Information Criterion Analysis Research Articles

Characterization of mechanical properties of shale constituent minerals using phase‐identified nanoindentation

AbstractCharacterization of mechanical properties of shale constituent minerals (viz., the mechanical genes of shale) has been challenging but of great significance for engineering applications in shale formations. In this study, a phase‐identified nanoindentation is proposed to decode the mechanical genes of shale using a large nanomechanical dataset. With the consideration of uniform prior probability density functions (PDFs) and Gaussian posterior PDFs, the evidence of the measured dataset generated by the candidate model classes was assessed by applying the expectation–maximization algorithm and solving the Hessian matrix of the likelihood function. In contrast with Bayesian information criterion analysis, which has been widely used in prior studies, the proposed phase‐identified nanoindentation approach is insensitive to the size of the dataset. Here, the identified clusters are well matched with the constituent phases measured by coupling grid nanoindentation and surface physicochemical identification.

Constraining the variable generalized Chaplygin gas model in matter creation cosmology

We explore the variable generalized Chaplygin gas (VGCG) model in the theory of matter creation cosmology within the framework of a spatially homogeneous and isotropic flat Friedmann—Lemaître—Robertson—Walker space-time. Matter creation cosmology is based on reinterpretation of the energy–momentum tensor in Einstein’s field equations. This creation corresponds to an irreversible energy flow from the gravitational field to the created matter constituents. The variable Chaplygin gas (VCG) is also studied as a particular solution. We use the Markov chain Monte Carlo method to constrain the free parameters of three models, namely, Lambda-Cold-Dark matter (ΛCDM), VGCG and VCG models with and without matter creation from the latest observational data from baryon acoustic oscillations, cosmic chronometer, type Ia supernovae (Pantheon) including gamma-ray bursts, quasars and the local measurement of H 0 from R21 data. Two different combinations of dataset provide a fairly tight constraint on the parameters of the ΛCDM, VGCG and VCG models. The present values of various cosmological parameters are obtained, which are very close to the ΛCDM model. Furthermore, we perform stability analysis, Bayesian evidence analysis and information criteria analysis for these models through studying the sound speed, Bayes factor, and Akaike information criteria (AIC) and Bayesian information criteria (BIC) selection criteria. The values of sound speed for VGCG and VCG models shows that both the models are stable. According to AIC, it is observed that VGCG and VCG models with matter creation are supported considerably less by current observations, while BIC shows that these models are not favoured by observational data.

Better performance of the APPLE score for the prediction of very early atrial fibrillation recurrence post-ablation

ObjectiveThe benefits of rhythm control in early atrial fibrillation (AF) are increasingly recognized. This study aimed to investigate whether early AF ablation contributes to long-term sinus rhythm maintenance and to identify a suitable predictive score. MethodsAccording to diagnosis-to-ablation time, this study prospectively enrolled 245 patients with very early AF, 262 with early AF, and 588 with late AF for radiofrequency ablation from June 2017 to December 2022. Clinical data, risk scores, and follow-up results were collected and analyzed. ResultsBaseline characteristics were similar among the three cohorts. During a median follow-up period of 26 months, AF recurrence was observed in 61 (24.9%), 66 (25.2%), and 216 (36.7%) patients in the very early, early, and late AF cohorts, respectively. In the multivariable-adjusted model, very early and early AF were associated with a reduced risk of AF recurrence, with hazard ratios of 0.72 (95% confidence interval [CI] 0.52–0.99) and 0.57 (95% CI 0.41–0.78), respectively. The APPLE score demonstrated the highest predictive power for very early AF, with an area under the curve (AUC) of 0.74. However, its predictive power decreased with time from diagnosis, showing low predictive power for late AF (AUC = 0.58). In addition, the time-dependent concordance index showed consistent results. For very early AF, the Akaike information criterion and decision curve analysis showed that APPLE had the highest predictive value. ConclusionVery early AF ablation was associated with a lower recurrence rate, and the APPLE score provided a higher predictive value for these patients. (URL: https://www.chictr.org.cn/; Unique identifier: ChiCTR-OIN-17013021)

Establishment and Validation of Risk Prediction Models for Postoperative Pain After Endoscopic Submucosal Dissection: A Retrospective Clinical Study.

