dataesr/scientific-paragraphs-categorization huggingface dataset:
The 2019 European Bioinformatics Community (EuBIC) Winter School was held from January 15th to January 18th 2019 in Zakopane, Poland. This year's meeting was the third of its kind and gathered international researchers in the field of (computational) proteomics to discuss (mainly) challenges in proteomics quantification and data independent acquisition (DIA). Here, we present an overview of the scientific program of the 2019 EuBIC Winter School. Furthermore, we can already give a small outlook to the upcoming EuBIC 2020 Developer's Meeting.
M. Eisenacher and J. Uszkoreit talked about "The essentials before and after spectrum identification: Selecting the appropriate database and inference strategy"  [3]  and guided participants through practicals exploring the protein database exports of the UniProt KB  [4] , analyses in the workflow environment KNIME  [5]  and performing protein inference using PIA  [6, 7]. In the workshop entitled" Quality Control and Benchmarking of Label-Free Quantification Workflows with LFQBench" S. Tenzer and M. Lacki explained how to apply the R package LFQBench  [8]  to proteomics data and how to interpret the results. Furthermore there were two workshops of our main sponsors: "Discovering the open-source Proline software suite, a new efficient and user friendly solution for label-free quantification" by ProFI (http://www.
profiproteomics. fr/) and a workshop organised by ThermoFisher Scientific about the new features of Proteome Discoverer 2. 3. On the afternoon of the third day, four more workshops were offered. L. J. Jensen presented the "Network visualization with Cytoscape and stringApp"  [9]  with hands-on practicals, while M. Wilhelm presented a "Validation of peptide identifications" using ProteomicsDB  [10]. In parallel, E. Petsalaki guided the participants through an analysis using SELPHI in "SELPHI: using data-driven approaches for analysis of phosphoproteomics datasets"  [11]  in the first half of the workshop before F. Meier who showed the "Advanced data acquisition methods with MaxQuant. Live"  [12]  during the second half.
Keynotes For this year's Winter School we had in total ten keynote slots with 45 min talk and discussion each: four on both Wednesday and Thursday and two on Friday before the closing session. In keeping with the tradition of the first Winter Schools, the presenters were asked to talk about problems and challenges in the field of bioinformatics for proteomics they encountered during their recent research. To emphasize this, this year's special focus was "Challenges in Quantification and DIA". On Tuesday, after the welcome and announcement, B. Searle (Institute for Systems Biology, Seattle, WA, USA) kicked off the keynotes with a talk about a new database search engine to detect genetic variants from DIA data using the tool XCorDIA  [13]. After this, H. Röst (The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada) spoke about developing tools for the personalized medicine revolution and using mass spectrometry for longitudinal molecular profiling  [14] [15] [16].
On the third day, the first talk was given by L. J. Jensen (University of Copenhagen, Copenhagen, Denmark) about STRING  [18]  and recent updates to its database. Exemplary stated in this regard were the text mining procedures and performances used to find protein-protein-interactions. M. Wilhelm (Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany) presented Prosit, a tool to predict MS/MS spectra from a given peptide sequence, which was trained on the data generated by the ProteomeTools project  [19, 20]. The following talks on Thursday were oriented towards MS-based data analysis and how to drive biological insights from the huge amount of data generated nowadays. E. Petsalaki (EMBL-EBI, Wellcome Genome Campus, Hinxton, UK) discussed re-analysing phosphoproteomics data ("Using phosphoproteomics data to study context-specific signalling") and illustrated the associated challenges  [21].
Acknowledgements The EuBIC likes to thank the the main sponsors EuPA and  Elsevier, platinum sponsor BSi , the gold sponsors ProFI,  ThermoFisher Scientific  and  SVA  and poster price sponsor  Molecular Omics  for their support of the Winter School. The organizing committee especially thanks the speakers and workshop organizers for bringing the Winter School to such a level of excellence, companies and exhibitors for their active participation in this community initiative, and all participants for the active and fruitful interactions.
