dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analyses The model was used to quantify the impact of generalized probiotic use versus no probiotic use on each of the following outcomes: number of ILI events, number of ILI days, number of antibiotic prescriptions, number of days missed from work, PCP visit costs, medication costs, and productivity loss. Subgroup analyses were conducted on age, vaccination status, smoking status (active and passive), and living or working in a shared indoor environment. Two additional scenario analyses were conducted: (1) comparing a population with generalized probiotic intake versus a population with current probiotic intake in the USA and (2) considering an alternative data source for productivity loss in ILI patients. Uncertainty around model results due to model assumptions was further explored in a one-way sensitivity analysis, which considered two key parameters: avoided RTI days and saved total societal cost with probiotic use versus no probiotic use (Table  2 ).
, 2018). The CDC estimates that of the 44% of outpatient antibiotics prescribed to treat patients with respiratory conditions, half are unneeded  (CDC Newsroom, 2016). Reduced antibiotic use and the associated risk of antibiotic resistance may have considerable public health relevance as well as additional cost consequences  (Michaelidis et al. , 2016). A limitation of our analysis is that probiotics were considered as a general category and not included in our assessment at the strain level. Different probiotic strains may have different effects on RTIs, but because our evaluation was based on two specific meta-analyses, we effectively included all relevant probiotic strains evaluated in one or more of the clinical trials that were pooled via these meta-analyses. Further, data available for probiotic use in the USA are not segregated based on specific strains.
2011; Zinchuk and Zinchuk 2008). Tools for quantifying these measures can be found in many image analysis open-source and commercial software packages, to name just a few: Fiji's JACoP plugin and Coloc 2, CellProfiler, BioImageXD, Huygens, Imaris, Metamorph, Volocity. Most object-based colocalisation methods first segment and identify objects, and then account for objects' inter-distances to analyze possible colocalisation. Usually, two objects are considered colocalised, if the centroids of the objects are within certain distance (Bolte and Cordelières 2006; Cordelières and  Bolte 2014; Obara et al. 2013), or if two objects with certain percentage of area/volume overlap (Rizk et al. 2014; Wörz et al. 2010). We will implement some specific methods for both categories in two case studies described in 7 Sects.
Fig. 3. 3. 5 DeconvolutionLab and DeconvolutionLab2 (Sage et al. 2017). 5 To further remove background noise, you can try [Process -> Subtract Background] (for our images, the rolling ball radius can be set to 10 pixels) However, deconvolution is not the focus of this module. Therefore, we would assume that the images to be processed during this module are either already deconvolved or are acquired with high image quality without the need of deconvolution. Other issues that may need to be dealt with during the preprocessing include: illumination correction, noise removal, background or artifacts disturbance. Since we have already been practicing techniques to handle these situations, here we would as well rather not to discuss them.
Datasets Let's first have a look at the data. The images display a large number of dash-like structures. The life scientist informs us they result from the labeling of two proteins which both locate at the most dynamic end of microtubules: they belong to the + TiPs family (plus-end tracking proteins). When overlaying both images (EB1 on channel 1 and CLIP-170 on channel 2, available in this module's folder: Images/+ TIPs. zip and as a smaller version Images/+ TIPs_mini. zip), it seems that the overlap is only partial, which justifies the scientist's first impression. Now we have to "put numbers" and try to evaluate this partial colocalisation to either demonstrate it or prove it to be a simple visual artifact (. Fig. 3. 4). Of course, most of us would rule out this latter hypothesis (. Fig.
3. 5). So let's take an example from Fiji's website: do you think some of the red and green pixels colocalise on this image? You may try zooming at the image and realize that yellow pixels are in fact resulting from the close proximity between green and red pixels: our brain simply blends one tone into the other. In case you do not believe this is an optical trick, try opening it under ImageJ then moving the cursor over the "yellowish" pixels: the status bar will display the values of the green and red components of the image. As a result, you'll figure out that red pixels are not green and the reverse way round. Word of advise: in colocalisation studies, as you can't trust your eyes and the brain that lays (or lies) behind, better be ready to build a proper quantification strategy! In addition to the requirement of a proper quantification strategy, this example also points out the need for a well characterized dataset.
When building a workflow,. Fig. 3. 5 Don't trust your eyes and brain on colocalisation!. Fig. 3. 4 Dataset on which colocalisation will be evaluated (Left: EB1, Middle: CLIP-170, Right: Overlay, Right-most: magnified sub-images) 3 the image analyst might benefit from use of a synthetic dataset. This somewhat barbarian terms designate a computer-generated dataset where all parameters are human controlled. In microscopy, the images result from single captions of a scene, which are impaired by the optics and corrupted by noise. To simplify the prototyping process, we could generate two images, one per channel, containing simple shapes (circles, rectangles etc…) which sizes, and degree of overlap are controlled. Such a dataset, extended to the 3D case is provided. Figure 3. 6  shows two 3D views of the synthetic dataset with two channels, where channel 1 (red) has six objects and channel 2 (blue) seven.
