dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, these models are tailored to or heavily dependent on the species characteristics  [6] [7] [8] [9]. Especially in the bee world, in which most studies focused on honey bee populations, no previous work has reported the development of an index summarizing their exposure to multiple IPAs (the PRISMA bibliographic survey is detailed in Appendix A, supplementary material). Only two publications described a synthetic approach to explore the relationship between pesticide exposure and pathogen prevalence in honey bees  [10, 11]. The study of large datasets is possible using factor analyses, also known as component-based analyses  [12, 13]. These analyses are usually used as they provide synthetic graphical displays illustrating relationships between variables and between observations. Principal Component Analysis -PCA  [12, 14]  or Multiple Correspondence Analysis -MCA  [15, 16]  are some of the most popular methods of this family.
However, factor analyses, and more precisely components sought from factor analyses, could also be used to seek indices. In this paper, we focused our statistical analyses on IPAs. Our goal was not to include other factors (climate, landscape) in the indices. The proposed synthetic indices are not designed to describe multiple stressors affecting bees coming from a large dataset. We propose to apply a standard and transferable procedure based on factor analysis and adapted to the configuration of biological data, to seek an index, regardless of the nature and structure of the data. Depending on the needs of the biologists and/or the goal of communication, this index can be either numerical, ordinal (=ordered qualifier) or nominal (=unordered qualifier). These three types of indices were calculated on data of honey bee exposure to IPAs produced by the European project PoshBee on 128 sites (https:// poshbee.
Data Data were produced in the frame of a Europe-wide project (PoshBee) which aimed at assessing, monitoring and mitigating stressors on bee health. PoshBee involved 14 European Union member countries over a five-year period  (2018) (2019) (2020) (2021) (2022) (2023). By "developing a site network for assessing exposure of bees to chemical, nutritional, and pathogen stressors" (Work Package 1), and by "measuring chemical exposure, pathogens and aspects of nutrition in honey bees, bumble bees and solitary bees" (Work Package 2), PoshBee provided several datasets with unique, robust and complete information  [17]. Data were collected in 128 sites, with two focal crops, across eight European countries: Estonia (site numbers beginning with EST), Germany (GER), Ireland (IRL), Italy (ITA), Spain (ESP), Sweden (SWE), Switzerland (CHE), and United Kingdom (GBR).
This means that eight sites per country for two crops were selected: 8 sites × 8 countries × 2 crops, leading to a total of 128 sites. At each site, three honey bee colonies were installed following the PoshBee protocols and over 50 measurements and samples related to the nutritional, toxicological, pathogenic and landscape components of the bees' environment were collected  [18]. In the following list, we provide examples of collected information: data on the abundance and diversity of field margin floral resources (floral survey); survey of wild pollinators as an indicator of general 'pollinator community health (pollinator survey); total and per hectare crop yields and inputs of agrochemicals (farmer survey)  [18]. Adult honey bees were sampled in each colony and nest two times in 2019 for the detection and quantification of various IPAs at two sampling times: before bees were deployed on the sites (T0) and after bloom of the focal crop (T1).
The dataset contained 2805 observations, corresponding to the loads of 11 IPAs (initially 11 and subsequently reduced to 8 IPAs, see below) quantified at two sampling times (T0 and T1) on 128 sites: six viruses (Acute bee paralysis virus -ABPV, Black queen cell virus -BQCV, Chronic bee paralysis virus -CBPV, Deformed wing virus type A -DWV-A, Deformed wing virus type B -DWV-B, Sacbrood bee virus -SBV), the bacterial agents of the American foulbrood (Paenibacillus larvae) and the European foulbrood (Melisococcus plutonius) and three Nosema microsporidia (Nosema apis, N. ceranae and N. bombi). Qualitative variables which described the country and the focal crop were also included in the dataset. The theoretical number of analyses was 2816 (11 IPAs sampled in 128 sites at 2 times). After quality control, some samples were discarded (0.
