dataesr/scientific-paragraphs-categorization huggingface dataset:
We filtered the gut and saliva microbiome datasets separately. Filtering for beta diversity analyses resulted in 282 ASVs for the fecal and 110 ASVs for the saliva dataset (Supplementary Tables  9  and 10 ). This filtering step reduced the number of reads by 31% and 9% in the fecal and saliva datasets respectively, while eliminating on average 90% and 88% of the ASVs/OTUs/genera, regardless of the clustering method and cutoff. As the compositional methods that we used to statistically analyze the data do not allow zero values, we replaced zeros utilizing the Bayesian multiplicative replacement function (cmult. repl, method = "GBM") implemented in the zCompositions R package  74. For distance-based analyses, we performed a centered log ratio (clr) transformation  75  implemented in the CoDaSeq R package  76, 77  and calculated the Euclidean distances, corresponding to Aitchison distances of raw compositional data  78.
For community type determination, we grouped the non-filtered ASV dataset at the genus level with the phyloseq "tax_glom" command  79. Statistical analysis: microbiome diversity and composition along the urbanization gradient. To examine how the human microbiome diversity changes along the urbanization gradient, we analyzed the gut and saliva microbiome separately. For alpha diversity, we fitted generalized least squares (gls) linear models with the package nlme  80  , with weights allowing different variances for each population in case of heteroscedasticity, i. e. if the Levene's test of homogeneity of variance (car package  81  ) was significant at alpha = 0. 05. To identify the taxa (ASVs and OTUs) whose relative abundances differed between the populations, we used the ALDEx2 package  76, 82.
We considered the taxa as differentially abundant only if Benjamini-Hochberg adjusted p-values of both Kruskal-Wallis and glm test were significant (p <= 0. 05). Finally, in order to identify the lineages that most predictably vary along the urbanization gradient regardless of the classification level, we performed a phylofactorization using the phylofactor package  83  , with a custom objective function (the same gls model as used in the alpha diversity analysis), F-statistic as a criterion for choosing the best edge, and KS test as the stopping function. Statistical analysis: community types. In order to classify the microbiomes into the optimal number of categories, we performed Dirichlet-multinomial mixture (DMM) modelling  84  on the genus-level data with the DirichletMultinomial package  85.
g. linearity), we used random forest classification and regression for the prediction of community types and alpha diversity, respectively. Random forest regression/classification identifies the best set of predictors, without assuming any particular relationship between the independent and response variables. We used conditional forest algorithm  87  implemented in the partykit package  88. We followed the guidelines from  48  and calculated conditional variable importance with the party package and cutoff = 0. 2  49  in order to avoid biases due to different scale of predictors and correlations between them. We used the caret package  89  to split the dataset into a training (80%) and test (20%) subsets and to find the best value for the "mtry" (i. e. available number of variables for splitting at each node) parameter.
We used the minimization of RMSE (root mean square error) as the selection criterion for regression and the maximization of Cohen's Kappa for classification. We ran ten independent iterations on each dataset to get realistic estimates of the mean and variability of the variable importance and the goodness of fit. As there is no clear cutoff for variable selection based on their importance, we applied several approaches. The most relaxed approach was the inclusion of random variables in the models: the factors with lower mean importance than the random variable with the highest value were considered unimportant. The second approach was to keep all the variables with a mean importance above the 95% quantile of all mean importance values. The final approach was to split the mean importances into two groups by kmeans clustering.
In the last two approaches, we discarded all predictors if any of the random variables was in the "important" group. We used both variable importance and goodness-of-fit estimates (including R-squared and variance explained by the model). If the proportion of the variance explained or the model accuracy was low (R-squared < 0. 1 or Kappa < 0. 3), we did not interpret the results. We analyzed potential associations between the community composition and the metadata by a distance-based approach inspired by Gloor et al. 90  and Falony et al. 25. Briefly, we performed a Principal Component Analysis on the equivalents of Aitchison distances with the package vegan  91. We found the variables significantly associated with the first three axes of variation using the envfit function. If some of the significant variables were correlated (correlation coefficient >= 0.