Postoperative pain is a common complication in endoscopic submucosal dissection (ESD) patients. This study aimed to develop and validate predictive models for postoperative pain associated ESD. We retrospectively constructed a development cohort comprising 2162 patients who underwent ESD at our hospital between January 2015 and April 2022. The dataset was randomly divided into a training set (n = 1541) and a validation set (n = 621) in a 7:3 ratio. The bidirectional stepwise regression with Akaike's information criterion (AIC) and multivariate logistic regression analysis were used to screen the predictors of post-ESD pain and construct three nomograms. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, Hosmer-Lemeshow (HL) goodness-of-fit test and decision curve analysis (DCA) in internal validation. The proportion of patients developing postoperative pain in the training and testing data set was 25.6% and 28.5%, respectively. Three nomograms were constructed according to the final logistic regression models. The clinical prediction models for preoperative risks, preoperative and intraoperative risks, and perioperative risks consisted of seven, nine and six independent predictors, respectively, after bidirectional stepwise elimination. The models demonstrated the AUC of 0.794 (95% CI 0.768-0.820), 0.823 (95% CI 0.799-0.847) and 0.817 (95% CI 0.792-0.842) in the training cohort and 0.702 (95% CI 0.655-0.748), 0.705 (95% CI 0.659-0.752) and 0.747 (95% CI 0.703-0.790) in the validation cohort. The calibration plot, HL and DCA demonstrated the model's favorable clinical applicability. We developed and validated three robust nomogram models, which might identify patients at risk of post-ESD pain and promising for clinical applications.

Performance of a Set of Soil Water Retention Models for Fitting Soil Water Retention Data Covering All Textural Classes

A clean environment is an essential component of sustainable development, which is based on a comprehensive understanding of the behavior of water, soil, and air. The soil water retention (SWR) curve is a crucial function that describes how soil retains water, playing a fundamental role in irrigation and drainage, soil conservation, as well as water and contaminant transport in the vadose zone. This study evaluates the accuracy, performance, and prediction capabilities of 15 SWR models. A total of 140 soil samples were collected from different sites, covering all textural classes. Standard suction tests, using both hanging column and ceramic pressure plate extractors, were conducted to compile the SWR databank. 15 SWR models were selected and fitted to the SWR data points. Soil texture, bulk density, and organic matter were used to determine their effect on the performance of the SWR models. The results indicate that the Tani and Russo models exhibit the lowest levels of accuracy and performance among the selected models. Based on the Akaike and Bayesian information criteria analysis, the van Genuchten model exhibits the lowest values among the selected models, with poor prediction capabilities in estimating the SWR curve. The significance test at the 0.05 level (95% confidence interval) shows that according to the calculated p-values for the Pearson correlation coefficient between RMSE and texture, the Brooks-Corey and van Genuchten models are poorly influenced by soil properties. The performance of the models is not significantly affected by the soil organic matter. Similarly, bulk density does not significantly affect model performance except for the Brooks–Corey, van Genuchten, Tani, and Russo models. Among the SWR models considered, the double exponential, Groenevelt and Grant, and Khlosi et al. models demonstrate superior accuracy and performance in predicting the SWR curve. This is supported by lower values for RMSE, Akaike, and Bayesian information criteria.

