Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Plasma and Beam Physics
Title: Generation of atomic oxygen in the effluent of an atmospheric pressure plasma jet
Abstract: The planar 13.56 MHz RF-excited low temperature atmospheric pressure plasma jet (APPJ) investigated in this study is operated with helium feed gas and a small molecular oxygen admixture. The effluent leaving the discharge through the jet's nozzle contains very few charged particles and a high reactive oxygen species' density. As its main reactive radical, essential for numerous applications, the ground state atomic oxygen density in the APPJ's effluent is measured spatially resolved with two-photon absorption laser induced fluorescence spectroscopy. The atomic oxygen density at the nozzle reaches a value of ~1016 cm−3. Even at several centimetres distance still 1% of this initial atomic oxygen density can be detected. Optical emission spectroscopy (OES) reveals the presence of short living excited oxygen atoms up to 10 cm distance from the jet's nozzle. The measured high ground state atomic oxygen density and the unaccounted for presence of excited atomic oxygen require further investigations on a possible energy transfer from the APPJ's discharge region into the effluent: energetic vacuum ultraviolet radiation, measured by OES down to 110 nm, reaches far into the effluent where it is presumed to be responsible for the generation of atomic oxygen.
Extracted statement — Excitation_frequency: 13.56
Research field: Plasma and Beam Physics
Title: Absolute ozone densities in a radio-frequency driven atmospheric pressure plasma using two-beam UV-LED absorption spectroscopy and numerical simulations
Abstract: The efficient generation of reactive oxygen species (ROS) in cold atmospheric pressure plasma jets (APPJs) is an increasingly important topic, e.g. for the treatment of temperature sensitive biological samples in the field of plasma medicine. A 13.56 MHz radio-frequency (rf) driven APPJ device operated with helium feed gas and small admixtures of oxygen (up to 1%), generating a homogeneous glow-mode plasma at low gas temperatures, was investigated. Absolute densities of ozone, one of the most prominent ROS, were measured across the 11 mm wide discharge channel by means of broadband absorption spectroscopy using the Hartley band centered at λ = 255 nm. A two-beam setup with a reference beam in MachZehnder configuration is employed for improved signal-to-noise ratio allowing highsensitivity measurements in the investigated single-pass weak-absorbance regime. The results are correlated to gas temperature measurements, deduced from the rotational temperature of the N2 (C Π u → B Π g , υ = 0 → 2) optical emission from introduced air impurities. The observed opposing trends of both quantities as a function of rf power input and oxygen admixture are analysed and explained in terms of a zerodimensional plasma-chemical kinetics simulation. It is found that the gas temperature as well as the densities of O and O2(b Σ g ) influence the absolute O3 densities when the rf power is varied. ‡ Current address: KROHNE Innovation GmbH, Ludwig-Krone-Str.5, 47058 Duisburg, Germany Page 1 of 26 AUTHOR SUBMITTED MANUSCRIPT PSST-101801.R1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A cc e d M an us cr ip t
Extracted statement — Excitation_frequency: 13.56
Research field: Plasma and Beam Physics
Title: 2D spatially resolved O atom density profiles in an atmospheric pressure plasma jet: from the active plasma volume to the effluent
Abstract: Two-dimensional spatially resolved absolute atomic oxygen densities are measured within an atmospheric pressure micro plasma jet and in its effluent. The plasma is operated in helium with an admixture of 0.5% of oxygen at 13.56 MHz and with a power of 1 W. Absolute atomic oxygen densities are obtained using two photon absorption laser induced fluorescence spectroscopy. The results are interpreted based on measurements of the electron dynamics by phase resolved optical emission spectroscopy in combination with a simple model that balances the production of atomic oxygen with its losses due to chemical reactions and diffusion. Within the discharge, the atomic oxygen density builds up with a rise time of 600 µs along the gas flow and reaches a plateau of 8 × 1015 cm−3. In the effluent, the density decays exponentially with a decay time of 180 µs (corresponding to a decay length of 3 mm at a gas flow of 1.0 slm). It is found that both, the species formation behavior and the maximum distance between the jet nozzle and substrates for possible oxygen treatments of surfaces can be controlled by adjusting the gas flow.
