dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectral features impact on CIR The inherent idea of multispectral imaging is that a few relevant wavelengths can explain most of the spectral variability between different species. Using fewer wavelengths not only reduces acquisition and processing delays, but also implies that fewer light sources will be required. It hereby simpli es the optical setup, which brings down the instrument cost. Thus, restricting the wavelength set will have practical consequences. Here, a rst selection of nine different wavelengths was used (Table  1 , Fig. 3 ). To test the robustness of analysis, strain-level SVM classi cations were run several times with partial data. First, nine classi cations were run, each time missing data related to a single wavelength. At the end of this rst row, the wavelength introducing the highest level of confusion (or providing the least information) is identi ed.
Discussion Here, a DFIR multispectral imaging technique has been proposed as a mean to identify bacterial colonies. It relies on a simple instrumentation, made of a microbolometer imager and a QCL system as light source, and allows acquisition of multispectral images of several cm² in matter of minutes, instead of hours with a FTIR imaging system, at a fraction of the cost  [13, 44, 45]. By using simple image descriptors, both morphological and spectral data can be extracted from the resulting stacks of images. A rst database containing 10 classes and 2253 examples was used to test the feasibility of identi cation, by means of machine-learning classi cation. With 94% of correct classi cation, the proposed technique already shows extremely promising results. Strain typing is also possible, although results will probably greatly vary from one species to another.
Hereby, restricting the set of wavelengths to its strict minimum will have practical and nancial implications, rendering it a key parameter. However, the reduced set of wavelengths determined here may be speci c to the current database. The same analysis performed with a database representing other species may have led to a different optimal set. Wavelengths inducing confusion on the current database may be pertinent to discriminate species currently not considered, which means that there may be a trade-off between practical aspects and analysis speci city. Current results show that a set of ve wavelengths provides good discriminating power between most species but, for optimal results, a speci c set of wavelengths may have to be determined for a given application.
Conclusion We report the creation of a simpli ed system to achieve multispectral lensless imaging in the midinfrared. This speci c range of wavelengths allow to probe the biochemical composition of the colonies, thus providing more data than an equivalent analysis in the visible range. In the same time, the speci cities of DFIR multispectral imaging allow to gain roughly the same information as with a FTIR hyperspectral scan, at a fraction of the time and instrumental costs. A database containing 2253 hypercubes of colonies enabled us to reach an average correct identi cation rate of 94. 4% based on images collected at nine different wavelengths. Careful selection allowed to further reduce this set to six wavelengths with minimal performance loss (less than 1%). This lensless setup already proved its ability to capture several colonies in a single row, making fast analysis of a whole Petri dish feasible.
The L*, a* and b* values were made according to the CIE Standard Illuminant D65 and analyzed using SpectraMagic NX software (Konica Minolta Sensing Americas, Inc, USA). The total change in color, E * ab , for each time point was calculated using E * ab = (L * 2 -L * 1 ) 2 + (a * 2 -a * 1 ) 2 + b * 2 -b * 1 2 , where values with subscript 1 denote the median of each color parameter from the first time point (t = 0), while those with subscript 2 were the median values for subsequent time points.
Indeed, in the case of HBP, most participants were convinced from the start of the project that the simulation of a fully digitized human brain was unlikely to be achieved in a 10-year span. The fourth goal was to foster interdisciplinarity in Europe and future emerging technologies. The new frontier targeted by HBP was at the interface between brain sciences and ICT fields, the hallmark of previous Bio-ICT and Neuro-IT proactive FET initiatives of the European Community  (de Touzalin, 2013). The fifth was to increase the societal return of scientific research. For HBP, the obvious targets were the domains of public health (curing brain diseases), digital personalized medicine and brain-inspired ICT technologies. The ranking among these motivations at the start of the project depended most likely on who you were, and what your primary domain of expertise was: a neuroscientist or a physicist in search of heavy funding to sustain risky research, a theoretician in search of the ultimate database to crunch, a computer scientist in need of exaflops, an IT entrepreneur in search of a new marketing bubble, an EC Eurocrat manager in charge of global policy design, a politician in search of a banner.
