dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, correction countermeasures should also be adopted. Thus, this work aims to indicate that new lightweight cryptographic and non-cryptographic solutions are required to maintain the security aspect and preserve data privacy, which are the main functionality of IoT devices. These solutions should be considered primordially especially for the resource-constrained IoT devices, as they require maintaining a high level of compatibility and accuracy (reducing false positives and false negatives). Finally, this work offers new perspectives, ideas, and insights toward achieving a much more secure and safe FL-based environment that can be adopted in a risk-free IoT environment. We hope that in the future, following our conducted in-depth research and investigation, the presented suggestions and recommendations are taken into consideration, applied, and further expanded to achieve a safer and more secure FL-based IoT environment.
Model Inversion Attack: by providing inputs and noting the matching output, an attacker can use a model inversion attack to get build an inversion model to reconstruct the original input sample with high accuracy. -Membership Inference Attack: occurs when an attacker queries a trained machine learning model to check if a specific example was contained in the model's training dataset or not [130]. Therefore, the main goal of this attack is to determine whether a particular input was part of the model's training set by just applying the trained model to specific inputs and evaluating the outcome. For example, we might wish to look through the training set to see whether a certain hospital discharge record is included, to see if a certain person has had treatment for a certain medical condition, or to see if a certain financial transaction is included in the training set for fraud detection.
In this way, a set of up to ten smartphones can be calibrated simultaneously (Figure  2 ). Noise mapping methodologies based on the Noise-Capture application have been proposed, including a calibration and interpolation phase  [26]. In addition, a dense collection protocol has been designed, called Noise-Capture Party, which has been widely tested (cities of Bastia, La Coruña, Salerno, etc. )¹. A NoiseCapture Party is an event that brings together a large number of participants, which makes it possible to cover a large area in a short period and to mobilize the residents around the issue of noise environments. In total, the application has nearly 100,000 contributors worldwide at the date of publication and over 5 years of data collected. All the data from the application are returned to the community as open data under the ODbL license² and constitute, to our knowledge, the largest open database in the world in the field of environmental acoustics.
Data collection Data collection lasted 7 months, extending from December 2021 to June 2022 inclusive. The participants were free to take measurements whenever they wished, with the instruction either to take measurements in spots of at least 1 min or to take mobile measurements, taking care not to pollute the measurements with their own steps and to pay attention to the weather conditions (avoid either rain or gusts of wind). In addition, a page dedicated to perception, present in the NoiseCapture application, allowed participants to fill in perceptual variables: tag the sound sources heard (on/off), and give a sound environment pleasantness rating. A smartphone calibration was carried out at each organized event for participants who wished to do so,  in order to improve the quality of the collected data.
Production of sound levels maps Each raw L Aeq,1s collected value is associated with a position, a time value, and Meta data such as a participant Id number, and possibly a calibration value. Each of the raw L Aeq,1s value is then processed in two steps: • Step 1: Correction with the calibration value. For participant Id numbers with no specified calibration value, a calibration value is estimated, in order to counteract the fact that data from non-calibrated smartphones have a higher variability. The method consists of applying the difference for each non-calibrated smartphone, between the median of the L Aeq,1s values collected with the smartphone and the overall median of the L Aeq,1s values calculated over all the smartphones. This method is a simplified approach derived from the method presented by Can et al.
Finally, spatial processing enables the production of the L A50,  [8-18h]  noise map on a regular hexagonal grid of 50 m sides. The spatial processing consists of a Kriging described in the study by Aumond et al. [35]. The variogram and Kriging algorithms presented in this study are computed using the functions vgm (computation of the variogram), fit, variogram (best fit of the variogram), and kriege (Kriging function) of the package gstat  [36].
Produced sound environment maps Sound environment maps were produced following the method described in Section 2. 3. 3. The maps depicted in Figures  6  and 7  are those that were presented and discussed with the residents during the final seminar on 25/06/2022. Possible improvements to the method are discussed in Section 4. 2. The L A50,  [8-18h]  map depicted in Figure  6  can be compared to the multi-sources (road traffic + rail traffic + aircraft traffic + main industries) L den strategic noise map. The produced L A50,  [8-18h]  map is in accordance with expectations. It highlights the contrast in sound levels between major and minor roads, although the lack of data around the ring road in the South prevents highlighting the high sound levels here. Interestingly, the produced L A50  [8-18h]  map is sensitive to sound level variations in no traffic zones as parks (see North East along the river for instance), whereas the strategic noise map depicts no data here.
