dataesr/scientific-paragraphs-categorization huggingface dataset:
Principal Results The primary purpose of this study was to leverage NLP features and machine learning strategies to detect reminiscence in the conversations of older adults in German in a naturalistic observation study. We used the written transcripts of the conversations and a manually coded variable (reminiscence or not reminiscence) as the basis for the prediction. We considered a wide array of methodologies, including different NLP features (ie, bag-of-words, POS tagging, and pretrained German word embeddings), multiple machine learning algorithms, and learning strategies to handle class imbalance. Results indicate that selected combinations (see below) of learning strategies, NLP features, and machine learning models show the potential to detect reminiscence. We argue that their performance can be further improved through feature engineering, by combining NLP features, using NLP-driven data augmentation techniques and collecting more data.
Learning Strategies Class weighting SVM outperforms others machine learning models, with the highest performance seen when trained on bag-of-words (F1=0. 48) with a balanced precision-recall trade-off, high specificity (0. 98), AUC=0. 91, and AP=0. 56. MC strategies show lower F1 scores than class weighted and data augmentation ones, with highly imbalanced precision-recall trade-offs, resulting in low precision and, consequently, low F1 score and specificity. On the other hand, data augmentation with SMOTE delivers performance comparable to the one of class weighting, although only when using word embeddings. However, SMOTE is a purely computational approach to data augmentation, as it generates new data points from any numerical representation of transcripts (eg, on word embeddings). On the other hand, data augmentation algorithms, such as replacing with synonyms, random inserting, swapping, or deleting words  [71] , process transcripts directly and have improved text classification performance across multiple data sets  [71].
NLP Features Bag-of-words and word embeddings delivered the highest performance of all learning strategies; machine learning models trained on POS features delivered low performance due to their low precision (combined with low recall, such as in the case of ADA for class weighting and data augmentation strategies). Considering models trained on bag-of-words features, German stopwords related to past tense (eg, "gewesen"), personal pronouns (eg, "ich" and "wir"), and connecting words (eg, "und" and "aber") showed high feature importance, suggesting that they can encode the time, personal narrative, and structure of reminiscence. Models trained on POS tags, however, use both 1-gram and 2-grams; in particular, 2-grams comprising punctuation signs followed by a conjunction, such as ". Und" and ".
Limitations This study has several limitations. The data set of conversations used to classify reminiscence had a limited number of records due to the short duration of the naturalistic observation study (4 days)  [6]. Moreover, as we considered data from a single observation study, we cannot infer the generalizability of the presented results to other data sources. The results are based on a single partitioning of data into train and test sets. In this study, we used SpaCy pretrained German word embeddings; clearly, these embeddings are trained on corpora that may not fully encode the linguistic specificities characterizing conversations among older adults. In addition, due to the small data set available for this study, we refrained from training embeddings as well as performing machine learning with data-intensive deep learning models  [79].
The reactions of genes and the interactions between them are not fully discovered. Consequently, the knowledge is not complete and could be inaccurate. Thus, we need to correct and complete it. Another feature in gene networks modeling is the integration of information coming from different sources that could be contradictory. The construction of a knowledge base expressing molecular biology and in particular gene networks has always been at the center of research in artificial intelligence. There exist several works using logical approaches. For instance, the system HYPGENE  [13, 14]  where the knowledge base is implemented using a frame language  1. This approach aims to resolve the mismatch between predictions calculated from the knowledge base and experimental observations. On the other hand, TRANSGENE  [3, 4]  is an approach for the representation of knowledge based on a "functional representation" language.
This language is chosen to overcome the shortcomings of systems using the frame language. The goal is to determine if a knowledge base can predict an observation. If not, then the knowledge base must contain faulty items that can be found and corrected. We also have the system GENEPATH  [26]  which uses a set of inference rules which have been formalized and implemented using Prolog. The rules are applied on the basis of the initial knowledge to build a plausible network that explains the observations. These works share a common characteristic: they are all built on knowledge representation languages limited to monotonous reasoning. In this paper, we represent gene networks and address all the features previously mentioned by using the Answer Set Programming (ASP) paradigm  [20]. ASP is a knowledge representation and reasoning framework.
