dataesr/scientific-paragraphs-categorization huggingface dataset:
We validated changes of selected genes by qRT-PCR in organoids of all genotypes  (Fig 4C). The loss of Cftr mRNA was more pronounced at the protein level, since CFTR protein was lost in double mutant organoids  (Fig 4D). To see if similar changes occur upon inhibiting CDK8/19 kinase activity, we performed RNA-seq analysis of WT organoids compared with WT organoids treated with Senexin B for 16 h or 24 h. 1,505 genes were identified as deregulated after 16 h of CDK8/19 inhibition, 189 of which by a factor of two or more, compared to 4,268 genes after 24 h (486 changes more than two-fold; Fig 4E, Dataset EV3). Since longer Senexin B treatments might have indirect effects, we performed GSEA on genes with altered expression in 16-h samples. This showed a strong overlap with genes deregulated in knockout of Cftr as well as with genes downregulated in the human colorectal cancer cell line HCT-116 upon treatment with a different CDK8/19 inhibitor, cortistatin A (Fig 4F ).
CDK8 and CDK19 regulate the CFTR pathway in the small intestine. A Gene set enrichment analysis (using Enrichr database) of highly deregulated genes in Cdk8 À/À /Cdk19 À/À organoids. Manually curated signatures extracted from RNAseq studies in GEO where gene expression was measured before and after drug treatment, gene perturbation or disease. B Venn diagrams indicating intersection of genes with altered expression in the Cftr mouse knockout small intestine (ileum, top, and duodenum, bottom) and Cdk8 À/À / Cdk19 À/À organoids. Numbers below give the percentage of each gene set in the intersection. C qRT-PCR analysis of indicated mRNA levels in Wt, Cdk8 À/À , Cdk19 À/À and Cdk8 À/À /Cdk19 À/À organoids, showing mean AE SEM. Three technical replicates were used for the analysis. D WB of indicated proteins extracted from Wt and Cdk8 À/À /Cdk19 À/À organoids.
Included are CFTR positive and negative control cell line samples obtained from the Cystic Fibrosis Foundation (CFF). Amidoblack was used as loading control. E Dot plot analysis of differentially expressed genes (DEGs) in Senexin B (Snx B)-treated (10 lM; for 16 h, top, and 24 h, bottom) organoids. Red dots: DEGs with P-value ≤ 0. 05; purple dots: log 2 fold change (LFC) > 1 or < À1, P-value ≤ 0. 05; grey dots: not significant, NS. Numbers inside plots indicate the number of genes deregulated more than 2-fold. F Gene set enrichment analysis (using Enrichr database) of highly deregulated genes in Senexin B-treated (16 h) organoids. The signatures were extracted from RNAseq studies in GEO where gene expression was measured before and after drug treatment, gene perturbation or disease, and an additional step of manual curation was performed to select those related to the intestine.
Briefly, 4 lm thick sections were dewaxed in xylene and rehydrated in graded alcohol baths. Slides were incubated in 3% H 2 O 2 for 20 min and washed in PBS to quench endogenous peroxidase activity. Antigen retrieval was performed by boiling slides for 20 min in 10 mM sodium citrate buffer, pH 6. 0. Nonspecific binding sites were blocked in blocking buffer (TBS, pH 7. 4, 5% dried milk, 0. 5% Triton X-100) for 60 min at RT. Sections were incubated with anti-b-catenin antibody diluted in blocking buffer overnight at 4°C. Envision+(Dako) was used as a secondary reagent. Signals were developed with Fast DAB (Sigma-Aldrich). After dehydration, sections were mounted in Pertex (Histolab), imaged using the Nanozoomer-XR Digital slide Scanner C12000-01 (Hamamatsu) and analysed using NDP. view 2 program (Hamamatsu).