Building a Flagship De Novo From a historical perspective, a difficulty for a European flagship specific to the interface field between brain sciences and ICT, was to define the starting point. With the exception of the European branch of the International Neuroinformatics Coordinating Facility (INCF), this was almost zero in terms of global shared infrastructure. Almost zero, in terms of global science management. Uncoordinated, in terms of completed databases. Fragmented, in terms of database ontologies. Elusively weak, in terms of integration across scales (from micro to macro). Lacking a coherent unifying theory. Weak and undecided, in terms of industrial investment intent. The initial blue-sky feature of the HBP agenda, the one which won the flagship contest, was in sharp contrast with the translationally-driven orientation of the two other successful flagships, "Graphene" and, more recently, "Quantum," for which industrial applications naturally prevailed.
It is important to note that the content of the final document contracted by Henry Markram and EPFL with the EC in 2012 ended up being quite different from the one submitted for the flagship contest. The amended version strengthened the focus on databases and high-performance computing (HPC) infrastructures, and re-established the preeminence of IT over fundamental brain sciences. The restrictions on nonhuman primate (NHP) experimentation, which quickly followed, were decided without the unanimous consent of the board of scientific directors. These moves were part of the negotiation with the EC (from which most of the funding originated). They were most likely made to accommodate the IT-division directorate and reduce pressure from the European anti-vivisectionist lobby. In contrast, competing international mega-science projects in the field of brain sciences were, from the start, more technically targeted  (Jorgenson et al.
Building novel datasets In terms of databases, considerable efforts in defining ontologies, including exhaustive analysis of metadata, are necessary ahead of any sharing of experimental work. It is only after agreeing on common classification labels that labs can interchange and convert their data around a common referential (Petilla Interneuron Nomenclature Group, 2008). Impressive attempts have been made beyond HBP, in particular by the Allen Institute  (Koch and Reid, 2012) , but large-scale initiatives have often kept their agenda and classification criteria separate, making a direct comparison difficult  (Frégnac, 2017; Fairhall, 2021). In the case of HBP, no significant funding was allocated for the full implementation of interoperability of preexisting databases, including revisiting experimental lab notebooks according to accepted standards.
This may have a cost in the long run: important structural and in vivo functional data, already acquired in species which have become out of fashion (cat, electric fish, giraffe. ), might remain noncurated on some lab shelves. This difficult issue (no one wants to go back to old data formats) was already discussed between in vivo and in vitro neuroscientists at the time of exploratory projects of the FET initiative (Daisy, FACETS, BrainScaleS) in collaboration with the experts from the Blue Brain Project (BBP) at EPFL. Some aspects were partially explored by the INCF community (mostly neuroimaging and calcium imaging), leading to the widespread adoption of the Neurodata Without Borders format for new data acquisition and a solid semantic metadata framework (developed in the second half of HBP).
In reality, HBP limited the amount of animal experimentation in higher mammals (the species the closest to humans), focusing instead on rodents and tried to remove nonhuman primate (NHP) experimentation from its objectives, mostly for political correctness. Although this exclusion rule was relaxed in the second part of the grant, it greatly weakened interspecies comparisons and led to overreaching transfer of coding concepts, developed in rodents, to the human brain  (Hodge, 2019; Lowe, 2019; Loomba et al. , 2022). By developing centralized database infrastructures, one benefit, foreseen by Henry Markram  (Markram, 2012) , was that the aggregation and mining of diverse long-tail data, as well as conversion of numerous small data sources into big data, would improve knowledge about neuroscience-related disorders  (Markram, 2012; Ferguson et al.