Extracted statement — Excitation_frequency: 13.56
Research field: Science
Title: Intermetallic Compound Pd 2 Ga as a Selective Catalyst for the Semi-Hydrogenation of Acetylene: From Model to High Performance Systems
Abstract: A novel nanostructured Pd2Ga intermetallic catalyst is presented and compared to elemental Pd and a macroscopic bulk Pd2Ga material concerning physical and chemical properties. The new material was prepared by controlled co-precipitation from a single phase layered double hydroxide precursor or hydrotalcite-like compound, of the composition Pd0.025Mg0.675Ga0.3(OH)2(CO3)0.15 ∙ m H2O. Upon thermal reduction in hydrogen, bimetallic nanoparticles of an average size less than 10 nm and a porous MgO/MgGa2O4 support are formed. HRTEM images confirmed the presence of the intermetallic compound Pd2Ga and are corroborated by XPS investigations which revealed an interaction between Pd and Ga. Due to the relatively high dispersion of the intermetallic compound, the catalytic activity of the sample in the semi-hydrogenation of acetylene was more than five thousand times higher than observed for a bulk Pd2Ga model catalyst. Interestingly, the high selectivity of the model catalysts towards the semi-hydrogenated product of 74% was only slightly lowered to 70% for the nano-structured catalyst, while an elemental Pd reference catalyst showed only a selectivity of around 20% under these testing conditions. This result indicates the structural integrity of the intermetallic compound and the absence of elemental Pd in the nano-sized particles. Thus, this work serves as an example of how the unique properties of an intermetallic compound, well-studied as a model catalyst, can be made accessible as a real high performing materials allowing establishment of structure-performance relationships and other application-related further investigations. The general synthesis approach is assumed to be applicable to several Pd-X intermetallic catalysts for X being elements forming hydrotalcite-like precursors in their ionic form.
Extracted statement — P (MPa): 0.1
Research field: Science
Title: Prediction of Recurrent Clostridium Difficile Infection Using Comprehensive Electronic Medical Records in an Integrated Healthcare Delivery System
Abstract: BACKGROUND Predicting recurrent Clostridium difficile infection (rCDI) remains difficult. METHODS. We employed a retrospective cohort design. Granular electronic medical record (EMR) data had been collected from patients hospitalized at 21 Kaiser Permanente Northern California hospitals. The derivation dataset (2007–2013) included data from 9,386 patients who experienced incident CDI (iCDI) and 1,311 who experienced their first CDI recurrences (rCDI). The validation dataset (2014) included data from 1,865 patients who experienced incident CDI and 144 who experienced rCDI. Using multiple techniques, including machine learning, we evaluated more than 150 potential predictors. Our final analyses evaluated 3 models with varying degrees of complexity and 1 previously published model. RESULTS Despite having a large multicenter cohort and access to granular EMR data (eg, vital signs, and laboratory test results), none of the models discriminated well (c statistics, 0.591–0.605), had good calibration, or had good explanatory power. CONCLUSIONS Our ability to predict rCDI remains limited. Given currently available EMR technology, improvements in prediction will require incorporating new variables because currently available data elements lack adequate explanatory power. Infect Control Hosp Epidemiol 2017;38:1196–1203
Extracted statement — C Statistic: 0.605
Research field: Science
Title: A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers
Abstract: OBJECTIVE An estimated 293,300 healthcare-associated cases of Clostridium difficile infection (CDI) occur annually in the United States. To date, research has focused on developing risk prediction models for CDI that work well across institutions. However, this one-size-fits-all approach ignores important hospital-specific factors. We focus on a generalizable method for building facility-specific models. We demonstrate the applicability of the approach using electronic health records (EHR) from the University of Michigan Hospitals (UM) and the Massachusetts General Hospital (MGH). METHODS We utilized EHR data from 191,014 adult admissions to UM and 65,718 adult admissions to MGH. We extracted patient demographics, admission details, patient history, and daily hospitalization details, resulting in 4,836 features from patients at UM and 1,837 from patients at MGH. We used L2 regularized logistic regression to learn the models, and we measured the discriminative performance of the models on held-out data from each hospital. RESULTS Using the UM and MGH test data, the models achieved area under the receiver operating characteristic curve (AUROC) values of 0.82 (95% confidence interval [CI], 0.80–0.84) and 0.75 ( 95% CI, 0.73–0.78), respectively. Some predictive factors were shared between the 2 models, but many of the top predictive factors differed between facilities. CONCLUSION A data-driven approach to building models for estimating daily patient risk for CDI was used to build institution-specific models at 2 large hospitals with different patient populations and EHR systems. In contrast to traditional approaches that focus on developing models that apply across hospitals, our generalizable approach yields risk-stratification models tailored to an institution. These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies. Infect Control Hosp Epidemiol 2018;39:425–433
Extracted statement — AUROC: 0.82
Research field: Science
Title: Improving Prediction of Surgical Site Infection Risk with Multilevel Modeling