Data Acquisition, where raw data is acquired from the BLE devices. 2. Data Mining, where the raw data undergoes a data mining process provisioned by HyPo to produce a set of features that is fed to Feature Selection algorithms to obtain a Sanitized Dataset. The Feature Selection is done to improve the execu- tion performance of the Machine Learning (ML) methods that will follow by determining the most relevant features and removing the others from the Sanitized Dataset. 3. Feedback Service, where the ML models are run over the Sanitized Dataset to produce feature set that can be used to provide timely feedback to healthcare providers. Paper Organization. The remainder of this paper is organized as follows: Section 2 defines key terms frequently referenced throughout the paper; Section 3 describes the data collection setup; Section 4 details the data preparation and mining process; Section 5 describes Hypo's feedback service and how it complements the direct observation approach; Section 6 surveys and compares existing research in the area of hand hygiene compliance improvement and monitoring applications; and Section 7 presents concluding remarks and outlines future extensions of this work.
Instrumentation Configurations The ICU with HyPo deployment was equipped with a Bluetooth Low-Energy (BLE) indoor positioning system that provides room-level accuracy for reporting staff locations in real-time. The system produced the location data for all staff with BLE badges. The ICU also deployed Gojo Active Monitoring handwashing stations, which record each dispenser activation. These activation events were then combined with real-time location data to track individual staff handwashing compliance. The system expected to receive at least one handwashing event from either a sanitation station inside of the room or a station immediately outside the room within two minutes prior to entry, abiding the facility rules described in Section 2. Similarly, two minutes before or after room exit, the system expected one handwashing event from either sanitation stations.
Dataset Limitations Although real-time location data was acquired and handwashing station activations recorded at the ICU, the dataset was still an estimate rather than a ground truth of hand hygiene compliance. The dataset collected has a number of key threats to validity, including: (1) not all staff wore their BLE badges at all times, (2) the system could not differentiate activations from badged vs. non-badged visitors/staff, and (3) subsets of the monitoring equipment went offline at some intervals and prevented data capture in certain rooms. However, we did not consider these limitations as fatal to our study results because we could either easily eliminate the data entries associated with these threats or discard the marginal impact that the threats had on our findings.
DATA PREPARATION This section discusses how we prepared the collected data to maximize the utilization of our machine learning classifiers, which is an important capability offered by HyPo. This process is the same as that employed in  (Zhang et al. , 2016)  to assist the analyses and characterization of hand hygiene compliance. Other influencing factors of hand hygiene compliance may be discovered as more relevant data becomes available, such as patient admittance details, medical records of admitted patients, facility regulations of compliance, etc. Despite the specificity of the dataset used throughout this paper, the data mining process provided by HyPo as described below can be generalized to support transformations of different forms of data collected in other facilities.
Data Transformation Most machine learning (ML) classifiers yield better results when the input dataset is structured in certain ways. For example, suppose we want to know if the day of week (Monday to Sunday) influences compliance, some ML classifiers will yield better results if we express date as a set of integers ranging from 1 to 7, as opposed to a real continuous stream of timestamps expressed in milliseconds. As another example, our location data consists of room numbers, which provides little information regarding spatial distribution of the rooms. If we want to know whether compliance decreases in nearby locations, we must first transform the room numbers into coordinates on the facility's floor plan, for instance. To obtain a transformed schema that can be better handled by our classifiers, we took the collected dataset and performed the following transformations over it: 1.
We converted all event data from the original timestamp format into an integer field with range 1 to 7 to represent day of week, an integer field with range 1 to 4 to represent time of day in morning, afternoon, evening, bedtime, and another integer data field of 0-23 to represent hour of day. The numeric representations of the original nominal time stamp data will allow our classifiers to achieve higher classification accuracy. 2. We mapped each patient room on the ICU floor plan to a set of x and y coordinates to identify the spacial location. We then extended each entry in the dataset to include these corresponding coordinates of the patient room. 3. For each data point we added new fields to include the previous record of the corresponding badged staff's handwashing data, i. e. , duration, location, washed on entry, and washed on exit.
