dataesr/scientific-paragraphs-categorization huggingface dataset:
FUTURE PLANS The Neo object model was designed after discussion between a group of scientists and engineers, both developers and potential users, from several laboratories. We consider the essential elements of the object model to be stable. However, as users and developers gain experience with the software, some evolution is to be expected. It will be necessary to find a compromise between changes to enhance usability and support new use cases, and the stability needed for a low-level library on top of which other software is built. Among the improvements already envisaged are the following: (i) some simplification of the object model, in particular merging of the scalar and array representations (e. g. , AnalogSignal and AnalogsignalArray); (ii) addition of an image sequence object, to support calcium-and voltagesensitive-dye imaging experiments, which are closely related to electrophysiology.
We also plan a number of improvements to the Python implementation: (i) improved memory management for IO. Despite the lazy/cascade mechanism, most Neo IO classes do not allow loading only a chunk or a subset of data; (ii) efficiency improvements. Some relationships are not efficient for querying (filtering) an object when the dataset is large. For instance, linking a SpikeTrain with its associated AnalogSignal is not currently straightforward. The object model is intended to be implementable in other languages besides Python. We have concentrated our initial efforts on one language in order to build up a critical mass of code to support the object model. An important future direction will be reaching out to users of other languages, such as MATLAB, and providing support for the object model.
To this end, we have created documentation for using Neo-structured data in MATLAB at http://pythonhosted. org/neo/io. html#neo. io. NeoMatlabIO. The development of the Neo object model has so far focused on the representation of recorded data, as typically stored in data files during an electrophysiology experiment. For broader use it would be desirable to also support data resulting from the processing and analysis of such data. Ideally, the object model should enable representing the relationship between analysis results and the original data. Instead of leaving the definition of corresponding objects to each user, it would make sense to extend the Neo model by a minimal set of definitions for generic scientific data along with provenance information. Such developments should be coordinated with international standardization efforts, such as those of the INCF Data Sharing Program  (Teeters et al.
e. , reverse complement of concatenated primer + index + transposase sequences), followed by a short stretch of As, and next by apparently random sequence with low Phred  (Ewing and Green, 1998)  quality scores Q (see Figure  1A ). In order to trim off these exogenous oligonucleotide sequences, Turner (2014) compared the respective accuracy of cutadapt and AlienTrimmer. Yet, the two programs were run using as input to be trimmed off, only the transposase sequence, but not the other alien oligonucleotide sequences (see Figure  1A ). With this incomplete setting, each trimming program performed quite differently. As cutadapt was set to perform 3 trimming only, it detected the transposase sequence as well as downstream sequences. In order to provide more flexible functionality and simpler usage, AlienTrimmer's strategy is to detect the specified alien oligonucleotides within reads and perform trimming when the matched region is close enough to a read end.
The program cutadapt was launched with the two reverse complemented transposase sequences to perform 3 end trimming, as it would run slower when specifying more oligonucleotide sequences, which is unnecessary (see above). As AlienTrimmer running times are not affected by the number of specified alien oligonucleotides to trim off, it was launched with all combination of exogenous bps (i. e. , reverse complement of concatenated primer + index + transposase sequences for each of the 16 used indexes + poly-A). Quality-trimming option (cut-off Q = 20) was also set for both programs in order to trim off low quality random bps in 3 , sometimes occurring when the insert size is very small (see Figure  1A ). Following Turner (2014) criteria, true positive and false negative results were defined as trimmed reads leaving less and more than five bps to be trimmed, respectively.
Denoting TP and FN as the number of true positive and false negative results, respectively, the sensitivity TP / (TP + FN) was estimated for each number of bps to be trimmed (Figure  1B ). All read pairs with insert size <250 bps were processed by cutadapt and AlienTrimmer in ∼17 and ∼8 s, respectively, confirming that AlienTrimmer runs fast with multiple input alien oligonucleotides. Note that we ran the program AlienTrimmer compiled with gcj to native machine code to observe such fast running times: different Java virtual machines were tested to execute AlienTrimmer, each running from ∼2 (Oracle JRE 7) to ∼6 (gij version 4. 8) times slower than the gcjcompiled version on these read data. Clearly, AlienTrimmer had high sensitivity. As expected, AlienTrimmer had moderate sensitivity only when the number of bps to be trimmed was small (i.