, 2014). However, the shift in emphasis, during HBP, from efforts in data acquisition (mostly fine-scale multispecies physiology) and ontologies to the development of ized database infrastructure (involving mostly exclusively neuroinformatics and HPC) and human neuroimaging had, in my view, an additional side-effect. Some governmental institutions in Europe (including France!) suggested that enough data may already be available on the laboratory shelves, constituting a pile of "siloed" dormant sources that just need to be curated (for review, see  Choudhury et al. , 2014; Frégnac, 2017). It seemed indeed easier in terms of budget control by institutions to turn scientists into hightech engineers seeking patterns in existing data rather than to continue to fund basic research which requires new experimentation and costly animal care facilities.
In the end, this strategy generated a conglomerate of fictive data, which may be related to the models from which they originate, but cannot be attributed to the biological system under study. The situation is different in physics, where theoretical models are strong enough to correctly predict experimental facts. This challenge may be one bridge too far in brain sciences. Strictly speaking, "virtual" experiments are experiments that test new hypotheses on real biological data that were collected to test other hypotheses. Classically, the qualifier "virtual" is used to refer to data collected from existing datasets (for instance, from different labs) and collated into a unique data set rather than taken from a new set of animals  (Peterson, 1995). This concept in HBP, and in deep learning models, has been extended to the read-out of sets of local internal "hidden" variables, which are the exclusive results of simulations for different contexts, without being systematically validated by the experimenter.
In most cases, the global parameters of the model were initially defined by the specific context of the biological experiment. They were trained to fit the correlation matrix between recorded real data, i. e. , external input, biological data recorded in sparse specific brain sites and the global output performance. The same model can thus be replayed for other classes of simulated inputs, and virtual responses can be predicted under various types of perturbations produced by modulatory-like or electrical-like artificial manipulations, or even following in silico lesions  (Markram, 2014). This data-enriching approach has generated a lot of interest in the modeling field. It has also been applied with some success to psychology and neurolinguistics  (Jain et al. , 2023). In physics of materials, deep learning algorithms trained by extensive structure-property datasets have recently been optimized to predict the capability of generating new materials with targeted properties  (Honda et al.
, 2008). This hybrid approach, already applied in human medicine, has been merged during HBP with the concept of "digital twin," which originates from the engineering industry, especially aeronautics  (Grieves, 2019). A "digital twin" is a type of personalized computational brain model, based on measured real-world data obtained from its real-life counterpart, i. e. , the patient. This concept has been applied, with impressive success, by Viktor Jirsa and colleagues in predictive modeling of focal epilepsy  (Jirsa et al. , 2023). In this case, sensor data are recorded in the real world and then used to build a virtual model of the individual brain. However, by feeding the databases with virtual data simulated in a metaverse world, the scientific project tends to suffer from a kind of poor man's logic (I cannot record for real, so I simulate).
To what extent is it a valid funding strategy to encourage "virtualization" as a way to replace in situ experimentation? Recently, the HBP consortium has answered to these criticisms by distancing its current objectives from the multiomic initial approach of the ramp-up phase of the project  (Markram et al. , 2011; Markram, 2012). In Section 6 of the white Zenodo paper generated at the end of the HBP flagship  (Amunts, et al. , 2023) , the reformed consortium brings a clear semantic clarification: "We distinguish purpose-driven digital twins from the abstract idea of a full digital replica (or duplicate/copy) of the brain, the latter being the complete representation of all aspects of the brain at all levels. A full replica of the brain is neither achievable nor does it seem of clear practical use.
The reductionist phase  (Markram, 2012)  progressively waned, when the triumvirate reduced experimental funding to the benefit of HPC, neuromorphic technology and future database infrastructures  (Amunts et al. , 2016 (Amunts et al. , , 2019)) , as if the community had siloed from the start enough low-level biological information to crack the brain. In the second part of the flagship, mean field (or neural mass) approaches and human brain imaging became dominant. In my view, this progressive shift in focus reflects the mid-flagship restructuration in governance, proposed by the  Marquardt (2015)  report, rather than a fundamental change in the way of looking at the brain. Science historians might make a finer diagnosis. Still, in a more scientific context, the shift could be interpreted as a down-grading of the initial ambitions, signaling the defeat of the theoretical field's ambition to provide a multiscale view of the brain.
