dataesr/scientific-paragraphs-categorization huggingface dataset:
Birefringence Analysis Birefringence was used to quantitatively assess the daily progression of dystrophy  (Berger et al. , 2012). Zebrafish were placed in tricaine (612 µM) immediately prior to imaging and then transferred to a 35mm glass bottom dish. Birefringence images were taken on a Leica MZ10 F Stereomicroscope with a Zeiss AxioCam MRm or Leica DMC5400 camera attached. An analyzer in a rotatable mount (Leica) was attached to the objective and the glass-bottom petri dish was placed on the polarized glass stage. Images were taken at the same time every day within an experiment. Imaging parameters were consistent for all zebrafish and across all days. Mean gray values were calculated using FIJI software as described previously  (Berger et al. , 2012). Briefly, the body of the zebrafish was outlined from the 6th to the 25th myotome using the "Polygon selections" tool and then the mean gray value was measured.
DanioVision Analysis The DanioVision system and EthoVision XT 13. 0 software (Noldus, Virginia, USA) was used to conduct high-throughput locomotion tracking studies to better characterize the impact of NMES on zebrafish swim function. A clear 12-well plate was placed into the DanioVision observation chamber. The temperature control unit was set to 28. 5 degrees Celsius, ensuring that the temperature of the ERM in the well plate was maintained. Zebrafish had a 5 minute acclimation period to the observation chamber prior to the beginning of the recording period. Using the EthoVision software, we created a white-light routine that included 5 minutes in the dark followed by two light-on/off cycles, where the white light turned on at 100% intensity for 5 minutes and then turned off for 5 minutes.
Image Analysis All images were blinded using a Perl script prior to analysis. The percent of myotomes with muscle fiber detachments was calculated manually by counting the number of muscle segments with visibly detached fiber(s). Muscle segments are defined as half myotomes. Additionally, we used machine learning to identify healthy versus unhealthy muscle fibers. For these analyses, we used MATLAB to implement a deep learning approach to segment images of phalloidin stained fish into healthy muscle, sick muscle, and background. We used the DeepLab v3+ system with an underlying Resnet18 network  (Chen et al. , 2017). We defined the ground truth dataset manually using  LabelBox (labelbox. com). Training images and ground truth images were broken down into 256 x 256 pixel images for training.
The training dataset was divided into 60% training, 20% validation and 20% test data. Median frequency weighting was used to balance the classes. Each fish was oriented such that the head of the fish would be at the left of the image. Data was augmented to translate the images by 10 pixels vertically and horizontally. Rotation was found to make the network less accurate as orientation angle of the muscle fibers relative to the body orientation is important to assessing their health. The stochastic gradient descent with momentum (SGDM) optimizer was selected with 0. 9 momentum. The maximum number of epochs was 100, and the mini-batch size was 8. In every epoch, the training dataset was shuffled. The number of iterations between evaluations of validation metrics was 315. The patience of validation stopping of network training is set up to 4.
The initial learning rate used for training was 0. 001. The learning rate was dropped 0. 3 fold piecewise during training every 10 epochs. The Factor for L2 regularization (weight decay) was 0. 005. The training set reached an accuracy of 97%. Images were then segmented by the MATLAB semanticseg command, which produced 8-bit unsigned integer segmentations. The fraction of each fish that was determined to be healthy was reported as a fraction of the total muscle. Pixels determined to be background (i. e. not muscle) were excluded from this calculation. For NMJ analyses, we used a method previously published by our laboratory  (Bailey et al. , 2019). To prepare images for analysis, a custom Fiji macro was written to keep image processing consistent throughout all experiments. First, the raw.
lif file was opened in FIJI and the image was split into its respective channels (phalloidin, AChR, and SV2). The phalloidin channel was immediately saved as a. tif file and closed. For the AChR and SV2 channels, duplicate z stacks were created and a 10-pixel radius Gaussian blur was applied. These blurred images were then subtracted from their original images, respectively. The resulting images were then merged to a single image and a maximum intensity projection was generated. This maximum intensity projection was saved as a. tif file and closed. For each experiment, the maximum intensity projections were combined into a single. tif file using a custom MATLAB script. This combined. tif file was then opened in FIJI and three separate masks, marking the fish body, horizontal myoseptum and myoseptal innervation, were drawn on the projected images using the Pencil tool.
