dataesr/scientific-paragraphs-categorization huggingface dataset:
Parental age influences locomotory behavior in C. elegans progeny To assess whether parental age affects other aspects of the physiology of C. elegans, we examined locomotion as a marker of overall health. For this, we used the CeLesT computational program  [31, 32]  to measure the animals' capacity to swim continuously over a defined period of time. Consistent with previous data  [31] , we observed age associated alterations in several swimming parameters. PCA analysis of data from N2 lines showed that travel speed, brush stroke, and wave initiation rate decreased with age, while asymmetry and stretch and curling rates increased (Figure  4A ). Of the parameters analyzed, we identified wave initiation rate as the best discriminant to investigate the effects of generational history (SG4, LG2, and LG2.
Therefore, we changed to another protocol: males used for mating were pre-labeled with a fluorescent marker by placing L4 males on NGM plates containing 1 μg/ml of red fluorescent Mitotracker CMXRos (Life Technologies) for 17 h  [28] [29] [30]. A total of 15 unlabeled hermaphrodites were mated with 30 labeled males for 12 h and individual hermaphrodites were then sorted based on the presence of fluorescent sperm in the spermatheca to identify cross-fertilized hermaphrodites. Data was submitted to the non-parametric Kruskal-Wallis analysis of variance. Developmental stage was estimated by observation of the vulva  [48]. Animals were synchronized by transfer after a 2-h laying period, as described above for the lifespan analysis, and incubated at 20°C. At the L4 to adult transition, animals were washed from the plates at three time points, mounted on agarose pads, and examined using a compound microscope.
Quantification of C. elegans swimming behavior Four or five adult hermaphrodites (day 3 or day 7 of adulthood) per condition were randomly selected and deposited in 60 μl M9 buffer on a 10-mm etched circular slide (Delta Microscopies). This low density minimized overlap between worm movements and allowed each worm to be accurately tracked. Worms were allowed to acclimate for 30 s and their movements were then recorded for 45 s. Videos were processed using ImageJ software and analyzed with the CeleST program implemented in MATLAB. Eight videos per condition were realized. The recording system consisted of a Leica M205FA stereomicroscope, a charge-coupled device camera recording at 21 frames/s, and LAS AF software to control image acquisition. A minimum of 30 worms per sample was analyzed.
Statistical analysis Lifespan analysis and calculations were performed using Oasis online software  [50]  and p values were calculated using the Mantel-Cox log-rank method. All other comparisons were performed using variance analysis and t-tests with MATLAB software. Motility data were subjected to principal component analysis and analysis of variance using MATLAB. Levene test was used to assess equality of variances. A p value of 0. 05 was considered statistically significant. Replicate experimental data are shown in Tables  1  and 2  for D. melanogaster and C. elegans, respectively. When Kruskal-Wallis or ANOVA gave significant results, post-hoc tests (Dunnett test) handling multiple comparisons were performed.
ACKNOWLEDGMENTS We thank  Pauline Joncour ,  Catherine Cerutti  for help with statistical analysis,  Françoise Gay  and  Pauline Gresset  for technical support,  Marie Delattre ,  Francesca Palladino ,  Marie-Anne Felix ,  Philippe Baret ,  Mathieu Gautier  and  Christophe de la Roche Saint André  for fruitful discussions and advices, and  Abderrahman Khila  and  Jonathan Enriquez  for suggestions on the manuscript.
| Data We test the hypotheses using three datasets (Business Registry [BSD], Business Enterprise Research and Development Survey [BERD], U. K. Innovation Survey [UKIS]), and eight cross-sectional surveys with a panel element over  2004 -2020 (Office for National Statistics, 2021a , 2021b). Although three datasets were pooled together and constructed from three different sources, they are matchable by identifying a reporting unit. First, we collected and matched eight consecutive UKIS waves between 2004 and 2020, each conducted every second year by the Office of National Statistics (ONS) in the United Kingdom. Second, we used BSD and BERD data for the years  2004, 2006, 2008, 2010, 2012, 2014, 2016, and 2018. The data were matched to a correspondent CIS survey wave for the initial year of the UKIS period.