This exercise, inspired by CASP (Critical Assessment of Protein Structure Prediction) explored the current state of the art in antibody 3D modeling and compared the results with a first assessment (AMA-I) conducted two years ago. 1  For AMA-II, modelers tackled eleven antibody Fv regions whose structures were solved, but were not yet deposited in the Protein Data Bank (PDB; www. rcsb. org/). Dr. Gilliland (Janssen R&D, LLC) expanded on the antibody discovery and optimization processes and discussed the antibody structures used as benchmark. Nine structures were contributed by Janssen R&D, LLC and two structures were provided by Robyn Stanfield of The Scripps Research Institute (TSRI). Dr. Gilliland provided a detailed description of the x-ray statistics and refinement parameters of the benchmark structures, as well as the canonical structure classification of their hypervariable loops (HVLs).
2, 3 He explained that the set of benchmark Fabs used in AMA-II was larger and more diverse than the one used in the first assessment. For AMA-II, the organizers were able to include the Fab structures of antibodies from three different sources: four human, including a lambda type antibody (the first study only included kappa type antibodies), six mouse and one rabbit. Six modeling groups participated in AMA-II: Accelrys Software, Inc. , Chemical Computing Group, Inc. , Johns Hopkins University (Gray's lab), Astellas Pharma, Macromoltek, and Schrödinger, Inc. Also, the sequences of the benchmark structures were submitted to the PIGS  4  web server to generate models for comparison. The models were evaluated by the organizers relative to the crystal structures which also were provided to the participants for self-evaluation.
). In the final presentation, Jinquan Luo and Alexey Teplyakov, both at Janssen R&D, LLC, described the evaluation strategy, summarized the results, compared the quality of the models obtained by all the participants, provided an overview of progress with respect to AMA-I and discussed challenges for the future of antibody 3D modeling. A summary of these talks follows. Accelrys Software, Inc. Marc Fasnacht described the methodology and results obtained at Accelrys (http://accelrys. com/). Accelrys' Antibody modeling module, distributed as part of Discovery Studio, is centered on template-based modeling. The templates are selected from an antibody database using sequence similarity to the target in the framework regions (FRs). Depending on the quality of the template hits, models of the FRs are built either using a single, chimeric or multiple template approach.
(www. schrodinger. com/). They used the antibody structure prediction methods as implemented in BioLuminate and Prime. Dr. Zhu explained that Schrödinger developed a novel knowledge-based approach for modeling the HVLs using a combination of sequence similarity, geometry matching, and the clustering of database structures. The homology models are further optimized with a physical-chemistry based energy function VSGB2. 0, which improves the model quality significantly. He pointed out that although HCDR3 modeling remained the most challenging task, their ab initio loop prediction performed very well in predicting the HCDR3 structures in the crystal structure context (Stage 2), and allowed improved results when refining the HCDR3 loops in the context of homology models. Excluding the rabbit antibody target-a challenge for all the modeling groups involved in AMA II-the average RMSDs for the Fv models and the FRs for the 10 targets were 1.
Tools for clonal analysis in IMGT allow for description of clonal diversity and expansion. The talk, "Using Phylogenetic Techniques to Identify B-cell Lineages," was then delivered by Felix Breden, who reviewed approaches to defining species as independent lineages in evolutionary biology. He applied these concepts to defining B-cell clonal lineages in Ab repertoires. He first separates large Ab datasets into "clusters," defined as sequences using the same V and J gene, and with the same CDRH3 length. This is a common clustering technique, and few Ab sequences from the same B-cell clone should fall into different clusters defined in this way. Within such clusters, inter-sequence divergences fall into two major categories: (1) those representing small divergences of sequences from the same presumed clonal lineage, and (2) those representing inter-clonal differences.
