dataesr/scientific-paragraphs-categorization huggingface dataset:
Alternatively, the sequence conservation between closely related species and lack of homologs in distant ones may indicate the recent origin of the identified novel snoRNA genes. Targets for C/D and H/ACA snoRNAs were predicted using the PLEXY  [43]  and RNAsnoop  [44]  programs, respectively. Seven snoRNAs were shown to target the methylation of 18S or 25S rRNA-specific residues (Table  1 ). In addition, 13 other predicted C/D box snoRNAs were found to target the spliceosomal snRNAs U2, U4, U5 and U6. Therefore, these should be considered as scaRNAs, as the modification of these snRNAs mainly occurs in Cajal bodies  [8]. We estimated the minimal size of the predicted canonical snoRNA and scaRNA transcripts as the distance between the C and D boxes for the C/D snoRNAs and between the stem structure and the 3' terminal ACANNN element for the H/ACA snoRNAs (Table  1 ).
Cluster 1 was previously reported to be expressed as individual snoRNAs, represented by independent lincR-NAs, rather than as a polycistronic transcript. To confirm our prediction of the clustered expression of these snoR-NAs, we amplified the polycistronic precursor by RT-PCR. Our result clearly shows the amplification of one precursor encoding predicted snoRNAs (Additional file 2: Figure  S1 ). A similar result was observed for cluster 10, showing amplification of the predicted dicistronic precursor. We also confirmed the expression of the predicted snoRNA gene loci encoding ncR18, for which no evidence of expression was available in the expression databases, by RT-PCR. The result clearly shows the specific amplification product of an ncR18 transcript, confirming its expression in 2-week-old seedlings (Additional file 2: Figure  S1 ).
Their expression was shown to be very low but was confirmed by northern blot hybridization using LNA oligonucleotide probes. Additionally, the biogenesis of both miRNAs was shown to be specifically dependent on the DCL1 enzyme  [46]. The region upstream of the predicted miR775 precursor (pre-miR775) contains a classical TeloSII motif associated with a TATA box element (Fig. 3a ). Screening of the flanking genomic sequences with the SnoReport program revealed a sequence that can fold into a typical H/ACA box snoRNA structure, including two stems separated by the conserved ANANAA motif, a 3' terminal ACANNN motif, and an internal loop sequence that could direct the pseudouridylation of the 25S rRNA U1465 residue (Fig. 3b ). We confirmed the expression of the mature 150-nt H/ACA snoRNA by northern blotting in seedlings, leaves and flowers (Fig.
3d ). Based on the close proximity of this snoRNA to miR775, we named it snoR775. Several lines of evidence indicate that miR775 and snoR775 are co-transcribed and produced from the same precursor. The first line of evidence is the presence of a 605 nt cDNA (BX818024) that encompasses both snoR775 and pre-miR775 (Fig. 3a ). The single-transcript organization was confirmed by RT-PCR using a pair of primers encompassing snoR775 and miR775 (data not shown). Finally, we mapped the transcription start site (TSS) and transcription termination site (TTS) using 5' RACE and 3' RACE, respectively (Fig. 3a ). A single major signal clearly mapped the TSS upstream of snoR775, whereas the TTS was mapped downstream of pre-miR775 (Additional file 4: Figure  S2 ). In conclusion, the snoR775 and miR775 precursors are encoded by a single gene that is transcribed as a 655-nt dicistronic transcript (Fig.
Identification of novel ncRNAs The 53 remaining ncRNAs located downstream of TeloSII elements (Fig. 1 ) did not show similarity to any known RNA family reported in the Rfam database. Among these ncRNAs, 16 loci were supported by RNA-Seq or MPSS signatures and/or corresponding ESTs or cDNAs, but had no predicted protein-coding capabilities (Table  2 ). The presence of corresponding MPSS/EST/cDNA sequences indicates that, in contrast to the identified snoRNAs (Table  1 ), these ncRNAs are polyadenylated. ncR40 is an interesting example; its expression is supported by short reads from RNA-Seq and RT-PCR experiments (Fig. 5 ). Another seven RT-PCR-validated ncRNAs are shown in Additional file 9: Figure  S6. We also identified 60 lincRNAs located downstream of TeloSII elements (Additional file 10: Table  S4 ).
