dataesr/scientific-paragraphs-categorization huggingface dataset:
Improved SONAR models penalize low PPI neighborhood and increasing network density. We previously developed SONAR (1. 0), an SVM-based machine learning algorithm that leverages large-scale experimental protein-protein interaction (PPI) data (without protein sequence information) to predict unannotated RBPs  16. Employing the BioPlex PPI dataset  17  , SONAR was able to predict RBPs with high specificity and sensitivity with BioPlex proteins and previously compiled RBP list. The success of SONAR was based on observations that an unusually high proportion of proteins that associate with RBPs (via RNA-dependent and independent interactions) are themselves RBPs. As such, SONAR's ability to accurately identify RBPs depended on the density of protein-protein interactions centered on an interrogated protein (the number of "edges") or the completeness of the local PPI network.
016 (from 0. 767 to 0. 783) for the SONAR model with BioPlex network (Figures  1C  and 1D ) and an increase in Precision-Recall-Area Under Curve (PR-AUC) value by 0. 108 (from 0. 347 to 0. 455) (Figure  S1B ). We refer to this revised model as SONAR2. 0. By replacing BioPlex network with the denser Mentha-BioPlex network (Figures  1C  and S1A ), we improve SONAR1. 0's performance by another 0. 078 (from 0. 783 to 0. 861, Figure  1D ) in ROC-AUC and 0. 108 (from 0. 455 to 0. 563) in PR-AUC (Figure  S1B ). Next, we evaluated if adding functional protein association data from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database would further improve SONAR2. 0 as functional protein association data had previously been successfully applied in other PPI-based protein prediction tasks  22.
Fully connected layers with non-linear activation units were added to further process the compressed features and generate final predictions. To avoid convergence of suboptimal solutions and accelerate the training process, seqCNN was initialized with weights from a convolutional autoencoder pretrained with all the protein sequences in our dataset before model training (see Methods). For the third classifier called ProteinBERT-RBP, protein sequences are fed into an attentionbased Transformer architecture designed for proteins (ProteinBERT)  15  to obtain predicted RNA-binding propensities (Figure  2C ). ProteinBERT-RBP was pretrained with ~106 million UniRef90 protein sequences and their corresponding functional annotations from the Gene Ontology database in a self-supervised manner, and then fine-tuned using our human RBP annotations.
We computed HydRA-seq classification scores for sequences collected from Pfam database corresponding to RNA binding domains (K homology domain, RNA recognition motif, DEAD box, cold-shock domain and like-SM domain) in different species. Expectedly, these sequences generally resulted in high classification scores (Figure  2F ). In contrast, sequences from protein domains that are not commonly found in RBPs such as protein-protein interaction (BTB, IgG), membrane associated (transmembrane, C2), and src homology 2 domains overall had lower classification scores than the known RBDs (Figure  2F ). These results demonstrate the capacity of our sequence-based RBP classifiers to distinguish sequence patterns that contribute to RNA-binding activity with domain level resolution.
The sensitivity of the Occlusion Map approach from the sequence-based component of HydRA potentially extends HydRA's applicability to in silico perturbation analyses to evaluate how protein sequence variations influence RNA-binding capacity. Moreover, since the HydRA framework does not depend on prior knowledge of RBPs (such as RNA-binding domains), this framework can be easily transferred and applied to RBP prediction in other species and even other protein function prediction problems with appropriate training dataset. We evaluated a representative set of HydRA-predicted RBPs by eCLIP to characterize transcriptome-wide targets. The previously uncharacterized RBP HSP90AA1 preferentially binds to transcripts encoding heat response proteins involved in the HSP90 protein folding pathway, and the uncharacterized RBP members of the 14-3-3 (YWHA) family of proteins targets genes involved in RNA splicing and translation processes.
E-G. We explored the performance of each sub-classifiers of HydRA, strucGNN+HydRA and other available RBP classifiers by looking at their recognition capacity on sub-categories of known RNA-binding proteins (i. e. "characterized" and "uncharacterized" known RBPs) via ROC-AUC and precision-recall analysis within the test dataset. H. The enriched GO terms in HydRA candidate RBPs.
