dataesr/scientific-paragraphs-categorization huggingface dataset:
Non-synonymous single nucleotide variation and polymorphic glycoproteins The structural analysis shows that NGS are over-represented on the surface of proteins. We find that 91% of the annotated sites in humans and 93% in mouse are solvent-accessible, compared to 67% and 70% of the unannotated sites. We used informatic tools to extract information on polymorphic N-glycosylation variants from the UniProtKB/Swiss-Prot database and dbSNP  [20]. Previously we have shown using pathway and function enrichment analysis that a significant number of proteins that gain or lose the glycosylation motif are involved in kinase activity, immune response and blood coagulation  [4]. However, it remains to be investigated whether a polymorphic site can indeed be glycosylated when there is gain of the NGS motif.
The major molecular functions represented are hydrolases, receptors and transferases. Out of 2299 proteins that do not have structures, 2247 proteins are predicted to be glycosylated at the gain of glycosylation site by the SFAT tool (total NXS/T sites: 12,623; sites predicted as yes: 11,651 and sites predicted as no: 972). Based on GO analysis using Panther tools  [21] , the over-represented GO biological processes in this dataset include immune response, response to stimulus, signaling and blood coagulation, which agrees with the our results obtained previously  [4].
Prediction of N-linked glycosylation sites The prediction is performed using the following four basic rules: (i) presence of Endoplasmic Reticulum targeting sequence; (ii) not nuclear, mitochondrial or cytoplasmic; (iii) present in a loop or turn and (iv) exposed. Data for the first two rules are extracted from the UniProtKB flat file. The third piece of information is extracted from the PDB file and the relative solvent accessibility is obtained from Define Secondary Structure of Proteins (DSSP) database  [22]. When structure is not available, the last two pieces of information is predicted. We identified 96 new (currently not annotated in UniProt) NGS in the human proteome that matches all four rules (3. 6% of the total unannotated NXS/T sites). Instead of using a set of strict rules for prediction which can potentially lead to large numbers of false negatives, we implemented these rules into a machine learning framework for better prediction accuracy.
In addition, GPP  [24]  uses the random forest algorithm and pairwise patterns to predict glycosylation sites with an accuracy of 90. 8% for Ser sites, 92. 0% for Thr sites and 92. 8% for Asn sites. It is important to note here that the authors used their own training and test datasets to determine the accuracy of their tools. A direct comparison between different tools is thus difficult because the tools were developed and tested on different training and test datasets. Furthermore, the definition of negative NGS is an open discussion, because it is difficult to prove definitively that a particular residue is not glycosylated under any conditions, although experiments can verify that a particular residue can be glycosylated.
Materials and methods By 'annotated' glycosylation sites, we mean NXS/T sites that are indicated as the N-linked glycosylation site in Uni-ProtKB/Swiss-Prot  [25]  protein record, while unannotated glycosylation sites include all other NXS/T sites. We consider these annotated glycosylation sites as potential NGS, as they have been manually curated by UniProtKB/Swiss-Prot curators based on experimental evidence, similarity to experimentally-validated NGS in homologous proteins and/or in-depth sequence and functional analysis. UniProtKB/Swiss-Prot human proteome is considered the gold standard set of manually-curated human proteins and in our opinion provides the best positive and negative datasets, since UniProtKB/Swiss-Prot curators have manually curated all entries for the human proteome.
Datasets All data were collected from UniProtKB and Protein Data Bank (PDB)  [26]. For the human proteome, the complete proteome available from UniProtKB/Swiss-Prot was used. For mouse, the file rp-seqs-15. fasta. gz from http://pir. georgetown. edu/rps/data/current/15/ was downloaded and parsed to obtain the complete proteome. This was done because the complete proteome of mouse from UniProtKB has several potential splice variants as separate entries. All other proteomes were obtained from UniProtKB using the 'complete proteome' keyword tag. The access dates for all data retrievals are between 15th February and 15th June, 2012. Experimentally-validated datasets were obtained from UniProtKB and supplementary materials in Zielinska et al. [10, 11].