These RNAs were among the 6480 lincRNAs reported by Liu et al. [38]  for which no function or structural similarity has been found. Using the Rfam database, we were able to identify 12 of these RNAs as snoRNAs and 1 as plant SRP RNA, which is the subunit of the Signal Recognition Particle RNP that is involved in protein translocation to the endoplasmic reticulum (Additional file 10: Table  S4 ). In summary, we have identified 60 non-coding RNAs (including 45 lincRNAs without any similarities to known RNAs; Additional file 10: Table  S4 ) whose transcription seems to be under the control of TeloSII regulatory elements.
snoRNAs and scaRNAs The first group includes 26 snoRNAs or scaRNAs encoded by ten polycistronic clusters and two monocistronic genes. Seven of these ncRNAs represent canonical C/D snoRNAs that target rRNA for methylation, adding previously unidentified members to the numerous snoRNA families that have already been found in Arabidopsis. Thirteen of the predicted molecules represent canonical scaRNAs that are predicted to target snRNAs. This result considerably increases the number of Arabidopsis scaRNAs because only 11 have been reported previously  [22, 23, 49]. Finally, the remaining six predicted molecules represent orphan snoR-NAs for which no RNA target could be found (Table  1 ). Notably, in the case of orphan snoRNAs, the transcripts may be much longer than the minimal size predicted by the SnoReport program, estimated as the distance between the conserved elements of canonical snoRNAs.
Acknowledgements The authors would like to thank  Prof. Yutaka Sato  for providing rice RNA. This work was supported by grants from the  National Science Centre  ( 2011/ 03/B/NZ2/01416  to WMK and  2013/11/B/NZ1/02099  to KK and ZS-K). GQ's PhD fellowship is a part of the  International PhD Program entitled "From genome  to phenotype: a multidisciplinary approach to functional genomics" ( MPD/2010/3 ) by the  Foundation for Polish Science (FNP)  and is co-financed by the  European Union Regional Development Fund  ( Innovative Economy Operational Program   2007-2013 ). This project was also funded by  KNOW Poznan RNA Centre  ( 01/KNOW2/2014 ).
We begin by briefly highlighting some key protein-ligand interactions from a biomedical perspective. In Figure  1  we focus on four examples of proteins bound to diverse types of ligands, which are important in health and disease. This includes Cytochrome P450 bound to the drug N-Benzylformamide (Figure  1A -PDB ID 4i8v). The enzyme Cytochrome P450 has an essential role in the electron transfer chain, and is therefore ubiquitous in all kingdoms of life  [5]. The human Cytochrome P450 (CYP1A1) is known to play a role in the biotransformation of polycyclic aromatic hydrocarbons into carcinogens  [6]. In addition, CYP1A1 (PDB ID 4i8v) is responsible for the metabolism of theophylline  [7] , a drug used to provide symptomatic relief from asthma. Cyclooxygenase-2 from Mus musculus, which is involved in the biosynthesis of prostaglandins, is a target of non-steroidal anti-inflammatory drugs such as Ibuprofen (Figure  1B ).
Furthermore, molecular docking methods are beyond the scope of this review, which have been recently reviewed by Yuriev et al. [10]. In this review, the term ligand is used to refer to molecules capable of binding to a protein, such as metal ions, small organic (e. g. , ATP) and inorganic compounds (e. g. , NH4), peptides, and DNA/RNA; not large macromolecules such as proteins. We begin by briefly highlighting some key protein-ligand interactions from a biomedical perspective. In Figure  1  we focus on four examples of proteins bound to diverse types of ligands, which are important in health and disease. This includes Cytochrome P450 bound to the drug N-Benzylformamide (Figure  1A -PDB ID 4i8v). The enzyme Cytochrome P450 has an essential role in the electron transfer chain, and is therefore ubiquitous in all kingdoms of life  [5].
