dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, the approach benefits from multiple replicates per feature. As PTMs usually appear low abundantly, this is often challenging to achieve in practice  (26). Although we recommend the addition of a global profiling counterpart to an enriched PTM dataset, this is conceptually not required as normalisation can be done using all peptidoforms mapping to the same protein. However, we showed that this approach has the risk of partially diluting the effect of the PTM as their underlying peptidoforms are now involved in the calculation of the PTM usage. As opposed to MSstatsPTM we do not make use of converters. Hence, msqrob2 input is not restricted to certain search engines or quantification algorithms, providing the user with full flexibility. However, this does require the user to convert their data into appropriate input format, which is a simple flat text file format (as exportable from a spreadsheet) or a data frame in R that can be used by the constructor for QFeatures objects.
Furthermore, our workflows are modular and provide the user with the flexibility to use custom pre-processing steps. Default workflows are presented in our package vignettes, but these can easily be altered by building upon methods in the QFeatures package. Moreover, the use of the QFeatures infrastructure also guarantees that input data is never lost during processing, but remains linked to the pre-processed and normalised assays as well as to the model output, insuring transparency, traceability, and reproducibility. This allows the user to perform differential usage (and/or abundance) analysis on both PTM and peptidoform (or even protein) level, while storing and linking all these different results in a structured manner in the same object. Another advantage of msqrob2PTM is that it can manage multiple modification sites per peptidoform.
Acknowledgements This research was funded by the  Research Foundation Flanders (FWO)  as a mandate awarded to ND ( 1S77220N ), and as project funding awarded to LM ( G010023N ,  G028821N ) and LC ( G062219N ), funding from the  European Union 's  Horizon 2020 Programme  to LM ( H2020-INFRAIA-2018-1 ) [ 823839 ], and funding from a  Ghent University Concerted Research Action  to LM [ BOF21/GOA/033]  and  LC [BOF20/GOA/023 ]. LCG and PL were supported by the  Bundesministerium für Bildung und Forschung  ( 01GM1917A , " Multi-omic analysis of axonosynaptic degeneration in motoneuron disease (MAXOMOD) ", which was funded in the scope of the  E-Rare Joint Transnational   Call for Proposals 2018  " Transnational research projects on hypothesis-driven use of multi-omic integrated approaches for discovery of disease causes  and/or  functional validation in the context of rare diseases.
Study design and setting This was an international, prospective, inception cohort study conducted and analysed in compliance with a published protocol and statistical analysis plan with minor modifications (electronic supplementary material (ESM) 1)  [16]. The manuscript was prepared according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement  [17]  (ESM 2). The study was performed in 52 ICUs across 10 countries in Europe and North America (ESM 3 and 4) and overseen by a steering committee  (ESM 5). Study ICUs were invited to participate through the research network Caring for critically ill immunocompromised patients: Multinational Network (Nine-I). The study was approved by the Danish Centre for Regional Development (R-21012287) and registered at the Capital Region's Centre for Data Compliance (P-2021-262).
Daily data were collected during ICU stay and resumed upon readmission or transfer to another study ICU for a maximum of 90 days. We collected data on the use of lifesupport [vasopressor/inotropes, mechanical ventilation, renal replacement therapy, extracorporeal membrane oxygenation (ECMO)], selected treatments, platelet counts, bleeding events, thrombotic events, and transfusions (ESM 9). For platelet transfusions used within ICU departments, we collected data on indications for transfusion (pre-procedural: covering invasive procedures or surgery; prophylactic: reducing the risk of bleeding; therapeutic: treating bleeding) and platelet count before transfusion (ESM 1 and 9). On day 90, we assessed the vital status and number of days in the hospital (ESM 10).
Funding Open access funding provided by  Royal Library, Copenhagen University Library. This study was funded by the  Research Council of Rigshospitalet , the  Ehrenreich's Foundation , and the  Dagmar Marshalls Foundation. The  Memorial Sloan Kettering Cancer Center  part of the study was supported by the  Core Grant ,  Grant/Award  Number:  P30CA008748 ;  Department of Anaesthesiology & Critical Care Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America. None of the funders had any influence on the design, conduct or reporting of the study.
IV. RESULTS To solve the MILP problem presented in this study, MATLAB has been employed, coupled with the Optimization toolbox, a powerful optimization tool designed for mathematical programming and linear programming tasks. The performance of the proposed approach is compared to an offline scheduling algorithm, deeply explained in  [24]. An offline scheduling approach incurs higher computational demands and necessitates complete, deterministic knowledge of the system for its operation. Despite this, it has the potential to yield superior makespan performance. Conversely, an MPC-based scheduling approach can achieve performance levels on par with offline scheduling, while also demonstrating the capability to adapt to unanticipated job arrivals or machinery malfunctions. Machine breakdowns, although relevant, have not been addressed in this study.
