dataesr/scientific-paragraphs-categorization huggingface dataset:
Tackling the broader sociopolitical anxieties about public powerlessness in the face of powerful political and economic interests-which provide fodder for conspiracy narratives-may also indirectly strengthen trust in scientists. Although our findings offer important insight into the dynamics of trust in scientists across selected European countries, they also underscore complexity of this trust. Given its crucial implications for public health policy and communication strategies, more granular investigations of the sociocultural, historical factors influencing public trust at national level are needed. Further research can guide more effective and nuanced science communication in the future. Twitter Tamara Giles-Vernick @GilesVernick Contributors TG-V and LWH designed the study and data collection tool, with contributions from SA.
The 'N-term helix' is a hotspot of RYBP cancer-relevant mutations, which destabilize the structural integrity of vPRC1 To connect the structural role of the RYBP 'N-term helix' to vPRC1 functions, we scouted available databases to screen if this newly-identified motif is in any way related to human diseases. Indeed, using the cBioPortal, Biomuta and Cosmic databases, we have found that the RYBP 'N-term helix' is a hotspot of cancer-related mutations. For instance, mutations S59F, Q60* or Q60H (equivalent to Q58H in YAF2), A63T and Q69* or Q69K (equivalent to Q67K in YAF2) are connected to uterine, central nervous system, stomach, lung, head and neck squamous cell, liver and bladder carcinomas or adenocarcinomas, as well as skin melanomas. Interestingly, as evident from the corresponding amino acid numbering, these mutations occur with a very peculiar pattern along the RYBP 'N-term helix', every 3-4 amino acids.
8-2. 5 mg/mL was injected. The elution profiles were followed on-line at 280 nm (SPD-M20A Shimadzu) with both static and dynamic light scattering being detected by a miniDAWN-TREOS and a Dynapro Nanostar detector (Wyatt Technology Corp. , Santa Barbara, CA), respectively, using a laser emitting at 658 nm. Protein concentration was measured on-line with the use of differential refractive-index measurements, with an Optilab rEX detector (Wyatt Technology Corp. , Santa Barbara, CA) and an RI increment, dn/dc, of 0. 187 mL/g. Data were analysed and weight-averaged molar masses (Mw) calculated using ASTRA software (Wyatt Technology Corp. , Santa Barbara, CA).
Analytical Ultracentrifugation (AUC) Purified vPRC1. 4 sample was subjected to analytical ultracentrifugation experiments at a protein concentration of 1mg/mL. Sedimentation-velocity experiments were performed overnight at a rotor speed of 42000rpm and 4C, using an analytical ultracentrifuge XL-I with an Anti-60 rotor (Beckman Coulter) and doublesector cells (Nanolytics) of optical path length 12mm with Saphire windows. Data acquisitions were made using absorbance at 280nm and interference optics. A partial specific volume (ῡ) of 0. 718 mL/g, buffer density (ρ) of 1. 01 g/mL and viscosity (η) of 1. 61 cp were calculated from the sample composition with the programs Sedfit and Sednterp. Data were analysed using Sedfit software, using continuous c(s) distribution model of sedimentation coefficients (s)  (Schuck, 2000).
SAXS) Size exclusion chromatography coupled with small-angle X-ray scattering experiments were conducted on the BioSAXS BM29 beamline at the European Synchrotron Radiation Facility (ESRF, Grenoble, France). An on-line HPLC system was attached directly to the sample inlet valve of the beamline sample changer. Purified vPRC1. 4 protein sample (50µL) at 1mg/mL, previously spun down  (Eppendorf 5427R, FA-45-30-11, 18000g, 10min) , was manually injected onto a Superdex 200 increase 5/150 GL column (GE Healthcare), pre-equilibrated with 50mM Tris pH 9. 0, 150mM NaCl, 1mM DTT buffer. Buffers were degassed prior to the run and a flow rate of 0. 2mL/min at 4C was used. The baseline was monitored prior to the experimental run. All data from the run were collected at a wavelength λ=0. 99 Å, using a sample-todetector (PILATUS 1M; Dectris AG, Baden, Switzerland) distance of 2.