These masks were used to define individual muscle segments, where each muscle segment represents half of a single myotome. Using a custom MATLAB script, skeleton number and skeleton length were calculated for each muscle segment across all zebrafish analyzed. Muscle nuclei were analyzed using FIJI's 3D Objects Counter as well as the Measure tool. To prepare images for analysis we first reduced background noise by duplicating the z stack, performing a 10-pixel Gaussian blur on the duplicated image, and subtracting the blurred image from the original image. We then performed a 1-pixel Gaussian blur on the resultant image and set a threshold using 'max entropy' setting. With this image, we used the Analyze Particles tool to generate masks to use with the 3D Objects Counter tool as well as the Measure tool.
Final library quality and quantity were analyzed by Agilent Bioanalyzer 2100 and Life Technologies Qubit3. 0 Fluorometer. Each library was sequenced using 150 bp paired-end reads using an Illumina HiSeq4000 (Illumnia Inc. , San Diego, CA). Analyses of RNA-Seq reads were completed on the Advanced Computing Group Linux cluster at the University of Maine. To determine the quality of the RNA sequencing reads before further processing, FastQC version 0. 11. 7 was utilized. Following this quality assessment, reads were concatenated tail-tohead to produce one forward FASTQ file and one reverse FASTQ file for each sample. These FASTQ files were then trimmed of adapter sequences, and low quality leading and trailing ends were removed using Trimmomatic version 0. 36. 0  (Bolger et al. , 2014). Trimmed paired-end reads mapped to the Ensembl-annotated zebrafish transcriptome (Ensembl version 95) to generate read counts per gene using RSEM version 1.
cs. technion. ac. il/). For this analysis, the entire set of expressed genes were used as a background. In cases where GOrilla found no enriched terms, PantherDB's overrepresentation test on Biological Processes (http://pantherdb. org/) was used. Again, the entire set of expressed genes list was used as the background, and results were evaluated using Panther's Fisher's Exact Test and p-values were adjusted for multiple testing using FDR. Ensembl gene IDs were mapped to gene symbols and names using zebrafishMine's Analyse feature (http://www. zebrafishmine. org/). In some cases, manual mapping was used by comparing Zfin. org gene search and Ensembl gene search results. Summarized gene expression data are available at the Gene Expression Omnibus (accession number GSE155465), and FASTQ files are available at the Short Read Archive (accession number SRP274405).
Statistical Analysis All statistical analyses were performed in Graphpad Prism. Normality was first assessed for all data using the Shapiro-Wilk test. If data passed this normality test, an unpaired two-tailed t test was performed between two data sets (i. e. , dmd mutant control versus dmd mutant eNMES) while an ordinary one-way ANOVA was performed followed by a Tukey's multiple comparison test between three data sets (i. e. , WT sibling control versus dmd mutant control versus dmd mutant eNMES). Conversely, if data failed the Shapiro-Wilk normality test, a Mann-Whitney U test was performed for comparing two data sets while a Kruskal-Wallis test was performed for comparing three data sets. Significance for all tests was set to p < 0. 05.
RESULTS Longitudinal studies indicate that muscle structure, degeneration, and regeneration in dmd mutant zebrafish are variable The zebrafish model of DMD has been used extensively in studies elucidating the underlying mechanisms of disease as well as screening for potential therapeutic compounds  (Maves, 2014). We found that dmd mutant zebrafish exhibit some phenotypic variation. We used longitudinal birefringence imaging to track muscle structure and DanioVision to analyze swimming activity (as one metric of muscle function). We took two approaches to improve consistency in the data. For a given experiment, all birefringence imaging and DanioVision sessions were conducted at the same time every day. All birefringence data were also normalized to the average WT birefringence in each imaging session (Fig.