ACKNOWLEDGMENTS The authors acknowledge the use of ChatGPT by OpenAI to refine the academic language and accuracy of this work. The authors have made the code accessible for future developments and the addition of new features through the following GitHub repository: https://github. com/Bozzi96/MPC_scheduling. git This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/ACCESS. 2023. 3341504 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4. 0 License. For more information, see https://creativecommons. org/licenses/by-nc-nd/4 Bozzi et al. : Dynamic MPC-based scheduling in a smart manufacturing system problem SIMONE GRAFFIONE , master's degree in Computer Engineering (2019) is a PhD student at  University of Genoa in System Engineering.
Study population This study analyzed data of mother-child pairs from the six European birth cohorts of the Human Early-Life Exposome (HELIX) project: BiB (Born in Bradford; United Kingdom), EDEN ( Étude des Déterminants pré et postnatals du développement et de la santé de l'ENfant; France), INMA (INfancia y Medio Ambiente; Spain) sub-cohort conducted in Sabadell municipality in Barcelona province, KANC (Kaunas Cohort; Lithuania), MoBa  (Norwegian Mother, Father and Child Cohort Study; Norway)  and Rhea (The Mother-Child Cohort in Crete, Greece). The project has been described in more detail elsewhere  (Maitre et al. , 2018). The HELIX Subcohort comprises 1301 mother-child pairs (around 200 children in each cohort) in which a new follow-up examination of the children between ages 6 and 11 years was carried out.
The subcohort sample was selected according to the following criteria of eligibility: a) primary school ages, b) stored pregnancy blood and urine samples available, c) complete address history available, and d) no serious health problems that may affect the clinical testing or the child's safety. In addition, n = 347 extra children (n = 271 INMA, n = 26 BiB, n = 7 EDEN, n = 5 KANC, n = 21 MoBa, and n = 17 RHEA) without biomarkers available were invited and examined following the same protocols for clinical examination, sample collection, and questionnaires. The present study included n = 1607 children with complete data for the exposures and the cognitive outcomes of interest (see Supplemental Material, Figure  A1 ). In the follow-up, the mother-child pairs were assessed by trained personnel according to the same protocol.
The assessment included a maternal computer-based main questionnaire, a geographical survey to geocode daily activity locations (i. e. , home, school, commute route from/to school, and other daily life places regularly visited by the children) conducted in QGIS software©  (QGIS Development Team, 2013 ; QGIS Geographic Information System), child computer-based cognitive tests, and clinical examination. The mothers also completed a short version of the cognitive tests. The ethics committee for each cohort approved the consent form. We obtained written informed consent for all participants, signed by the parent. The current study was approved by the HELIX Ethical committee.
Exposure assessment Based on the main questionnaire, qGIS survey, and GIS data we derived multiple dimensions of the green space exposures. Daily activity locations measurement. We first measured children's daily activity locations including their home, school, commuting routes from home to school and from school to home, and places other than home and school, which they visited regularly and/or in the seven days before the follow-up assessment ("Did your child go to any place, apart from home and school, for more than 2 h in the last week?"). Parents/carers were invited to answer an electronic survey using qGIS (Version 1. 8. 0)a free and open-source Geographic Information System (GIS) software. Using different map services (such as Open Street Maps or Google Maps) with QGIS software we geocoded specific locations or paths by searching directly on the map or using a search motor.
Trained researchers conducted the survey with parents/carers to geocode the child's current home(s), school(s), commuting route(s), regular places, and all places visited in the seven days before the survey (see Supplemental Material, Green space, and qGIS questionnaires). The main home address and any secondary residence where the child spent more than two days in a usual week were recorded, as well as the school address [What is the address of your child  's school?]. Common commuting routes between home-school and school-home were geocoded if children repeated each one more than 2 days per week. The trained researcher drew the route on the map following the information given by the parent/carer. Following the criteria, all the commuting routes from home(s) to school and from school to home(s) were geocoded (if the children had two households, both routes were geocoded; i.
Data pre-processing For each exposure variable, the optimal transformation to approach normality was applied (i. e. , NDVI was log-transformed and Straight line to green space square root transformed) or the variable was categorized if normality could not be achieved (i. e. , visits to green spaces). Missing data for all exposures and confounders were imputed using the method of chained equations  (White et al. , 2011). A total of 20 imputed datasets were generated and used in all the analyses mentioned hereafter. We restricted the number of predictors in the imputation models to fewer than 25 variables while ensuring that two of the three outcomes were considered predictors (van Buuren, 2012). Rubin's rule was used to aggregate the results from the 20 imputed datasets  (White et al. , 2011).
