dataesr/scientific-paragraphs-categorization huggingface dataset:
Users can use a guest account or register http://milou. science. uu. nl/se Binding affinity Main input: provide PDB ID of the Different situations if rvices/PRODIGY-LIG protein-small-molecule complex or can users are registered or not upload a file in PDB or mmCIF format. An archive of multiple PDB/mmCIF files (. tar,. tgz,. zip,. bz2, or. tar. gz) can also be uploaded Major output: results can be visualized online and archive file of all the outputs (. tgz) can be downloaded Open http://stats. drugdesign. fr Analysis of screening results, Major input: supply a csv file with n/a consensus scoring compounds identifiers, docking scores and Activity tags Major output: Interactive charts & screening metrics can be visualized online and downloaded Open http://www. swissdock. ch Protein-ligand docking and Main input: provide either PDB code, protein The institute does not advanced binding affinity name, sequence, URL or upload PDB file or a look at user data prediction ZIP file containing the target protein in the CHARMM format; Ligand information can be supplied as ZINC accession number, ligand name, URL or by uploading ligand file in the mol2 format or as a ZIP file containing files in the CHARMM format Main output: docking poses can be visualized online and downloaded This server is free for academic use https://durrantlab.
ccnu. edu. cn/ Consensus inverse docking Major input: select a protein set of interest; n/a ccb/server/ACID/ strategy. Docking with for ligand information is fetched by drawing a instance AutoDock Vina, molecule or by uploading a file in sdf, pdb or LEDOCK, PLANTS and mol2 format Major output: results can be PSOVina visualized online Open http://pbil. kaist. ac. kr/CRDS Consensus inverse docking Major input: PubChem or ZINC ID or a ligand n/a file can be uploaded in mol2 or sdf format (3D structure). Compounds are docked to a selected set of targets Major output: the results from docking and the predicted binding pose can be visualized online and the structure files can be downloaded. Information about pathways are also reported. Target UniProt ID and PDB id are provided Open, results are sent by email http://bio-hpc.
Along with the occurrence of the big data era, digital transformation has had a transformative effect on modern education tremendously in higher education. It transforms an institutional core value of education to better meet students' needs by leveraging big data and digital technology. Based on this background, this study attempts to catch the principal trends, or new directions, paradigms as predictors with an association of each topic by discovering the up-to-date research trends on teaching and learning in higher education via text mining technique. For this, 285 research articles in the area of teaching and learning in higher education were collected from several big databases (distinguishable publishers' web platforms) through search engines for two years in 2018 ~ 2019. Then it was analyzed using a semantic network analysis that processes natural human language.
Semantic Network Analysis using big data of the unstructured text We live in an era where all aspects of our lives are uncertain and rapidly changing (Levine, 2019; Kim & SNU Consumer Trend Analysis Center, 2019; Park, 2019). The best way to cope with this uncertain and unknown future is to predict and prepare for the future based on a variety of historical big data by reducing this prediction error. In this regard, people focus on using big data to read trends and prepare for the unknown future. This substantial phenomenon is well represented in diverse and separate research elds as well. Many scholars in a very different area are working actively to discover insights into big data using various data mining techniques  (Algarni, 2016; Doerfel, 1998   Park, 2018). Due to the breakthrough technology, we can deal with big data or data sets, which are too complicated or broad to be dealt with by traditional data-processing approaches.
Until recently, network analysis required data structured by nodes and ties ahead of time, and the subsequent processes were performed by individual programs, which required plenty of human efforts and time. However, for now, with the development of state-of-the-art technology, natural language processing is built into data mining programs, which can directly enter unstructured text data and extract words (nodes) in morphological units and create network data encompassing words. This broadens the horizon of network analysis with massive unstructured text data (Cyram NetMiner, 2019; Kim, Choi, & Youm, 2017). Accordingly, a large number of scholars has ripened into a semantic network analysis as a powerful tool of text mining in numerous ways since Rice & Danowski (1991) built a basic framework for network analysis  (Doerfel, 1998; Monge & Eisenberg, 1987; Rice & Danowski, 1991; Stohl, 1993).