87m corresponding to a q-range of 0. 0035-0. 167 Å -1 where q is the momentum transfer (q= 4πλ sinθ) and 2θ the scattering angle. Acquisitions with an exposure time of 1s/frame were recorded during 1000s. One hundred initial frames were averaged to create the reference buffer and the frames collected at the elution peak, corresponding to the scattering of an individual purified species, were also averaged (to give the average scattering curve) and subtracted of the reference buffer using the program PRIMUS  (Konarev et al. , 2003). Primary data reduction was performed and Radii of gyration (Rg) and pairwise distance distribution functions [P(r)] (Dmax) were extracted based on the Guinier approximation.
5 M. Samples were then acidified with 10% (v/v) trifluoroacetic acid (TFA) and desalted using OASIS® HLB µElution Plate. in 0. 1% (v/v) TFA] and fractionated using a Superdex Peptide PC 3. 2/30 column (GE Healthcare). Collected fractions were analysed by liquid chromatography-coupled tandem mass spectrometry (MS/MS) using a nanoAcquity UPLC system connected on-line to LTQ-Orbitrap Velos Pro instrument. To assign the fragment ion spectra, raw files were converted to centroid mzXML using a raw converter and then searched using xQuest  (Leitner et al. , 2014)  against a FASTA database containing the sequences of the crosslinked proteins. Posterior probabilities were calculated using xProphet, and results were filtered using the following parameters: FDR = 0. 05, min delta score = 0. 95, MS1 tolerance window of 4-7 p.
Evolutionary Sequence Alignment Analysis Sequence alignments were performed with Clustal Omega  (Madeira et al. , 2022)  using the default parameters for multiple alignment sections. Protein sequences used were obtained from GenBank. Resulting alignments and secondary structure predictions were rendered with ESPript3  (Robert and Gouet, 2014). The sequences used for RYBP sequence alignments are as follows: Human (Homo sapiens, AAH36459. 1), Gorilla (Gorilla gorilla, XP_030865299. 1), Orangutan (Pongo abelii,.
Nuclear Magnetic Resonance (NMR) NMR spectra were recorded of free RYBP at 5C and a 1 H frequency of 950 MHz. The NMR spectral assignment was carried out on a 13 C/ 15 N-labeled sample (concentration of 293 M) in 50 mM Tris pH 6. 5, 150 mM NaCl using a series of BEST-TROSY triple resonance experiments  (Solyom et al. , 2013) : HNCO, intra-residue HN(CA)CO, HN(COCA)CB and intra-residue HN(CA)CB. The assignment was done manually and included all residues of RYBP, except those belonging to the Zn-finger domain for which magnetization transfer was incomplete in the triple resonance experiments. The secondary structure propensities of RYBP were calculated from the experimental 13 C and 13 C chemical shifts using the SSP program  (Marsh et al. , 2006). The 1 H-15 N HSQC spectrum of the free RYBP was compared to that of RYBP in the context of the trimeric complex with the RING1B/PCGF4 catalytic dimer of vPRC1.
Mass Photometry Mass photometry measurements were performed for vPRC1. 4 WT and vPRC1. 4-RYBP point mutants samples using a One MP mass photometer (Refeyn Ltd). Immediately prior to mass photometry measurements, protein stocks were diluted to concentrations between 15-20nM (adjusted to maximize camera counts while avoiding saturation) in filtered 50mM Tris pH 9. 0, 150mM NaCl buffer, into a final 20μL droplet placed on the gasket. Data acquisition was started within 10s after addition of the protein. Each sample was measured three times independently with similar results. Contrast-to-Mass (C2M) calibration was performed under the same buffer conditions as our samples and using a Native Marker (NM) composed of 4 proteins with molecular weights of 66, 146, 480 and 1048 kDa (Native Mark unstained protein standard, LC0725, Life Technologies).
Ratiometric movies were collected for the buffer, native marker and the measured samples for 1min. All measurements were performed using the regular field of view (18μm 2 of detection area). Data acquisition was performed using AcquireMP (Refeyn) and images processed and analysed using DiscoverMP (Refeyn). Mass Photometry technique has a detection size limit range of 40 kDa to 5 MDa.