Statistical analyses We explored associations between measures of green space exposure and children's fluid intelligence, inattention, and working memory test scores. For each outcome variable, we conducted separate multiple linear regression analyses on pooled data adjusted for confounders. We estimated associations with each exposure variable individually using independent regression models. To provide an easier comparison of the effects, we estimated the change in average outcome scores associated with one interquartile range (IQR) increase in average NDVI (based on all study participants). Thus, in our models, the resulting coefficient can be interpreted as the change in the mean when moving from the 1st to the 3rd quartile of NDVI and straight-line distance to the nearest major green space.
Study population Of 1648 participants, 1639 answered the qGIS questionnaire and had daily activity locations geocoded. Of these, 1607 had available data on fluid intelligence (Raven's test) and on the inattention test (Attention Network Test [ANT]) (See Supplemental Material, Figure  A1 ). Due to technical problems with the test software, the N-back test for working memory was completed by 1325 children. Descriptive statistics for the characteristics of study participants, exposures, and the prevalence of our investigated outcomes are presented in Table  1. We presented the descriptive statistics per cohort in the Supplemental Material, Table  A2. The mean age of children was higher in  EDEN [11 (±0. 59) ], INMA [9. 3 (±0. 72)], and MoBA [9. 0 (±0. 62)], which probably explained the better cognitive test scores in these cohorts.
, 2022; Amoly et al. , 2014; Dadvand et al. , 2015). Also, it evaluated a more comprehensive set of green space exposure variables, including a temporal dimension, that was identified as a main methodological gap in the available literature  (Labib et al. , 2020). In addition, the outcome assessment was based on cross-culturally validated computerized tests to objectively characterize multiple cognition functions for each study participant. A pilot was conducted in each cohort to guarantee the quality of the measures and the standardized assessment protocol. We analyzed data from six European birth cohorts, which might increase the external validity or generalizability of the present findings. Furthermore, we were able to control for the effect of maternal working memory a major confounder not addressed in previous studies  (Dadvand et al.
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (NIHR) applied research collaboration for Yorkshire and Humber (NIHR200166) and by the UK Prevention Research Partnership (MR/ S037527/1). Jose Urquiza is supported by Catalan program PERIS (Ref. : SLT017/20/000119), granted by Departament de Salut de la Generalitat de Catalunya (Spain). We acknowledge support from the grant CEX2018-000806-S funded by MCIN/AEI/ 10. 13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program. This publication reflects only the authors' view and the European Commission is not responsible for any use that may be made of the information it contains.
Such approaches can be leveraged as the software overlay of widespread IoT applications. The Space Broker  [5]  and SemIoTic  [35]  frameworks define smart space characteristics and contextual data such as spatial representation of spaces. However, both works have focused on a single smart space and do not offer a solution in a federation of smart communities/spaces. This paper introduces ComDeX, a context-aware federated architecture and IoT platform for enabling data exchange between smart communities. ComDeX solves an information integration problem for enabling widespread IoT applications in smart cities by providing a novel underpinning architecture with context-aware data modeling and dissemination mechanism for cross-community data exchange. It uses property graphs to model data by including the communities' context (dynamic information coming from IoT devices), while scalable information dissemination mechanisms ensure selective-sharing of IoT data.
NGSI-LD as ComDeX's information model Recall that the ComDeX information model consists of modeling entities, their static and dynamic properties, and relationships using property graphs. The NGSI-LD information model is a good fit for it as it also derives from property graphs  [27] : The core element is the entity (known as a node in property-graph language), which corresponds to a real-world concept. Every entity must have a unique identifier, which must be a URI (typically a URN), as well as a type, likewise a URI. This URI should point to a Web-based data model. Entities are associated with properties and relationships. To enable context awareness, each property's name should ideally be a well-defined URI that corresponds to a widely used notion on the Web. This knowledge graph is well-defined and infinitely expandable.
This process is similar to entity creation but has additional complexity. When creating a subscription, the action handler validates the command and the 'subscription. file', which must contain a valid JSON with at least one of the following: id, type, watched_attributes. The handler establishes a connection with the specified broker and subscribes to provider messages (advertisements) that align with the subscription. For each newly received provider message, a parallel connection/subscription is created with the broker that published the relevant information, provided it has not been received before. If an advertisement is deleted while a subscriber is still connected, the corresponding subscription is terminated. The fragmented entity data received is reconstructed accordingly. Our ComDeX prototype implementation is in Python but can be easily adapted to other programming languages.
