The biological sink of atmospheric H2 is more sensitive to spatial variation of microbial diversity than N2O and CO2 emissions in an agroecosystem

The relationship between soil microbial diversity and agroecosystem functioning is controversial due to the elevated diversity level and the functional redundancy of microorganisms. A field trial was established to test the hypothesis that enhanced crop diversity with the integration of winter cover crops (WCC) in a conventional maize-soy rotation promotes microbial diversity and the biological sink of H2 in soil, while reducing N2O emissions to the atmosphere. Vicia villosa (hairy vetch), Avena sativa (oat), and Raphanus sativus (Daikon radish) were cultivated alone or in combinations and flux measurements were performed throughout two subsequent growing seasons. Soil acted as a net sink for H2 and as a net source for CO2 and N2O. CO2 flux was the most sensitive to WCC whereas a significant spatial variation was observed for H2 flux with soil uptake rates observed in the most productive area two-fold greater than the baseline level. Sequencing and quantification of taxonomic and functional genes were integrated to explain variation in trace gas fluxes with compositional changes in soil microbial communities. Fungal communities were the most sensitive to WCC, but neither community abundance nor beta diversity were found to be indicative of fluxes. The alpha diversity of taxonomic and functional genes, expressed as the number of effective species, was integrated into composite variables extracted from multivariate analyses. Only the composite variable computed with the inverse Simpson's concentration index displayed a reproducible pattern throughout both growing seasons, with functional genes and bacterial 16S rRNA gene defining the two most contrasting gradients. The composite variable was decoupled from WCC treatment and explained 19-20% spatial variation of H2 fluxes. Sensitivity of the trace gas exchange process to soil properties at the local scale was inconsistent among H2, N2O and CO2, with the former being the most related to microbial diversity distribution pattern.

Predicting hotel reviews from sentiment: a multinomial classification framework

Purpose Machine learning algorithms are useful to effectively analyse, and therefore automatically classify online reviews. The purpose of this paper is to demonstrate a novel text-mining framework and its potential for use in the classification of unstructured hotel reviews. Design/methodology/approach Well-known data mining methods (i.e. boosted decision trees (BDT), classification and regression trees (C&RT) and random forests (RF)) in conjunction with incorporating five-fold cross-validation are used to predict the star rating of the hotel reviews. To achieve this goal, extracted features are used to create a composite variable (CV) to deploy into machine learning algorithms as the main feature (variable) during the learning process. Findings BDT outperformed the other alternatives in the exact accuracy rate (EAR) and multi-class accuracy rate (MCAR) by reaching the accuracy rates of 0.66 and 0.899, respectively. Moreover, phrases such as “clean”, “friendly”, “nice”, “perfect” and “love” are shown to be associated with four and five stars, whereas, phrases such as “horrible”, “never”, “terrible” and “worst” are shown to be associated with one and two-star hotels, as it would be the intuitive expectation. Originality/value To the best of the knowledge, there is no study in the existent literature, which synthesizes the knowledge obtained from individual features and uses them to create a single composite variable that is powerful enough to predict the star rates of the user-generated reviews. This study believes that the proposed method also provides policymakers with a unique window in the thoughts and opinions of individual users, which may be used to augment the current decision-making process.

Present value of the Urethral mobility test as a tool to assess Stress urinary incontinence due to Intrinsic sphincteric deficiency

Q-tip test offers a simple approach for identifying urethral hypermobility. Considering surgical treatment, stress urinary incontinence (SUI) must be classified and the contribution of intrinsic sphincter deficiency (ISD) and/or urethral hypermobility must be determine. We believe there's a correlation between abdominal leak point pressure (ALPP) and urethral mobility degree, and the aim of this study is to explore it using Q-tip. We conducted a prospective study, between years 2014 and 2016. Females over 18 years presenting with signs and symptoms of SUI according to the 2002 ICS Standardization of Terminology were included. Assessment was made with the International Consultation on Incontinence Questionnaire Short Form (ICIQ-SF), the Q-tip test and invasive urodynamics. Urethral mobility (UM) and ALPP were analyzed. We built two composite variables based on reported risk factors for ISD, defined as composite variable A (equal to a Q-tip test < 30° AND ICIQ-SF ≥ 10 points) and composite variable B (equal to low urethral mobility AND/OR hypoestrogenism AND/OR history of radiotherapy AND/OR previous pelvic surgery). Correlation analyzes were made according to the type of variable. A total of 221 patients were included. Incontinence was rated as moderate and severe by 65.3% and 6.8%, respectively. The analysis showed a 61.75%, 51.61% and 70.6% agreement between ALPP and UM, ALPP and composite variable A and ALPP and composite variable B respectively. Correlation and concordances were low (r = 0.155, r_s = − 0.053 and r_s = − 0.008), (rho_c = 0.036, k = 0.116 and k = 0.016). Neither the degree of UM, nor the composite variables, correlate or agree with urethral function tests in UDS, suggesting that the ALPP cannot be predicted using the Q-tip test or the ICIQ-SF for classifying patients with SUI.