The Business Structure Database has data on firms' legal status, ownership (foreign or national), alliance information, exports, turnover, employment, industry, and postcodes. The UKIS offers the most comprehensive data, including direct measures of innovation performance and a wide variety of factors influencing innovation. Our match resulted in 25,813 observations with 14,784 firms. In our sample, 8984 firms were observed only once; 3071 firms were observed twice; 1712 firms were observed three times; 510 firms were observed four times; 289 firms were observed in five surveys; 122 firms were observed in six surveys; 69 firms were observed in seven surveys; and 29 firms were observed in all eight surveys. All questions related to the variables of interest need to be completed with no missing values to be included in a sample.
Consequently, firms from one wave may not always participate in the next survey. Each wave is selected as a stratified sample of a pull of firms by industry, region, and size. The panel element in a sample is treated using a multilevel estimation approach. Multilevel analysis is sometimes called a hierarchical, random coefficient, or mixed-effect model. First, the macro-level contains the eight waves of the BDS-BERD-UKIS dataset; there are 12 U. K. regions at the meso-level. Finally, there are 14,516 firms in the sample assumed to be randomly sampled per unit (micro-level). Given that the dependent variable is "product innovation," it was measured as a fraction of the firm's annual sales of new-to-market products. The resulting dependent variable was defined on the interval [0, 100] F I G U R E 3 Predictive margins of R&D collaboration on product (left) and process innovation (right) for firms that used (a) regional partners; (b) national partners; (c) international partners and a share of STEM employees (95% confidence intervals).
ACKNOWLEDGMENTS We would like to thank a distinguished  Professor David Bruce Audretsch , American economist and  Ameritech Chair of Economic Development  for his valuable contribution to our resrearch and understanding the knowlege spillover of innovation. Furthemore, we thank the JPIM Editor-in-Chief  Professor Jelena Spanjol  and the three anonymous reviewers for their valuable comments and suggestions throughout the manuscript developemnt. This research was funded by the  British Academy  within the project titled  Tackling the UK's International Challenges  number  IC160084  from  17/02/2017.
0 kit according to the manufacturer's instructions running on a Sequel IIe platform. The resulting data were analyzed with SMRT Link software using the HiFiViral SARS-CoV-2 Analysis Application with the default parameters (https://www. pacb. com/wp-content/uploads/SMRT_Link_ User_Guide_v11. 0. pdf), except for the minimum base coverage, which was changed to 10 reads (ECDC recommendations for SARS-CoV-2 sequencing). 30 Consensus sequences were then analyzed with Pangolin lineages (v4. 1. 1, https://cov-lineages. org/resources/pangolin. html) and Nextstrain clades (v2. 3. 1, https://nextstrain. org/sars-cov-2). Finally, customized python script (v3. 8. 8) analysis was used to generate a user-friendly report, including the number of mapped reads, median coverage, list of S gene mutations (substitutions, insertions and deletions), S gene missing positions, multiple strains probability and previously found clades and lineages.
The cut-off was more than 10 mutations between 10% and 90% of the quasispecies. We used our inhouse python script (v3. 8. 8) analysis to generate a user-friendly report that included the number of mapped reads, the median coverage, the number of mutations between 10% and 90% of the quasispecies, a list of S gene mutations (substitutions, insertions and deletions), S gene missing positions, and the previously found clades and lineages. See Supporting Information: Table  S1  for GISAID accession numbers.
001) but both failed to sequence nine samples. The amplicon protocol did not sequence 42 samples, including 21 (50%) that had a genome coverage of >80% with the target capture protocol, allowing clade and lineage assignment. Sequence data for 97 samples were obtained with both protocols. Whole genome coverage for samples with a high Ct value decreased for both protocols (Figure  2A ). The Ct for samples that were sequenced by both protocols were significantly lower than those for samples that were not sequenced by either protocol (Figure  3 ). The Ct of the samples sequenced with target capture protocol but not with amplicon protocol were significantly lower than those of samples not sequenced by either protocol (Figure  3 ). samples (Supporting Information: Table  S2 ). Some mutations in 5 samples were missed by the target capture and/or amplicon protocols (Supporting Information: Table  S2 ).