ACKNOWLEDGEMENTS We thank all members of the  Marcia lab  for helpful discussion, the  EMBL Proteomic core facility in Heidelberg  for support with crosslink-and PMF-MS,  Alice Aubert  and  Dr. Martin Pelosse  for support in using the Eukaryotic Expression Facility at  EMBL Grenoble ,  Aline Leroy  and  Dr. Christine Ebel  ( IBS Grenoble ) for support with AUC,  Anna Katharina Hoefler  for performing initial evolutionary alignments;  Sabina Colombo  for help with vPRC1 purification; beamline scientists at the  ESRF BioSAXS beamline  BM29 for support with SAXS data collection, and  Prof. Song Tan  ( Penn State University ) for sharing the pST55 plasmid for nucleosome production. Work in the Marcia lab is partly funded by  ITMO Cancer  ( 18CN047-00 ),  Région Auvergne Rhône Alpes  (project  R21105CC ; allocation  RPH21004CCA ),  FINOVI  ( AAP15 ),  Canceropole CLARA  (Oncostarter), and by the  Fondation ARC pour la recherche sur le cancer  ( PJA-20191209284 ).
Financial support from the  IR INFRANALYTICS   FR2054  for conducting the research is gratefully acknowledged. M. R. J. is laureate of the  Impulscience ® program  of the  Fondation Bettencourt Schueller. This work used the platforms of the Grenoble Instruct Center (ISBG:  UAR 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INBS-0005-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology (PSB). The  Institute of Structural Biology  acknowledges integration into the  Interdisciplinary Research Institute of Grenoble.
XP_024100611. 1), Mouse (Mus musculus, XP_036014155. 1), Rat (Rattus norvegicus, NP_001386529. 1), Sheep (Ovis aries, XP_027813966. 1and Placozoa (Trichoplax adhaerens, XP_002111440. 1). For RING1B sequence alignments the sequences used are: Human (Homo sapiens, NP_009143. 1), Gorilla (Gorilla gorilla, XP_030859631. 1), Orangutan (Pongo abelii, NP_001127433. 1), Mouse (Mus musculus, NP_001347773. 2), Rat (Rattus norvegicus, NP_001388309. 1), Sheep (Ovis aries, XP_027831758. 1)Frog (Xenopus laevis, XP_018116603. 1), Zebrafish (Danio rerio, NP_571288. 2), Platy fish (Xiphophorus maculatus, XP_005809264. 1) and Placozoa (Trichoplax adhaerens, XP_002108615. 1). Numbers indicated correspond to GenBank accession numbers.
Acknowledgements We thank  Michael Overton  for his comments on an early draft of this article. We are also grateful for the thorough work of the copy-editor. This work was partly supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme  -Grant Agreements  810367  and  101001995. It was also partly supported by the  NuSCAP  project ( ANR-20-CE48-0014 ) of the  French national research agency (ANR).
Cold boot attacks A cold boot attack is a kind of data remanence attack by which an adversary could fetch sensitive data from an electronic device's main memory after the device has supposedly deleted the memory data. This attack vector exploits the data remanence property of Dynamic RAM (DRAM). Through it, an adversary might recover readable memory content after the device's power is off for a while. This attack vector, introduced in  [1] , has been explored extensively against multiple cryptographic schemes, as we will discuss in Section 2. 3. In this setting, an adversary, who has physical access to a device, might retrieve chunks of memory content from the device via carrying out a cold-rebooting on it  [1] [2] [3]. In general terms, the adversary forces the operating system to shut down, which causes it to go past all tasks that typically execute during a normal shutdown, such as the file system synchronization.
The SISOT Evaluation Matrix describes a tool or resource using a standardized set of criteria that can be used to evaluate whether it is fit for a given purpose. The matrix can be applied to all tools and resources, which can assist in completing any step toward creation of a coordinated zoonotic surveillance system. The criteria are used to identify the strengths and weaknesses in an objective and unbiased way. There are nine categories of criteria: (1) accessibility, (2) language, (3) data needs and management, (4) data analysis and interpretation, (5) ease of use, (6) flexibility, (7) acceptability, (8) One Health, and (9) tool impact. For each category, the evaluation must address a series of predefined questions. There are between 3 and 10 questions per category, and for each question a scale of 1-5 is used depending on the situation observed.