Data analysis Python scripts were used to extract information from Uni-ProtKB flat file feature (FT) lines, cross-reference (DR) lines for PDB database and sequence (SQ) lines. Information was also extracted from the PDB files in order to get secondary structure information. For annotated N-linked glycosylation sites, all UniProt FT lines annotated as ''N-linked'' whether ''confirmed'', ''potential'' or ''by similarity'' were retained. The positions of these sites were retrieved and the corresponding sequences were checked for the NXS/T motif. PDB IDs were extracted from UniProtKB flat files. As there can be more than one PDB file mapped to any UniProtKB protein, the PDB structure which meets the following criteria was selected: the structure was determined by x-ray diffraction, highest resolution, and the site of interest is contained within the solved structure.
Once selected, PDB files were downloaded from PDB and the positions aligned to the PDB sequence and the secondary structure was determined based on the secondary structure assignment in the PDB file. Relative solvent accessibility was calculated using the information in DSSP database  [22]. Based on the value of the relative accessible surface area (ASA), the residues were grouped as buried (0. 0-0. 25) or exposed (0. 25-1. 0). The choice of the threshold was based on previous studies  [10, 18, 27]. For machine learning using classification and regression tree (CART), we allow CART to automatically select the relative ASA based on the test set. MUSCLE was used to perform pairwise alignment to map UniProtKB protein sequences to PDB  [28]. Prediction of the N-terminal targeting sequences was performed using Predotar  [29].
Identification of polymorphic glycosylation sites Gain of glycosylation sites were identified by using variation data from UniProtKB FT lines and dbSNP  [20, 33]. Entries were first mapped to UniProtKB accessions using the ID mapping service  [34]  followed by sequence mapping. This resulted in a table with UniProtKB accession numbers and position of variation, the variation and the data source (Table  S2 ).
Machine learning using CART This method consists of creating a model that predicts the value or a class for a predictive variable based on several input variables. The algorithms of this decision tree usually work top-down by choosing a variable at each step that is the next best variable to use in splitting the set of items. ''Best'' is defined by how well the variable splits the set into homogeneous subsets that have the same value of the predictive variable  [35]. As the number of unannotated sites is higher than that of the annotated sites in our analysis of the human proteome (2:3 ratio), the training dataset for our prediction model contains 200 experimentally-validated sites (noted as positive) and 300 unannotated sites (noted as negative). Training classifiers are challenging because the positive and negative datasets are unbalanced with more negative sites than positive sites, which can result in poor classification of the minority class (in this case positive sites).
One solution is to change the distribution of major and minor classes during training by randomly selecting a subset of the training data. However, this approach does not then take into consideration all available information in the real dataset. Therefore, we chose our training dataset to contain original data in a ratio that reflects the natural dataset. First, a set of patterns is generated from the training data for each of the glycosylation sites and then used to generate a value for each instance. Multiple runs are performed with each instance collecting weights to determine the positive or negative NGS class. Each of the runs used dataset comprising randomly chosen positive and negative instances from the cross validation fold. The accuracy of the prediction was evaluated by cross validation (described below).
The variables that describe the dataset are selected based on the rules derived from this study: (i) ER targeting sequence (ii) sub-cellular location, (iii) secondary structure and (iv) exposed/buried. To build the classification model, the function 'rpart' in statistical language R was used. Twenty-fold internal cross validation was performed to validate the training dataset as described previously  [24]. More specifically, the dataset was partitioned randomly into 20 sections and the training procedure was carried out using 19 of these while the 20th section provides a test dataset. This was repeated 20 times on each occasion with a different section of the data acting as the test set. The evaluation of the model is based on the number of true positives (TP), false positives (FP), true negatives (TN), false negatives (FN).
Acknowledgements We want to thank  Hayley Hamilton  of the  George Washington University and Nagarajan Pattabiraman of MolBox LLC  for critical reading of the manuscript and providing useful comments. All computations were performed at High-performance Integrated Virtual Environment (HIVE) located at The  George Washington University  and co-developed by  Drs. Raja Mazumder  and  Vahan Simonyan. Support  for this work came from the  George Washington University  funds to RM. RG's participation is supported by  RO1 CA135069  and  U01 CA168926. This project is also supported in part by an appointment to the  Research Participation Program at the Center for Biologics Evaluation  and Research administered by the  Oak Ridge Institute for Science and Education through an interagency agreement between the U.