8, Supplementary Table  3 ), we selected the one that explained more variation in envfit and discarded the rest. We then used these selected variables as a scope for ordistep (bidirectional) variable selection. Finally, we identified the ASVs (or OTUs) whose coordinates were in the lower 2. 5% and the upper 97. 5% quantile on the first three PCA axes as well as those with the distances in the 2. 5% and the upper 97. 5% quantile from the coordinates of the selected environmental variables (calculated with the cdist function from the rdist package  92. Statistical analysis: differences between the gut and saliva microbiome. We investigated a potential correlation between the gut and saliva microbiome diversity within individual using Pearson's r coefficient, with nonparametric bootstrap 95% confidence intervals calculated with the boot package  93, 94.
Acknowledgements We thank the technical platform " Paléogénomique et génétique moléculaire " of the  MNHN at the Musée de l'Homme (Paris, France)  for their assistance with the processing of the fecal and saliva samples, as well as the  Centre Pasteur Cameroon  for their help with the parasitological analyses. This work was supported by an  ANR  grant ( MICROREGAL ,  ANR-15-CE02-0003 ), a  CNRS   INEE grant (PEPS MiOr ) as well as grants from the  Center for Microbiome Informatics and Therapeutics at MIT  and the  Rasmussen Family Foundation  to the  Global Microbiome Conservancy  (http://www. microbiomeconservancy. org).
The presence of Entamoeba sp. and smoking habits were encoded both as binary, as well as ordered factors (specifying Entamoeba sp. load and smoking intensity and duration), which resulted in a total of 25 non-dietary variables tested for correlations. After correcting for multiple testing, 403 out of  1431  ( 28% ) and 555 out of 5253 ( 11% ) pairwise correlations between these variables were significant in the complete and rural-urban dataset, respectively (alpha = 0. 05, Supplementary Table 4). Strong correlations (abs(Pearson) >= 0. 8, Cramer's V or Omega >= 0. 4) were mainly found between 24h-recall dietary variables and between habitat-related (e. g. sanitary) variables (Supplementary Fig. 1). In addition, the urbanization level was associated with some habitat-related variables, including water source, access to electricity, type of floor and presence of animals in the house.
Any discrepancy was resolved by consensus discussion between the 2 coordinators, or the whole team if necessary. Once the questionnaire was finalized, a guide was edited to standardize data collection. The research team recruited two undergraduate dental students in each Faculty involved, to gather data. After training in both presentation of the study to eligible participants and filling in the questionnaire, the students conducted the questionnaire survey at the relevant places and times identified by their local supervisor. Students recorded the anonymous responses to questionnaires on an iPad®. On return to the Faculty, they transferred the data collected for their storage in a secure, web-based program. In order to get a large sample of the general French population, adult passers-by were invited to participate in the study in public places (outside shopping centres, in city centre streets, on railway platforms).
Ethical considerations For this type of street-survey study, the approval of an Ethics Committee was not mandatory in France during the period of construction and conduct of the study (Article L1121-1 of the Code de la santé publique, corresponding period). The data were collected anonymously. Each subject was first informed orally about the study (objective, method, interest and duration) and could ask any question. He or she was also informed of the possibility to stop and/or retry at any time during his/her participation in the study, and that the data were collected anonymously. Each person who agreed to participate gave his/her informed oral consent prior to participation in the study. Each subject was compensated for the time spent by the delivery of a brushing kit.
Statistical analysis The database was accessed directly in Excel® format (Excel®11. 0, Microsoft Corp, Washington, USA) once completed questionnaires had been downloaded from the iSURVEY® application for iPad® to the internet iSUR-VEY® site (iSURVEY, Wellington, New Zealand) via an internet connection. Statistical analyses were performed using Stata®9. 0 software (Stata Corp LP, Texas, USA). No imputation on potential missing data was planned because the electronic questionnaire was designed to require an answer to each question, except those about ST characteristics. The descriptive analysis of the sample was carried out using frequencies for categorical variables, and means and standard deviations for quantitative variables. Age was divided into 3 categories (18-34, 35-64, 65 years and over).