0191) and pNMES (n = 12 control, n = 20 pNMES; p = 0. 0033) resulted in fewer fibers degenerating compared to control dmd mutants (Figure  6C2  and F2 ). Taken together, the above data indicate that eNMES and pNMES improve muscle structure in dmd larvae. We hypothesized that improved muscle structure would correlate with improved function. We tested this hypothesis by assessing swim activity as a gross readout of muscle function. Swim activity was tested using DanioVision. Swimming activity was recorded for 25 minutes with alternating 5 minute light/dark periods. As predicted, eNMES resulted in increased distance (n = 7 control, n = 11 eNMES; p < 0. 0001) and mean velocity (n = 7 control, n = 11 eNMES; p < 0. 0001) compared to control dmd larvae (Figure  7A4  and B4 ). Surprisingly, though, pNMES negatively affected swimming activity (for both total distance and mean velocity: n = 20 control, n = 19 pNMES; p < 0.
Indeed, the percent of muscle segments with detachments trends lower in dmd mutants that completed eNMES compared to control dmd mutants (Figure  11D4 ). The most striking difference in appearance between the control and eNMES dmd mutants after the hard stimulation was the improved organization of muscle fibers in eNMES treated dmd mutants. Whereas control dmd mutants had lots of disorganized fibers (red arrow, Fig. 11D2a ), eNMES dmd mutants had more organized fibers (green arrow, Fig. 11D3a ). We used machine learning to quantify overall muscle health. This approach showed that dmd mutants that completed eNMES had a significantly higher percentage of healthy muscle compared to control dmd mutants (n = 21 control, n = 22 eNMES; p = 0. 0496) (Figure  11D5 ). Taken together, these data indicate that eNMES-treated dmd mutants can withstand contraction-induced injury better than dmd mutant controls.
(C) Selected differentially expressed genes in dmd mutants following eNMES to show how expression levels return to those of WT controls. reduced in dmd mutants that complete eNMES training compared to dmd mutant controls. For this analysis a muscle segment was defined as half of a myotome. (D1b, D2b, D3b) Machine learning was used to quantify muscle health pixel-by-pixel. Green indicates healthy pixels while red indicates unhealthy pixels. (D5) The percent of healthy muscle following the hard stimulation is significantly higher in dmd mutants that completed eNMES compared to dmd mutant controls. All data were analyzed using two-sided t tests. * p < 0. 05.
Acknowledgements The authors would like to thank  Drs. Sharon Amacher  and  Jared Talbot  for developing the transgenic 3MuscleGlow zebrafish and sharing this valuable tool with us;  Dr. Joy-El Talbot  at  Iris Data Solutions  for her expertise in RNAseq analysis;  NVIDIA Corporation  for donating the Quadro P6000 used for deep learning analyses;  Keegan Kilroy  for designing the NMES gym and assistance with designing the.
Methodology The main goal of this paper is to examine how ML and AI technologies are leveraged to improve business strategies, through the creation of a taxonomy of applications used to solve marketing-specific needs. We have first collected a large number of use cases, following a systematic search process. Given the broad range of possible applications that we could consider, we based our choice on a set of four specific selection criteria: we collected only (1) real-life use cases from existing companies, (2) with the availability of references on the case, (3) from published works in business or scholar literature, and (4) with some information about the ML implementations (such as the class of algorithms that have been adopted). We conducted a systematic, wide-ranging research of literature on Scopus and Google Scholar bibliometric databases.
Walmart's Social Genome Project for example enables the monitoring of public social media conversations to get insights on people's preferences and predict future trends  (Marr, 2016). On the consumer sensing side, companies leverage similar technologies to go beyond traditional interview-based information and incorporate unstructured consumer data, in order to gain a deeper understanding of the consumer's needs and wants. In addition, when consumers interact with AI (e. g. , conversational bots), computer vision and deep learning techniques can detect their emotions from facial expressions, body gestures, voice, and eye movements  (Campbell et al. , 2020) , providing companies with richer insights about consumers preferences. The software company Autodesk for instance monitors and tracks every interaction of the consumers with their product, allowing them to have a better consumer understanding, and to provide updates or solve problems at any time  (Marr, 2016).
Computation time must then be negligible relatively to the time of completion of a tour in order to be consistent with a real-time decision support tool. However NP-completeness in the strong sense problem's are expected to become intractable when the problem size grows even for moderately small instances. Furthermore we have observed in [? ] that the Mixed Integer Program with Quadratic terms (MIQP) we developed indeed has exponential computation time, which explains the choice that was made to solve this problem through heuristic algorithms. In this paper we present the formulation of the model as a MIQP but as stated above, an exact method is not suited to answer the real-life problem in terms of computation time. However this formulation allows to characterize a solution to the problem in order to confirm the results that heuristics algorithms produce and verify that the constraints of the model are verified.