Small intestine organoids and CRISPR-Cas9-mediated Cdk19 targeting The genetic background of intestinal organoids and mice used in this study was identical. Cdk8 lox/lox and Cdk8 lox/lox /Villin-Cre-ERT2 mice were used to obtain small intestine organoids. Establishment, expansion, and maintenance of organoids were performed as described previously  (Sato et al, 2009). To induce the Cre-mediated recombination of Cdk8 in vitro, organoids were cultured during 7 days in medium supplemented with 600 nM 4-Hydroxytamoxifen (Sigma H7904) resuspended in ethanol. Evaluation of knockout efficiency was performed using genotyping, qPCR, and Western blotting. CRISPR/Cas9-mediated genome editing was employed to remove CDK19 from the organoids. CRISPR single guide RNA (sgRNA) targeting exon 1 in mouse Cdk19 (CDK19_Mouse: 5 0 -AAAGTGGGACGCGGCACCTA-3 0 from Zhang lab database) was cloned as synthetic dsDNA into lentiCRISPRv2 vector as described  (Sanjana et al, 2014 ; provided by F.
The same kit was used for all other rabbit primary antibodies to amplify the signal. Slides were incubated with DAB (cat# 05266645001), then counterstained with haematoxylin II (cat# 790-2208) for 8 min, followed by Bluing reagent (cat# 760-2037) for 4 min. Slides were then dehydrated with Leica autostainer and coverslipped with Pertex mounting medium with CM6 coverslipper (Microm). Brightfield stained slides were digitalised with a Hamamatsu NanoZoomer 2. 0-HT scanner and images were visualised with the NDP. view 1. 2. 47 software. For Paneth cells, tuft cells, b-catenin, and Brdu detection, the IHCs were performed manually. After deparaffination and rehydration, demasking of antigenic sites was performed by boiling the slides for 20 0 in 10 mM Na-Citrate pH 6. 4. After cooling down, slides were treated with 3% H 2 O 2 , 5 0 at RT for peroxidase inhibition.
Bioinformatic analysis The raw reads obtained in fastq format were subject to quality control using the FastQC software. The reads passing the quality control were aligned to the mouse reference genome (GRCm38. p6) and the counts per gene were quantified using the tool STAR 2. 6. 0a(2). The Ensembl mouse genome annotations (release 93) were used to map the reads to each gene and their corresponding transcripts. Differential gene expression analysis was performed in R using the DESeq2 library. MA-plots showing the deferentially expressed genes were generated with an in-house script and the gene set enrichment analysis were performed using the "enrichR" library, in both cases using the R programming language.
Time-lapse microscopy Organoids (Wt, Cdk8 Lox/Lox , Cdk19 À/À and Cdk8 Lox/Lox /Cdk19 À/À ) were treated with 600 nM 4-hydroxytamoxifen during 7 days. Once recombination of the LoxP sites flanking Cdk8 exon 2 was obtained, images were taken every 4 h during 7 days on an inverted microscope (Axio Observer, Zeiss) equipped with a heated chamber allowing constant temperature (37°C) and CO 2 flow (5% CO 2 ). CCD camera (Princeton Instruments (Micromax), pixel = 6. 7 lm), with 10x/0. 3 DRY PH1objective, correction ECPLAN Neofluar, 5. 2 mm working distance. Acquisition software was MetaMorph 7. 8 (Molecular Devices, LLC). Images were analysed using Image J software to calculate the time for release of mucus in the lumen of the organoid (observed as a dark staining in the centre of the organoid).
Statistics Graphs and statistical analyses were performed using Microsoft Excel 16. 50 and GraphPad Prism6 using analyses described in legends. WB with intestine epithelium samples from mice presented in (A) confirm the absence of CDK8 protein in Cdk8 À/À mice. Amido-black staining was used as loading control. C, D IHC staining of (C) CDK8 and (D) Ki-67 in small intestine and colon samples from Apc À/À /Cdk8 +/+ and Apc À/À /Cdk8 À/À mice. Scale bars, 100 lm. (D), right: quantification of the Ki-67 positive area (% of the total area of the intestine presenting positive staining, quantified using QuPath  (Bankhead et al, 2017)  and Image J software; mean AE SD). Two-tailed P-value of unpaired t-test is indicated: ns, not significant (P > 0. 05; n = 6 biological replicates).
Acknowledgements LA was funded by the  Montpellier FHU Cancer  programme and by the  Fondation pour la Recherche Medicale ; GD was funded by the  French National Cancer Institute (INCa) ;  SP, CB-P , and FG are CNRS employees; DF, PJ, and LK are Inserm employees. This work was undertaken with support from  INCa  ( PLBIO10-068  and  PLBIO15-005 ), and the  LNCC  ( EL2013. LNCC/DF  and  EL2018. LNCC/DF ). RHEM histology facility was supported by  SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553 , the  European regional development foundation  and the  Occitanie region (FEDER-FSE 2014-2020 Languedoc Roussillon ). We thank  Sylvain de Rossi  and  Volker Baecker  of the Montpellier Resources in Imaging (MRI) platform for help with microscopy and analysis. We thank  Max Chen  and  Igor Roninson  for providing Senexin B, SNX-631 and for helpful advice, and  J.