Optical coherence tomography (OCT) To obtain cross-section images of the ablated holes and lines, we used an optical coherence tomography (OCT) system built in our laboratory. The OCT system has an Axsun swept laser Fig. 2. Example showing manual measurement of the crater depth (yellow vertical line from the crater's deepest point to the surface, depicted with a red horizontal line) and width as the FWHM (green horizontal line) from the OCT image using ImageJ software. In this example, the depth and width were measured as 0. 74 and 1. 59 mm, respectively. source with central wavelength λ 0 = 1060 nm, bandwidth ∆λ = 100 nm, and swept rate 100 kHz. The acquired volume size is 7 x 3. 56 x 7 mm 3 , and the volume rate is 0. 37 volumes s. The axial and lateral resolution of the OCT are 11 and 40 µm, respectively.
We concluded that Hermite based textures features are more robust than curvelet, contourlet and gray level co-occurrence matrix-based texture feature descriptors to classify edematous and normal images  [12, 34]. In this study we proposed a fully automatic CAD system based on CNN for diagnosis of bone pathologies of proximal humerus as normal, edematous and humeral head with Hill-Sachs lesion. We extended our dataset by adding MR images including Hill-Sachs lesion in the dataset. The whole dataset was segmented with the same manner. As would be predicted the presence of edema might interfere with the diagnosis of subtle fractures like a small Hill-Sachs lesion. Therefore the success rate of the classification of Hermite based features decreased to 94. 7% in the extended dataset compared to the 98.
This study has contributions by several aspects: • To the best of our knowledge there is no study in the literature addressing computer based diagnosis of pathologies of bony structures of shoulder utilising CNN. • We proposed a new computer aided diagnosis system for detection of bone pathologies of shoulder which is unique in both utilising PD weighted MR images and introducing deep learning to the diagnosis of bone pathologies from MRI and studying classification of shoulder pathologies from automatically segmented image patches. • This study includes comparative results of state of art hand-crafted methods with CNN. • A new dataset composed of axial PD weighted shoulder MR images of 219 patients were included in the study. The remainder of paper is organized as follows: Section III describes the dataset and proposed method in detail.
Image dataset and pre-processing operation We included 219 shoulder MR images of randomly selected patients who admitted with painful shoulder due to traumatic and acute conditions. 38 of the patients had the complaint of anterior shoulder instability. 100 patients were labeled as having bone edema by the orthopedist. All of the included images were 1. 5 Tesla PD weighted axial MRI images. The slice thickness was 4 millimeters and the size of the images were 256x256 pixels. A healthy humeral head, being composed of cancellous bone, has an almost homogenous architecture presented as a dark round bony structure (Fig  1a ). An edematous humeral head contains intensity changes in the form of whitish  The same as the many clinicians preference, we also selected PD weighted images due to their superiority to the other imaging modalities and other sequences of MRI in presenting bone edema and the anatomic details.
Recently the rectified linear unit (RELU) is the mostly used function in this step. RELU replaces negative values of the feature maps with zero which considerably increase the speed of the convergence of the CNN  [35]. The pooling layer is another important parameter of the CNN which is actually a form of down-sampling. It provides to reduce feature maps with using several non-linear functions of which the max and average pooling are the mostly used. The pooling layer prevents overfitting by decreasing the amount of parameters and computation. The output of the convolution and pooling layers are high level features extracted from image. The fully connected layer of CNN utilizes these discriminative features as input and provides classification of the test data according to training dataset.
Classification 335  The focus of this study is to discriminate between normal, edematous humeral heads and humeral heads with Hill-Sachs lesion. All of the experiments were carried out on a server having a NVIDIA Geforce GTX Titan X (6 GB on board memory). The CNN algorithms were performed with the TensorFlow. We evaluated traditional feature extraction methods as pyramid HOG, GLCM and Hermite based GLCM in order to compare the performance of our proposed CNN method. Each image patch of shoulder MRI was divided into cells and histogram of gradient directions were calculated for each cells. Pyramid of HOG is a useful shape feature extraction method but its success rate was 85% for our dataset. Although the main differences of HSL group from normal or edematous groups was the shape of the humeral head the texture information also has a positive effect on the recognition.