Mentha-BioPlex network We obtained a comprehensive protein-protein interaction (PPI) network, i. e. Mentha-BioPlex, by combining BioPlex2. 0 50 a high-quality PPI dataset generated by robust affinity purificationmass spectrometry, with a meta-database Mentha 18 that collects experimental evidence from different PPI database such as MINT  19  , IntAct 21 , DIP  51  , MatrixDB 52 and BioGRID  20. Each interaction in the network was assigned a reliability score taking into account all the aggregated experimental evidence, according to the formula defined in MINT database and Mentha: " = 1 -& !" a is a constant. It determines the growth rate of the curve. To keep the scores in a convenient range, we chose a=1. 4 as Mentha did. x is calculated by adding up all the experimental evidence considering type and size of the experiments and the number of publications that support the interaction: ' = ( ) # * # # + ,/10 i is the index of all experimental evidence supporting the interaction, while d takes into consideration the experiment size (d=0.
The functional protein association network To supplement current PPI network with extra extrinsic association information of proteins, we introduced functional protein association data into our modelling. We take STRING  53  as our functional protein association resource in this study. STRING is a database consisting of protein-protein association data from several sources, i. e. genomic context predictions, highthroughput lab experiments, conserved co-expression, automated text-mining and other PPI databases. To construct the functional protein association network, we kept all the STRING interactions that has at least one protein existing in Mentha-BioPlex network, while the experimentally demonstrated physical protein-protein interactions from STRING are removed to avoid redundancies.
Protein dataset and Known RBPs collection On one hand, all the proteins in Mentha-BioPlex network constituted our protein dataset and the corresponding protein sequences were retrieved from Uniprot database. On the other hand, we collected currently known RBPs from published studies  1, 30, 40, 41, [54] [55] [56] [57] [58]  , and merged them with proteins in the GO term of "RNA-binding" (GO:0003723) and its descendent terms from AmiGO 2 database (version released in July 2016)  59, 60  , where we removed those proteins assigned by automated methods without curatorial judgement in order to guarantee the high quality. After that, each protein in our protein dataset was assigned a class label, viz. known RBP or not known for RBP (we referred to it as non-RBP for simplicity), based on this RBP list.
Network properties' effects on SONAR We studied the effects of PPI network's properties on the performance of SONAR, such as network density (also referred to as completeness) and the reliability of PPI edges. In this analysis, we first randomly removed certain numbers of edges from the original network that led to a couple of induced networks with retained edges in percentage of 20%, 40%, 60% and 80% respectively from the whole network. For each percentage, we generated 10 different networks as replicates. SONAR was then run with each of these induced networks and the performance was evaluated by ROC-AUC analysis with the output from SONAR software which was obtained by a cross-validation-like (10-fold) approach  16. To test if the reliability of PPI edges in the network matters, we also generated a group of induced networks in the same way as above but from a random network that came from an edges-shuffle operation with the original network.
Dataset for modeling Under the criterion where the class ratio (i. e. known RBPs versus other proteins) in each set keeps same, we first randomly took out 20% of the data in our protein dataset as the hold-out test set, which is only used for the final evaluation of the final RBP classifier and the comparison among different RBP classifiers/software. The rest 80% of data constitutes the training/validation set that is used to train and select model, select features and tune the hyperparameters of the RBP classifiers. In the evaluation and comparisons of classifiers that utilize proteins sequences, we discarded the proteins in the test dataset whose sequences are highly similar to any protein's sequence (>90% similarity and >90% coverage) in the training/validation dataset. The similarity between protein sequences was calculated by cd-hit program  61.
