dataesr/scientific-paragraphs-categorization huggingface dataset:
Ecological network analysis The scenarios built in  Nogues et al. (2022)  were used to compute maps of ENA indices using the "EnaR" Ecospace plugin  (Nogues et al. , 2022). This plugin available in a modified needed to create maps of ENA indices using the R package "EnaR"  (Borrett and Lau, 2014). Each cell of the Ecospace model had ENA values that were used to build ENA maps using the same resolution as Ecospace (Supplementary materials 1 Figure  S1 -1 ). We selected four ENA indices to describe and understand the functioning and organization of the food web (Table -2), based on previous lists of ENA indices considered promising for ecosystem management due to their insights into ecosystem functioning and ecosystem resilience  (Fath et al. , 2019; Safi et al. , 2019). Each cumulative scenario had three sets of ENA results -one per fishing scenario.
Acknowledgements This work was funded by the  Normandy Region  ( RIN Trophi-Services  project), and follows the  TROPHIK  project, which benefited from  France Energies Marines and State  financing managed by the  National Research Agency  under the  Investments for the Future program  (reference  ANR/FEM EMR-ITE ANR-10-IED-0006-12 ). This study is also supported by the  APPEAL  project ( ANR-10-IED-0006-25 ). Finally, we would like to thank the reviewers.
2050 and 2100), in each cell of the eBoS Ecospace model (4,907 comparisons). It was chosen because it is a non-parametric effect size metric that can be used to compare datasets with different treatments. The Cliff delta determined if the difference between the ∑ ∆𝐼 𝑠 𝑠𝑒𝑝 and the ∆𝐼 𝑐 𝑐𝑢𝑚 (per cumulative scenario) was large, medium, small or negligible. If the difference was considered large, based on the threshold given by Romano et al. (2006) (| ∂Cliff | < 0. 474), then the CE was considered significant in the cell. This allowed a more precise discrimination of CEs within the eBoS, and enabled the mapping of CEs despite variability inside the regions and between fishing scenarios. In Ecospace, the habitat suitability drives the distribution and dynamic of trophic groups. It is the product of the group responses to each environmental driver: here climate change and to the OWF reef effect.
The Character Variety for the Mediating Manifold W The fundamental group π 1 ( W) of the h-cobordism W is as follows: [13] π 1 ( W) = a, b|a 3 b 2 AB 3 Ab 2 , (ab) 2 aB 2 Ab 2 AB 2. ( 5 ) The cardinality structure of subgroups of this fundamental group is η d [π 1 ( W)] = η d [π 1 (W)] = [1, 0, 0, 0, 1, 1, 2, 0, 0, 1, 0, 5, 4, 9, 7, 1 • • • ], where the bold digits refers to our investigation in (  [13] , Table  1 ) concerning the existence of a finite geometry obtained with a subgroup of the corresponding index. The smallest case occurs at index 10, and the geometry is the Mermin pentagram, a type of contextual geometry. As before, with Sage software in Reference  [15] , we compute the corresponding character variety. Then, from Magma, the Groebner basis is found in the form G W = (z -2)(z 2 -z -1)[x + g 1 (z)][y + g 2 (z)]g 3 (z), g 1 (z) = -z 9 + 3z 8 + 5z 7 -18z 6 -10z 5 + 43z 4 -2z 3 -27z 2 + 3z + 4, (6) g 2 (z) = -z 8 + 2z 7 + 6z 6 -11z 5 -15z 4 + 24z 3 + 9z 2 -10z -2, g 3 (z) = z 7 + z 6 -5z 5 -6z 4 + 6z 3 + 5z 2 -2z -1.
For details about the miRNA sequences, we use the Mir database  [33, 34]. Disregulation of miRNAs may lead to a disease such as cancer. A key microRNA known as an oncomir (involved in immunity and cancer) is mir-155  [12]. Here, we select examples of human miRNAs from the perspective of evolution. The generator of the group π to be considered is a short (about 8-letter long) seed made of two to four distinct bases in the set {A,U,G,C}. Most of the time, π is close to a free group of rank equal the number of distinct bases in the seed minus one. This point can be checked by the cardinality structure of conjugacy classes subgroups of π (denoted card seq). Exceptions to this rule and the occurrence of singularities (isolated or not) in the corresponding character variety built from π are a witness of a potential disease.
