dataesr/scientific-paragraphs-categorization huggingface dataset:
The model inputs (i. e. , outcomes, utility values, and costs) were derived from RCTs (n = 3), from literature (n = 4), and from national databases (n = 3). A natural experiment was used in Frew et al. (39)  (Supplementary Table  2 ). In all studies, the reported costs and benefits were combined in an ICER (n = 5) or an incremental cost per utility ratio (ICUR) (n = 1). The additional costs per QALY gained were estimated in most studies. Only  (38)  estimated the ICER per DALY for the diet and exercise interventions. Final estimates were available in the country currency and price-year (n = 5). The time horizon used in the studies varied from 5 years to the lifetime horizon of the population studied. Different time horizons were used in the sensitivity analysis. In all studies but one, the cost-effectiveness analysis was presented from the perspective of the society, and in half of the studies, both the society and the healthcare payer perspectives were included.
Limitations Our study had several limitations. Firstly, the number of studies that could be included was limited. Only two types of interventions were identified: physical activity (in five studies) and diet (in two studies). The small number of interventions did not permit the differentiation of the primary-preventionrelated impact of intervention on breast cancer. More studies might be required to reach such an impact of public health interventions. The lack of sufficient evidence on the primary prevention interventions in reducing breast cancer might hinder the economic evaluations of lifestyle-related interventions. Also, it might be a result of our rapid review strategy and the limited number of databases searched. However, similar limitations were observed in previous systematic reviews in a number of studies retrieved  (19).
Results indicate that the coated nanoplatform may be suitable for the use as MRI contrast agent and also as a drug delivery platform, in particular for liver indications. Further studies are underway. to acknowledge the Ramsay Memorial Trust for provision of a Ramsay Fellowship. We also thank the ImagoSeine core facility of the Institut Jacques Monod, member of IBiSA and France-Bioimaging (ANR-10-INBS-04) infrastructures, particularly Dr Rémi le Borgne for sample preparation and assistance with TEM imaging.
Acknowledgements A. Barosi  acknowledges the NanoK grant of the IdF region. Authors wish to thank to the  CNanoMat platform of the University Paris 13  for the use of UV-vis, FTIR and  Zetasizer Nano ZS  and the  FRIM Imaging platform at Université Paris 7  ( CHU X Bichat ) for the MRI 7T measurements. A. M. would like.
Two examples of the use of 3D-InSight™ human liver microtissues (MTs; InSphero, Swizerland) for untargeted hepatolipidomics were described. In the first, the effect of fusariotoxins (deoxynivalenol/DON, fumonisin B1/FB1 and T-2 toxin) on MTs was discussed, showing how the proposed method is sensitive to mycotoxin concentration and duration of treatment. In the second example, a cheminformatics approach to assess the risk of clinical hepatotoxicity based a dataset of drugs belonging to five different chemical classes and with various drug-induced liver injury effects (Goracci et al. , 2020) was discussed. In both cases, Lipostar software (Molecular Discovery, United Kingdom) was used to cover all the steps of the analysis (raw data file conversion, peak detection, statistical analysis, lipid identification, lipid pathways interpretation).
, 2020) , pesticide residues in water, food contact materials, parabens and PCBs, food additives and drugs, in particular a drug for multiple sclerosis. In all cases, a mixed set of computational techniques was applied, including ligand-based screening, docking/scoring, consensus scoring and molecular dynamics, based on a reliable 3D database. Federica Madia from the European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) of the European Commission's Joint Research Centre introduced the JRC's non-animal approach to cancer. The rising incidence and prevalence of cancer identified by the WHO are of serious concern. Scientific advances of the past twenty years have helped to describe major properties of cancer, enabling more sophisticated therapies. It is also clear that the management of relevant risk factors, including chemical exposure, can significantly reduce cancer occurrence worldwide.
Statistical analysis All results were expressed as mean ± standard deviation (SD) from at least three independent experiments, each one performed in triplicate for each tested condition, unless stated otherwise. The significance of variability between the results from various groups was determined by one-way ANOVA when the normality assumption was met, and the Kruskal-Wallis test if not. Post-test analysis was verified by Dunn's test. This analysis was performed using the SigmaPlot 11. 0 software. The results were considered statistically different when p <. 05. The association between LY permeability and U concentration was analyzed by a generalized estimating equation (GEE) regression  (Zeger and Liang 1986 ) and conducted using the Zelig Package of R software (R Software 2016). The associated symbols are Ã for p <.