It is used to express and handle a variety of combinatorial problems. Among them graph problems, planning, and model checking. It is a powerful modeling tool that can encode a significant number of problems. There is a growing number of practical applications of ASP in different fields  [7] , especially those related to biology  [8, 17, 18]. Using ASP provides several advantages. First, it is appropriate to model biology information. It is also a robust declarative language for knowledge representation. It offers the possibility of reasoning on incomplete knowledge, and the last ASP solvers  [11]  are very efficient and able to solve significant size problems. We consider a qualitative approach to design gene networks. For instance, the representation of a protein concentration is assumed to have a finite number of states  [15].
Answer Set Programming The paradigm ASP  [20]  is fully declarative, it has a high level capacity of knowledge representation and efficient solvers. The basic idea of ASP is to represent the knowledge base as a set of rules forming a logic program and then reason on that program by computing its stable models  [9]  or its answer sets  [10]. A logic program π is a finite set of rules of the form r:head(r) ← body(r). A positive logic program π is a set of rules of the form : r = A 0 ← A 1 , A 2 ,. , A m , with (m ≥ 0) and where A i∈{0,. ,m} is an atom. A general logic program π is a set of rules of the form : r = A 0 ← A 1 , A 2 ,. , A m , not A m+1 ,. , not A n , (0 ≤ m < n) where A i∈{0. n} is an atom and not the symbol expressing the negation as failure. The positive body of r is body + (r) = {A 1 , A 2 ,.
An extended program is composed by rules of the form r = L 0 ← L 1 , L 2 ,. , L m , not L m+1 ,. , not L n , (0 ≤ m < n) where L i∈{0. n}. To reduce an extended logic program into an equivalent general logic program, we generally replace any negative literals ¬L appearing in the representation with a new atom L. We add the clauses of integrity constraint ← L, L. This constraint prevents both L and ¬L to be simultaneously in the same stable model. The constraints prohibit that L and ¬L to be true at the same time. We compute the stable models of the resulting general program from which we can obtain the answer sets of the original extended program. For the present work, we used the ASP solver presented in  [19, 16].
Representation of gene networks In this section, we show how we express gene networks as a logic program that could be handled by ASP solvers. The use of such logical formalism could naturally express all the information that biologists want to represent. The advantage of such a logical framework is the possibility of representing knowledge in a declarative way that is close to a natural language. In our approach, genes and proteins are considered as the same objects. We assume that a protein is present only if a gene is activated. The protein interactions are described by using a propositional logic Programming language. This logical representation could be seen as an abstraction of protein interaction reality. We can use, for example, p53 to say that the protein is expressed. Indeed, the protein concentration is rarely precise, and in practice, the biologists' experiments show a qualitative interpretation of increasing or decreasing of the concentration.
This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13 C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.
In practice, this means that all metabolic models must explicitly include the labeled nutrient(s) initially provided to the biological system and all the pathways that distribute the isotopic tracer up to the pathway of interest. To ensure fluxes are identifiable, the extracellular fluxes and the intracellular concentrations and labeling of upstream metabolites must also be measured. This is a major limitation for investigating i) pathways far downstream of the labeled nutrient(s), ii) networks with reaction gaps (e. g. an uncertain network topology), iii) incomplete datasets, iv) experiments performed in rich media, or v) situations where the isotopic transitions remain uncertain or complex (e. g. 2 H tracer)  [1, 18]. This also makes the entire experimental and computational workflow very time consuming, costly and error prone.
e. metabolite concentrations are constant). Each minimal subsystem is self-consistent and can be incorporated into a flux model to estimate fluxes through the included reactions. This modular representation is the essence of the scalability of ScalaFlux. We used this procedure to decompose the example network shown in   For the reversible reaction r6, both the forward and reverse reaction rates were determined. The robustness of ScalaFlux to measurement noise was assessed by estimating fluxes from 200 datasets in which Gaussian noise was added to the theoretical data, assuming a typical precision 0. 02 for 13 C-enrichments and of 10 % for concentrations  [30, 31]. The distribution of fluxes estimated from these datasets indicates that the precision of the method is good, with an average relative standard deviation of 13 % (Fig 3C ).