Evidence of dynamical dark energy in a non-flat universe: current and future observations

We investigate the dark energy phenomenology in an extended parameter space where we allow the curvature density of our universe as a free-to-vary parameter. The inclusion of the curvature density parameter is motivated from the recently released observational evidences indicating the closed universe model at many standard deviations. Here we assume that the dark energy equation-of-state follows the PADE approximation, a generalized parametrization that may recover a variety of existing dark energy models. Considering three distinct PADE parametrizations, labeled as PADE-I, SPADE-I and PADE-II, we first constrain the cosmological scenarios driven by them using the joint analyses of a series of recently available cosmological probes, namely, Pantheon sample of Supernovae Type Ia, baryon acoustic oscillations, big bang nucleosynthesis, Hubble parameter measurements from cosmic chronometers, cosmic microwave background distance priors from Planck 2018 and then we include the future Gravitational Waves standard sirens (GWSS) data from the Einstein telescope with the combined analyses of these current cosmological probes. We find that the current cosmological probes indicate a very strong evidence of a dynamical dark energy at more than 99% C.L. in both PADE-I, and PADE-II, but no significant evidence for the non-flat universe is found in any of these parametrizations. Interestingly, when the future GWSS data from the Einstein telescope are included with the standard cosmological probes an evidence of a non-flat universe is found in all three parametrizations together with a very strong preference of a dynamical dark energy at more than 99% C.L. in both PADE-I, and PADE-II. Although from the information criteria analysis, namely, AIC, BIC, DIC, the non-flat Λ-Cold Dark Matter model remains the best choice, however, in the light of DIC, PADE parametrizations are still appealing.

Bayesian Information Criterion Analysis for Accuracy Improvement of Multivariate Time Series Data Analysis

Bayesian Information Criterion Analysis for Accuracy Improvement of Multivariate Time Series Data Analysis

Identification of NIFTP-Specific mRNA Markers for Reliable Molecular Diagnosis of Thyroid Tumors.

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) is a low-risk thyroid tumor with a favorable prognosis. Nonetheless, differentiating NIFTP from other thyroid tumors remains challenging, necessitating reliable diagnostic markers. This study is aimed at discovering NIFTP-specific mRNA markers through RNA sequencing analysis of thyroid tumor tissues. We performed mRNA expression profiling for 74 fresh frozen thyroid tissue samples, including NIFTP and benign and malignant follicular-cell-derived tumors. NIFTP/malignant tumors showed 255 downregulated genes and 737 upregulated genes compared to benign tumors. Venn diagram analysis revealed 19 significantly upregulated and 7 downregulated mRNAs in NIFTP. Akaike information criterion analysis allowed us to select OCLN, ZNF423, LYG1, and AQP5 mRNA markers. We subsequently developed a predictive model based on logistic regression analysis using these four mRNAs, which we validated in independent samples (n = 90) using a qRT-PCR assay. This model demonstrated high accuracy in predicting NIFTP in discovery dataset (AUC (area under the receiver operating characteristic) = 0.960) and the validation dataset (AUC = 0.757). Our results suggest that OCLN, ZNF423, LYG1, and AQP5 mRNA markers might serve as reliable molecular markers for identifying NIFTP among other thyroid tumors, ultimately aiding in accurate diagnosis and management of NIFTP patients.

Prognostic value of a predictive model comprising preoperative inflammatory response and nutritional indexes in patients with gastric cancer