This analysis package, which is available from the Burton lab at Scripps, integrates with major cloud computing platforms, ensuring that the analysis tools can scale with future increases in sequencing throughput. Dr. Briney used these tools to show that Abs with long CDR-H3 regions were produced by the process of V-D-J recombination, and do not appear later in the development of the B-cell lineage, via processes such as insertions or secondary rearrangement. The workshop ended with a panel discussion that included all of the presenters and questions and discussion with the audience. The topics that the panel were asked to consider included: (1)  Making publicly available expressed VH and VL gene sequence data; (2) Agreement on a "universal" definition of "clonal lineage" based on analysis of expressed VH gene sequence data; (3) Making publicly available the tools for clonal-lineage analysis; (4) A comparative analysis of different approaches to clonal lineage analysis, using shared expressed VH (and associated VL) gene datasets; and (5) Recommending "best practices" for assignment of B-cell clonal lineage based on expressed VH (and VL) gene datasets.
The panel engaged in a vigorous discussion regarding definitions of the terms clonotype and clonal lineage within Ab repertoires. There was no consensus, and some discussion as to the practical necessity of such a definition. In other words, as long as similar approaches and definitions were adopted, comparisons between repertoires of healthy and unaffected individuals, for example, should be valid. The talks showed that most of the Ab analysis pipelines, sets of programs for analyzing large-scale Ab repertoires in terms of V-gene usage or clonal expansion, were available from the developers of the tools. There was little consensus on if it would be possible to share these Ab repertoire data sets, given their large size, and the associated patient identifiers and clinical data. This is clearly a field that will be growing in size of data sets and sophistication of analysis approaches, and it was agreed that there was a need for further meetings to discuss these issues.
Specifically, the use of an universal 5' priming system results in amplification of all antibody cDNA, even cDNA with 5' variable region mutations arising from affinity maturation. Based on Atreca's plasmablast datasets, up to 10% of all identified antibodies would have not been amplified by V-gene specific primers. In addition, the barcodes enable accurate determination of clonal proportions, which in the absence of single-cell barcoding become distorted by the PCR. Further, the incorporated barcodes are used to correct PCR and NGS errors by oversampling, followed by determination of the consensus sequence, while negative control barcodes identify contaminants. This technology has thus far been primarily used to generate antibody sequences from short-lived plasmablasts, thereby yielding functional repertoires comprised of both immuno-dominant and rare clonal antibody families generated in response to recent physiological events.
Dr. Pettersson presented the selection strategy for PC-mAb, which was done in collaboration with Dyax Corp. Binding to PC, oxidized LDL (oxLDL) and cell membranes was assessed, with positive selection for PC-BSA, PC-transferrin, Cu-oxidized LDL and apoptotic cells, and deselection for linker-BSA, linkertransferrin, native LDL and native cells. Function was assessed via ox-LDL uptake in macrophages, with positive selection for molecules with better inhibition than IgM anti-PC. Antiinflammatory and anti-restenosis properties were assessed in a mouse cuff model, with positive selection for inhibition of white cell accumulation and neointima formation. Dr. Pettersson noted that recent preclinical data show that PC-mAb reduces infarction size and prevents LV remodeling after experimental myocardial infarction.
Dr. Li also noted that the antibody treatment also improved insulin sensitivity in rhesus monkey. MLDL1278A was evaluated in a Phase 1 and a Phase 2 study. In the Phase 2 GLACIER study (NCT01258907), the primary endpoint was the relative change in inflammatory activity in an index arterial vessel after twelve weeks, as measured by [18F]-2-deoxyglucose positron emission-tomography/computed tomography. No statistically significant reduction between placebo and the two active treatment arms was observed. Kai Y. Xu (University of Maryland) presented data on potent inotropic antibodies that specifically bind to the activation sites of the (Na + +K + )-ATPase (NKA) and markedly promote NKA activity, resulting in increased myocyte contraction in vitro and generating positive inotropic effect in mouse heart in vivo.