Biosynthesis of prenyl pyrophosphates in yeast ScalaFlux provides the opportunity to reconsider published datasets from which fluxes could not be calculated because of the lack of an appropriate modeling framework. As an example application, we analyzed a published dataset on the metabolism of prenyl pyrophosphates, the precursors of isoprenoids, in the yeast Saccharomyces cerevisiae  [32]. Isoprenoid biosynthesis starts with isopentenyl pyrophosphate (IPP), which is isomerized into  The published dataset contains i) steady-state concentrations of three prenyl pyrophosphate intermediates (GPP, FPP and GGPP) measured during exponential growth on glucose and ii) 44 transient 13 C-enrichments following a switch from unlabeled to U-13 C-glucose (11 time points for GPP, FPP, GGPP, and combined pools of IPP and DMAPP).
The flux was similar in the phytoene producing strain S023 (0. 93±0. 04 nmol/g DCW /min). This indicates that the increased demand for GGPP does not propagate upstream and does not affect its production, in agreement with the low reversibility of the prenyl transferase reactions. Importantly, we verified that the flux estimated by ScalaFlux in S023 was consistent with the GGPP demand for phytoene synthesis estimated from phytoene accumulation (1. 33±0. 16 nmol/g DCW /min, Fig  5D ). The good agreement between these two independent methods demonstrates that ScalaFlux provides accurate flux measurements from datasets collected on just a few metabolic intermediates. Finally, while qualitative interpretations suggested that the turnover rate of GGPP was stable in the different strains  [32] , this could not be verified because the fluxes could not be estimated.
It is important to note that flux identifiability depends on the experimental setup used (e. g. type of isotopic data, accessible measurements, sampling frequency) and on biological constraints (e. g. network topology, fluxes). We refer to previous work  [12-14, 22, 24, 33]  for extensive discussion on these topics. We validated the practical applicability of ScalaFlux by reanalyzing a published dataset on the metabolism of prenyl pyrophosphates, from which fluxes could not be calculated using current MFA approaches. Indeed, GGPP is continuously used by different processes (such as protein geranylgeranylation and membrane biosynthesis) and does not accumulate in cells. Its biosynthetic flux cannot therefore be measured in vivo without using isotopic tracers. Moreover, measuring this flux using stationary 13 C-MFA approaches would have been impossible because of the topology of the prenyl pyrophosphate pathway.
For instance, fluxes through individual reactions in a linear pathway can be estimated independently using different datasets. ScalaFlux can thus potentially verify (or disprove) assumptions that are usually made in 13 C-MFA (e. g. that all the reactions in a given linear pathway carry the same flux) and to identify gaps in the current knowledge (e. g. that an intermediate of an apparently linear pathway is actually consumed by another unknown reaction, or that the assumed network topology is not sufficient to explain the labeling dynamics of some of the intermediates). ScalaFlux is also highly versatile in terms of the pathways that can be monitored. It can be used to measure fluxes through virtually any metabolic subsystem of interest: a single reaction, a pathway, or larger networks. Because it exploits concepts from non-stationary 13 C-MFA, ScalaFlux can be used to investigate C 1 -metabolism (e.