Objective: To investigate the prognostic value of preoperative inflammatory and nutritional condition detection in the postoperative survival, and establish a prognostic model for predicting the survival of patients with gastric cancer. Methods: The clinicopathological data of 1123 patients with gastric cancer who had undergone radical gastrectomy in Tianjin Medical University Cancer Institute & Hospital from January 2005 to December 2014 were retrospectively analyzed. Patients with history of other malignancy, with history of gastrectomy, who had received preoperative treatment, who died during the initial hospital stay or first postoperative month, and missing clinical and pathological information were excluded. Cox univariate and multivariate analyses were used to identify independent clinicopathological factors associated with the survival of these gastric cancer patients. Cox univariate analysis was used to identify preoperative inflammatory and nutritional indexes related to the survival of patients with gastric cancer after radical gastrectomy. Moreover, the Cox proportional regression model for multivariate survival analysis (forward stepwise regression method based on maximum likelihood estimation) was used. The independent clinicopathological factors that affect survival were incorporated into the following three new prognostic models: (1) an inflammatory model: significant preoperative inflammatory indexes identified through clinical and univariate analysis; (2) a nutritional model: significant preoperative nutritional indexes identified through clinical and univariate analysis; and (3) combined inflammatory/nutritional model: significant preoperative inflammatory and nutritional indexes identified through clinical and univariate analysis. A model that comprised only pT and pN stages in tumor TNM staging was used as a control model. The integrated area under the receiver operating characteristic curve (iAUC) and C-index were used to evaluate the discrimination of the model. Model fitting was evaluated by Akaike information criterion analysis. Calibration curves were used to assess agreement between the predicted probabilities and actual probabilities at 3-year or 5-year overall survival (OS). Results: The study cohort comprised 1 123 patients with gastric cancer. The mean age was 58.9±11.6 years, and 783 were males. According to univariate analysis, age, surgical procedure, extent of lymph node dissection, tumor location, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, and nerve invasion were associated with 5-year OS after radical gastrectomy for gastric cancer (all P<0.050). Multivariate analysis further identified age (HR: 1.18, 95%CI: 1.03-1.36, P=0.019), maximum tumor size (HR: 1.19, 95%CI: 1.03-1.38, P=0.022), number of examined lymph nodes (HR: 0.79, 95%CI: 0.68-0.92, P=0.003), pT stage (HR: 1.40, 95%CI: 1.26-1.55, P<0.001) and pN stage (HR: 1.28, 95%CI: 1.21-1.35, P<0.001) as independent prognostic factors for OS of gastric cancer patients. Additionally, according to univariate survival analysis, the preoperative inflammatory markers of neutrophil count, percentage of neutrophils, neutrophil/lymphocyte ratio, platelet/neutrophil ratio and preoperative nutritional indicators of serum albumin and body mass index were potential prognostic factors for gastric cancer (all P<0.05). On the basis of the above results, three models for prediction of prognosis were constructed. Variables included in the three models are as follows. (1) Inflammatory model: age, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, percentage of neutrophils, and neutrophil-lymphocyte ratio; (2) nutritional model: age, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, and serum albumin; and (3) combined inflammatory/nutritional model: age, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, percentage of neutrophils, neutrophil-lymphocyte ratio, and serum albumin. We found that the predictive accuracy of the combined inflammatory/nutritional model, which incorporates both inflammatory indicators and nutrition indicators (iAUC: 0.676, 95% CI: 0.650-0.719, C-index: 0.698),was superior to that of the inflammation model (iAUC: 0.662, 95% CI: 0.673-0.706;C-index: 0.675), nutritional model (iAUC: 0.666, 95% CI: 0.642-0.698, C-index: 0.672), and TNM staging control model (iAUC: 0.676, 95% CI: 0.650-0.719, C-index: 0.658). Furthermore, the combined inflammatory/nutritional model had better fitting performance (AIC: 10 762) than the inflammatory model (AIC: 10 834), nutritional model (AIC: 10 810), and TNM staging control model (AIC: 10 974). Conclusions: Preoperative percentage of neutrophils, NLR, and BMI have predictive value for the prognosis of gastric cancer patients. The inflammatory / nutritional model can be used to predict the survival and prognosis of gastric cancer patients on an individualized basis.

Child sleep problems, maternal sleep and self-efficacy: Sleep's complicated role in maternal depression.

Depression, poor sleep duration and low self-efficacy are common in mothers of children with sleep problems. However, research rarely extends beyond the postpartum period. This study investigated the multifaceted relationship between child sleep and maternal depression in early motherhood. A confidential survey assessed child sleep problems, maternal sleep duration, parental self-efficacy and depressive symptoms in 477 Australian mothers of children aged 3 months to 5 years. We found no relationship between child age and maternal depression, supporting our decision to look beyond postpartum depression. Robust bootstrapped mediation modelling tested the hypothesis that both maternal sleep duration and parental self-efficacy would mediate child sleep problems as predictors of maternal depression. After controlling for child age, results showed a significant parallel mediation effect, demonstrating that maternal sleep duration and parental self-efficacy both mediate the relationship between child sleep problems on maternal depression. While the total effect of child sleep problems on maternal depression was statistically significant, after partialling out the effects of other variables, child sleep problems no longer predicted maternal depression. Akaike information criterion analyses supported the full model, with both mediators explaining meaningful variance in maternal depression. This study expands our knowledge beyond the postpartum period, and divulges the disparate effects of sleep deprivation and parental self-efficacy on the relationship between child sleep and depression in early motherhood. Maternal sleep duration and self-efficacy are modifiable risk factors of maternal depression, indicating possible efficacious treatments. Parental self-efficacy stands out as a direction for clinical practice and further psychobiological study.