The library for deep sequencing was made in an unbiased manner using RACE technology, in which an adapter was ligated to the end of the mRNA and PCR amplification of cDNA was made with primers complementary to the adaptor and to the mu constant region of the heavy chain, Cμ. Each library consisted of a pool of ten independent PCR reactions, and six independent libraries were pooled to obtain over 17,000 unique rearrangements for analysis. This dataset allowed detailed analysis of the factors that influence the frequencies of V, D and J gene rearrangements at the IgH locus. It was observed that the two most frequently rearranging D gene segments (DFL16. 1 and DQ52) are located at the two ends of the ~150 kb DH gene region of the IGH locus. Dr. Feeney and her colleagues had previously demonstrated by chromosome conformation capture (3C) that the CTCF sites, which they had identified as being just upstream of the most 5' D gene, interact directly with the CTCF sites at the 3' end of the IGH locus, and with the Eμ enhancer.
With the current deep-sequencing data set, it was clearly observed that V genes are rearranging at very different frequencies throughout the IGH locus. The VH dataset allowed a detailed bioinformatic analysis of the factors that could impact V-gene accessibility, and therefore gene rearrangement frequency. ChIP-seq data for several histone post-translational modifications has already been done in the Feeney lab. They also published the first RNA-seq analysis (i. e. , deep sequencing analysis of total RNA) of pro-B cells, revealing all of the non-coding "germline transcription" throughout the IGH locus. It had been proposed over two decades ago that one role of this ncRNA was to make the regions being transcribed accessible to VDJ gene rearrangement. In order to determine how the local transcriptional environment and the local epigenetic environment influence V-gene rearrangement, the number of ChIP-seq or RNA-seq reads aligning to a 2.
Somatic mutation levels in VH regions of memory B cells and plasmablasts were higher in HIV+ individuals compared to healthy controls (6-10/VH in HIV-positive versus 1-2/VH in healthy individuals, respectively). The results of B-cell phenotype analysis supported previous studies in showing that, compared to healthy controls, HIV-infected individuals have perturbations in the distributions of naïve B cells (reduced), resting and activated memory B cells (elevated) and plasmablasts (elevated);  69, 70  however, NGS of these subsets showed no difference between the HIV+ and healthy individuals on VH gene usage, or CDR-H3 length in the VH repertoire of any B-cell subsets. In addition, there was weak or no evidence of increased IGHV1-69, IGHV5-51 or IGHV4-34 usage among HIV+ individuals. Overall, the data suggest that VH repertoire diversity among memory cells expands by somatic mutation driven by with non-specific immunomodulatory mechanisms associated with chronic infection, whereas features observed both for bNt mAbs and for weakly or non-Nt mAbs cloned from chronically infected HIV+ individuals, such as very heavy somatic mutation levels, long CDR-H3s and biased VH gene usage, may be driven by prolonged antigenic stimulation associated with persistent infection.
Dr. Lachowicz then presented data for ANG4043, which is a chemical conjugate of trastuzumab with Angiopep-2. The conjugation does not affect target affinity, and ANG4043 retains anti-proliferative efficacy in BT-474 breast cancer cells (IC 50 = 4. 99 +76 for trastuzumab and 3. 16 +97 for ANG4043). In a PK study in C57 mice, trastuzumab and ANG4043 demonstrated a similar profile. T1/2β was 11. 4 and 8. 5 days, Cl was 0. 43 and 0. 97 ml/day, and Cmax was 166 and 91. 5 μg/ml for trastuzumab and ANG4043, respectively. In vivo studies in mice showed that animals treated with 15 mg/kg ANG4043 had intracranial BT-474 tumor shrinkage, as assessed by near-infrared imaging. Dr. Lachowicz concluded by noting that ANG1005, a conjugate of paclitaxel with the Angiopep-2 peptide, is undergoing evaluation in Phase 2 studies of patients with glioma, non-small cell lung cancer with brain metastases or breast cancer with brain metastases.
Due to the above findings, ABT-736 development was stopped. To identify the cross-reactivity partner, an ABT-736 immunoprecipitation of cynomolgus monkey serum was performed. The immunoprecipitate was analyzed by silver-stained SDS-PAGE and revealed a specific cross-reactivity partner of ~8 kDa. The molecular weight was further specified with 8,144 Da by MS. This mass could be assigned by database searches to the cytokine platelet factor-4 (PF4), which was confirmed by positive anti-PF4 Western blot staining and ESI-MS-MS peptide sequencing of the ABT-736 immunoprecipitate. Physiologically, PF4 acts as a strong chemo-attractant for neutrophils and fibroblasts and also neutralizes the anticoagulant effect of heparin. Pathophysiologically, PF4 acts as antigen in heparin-induced thrombocytopenia (HIT), an autoimmune reaction that occurs upon heparin administration, resulting in thrombocytopenia and a worsening of the thrombosis.
