e. , the tuples of relations) are stored on disk and read/written only in a sequential way. However, all other data (e. g. , distinct attribute-value sets and the mass of each attribute value) are assumed to be stored in memory.
Datasets We describe the real-world and synthetic tensors used in our experiments. Real-world tensors are categorized into four groups: (a) Rating data (SWM, Yelp, Android, Netflix, and YahooM. ), (b) Wikipedia revision histories (KoWiki and EnWiki), (c) Temporal social networks (Youtube and SMS), and (d) TCP dumps (DARPA and AirForce). Some statistics of these datasets are summarized in Table  3. Rating data. Rating data are relations with schema (user, item, timestamp, score, #ratings). Each tuple (u,i,t,s,r) indicates that user u gave item i score s, r times, at timestamp t. In the SWM dataset  [1] , the timestamps are in dates, and the items are entertaining software from a popular online software marketplace. In the Yelp dataset, the timestamps are in dates, and the items are businesses listed on Yelp, a review site.
In the Android dataset  [27] , the timestamps are hours, and the items are Android apps on Amazon, an online store. In the Netflix dataset  [8] , the timestamps are in dates, and the items are movies listed on Netflix, a movie rental and streaming service. In the YahooM. dataset  [12] , the timestamps are in hours, and the items are musical items listed on Yahoo! Music, a provider of various music services. Wikipedia revision history. Wikipedia revision histories are relations with schema (user, page, timestamp, #revisions). Each tuple (u,p,t,r) indicates that user u revised page p, r times, at timestamp t (in hour) in Wikipedia, a crowd-sourcing online encyclopedia. In the KoWiki dataset, the pages are from Korean Wikipedia. In the EnWiki dataset, the pages are from English Wikipedia. Temporal social networks.
Temporal social networks are relations with schema (source, destination, timestamp, #interactions). Each tuple (s,d,t,i) indicates that user s interacts with user d, i times, at timestamp t. In the Youtube dataset  [28] , the timestamps are in hours, and the interactions are becoming friends on Youtube, a video-sharing website. In the SMS dataset, the timestamps are in hours, and the interactions are sending text messages. TCP Dumps. The DARPA dataset  [25] , collected by the Cyber Systems and Technology Group in 1998, is a relation with schema (source IP, destination IP, timestamp, #connections). Each tuple (s,d,t,c) indicates that c connections were made from IP s to IP d at timestamp t (in minutes). The AirForce dataset, used for KDD Cup 1999, is a relation with schema (protocol, service, src bytes, dst bytes, flag, host count, srv count, #connections).
Q1. Memory Efficiency We compare the amount of memory required by different methods for handling the real-world datasets. As seen in Figure  4 , D-Cube, which does not require tuples to be stored in memory, needed up to 1,561× less memory than the second most memory-efficient method, which stores tuples in memory. Due to its memory efficiency, D-Cube successfully handled 1,000× larger data than its competitors within a memory budget. We ran methods on 3-way synthetic tensors with different numbers of tuples (i. e. , |R|), with a memory budget of 16GB per machine. In every tensor, the cardinality of each dimension attribute was 1/1000 of the number of tuples, i. e. , 10 10 tuples (240GB), which was the largest tensor that can be stored on a disk. However, all other methods ran out of memory even on a tensor with 10 9 tuples (21GB).
Q2. Speed and Accuracy in Dense-subtensor Detection We compare how rapidly and accurately D-Cube (the serial version) and its competitors detect dense subtensors in the real-world datasets. We measured the wall-clock time (average over three runs) taken for detecting three subtensors by each method, and we measured the maximum density of the three subtensors found by each method using different density measures in Section 2. 2. For this experiment, we did not limit the memory budget so that every method can handle every dataset. D-Cube also utilized extra memory space by caching tuples in memory, as explained in Section 3. 1. 4. Figure  3  shows the results averaged over all considered datasets. 6 The results in each data set can be found in the appendix. D-Cube provided the best trade-off between speed and accuracy.
Network Intrusion Detection from TCP Dumps D-Cube detected network attacks from TCP dumps accurately by spotting corresponding dense subtensors. We consider two TCP dumps that are modeled differently. The DARPA dataset is a 3-way tensor where the dimension attributes are source IPs, destination IPs, and timestamps in minutes; and the measure attribute is the number of connections. The AirForce dataset, which does not include IP information, is a 7-way tensor where the measure attribute is the same but the dimension attributes are the features of the connections, including protocols and services. Both datasets include labels indicating whether each connection is malicious or not. Notice that the dense subtensors are mostly composed of various types of network attacks. Based on this observation, we classified each connection as malicious or benign based on the density of the densest subtensor including the connection (i.
