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Social media is more and more dominant in everyday life for people around the world. YouTube content is a resource that may be useful, in social computational science, for understanding key questions about society. Using this resource, we performed web scraping to create a dataset of 644,575 video transcriptions concerning net activism and whistleblowing. We automatically performed linguistic feature extraction to capture a representation of each video using its title, description and transcription (downloaded metadata). The next step was to clean the dataset using automatic clustering with linguistic representation to identify unmatched videos and noisy keywords. Using these keywords to exclude videos, we finally obtained a dataset that was reduced by 95%, i.e., it contained 35,730 video transcriptions. Then, we again automatically clustered the videos using a lexical representation and split the dataset into subsets, leading to hundreds of clusters that we interpreted manually to identify a hierarchy of topics of interest concerning whistleblowing. We used the dataset to learn a lexical representation for a specific topic and to detect unknown whistleblowing videos for this topic; the accuracy of this detection is 57.4%. We also used the dataset to identify interesting context linguistic markers around the names of whistleblowers. From a given list of names, we automatically extracted all 5-g word sequences from the dataset and identified interesting markers in the left and right contexts for each name by manual interpretation. The results of our study are the following: a dataset (raw and cleaned collections) concerning whistleblowing, a hierarchy of topics about whistleblowing, the automatic prediction of whistleblowing and the semi-automatic semantic analysis of markers around whistleblower names. This text mining analysis can be exploited for digital sociology and e-democracy studies., Competing Interests: Conflict of interestNone., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Perseverance's Mastcam-Z instrument provides high-resolution stereo and multispectral images with a unique combination of spatial resolution, spatial coverage, and wavelength coverage along the rover's traverse in Jezero crater, Mars. Images reveal rocks consistent with an igneous (including volcanic and/or volcaniclastic) and/or impactite origin and limited aqueous alteration, including polygonally fractured rocks with weathered coatings; massive boulder-forming bedrock consisting of mafic silicates, ferric oxides, and/or iron-bearing alteration minerals; and coarsely layered outcrops dominated by olivine. Pyroxene dominates the iron-bearing mineralogy in the fine-grained regolith, while olivine dominates the coarse-grained regolith. Solar and atmospheric imaging observations show significant intra- and intersol variations in dust optical depth and water ice clouds, as well as unique examples of boundary layer vortex action from both natural (dust devil) and Ingenuity helicopter-induced dust lifting. High-resolution stereo imaging also provides geologic context for rover operations, other instrument observations, and sample selection, characterization, and confirmation.

Before Perseverance, Jezero crater's floor was variably hypothesized to have a lacustrine, lava, volcanic airfall, or aeolian origin. SuperCam observations in the first 286 Mars days on Mars revealed a volcanic and intrusive terrain with compositional and density stratification. The dominant lithology along the traverse is basaltic, with plagioclase enrichment in stratigraphically higher locations. Stratigraphically lower, layered rocks are richer in normative pyroxene. The lowest observed unit has the highest inferred density and is olivine-rich with coarse (1.5 millimeters) euhedral, relatively unweathered grains, suggesting a cumulate origin. This is the first martian cumulate and shows similarities to martian meteorites, which also express olivine disequilibrium. Alteration materials including carbonates, sulfates, perchlorates, hydrated silicates, and iron oxides are pervasive but low in abundance, suggesting relatively brief lacustrine conditions. Orbital observations link the Jezero floor lithology to the broader Nili-Syrtis region, suggesting that density-driven compositional stratification is a regional characteristic.

The Mars 2020 Perseverance rover landing site is located within Jezero crater, a ∼ 50 km diameter impact crater interpreted to be a Noachian-aged lake basin inside the western edge of the Isidis impact structure. Jezero hosts remnants of a fluvial delta, inlet and outlet valleys, and infill deposits containing diverse carbonate, mafic, and hydrated minerals. Prior to the launch of the Mars 2020 mission, members of the Science Team collaborated to produce a photogeologic map of the Perseverance landing site in Jezero crater. Mapping was performed at a 1:5000 digital map scale using a 25 cm/pixel High Resolution Imaging Science Experiment (HiRISE) orthoimage mosaic base map and a 1 m/pixel HiRISE stereo digital terrain model. Mapped bedrock and surficial units were distinguished by differences in relative brightness, tone, topography, surface texture, and apparent roughness. Mapped bedrock units are generally consistent with those identified in previously published mapping efforts, but this study's map includes the distribution of surficial deposits and sub-units of the Jezero delta at a higher level of detail than previous studies. This study considers four possible unit correlations to explain the relative age relationships of major units within the map area. Unit correlations include previously published interpretations as well as those that consider more complex interfingering relationships and alternative relative age relationships. The photogeologic map presented here is the foundation for scientific hypothesis development and strategic planning for Perseverance's exploration of Jezero crater.

Rumour is an old social phenomenon used in politics and other public spaces. It has been studied for only hundred years by sociologists and psychologists by qualitative means. Social media platforms open new opportunities to improve quantitative analyses. We scanned all scientific literature to find relevant features. We made a quantitative screening of some specific rumours (in French and in English). Firstly, we identified some sources of information to find them. Secondly, we compiled different reference, rumouring and event datasets. Thirdly, we considered two facets of a rumour: the way it can spread to other users, and the syntagmatic content that may or may not be specific for a rumour. We found 53 features, clustered into six categories, which are able to describe a rumour message. The spread of a rumour is multi-harmonic having different frequencies and spikes, and can survive several years. Combinations of words (n-grams and skip-grams) are not typical of expressivity between rumours and news but study of lexical transition from a time period to the next goes in the sense of transmission pattern as described by Allport theory of transmission. A rumour can be interpreted as a speech act but with transmission patterns.