The approach consists of creating a multifunctional MISIIR/MISIR antibody that would impart anti-ALK2/3 (MISIR) specificity onto the light chain. This involves redesign of the irrelevant light chain using computer algorithms to identify docking combinations of the light chain CDRs that bind ALK2 or ALK3. Dr. Adams concluded that protein design represents a promising approach for developing antibodies against difficult to target functional antigens. Roland L. Dunbrack Jr. (Fox Chase Cancer Center) presented new methods for computational design of antibodies. Dr. Dunbrack first explained that several different antibody design scenarios are possible, depending on the available data. A database of clustered antibody CDRs from known structures was generated providing germline assignment for PDB antibody structures.
88 He showed basic principles for antibody design using the structure and sequence information from CDR "canonical" clusters from the PDB database with a two-stage computational design: Stage 1 is called GraftDesign and allows CDR sampling, grafting and docking based on input "rules" for CDR lengths and clusters. Stage 2 is called SequenceDesign and allows antibody-specific sequence design of top-scoring hits from GraftDesign using observed sequence variation in antibody structural clusters. This intensive structural bioinformatics analysis of antibodies enables computational design to improve the affinity of a given existing Ab/Ag complex, switching affinity to a homologous antigen, and adding new CDR contacts in one mAb by grafting longer CDRs. Dr. Dunbrack concluded that this database of antibody CDRs from known structures could serve as a source of design templates, guiding design in a highly reliable and predictable manner.
The group used computational design algorithms in Rosetta to explore antibody sequence repertoires in order to predict immunoglobulin sequences that would exhibit capability for polyspecific binding. The resulting Rosetta designed antibody sequences identified residues critical for achieving polyspecificity. Remarkably, the computationally designed amino acid residues at positions critical for flexibility match those in the germline gene segment sequences to a high degree. Specifically, the computational experiments examined groups of somatically mutated antibodies from antigen-antibody complexes in the Protein Data Bank that were encoded by a common V H gene, but bound to different antigens. The information for the specific amino acid residues from somatically mutated positions in matched sets of antibodies was removed, and the antibody heavy chain variable regions were redesigned in Rosetta to predict antibodies that could engage the diverse antigens in the common V H panels all at once.
The Rosetta multi-state design process predicted antibody sequences for the entire heavy chain variable region, including mutated residues in framework, CDR1 and CDR2 regions. The "multi-state design" protocol predicted sequences matched the germline gene sequences to a remarkable degree, revealing by computational design the residues that are predicted to enable polyspecificity. Conversely, when Rosetta designed antibodies to bind a single antigen ("singlestate design"), the program revealed a preferred sequence similar to that of the affinity matured antigen-specific antibody. These results reveal how a limited set of antibody germline gene segments encodes immunoglobulins that bind to diverse antigens. Aspects of this work are published. 132 ric Krauland (Adimab LLC) provided an update on work at Adimab to assess the developability of therapeutic antibodies at the earliest stages of the discovery process.
The proteins are biotinylated and subjected to size exclusion chromatography to separate monomeric proteins and multi-molecular complexes. A mixture of color-coded microspheres is added to 96 fractions. After overnight incubation, the microspheres are labelled with phycoerythrin-conjugated streptavidin and analyzed with a flow cytometer equipped with a device that harvests samples directly from microwell plates. The data obtained by analysis of 24 size fractions are cumulated to generate size distribution profiles for the antibody targets. These profiles may be complex if the antibody target occurs both as a monomer and in one or more complexes. 4] [145]  Intended antibody targets are detected as reactivity peaks that are observed with all the antibodies. The utility of MAP depends on the extent and precision of protein separation and on the coverage of the antibodies in the arrays.