Radar diagrams are used to provide a graphical presentation of the results of scoring, illustrating the scores on nine different axes corresponding to each category. An evaluation criteria score is given up to a maximum of 100%. FAO has been undertaking country pilots using the SISOT Evaluation Matrix  (7). Based on the investigation of the SISOT Evaluation Matrix and discussions in the CoEvalAMR network, we identified that the addition of the following functional aspects would make the CoEvalAMR methodology more complete: • Type of approach: framework or tool, • Scoring-system method (quantitative, semi-quantitative or qualitative), • Required level of knowledge of users regarding surveillance, epidemiology, etc. • Required training to be acquainted with the tool, • Coverage of the tools: human domain, animal domain, environmental, and food domain and combinations thereof, • Coverage of gender aspects, • Accessibility, and • Languages in which the tool is available.
fp7-risksur. eu/case-studies/). The Excel matrix using the revised methodology was pilot tested as part of the French case study on the evaluation of collaboration within the French surveillance system for AMR, AMU and antimicrobial residues using ECoSur  (11). The completed matrix can be consulted on the CoEvalAMR case studies repository (Please see Case 9 on https://guidance. fp7-risksur. eu/ case-studies/). Briefly, the assessment demonstrated that despite ECoSur being somewhat difficult to use (collection of complex data and need for prior knowledge/training before use), it covered a large part of One Health aspects and generated actionable outputs (Figure  1A ). In addition, most content themes identified by the CoEvalAMR consortium as relevant to the evaluation of integrated surveillance of AMU and AMR were covered by ECoSur, with the exception of AMU/AMR specific aspects (ECoSur being a generic tool) and impacts (Figure  1B ).
Based upon our own experience as well as the French case study  (11) , the CoEvalAMR methodology appears simpler and quicker to use than the SISOT matrix, while it still contains most of the elements that form part of the SISOT matrix. In conclusion, each approach was developed for its own objectives and has its value. The case studies reported by Sandberg et al. Multisectoral means that more than one sector is working together in a joint program or response to an event. Similarly, multidisciplinary means collaboration across several disciplines. Taking a One Health approach means that all relevant sectors and disciplines are involved  (15). However, it does not imply that all sectors must work together and at all stages of surveillance. The key regarding the degree of integration is relevance.
An ongoing common activity in WG4 of CoEvalAMR is an evaluation of the OH-EpiCap tool, which is under development by the MATRIX consortium, funded by the One Health European Joint Program  (17). In a common paper about OH-EpiCap, it will be investigated how we can combine the scores of the different assessors and case studies in a way which ensures that the variation is reported. Other persons involved in surveillance evaluation are welcome to make use of our methodology. Moreover, the tool developers can get valuable information from our case studies for further improvements of their tools.
They also introduced a dataset for mask-wearing detection, which included 7804 realistic images. The results show that this approach achieves an overall accuracy of 97. 32% for simple scenes and 91. 13% for complex scenes, outperforming the compared methods. They suggest that the proposed method can help curb the spread of COVID-19 by detecting masked faces in public spaces, such as hospitals and airports. In  [8] , Sharma et al. proposed an eigenvalue decomposition of the Hankel matrix (EVDHM)-based autoregressive integrated moving average (ARIMA) model to predict the number of COVID-19 cases. They decomposed the data into subcomponents using EVDHM to reduce non-stationarity, applied ARIMA to forecast future values for each subcomponent, and combined the results to generate the final output values.
They employed a genetic algorithm to obtain the optimal values of ARIMA parameters based on the minimum Akaike information criterion. This technique is applied to predict daily new COVID-19 cases in India, the USA, and Brazil, showing high effectiveness. In  [9] , Lam et al. used semi-supervised learning (SSL) techniques to develop and validate machine learning algorithms for predicting the development of acute respiratory distress syndrome (ARDS) in hospitalized patients  [9]. The study employed a dataset of 29,127 encounters with patients admitted to seven US hospitals, and a recurrent neural network was utilized to predict ARDS development based on electronic health record data. The study's outcomes indicate that SSL techniques can enhance the model performance, and unlabeled data can be used for predicting ARDS development in scenarios where labeled data are relatively inexpensive.
The area under the receiver operating characteristic curve increases from 0. 73 to 0. 84 when using the labeled dataset. The paper concludes that unlabeled data can be valuable for enhancing the efficiency of machine learning models in predicting ARDS development. In  [10] , W. Wu et al. presented a method for reconstructing high-quality computed tomography (CT) images in low-dose cases, based on a technique called tensor gradient L0-norm minimization (TGLM). They developed a TGLM strategy to reduce radiation dose by considering the 3D spatial information of CT images. The optimization of the TGLM model was carried out using the split Bregman technique, and its performance was subsequently evaluated using two COVID-19 patients as test cases. Results revealed that the TGLM model exhibits accurate detection performance in scenarios involving low-dose cases, and it effectively preserves image edges while minimizing sparse undersampling artifacts.
