dataesr/scientific-paragraphs-categorization huggingface dataset:
Contribution of direct Neat1 RNA targets to the total RNAs targets of paraspeckles To evaluate the proportion of paraspeckle RNA targets in GH4C1 cells that were directly connected by Neat1 through RNA-RNA interactions, we compared our present list of direct Neat1 targets with the dataset of total paraspeckle RNA targets we have previously established using Neat1 RNA pull-down experiments performed on cells fixed by PFA  (12). Indeed, while psoralen used to fix the cells in the present study allowed to selectively crosslink only RNA-RNA interactions, PFA permitted to crosslink both RNA-RNA and RNA-protein interactions leading to the pull-down of both direct and indirect Neat1 targets. By crossing over the list of 1791 RNAs directly targeted by Neat1 (List-AB) with the list of 4268 RNAs previously identified as paraspeckle targets  (12) , 1398 genes appeared common to the two lists (Figure  4A ; Supplemental Table  3 ).
Among these 4 zones, only Neat1-zone1 was able to bind the 6 RNAs tested. Furthermore, while Neat1-zone1 was shown to be G-rich (37%), the nucleotide composition of the 3 other zones didn't display any specificity. In addition, none of the 4 zones contained interspersed repeat as determined by using the program RepeatMasker (http://www. repeatmasker. org). Finally, after alignment no consensus sequence was found in the 6 RNA targets that can bind any of the 4 zones of Neat1.
Overall, these data show that the LRRK2-G2019S mutation causes an acceleration of neuronal differentiation and mDA (B) Histograms showing cumulative gene expression distributions, for mDA-specific genes, during differentiation. 250 cells per population per day were analyzed. Statistical analysis was performed via z-test followed by Bonferroni post hoc test, ****p < 0. 0001. Differentially expressed genes and percentages of particular lists are presented in Figure  S4B  and in Table  S2. (C) Branching analysis of cell transitions using the Monocle 2. 0 R package based on the complete pool of data. (D) Distribution of corresponding cellular pseudotimes for PD2/WT and PD2/G2019S. Experiments were performed one time with one replicate per time point. specification. Additionally, these results demonstrate that prior to differentiation, the gene expression pattern in LRRK2-G2019S NESCs already differs from that of the isogenic control cells.
Analysis of differentially regulated genes The output of the DropSeq experimental setup (Figure  S4A ) is the expression matrix where rows represent genes, and each column is an individual cell. The elements of this matrix represent the measured intensity of gene expression, i. e. , the measured number of mRNA molecules leading to the gene expression matrix. To extract the relevant information on the developmental status of cells from the large dataset, we defined five lists of specific genes (Table  S1 ). In particular, these lists contain genes covering stemness (NESC), dopaminergic neurons (mDA neurons), cell cycle, and related to pro-apoptotic and caspases genes. For each of these lists, we compared the expression of the corresponding genes between genotypes within sampling points. Since the gene expression levels were measured at the single cell level, we analyzed the distribution of gene expression across cells.
Cluster detection, cell identity mapping, and quantification For a deeper analysis of cell states and the differentiation process, we developed a custom analysis pipeline using MATLAB (version R2017a; Mathworks) scripts. To preprocess the gene expression matrices as described. We restricted the number of cells (columns) to a subset of the most maximally expressed cells, according to various factors such as the minimum level of expression, the cumulative level of expression as described. This led to the number of cells used for each group and day as indicated  (Figures S4G and S4H). Then, we performed a cluster analysis of this high dimensional data to investigate cell type-specific gene expression across different days, and for the different cell lines. We first reduced the dimensionality of the data by principle component analysis (PCA)  (Bishop, 2006).
Then, using this shuffled vector, we counted again the number of cells of each genotype contained in the particular cluster. We repeated this permutation 20,000 times, finally leading to the distributions of a number of cells for each group in the cluster. Hence, given these distributions and the original number of cells of each genotype in the cluster, we computed a p value that accounts for an abnormal number of cells of one specific type in the highlighted cluster. Data pre-processing scRNA-seq in organoids From midbrain organoids datasets, cells having unique feature counts over 2,500 were removed as probable doublets or multiplets. Similarly, low-quality cells or empty droplets, were further filtered out with unique feature counts below 100 (for day35 data) -200 (for day70 data), and mitochondrial transcripts above 30% (Figure  S1 ).