Abstract: Background Surgical site infection (SSI) surveillance is a key factor in the elaboration of strategies to reduce SSI occurrence and in providing surgeons with appropriate data feedback (risk indicators, clinical prediction rule). Aim To improve the predictive performance of an individual-based SSI risk model by considering a multilevel hierarchical structure. Patients and Methods Data were collected anonymously by the French SSI active surveillance system in 2011. An SSI diagnosis was made by the surgical teams and infection control practitioners following standardized criteria. A random 20% sample comprising 151 hospitals, 502 wards and 62280 patients was used. Three-level (patient, ward, hospital) hierarchical logistic regression models were initially performed. Parameters were estimated using the simulation-based Markov Chain Monte Carlo procedure. Results A total of 623 SSI were diagnosed (1%). The hospital level was discarded from the analysis as it did not contribute to variability of SSI occurrence (p = 0.32). Established individual risk factors (patient history, surgical procedure and hospitalization characteristics) were identified. A significant heterogeneity in SSI occurrence between wards was found (median odds ratio [MOR] 3.59, 95% credibility interval [CI] 3.03 to 4.33) after adjusting for patient-level variables. The effects of the follow-up duration varied between wards (p<10−9), with an increased heterogeneity when follow-up was <15 days (MOR 6.92, 95% CI 5.31 to 9.07]). The final two-level model significantly improved the discriminative accuracy compared to the single level reference model (p<10−9), with an area under the ROC curve of 0.84. Conclusion This study sheds new light on the respective contribution of patient-, ward- and hospital-levels to SSI occurrence and demonstrates the significant impact of the ward level over and above risk factors present at patient level (i.e., independently from patient case-mix).
Extracted statement — AUROC: 0.84
Research field: Science
Title: Development and validation of a Clostridium difficile infection risk prediction model
Abstract: Objective. To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease. Design and Setting. Retrospective cohort study in a tertiary care medical center. Patients. Patients admitted to the hospital for at least 48 hours during the calendar year 2003. Methods. Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs. Results. A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009). Conclusions. The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.
Extracted statement — AUROC: 0.88
Research field: Science
Title: A diagnostic algorithm for the surveillance of deep surgical site infections after colorectal surgery
Abstract: Abstract Objective: Surveillance of surgical site infections (SSIs) is important for infection control and is usually performed through retrospective manual chart review. The aim of this study was to develop an algorithm for the surveillance of deep SSIs based on clinical variables to enhance efficiency of surveillance. Design: Retrospective cohort study (2012–2015). Setting: A Dutch teaching hospital. Participants: We included all consecutive patients who underwent colorectal surgery excluding those with contaminated wounds at the time of surgery. All patients were evaluated for deep SSIs through manual chart review, using the Centers for Disease Control and Prevention (CDC) criteria as the reference standard. Analysis: We used logistic regression modeling to identify predictors that contributed to the estimation of diagnostic probability. Bootstrapping was applied to increase generalizability, followed by assessment of statistical performance and clinical implications. Results: In total, 1,606 patients were included, of whom 129 (8.0%) acquired a deep SSI. The final model included postoperative length of stay, wound class, readmission, reoperation, and 30-day mortality. The model achieved 68.7% specificity and 98.5% sensitivity and an area under the receiver operator characteristic (ROC) curve (AUC) of 0.950 (95% CI, 0.932–0.969). Positive and negative predictive values were 21.5% and 99.8%, respectively. Applying the algorithm resulted in a 63.4% reduction in the number of records requiring full manual review (from 1,606 to 590). Conclusions: This 5-parameter model identified 98.5% of patients with a deep SSI. The model can be used to develop semiautomatic surveillance of deep SSIs after colorectal surgery, which may further improve efficiency and quality of SSI surveillance.
Extracted statement — AUROC: 0.95
Research field: Science
Title: Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data
Abstract: This study describes a novel approach to solve the surgical site infection (SSI) classification problem. Feature engineering has traditionally been one of the most important steps in solving complex classification problems, especially in cases with temporal data. The described novel approach is based on abstraction of temporal data recorded in three temporal windows. Maximum likelihood L1-norm (lasso) regularization was used in penalized logistic regression to predict the onset of surgical site infection occurrence based on available patient blood testing results up to the day of surgery. Prior knowledge of predictors (blood tests) was integrated in the modelling by introduction of penalty factors depending on blood test prices and an early stopping parameter limiting the maximum number of selected features used in predictive modelling. Finally, solutions resulting in higher interpretability and cost-effectiveness were demonstrated. Using repeated holdout cross-validation, the baseline C-reactive protein (CRP) classifier achieved a mean AUC of 0.801, whereas our best full lasso model achieved a mean AUC of 0.956. Best model testing results were achieved for full lasso model with maximum number of features limited at 20 features with an AUC of 0.967. Presented models showed the potential to not only support domain experts in their decision making but could also prove invaluable for improvement in prediction of SSI occurrence, which may even help setting new guidelines in the field of preoperative SSI prevention and surveillance.