Development and validation of nomograms to predict preoperative anxiety and postoperative pain in patients undergoing gynecological surgery: An observational analysis

BackgroundPerioperative anxiety and pain are associated with patient dissatisfaction, postoperative complications, and prolonged hospital stay. Early identification of high-risk patients with preoperative anxiety and postoperative pain will be useful for the implementation of preventive management. MethodsPatients, who underwent gynecological surgery in our hospital between March 2022 and September 2022, were consecutively enrolled. Perioperative anxiety and pain were evaluated with the Visual Analogue Scale of Anxiety (VAA) and Visual Analogue Scale of Pain (VAS), respectively. Step Akaike Information Criterion analysis was performed to identify risk factors and logistic regression was used to establish nomograms, followed by discrimination, calibration, and clinical utility evaluation. ResultsA total of 197 patients were included for analysis, including 116 and 81 patients who were randomized to training and test groups, respectively. The prediction model of preoperative moderate to severe anxiety identified four preoperative relevant factors: age, sleep duration, preoperative pain, and regular exercise before gynecological surgery. The model had an area under the receiver operating characteristics curve of 0.808 (0.729, 0.887) and 0.754 (0.634, 0.875) in the training and test groups, respectively. The prediction model of postoperative moderate to severe pain identified four relevant factors: preoperative pain, surgery type, VAA before anesthesia, and patient-controlled analgesia. The model had an area under the receiver operating characteristics curve of 0.867 (0.798, 0.935) and 0.852 (0.761, 0.943) in the training and test groups, respectively. ConclusionsThe established nomograms accurately identified high-risk patients with preoperative anxiety and postoperative pain before gynecological surgery.Clinical registration at: www.chictr.org.cn (ChiCTR2200057757).

Bayesian binary regression using power and power reverse link functions: an application to premature birth data

This study aims to determine factors associated (and quantify) with prematurity through binary regression models, considering power and reverse power link functions, with asymmetric characteristics. As criteria for the model selection, the Bayesian Deviance Information Criterion (DIC), predictive evaluation, and residual analysis. All models analyzed presented similar predictive capacity, however, the model with a reverse power logit link function, with asymmetry parameter =0.336 was chosen, since it presented the lowest value of DIC=3,203, residues that indicated a good fitted of the model. There was an association of prematurity with the following variables: maternal - age over 35 years (OR=1.485), with a partner (OR=0.731), and primiparous (OR=1.307); of pregnancy and childbirth - multiple pregnancy (OR=36.360), cesarean childbirth (OR=1.337) and number of prenatal consultations less than seven (OR=3.305); and newborns of white race/skin (OR=0.731) and presence of congenital malformation (OR=2.663). Considering the proposed criteria, an asymmetric link function (reverse power logit) was the most parsimonious for the model. From this, there were high chances of factors associated with the occurrence of prematurity, indicating the need for actions to minimize them.