In ruminants, more than 30% of the embryonic loss observed after artificial insemination has an early origin that is coincident with the marked elongation of the conceptus that occurs before implantation. During this developmental phase, physiological interactions are established between the conceptus and the uterus which are essential for the establishment of pregnancy and the elongation process. Our molecular knowledge of elongating conceptuses in cattle has long been focused on its analysis in view of its interactions with the uterus with the elongating stages being defined, like the uterus stages, by days post insemination or conception. The gene clusters reported so far indicate important pathways, some being shared by the non-elongating conceptuses of other mammals. However, to identify the key components of the elongation process - that could be specific to ungulates - new models are needed. Somatic nuclear transfer could be one of them as it provides complementary insights on differentiation beyond the blastocyst stage. Nonetheless, other models are necessary to convert gene lists or networks in elongating phenotypes. This review partly summarizes information on these topics, but data on the impact of the uterus on the elongation process or on the differentiation of the embryonic tissues are reviewed elsewhere., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Background: Since processes in well-known model organisms have specific features different from those in Bos taurus, the organism under study, a good way to describe gene regulation in ruminant embryos would be a species-specific consideration of closely related species to cattle, sheep and pig. However, as highlighted by a recent report, gene dictionaries in pig are smaller than in cattle, bringing a risk to reduce the gene resources to be mined (and so for sheep dictionaries). Bioinformatics approaches that allow an integration of available information on gene function in model organisms, taking into account their specificity, are thus needed. Besides these closely related and biologically relevant species, there is indeed much more knowledge of (i) trophoblast proliferation and differentiation or (ii) embryogenesis in human and mouse species, which provides opportunities for reconstructing proliferation and/or differentiation processes in other mammalian embryos, including ruminants. The necessary knowledge can be obtained partly from (i) stem cell or cancer research to supply useful information on molecular agents or molecular interactions at work in cell proliferation and (ii) mouse embryogenesis to supply useful information on embryo differentiation. However, the total number of publications for all these topics and species is great and their manual processing would be tedious and time consuming. This is why we used text mining for automated text analysis and automated knowledge extraction. To evaluate the quality of this "mining", we took advantage of studies that reported gene expression profiles during the elongation of bovine embryos and defined a list of transcription factors (or TF, n = 64) that we used as biological "gold standard". When successful, the "mining" approach would identify them all, as well as novel ones., Methods: To gain knowledge on molecular-genetic regulations in a non model organism, we offer an approach based on literature-mining and score arrangement of data from model organisms. This approach was applied to identify novel transcription factors during bovine blastocyst elongation, a process that is not observed in rodents and primates. As a result, searching through human and mouse corpuses, we identified numerous bovine homologs, among which 11 to 14% of transcription factors including the gold standard TF as well as novel TF potentially important to gene regulation in ruminant embryo development. The scripts of the workflow are written in Perl and available on demand. They require data input coming from all various databases for any kind of biological issue once the data has been prepared according to keywords for the studied topic and species; we can provide data sample to illustrate the use and functionality of the workflow., Results: To do so, we created a workflow that allowed the pipeline processing of literature data and biological data, extracted from Web of Science (WoS) or PubMed but also from Gene Expression Omnibus (GEO), Gene Ontology (GO), Uniprot, HomoloGene, TcoF-DB and TFe (TF encyclopedia). First, the human and mouse homologs of the bovine proteins were selected, filtered by text corpora and arranged by score functions. The score functions were based on the gene name frequencies in corpora. Then, transcription factors were identified using TcoF-DB and double-checked using TFe to characterise TF groups and families. Thus, among a search space of 18,670 bovine homologs, 489 were identified as transcription factors. Among them, 243 were absent from the high-throughput data available at the time of the study. They thus stand so far for putative TF acting during bovine embryo elongation, but might be retrieved from a recent RNA sequencing dataset (Mamo et al. , 2012). Beyond the 246 TF that appeared expressed in bovine elongating tissues, we restricted our interpretation to those occurring within a list of 50 top-ranked genes. Among the transcription factors identified therein, half belonged to the gold standard (ASCL2, c-FOS, ETS2, GATA3, HAND1) and half did not (ESR1, HES1, ID2, NANOG, PHB2, TP53, STAT3)., Conclusions: A workflow providing search for transcription factors acting in bovine elongation was developed. The model assumed that proteins sharing the same protein domains in closely related species had the same protein functionalities, even if they were differently regulated among species or involved in somewhat different pathways. Under this assumption, we merged the information on different mammalian species from different databases (literature and biology) and proposed 489 TF as potential participants of embryo proliferation and differentiation, with (i) a recall of 95% with regard to a biological gold standard defined in 2011 and (ii) an extension of more than 3 times the gold standard of TF detected so far in elongating tissues. The working capacity of the workflow was supported by the manual expertise of the biologists on the results. The workflow can serve as a new kind of bioinformatics tool to work on fused data sources and can thus be useful in studies of a wide range of biological processes.

Processing literature (i.e., text corpora) to capture gene regulation events is not easy and can be driven by the final data representation. We propose to build, manually, an example of temporal representation (whole gene networks for coat formation in Bacillus Subtilis). Our temporal representation is based on a generalised formal language theory (S-languages). We propose an algorithm to link bags of relations with representation, by ordering interactions. In this paper, starting from the network made manually from text data, we show that S-languages are quite relevant to encapsulate gene properties, and infer knowledge across timestamped gene relations found in texts.