The present view further includes the coordination of teams and tasks at a macroscale level, adding further coherence to the collective agenda and partnership, independent of individual merit. • Most importantly, the flagship should include an "integrator" consortium of interdisciplinary labs and theoretical centers, with a full-time focus on the final integration of the flagship research findings. The aim would be to produce a strong theoretical framework that generates testable predictions for the future. To feed the model(s), the whole community should first agree on common standards of data and metadata. Significant funding should be devoted to ensuring that new datasets and relevant existing datasets are interoperable. Consolidated databases should be open and not privately owned. When possible, theoreticians should partner with the experimenters from start in defining the dataset which will feed their simulations.
• Priorities should also include the production of appropriate tools for the community to ensure interoperability between the databases and models produced by the various actors. Note that this issue motivated the creation of EBRAINS as a tool and infrastructure provider following HBP. • This coordination would require, from the start, the active and direct participation of the main national and federal research agencies (e. g. , CNRS, Max DFG, CNR) and private centers already funding scientific excellence (e. g. ,, International Brain Laboratory, ELSC among many), as well as the main European scientific coordinating networks (e. g. , Bernstein Network). Note here that, in contrast to the HBP, this coordination strategy was fully operational from the start of the United States BRAIN initiative.
Acknowledgments We thank  Diego Guadagnoli  and  Méril Reboud  for discussions about related topics. We thank  Quim Matias  for information about the level of disagreement with the SM of the latest b → sµ + µ -data. This work was partially funded by the program " Aide aux étudiants et développement international de la Région Auvergne Rhone-Alpes ," project number.
No evidence of a particle signal, coincident with a GW signal, has been reported yet. Here, we use data from the XENON1T dark matter search  [12]  to extend this neutrino energy window down to 17 keV. Further, the low threshold of 1 keV electronequivalent (keV ee ) offered by XENON1T, enables us to search for axion-like particles (ALPs) in the keV energy range. There have been studies proposing the emission of relativistic axions  [13, 14]  from the merger of neutron stars. Since blackbody radiation peaks at several to hundreds of keV for binary neutron stars  [15] , ALPs might exist in thermal equilibrium with the blackbody radiation with keV energy. Additionally, ALPs can be accelerated to keV energies by the 511-keV line associated with positrons produced by mergers  [16]. Not only are the low-energy thresholds of XENON1T helpful in looking for these keV ALPs, but they also allow to look for MeV beyond-Standard-Model particles (like sterile neutrinos  [17, 18] ) which deposit only a fraction of their energy via interactions like coherent scattering  [19].
II. DATA SETS The LIGO and Virgo observatories conducted their second observation run from November 30, 2016, to  August 25, 2017 [20]. Overlapping with this time frame, the XENON1T detector took Science Run 0 (SR0) data from November 22, 2016 to January 18, 2017, and Science Run 1 (SR1) data from February 2, 2017 to February 8, 10 5 MeV-10 11 MeV Super-K  [7]  3. 5 MeV-10 11 MeV XMASS  [8]  14 MeV-100 MeV KamLAND  [9]  1. 8 MeV-111 MeV NOvA  [10]  10 MeV-100 MeV Borexino  [11]  0. 5 MeV-5 MeV XENON1T (this work) 0. 017 MeV-50 MeV 2018. This data has already been analyzed in several modes that we revisit here in order to check for particle signals coincident with LIGO and Virgo GW observations. The XENON1T experiment has been described in  [12]. There are two channels via which a particle can interact with the detector.