Metastasis potential of TDEs secreted by FAM3C-overexpressing carcinoma cells was validated in mouse models. Results: Transcriptomic meta-analysis of pan-cancer datasets confirmed the overexpression of FAM3C -a gene encoding for interleukin-like EMT inducer (ILEI) -in NSCLC tumors, with strong association with poor patient prognosis and cancer metastasis. Aberrant expression of FAM3C in lung carcinoma cells enhances cellular transformation and promotes distant lung tumor colonization. In addition, higher FAM3C concentrations were detected in EVs extracted from plasma samples of NSCLC patients compared to those of healthy subjects. More importantly, we defined a hitherto-unknown mode of microenvironmental crosstalk involving FAM3C in EVs, whereby the delivery and uptake of FAM3C via TDEs enhances oncogenic signaling -in recipient cells that phenocopies the cell-endogenous overexpression of FAM3C.
Duolink Proximity Ligation in situ Assay (P-LISA) For Duolink in situ, all incubations were performed in a 37 °C humidity chamber. Cells were plated onto glass coverslips and fixed with 4% paraformaldehyde at room temperature for 30 min. PLA staining was conducted according to the manufacturer's protocol (Duolink, #DUO92101). Preparations were mounted with prolong gold antifade reagent with DAPI (Life Technologies). PLA images were analyzed using ImageJ software (NIH), expressed with the mean PLA/cell intensity ± SEM.
Statistical analysis All data analysis was conducted using Graph Pad Prism 9 software (GraphPad Software, La Jolla, CA, USA). All results are reported as mean ± SEM. Statistical differences between two groups were evaluated using two-sided paired Student's t-test, while comparison between multiple groups was conducted using one-way ANOVA. Differences with a p value < 0. 05 were considered statistically significant. The non-parametric one-tailed Mann-Whitney U test was carried out for the mouse lung tumor nodule analyses.
More importantly, higher FAM3C concentrations in the EVs extracted from patient plasma was associated with more advanced stages of NSCLC (Figure  S2B ). In addition, the other top hits identified from the cell line-derived EVs (ANXA4, RPS9, RPLP1, and PSME3) were detected in plasma EVs as well (Figure  S2C-F ), thus validating the robustness of our proteomic approach. Given the known role of FAM3C in promoting EMT in a few cancers, we hypothesized that FAM3C carried on TDEs could promote proliferation of NSCLC at secondary sites. To unravel the clinical significance of FAM3C in carcinomas, we began by delineating its clinicopathological and molecular correlation by leveraging on publicly available transcriptomic datasets as well as immunohistochemical evaluation of FAM3C expression in tissue microarrays.
Notably, FAM3C expression is elevated in malignant lung tissues (lung adenocarcinoma, LUAD; and lung squamous cell carcinoma, LUSC) compared to the non-malignant tissues based on The Cancer Genome Atlas (TCGA) database (Figure  1A ). Furthermore, significantly higher mRNA expression of FAM3C in carcinoma tissues was observed as compared to the normal lung counterparts based on five independent lung carcinoma datasets (Figure  1B ). Using one-stage meta-analysis on patient survival data from these datasets, we found high FAM3C mRNA expression (stratified at 239 for both high and low median) to be correlated with poorer prognosis (marginal HR = 1. 40; 95% CI: 1. 04-1. 89; p = 0. 028) (Figure  1C ), thereby suggesting its pertinence in NSCLC. To confirm the specificity of FAM3C in tumor cells, its expression profile across normal lung tissues and early to late stage carcinomas was assessed in a scRNA-Seq readout of 44 LUAD patients  [26].
Aberrant FAM3C promotes NSCLC cell motility and anchorage-independent growth Endogenous FAM3C expression has been reported to drive metastatic outgrowth via EMT induction in breast and gastric carcinomas  [22, 24, 27]. However, its pathophysiologic role in NSCLC has not been investigated in depth. Here, we screened FAM3C protein expression in 12 NSCLC cell lines. FAM3C expression was low in the normal lung fibroblast (IMR90) cell line. In contrast, most of adenocarcinoma or squamous cell lines showed high FAM3C content (Figure  S4A ), which is consistent with the scRNA-Seq database (Figure  S3A ). To delineate the role of FAM3C in NSCLC, we modulated its expression in the FAM3C-high SKMES-1 (using shRNA-mediated knockdown), FAM3C-low H2170 (using plasmid-based overexpression), and FAM3Cintermediate A549 cell lines (knockdown or overexpression).