ScalaFlux can be applied alone or in combination with other methods to address a broad range of biological questions. Combined with untargeted MS(/MS) approaches  [34] [35] [36] , ScalaFlux paves the way to 13 C-flux studies at the cellular level. The network coverage of untargeted MS(/MS) approaches is in general low and sparse, which results in poor flux identifiability when the complete dataset is integrated into metabolic reconstructions. In ScalaFlux, incomplete datasets can still be exploited to estimate fluxes through subsystems, and these flux measurements can be used to constrain genome scale metabolic models. Our approach should also be helpful to study poorly characterized organisms, for which simulations from carbon entry up to the pathway of interest may not be possible. From a computational point of view, the proposed approach shares many elements with traditional approaches, and is compatible with all current simulation frameworks -EMUs, cumomers, fluxomers, etc -  [1, 14, 25].
Implementation of the ScalaFlux workflow We implemented the ScalaFlux workflow (Fig 1C ) in a major update of IsoSim, an R software previously developed to couple kinetic and isotopic models of metabolism  [21]  Briefly, IsoSim includes functions to i) construct flux models, ii) design isotope labeling experiments, iii) define local label inputs, iv) simulate label propagation, and v) fit experimental data in order to estimate fluxes. Each of these steps is explained in detail in the following sections. All the scripts we used to construct the models, to perform the simulations and to generate the figures are provided at https://github. com/MetaSys-LISBP/IsoSim/ to ensure reproducibility and reusability.
To illustrate the value of this scalability, we explored different options to estimate the flux through the pathway composed of the seven reactions {r10, …, r16} (Fig 4A). We identified a total of 29 subsystems (and associated datasets, Fig 4B) that potentially enable flux evaluation through this pathway. Of course, this flux can be estimated through each reaction individually, as demonstrated above, corresponding to subsystems S G , S H , S IJ , S K , S L , S M , and S N in Fig 4B. Several reactions in this pathway can also be combined into a single flux model (following the rules defined in section Construction of flux models), e. g. by merging two connected subsystems as done for subsystems S GH , S HK , S LM and S MN.
Acknowledgements We thank  MetaToul (Metabolomics & Fluxomics Facilities, Toulouse, France , www. metatoul. fr),which is part of the  MetaboHUB-ANR-11-INBS-0010 national infrastructure  (www. metabohub. fr), for providing free access to its computational resources. We thank  Matthieu Guionnet  for technical assistance and  Baudoin Delépine  for insightful.
The source code of IsoSim v2 is freely distributed under open-source license at https://github. com/MetaSys-LISBP/IsoSim/. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. bioRxiv preprint first posted online Aug. 28, 2019; doi: http://dx. doi. org/10. 1101/735308. The copyright holder for this preprint It is made available under a. CC-BY 4. 0 International license.
Fauré and S. Kaji study the conversion based on Summing code (Section 4), which provides several alternative Boolean profiles for encoding an integer, such that the resulting Boolean dynamics is deployed on the whole Boolean state space  (Fauré and Kaji, 2018). Following similar motivations, E. Tonello also studies the conversion based on this code  (Tonello, 2019). Regarding conversion tool, A. Naldi developed the Java library biolqm  (Naldi, 2018) , which is a toolkit dedicated to qualitative analysis of biological regulatory networks, and which includes a multivalued to Boolean converter. The converter is based on Van Ham coding and ensures that the trajectories always converge to the region of the valid Boolean codings, called the admissible region (Section 3), thereby avoiding potential spurious equilibria outside this region.
This converter is also available in ginsim 3. 0 environment  (Chaouiya et al. , 2012) , which relies on biolqm. These research works elegantly pave the formal foundation of the multivalued to Boolean network conversion. However, the results are intrinsically dependent on a specific coding, the Summing and Van Ham code. Moreover they are mainly designed for the asynchronous mode. Therefore, it appears interesting to generalise this approach by distinguishing the properties that purely relate to the conversion process from those depending on the code for highlighting the foundations of this process. The behavioural equivalence is formally defined by the reachability preservation property, namely: whenever an integer state is reachable from another one, the equivalent Boolean state of the former is also reachable by the equivalent Boolean state of the latter, and conversely.