Vector Autoregressive (VAR) Method in Analyzing the Effect of Inflation on Food Price Volatility (FPV) in Palopo City, Indonesia

Purpose: This study aimed to analyze the effect of Inflation on food prices in Palopo City, where the inflation rate is calculated based on index numbers collected from several commodities traded at each price level in the market, namely the Consumer Price Index (CPI). Theoretical framework: The Vector Autoregressive (VAR) method is simultaneous equation modeling that has several endogenous variables simultaneously, but each endogenous variable is explained by lags of its own value and other endogenous variables in the model. Forecasting using the VAR method is in some cases better than complex simultaneous equations. Design/methodology/approach: The stationarity test in this study uses the Augmented Dickey-Fuller (ADF) method, followed by determining lag optimal Akaike Information Criteria (AIC) and Schwarz information Criterion (SC) analysis. The stability test of the VAR model was carried out through the Johansen Cointegration Test at a 5% significance level which produced six equations with a trace statistically more significant than the critical value. Then an analysis of Impulse Response Functions (IRF) was carried out to see the dynamic response of the CPI variable to food commodity prices. The data used are time series for January 2018 to December 2020. Findings: The inflation response to food price fluctuations in Palopo City resulted in three conditions. The Palopo City CPI responded positively to the price fluctuations of garlic and red chili. In contrast, shallots and chicken meat price fluctuations responded negatively to the Palopo City CPI. The Palopo City CPI did not permanently affect the price fluctuations of cayenne pepper and chicken eggs. Research, Practical &amp; Social implications: The practical implication of this research is that food commodity prices still have an influence on inflation movements. Local governments should cooperate with city governments in the production of food commodities by implementing cropping patterns on these commodities to overcome excess production, and it is necessary to carry out off-season production to achieve harvest deficits so that prices remain stable. Improvements to the trade system also need to be improved in addition to the production system. This is intended so that the distribution of food commodity products can be more efficient by implementing supply chain management. Originality/value: Collecting time series data of the Consumer Price Index (CPI) for 6 commodities, namely shallots, garlic, chili peppers, cayenne pepper, broiler meat, and broiler eggs, in a modern model, namely a multivariate time series model with a high level of predictive accuracy of the model among these variables to predict and analyze the effects among these series and estimate the interactions among these series with the latest data is the value of this research, and then provide recommendations for the government in making economic strategies, especially for those related to inflation and food prices.

Data-driven reliability assessment with scarce samples considering multidimensional dependence

This study proposes a data-driven method for assessing reliability, based on the scarce input dataset with multidimensional correlation. Since considering the distribution parameters estimated from the scarce dataset as those of the population may lead to epistemic uncertainty, the bootstrap resampling algorithm is adopted to infer the distribution parameters as interval parameters. To account for the variable dependence, vine copula theory is utilized to construct the joint probability density function (PDF) of input variables, and maximum likelihood estimation (MLE) and Akaike information criterion (AIC) analysis are employed to select optimal copulas based on the samples for the vine structure. Subsequently, the failure probability bounds of a response function are calculated based on the constructed joint PDF with interval distribution parameters by the active learning Kriging (AK) method combining the sparse grid integration (SGI) method. Finally, several examples are provided to demonstrate the feasibility and efficiency of the proposed method.

Circulating Interleukin-8 Dynamics Parallels Disease Course and Is Linked to Clinical Outcomes in Severe COVID-19.

Severe COVID-19 frequently features a systemic deluge of cytokines. Circulating cytokines that can stratify risks are useful for more effective triage and management. Here, we ran a machine-learning algorithm on a dataset of 36 plasma cytokines in a cohort of severe COVID-19 to identify cytokine/s useful for describing the dynamic clinical state in multiple regression analysis. We performed RNA-sequencing of circulating blood cells collected at different time-points. From a Bayesian Information Criterion analysis, a combination of interleukin-8 (IL-8), Eotaxin, and Interferon-γ (IFNγ) was found to be significantly linked to blood oxygenation over seven days. Individually testing the cytokines in receiver operator characteristics analyses identified IL-8 as a strong stratifier for clinical outcomes. Circulating IL-8 dynamics paralleled disease course. We also revealed key transitions in immune transcriptome in patients stratified for circulating IL-8 at three time-points. The study identifies plasma IL-8 as a key pathogenic cytokine linking systemic hyper-inflammation to the clinical outcomes in COVID-19.