Insights from the focus group and the semi-directive interviews The transcripts of the focus group and the semi-directive interviews were analysed according to a reading grid stressing the dimensions of diagnosis, impact of the experimentation, and empowerment. The two focus group interviews involved six and four residents. The semi-directive interviews involved ten residents (Section 2. 3. 5). These were not necessarily those who were most active during the data collection phase. For example, two participants said that they had not taken any measurements because they did not own an Android smartphone. Others acknowledged that they had taken very few measurements. Different profiles of participants can be drawn from the participants in these focus groups. Some participants came to the experiment clearly with a negative a priori about the city's noise environments, some belonging to the anti-airport noise association.
Acknowledgements: The  Sonorezé  project is funded by the  I-Site Future  under the programme "Chercheur. e. s/ Citoyen. ne. s. " The semi-directive interviews with participants took profit of funding from  IRSTV. The authors would like to thank  Antonin Tribaleau , who contributed a lot to the relations with the residents during the experiment, and  Myriam Pedessaud , who did very valuable work of transcribing the focus group interviews and who conducted the semi-structured interviews. The authors would like to thank  Gwladys Diquelou , from the  City de Rezé , for her precious technical and scientific contribution. Finally, the authors would like to warmly thank the participants of the city of  Rezé  who volunteered their time for collecting data and participating in the focus group interviews and for sharing their opinions and experiences.
These simulations were made possible by the ongoing development of the LAMMPS program for molecular modeling, which is a computational tool that continuously improves its ability to accurately represent the physical, chemical, and biological characteristics of systems composed of interacting molecules and atoms  [11]. In addition, innovative experimental tests were conducted for measuring electrical current in prismatic mortar samples of 40x40x80 mm with carbon nanotubes in weight percentages (wt. %) of 0. 00wt. %, 0. 006wt. % and 0. 018wt. %. Thus wise, the subject presented in this paper is to focus on the opportunity for cement past structure to be modify by addition of CNTs. For that, we are to determine the atomic structure of the C-S-H/CNT composite and simulate it.
0; f: 4. 8 mm -∞). The GUI was developed by the first author using python and the OpenCV library (accessible on demand). The "target" window (Fig. 1. c) separating the index finger from the rest of the hand was positioned and sized by the experimenter at the beginning of the experiment, just after the picture of the hand was taken. The participant was instructed to adjust the slider, using a standard mouse, until the picture of their finger matched their perceived length of their finger. Adjusting the slider resized the "target" window laterally, thus shrinking or expanding the represented length of the finger. In each condition, perceived finger length was measured as the mean of two trials, one with the starting position of the slider at the maximum distortion (three times the normal size) and one starting at the minimum distortion (one third of the normal size).
Statistical analyses No multivariate outliers (>M + 3 SDs in 2 conditions) were identified in the dataset. Data were analysed using linear mixed effects models, t-tests (frequentist and Bayesian) and correlation comparisons using R (Version 4. 0. 3) with the lme4, car and cocor libraries  (Bates et al. , 2015; Diedenhofen & Musch, 2015) , respectively. All other analyses were performed using Python 3 and the scipy library. All data are publicly available on the Open Science Framework (https://osf. io/gy7u9/).
Genome sequence data The reference-quality assembled genome sequences of T. urartu cultivar G1812 v2. 0 (PRJNA337888)  (Ling et al. , 2018) , Ae. tauschii subsp. strangulata cultivar AL8/78 v4. 0 (PRJNA182898)  (Luo et al. , 2017) , T. turgidum subsp. durum cultivar Svevo v1. 0 (PRJEB22687)  (Maccaferri et al. , 2019) , T. turgidum subsp. dicoccoides isolate Atlit2015 ecotype Zavitan v2. 0 (PRJNA310175)  (Zhu et al. , 2019)  and T. aestivum RefSeq v1. 0 (PRJEB27788) (IWGSC, 2018) were downloaded from NCBI (https://www. ncbi. nlm. nih. gov/). Reference-quality assembled genome sequences of T. aestivum accessions ArinaLrFor, CDC Landmark, CDC Stanley, Jagger, Julius, LongReach Lancer, Mace, Norin61, Spelt, and SY_Mattis were downloaded from https://wheat. ipk-gatersleben. de/ (Walkowiak et al.