Intracellular localization of U by microscopical analyses Using ICP MS analysis, U was quantified inside each type of cell and represented only 1% of added mass at the beginning of the experiment (data not shown). In order to identify U localization, transmission electronic microscopy (TEM) analyses were performed in control conditions and after different times of U exposure. In controls, one or two layers of hCMEC/D3 cells were detected with no signs of damage: TJ, endosomal activity and vesicles with multilamellar/multivesicular/cottony-like bodies were detected. After U exposure, TJ were always detected as correlated with the LY permeability results (Figure  4 ). To distinguish the different types of cells in model B, we characterized the presence of pericytes in one side of the filter by extensive granular endoplasmic reticulum.
After the items passed these two panels, the items were sent to the organization collecting the data (Kantar; see below for more information). Two researchers of Kantar with extensive experience in collecting survey data among organizations reviewed the items (along with the whole questionnaire). Finally, the items were pre-tested among a small selection of organizations. Respondents were asked to provide comments about the measures. The resulting measures are provided under the heading "measures" below. The data for this study are available through the innovative HRM Survey  (Koster et al. , 2017). This data were collected among a random sample of Dutch firms. The questionnaire was developed to measure several characteristics of organizations, related to their innovativeness. Among these measures are the innovative HRM scale introduced in this study, the composition of the workforce and a number of core characteristics of the organizations.
Kantar Public collected the data using their panel with private organizations (NIPObase Business). The panel consists of 15,000 representatives from Dutch firms. From this panel, a random selection of 3,000 organizations was drawn. The response rate was 25% (752 responding organizations). Some variables are not available for all organizations. For 33 organizations not all variables included in this study were missing, meaning that the analyzes could be conducted on a sample of 719 organizations. These organizations operate in different economic sectors and differ in size. Altogether, these organizations provide a cross-section of the Dutch economy.
Step 5 is merged with the exploratory factor analysis and the confirmatory analysis as all items are analyzed to investigate whether they actually measure three distinct dimensions of HRM. In the construction of the innovative HRM scale, two analytical techniques were used, namely, principal component analysis (PCA) and CFA. The PCA was conducted with the psych package  (Revelle, 2018)  in R and the CFA was conducted with the lavaan package  (Rosseel, 2012)  in R. First, a PCA was performed on the four Likert-items that measure innovative HRM. To verify that the developed innovative HRM scale is not a proxy for the presence of HRM in general or specific HRM policies and practices, another PCA was performed. In total, 14 items were included that measure organizational practices related to learning and collaboration in HRM between organizations.
Data acquisition and analysis Electrophysiological recordings were digitized at 25 kHz for intracellular signals and at 3 kHz for ECoG waveforms (CED 1401plus, Spike2 software version 7. 06; Cambridge Electronic Design, Cambridge, UK). Cross-correlograms between subthreshold striatal intracellular activity (1-5 s of continuous recording down-sampled at 3 kHz) and the corresponding ECoG signal (taken as the temporal reference) were computed using Spike2 software. The value of neuronal membrane potential (Vm) was calculated as the mean of the distribution of spontaneous subthreshold activity (10 s duration). The corresponding magnitude of Vm fluctuations was quantified as the standard deviation of the distribution (SD Vm). Vm values were eventually corrected when a tip potential was recorded after termination of the intracellular recording.
Introduction The areas presented in this paper range from plan goal decision tree satisfiability with competitive business models to predictive analytics models that accomplish goals on a 3-tier business systems design models. The decision-making process and business analytics are explored with an augmentation of decision processes to accomplish goals modeled. Attention spanning trees are applied to focus on plan goal models that can be processed on a vector state machine coupled with a database pre-processor data mining interfaces  (Nourani 200? Pedersen 200?). Modeling, objectives, and planning issues are examined to present precise decision strategies. Competitive decision tree models are applied to agent learning. Enterprise systems stage sequence communications with business objects and basic content management with multitier interfaces are being explored, focusing on attention spanning  (Nourani-Lauth-Pedersen 2013)  with specific examples developed in (Lauth-Nourani-Pederson-Bloom 2013),  (Lauth 2013).
The field of automated learning and discovery has obvious financial and organizational memory applications. There are basic applications to data discovery and model discovery. Augmentation is a process that enhances decision making by providing cognitive decision support that provides AI argumentation that affects behavior and sentiments to make sensible decisions. Hence it does not replace the human decision-making process with an automated machine. A competitive business modeling technique, based on the first author's planning techniques are stated in brief. Systemic decisions are based on common organizational goals, and as such business planning and resource assignments should strive to satisfy such goals. Heuristics on predictive analytics are examined with brief applications to decision trees.
The basic multi-tiered designs are based on the following layers. The presentation layer contains components dealing with user interfaces and user interaction. Example a visual JAVA standalone application. A business logic layer contains components that work together to solve business problems. The data layer is caused by the business logic layer to persist state permanently. More and more enterprises recognize that in the electronically archived databases a there is a potential for knowledge that could be processed up to now only insufficiently example application for competitive models appears in the transactional business models. Alternate models can be designed based on where assets, resources, and responsibility are assigned; how to control and coordination are distributed; and where the plan goals are set.