Skeletal muscles have also an abundant supply of blood vessels that, together with the endothelial cells, nourishes the tissue and the different cell populations associated with it  13. Based on this presupposition, we characterized the best possible culture conditions to generate an X-MET that possesses most of the molecular, morphological and functional properties of a muscle fiber and therefore making it more suitable for an efficient therapeutic approach. Of note, the heterogeneity of cell populations isolated from skeletal muscle was the prerequisite for the formation of the X-MET, since pure cultures of either satellite cells or fibroblasts failed to generate the X-MET structure (data not shown). To define the type of progenitor cells associated with muscle niche participating to the generation of X-MET, we used flow cytometry assay.
Interestingly, while 2D cell culture system did not survive more than 15 days in DM, X-MET structure was maintained viable in culture for over 40 days (data not shown). To analyze potential morphological changes of the X-MET in culture over time, we performed histological and morphometric analysis on it at 5day, 10-day and 40 days in DM. We did not observe significant differences in the X-MET myotube size between 5 days and 10 days in DM (Fig. 3e ), as well as we did not observe a significant difference of the general size and the myotube length between 2D  Of note, a median value shift toward the small size has been observed for the X-MET after 40-days in culture (Fig. 3e ). All of these data suggest that: i) the 3D X-MET structure survives in culture for an extended period of time if compared to the classical 2D structure; ii) The X-MET after 40-days in culture might represent an ideal in vitro muscle model of atrophy-associated senescence and aging, typical of sarcopenia, the age-related loss of skeletal muscle mass  24.
For each analysis, data were collected from 30,000 cells and analyzed using Win MDI 2. 8 software (Scipps Research Institute, La Jolla, CA). Histological and immunofluorescence analysis. X-METs were embedded in tissue freezing medium and snap frozen in nitrogen-cooled isopentane. Samples were mounted on a cryostat and cut at 7 mm thick sections. Frozen sections were stained for hematoxylin & eosin and X-gal  36. For immunofluorescence analysis, samples were fixed with 4% PFA and incubated overnight at 4uC with primary antibodies: MyoD (BD Biosciences Pharmingen), myogenin (Dako), MyHC (MF20 Hybridoma Bank), laminin (Sigma), ERTR7 (Acris), CD31 (Abcam), a-SMA (Sigma), Pax-7 (Hybridoma Bank), e-MyHC (Hybridoma Bank); nuclei were visualized using Hoechst staining. Samples were visualized using an inverted microscope (Axioskop 2 plus; Carl Zeiss MicroImaging, Inc.
); images were processed using Axiovision 3. 1. RNA extraction and real time PCR analysis. Total RNA extraction was performed using TriRiagentTM (SIGMA) and was reverse-transcribed using the QuantiTec Reverse Transcription kit (QIAGEN). Relative quantitative PCR was performed on ABI PRISM 7500 SDS (Applied Biosystems, USA), using premade 6carboxyfluorescein (FAM)-labeled TaqMan assays for HPRT1, MyoD, Myogenin, Pax-7, CD31, VE-cadherin, Flik1, VEGFA (Applied Biosystem, USA). Relative quantitative RT-PCR sample value was normalized for the expression of HPRT1mRNA. The relative level for each gene was calculated using the 2-ddCt method  38  and reported as mean fold change in gene expression. Morphometric analysis and statistics. The size of MyHC positive myotubes was calculated using Scion Image software (v.
Beta 4. 0. 2, Scion, Frederick, MD). Statistical analysis was performed with GraphPad Prism v5. 0 software; groups were compared by Mann-Whitney Rank Sum test and the difference in the median values between the two groups was considered significant for p value , 0. 05. X-MET transplantation in skeletal muscle. Muscle Graft model 1: Transplanting X-MET into injured muscle. C57BL/6 wild type mice were anesthetized with an intraperitoneal injection of 250 mg/Kg of Tribromoethanol (AvertinH). The skin on the hind limbs was opened, and the tibialis anterior (TA) muscles were gently exposed. In host TA muscles, we created a local injury (a ''bed'') in the middle region of the muscle by removing a small number of fibers. The bed was 5-7 mm long and 3 mm in deep. We transplanted an X-MET fragment, obtained from MLC/hAP mice, into the bed obtained in the right TA muscle.