MT genes were removed from midbrain organoid datasets after QC by deletion of all row names with pattern 'MT'. After QC, WT35 midbrain organoids included 2864 cells, WT70 -2005 cells, MUT35 -2946 cells and MUT70 -2660 cells. To better transmit the biological information between in vivo and in vitro ventral midbrain datasets, midbrain organoid data (WT35, WT70, MUT35 and MUT70) were integrated with embryonic midbrain data (La  Manno et al. , 2016)  using Seurat integration analysis workflow  (Stuart et al. , 2019). Integration was performed based on the top 20 dimensions. RNA assay data of integrated object were log normalized and scaled to 10'000 transcripts per cell. Cell type identification scRNA-seq in organoids After integration of embryonic midbrain and midbrain organoid datasets, integrated object was scaled and principal component analysis (PCA) was applied.
Then, a regularized logistic regression classifier is trained to discriminate cells from different prototypes. The model formulation is probabilistic and can output a probability for each possible assignment. The assigned prototype is the one of maximum likelihood. With respect to the original paper (La  Manno et al. , 2016) , we made the following minor changes. We performed a normalization of the NESC dataset. This was a normalization by total molecules across the cells of the sample and normalized the whole dataset to the same scale of the training dataset (the atlas). In particular, the normalization is this is: xij = and y ij indicate the value of the i th gene for the j th cell for the test and train set respectively. This size normalization was meant to buffer two effects: cell-to-cell variation in RNA detection that in the DropSeq can be stark, and the difference in average sequencing depth of the test with respect to the reference dataset since they were generated with different technologies (STRT versus DropSeq).
Resulting flow cytometric data was further processed using FlowJo, LLC software. First, we gatedout doublets using conservative double gating via SSH-A/SSH-H and SSH-A/SSC-W). Samples were further analyzed while considering appropriate negative, compensation, and isotype controls. Gated quantitative numbers were subjected to different groupings e8 Cell Reports 37, 109864, October 19, 2021 Article and further statistically analyzed as indicated. Cells were dissociated using Accutase incubation for 8 min, at 37 C. Accutase activity was reduced using DMEM/F12 w/o phenol-red. After 5 min 400 rcf. centrifugation cells were washed once in 1 mL ice cold DMEM/ F12 w/o phenol-red. In the last step, cells were dissociated in 300 ml DMEM/F12 w/o phenol-red and stained with final concentration propidium iodide 0.
For cell percentage/number quantification after immunofluorescence (IF), measurements were performed on at least 9 sections coming from 2 to 3 different animals. Fixed embryos with damaged tissues were excluded from any further analysis/processing. Microscope images were processed with Photoshop or Im-ageJ software, by randomly overlapping fixed-width (100mm) rectangular boxes on the area of interest, then quantifying positive cells or pixel intensity inside the boxes. When calculating percentages over the total cell number, the latter was quantified by counting DAPI + nuclei, unless otherwise specified.
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical analysis was assay and purpose dependent as indicated in the figure legends. We applied: unpaired or paired (isogenic controls) Student's t test when sample size was small and data normally distributed. For large datasets like HCS imaging data that was not normally distributed Mann-Whitney-Test without further cleaning of the data was applied. Two-way ANOVA with Bonferroni correction was applied when indicated. Tests applied indicated in the particular legends. All data are presented as mean ± SEM. Significance levels were set at*/y p < 0. 05, **/yy p < 0. 01, ***/yyy p < 0. 001, ****/yyyy p < 0. 0001. For statistical analysis, GraphPad Prism was used. Statistical analysis of single-cell RNaseq data was handled differently and as indicated.
'' For the scRNaseq data in organoids, the statistical analysis was performed with RStudio R version 3. 6. 2 using ggplot2 package. For all comparison non-parametric Kruskal Wallis test was performed. Statistical significance between comparisons are represented with asterisks: p < 0. 05 *, p < 0. 01**, p < 0. 001***, p < 0. 00001****. For the immunohistochemistry analysis in animals, data were statistically analyzed and graphically represented using Microsoft Office Excel software and GraphPad Prism (version 7. 00). Quantitative data are shown as the mean ± standard error (SEM). Data were compared by 2-way ANOVA (analysis of variance; for comparison of three or more groups) and statistical significance was set as follows: * = p % 0. 05; ** = p % 0. 01; *** = p % 0. 001.