Statistical methods It should be reminded that although the statistical methods presented here were not novel, this was the first time that these methods were used to characterize the exposure of bees to IPAs. This synthesis was a first step to interpret the exposure data. Subsequent steps may involve the use of structural equation modeling (SEM) for example. The index was defined as a value assigned to a site, that summarises all the variables measured for that given site. Because components of factorial analyses are the best summary of several variables into a single one, these statistical methods were applied. Indeed, components are built as linear combinations of the variables obtained from a Singular Value Decomposition of the overall dataset  [21]. Therefore, the first component explains the maximum amount of data variability.
The statistical procedure was organised in two steps. The first step consisted of reducing the dataset into a single (or into its main) component(s). The second step involved the calculation of the three indices from the first (or the main) component(s). Depending on the biologist's aim, three types of indices are proposed (numerical, ordinal or nominal). The first component was directly used to seek the numerical index, quantiles of the first component were used to seek the ordinal index, whereas several main components were used to seek the nominal index. Step 1: Seek the first or main component(s) that best summarise the data. To summarise the large dataset into a single index, a factor analysis was performed and its first component was extracted. For our data, the aim is to rank or classify into categories the 128 sites according to the bee exposure to the various IPAs.
Since the variables are grouped into two sampling times (i. e. T0 and T1), a Multiple Factor Analysis (MFA) was applied  [16]. Step 2: Process the index from the component(s). Three types of indices could be calculated and selected by the biologist depending on the aim pursued: (i) a numerical index directly obtained from the first MFA component. It was the best summary of the whole dataset into a single dimension, the one that explained the greatest variability of the data; (ii) an ordinal index from the quantiles of the first MFA component. This index was a transformation of the numerical index into ordinal levels using quantiles; (iii) a nominal index produced from a clustering analysis (Hierarchical Clustering on Principal Components -HCPC  [14] ) applied to the first main MFA components.
This method gave the best multidimensional summary of the whole dataset. The choice of the final number of clusters to be retained was discussed among statistical, biological and bee health experts. The MFA method was applied to the eight IPAs measured at two different times. Country and focal crop of each site were considered as supplementary variables in the analysis. They were not used to process the components but only for additional interpretation. The factor analyses were implemented in the R software version 4. 1. 2 (R Core Team, 2017) from the FactoMineR package  [22].
Among low-index-value sites, Ireland differed from Spain and Sweden in having an overall lower load of BQCV while the prevalence was rather high in those sites (cluster B). Among highindex-value sites, Estonia and United Kingdom differed from the other sites with a higher prevalence of N. apis. In addition, nuances Fig. 2. The numerical and ordinal synthetic indices calculated to summarise the exposure of honey bees to IPAs using the same dataset. related to the sampling times were better highlighted with the nominal index than with the numerical or the ordinal ones. A summary table (Appendix C) gave a full disclosure of the three types of indices for each PoshBee site.
In our example and for this index, each site belonged to a 4-level ordered scale of exposure. The amount of information summarised in the nominal index was the highest among the three types of indices (94%) but the index was less practical to use in subsequent statistical analyses as only a reduced number of statistical analyses are currently devoted to nominal data. If the biologist needed to interpret data with deep insight, the nominal index would be the most relevant tool. In our example, the dataset resulted in four distinct categories of exposure configuration, including the same antagonistic relationship between N. ceranae and DWV-B as observed in the numerical index (Appendix D). However, the information was completed with more nuances found for the remaining IPAs. These results support the previous hypothesis that variation in tolerance might be much more prevalent in systems with less pathogenic parasites (in our study: BQCV, DWV, SBV or N.
ceranae infection  [28]. Contrary to country, focal crops were not significantly linked to IPA exposure in honey bees. Nevertheless, time of colony exposure on sites was linked to IPA exposure. There was an evolution in pathogen loads between installation of colonies on sites and end of the flowering bloom. One other study has also shown the impact of IPA exposure on variation of morphological traits in honey bees at site level  [29]. Interestingly, crop surrounding sites also impacted this variation. To conclude, we proposed the calculation of three different types of indices to summarise a large number of variables collected by biologists. This calculation could be applied to a wild range of data; an example on bee data being given here. As factor analyses summarise the information contained in the overall data into components (the first one being the best summary into a single dimension, the first two ones into two dimensions, and so on) they are relevant tools for biologists to summarise their data.