V. DATA ANALYSIS With the objective of reproducing real-life experiment to validate the model and heuristics, data has been extracted from Experience Feedback from rescue teams of SDIS 31. SDIS 31 is responsible for the rescue operations for Haute-Garonne Department in South of France. We conceived the model to fit their logistic and therefore the priority categories that they usually use. The values of the priority coefficients are fixed to: 1) Can remain on the spot : 1 2) Have to be rescued within 12 hours : 2 3) Have to be rescued within 6 hours : 4 4) Need to be rescued in emergency : 10 These values have been arbitrary picked to represent the relative importance of each priority categories one from another. They can be adapted consequently to discussions with the rescue teams in order to fit the situation requirements.
Introduction AlphaFold2  [1]  and RoseTTAfold  [2]  have recently achieved an impressive breakthrough in the field of structural biology, providing accurate models of three-dimensional (3D) structures of proteins based on only knowledge of their amino acid sequences alone. Based on deep-learning techniques, they take advantage of the vast existing knowledge of protein sequences and 3D structures, recently expanded through environmental genomics and structural genomics approaches. In particular, they extensively used evolutionary information to detect co-variation of residues (or correlated mutations), the underlying idea being that residues that have co-evolved are close in 3D space. The first version of the AlphaFold2 database (AFDB v1)  [3]  included predictions for a very large part of proteomes from 21 widely studied organisms.
High values of pLDDT are observed for folded domains, contrasting with low values typically associated with linkers and unstructured or disordered regions  [11]. The relevance of pLDDT as a predictor of disorder has been supported on the CAID benchmark dataset  [12]  and compared to other state-of-the-art disorder predictors, such as SPOT-Disorder2 or IUPred2  [1, 4, 11]. At least two questions need to be considered when focusing on very low confidence regions (pLDDT < 50) in AF2 predictions, which are assumed to be globally disordered. The first question is whether it is possible to reveal conditional order within these intrinsically disordered regions (IDRs), from amino acid sequence information alone. Such IDRs may be involved in molecular recognition, to which hydrophobic interactions make major contributions  [13, 14].
This hypothesis is conceivable as the co-evolutionary information, necessary to predict inter-residue contacts, is lacking for some protein sequences. These proteins, not predicted as disordered, escape any annotation coming from sequence or structure databases and constitute the dark proteome  [30, 31]. They still represent 10% of the human proteome after annotation with the AlphaFold2 predictions  [7]. We recently analyzed the pLDDT values observed for the AF2 3D structure predictions on the 21 reference proteomes (AFDB v1) in light of another metric, called the HCA score (Bruley et al. [32] ). The HCA score is based on Hydrophobic Cluster Analysis (HCA), a two-dimensional approach allowing the analysis of the content of an amino acid sequence in regular secondary structures (see  [33]  for a recent review of the methodology).
Particular focus was on segments of length > 30 amino acids, which corresponds to the minimum length considered for globular domains  [38]. Moreover, this minimal length excludes a large number of short motifs (SLiMs) undergoing induced folding and which are otherwise associated with higher HCA score values (Bruley et al. [32] ). To analyze these long, soluble-like full-VL segments, we considered four features related to their amino acid sequences and 3D structures, as predicted by AF2. We described these 3D structure models by the proportion of residues involved in a regular secondary structure (RSS) and by the proportion of residues accessible to the solvent. In addition, we described the protein sequences on which these predictions were based, by the proportion of residues predicted as disordered by IUPred2 and by the average number of homologs per residue as found in the large environmental BFD database.
Proteomes from AlphaFold Protein Structure Database v1 Amino acid (aa) sequences and predicted 3D structures were downloaded from the AlphaFold Protein Structure database (AFDB) v1 (  [3] , https://alphafold. ebi. ac. uk, accessed on 21 July 2021) for the 21 reference model-organism proteomes. The per-residue model confidence values (pLDDT) were extracted from the 3D coordinate files (B-factor column in PDB format).
