dataesr/scientific-paragraphs-categorization huggingface dataset:
Design of inhibitors Lymphocyte function-associated antigen-1 (LFA-1) is an integrin that plays a crucial role in antigen-specific responses  [53]  by recognizing intercellular adhesion molecule 1 (ICAM-1) (1MQ8, PPDBv4). By using SC1, JET 2 successfully predicted ICAM-1 binding site of LFA-1 (Sens = 67% and PPV = 48%). Another patch was predicted by using SC3 and the overlap between the two patches is 50%. We could identify five residues in the SC3-predicted patch that are not involved in the interaction with ICAM-1 but are found in the interface with Efalizumab (PDB code: 3EOA) (S7 Fig). Efalizumab is an antibody drug used in the treatment of psoriasis which inhibits the association between LFA-1 and ICAM-1  [54]. This example suggests that JET 2 , by exploiting surface local geometry and physico-chemical properties, could be useful for identifying putative sites at the surface of therapeutic targets.
This opens new perspectives for the design of highly specific drugs These examples illustrate the predictive potential of JET 2 to detect multiple interactions involving different partners of a protein. By manually inspecting the predictions and looking for additional experimental data reported in the literature, we could assess JET 2 predictive power beyond the interactions present in the studied datasets. It is likely that the increasing availability of experimental structures of protein complexes will further validate JET 2 predictions in the future. Let us stress however that many aspects of protein regulation (e. g. post-translational modification, differential expression. ), which are out of the scope of this work, may also influence protein binding specificity. We have carefully compared the performance of JET 2 with state-of-the-art methods for predicting protein-protein interfaces based on machine learning algorithms.
Datasets of protein-protein interactions Two datasets were used to analyze experimental protein interfaces and evaluate JET 2 performance: the Huang dataset of 62 protein complexes  [20]  which was previously used to assess JET performance  [39] , and the Protein Protein Docking Benchmark version 4 (PPDBv4) of 176 protein complexes  [41]  (S1 Table ). The Huang dataset comprises 41 homodimeric chains, 24 heterodimeric chains and 19 transient chains. PPDBv4 proteins can be grouped in four functional classes-antibody-antigen  (26) , bound antibody-antigen  (24) , enzyme-inhibitor (104) and proteins with other functions (198). PPDBv4 collects both their free and complexed states. Experimental interfaces were defined using the complexed conformations while JET 2 predictions were performed on the free conformations.
JET 2 workflow The JET 2 method requires as input a protein query sequence for which three-dimensional structural coordinates are available in the Protein Data Bank (PDB)  [56]. In a first step, JET 2 determines the conservation level (T JET ) of every amino acid in the query sequence. The calculation is performed on a set of homologous sequences (ideally 100 or more). This step, essentially unchanged compared to JET, was extensively described in  [39]. In a second step, JET 2 detects and extends putative binding patches at the surface of the three-dimensional structure of the query protein. For this, residues are scored using a mixture of three descriptors: the evolutionary traces (T JET ) computed in step 1, interface propensities (PC) specific to each amino acid and circular variances (CV) computed from the protein 3D structure and representing the density of protein around each residue.
Expected size of the interface The expected size of a protein interface is computed as f intfrac (N) = (26. 54/N) + 0. 03, where N is the number of surface residues  [39]. The parameters in this analytical expression were determined in  [39]  based on a dataset of 1256 protein chains collected from the PDB  [62]. The function also approximates well the experimental data from PPDBv4 (S8 Fig). f intfrac (N) is used in JET 2 clustering procedure to define two thresholds (Table  2 ). The score CUT  res threshold is the score determined with a confidence level of 2 × f intfrac (N) on the distribution of score values. It is used to select appropriate residues for constructing the clusters. The score CUT clus threshold is the score determined with a confidence level of f intfrac (N)/x, where x values 2 or 4, on the same distribution.
Iterative mode For a given query protein and a fixed set of homologous protein sequences, different independent runs of JET 2 may produce slightly different results (typically differing by a few amino-acid residues). This is due to the heuristics employed to compute evolutionary traces T JET and to filter out small cluster seeds (see  [39] ). To get more robust predictions, JET 2 can be run multiple times, in an iterative mode of the program which we call iJET 2. For each residue, we count the number of runs where it was detected in a cluster and divide it by the total number of runs. The resulting number is comprised between 0 and 1 and corresponds to the probability of each residue to be predicted at an interface. In order to define interaction patches, we set up a threshold from which we get a consensus prediction.