ACKNOWLEDGMENTS The authors would like to thank  Thea van Wuellen ,  Marie Fossepre ,  Antoine Treff , and  Annegraet Daujeumont  for technical assistance. We thank  Prof. Dr. Hans R. Scho  ¨ler of the  Max-Planck-Gesellschaft ,  Dr. Jared Sterneckert  of the  CRTD ,  Prof. Dr. Thomas Gasser  of the  Hertie Institute in Tuebingen , and the  Coriell Institute  for providing the cell lines and  EMBL Gene Core at Heidelberg  for support with high-throughput sequencing. The experiments presented in this paper were carried out using the HPC facilities of the  University of Luxembourg. We also thank  Prof. Dr. Ernest Arenas  and  Gioele La Manno  for their help with Figure 1B and feedback on the manuscript. This project was supported by the  Luxembourg Centre for Systems Biomedicine (LCSB)  pluripotent stem cell core facility.
The J. C. S. lab is supported by the  Fonds National de la Recherche(FNR)  ( CORE ,  C13/BM/5791363  and Proof-of-Concept program  PoC15/11180855  and  PoC16/11559169 ). This is an  EU   Joint Programme -Neurodegenerative Disease Research (JPND) project  ( INTER/JPND/14/02  and  INTER/JPND/15/11092422 ). Further support comes from the  SysMedPD  project, which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 668738. J. W. , D. G. , X. Q. , L. G. C. , J. J. , and A. M. were supported by fellowships from the  FNR  (AFR, Aides ala Formation-Recherche). S. M. is supported by the  FNR PRIDE DTU CriTiCS , reference 10907093. M. B. and M. S. were supported by the  Jerome Lejeune Foundation  (grant  199162 ). S. B. is supported by the  FNR Core Jr   C19/BM/13626885/IDeM.
d QUANTIFICATION AND STATISTICAL ANALYSIS 100-36E Paraformaldehyde Sigma-Aldrich P6148 Antimycin A Abcam SUPPLEMENTAL INFORMATION ab141904 D-(+)-Glucose Sigma-Aldrich G8270-100 g Dulbecco 0 s Modified Eagle 0 s Medium Sigma-Aldrich Supplemental information can be found online at https://doi. org/10. 1016/j. celrep. 2021. 109864. D5030-10X1L CyQuant Thermo Fisher Scientific C7026 NUNCLON cell culture ware Thermo Fisher Scientific -- MatriGel Corning, Inc. 354277 Tween20 Sigma-Aldrich P7949 TritonX Carl Roth 3051. 3 Bovine Serum Albumin Sigma-Aldrich A4503 Fetal Bovine Serum (FBS) Thermo Fisher Scientific 10270106 Fluoromount-G Southern Biotech SOUT0100-01 EBSS Thermo Fisher Scientific 24010043 HBSS (Hanks Balanced Salt Thermo Fisher Scientific 14025-092 Solution w/ Ca and Mg) L-Glutamine Thermo Fisher Scientific 25030-024 Sodium Pyruvate 100 mM Thermo Fisher Scientific 11360-039 Deposited data Single cell RNA sequencing data This paper GEO: GSE128040 Figures and Source codes This paper https://doi.
org/10. 17881/y9k6-xa72 Experimental models: Cell lines iPSC line H1 Reinhardt et al. , 2013b N/A iPSC line H3 Dr. Bill Skarnes (The Wellcome Trust N/A Sanger Institute, Cambridge, UK) iPSC line H2 Reinhardt et al. , 2013b N/A iPSC line H4 Coriell GM23338 iPSC line P1 Reinhardt et al. , 2013b N/A iPSC line P1. 1 Reinhardt et al. , 2013b N/A iPSC line P2 Reinhardt et al. , 2013b N/A iPSC line P4 Coriell ND35367 Software and algorithms SEURAT R package https://satijalab. org/seurat/ - Monocle R package http://cole-trapnell-lab. github. 2. 4. 0 io/monocle-release/ GraphPad Prism GraphPad Software, Inc. 7. 03 MATLAB The MathWorks, Inc. R2017a (9. 2. 0. 556244) Illustrator Adobe Systems, Inc. 19. 2. 1 Photoshop Adobe Systems, Inc. 2015. 1. 2 20160113. r. 355 ImageView PerkinElmer, Inc. 1. 1. 0.