The human Cytochrome P450 (CYP1A1) is known to play a role in the biotransformation of polycyclic aromatic hydrocarbons into carcinogens  [6]. In addition, CYP1A1 (PDB ID 4i8v) is responsible for the metabolism of theophylline  [7] , a drug used to provide symptomatic relief from asthma. Cyclooxygenase-2 from Mus musculus, which is involved in the biosynthesis of prostaglandins, is a target of non-steroidal anti-inflammatory drugs such as Ibuprofen (Figure  1B ). The Plasmodium vivax TRAP protein, bound to magnesium, is involved in phosphate ester hydrolysis (Figure  1C ). Finally, Figure  1D  shows the protein-ligand binding site of the aminopeptidase N family protein Q5QTY1, from Idiomarina loihiensis bound to zinc (its cofactor), which can be used as a biomarker to detect kidney damage.
This results in the prediction of residues comprising a putative ligand binding site. In contrast, LigandRFs  [13]  uses a random forest-based algorithm to predict protein-ligand binding site residues. LigandRFs extracts 544 amino acid properties from the AAindex database  [21] , which are then compared using the Matthews correlation coefficient. Each of the 544 properties are ranked in relation to the number of their related properties. The properties are filtered to remove all properties related to the top property; this removes redundant properties, which do not add any new information. This process is continued through the list until 34 properties remain. These properties relate to specific features crucial for determining putative binding site residues. The properties are then applied over a seven residue sliding window of a PSI-BLAST  [22]  profile.
The method is available as a webserver and also uses text-based searches to find proteins that may bind to a particular ligand of interest  [26]. This results in the prediction of putative protein-ligand binding sites. In contrast to FINDSITE, LigDig, and the majority of geometric-based approaches, LISE  [27]  is an algorithm that utilizes a novel concept of binding site-enriched protein triangles in order to predict protein-ligand binding site locations. LISE uses ideas developed in a previous method, called MotifScore  [61] , that determined motifs in a protein-ligand interaction database, composed of 6276 protein-ligand structures. The motifs contain the interactions between three atoms of a protein and two atoms of a ligand. Thus, the three protein atoms of these motifs compose the "protein triangles".
FunFOLD firstly superposes, using TM-align  [66] , a list of structural templates containing biologically relevant ligands (determined using the BioLip database  [67] ) onto the target 3D model. Template-model superpositions with a TM-score ě 0. 4 are retained. The next step is to superimpose all retained templates onto the target model and assign ligands from the template files into clusters using agglomerative hierarchical clustering. The identified ligand clusters are located at the potential ligand-binding sites. Ligands are determined to be components of a cluster if the contact distance is less than or equal to 0. 5 Å plus the Van der Waal radii of the contacting atoms. The putative ligand-binding site containing the largest ligand cluster is determined to be the most probable ligand-binding site of the protein.
The COACH  [49]  method is similar to FunFOLD and is one of the most accurate ligand-binding site prediction methods that utilizes both sequence and structural homology in the prediction pipeline. The structure component (TM-SITE) of the pipeline firstly locates putative ligand-binding pockets using ConCavity  [72]. TM-SITE then uses fifteen residues within the binding pocket structure to search against the BioLip database to find structures containing similar binding pockets, in addition to searching for similar structures (using TM-align  [66] ) to the target protein containing biologically relevant ligands within BioLip  [67]. All templates and sub-structural templates are superposed onto the target and scored based on empirically determined cutoffs. Ligand binding site residues are then determined using a similar strategy to FunFOLD  [4] , but using average linkage clustering and assigning a confidence score to each predicted ligand binding site residue.
For example, the method by  Andersson et al. (2010, [56] ) works initially by identifying solvent accessible patches. In the second step, data is collected from each patch based on 408 surface descriptors, divided into eight categories. These descriptors include neighbouring amino acids, secondary structure, polarity of adjacent amino acids, close hydrogen bond donors and acceptors, electrostatic potential, shape, polarity, and flexibility. In the third step, the descriptor results are divided into bins and scaled to be usable for Principal Component Analysis (PCA). In the fourth step, PCA is carried out and the relationships between pockets are analysed. This method produces results for all putative pockets, leaving the user to determine which pocket is the most suitable ligand binding pocket for their particular task  [56].