Local hydraulics influence habitat selection and swimming behavior in adult California Central Valley Chinook salmon at a large river confluence

Migratory habitat selection in adult anadromous salmonids occurs in response to a combination of physical, chemical, and biological cues. Migratory behavioral responses to localized hydraulics are not well understood and hydraulic flow features can be particularly complex at confluence junctions. In some cases, hydraulics may play a partial role in migratory routing, with implications for population structure where wild- and hatchery-origin fish hybridize. This study investigated two questions about such confluences: (1) Can patterns in migratory microhabitat selection or migratory swimming behavior in adult Chinook salmon be attributed to micro-scale hydraulic conditions driven by discharge magnitude and ratio at a confluence? (2) What is the relative influence of selectivity for hydraulic conditions compared to temperature and/or turbidity in micro-scale habitat selection or migratory swimming behavior at a confluence? The fall 2019 migration of California Central Valley Chinook salmon (Oncorhynchus tshawytscha) at the confluence of the Feather and Yuba Rivers in northeastern California served as a testbed. Using two dual-frequency identification sonars, 12 monitoring sites representing distinct physical microhabitats upstream of, within, and downstream of the confluence were repeatedly sampled during two four-day flow periods (mean flow ratios between the Feather and Yuba Rivers were 8.66 and 4.02, respectively). Temperature magnitudes and ratios flipped between these sampling periods. We used a multiple regression analysis using the F test for significance and a corrected Akaike information criteria (AICc) analysis to identify predictors of both detection rate (# individuals/m3/min) and percent occurrence of directed, milling, and backtracking swimming behaviors. A combination of conveyance (m2/s), temperature, and turbidity was found to perform best in predicting detection rate (p < 0.001). No suitable model was found to predict directed behavior. Milling was best predicted by a combination of all hydraulic variables (p < 0.001) and although temperature alone was found to best predict backtracking (p < 0.01), we identified a candidate model including conveyance and temperature as predictors (ΔAICc = 3.66, p = 0.02) which aided in the interpretation of our results. This study provides evidence that channel hydraulics play an active role in the sum of navigation cues that are utilized by migrating adult salmon en route to spawning grounds and should be considered in future investigations of homing and straying patterns in anadromous salmonids.

Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models

BackgroundPreoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of personalized diagnosis and treatment plans. This study aimed to build radiomics deep learning (DL) models using computed tomography (CT) data for the preoperative differential diagnosis of common cystic tumors of the pancreas.MethodsClinical and CT data of 193 patients with PCN were collected for this study. Among these patients, 99 were pathologically diagnosed with pancreatic serous cystadenoma (SCA), 55 were diagnosed with mucinous cystadenoma (MCA) and 39 were diagnosed with intraductal papillary mucinous neoplasm (IPMN). The regions of interest (ROIs) were obtained based on manual image segmentation of CT slices. The radiomics and radiomics-DL models were constructed using support vector machines (SVMs). Moreover, based on the fusion of clinical and radiological features, the best combined feature set was obtained according to the Akaike information criterion (AIC) analysis. Then the fused model was constructed using logistic regression.ResultsFor the SCA differential diagnosis, the fused model performed the best and obtained an average area under the curve (AUC) of 0.916. It had a best feature set including position, polycystic features (≥6), cystic wall calcification, pancreatic duct dilatation and radiomics-DL score. For the MCA and IPMN differential diagnosis, the fused model with AUC of 0.973 had a best feature set including age, communication with the pancreatic duct and radiomics score.ConclusionsThe radiomics, radiomics-DL and fused models based on CT images have a favorable differential diagnostic performance for SCA, MCA and IPMN. These findings may be beneficial for the exploration of individualized management strategies.