Further, all FIG. 3. The upper limit on the fluence (time-integrated flux) of coincident neutrinos emitted in GW170817, the merger of neutron stars using the electronic recoil signals in XENON1T, obtained for electron neutrinos, muon or tau neutrinos (ν e and ν μ;τ , respectively, shown in dashed), electron antineutrinos, and muon or tau antineutrinos (ν e and νμ;τ , respectively, shown in solid). We also show the limits obtained by Borexino  [11] , KamLAND  [9] , and Super-K  [7]  for comparison. FIG. 4. The upper limit on the fluence (time-integrated flux) of coincident neutrinos emitted in GW170817, the merger of neutron stars using the nuclear recoil signals in XENON1T, placed on the sum of the fluence of all flavors of neutrinos and antineutrinos. 3-fold, 2-fold, and S2-only refer to different modes in which the data is analyzed in the search for nuclear recoils, as described in Sec.
ACKNOWLEDGMENTS We gratefully acknowledge support from the  National Science Foundation ,  Swiss National Science Foundation ,  German Ministry for Education and Research ,  Max Planck Gesellschaft ,  Deutsche Forschungsgemeinschaft ,  Helmholtz Association ,  Dutch Research Council (NWO) ,  Weizmann Institute of Science, Israeli Science Foundation ,  Binational Science Foundation ,  Région des Pays de la Loire ,  Knut and Alice Wallenberg Foundation ,  Kavli Foundation ,  JSPS Kakenhi  and  JST FOREST Program in Japan ,  Tsinghua University   Initiative Scientific Research Program  and  Istituto Nazionale di Fisica Nucleare. This project has received funding/support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No.
The other arm tested an active control, namely massage therapy MT. MT  [24]  takes shape through touch and a sequence of movements on all or part of the body, that allows relaxation, fitness, reassurance, communication or simply well-being". The study was approved by the CPP (personal protection committee) Sud Ouest et Outre Mer III, Decembre 16, 2020. The number of the Clinical Trials registration is NCT04561271, 23/09/2020. The study took place in the PCU of the Clermont-Ferrand University Hospital. Patients were included between January 2021 and June 2021.
It extends along a continuum from feelings of vulnerability, sadness, fears, to more serious issues such as anxiety, panic attacks, depression or identity crisis  [29, 30]. It is assessed by the Distress Thermometer (DT), a visual analogue scale that asks the patient to rate their feelings on a continuum from "no distress, 0" to "extreme distress, 10". The French version is validated and used in palliative care  [31]. The study evaluators were blinded. The data were collected using the RedCap software. They were age, sex, occupational category, whether or not death had occurred at the time of analysis, involvement of close relatives in accompanying the patient, pathology, patient's knowledge of the diagnosis and prognosis, ESAS anxiety/pain/sleep, distress state measured by the DT, status with regard to COVID (patient and close family), state of alertness at the time of treatment (measured on the Richmond scale), use of analgesics and anxiolytics, position during treatment, duration of treatment, and whether or not a further session was scheduled.
At the end of the first stage, 30 patients were included (15 in the reflexology group, 15 in a control group). If 12 or more patients out of the 15 presented the primary endpoint, then the study could be stopped and the intervention considered feasible. If 8 or more patients out of the 15 failed to present the primary endpoint, then the study could be stopped for non-feasibility. Data were recorded anonymously with REDCap software  [23]. Data storage and management were performed according to international guidelines relevant in French institutions. All data were entered using electronic case report form and data accuracy was analyzed by data manager. Data quality control measures were included queries to identify outliers and missing data. Only the research assistant and study principal investigator (PI) were access to protected personal health information.
The comparisons concerning categorical variables were performed with chi-squared or Fisher's exact tests. Paired comparisons (within randomization group analyses) were conducted with the paired Student test or the Wilcoxon test. As the primary objective of this study was feasibility, primary analysis was carrie out in a per-protocol population. However, a sensitivity analysis was carried out in the intention-totreat population with a last-observation-carried-forward imputation approach. All analyses were generated with Stata software, version 15. 0 (StataCorp, College Station, US). A two-sided p value of less than 0. 05 was taken to indicate statistical significance. Results were also expressed as standardised median differences (SMD for between-group differences) and effect-size (ES for within group differences) with 95% confidence intervals.