D. The decision to decompress the foramen magnum should be made using a combination of clinical, neurological and imaging assessments, evaluated Rates of cervicomedullary decompression vary widely between centres. Analysis of a US database found a range of decompression rates from 4. 6 to 43%  [14]. Some guidance is given in the literature, but there is no clear criteria for when cervicomedullary decompression is indicated  [6]. Among other indications, White et al. states that decompression is indicated in the presence of significant stenosis and correlative changes in polysomnography and/or pathological neurologic examination, as well as in MRI-defined FMS in instances where there are spinal cord changes  [4]. This is echoed in the protocol adopted by Cheung et al. when assessing the AFMS,  [17]  while Savarirayan et al.
Mass spectrometry analysis of SCAF proteome After FACS isolation, GFP positive SCAFs were centrifuged at 400g for 5 minutes at room temperature and the pellet was snap-frozen in liquid nitrogen. Proteins were then precipitated using trichloroacetic acid, followed by acetone wash. Mass spectrometry was performed on Orbitrap Elite using the Top20CIDr method, with 2 hours run and 3 repetitions. Results were analyzed using Proteome Discoverer 1. 4 and compared with homo sapiens_181015 database. Thresholds of 1% FDR and minimum of 2 peptides per protein were applied. Pathway analysis was performed using gProfiler (https://biit. cs. ut. ee/gprofiler/gost). Gene Ontology Databases and KEGG biological pathway database were considered. Adjusted p-value of <0. 05 was used as a threshold to select the significant enrichment.
10. Remaining reads were mapped onto the hg38 assembly of Homo sapiens genome using STAR version 2. 5. 3a. Gene expression quantification was performed from uniquely aligned reads using htseq-count version 0. 6. 1p1, with annotations from Ensembl version 98 and "union" mode. Read counts have been normalized across samples with the median-of-ratios method proposed by Anders and Huber, to make these counts comparable between samples. Differential gene expression analysis was performed using Wald test, as proposed by Love et al. and implemented in the Bioconductor package DESeq2 version 1. 16. 1. P-values were adjusted for multiple testing using the Benjamini and Hochberg method. Genes with an adjusted p-value <0. 05 were considered significantly differentially expressed. Pathway analysis was performed using gProfiler (https://biit.
Cell motility assay Proliferating IMR90 cells were seeded in a 4 chamber 30 mm diameter glass-bottom dish (IBL, Ref 220. 120. 022) with 8000 cells per chamber. For acquisition, Zeiss Axio Observer Z1 microscope with 20x dry objective was used. Cells were treated with Hoechst 33342 0. 5 µg/ml, 30 000 SCAFs or inert beads and were placed into a humid heated chamber with 5% CO2 and 80% humidity in the microscope. Four positions were selected for each condition and pictures were taken for 8 hours every 5 minutes. Cell nuclei were automatically detected using StarDist and subsequently tracked using the TrackMate ImageJ modules.
First, the social-cognition hypothesis of MNS and its criticisms were not originally formulated in the eld of psychiatry  [4]. Second, the neuropsychological mechanisms of psychiatric disorders are insu ciently understood and the social-cognition hypothesis of MNS applied to psychiatric disorders had gained signi cant popularity in the media  [5]. An essential characteristic of the dynamics of scienti c research is to progress iteratively, by trial and error. Therefore, exibility is expected from researchers when hypotheses, which were considered fruitful, turn out to be doubtful  [6]. A bibliometric analysis of the Scopus database by Heyes et al. showed a decrease in the number of scienti c publications on MNS since 2014  [1]. However, the decrease in scienti c publications on the social-cognition hypothesis of MNS was not examined separately in the eld of research in psychiatry.