Study on Thermodynamics and Kinetics of Cephalexin Enzymatic Hydrolysis and Its Process Development to Prepare 7-ADCA

Cephalosporin enzymatic hydrolysis technology is a green technology for recovering 7-amino-3-deacetoxycephalosporanic acid (7-ADCA) from cephalosporin mother liquor. Solubility is critical for the production and purification of 7-ADCA. In this paper, the solubility of 7-ADCA and phenylglycine was measured. Solubility-temperature correlation model and solubility-pH correlation model were investigated, and Akaike information criterion (AIC) analysis was performed. The kinetic parameters of the enzymatic hydrolysis reaction of cephalexin, cefradine, and cefadroxil were determined, and the reaction rates under different substrate concentrations were measured, and the Lineweaver–Burk double-reciprocal equation was used to draw a graph. The Michaelis constants Km/(mg/mL) were 73.98, 583.84, 38.66, Vmax/(mg/mL·min) 4.20, 16.00, 1.96, respectively. The experimental results show that amphoteric compounds and buffers can prompt the reaction, low concentration of methanol promotes the reaction, while high concentration of methanol inhibits the reaction, and ethanol, isopropanol, tetrahydrofuran (THF), dimethyl sulfoxide (DMSO), ethylene glycol (EG), 1,4-dioxane all have different degrees of inhibition on the reaction speed. Finally, based on thermodynamic and kinetic studies, a process technology for the preparation of 7-ADCA by hydrolysis catalyzed of cephalexin was developed. It was confirmed that the proposed process route of preferential removal of phenylglycine by elution and/or cooling crystallization was reasonable and effective. The 7-ADCA crystal products obtained by crystallization were characterized by PXRD, thermal analysis, infrared, electron microscope, and high-performance liquid chromatography (HPLC).

Barrow holographic dark energy model with GO cut-off - An alternative perspective

Recently, Barrow holographic dark energy (BHDE), based on Barrow entropy, has been proposed to describe the late acceleration of the universe. Contrary to the earlier analysis of this model in the literature, we consider the BHDE with the Granda–Oliveros length as IR cut-off, as a dynamical vacuum, having a constant equation of state [Formula: see text] We have analytically solved for the Hubble parameter and studied the evolution of cosmological parameters. The model is compared with the observational data on Hubble parameter (OHD36) and Supernovae type Ia (SN Ia), the pantheon data. In the absence of interaction between the dark sectors, we found that the model predicts a [Formula: see text]CDM-like evolution of the universe with an effective cosmological constant. In this case, the model is found to satisfy the generalized second law (GSL), irrespective of the value of the Barrow index. The interaction also shows the safe validity of GSL, for the extracted value of the Barrow index, [Formula: see text]. The thermodynamic analysis of the model predicts an end de Sitter phase of maximum entropy. We performed a dynamical system analysis, which reveals that the end de Sitter phase is stable. Furthermore, we performed the Information Criterion analysis using Akaike and Bayesian Information Criterion to compare the statistical compatibility of the present model with the standard [Formula: see text]CDM model.

Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments

Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The most appropriate MLR model has been selected on the basis of the Akaike information criterion, sensitivity and uncertainty analyses. The performance of the MLR model was then evaluated by a variable aggregation and disaggregation approach, for upscaling and downscaling proposes, using the data from four very large- and three large-sized catchments and from eight medium-, three small- and seven very small-sized catchments, where they are located in the southern basin of the Caspian Sea. The performance of seven artificial neural network training algorithms, including Quick Propagation, Conjugate Gradient Descent, Quasi-Newton, Limited Memory Quasi-Newton, Levenberg–Marquardt, Online Back Propagation, and Batch Back Propagation, has been evaluated to predict the water quality index. The results show that the highest mean absolute error was observed in the WQI, as predicted by the ANN LM training algorithm; the lowest error values were for the ANN LMQN and CGD training algorithms. Our findings also indicate that for upscaling, the aggregated MLR model could provide reliable performance to predict the water quality index, since the r2 coefficient of the models varies from 0.73 ± 0.2 for large catchments, to 0.85 ± 0.15 for very large catchments, and for downscaling, the r2 coefficient of the disaggregated MLR model ranges from 0.93 ± 0.05 for very large catchments, to 0.97 ± 0.02 for medium catchments. Therefore, scaled models could be applied to catchments that lack sufficient data to perform a rapid assessment of the water quality index in the study area.