Geometry-based clustering of interface residues Multiple recognition patches within protein experimental interfaces from the Huang dataset and PPDBv4 were detected by applying hierarchical clustering to interface residues using the average linkage method, as shown in  [25]. Calculations were performed with the R software  [63]. A threshold distance of 20Å enabled to obtain values similar to those reported in  [25]. However, contrary to  [25] , for each complex we consider the two interfaces from the receptor and the ligand individually. The two datasets show very different trends: while the majority of the proteins from all functional classes of PPDBv4 contain one single recognition patch, more than 70% of the homodimers, heterodimers and transients from the Huang dataset have multipatch interaction sites, containing up to 7 patches (S16 Table ).
Comparison with other tools The Multi-VORFFIP webserver  [64]  was used to predict binding sites on the proteins from PPDBv4 and Huang. The proteins listed in the O333 dataset  [65] , which was used to train the method, were excluded. Given a query protein, Multi-VORFFIP gives four predictions corresponding to putative interfaces with a protein, DNA, RNA or a peptide. We considered only predictions related to protein-protein binding sites. The prediction consists in two scores assigned to each residue of the query protein: a raw score and a normalized score (or probability). Following the recommandations of the authors of the method, we used the normalized score and set up a threshold of 0. 5, which seemed to yield the best balance between sensitivity and precision. The PredUs web server  [47]  was also used to predict binding sites on the proteins from Huang and PPDBv4.
This work was supported by the  MAPPING  project ( ANR-11-BINF-0003 ,  Excellence Programme "Investissement d'Avenir" in Bioinformatics ); the  Institut Universitaire de France ; access to the  HPC resources of the Institute for Scientific Computing and Simulation at UPMC  was provided by  Equip @ Meso  project  -ANR-10-EQPX-29-01 ,  Excellence Program  " Investissement d'Avenir ". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The response of an individual B. bairdii was similar to that of the other two species, but was of shorter duration after sound transmission ceased  [11]. Responses of individuals tagged with high-resolution DTAGs were consistent with larger scale trends in movement  [4]  and echolocation behaviour of whales during actual exercises on the AUTEC range  [4, 10]. These small but crucial datasets indicate that beaked whales are particularly sensitive to sonar, supporting the suggestion by Cox et al. [3]  that behavioural responses may be an important factor leading to their stranding. Thus far, such studies have been carried out exclusively in the vicinity of navy underwater training ranges, and the high level of sonar activity in such areas is clearly an important contextual feature limiting our ability to predict responses of beaked whales in more pristine habitats.
Material and methods We conducted a controlled exposure of naval sonar to an aggregation of bottlenose whales in which one individual was tagged with a sound and movement-recording datalogger attached to the whale by suction cups (DTAG  [23] ). The protocol for sonar exposure specified that the sonar source was to start transmissions at a planned distance of approximately 1 km from the tagged whale after at least 8 h of baseline tag data were recorded. The tag was programmed to release after being attached to the whale for 18 h. During the sonar exposure, the source vessel (RV HU Sverdrup II) followed a pre-determined course, independent of the movement of the tagged whale or other whales, while towing a sonar sound source at 90-100 m depth. The sonar transmission consisted of one hundred and four 1 s duration 1-2 kHz hyperbolic upsweep pulses at 20 s intervals.
Estimated speed was combined with pitch and heading data to estimate a dead-reckoned track of the whale  [23, 28]. Sonar arrival times were marked in the audio record by cross-correlation with the transmitted waveform. Behaviour of the tagged whale during the pre-exposure baseline period was summarized using two sets of quantitative variables calculated at a common sampling rate of 5 Hz. The first set of variables, movement parameters, was designed to detect predicted movements during avoidance and consisted of dive profile wiggliness (proportion zero crossings in the first difference of the depth time series), whale heading (decomposed into sine and cosine components) and variability of animal pitch and heading. The second set, energetic parameters, was designed to track locomotion effort of the whale and consisted  of overall dynamic body acceleration (ODBA  [29] ) and pitching movements relative to the body axis.