Hence, the CASP FN category moved to the CAMEO continuous assessment project  [65]. The move to fully automated assessment resulted in a change of prediction format, with the additional prediction of which ligand category (I-Ion, O-Organic, N-Nucleotide, and P-Peptide) a protein may bind. Participating servers must also provide a p-value representing the likelihood that each residue (or atom) binds a ligand in each category. The CAMEO assessment runs weekly on structures containing biologically-relevant ligands using target sequences of structures that are on hold for release by the Protein Data Bank (PDB)  [65]. The CAMEO project provides a better picture of how each method performs on a large and diverse dataset, containing a wide variety of proteins bound to a wide variety of ligands. Complementary to CAMEO and CASP is the CAFA  [85]  experiment, which has also been a major driver for the development of function prediction methods.
The goal of CAFA is to functionally annotate proteins on a large scale using GO terms  [70, 71]. The CAFA1 dataset contained >48,000 proteins as of October 2010, for which predictions were made. Following the prediction season, methods were evaluated on 866 of the proteins, which had acquired annotations over the eleven months following the close of the prediction season. Methods that compete in CAFA  [85]  include a large number of the methods described in the proceeding sections, comprising sequence-based methods, structure-based methods and combinations of both.
Many studies have focused on the impacts of climate change on biological assemblages, yet little is known about how climate interacts with other major anthropogenic influences on biodiversity, such as habitat disturbance. Using a unique global database of 1128 local ant assemblages, we examined whether climate mediates the effects of habitat disturbance on assemblage structure at a global scale. Species richness and evenness were associated positively with temperature, and negatively with disturbance. However, the interaction among temperature, precipitation and disturbance shaped species richness and evenness. The effect was manifested through a failure of species richness to increase substantially with temperature in transformed habitats at low precipitation. At low precipitation levels, evenness increased with temperature in undisturbed sites, peaked at medium temperatures in disturbed sites and remained low in transformed sites.
Introduction Although considerable debate exists about the forces that structure ecological assemblages (e. g. [1, 2] ), there is little doubt that, at global scales, climate and disturbance are key drivers. For instance, numerous studies have demonstrated that species richness at both regional (e. g. 10 Â 10 km grids) and local (i. e. the scale of local assemblages) scales tracks contemporary climatic conditions  [3] [4] [5] , and many studies have documented predominantly negative effects of anthropogenic disturbance on diversity at local scales  [6, 7]. Although anthropogenic disturbance and climate are key drivers of assemblage structure, surprisingly few studies have addressed their interaction as a driver of biological change. Here, we use data from a global database of the abundances of ant species from 1128 local assemblages to determine how assemblage structure changes with climate and disturbance.
Material and methods (a) Assemblage data We compiled species abundance data from local ant assemblages from 1128 sites distributed throughout the world (figure  1 ). rspb. royalsocietypublishing. org Proc. R. Soc. B 282: 20150418 The data used here were largely collected by the authors and built upon a database originally created by Dunn et al. [5, 16]. Additional studies were added after searches of the Web of Science and Google Scholar for published datasets on ant assemblages that included site-specific details of species abundances. Assemblages included in this analysis met the following criteria: (1) the ground-foraging ant assemblage was sampled using standardized passive field methods, with all studies including pitfall trapping and some studies also including Winkler or Berlese funnel sampling (both of which involve sampling from leaf litter); (2) sampling was not trophically or taxonomically limited (e.
(b) Environmental variables: climate and disturbance Contemporary environmental variables were obtained from the WorldClim database  [17]  at a spatial resolution of 30-arcsecond resolution (approx. 1 Â 1 km) and were extracted using ARCGIS (ESRI, Redlands, CA; 2010). The 1 km resolution was selected so that the environmental data would describe the conditions with high specificity for the site at which ants were sampled and the surrounding environment. We used mean annual temperature (MAT; range: 0. 1-28. 58C), annual precipitation (157-3303 mm), temperature range (9. 7-52. 28C), hemisphere, continent, trap days (range: 2-18 360) and transect length (range: 1 -1000 m) in our analyses. Sampling grain and extent can affect the outcome of analyses of diversity metrics  [18] , so including details of trap days and total transect length in all analyses accounted for differences in sampling protocols among studies.