Cells were rinsed with PBS and microfluidic devices were topped with 90% glycerol. Samples were imaged under a LSM 880 confocal microscope (Zeiss, Oberkochen, Germany) equipped with a 63Â 1. 4 numerical aperture objective. Images were acquired at zoom 2 with a z-stack interval of 0. 5 mm. Typically, four images were acquired from each synapse chamber, each showing multiple dendrites arriving from the postsynaptic chamber. Images were deconvoluted using AutoQuantX3 software (BitPlane, Zurich, Switzerland) for synaptic connectivity analysis. In a subset of experiments, neurons were plated only in the presynaptic chamber, only in the postsynaptic chamber or only in the synaptic chamber. Neurons were fixed and immunostained at DIV14 as described to reveal nuclei, glial fibrillary acidic protein (ABD95; Millipore) and b3-tubulin (MAB1637; Millipore).
Quantification of synapse integrity We developed an image analysis workflow based on image segmentation using Imaris software (BitPlane) and assignment of postsynaptic signals to the nearest presynaptic signal using a custom Matlab (Mathworks, Natick, MA, USA) code. Briefly, signals obtained for preand postsynaptic structures were first deconvoluted in Autoquant X3 (Media Cybernetics, Rockville, MD, USA) and segmented into distinct volumes using the surfaces function of Imaris in the batch mode that permits the same parameters to be applied to all images (Supplementary Fig. 1A-D ). Postsynaptic spots were then assigned to the nearest presynaptic spot (according to the three-dimensional Euclidean distance between intensity centres) within a pre-defined cut-off distance (Supplementary Fig. 1E ).
It is important to note that preand postsynaptic structures are smaller than the axial resolution of the confocal microscope; the distances measured are thus not true physical distances between synaptic structures but the distances between respective intensity centres. We empirically determined the cut-off distance by testing a range of values on a large set of control cultures (Supplementary Fig. 1F  and G ). The fraction of Syp puncta with at least one Homer assignments and the average number of Homer assignments per Syp were determined to be the most robust read-outs of synapse connectivity. The numbers of assigned pre-and postsynaptic puncta per image area (in the xy-plane) were also provided. Note that a small fraction of control samples for CHO-APP WT and CHO-APP LDN co-cultures (three and two devices, respectively) do overlap in the data reported.
Analysis of gene expression alterations in human brain samples RNA sequencing (RNAseq) data from the Mount Sinai/JJ Peters VA Medical Center Brain Bank (MSBB)  (Wang et al. , 2018) , the ROSMAP database  (De Jager et al. , 2018)  and the Mayo Clinic whole genome and transcriptome data  (Allen et al. , 2016)  were downloaded from AMP-AD Knowledge Portal (https://adknowledgeportal. syn apse. org/) according to the terms and conditions concerning the use of the data. Data were aligned using pseudoaligner Kallisto version 0. 43. 1  (Bray et al. , 2016)  using a pre-built index to align fastq files. Differential gene expression analysis was performed using DESeq2  (Love et al. , 2014). First, a DESeq2 object was created using DESeqDataSetFromTximport function and rows with sum of all counts <10 were filtered out.
Next, DESeq function was used with default parameters. Temporal cortex was analysed in the Mayo Clinic dataset (82 cases and 78 healthy controls). Dorsolateral prefrontal cortex was analysed in the ROSMAP dataset (222 cases and 201 controls). The following brain areas were analysed in the MSBB dataset: Brodmann area (BA) 10, which corresponds to the anterior prefrontal cortex (105 cases and 71 controls); BA 22, which is part of the Wernicke's area in the superior temporal gyrus (98 cases and 61 controls); BA 36, which corresponds to the lateral perirhinal cortex (88 cases and 64 controls); and BA 44, which corresponds to the inferior frontal gyrus (90 cases and 63 controls).