Synchronized Behavior Detection in Rating Data D-Cube spotted suspicious synchronized behavior accurately in rating data. Specifically, we assume an attack scenario where fraudsters in a review site, who aim to boost (or lower) the ratings of the set of items, create multiple user accounts and give the same score to the items within a short period of time. This lockstep behavior forms a dense subtensor with volume (# fake accounts × # target items × 1 × 1) in the rating dataset, whose dimension attributes are users, items, timestamps, and rating scores. We injected 10 such random dense subtensors whose volumes varied from 15×15×1×1 to 60×60×1×1 in the Yelp and Android datasets. We compared the ratio of the injected subtensors detected by each dense-subtensor detection method. We considered each injected subtensor as overlooked by a method if the subtensor did not belong to any of the top-10 dense subtensors spotted by the method or it was hidden in a natural dense subtensor at least 10 times larger than the injected subtensor.
That is, we measured the recall at top 10. We repeated this experiment 10 times, and the averaged results are summarized in Table  4. For each method, we report the results with the density measure giving the highest recall. In both datasets, D-Cube detected a largest number of the injected subtensors. Especially, in the Android dataset, D-Cube detected 9 out of the 10 injected subtensors, while the second best method detected only 7 injected subtensors on average. D-Cube successfully spotted spam reviews in the SWM dataset, which contains reviews from an online software marketplace. We modeled the SWM dataset as a 4-way tensor whose dimension attributes are users, software, ratings, and timestamps in dates, and we applied D-Cube (with ρ = ρ ari ) to the dataset. Table  6  shows the statistics of the top-3 dense subtensors.
Anomaly Detection in Wikipedia Revision Histories D-Cube detected interesting anomalies in Wikipedia revision histories, which we model as 3-way tensors whose dimension attributes are users, pages, and timestamps in hours. Table  6  gives the statistics of the top-3 dense subtensors detected by D-Cube (with ρ = ρ ari and the maximum cardinality policy) in the KoWiki dataset and by D-Cube (with ρ = ρ geo and the maximum density policy) in the EnWiki dataset. All three subtensors detected in the KoWiki dataset indicated edit wars. For example, the second subtensor corresponded to an edit war where 4 users changed 4 pages, 1,011 times, within 5 hours. On the other hand, all three subtensors detected in the Enwiki dataset indicated bot activities. For example, the third subtensor corresponded to 3 bots which edited 1,067 pages 973,747 times.
Acknowledgments This research was supported by  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  (No. NRF-2020R1C1C1008296 ) and  Institute of Information & Communications Technology Planning & Evaluation (IITP)  grant funded by the  Korea government (MSIT)  (No. 2019-0-00075 ,  Artificial Intelligence Graduate School Program (KAIST) ). This research was also supported by the  National Science Foundation  under Grant No. CNS-1314632  and  IIS-1408924. This research was sponsored by the  Army Research Laboratory  and was accomplished under Cooperative Agreement Number  W911NF-09-2-0053. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the  National Science Foundation , or other funding parties.
Results and Discussion We designed and conducted experiments to answer the following questions:   We investigate the effects of the mass-threshold parameter θ on the speed and accuracy of D-Cube in dense-subtensor detection. We used the serial version of D-Cube with a memory budget of 16GB, and we measured the relative density of detected subtensors and its running time, as in Section 4. 3. Figure  7  shows the results averaged over all considered datasets. Different θ values provided a trade-off between speed and accuracy in dense-subtensor detection. Specifically, increasing θ tended to make D-Cube faster but also make it detect sparser subtensors. This tendency is consistent with our theoretical analyses (Theorems 1-3 in Section 3. 2). The sensitivity of the dense-subtensor detection accuracy to θ depended on the used density measures.