Instead of exploiting traditional texture and shape features we classified our dataset with pre-trained CNNs of GoogLeNet and AlexNet which are two successful CNNs used very commonly for pattern recognition problems. Nonetheless optimization of the hyperparameters and determining the optimal learning rate of AlexNet and GoogLenet for different layers are very challenging tasks due to the limited knowledge about the relevance of the large number of architectural and training hyperparameters. We followed the approach of Sharif et al. in which they assumed to utilise very small learning rates compared to default. After meticulous parameter tuning, we obtained the most successful model for our problem, which was very difficult to further improve. The accuracy rates of GoogLeNet and AlexNet were 82.
25% and 85. 48% respectively as demonstrated in Table  4. The GoogLeNet and AlexNet were also popular for their success in classification of images however their success rates were not in desired levels in differentiation of edematous humeral heads from humeral heads with Hill-Sachs lesion. Another possible reason might be the fact that GoogLenet and AlexNet were pre-trained on natural image datasets which are completely different from the MR images of the humeral head. GoogLeNet and AlexNet were originally designed for the fixed image dimension of 256x256x3 pixels whereas our dataset was composed of MR images of 64x64x3 pixels. Utilizing pre-trained CNNs may be a solution for problem of limited number of samples. Unfortunately there is no existing pre-trained CNN model in the literature on medical images or images with similar structure.
Conclusions Humeral head defects is a challenging topic in the orthopedic practice. There are ongoing studies on this subject in the orthopaedic literature which address both of the humeral and glenoid defects in the shoulder also called bipolar lesions. The treatment of the bipolar lesions is still under research. This study is concentrated on the humeral pathologies and detection of humeral bone defects. Further research may be on measuring the bone defects and the combined effect of bipolar bone lesions on the MR images. We proposed a CNN architecture to classify bone pathologies of the humeral head. To increase the success of the classification by the CNN, increasing either the number of filters or the number of convolution layers may be addressed. The small number of data present in our dataset was one of the limitations of this study.
128 features maps of size 32x32 were generated with parameters F=7x7, S=1 and padding (P) size of P=1 in the first, and 256 features maps of size 16x16 F=9x9, S=1, P=0 in the second convolution layer. In the last stage one convolution layer was used to obtain 64 feature maps of size 16x16 with F=5x5, S=1, P=0. Finally, a fully connected layer with 128 hidden units with max-out activation function was stacked to label each image patches as normal, edematous and Hill-Sachs based on training dataset (Table.
9 keV). The mean value of the Ca/P atomic ratio of needles was determined from EDX microanalyses performed on more than 10 different needles. Quantitative analyses were obtained on the basis of thin film approximation, and the calcium and phosphorus experimental Cliff-Lorimer K factors were standardized from analyses performed under the same conditions on β-TCP [β-Ca 3 (PO 4 ) 2 ] and octa calcium phosphate [Ca 8 H 2 (PO 4 ) 6 •5H 2 O] standards. Schematic 2D representations of the reciprocal lattice compared to ED patterns were simulated using CaRIne Crystallography 3. 1 software with a hexagonal crystallographic cell corresponding to the HA crystalline structure (a = 9. 42 Å, c = 6. 88 Å, space group P 6 3 /m).
ACKNOWLEDGMENTS The authors thank  Prof. C. Rey  for his fruitful discussion concerning the FTIR results,  F-X Lefevre  from  CEISAM -UMR CNRS 6230, Nantes University  for his technical help for atomic absorption analyses, and " Département Systèmes Energétiques et Environnement de l'École des Mines de Nantes " ( IMT, Atlantique -Bretagne -Pays de la Loire ) for SSA analyses. The authors also thank  Nicola Piveteau  for her linguistic revision of the manuscript.