Data Collection This study aims to identify the most recent educational trends and predict future directions or shifts by recognizing the main issues of teaching and learning in Higher education. For that, the data collected from 285 research articles of four international 'top-tier' journals ranked as ISI/SCOPUS Q1 in this eld for two years (2018~2019) in the big databases of each publisher's web platform. Moreover, the data were selected according to the following criteria using the search engine: ISI/SCOPUS indexed, reputable publishers / open-access journals, and international peer-reviewed journals in Table  1. Then, semantic network analysis, a powerful and compelling technique in a signi cant data era, is used to extract patterns or directions with uncovering data-empowered insights.
Consequently, 587 unique keywords, 1743 sentences, and 285 paragraphs and documents were identi ed in 285 abstracts of research articles through the program of NetMiner4' 's semantic network analysis. NetMiner is one of the eminent application software tools for exploratory analysis and also visualization of large network data. It also includes several functions and features for data analytics such as machine learning algorithm, semantic network analysis, etc. It has optimized functions and structure that can e ciently process large amounts of unstructured text data. Accordingly, the study uses this software program, particularly, semantic network analysis which is the most appropriate method to achieve the research questions.
Then, it considers the relationship and features of keywords in more detail through network map and topic modeling. Word Cloud is a visualization tool that illustrates the size of letters according to the importance of keywords. Based on that, we can notice the difference between relatively meaningful words, in brief, to understand how much difference is there. In this study, the word cloud node attributes are used to display information as frequency and a large number of words by entering the maximum number of words as 100. The result of the word cloud is as follows. Meanwhile, the word-to-word network is visualized with a network map to understand the data analysis results intuitively. It means that the network data is calculated and arranged according to the program of NetMiner's layout algorithm.
The layout of the analysis results is as follows. After importing unstructured data, a 2-mode network between words and sentences/paragraphs/documents generated in the program of NetMiner transforms into the keyword-keyword 1-mode system of the research presented in the major journals about teaching and learning. Here's a look at the detailed layout with keywords from the three core types of network maps in Figure  4. In the NetMiner program, a 1-mode network generates by using word-to-word distance information. In this study, the nearness of two words is calculated and based on that, a method of creating links between words located close together is used. The number of words includes when setting the link generation range between words is called 'Window Size. ' In this study, the maximum of 3 words are included in the link generation range by entering Window Size as 3.
Given that, this study allows us to identify the current ow of education and educational issues at a glance by leveraging advanced technology and big data. To best support this current situation, this study focuses on the most recent two years of big research data, rather than looking at old panel data such as ten or twenty-year past data. Recent big data generated by reputable publishers' web database platforms coupled with analytics has become an essential component for a higher education sector. In this way, this research re ects best the current educational situations in teaching and learning of higher education and contribute to preparing for the future with data-empowered intuitions and insights. Secondly, the method of semantic network analysis, a data-driven approach used in this paper, may shed light on the development of new techniques using the machine learning algorithm to get the whole picture of a new paradigm or shift in higher education.
al. , 2012; Srivastava & Srivastava, 2014; Steyvers & Tenenbaum, 2005; Zaki & Meira, 2014). As previous literature shows, a researcher can collect big data from various sources and platforms. For example, news channels (such as BBC, CNN, ABC, etc. ), social media (such as Facebook, Twitter, Instagram, YouTube, etc. ), web or internet search engines (such as Google, Bing, Yahoo, AOL, Journal databases or publishers' databases, etc. ), other nancial reports (such as nancial statements, press releases, conference calls regarding earnings and related information, etc. ), and so on. (Baker, 2010; Bose, 2009; Mishra et al. , 2017; Srivastava & Srivastava, 2014;.