We developed a Python script (Get_Collinear_Region. py) in order to retrieve all orthologous intergenic regions (oIGRs) that were flanked by the same pair of neighbor orthologs (collinearity) in Chinese Spring and in the compared genome. Cases of tandem duplicated gene copies that mapped at the same positions in a compared genome were dealt with by the script so that it specifically determined which copies delimitate an oIGR. Chromosome positions of these pairs of neighbor orthologs were used to extract the corresponding genomic segments (including the genes at both extremities of oIGRs) in both compared genomes. oIGRs were then aligned with  BLASTN (v2. 11. 0+; Camacho et al. , 2009 , threshold evalue: 1e-5) and we filtered out HSPs (High Scoring Pairs) with a cutoff at 90% identity. We excluded HSPs whose coordinates were included in a larger one.
We developed Python scripts (1 per superfamily; available at https://forgemia. inra. fr/umr-gdec/scripts_ files/) to identify TSDs of variable size, depending on the superfamily considered  (Wicker et al. , 2007) , immediately flanking terminal motifs: 5′-TG and CA-3′ for RLGs, RLCs, and RLXs; 5′-CACT[AG] and [CT]AGTG-3′ for DTCs. For the other superfamilies with undefined terminal motifs, the entire subsequence of 20 nts was scanned for the presence of a TSD. The expected TSD size was 5 nts for RLGs, RLCs, and RLXs, 3 nts for DTCs and DTHs, 8 nts for DTAs, varying between 9 and 11 nts for DTM, and a TA duplication was expected for DTTs. We tolerated one single nucleotide polymorphism (SNP) between 5′ and 3′ TSDs. No TSD was searched for LINEs/SINEs. Insertion dates of LTR retrotransposons were estimated by aligning the two LTRs of an element with BLASTN and we used a mutation rate of 1.
Extent of structural variations affecting TEs through the mapping of ISBP markers Although TEs are repeated, each copy is inserted into a different locus, which makes the 5′ and 3′ junctions between TE extremities and the insertion site, unique kmers at the whole genome level. We used this valuable feature to address presence/absence variations (PAVs), also called transposon insertion polymorphism (TIPs) or ISBPs among the wheat research community, between orthologous loci. We extracted 150 bps encompassing each TE extremity of all genomes which provided a dataset of, on average, 1. 7, 1. 9, and 1,5 million uniquely mappable ISBP markers from the A, B, and D subgenomes, respectively. ISBPs mapped with at least 95% identity over 95% of their length were considered present while no match revealed a PAV.
TE variability assessed by whole genome alignments Aligning Gb-sized genomes containing 85% of TEs is not trivial. In this regard, we developed a dedicated approach aiming at identifying collinear orthologous genes in order to target the pairwise alignment of pre-identified orthologous intergenic regions (oIGRs). Therefore, we mapped the 105,200 predicted genes of T. aestivum Chinese Spring  (35,345, 35,643, and 34 ,212 carried by subgenomes A, B, and D, respectively) on related genomes with high stringency and found unambiguous orthologs for 79%, 96%, 94%, and 94% of them in T. urartu A , Ae. tauschii D , T. dicoccoides AB , and T. durum AB , respectively. These genes were used as anchors to guide all genome sequence alignments. From this, we extracted all intergenic regions flanked by collinear neighbor orthologs, representing a dataset of 17,904, 30,623, 59,048, and 59,812 oIGRs, respectively.