METHODS We considered all publications in the PubMed database mentioning the term "Mirror Neuron" from 1995. We set the end on the 27th of May 2023. We aim to extract the publications: (i) on MNS, (ii) MNS in the eld of psychiatric disorders, and (iii) the social-cognition hypothesis of MNS in the eld of psychiatric disorders. These publications were classi ed in the eld of research on "psychiatric disorders" using the following MeSH (Medical Subject Headings) in abstract or keywords: ["autis*", "asd", "schiz*", "psychoti*", "psychos*", "disorder*", "psychiatry*"]. The rst ve terms refer to the disorders most concerned by the research on MNS  [7] [8] [9]. Publications were classi ed in the eld of research of "social-cognition hypotheses" using the following MeSH ["empath*", "self-*", "theory of mind", "mind read", "mind-read", "action understanding*", "action interp*"], referring to the three main sub elds of high-level hypothesis (empathy, mind-reading and action understanding)  [10, 11].
Inclusion and Exclusion Criteria In 2019, 34,203,087 persons were managed following an EMS activation (Figure  1 ). We identified crash injuries using the International Classification of Disease version 10 (ICD-10) codes V00 to V89. Of this population, 1,200,018 (3. 5%) persons were involved in crash events. Of this crash population, 831,999 persons (69. 3%) had documentation stating whether substance use was or was not detected. After excluding respondents without a substance use status, 502,866 persons (60. 4%) had a reported injury status. We restricted the data to participants 65 years and older, excluding 444,628 persons (88. 4%). The final dataset included 58,238 older adults who sustained crash injuries and whose substance use status at the time of the EMS review was known.
13  this study, substance use among older adults in rural areas was associated with worse injury outcomes in the unadjusted model but the odds were attenuated and no longer significant in the adjusted model. The significant association in suburban and urban areas persisted after controlling for patient and injury characteristics. 1] [52]  These metrics would affect all injured rural patients and may blur the difference between the injury severity categories of crash victims in rural areas. This study has its limitations. The study is of a cross-sectional design and causal inferences cannot be made. Also, we used a large dataset and statistical significance may not suggest clinical significance. The study did not report the racial/ethnic characteristics of the study population due to the large proportion of missing variables.
Device Analysis The device for measuring electrical stimulation had an electrode/FCM/rhodamine B/(electric facial mask)/electrode structure (Figures  S1-S4 ). To create an environment for smooth current flow and practical device use, an FCM and facial mask were used between the devices. The simulation was developed in the AC/DC module of COMSOL Multiphysics and was performed to model the application of the drug delivery system on the skin. Fluorescence images were captured to observe the relative absorption intensity using Image J (National Institutes of Health, Bethesda, MD, USA). Moldex3D ® software (CoreTech System Co. , Ltd. , Republic of Korea) based on the finite element method (FEM) was used to simulate the injection molding process. The fluorescent dye, rhodamine B, penetrating the skin was traced before and after the simple diffusion, microneedle, and galvanic ionic device treatments in vitro.
The exposed side of the skin was examined under a microscope to evaluate the penetration and depth. The porcine cadaver skin samples were treated with a cryoblock and were cut into 10 µm sections along the z-axis direction and frozen. The sections were visualized under a fluorescence microscope (OLYMPUS, CKX41) with 4× and 20× objective lenses. The results were expressed as the mean ± standard deviation. All statistical data were analyzed by performing the Student's t-tests. In all cases, p ≤ 0. 001 was considered significant. The moisture measurements were conducted with a skin moisture and oil content analyzer (Vcare SK-8, VCARE Co. , Seoul, Republic of Korea).
Numerical Modeling The injection molding processes were analyzed numerically using the commercial simulation software Moldex3D ® based on the FEM. The governing equations for the numerical model considered the conservation of mass, linear momentum, and energy equations as follows  [30, 31] : dρ dt + ρ(∇• v) = 0, (1) ρ d v dt = -∇P + ∇• τ + ρ g, ( 2 ) ρC p dT dt = βT dP dt + η. γ 2 + ∇• q, ( 3 ) where ρ is the density, v is the velocity vector, P represents the pressure, τ is the stress tensor, g is the gravity vector, C p is the specific heat at constant pressure, β is the thermal expansion coefficient, η is the generalized Newtonian viscosity,. γ is the magnitude of the shear rate tensor, and q is the heat flux vector. The numerical simulation was performed using the steady-state AC/DC module of COMSOL Multiphysics.