However, it is possible to easily extend the X-NETAD architecture in order to exploit the presence of dedicated OBUs within the targeted vehicles. While our previous work  [10]  aimed at presenting the "big picture" of the X-NETAD architecture without disclosing any specific implementation detail, in this manuscript, a comprehensive overview of the main research and scientific aspects of the X-NETAD project is provided. To summarize, the goal of this manuscript is twofold: • to provide an in-deep description of the X-NETAD architecture and its implementation on a VANET formed by an Android smartphone; • to present the experimental results, obtained by implementing X-NETAD on a testbed of commercial off-the-shelf (COTS) Android smartphones, with no high-end requirements, shedding light on the strengths and weaknesses of the proposed architecture.
The Big Picture The goal of the X-NETAD project was to design and implement an innovative traffic alerts dissemination system based on a cross-network (cellular and WiFi) software application, installable on dual-interface (UMTS and WiFi) smartphones  [11]. An illustrative representation of the envisioned cross-network traffic alert dissemination system is shown in Figure  1. The key idea of the X-NETAD approach is that of leveraging on the spontaneous formation of WiFi local VANETs, with direct connections between neighboring vehicles, in order to disseminate traffic alerts and GPS location in the VANETs very quickly and inexpensively. It is important to remark that X-NETAD has been conceived of for a unidirectional exchange of information from a centralized data center to mobile vehicles, but it does not allow collecting information from the nodes.
However, it is possible to overcome this limitation, since most wireless network adapters inside Android devices are compliant with the full IEEE 802. 11 standard and, thus, support the ad hoc mode. This functionality can be exploited running the OS as administrator with super-user rights, using a procedure called rooting and properly configuring the wireless adapter to connect to ad hoc networks using the classic ifconfig Linux command. Furthermore, the access to network interfaces is natively exclusive, i. e. , only one between the cellular and the IEEE 802. 11 interfaces is active at a time. In X-NETAD, we have introduced suitable software routines that overcome this limitation by properly altering the routing table of the devices, making it a true multi-homed device. We designed the X-NETAD in order to process and transfer large information amounts.
In order to reduce the impact of the problems mentioned above, the compressed archive received by a PU through the cellular interface is not directly transmitted to SUs, but, instead, is split in many small pieces that are disseminated independently. On the basis of an analysis of a VANET's peculiar characteristics, such as high node mobility and limited transmission range, we adopted a broadcast (starting from a primary node) approach for our system. In X-NETAD, all vehicles act as independent routers and cooperate together, fostering the complete reception of the traffic update. All transmissions in the system are broadcast, in order to reach the largest number of nodes at a time and to avoid any overhead related to routing tables' update issues. The IF protocol controls message rebroadcasting, guaranteeing an efficient network-wide data dissemination.
Ad Hoc Domain As previously mentioned, we designed the X-NETAD system in order to be able to disseminate a (relatively) large content, adapting to the constraints imposed by the VANET scenario. The strategy we adopted to enforce this feature split the archive that comes from the cellular network into smaller pieces, called chunks, which are disseminated independently. Chunks are small data units of fixed size, exchanged during a single contact of limited duration with high probability. If a chunk is only partially received from a node (e. g. , due to a connection breakdown), it is discarded. If, on the one hand, splitting the traffic update into several chunks allows nodes to receive parts of the same traffic update from different nodes, on the other hand, this adds complexity to the system, because nodes should be able to recognize which chunks are missing and, if needed, should request them back.