Feature Selection After we transformed the dataset into a features set, we executed a feature selection process to automatically select feature subsets in our transformed data that best (1) reduced overfitting of data, (2) improved classification accuracy, and (3) decreased model training time  (Guyon and Elisseeff, 2003 ). Although we do not have a significantly large feature list produced for this ICU, it is still useful to apply this technique to select the most relevant subsets of features to help produce the most accurate feedback in the next step. To automatically select features from the transformed dataset, HyPo applies a supervised attribute selection filter from the open source Weka Java library  (Hall et al. , 2009). The filter is composed of two pieces: (1) a feature Evaluator to determine how features are evaluated and (2) a Search Method to navigate the feature's search space.
Search Method: GeneticSearch that performs a search using the simple genetic algorithm (Goldberg, 1989) Our previous study  (Zhang et al. , 2016)  observed two highly correlating factors of compliance using the data collected in the same 30-bed ICU. We could configure HyPo to select only these two features from the dataset to use for determining feedback provision. However, automatic feature selection is an integral piece because as the dataset increases in size and dimension, our enhanced app can continuously combine features or intelligently adjust the correlating features to maximize classification accuracy.
Machine Learning Models After preparing the dataset, we split the data to 65% for training, 10% for cross validation, and the remaining 25% for testing the ML models. Based on the compliance prediction observations from the previous study in  (Zhang et al. , 2016) , we employed the top three classifiers, one from Weka  (Hall et al. , 2009)  and two deep nets from DeepLearning4J (DL4J)  (Team, 2016)  to serve as our models for classifying washed on entry and washed on exit. HyPo then uses the results with highest accuracy. • The Sequential Minimal Optimization (SMO) implementation of the Support Vector Machine (SVM), which uses heuristics to partition the training problem into smaller sub-problems and uses pairwise linear regression to classify. This method is usually resilient to data overfitting and by default normalizes the input data  (Platt et al.
, 2010). Similarly, Marra et al employed a wireless network with sensors on the alcohol dispensers that provide realtime flashing light feedback to HCWs for hygiene activity detection  (Marra et al. , 2014). Most recently, Ellison et al proposed a prospective electronic hand hygiene room entry/exit audible reminder system  (Ellison et al. , 2015)  that provides a combination of 24hour-a-day recording of hand hygiene activities and real-time computer monitor performance feedback. Differentiating Factors of Our Approach. All the prior research we reviewed collected data to propose strategies that increased hand hygiene performance or gather conclusions regarding the efficacy of a specific approach. Our HyPo app is unique since it uses the gathered data to predict future compliance behavior instead of notifying appropriate caregivers after noncompliance has been detected.
CONCLUDING REMARKS This paper presented a hand hygiene monitoring app called Hygiene Police (HyPo) that can be deployed as a service to complement the current monitoring approach and improve compliance. We showed an example data collection process taken place at a 30-bed ICU where we acquired the handwashing compliance data. We also described the data transformation process HyPo employs to maximize the utilization of the selected machine learning (ML) classifiers. Combining the results of real-time compliance predictions using the correlations identified in  (Zhang et al. , 2016) , HyPo can provide three types of services: (1) just-in-time alerting to remind predicted non-compliant staff to perform hand hygiene, (2) recommending training materials to habitually noncompliant staff via email, and (3) assisting the direct observation approach by deploying human auditors at the opportune time and place when and where noncompliance is frequently predicted to occur.
e. , ecological status, profitability of the fisheries, and food security. Four storylines of plausible futures of the coast were created (Appendix 1). These stories integrated secondary data on key indicator trends (e. g. , population, fishing effort, economic development), system understanding (from first workshop), and ecological projections. The four stories were written by scientist groups with the support of local experts, and they were designed to incorporate the vast majority of systems components and dynamics that were discussed in the first workshop. Each story also featured potential trade-offs between primary stakeholders and systems objectives. These drafts were sent to local experts to verify the plausibility of the stories and to incorporate additional features. A graphic artist visually represented each story.