Various studies have been conducted to detect COVID-19 infection using chest X-rays. For instance, Han et al. present a semi-supervised deep neural network method for improving the detection of COVID-19 using CT images in  [11]. The method utilizes labeled and unlabeled CT images to enhance the accuracy and robustness of COVID-19 diagnosis. This approach achieves an overall accuracy of 99. 83%, a sensitivity of 0. 9286, a specificity of 0. 9832, and a positive predictive value (PPV) of 0. 9192 in distinguishing COVID-19 from non-COVID-19 CT images. Additionally, the method obtains 97. 32% accuracy, 0. 9971 sensitivity, 0. 9598 specificity, and 0. 9326 PPV in discriminating between COVID-19 and common pneumonia CT images. The results are consistent across different datasets, and the proposed method improves diagnostic accuracy and robustness without exhaustive labeling.
In  [12] , Khobahi et al. considered a deep learning-based technique for analyzing chest X-ray images to diagnose COVID-19. This method uses a semi-supervised learning approach based on autoencoders to extract infected legions in chest X-ray images. Then it utilizes a deep architecture to extract relevant features and train a classifier for automatic diagnosis. The results showed that this approach achieved an overall class average accuracy of 93. 5%. Brunese et al. proposed a deep learning-based approach for the rapid and automatic detection of COVID-19 from X-rays in  [13]. The approach consists of three phases: pneumonia detection, discrimination between COVID-19 and pneumonia, and localization of the COVID-19 presence in symptomatic areas of the X-ray. The method was evaluated on a dataset comprising 6523 chest X-rays from multiple institutions, and it demonstrated an average detection time of 2.
Moreover, in  [15] , Alves et al. investigated machine learning techniques for COVID-19 diagnosis through routine blood tests, using a public dataset from a Brazilian hospital. The random forest (RF) performed the best among the classifiers tested. A decision tree explainer (DTX) was utilized for localized explanations to improve interpretability. A criteria graph was employed to aggregate these explanations and offer a comprehensive understanding of the results. The authors argue that this approach closely mimics the decision-making process of healthcare professionals, making it more practical for real-world use. The study findings indicate that simple blood tests can assist in identifying false positives/negatives in RT-PCR tests. In  [16] , AlJame et al. introduced an ensemble learning technique, named ERLX, for identifying COVID-19 from routine blood tests.
This model incorporates three distinct classifiers (extra trees, random forest, and logistic regression) in the initial stage, and then utilizes a second-level classifier, known as extreme gradient boosting (XGBoost), to merge the predictions and enhance the performance. The data preparation process includes handling null values with KNNImputer, removing outliers with iForest, and balancing data distribution with SMOTE. The model's interpretability is enhanced using the SHapley Additive exPlanations (SHAP) technique to report feature importance. The results from a publicly available dataset from the Albert Einstein Hospital in Brazil show that the ERLX model achieved an overall accuracy of 99. 88%, an AUC of 99. 38%, a sensitivity of 98. 72%, and a specificity of 99. 99%. This model is considered more robust and reliable than existing state-of-the-art models for early and rapid screening of COVID-19 patients.
By using KPCA to extract relevant features, this approach reduces the high computation cost associated with nonlinear optimization compared to other nonlinear methods. Additionally, the use of kernel tricks allows the KPCA algorithm to handle nonlinearity without the need for explicit computation of nonlinear functions, which can be computationally expensive. Furthermore, the use of KPCA with one-class SVM also reduces the need for labeled data and is less sensitive to the choice of the kernel function, making it a more robust method for anomaly detection. In this study, the original training dataset, denoted as x1, x2. , xn ∈ R m , where n is the number of samples and m is the number of process variables, is used to construct a feature space through a nonlinear mapping function Φ(•) : R m → F h.
One-Class SVM The OCSVM is a popular technique used for anomaly detection, which identifies abnormal data instances in a dataset. OCSVM is a type of SVM that learns a decision boundary that separates the normal data from the rest, and is considered anomalous  [22]. OCSVM is a semi-supervised learning algorithm used for detecting anomalies in a given dataset. Unlike traditional supervised learning methods, OCSVM does not require labeled data for model construction and is trained using only "normal" observations  [23]. The basic idea behind OCSVM is to identify a boundary that maximizes the gap between the origin and the normal observations in the training data points in their original space. This boundary is then utilized to classify new test data as comparable or distinct from the training data  [24].