A vector of 20 descriptors per protein was gained. (3) k-mer of predicted secondary structure (SS-kmer): Secondary structure type of each amino acid in the protein was predicted by SPIDER2 software  63. The induced sequence of secondary structures of the protein was then used to generate the k-mer (k=11, 15) feature vector in the same way described in (1) except that we look at the 1500 most frequent SS-kmers that appeared in the known RBPs. Secondly, feature selection was done in order to reduce the dimension of feature vectors. Chi-square test was used to filter out unimportant features within each feature category respectively via their p values controlled for false discovery rate (FDR) at level 0. 01. For kmer and SS-kmer, features were further selected by linear SVM model by keeping the features the absolute value of whose weights are larger than the average of those of all input features.
Convolutional neural network for sequence-based RBP classification Parallel to seqSVM, we also created a feed forward convolutional neural network (CNN) to recognize RBPs by automatically learning the pattern underlying the protein sequence with convolutional layers. We referred to this CNN model as seqCNN. The seqCNN model consisted of 8 different hidden layers of neuron-like computational units that are stacked up in a certain order (shown in Figure  2A ). The first layer was an embedding layer that mapped amino acids to dense vectors encoded with biophysical and biochemical properties of the amino acids using a pre-trained weight matrix called ProtVec 23. This layer not only enriches protein sequence information with the knowledge from available database, but also alleviates the difficulties on deep learning caused by sparse one-hot encoded matrix of the protein sequence.
Attention-based protein language model for RBP classification ProteinBERT is an attention-based Transformer/BERT architecture specifically designed for protein classification problems  15   Adam optimizer for the backpropagation, and we reduced the learning rate on plateau and used early-stopping base on the "validation set" to avoid overtraining. This ProteinBERT model for RBP classification (referred to as ProteinBERT-RBP) was implemented using Keras  66  with Tensorflow kernel  65.
Graph neural network modelling on protein structures The protein structure backbone is represented as graphs G = (V, E), where each node 3 ∈ V is an amino acid connected by edge 4 ∈ E to its amino acid neighbors in the 3D space whose corresponding Ca atoms are less than 10Å distant from the C! atom of 3 or within the kneareast neighbor set of the C! atom of 3. To obtain the optimal features to represent each amino acid and their spatial relationship to their neighbors, we select node features from various types of amino acid features including (1) one-hot representation, (2) main chain torsion angles (Phi and Psi), (  3 ) the structural features encoded by FoldSeek 68 , and the latent representations learned by large pre-trained protein language models, such as (4) DeepFRI To update the node features of each amino acid according to the protein structure graph, strucGNN1 use 3 PointTransformer blocks.
Finetuning pretrained Graph neural network on protein structures To exploit the recent advancement in the structure-based protein pretraining models, we adjusted and finetuned Masked Inverse Folding (MIF) model for RBP classification task and referred this new model as strucGNN-MIF. MIF is a pretrained graph neural network model which achieved state-of-the-art performance in many protein prediction tasks  73  , MIF was pretrained with the CATH4. 2 database  74  , and is built on structured graph neural network architecture with the pretraining task of reconstructing a corrupted protein sequence conditioned on its backbone structure. Similar to the graph construction in strucGNN1 and strucGNN2, the protein structure backbone is represented as graphs G = (V, E) with each amino acid as a node 3 ∈ V.
Protein domain annotation The domain annotations in each protein are obtained by homology search using hmmscan (HMMER 3. 1b1) against Pfam-A. hmm from Pfam database (v27. 0). Overlapped and redundant domain hits are merged using hmmscan-parser. sh (from https://github. com/carden24/Bioinformatics_scripts/blob/master/hmmscan-parser. sh). Only domain annotations with E-value < 0. 1 were kept for downstream analysis.
Criteria of RBP candidacy After all the aforementioned steps of model selection and optimization, we got the final RBP classifier (i. e. HydRA) and trained it with all the samples in our protein dataset. With this trained classifier, we got a classification score to each protein in the protein dataset. We set a cutoff as 0. 8927 for the classification scores by fixing the false positive rate to 10% within the test dataset. Thus, all those proteins with classification scores higher than the cutoff but with negative class labels (i. e. previously not known for RNA-binding) were collected in the list of candidate RBPs.