To compare dives of the exposed whale with those of other tagged northern bottlenose whales, we analysed data from eight other tag records obtained from the Gully, eastern Canada, and five other Four Gully records used Little Leonardo 3MPD3GT loggers  [30]  and two were time-depth recorder data previously reported by Hooker & Baird  [20]. All dives greater than 10 m depth were extracted from timedepth records. Dives were segmented into three phases: descent (from the surface until first excursion greater than 85% of maximum depth), ascent (from the final excursion greater than 85% of maximum depth to surfacing) and bottom (from end of descent to start of ascent); we also measured the surface interval after each dive (from surfacing until the start of the next dive)  [31]. For each dive across all tag types, basic dive parameters were calculated, including: maximum depth, dive duration, descent rate, ascent rate, bottom duration and surface interval  [31].
For tag types including acceleration sensors (DTAGs and 3MPD3GT), kinematic dive parameters included ascent pitch, descent pitch, variability of heading, variability of pitch and ODBA. ODBA values were normalized by the whale-specific median value to help account for the effects of tag locations on the animals. Dives were clustered into shortshallow or long-deep dive types by k-means clustering based upon dive duration, maximum depth, and descent and ascent rates. For basic dive parameters, all dives by all tagged whales were included in the analysis: 477 shallow dives and 79 deep dives. For the kinematic dive parameters, only data from whales tagged with DTAGs and 3MPD3GTs were included: 379 shallow dives and 57 deep dives. The Mahalanobis distance between each dive and the dive-type-specific mean value were calculated for both basic and kinematic dive parameters.
Discussion The results of our experiment demonstrate that northern bottlenose whales may be highly sensitive to disturbance by underwater noise, with a scale of behavioural response at least as strong as that exhibited by other beaked whales  [4, 7]. The behavioural response of the tagged animal was typified by a strong change in swimming and diving behaviour starting at received levels (SPL) of 107 dB re 1 µPa, indicating that its movement was driven by response to the sonar signals received up to that point rather than part of natural functional behaviours. Deep dives before the sonar exposure had variable movements and were characterized by production of foraging clicks and buzzes, typical of the deep feeding behaviour of other tagged northern bottlenose whales in our baseline dataset from Jan Mayen (table 1 and figure  4 ) and other beaked whales  [21].
Data accessibility. Supporting data are available from dryad. org. Authors' contributions. P. M. , P. K. , F. L. and P. T. jointly conceived and designed the study. P. M. , P. K. , F. L. , C. C. , L. K. , L. S. , S. v. IJ. , F. V. , P. W. , L. M. L. , T. N. and S. H. acquired data during fieldwork. P. M. , C. C. , S. D. , L. S. , S. v. IJ. , F. V. , P. W. , A. v. B. , L. M. L. , F. L. , T. N. and S. H. analysed data and interpreted results. P. M. and S. H. drafted the first version and all authors revised the article. All authors gave final approval for publication.
The ANCOVA, LOCF analysis was used as a sensitivity analysis with no direct or indirect comparison of the 2 analysis methods. The standardized mean differences to placebo interpreted as Cohen's d standardized effect size (SES) were used (using the standard 0. 2 threshold for clinical relevance, based on the research of Leucht et al  69  ). This method allows a comparison of the magnitude of the effect sizes between studies and the different assessment tools. All analyses were performed by using SAS version 9. 1 (SAS Institute, Inc, Cary, North Carolina).
The clinical relevance of the results was supported by the SES, which exceeded the standard 0. 2 threshold (vortioxetine 5 mg, SES of 0. 22 [range in the individual studies (Table  II ), 0. 06-0. 38]; vortioxetine 10 mg, SES of 0. 42 [range in the individual studies, 0. 35-0. 49]; FAS, MMRM) (Table  III ). The ANCOVA, LOCF analyses of the individual studies produced results similar to those in the MMRM analyses, with the exception that the 5-and 10-mg doses did not separate from placebo in study NCT00839423. The ANCOVA, LOCF meta-analysis yielded results similar to the MMRM meta-analysis.
HSQ-12 Scores The HSQ-12 was included in the dedicated elderly study (NCT00811252) to assist in understanding the functional benefits of vortioxetine in elderly patients with MDD. In the MMRM analyses of the mean change from baseline in HSQ-12 domain scores at week 8, vortioxetine 5 mg demonstrated a statistically significant separation from placebo on the domains of health perception (change of 10. 35; P o 0. 0001; SES of 0. 54; n ¼ 137), mental health (change of 7. 94; P o 0. 001; SES of 0. 44; n ¼ 137), and energy (change of 6. 39; P o 0. 05; SES of 0. 28; n ¼ 137). The SES supported the results of the MMRM analysis, with all separations from placebo in favor of vortioxetine. Analysis of the results using ANCOVA, LOCF methods yielded similar results.