Extracted statement — AUROC: 0.967
Research field: Science
Title: Facultative catadromy of the eel Anguilla japonica between freshwater and seawater habitats
Abstract: To confirm the occurrence of marine residents of the Japanese eel, Anguilla japonica, which have never entered freshwater ('sea eels'), we measured Sr and Ca concentrations by X-ray electron microprobe analysis of the otoliths of 69 yellow and silver eels, collected from 10 localities in seawater and freshwater habitats around Japan, and classified their migratory histories. Two-dimen- sional images of the Sr concentration in the otoliths showed that all specimens generally had a high Sr core at the center of their otolith, which corresponded to a period of their leptocephalus and early glass eel stages in the ocean, but there were a variety of different patterns of Sr concentration and concentric rings outside the central core. Line analysis of Sr/Ca ratios along the radius of each otolith showed peaks (ca 15 × 10 -3 ) between the core and out to about 150 µm (elver mark). The pattern change of the Sr/Ca ratio outside of 150 µm indicated 3 general categories of migratory history: 'river eels', 'estuarine eels' and 'sea eels'. These 3 categories corresponded to mean values of Sr/Ca ratios of ≥ 6.0 × 10 -3 for sea eels, which spent most of their life in the sea and did not enter freshwater, of 2.5 to 6.0 × 10 -3 for estuarine eels, which inhabited estuaries or switched between different habitats, and of <2.5 × 10 -3 for river eels, which entered and remained in freshwater river habitats after arrival in the estuary. The occurrence of sea eels was 20% of all specimens examined and that of river eels, 23%, while estuarine eels were the most prevalent (57%). The occurrence of sea eels was confirmed at 4 localities in Japanese coastal waters, including offshore islands, a small bay and an estuary. The finding of estuarine eels as an intermediate type, which appear to frequently move between different habitats, and their presence at almost all localities, suggested that A. japonica has a flexible pattern of migration, with an ability to adapt to various habitats and salinities. Thus, anguillid eel migrations into freshwater are clearly not an obligatory migratory pathway, and this form of diadromy should be defined as facultative catadromy, with the sea eel as one of several ecophenotypes. Furthermore, this study indicates that eels which utilize the marine environment to various degrees during their juve- nile growth phase may make a substantial contribution to the spawning stock each year.
Extracted statement — Freshwater: 2.5
Research field: Science
Title: Use of otolith Sr:Ca ratios to study the riverine migratory behaviors of Japanese eel Anguilla japonica
Abstract: To understand the migratory behavior and habitat use of the Japanese eel Anguilla japonica in the Kaoping River, SW Taiwan, the temporal changes of strontium (Sr) and calcium (Ca) contents in otoliths of the eels in combination with age data were examined by wavelength dispersive X-ray spectrometry with an electron probe microanalyzer. Ages of the eel were determined by the annulus mark in their otolith. The pattern of the Sr:Ca ratios in the otoliths, before the elver stage, was similar among all specimens. Post-elver stage Sr:Ca ratios indicated that the eels experienced different salinity histories in their growth phase yellow stage. The mean (±SD) Sr:Ca ratios in otoliths beyond elver check of the 6 yellow eels from the freshwater middle reach were 1.8 ± 0.2 x 10 -3 with a maximum value of 3.73 x 10 -3 . Sr:Ca ratios of less than 4 x 10-3 were used to discriminate the freshwater from seawater resident eels. Eels from the lower reach of the river were classified into 3 types: (1) freshwater contingents, Sr:Ca ratio <4 x 10 -3 , constituted 14 % of the eels examined; (2) seawater contingent, Sr:Ca ratio 5.1 ± 1.1 x 10-3 (5%); and (3) estuarine contingent, Sr:Ca ratios ranged from 0 to 10 x 10 -3 , with migration between freshwater and seawater (81 %). The frequency distribution of the 3 contingents differed between yellow and silver eel stages (0.01 < p < 0.05 for each case) and changed with age of the eel, indicating that most of the eels stayed in the estuary for the first year then migrated to the freshwater until 6 yr old. The eel population in the river system was dominated by the estuarine contingent, probably because the estuarine environment was more stable and had a larger carrying capacity than the freshwater middle reach did, and also due to a preference for brackish water by the growth-phase, yellow eel.
Extracted statement — Seawater: 5.1