F. We explored the performance of each available RBP classifiers by looking at their recognition capacity on sub-categories of known RNA-binding proteins via ROC-AUC analysis within the test dataset. G. We obtained a list of 1,487 candidate RBPs (previously unknown for RNA-binding) with HydRA by setting the false positive rate as 0. 1. We compared the physicochemical properties (i. e. the isoelectric points of the proteins, the proportion of intrinsic disordered regions in the protein and the amino acid composition of the intrinsic disordered regions in the proteins) among known RBPs, candidate RBPs, low scored proteins by HydRA (referred to as noncandidate proteins) and all proteins in our protein set. Density curve was plotted to show the difference of each categories in isoelectric points and reverse cumulative frequency curves are showing the difference of each categories in the relative size of intrinsically disordered regions (IDRs) in each proteins and the composition of several RNA-binding-related amino acids in the IDRs of each proteins.
domain sequences and RNA-binding peptide 7  , SPOT-seq 8  , catRAPID signature 9  and RBPPred 10 to predict all the proteins in our test dataset. The predicted scores and class labels were then collected to calculate model evaluation metrics such as accuracy, Amino acid sequences corresponding to different protein domains (i. e. KH, RRM, DEAD, CSD, LSM, BTB, SH2, Ig, UBA and 7 transmembrane domains)) are downloaded from Pfam database. RNA-binding peptides are collected from five recent studies of four technologies that experimentally identify the regions that interact with RNA molecules in RNA-binding proteins: RBDmap 6,44 , pCLAP 45  , RBR-ID 75  and RNP xl 46. sensitivity, specificity, precision, ROC-AUC, F1 score, Matthews Correlation Coefficient and balanced accuracy. RNA-binding.
This is reflected in visual and audio media pipelines which are well established and highly streamlined, while tactile processes are only recently being identified  [33, 41, 46]. Second, design processes for all modalities rely heavily on collaboration, which remains one of the most significant challenges for both expert and novice haptic design  [41, 46]. In practice and in research, it is comparatively easy to communicate visual or acoustic information through pictures and recordings; however, the same is not true for tactile feedback. In this paper, we address the collaboration problem as a step towards improving our ability to design tactile information throughout the design process. We introduce TactJam (Figure  1 ), an open source suite of software and hardware tools for designing, storing, sharing, and testing on-body vibrotactile feedback patterns.
With TactJam, we expand upon these by offering end-to-end support for collocated and remote collaborative work-from inception of a design, to recording and sharing-throughout the haptic design pipeline. TactJam enables prototyping, recording, and sharing tactile icons within the same environment. It supports in-situ exploration of haptic experiences and allows design outcomes to feed back into design processes. This particularly suits haptic design as we often lack a language which fully captures the richness of tactile experience  [35]. It also supports the design of on-body haptics, as designers are faced with a plethora of bodily shapes and sizes, which interact with how a given tactile design is experienced. Fast sharing mechanisms enable designers to explore a range of multiple designs rapidly.
In industry, companies have long used in-house tools (e. g. , D-Box's and Immersion's editors for motion planning and vibrotactile feedback), but more recently some companies have emerged as software-focused to meet this growing need for haptic design (e. g. ,  Lofelt, Interhaptics). Custom DIY kits have also emerged, such as Stereo Haptics [58] for accessible 2-actuator control, and Syntacts  [38]  for more general wearable vibrotactile feedback. This mirrors similar DIY kits for other haptic modalities, like the Hapkit and Haply for force feedback display  [15, 31]. TactJam is similarly designed to be easily replicable by other makers and tinkerers, its main contribution over existing kits is its intended use in remote collaboration settings.
The global amplitude of vibration can be modulated with a rotary controller. (2) Record/Play, where users can record button presses and amplitude modulations. These can then be played back. (3) Data Transfer, where the physical device connects to the GUI. From it, recorded vibration designs can be uploaded to or downloaded from a server to the device. To compare and contrast designs, TactJam has three independent 'slots' to work with. This allows, for example to record patterns in slot one and slot two and switching back and forth between them. Concurrently one might use slot three for testing new ideas, or download a third pattern created by someone else using the TactJam GUI. The TactJam GUI handles the persistent storage in a remote database as well as viewing, sorting, and retrieving vibrotactile patterns from this database.