Studies on nonproprietary technologies, such as the experimental study described in  [7] , tend to explore the uncertainty when using experimental data to validate a numerical model or extrapolate to full scale. What generally has not been captured in previous works is how facility variability manifests in the model outputs of most interest to a wave energy developer. A round robin programme across multiple facilities provides this opportunity, while also providing a dataset that may be interrogated in detail by future projects to further explore the sources of this uncertainty. The previous attempt at a wave round robin programme followed an approach more typical of a commercial procurement programme, with each laboratory building the model, executing the test and conducting the analysis to a common specification.
In practice, this made a meaningful technical comparison difficult. In this test programme, the variables were reduced to a more manageable level by testing with the same model and conducting common analyses to the combined dataset from all four facilities. In this particular programme, a hinged raft device was designed and built by ECN. Hinged rafts have been explored commercially by several developers and so present a relevant and representative case study. The device deployed in this programme had a relatively simple generic geometry, but incorporated a controllable power takeoff (PTO) and force instrumentation typical of a higher technology readiness level (TRL) test programme. A parallel round robin programme explored lower TRL testing using an oscillating water column, the uncertainty analysis of which was presented in  [8].
Hinged Raft WEC Model Properties A two-body hinged raft was selected to be the higher TRL concept for the MaRINET2 wave round robin tests. This device includes a PTO emulator that produces a controlled torque. Such advanced modelling of the PTO is usually implemented for concepts having reached a TRL of 3. At TRL 3, the model scale is generally between 1:30 and 1:10. For this study, a 1:25 scale factor was chosen to model this deep-water device. The depths of the wave tanks involved vary, and therefore, the corresponding full-scale configurations will have different depths. For most of the tests, the waves of interest were deep-water waves in the different tanks. In order to provide some inputs for the design of the model and its PTO system, preliminary numerical simulations were performed using Centrale Nantes internal codes and InWave and OrcaFlex software, following the work performed in  [9].
Measurement and Instrumentation The measurement equipment can be divided into two different types: (a) directly connected to the model and used in all the facilities, as well as (b) additional measurement systems provided by the facility itself. The latter were captured by the individual data capturing systems, but in all cases, the synchronisation was based on an electrical pulse provided by the tank. Different measurement frequencies were used across the different data capturing systems and facilities, but all data were resampled to 100Hz for the combined dataset and data analysis.
Model Instruments and Data Acquisition In order to provide commonality and avoid calibration discrepancies across the infrastructures, the same National Instruments CompactRIO data acquisition system was used for all tests. A custom LabView programme, developed by ECN, provided the acquisition and control of the model. Power was inferred from hinge torque and hinge velocity, avoiding any losses through bearings and couplings to the motor. This approach is also consistent with current best practice and guidance (e. g. ,  [10] ). The torque transducer was a DRBK model from ETH Messtechnik with a 100 Nm range. The angular velocity was given by the encoder associated with the motor. Four Applied Measurements DBBSMM 250 N axial load cells were mounted at the mooring attachment points on the raft and were also captured by the common data acquisition system.
The key parameters examined across the four facilities related to the motions of the fore and aft rafts (pitch, heave and surge), hinge angle, mooring loads and power. These outputs were deemed to provide the key dynamic and kinematic information typically used in the characterisation of a WEC (e. g. ,  [10] ). The analysis could be further expanded as required using the collated dataset, as noted and discussed below. In broad terms, the agreement across the facilities was good and the fundamental behaviours of the WEC were consistent across test programmes. This was observed both in the frequency domain characterisation (RAOs) and summary parameters (e. g. , mean, median, significant value). Where significant deviations did occur (e. g. , forward raft RAO at higher wave heights), this could be traced to measurement issues, rather than a change in response of the WEC itself.