Proximity to genes and insertion dates In total, our bp-resolved alignments revealed the presence of 51,928 elements (39,966 class 1, 11,048 class 2, and 914 unclassified) in the Chinese Spring genome that are absent from the corresponding insertion site in one or more compared genomes. They represent a large and clean dataset of novel insertions, although some of the class 2 elements detected here may be traces of recent excision in the compared genome. We used this dataset in order to investigate the global distribution of new insertions and the potential preferential insertion in gene vicinity. Among the 102,601 analyzed oIGRs, 26,862 contained one or more new insertions, showing that there was no insertion hotspot but rather that novel insertions spread the genome homogeneously. In wheat, genes tend to be clustered into small islands separated by large TE clusters, so it was important to distinguish small IGRs (≤ 40 kb; 51,120 regions corresponding to gene islands) from large ones (>40 kb; 51,481 regions corresponding to large TE clusters).
, 2018). In contrast, Gypsy and Copia insertions tend to be excluded from the gene vicinity and insert preferentially in the core of large IGRs. Finally, we estimated the age of these novel insertions with the approach, that is, widely used by the community: aligning 5′ and 3′ LTRs of LTR-retrotransposons and applying a molecular clock, considering that both LTRs are replicated (and thus identical) during transposition process. The purpose was to validate the reliability of this approach since we have sampled here the largest dataset of recently inserted LTR-RTs. The average insertion time estimated for the 88,269 newly inserted LTR-RTs identified in all our comparisons, is 590,000 years. Only 4% are estimated to be younger than 100,000 years ago. This estimate is surprisingly older than expected and not in accordance with the divergence time estimated for these species/accessions.
For instance, the extent of variability between CS and T. durum is not substantially higher than between two T. aestivum accessions. The important point in our study was to explore variability at different time scales across the tree, whatever the species definition, as long as one can align orthologous TEs to retrieve structural variations corresponding to obvious events of TE insertions and deletions. The four genotypes of tetraploid and diploid species might be not the best representative and one cannot exclude that some genotypes may not follow the evolutionary rules we commented here. Intraspecific level was assessed for T. aestivum only and it would be interesting to estimate the extent of intraspecific variability of the wild species also when more data will be available. Rare cases of families that contribute more than others to recent genome diversification were observed.
Several use cases required more sophisticated representation. It was then possible to study membrane proteins interacting with lipids and/or small molecules  (Saam et al. , 2002; Tang and Xu, 2002; Grossfield et al. , 2006)  (Figures  2A, B ). The growing interest in ion channels and transporters has necessitated the visualization of pores in proteins. It was possible to render the protein surface to estimate the pore shape  (Law et al. , 2003; Sotomayor and Schulten, 2004) , but this visualization was not optimal to also visualize important residues inside the proteins and dedicated tools, such as the program HOLE  (Smart et al. , 1996) , were developed to both visualize and quantify pore shape  (Doyle et al. , 1998; Oloo and Tieleman, 2004 ) (Figure  2C ). However, due to the increasing size of the model systems, it was necessary to select a portion of the model to zoom in and understand the interactions between specific residues  (Gullingsrud et al.
The development of CG force fields and the increase in system complexity required new visualization methods A key development in this period has been the introduction of coarse-grained (CG) force fields. Early examples of these force fields focused on phospholipids  (Shelley et al. , 2001)  and led to impressive   (Saam et al. , 2002). With the permission of the Theoretical and Computational Biophysics Group, University of Illinois Urbana-Champaign. (B) Trajectories representation of the anesthetic halothane in a DMPC membrane (acyl chains not shown) along a gramicidin A peptide  (Tang and Xu, 2002). (C) Ribbon representation of the BtuCD protein and representation of the pathway of vitamin B 12 in yellow is HOLE program  (Oloo and Tieleman, 2004). (D) Rendering of a transmembrane water pore  (Marrink et al.
At the molecular level, one solution is to apply a method that automates the evaluation of specific parameters. These programs are not only fast and reproducible, but allow the user to easily create impressive graphs and structural representations. For example, the tool ProLint  (Sejdiu and Tieleman, 2021)  is a powerful method for identifying protein-lipid interactions that provides a web server with interactive visualization (Figure  4A ). PyLipID  (Song et al. , 2022)  can also identifies and provides statistics on protein-lipid binding sites and automatically generates diagrams and scripts for molecular visualization (Figure  4B ). These tools have been used to study large membrane systems, such as in a recent study of 144 Kir potassium channels in several complex membranes, which allowed us to analyze the preferential interactions of this protein with different lipids  (Duncan et al.