Firmware & Data Format: The firmware is implemented using PlatformIO  [27] , the Espressif 32 platform  [28]  and the Arduino framework  [2]. The firmware has three major tasks: (1) handle inputs (e. g. button presses), (2) record and play vibrotactile patterns, and (3) communicate with the PC to transfer data between both entities. The device can record and store up to three tactile patterns in the on-board flash memory of the ESP32. Patterns are stored as a series of 32bit binary instructions. The encoding and decoding system is implemented using libvtp  [20]  for memory efficient binary encoding and decoding. Each instruction consists of a instruction type (increment time, set amplitude, or set frequency), actuator ID, time offset to last instruction and finally the value to set. For example, a command to change the amplitude is structure as follows: • 4 bit instruction code (0010) • 8 bit actuator id (all actuators if zero) • 10 bit time offset in milliseconds to last instruction • 10 bit amplitude If the time offset exceeds the 10-bit limit of 1023 ms the increment time instruction can be used with the following structure: • 4 bit instruction code (0000) • 28 bit time offset in milliseconds to last instruction As frequency and amplitude are coupled for ERMs (the parameters influence eachother, and can therefore not be tuned separately from each other), the current iteration of TactJam does not use the set frequency command, however it is implemented for compatibility with other vibrotactile pattern generation tools such as that provided by bARefoot  [50].
P13 hinted at a lack of training regarding vibrotactile sensitivities of participants. I wanted to share that it's really hard to imagine how stuff feels. [. ] I think humans are just really not used to imagine how tactile input feels and tactile output on the skin. -P13 Participants also provided the development team with a range of suggestions for further improvements on the interaction of TactJam hardware and software parts for design iterations. Overall, though, participants largely explored sensory experiences for themselves during the second workshop, re-evaluated their prior designs and sought out explanations for the meaning behind sharing on-body experiences as well as the divergence between assumptions driving designs in the first workshops and experiences made in the second.
Limitations and Future Work This is not to say that everything worked perfectly during the workshops. As with any first deployment of a system, we encountered bugs, as well as areas where we wish we would have done things differently or done more. Here we wish to again stress that TactJam is open source, and that anyone is invited to reproduce the devices, use the software and iterate on it, maybe even addressing some of our own critique (see also https://github. com/TactileVision). While TactJam was explicitly designed to support rapid creation of a breadth of lo-fi prototypes, rather than as a tool for editing and refining designs towards higher fidelity, this was perceived as a major missing feature of the TactJam suite. The ability to edit time profiles of actuators using the GUI (as is done with the haptics composer by interhaptics,  [25]  or in work by Schneider et al  [44] ) and copying, pasting, and swapping profiles between actuators was the feature most commonly requested.
ACKNOWLEDGMENTS We would like to thank all participants of the two workshops, without them this paper would not exist. We also thank  Alexander Mamedow ,  Richard Adler ,  Martin Johne ,  Christopher Praas ,  Lucas Hindenberger  and  Jonas Bruschke  for their support while building TactJam. This project received funding from the  European Research Council  ( ERC StG Interactive Skin 714797 ) and the  Hertha-Firnberg  Grant T 1146-G by the  Austrian Science Fund (FWF).
Modern trends in data collection are bringing current mainstream techniques for database query processing to their limits. Consequently, various novel approaches for efficient query processing are being actively studied. One such approach is based on hypertree decompositions (HDs), which have been shown to carry great potential to process complex queries more efficiently and with stronger theoretical guarantees. However, using HDs for query execution relies on the difficult task of computing decompositions of the query structure, which guides the efficient execution of the query. From theoretical results we know that the performance of purely sequential methods is inherently limited, yet the problem is susceptible to parallelisation. In this paper we propose the first algorithm for computing hypertree decompositions that is well-suited for parallelisation.