Volunteers Ethical approval was received from the local ethical committee "Comites de protection des personnes en Ile-de-France" (CPP IDF VI, No 90-06). Patients had provided written informed consent for the original study. We used a total of 48 biplanar radiographs from 12 patients, which were acquired with a biplanar low-dose X-ray imaging system, initially for spinal assessments with various upper extremity positions (patients provided informed consent for the original study). The images included 24 data sets (12 posteroanterior and 12 lateral) from patients in a standing position with hands in front of their face (Group A), and 24 data sets (12 posteroanterior and 12 lateral data sets) from the same patients without hands in front of their face (Group B). Depicted in Figs  1  and 2  is the exact position of the patient in the biplanar lowdose X-ray device.
The photosensitive formulations were deposited on a polypropylene film (thickness of 125 μm) and the sample thickness was controlled using a mold (1. 4 mm -thick samples). The samples were polymerized under air with the blue LED. The evolution of the double bond content of the methacrylate function was continuously followed by real-time FTIR spectroscopy (JASCO FTIR 6600) at about 6165 cm -1 for thick samples. The procedure used to  monitor the photopolymerization profiles has been already described in detail in. [14, 15] MR spectra were recorded on Oxford Varian 300 spectrometer at 300 MHz for 1 H NMR. Chemical shifts (δ) are reported in ppm relative to the residual solvent signal (δ = 7. 27 for 1 H NMR). Data for 1 H NMR spectra are reported as follows: chemical shift (multiplicity, coupling constants, integrations).
Legal Obligations of Manufacturers Regulatory bodies in different countries establish the obligations that manufacturers of MDs should comply with to ensure that all the MDs produced and placed on the market remain in conformity with current regulations. In USA, first, manufacturers must be registered with the FDA. [176] Besides, the Code of Federal Regulation Part 820 establishes Quality System Regulation, also known as current good manufacturing practices, (International Organization for Standardization (ISO) 9001:2015 "Quality Management Systems -Requirements"). [177, 178] Similarly, the European Union approved new Regulation 2017/745 on medical devices (European Union Medical Device Regulation, EU MDR) obliges manufacturers to register in the Unique Device Identifier database (for more details, see Section 7).
Acknowledgements The authors wish to thank  Châu Lê Bao  and  Ian T. Whelan  for their help in revising the paper. The authors also want to thank  Thierry Bastogne  ( Cybernano ) for mentoring them on QbD. This work was in part funded by the  H2020-NMBP-HUBS-2018   TBMED  project: Grant Agreement No. 814439.
First molars were selected as these could be readily dissected at these early stages in tooth formation. RNA-seq data (Supplementary Table  S4  lists relevant down-regulated transcripts) confirmed the down-regulation of Smoc2 transcripts, and down-regulation of other gene transcripts including bone gamma-carboxyglutamate protein (Bglap), that could potentially affect bone turnover  25  , and dentin matrix acidic phosphoprotein 1 (Dmp1) potentially causing dental and osteogenic differentiation defects  26  or dentin dysplasia  12. Reduction of odontogenic ameloblast-associated protein (Odam), encoding a secreted protein guiding early enamel formation, could perturb enamel compaction in Smoc2 -/- mutants (see Fig. 3 ). mRNA-seq analysis was also performed on E18. 5 microdissected mandibular bone.
Quantitative real-time PCR (RT-PCR). To quantitate gene expression changes in tooth and bone, RT-PCR was performed on age-matched RNA samples for either control or Smoc2 -/-mice. For this, total RNA was extracted with the RNeasy Micro-kit and amplified (1 μg per reaction) by real-time RT-PCR using SYBR Green Reagents (Qiagen). cDNA templates were generated using the Oligo-dT primed Superscript II kit (Invitrogen). SYBR Green incorporation into amplified PCR products was detected using a Roche 480 LightCycler. Primer sequences (listed in Supplementary Table  S1 ) were obtained from "Harvard primer" website (https ://pga. mgh. harva rd. edu/prime rbank /, Primerbank), or designed using the Primer3web program. GFP primers were previously reported  52. Expression levels were normalized to glyceraldehyde-3-phosphate dehydrogenase (Gapdh) levels.