, 2020)  (Figure  4D ). Another example is the study of a large number of membrane systems, showing the local enrichment of different lipid species around each membrane protein, so-called fingerprints  (Corradi et al. , 2018)  (Figure  4C ). The MemProtMD database also allows the visualization of protein-lipid interactions and getting statistics on a very large number of membrane proteins  (Newport et al. , 2018). Reducing the complexity of the system by creating simplified 2D images has been used to characterize the phase separation of lipids  (Fowler et al. , 2016)  and lipid packing defects  (Wildermuth et al. , 2019). Aside from reducing the complexity of simulation data, the two-dimensional array format is likely to become increasingly important as it is integrated into machine learning (ML) workflows to bridge the different scales  (Ingólfsson et al.
Shedding new light on large and crowded systems Increasing efforts have been made to improve the visual quality of snapshots and molecular poses from large molecular simulations using software such as VMD  (Jefferies and Khalid, 2020; Vermaas et al. , 2021; Casalino et al. , 2022 ) (Figure  5A ), which is constantly evolving and becoming more powerful over time  (Stone et al. , 2013; Stone et al. , 2016; Stone, 2019). In parallel, other visualization methods are increasingly being used and have recently been updated, such as the new version of the Chimera software, called ChimeraX  (Goddard et al. , 2018b) , including extensions such as ArtiaX  (Ermel et al. , 2022)  (Figure  5B ), both of which set new standards in terms of visual representation of membranes and membrane proteins. Similarly, tools such as Blender3D  (Community, 2018)  MolecularNodes plugin allow for the rapid rendering of very complex systems that produce impressive results  (Johnston, 2022)  (Figure  5C ).
Due to its optimized rendering algorithms and, user-friendly and exhaustive user interface, Blender3D program allowed efficiently managing the rendering of an entire minimal cell model  (Stevens et al. , 2023). New molecular renders are also emerging, directly integrating advanced lighting and rendering  (Chavent et al. , 2011; Lv et al. , 2013; Maria et al. , 2022). When the simulated environments become even more crowded  (Yu et al. , 2016; Bülow et al. , 2019; Bari et al. , 2023) , more artistically inclined methods can be used, such as in the work of Dr. David Goodsell  (Goodsell et al. , 2018). This visualization was then stratified into a set of tools, such as CellPAINT  (Gardner et al. , 2021) , that combines visualization and modelling to create models such as a mycoplasma cell  (Maritan et al.
Acknowledgments We especially would like to thank the  Theoretical and Computational Biophysics Group, University of Illinois Urbana-Champaign  to grant us permission to use the figure of the Rhodopsin system (Figure  2A ) in your article. MC would like to thank  SJ Marrink  for the use of the picture in Figure 2D. We also would like to thank  Sebastien Buchoux  for the use of the FATSLiM picture (Figure  3C ).
Funding This work was supported by the  ITMO Cancer of Aviesan  " Mathematiques et Informatique " on funds managed by  Inserm (MC). RC is supported by  Wellcome  ( 208361/Z/17/Z ). MB thanks  Sesame Ile-de-France  for co-funding the display wall. Part of this research was funded by the " Initiative d'Excellence" program  from the  French State , grants " DYNAMO ,"  ANR-11-LABX-0011 , and " CACSICE ,"  ANR-11-EQPX-0008 ).
Large-scale quantification of yeast replicative lifespan in a microfluidic platform Using the traditional micromanipulation assay, previous studies examined the lifespan impact of most of the non-essential genes in haploid yeast, identifying several hundred genes whose deletion extended lifespan compared to the wild-type  19, 20, 24, 25. We compiled a set of 307 haploid knockout strains based on their increased longevity as published in the literature (Supplementary Data 9) and measured the RLS of each strain using our Yeast Replicator microfluidic platform. The measurements were made by tracking 200 newly born cells of each strain for 3 days in our Yeast Replicator microfluidic device  13  (Fig. 1a ). While some long-lived cells take as long as 5 days to cease to divide or die, we chose to use the 3-day tracking approach to reduce the time and cost of the experiments.