FAM3C induces transcriptomic activation of oncogenic signaling We next sought to elucidate the molecular mechanisms underpinning FAM3C function. RNA-Seq analyses identified 4210 upregulated genes and 4702 downregulated genes in FAM3C_ox A549 cell lines compared to control cells (Figure  5A ). Unsurprisingly, the transcriptomic perturbations evoked by FAM3C-overexpression were found to closely resemble curated gene signatures relevant to lung carcinoma, breast carcinoma, and prostate carcinoma, based on enrichment analyses using the Human Disease Ontology (DO) (Figure  5B , left panel). These differentially-expressed genes were also found to be overrepresented in KEGG-pathway ontologies related to focal adhesion and lysosome (Figure  5B , right panel), while gene-set enrichment analyses (GSEA) in the Molecular Signatures Database (MSigDB) further demonstrated significant overrepresentation of input genes in the hallmark gene set (identifier: M2572) for epithelialto-mesenchymal transition (FDR q-value = 0), and two curated gene sets related to KRAS signaling (identifier: M2448; FDR q-value = 0.
RalA is a functional interacting partner of FAM3C Though FAM3C is known to induce metastasis, the signaling pathway mediating this is unclear. As FAM3C lacks a functional kinase domain, it is likely that interaction with other signaling molecules mediate this effect. Accordingly, we sought to identify potential interacting partners of FAM3C by employing the GPS-Prot bioinformatics database (GPS-Prot: Data Visualization for Protein-Protein Interactions (gpsprot. org)) (Figure  S10 ). The Rasrelated proto-oncogene A (RalA), a GTPase normally involved in regulating cell motility, vesicular trafficking, and endo/exocytosis  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] , was identified as a plausible binding partner of FAM3C. Activation of RalA through phosphorylation at the C-terminal Ser194  [35]  residue is integral in Ras-mediated tumorigenesis, invasion, and metastasis  [38, 39].
Funding This research is supported by the  National Research Foundation Singapore  and the  Singapore Ministry of Education  under its  Research Centres of Excellence initiative. This work was also funded by the  Singapore Ministry of Health's National Medical Research Council  ( NMRC/CSA-SI/0006/2016 ), the  National Research Foundation Singapore  and the  Singapore Ministry of Education  under its  Research Centres of Excellence initiatives (Boon-Cher Goh) ; the  Singapore Ministry of Health's National Medical Research Council  ( NMRC/CNIG/1146/2016 ,  Lingzhi Wang ),  NCIS-N2CR Seed Grant  ( Boon-Cher Goh ,  Lingzhi Wang ) and the  Singapore Ministry  of.
Synapse model under AICD and Aβ conditions We employed a phenomenological voltage-dependent NMDAR-based synaptic plasticity model, developed in our previous study  (Dainauskas et al. , 2023)  (ModelDB accession number 267680), and extended it to incorporate the effects of AICD and Aβ in AD. The model relies on the functioning of the GluN2A-NMDAR and GluN2B-NMDAR subunits as the separate mediators of LTD and LTP, respectively. It is assumed that LTP is mainly dependent on GluN2B-NMDAR  (Morishita et al. , 2007; Andrade-Talavera et al. , 2016; Pousinha et al. , 2017) , and GluN2A-NMDAR is the main mediator of LTD  (Morishita et al. , 2007). Schematic diagram of the synapse model in control conditions and under the influence of increased AICD and Aβ is presented in Figure  1. In the model, active NDMARs, consisting of GluN2B-NMDAR and GluN2A-NMDAR subunits, trigger LTP and LTD functions φ NMDA LTP and φ NMDA LTD , respectively, that represent second messenger pathways responsible for LTP and LTD induction, such as CaMKII and phosphatase activation, in a phenomenological manner.
Multicompartmental model of CA pyramidal neuron We used a morphology reconstruction of a CA1 pyramidal neuron downloaded from: http://www. neuromorpho. org  (Peng et al. , 2016 ) (cell fx_CA1_7. CNG. swc). The channel kinetics were based on those used in previously published articles on CA1 hippocampal neurons and validated against a number of experimental findings. The model was implemented with channel kinetics used by  Migliore et al. (2018)  (ModelDB accession number 244688). In particular, we used a delayed-rectifier type current (K DR ), two A-type potassium (K A ) currents (for proximal and distal dendrites), a delayed type current (K D ), three types of voltage-dependent Ca 2+ currents (CaL, CaT, and CaN), the slow afterhyperpolarization (AHP) Ca 2+ -dependent K + current (kCa), a Na + current, and a calcium pump.