Section 3 briefs on goals, plans, and realizations with databased and knowledge bases. There a function key interface to the database is presented with applications to model discovery and data mining. Competitive model goal satisfiability with model diagrams is briefed with examples. Section 4 presents the applications to decision trees and practical systems design with splitting agent decisions trees. Cognitive spanning with decision trees and state vector machine computations applications are presented. Section 5 presents spanning applications with multitier business interfaces. Social media applications with Gatesense example from the accompanying author's is briefed. The paper concludes with heuristics for competitive models and goals for decision tree accomplishment from the first author's newer game decision tree bases since (Nourani-Schulte 2014).
The current pertinent rules are available at an 'agenda' store. The rules carried out will modify the database. Backward chaining is an alternative based on an opportunistic response to changing information. It starts with the goal and looks for available premises that might be satisfied to have gotten there. Goals are objects for which there is automatic goal generation of missing data at the goal by recursion backward chaining on the missing objects as sub-goals. Data unavailability implies a search for new goal discovery. A basis to model discovery and prediction planning is presented in  (Nourani 2002 ) and is briefed here. The new AI agent computing business bases defined during the last several years can be applied to present precise decision strategies on multiplayer games with only perfect information between agent pairs.
Modeling with effector and sensor uncertainty, incomplete knowledge of the current state, and how the world operates is treated with agents and competitive models. Bounds on game trees were developed based on the first author's preceding publications on game trees generalizations on VMK to on (Nourani-Schulte 2013). Partial deductions in this approach correspond to proof trees that have free Skolemized trees in their representation. Our past decade developments have applied diagrams do for knowledge discovery knowledge management. Diagrams allow us to modeltheoretically characterize incomplete KR. To key into the incomplete knowledge base. The following figure depicts selector functions Fi from an abstract view grid interfaced via an inference engine to a knowledge base and in turn onto a database.
1 , and data is retrieved Nourani  [20]. Visual object views to active databases might be designed with the above. The trees defined by the notion of provability implied by the definition might consist of some extra Skolem functions {g1,. ,gn}, that appear at free trees. The f terms and g terms, tree congruences, and predictive diagrams then characterize deduction with virtual trees Nourani  [12]  as intelligent predictive interfaces. Data discovery from knowledge diagrams might be viewed as satisfying a goal by getting at relevant data which instantiates a goal. The goal formula states what relevant data is sought.
CART system deploys a binary recursive partitioning that for our system is applications for the agent and/or trees  (Nourani 1999)  The term "binary" implies that each group is represented by a "node" in a decision tree, can only be split into two groups. Thus, each node can be split into two child nodes, in which case the original node is called a parent node. The term "recursive" refers to the fact that the binary partitioning process can be applied over and over again. Thus, each parent node can give rise to two child nodes and, in turn, each of these child nodes may themselves be split, forming additional children. The term "partitioning" refers to the fact that the dataset is split into sections or partitioned. CART trees are much simpler to interpret than the multivariate logistic regression model, making it more likely to be practical in a clinical setting.
Multitier Business Models From e. g. (Nourani 2012 ) Presentation Layer o Runs with the address space if one or more web servers. Business Logic layer Runs with the address space of one or more application servers, and a Backend and DataBase layer. The presentation layer contains components dealing with user interfaces and user interaction. Example a visual JAVA standalone application. A business logic layer contains components that work together to solve business problems. The components can be high -performance engines. Data layer is used by the business logic layer to persist state permanently. Control f the data layer is one or more databases that home the standalone. From an example content processing prototype  (Nourani 2007 (Nourani ,2013) )  we can glimpse on the applications for the above section.
A basic keyed database view provides the presentation of the model. It is the look The presentation of the model. It is the look of the application. We apply predefined user know functions on the view to present the applications look. The view should be notified when changes to the model. The business logic updates the state of the model and helps control the flow of the application. With Struts, this is done with an Action class as a thin wrapper to the actual business logic. The model represents the world model for the actual business state of the application. The business objects update the application state. application. The business objects update the Ap. Action Form bean represents the Model state at a session or request level, bean represents the Model state at a session or request level, and not at a persistent level.