(c) Data analysis All statistical analyses were carried out in the R v. 3. 0. 3 statistical environment  [19]. We selected two commonly used metrics to describe assemblage structure: species richness and a measure of species evenness, the probability of interspecific encounter (PIE)  [20, 21]. We calculated PIE from Simpson's diversity index (PIE ¼ 1 2 Simpson's diversity index) using the vegan package  [22]. PIE gives the probability that two randomly sampled individuals from an assemblage represent two different species. PIE is equivalent to the slope of an individual-based rarefaction curve measured at its base  [23] , and ranges from 1. 0 when all species are equally abundant in an assemblage to 0 when there is only a single species in an assemblage. PIE is also robust to variation in abundance among assemblages  [24]  and is a scale-independent metric  [18].
Additionally, PIE was strongly and inversely correlated with a measure of dominance (number of individuals of the most abundant species divided by the number of individuals of all species) (t 748 ¼ 287. 0, p , 0. 0001, r ¼ 20. 95) and positively correlated with a range of other diversity measures for our dataset, including Shannon's H and Pielou's evenness. PIE and species richness were correlated, but the relationship was weak (r ¼ 0. 13). We henceforth refer to PIE as 'species evenness'. We tested the effect of climate (MAT, mean annual precipitation and temperature range) and disturbance (three levels: disturbed, undisturbed and transformed) on species richness and evenness of ant communities. Additionally, to control for sampling differences, we included the number of trap days and transect length in all models.
rspb. royalsocietypublishing. org Proc. R. Soc. B 282: 20150418 differences in ant assemblages. For species richness, we used the lme4 package  [25]  to fit generalized linear mixed models, specifying a Poisson error distribution. Fitted models for species richness showed evidence of over-dispersion, so to control for this we included an observation-level random effect  [26, 27]. To model the effects of disturbance and climate on species evenness (PIE), we built linear mixed-effects models in the lme4 package. Because PIE represents a bounded variable (between 0 and 1), we used a logit transformation  [28]. The minimum non-zero value (3. 35 Â 10 24 ) was added to the denominator and the numerator of the logit transform equation to allow transformation of values equal to zero and 1, which would otherwise transform to 21 and 1, respectively.
Simulation Environment The proposed algorithms in this paper were evaluated using a 1. 70 GHz server with 6 GBytes of available RAM. We used data files with sizes ranging from 100 Megabytes to 4 Gigabytes. These data were stored in a distributed manner over a number of available data centers or providers ranging from 10 to 50. We associate with each data center, a data price per Gigabyte and per month, uniformly generated between 0 $ and 1 $. Each data is splitting multiple chunks and each chunk size is equal to 1 Megabyte. This configuration leads to construct a full mesh bipartite graph as described above. The number of generated bipartite graphs was set to 100 in our simulations yielding an average value reported in the following curves and tables. Without loss of generality, we suppose that each data center has an unlimited storage capacity.
A Private Information Retrieval (PIR) scheme is a protocol in which a user retrieves a record from a database while hiding which from the database administrators. PIR can be achieved using mutuallydistrustful replicated databases, trusted hardware, or cryptography. In this paper we focus on the later setting which is known as single-database computationally-Private Information Retrieval (cPIR). Classic cPIR protocols require that the database server executes an algorithm over all the database content at very low speeds which impairs their usage. In [1], given certain assumptions, realistic at the time, Sion and Carbunar showed that cPIR schemes were not practical and most likely would never be. To this day, this conclusion is widely accepted by researchers and practitioners. Using the paradigm shift introduced by lattice-based cryptography, we show that the conclusion of Sion and Carbunar is not valid anymore: cPIR is of practical value.
Introduction Homomorphic encryption has followed a curious path in the history of cryptography. Since the very beginning of public key cryptography, it has been presented as a holy grail able to provide the most incredible and powerful applications. Yet, even with the recent break-throughs due to lattice based cryptography, homomorphic encryption is almost never used in practice. Among the potential applications of homomorphic encryption, one of the oldest and most emblematic is single-database computationally-Private Information Retrieval. With such a protocol, a user can retrieve a record out of n from a database, without having to reveal which one to the database administrators (security being derived from computational hardness assumptions). A trivial way to obtain such privacy is to simply download the whole database and to dismiss the elements the client is not interested in.