Mobile networks face challenges such as data traffic growth, increasing connected devices, spectrum limitations, and costly infrastructure updates. In this context, opportunistic communication strategies appear to assist in scenarios such as data offloading, expanding the capillarity given by users' mobility, delivering content in challenging situations (e. g. , emergency, rural areas, or crowded places), and fostering innovative applications. State-of-art opportunistic strategies extract human mobility characteristics (e. g. , social and spatial) to improve content delivery's cost-effectiveness. Nevertheless, due to constraints, such as the limited availability of real-world datasets or the lack of a human-centered vision in networking, most previous works relied on traditional or simplified human-aware metrics to reach their purposes.
Next-generation proposals need a more in-depth vision of peculiar aspects hidden into mobility datasets while working with more realistic scenarios. While building TOOTS, a novel human-aware opportunistic communication strategy for improved content delivery cost-effectiveness, we learned that several phases play a significant role in reaching strategy's superior performance. This work guides the reader through the whole process necessary for building the Tactful Opportunistic Communication Strategy (TOOTS). The proposal consists of the following steps: learning humanaspect best practices from state-of-art; characterizing the traces while obtaining strategy's insights targeting superior performance; using, proposing, and analyzing human-aware metrics; combining such metrics and insights into a complete strategy for cost-effective content delivery in a more realistic scenario, and finally, evaluating the strategy through discussions of its best practices and shortcomings for future work.
Introduction Opportunistic networks (OppNets) and Device-to-Device (D2D) communication play an essential role as enablers of emerging applications and high-demanding requirements of the modern and future mobile network generations, such as 5G  [1, 2]  and 6G  [3]. In many of the envisaged scenarios with low-latency and high-speed data transmission, or even with delay-tolerant communication, D2D  [4]  should benefit from AI techniques boosted by human-aware information and edge technologies, such as mobile edge computing (MEC)  [3, 5]. In this context, D2D-enabled OppNets have the potential to assist in tasks such as data offloading (given the mobile data traffic and the number of connected devices growth), increasing network capillarity by its users' mobility, providing connectivity in challenging situations One of the challenges that state-of-art work had in this context was the limited availability of real-world datasets  [6, 11]  and the lack of a human-centered vision in computer networking.
The works mentioned above demonstrate a lack of initiatives beyond traditional techniques or limited human-mobility features. So, this work identifies spatiotemporal routines, related consequences (e. g. , wireless encounters), and movement decisions (e. g. , motion direction) with more detail and precision. In this paper, we characterize and evaluate NCCU  [13]  and GRM  [14]  datasets to extract human features and metrics later combined in an opportunistic communication strategy that targets improved performance in content delivery cost-effectiveness. As a matter of fact, we learned that several preliminary phases play a significant role in reaching strategy's superior performance. Whenever the human factor is part of the solution, we must have a broader view of each step towards building the strategy.
Thus, The main contribution of this work is the entire process for developing a tactful (i. e. , human-aware) opportunistic communication strategy able to improve content delivery costeffectiveness. This process (Fig. 1 ) was used for building and introducing the Tactful Opportunistic COmmunicaTion Strategy (TOOTS)  [15]  and consists of: • Discussing how state-of-art work dealt with the human aspect in their solutions and what kind of improvements can come from their approaches; • Analyzing real-world and synthetic datasets while transforming the obtained information into insights for later improving the performance of the opportunistic strategy; • Combining tactful metrics and intuitions with more granularity and precision than state-of-art to build a full strategy (TOOTS) aiming at improving content delivery cost-effectiveness; • Comparing TOOTS' effectiveness on its two phases with stateof-art enhanced Store-wait-forward, Epidemic, and Bubble Rap algorithms through real and synthetic traces.