The patients included in the short-term vortioxetine studies in adults with MDD were generally in good physical health (as prespecified on the clinical trial inclusion criteria), and therefore no or limited improvement in the score was expected or reported. The improvement in HRQoL observed with the SF-36 MCS score is supported by the separation from placebo (P o 0. 05) at week 6/8 in favor of vortioxetine in the EQ-5D score and Q-LES-Q total and life-satisfaction scores. The effect on the Q-LES-Q and EQ-5D in NCT01140906 and NCT00839423, respectively, were supported by the SES for vortioxetine 10, 15, and 20 mg/d. These improvements in HRQoL were replicated in the dedicated elderly study, in which vortioxetine 5 mg/d separated from placebo (P o 0. 05) on the HSQ-12 subscale scores for health perception, mental health, and energy at week 8.
The overall improvements in HRQoL in the elderly populations were similar to those seen in the overall adult MDD population and are of particular importance given the prevalence and burden of depressive symptoms in this specific patient group. This evaluation of HRQoL in the vortioxetine clinical trial program does have certain limitations. Not all instruments were used in all clinical trials, with 3 of the 4 assessment tools only used in 1 study each. Furthermore, the trial designs include doses ranging from 1 to 20 mg, with only 1 trial that assessed quality of life using the 15-and 20-mg doses. In addition, the SF-36 was not evaluated in the higher dose study, and the overall negative clinical efficacy results of the NCT00672958 trial also directly reflect a lack of effect on the SF-36 MCS in that trial; this potentially confounds the perceived treatment benefit of vortioxetine on the SF-36 MCS.
ACKNOWLEDGMENTS The primary studies and subsequent analyses were sponsored by H. Lundbeck A/S  and the  Takeda Pharmaceutical Company, Ltd. The authors thank  Philip Sjostedt , BPharm, and The Medicine Group for editorial assistance. The author contributions were as follows:  Mr. Loft  was responsible for the acquisition of data;  Drs. Brignone ,  Danchenko , and  Florea  drafted the manuscript; and all authors were involved in analysis design and the statistical analysis plan, analysis and interpretation of data, and critical revisions to the manuscript.
87 3. 44 1. 35 0. 78 6. 10 0. 32 0. 011 - Vortioxetine 10 mg 8 124 14. 37 4. 94 1. 37 2. 26 7. 63 0. 46 o0. 001 - NCT00672958 Placebo 6 4. 82 (1. 16 to 8. 48) 247 11. 80 98:90 - - - - - - - Vortioxetine 5 mg 6 6. 21 (2. 41 to 10. 01) 250 12. 65 84:90 0. 84 1. 2 -1. 51 3. 20 0. 06 0. 481 - Meta-analysis Placebo 6/8 591 - - - - - - - - Vortioxetine 5 mg 6/8 604 - 2. 62 0. 82 1. 02 4. 22 0. 22 0. 001 0. 268 Vortioxetine 10 mg 6/8 2. 60 (-0. 21 to 5. 42) 328 - 126:125 4. 84 0. 88 3. 10 6. 57 0. 42 0. 001 0. 642 3. 95 (1. 11 to 6. 79) 120:125 3. 44 (0. 78 to 6. 10) 130:129 4. 94 (2. 26 to 7. 63) 124:129 0. 84 (-1. 51 to 3. 20) 250:247 2. 62 (1. 02 to 4. 22) 604:591 4. 84 (3. 10 to 6. 57) 328:344 -4 0 4 8 12 16 20 Worse Than Placebo Better Than Placebo Difference From placebo Figure 1. Meta-analysis of change from baseline on the 36-item Short-Form Health Survey mental component summary (full analysis set, mixed model for repeated measures).
Most previous studies that aimed to identify biomarkers for AD diagnosis have predominantly employed positron emission tomography (PET) and magnetic resonance imaging (MRI) approaches  13. However, new tools have now been developed which are highly more sensitive and specific and are able to detect biomarkers relevant to disease progression and the signalling pathways underlying AD. Thus, in the current study, we combined mass spectrometry-based proteomic analysis with label-free quantification for identification and quantification of proteins from AD and control samples. We dissected the CA4 and DG fields from human hippocampal tissues and used one-dimensional liquid chromatography-tandem mass spectrometry (LC-MS/MS) with subsequent database searches to identify peptides. Using this methodology, we identified previously unknown pathological mechanisms of AD.