For this set of experiments, we created a data model with simple attributes of no semantic meaning (see Table  2 ), following an approach similar to  [16]. We developed a data generator in Python for the above data model, and used it to generate 2000 entities of 10 different entity types and 3 different attributes. The generated entities are entered into the system and stored at the edge brokers of each setup (Orion-LD, Scorpio, ComDeX (Mosquitto) 1-, 3-and 6-broker-wide) (Fig. 9 ). We then deploy a workload generating synthetic sensor requests on 10 subscriber VMs, each request selecting a random attribute of a random entity approximately every 100ms and patching its value to a random  value (e. g. , entity_500, Attribute3, 42). We dedicate one subscriber VM to each data type in all cases (e.
Devices can exist within rooms, but they can also be embedded on windows and doors. A generation run for the entities of a single community sub-broker (Port Area B) gave 82 buildings, 212 floors, 1199 rooms, 1796 windows, 1781 doors, and 588 devices. with a rough total of 1700 synthetic devices across all communities. To create a realistic scenario for the experiment, the IoT devices and publishers are modeled using device characteristics reported by Kumar et al. [20] , which process a dataset of 20 days of network traces generated by 20 IoT devices and extract their significant features. This dataset was chosen to generate synthetic traces because of its thorough analysis of real-world IoT device characteristics over a significant period, providing a comprehensive representation of IoT device behaviors and communication patterns.
Visual predictive checks As a verification criterion of calibration success, as well as correct estimation of parameter values and distribution amongst the population, visual predictive checks were performed on the experimental dataset used for calibration (Figs. 1, 2 and 3). For the in vitro calibration of the model, the model faithfully reproduced the time at which ERK and AKT proteins reach their maximal activation levels, and the maximal activation observed (following epidermal growth factor (EGF) or hepatocyte growth factor (HGF) stimulation) as shown in Fig. 1. On in vitro KRAS mutated spheroid simulation, the model output matched the experimental data, both in terms of tumor radius evolution, and maximal depth for cell viability observed in these conditions (Fig. 2a, b ). On the mice xenografted with patient-derived tumor (carrying exon 19 deletion mutation, with or without co-occurrence of PIK3CA mutation), either treated with gefitinib or untreated and based on the dynamics observed in literature, the ISELA model reproduces accurately the evolution of tumor volume over time (Fig.
2c-f ). The selected range of acceptable model output variation, materialized by the error bars, was defined as the maximum between two times the associated standard deviations and 20% of the mean. This approach allows coverage of heterogeneous calibration datasets: we were able to constrain the model on datasets composed of one or few numbers of experiments and/or datapoints, as well as datasets lacking standard deviation. Finally, the penalizations were applied to the selected range of variation without assigning specific weight to the mean. To note, in one condition (namely EGFR mutant with placebo), the simulation tended to provide a tumor volume that was slightly lower than the observed one, while remaining in the experimental uncertainty that was huge in this particular setting. In the human setting, the ISELA model was able to match a realistic TTP for 97% patients included in the calibration process (one virtual patient displaying a higher TTP than their real world counterpart), supporting its reliability with respect to clinical outcome prediction (Fig.
1  Quantitative visual predictive check of calibration step 1 results. The correspondence between simulation outcome and observed data, after calibration, was assessed (a, b) for the activation of pathways downstream to EGFR and cMET (cellular mesenchymal epithelial transition). The observational data corresponds to the area defined by both maximal phosphorylation rate (dashed line) and time of that maximum following the indicated stimulation. Fig. 2  Quantitative visual predictive check of calibration step 2 results. The correspondence between simulation outcome and observed data, after calibration, was assessed for tumor evolution. a Evolution of spheroid tumor size; the observational data corresponds to the mean tumor radius overtime with estimated error bars (taken as maximum between 2 standard deviations and 20% of the mean).
Validation process We performed a validation process to assess the reliability of the ISELA model predictions. We aimed to ensure that the model is able to reproduce biological and clinical behaviors extracted from independent clinical datasets that were not used to build nor calibrate the model. In the following, we compare the ISELA model predictions with the data extracted from ref. 23. The model is deemed as successfully validated if it respects the thresholds set in the "Materials and methods" section on raw coverage and bootstrapped LR-test thresholds.
DISCUSSION The in silico EGFR-mutant lung adenocarcinoma (ISELA) model presented in this paper is a predictive and reliable mechanistic model of tumor growth evolution for patients treated with gefitinib with TTP as primary outcome. This model includes patients' individual characteristics variability observed in literature. The model was designed with knowledge and data available in public literature. The calibration outcome and the corresponding visual predictive checks show the successful calibration with more than 85% of patients accurately reproduced. Visual predictive checks are a valuable tool widely used in the field of modeling in particular in pharmacodynamics  25, 26  , which helped build credibility for the calibrations performed. To go even further, and to assess the credibility of the ISELA for prediction, a formal validation of the model output with respect to an independent dataset showed agreement with the real world clinical data.