For instance, in a scenario with a 30 m communication range, TOOTS can reach a 100% delivery ratio with 28%, and 73% reduced delivery latency; and 16% and 27% reduced overhead, respectively, in the realworld and synthetic datasets. In the next scenario with a more restricted communication range (10 m), TOOTS is the fastest strategy and the only one that delivers 100% of the contents in the real trace. Through the whole process for building the strategy, this paper shows that improving the performance of an opportunistic scenario in terms of overhead, delivery rate, and delivery latency (i. e. , its costeffectiveness) is possible. Since previous work in this context has not necessarily followed some best practices presented herein, this process can serve as a guideline for other opportunistic strategies and future human-aware networking solutions.
The results showed herein cope with  [5] , which discuss the need to have a more in-depth look into human characteristics to build tactful networking solutions. In  [15]  we introduced the preliminary results of this research. Differently from the previous publication, this paper includes: (i) a thorough review of stateof-art with discussions of each related work; (ii) characterization results from the NCCU and GRM datasets, discussions, and insights towards reaching strategy's improved performance; (iii) metrics descriptions, mathematical formulations, and evaluations throughout the datasets' populations; (iv) TOOTS performance evaluation in terms of delivery rate, delivery latency, and overhead in a mobile scenario, including parameters such as varying communication range. To the best of our knowledge, this is the only work in its context that evaluates different communication ranges, which influences the strategy's effectiveness.
The steps in Fig. 1  also reflect the sequence of this paper's remaining organization. Section 2 discusses background work. Section 3 features the mobility models (datasets) description and characterization. Section 4 presents the novel temporal approach, the metrics of the strategy, results, and discussions from metrics analysis, and a state-of-art algorithm evaluation as a motivation to build TOOTS. Section 5 introduces TOOTS, including its scenario architecture and the proposed algorithms for content dissemination and forwarding. Section 6 presents the experimental results and analysis. Finally, Section 7 concludes this paper and points out future work.
Related work The state-of-art opportunistic forwarding algorithms extracted metrics or decision factors from real-world or synthetic mobility datasets  [6, 7, 10, 11, 16]. Human mobility hidden into the datasets is dynamic but shows regularity  [17, 18]  of visited locations  [19] , encounters  [20] , displacements  [21] , time  [18, 19] , space  [18, 21] , and other aspects  [22]. Despite the high predictability of human mobility  [23, 24] , there is a challenge in choosing the forwarders through the combination of human-aware decision factors in the context of opportunistic forwarding. Bubble Rap: A social-aware forwarding algorithm for delay-tolerant networks  [6]. It relies on two characteristics extracted from human mobility: centrality, related to social bounds (i. e. , encounters), and community detection, defined as a socially connected set of people with meaningful relationships.
GROUPS-NET: A social-aware forwarding strategy that relies on the regularity of group meetings  [10]. This algorithm is parametercalibration-free and does not require community detection. Compared to Bubble Rap in a larger scenario, GROUPS-NET improved the overhead with a similar delivery rate. Despite the contributions, the algorithm relies on a group-to-group path centralized calculation every time a given content needs to be forwarded opportunistically from a source to a destination. In realworld larger populations, performing these calculations is challenging. Finally, GROUPS-NET also requires validations in other datasets than campus, where mobility is restricted. SAMPLER: This forwarding algorithm  [11]  relies on social-aware, spatial-aware, and individual mobility extracted from a real-world dataset NCCU.
TOOTS positioning: According to the initiatives discussed herein, we found that learning from human routines hidden into mobility datasets is essential to develop improved opportunistic networking solutions. This work's first insights came through analyzing and discussing how the human aspect is part of the state-of-art solutions. Unlike from state-of-art, in this work, we format our metrics and decision factors with a more precise link with time. Our algorithm relies on different human activities present in human mobility over periods of the day. In previous work  [12] , we confirmed that human mobility varies according to different periods of the day. Among the human-mobility features considered are the routines (i. e. , spatiotemporal patterns), related consequences (e. g. , encounters, PoI visiting), and displacement (e.
g. , the quantity of movement and motion direction). TOOTS (i. e. , the strategy described in this work) relies on probabilistic spatiotemporal aspects, social information (i. e. , wireless contacts), PoI awareness (related to network cells), and individual's mobility (displacement and direction awareness). In Table  1 , we resume the properties of the state-of-art solutions discussed herein. Generic insights, metrics, and decision factors might not suit different kinds of populations. Aspects such as culture, environment, node density, and urban transportation planning can impact a population's mobility (including the contact dynamics). Each population has particular and common characteristics, calling for the need to evaluate datasets of different kinds with proper temporal aspects chosen accordingly.