Virology Laboratory. The Ct values were obtained with ID™ SARS-CoV-2/VOC Revolution Pentaplex (ID solutions) using one target sequence in the N gene and two in the RdRp gene. The consecutive samples were taken between May 30 and June 19, 2022 during an Omicron wave in France, at the beginning of the switch from BA. 2 to BA. 5 variants. Two strains of SARS-CoV-2 B. 1. 1. 254/ 20B (EPI_ISL_804374) and B. 1. 617. 2/21I (EPI_ISL_10316331) from a culture supernatant and one SARS-CoV-2 negative sample were included as controls in each 96-well plate.
Samples were pooled (1 µL of each sample from Pool 1 and Pool 2), quantified with the Quantifluor DSDNA system running on Roche LC480 instrument and normalized. SMRT bell libraries were constructed by pooling the normalized barcoded samples. SMRT bell libraries were sequenced with SMRTbell Express Template Prep 2. 0 kits according to the manufacturer's instructions on a Sequel IIe platform. The data were analyzed with the Snakemake pipeline for complete genome construction. This starts by demultiplexing HiFi reads with lima (v. 2. 6. 0, https://github. com/PacificBiosciences/barcoding) and then creates the VCF files from pbAA clusters (PacBio tool, v. 0. 1. 3 https://github. com/PacificBiosciences/pbAA). These files, along with the samtools (v1. 11) depth file are used to build consensus sequences (CoSA, coronavirus Sequence Analysis, v9.
0, https://github. com/Magdoll/ CoSA). The minimum base coverage required to define a position was 10 reads. The consensus sequences were then analyzed with Pangolin (v4. 1. 1, https://cov-lineages. org/resources/pangolin. html) for lineages and Nextstrain (v2. 3. 1, https://nextstrain. org/sars-cov-2) for clades. The presence of multiple strains or of sample cross-over was checked by.
Hospital Virology Laboratory. The Ct values were obtained with ID™ SARS-CoV-2/VOC Revolution Pentaplex (ID solutions) using one target sequence in the N gene and two in the RdRp gene. The consecutive samples were taken between May 30 and June 19, 2022 during an Omicron wave in France, at the beginning of the switch from BA. 2 to BA. 5 variants. Two strains of SARS-CoV-2 B. 1. 1. 254/ 20B (EPI_ISL_804374) and B. 1. 617. 2/21I (EPI_ISL_10316331) from a culture supernatant and one SARS-CoV-2 negative sample were included as controls in each 96-well plate.
5 ). The diffractogram obtained from the nitridated ZrN thin film (red curve in Fig. 5 ) displayed five characteristic peaks centered respectively at angles 2θ equal to 34. 02, 39. 50, 57. 10, 68. 14 and 71. 74 •. After background subtraction, their relative intensities normalized to the most intense peak (39. 50 • ) were 95. 9, 100. 0, 58. 1, 39. 8 and 18. 2%. A Bragg index was applied using the COD database  [44]. The most probable phase in agreement with the diffractogram is the cubic phase Fm3m (225) of the ZrN, with a cell parameter of 0. 4563 nm. The diffraction peaks within the diffraction range investigated correspond to the crystalline planes (111), (  200 ), (  220 ), (311), (222). Table  1  compares the values of inter-reticular distance d (hkl) and relative intensity of the peaks (I) deduced from the GIXRD measurements of the ZrN layer and the reference extracted from the COD data base  [40].
Here, we investigate the interannual trends of SM and VOD at a global scale to identify the distinct factors driving ecosystems out of balance in recent times. We use the Mann-Kendall trend test (see the Methods section) with a spatio-temporal approach to finding significant clusters with concurrent monotonic changes in SM and VOD over the last decade (2010-22); see the Data section. We cross-validate results by comparing the extracted trends with precipitation records and vegetation greenness data (NDVI; see the online supplementary material). Different spatially homogeneous groups emerge, allowing us to identify causes/drivers of monotonic changes in five semantically compact regions using only satellite-based estimates of soil and vegetation water content.
Data Soil moisture is an essential climate variable closely related to other relevant land climate variables and atmospheric fluxes such as surface temperature, evapotranspiration and precipitation. SM and VOD are linked to the water and energy cycles over land and are important drivers of ecosystem variability. Quantifying and understanding the spatiotemporal distribution of global SM and VOD and their changes is crucial for hydrological, ecological and climate models. We use global SM and VOD maps from the ESA's SMOS mission for 2010-20. Since its launch in November 2009, SMOS is providing multiangular and full polarimetric L-band brightness temperatures that enable the estimation of global maps of the Earth's surface SM moisture (top 5 cm) every three days with a spatial resolution of ∼25 km and a target accuracy of 0.