Then, the absorption of the ionizing essence and vitamin C by the skin was investigated. The results showed an effective improvement in the penetration depth of the facial skin. Through this study, it was possible to investigate the effect of an efficient transdermal drug delivery system applicable to wearable devices. A system was constructed to supply electricity to the facial mask pack for effective drug delivery. Figure  8a  provides a schematic of the electric facial mask. Figure  8b, c  present the effects of drug delivery to the skin as obtained using COMSOL Multiphysics software. The images depict the model used to study the current flowing in the ion devices when vitamin C was applied to the skin. Negatively charged vitamin C was applied, and negatively charged ionized essence and vitamin C were applied to the actual mask pack  [32, 33].
[6]  propose an optimization of  [24]  by considering physical layer interactions in the attacker model. Mesli-kesraoui et al. [20]  model the whole control-command chain (control program, supervision interface, physical equipment and human tasks) in a timed automaton. Then, a set of usage and safety properties are verified on the timed controller of a simple use case (a single task consisting of opening a valve) with the UPPAAL model checker. Rocchetto and Tippenhauer  [28]  present a method based on the cryptographic protocol verification tool CL-Atse  [30]  and the ASLAN++ language. The authors extend the Dolev-Yao attaker model, notably to take into account possible physical interactions with the process. LTL properties on the global state of the system are specified and translated by ASLAN++ into properties that can be checked by CL-Atse.
In this set of possible paths of the data, some can be secured and others not, and thus the same logical data (Mealy machine) can be secured or not according to the physical context. To overcome this problem, it would be necessary to distinguish each physical context of the data, which is equivalent to distinguish each PLC in the Mealy machine. • An actuator is driven by a single PLC. Thus, the commands (outputs) of each PLC are unique and therefore independent of other PLCs. Our method can be extended to the global logic of the system, by performing the Cartesian product of all the PLC logics (Mealy machine) of the system. Thus, restricting to a single PLC is a performance choice to decrease the calculation and the analysis time of the results and not a limitation. However, this choice eliminates the possibility of handling distributed safety functions.
For example, if the process goes from 1 to 2 (critical transition), an attacker can force 2 value to the PLC (event that force the PLC logic to go from 2 to 3) and compromises the safety function. If we analyse the minimal data set to secure, obtained from our method, which permits to determine if there are data in common to all downstream attacks, we notice that all attacks require that the attacker must be able to modify or forge either FrontP2 or P1Back. In a remediation approach, cybersecurity experts will be able to prioritize their remediation by protecting the FrontP2 or P1Back data and thus protects the system from all downstream attacks.
Different implementations of this process have been proposed  [4, 5, [25] [26] [27]. The one we use includes close to 70 different variables, including flow rates, pressures, temperatures, levels, mole fractions, and compressor power outputs. The operator may control over 36 of these variables (flow rates, valve positions, and reaction agitator speed) to make sure the chemical process is operating under control. Other variables are data collected from various sensors (liquid level, etc. ). In our reference implementation, the process is divided into three sub-processes, each controlling a part of Figure  6. The total implementation is made of eight SFC with eight associated ladder graphs. We analyzed the model with respect to three safety functions: "preventing the overflowing of S3", "preventing the overflowing of S1", and "preventing the overflowing of S2".
CONCLUSION AND PERSPECTIVES In this paper, we proposed and applied a new methodology to identify cybersecurity risks on industrial systems. This method is based on the conversion of the PLC logic into a mathematical model called Mealy machine that allows us to exhaustively define the behavior of the PLC. Then, we showed how to exploit, from a cyberattack on the network, the legitimate behavior of this PLC to compromise safety functions. Moreover, our safety-security model, based on the PLC logic, reduces combinatorial explosion and automates the analysis. Due to the exploitation of the legitimate behavior of the PLC, the attack scenarios that we are able to identify are difficult to detect for the standard solutions used in industrial systems. Finally, our methodology provides two quantitative results: the minimal set of data to secure and the ranked set of critical communication channels for the analyzed system.