2. 1. ) using the cellular interface. The Wireless Manager block, on the other hand, is present at any node, because it drives the IEEE 802. 11 interface and is fundamental in the dissemination process taking place in the ad hoc domain. The X-NETAD Dissemination Manager is the key component of the whole system, due to the fact that it is in charge of the logical system management. If a new update is received from the AS, its content is stored locally, split in smaller chunks and the dissemination process is started. Upon the reception of an X-NETAD message on the IEEE 802. 11 interface, the Dissemination Manager performs all the actions required to handle the message (see Section 4. 2. 2. ). For example, when the system receives a DATA message, the Dissemination Manager checks if the received chunk is missing, updates (if required) its local bit-vector and, with the help of the IF and the GPS blocks, decides if the message has to be re-broadcast.
Experimental Results In this section, we analyze the experimental results in different environments, obtained with a test-bed composed by four LG Optimus One (P-500) smartphones and a Samsung Galaxy Tab tablet, all equipped with release 2. 3. 3 of the Android OS. The experimental campaign is conducted in three different phases. In the first phase, we preliminarily investigate the relationship between the Packet Error Rate (PER) and a couple of physical parameters, namely, the IEEE 802. 11 transmission data rate (in Subsection 5. 2. 1) and the inter-node distance (in Subsection 5. 2. 2). In the second phase, described in Subsection 5. 3, we evaluate the performance of the X-NETAD application in a couple of ideal static scenarios, where the nodes are fixed. In the third phase, described in Subsection 5.
PER as a Function of the Distance Besides the data rate, the distance between communicating nodes in the system has a major impact on the performance of the X-NETAD dissemination protocol. Even if the IF protocol assigns higher rebroadcasting probabilities to the farthest nodes, packet loss probability is an increasing function of the distance. Propagation losses affect all packets, depending on a multitude of factors (e. g. , fading, shadowing, absorption, multipath interference, distance). Unlike other factors that are strictly related to the surrounding environment, one can estimate the impact of the distance between sender and receiver nodes through the Friis transmission formula  [32] : P r P t ∝ λ d n (3) where P t represents the transmission power (dimension: [mW]); P r is the received power [dimension: (mW)], λ is the wavelength [dimension: (m)]; d is the distance between receiver and sender [dimension: (m)]; and n is an experimental coefficient that can vary (typically, it lies in the range  [2, 4] -n = 2 for free space transmission).
Discussion The results obtained from the experimental campaign have confirmed the good performance exhibited by the data dissemination protocol adopted in X-NETAD. In fact, it has been shown that a small number of rounds of requests (at most five) is sufficient to disseminate the whole information archive to all network nodes. This happens also in the case of unfavorable network conditions (as in the case of Scenario 2), where SUs are sparse and distant from the PU (up to three hops away). Remarkably, these results have been obtained limiting the overall number of transmissions in the network. The core retransmission mechanism of X-NETAD is the IF broadcast protocol, which is designed to reduce the channel congestion and, thus, scales well with the number of nodes, as shown in  [26]. The presented results have been obtained in scenarios with a small number of nodes, where the impairments of the wireless channel are the principal cause of the packet losses, while the losses due to channel congestion are negligible.
Acknowledgments The work of  S. Busanelli ,  N. Iotti  and  G. Ferrari  was carried out under the one-year project " Cross-Network Effective Traffic Alerts Dissemination " ( X-NETAD ,  Eureka Label E! 6252  [11], 2011-2012), sponsored by the  Ministry of Foreign Affairs (Italy)  and  The Israeli Industry Center for R&D (Israel)  under the " Israel-Italy Joint Innovation Program for Industrial, Scientific and Technological Cooperation in R&D. " F. Rebecchi was at the  University of Parma  (2011) while collaborating with  S. Busanelli  and  G. Ferrari  within the X- NETAD  project.
In this paper, we compare three methods to reconstruct galaxy cluster density fields with weak lensing data. The first method called FLens integrates an inpainting concept to invert the shear field with possible gaps, and a multi-scale entropy denoising procedure to remove the noise contained in the final reconstruction, that arises mostly from the random intrinsic shape of the galaxies. The second and third methods are based on a model of the density field made of a multi-scale grid of radial basis functions. In one case, the model parameters are computed with a linear inversion involving a singular value decomposition. In the other case, the model parameters are estimated using a Bayesian MCMC optimization implemented in the lensing software Lenstool. Methods are compared on simulated data with varying galaxy density fields.