Figures 9-19 show the speed and accuracy of the considered algorithms in 11 different datasets. D-Cube (maximum density) D-Cube (maximum cardinality) M-Zoom M-Biz CrossSpot CPD 10 9 Ideal 10 4 Ideal 10 4 Ideal 10 9 Ideal 10 9 Ideal Density 10 7 10 8 Density 10 3 Density 10 3 Density 10 6 10 7 10 8 Density 10 6 10 7 10 8 10 6 5X 10 2 4X 10 2 4X 10 5 5X 10 5 7X 10 1 10 2 10 3 10 4 10 1 10 2 10 3 10 4 10 1 10 2 10 3 10 1 10 2 10 3 10 4 10 1 10 2 10 3 10 4 Elapsed Time (sec) Elapsed Time (sec) Elapsed Time (sec) Elapsed Time (sec) Elapsed Time (sec) A. ρ susp B. ρ ari C. ρ geo D. ρ es(1) E. ρ es(10).
This is particularly true when the companion/satellite galaxy ultimately plunges into the host galaxy and the host galaxy readjusts after the merger is completed. The exact role of different orbital parameters, Hubble type of the companion, gas fraction in disc galaxy is not known either in context of reshaping the  = 2 bar mode during a minor merger event. In this paper, we carry out a systematic study of the temporal evolution of bar properties and the associated disc kinematics during a minor merger event while varying different orbital parameters, nature of satellite galaxies. For this, we make use of the publicly available minor merger simulation models from the GalMer database  (Chilingarian et al. 2010). This library offers to study the physical effects of minor merger process, encompassing a wide range of cosmologically motivated initial conditions; thus, it is well-suited for the goal of this paper.
MODELS OF MINOR MERGERS -GALMER DATABASE GalMer 1  is a publicly available library of -body+smooth particle hydrodynamics (SPH) simulation of galaxy mergers to probe the details of galaxy formation through hierarchical merger process. The morphology of galaxy models ranges from ellipticals to late-type, gas-rich spirals. A galaxy model consists of a non-rotating spherical dark matter halo, a stellar and a gaseous disc (optional), and a central non-rotating bulge (optional). The dark matter halo and the central bulge (if present) are modelled as Plummer sphere  (Plummer 1911)  whereas the stellar and the gaseous disc are modelled as Miyamoto-Nagai density profiles  (Miyamoto & Nagai 1975). GalMer offers three different galaxy interaction/merger scenarios, namely, the giant-giant major merger (mass ratio of 1:1), giant-intermediate merger (mass ratio of 1:2), and the giant-dwarf minor merger (mass ratio of 1:10).
6 × 10 9 M (20 per cent of the total stellar mass; for details see  Chilingarian et al. 2010). The merger simulations are evolved using a TreeSPH code by  Semelin & Combes (2002). The gravitational forces are calculated using a hierarchical tree method  (Barnes & Hut 1986 ) with a tolerance parameter  = 0. 7 and include terms up to the quadrupole order in the multiple expansion. The gas evolution is followed by means of smoothed particle hydrodynamics (e. g. Lucy 1977). A Plummer potential is used to soften gravitational forces, with a constant softening length  = 200 pc. The galaxy models are evolved in isolation for 1 Gyr before the start of merger simulation  (Chilingarian et al. 2010). Here, we consider a set of giant-dwarf minor merger models where the host galaxy is of Sa-type and the morphology of the satellite galaxy varies from dE0l to dSb.
EVOLUTION OF STELLAR BARS IN GALMER MODELS Here, we investigate how a central stellar bar, initially present in a host galaxy, evolves after it suffers a minor merger (mass ratio 1:10) with a satellite galaxy. To do that, we choose a minor merger model gSadE001dir33 from the GalMer database where a dwarf E0-type galaxy merges with a host giant Sa-type galaxy. In the beginning, the host galaxy (gSa) harbours a prominent central stellar bar; thereby serving an ideal testbed for this work. Fig. 1  shows the temporal evolution of distance between the centres of mass of these two galaxies. After each pericentre passage, the satellite loses a part of its orbital angular momentum due to the dynamical friction and the tidal torque. Consequently, it falls deep in the gravitational potential of the host galaxy and ultimately merges with the host galaxy.
In precision agriculture and plant breeding, the amount of data tends to increase. This massive data is becoming more and more complex, leading to difficulties in managing and analysing it. Optical instruments such as NIR Spectroscopy or hyperspectral imaging are gradually expanding directly in the field, increasing the amount of spectral database. Using these tools allows access to non-destructive and rapid measurements to classify new varieties according to breeding objectives. Processing this massive amount of spectral data is challenging. In a context of genotype discrimination, we propose to apply a method called parSketch-PLSDA to analyse such a massive amount of spectral data. ParSketch-PLSDA is a combination of an indexing strategy (parSketch) and the reference method (PLSDA) for predicting classes from multivariate data.