Workflow 1: Objects Overlap Volume Quantification Let's imagine a typical conversation between an image analyst and a life scientist who has a colocalisation analysis request for the dataset to be reviewed under 7 Sect. 3. 2: » I've got a set of two-channel 3D images where objects are overlapping. I think the overlap might not be the same from object to object. Therefore, I would like to quantify the overlap and get a map of quantification. A user comes to the Facility, asking for help  The key words here are: objects, overlapping, map of quantification. So the first task is to identify objects of interest in the image, i. e. segmentation. Then, overlapping regions between objects in the two channels should be extracted. In order to obtain a map of quantification on overlap, one of the quantification metrics could be calculating the volume of objects and overlapping regions.
For the kind of processing we are planning to do, each channel should be handled separately. Channels are splitted by using the function [Image > Color > Split channels] that is macro recordable. ImageJ/Fiji applies specific naming rules to each image when splitting the channels: the resulting titles are built by concatenating the letter "C" (for channel), the number of the channel (starting at 1), a dash, and finally the original image's title. We therefore have a way to precisely activate the first or the second channel. However, to make the process a bit easier, we rename each channel in the form of "ChannelXX". Finally, once all operations have been recorded, and the code cleaned up, the corresponding lines can be encapsulated into a function. To generate a function, you simply need to append the keyword "function" with a proper name, a pair of empty parentheses (or contain required parameters) and a pair of curly brackets: the few lines of code we've just written should be copied/pasted in between.
", "threshold="+lower+ "slice=5 min. = "+minSize+ " max. =100000 objects"); 9 } Again, in order to run the macro properly with this function, we need to call it in the main code body of the macro. For this step, since we need to tag objects in both channels, thus the function will be called twice. The advantage of creating user-defined function is nicely visible here: we won't have to re-type all the code from channel 1 to use it on channel 2. Functions should be called after Step 1 (3. 4. 2) is done, as shown in Code 3. 2. k The main macro till Step 2   ------------------------------------  5 Isolate object parts that are overlapping. 5 Generate user-defined functions to perform the task: getCommonParts and maskFromObjects with proper arguments, if needed. 1 //-------------------------------------- 2 //.
3 Working Plan: 5 Part 1: Convert an object map into a mask scaled between 0 and 1 1. Select the tagged map 2. Duplicate it, giving it a name in the form "Mask_"+original name (be careful: we want the full stack, not just the current slice) 3. Set the threshold between 1 and the maximum (65535) 4. Convert the thresholded image to a mask 5. Normalize the intensities between 0 and 1 (divide by 255) 6. Pack everything into a function, thinking about the proper arguments, if any, that should be entered for the process to be run 5 Part 2: Isolate common parts from both images 1. Generate the normalized mask for channel 1 (i. e. 0-1 scaled) 2. Generate the normalized mask for channel 2 (i. e. 0-1 scaled) 3. Use logical operators between both mask to retrieve the overlapping voxels 4. Pack everything into a function, thinking about the proper arguments, if any, that should be entered for the process to be run The correct code of the two functions are: 1 //Generate an image of the overlapped parts from channel 1 and 2 2 function getCommonParts(){ 3 //Generate the mask for channel 1 4 maskFromObjects("Channel1"); 1 A B AND OR XOR 1 1 1 0 1 0 0 1 1 0 1 0 1 1 0 0 0 0 0.
3 i. Activate the image where the quantification will be done ii. Activate the proper ROI iii. Retrieve the region's statistics iv. Modify the temporary variable accordingly (g) Push the temporary variable's content to the corresponding array compartment 5. Pack everything into a function, thinking about the proper arguments, if any, that should be entered for the process to be run, and the output that should be made by the "return" statement Here is the correct code for the function getValues:.
These two hospitals provide general health care to the population of Niamey estimated at 1. 1 million in 2015, as well as to patients referred from across the country. Laboratory detection of respiratory viruses and bacteria were conducted at the Centre de Recherche Médicale et Sanitaire (CERMES), Niamey, Niger (the National Reference Laboratory for influenza). Demographic and clinical data, as well as the use of antibiotics before and during admission, were collected using standard data collection forms. Malaria testing was implemented on site using thick blood film microscopy. Chest X-rays were performed at the discretion of the attending physician, and typical abnormal radiological findings assessed included pulmonary opacities, acute bronchiolitis, and enlargement of intercostal spaces.
In particular, we will tackle the word categorization task and compare the performance of classification model on different feature sets: standard linguistic and non-linguistic features described in section 4, features obtained using different deep learning approaches, and the combinations of all of them. We also investigate how different feature sets perform with three different cross-validation settings, described in section 5. The medical data used in this work are in French. Three human annotators participated in the creation of the reference data (specifying the understandability of words).