Acknowledgments: The primary and secondary stakeholders of the  Nyali Beach  fishery in focus groups and workshops is gratefully acknowledged. Arthur Tuda ,  Sheila Heymans ,  Allister McGregor , and  Raphaël Mathevet  advised on the design of the project. This paper is a result of the project " Participatory Modelling Frameworks to Understand Wellbeing Trade-offs in Coastal Ecosystem Services " (P-mowtick) funded by the  Ecosystem Services and Poverty Alleviation (ESPA) program  (ref number:  NE/I00324X/1 ) and received further support from  K. Brown 's  ESRC Professorial Fellowship  ( RES-051-27-0263 ). The ESPA program is funded by the  Department for International Development (DFID) , the  Economic and Social Research Council (ESRC) , and the  Natural Environment Research Council (NERC).
Sample porosity assessment An in-house python script was developed to make local measurements of sample porosity. For practical reasons, the porosity is preferred over void ratio for the simple reason that it is a ratio of occupancy with respect to a total (local) volume which is convenient to set when using 3D images. Concerning the issue of the size of the representative sample sub-volume, this is set by successively measuring the porosity of a cube with an increasing volume size. When the porosity measurements all converge and become stable this is taken as the sub-volume size for the analysis, which in this case represents a cube of 55 × 55 × 55 pixels (approximately 2. 5×2. 5×2. 5 D 50 ). The mesh of points that has been chosen as a basis for measurements (on which the sub-volumes will be centred) has a spacing of 4 pixels in each direction, which means there is a very significant overlap of the measurement windows.
For a granular material, the distance over which the local porosity is affected was estimated to be about 4-5 average particle size diameters from a vertical sample wall  [42]. However, for fibre-reinforced soils, De Larrard  [43]  estimated the perturbed zone from the face of rigid cylindrical fibres mixed with a granular soil (having an average particle size diameter of the same order of magnitude of the fibre diameter) to be much smaller, about 10% of the average particle size. In order to quantify the porosity in the vicinity of the fibres, an additional bespoke image analysis script was written in python to measure the evolution of porosity with increasing distance to each fibre. To achieve this direct calculation, a trinarised image is used (Fig. 7a ); for n increments of one pixel up to a maximum fixed scanning distance in pixels, the fibre phase is dilated and the volume defined by the previous dilation (n -1) is subtracted, leaving a one-pixel thick shell at a distance n from the surface of all fibres (Fig.
The 3D skeletonised fibre network architecture reconstructed by the X-ray CT (Fig. 5b ) contains all the fibre spatial information. For each fibre-reinforced sample, the 3D spatial information was transferred into an in-house Matlab program and the 1 mm fibre length segments were automatically traced along the fibres. For each segment, a set of (θ , α) angles used to analyse the spatial geometry and orientation of individual fibres was subsequently produced. In the context of cylindrical soil samples, the axisymmetry of fibre orientation with respect to the vertical axis is an important characteristic of the 3D fibre architecture network. The laboratory fabrication methods are expected to produce an axisymmetric distribution of fibre orientation and the distribution of α angles provides information on the randomness of in-plane fibre orientation.
Acknowledgements The first author thanks the  LabEx Tec 21 (Investissements d'Avenir , Grant Agreement No. ANR-11-LABX-0030 ) for full financial support of her master research project, which is an important part of this work. Part of this work was possible with the financial support of  University of Bristol  and by the  UK Engineering and Physical Sciences Research Council (EPSRC)  Grant  EP/J010022/1. Erdin Ibraim  initiated and developed this work during a  Visiting Professorship (IFSTTAR Nantes ) supported by  La Région Des Pays de la Loire, France. Compliance with ethical standards.