Dataset Availability and Applications The dataset obtained from this experimental programme will be made publicly available through the MaRINET2 project's data preservation tasks. In addition to the wave and model parameters examined here, it also includes additional wave gauge data (as illustrated in Figure  3 ), more detailed PTO data (e. g. , damping coefficients, hinge torque, motor/generator output) and full 6-DoF motions for both rafts. Regular wave tests, as analysed in [2], will also be made available. Additional irregular sea data are also available, primarily repeats of the seas outlined here. These data form the basis of a parallel MaRINET2 study quantifying the uncertainty associated with the physical model testing of WECs.
Acknowledgments: This paper summarised the work of many individuals of  ECN, IHC, UCC, UoE and UoP  who may have not been named as authors, but whose contributions were important. The authors would like to thank them all. UCC is particularly obliged to  Tom Walsh  and  Christian van den Bosch  for their contributions. ECN is grateful to  Virginie Baudry  and  Aurelien Babarit  for their contributions to the numerical simulation of the raft and to  Sylvain Bourdier  for the definition of the test program. UoP would like to acknowledge the contribution of  Kieran Monk  and  Federica Buriani  from  COAST. UoE would like to thank  Martyn Lennon  for his contribution to the execution of the FloWave test programme.
In the context of shrinking non-renewable energy resources and reducing greenhouse gas emissions, it turns out necessary to drop energy consumption and move towards renewable energy sources. In this regard, solar energy being an abundant source of energy, it can play an important role in the building energy supply. This energy supplement can come from passive solar technologies, of which the Trombe wall is one of the passive models most used in the building envelope, whose main role is to ensure net gains. Thereby, the main aim of this study is to highlight the use of a simple method to reduce the buildings heating demand. For this, a 3-D model was developed through scSTREAM V2020 software to investigate the thermal performance of a classical Trombe wall in winter while targeting desirable indoor thermal comfort conditions.
IoT technologies facilitate the development and the improvement of pervasive computing by enabling effective context-awareness features. ese features enable the IoT applications to detect the user's situation and adapt their behavior. ey also enable context-aware security and privacy, which consist in adapting security and privacy mechanisms' deployment to the user's situation. Research studies on context-aware security and privacy focus on security and privacy mechanisms' implementation but do not consider the secure and trustworthy context management. In this paper, we introduce a new secure and trustworthy context management system for context-aware security and privacy in the smart city: "SETUCOM. " SETUCOM is the implementation of the DTM (Device Trust Management) module of the CASPaaS (Context-Aware Security and Privacy as a Service) architecture.
To achieve that, they propose the use of mechanisms provided for this purpose in the IEEE 802. 15. 6 standard. Indeed, this standard provides the implementation of authenticated and confidential WBAN (Wireless Body Area Network) intrabody network communications. However, the range of a WBAN is limited to the carrier's body. It does not ensure information security for extra-WBAN communications. In addition, the solutions proposed in  [3, 18]  were neither implemented nor evaluated. Chouhan et al. [19]  highlighted the security issues in IoT environments and proposed to use the concept of situation evaluation to ensure the security of IoT applications. is concept is based on the detection of events that can help to evaluate the situation and propose appropriate security mechanisms. However, the security of the data provision allowing situation evaluation and events' detection has not been addressed.
anks to some new architectures/technologies (NFV: Network Function Virtualization, SDN: Software-Defined Networking, SFC: Service Function Chaining, etc. ), CASPaaS can be considered as a service that can be placed at some strategic nodes of the network infrastructure (e. g. , edge nodes with an edge architecture). CASPaaS will thus be available at all times as close as possible to the users, while supporting their mobility. CASPaaS is composed of two plans: the Knowledge Plan (KP) and the Security and Privacy Plan (SPP) (Figure  2 ). KP is responsible for the management of context awareness. Based on the context delivered by the KP, SPP implements the security and privacy mechanisms. SPP also has the role of ensuring the security of the architecture. To do so, it has several modules, including the Device Trust Management (DTM) module, which is responsible for the security management, the reliability of context information, and the trust of context sources.
General Framework. e proposed solution protects the CASPaaS architecture against identity spoofing, eavesdropping, data tampering, and replay attacks. It also preserves privacy, thanks to the secure exchange of context information. As we will see in Section 5. 2, the solution is also adapted to the smart city's constrained devices. Indeed, it has a low impact on energy consumption and offers better performance compared to existing solutions. Figure  3  illustrates the principle of secure exchange of context information where CASPaaS is placed in an edge infrastructure.