Background Few studies have evaluated the prevalence of potentially inappropriate medications (PIMs) and its association with postoperative outcomes in a geriatric population in the preoperative setting. Objectives The purpose of this study was to evaluate the prevalence of PIMs in an older elective surgery population and to explore associations between PIMs and postoperative length of stay (LOS) and emergency department (ED) visits in the 90 days post hospital discharge, depending on frailty status. Methodology We performed a retrospective cohort study of older adults awaiting major elective noncardiac surgery and undergoing an evaluation in the preoperative clinic at a tertiary academic center between 2017 and 2018. We identified PIMs using MedSafer, a software tool built to improve the safety of prescribing.
Results The MedSafer software generated 394 recommendations on PIMs in 1619 medications for 252 patients. In total, 197 (78%) patients had at least one PIM. The cohort included 138 (51%) robust, 87 (32. 2%) vulnerable and 45 (16. 7%) frail patients. The association between PIMs and LOS was not significant for the robust and frail subgroups. For the vulnerable patients, every additional PIM increased LOS by 20% (incidence rate ratio 1. 20; 95% confidence interval 0. 90-1. 44; p = 0. 089) without reaching statistical significance. No association was found between PIMs and ED visits. Conclusion PIMs identified by the MedSafer software were prevalent. Preoperative evaluation represents an opportunity to plan deprescribing of PIMs.
Consent was obtained at the time of the pre-operative evaluation and then patients were contacted via telephone to complete data collection and specifically a frailty evaluation. Eligible surgeries included orthopedic surgery (hip and knee arthroplasty, sarcoma resection), vascular surgery (aortic, aortobifemoral, iliofemoral bypass, carotid endarterectomy) and general surgery (hernia repair, hemicolectomy, colostomy, ileostomy closure, low anterior rectum resection). All patients were seen by a nurse and an internist as part of their routine evaluation. Medication lists were obtained from the patient's community pharmacy and reviewed by the nurse and internist for preoperative management. A formal review process of PIMs and deprescribing was not part of the routine assessment in our center.
Data Collection The following variables, collected in the preoperative period, were available in the existing database: demographics, comorbidities, Charlson comorbidity score (computed from the known variables of the Charlson Comorbidity Index, except age  [14] ), creatinine level at the time of preoperative assessment, American Society of Anesthesiologists score  [15] , surgical specialty and frailty score. We also presented the most frequent comorbidities found in our cohort. Frailty assessment was performed by a trained research assistant using the 7-point Clinical Frailty Scale, a validated and reliable frailty instrument  [11]  that considers whether patients' chronic illnesses are under control and their independence in activities of daily living and instrumental activities of daily living.
Patients were classified on a numerical scale  (1) (2) (3) (4) (5) (6) (7) , where scores of 1-3 indicated robust, 4 indicated vulnerable, and ≥ 5 indicated frail. Vulnerability is defined in the Clinical Frailty Scale as being slowed up but not dependent on others to accomplish daily activities. Frailty was assessed during the preoperative visit through a semi-structured interview. We collected medication data using the patient's medication list provided at the time of preoperative evaluation. Only chronic medications were considered and were classified according to the Anatomical Therapeutic Chemical classification system. We excluded ophthalmological drops, dermatological preparations and short-term treatments such as antibiotics.
Identification of Potentially Inappropriate Medications (PIMs) We identified PIMs using MedSafer, which is a Canadian software tool built to improve the safety of prescribing. It is an electronic application that identifies PIMs in older adults based on existing evidence-based lists  [16]. It includes all of the recommendations for deprescribing contained in the Beers Criteria  [17] , the Screening Tool of Older People's Prescriptions (STOPP)  [18] , Choosing Wisely  [19]  and some emerging evidence. The development of the software has been previously described  [20]. MedSafer identifies specific triggering conditions, relevant drug-drug interactions and drug-disease interactions using patients' comorbidities and usual home medications. For each PIM, a recommendation and rationale to stop or reconsider the medication is generated, with a tapering regimen when necessary (Electronic Supplementary Material [ESM] 1).