NLME models are also being used by advising groups such as the Food Animal Drug Residue Avoidance Databank (FARAD) to provide a robust estimate of the withdrawal time for edible tissues in situations when a drug or drug product has been used in an extralabel manner (i. e. using a route, dosage, frequency, or in species other than that indicated on the approved drug product label). The Animal Health Modeling & Simulation (AHM&S) Society was founded in 2012 to create an international forum promoting the use of in silico tools to support veterinary drug development  (Mochel et al. , 2013a). This review article is part of a series of white papers designed to illustrate the value and applications of mathematical models in veterinary pharmacology and toxicology. In previous reviews, an introduction to modeling and simulation  (Riviere et al.
Author Manuscript This article is protected by copyright. All rights reserved analyzing sparse clinical data and recognized that the relationship between observed data and model parameters was non-linear by nature. Therapeutic drug monitoring for individualized digoxin dosing in heart-disease patients later crystallized the first human clinical application of NLME by Sheiner in the early 1970's  (Peck et al. , 1973; Sheiner et al. , 1975). Subsequently, Sheiner and  Beal (1983)  contributed to the dissemination of the NLME approach by developing the computer software NONMEM® (Non-Linear Mixed Effect Modeling, ICON Development Solutions), providing a unique analytical platform for pharmacokineticists  (Mould & Upton, 2012). Prior to the availability of the NLME approach, population parameters were expressed as either means or medians based upon a pooling of individual data into a single concentration-time course (i.
Drug Use Several NLME models have been published as tools for estimating the time when FDA-approved marker residues deplete below the tolerance or detectable levels in extralabel use situations. This is well exemplified by a study from  Li et al. (2014)  where the authors established a robust NLME model of penicillin for a large and diverse population of food-producing animals (Figure  3 ). The model was developed using published data from the literature and validated by comparing the concentrations predicted by the model with separate, both published and unpublished independent datasets. Usage of drugs approved for major species in minor species requires that dosing regimens and withdrawal times are extrapolated between species. Allometric models of drug PK across animal species are usually formulated empirically, building equations that relate PK parameters to factors like bodyweight, brain weight or maximum lifespan  (Riviere et al.
, 1997). Using retrospective data from the FARAD, Martin-Jimenez and Riviere  (2001, 2002)  applied NLME for analyzing the disposition of gentamicin and oxytetracycline across several mammalian species. In these studies, various covariates were incorporated into NLME PK models, using data from six and seven species for oxytetracycline and gentamicin, respectively. PK parameters were found to be exponentially related to bodyweight for both oxytetracycline and gentamicin. Interestingly, brain weight was also found to be a predictor of gentamicin clearance in the model. An advantage of this approach is that it maximizes the information that can be gleaned from limited available data, and facilitates the estimation of population variability by combining data from several sources (e. g. literature and raw data from new studies).
Author Manuscript This article is protected by copyright. All rights reserved approach combining data from three prospective, multi-center field studies in 208 osteoarthritic dogs, Fink and colleagues  (2013)   For mavacoxib, simulations from a NLME PK model showed that a 2 mg/kg dose was sufficient to maintain trough concentrations above a threshold efficacy level in dogs. These results were key to reduce the recommended dose of mavacoxib from 4 to 2 mg/kg, which ultimately increased the therapeutic index of mavacoxib in dogs  (Cox et al. , 2011). for efficacy and safety assessment, respectively. This exposure index will be further used as a covariate to support the perioperative clinical efficacy and safety evaluation of robenacoxib in cats.
In this paper we describe the MUMULS system that participated to the 2017 shared task on automatic identification of verbal multiword expressions (VMWEs). The MUMULS system was implemented using a supervised approach based on recurrent neural networks using the open source library TensorFlow. The model was trained on a data set containing annotated VMWEs as well as morphological and syntactic information. The MU-MULS system performed the identification of VMWEs in 15 languages, it was one of few systems that could categorize VMWEs type in nearly all languages.