The enhancement and partial blockade of the GluN2B-NMDAR-gated channel in AICD and Aβ conditions was simulated by varying the conductance g NMDA GluN2B (Equation S14 in Supplementary material). All simulations were carried out the NEURON simulation environment  (Hines and Carnevale, 1997)  integrated with Python  (Van Geit et al. , 2016). Optimization of the neuron parameters was performed as a parallel code executed on different high-performance computing systems: JURECA (Juelich Supercomputing Center, Germany), Galileo100 (CINECA, Italy), and Piz Daint (Swiss National Supercomputing Center CSCS). All model files in NEURON and Python are available for public download under the ModelDB section of the Senselab database, accession numbers 2014822 and 2015000 (https://senselab. med. yale. edu/ModelDB/).
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the  Research Council of Lithuania ,  Agence Nationale de la Recherche (France)  ( Flagship ERA-NET Joint Transnational Call JTC 2019 in synergy  with the  Human Brain Project , No. S-FLAG-ERA-20-1/2020-PRO-28 ), the  EU Horizon 2020 Framework Program for Research and Innovation  ( Specific Grant   945539 ,  Human Brain Project SGA3 ), the Fenix computing and storage resources was provided under Specific Grant Agreement No. 800858  ( Human Brain Project ICEI ), and a grant from the  Swiss National Supercomputing Center (CSCS)  under project ID ich011. MM also acknowledges administrative and technical assistance from  Alessia Bonafede  and a contribution from the  Italian National Recovery and Resilience Plan (NRRP) , M4C2 and funded by the  European Union -NextGenerationEU  (Project  IR0000011 ,  CUP B51E22000150006 ,  EBRAINS-Italy ).
Further, there is great potential for rigorous video assessment to enhance clinical trial performance, which could reduce the burden on study participants and thereby enhance recruitment and retention. The Myasthenia Gravis Core Examination (MG-CE)  [7]  was recommended for telemedicine evaluation of patients with myasthenia gravis (MG), and it involves specific aspects of neurological examinations critical to the comprehensive assessment of patients with MG. The National Institutes of Health Rare Disease Clinical Research Network dedicated to MG, MGNet, initiated an evaluation to assess the feasibility and validity of MG-CE for use in future clinical trials. These assessments were video recorded using the software Zoom (Zoom Video Communications), and we used the evaluations performed at George Washington University with the following 2 objectives: (1) assess workflow efficiency by making the data acquisition and analytics fully automatic and (2) evaluate the potential to quantitate the evaluations.
MG-CE and Automatic Data Acquisition The study used recorded telemedicine evaluations of individuals with a clinical-and laboratory-confirmed diagnosis of MG. The patients were provided instructions regarding their position in relation to the cameras and level of illumination, and were told to follow the examiner's instructions. We used videos of 6 subjects recorded twice within 7 days to develop our algorithms. One normal control subject was used to evaluate the methodology prior to evaluating MG subject videos. The MG-CE is summarized in Table  1 , and a full description has been provided previously  [7]. The examination required 2 broad categories of processing:  (1)  the computer vision algorithm to analyze video focusing on eye or body motions and (2) the analysis of the voice signal, which requires a completely different category of signal processing methods.
In the one-side tongue-to-cheek push, the upper lip is deformed. All these changes can be monitored in time easily by tracking the relative position of the points in the facial model that mark the mouth (Figure  1A ). We describe our computer vision methods based on an analysis performed with 3 different formats of videos. The first was acquired with our new telehealth platform using a high-definition camera with a patient who has a normal cheek puff response. The second was acquired on a control subject with a cell phone camera (Apple 13 system, Apple Inc), and the third was extracted from the MGNet data set. We tested the impact of diversity with White subjects, subjects with dark sun tan, and subjects who were African American. We demonstrate in the Results section which metrics appeared to provide the best assessment.
An alternative solution is to use a fractal feature as reported previously  [33]. This methodology seems to reach a greater accuracy of about 90% and does not require a training set. Lip and jaw movements are related to dysarthria  [34]. We are not aware of any systematic study that combines automatic lip motion tracking and speech digital analysis to assess breathing and dysarthria in patients with MG. We assessed more than half a dozen algorithms producing various sound metrics to check for the potential best voice analysis candidate to assess MG patients. As the analysis of the pitch of voice did not show any outliers in the data set and the energy metric analysis was impacted by the environment and control of the exercise, we restricted the description to the most promising algorithm. To compute voice features, we used the following steps.