Statistical Methods We used SPSS Statistics (version 25) for analysis. We reported descriptive statistics for the complete cohort of surgical patients evaluated at our preoperative clinic included in the prospective study. Kolmogorv-Smirnov or Shapiro-Wilk tests were used to test for normality distribution of continuous variables, which were then reported as mean or median with their central tendency distribution. Categorical variables were reported as proportions. We used two independent sample t tests and the Wilcoxon-Mann-Whitney test to compare continuous variables and the Chi-squared and Fisher's exact test for categorial variables for comparison between two groups (Kruskal-Wallis and one-way analysis of variance for comparisons between three groups). We reported the total number of PIM recommendations for the surgical cohort and the percentage of PIM recommendations per priority 1, 2 and 3.
Discussion PIMs identified by the MedSafer software were prevalent in our cohort of older adults awaiting elective surgery. An increasing number of PIMs in vulnerable older adults seemed to be associated with a longer hospital LOS in our adjusted model but did not reach statistical significance. The prevalence of PIMs in our cohort was higher than in past studies. In a 2016 retrospective study of a colorectal cancer surgery population (n = 7279, aged ≥ 75 years), the prevalence of PIMs was 22. 5%  [9]. Their definition of PIMs was more selective than the MedSafer software and included fewer medication class (psycholeptics, analgesics, anticholinergics, opioids, antidepressants, non-steroid anti-inflammatories). In our study, the vulnerable and frail populations had more PIMs than the robust group even when the disease burden, as measured by the Charlson comorbidity score, was comparable.
g. , angiotensinconverting enzyme inhibitor for left ventricular dysfunction). We were unable to assess patients' compliance with medication and or consider over-the-counter PIMs. Peri-and postoperative medication could have been associated with the outcomes but was not assessed. The strengths of this study included the novel use of comprehensive electronic software to identify PIMs in patients undergoing a preoperative assessment from different surgical specialties. To our knowledge, this is the first study to assess opportunities for deprescribing in the preoperative setting. The use of MedSafer has previously been shown to decrease PIMs in the acute care setting by prioritizing PIMs for deprescribing and providing a rationale and withdrawal recommendations to clinicians. With that information in hand, deprescribing could be easier to implement in the preoperative period and beyond.
The geo-referenced fisheries dataset used for model calibration includes effort, catch and length-frequency distributions, constituting the total fishing pressure on the South Pacific albacore population over more than thirty years of exploitation. The model is then validated on the fishing data for Atlantic albacore, thus providing evidence of estimated model parameter invariance with respect to space and time. Given the robustness of the results, the quantitative approach presented here could be used to assist stock assessment and to improve management advice.
The spatial distributions of longline catch rates indicate that adult albacore migrate to, and remain in, temperate waters for feeding during other times of the year. However, despite availability of detailed fisheries datasets collected during the historical period of industrial fishing, the migration routes of albacore are poorly understood. Tagging of immature albacore caught by trolling in the South Pacific since the mid-1980s has not greatly improved knowledge about albacore seasonal movements due to low tag recaptures  (Hampton and Fournier, 2000; Hoyle et al. , 2008; Harley et al. , 2015). Due to its complex seasonal migration patterns, albacore are found in all ocean basins from the tropics to temperate regions to 45-50 � latitude  (Jones, 1991). Despite this wide geographical range of preferred habitat, albacore are known to be strongly influenced by ocean temperature and variables such as dissolved oxygen, currents and prey concentration  (Briand et al.
, 2014). Parameterization of Lagrangian models can only be done by manual calibration, using empirically derived parameter values or relying on the estimates from external models  (Scutt Phillips et al. , 2018). Being continuous, ADREs allow implementation of quantitative methods to estimate model parameters from available observations  (Sibert et al. , 1999; Senina et al. , 2008). Nevertheless, quantitative modelling of the population spatial dynamics requires large datasets allowing observation of all modelled processes and dimensions, so their implementation remains complex and similar model applications are still rare  (Faugeras and Maury, 2005; Dueri et al. , 2012). The Spatial Ecosystem And Population Dynamics Model (SEAPO-DYM) describes spatial and temporal population dynamics while integrating relationships between the dynamic processes and key environmental variables (temperature, dissolved oxygen, currents and prey density).