Then the lemmatization was checked with FLEMM  (Namer, 2000). After that we received 29,641 unique words. For instance, the term trisulfure d'hydrogène provided three words (trisulfure, de, hydrogène). The final dataset contains compound words which contain several bases (abdominoplastie (abdominoplasty), dermabrasion (dermabrasion)), constructed words which contain one base and at least one affix (lipoïde (lipoid), cardiaque (cardiac)), simple words which contain one base, no affixes and possibly infections when the lemmatization fails (acné (acne), fragment (fragment)).
Standard NLP features The standard NLP features include 24 linguistic and extra-linguistic features related to general and specialized languages. The features are computed automatically and can be grouped into ten classes: • Syntactic categories. Syntactic categories and lemmas are computed by TreeTagger  (Schmid, 1994)  and then enriched by FLEMM  (Namer, 2000). • Presence of words in reference lexica. Two reference lexica of the French language were exploited: TLFi 3  and lexique. org 4. TLFi is a dictionary of the French language covering XIX and XX centuries. It contains almost 100,000 entries. lexique. org is a lexicon created for psycholinguistic experiments. It contains over 135,000 entries, among which inflectional forms of verbs, adjectives and nouns, and almost 35,000 lemmas. • Frequency of words through a non specialized search engine.
Each word were queried on Google to find out the frequency of the word on the web. • Frequency of words in the medical terminology. The frequency of words in the medical terminology Snomed Int corresponds to the number of different terms containing a given word. • Number and types of semantic categories associated to words. The information on the semantic categories of Snomed Int was exploited. • Length of words in number of their characters and syllables. For each word, the number of its characters and syllables was computed. • Number of bases and affixes. Each lemma was analyzed by the morphological analyzer Dérif  (Namer and Zweigenbaum, 2004) , adapted to the treatment of medical words. It performs the decomposition of lemmas into bases and affixes known in its database and it provides also semantic explanation of the analyzed lexemes.
FastText word embeddings usage. FastText word embeddings  (Bojanowski et al. , 2017 ) is a good candidate feature for the detection of word difficulty because they are able to use the morphological information of words and generalize over it. Since the word embeddings capture context and morphological information, we assume that using them as features will improve classification accuracy for our specific problem. We note that FastText word embeddings trained on Wikipedia and Common Crawl 5  texts have an important part of words from our dataset. According to our analysis, the currently published Fast-Text 6  model for French contains 44. 26% (13,118 out of 29,641) medical words from our dataset and up to 56. 00% (16,598 out of 29,641) lowercased medical words from our dataset.
French RNN Medical Understandability Text Embeddings (FrnnMUTE). According to the general functionality of RNNs, the final hidden state aggregates the informa-tion about all input sequence. This idea is frequently used to receive hidden representations of sequences. Sequence-to-sequence model is a wellknown example of how this idea works in practice  (Sutskever et al. , 2014). Such models consist of two parts: an encoder is an RNN which encodes the input sequence into a representation in hidden space (which is also called thought vector), and a decoder which generates a new sequence out of the hidden representations. We used this idea for representing words from our dataset. To receive words representations from an RNN, we first trained it to classify words based on labels by one annotator (we chose O1), then for each word we collect values of the last hidden state of the RNN and use this vector as features during the detection of words understandability for different users (or annotators).
Train/test split was 70%/30% of randomly shuffled samples. As a direct classifier, we trained a characterlevel RNN using PyTorch framework 7 and one GPU Tesla K80. We lowercased all words, lemmatized them and substituted all Unicode symbols with their ASCII analogs. We tested several RNN architectures and hyperparameter sets. The best performance was reached with a model consisting of two unidirectional long short-term memory (LSTM) units, each with 50 hidden units. The dropout of the model is 0. 7. The input size is 57 as the number of unique characters in lowercased and converted to ASCII input words. The output size is 3 as the number of classes in our data. This model reached the best performance on the eighth epoch with F 1 = 78. 94 and accuracy = 81. 21% on development set. Using this model we received 50dimensional word representations which we called FrnnMUTE (French RNN Medical Understandability Text Embeddings).
User-in vocabulary-out cross-validation The cross-validation is performed on each dataset (i. e. , each user annotation) separately. We aim to measure the ability of the classification model to generalize class recognition on the known user and to predict annotations for unknown words. From the practical perspective, user-in means learning the profile of a user. Hence, a model trained by such scenario represents the word understanding or knowledge of the annotator. The experiments use (i) the standard features only, (ii) the FastText word embeddings only and (iii) their combination. The experiments with isolated FastText word embeddings as features resulted in poor F1 scores (Table  2 ), that can be explained by the fact that contextual information, which is dominant in these word embeddings, is not enough to define the word understandability.