04 m 3 •m -3 , and of    [24, 78]  and considers the pixel homogeneous. The SMOS-IC version 2 SM and VOD products provided in the 25-km EASEv2 grid and in NetCDF format are used here  [24]. The NDVI from MODIS and rainfall from the precipitation estimation from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR) were used as ancillary data for this study. The terra moderate resolution imaging spectroradiometer (MODIS) vegetation indices (MOD13Q1) version 6 data are generated every 16 days at 250-m spatial resolution as a level 3 product. It provides the NDVI choosing the best available pixel value from all the acquisitions within the 16 days. The rainfall dataset comes from PERSIANN-CDR. The precipitation estimation comes from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR) version 2.
2 monthly global rainfall data with a spatial resolution of 0. 25 SM (m 3 •m -3 ) VOD (standard units) Q 10 Q 25 Q 50 Q 75 Q 90 Q 10 Q 25 Q 50 Q 75 Q 90 North America ( • between 60S-60N latitudes. The precipitation estimate is produced using the PERSIANN algorithm on GridSat-B1 infrared satellite data, and the artificial neural network training is done using the National Centers for Environmental Prediction stage IV hourly precipitation data. The MODIS also provides yearly global land cover types (2001-20) at 500-m spatial resolution (MCD12Q1), including six different classification schemes. The MCD12Q1 collection 6 product was derived using supervised machine learning methods with MODIS surface reflectances from the terra and aqua platforms for improved classification accuracy  [79]. For this work, we have considered the annual plant functional types classification scheme (type 5), which divides the terrestrial surface into eleven broad categories: water bodies, evergreen needle leaf forest, evergreen broadleaf forest, deciduous needle leaf forest, deciduous broadleaf forest, grasslands, shrub lands, cereal croplands, broadleaf croplands, urban and built-up lands, permanent snow/ice and barren or sparsely vegetated.
FUNDING M. P. was supported by the project  LEAVES  under grant  RTI2018-096765-A-100  funded by  MCIN/AEI/10. 13039/501100011033  and by the  European Regional Development Fund (ERDF, EU). G. C. -V. was also supported by the  European Research Council (ERC) Synergy  Grant "Understanding and Modelling the Earth System with  Machine Learning  ( USMILE) " under the  Horizon 2020 research and innovation programme  ( 855187 ).
Cyber-Physical Systems (CPS), in the framework of the Internet of Things (IoT), are good candidates to implement the Structural Health Monitoring of civil engineering structures  [3]. Indeed, these can monitor and/or control the physical world, as well as connect the physical and digital worlds (for instance by updating the digital/virtual models/twins/representations with the data collected by the nodes, but also by commanding the nodes based on the needs of the digital/virtual models/twins/representations). The physical part of the Cyber-Physical Systems can be based on the use of Wireless Sensor Networks (WSN)  [4] , which are able to wirelessly exchange (with the humans and/or machines) data commonly generated with Non-Destructive Testing (NDT) methods that do not alter the element under test  [1, 5, 6].
By considering both Wireless Power Transmission and wireless communication, Wireless Sensor Networks can answer to the Simultaneous Wireless Information and Power Transfer (SWIPT) paradigm  [9]. In addition, the cyber-security considerations (especially in terms of data integrity, availability, and confidentiality, but also of alteration or interruption of service) are usually not addressed during the design and implementation phases of the Cyber-Physical Systems and of its Wireless Sensor Network, but only a posteriori, or when necessary (e. g. , after an attack, or an attempt of attack). However, the hardware and software solutions to protect a Cyber-Physical System have a significant cost, in terms of energy consumption and money, which must be considered at the earliest stages of a project  [10].