We separated the interval of time when the subject spoke from when the subject was silent. We used the MATLAB function "detectSpeech"  [35]  on the original signal. The function "detectSpeech" provides the start and end times of each so called "speech segment. " The frequency of signal acquisition was about 1000 Hz. For comparison, we used our own custom-made algorithm to extract speech segments using sampling of size 60 of the voice signal. The signal now had an equivalent frequency of acquisition of about 17 Hz. We then used averaging on each sample of the original signal to dampen noise. The signal was then smoother, and we could use a threshold to filter out noise without building up a large number of small gaps corresponding to "no sound. " We looked in the sound track of "counting to 50" exercises for the largest 50 time intervals of sound above noise level.
Controlling the Setting and Hardware To avoid changes in the quality of the recording (frames and audio), it is important that the hardware used is identical for all sessions and is calibrated. In an attempt to improve the quality of data acquisition with the future development of clinical studies, we built a new telehealth system with a high-definition camera and microphone that can be controlled remotely by the examiner. The recording is now performed on the patient side to obtain the raw footage of the video and audio in order to optimize maximum resolution and avoid any issues with network connection quality during the examination. Different interfaces and tools are added compared to Zoom's control system to assist the patient and the doctor to focus on the consultation and not the technology.
Standardization of the Data Acquisition We evaluated our methods and identified large variability in the data acquisition and mode of operation for the assessments performed by the single examiner. The purpose of the primary study from which we obtained the videos was to assess test/retest variability and interexaminer variation in performance of telemedicine evaluations (manuscript in preparation). Our goal was to assess accurate and robust measurements from the MG-CE in order to remove human bias. The videos from the clinical study are quickly showing some limitations as the hardware used to film was not identical and the recording was performed on the doctor side, linking the quality of the frames to the quality of the network on both sides. We will next present our methodology and the platform.
Eyelid Position and Eye Movements We have used a data set of images of 6 patients and 3 healthy subjects with broad diversity in skin color, eye color, ocular anatomy, and image frame resolution to test the accuracy of our approach. We identified 72 ROIs of patients' eye movements and then annotated them with ground true measures obtained by manually zooming in the computer images. On average, we found the pupil location within 3 pixels and the distance from the pupil to the upper eyelid within 2 pixels independent of the image frame resolution. The relative accuracy in pixels was independent of the camera used. The standard Zoom video has 450×800 pixels per frame, a smartphone has 720×1280 pixels, and the Lumens B30U PTZ camera (Lumens Digital Optics Inc) has a resolution of 1080×1920. Overall, Inteleclinic doubles the resolution of a standard Zoom call and provides a submillimeter accuracy of lid position and eye motion.
Arm Position and Sit-to-Stand Movement Most videos of the MGNet data set offered only partial views of the body during these exercises and showed great variability. The model (Figure  1 ) failed under such conditions. Figure  10  shows a representative example of the arm angle decay due to weakening during the 120-second assessment of one of the ADAPT patients. The measurement exhibited some minor noise. We used a high-order filtering method  [43]  to provide a meaningful graphic to limit the noise of the method and maintain the trend that could be used for the physical examination assessment. The decay of both arms was linear and significant. It was however difficult for the medical examiner to quantify the slope or even notice it. Figure  11  shows an example of the vertical elevation examination with respect to time for both hips, involving tracking the elevation of landmark points (  23 ) and (  24 ) (Figure  1B ) as a function of time.
Counting Exercise This evaluation is used to assess breathing and speech quality. We used a data set of 6 patients involving 2 sessions and 9 additional healthy subject voice exercises. Audio files were cut to start and end approximately within 1 to 3 seconds of initiation and the end of counting. Based on the evaluation of patients performed by the physician according to the protocol  [7] , there were rarely differences between the first visit and the second controlled visit for ptosis and diplopia grading. We found however that most of the metrics described above had some variability from one visit to another, and might be considered as more sensitive metrics than the current physician examination. We will report here on our main findings with these metrics. Instead of using the maximum number reached during the single-breath counting exercise, we used the duration of the exercise itself to grade the exercise.