Conclusions New bases for augmented model decision techniques with splitting decision tree for enterprise modeling and business planning with predictive analytics models were presented. Decision tree applications to analytics towards designing cognitive augmented business interfaces with applications to social media were presented. Augmented reasoning on business systems reflect on the innovation ecosystems. Data filtering is applied with function keyed knowledge bases for goal satisfiability with competitive business models coupled with predictive analytics models accomplish goals on business models. Spanning trees focus on plan goal models that can be processed on a vector state machine coupled with database preprocessor interfaces. Cognitive views and augmented models with heuristics on predictive analytics are developed based on ranked game trees from the first authors preceding publications on economic games and big data towards newer applications to decision tree heuristics.
• Energy depositions below noise thresholds: energy from particles that do not form calorimeter clusters or have energy depositions not included in these clusters due to the noise suppression in the cluster formation algorithm. • Pile-up: energy deposition in jets is affected by the presence of multiple   collisions in the same   bunch crossing as well as residual signals from other bunch crossings. A first estimate of the jet energy scale (JES) uncertainty of 5%-9% was based on information available prior to   collision data and initial analysis of early data taken in 2010  [4]. An improved jet calibration with an uncertainty evaluated to be about 2. 5% for jets with pseudorapidity 1 || < 0. 8 over a wide range of transverse momenta ( T ) was achieved with the full 2010 dataset using test-beam measurements, single-hadron response measurements, and in situ techniques  [5].
A much larger dataset, recorded during the 2011 data-taking period, improved the precision of JES measurements to 1%-3% for jets with  T > 40 GeV within || < 2. 5 using a statistical combination of several in situ techniques  [6]. This paper describes the derivation of the ATLAS jet calibration and jet energy resolution using the full 2012   collision dataset, which is more than four times larger than the 2011 dataset used for the previous calibration  [6]. Due to the increased instantaneous luminosity, the beam conditions in 2012 were more challenging than those in 2011, and the ability to mitigate the effects of additional   interactions is of 1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the -axis along the beam pipe.
4 jets recorded during the 2015 data-taking period  [8] , and for  = 1. 0 jets recorded during the 2015-2016 data-taking period  [9]. The outline of the paper is as follows. Section 2 describes the ATLAS detector and the dataset used. The MC simulation framework is presented in Section 3, and the jet reconstruction and calibration strategy is summarized in Section 4. Section 5 describes the global sequential calibration method, which exploits information from the tracking system (including the muon chambers) and the topology of the energy depositions in the calorimeter to improve the JES uncertainties and the JER. The in situ techniques based on a  T balance are described in Sections 6 to 8. First, the intercalibration between the central and forward detector, using events with dĳet-like topologies, is presented in Section 6.
Events are retained for analysis using a trigger system  [10]  consisting of a hardware-based level-1 trigger followed by a software-based high-level trigger with two levels: level-2 and subsequently the event filter. Jets are identified using a sliding-window algorithm at level-1 that takes coarse-granularity calorimeter towers as input. This is refined with an improved jet reconstruction based on trigger towers at level-2 and on calorimeter cells in the event filter  [11]. The dataset consists of   collisions recorded from April to December 2012 at a centre-of-mass energy ( √ ) of 8 TeV. All ATLAS subdetectors were required to be operational and events were rejected if any data quality issues were present, resulting in a usable dataset with a total integrated luminosity of 20 fb -1. The LHC beams were operated with proton bunches organized in bunch trains, with bunch crossing intervals (bunch spacing) of 50 ns.
Calibration and resolution measurement using 𝜸+jet and 𝒁+jet events This section describes the determination of the final jet calibration that corrects the absolute energy scale of the jets to achieve a data-to-MC agreement within the associated uncertainties. The jet calibration is based on measurements conducted by in situ techniques that exploit the transverse momentum balance between a well-calibrated object and the hadronic recoil (jet). The well-calibrated object is either a photon or a  boson that decays leptonically, either → or →. Three separate datasets are used: (→)+jet, (→)+jet and +jet, and two different in situ techniques are used for each dataset, namely the direct balance technique (DB) and the missing projection fraction method (MPF). The three independent datasets and the two analysis methods provide six separate measurements of the jet calorimeter response that can be cross-checked with each other, allowing detailed studies of systematic uncertainties.
For each dataset, the method that gives the smallest overall uncertainty is chosen and is used as input to the final combination of the absolute jet calibration (Section 9). Due to the steeply falling  boson  T spectrum, the +jet data provide sufficient statistics to calibrate jets at lower  T and are used in the range 17 GeV ≤  T < 250 GeV. The  boson four-momentum is reconstructed by four-vector addition of its decay products (leptons). The +jet process has a higher cross section and covers the jet  T range 25 GeV ≤  T < 800 GeV. However, at low  T the photon sample has a large contamination from events that do not contain any true prompt photon and hence a sizeable systematic uncertainty. As discussed in Section 9. 1, a combination of both the +jet and +jet channels covers the full momentum range 17 GeV ≤  T < 800 GeV.