User-out vocabulary-out cross-validation Finally, we consider (k-1) folds of data from one user for training and use k-th fold for testing from the remaining user. We aim to measure the ability of the method to generalize both on unknown users and unknown vocabulary. This experiment should be helpful in identifying the number of words needed for determining whether the profile of one user is the same as profile of other users in case the model achieves good performance. In these experiments, FastText word embeddings provide approximately 0. 5% higher F1 score in case of learning on users O1 and O3 (Table  4 ). When learning on user O2, embeddings decrease F by 0. 5, which means that annotations and health literacy of user O2 are different from users O1 and O3. It seems that adding embeddings makes overfitting the machine learning model to the dataset.
The Frn-nMUTE embeddings and the categorization code are being made publicly available for scientific non-commercial purposes 8. We have several directions for future work. Currently we use the existing word embeddings pretrained on Wikipedia and Web Crawl. We assume that training words embeddings on medical data may improve their impact on the results from categorization of medical terms. Another issue is that, after analysis of results of the application of Fast-Text word embeddings in a categorization task, we assumed the existence of a robust nonlinear dependency between some subsets of standard features and subword-level components of FastText word embeddings. We plan to test this hypothesis in further research. Finally, while the annotations go forward, the annotators usually show learning progress in decoding the morphological structure of terms and their understanding.
Numerical experimentations The aim of this section is to present several numerical simulations which illustrate the performance and the efficiency of our tool, Rock-N-Roll, introduced in Section 3. Obviously, we aim at evaluating how much the numerical accuracy is improved but also the impact on the execution time. To do this, we have taken two examples based on two physical problems arising in Mechanics: The flexion of a beam fixed on its extremities and the compression of a viscoelastic body against a moving foundation. In both cases, the discretization is based on the finite element method (FEM) that was usually used to solve complicated problems in engineering, notably in elasticity and structural Mechanics modeling involving elliptic PDEs and complicated geometries. Note that the linear systems come from a Fortran computer code based on a MODULar Finite Element library (MODULEF) 1.
In such type of problem, it is well known that the previous linear system is ill-conditioned and the condition number of the matrix is related to the max 1≤i≤N { 1 hi }. For this reason, it is an interesting example to test the Gauss Pivoting algorithm developed in Section 3. For our experiment, we considered that f = -20N/m 2 , max 1≤i≤N { 1 hi } = 10 6 , the penalization value C = 10 6 and that the beam is fixed on its extremities (α = β = 0). First we created different linear systems of size 4 N 40. Then, we calculated the solution of each system by the C-program given by our tool Rock-N-Roll: x RN R and by a classical Gauss pivoting method program: x CG. Finally, in order to highlight the differences between solutions, we computed and displayed in Figure  7  the relative error We can observe a significant difference between the curves corresponding to RelErr(x RN R ) and RelErr(x CG ), which are calculated with x RN R and x CG respectively.
We see that the difference in accuracy between the results of the two methods is of the order of 5 × 10 -6 on average. We can also see that the increase of the error is more regular and smoother with the solution calculated by the program generated by Rock-N-Roll. For execution time measurements, all the programs have been written in the C language and compiled with GCC 4. 9. 2-03, and executed on Intel Core i7 in IEEE754 single precision in order to emphasize the effect of the finite precision. The results displayed in Figure  8  show that by synthesizing Gauss pivoting code, we improve not only its accuracy but we reduce its execution time too. This is mainly due to the fact that the systems from coming from our mechanical problem are sparse and that is the case, Rock-N-Roll is able to simplify the computation and to remove all the zero terms.
For instance, in the examples of Section 3. 3 some zero terms have been removed in the expression of x 2 and B 4 otherwise we would have a far more larger expression. The program synthesized by Rock-N-Roll contains far less computations then the original one. Remark that the computing time necessary for the obtention of the synthesizing Gauss pivoting code is not taken into account for the execution time measurements of the linear systems. RelErr(x) = A * x -b 2 b 2 of each solution.
The ever-increasing bacterial genomic repositories reveal a great number of uncharacterized biosynthetic gene clusters, representing a tremendous resource for natural product discovery. Genome mining of the marine Streptomyces sp. NA03103 indicates the presence of an orphan nonribosomal peptide synthetase (NRPS) gene cluster (asm), to which there are no homologous gene clusters in the public genome databases. Heterologous expression of the asm gene cluster in the S. lividans SBT18 strain led to the discovery of two novel cyclopeptides, ashimides A and B (1 and 2), with 2 showing cytotoxic activity. In addition, we use bioinformatic analysis, gene inactivation and stable isotope labelling experiments, as well as in vitro biochemical assays, to present a coherent and novel assembly line for ashimide biosynthesis, featuring an unusual desaturation, halogenation and cyclization cascade catalyzed by a P450 monooxygenase and a FAD-dependent halogenase.