A radiative electromagnetic Wireless Power Transfer system is used to achieve this Wireless Power Transfer. The Sensing Nodes are the core elements of this Cyber-Physical System, because gathering the main constraints: inaccessible, energy-autonomous, fully wireless, and longterm usable, resilient, and reliable, during their entire lifetime expressed in decades. Thus, these are designed as simply as possible in order to minimize the risk of failure, and are also battery-free and able to cold-start. In their current implementation, which is fully presented in  [12] [13] [14] , the Sensing Node is completely inaccessible and cannot be changed, repaired, or updated. Indeed, there is no physical access (these are encapsulated in the core of the reinforced concrete), nor wireless access (no data downlink is implemented), with an exception for the Wireless Power Transfer that can control the periodicity of activation.
It could be noted that in the implementation based on the LoRa technology, a unique antenna is used both for harvesting the electromagnetic power transmitted by the Communicating Node(s) and for sending the collected data to the Communicating Node(s). Due to their limited resources (in terms of processing and energy), their inaccessibility, and their targeted lifespan, the Sensing Nodes are the focus of this low-level security analysis, and in particular their wireless communications with the Communicating Node(s). Because of the specific implementation and design constraints, this case study of communicating concrete differs from more usual deployments of Wireless Sensors Network, especially because the Sensing Nodes have a very limited (physical and/or wirelessly) and resources (mainly in terms of available energy but also in terms of processing and data storage resources).
Firstly, as there is no physical access to the Sensing Nodes once deployed (as these are encapsulated in reinforced concrete), it is assumed that the attacker cannot physically access it either. Thus, it seems useless to consider the physical attacks for this case study. Nevertheless, this makes impossible to change, repair, update, or upgrade the hardware part of the Sensing Nodes, once deployed and/or after an attack or a compromise. Then, because of the specific design of the Sensing Nodes: with limited energy resources, limited processing resources, and limited data storage resources, but also without a data downlink; it is assumed that the attacker cannot wirelessly access it to reconfigure or compromise its firmware or exploit its processing resources (e. g. , hijacking the network for others activities).
Native Security Features A LoRaWAN device has a unique 64 bits identifier (DevEUI) and a unique 32 bits address (DevAddr), and must be authenticated in order to transmit data to a network. This authentication can be achieved by over-the-air activation (OTAA) (not implemented in the McBIM project) or by activation by personalization (ABP) (implemented in the McBIM project). In both cases, the device obtains two unique AES-128 symmetric session keys named AppSKey and NwkSKey assigned before data communication for a unique communication session. NwkSKey is shared with the network server and is used to calculate and verify the MIC (Message Integrity Code) of all data frames to ensure data integrity; and to encrypt and decrypt the payload field of a MAC (Medium Access Control) data frames. Whilst, AppSKey is shared with the application server and is used to encrypt, by an XOR operation, and decrypt the payload field of application-specific data frames.
1. Physical access to devices By having physical access to devices, it becomes possible to extract the device and network security keys (e. g. , through reverse engineering by deriving the key from public information, etc. ), especially AppSKey and NwkSKey which are necessary to decrypt the communications; and thus, to compromise both the device and the network. The consequences are that: the communications could be decrypted; an attacker could create a mock device with the same credentials to impersonate a legitimate device; the data payload can be manipulated; etc. It is also possible to use hardware, especially a radio module, near the targeted device to intercept its communications. To prevent compromises, the critical data should not be shared.
Acknowledgement spoofing This attack results from the lack of association between a frame and its acknowledgment. The attacker prevents the reception (e. g. , via jamming) and captures the downlink acknowledgement in order to acknowledge another uplink frame from the same device. The purpose of this attack is mainly: to take control of the gateways; to damage the network; or to provoke Denial of Service. This is also possible on uplink frames if the attacker can prevent their reception by gateways.
Intrusion Detection System An Intrusion Detection System can be another alternative way to secure the Wireless Sensor Network. This one is based on two detection methods: the signature-based and the anomaly-based methods. The first is not yet adapted for the low-power Wireless Sensor Networks, as we do not yet have enough knowledge of malicious behavior to propose a database of signatures of malicious activities. The second method uses learning systems to model the legitimate behaviors and detect the suspicious behaviors, by comparing observation with the reference model. In the McBIM project, two learning phases can be imagined: one during the manufacture of an element made of communicating reinforced concrete, during which the Communicating Node(s) detect the legitimate Sensing Nodes in its neighborhood; and the other during the construction of a complete structure made of several elements, during which each Communicating Node detects the legitimate Communicating Nodes in its neighborhood.