Datasets: Description and characterization As for acquiring insights, building and evaluating our opportunistic communication strategy, we worked with two mobility datasets: NCCU  [13]  and GRM  [14]. Both datasets provide anonymized user information. NCCU: Is a real-world mobility trace featuring 115 users' displacements inside the campus of the National Chengchi University (NCCU) in China over two weeks. This campus measures 3764 m × 3420 m. The NCCU dataset came through an Android app named NCCU Trace Data, which recorded GPS data, application usage, Wi-Fi access points, and Bluetooth devices in proximity. In mobile sensing applications like NCCU Trace Data or online D2D games, the user consents to share sensitive information (e. g. , mobility) to participate. According to the authors, considering the consumption of a smart handheld device, the app was configured to collect data every 10 min, store it locally and upload it to a server every day.
In case of GPS data loss (e. g. , due to weak indoor signal), they used interpolation between available coordinates. NCCU also collected users' data throughout questionnaires, including gender, grade, majors, personal interests, places on campus they would go most frequently, Facebook ID, and Facebook usage behavior. Despite this varied amount of data collected, only the anonymized coordinates of the 115 users are publicly available. GRM: This dataset comes from a synthetic mobility model (Group Regularity Mobility Model). According to the authors, this dataset can capture users' social (i. e. , encounters) regularity. The authors made available GRM synthetic mobility traces with 100, 1000, and 2000 users' movements inside an area of 1500 m x 1500 m. We use the version with 1000 users to simulate a larger-scale network in this work.
The GRM model receives as input a social network which can be a real social network or a synthetic one generated by a social network model. This social network applies for defining the nodes that will be present in the groups of users. Additionally, GRM receives as input the simulation configurations for the dataset, including the dataset area, duration (e. g. , two weeks), number of nodes, and the number of groups. Finally, GRM's last input is a set of statistical parameters which can be extracted, for example, from other real datasets. Since this research deals with human-aware sensitive information, we remark that privacy-preserving mechanisms are essential. Novel human-aware applications or solutions such as the one proposed herein need to guarantee that individual rights and user-sensitive data are protected.
As the datasets used are already anonymized, we skipped privacy discussions on this paper. We present more detailed privacy discussions in  [5]. Among the contributions, we proposed a general framework for processing humanbehavior raw data and preserving its privacy while assisting Tactful (i. e. , human-aware) Networking applications. The privacy discussions feature recent advances in privacy-preserving big data, including data mining, management, and publishing. Below we discuss the results of both NCCU and GRM datasets' manipulation. The first analysis herein performed is of pairwise contact information. We define a contact as two network nodes in a communication range at the same temporal instant. To the best of our knowledge, this work is the only in its context that analyzes different contact ranges.
We use 10 m and 30 m. The first commonly appears in stateof-art contextualized as the D2D Bluetooth range  [11]. The latter is an average range for WiFi Direct, which works with up to 200 m communication range  [28]. We used these different contact ranges to evaluate how this factor impacts the opportunistic communication strategy results and how they link to dataset social aspects. The proposed solution does not depend on the above technologies since the chosen communication ranges are only for simulation purposes with more realistic assumptions. Fig. 2 (a) plots a Complementary Cumulative Distribution Function (CCDF) of the aggregated contact duration per day on both datasets. For each unique pair of users that gets into contact, we sum all their contact durations and divide by the number of days evaluated (i.
e. , 11) in each dataset. In Section 6, we present the evaluation setup and detail the use of the 11 days of data from the traces. In Fig. 2 (a), we see that NCCU has longer contact durations when the contact range is higher (30 m), while in the GRM trace, the behavior looks quite similar for both ranges. The aggregated contact duration per day in the GRM dataset is shorter, with only an average of 5% of those over 10 min. This dataset characteristic can impact a forwarding strategy's effectiveness if, for example, there is less bandwidth available and heavier content to forward opportunistically. Following up in the contact analysis, Fig. 2 (b) shows the evaluation of the traditional mobility metric Centrality Degree (CD)  [6]  in both datasets. The plot shows the nodes' normalized CD as the total amount of unique contacts during 11 days of mobility data.