Delineation of Soluble-Like Foldable Segments within Protein Sequences The segment function of the pyHCA tool (provided at https://github. com/DarkVador-HCA/pyHCA, accessed on 14 September 2022) was used to automatically delineate foldable segments (FS), i. e. , segments with a high density of hydrophobic clusters (HC), as defined by the Hydrophobic Cluster Analysis (HCA)  [33]. HC consist of strong hydrophobic amino acids (V,I,L,M,F,Y,W) and are separated from each other by at least four other amino acids or a proline. For FS delineation, cysteine (C) is integrated into the hydrophobic alphabet and HC consist of only one or two consecutive hydrophobic amino acid(s) are not considered, as they are mainly associated with coils  [36]. The HCA score, which measures the density of hydrophobic clusters and strong hydrophobic amino acids of foldable segments (Bruley et al.
Description of Sequence and Structural Features Our final dataset consisted of proteins from AFDB v1, encompassing at least one long (>30 a. a. ), globular soluble-like (-1 ≤ HCA score ≤ 3. 5) foldable segments, entirely made of residues with very low (VL) or very high (VH) 3D prediction confidence (pLDDT ≤ 50 and pLDDT > 90, respectively). We considered four different features to characterize the amino acid sequence and AF2 3D models of these segments, as detailed below. For each feature, we defined a threshold value based on the distribution of these full-VH segments and delimiting an interval encompassing at least 95% of them. These threshold values were further used for the dataset description by binary trees.
Known Homologs The multiple sequence alignments used to build the AlphaFold2 models were not provided in AFDB repositories. Therefore, a search for known homologs in the reduced Big Fantastic Database (BFD) was performed using jackhmmer (from HMMER 3. 3. 2 [40] , http://hmmer. org/, accessed on 14 September 2022). The parameters (e-value threshold of 0. 0001, 1 iteration) were those used by AF2 in the similarity search step. The Big Fantastic Database (BFD)  [1]  (https://bfd. mmseqs. com, accessed on 24 May 2022) is a database containing 2. 5 billion clustered protein sequences. It is the most comprehensive database used by AF2 in order to build multiple sequence alignments, gathering sequences from genomic and metagenomic databases (UniprotKB  [41]  and metaclust  [42]  and datasets assembled with Plass  [43] ).
The reduced version of BFD contains only representative sequences of each cluster (65,984,053 sequences). This one was downloaded following the recommendations given on the AF2 github (https://github. com/deepmind/alphafold, accessed on 24 May 2022). In this work, the sequence similarity search was performed on the whole protein sequences. The number of aligned sequences per FS position was then calculated and averaged over the length of the FS. The mean number threshold was set to 23. 5 BFD homologs per segment residue. The number of FS with a value above this threshold were as follows: 3347 segments over 30,644 and 10,829 segments over 11,395 in case of full-VL and full-VH, respectively.
Secondary Structure Assignment Secondary structures were assigned from the coordinates of the AF2 3D structure models (PDB files, full-length proteins) using the DSSP program  [44]  available in the biopython module v1. 78 for python v3. 6. 3. All amino acids found in alpha helices (encoded as "H" in DSSP), 3-10 helices ("G"), Pi helices ("I"), strands ("E"), and isolated beta-bridge residues ("B") were considered to participate in regular secondary structures (RSS). The percentage of the FS residues participating in a RSS was then calculated. The number of FS with at least 1 RSS were as follows: 10,993 segments out of 30,644 and 11,393 segments out of 11,395 in case of full-VL and full-VH, respectively.
Solvent Accessibility Using the same module, the residues relative accessible surface area was calculated. This value was obtained by normalizing the residue accessible surface area (ASA) by the maximum ASA for the residue, computed on Gly-X-Gly tripeptides (where X is the residue of interest). By default, DSSP referred to the Sander and Rost scale for maximum ASA values per residue  [45]. We considered a residue to be solvent accessible if the relative ASA was above 0. 36 (based on Rost and Sander  [45] ). The percentage of accessible residues was calculated on each FS. The feature threshold was set to 82. 9%. The number of FS with a value below this threshold were as follows: 1908 segments over 30,644 and 10,823 segments over 11,395 in case of full-VL and full-VH, respectively.