This led to Fig. 1. Functions of seasonal switch between feeding and spawning migrations. The black dots that form the lines show dates of switch between spawning or feeding migrations depending on latitude (left y-axis). The dotted vertical lines denote the estimated middate of spawning season. The dashed curve shows the standard deviation of the Gaussian thermal function (right y-axis) used in the seasonal spawning habitat for mature adults. an increase in total annual albacore catch to 80,000 metric tonnes. A large dataset of geo-referenced catch and effort by fishery with the monthly temporal and 5 � square spatial resolution as well as associated size frequency of catch (quarterly and 5 � � 5 � or 10 � � 20 � ) was provided by the Western and Central Pacific Fisheries Commission (WCPFC) through the Pacific Community (SPC).
Parameter estimation The MLE approach previously implemented in  Senina et al. (2008)  was used to estimate SEAPODYM model parameters based on fishing data. The MLE is obtained by minimizing the negative log likelihood L À ¼ À lnL until the conditions defining a minimum are fulfilled, namely a convergence criterion on the likelihood gradient max i fr i L À gh10 À 2 is satisfied for each i-th parameter and that the matrix of second derivatives is positive definite, Hþþ. The accuracy and efficiency of the likelihood gradient computation is achieved using adjoint model code  (Senina et al. , 2008)  providing exact analytical gradient subject to a computer precision error only  (Griewank and Corliss, 1992). The function minimizer as well as efficient tools to handle storage and access of intermediate variables during direct and reverse model runs are provided by the AUTODIF library  (Fournier et al.
Validation methods The validation of model results includes 1) evaluation of the improvement of the fit to the data due to optimization; 2) analysis of model dynamics under estimated parameter values; and 3) measuring the fit with the optimal solution to an independent dataset. The fit can be measured using three statistical metrics ðr 2 ; σ rel ; NMSEÞ, where r 2 is the squared Pearson correlation coefficient, showing the percentage of predicted variability that is consistent with observations, σ rel is the standard deviation of model predictions relative to those of data and NMSE is the normalized mean square error  (Taylor, 2001). The metrics (1,1,0) is the best validation score. The independent data are the data that were not used in the likelihood. Although we can extend the data time series by including the first five years of data to compute the validation scores, the best validation of the model with spatial dynamics is its application to an independent set of data in time and space.
To verify that seasonal migrations can be observed with fisheries data, we performed optimizations with and without the seasonal switch. The latter scenario provided the worst fit to the data with deterioration of both catch and length frequency likelihood components: L À C ¼ 258; L À Q ¼ 2423 and L À C ¼ 317; L À Q ¼ 2592 for the optimization with and without seasonal migrations, respectively. Another indicator that data constrain the modelled process is the parameter observability. Both seasonal switch parameters were well estimated for albacore using the day-night ratio to trigger the switch between the two habitats. The critical day-night ratio value ϱ s ¼ 1:05 provides the beginning of spawning migrations in August in the   A1 ). southernmost latitudes of SPO albacore feeding habitat.
Validation Three validation metrics for each SPO fishery corresponding to the best model solution and computed over the entire simulation period, i. e. 1979-2010 (thus including the first five years that were not used in the likelihood) are shown in Fig. 5. The best scores (1,1,0) for fisheries L4, L6, T8 and L14 (Table  A1 ) correspond to the catch prediction by removing observed quantities of fish from the modelled biomass and the case when the local model biomass is always higher than observed catches. The gillnet fishery G9, however, obtained the score (1, 0. 94, 0. 05), indicating slightly lower model biomass and its variability than the observed catch. Overall model fit to the catch data is satisfactory, with r 2 ¼ 0:74, σ rel ¼ 0. 68 and NMSE ¼ 0. 51. The worst fits are obtained for Japanese longline east of 150 � W (L10) and in the equatorial area (L12), both targeting primarily bigeye tuna, New Zealand longline (L7), likely changing target species seasonally with 50% of its catches being swordfish, and Korean longline (L2).