Analisis Perbandingan ¬Tingkat Kesehatan Bank BUMN dan Bank BUSN Dengan Menggunakan Pendekatan Risk Based Bank Rating (RBBR)

This study aims to determine the effect of the Comparison of Health Levels of BUMN and BUSN Banks Using a Risk Based Bank Rating (RBBR) Approach. The data used in this study are secondary data obtained from the financial statements of BUMN and BUSN. Samples taken were 6 banks (3 BUMN Banks and 3 BUSN Banks) with purposive sampling method. The data obtained is then processed using the calculation of each variable based on the RBBR approach which refers to Circular Letter of OJK No.14/SEOJK.03/2017 covering components: Risk profile (using NPL ratio, LDR), Governance, Rentability (using ROA, ROE) and Capital (using CAR ratio).The results of the study show that in 2013-2017 the total composite ranking score of the entire BUMN Bank variable was higher than the BUSN Bank. The composite variable total score of ROA and ROE of BUMN banks is higher than BUSN. Total NPL, LDR, and GCG composite rating scores of BUSN Banks are higher than BUMN Banks. In the total composite rating score, the CAR variable of the BUMN Bank and BUSN Bank gets the same score.

The Importance of Tumor Length in Needle Biopsies of the Prostate

Objectives To form a composite predictor variable that combines the effects of tumor length, serum prostate-specific antigen (PSA), and International Society of Urologic Pathologists (ISUP) grade on the observation of adverse prostatectomy pathology. Methods Logistic regression analysis was used to demonstrate how tumor length, serum PSA, and ISUP grade related to adverse prostatectomy results and to derive weighting factors for a composite variable, cx. Results The composite variable, cx, relates closely to adverse prostatectomy results as well as to observed PSA failure. Conclusions The composite variable cx uses preoperative information that may allow the sorting of patients into low, intermediate, and high risk for adverse outcomes after prostatectomy.

A cocktail fallacy in research with composite variables: a critical review of job strain, effort–reward imbalance (ERI) and body mass index (BMI)

To estimate a combined effect of two different exposures, the standard method is to include both exposures as independent variables in a statistical model, together with an interaction term. Journals such as Epidemiology recommend this method as best practice in their instructions for authors. However, in occupational stress research it is common to combine two exposures into a single composite variable, and then use associations observed on that variable to claim support for theories implying an interaction (or a combined additive effect). Here we provide a non-technical illustration of how such composite variable models lead to a logical fallacy, when they are used to try to argue support for theories such as job strain and effort–reward imbalance (ERI). We discuss different types of composite variables, illustrate their susceptibility to bias, and show why researchers should be critical when interpreting associations observed on the body mass index (BMI). We also present the proper statistical models that should be used when estimating associations in job strain and effort–reward imbalance or similar research.

History of Depression and Increased Risk of Sternal Wound Infection After Cardiothoracic Surgery: A Novel and Potentially Modifiable Risk Factor

Background Sternal wound infection (SWI) is a leading cause of postoperative disease and death; the risk factors for SWI remain incompletely understood. The goal of the current study was to investigate the relationship between a preoperative history of depression and the risk of SWI after cardiothoracic surgery. Methods Among patients undergoing cardiothoracic surgery in a major academic medical center between 2007 and 2012, those in whom SWI developed (n = 129) were matched, by date of surgery, with those in whom it did not (n = 258). Multivariable logistic regression was used to examine the strength of relationships between risk factors and development of infection. History of depression was defined as a composite variable to increase the sensitivity of detection. Results History of depression as defined by our composite variable was associated with increased risk of SWI (adjusted odds ratio, 2.4; 95% confidence interval, 1.2–4.7; P = .01). Staphylococcus aureus was the most common organism isolated. Conclusions History of depression was associated with increased risk of SWI. Future prospective studies are warranted to further investigate this relationship. Depression is highly treatable, and increased efforts to identify and treat depression preoperatively may be a critical step toward preventing infection-related disease and death.