MALDI-TOF MSI. The MALDI matrix was applied using an ImagePrep device as described above. Protein imaging was then performed on an Ultraflex II MALDI-TOF/TOF instrument (Bruker Daltonics, Bremen, Germany) equipped with a smartbeam laser (Nd:YAG, 355 nm). Protein mass spectra were acquired in linear positive ion mode within a mass range of m/z 3,000-30,000. The distance between raster points was set to 80 μ m with a total of 500 laser shots accumulated at a 200-Hz repetition rate for each pixel. Spectra were processed by baseline correction and smoothed using FlexAnalysis 3. 2 software (Bruker Daltonics, Bremen, Germany). Image analysis and data visualization were performed with FlexImaging 2. 1 software (Bruker Daltonics, Bremen, Germany). For statistical analysis, datasets obtained from FlexImaging were loaded into ClinProTools 2.
MALDI image normalization. Normalization of MALDI imaging data is not appropriate for single spectra, but instead requires the dataset as a whole. Here, we normalized data with the aim of equalizing the total ion count for all spectra to counter the effect of image 'hot spots'. One example of a hot spot occurring is when a large matrix crystal had been hit directly by the laser beam, resulting in higher spectrum intensity at this point. However, normalization has the disadvantage of artificially increasing the intensity of pure noise spectra. It is therefore possible to exclude noise spectra by defining a Ymean/ Ymax cutoff. Normalization was performed according to the following calculation: = + + … + Ymean y y y n n 1 2 2 2 2 y = y value at a point in the spectrumn = number of data points in the spectrumIf Ymean < threshold factor × maximum value of the y-axis, then the spectrum will be normalized.
Collision energy was set to 35%. Heated capillary temperature and electrospray voltage were 160 °C and 1. 5 kV respectively  62. Protein identification using label-free quantification and statistical analysis. Raw MS data from patients with AD and controls were processed for protein identification using Mascot Daemon (v. 2. 3. 2) with a peptide mass tolerance of 100 ppm and a fragment mass tolerance of ±0. 8 Da at a false discovery rate (FDR) of 1% for both proteins and peptides. All peak lists were identified with the human International Protein Index (IPI) database (v3. 87; containing 91,491 entries) with a decoy using the following parameters: enzyme: trypsin, missed cleavages: ≤2, fixed modification: carbamidomethyl (C), variable modifications: oxidation (M), and phosphorylation (S, T, Y).
Primary antibodies were incubated with peroxidase-conjugated anti-rabbit IgG (1:5000, Sigma) or peroxidase-conjugated anti-mouse I gG (1:5000, Sigma) secondary antibodies at RT. Antibody-reactive protein bands were visualized with an ECL detection reagent (GE Healthcare). Optimization for peptide synthesis and SRM analysis in the MS instrument. To perform SRM measurements, isotopically-labelled peptides were optimized with Optimizer software (MassHunter optimizer, version B. 0. 3, Agilent, Palo Alto, CA, USA) to obtain the best analysis conditions for the triple quadrupole MS instrument. The optimization process selected fragment moieties from precursor ions and set the collision energy of fragment ions in each peptide. These samples were analysed with a nanoLC chip SRM/triple quadrupole MS, coupled with a 1200 Nano HPLC-chip microfluidic device (Agilent Technologies, Santa Clara, CA, USA).
Acknowledgments This research was supported by grants from the  Ministère de L'Education Nationale, de L'Enseignement Supérieur et de la Recherche ,  Internationale Stichting Alzheimer Onderzoek  (# 09515 ; YMP, HS, IF). This work was also supported by the  Institute for Basic Science  ( IBS-R001-D1 , YMP), Koranet (2010, IF, YMP), the  Université de Lille  (IF, MS),  National Research Foundation of Korea  (2009, and  University-Institute cooperation program , YMP). This work was supported by the  National Research Foundation of Korea  Grant funded by the  Korean Government(MSIP)  ( 2014, R&D Equipment Engineer Education Program ,  2013R1A6A9067028 ).